|
| 10. 기초 확률론3 - 확률 분포 모형/07. F 분포.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "b844fca047d24f2c8d4e6be1be201599"
},
"source": [
"# F 분포"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "8d7bd8b8ec1f4c9b8ba8bf4e6ba36da3"
},
"source": [
"student-t 분포와 카이 제곱 분포는 가우시안 정규 분포를 따르는 하나의 확률 변수 $X$ 의 $n$개의 샘플로부터 생성할 수 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "4a957a9c788745709a55ec2eab0e2adb"
},
"source": [
"[[school_notebook:8956e37db86c44b3b1b3a4c3357e590c]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "240a30e5aab84ddcaa90b358555af634"
},
"source": [
"[[school_notebook:683cfb97b17041f3a9a0e6cbee5f1fef]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "8c616d6cd61744fd8f0fdd38f1dfc05f"
},
"source": [
"이와 비슷하게 F 분포도 카이 제곱 분포를 따르는 독립적인 두 개의 확률 변수 $\\chi^2_1(n_1)$와 $\\chi^2_2(n_2)$의 확률 변수 샘플로부터 생성할 수 있다. 두 카이 제곱 분포의 샘플을 각각 $x_1$, $x_2$이라고 할 때 이를 각각 $n_1$, $n_2$로 나누어 그 비율을 구하면 $F(n_1, n_2)$ 분포가 된다. $n_1$, $n_2$는 F 분포의 자유도 인수이다.\n",
"\n",
"$$ \\dfrac{x_1 / n_1}{x_2/ n_2} \\sim F(n_1, n_2) $$"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "6a2a3b4be5444af591e3b89e312cd40c"
},
"source": [
"F 분포의 확률 밀도 함수는 다음과 같이 정의된다.\n",
"\n",
"$$ \n",
"f(x; n_1,n_2) = \\dfrac{\\sqrt{\\dfrac{(n_1\\,x)^{n_1}\\,\\,n_2^{n_2}} {(n_1\\,x+n_2)^{n_1+n_2}}}} {x\\,\\text{Beta}\\!\\left(\\frac{n_1}{2},\\frac{n_2}{2}\\right)} \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "8c1073971f96406c9fa909aa75ca0fca"
},
"source": [
"SciPy stats 서브패키지의 `f` 클래스는 F 분포를 지원한다."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"school_cell_uuid": "2913794697424caba1e55428e96ef7f0"
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
"<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
"<svg height=\"342pt\" version=\"1.1\" viewBox=\"0 0 488 342\" width=\"488pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
" <defs>\n",
" <style type=\"text/css\">\n",
"*{stroke-linecap:butt;stroke-linejoin:round;stroke-miterlimit:100000;}\n",
" </style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 342.017969 \n",
"L 488.65 342.017969 \n",
"L 488.65 0 \n",
"L 0 0 \n",
"z\n",
"\" style=\"fill:#ffffff;\"/>\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 28.1 318.672656 \n",
"L 474.5 318.672656 \n",
"L 474.5 11.772656 \n",
"L 28.1 11.772656 \n",
"z\n",
"\" style=\"fill:#eaeaf2;\"/>\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 318.672656 \n",
"L 28.1 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_2\">\n",
" <defs>\n",
" <path d=\"M 0 0 \n",
"L 0 0 \n",
"\" id=\"m4ead70e3ba\" style=\"stroke:#262626;\"/>\n",
" </defs>\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_3\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" <!-- 0.0 -->\n",
" <defs>\n",
" <path d=\"M 4.15625 35.296875 \n",
"Q 4.15625 48 6.765625 55.734375 \n",
"Q 9.375 63.484375 14.515625 67.671875 \n",
"Q 19.671875 71.875 27.484375 71.875 \n",
"Q 33.25 71.875 37.59375 69.546875 \n",
"Q 41.9375 67.234375 44.765625 62.859375 \n",
"Q 47.609375 58.5 49.21875 52.21875 \n",
"Q 50.828125 45.953125 50.828125 35.296875 \n",
"Q 50.828125 22.703125 48.234375 14.96875 \n",
"Q 45.65625 7.234375 40.5 3 \n",
"Q 35.359375 -1.21875 27.484375 -1.21875 \n",
"Q 17.140625 -1.21875 11.234375 6.203125 \n",
"Q 4.15625 15.140625 4.15625 35.296875 \n",
"M 13.1875 35.296875 \n",
"Q 13.1875 17.671875 17.3125 11.828125 \n",
"Q 21.4375 6 27.484375 6 \n",
"Q 33.546875 6 37.671875 11.859375 \n",
"Q 41.796875 17.71875 41.796875 35.296875 \n",
"Q 41.796875 52.984375 37.671875 58.78125 \n",
"Q 33.546875 64.59375 27.390625 64.59375 \n",
"Q 21.34375 64.59375 17.71875 59.46875 \n",
"Q 13.1875 52.9375 13.1875 35.296875 \n",
"\" id=\"ArialMT-30\"/>\n",
" <path d=\"M 9.078125 0 \n",
"L 9.078125 10.015625 \n",
"L 19.09375 10.015625 \n",
"L 19.09375 0 \n",
"z\n",
"\" id=\"ArialMT-2e\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(21.15 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_4\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 102.5 318.672656 \n",
"L 102.5 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_5\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"102.5\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_6\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"102.5\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" <!-- 0.5 -->\n",
" <defs>\n",
" <path d=\"M 4.15625 18.75 \n",
"L 13.375 19.53125 \n",
"Q 14.40625 12.796875 18.140625 9.390625 \n",
"Q 21.875 6 27.15625 6 \n",
"Q 33.5 6 37.890625 10.78125 \n",
"Q 42.28125 15.578125 42.28125 23.484375 \n",
"Q 42.28125 31 38.0625 35.34375 \n",
"Q 33.84375 39.703125 27 39.703125 \n",
"Q 22.75 39.703125 19.328125 37.765625 \n",
"Q 15.921875 35.84375 13.96875 32.765625 \n",
"L 5.71875 33.84375 \n",
"L 12.640625 70.609375 \n",
"L 48.25 70.609375 \n",
"L 48.25 62.203125 \n",
"L 19.671875 62.203125 \n",
"L 15.828125 42.96875 \n",
"Q 22.265625 47.46875 29.34375 47.46875 \n",
"Q 38.71875 47.46875 45.15625 40.96875 \n",
"Q 51.609375 34.46875 51.609375 24.265625 \n",
"Q 51.609375 14.546875 45.953125 7.46875 \n",
"Q 39.0625 -1.21875 27.15625 -1.21875 \n",
"Q 17.390625 -1.21875 11.203125 4.25 \n",
"Q 5.03125 9.71875 4.15625 18.75 \n",
"\" id=\"ArialMT-35\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(95.55 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_3\">\n",
" <g id=\"line2d_7\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 176.9 318.672656 \n",
"L 176.9 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_8\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"176.9\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_9\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"176.9\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" <!-- 1.0 -->\n",
" <defs>\n",
" <path d=\"M 37.25 0 \n",
"L 28.46875 0 \n",
"L 28.46875 56 \n",
"Q 25.296875 52.984375 20.140625 49.953125 \n",
"Q 14.984375 46.921875 10.890625 45.40625 \n",
"L 10.890625 53.90625 \n",
"Q 18.265625 57.375 23.78125 62.296875 \n",
"Q 29.296875 67.234375 31.59375 71.875 \n",
"L 37.25 71.875 \n",
"z\n",
"\" id=\"ArialMT-31\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(169.95 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_4\">\n",
" <g id=\"line2d_10\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 251.3 318.672656 \n",
"L 251.3 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_11\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"251.3\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_12\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"251.3\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" <!-- 1.5 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(244.35 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_5\">\n",
" <g id=\"line2d_13\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 325.7 318.672656 \n",
"L 325.7 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_14\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"325.7\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_15\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"325.7\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" <!-- 2.0 -->\n",
" <defs>\n",
" <path d=\"M 50.34375 8.453125 \n",
"L 50.34375 0 \n",
"L 3.03125 0 \n",
"Q 2.9375 3.171875 4.046875 6.109375 \n",
"Q 5.859375 10.9375 9.828125 15.625 \n",
"Q 13.8125 20.3125 21.34375 26.46875 \n",
"Q 33.015625 36.03125 37.109375 41.625 \n",
"Q 41.21875 47.21875 41.21875 52.203125 \n",
"Q 41.21875 57.421875 37.46875 61 \n",
"Q 33.734375 64.59375 27.734375 64.59375 \n",
"Q 21.390625 64.59375 17.578125 60.78125 \n",
"Q 13.765625 56.984375 13.71875 50.25 \n",
"L 4.6875 51.171875 \n",
"Q 5.609375 61.28125 11.65625 66.578125 \n",
"Q 17.71875 71.875 27.9375 71.875 \n",
"Q 38.234375 71.875 44.234375 66.15625 \n",
"Q 50.25 60.453125 50.25 52 \n",
"Q 50.25 47.703125 48.484375 43.546875 \n",
"Q 46.734375 39.40625 42.65625 34.8125 \n",
"Q 38.578125 30.21875 29.109375 22.21875 \n",
"Q 21.1875 15.578125 18.9375 13.203125 \n",
"Q 16.703125 10.84375 15.234375 8.453125 \n",
"z\n",
"\" id=\"ArialMT-32\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(318.75 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_6\">\n",
" <g id=\"line2d_16\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 400.1 318.672656 \n",
"L 400.1 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_17\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"400.1\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_18\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"400.1\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_6\">\n",
" <!-- 2.5 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(393.15 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_7\">\n",
" <g id=\"line2d_19\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 474.5 318.672656 \n",
"L 474.5 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_20\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_21\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_7\">\n",
" <!-- 3.0 -->\n",
" <defs>\n",
" <path d=\"M 4.203125 18.890625 \n",
"L 12.984375 20.0625 \n",
"Q 14.5 12.59375 18.140625 9.296875 \n",
"Q 21.78125 6 27 6 \n",
"Q 33.203125 6 37.46875 10.296875 \n",
"Q 41.75 14.59375 41.75 20.953125 \n",
"Q 41.75 27 37.796875 30.921875 \n",
"Q 33.84375 34.859375 27.734375 34.859375 \n",
"Q 25.25 34.859375 21.53125 33.890625 \n",
"L 22.515625 41.609375 \n",
"Q 23.390625 41.5 23.921875 41.5 \n",
"Q 29.546875 41.5 34.03125 44.421875 \n",
"Q 38.53125 47.359375 38.53125 53.46875 \n",
"Q 38.53125 58.296875 35.25 61.46875 \n",
"Q 31.984375 64.65625 26.8125 64.65625 \n",
"Q 21.6875 64.65625 18.265625 61.421875 \n",
"Q 14.84375 58.203125 13.875 51.765625 \n",
"L 5.078125 53.328125 \n",
"Q 6.6875 62.15625 12.390625 67.015625 \n",
"Q 18.109375 71.875 26.609375 71.875 \n",
"Q 32.46875 71.875 37.390625 69.359375 \n",
"Q 42.328125 66.84375 44.9375 62.5 \n",
"Q 47.5625 58.15625 47.5625 53.265625 \n",
"Q 47.5625 48.640625 45.0625 44.828125 \n",
"Q 42.578125 41.015625 37.703125 38.765625 \n",
"Q 44.046875 37.3125 47.5625 32.6875 \n",
"Q 51.078125 28.078125 51.078125 21.140625 \n",
"Q 51.078125 11.765625 44.234375 5.25 \n",
"Q 37.40625 -1.265625 26.953125 -1.265625 \n",
"Q 17.53125 -1.265625 11.296875 4.34375 \n",
"Q 5.078125 9.96875 4.203125 18.890625 \n",
"\" id=\"ArialMT-33\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(467.55 332.83046875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-33\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_22\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 318.672656 \n",
"L 474.5 318.672656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_23\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_24\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"318.67265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_8\">\n",
" <!-- 0.0 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 321.2578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_25\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 284.572656 \n",
"L 474.5 284.572656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_26\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"284.57265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_27\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"284.57265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_9\">\n",
" <!-- 0.2 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 287.1578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-32\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_3\">\n",
" <g id=\"line2d_28\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 250.472656 \n",
"L 474.5 250.472656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_29\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"250.47265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_30\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"250.47265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_10\">\n",
" <!-- 0.4 -->\n",
" <defs>\n",
" <path d=\"M 32.328125 0 \n",
"L 32.328125 17.140625 \n",
"L 1.265625 17.140625 \n",
"L 1.265625 25.203125 \n",
"L 33.9375 71.578125 \n",
"L 41.109375 71.578125 \n",
"L 41.109375 25.203125 \n",
"L 50.78125 25.203125 \n",
"L 50.78125 17.140625 \n",
"L 41.109375 17.140625 \n",
"L 41.109375 0 \n",
"z\n",
"M 32.328125 25.203125 \n",
"L 32.328125 57.46875 \n",
"L 9.90625 25.203125 \n",
"z\n",
"\" id=\"ArialMT-34\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 253.0578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-34\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_4\">\n",
" <g id=\"line2d_31\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 216.372656 \n",
"L 474.5 216.372656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_32\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"216.37265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_33\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"216.37265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_11\">\n",
" <!-- 0.6 -->\n",
" <defs>\n",
" <path d=\"M 49.75 54.046875 \n",
"L 41.015625 53.375 \n",
"Q 39.84375 58.546875 37.703125 60.890625 \n",
"Q 34.125 64.65625 28.90625 64.65625 \n",
"Q 24.703125 64.65625 21.53125 62.3125 \n",
"Q 17.390625 59.28125 14.984375 53.46875 \n",
"Q 12.59375 47.65625 12.5 36.921875 \n",
"Q 15.671875 41.75 20.265625 44.09375 \n",
"Q 24.859375 46.4375 29.890625 46.4375 \n",
"Q 38.671875 46.4375 44.84375 39.96875 \n",
"Q 51.03125 33.5 51.03125 23.25 \n",
"Q 51.03125 16.5 48.125 10.71875 \n",
"Q 45.21875 4.9375 40.140625 1.859375 \n",
"Q 35.0625 -1.21875 28.609375 -1.21875 \n",
"Q 17.625 -1.21875 10.6875 6.859375 \n",
"Q 3.765625 14.9375 3.765625 33.5 \n",
"Q 3.765625 54.25 11.421875 63.671875 \n",
"Q 18.109375 71.875 29.4375 71.875 \n",
"Q 37.890625 71.875 43.28125 67.140625 \n",
"Q 48.6875 62.40625 49.75 54.046875 \n",
"M 13.875 23.1875 \n",
"Q 13.875 18.65625 15.796875 14.5 \n",
"Q 17.71875 10.359375 21.1875 8.171875 \n",
"Q 24.65625 6 28.46875 6 \n",
"Q 34.03125 6 38.03125 10.484375 \n",
"Q 42.046875 14.984375 42.046875 22.703125 \n",
"Q 42.046875 30.125 38.078125 34.390625 \n",
"Q 34.125 38.671875 28.125 38.671875 \n",
"Q 22.171875 38.671875 18.015625 34.390625 \n",
"Q 13.875 30.125 13.875 23.1875 \n",
"\" id=\"ArialMT-36\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 218.9578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-36\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_5\">\n",
" <g id=\"line2d_34\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 182.272656 \n",
"L 474.5 182.272656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_35\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"182.27265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_36\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"182.27265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_12\">\n",
" <!-- 0.8 -->\n",
" <defs>\n",
" <path d=\"M 17.671875 38.8125 \n",
"Q 12.203125 40.828125 9.5625 44.53125 \n",
"Q 6.9375 48.25 6.9375 53.421875 \n",
"Q 6.9375 61.234375 12.546875 66.546875 \n",
"Q 18.171875 71.875 27.484375 71.875 \n",
"Q 36.859375 71.875 42.578125 66.421875 \n",
"Q 48.296875 60.984375 48.296875 53.171875 \n",
"Q 48.296875 48.1875 45.671875 44.5 \n",
"Q 43.0625 40.828125 37.75 38.8125 \n",
"Q 44.34375 36.671875 47.78125 31.875 \n",
"Q 51.21875 27.09375 51.21875 20.453125 \n",
"Q 51.21875 11.28125 44.71875 5.03125 \n",
"Q 38.234375 -1.21875 27.640625 -1.21875 \n",
"Q 17.046875 -1.21875 10.546875 5.046875 \n",
"Q 4.046875 11.328125 4.046875 20.703125 \n",
"Q 4.046875 27.6875 7.59375 32.390625 \n",
"Q 11.140625 37.109375 17.671875 38.8125 \n",
"M 15.921875 53.71875 \n",
"Q 15.921875 48.640625 19.1875 45.40625 \n",
"Q 22.46875 42.1875 27.6875 42.1875 \n",
"Q 32.765625 42.1875 36.015625 45.375 \n",
"Q 39.265625 48.578125 39.265625 53.21875 \n",
"Q 39.265625 58.0625 35.90625 61.359375 \n",
"Q 32.5625 64.65625 27.59375 64.65625 \n",
"Q 22.5625 64.65625 19.234375 61.421875 \n",
"Q 15.921875 58.203125 15.921875 53.71875 \n",
"M 13.09375 20.65625 \n",
"Q 13.09375 16.890625 14.875 13.375 \n",
"Q 16.65625 9.859375 20.171875 7.921875 \n",
"Q 23.6875 6 27.734375 6 \n",
"Q 34.03125 6 38.125 10.046875 \n",
"Q 42.234375 14.109375 42.234375 20.359375 \n",
"Q 42.234375 26.703125 38.015625 30.859375 \n",
"Q 33.796875 35.015625 27.4375 35.015625 \n",
"Q 21.234375 35.015625 17.15625 30.90625 \n",
"Q 13.09375 26.8125 13.09375 20.65625 \n",
"\" id=\"ArialMT-38\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 184.8578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-38\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_6\">\n",
" <g id=\"line2d_37\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 148.172656 \n",
"L 474.5 148.172656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_38\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"148.17265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_39\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"148.17265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_13\">\n",
" <!-- 1.0 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 150.7578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_7\">\n",
" <g id=\"line2d_40\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 114.072656 \n",
"L 474.5 114.072656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_41\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"114.07265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_42\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"114.07265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_14\">\n",
" <!-- 1.2 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 116.6578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-32\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_8\">\n",
" <g id=\"line2d_43\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 79.972656 \n",
"L 474.5 79.972656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_44\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"79.97265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_45\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"79.97265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_15\">\n",
" <!-- 1.4 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 82.5578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-34\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_9\">\n",
" <g id=\"line2d_46\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 45.872656 \n",
"L 474.5 45.872656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_47\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"45.87265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_48\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"45.87265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_16\">\n",
" <!-- 1.6 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 48.4578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-36\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_10\">\n",
" <g id=\"line2d_49\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 28.1 11.772656 \n",
"L 474.5 11.772656 \n",
"\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n",
" </g>\n",
" <g id=\"line2d_50\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"28.1\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_51\">\n",
" <g>\n",
" <use style=\"fill:#262626;stroke:#262626;\" x=\"474.5\" xlink:href=\"#m4ead70e3ba\" y=\"11.77265625\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_17\">\n",
" <!-- 1.8 -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(7.2 14.3578125)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"55.615234375\" xlink:href=\"#ArialMT-2e\"/>\n",
" <use x=\"83.3984375\" xlink:href=\"#ArialMT-38\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_52\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 32.564 14.460434 \n",
"L 33.006378 29.333974 \n",
"L 33.448757 42.352569 \n",
"L 34.333514 64.173196 \n",
"L 35.21827 81.865587 \n",
"L 36.545405 103.10148 \n",
"L 37.872541 119.950092 \n",
"L 39.199676 133.755937 \n",
"L 40.526811 145.34742 \n",
"L 41.853946 155.266756 \n",
"L 43.623459 166.518058 \n",
"L 45.392973 176.048589 \n",
"L 47.162486 184.261449 \n",
"L 48.932 191.437935 \n",
"L 50.701514 197.781232 \n",
"L 52.471027 203.442339 \n",
"L 54.682919 209.731926 \n",
"L 56.894811 215.303369 \n",
"L 59.106703 220.283953 \n",
"L 61.318595 224.771158 \n",
"L 63.972865 229.610585 \n",
"L 66.627135 233.950918 \n",
"L 69.281405 237.871564 \n",
"L 71.935676 241.435134 \n",
"L 75.032324 245.207683 \n",
"L 78.128973 248.625833 \n",
"L 81.225622 251.7404 \n",
"L 84.764649 254.980537 \n",
"L 88.303676 257.927245 \n",
"L 92.285081 260.941178 \n",
"L 96.266486 263.680494 \n",
"L 100.69027 266.447329 \n",
"L 105.114054 268.96275 \n",
"L 109.980216 271.479495 \n",
"L 115.288757 273.967527 \n",
"L 121.039676 276.402836 \n",
"L 127.232973 278.766899 \n",
"L 133.868649 281.04601 \n",
"L 140.946703 283.230576 \n",
"L 148.467135 285.314433 \n",
"L 156.429946 287.294209 \n",
"L 165.277514 289.261943 \n",
"L 175.009838 291.188165 \n",
"L 185.626919 293.05046 \n",
"L 197.128757 294.832702 \n",
"L 209.515351 296.52414 \n",
"L 223.229081 298.168165 \n",
"L 238.269946 299.743315 \n",
"L 255.080324 301.272596 \n",
"L 273.660216 302.731978 \n",
"L 294.452 304.133346 \n",
"L 317.455676 305.455608 \n",
"L 343.556 306.725845 \n",
"L 372.752973 307.919347 \n",
"L 405.931351 309.049374 \n",
"L 443.975892 310.118094 \n",
"L 474.5 310.839192 \n",
"L 474.5 310.839192 \n",
"\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_53\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 32.564 162.442242 \n",
"L 35.21827 170.010183 \n",
"L 37.872541 176.986267 \n",
"L 40.969189 184.460339 \n",
"L 44.065838 191.299659 \n",
"L 47.162486 197.577216 \n",
"L 50.259135 203.35555 \n",
"L 53.355784 208.688526 \n",
"L 56.452432 213.622762 \n",
"L 59.991459 218.825378 \n",
"L 63.530486 223.612303 \n",
"L 67.069514 228.028594 \n",
"L 70.608541 232.113218 \n",
"L 74.589946 236.353955 \n",
"L 78.571351 240.259386 \n",
"L 82.552757 243.865488 \n",
"L 86.976541 247.558965 \n",
"L 91.400324 250.958208 \n",
"L 95.824108 254.095018 \n",
"L 100.69027 257.275126 \n",
"L 105.556432 260.202535 \n",
"L 110.864973 263.139783 \n",
"L 116.615892 266.054138 \n",
"L 122.366811 268.722214 \n",
"L 128.560108 271.352336 \n",
"L 135.195784 273.92359 \n",
"L 142.273838 276.41926 \n",
"L 149.79427 278.8265 \n",
"L 157.757081 281.13594 \n",
"L 166.16227 283.341239 \n",
"L 175.452216 285.538049 \n",
"L 185.184541 287.60555 \n",
"L 195.801622 289.626596 \n",
"L 207.303459 291.580035 \n",
"L 219.690054 293.450298 \n",
"L 232.961405 295.226729 \n",
"L 247.559892 296.951981 \n",
"L 263.485514 298.605209 \n",
"L 280.73827 300.172052 \n",
"L 299.760541 301.676306 \n",
"L 320.994703 303.128692 \n",
"L 344.440757 304.506543 \n",
"L 370.541081 305.816 \n",
"L 400.180432 307.074735 \n",
"L 433.358811 308.257036 \n",
"L 471.403351 309.383798 \n",
"L 474.5 309.466522 \n",
"L 474.5 309.466522 \n",
"\" style=\"fill:none;stroke:#55a868;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_54\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 32.564 301.679789 \n",
"L 36.987784 280.893597 \n",
"L 39.199676 271.503765 \n",
"L 41.411568 263.062355 \n",
"L 43.623459 255.60335 \n",
"L 45.392973 250.326558 \n",
"L 47.162486 245.632888 \n",
"L 48.932 241.486702 \n",
"L 50.701514 237.849236 \n",
"L 52.471027 234.680892 \n",
"L 54.240541 231.942675 \n",
"L 56.010054 229.597078 \n",
"L 57.779568 227.608603 \n",
"L 59.549081 225.944023 \n",
"L 61.318595 224.572501 \n",
"L 63.088108 223.465588 \n",
"L 64.857622 222.597165 \n",
"L 66.627135 221.943335 \n",
"L 68.839027 221.394793 \n",
"L 71.050919 221.109301 \n",
"L 73.262811 221.052864 \n",
"L 75.474703 221.195388 \n",
"L 78.128973 221.591813 \n",
"L 81.225622 222.315011 \n",
"L 84.32227 223.267725 \n",
"L 87.861297 224.579718 \n",
"L 92.285081 226.473913 \n",
"L 97.593622 229.007609 \n",
"L 104.671676 232.648186 \n",
"L 133.868649 247.940796 \n",
"L 142.716216 252.193196 \n",
"L 151.121405 255.991585 \n",
"L 159.526595 259.548709 \n",
"L 167.931784 262.868229 \n",
"L 176.779351 266.115915 \n",
"L 185.626919 269.124816 \n",
"L 194.916865 272.044672 \n",
"L 204.649189 274.86084 \n",
"L 214.823892 277.562328 \n",
"L 225.440973 280.141441 \n",
"L 236.500432 282.593386 \n",
"L 248.00227 284.915857 \n",
"L 260.388865 287.185884 \n",
"L 273.660216 289.382742 \n",
"L 287.373946 291.427875 \n",
"L 302.414811 293.441134 \n",
"L 318.340432 295.345073 \n",
"L 335.593189 297.179626 \n",
"L 354.173081 298.927965 \n",
"L 374.522486 300.612829 \n",
"L 396.641405 302.214226 \n",
"L 420.529838 303.718654 \n",
"L 447.072541 305.162963 \n",
"L 474.5 306.448186 \n",
"L 474.5 306.448186 \n",
"\" style=\"fill:none;stroke:#c44e52;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_55\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 32.564 314.658525 \n",
"L 33.448757 312.203015 \n",
"L 34.775892 307.603269 \n",
"L 36.545405 300.302165 \n",
"L 43.181081 271.535527 \n",
"L 44.950595 265.089273 \n",
"L 46.720108 259.397322 \n",
"L 48.489622 254.458374 \n",
"L 50.259135 250.238016 \n",
"L 52.028649 246.683813 \n",
"L 53.798162 243.735091 \n",
"L 55.567676 241.329065 \n",
"L 57.337189 239.404501 \n",
"L 59.106703 237.903783 \n",
"L 60.876216 236.773945 \n",
"L 62.64573 235.967068 \n",
"L 64.415243 235.440271 \n",
"L 66.184757 235.155497 \n",
"L 67.95427 235.07916 \n",
"L 70.166162 235.232251 \n",
"L 72.378054 235.615485 \n",
"L 75.032324 236.32055 \n",
"L 78.128973 237.406511 \n",
"L 81.668 238.903151 \n",
"L 86.091784 241.032118 \n",
"L 92.285081 244.281762 \n",
"L 113.076865 255.384333 \n",
"L 121.039676 259.289283 \n",
"L 128.560108 262.724989 \n",
"L 136.080541 265.911654 \n",
"L 143.600973 268.857242 \n",
"L 151.563784 271.728707 \n",
"L 159.526595 274.364781 \n",
"L 167.931784 276.914213 \n",
"L 176.779351 279.364071 \n",
"L 186.069297 281.705315 \n",
"L 195.801622 283.932274 \n",
"L 206.418703 286.129189 \n",
"L 217.478162 288.19145 \n",
"L 229.422378 290.192983 \n",
"L 242.251351 292.116788 \n",
"L 255.965081 293.950687 \n",
"L 271.005946 295.736209 \n",
"L 287.373946 297.451692 \n",
"L 305.069081 299.081787 \n",
"L 324.53373 300.649978 \n",
"L 346.21027 302.166854 \n",
"L 370.098703 303.608884 \n",
"L 396.641405 304.98199 \n",
"L 426.280757 306.286294 \n",
"L 459.459135 307.518589 \n",
"L 474.5 308.011835 \n",
"L 474.5 308.011835 \n",
"\" style=\"fill:none;stroke:#8172b2;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_56\">\n",
" <path clip-path=\"url(#p1807296224)\" d=\"M 32.564 318.672655 \n",
"L 46.27773 318.577462 \n",
"L 50.259135 318.319429 \n",
"L 52.913405 317.954139 \n",
"L 55.125297 317.469749 \n",
"L 57.337189 316.771004 \n",
"L 59.549081 315.808903 \n",
"L 61.318595 314.817506 \n",
"L 63.088108 313.60437 \n",
"L 64.857622 312.149228 \n",
"L 66.627135 310.434846 \n",
"L 68.396649 308.447525 \n",
"L 70.166162 306.17748 \n",
"L 72.378054 302.934145 \n",
"L 74.589946 299.238806 \n",
"L 76.801838 295.099892 \n",
"L 79.01373 290.535452 \n",
"L 81.668 284.534719 \n",
"L 84.764649 276.890313 \n",
"L 88.303676 267.450982 \n",
"L 92.727459 254.894341 \n",
"L 108.653081 208.88007 \n",
"L 112.192108 199.65075 \n",
"L 115.288757 192.128148 \n",
"L 118.385405 185.190924 \n",
"L 121.039676 179.748792 \n",
"L 123.693946 174.796174 \n",
"L 126.348216 170.348188 \n",
"L 128.560108 167.033146 \n",
"L 130.772 164.075798 \n",
"L 132.983892 161.474845 \n",
"L 135.195784 159.226543 \n",
"L 137.407676 157.324978 \n",
"L 139.619568 155.762339 \n",
"L 141.831459 154.529177 \n",
"L 143.600973 153.772629 \n",
"L 145.370486 153.21394 \n",
"L 147.14 152.84662 \n",
"L 148.909514 152.663843 \n",
"L 151.121405 152.684054 \n",
"L 153.333297 152.967206 \n",
"L 155.545189 153.49864 \n",
"L 157.757081 154.263494 \n",
"L 159.968973 155.246831 \n",
"L 162.623243 156.694275 \n",
"L 165.277514 158.409458 \n",
"L 168.374162 160.715676 \n",
"L 171.470811 163.314042 \n",
"L 175.009838 166.594221 \n",
"L 178.991243 170.618554 \n",
"L 183.415027 175.424557 \n",
"L 188.723568 181.540025 \n",
"L 195.801622 190.072425 \n",
"L 219.690054 219.190639 \n",
"L 226.768108 227.307712 \n",
"L 232.961405 234.083802 \n",
"L 239.154703 240.521244 \n",
"L 244.905622 246.178544 \n",
"L 250.656541 251.519428 \n",
"L 256.407459 256.54233 \n",
"L 262.158378 261.25046 \n",
"L 267.909297 265.650718 \n",
"L 273.660216 269.752783 \n",
"L 279.411135 273.56835 \n",
"L 285.162054 277.11051 \n",
"L 290.912973 280.393241 \n",
"L 296.663892 283.431 \n",
"L 302.857189 286.445231 \n",
"L 309.050486 289.210418 \n",
"L 315.686162 291.916875 \n",
"L 322.321838 294.37879 \n",
"L 329.399892 296.757842 \n",
"L 336.477946 298.904346 \n",
"L 343.998378 300.954089 \n",
"L 351.961189 302.890859 \n",
"L 360.366378 304.702455 \n",
"L 369.213946 306.380604 \n",
"L 378.503892 307.920731 \n",
"L 388.678595 309.380621 \n",
"L 399.738054 310.736213 \n",
"L 411.68227 311.97103 \n",
"L 424.953622 313.110317 \n",
"L 439.552108 314.131893 \n",
"L 455.920108 315.046218 \n",
"L 474.5 315.853076 \n",
"L 474.5 315.853076 \n",
"\" style=\"fill:none;stroke:#ccb974;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 28.1 11.772656 \n",
"L 474.5 11.772656 \n",
"\" style=\"fill:none;\"/>\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 474.5 318.672656 \n",
"L 474.5 11.772656 \n",
"\" style=\"fill:none;\"/>\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 28.1 318.672656 \n",
"L 474.5 318.672656 \n",
"\" style=\"fill:none;\"/>\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 28.1 318.672656 \n",
"L 28.1 11.772656 \n",
"\" style=\"fill:none;\"/>\n",
" </g>\n",
" <g id=\"legend_1\">\n",
" <g id=\"line2d_57\">\n",
" <path d=\"M 399.710938 24.552344 \n",
"L 419.710938 24.552344 \n",
"\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_58\"/>\n",
" <g id=\"text_18\">\n",
" <!-- F(1,1) -->\n",
" <defs>\n",
" <path d=\"M 23.390625 -21.046875 \n",
"Q 16.109375 -11.859375 11.078125 0.4375 \n",
"Q 6.0625 12.75 6.0625 25.921875 \n",
"Q 6.0625 37.546875 9.8125 48.1875 \n",
"Q 14.203125 60.546875 23.390625 72.796875 \n",
"L 29.6875 72.796875 \n",
"Q 23.78125 62.640625 21.875 58.296875 \n",
"Q 18.890625 51.5625 17.1875 44.234375 \n",
"Q 15.09375 35.109375 15.09375 25.875 \n",
"Q 15.09375 2.390625 29.6875 -21.046875 \n",
"z\n",
"\" id=\"ArialMT-28\"/>\n",
" <path d=\"M 8.890625 0 \n",
"L 8.890625 10.015625 \n",
"L 18.890625 10.015625 \n",
"L 18.890625 0 \n",
"Q 18.890625 -5.515625 16.9375 -8.90625 \n",
"Q 14.984375 -12.3125 10.75 -14.15625 \n",
"L 8.296875 -10.40625 \n",
"Q 11.078125 -9.1875 12.390625 -6.8125 \n",
"Q 13.71875 -4.4375 13.875 0 \n",
"z\n",
"\" id=\"ArialMT-2c\"/>\n",
" <path d=\"M 12.359375 -21.046875 \n",
"L 6.0625 -21.046875 \n",
"Q 20.65625 2.390625 20.65625 25.875 \n",
"Q 20.65625 35.0625 18.5625 44.09375 \n",
"Q 16.890625 51.421875 13.921875 58.15625 \n",
"Q 12.015625 62.546875 6.0625 72.796875 \n",
"L 12.359375 72.796875 \n",
"Q 21.53125 60.546875 25.921875 48.1875 \n",
"Q 29.6875 37.546875 29.6875 25.921875 \n",
"Q 29.6875 12.75 24.625 0.4375 \n",
"Q 19.578125 -11.859375 12.359375 -21.046875 \n",
"\" id=\"ArialMT-29\"/>\n",
" <path d=\"M 8.203125 0 \n",
"L 8.203125 71.578125 \n",
"L 56.5 71.578125 \n",
"L 56.5 63.140625 \n",
"L 17.671875 63.140625 \n",
"L 17.671875 40.96875 \n",
"L 51.265625 40.96875 \n",
"L 51.265625 32.515625 \n",
"L 17.671875 32.515625 \n",
"L 17.671875 0 \n",
"z\n",
"\" id=\"ArialMT-46\"/>\n",
" </defs>\n",
" <g style=\"fill:#262626;\" transform=\"translate(427.7109375 28.05234375)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-46\"/>\n",
" <use x=\"61.083984375\" xlink:href=\"#ArialMT-28\"/>\n",
" <use x=\"94.384765625\" xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"150.0\" xlink:href=\"#ArialMT-2c\"/>\n",
" <use x=\"177.783203125\" xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"233.3984375\" xlink:href=\"#ArialMT-29\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_59\">\n",
" <path d=\"M 399.710938 38.936719 \n",
"L 419.710938 38.936719 \n",
"\" style=\"fill:none;stroke:#55a868;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_60\"/>\n",
" <g id=\"text_19\">\n",
" <!-- F(2,1) -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(427.7109375 42.43671875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-46\"/>\n",
" <use x=\"61.083984375\" xlink:href=\"#ArialMT-28\"/>\n",
" <use x=\"94.384765625\" xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"150.0\" xlink:href=\"#ArialMT-2c\"/>\n",
" <use x=\"177.783203125\" xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"233.3984375\" xlink:href=\"#ArialMT-29\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_61\">\n",
" <path d=\"M 399.710938 53.321094 \n",
"L 419.710938 53.321094 \n",
"\" style=\"fill:none;stroke:#c44e52;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_62\"/>\n",
" <g id=\"text_20\">\n",
" <!-- F(5,2) -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(427.7109375 56.82109375)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-46\"/>\n",
" <use x=\"61.083984375\" xlink:href=\"#ArialMT-28\"/>\n",
" <use x=\"94.384765625\" xlink:href=\"#ArialMT-35\"/>\n",
" <use x=\"150.0\" xlink:href=\"#ArialMT-2c\"/>\n",
" <use x=\"177.783203125\" xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"233.3984375\" xlink:href=\"#ArialMT-29\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_63\">\n",
" <path d=\"M 399.710938 67.705469 \n",
"L 419.710938 67.705469 \n",
"\" style=\"fill:none;stroke:#8172b2;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_64\"/>\n",
" <g id=\"text_21\">\n",
" <!-- F(10,1) -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(427.7109375 71.20546875)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-46\"/>\n",
" <use x=\"61.083984375\" xlink:href=\"#ArialMT-28\"/>\n",
" <use x=\"94.384765625\" xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"150.0\" xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"205.615234375\" xlink:href=\"#ArialMT-2c\"/>\n",
" <use x=\"233.3984375\" xlink:href=\"#ArialMT-31\"/>\n",
" <use x=\"289.013671875\" xlink:href=\"#ArialMT-29\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_65\">\n",
" <path d=\"M 399.710938 82.089844 \n",
"L 419.710938 82.089844 \n",
"\" style=\"fill:none;stroke:#ccb974;stroke-linecap:round;stroke-width:1.75;\"/>\n",
" </g>\n",
" <g id=\"line2d_66\"/>\n",
" <g id=\"text_22\">\n",
" <!-- F(20,20) -->\n",
" <g style=\"fill:#262626;\" transform=\"translate(427.7109375 85.58984375)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#ArialMT-46\"/>\n",
" <use x=\"61.083984375\" xlink:href=\"#ArialMT-28\"/>\n",
" <use x=\"94.384765625\" xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"150.0\" xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"205.615234375\" xlink:href=\"#ArialMT-2c\"/>\n",
" <use x=\"233.3984375\" xlink:href=\"#ArialMT-32\"/>\n",
" <use x=\"289.013671875\" xlink:href=\"#ArialMT-30\"/>\n",
" <use x=\"344.62890625\" xlink:href=\"#ArialMT-29\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p1807296224\">\n",
" <rect height=\"306.9\" width=\"446.4\" x=\"28.1\" y=\"11.77265625\"/>\n",
" </clipPath>\n",
" </defs>\n",
"</svg>\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6ba0d5f590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xx = np.linspace(0.03, 3, 1000)\n",
"plt.hold(True)\n",
"plt.plot(xx, sp.stats.f(1,1).pdf(xx), label=\"F(1,1)\")\n",
"plt.plot(xx, sp.stats.f(2,1).pdf(xx), label=\"F(2,1)\")\n",
"plt.plot(xx, sp.stats.f(5,2).pdf(xx), label=\"F(5,2)\")\n",
"plt.plot(xx, sp.stats.f(10,1).pdf(xx), label=\"F(10,1)\")\n",
"plt.plot(xx, sp.stats.f(20,20).pdf(xx), label=\"F(20,20)\")\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |
|
TheOregonian/long-term-care-db | notebooks/analysis/.ipynb_checkpoints/facilities_analysis-checkpoint.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a dataset of Assisted Living, Nursing and Residential Care facilities in Oregon, open as of January, 2017. For each, we have:\n",
"1. <i>facility_id:</i> Unique ID used to join to complaints\n",
"2. <i>fac_ccmunumber:</i> Unique ID used to join to ownership history\n",
"3. <i>facility_type:</i> NF - Nursing Facility; RCF - Residential Care Facility; ALF - Assisted Living Facility\n",
"4. <i>fac_capacity:</i> Number of beds facility is licensed to have. Not necessarily the number of beds facility does have.\n",
"5. <i>offline:</i> created in munging notebook, a count of complaints that DO NOT appear when facility is searched on state's complaint search website (https://apps.state.or.us/cf2/spd/facility_complaints/).\n",
"6. <i>online:</i> created in munging notebook, a count of complaints that DO appear when facility is searched on state's complaint search website (https://apps.state.or.us/cf2/spd/facility_complaints/)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>.container { width:100% !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from IPython.core.display import display, HTML\n",
"display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('/Users/fzarkhin/OneDrive - Advance Central Services, Inc/fproj/github/database-story/data/processed/facilities.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities have accurate records online?</h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those that have no offline records."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"57"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['offline'].isnull())].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities have inaccurate records online?<h/3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those that have offline records."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"585"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['offline'].notnull())].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities had more than double the number of complaints shown online?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"357"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['offline']>df['online']) & (df['online'].notnull())].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities show zero complaints online but have complaints offline?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"59"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['online'].isnull()) & (df['offline'].notnull())].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities have complaints and are accurate online?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['online'].notnull()) & (df['offline'].isnull())].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>How many facilities have complaints?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"599"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['online'].notnull()) | df['offline'].notnull()].count()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>What percent of facilities have accurate records online?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8.8785046728971952"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['offline'].isnull())].count()[0]/df.count()[0]*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>What is the total capacity of all facilities with inaccurate records?</h3>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"35238.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['offline'].notnull()].sum()['fac_capacity']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>facility_id</th>\n",
" <th>fac_ccmunumber</th>\n",
" <th>facility_type</th>\n",
" <th>fac_capacity</th>\n",
" <th>facility_name</th>\n",
" <th>offline</th>\n",
" <th>online</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [facility_id, fac_ccmunumber, facility_type, fac_capacity, facility_name, offline, online]\n",
"Index: []"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['fac_capacity'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#df#['fac_capacity'].sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_artifacts_correction_ssp.ipynb |
|
| {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n\n\nArtifact Correction with SSP\n============================\n\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"import numpy as np\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.preprocessing import compute_proj_ecg, compute_proj_eog\n\n# getting some data ready\ndata_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.set_eeg_reference()\nraw.pick_types(meg=True, ecg=True, eog=True, stim=True)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Compute SSP projections\n-----------------------\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, average=True)\nprint(projs)\n\necg_projs = projs[-2:]\nmne.viz.plot_projs_topomap(ecg_projs)\n\n# Now for EOG\n\nprojs, events = compute_proj_eog(raw, n_grad=1, n_mag=1, average=True)\nprint(projs)\n\neog_projs = projs[-2:]\nmne.viz.plot_projs_topomap(eog_projs)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Apply SSP projections\n---------------------\n\nMNE is handling projections at the level of the info,\nso to register them populate the list that you find in the 'proj' field\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw.info['projs'] += eog_projs + ecg_projs"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Yes this was it. Now MNE will apply the projs on demand at any later stage,\nso watch out for proj parmeters in functions or to it explicitly\nwith the ``.apply_proj`` method\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"Demonstrate SSP cleaning on some evoked data\n--------------------------------------------\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"events = mne.find_events(raw, stim_channel='STI 014')\nreject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)\n# this can be highly data dependent\nevent_id = {'auditory/left': 1}\n\nepochs_no_proj = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,\n proj=False, baseline=(None, 0), reject=reject)\nepochs_no_proj.average().plot(spatial_colors=True)\n\n\nepochs_proj = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5, proj=True,\n baseline=(None, 0), reject=reject)\nepochs_proj.average().plot(spatial_colors=True)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Looks cool right? It is however often not clear how many components you\nshould take and unfortunately this can have bad consequences as can be seen\ninteractively using the delayed SSP mode:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"evoked = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,\n proj='delayed', baseline=(None, 0),\n reject=reject).average()\n\n# set time instants in seconds (from 50 to 150ms in a step of 10ms)\ntimes = np.arange(0.05, 0.15, 0.01)\n\nevoked.plot_topomap(times, proj='interactive')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"now you should see checkboxes. Remove a few SSP and see how the auditory\npattern suddenly drops off\n\n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.13",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} |
|
mercybenzaquen/foundations-homework | foundations_hw/11/Homework 11_.ipynb |
|
|
|
|
pattu777/Algorithms-and-Data-structures |
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This is an array based implementation of a Queue.\n",
"\n",
"import unittest\n",
"\n",
"class Queue(object):\n",
" def __init__(self, max_size=0):\n",
" self.size = max_size\n",
" self.arr = []\n",
"\n",
" def put(self, item):\n",
" '''Enqueue an element.'''\n",
" if self.is_full():\n",
" print \"Queue is full.\"\n",
" else:\n",
" self.arr.append(item)\n",
"\n",
" def get(self):\n",
" '''Pop an item.'''\n",
" if self.is_empty():\n",
" print \"Queue is empty.\"\n",
" else:\n",
" self.arr.pop(0)\n",
"\n",
" def is_empty(self):\n",
" '''Return if the Queue is empty or not.'''\n",
" return len(self.arr) == 0\n",
"\n",
" def is_full(self):\n",
" '''Return if the Queue is full or not.'''\n",
" return len(self.arr) == self.size\n",
"\n",
" def front(self):\n",
" '''Return the front item.'''\n",
" if self.is_empty():\n",
" print \"Queue is empty.\"\n",
" else:\n",
" print self.arr[0]\n",
"\n",
" def rear(self):\n",
" '''Return the rear item.'''\n",
" if self.is_empty():\n",
" print \"Queue is empty.\"\n",
" else:\n",
" print self.arr[len(self.arr)-1]\n",
" \n",
" def length(self):\n",
" \"\"\"Return the number of items in the queue.\"\"\"\n",
" return len(self.arr)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"test_get (__main__.TestQueue) ... ok\n",
"test_length (__main__.TestQueue) ... ok\n",
"test_push (__main__.TestQueue) ... ok\n",
"\n",
"----------------------------------------------------------------------\n",
"Ran 3 tests in 0.005s\n",
"\n",
"OK\n"
]
}
],
"source": [
"# Unitt tests for Queue data structure.\n",
"\n",
"import unittest\n",
"\n",
"class TestQueue(unittest.TestCase):\n",
" def __init__(self, *args, **kwargs):\n",
" super(TestQueue, self).__init__(*args, **kwargs)\n",
" self.queue = Queue(100)\n",
" for i in xrange(10):\n",
" self.queue.put(i)\n",
"\n",
" def test_length(self):\n",
" # Test the length of the Queue.\n",
" self.assertEqual(self.queue.length(), 10)\n",
"\n",
" def test_push(self):\n",
" # Test put operation.\n",
" self.queue.put(191)\n",
" self.assertEqual(self.queue.length(), 11)\n",
"\n",
" def test_get(self):\n",
" # Test get operation.\n",
" self.queue.get()\n",
" self.assertEqual(self.queue.length(), 9)\n",
" \n",
" \n",
"if __name__ == '__main__':\n",
" unittest.main(argv=['ignored', '-v'], exit=False)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| examples/nb/mef2cMulti/dugla-tabs.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"import ipywidgets as widgets\n",
"import time\n",
"from IPython.display import display, HTML\n",
"from traitlets import Int, Unicode, observe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class TabsWidget(widgets.DOMWidget):\n",
" \n",
" _view_name = Unicode('TabsView').tag(sync=True)\n",
" _view_module = Unicode('tabsDemo').tag(sync=True)\n",
" frameHeight = Int(300).tag(sync=True)\n",
"\n",
" def setHeight(self, height):\n",
" print(\"setHeight(%d) \"% height)\n",
" self.frameHeight = height"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" div#notebook-container { width: 97%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(HTML(data=\"\"\"\n",
"<style>\n",
" div#notebook-container { width: 97%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n",
"\"\"\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<link rel=\"stylesheet\" type=\"text/css\" href=\"//igv.org/web/release/1.0.6/igv-1.0.6.css\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(HTML('<link rel=\"stylesheet\" type=\"text/css\" href=\"//igv.org/web/release/1.0.6/igv-1.0.6.css\">'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"\"use strict\"\n",
"require.config({\n",
" 'shim': {'bootstrap': {'deps' :['jquery']},\n",
" 'igv': {'deps' :['jquery', 'jquery-ui', 'bootstrap']}\n",
" },\n",
" \n",
" paths: {'jquery' : 'http://code.jquery.com/jquery-1.12.4.min',\n",
" 'jquery-ui' : 'http://code.jquery.com/ui/1.12.1/jquery-ui.min',\n",
" 'bootstrap' : 'http://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min',\n",
" 'igv' : 'http://igv.org/web/release/1.0.6/igv-1.0.6'\n",
" }\n",
" });\n",
"\n",
"require.undef('tabsDemo')\n",
"\n",
"define('tabsDemo', [\"jupyter-js-widgets\", \"jquery\", \"igv\"], \n",
" function(widgets, $, igv) {\n",
" \n",
" var TabsView = widgets.DOMWidgetView.extend({\n",
"\n",
" initialize: function() {\n",
" this.options = {}\n",
" console.log(\"constructing TabsView\");\n",
" this.frameHeight = \"800px\";\n",
" },\n",
"\n",
" resizeHandler: function(){\n",
" console.log(\"TabsView resizeHandler\") \n",
" },\n",
" \n",
" createMasterTabsDiv: function(){\n",
" var masterTabsDiv = $(\"<div id='masterTabsDiv' style='border:1px solid gray; height: 400px; width: 97%'></div>\");\n",
" \n",
" var list = $(\"<ul/>\");\n",
" list.append(\"<li><a href='#tab-2'>igv</a></li>\");\n",
" list.append(\"<li><a href='#tab-3'>three</a></li>\");\n",
" masterTabsDiv.append(list);\n",
" \n",
" var tab2 = $(\"<div id='tab-2'></div>\");\n",
" tab2.append(\"<div id='igvDiv' style='border:1px solid blue; height:500px;'></div>\");\n",
" \n",
" var tab3 = $(\"<div id='tab-3'>contents 3</div>\");\n",
" \n",
" masterTabsDiv.append(tab2);\n",
" masterTabsDiv.append(tab3);\n",
" \n",
" return(masterTabsDiv);\n",
" },\n",
" \n",
"\n",
" render: function() { \n",
" console.log(\"entering render\");\n",
" this.masterTabsDiv = this.createMasterTabsDiv();\n",
" this.$el.append(this.masterTabsDiv);\n",
" this.listenTo(this.model, 'change:frameHeight', this.frameDimensionsChanged, this);\n",
" var igvOptions = {\n",
" palette: [\"#00A0B0\", \"#6A4A3C\", \"#CC333F\", \"#EB6841\"],\n",
" locus: \"7:55,085,725 - 55,276,031\",\n",
" reference: {id: \"hg19\",\n",
" fastaURL: \"http://igv.broadinstitute.org/genomes/seq/1kg_v37/human_g1k_v37_decoy.fasta\",\n",
" cytobandURL: \"http://igv.broadinstitute.org/genomes/seq/b37/b37_cytoband.txt\"\n",
" },\n",
" trackDefaults: {\n",
" bam: {coverageThreshold: 0.2,\n",
" coverageQualityWeight: true\n",
" }\n",
" },\n",
" tracks: [\n",
" {name: \"Genes\",\n",
" url: \"http://igv.broadinstitute.org/annotations/hg19/genes/gencode.v18.collapsed.bed\",\n",
" index: \"http://igv.broadinstitute.org/annotations/hg19/genes/gencode.v18.collapsed.bed.idx\",\n",
" displayMode: \"EXPANDED\"\n",
" }\n",
" ]\n",
" }; // igvOptions\n",
" \n",
" setTimeout(function(){\n",
" console.log(\"about to call tabs()\");\n",
" $(\"#masterTabsDiv\").tabs();\n",
" window.browser = igv.createBrowser($(\"#igvDiv\"), igvOptions);\n",
" }, 0);\n",
" },\n",
"\n",
" \n",
" frameDimensionsChanged: function(){\n",
" console.log(\"frameDimensionsChanged\");\n",
" var oldHeight = $(\"#mainDiv\").height()\n",
" var oldWidth = $(\"#mainDiv\").width()\n",
" var newHeight = this.model.get(\"frameHeight\");\n",
" var msg = \"<center>tabs demo, height: \" + oldHeight + \" -> \" + newHeight + \"</center>\";\n",
" $(\"#mainDiv\").html(msg);\n",
" $(\"#masterTabsDiv\").height(newHeight);\n",
" }, \n",
" \n",
"\n",
" });\n",
" return {\n",
" TabsView: TabsView\n",
" };\n",
"});"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"\"use strict\"\n",
"require.config({\n",
" 'shim': {'bootstrap': {'deps' :['jquery']},\n",
" 'igv': {'deps' :['jquery', 'jquery-ui', 'bootstrap']}\n",
" },\n",
" \n",
" paths: {'jquery' : 'http://code.jquery.com/jquery-1.12.4.min',\n",
" 'jquery-ui' : 'http://code.jquery.com/ui/1.12.1/jquery-ui.min',\n",
" 'bootstrap' : 'http://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min',\n",
" 'igv' : 'http://igv.org/web/release/1.0.6/igv-1.0.6'\n",
" }\n",
" });\n",
"\n",
"require.undef('tabsDemo')\n",
"\n",
"define('tabsDemo', [\"jupyter-js-widgets\", \"jquery\", \"igv\"], \n",
" function(widgets, $, igv) {\n",
" \n",
" var TabsView = widgets.DOMWidgetView.extend({\n",
"\n",
" initialize: function() {\n",
" this.options = {}\n",
" console.log(\"constructing TabsView\");\n",
" this.frameHeight = \"800px\";\n",
" },\n",
"\n",
" resizeHandler: function(){\n",
" console.log(\"TabsView resizeHandler\") \n",
" },\n",
" \n",
" createMasterTabsDiv: function(){\n",
" var masterTabsDiv = $(\"<div id='masterTabsDiv' style='border:1px solid gray; height: 400px; width: 97%'></div>\");\n",
" \n",
" var list = $(\"<ul/>\");\n",
" list.append(\"<li><a href='#tab-2'>igv</a></li>\");\n",
" list.append(\"<li><a href='#tab-3'>three</a></li>\");\n",
" masterTabsDiv.append(list);\n",
" \n",
" var tab2 = $(\"<div id='tab-2'></div>\");\n",
" tab2.append(\"<div id='igvDiv' style='border:1px solid blue; height:500px;'></div>\");\n",
" \n",
" var tab3 = $(\"<div id='tab-3'>contents 3</div>\");\n",
" \n",
" masterTabsDiv.append(tab2);\n",
" masterTabsDiv.append(tab3);\n",
" \n",
" return(masterTabsDiv);\n",
" },\n",
" \n",
"\n",
" render: function() { \n",
" console.log(\"entering render\");\n",
" this.masterTabsDiv = this.createMasterTabsDiv();\n",
" this.$el.append(this.masterTabsDiv);\n",
" this.listenTo(this.model, 'change:frameHeight', this.frameDimensionsChanged, this);\n",
" var igvOptions = {\n",
" palette: [\"#00A0B0\", \"#6A4A3C\", \"#CC333F\", \"#EB6841\"],\n",
" locus: \"7:55,085,725 - 55,276,031\",\n",
" reference: {id: \"hg19\",\n",
" fastaURL: \"http://igv.broadinstitute.org/genomes/seq/1kg_v37/human_g1k_v37_decoy.fasta\",\n",
" cytobandURL: \"http://igv.broadinstitute.org/genomes/seq/b37/b37_cytoband.txt\"\n",
" },\n",
" trackDefaults: {\n",
" bam: {coverageThreshold: 0.2,\n",
" coverageQualityWeight: true\n",
" }\n",
" },\n",
" tracks: [\n",
" {name: \"Genes\",\n",
" url: \"http://igv.broadinstitute.org/annotations/hg19/genes/gencode.v18.collapsed.bed\",\n",
" index: \"http://igv.broadinstitute.org/annotations/hg19/genes/gencode.v18.collapsed.bed.idx\",\n",
" displayMode: \"EXPANDED\"\n",
" }\n",
" ]\n",
" }; // igvOptions\n",
" \n",
" setTimeout(function(){\n",
" console.log(\"about to call tabs()\");\n",
" $(\"#masterTabsDiv\").tabs();\n",
" window.browser = igv.createBrowser($(\"#igvDiv\"), igvOptions);\n",
" }, 0);\n",
" },\n",
"\n",
" \n",
" frameDimensionsChanged: function(){\n",
" console.log(\"frameDimensionsChanged\");\n",
" var oldHeight = $(\"#mainDiv\").height()\n",
" var oldWidth = $(\"#mainDiv\").width()\n",
" var newHeight = this.model.get(\"frameHeight\");\n",
" var msg = \"<center>tabs demo, height: \" + oldHeight + \" -> \" + newHeight + \"</center>\";\n",
" $(\"#mainDiv\").html(msg);\n",
" $(\"#masterTabsDiv\").height(newHeight);\n",
" }, \n",
" \n",
"\n",
" });\n",
" return {\n",
" TabsView: TabsView\n",
" };\n",
"});"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2bb5047ba134cab8fce282bd81f11ae"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app = TabsWidget()\n",
"display(app)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| python-ml-book/ch12/ch12.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 12: Training Artificial Neural Networks for Image Recognition\n",
"\n",
"In this notebook I work through chapter 12 of Python Machine Learning—see [the author's definitive notes](http://nbviewer.jupyter.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb).\n",
"\n",
"## Loading in the MNIST hand written image data set"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import struct\n",
"import numpy as np\n",
"\n",
"def load_mnist(path, kind='train'):\n",
" \"\"\"Load MNIST data from `path`\"\"\"\n",
" labels_path = os.path.join(path, \n",
" '%s-labels-idx1-ubyte' % kind)\n",
" images_path = os.path.join(path, \n",
" '%s-images-idx3-ubyte' % kind)\n",
" \n",
" with open(labels_path, 'rb') as lbpath:\n",
" magic, n = struct.unpack('>II', \n",
" lbpath.read(8))\n",
" labels = np.fromfile(lbpath, \n",
" dtype=np.uint8)\n",
"\n",
" with open(images_path, 'rb') as imgpath:\n",
" magic, num, rows, cols = struct.unpack(\">IIII\", \n",
" imgpath.read(16))\n",
" images = np.fromfile(imgpath, \n",
" dtype=np.uint8).reshape(len(labels), 784)\n",
" \n",
" return images, labels"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows: 60000, columns: 784\n"
]
}
],
"source": [
"X_train, y_train = load_mnist('mnist', kind='train')\n",
"print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows: 10000, columns: 784\n"
]
}
],
"source": [
"X_test, y_test = load_mnist('mnist', kind='t10k')\n",
"print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAIxCAYAAACsBjJEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1071099e8>"
]
},
"metadata": {
"image/png": {
"height": 280,
"width": 424
}
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"\n",
"fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)\n",
"ax = ax.flatten()\n",
"for i in range(10):\n",
" img = X_train[y_train == i][0].reshape(28, 28)\n",
" ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
"\n",
"ax[0].set_xticks([])\n",
"ax[0].set_yticks([])\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show a bunch of 4s"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAIxCAYAAACsBjJEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRAEAAGT4BRe+N3I2gqK7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107109630>"
]
},
"metadata": {
"image/png": {
"height": 280,
"width": 424
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n",
"ax = ax.flatten()\n",
"for i in range(25):\n",
" img = X_train[y_train == 4][i].reshape(28, 28)\n",
" ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
"\n",
"ax[0].set_xticks([])\n",
"ax[0].set_yticks([])\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classifying with tree based models\n",
"\n",
"Let's see how well some other models do before we get to the neural net."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"tree10 = DecisionTreeClassifier(criterion='entropy', max_depth=10, random_state=0)\n",
"tree100 = DecisionTreeClassifier(criterion='entropy', max_depth=100, random_state=0)\n",
"\n",
"rf10 = RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=1)\n",
"rf100 = RandomForestClassifier(criterion='entropy', n_estimators=100, random_state=1)\n",
"\n",
"labeled_models = [\n",
" ('decision tree depth 10', tree10),\n",
" ('decision tree depth 100', tree100),\n",
" ('random forest 10 estimators', rf10),\n",
" ('random forest 100 estimators', rf100),\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"decision tree depth 10 fit the dataset in 15.2 seconds\n",
"decision tree depth 100 fit the dataset in 19.1 seconds\n",
"random forest 10 estimators fit the dataset in 5.3 seconds\n",
"random forest 100 estimators fit the dataset in 52.6 seconds\n"
]
}
],
"source": [
"import time\n",
"import subprocess\n",
"\n",
"def say_done(label):\n",
" subprocess.call(\"say 'done with {}'\".format(label), shell=True)\n",
"\n",
"for label, model in labeled_models:\n",
" before = time.time()\n",
" model.fit(X_train, y_train)\n",
" after = time.time()\n",
"\n",
" print(\"{} fit the dataset in {:.1f} seconds\".format(label, after - before))\n",
" say_done(label)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"decision tree depth 10 training fit: 0.912\n",
"decision tree depth 10 test accuracy: 0.872\n",
"decision tree depth 100 training fit: 1.000\n",
"decision tree depth 100 test accuracy: 0.886\n",
"random forest 10 estimators training fit: 0.999\n",
"random forest 10 estimators test accuracy: 0.946\n",
"random forest 100 estimators training fit: 1.000\n",
"random forest 100 estimators test accuracy: 0.969\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"for label, model in labeled_models:\n",
" print(\"{} training fit: {:.3f}\".format(label, accuracy_score(y_train, model.predict(X_train)))) \n",
" print(\"{} test accuracy: {:.3f}\".format(label, accuracy_score(y_test, model.predict(X_test)))) "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| to_do/vax_temp/test.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# conversion, drawing, saving, analysis\n",
"- copy of dan's thing\n",
"- converts .csv to .gml and .net\n",
"- draws graph, saves graph.png\n",
"- try to combine into this"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import networkx as nx\n",
"from copy import deepcopy\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from matplotlib.backends.backend_pdf import PdfPages\n",
"\n",
"from glob import glob\n",
"fileName = 'article0'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def getFiles(fileName):\n",
" matches = glob('*'+fileName+'*')\n",
" bigFile = matches[0]\n",
" data = pd.DataFrame.from_csv(bigFile)\n",
" return clearSource(data)\n",
" \n",
"\n",
"def clearSource(data):\n",
" columns = ['source','target']\n",
" pre = len(data)\n",
" for column in columns:\n",
" data = data[pd.notnull(data[column])]\n",
" post = len(data)\n",
" print \"Filtered %s rows to %s rows by removing rows with blank values in columns %s\" % (pre,post,columns)\n",
" return data\n",
" \n",
" \n",
"#data = getFiles(fileName)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def getStuff(data,labels):\n",
" forEdges = labels == ['edge']\n",
" columns = list(data.columns.values)\n",
" items = dict()\n",
" \n",
" nameFunc = {True: lambda x,y: '%s - %s - %s' % (x['source'],x['edge'],x['target']),\n",
" False: lambda x,y: x[y]}[forEdges]\n",
" \n",
" extra = ['source','target'] * forEdges\n",
" \n",
" for label in labels:\n",
" relevant = [col for col in columns if label+'-' in col] + extra\n",
" #relevant = extra\n",
" print \"Extracting %s data from %s\" % (label,relevant)\n",
" for i in data.index:\n",
" row = data.ix[i]\n",
" for col in relevant:\n",
" if str(row[col]).lower() != 'nan':\n",
" name = nameFunc(row,label)\n",
" if name not in items:\n",
" items[name] = dict()\n",
" items[name][col.replace(label+'-','')] = row[col]\n",
" return items\n",
" \n",
"\n",
"def getNodes(data):\n",
" return getStuff(data,['source','target'])\n",
"\n",
"\n",
"def getEdges(data):\n",
" return getStuff(data,['edge'])\n",
" \n",
" \n",
"#allNodes = getNodes(data); allEdges = getEdges(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def addNodes(graph,nodes):\n",
" for key,value in nodes.iteritems():\n",
" graph.add_node(key,attr_dict=value)\n",
" return graph\n",
" \n",
"def addEdges(graph,edges):\n",
" for key,value in edges.iteritems():\n",
" value['label'] = key\n",
" value['edge'] = key.split(' - ')[1]\n",
" graph.add_edge(value['source'],value['target'],attr_dict = value)\n",
" return graph\n",
" \n",
"\n",
"#########\n",
"\n",
"def createNetwork(edges,nodes):\n",
" graph = nx.MultiGraph()\n",
" graph = addNodes(graph,nodes)\n",
" graph = addEdges(graph,edges)\n",
" return graph\n",
"\n",
"\n",
"#fullGraph = createNetwork(allEdges,allNodes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def drawIt(graph,what='graph', save_plot=None):\n",
" style=nx.spring_layout(graph)\n",
" size = graph.number_of_nodes()\n",
" print \"Drawing %s of size %s:\" % (what,size)\n",
" if size > 20:\n",
" plt.figure(figsize=(10,10))\n",
" if size > 40:\n",
" nx.draw(graph,style,node_size=60,font_size=8)\n",
" if save_plot is not None:\n",
" print('saving: {}'.format(save_plot))\n",
" plt.savefig(save_plot)\n",
" else:\n",
" nx.draw(graph,style)\n",
" if save_plot is not None:\n",
" print('saving: {}'.format(save_plot))\n",
" plt.savefig(save_plot)\n",
" else:\n",
" nx.draw(graph,style)\n",
" if save_plot is not None:\n",
" print('saving: {}'.format(save_plot))\n",
" plt.savefig(save_plot)\n",
" plt.show()\n",
" \n",
" \n",
"def describeGraph(graph, save_plot=None):\n",
" components = nx.connected_components(graph)\n",
" components = list(components)\n",
" isolated = [entry[0] for entry in components if len(entry)==1]\n",
" params = (graph.number_of_edges(),graph.number_of_nodes(),len(components),len(isolated))\n",
" print \"Graph has %s nodes, %s edges, %s connected components, and %s isolated nodes\\n\" % params\n",
" drawIt(graph, save_plot=save_plot)\n",
" for idx, sub in enumerate(components):\n",
" drawIt(graph.subgraph(sub),what='component', save_plot='{}-{}.png'.format('component', idx))\n",
" print \"Isolated nodes:\", isolated\n",
"\n",
"def getGraph(fileRef, save_plot=None):\n",
" data = getFiles(fileName)\n",
" nodes = getNodes(data)\n",
" edges = getEdges(data)\n",
" graph = createNetwork(edges,nodes)\n",
" fileOut = fileRef.split('.')[0]+'.gml'\n",
" print \"Writing GML file to %s\" % fileOut\n",
" nx.write_gml(graph, fileOut)\n",
" \n",
" fileOutNet = fileRef.split('.')[0]+'.net'\n",
" print \"Writing net file to %s\" % fileOutNet\n",
" nx.write_pajek(graph, fileOutNet)\n",
" \n",
" describeGraph(graph, save_plot)\n",
" return graph, nodes, edges"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fileName = 'data/csv/article1'\n",
"graph, nodes, edges = getGraph(fileName, save_plot='graph.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"plt.figure(figsize=(12, 12))\n",
"nx.draw_spring(graph, node_color='g', with_labels=True, arrows=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# return a dictionary of centrality values for each node\n",
"nx.degree_centrality(graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## degree centrality\n",
"for a node v is the fraction of nodes it is connected to"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# the type of degree centrality is a dictionary\n",
"type(nx.degree_centrality(graph))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# get all the values of the dictionary, this returns a list of centrality scores\n",
"# turn the list into a numpy array\n",
"# take the mean of the numpy array\n",
"np.array(nx.degree_centrality(graph).values()).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## closeness centrality\n",
"of a node u is the reciprocal of the sum of the shortest path distances from u to all n-1 other nodes. Since the sum of distances depends on the number of nodes in the graph, closeness is normalized by the sum of minimum possible distances n-1. Notice that higher values of closeness indicate higher centrality."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nx.closeness_centrality(graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## betweenness centrality\n",
"of a node v is the sum of the fraction of all-pairs shortest paths that pass through v"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"nx.betweenness_centrality(graph)\n",
"np.array(nx.betweenness_centrality(graph).values()).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## degree assortativity coefficient\n",
"Assortativity measures the similarity of connections in the graph with respect to the node degree."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nx.degree_assortativity_coefficient(graph)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| docs/examples/testsuite1.ipynb |
|
|
|
|
|
| LOTlib/Examples/GrammarInference/NumberGame/Untitled0.ipynb |
|
| {
"metadata": {
"name": "",
"signature": "sha256:9bf3105f1003d7edc6faad3f8129bdd3fba0eb3bd5f1b017915a15eb2e7eea0d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from SimpleDemo import *"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"run(grammar=mix_grammar, data=toy_3n, domain=20, alpha=0.99, enum_d=5, grammar_n=1000, cap=100, \n",
" print_stuff=True, plot_type='violin', pickle_data='load')"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
|
| code/08-cooccurrence-nestedness/nestedness_binary_heatmaps.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**author**: [email protected]<br>\n",
"**date**: 19 Sep 2017<br>\n",
"**language**: Python 3.5<br>\n",
"**conda environment**: emp-py3<br>\n",
"**license**: unlicensed<br>\n",
"\n",
"## nestedness_binary_heatmaps.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%run ../../code/colors-and-styles/empcolors.py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# seaborn plot settings\n",
"sns.set_context('talk')\n",
"sns.set_style('white')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# input file paths (.csv removed)\n",
"paths_phylum_empo2 = [\n",
" '../../data/nestedness/nest_phylum_Animal',\n",
" '../../data/nestedness/nest_phylum_Plant',\n",
" '../../data/nestedness/nest_phylum_Saline',\n",
" '../../data/nestedness/nest_phylum_Non-saline']\n",
" \n",
"paths_phylum_all = ['../../data/nestedness/nest_phylum_allsamples'] "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def plot_nestedness_big(path, taxlevel, xmax, ymax, legendloc, legendcol):\n",
" df = pd.read_csv('%s.csv' % path)\n",
" # df = df[df['OBSERVATION_ID'] != 'unclassified'] # if we want to remove unclassified phylum\n",
" empolevel = path.split('/')[-1].split('_')[-1]\n",
" if xmax == 'auto':\n",
" xmax = df.SAMPLE_RANK.max()\n",
" if ymax == 'auto':\n",
" ymax = df.OBSERVATION_RANK.max()\n",
" fig = plt.figure(figsize=(500/30, 80/12.7), facecolor=None) # xmax/30 ymax/12.7\n",
" for empo3 in np.sort(df.empo_3.unique()):\n",
" plt.scatter(df[df.empo_3 == empo3].SAMPLE_RANK, df[df.empo_3 == empo3].OBSERVATION_RANK, marker='|', \n",
" linewidths=2, label=empo3, color=get_empo_cat_color(empo3))\n",
" plt.xlabel('%s samples (sorted by richness)' % empolevel, fontsize=24)\n",
" plt.ylabel('%s (sorted by prevalence)' % taxlevel, fontsize=24)\n",
" plt.tick_params(axis='both', which='major', labelsize=20)\n",
" plt.axis([0, xmax+1, 0, ymax+0.8])\n",
" #plt.legend(loc=legendloc, ncol=legendcol, markerscale=4, handletextpad=0, fontsize=18) # bbox_to_anchor=(0.5, 1+0.08*80/ymax), \n",
" #plt.tight_layout()\n",
" fig.patch.set_alpha(0.0)\n",
" plt.savefig('%s.pdf' % path)\n",
" return df, empolevel"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def plot_nestedness(path, taxlevel, xmax, ymax, legendloc, legendcol):\n",
" df = pd.read_csv('%s.csv' % path)\n",
" # df = df[df['OBSERVATION_ID'] != 'unclassified'] # if we want to remove unclassified phylum\n",
" empolevel = path.split('/')[-1].split('_')[-1]\n",
" if xmax == 'auto':\n",
" xmax = df.SAMPLE_RANK.max()\n",
" if ymax == 'auto':\n",
" ymax = df.OBSERVATION_RANK.max()\n",
" fig = plt.figure(figsize=(400/30, 80/12.7), facecolor=None) # xmax/30 ymax/12.7\n",
" for empo3 in np.sort(df.empo_3.unique()):\n",
" plt.scatter(df[df.empo_3 == empo3].SAMPLE_RANK, df[df.empo_3 == empo3].OBSERVATION_RANK, marker='|', \n",
" linewidths=2, label=empo3, color=get_empo_cat_color(empo3))\n",
" plt.xlabel('%s samples (sorted by richness)' % empolevel, fontsize=24)\n",
" plt.ylabel('%s (sorted by prevalence)' % taxlevel, fontsize=24)\n",
" plt.tick_params(axis='both', which='major', labelsize=20)\n",
" plt.axis([0, xmax+1, 0, ymax+0.8])\n",
" #plt.legend(loc=legendloc, ncol=legendcol, markerscale=4, handletextpad=0, fontsize=18) # bbox_to_anchor=(0.5, 1+0.08*80/ymax), \n",
" #plt.tight_layout()\n",
" fig.patch.set_alpha(0.0)\n",
" plt.savefig('%s.pdf' % path)\n",
" return df, empolevel"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def top_taxa(df):\n",
" return df[['OBSERVATION_RANK', 'OBSERVATION_ID']].sort_values(\n",
" 'OBSERVATION_RANK', ascending=False).drop_duplicates().reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABVsAAAJICAYAAAB/vDTHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e1d9f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"panels_dict = {}\n",
"for path in paths_phylum_all:\n",
" df, empolevel = plot_nestedness_big(path, 'Phyla', 'auto', 'auto', 3, 4)\n",
" panels_dict[empolevel] = df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABFQAAAJICAYAAABPObCKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSJIkSZIkdcmAiiRJkiRJUpf+BZpMJ6xmDNNjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f43d410>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABFQAAAJICAYAAABPObCKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQRAEQRAEQRB0yP8H21bbZmT87aQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eadb490>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABFQAAAJLCAYAAADJrcIkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREGqSAioiIiIiIiIhIg/4fNPmIpvrcQ0cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11184fb50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABFQAAAJRCAYAAABr4OPZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRhjjDHGGGOMMWZMfg9SYZrkCGgdOAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e778910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for path in paths_phylum_empo2:\n",
" df, empolevel = plot_nestedness(path, 'Phyla', 'auto', 'auto', 3, 1)\n",
" panels_dict[empolevel] = df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Animal\n",
"k__Bacteria;p__Proteobacteria 639\n",
"k__Bacteria;p__Firmicutes 631\n",
"k__Bacteria;p__Actinobacteria 608\n",
"k__Bacteria;p__Bacteroidetes 592\n",
"k__Bacteria;p__Cyanobacteria 468\n",
"unclassified 446\n",
"k__Bacteria;p__Verrucomicrobia 377\n",
"k__Bacteria;p__Fusobacteria 321\n",
"Name: OBSERVATION_ID, dtype: int64\n",
"\n",
"Non-saline\n",
"k__Bacteria;p__Proteobacteria 595\n",
"k__Bacteria;p__Bacteroidetes 587\n",
"k__Bacteria;p__Actinobacteria 571\n",
"unclassified 565\n",
"k__Bacteria;p__Cyanobacteria 561\n",
"k__Bacteria;p__Acidobacteria 550\n",
"k__Bacteria;p__Verrucomicrobia 548\n",
"k__Bacteria;p__Chloroflexi 546\n",
"Name: OBSERVATION_ID, dtype: int64\n",
"\n",
"Plant\n",
"k__Bacteria;p__Proteobacteria 377\n",
"k__Bacteria;p__Cyanobacteria 369\n",
"k__Bacteria;p__Actinobacteria 322\n",
"k__Bacteria;p__Bacteroidetes 310\n",
"k__Bacteria;p__Planctomycetes 286\n",
"k__Bacteria;p__Verrucomicrobia 282\n",
"unclassified 278\n",
"k__Bacteria;p__Firmicutes 258\n",
"Name: OBSERVATION_ID, dtype: int64\n",
"\n",
"Saline\n",
"k__Bacteria;p__Proteobacteria 386\n",
"k__Bacteria;p__Bacteroidetes 384\n",
"k__Bacteria;p__Cyanobacteria 368\n",
"k__Bacteria;p__Actinobacteria 360\n",
"k__Bacteria;p__Verrucomicrobia 359\n",
"unclassified 352\n",
"k__Bacteria;p__Planctomycetes 337\n",
"k__Bacteria;p__Firmicutes 298\n",
"Name: OBSERVATION_ID, dtype: int64\n",
"\n",
"allsamples\n",
"k__Bacteria;p__Proteobacteria 1997\n",
"k__Bacteria;p__Bacteroidetes 1873\n",
"k__Bacteria;p__Actinobacteria 1861\n",
"k__Bacteria;p__Cyanobacteria 1766\n",
"k__Bacteria;p__Firmicutes 1730\n",
"unclassified 1641\n",
"k__Bacteria;p__Verrucomicrobia 1566\n",
"k__Bacteria;p__Planctomycetes 1410\n",
"Name: OBSERVATION_ID, dtype: int64\n"
]
}
],
"source": [
"for key in sorted(panels_dict):\n",
" print('\\n' + key)\n",
" print(panels_dict[key]['OBSERVATION_ID'].value_counts().head(8))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:emp-py2]",
"language": "python",
"name": "conda-env-emp-py2-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n",
"and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n",
"merged properties. This allows you to answer questions like\n",
"\n",
"> What are the demographics of the census tracts within 1000 ft of the highway?\n",
"\n",
"The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n",
"not on individual geometries, and the properties from both are retained"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:34.256318Z",
"start_time": "2017-12-15T21:09:34.226318Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import Image\n",
"Image(url=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can load up two GeoDataFrames containing (multi)polygon geometries..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:36.236298Z",
"start_time": "2017-12-15T21:09:34.256318Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from shapely.geometry import Point\n",
"from geopandas import datasets, GeoDataFrame, read_file\n",
"from geopandas.tools import overlay\n",
"\n",
"# NYC Boros\n",
"zippath = datasets.get_path('nybb')\n",
"polydf = read_file(zippath)\n",
"\n",
"# Generate some circles\n",
"b = [int(x) for x in polydf.total_bounds]\n",
"N = 10\n",
"polydf2 = GeoDataFrame([\n",
" {'geometry': Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}\n",
" for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n",
" range(b[1], b[3], int((b[3] - b[1]) / N)))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first dataframe contains multipolygons of the NYC boros"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:36.526295Z",
"start_time": "2017-12-15T21:09:36.236298Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x512ee48>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x512e2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"polydf.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a [different color palette](https://matplotlib.org/examples/color/colormaps_reference.html)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:36.756293Z",
"start_time": "2017-12-15T21:09:36.526295Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x512e0f0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARoAAAD8CAYAAACo2WuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzhKNMcZzlmiMMZ77/2SB+rI5WiaWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12caea58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"polydf2.plot(cmap='tab20b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `geopandas.tools.overlay` function takes three arguments:\n",
"\n",
"* df1\n",
"* df2\n",
"* how\n",
"\n",
"Where `how` can be one of:\n",
"\n",
" ['intersection',\n",
" 'union',\n",
" 'identity',\n",
" 'symmetric_difference',\n",
" 'difference']\n",
"\n",
"So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` tool. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:39.796263Z",
"start_time": "2017-12-15T21:09:36.756293Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12978940>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARwAAAD8CAYAAAClxxvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4dd50f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from geopandas.tools import overlay\n",
"newdf = overlay(polydf, polydf2, how=\"intersection\")\n",
"newdf.plot(cmap='tab20b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:40.416257Z",
"start_time": "2017-12-15T21:09:39.796263Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Brooklyn</td>\n",
" <td>741080.523166</td>\n",
" <td>1.937479e+09</td>\n",
" <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Manhattan</td>\n",
" <td>359299.096471</td>\n",
" <td>6.364715e+08</td>\n",
" <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BoroCode BoroName Shape_Leng Shape_Area \\\n",
"0 5 Staten Island 330470.010332 1.623820e+09 \n",
"1 4 Queens 896344.047763 3.045213e+09 \n",
"2 3 Brooklyn 741080.523166 1.937479e+09 \n",
"3 1 Manhattan 359299.096471 6.364715e+08 \n",
"4 2 Bronx 464392.991824 1.186925e+09 \n",
"\n",
" geometry \n",
"0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
"1 (POLYGON ((1029606.076599121 156073.8142089844... \n",
"2 (POLYGON ((1021176.479003906 151374.7969970703... \n",
"3 (POLYGON ((981219.0557861328 188655.3157958984... \n",
"4 (POLYGON ((1012821.805786133 229228.2645874023... "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polydf.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:40.446256Z",
"start_time": "2017-12-15T21:09:40.416257Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POLYGON ((923175 120121, 923126.847266722 1191...</td>\n",
" <td>1033296</td>\n",
" <td>793054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
" <td>1063988</td>\n",
" <td>793202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
" <td>1094680</td>\n",
" <td>793350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POLYGON ((969435 165937, 969386.847266722 1649...</td>\n",
" <td>1125372</td>\n",
" <td>793498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POLYGON ((984855 181209, 984806.847266722 1802...</td>\n",
" <td>1156064</td>\n",
" <td>793646</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry value1 value2\n",
"0 POLYGON ((923175 120121, 923126.847266722 1191... 1033296 793054\n",
"1 POLYGON ((938595 135393, 938546.847266722 1344... 1063988 793202\n",
"2 POLYGON ((954015 150665, 953966.847266722 1496... 1094680 793350\n",
"3 POLYGON ((969435 165937, 969386.847266722 1649... 1125372 793498\n",
"4 POLYGON ((984855 181209, 984806.847266722 1802... 1156064 793646"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polydf2.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:40.586255Z",
"start_time": "2017-12-15T21:09:40.446256Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>1033296</td>\n",
" <td>793054</td>\n",
" <td>POLYGON ((916755.4256330276 129447.9617643995,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>1063988</td>\n",
" <td>793202</td>\n",
" <td>POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>1125372</td>\n",
" <td>793498</td>\n",
" <td>POLYGON ((961436.3049926758 175473.0296020508,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>1094680</td>\n",
" <td>793350</td>\n",
" <td>POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" <td>1309524</td>\n",
" <td>794386</td>\n",
" <td>POLYGON ((1043287.193237305 260300.0289916992,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BoroCode BoroName Shape_Leng Shape_Area value1 value2 \\\n",
"0 5 Staten Island 330470.010332 1.623820e+09 1033296 793054 \n",
"1 5 Staten Island 330470.010332 1.623820e+09 1063988 793202 \n",
"2 5 Staten Island 330470.010332 1.623820e+09 1125372 793498 \n",
"3 5 Staten Island 330470.010332 1.623820e+09 1094680 793350 \n",
"4 2 Bronx 464392.991824 1.186925e+09 1309524 794386 \n",
"\n",
" geometry \n",
"0 POLYGON ((916755.4256330276 129447.9617643995,... \n",
"1 POLYGON ((938595 135393, 938546.847266722 1344... \n",
"2 POLYGON ((961436.3049926758 175473.0296020508,... \n",
"3 POLYGON ((954015 150665, 953966.847266722 1496... \n",
"4 POLYGON ((1043287.193237305 260300.0289916992,... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"newdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's look at the other `how` operations:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:44.026220Z",
"start_time": "2017-12-15T21:09:40.586255Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a72e48>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARoAAAD8CAYAAACo2WuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxudoodFoND5HC41Go/E5/w/hJcoDL8piaQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12ae5f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"newdf = overlay(polydf, polydf2, how=\"union\")\n",
"newdf.plot(cmap='tab20b')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:47.366187Z",
"start_time": "2017-12-15T21:09:44.026220Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12bccda0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhTJaCoViWPH/2UAwmuqzL+4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12d79940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"newdf = overlay(polydf, polydf2, how=\"identity\")\n",
"newdf.plot(cmap='tab20b')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:50.556155Z",
"start_time": "2017-12-15T21:09:47.366187Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12dc2710>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARoAAAD8CAYAAACo2WuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12d3a7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"newdf = overlay(polydf, polydf2, how=\"symmetric_difference\")\n",
"newdf.plot(cmap='tab20b')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:09:53.566125Z",
"start_time": "2017-12-15T21:09:50.556155Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12e13630>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKpzRcjgcUYUzWg6HI6pwRsvhcEQV/w+iixtNUc2vPQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12d3aac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"newdf = overlay(polydf, polydf2, how=\"difference\")\n",
"newdf.plot(cmap='tab20b')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
AdityaSoni19031997/Machine-Learning | AV/AV_Enigma_ML_ml_bazzokass.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## the boring stuff"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:37:52.365719Z",
"start_time": "2018-08-31T08:37:51.888619Z"
}
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:53:57.284178Z",
"start_time": "2018-08-31T09:53:56.974011Z"
}
},
"outputs": [],
"source": [
"import time\n",
"import xgboost as xgb\n",
"import lightgbm as lgb\n",
"import category_encoders as cat_ed\n",
"import gc, mlcrate, glob\n",
"\n",
"from gplearn.genetic import SymbolicTransformer\n",
"from fastai.imports import *\n",
"from fastai.structured import *\n",
"from pandas_summary import DataFrameSummary\n",
"from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, RandomForestRegressor\n",
"from IPython.display import display\n",
"from catboost import CatBoostClassifier, CatBoostRegressor\n",
"from scipy.cluster import hierarchy as hc\n",
"from collections import Counter\n",
"\n",
"from sklearn import metrics\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.metrics import roc_auc_score, log_loss\n",
"from sklearn.model_selection import KFold, StratifiedKFold\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.decomposition import PCA, TruncatedSVD, FastICA, FactorAnalysis\n",
"from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection\n",
"from sklearn.cluster import KMeans\n",
"\n",
"from sklearn.metrics import accuracy_score, log_loss\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.gaussian_process import GaussianProcessClassifier\n",
"from sklearn.gaussian_process.kernels import RBF\n",
"\n",
"# will ignore all warning from sklearn, seaborn etc..\n",
"def ignore_warn(*args, **kwargs):\n",
" pass\n",
"warnings.warn = ignore_warn\n",
"\n",
"pd.option_context(\"display.max_rows\", 1000);\n",
"pd.option_context(\"display.max_columns\", 1000);"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:38:04.720536Z",
"start_time": "2018-08-31T08:38:04.430684Z"
}
},
"outputs": [],
"source": [
"PATH = os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:38:38.478838Z",
"start_time": "2018-08-31T08:38:35.884072Z"
}
},
"outputs": [],
"source": [
"df_raw = pd.read_csv(f'{PATH}\\\\train_new_agg_feats.csv', low_memory=False)\n",
"df_test = pd.read_csv(f'{PATH}\\\\test_new_agg_feats.csv', low_memory=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:38:45.306630Z",
"start_time": "2018-08-31T08:38:44.956293Z"
}
},
"outputs": [],
"source": [
"def display_all(df):\n",
" with pd.option_context(\"display.max_rows\", 100): \n",
" with pd.option_context(\"display.max_columns\", 100): \n",
" display(df)\n",
"\n",
"def make_submission(probs):\n",
" sample = pd.read_csv(f'{PATH}\\\\sample_submission.csv')\n",
" submit = sample.copy()\n",
" submit['Upvotes'] = probs\n",
" return submit"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:38:50.801706Z",
"start_time": "2018-08-31T08:38:50.498752Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"((330045, 20),)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_raw.shape,"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:38:57.495698Z",
"start_time": "2018-08-31T08:38:57.176839Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"float64:dense 20\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_raw.get_ftype_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T08:39:05.889838Z",
"start_time": "2018-08-31T08:39:05.508350Z"
},
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Answers 0.0\n",
"Reputation 0.0\n",
"Tag 0.0\n",
"Username 0.0\n",
"Views 0.0\n",
"agg_answers 0.0\n",
"agg_count 0.0\n",
"agg_repo 0.0\n",
"agg_views 0.0\n",
"log_trans_Answers 0.0\n",
"log_trans_Reputation 0.0\n",
"log_trans_Username 0.0\n",
"log_trans_Views 0.0\n",
"log_trans_agg_answers 0.0\n",
"log_trans_agg_count 0.0\n",
"log_trans_agg_repo 0.0\n",
"log_trans_agg_views 0.0\n",
"repo_per_Answers 0.0\n",
"repo_per_Views 0.0\n",
"target 0.0\n",
"dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_all(df_raw.isnull().sum().sort_index()/len(df_raw))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## random"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:35:37.205250Z",
"start_time": "2018-08-31T09:35:36.914314Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tag</th>\n",
" <th>Reputation</th>\n",
" <th>Answers</th>\n",
" <th>Username</th>\n",
" <th>Views</th>\n",
" <th>agg_count</th>\n",
" <th>agg_answers</th>\n",
" <th>agg_views</th>\n",
" <th>agg_repo</th>\n",
" <th>log_trans_Reputation</th>\n",
" <th>log_trans_Answers</th>\n",
" <th>log_trans_Username</th>\n",
" <th>log_trans_Views</th>\n",
" <th>log_trans_agg_count</th>\n",
" <th>log_trans_agg_answers</th>\n",
" <th>log_trans_agg_views</th>\n",
" <th>log_trans_agg_repo</th>\n",
" <th>repo_per_Answers</th>\n",
" <th>repo_per_Views</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>4271.0</td>\n",
" <td>4.0</td>\n",
" <td>112223.0</td>\n",
" <td>13986.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>20598.0</td>\n",
" <td>4271.0</td>\n",
" <td>8.359838</td>\n",
" <td>1.609438</td>\n",
" <td>11.628252</td>\n",
" <td>9.545883</td>\n",
" <td>1.386294</td>\n",
" <td>1.098612</td>\n",
" <td>9.932998</td>\n",
" <td>8.359838</td>\n",
" <td>854.200012</td>\n",
" <td>0.305377</td>\n",
" <td>84.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4.0</td>\n",
" <td>2269.0</td>\n",
" <td>2.0</td>\n",
" <td>54623.0</td>\n",
" <td>312.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>4168.0</td>\n",
" <td>2269.0</td>\n",
" <td>7.727535</td>\n",
" <td>1.098612</td>\n",
" <td>10.908229</td>\n",
" <td>5.746203</td>\n",
" <td>2.079442</td>\n",
" <td>1.098612</td>\n",
" <td>8.335431</td>\n",
" <td>7.727535</td>\n",
" <td>756.333313</td>\n",
" <td>7.272436</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.0</td>\n",
" <td>111.0</td>\n",
" <td>2.0</td>\n",
" <td>172926.0</td>\n",
" <td>53738.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>53738.0</td>\n",
" <td>111.0</td>\n",
" <td>4.718499</td>\n",
" <td>1.098612</td>\n",
" <td>12.060625</td>\n",
" <td>10.891894</td>\n",
" <td>0.693147</td>\n",
" <td>1.098612</td>\n",
" <td>10.891894</td>\n",
" <td>4.718499</td>\n",
" <td>37.000000</td>\n",
" <td>0.002066</td>\n",
" <td>80.000008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1.0</td>\n",
" <td>7952.0</td>\n",
" <td>2.0</td>\n",
" <td>62155.0</td>\n",
" <td>29191.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>28314.0</td>\n",
" <td>7952.0</td>\n",
" <td>8.981304</td>\n",
" <td>1.098612</td>\n",
" <td>11.037403</td>\n",
" <td>10.281650</td>\n",
" <td>1.609438</td>\n",
" <td>1.386294</td>\n",
" <td>10.251147</td>\n",
" <td>8.981304</td>\n",
" <td>2650.666748</td>\n",
" <td>0.272413</td>\n",
" <td>224.000015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>3.0</td>\n",
" <td>731.0</td>\n",
" <td>4.0</td>\n",
" <td>43559.0</td>\n",
" <td>5622.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>6730.0</td>\n",
" <td>731.0</td>\n",
" <td>6.595780</td>\n",
" <td>1.609438</td>\n",
" <td>10.681894</td>\n",
" <td>8.634621</td>\n",
" <td>1.098612</td>\n",
" <td>1.098612</td>\n",
" <td>8.814479</td>\n",
" <td>6.595780</td>\n",
" <td>146.199997</td>\n",
" <td>0.130025</td>\n",
" <td>14.000001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tag Reputation Answers Username Views agg_count agg_answers \\\n",
"4 1.0 4271.0 4.0 112223.0 13986.0 3.0 2.0 \n",
"7 4.0 2269.0 2.0 54623.0 312.0 7.0 2.0 \n",
"8 4.0 111.0 2.0 172926.0 53738.0 1.0 2.0 \n",
"15 1.0 7952.0 2.0 62155.0 29191.0 4.0 3.0 \n",
"16 3.0 731.0 4.0 43559.0 5622.0 2.0 2.0 \n",
"\n",
" agg_views agg_repo log_trans_Reputation log_trans_Answers \\\n",
"4 20598.0 4271.0 8.359838 1.609438 \n",
"7 4168.0 2269.0 7.727535 1.098612 \n",
"8 53738.0 111.0 4.718499 1.098612 \n",
"15 28314.0 7952.0 8.981304 1.098612 \n",
"16 6730.0 731.0 6.595780 1.609438 \n",
"\n",
" log_trans_Username log_trans_Views log_trans_agg_count \\\n",
"4 11.628252 9.545883 1.386294 \n",
"7 10.908229 5.746203 2.079442 \n",
"8 12.060625 10.891894 0.693147 \n",
"15 11.037403 10.281650 1.609438 \n",
"16 10.681894 8.634621 1.098612 \n",
"\n",
" log_trans_agg_answers log_trans_agg_views log_trans_agg_repo \\\n",
"4 1.098612 9.932998 8.359838 \n",
"7 1.098612 8.335431 7.727535 \n",
"8 1.098612 10.891894 4.718499 \n",
"15 1.386294 10.251147 8.981304 \n",
"16 1.098612 8.814479 6.595780 \n",
"\n",
" repo_per_Answers repo_per_Views target \n",
"4 854.200012 0.305377 84.000000 \n",
"7 756.333313 7.272436 4.000000 \n",
"8 37.000000 0.002066 80.000008 \n",
"15 2650.666748 0.272413 224.000015 \n",
"16 146.199997 0.130025 14.000001 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_raw.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:37:23.753934Z",
"start_time": "2018-08-31T09:37:23.424519Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"df_raw = pd.get_dummies(df_raw, 'tag', columns=['Tag'])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:43:21.466894Z",
"start_time": "2018-08-31T09:43:21.268393Z"
}
},
"outputs": [],
"source": [
"df_test = pd.get_dummies(df_test, 'tag', columns=['Tag'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bazooka ! (anokas)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:26:20.659980Z",
"start_time": "2018-08-31T09:26:20.176332Z"
}
},
"outputs": [],
"source": [
"man_train_list = df_raw.Username.unique()\n",
"man_test_list = df_test.Username.unique()\n",
"\n",
"man_not_in_test = set(man_train_list) - set(man_test_list)\n",
"man_not_in_train = set(man_test_list) - set(man_train_list)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:26:26.642909Z",
"start_time": "2018-08-31T09:26:26.107529Z"
}
},
"outputs": [],
"source": [
"df_raw.drop(index = df_raw.loc[list(man_not_in_test)].index, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:13:41.275773Z",
"start_time": "2018-08-31T10:13:40.996022Z"
}
},
"outputs": [],
"source": [
"model=CatBoostRegressor(iterations=500, learning_rate= 0.06, depth = 8, loss_function='RMSE')"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:15:51.437328Z",
"start_time": "2018-08-31T10:13:42.818180Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 3063.2371640\ttotal: 241ms\tremaining: 2m\n",
"1:\tlearn: 2960.8726708\ttotal: 482ms\tremaining: 1m 59s\n",
"2:\tlearn: 2859.8232707\ttotal: 725ms\tremaining: 2m\n",
"3:\tlearn: 2763.7387297\ttotal: 962ms\tremaining: 1m 59s\n",
"4:\tlearn: 2674.4727651\ttotal: 1.2s\tremaining: 1m 58s\n",
"5:\tlearn: 2593.9050737\ttotal: 1.45s\tremaining: 1m 58s\n",
"6:\tlearn: 2518.4998525\ttotal: 1.68s\tremaining: 1m 58s\n",
"7:\tlearn: 2447.5342017\ttotal: 1.92s\tremaining: 1m 57s\n",
"8:\tlearn: 2377.0639722\ttotal: 2.14s\tremaining: 1m 57s\n",
"9:\tlearn: 2309.8472468\ttotal: 2.36s\tremaining: 1m 55s\n",
"10:\tlearn: 2252.9853105\ttotal: 2.59s\tremaining: 1m 54s\n",
"11:\tlearn: 2201.9552468\ttotal: 2.81s\tremaining: 1m 54s\n",
"12:\tlearn: 2155.0355269\ttotal: 3.05s\tremaining: 1m 54s\n",
"13:\tlearn: 2109.8449006\ttotal: 3.29s\tremaining: 1m 54s\n",
"14:\tlearn: 2063.1868582\ttotal: 3.51s\tremaining: 1m 53s\n",
"15:\tlearn: 2018.2463731\ttotal: 3.74s\tremaining: 1m 53s\n",
"16:\tlearn: 1980.3793067\ttotal: 3.96s\tremaining: 1m 52s\n",
"17:\tlearn: 1941.8133946\ttotal: 4.18s\tremaining: 1m 52s\n",
"18:\tlearn: 1909.5766205\ttotal: 4.4s\tremaining: 1m 51s\n",
"19:\tlearn: 1875.9087949\ttotal: 4.63s\tremaining: 1m 51s\n",
"20:\tlearn: 1842.5364903\ttotal: 4.85s\tremaining: 1m 50s\n",
"21:\tlearn: 1810.7217731\ttotal: 5.08s\tremaining: 1m 50s\n",
"22:\tlearn: 1783.7335448\ttotal: 5.3s\tremaining: 1m 49s\n",
"23:\tlearn: 1752.4532362\ttotal: 5.53s\tremaining: 1m 49s\n",
"24:\tlearn: 1729.7528946\ttotal: 5.76s\tremaining: 1m 49s\n",
"25:\tlearn: 1706.3781921\ttotal: 5.99s\tremaining: 1m 49s\n",
"26:\tlearn: 1680.6953070\ttotal: 6.21s\tremaining: 1m 48s\n",
"27:\tlearn: 1655.2732000\ttotal: 6.44s\tremaining: 1m 48s\n",
"28:\tlearn: 1630.7140227\ttotal: 6.66s\tremaining: 1m 48s\n",
"29:\tlearn: 1614.4246819\ttotal: 6.88s\tremaining: 1m 47s\n",
"30:\tlearn: 1593.9704031\ttotal: 7.14s\tremaining: 1m 48s\n",
"31:\tlearn: 1577.7354516\ttotal: 7.38s\tremaining: 1m 47s\n",
"32:\tlearn: 1556.5865803\ttotal: 7.6s\tremaining: 1m 47s\n",
"33:\tlearn: 1545.4092596\ttotal: 7.83s\tremaining: 1m 47s\n",
"34:\tlearn: 1527.4291772\ttotal: 8.04s\tremaining: 1m 46s\n",
"35:\tlearn: 1512.1750109\ttotal: 8.3s\tremaining: 1m 47s\n",
"36:\tlearn: 1500.5867365\ttotal: 8.57s\tremaining: 1m 47s\n",
"37:\tlearn: 1490.7415966\ttotal: 8.86s\tremaining: 1m 47s\n",
"38:\tlearn: 1476.8908032\ttotal: 9.14s\tremaining: 1m 47s\n",
"39:\tlearn: 1468.1102551\ttotal: 9.41s\tremaining: 1m 48s\n",
"40:\tlearn: 1456.8536919\ttotal: 9.7s\tremaining: 1m 48s\n",
"41:\tlearn: 1446.7406451\ttotal: 9.97s\tremaining: 1m 48s\n",
"42:\tlearn: 1431.9397542\ttotal: 10.2s\tremaining: 1m 48s\n",
"43:\tlearn: 1424.4922563\ttotal: 10.5s\tremaining: 1m 49s\n",
"44:\tlearn: 1408.6396887\ttotal: 10.8s\tremaining: 1m 49s\n",
"45:\tlearn: 1401.5429193\ttotal: 11.1s\tremaining: 1m 49s\n",
"46:\tlearn: 1388.1175988\ttotal: 11.3s\tremaining: 1m 49s\n",
"47:\tlearn: 1378.1062350\ttotal: 11.6s\tremaining: 1m 49s\n",
"48:\tlearn: 1371.2113914\ttotal: 11.8s\tremaining: 1m 48s\n",
"49:\tlearn: 1365.5885524\ttotal: 12s\tremaining: 1m 48s\n",
"50:\tlearn: 1359.1910504\ttotal: 12.3s\tremaining: 1m 48s\n",
"51:\tlearn: 1350.9557096\ttotal: 12.5s\tremaining: 1m 48s\n",
"52:\tlearn: 1345.4958794\ttotal: 12.8s\tremaining: 1m 47s\n",
"53:\tlearn: 1340.4850279\ttotal: 13s\tremaining: 1m 47s\n",
"54:\tlearn: 1329.3792124\ttotal: 13.2s\tremaining: 1m 46s\n",
"55:\tlearn: 1321.5710854\ttotal: 13.4s\tremaining: 1m 46s\n",
"56:\tlearn: 1315.8275091\ttotal: 13.7s\tremaining: 1m 46s\n",
"57:\tlearn: 1312.3330157\ttotal: 13.9s\tremaining: 1m 45s\n",
"58:\tlearn: 1305.4889036\ttotal: 14.1s\tremaining: 1m 45s\n",
"59:\tlearn: 1299.8737494\ttotal: 14.4s\tremaining: 1m 45s\n",
"60:\tlearn: 1293.4323469\ttotal: 14.6s\tremaining: 1m 45s\n",
"61:\tlearn: 1289.7888619\ttotal: 14.8s\tremaining: 1m 44s\n",
"62:\tlearn: 1285.7002114\ttotal: 15.1s\tremaining: 1m 44s\n",
"63:\tlearn: 1281.5030388\ttotal: 15.4s\tremaining: 1m 44s\n",
"64:\tlearn: 1274.4544924\ttotal: 15.7s\tremaining: 1m 44s\n",
"65:\tlearn: 1270.7663378\ttotal: 15.9s\tremaining: 1m 44s\n",
"66:\tlearn: 1264.1172399\ttotal: 16.2s\tremaining: 1m 45s\n",
"67:\tlearn: 1258.6797254\ttotal: 16.5s\tremaining: 1m 44s\n",
"68:\tlearn: 1251.2412341\ttotal: 16.8s\tremaining: 1m 44s\n",
"69:\tlearn: 1246.0455960\ttotal: 17.1s\tremaining: 1m 44s\n",
"70:\tlearn: 1237.9917166\ttotal: 17.3s\tremaining: 1m 44s\n",
"71:\tlearn: 1231.8716965\ttotal: 17.6s\tremaining: 1m 44s\n",
"72:\tlearn: 1227.3734414\ttotal: 17.9s\tremaining: 1m 44s\n",
"73:\tlearn: 1222.5464278\ttotal: 18.2s\tremaining: 1m 44s\n",
"74:\tlearn: 1218.6713565\ttotal: 18.4s\tremaining: 1m 44s\n",
"75:\tlearn: 1210.5758236\ttotal: 18.6s\tremaining: 1m 43s\n",
"76:\tlearn: 1208.5395225\ttotal: 18.9s\tremaining: 1m 43s\n",
"77:\tlearn: 1204.9079939\ttotal: 19.1s\tremaining: 1m 43s\n",
"78:\tlearn: 1200.3180198\ttotal: 19.3s\tremaining: 1m 42s\n",
"79:\tlearn: 1198.9817667\ttotal: 19.6s\tremaining: 1m 42s\n",
"80:\tlearn: 1193.3297295\ttotal: 19.8s\tremaining: 1m 42s\n",
"81:\tlearn: 1188.3144440\ttotal: 20s\tremaining: 1m 42s\n",
"82:\tlearn: 1184.7965892\ttotal: 20.2s\tremaining: 1m 41s\n",
"83:\tlearn: 1179.2173251\ttotal: 20.5s\tremaining: 1m 41s\n",
"84:\tlearn: 1174.7655945\ttotal: 20.7s\tremaining: 1m 41s\n",
"85:\tlearn: 1172.4001541\ttotal: 20.9s\tremaining: 1m 40s\n",
"86:\tlearn: 1168.8771520\ttotal: 21.2s\tremaining: 1m 40s\n",
"87:\tlearn: 1167.2213346\ttotal: 21.4s\tremaining: 1m 40s\n",
"88:\tlearn: 1163.8483743\ttotal: 21.7s\tremaining: 1m 40s\n",
"89:\tlearn: 1159.9279305\ttotal: 21.9s\tremaining: 1m 39s\n",
"90:\tlearn: 1156.3111194\ttotal: 22.2s\tremaining: 1m 39s\n",
"91:\tlearn: 1148.0922752\ttotal: 22.5s\tremaining: 1m 39s\n",
"92:\tlearn: 1146.5745636\ttotal: 22.7s\tremaining: 1m 39s\n",
"93:\tlearn: 1142.6724054\ttotal: 23s\tremaining: 1m 39s\n",
"94:\tlearn: 1141.0029265\ttotal: 23.3s\tremaining: 1m 39s\n",
"95:\tlearn: 1136.9930913\ttotal: 23.6s\tremaining: 1m 39s\n",
"96:\tlearn: 1134.5355230\ttotal: 23.8s\tremaining: 1m 39s\n",
"97:\tlearn: 1131.7039787\ttotal: 24.1s\tremaining: 1m 38s\n",
"98:\tlearn: 1126.9852997\ttotal: 24.4s\tremaining: 1m 38s\n",
"99:\tlearn: 1122.4116532\ttotal: 24.7s\tremaining: 1m 38s\n",
"100:\tlearn: 1118.8205045\ttotal: 24.9s\tremaining: 1m 38s\n",
"101:\tlearn: 1116.9513704\ttotal: 25.2s\tremaining: 1m 38s\n",
"102:\tlearn: 1109.2849838\ttotal: 25.5s\tremaining: 1m 38s\n",
"103:\tlearn: 1108.1590024\ttotal: 25.8s\tremaining: 1m 38s\n",
"104:\tlearn: 1106.5731717\ttotal: 26s\tremaining: 1m 37s\n",
"105:\tlearn: 1106.0268385\ttotal: 26.3s\tremaining: 1m 37s\n",
"106:\tlearn: 1105.3845213\ttotal: 26.5s\tremaining: 1m 37s\n",
"107:\tlearn: 1103.5649036\ttotal: 26.8s\tremaining: 1m 37s\n",
"108:\tlearn: 1097.3003217\ttotal: 27s\tremaining: 1m 36s\n",
"109:\tlearn: 1094.3846386\ttotal: 27.2s\tremaining: 1m 36s\n",
"110:\tlearn: 1092.4441536\ttotal: 27.4s\tremaining: 1m 36s\n",
"111:\tlearn: 1091.1007582\ttotal: 27.7s\tremaining: 1m 35s\n",
"112:\tlearn: 1090.1837888\ttotal: 27.9s\tremaining: 1m 35s\n",
"113:\tlearn: 1089.2574779\ttotal: 28.1s\tremaining: 1m 35s\n",
"114:\tlearn: 1087.6931682\ttotal: 28.4s\tremaining: 1m 34s\n",
"115:\tlearn: 1085.9414680\ttotal: 28.6s\tremaining: 1m 34s\n",
"116:\tlearn: 1081.4751456\ttotal: 28.8s\tremaining: 1m 34s\n",
"117:\tlearn: 1074.4546997\ttotal: 29.1s\tremaining: 1m 34s\n",
"118:\tlearn: 1072.4769458\ttotal: 29.3s\tremaining: 1m 33s\n",
"119:\tlearn: 1068.6691068\ttotal: 29.5s\tremaining: 1m 33s\n",
"120:\tlearn: 1061.7923280\ttotal: 29.8s\tremaining: 1m 33s\n",
"121:\tlearn: 1060.9245520\ttotal: 30.1s\tremaining: 1m 33s\n",
"122:\tlearn: 1059.9465634\ttotal: 30.3s\tremaining: 1m 32s\n",
"123:\tlearn: 1059.9456174\ttotal: 30.4s\tremaining: 1m 32s\n",
"124:\tlearn: 1058.3410714\ttotal: 30.7s\tremaining: 1m 32s\n",
"125:\tlearn: 1051.2972779\ttotal: 31s\tremaining: 1m 32s\n",
"126:\tlearn: 1049.8853857\ttotal: 31.3s\tremaining: 1m 31s\n",
"127:\tlearn: 1045.5041186\ttotal: 31.6s\tremaining: 1m 31s\n",
"128:\tlearn: 1043.4805824\ttotal: 31.8s\tremaining: 1m 31s\n",
"129:\tlearn: 1040.6572685\ttotal: 32.1s\tremaining: 1m 31s\n",
"130:\tlearn: 1034.6188054\ttotal: 32.4s\tremaining: 1m 31s\n",
"131:\tlearn: 1031.0899417\ttotal: 32.7s\tremaining: 1m 31s\n",
"132:\tlearn: 1029.9232118\ttotal: 32.9s\tremaining: 1m 30s\n",
"133:\tlearn: 1028.4953615\ttotal: 33.2s\tremaining: 1m 30s\n",
"134:\tlearn: 1026.8957748\ttotal: 33.5s\tremaining: 1m 30s\n",
"135:\tlearn: 1026.8611494\ttotal: 33.7s\tremaining: 1m 30s\n",
"136:\tlearn: 1025.2347461\ttotal: 34s\tremaining: 1m 30s\n",
"137:\tlearn: 1022.5856273\ttotal: 34.2s\tremaining: 1m 29s\n",
"138:\tlearn: 1020.1729423\ttotal: 34.5s\tremaining: 1m 29s\n",
"139:\tlearn: 1018.8064202\ttotal: 34.7s\tremaining: 1m 29s\n",
"140:\tlearn: 1017.7358998\ttotal: 34.9s\tremaining: 1m 28s\n",
"141:\tlearn: 1015.6226926\ttotal: 35.1s\tremaining: 1m 28s\n",
"142:\tlearn: 1013.5023750\ttotal: 35.4s\tremaining: 1m 28s\n",
"143:\tlearn: 1013.4908655\ttotal: 35.5s\tremaining: 1m 27s\n",
"144:\tlearn: 1010.8018501\ttotal: 35.7s\tremaining: 1m 27s\n",
"145:\tlearn: 1007.8380884\ttotal: 36s\tremaining: 1m 27s\n",
"146:\tlearn: 1007.7185928\ttotal: 36.1s\tremaining: 1m 26s\n",
"147:\tlearn: 1005.3273478\ttotal: 36.4s\tremaining: 1m 26s\n",
"148:\tlearn: 1000.0062962\ttotal: 36.6s\tremaining: 1m 26s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"149:\tlearn: 999.9606264\ttotal: 36.8s\tremaining: 1m 25s\n",
"150:\tlearn: 997.7357668\ttotal: 37s\tremaining: 1m 25s\n",
"151:\tlearn: 992.5900040\ttotal: 37.2s\tremaining: 1m 25s\n",
"152:\tlearn: 991.7642291\ttotal: 37.4s\tremaining: 1m 24s\n",
"153:\tlearn: 991.4635386\ttotal: 37.6s\tremaining: 1m 24s\n",
"154:\tlearn: 989.8268487\ttotal: 37.9s\tremaining: 1m 24s\n",
"155:\tlearn: 989.2420901\ttotal: 38.2s\tremaining: 1m 24s\n",
"156:\tlearn: 987.3081136\ttotal: 38.4s\tremaining: 1m 23s\n",
"157:\tlearn: 986.7190499\ttotal: 38.7s\tremaining: 1m 23s\n",
"158:\tlearn: 985.3585718\ttotal: 39s\tremaining: 1m 23s\n",
"159:\tlearn: 985.0191420\ttotal: 39.3s\tremaining: 1m 23s\n",
"160:\tlearn: 981.9221194\ttotal: 39.5s\tremaining: 1m 23s\n",
"161:\tlearn: 981.4420216\ttotal: 39.8s\tremaining: 1m 23s\n",
"162:\tlearn: 980.7993257\ttotal: 40.1s\tremaining: 1m 22s\n",
"163:\tlearn: 979.4011786\ttotal: 40.3s\tremaining: 1m 22s\n",
"164:\tlearn: 979.3289771\ttotal: 40.6s\tremaining: 1m 22s\n",
"165:\tlearn: 978.9966721\ttotal: 40.9s\tremaining: 1m 22s\n",
"166:\tlearn: 978.2825344\ttotal: 41.2s\tremaining: 1m 22s\n",
"167:\tlearn: 978.1990025\ttotal: 41.4s\tremaining: 1m 21s\n",
"168:\tlearn: 978.1123669\ttotal: 41.6s\tremaining: 1m 21s\n",
"169:\tlearn: 974.9072379\ttotal: 41.9s\tremaining: 1m 21s\n",
"170:\tlearn: 973.2546655\ttotal: 42.2s\tremaining: 1m 21s\n",
"171:\tlearn: 968.6653026\ttotal: 42.5s\tremaining: 1m 20s\n",
"172:\tlearn: 965.1675566\ttotal: 42.7s\tremaining: 1m 20s\n",
"173:\tlearn: 963.6626781\ttotal: 43s\tremaining: 1m 20s\n",
"174:\tlearn: 962.7360495\ttotal: 43.3s\tremaining: 1m 20s\n",
"175:\tlearn: 962.7281514\ttotal: 43.4s\tremaining: 1m 19s\n",
"176:\tlearn: 959.9471949\ttotal: 43.7s\tremaining: 1m 19s\n",
"177:\tlearn: 959.2961580\ttotal: 44s\tremaining: 1m 19s\n",
"178:\tlearn: 955.2740549\ttotal: 44.4s\tremaining: 1m 19s\n",
"179:\tlearn: 953.8410444\ttotal: 44.7s\tremaining: 1m 19s\n",
"180:\tlearn: 953.3603333\ttotal: 45s\tremaining: 1m 19s\n",
"181:\tlearn: 951.7340452\ttotal: 45.2s\tremaining: 1m 19s\n",
"182:\tlearn: 947.0774181\ttotal: 45.5s\tremaining: 1m 18s\n",
"183:\tlearn: 945.8715540\ttotal: 45.8s\tremaining: 1m 18s\n",
"184:\tlearn: 943.5421918\ttotal: 46.1s\tremaining: 1m 18s\n",
"185:\tlearn: 943.4836069\ttotal: 46.3s\tremaining: 1m 18s\n",
"186:\tlearn: 942.6346337\ttotal: 46.6s\tremaining: 1m 18s\n",
"187:\tlearn: 941.3982365\ttotal: 46.8s\tremaining: 1m 17s\n",
"188:\tlearn: 939.5919895\ttotal: 47.1s\tremaining: 1m 17s\n",
"189:\tlearn: 935.3826764\ttotal: 47.3s\tremaining: 1m 17s\n",
"190:\tlearn: 933.0288560\ttotal: 47.5s\tremaining: 1m 16s\n",
"191:\tlearn: 932.9810286\ttotal: 47.7s\tremaining: 1m 16s\n",
"192:\tlearn: 931.7432984\ttotal: 47.9s\tremaining: 1m 16s\n",
"193:\tlearn: 930.2959789\ttotal: 48.1s\tremaining: 1m 15s\n",
"194:\tlearn: 926.0605251\ttotal: 48.3s\tremaining: 1m 15s\n",
"195:\tlearn: 925.6656904\ttotal: 48.5s\tremaining: 1m 15s\n",
"196:\tlearn: 922.9097757\ttotal: 48.8s\tremaining: 1m 15s\n",
"197:\tlearn: 922.8134588\ttotal: 48.9s\tremaining: 1m 14s\n",
"198:\tlearn: 919.8295041\ttotal: 49.1s\tremaining: 1m 14s\n",
"199:\tlearn: 918.6970301\ttotal: 49.3s\tremaining: 1m 13s\n",
"200:\tlearn: 913.5940386\ttotal: 49.5s\tremaining: 1m 13s\n",
"201:\tlearn: 913.5831380\ttotal: 49.6s\tremaining: 1m 13s\n",
"202:\tlearn: 913.0023541\ttotal: 49.9s\tremaining: 1m 12s\n",
"203:\tlearn: 910.4114667\ttotal: 50.1s\tremaining: 1m 12s\n",
"204:\tlearn: 906.4983096\ttotal: 50.4s\tremaining: 1m 12s\n",
"205:\tlearn: 905.3112278\ttotal: 50.6s\tremaining: 1m 12s\n",
"206:\tlearn: 905.0169408\ttotal: 50.9s\tremaining: 1m 12s\n",
"207:\tlearn: 903.1707354\ttotal: 51.1s\tremaining: 1m 11s\n",
"208:\tlearn: 903.1527044\ttotal: 51.3s\tremaining: 1m 11s\n",
"209:\tlearn: 902.0290371\ttotal: 51.5s\tremaining: 1m 11s\n",
"210:\tlearn: 900.6167164\ttotal: 51.8s\tremaining: 1m 10s\n",
"211:\tlearn: 897.9693552\ttotal: 52.1s\tremaining: 1m 10s\n",
"212:\tlearn: 897.3718981\ttotal: 52.3s\tremaining: 1m 10s\n",
"213:\tlearn: 893.1908966\ttotal: 52.6s\tremaining: 1m 10s\n",
"214:\tlearn: 890.4804645\ttotal: 52.9s\tremaining: 1m 10s\n",
"215:\tlearn: 888.4615734\ttotal: 53.1s\tremaining: 1m 9s\n",
"216:\tlearn: 885.0487848\ttotal: 53.4s\tremaining: 1m 9s\n",
"217:\tlearn: 885.0187501\ttotal: 53.6s\tremaining: 1m 9s\n",
"218:\tlearn: 884.0080252\ttotal: 53.8s\tremaining: 1m 9s\n",
"219:\tlearn: 881.1484999\ttotal: 54s\tremaining: 1m 8s\n",
"220:\tlearn: 880.3085053\ttotal: 54.2s\tremaining: 1m 8s\n",
"221:\tlearn: 878.7649068\ttotal: 54.4s\tremaining: 1m 8s\n",
"222:\tlearn: 877.2014133\ttotal: 54.7s\tremaining: 1m 7s\n",
"223:\tlearn: 875.4985617\ttotal: 54.9s\tremaining: 1m 7s\n",
"224:\tlearn: 875.1659578\ttotal: 55.1s\tremaining: 1m 7s\n",
"225:\tlearn: 873.6639670\ttotal: 55.3s\tremaining: 1m 7s\n",
"226:\tlearn: 871.9225615\ttotal: 55.5s\tremaining: 1m 6s\n",
"227:\tlearn: 870.3859520\ttotal: 55.8s\tremaining: 1m 6s\n",
"228:\tlearn: 869.1560191\ttotal: 56s\tremaining: 1m 6s\n",
"229:\tlearn: 867.8265288\ttotal: 56.2s\tremaining: 1m 5s\n",
"230:\tlearn: 867.0562769\ttotal: 56.4s\tremaining: 1m 5s\n",
"231:\tlearn: 864.5923706\ttotal: 56.7s\tremaining: 1m 5s\n",
"232:\tlearn: 862.7084115\ttotal: 57s\tremaining: 1m 5s\n",
"233:\tlearn: 861.9328079\ttotal: 57.2s\tremaining: 1m 5s\n",
"234:\tlearn: 861.5034162\ttotal: 57.4s\tremaining: 1m 4s\n",
"235:\tlearn: 860.4756942\ttotal: 57.6s\tremaining: 1m 4s\n",
"236:\tlearn: 859.6641813\ttotal: 57.8s\tremaining: 1m 4s\n",
"237:\tlearn: 858.8796723\ttotal: 58.1s\tremaining: 1m 3s\n",
"238:\tlearn: 856.6945270\ttotal: 58.2s\tremaining: 1m 3s\n",
"239:\tlearn: 856.4444410\ttotal: 58.5s\tremaining: 1m 3s\n",
"240:\tlearn: 856.0229468\ttotal: 58.7s\tremaining: 1m 3s\n",
"241:\tlearn: 853.6472092\ttotal: 58.9s\tremaining: 1m 2s\n",
"242:\tlearn: 853.0159442\ttotal: 59.1s\tremaining: 1m 2s\n",
"243:\tlearn: 851.3207202\ttotal: 59.4s\tremaining: 1m 2s\n",
"244:\tlearn: 850.8282239\ttotal: 59.7s\tremaining: 1m 2s\n",
"245:\tlearn: 850.6857697\ttotal: 59.9s\tremaining: 1m 1s\n",
"246:\tlearn: 850.2031416\ttotal: 1m\tremaining: 1m 1s\n",
"247:\tlearn: 849.7407528\ttotal: 1m\tremaining: 1m 1s\n",
"248:\tlearn: 847.4252256\ttotal: 1m\tremaining: 1m 1s\n",
"249:\tlearn: 846.8468295\ttotal: 1m\tremaining: 1m\n",
"250:\tlearn: 845.0603637\ttotal: 1m 1s\tremaining: 1m\n",
"251:\tlearn: 843.5026304\ttotal: 1m 1s\tremaining: 1m\n",
"252:\tlearn: 842.5448558\ttotal: 1m 1s\tremaining: 1m\n",
"253:\tlearn: 841.4135580\ttotal: 1m 2s\tremaining: 1m\n",
"254:\tlearn: 841.1405446\ttotal: 1m 2s\tremaining: 59.9s\n",
"255:\tlearn: 841.0838664\ttotal: 1m 2s\tremaining: 59.6s\n",
"256:\tlearn: 838.6671860\ttotal: 1m 2s\tremaining: 59.4s\n",
"257:\tlearn: 838.0347710\ttotal: 1m 3s\tremaining: 59.2s\n",
"258:\tlearn: 837.1949330\ttotal: 1m 3s\tremaining: 59s\n",
"259:\tlearn: 836.3643697\ttotal: 1m 3s\tremaining: 58.7s\n",
"260:\tlearn: 835.2765737\ttotal: 1m 3s\tremaining: 58.5s\n",
"261:\tlearn: 835.2640811\ttotal: 1m 4s\tremaining: 58.2s\n",
"262:\tlearn: 834.4504443\ttotal: 1m 4s\tremaining: 58s\n",
"263:\tlearn: 833.6251991\ttotal: 1m 4s\tremaining: 57.7s\n",
"264:\tlearn: 831.1171839\ttotal: 1m 4s\tremaining: 57.5s\n",
"265:\tlearn: 829.8937156\ttotal: 1m 5s\tremaining: 57.3s\n",
"266:\tlearn: 829.6511155\ttotal: 1m 5s\tremaining: 57.1s\n",
"267:\tlearn: 829.1017726\ttotal: 1m 5s\tremaining: 56.8s\n",
"268:\tlearn: 828.3125841\ttotal: 1m 5s\tremaining: 56.6s\n",
"269:\tlearn: 826.8324490\ttotal: 1m 6s\tremaining: 56.4s\n",
"270:\tlearn: 825.0727420\ttotal: 1m 6s\tremaining: 56.2s\n",
"271:\tlearn: 823.8898608\ttotal: 1m 6s\tremaining: 55.9s\n",
"272:\tlearn: 823.3300078\ttotal: 1m 7s\tremaining: 55.7s\n",
"273:\tlearn: 822.3887549\ttotal: 1m 7s\tremaining: 55.5s\n",
"274:\tlearn: 821.0498375\ttotal: 1m 7s\tremaining: 55.2s\n",
"275:\tlearn: 820.4431865\ttotal: 1m 7s\tremaining: 54.9s\n",
"276:\tlearn: 819.1382485\ttotal: 1m 7s\tremaining: 54.7s\n",
"277:\tlearn: 817.0163142\ttotal: 1m 8s\tremaining: 54.4s\n",
"278:\tlearn: 815.8609074\ttotal: 1m 8s\tremaining: 54.1s\n",
"279:\tlearn: 815.1604222\ttotal: 1m 8s\tremaining: 53.9s\n",
"280:\tlearn: 813.0593184\ttotal: 1m 8s\tremaining: 53.6s\n",
"281:\tlearn: 811.6386339\ttotal: 1m 9s\tremaining: 53.3s\n",
"282:\tlearn: 811.2995560\ttotal: 1m 9s\tremaining: 53.1s\n",
"283:\tlearn: 808.7355966\ttotal: 1m 9s\tremaining: 52.8s\n",
"284:\tlearn: 807.5639631\ttotal: 1m 9s\tremaining: 52.6s\n",
"285:\tlearn: 806.8836833\ttotal: 1m 9s\tremaining: 52.3s\n",
"286:\tlearn: 805.7218120\ttotal: 1m 10s\tremaining: 52s\n",
"287:\tlearn: 805.7087099\ttotal: 1m 10s\tremaining: 51.7s\n",
"288:\tlearn: 804.4081444\ttotal: 1m 10s\tremaining: 51.4s\n",
"289:\tlearn: 803.9249634\ttotal: 1m 10s\tremaining: 51.2s\n",
"290:\tlearn: 803.4127689\ttotal: 1m 10s\tremaining: 51s\n",
"291:\tlearn: 803.1018264\ttotal: 1m 11s\tremaining: 50.7s\n",
"292:\tlearn: 802.2229518\ttotal: 1m 11s\tremaining: 50.5s\n",
"293:\tlearn: 799.6227524\ttotal: 1m 11s\tremaining: 50.3s\n",
"294:\tlearn: 797.6970062\ttotal: 1m 12s\tremaining: 50s\n",
"295:\tlearn: 797.5217486\ttotal: 1m 12s\tremaining: 49.8s\n",
"296:\tlearn: 796.6043327\ttotal: 1m 12s\tremaining: 49.6s\n",
"297:\tlearn: 795.1304315\ttotal: 1m 12s\tremaining: 49.3s\n",
"298:\tlearn: 795.1192720\ttotal: 1m 12s\tremaining: 49s\n",
"299:\tlearn: 795.0113934\ttotal: 1m 13s\tremaining: 48.8s\n",
"300:\tlearn: 794.6548785\ttotal: 1m 13s\tremaining: 48.6s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"301:\tlearn: 794.4154340\ttotal: 1m 13s\tremaining: 48.4s\n",
"302:\tlearn: 791.8650076\ttotal: 1m 14s\tremaining: 48.1s\n",
"303:\tlearn: 790.4335313\ttotal: 1m 14s\tremaining: 47.9s\n",
"304:\tlearn: 789.3009397\ttotal: 1m 14s\tremaining: 47.7s\n",
"305:\tlearn: 787.7721690\ttotal: 1m 14s\tremaining: 47.5s\n",
"306:\tlearn: 786.9612054\ttotal: 1m 15s\tremaining: 47.3s\n",
"307:\tlearn: 786.4413806\ttotal: 1m 15s\tremaining: 47.1s\n",
"308:\tlearn: 785.6216395\ttotal: 1m 15s\tremaining: 46.9s\n",
"309:\tlearn: 785.4690526\ttotal: 1m 16s\tremaining: 46.7s\n",
"310:\tlearn: 784.7599126\ttotal: 1m 16s\tremaining: 46.4s\n",
"311:\tlearn: 783.8655334\ttotal: 1m 16s\tremaining: 46.2s\n",
"312:\tlearn: 782.7737148\ttotal: 1m 16s\tremaining: 46s\n",
"313:\tlearn: 780.5268743\ttotal: 1m 17s\tremaining: 45.7s\n",
"314:\tlearn: 779.6913249\ttotal: 1m 17s\tremaining: 45.5s\n",
"315:\tlearn: 779.6776446\ttotal: 1m 17s\tremaining: 45.2s\n",
"316:\tlearn: 776.4204172\ttotal: 1m 17s\tremaining: 44.9s\n",
"317:\tlearn: 776.1585568\ttotal: 1m 18s\tremaining: 44.7s\n",
"318:\tlearn: 774.2847859\ttotal: 1m 18s\tremaining: 44.4s\n",
"319:\tlearn: 774.2826136\ttotal: 1m 18s\tremaining: 44.1s\n",
"320:\tlearn: 773.5678847\ttotal: 1m 18s\tremaining: 43.8s\n",
"321:\tlearn: 773.1950142\ttotal: 1m 18s\tremaining: 43.6s\n",
"322:\tlearn: 772.5131238\ttotal: 1m 19s\tremaining: 43.3s\n",
"323:\tlearn: 772.5107229\ttotal: 1m 19s\tremaining: 43s\n",
"324:\tlearn: 771.8116079\ttotal: 1m 19s\tremaining: 42.7s\n",
"325:\tlearn: 770.5739075\ttotal: 1m 19s\tremaining: 42.5s\n",
"326:\tlearn: 770.4987790\ttotal: 1m 19s\tremaining: 42.2s\n",
"327:\tlearn: 769.9167564\ttotal: 1m 19s\tremaining: 41.9s\n",
"328:\tlearn: 769.1093489\ttotal: 1m 20s\tremaining: 41.7s\n",
"329:\tlearn: 767.8221222\ttotal: 1m 20s\tremaining: 41.4s\n",
"330:\tlearn: 767.0687205\ttotal: 1m 20s\tremaining: 41.2s\n",
"331:\tlearn: 766.4929799\ttotal: 1m 20s\tremaining: 40.9s\n",
"332:\tlearn: 766.2006454\ttotal: 1m 21s\tremaining: 40.7s\n",
"333:\tlearn: 764.6423362\ttotal: 1m 21s\tremaining: 40.5s\n",
"334:\tlearn: 763.4328542\ttotal: 1m 21s\tremaining: 40.2s\n",
"335:\tlearn: 763.4314758\ttotal: 1m 21s\tremaining: 39.9s\n",
"336:\tlearn: 763.3161117\ttotal: 1m 22s\tremaining: 39.7s\n",
"337:\tlearn: 762.4746422\ttotal: 1m 22s\tremaining: 39.5s\n",
"338:\tlearn: 761.3450132\ttotal: 1m 22s\tremaining: 39.2s\n",
"339:\tlearn: 761.3435779\ttotal: 1m 22s\tremaining: 38.9s\n",
"340:\tlearn: 760.3540810\ttotal: 1m 23s\tremaining: 38.7s\n",
"341:\tlearn: 760.3524253\ttotal: 1m 23s\tremaining: 38.4s\n",
"342:\tlearn: 759.3497922\ttotal: 1m 23s\tremaining: 38.2s\n",
"343:\tlearn: 758.6646801\ttotal: 1m 23s\tremaining: 38s\n",
"344:\tlearn: 757.0072613\ttotal: 1m 23s\tremaining: 37.7s\n",
"345:\tlearn: 755.9663399\ttotal: 1m 24s\tremaining: 37.5s\n",
"346:\tlearn: 755.8308477\ttotal: 1m 24s\tremaining: 37.3s\n",
"347:\tlearn: 755.7380565\ttotal: 1m 24s\tremaining: 37s\n",
"348:\tlearn: 754.6844029\ttotal: 1m 25s\tremaining: 36.8s\n",
"349:\tlearn: 754.6842015\ttotal: 1m 25s\tremaining: 36.5s\n",
"350:\tlearn: 752.3372411\ttotal: 1m 25s\tremaining: 36.2s\n",
"351:\tlearn: 752.3360818\ttotal: 1m 25s\tremaining: 36s\n",
"352:\tlearn: 751.1189834\ttotal: 1m 25s\tremaining: 35.7s\n",
"353:\tlearn: 750.6071991\ttotal: 1m 25s\tremaining: 35.4s\n",
"354:\tlearn: 750.6068761\ttotal: 1m 26s\tremaining: 35.2s\n",
"355:\tlearn: 748.4114932\ttotal: 1m 26s\tremaining: 34.9s\n",
"356:\tlearn: 748.4104858\ttotal: 1m 26s\tremaining: 34.6s\n",
"357:\tlearn: 747.8627869\ttotal: 1m 26s\tremaining: 34.4s\n",
"358:\tlearn: 747.3041333\ttotal: 1m 26s\tremaining: 34.1s\n",
"359:\tlearn: 747.2448037\ttotal: 1m 27s\tremaining: 33.9s\n",
"360:\tlearn: 745.7153666\ttotal: 1m 27s\tremaining: 33.6s\n",
"361:\tlearn: 744.5484235\ttotal: 1m 27s\tremaining: 33.4s\n",
"362:\tlearn: 744.2973117\ttotal: 1m 27s\tremaining: 33.1s\n",
"363:\tlearn: 743.6131041\ttotal: 1m 27s\tremaining: 32.9s\n",
"364:\tlearn: 743.3009723\ttotal: 1m 28s\tremaining: 32.6s\n",
"365:\tlearn: 742.5127480\ttotal: 1m 28s\tremaining: 32.4s\n",
"366:\tlearn: 741.3479193\ttotal: 1m 28s\tremaining: 32.1s\n",
"367:\tlearn: 740.6784681\ttotal: 1m 28s\tremaining: 31.9s\n",
"368:\tlearn: 740.1043301\ttotal: 1m 29s\tremaining: 31.6s\n",
"369:\tlearn: 738.5934180\ttotal: 1m 29s\tremaining: 31.4s\n",
"370:\tlearn: 737.6052952\ttotal: 1m 29s\tremaining: 31.2s\n",
"371:\tlearn: 736.7764167\ttotal: 1m 29s\tremaining: 30.9s\n",
"372:\tlearn: 736.1445414\ttotal: 1m 30s\tremaining: 30.7s\n",
"373:\tlearn: 735.5051499\ttotal: 1m 30s\tremaining: 30.5s\n",
"374:\tlearn: 735.2123058\ttotal: 1m 30s\tremaining: 30.2s\n",
"375:\tlearn: 732.6481962\ttotal: 1m 30s\tremaining: 30s\n",
"376:\tlearn: 732.4242743\ttotal: 1m 31s\tremaining: 29.8s\n",
"377:\tlearn: 731.8573875\ttotal: 1m 31s\tremaining: 29.5s\n",
"378:\tlearn: 731.8492460\ttotal: 1m 31s\tremaining: 29.3s\n",
"379:\tlearn: 731.6684530\ttotal: 1m 31s\tremaining: 29s\n",
"380:\tlearn: 730.8704621\ttotal: 1m 32s\tremaining: 28.8s\n",
"381:\tlearn: 730.1708005\ttotal: 1m 32s\tremaining: 28.6s\n",
"382:\tlearn: 728.5928838\ttotal: 1m 32s\tremaining: 28.3s\n",
"383:\tlearn: 728.5688475\ttotal: 1m 33s\tremaining: 28.1s\n",
"384:\tlearn: 727.1116774\ttotal: 1m 33s\tremaining: 27.9s\n",
"385:\tlearn: 725.5070297\ttotal: 1m 33s\tremaining: 27.6s\n",
"386:\tlearn: 723.9825808\ttotal: 1m 33s\tremaining: 27.4s\n",
"387:\tlearn: 721.9312717\ttotal: 1m 33s\tremaining: 27.1s\n",
"388:\tlearn: 720.9438560\ttotal: 1m 34s\tremaining: 26.9s\n",
"389:\tlearn: 718.8186671\ttotal: 1m 34s\tremaining: 26.6s\n",
"390:\tlearn: 716.3172774\ttotal: 1m 34s\tremaining: 26.4s\n",
"391:\tlearn: 714.4640373\ttotal: 1m 34s\tremaining: 26.1s\n",
"392:\tlearn: 713.3978220\ttotal: 1m 35s\tremaining: 25.9s\n",
"393:\tlearn: 712.3299140\ttotal: 1m 35s\tremaining: 25.6s\n",
"394:\tlearn: 712.1237737\ttotal: 1m 35s\tremaining: 25.4s\n",
"395:\tlearn: 711.8743598\ttotal: 1m 35s\tremaining: 25.1s\n",
"396:\tlearn: 711.6651248\ttotal: 1m 35s\tremaining: 24.9s\n",
"397:\tlearn: 711.5190483\ttotal: 1m 36s\tremaining: 24.6s\n",
"398:\tlearn: 710.9531075\ttotal: 1m 36s\tremaining: 24.4s\n",
"399:\tlearn: 709.9624974\ttotal: 1m 36s\tremaining: 24.1s\n",
"400:\tlearn: 709.8192664\ttotal: 1m 36s\tremaining: 23.9s\n",
"401:\tlearn: 709.1007244\ttotal: 1m 37s\tremaining: 23.7s\n",
"402:\tlearn: 708.8538420\ttotal: 1m 37s\tremaining: 23.4s\n",
"403:\tlearn: 708.5390036\ttotal: 1m 37s\tremaining: 23.2s\n",
"404:\tlearn: 707.5683963\ttotal: 1m 37s\tremaining: 23s\n",
"405:\tlearn: 706.8190609\ttotal: 1m 38s\tremaining: 22.7s\n",
"406:\tlearn: 706.2547025\ttotal: 1m 38s\tremaining: 22.5s\n",
"407:\tlearn: 705.3271781\ttotal: 1m 38s\tremaining: 22.3s\n",
"408:\tlearn: 702.7091699\ttotal: 1m 38s\tremaining: 22s\n",
"409:\tlearn: 701.1335801\ttotal: 1m 39s\tremaining: 21.8s\n",
"410:\tlearn: 700.3531136\ttotal: 1m 39s\tremaining: 21.5s\n",
"411:\tlearn: 699.3498539\ttotal: 1m 39s\tremaining: 21.3s\n",
"412:\tlearn: 698.8914831\ttotal: 1m 40s\tremaining: 21.1s\n",
"413:\tlearn: 698.5826648\ttotal: 1m 40s\tremaining: 20.8s\n",
"414:\tlearn: 698.2652115\ttotal: 1m 40s\tremaining: 20.6s\n",
"415:\tlearn: 697.4302264\ttotal: 1m 40s\tremaining: 20.4s\n",
"416:\tlearn: 696.0684946\ttotal: 1m 41s\tremaining: 20.1s\n",
"417:\tlearn: 695.8898608\ttotal: 1m 41s\tremaining: 19.9s\n",
"418:\tlearn: 695.8591374\ttotal: 1m 41s\tremaining: 19.6s\n",
"419:\tlearn: 695.0255113\ttotal: 1m 41s\tremaining: 19.4s\n",
"420:\tlearn: 693.4513403\ttotal: 1m 42s\tremaining: 19.2s\n",
"421:\tlearn: 692.2110455\ttotal: 1m 42s\tremaining: 18.9s\n",
"422:\tlearn: 692.0232016\ttotal: 1m 42s\tremaining: 18.7s\n",
"423:\tlearn: 690.7677299\ttotal: 1m 42s\tremaining: 18.5s\n",
"424:\tlearn: 689.1396679\ttotal: 1m 43s\tremaining: 18.2s\n",
"425:\tlearn: 688.7648203\ttotal: 1m 43s\tremaining: 18s\n",
"426:\tlearn: 688.1189539\ttotal: 1m 43s\tremaining: 17.7s\n",
"427:\tlearn: 687.5303484\ttotal: 1m 44s\tremaining: 17.5s\n",
"428:\tlearn: 686.9117608\ttotal: 1m 44s\tremaining: 17.3s\n",
"429:\tlearn: 686.5077369\ttotal: 1m 44s\tremaining: 17s\n",
"430:\tlearn: 686.0521128\ttotal: 1m 44s\tremaining: 16.8s\n",
"431:\tlearn: 684.5345503\ttotal: 1m 45s\tremaining: 16.5s\n",
"432:\tlearn: 683.2911165\ttotal: 1m 45s\tremaining: 16.3s\n",
"433:\tlearn: 682.6764248\ttotal: 1m 45s\tremaining: 16.1s\n",
"434:\tlearn: 682.0506552\ttotal: 1m 45s\tremaining: 15.8s\n",
"435:\tlearn: 681.8755665\ttotal: 1m 46s\tremaining: 15.6s\n",
"436:\tlearn: 680.8694859\ttotal: 1m 46s\tremaining: 15.3s\n",
"437:\tlearn: 680.3207150\ttotal: 1m 46s\tremaining: 15.1s\n",
"438:\tlearn: 679.9425685\ttotal: 1m 46s\tremaining: 14.8s\n",
"439:\tlearn: 679.1157861\ttotal: 1m 46s\tremaining: 14.6s\n",
"440:\tlearn: 678.2798787\ttotal: 1m 47s\tremaining: 14.3s\n",
"441:\tlearn: 677.2959838\ttotal: 1m 47s\tremaining: 14.1s\n",
"442:\tlearn: 675.9050066\ttotal: 1m 47s\tremaining: 13.9s\n",
"443:\tlearn: 674.7484282\ttotal: 1m 47s\tremaining: 13.6s\n",
"444:\tlearn: 673.5700114\ttotal: 1m 48s\tremaining: 13.4s\n",
"445:\tlearn: 672.8481826\ttotal: 1m 48s\tremaining: 13.1s\n",
"446:\tlearn: 672.6932383\ttotal: 1m 48s\tremaining: 12.9s\n",
"447:\tlearn: 672.0764158\ttotal: 1m 48s\tremaining: 12.6s\n",
"448:\tlearn: 670.8348349\ttotal: 1m 49s\tremaining: 12.4s\n",
"449:\tlearn: 669.8428709\ttotal: 1m 49s\tremaining: 12.1s\n",
"450:\tlearn: 668.9224196\ttotal: 1m 49s\tremaining: 11.9s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"451:\tlearn: 668.7162450\ttotal: 1m 49s\tremaining: 11.7s\n",
"452:\tlearn: 667.6379633\ttotal: 1m 50s\tremaining: 11.4s\n",
"453:\tlearn: 667.2526721\ttotal: 1m 50s\tremaining: 11.2s\n",
"454:\tlearn: 666.8589603\ttotal: 1m 50s\tremaining: 10.9s\n",
"455:\tlearn: 665.6129627\ttotal: 1m 50s\tremaining: 10.7s\n",
"456:\tlearn: 665.4283978\ttotal: 1m 51s\tremaining: 10.5s\n",
"457:\tlearn: 664.9031034\ttotal: 1m 51s\tremaining: 10.2s\n",
"458:\tlearn: 664.4429432\ttotal: 1m 51s\tremaining: 9.97s\n",
"459:\tlearn: 662.7554730\ttotal: 1m 51s\tremaining: 9.73s\n",
"460:\tlearn: 661.7949838\ttotal: 1m 52s\tremaining: 9.49s\n",
"461:\tlearn: 661.1719274\ttotal: 1m 52s\tremaining: 9.24s\n",
"462:\tlearn: 661.0261829\ttotal: 1m 52s\tremaining: 9s\n",
"463:\tlearn: 660.4675751\ttotal: 1m 52s\tremaining: 8.76s\n",
"464:\tlearn: 659.2579658\ttotal: 1m 53s\tremaining: 8.52s\n",
"465:\tlearn: 658.8995971\ttotal: 1m 53s\tremaining: 8.28s\n",
"466:\tlearn: 658.3467436\ttotal: 1m 53s\tremaining: 8.04s\n",
"467:\tlearn: 658.0117440\ttotal: 1m 53s\tremaining: 7.79s\n",
"468:\tlearn: 656.8870599\ttotal: 1m 54s\tremaining: 7.55s\n",
"469:\tlearn: 656.5677388\ttotal: 1m 54s\tremaining: 7.31s\n",
"470:\tlearn: 655.9556828\ttotal: 1m 54s\tremaining: 7.07s\n",
"471:\tlearn: 654.5686416\ttotal: 1m 55s\tremaining: 6.82s\n",
"472:\tlearn: 654.4495835\ttotal: 1m 55s\tremaining: 6.58s\n",
"473:\tlearn: 653.8103551\ttotal: 1m 55s\tremaining: 6.34s\n",
"474:\tlearn: 652.6816129\ttotal: 1m 55s\tremaining: 6.09s\n",
"475:\tlearn: 651.3429969\ttotal: 1m 56s\tremaining: 5.85s\n",
"476:\tlearn: 650.8938832\ttotal: 1m 56s\tremaining: 5.61s\n",
"477:\tlearn: 649.9794305\ttotal: 1m 56s\tremaining: 5.37s\n",
"478:\tlearn: 649.6059601\ttotal: 1m 56s\tremaining: 5.12s\n",
"479:\tlearn: 649.4023308\ttotal: 1m 57s\tremaining: 4.88s\n",
"480:\tlearn: 648.4227969\ttotal: 1m 57s\tremaining: 4.64s\n",
"481:\tlearn: 646.8437027\ttotal: 1m 57s\tremaining: 4.39s\n",
"482:\tlearn: 646.4760202\ttotal: 1m 57s\tremaining: 4.15s\n",
"483:\tlearn: 646.2949553\ttotal: 1m 58s\tremaining: 3.9s\n",
"484:\tlearn: 645.9410739\ttotal: 1m 58s\tremaining: 3.66s\n",
"485:\tlearn: 644.3585616\ttotal: 1m 58s\tremaining: 3.41s\n",
"486:\tlearn: 643.7781365\ttotal: 1m 58s\tremaining: 3.17s\n",
"487:\tlearn: 643.4312812\ttotal: 1m 58s\tremaining: 2.92s\n",
"488:\tlearn: 642.9842251\ttotal: 1m 59s\tremaining: 2.68s\n",
"489:\tlearn: 642.7216593\ttotal: 1m 59s\tremaining: 2.44s\n",
"490:\tlearn: 642.5586526\ttotal: 1m 59s\tremaining: 2.19s\n",
"491:\tlearn: 642.4175068\ttotal: 1m 59s\tremaining: 1.95s\n",
"492:\tlearn: 642.1007069\ttotal: 2m\tremaining: 1.7s\n",
"493:\tlearn: 641.8468232\ttotal: 2m\tremaining: 1.46s\n",
"494:\tlearn: 641.2739622\ttotal: 2m\tremaining: 1.22s\n",
"495:\tlearn: 640.4798015\ttotal: 2m\tremaining: 974ms\n",
"496:\tlearn: 640.1637081\ttotal: 2m\tremaining: 730ms\n",
"497:\tlearn: 638.4174588\ttotal: 2m 1s\tremaining: 487ms\n",
"498:\tlearn: 637.8842430\ttotal: 2m 1s\tremaining: 244ms\n",
"499:\tlearn: 637.4574442\ttotal: 2m 1s\tremaining: 0us\n"
]
},
{
"data": {
"text/plain": [
"<catboost.core.CatBoostRegressor at 0x1ff001b8630>"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(df_raw, target)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:24:28.846104Z",
"start_time": "2018-08-31T10:24:27.627383Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 190.38626, 141.29875, 62.31854, 4.08713, 291.0547 , 36.24614, 16.04935, 76.81639,\n",
" 63.33431, 24.40927])"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = model.predict(df_test) - 1;\n",
"preds[:10]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:24:32.356890Z",
"start_time": "2018-08-31T10:24:31.973419Z"
}
},
"outputs": [],
"source": [
"submit = make_submission(preds)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:24:32.942765Z",
"start_time": "2018-08-31T10:24:32.356890Z"
}
},
"outputs": [],
"source": [
"submit.to_csv(f'{PATH}\\\\Adi_catboost_with rf_feats_310818.csv', index=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RF"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:44:57.780012Z",
"start_time": "2018-08-31T09:44:57.508442Z"
}
},
"outputs": [],
"source": [
"def rmse(x,y): return math.sqrt(((x-y)**2).mean())\n",
"\n",
"def print_score(m):\n",
" res = ['RMSLE X_train', rmse(m.predict(X_train), y_train), '\\n RMSLE X_valid', rmse(m.predict(X_valid), y_valid),\n",
" '\\n R**2 Train',m.score(X_train, y_train), '\\n R**2 Valid', m.score(X_valid, y_valid)]\n",
" if hasattr(m, 'oob_score_'): res.append(['\\n OOB_Score', m.oob_score_])\n",
" print(res)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:45:21.773635Z",
"start_time": "2018-08-31T09:45:21.507978Z"
}
},
"outputs": [],
"source": [
"target = df_raw.target"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:45:53.058554Z",
"start_time": "2018-08-31T09:45:52.756985Z"
}
},
"outputs": [],
"source": [
"df_raw.drop('target', axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:56:40.077108Z",
"start_time": "2018-08-31T09:56:39.759730Z"
}
},
"outputs": [],
"source": [
"df_raw.drop('Username', axis=1,inplace=True)\n",
"df_test.drop('Username', axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:56:42.809810Z",
"start_time": "2018-08-31T09:56:42.456754Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"((203657, 27), (203657,), (30000, 27))"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_valid, y_train, y_valid = train_test_split(df_raw, target, test_size=0.2, random_state=42)\n",
"\n",
"def split_vals(a,n): return a[:n].copy(), a[n:].copy()\n",
"\n",
"n_valid = 30000\n",
"n_trn = len(df_raw)-n_valid\n",
"raw_train, raw_valid = split_vals(df_raw, n_trn)\n",
"X_train, X_valid = split_vals(df_raw, n_trn)\n",
"y_train, y_valid = split_vals(target, n_trn)\n",
"\n",
"X_train.shape, y_train.shape, X_valid.shape"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:01:57.194446Z",
"start_time": "2018-08-31T10:01:56.897617Z"
}
},
"outputs": [],
"source": [
"df_raw.drop(['Reputation', 'Answers', 'Views'], axis=1, inplace=True)\n",
"df_test.drop(['Reputation', 'Answers', 'Views'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:02:07.599467Z",
"start_time": "2018-08-31T10:01:59.013493Z"
}
},
"outputs": [],
"source": [
"m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True, max_depth= 8)\n",
"m.fit(X_train, y_train)\n",
"print_score(m)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T09:58:27.509418Z",
"start_time": "2018-08-31T09:58:27.201243Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Reputation</th>\n",
" <th>Answers</th>\n",
" <th>Views</th>\n",
" <th>agg_count</th>\n",
" <th>agg_answers</th>\n",
" <th>agg_views</th>\n",
" <th>agg_repo</th>\n",
" <th>log_trans_Reputation</th>\n",
" <th>log_trans_Answers</th>\n",
" <th>log_trans_Username</th>\n",
" <th>...</th>\n",
" <th>tag_0.0</th>\n",
" <th>tag_1.0</th>\n",
" <th>tag_2.0</th>\n",
" <th>tag_3.0</th>\n",
" <th>tag_4.0</th>\n",
" <th>tag_5.0</th>\n",
" <th>tag_6.0</th>\n",
" <th>tag_7.0</th>\n",
" <th>tag_8.0</th>\n",
" <th>tag_9.0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4271.0</td>\n",
" <td>4.0</td>\n",
" <td>13986.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>20598.0</td>\n",
" <td>4271.0</td>\n",
" <td>8.359838</td>\n",
" <td>1.609438</td>\n",
" <td>11.628252</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2269.0</td>\n",
" <td>2.0</td>\n",
" <td>312.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>4168.0</td>\n",
" <td>2269.0</td>\n",
" <td>7.727535</td>\n",
" <td>1.098612</td>\n",
" <td>10.908229</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>111.0</td>\n",
" <td>2.0</td>\n",
" <td>53738.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>53738.0</td>\n",
" <td>111.0</td>\n",
" <td>4.718499</td>\n",
" <td>1.098612</td>\n",
" <td>12.060625</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>7952.0</td>\n",
" <td>2.0</td>\n",
" <td>29191.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>28314.0</td>\n",
" <td>7952.0</td>\n",
" <td>8.981304</td>\n",
" <td>1.098612</td>\n",
" <td>11.037403</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>731.0</td>\n",
" <td>4.0</td>\n",
" <td>5622.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>6730.0</td>\n",
" <td>731.0</td>\n",
" <td>6.595780</td>\n",
" <td>1.609438</td>\n",
" <td>10.681894</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" Reputation Answers Views agg_count agg_answers agg_views agg_repo \\\n",
"4 4271.0 4.0 13986.0 3.0 2.0 20598.0 4271.0 \n",
"7 2269.0 2.0 312.0 7.0 2.0 4168.0 2269.0 \n",
"8 111.0 2.0 53738.0 1.0 2.0 53738.0 111.0 \n",
"15 7952.0 2.0 29191.0 4.0 3.0 28314.0 7952.0 \n",
"16 731.0 4.0 5622.0 2.0 2.0 6730.0 731.0 \n",
"\n",
" log_trans_Reputation log_trans_Answers log_trans_Username ... \\\n",
"4 8.359838 1.609438 11.628252 ... \n",
"7 7.727535 1.098612 10.908229 ... \n",
"8 4.718499 1.098612 12.060625 ... \n",
"15 8.981304 1.098612 11.037403 ... \n",
"16 6.595780 1.609438 10.681894 ... \n",
"\n",
" tag_0.0 tag_1.0 tag_2.0 tag_3.0 tag_4.0 tag_5.0 tag_6.0 tag_7.0 \\\n",
"4 0 1 0 0 0 0 0 0 \n",
"7 0 0 0 0 1 0 0 0 \n",
"8 0 0 0 0 1 0 0 0 \n",
"15 0 1 0 0 0 0 0 0 \n",
"16 0 0 0 1 0 0 0 0 \n",
"\n",
" tag_8.0 tag_9.0 \n",
"4 0 0 \n",
"7 0 0 \n",
"8 0 0 \n",
"15 0 0 \n",
"16 0 0 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_raw.head()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:01:04.278876Z",
"start_time": "2018-08-31T10:01:04.084758Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Reputation', 'Answers', 'Views', 'agg_count', 'agg_answers',\n",
" 'agg_views', 'agg_repo', 'log_trans_Reputation', 'log_trans_Answers',\n",
" 'log_trans_Username', 'log_trans_Views', 'log_trans_agg_count',\n",
" 'log_trans_agg_answers', 'log_trans_agg_views', 'log_trans_agg_repo',\n",
" 'repo_per_Answers', 'repo_per_Views', 'tag_0.0', 'tag_1.0', 'tag_2.0',\n",
" 'tag_3.0', 'tag_4.0', 'tag_5.0', 'tag_6.0', 'tag_7.0', 'tag_8.0',\n",
" 'tag_9.0'],\n",
" dtype='object')"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_raw.columns"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"ExecuteTime": {
"end_time": "2018-08-31T10:00:05.322913Z",
"start_time": "2018-08-31T09:59:58.725959Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEKCAYAAAAxXHOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nceibmWXI/wfIGMQ5/sAp/gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA+137zazCK54AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAELCAYAAAD6AKALAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAH9pJREFUeJzt3X+U3HV97/Hna3ezG5JAgpsVEYgJ\nEm0XtVSWRGulFioJ2hruFdtA7wUtLbWF29tDPZfkqK0F6zmpt031AioeUIqXhpReS46iEQV/VgMb\nwUACgU1AiKGQkB+QBHYzu+/7x/ezyWSY2Znd/Sy7sq/HOTk785nP5/35zHd/vPKd73e+o4jAzMws\nl6bxXoCZmb2yOFjMzCwrB4uZmWXlYDEzs6wcLGZmlpWDxczMsnKwmJlZVg4WMzPLysFiZmZZtYz3\nAsbD7NmzY+7cueO9DDOzXyrr16/fGREd9fpNymCZO3cu3d3d470MM7NfKpJ+3kg/vxRmZmZZOVjM\nzCwrB4uZmWXlYDEzs6wcLGZmlpWDxczMsnKwmJlZVg4WMzPLysFiZmZZTcp33o/WLeueqNp+4cI5\nL/NKzMwmHu+xmJlZVg4WMzPLqqFgkbRY0mZJPZKWVXm8TdKt6fF1kuaWPbY8tW+WtKheTUnzUo1H\nU83W1H6mpJ9KKkk6v6z/aZJ+LGmjpA2S/mBkm8LMzHKoGyySmoFrgXOBTuACSZ0V3S4BdkfEKcBK\nYEUa2wksBU4FFgPXSWquU3MFsDIi5gO7U22AJ4APArdUzH0AuCgiBuf4J0mzGnv6ZmaWWyN7LAuA\nnojYGhF9wCpgSUWfJcBN6fZtwNmSlNpXRURvRDwG9KR6VWumMWelGqSa5wFExOMRsQEYKJ84Ih6J\niEfT7e3AM0DdzwswM7Ox0UiwnAA8WXZ/W2qr2iciSsBeoH2IsbXa24E9qUatuWqStABoBbY0OsbM\nzPJqJFhUpS0a7JOrvS5JxwM3Ax+KiIEqj18qqVtS944dOxopaWZmI9BIsGwDTiq7fyKwvVYfSS3A\nTGDXEGNrte8EZqUateZ6CUnHAF8HPhYRP6nWJyKuj4iuiOjq6PArZWZmY6WRYLkXmJ/O1mqlOBi/\npqLPGuDidPt84K6IiNS+NJ01Ng+YD9xTq2Yac3eqQap5+1CLS+O/CvxzRPxrA8/HzMzGUN1gScc7\nLgfWAg8BqyNio6SrJL0vdbsBaJfUA1wBLEtjNwKrgU3AN4HLIqK/Vs1U60rgilSrPdVG0hmStgEf\nAL4gabD/7wNnAh+UdH/6d9ootomZmY2Cip2EyaWrqyu6u7tHPN6XdDGzyUjS+ojoqtfP77w3M7Os\nHCxmZpaVg8XMzLJysJiZWVYOFjMzy8rBYmZmWTlYzMwsKweLmZll5WAxM7OsHCxmZpaVg8XMzLJy\nsJiZWVYOFjMzy8rBYmZmWTlYzMwsKweLmZll5WAxM7OsHCxmZpaVg8XMzLJysJiZWVYOFjMzy8rB\nYmZmWTlYzMwsKweLmZll5WAxM7OsGgoWSYslbZbUI2lZlcfbJN2aHl8naW7ZY8tT+2ZJi+rVlDQv\n1Xg01WxN7WdK+qmkkqTzK+a/OPV/VNLFw98MZmaWS91gkdQMXAucC3QCF0jqrOh2CbA7Ik4BVgIr\n0thOYClwKrAYuE5Sc52aK4CVETEf2J1qAzwBfBC4pWJ9rwL+BlgILAD+RtKxjW4AMzPLq5E9lgVA\nT0RsjYg+YBWwpKLPEuCmdPs24GxJSu2rIqI3Ih4DelK9qjXTmLNSDVLN8wAi4vGI2AAMVMy9CLgz\nInZFxG7gTooQMzOzcdBIsJwAPFl2f1tqq9onIkrAXqB9iLG12tuBPalGrblGsj4zM3uZNBIsqtIW\nDfbJ1T6UhsZIulRSt6TuHTt21ClpZmYj1UiwbANOKrt/IrC9Vh9JLcBMYNcQY2u17wRmpRq15hrJ\n+oiI6yOiKyK6Ojo66pQ0M7ORaiRY7gXmp7O1WikOxq+p6LMGGDwb63zgroiI1L40nTU2D5gP3FOr\nZhpzd6pBqnl7nfWtBc6RdGw6aH9OajMzs3FQN1jS8Y7LKf5YPwSsjoiNkq6S9L7U7QagXVIPcAWw\nLI3dCKwGNgHfBC6LiP5aNVOtK4ErUq32VBtJZ0jaBnwA+IKkjWmOXcDVFGF1L3BVajMzs3GgYidh\ncunq6oru7u4Rj79l3RNV2y9cOGfENc3MJjpJ6yOiq14/v/PezMyycrCYmVlWDhYzM8vKwWJmZlk5\nWMzMLCsHi5mZZeVgMTOzrBwsZmaWlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVg\nMTOzrBwsZmaWlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVgMTOzrBwsZmaWVUPB\nImmxpM2SeiQtq/J4m6Rb0+PrJM0te2x5at8saVG9mpLmpRqPppqtQ80haYqkmyQ9IOkhSctHujHM\nzGz06gaLpGbgWuBcoBO4QFJnRbdLgN0RcQqwEliRxnYCS4FTgcXAdZKa69RcAayMiPnA7lS75hzA\nB4C2iHgzcDrwp+XBZmZmL69G9lgWAD0RsTUi+oBVwJKKPkuAm9Lt24CzJSm1r4qI3oh4DOhJ9arW\nTGPOSjVINc+rM0cA0yW1AEcBfcBzDW8BMzPLqpFgOQF4suz+ttRWtU9ElIC9QPsQY2u1twN7Uo3K\nuWrNcRuwH3gKeAL43xGxq4HnZWZmY6CRYFGVtmiwT672oeZYAPQDrwXmAX8l6eTKjpIuldQtqXvH\njh1VSpmZWQ6NBMs24KSy+ycC22v1SS9JzQR2DTG2VvtOYFaqUTlXrTkuBL4ZEQcj4hngR0BX5ZOI\niOsjoisiujo6Ohp42mZmNhKNBMu9wPx0tlYrxcH4NRV91gAXp9vnA3dFRKT2pemMrnnAfOCeWjXT\nmLtTDVLN2+vM8QRwlgrTgbcBDze+CczMLKeWeh0ioiTpcmAt0AzcGBEbJV0FdEfEGuAG4GZJPRR7\nEUvT2I2SVgObgBJwWUT0A1Srmaa8Elgl6ZPAfak2teagOLvsS8CDFC+XfSkiNox4i5iZ2aio+E//\n5NLV1RXd3d0jHn/Luieqtl+4cM6Ia5qZTXSS1kfESw41VPI7783MLCsHi5mZZeVgMTOzrBwsZmaW\nlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVgMTOzrBwsZmaWlYPFzMyycrCYmVlW\nDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVgMTOzrBwsZmaWlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk1\nFCySFkvaLKlH0rIqj7dJujU9vk7S3LLHlqf2zZIW1aspaV6q8Wiq2drAHG+R9GNJGyU9IGnqSDaG\nmZmNXt1gkdQMXAucC3QCF0jqrOh2CbA7Ik4BVgIr0thOYClwKrAYuE5Sc52aK4CVETEf2J1qDzVH\nC/AV4MMRcSrwLuDgMLeDmZll0sgeywKgJyK2RkQfsApYUtFnCXBTun0bcLYkpfZVEdEbEY8BPale\n1ZppzFmpBqnmeXXmOAfYEBE/A4iIZyOiv/FNYGZmOTUSLCcAT5bd35baqvaJiBKwF2gfYmyt9nZg\nT6pROVetOd4AhKS1kn4q6X818JzMzGyMtDTQR1XaosE+tdqrBdpQ/YeaowX4TeAM4ADwHUnrI+I7\nRyxQuhS4FGDOnDlVSpmZWQ6N7LFsA04qu38isL1Wn3TMYyawa4ixtdp3ArNSjcq5hprjexGxMyIO\nAHcAb618EhFxfUR0RURXR0dHA0/bzMxGopFguReYn87WaqU4GL+mos8a4OJ0+3zgroiI1L40ndE1\nD5gP3FOrZhpzd6pBqnl7nTnWAm+RNC0Fzm8BmxrfBGZmllPdl8IioiTpcoo/4M3AjRGxUdJVQHdE\nrAFuAG6W1EOxF7E0jd0oaTXFH/oScNnggfVqNdOUVwKrJH0SuC/VZog5dkv6R4qwCuCOiPj6qLaK\nmZmNmIr/9E8uXV1d0d3dPeLxt6x7omr7hQt97MbMXrnS8euuev38znszM8vKwWJmZlk5WMzMLCsH\ni5mZZeVgMTOzrBwsZmaWlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVgMTOzrBws\nZmaWlYPFzMyycrCYmVlWDhYzM8vKwWJmZlk5WMzMLCsHi5mZZeVgMTOzrBwsZmaWlYPFzMyycrCY\nmVlWDhYzM8uqoWCRtFjSZkk9kpZVebxN0q3p8XWS5pY9tjy1b5a0qF5NSfNSjUdTzdZ6c6TH50ja\nJ+kjw90IZmaWT91gkdQMXAucC3QCF0jqrOh2CbA7Ik4BVgIr0thOYClwKrAYuE5Sc52aK4CVETEf\n2J1q15yjzErgG40+cTMzGxuN7LEsAHoiYmtE9AGrgCUVfZYAN6XbtwFnS1JqXxURvRHxGNCT6lWt\nmcaclWqQap5XZw4knQdsBTY2/tTNzGwsNBIsJwBPlt3fltqq9omIErAXaB9ibK32dmBPqlE5V9U5\nJE0HrgT+dqgnIelSSd2Sunfs2FHnKZuZ2Ug1Eiyq0hYN9snVPtQcf0vx0tm+Ko8f7hhxfUR0RURX\nR0fHUF3NzGwUWhrosw04qez+icD2Gn22SWoBZgK76oyt1r4TmCWpJe2VlPevNcdC4HxJfw/MAgYk\nvRgR1zTw3MzMLLNG9ljuBeans7VaKQ7Gr6noswa4ON0+H7grIiK1L01ndM0D5gP31KqZxtydapBq\n3j7UHBHxzoiYGxFzgX8CPuVQMTMbP3X3WCKiJOlyYC3QDNwYERslXQV0R8Qa4AbgZkk9FHsRS9PY\njZJWA5uAEnBZRPQDVKuZprwSWCXpk8B9qTa15jAzs4lFxU7C5NLV1RXd3d0jHn/Luieqtl+4cM6I\na5qZTXSS1kdEV71+fue9mZll5WAxM7OsHCxmZpaVg8XMzLJysJiZWVYOFjMzy8rBYmZmWTlYzMws\nKweLmZll5WAxM7OsHCxmZpaVg8XMzLJysJiZWVYOFjMzy8rBYmZmWTlYzMwsKweLmZll5WAxM7Os\nHCxmZpaVg8XMzLJysJiZWVYOFjMzy8rBYmZmWTlYzMwsq4aCRdJiSZsl9UhaVuXxNkm3psfXSZpb\n9tjy1L5Z0qJ6NSXNSzUeTTVbh5pD0rslrZf0QPp61kg3hpmZjV7dYJHUDFwLnAt0AhdI6qzodgmw\nOyJOAVYCK9LYTmApcCqwGLhOUnOdmiuAlRExH9idatecA9gJ/F5EvBm4GLh5eJvAzMxyamSPZQHQ\nExFbI6IPWAUsqeizBLgp3b4NOFuSUvuqiOiNiMeAnlSvas005qxUg1TzvKHmiIj7ImJ7at8ITJXU\n1ugGMDOzvBoJlhOAJ8vub0ttVftERAnYC7QPMbZWezuwJ9WonKvWHOXeD9wXEb0NPC8zMxsDLQ30\nUZW2aLBPrfZqgTZU/7rrkHQqxctj51Tph6RLgUsB5syZU62LmZll0MgeyzbgpLL7JwLba/WR1ALM\nBHYNMbZW+05gVqpROVetOZB0IvBV4KKI2FLtSUTE9RHRFRFdHR0dDTztxvWVBrLWMzP7ZdZIsNwL\nzE9na7VSHIxfU9FnDcWBc4DzgbsiIlL70nRG1zxgPnBPrZppzN2pBqnm7UPNIWkW8HVgeUT8aDhP\nPodtuw9w1dc28vNn97/cU5uZTUh1gyUdz7gcWAs8BKyOiI2SrpL0vtTtBqBdUg9wBbAsjd0IrAY2\nAd8ELouI/lo1U60rgStSrfZUu+Ycqc4pwMcl3Z/+vXqE22PYdjzfy0DAtzY9TZGLZmaTmybjH8Ou\nrq7o7u4e8fhb1j1x6PYPe3ZyxwNPFe1/vJDfOGX2qNdnZjYRSVofEV31+vmd96N0oLdEk2DmUVP4\nhzsf8V6LmU16DpZR2t/Xz7TWFt71xg7W/3w333tkx3gvycxsXDlYRml/b4lprc2c/rpjmT2jldvv\nrzxhzsxscnGwjNKBvhLT21poaWriV48/hi079o33kszMxpWDZZT29/UzvbUZgJNnT2frjv0+zmJm\nk5qDZZT295aY1la8n/Pkjhns6y2x43lfUcbMJi8HyygMRPBC+R5Lx3QAtuzwmyXNbPJysIzCi339\nBDC9bI8FYOtOH2cxs8nLwTIK+/v6AZjWWgTL8cdMZeqUJrZ6j8XMJjEHyyjs7y2u7j/4UlhTk5g3\newZbfWaYmU1iDpZRONCXgqXt8KcPnNwxna07vcdiZpOXg2UUDr8U1nyo7fWzp/PkrgP0lvrHa1lm\nZuPKwTIKB9JLYYPHWKA4gD8Q8PNnD4zXsszMxpWDZRT29/UzpVm0thzejIOnHPs4i5lNVg6WUdjf\nWzri+ArAvNl+L4uZTW6NfOa91XCgr5/pZS+DDX5Oy9FTW/jOQ09z7LRWAC5cOGdc1mdmNh68xzIK\n+/tKRxy4HzR7Rpsv62Jmk5aDZRSqvRQG0DGjjZ37+hjwxSjNbBJysIzCgbLrhJU7uWM6Lxzs58Ff\n7B2HVZmZjS8HywiV+gfoLQ0curJxuTedMJPXHDOVOzc9Tf+A91rMbHJxsIxQtTdHDmqSOKfzOJ7d\n38f6n+9+uZdmZjauHCwjdOhyLq3VT6x742uOZs6rpnHXw0/zQp/fhW9mk4eDZYT29xZhUe3gPYAk\nFp36Gp57scSHv7KePQf6Xs7lmZmNGwfLMD3y9PMMRBzaY6n2UtigebOns+S01/LjLc/yu//nhz6Y\nb2aTQkPBImmxpM2SeiQtq/J4m6Rb0+PrJM0te2x5at8saVG9mpLmpRqPppqtI50jty079vHez/6A\nux5+5vAl82vssQxaOK+d1R9+OwMDwX/93H9w671PjNXyzMwmhLrBIqkZuBY4F+gELpDUWdHtEmB3\nRJwCrARWpLGdwFLgVGAxcJ2k5jo1VwArI2I+sDvVHvYcw90QjXh9xwx+79dey90PP8Omp55DwFFT\n6k+1aftzfOgd85hz7DSu/LcHWHLND/nzr6znxh8+xv1P7hmLpZqZHfKfe1+k1D/wss3XyCVdFgA9\nEbEVQNIqYAmwqazPEuAT6fZtwDWSlNpXRUQv8JiknlSPajUlPQScBVyY+tyU6n5uBHP8uMFtMCxX\nL3kT39u8gy079nPUlGaam9TQuOltLXzwHXP59kNP8/1HdvCzbXu548H/BOC0k2bx7s7j2L7nBbbt\nfoFTXj2D33h9O6+ZOZUdz/eyr7fE3PbpnPLqGUjwzHO99Jb6OX7mUUxvayEiePFg8UMzdUoTxWaB\n/oGgSRy6b2YT08H+AVqadOh3tdQ/wL7eEkdPnUJzk4gInt3fx/Mvljh+5lSmTmlmX2+Jh556jlJ/\n0PnaYzi6rYX7ntzDdzc/w6xprZzTeRylgeBTdzzEnZue5g3HzeBj7+3kzDd0jPnzaSRYTgCeLLu/\nDVhYq09ElCTtBdpT+08qxp6Qbler2Q7siYhSlf4jmSO76W0tXLBgDp/73hamtw1vx6g4Dfk1/PYb\nX03/QHCwf4AHf7GX/9jyLJ9eu5mjpjQza9oUftSzkxt++NhLxktQ+Wb+o6e20FsaoK9UBMuUZjGt\ntYUXD/bTWxqgScWZa60tTfSVBujtL9pamppoaRYtTaK5SbQ0NdHcJPoHgt7SAP0DA2XzKq2/uN2k\n4rk0SSjdlqA8voJirUEUXwMigoHUBiAOjwdoajrclisKBzfX4HYbnPvQ/SpvM4rUWPnQ4Dql9C+t\ndbj1Kw3WOPQVHXG/vHa1uhHxkrW+ZI6q86ri/kvXUd5lrJ9j+c9K0Xb4ealsvZXbfyCq/4xFHO43\n+LM7OLZJIuLw2IEI+gfS2AiamkRrcxNNTdBXGuBgf9Ak0dbShAS9pQEO9g8wpbmJtpYmBgaCFw72\nU+oPprY205Z+3w709TMQwbTWFqZOaeJAXz/Pv3iQJomjp06htVnseeEgB9KV0mdNayUCdu3vZSCg\nuUm0T29lf2/p0FscJGif3sqz+/uO2PYz2lrY11uiSTAQcPXXNtGk4lWVP3nnPL616WkuuvEeFp16\nHJ//b6eP6X84GwmWarNX/ijV6lOrvdpLcEP1H8kcRy5QuhS4NN3dJ2lzlXGNmg3sBPjDj4+iytg6\ntMYJzGvMw2scvQm7vq2Hbx5a4+PDrPGxstvXA9dfNOLlvK6RTo0EyzbgpLL7JwLba/TZJqkFmAns\nqjO2WvtOYJaklrTXUt5/JHMcEhHXU2zTUZPUHRFdOWqNFa8xD68xj4m+xom+PvjlWOOgRs4KuxeY\nn87WaqU4UL6mos8a4OJ0+3zgriheT1gDLE1ndM0D5gP31KqZxtydapBq3j7COczMbBzU3WNJxzMu\nB9YCzcCNEbFR0lVAd0SsAW4Abk4HzndRBAWp32qKA/0l4LKI6AeoVjNNeSWwStIngftSbUYyh5mZ\nvfwUjRx5syNIujS9tDZheY15eI15TPQ1TvT1wS/HGgc5WMzMLCtf0sXMzPKKCP8bxj+Kd/dvBnqA\nZWM0x+PAA8D9FMexAF4F3Ak8mr4em9oFfDatZwPw1rI6F6f+jwIXl7Wfnur3pLEaao702I3AM8CD\nZW3jtqZqc9RY4yeAX6RteT/wnrLHlqfxm4FF9b7HwDxgXVrLrUBram9L93vS43NrzPGHFCenPARs\nBP7nBNyO59ZY40Tajr9LcYLOz9Ia/zZT3Zxrf2ONNX4ZeKxsO542nr8zY/Z3cqwKvxL/UZxosAU4\nGWhNPzSdYzDP48Dsira/H/wBB5YBK9Lt9wDfSD80bwPWlf1wbU1fj023B3/A7gHensZ8Azh3qDnS\n/TMp/ng/OBHWVG2OGmv8BPCRKtu4M33/2ij+WGxJ39+a32NgNbA03f488Gfp9p8Dn0+3lwK31pjj\ncaArPXY08EjqM5G2409Jf3Aq1jiRtuMW4Jj02JT0vX9bprrZ1g7MqLLGLwPnV9mO4/I7M2Z/K8eq\n8CvxX/omri27vxxYPgbzPM5Lg2UzcHy6fTywOd3+AnBBZT/gAuALZe1fSG3HAw+XtR/qV2uOsr5z\nOfKP9ritaYg5Ktf4Car/QTzie0dxhuLba32P0y/jTqCl8mdhcGy63ZL6qdYcZfdvB949EbdjlTVO\nyO0ITKMIw4W56uZce5U1fpnqwTLu3+uc/3yMZXiqXd5mLC4fE8C3JK1PVwwAOC4ingJIX19dZ01D\ntW+r0j7UHLWM55qG8724XNIGSTdKOnaEa2z4ckNA+eWGqq4xXZ371yn+Jzsht2PFGmFibceTJN1P\n8dLnnRR7GLm+P7nW3lG+xogY3I5/l7bjSkltI9yOY/07MyoOluFp6PIxGbwjIt5K8Xr3ZZLOHMGa\nRnUJnFF6OdbU6JjPAa8HTgOeAv5hDNY4rDGSZgD/BvxlRDxXpc+gcduOVdY40bZjf0ScRnGljQXA\nr2aqm3PtR6xR0pso9nx+BTiD4uWtKzOvcSgv198vB8swNXT5mNGKiO3p6zPAVyl+cZ6WdDxA+vpM\nnTUN1X5ijedQa45axnNNjV7K5+mI6I+IAeCLHL669nDXeOhyQ1XmOzSmgcsNPU3xB/v/RsT/G+Fz\nHOvt+EzlGifgdhz8HdkDfJfimEGuujnXXr7GxRHxVBR6gS8x8u04Zr8zOThYhqeRy9uMiqTpko4e\nvA2cAzzIkZe0uZgjL3VzkQpvA/am3d+1wDmSjk0vW5xD8XrwU8Dzkt6WPnbgIqpfNqd8jlrGc021\n5qjcnseX3f0vaVsOjh+Pyw39KfBQRPzjRN2OwN9VrnGCbcc3UhwfQNJRwO9QnMWW4/uTa+0/ogiX\n8jU+XPYHX8B5FdtxQvzOZDEWB25eyf8ozqx4hOI13Y+OQf2TKc5CGTxN8aOpvR34DsUphN8BXpXa\nRfGhaVsoTj3sKqv1RxSnFvYAHypr76L4gd4CXMPhg4xV50iP/QvFSyAHKf7nc8l4rqnaHDXWeHN6\nfAPFL1b5gemPpvGbSWfUDPU9Tt+be9La/xVoS+1T0/2e9PjJNeb4K4qXHjZQdtruBNuOl9RY40Ta\njn9GcbmnDem5/nWmujnXfm6NNd6VtuODwFc4fObYuPzOjNXfSb/z3szMsvJLYWZmlpWDxczMsnKw\nmJlZVg4WMzPLysFiZmZZOVjMzCwrB4vZBCapS9Jnx3sdZsPh97GYmVlW3mMxGwZJ/67iqtMbla48\nLekSSY9I+q6kL0q6JrW/XtJPJN0r6SpJ+4aoe6uk95Td/7Kk90t6l6SvpbbpKq4sfK+k+yQtSe13\nSHpLun2fpL9Ot6+W9MeSjpf0fUn3S3pQ0jvHbguZOVjMhuuPIuJ0istp/IWkE4CPU1wE8d0UV64d\n9BngMxFxBvUv9rcK+AOAdH2qs4E7Kvp8lOI6V2cAvw18Ol1P7vvAOyUdA5SAd6T+vwn8ALiQ4vpS\npwG/RnGZFrMx42AxG56/kPQz4CcUV4r978D3ImJXRBykuF7UoLeX3b+lTt1vAGep+HyOc4HvR8QL\nFX3OAZap+IyP71Jcn2oORXicSREkXwdmSJpG8RG8mykuqvghSZ8A3hwRzw//aZs1rqV+FzMDkPQu\niqvUvj0iDkj6LsXFC6t9FsiwRMSLqd4iij2Xf6m2BOD9KSzK19VKsQe1leJDr2YDfwKsT7W/r+Iz\nfd4L3Czp0xHxz6Nds1kt3mMxa9xMYHcKlV+hePlrGvBb6bLmLcD7y/r/pOz+0gbqrwI+BLyT4nLp\nldYC/yNdJh1Jvw4QEX0Unwz4+2nOHwAfSV+R9DrgmYj4InAD8NaGn7HZCDhYzBr3TaBF0gbgaoo/\n4r8APkXx8b3fBjZRfKYJwF8CV0i6h+Kzx/e+pOKRvkXxkta3U1hUuhqYAmyQ9GC6P+gHwNMRcSDd\nPjF9BXgXcL+k+yiC7jONPmGzkfDpxmajJGlGROxLeyxfBW6MiK+m4xwvRERIWgpcEBFLxne1ZmPP\nx1jMRu8Tkn6H4mD6t4B/T+2nA9ekl672UHxgk9krnvdYzF5Gkt5M8WmM5XojYuF4rMdsLDhYzMws\nKx+8NzOzrBwsZmaWlYPFzMyycrCYmVlWDhYzM8vq/wOgTLeXTC8vzgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGg5JREFUeJzt3X+QXeV93/H3d3e1EhJCAkk4QoAl\njFpbGMc2a37ETuwB20DSWsyUjgWxQ2I6jFMYNyUzBcbpJLU9aXHbUGeMbWigxqSKwNQ2GlyHNIDd\ncWMEK0zAgAUyYCHAlmQkISEh7Y9v/7iPxGW5d/fq0S4rlvdrZmfPfc5zvud5dFb7uefec89GZiJJ\n0oHqmuwBSJLemAwQSVIVA0SSVMUAkSRVMUAkSVUMEElSFQNEklTFAJEkVTFAJElVeiZ7ABNp/vz5\nuXjx4skehiS9oaxdu3ZLZi4Yq9+UDpDFixfT398/2cOQpDeUiPh5J/18CUuSVMUAkSRVMUAkSVUM\nEElSFQNEklTFAJEkVTFAJElVDBBJUhUDRJJUZUp/Ev1grVyzoWX7hacd/zqPRJIOPZ6BSJKqGCCS\npCoGiCSpigEiSapigEiSqhggkqQqBogkqYoBIkmqYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCS\npCoGiCSpigEiSarSUYBExDkRsS4i1kfElS3WT4+IW8r6NRGxuGndVaV9XUScPVbNiFhSajxRavaO\n2Nf5EZER0VczYUnS+BgzQCKiG7gWOBdYBlwQEctGdLsY2JqZJwLXAFeXbZcBK4CTgHOAr0RE9xg1\nrwauycylwNZSe99YZgOfAdbUTVeSNF46OQM5FVifmU9m5l5gFbB8RJ/lwE1l+TbgrIiI0r4qM/dk\n5lPA+lKvZc2yzZmlBqXmeU37+TzwReDlA5ynJGmcdRIgi4Bnmh5vLG0t+2TmILAdmDfKtu3a5wHb\nSo1X7Ssi3gMcl5l3jDbYiLgkIvojon/z5s0dTE+SVKOTAIkWbdlhn3Fpj4guGi+N/fEo42x0zrw+\nM/sys2/BggVjdZckVeokQDYCxzU9PhZ4rl2fiOgB5gAvjLJtu/YtwNxSo7l9NvBO4PsR8TRwOrDa\nN9IlafJ0EiD3A0vL1VG9NN4UXz2iz2rgorJ8PnB3ZmZpX1Gu0loCLAXua1ezbHNPqUGpeXtmbs/M\n+Zm5ODMXA/cCH8vM/sp5S5IOUs9YHTJzMCIuA+4EuoEbM/ORiPgc0J+Zq4EbgJsjYj2NM48VZdtH\nIuJW4FFgELg0M4cAWtUsu7wCWBURXwB+XGpLkg4x0XjSPzX19fVlf3/9ScrKNRtatl942vHVNSXp\nUBcRazNzzLcI/CS6JKmKASJJqmKASJKqGCCSpCoGiCSpigEiSapigEiSqhggkqQqBogkqYoBIkmq\nYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEiSapigEiSqhggkqQqBogkqYoBIkmq\nYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEiSapigEiSqhggkqQqBogkqYoBIkmq\nYoBIkqoYIJKkKgaIJKlKRwESEedExLqIWB8RV7ZYPz0ibinr10TE4qZ1V5X2dRFx9lg1I2JJqfFE\nqdlb2j8dEQ9HxIMR8cOIWHYwE5ckHZwxAyQiuoFrgXOBZcAFLX55XwxszcwTgWuAq8u2y4AVwEnA\nOcBXIqJ7jJpXA9dk5lJga6kNsDIzT87MdwNfBP6ics6SpHHQyRnIqcD6zHwyM/cCq4DlI/osB24q\ny7cBZ0VElPZVmbknM58C1pd6LWuWbc4sNSg1zwPIzBeb9jcLyAObqiRpPPV00GcR8EzT443Aae36\nZOZgRGwH5pX2e0dsu6gst6o5D9iWmYMt+hMRlwKXA700gkaSNEk6OQOJFm0jn/236zNe7Y2FzGsz\n823AFcCftBxsxCUR0R8R/Zs3b27VRZI0DjoJkI3AcU2PjwWea9cnInqAOcALo2zbrn0LMLfUaLcv\naLzkdV6rwWbm9ZnZl5l9CxYsGHNykqQ6nQTI/cDScnVUL403xVeP6LMauKgsnw/cnZlZ2leUq7SW\nAEuB+9rVLNvcU2pQat4OEBFLm/b3O8ATBzZVSdJ4GvM9kPKexmXAnUA3cGNmPhIRnwP6M3M1cANw\nc0Ssp3HmsaJs+0hE3Ao8CgwCl2bmEECrmmWXVwCrIuILwI9LbYDLIuLDwACNq7P2BZYkaRJE40n/\n1NTX15f9/f3V269cs6Fl+4WnHV9dU5IOdRGxNjP7xurnJ9ElSVUMEElSFQNEklTFAJEkVTFAJElV\nDBBJUhUDRJJUxQCRJFUxQCRJVQwQSVIVA0SSVMUAkSRVMUAkSVUMEElSFQNEklTFAJEkVTFAJElV\nDBBJUhUDRJJUxQCRJFUxQCRJVQwQSVIVA0SSVMUAkSRVMUAkSVUMEElSFQNEklTFAJEkVTFAJElV\nDBBJUhUDRJJUxQCRJFUxQCRJVQwQSVIVA0SSVMUAkSRVMUAkSVUMEElSlY4CJCLOiYh1EbE+Iq5s\nsX56RNxS1q+JiMVN664q7esi4uyxakbEklLjiVKzt7RfHhGPRsRDEXFXRLz1YCYuSTo4YwZIRHQD\n1wLnAsuACyJi2YhuFwNbM/NE4Brg6rLtMmAFcBJwDvCViOgeo+bVwDWZuRTYWmoD/Bjoy8x3AbcB\nX6ybsiRpPHRyBnIqsD4zn8zMvcAqYPmIPsuBm8rybcBZERGlfVVm7snMp4D1pV7LmmWbM0sNSs3z\nADLznszcVdrvBY498OlKksZLJwGyCHim6fHG0tayT2YOAtuBeaNs2659HrCt1Gi3L2iclXyvg7FL\nkiZITwd9okVbdtinXXur4Bqt/ys7ivgE0Ad8sEVfIuIS4BKA448/vlUXSdI46OQMZCNwXNPjY4Hn\n2vWJiB5gDvDCKNu2a98CzC01XrOviPgw8FngY5m5p9VgM/P6zOzLzL4FCxZ0MD1JUo1OAuR+YGm5\nOqqXxpviq0f0WQ1cVJbPB+7OzCztK8pVWkuApcB97WqWbe4pNSg1bweIiPcA19EIj01105UkjZcx\nX8LKzMGIuAy4E+gGbszMRyLic0B/Zq4GbgBujoj1NM48VpRtH4mIW4FHgUHg0swcAmhVs+zyCmBV\nRHyBxpVXN5T2/wwcDnyz8V47GzLzYwf9LyBJqhKNJ/1TU19fX/b391dvv3LNhpbtF57meyuSpq6I\nWJuZfWP185PokqQqBogkqYoBIkmqYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEi\nSapigEiSqhggkqQqBogkqYoBIkmqYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEi\nSapigEiSqhggkqQqBogkqYoBIkmqYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEi\nSapigEiSqhggkqQqBogkqUpHARIR50TEuohYHxFXtlg/PSJuKevXRMTipnVXlfZ1EXH2WDUjYkmp\n8USp2VvafysiHoiIwYg4/2AmLUk6eGMGSER0A9cC5wLLgAsiYtmIbhcDWzPzROAa4Oqy7TJgBXAS\ncA7wlYjoHqPm1cA1mbkU2FpqA2wAfh9YWTdVSdJ46uQM5FRgfWY+mZl7gVXA8hF9lgM3leXbgLMi\nIkr7qszck5lPAetLvZY1yzZnlhqUmucBZObTmfkQMFw5V0nSOOokQBYBzzQ93ljaWvbJzEFgOzBv\nlG3btc8DtpUa7fY1qoi4JCL6I6J/8+bNB7KpJOkAdBIg0aItO+wzXu0dy8zrM7MvM/sWLFhwIJtK\nkg5AJwGyETiu6fGxwHPt+kREDzAHeGGUbdu1bwHmlhrt9iVJOgR0EiD3A0vL1VG9NN4UXz2iz2rg\norJ8PnB3ZmZpX1Gu0loCLAXua1ezbHNPqUGpeXv99CRJE2XMACnvR1wG3Ak8BtyamY9ExOci4mOl\n2w3AvIhYD1wOXFm2fQS4FXgU+Fvg0swcalez1LoCuLzUmldqExHvi4iNwL8ErouIff0lSZMgGk/6\np6a+vr7s7++v3n7lmg0t2y887fjqmpJ0qIuItZnZN1Y/P4kuSapigEiSqhggkqQqBogkqYoBIkmq\nYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigEiSapigEiSqhggkqQqBogkqYoBIkmq\nYoBIkqoYIJKkKgaIJKmKASJJqmKASJKqGCCSpCoGiCSpigHSocwkMyd7GJJ0yOiZ7AG8Ufzvh5/n\nJ8+9yPJ3HzPZQ5GkQ4JnIB16cstLbN89wDd+9HMuv/VB9gwOTfaQJGlSeQbSgeFMtuzcw+knHMWM\nnm6+9cCz/Pqxc7noNxZP9tAkadJ4BtKB7bsGGBhKFh5xGB9Z9hZOeeuRXP9/n2RgaHiyhyZJk8YA\n6cCmHXsAmD97OhHBH37wbTy7bTfffej5SR6ZJE0eA6QDm3c2AuTo2dMBOPPtR7P06MP52g9+5pVZ\nkt60DJAObN7xMjN7u5k1vfGWUVdX8OkPvo2f/mIH31+3eZJHJ0mTwwDpwOYde1hQzj4AVq7ZwK69\nQ8ydOY2rvvUwN//o56xcs2ESRyhJrz8DpAObduxhweHTX9XW3RX8zskL+cWLL/Ojn22ZpJFJ0uQx\nQMbw0p5Bdu0d2v/+R7NlC4/g7b82m79/bBPbdu2dhNFJ0uQxQMawuVyBtaBFgEQE//xdx5Ak33nw\nWV58eeD1Hp4kTRoDZAz7rsBaMHtGy/VHzurlo8t+jcd/uZMz/vwuPn/Hozzy3HavzpI05XX0SfSI\nOAf4EtAN/FVm/qcR66cD3wBOAX4FfDwzny7rrgIuBoaAz2TmnaPVjIglwCrgKOAB4JOZuXe0fUyk\nzTv20NMVzJ05rW2f9584nyXzZ7Fx6y6+/g9Pc8MPn+KYOTN4/4nzecfCI3jnojm8b/GRRMRED1eS\nXjdjBkhEdAPXAh8BNgL3R8TqzHy0qdvFwNbMPDEiVgBXAx+PiGXACuAk4Bjg7yPin5Rt2tW8Grgm\nM1dFxNdK7a+228fB/gO08uAz2/j6/3uKkxfN3X8FVtcYv/yPmXsYx8w9jHcsPIJ1v9jBY8+/yHcf\nfp5vrt0IwMmL5nDFOW/nA0vn798mM9k9MMSMnm66ugwXSW8snZyBnAqsz8wnASJiFbAcaA6Q5cCf\nleXbgC9H4+n2cmBVZu4BnoqI9aUerWpGxGPAmcCFpc9Npe5X2+0jJ+C1oseef5E7Hnqee9ZtZmBo\nmBOPPrzjbWfPmEbf4qPoW3wUmcnOPYM8/ssd3PXTTXzihjVM6w56u7uICPYMDjEwlHR3BfMP72Xh\nnMNYPG8mb5kzg20vDfDLHS+ze+8QQ8PJYb3dLDvmCE5eNIc5h02juyuYMa2bOYdNY1pXF09u2cn6\nTTvZtXeI7q5gZm83i+fN4vh5M+npCgaGkuk9XRw5q5dZvd3s2DPItpcGmNYTHDmzl+k9XQwOJ3sH\nh4mArgi6IujuCoYzeXH3ANt3DzCtu4sjDpvGrN5uurviVWdVu/cOsWnHy2zbNcDcmdM4alYv03u6\niYCA/X0by3R8RjY4NMyOlwcZGB6mu4ypqyvo6XpljN0RrwnhzGTP4DB7h4aZOa2bnu6Jf8W2cdv/\nxrJPCt4cMpPhhK6mn+nMZHA46Wn6PzI4NLz//2FXV5CZ7No7xFAms3p76O5q/E7YvmuA7q5gzmHT\n6IrghV172bJzD4dP7+Ho2TMYHB7mZ5te4tltuznuqMN424LDefpXL3HXY5t4dttuPnDifH5z6Xxm\nz2j/qsl46SRAFgHPND3eCJzWrk9mDkbEdmBeab93xLaLynKrmvOAbZk52KJ/u32M+zW0F5x6PCcd\ncwSf+no/23cPvOYS3k5FBLNnTOOUtx7Frx87lwc2bGPLzj3sHRomM5nZ28OMad3sGRhix8uDbNu9\nl+8/vpkXdw8wa3oPs2f0ML2nm66A57cP8w/rf8XQOORlBIws06qtU10lcAaH6wrsC6yg8Z0SOF0R\nJMnLA53fc6y765VAGRgaftWYpvd07a+Z2Zjv/uXm8ZQxNZZfGc++sY7cdjiTpPW/X1dTGHeSlx31\nobNgyjKrfePKV1a8av1r6r76W3kCEK8aXzbVeFX9Nu37nuc173HkMe8q+2m1j/1DHzmXEZrHPHJe\nzW3D2XTcm47hvhr75zxiXMMlLPaFxlDTz1cETOvuIoA9g6/8zE7v6WI4k4GhV/8sNn4PvDKm3u5G\nW7OergP7fzWrt5uVazbQ0xV8/rx3csGpx3e8bY1OAqTVT+vIGbXr06691VPB0fp3Og4i4hLgkvJw\nZ0Ssa7Fdp+YDW24EbjyIIm8A85mAID4EOc+p580y1wOe54X/8ZWXciq8tZNOnQTIRuC4psfHAs+1\n6bMxInqAOcALY2zbqn0LMDciespZSHP/dvt4lcy8Hri+g3mNKSL6M7NvPGodypzn1PJmmSe8eeZ6\nqM6zkxeF7weWRsSSiOil8ab46hF9VgMXleXzgbvLexOrgRURMb1cXbUUuK9dzbLNPaUGpebtY+xD\nkjQJxjwDKe83XAbcSeOS2xsz85GI+BzQn5mrgRuAm8ub5C/QCARKv1tpvOE+CFyamUMArWqWXV4B\nrIqILwA/LrVptw9J0uQIn8S3FxGXlJfEpjTnObW8WeYJb565HqrzNEAkSVW8lYkkqYoB0kJEnBMR\n6yJifURcOdnjaScijouIeyLisYh4JCL+TWk/KiL+T0Q8Ub4fWdojIv6yzOuhiHhvU62LSv8nIuKi\npvZTIuLhss1flg+Itt3HBM+3OyJ+HBF3lMdLImJNGcMt5YIMykUbt5Qxr4mIxU01rirt6yLi7Kb2\nlse83T4mcI5zI+K2iPhpOa5nTOHj+W/Lz+1PIuJvImLGVDimEXFjRGyKiJ80tU3aMRxtHwet8clZ\nv/Z90XhT/2fACUAv8I/AsskeV5uxLgTeW5ZnA48Dy4AvAleW9iuBq8vybwPfo/GZmtOBNaX9KODJ\n8v3IsnxkWXcfcEbZ5nvAuaW95T4meL6XAyuBO8rjW4EVZflrwB+W5X8NfK0srwBuKcvLyvGcDiwp\nx7l7tGPebh8TOMebgH9VlnuBuVPxeNL4YPBTwGFN/86/PxWOKfBbwHuBnzS1TdoxbLePcZnrRP6Q\nvBG/yoG5s+nxVcBVkz2uDsd+O437i60DFpa2hcC6snwdcEFT/3Vl/QXAdU3t15W2hcBPm9r392u3\njwmc27HAXTRudXNH+c+wBegZedxoXN13RlnuKf1i5LHc16/dMR9tHxM0xyNo/FKNEe1T8Xjuu7PE\nUeUY3QGcPVWOKbCYVwfIpB3DdvsYj3n6EtZrtbp1y6I2fQ8Z5ZT+PcAa4C2Z+TxA+X506dZubqO1\nb2zRzij7mCj/Dfh3wL57PXR82xug+dY6BzL/0fYxEU4ANgP/Ixov1f1VRMxiCh7PzHwW+C/ABuB5\nGsdoLVPvmO4zmcdwwn6nGSCv1dEtUw4lEXE48L+AP8rMF0fr2qJttFvIHBL/FhHxz4BNmbm2ublF\n17Fue3Ooz7+HxksfX83M9wAv0Xgpop1DfT5tldfnl9N42ekYYBZwbouub/RjOpbXY/wTNmcD5LU6\nuXXLISMiptEIj/+Zmd8qzb+MiIVl/UJgU2lvN7fR2o9t0T7aPibC+4GPRcTTNP5WzJk0zkjmRuO2\nNiPHtn8+0dmtddq177+1Tot9TISNwMbMXFMe30YjUKba8QT4MPBUZm7OzAHgW8BvMPWO6T6TeQwn\n7HeaAfJandy65ZBQrr64AXgsM/+iaVXzbV9G3g7m98pVGacD28up7p3ARyPiyPLM8KM0Xhd+HtgR\nEaeXff0erW8t07yPcZeZV2XmsZm5mMbxuDszf5cDv+3NeN5aZyLm+QvgmYj4p6XpLBp3cZhSx7PY\nAJweETPLWPbNdUod0yaTeQzb7ePgjfebR1Phi8ZVC4/TuIrjs5M9nlHG+QEap6IPAQ+Wr9+m8Trv\nXcAT5ftRpX/Q+ENePwMeBvqaan0KWF++/qCpvQ/4Sdnmy7zy4dOW+3gd5vwhXrkK6wQavyzWA98E\nppf2GeXx+rL+hKbtP1vmso5y9cpox7zdPiZwfu8G+ssx/Q6NK3Cm5PEE/gPw0zKem2lcSfWGP6bA\n39B4X2eAxrP/iyfzGI62j4P98pPokqQqvoQlSapigEiSqhggkqQqBogkqYoBIkmqYoBIh4CI6J7s\nMUgHygCRxhAR34mItdG49fglpe3iiHg8Ir4fEf89Ir5c2t8WEfdGxP0R8bmI2DlK3Q9F43b8K2lc\nn09EfCIi7ouIByPiun3BEhE7I+K/RsQDEXFXRCwo7e8u+3soIr4dr8Nt2KV9DBBpbJ/KzFNofIDr\nMxGxCPj3NG6N/RHg7U19vwR8KTPfR2e3iziVxofclkXEO4CPA+/PzHcDQ8Dvln6zgAcy873AD4A/\nLe3fAK7IzHfRCKE/RXqdGCDS2D4TEf8I3EvjnkKfBH6QmS9k4z5O32zqe0bT45Ud1L4vM58qy2cB\npwD3R8SD5fEJZd0wcEtZ/mvgAxExB5ibmT8o7TfR+FsU0uuiZ+wu0ptXRHyIxo3/zsjMXRHxfRq3\nzHjHOO3ipebdATdl5lUdbOctJDTpPAORRjcH2FrC4+00XraaCXyw3OiuB/gXTf3vbXq84gD3dRdw\nfkQcDfv/ROlby7ouXrkB4IXADzNzO7A1In6ztH+Sxstb0uvCMxBpdH8LfDoiHqJx5nEv8Czw5zT+\neNdzNO4iu730/yPgryPij4HvNrWPKTMfjYg/Af4uIrpo3IzvUuDnNM5UToqItaXmx8tmFwFfi4iZ\nNP7s6R8cxFylA+LNFKUKEXF4Zu4sZyDfBm7MzG+XX+S7MzMjYgWNPyW6fBz2tzMzDz/YOtJ48gxE\nqvNnEfFhGrcZ/zsat16HxpvgXy5/q2EbjVtyS1OSZyDSBIuIk2n8vYtmezLztMkYjzReDBBJUhWv\nwpIkVTFAJElVDBBJUhUDRJJUxQCRJFUxQCRJVf4/MXCWk5tRPX8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAELCAYAAADZW/HeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npZOrAaQPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAELCAYAAAAiIMZEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAELCAYAAAAiIMZEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAELCAYAAAA1AlaNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRBEr+sYYE0Ws6BtjTBSxom+MMVHk/wMaDlUgIwrXhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAELCAYAAAAiIMZEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAELCAYAAAAiIMZEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAELCAYAAADeNe2OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS/zGGBNlLPEbY0yU+f9dijRuIchWKwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAELCAYAAAA1AlaNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAELCAYAAAD3HtBMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAELCAYAAADZW/HeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCJIkwECQJDUGgiQJMBAkSc3/Bw4tqjFjhBimAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEZFAKNBFRAKhQBcRCcT/B+j5RNAvlLi1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLiKSEEroIiIJ8f8B2ptUBJnIim8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noYAuIpIQCugiIgmhgC4ikhD/H05PeJVP86q8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFelJREFUeJzt3XuwnPV93/H3d3fPRQcJJJDAXAzC\nDcbGjK8KJnWT+poSpzFMazy4jks7tNRxk0knnYmdphcn6TSOJ7aTztBpGdsJTmIb4iZFwY4dQqDE\nHhsQBgMCgjCWZBkZXY8kdG57+faPfSTL6hFndc7ZPTo/3q+ZM2f32d8+z/d3dvezz/nt8/w2MhNJ\n0vJXW+oCJEmLw0CXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQjV4aRcRW4BDQBlqZuSEi\nzgRuBdYDW4H3ZOb+/pQpSZpL9HKmaBXoGzJzzzHLPgbsy8yPRsSHgTWZ+aEXWs/atWtz/fr1C6tY\nkl5kHnzwwT2ZuW6udj3toZ/A1cCbq8u3APcALxjo69evZ9OmTQvYpCS9+ETEtl7a9TqGnsBfRcSD\nEXFjteyczNwJUP0+++TLlCQtll730N+Umc9GxNnAnRHxZK8bqN4AbgS48MIL51GiJKkXPe2hZ+az\n1e9dwJ8DVwDPRcS5ANXvXSe4782ZuSEzN6xbN+cQkCRpnuYM9Ig4LSJWHbkM/DTwGLARuL5qdj1w\ne7+KlCTNrZchl3OAP4+II+0/l5lfiYgHgNsi4gZgO3Bt/8qUJM1lzkDPzGeA18yyfC/wtn4UJUk6\neZ4pKkmFMNAlqRAGuiQVYiFnig7U5+7bPuvyf/ZGj22XJHAPXZKKYaBLUiEMdEkqhIEuSYUw0CWp\nEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgph\noEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6\nJBWi50CPiHpEPBQRd1TXL46I+yJiS0TcGhHD/StTkjSXk9lD/2XgiWOu/w7wycy8BNgP3LCYhUmS\nTk5PgR4RFwA/C3yquh7AW4EvVk1uAa7pR4GSpN70uof+e8CvAp3q+lnAeGa2qus7gPMXuTZJ0kmY\nM9Aj4h8DuzLzwWMXz9I0T3D/GyNiU0Rs2r179zzLlCTNpZc99DcB74qIrcAX6A61/B6wOiIaVZsL\ngGdnu3Nm3pyZGzJzw7p16xahZEnSbOYM9Mz8tcy8IDPXA9cBf5OZ7wPuBt5dNbseuL1vVUqS5rSQ\n49A/BPxKRDxNd0z904tTkiRpPhpzN/mhzLwHuKe6/AxwxeKXJEmaD88UlaRCGOiSVAgDXZIKYaBL\nUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQV\nwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEM\ndEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklSIOQM9IkYj4v6I+HZEbI6I36iWXxwR90XEloi4NSKG\n+1+uJOlEetlDnwbempmvAV4LXBURVwK/A3wyMy8B9gM39K9MSdJc5gz07Hq+ujpU/STwVuCL1fJb\ngGv6UqEkqSc9jaFHRD0iHgZ2AXcC3wHGM7NVNdkBnN+fEiVJvegp0DOznZmvBS4ArgBeOVuz2e4b\nETdGxKaI2LR79+75VypJekEndZRLZo4D9wBXAqsjolHddAHw7Anuc3NmbsjMDevWrVtIrZKkF9DL\nUS7rImJ1dXkF8HbgCeBu4N1Vs+uB2/tVpCRpbo25m3AucEtE1Om+AdyWmXdExOPAFyLivwIPAZ/u\nY52SpDnMGeiZ+QjwulmWP0N3PF2SdArwTFFJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgph\noEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6\nJBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtS\nIQx0SSqEgS5JhZgz0CPipRFxd0Q8ERGbI+KXq+VnRsSdEbGl+r2m/+VKkk6klz30FvDvM/OVwJXA\nv42Iy4APA3dl5iXAXdV1SdISmTPQM3NnZn6runwIeAI4H7gauKVqdgtwTb+KlCTN7aTG0CNiPfA6\n4D7gnMzcCd3QB85e7OIkSb3rOdAjYiXwv4F/l5kHT+J+N0bEpojYtHv37vnUKEnqQU+BHhFDdMP8\nTzLzz6rFz0XEudXt5wK7ZrtvZt6cmRsyc8O6desWo2ZJ0ix6OcolgE8DT2TmJ465aSNwfXX5euD2\nxS9PktSrRg9t3gS8H3g0Ih6ulv0H4KPAbRFxA7AduLY/JUqSejFnoGfm14A4wc1vW9xyJEnz5Zmi\nklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5J\nhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQI\nA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgoxZ6BHxGciYldEPHbMsjMj\n4s6I2FL9XtPfMiVJc+llD/0PgauOW/Zh4K7MvAS4q7ouSVpCcwZ6Zt4L7Dtu8dXALdXlW4BrFrku\nSdJJmu8Y+jmZuROg+n324pUkSZqPvn8oGhE3RsSmiNi0e/fufm9Okl605hvoz0XEuQDV710napiZ\nN2fmhszcsG7dunluTpI0l/kG+kbg+ury9cDti1OOJGm+ejls8fPAN4BLI2JHRNwAfBR4R0RsAd5R\nXZckLaHGXA0y870nuOlti1yLJGkBPFNUkgphoEtSIQx0SeqjA5NNnt51iOlWu+/bMtAlqY/ufWo3\nb//EvWzfO9H3bRnoktRH45NNAM4YG+r7tgx0SeqjAxMzAJyxwkCXpGVtfKLJ2HCdkUa979sy0CWp\nj8Ynm6wewN45GOiS1FfjE03OGBseyLYMdEnqowOTM+6hS1IJxiearB7AES5goEtSXx2YbA7kCBcw\n0CWpbzKT8cnmQI5BBwNdkvpmqtlhptVh9Qo/FJWkZW18sntSkWPokrTMjU90T/v3KBdJWuaOBLpj\n6JK0zB04MuTiGLokLW9Hh1zcQ5ek5e3ApIEuSUUYn2wyVA9WDPV/pkUw0CWpb8YnmpyxYpiIGMj2\nDHRJ6pMDkzMDG24BA12S+mZ8YnBzoYOBLkl9M8iZFsFAl6S+6c60OJhj0MFAl6S+GZ9wDF2Slr1m\nu8PhmbZj6JK03A36pCIw0CWpL46c9n/6APfQGwPbkiQV7nP3bT96edvewwA8tH2cq197/kC2v6z2\n0HcemOSmu58++q+MJJ1KDk+3+B/3PM32vYeZnGkDMDY8mNP+YZkF+te27OH745M8sHXfUpciSf+f\nb23fz479k9y7ZQ8TzW6gD2oeF1hGgT450+bR7x8AYNPWfbQ7ucQVSdIPZebRnc0nf3CQXQenABgb\nHtzI9oICPSKuioi/i4inI+LDi1XUbB7+3n5aneQtl67j4FSLLc8dAuCvH3+OX/uzR5iq3g0laRCa\n7Q7/+fbHuOORZwG477v72PP8DG++dB2dhPu37iOAkaHB7TfP+60jIurATcA7gB3AAxGxMTMfX6zi\njui+8+3nvNWjvPUV5/DA1v3cv3UfK0cb/MZfbGa61WG61eHj174GgI3ffpbpVodr33DBwGY5k1Su\nzOzmSrPDtRu6ufJbdzzOZ7+xjc/fv52zV43y+fu3MzpU480vP5tteyf47p7DrBiqUxtgBi3kf4Er\ngKcz8xmAiPgCcDWw6IH+8PfG+cHBKa5+7XnUa8EbLlrDvU/t5vvjk6xdOcLPXP4SPvW17/LSNWM8\nvet5vvToTgD+avMP+C8/9yq+9OhO/ugb2zhv9SgffMuP8caLz+SuJ3bxt1t2c/n5Z/Azl5/LypEG\nD27bz9O7DvHql67m1eefQauTPLHzIAcmm1x23umcvWqU6VabrXsmiICLzhpjpFFnptVh54FJxoYb\nrF3ZnSqz2e6w7/AMZ6wYYrQaQ2u1OxyebrNqtEGt1n2Q252k1ekw0vjhOFu7kwQcbQPdJ5RvTlpO\njn/OdjpJAvVjntfTrTb1CBr12tH7HJxqMTZcZ6haNt1qMz7RZM3YMMONGpnJvsMzHJpqce7q0aOv\nwW17D9PqJBevPY3RoTp7n59m87MHOW2kwWXnns5wo8bmZw/w0PZxLjprjCsuPpOpZoevPPYDHtq+\nn7//Y2fxjstewre/N85Ndz/NM7sP8743Xsg1rzufj37lSb70SDdXvrr5B2xYfyaf/cY23n/lRXz9\nO3v4wB8/yPPTLV5/4RqGGzV+fP2ZfHfP4YF+IAoLC/Tzge8dc30H8MaFlTO7L9z/PYbqwWsuWA3A\nhovW8H+f2s10s8On/s0GXvGSVew8MMXv37WFRi340FWvYHSoxm9/+Ul+8mN3A/ATLzuLbXsP8y//\n4AHqtaDdSU4brnPbph18ZONmGrUaM+3O0W2ODXefJK1jxurXjA1xYLLJkUX1WnDmacPseX6arJat\nGKqzcrTxI8vOOq07l8O+iRkyoVEL1q4c6T5RJ5tkwmnDdc5YMcThmTYHp5oEcMaKIcaGGxyaanJo\nusVwvcbpK4YYqgWHpltMzLQZG6qzarRBAs9PtZhudzhtuM7YcINWp8PEdJt2JmPDDUaHaky3OkzO\ntAlgbKT7oplqtpmcaTPUqB3do5hutZludhhu1I6+IU022zTbHUaH6owO1Wi1k6lmmwRGGjWGGzWa\nrWS61SYiGG3UqNeD6WaHmXaHRi0YadSJgOlWh2a7w3C9Vr1Iu8s6mQzXaww1gnY7mW51H5PhRo1G\nPWi2kpl2h1rASKNOrQYzrQ7NdlKvBcP1GhEcfeyG6sFwo0an011/ZjJ0zPpn2h0yu/XXq/U32x0i\ngpFG7YTrn251aM+y/mPrb7WTmar+Y9ffrb+7/iO1zrQ7DNVrjBzzt2h1un+fkaHa0b9FO/NH/tZT\nre5jOTpUp1EPppodppptGrXulyrUasFU80cfy8xkotmm2eqwYrjO6FCdZrvDxEybTidZUT1Xppod\nJmZa1CKOBuzETIvDM22G6zVWjjSoBRyaajHR7D4XV442aHeSg1NNplsdVo40WDXSYKrVYXxihk7C\n6aMNVo40ODjV4vnpFtA9+Wa0UWfv4Wma7e4L58zThqkF7Hl+5uhrcO3KYSZn2hyujiCJgLNOG2F8\nYuboazUC1owNs+/wD+9Xi+5r88j9oPs6BGhVWfCnD+6gXnuEdic5e9UILz9nFR+/8yk+fudTNGrB\nr151KSuG6vz2Xz7JXU/u4s2XruMj73oV2/Ye5pqbvs5Mq8OPr18DwKvOO50VQ3VWDDjQI3N+Hy5G\nxLXAP8rMf1Vdfz9wRWb+0nHtbgRurK5eCvzdPGtdC+yZ532XK/v84mCfy7fQ/l6UmevmarSQPfQd\nwEuPuX4B8OzxjTLzZuDmBWwHgIjYlJkbFrqe5cQ+vzjY5/INqr8L+fj1AeCSiLg4IoaB64CNi1OW\nJOlkzXsPPTNbEfGLwFeBOvCZzNy8aJVJkk7Kgo54z8wvA19epFrmsuBhm2XIPr842OfyDaS/8/5Q\nVJJ0alk2p/5Lkl7YKRfoc00nEBEjEXFrdft9EbF+8FUurh76/CsR8XhEPBIRd0XERUtR52LqddqI\niHh3RGRELOsjInrpb0S8p3qcN0fE5wZd42Lr4Xl9YUTcHREPVc/tdy5FnYspIj4TEbsi4rET3B4R\n8d+rv8kjEfH6RS0gM0+ZH7ofrn4HeBkwDHwbuOy4Nh8E/md1+Trg1qWuewB9fgswVl3+hRdDn6t2\nq4B7gW8CG5a67j4/xpcADwFrqutnL3XdA+jzzcAvVJcvA7Yudd2L0O+fAl4PPHaC298J/CUQwJXA\nfYu5/VNtD/3odAKZOQMcmU7gWFcDt1SXvwi8LZb3OfFz9jkz787MierqN+ke87+c9fI4A/wW8DFg\napDF9UEv/f3XwE2ZuR8gM3cNuMbF1kufEzi9unwGs5zHstxk5r3AC83vfTXw2ez6JrA6Is5drO2f\naoE+23QCx3/Vx9E2mdkCDgBnDaS6/uilz8e6ge47/HI2Z58j4nXASzPzjkEW1ie9PMYvB14eEV+P\niG9GxFUDq64/eunzR4Cfj4gddI+W+yXKd7Kv95Nyqn0F3Wx72scfhtNLm+Wk5/5ExM8DG4B/2NeK\n+u8F+xwRNeCTwL8YVEF91stj3KA77PJmuv+B/W1EXJ6Z432urV966fN7gT/MzI9HxE8Af1T1uTPL\nfUvR1/w61fbQe5lO4GibiGjQ/VdtOX+FUU9TKETE24FfB96VmdMDqq1f5urzKuBy4J6I2Ep3rHHj\nMv5gtNfn9e2Z2czM79Kd8+iSAdXXD730+QbgNoDM/AYwSnfOk5L19Hqfr1Mt0HuZTmAjcH11+d3A\n32T1acMyNWefq+GH/0U3zJf72CrM0efMPJCZazNzfWaup/u5wbsyc9PSlLtgvTyv/w/dD7+JiLV0\nh2CeGWiVi6uXPm8H3gYQEa+kG+i7B1rl4G0E/nl1tMuVwIHM3Lloa1/qT4VP8CnwU3Q/If/1atlv\n0n1BQ/dB/1PgaeB+4GVLXfMA+vzXwHPAw9XPxqWuud99Pq7tPSzjo1x6fIwD+ATd7xN4FLhuqWse\nQJ8vA75O9wiYh4GfXuqaF6HPnwd2Ak26e+M3AB8APnDM43xT9Td5dLGf154pKkmFONWGXCRJ82Sg\nS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkDXshYRqyPig4u8zlsj4uHqZ2tEPHyCdj1NASwNiseha1mr\n5sO/IzMv79P6P073bL7fPG55ne5JM++gewLJA8B7M/PxftQh9eJUm5xLOlkfBf5etRd9N/BqYA0w\nBPzHzLwdICL+E/A+ujPd7QEezMzffaEVV9Myvwd46yw3H50etmp7ZHpYA11LxkDXcvdh4PLMfG01\nWdtYZh6s5kP5ZkRsBN4A/FPgdXSf898CHuxh3T8JPJeZW2a5bbZpUN+4gH5IC2agqyQB/LeI+Cmg\nQzd0zwH+Ad2ZDCcBIuIvelzfe+nOzXGibR3P8UstKQNdJXkfsA54Q2Y2q6l3R5k9fF9Qtbf/T+ju\n3c+mr9OgSvPhUS5a7g7RnT8dunPj76rC/C3AkS/T/hrwcxExGhErgZ/tYb1vB57MzB0nuL2X6WGl\ngXIPXctaZu6tvrbtMboh+4qI2ER3OtYnqzYPVGPp3wa2AZvofnXhC7mO44ZbIuI84FOZ+c7MbEXE\nLwJfpfuFyJ/JzM2L2TfpZHnYol4UImJlZj4fEWPAvcCNmfmtpa5LWkzuoevF4uaIuIzumPothrlK\n5B66XrQi4ibgTcct/v3M/IOlqEdaKANdkgrhUS6SVAgDXZIKYaBLUiEMdEkqhIEuSYX4fx6XU5JW\nmyQ4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngy2yBgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in df_raw.columns:\n",
" sns.distplot(df_raw[i])\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
},
"nbTranslate": {
"displayLangs": [
"*"
],
"hotkey": "alt-t",
"langInMainMenu": true,
"sourceLang": "en",
"targetLang": "fr",
"useGoogleTranslate": true
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| .ipynb_checkpoints/Final_Project-Copy1-checkpoint.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib as mpl\n",
"import matplotlib.cm as cm\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import time\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.notebook_repr_html', True)\n",
"import seaborn as sns\n",
"sns.set_style(\"whitegrid\")\n",
"sns.set_context(\"poster\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"# The \"requests\" library makes working with HTTP requests easier\n",
"# than the built-in urllib libraries.\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def rowInfoGrabber(r):\n",
" info = []\n",
" # Ranking\n",
" info.append(int(r.find(\"font\").get_text()))\n",
" # Title\n",
" info.append(r.find(\"a\").get_text())\n",
" # Gross\n",
" info.append(int(r.find(\"td\", attrs={\"align\":\"right\"}).find(\"b\").get_text().strip(\"$\").replace(\",\",\"\")))\n",
" # Total Number of Theaters\n",
" info.append(int(r.find_all(\"td\",attrs={\"align\":\"right\"})[1].find(\"font\").get_text().replace(\",\",\"\")))\n",
" # Opening Cost\n",
" info.append(int(r.find_all(\"td\", attrs={\"align\":\"right\"})[2].find(\"font\").get_text().strip(\"$\").replace(\",\",\"\")))\n",
" # Opening Number of Theaters\n",
" info.append(int(r.find_all(\"td\", attrs={\"align\":\"right\"})[3].find(\"font\").get_text().replace(\",\",\"\")))\n",
" # Date of Opening\n",
" info.append(r.find_all(\"td\", attrs={\"align\":\"right\"})[4].find(\"a\").get_text())\n",
" # Date of Closing\n",
" info.append(r.find_all(\"td\", attrs={\"align\":\"right\"})[5].find(\"font\").get_text())\n",
" return info\n",
"fields = [\"ranking\", \"title\", \"gross\", \"total_theaters\", \"opening\", \"opening_theaters\", \"open\", \"close\"]\n",
"\n",
"movies = [dict(zip(fields, rowInfoGrabber(row))) for row in movieRows]\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# $80 million\n",
"movie_df = pd.DataFrame(columns=['close', 'gross', 'open', 'opening', 'opening_theaters','ranking','title','total_theaters','year'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Scraping the past 26 years (1990-2016)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"IMDB was created in 1990, so we'll only go that far back in our scraping of Box Office Mojo.'"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"years = [str(1990 + i) for i in range(26)]\n",
"for year in years:\n",
" pageText = requests.get(\"http://www.boxofficemojo.com/yearly/chart/?yr=%(yr)d&p=.htm\" % {'yr':year})\n",
" soup = BeautifulSoup(pageText.text, \"html.parser\")\n",
" movieTable = soup.find(\"td\", attrs={\"colspan\":\"3\"})\n",
" movieRows = movieTable.find(\"table\").find_all(\"tr\")[2:102]\n",
" movie_dicts = [dict(zip(fields, rowInfoGrabber(row))) for row in movieRows]\n",
" year_df = pd.DataFrame(movie_dicts)\n",
" year_df['year'] = year\n",
" movie_df = movie_df.append(year_df)\n",
" time.sleep(1)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1200, 9)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movie_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>close</th>\n",
" <th>gross</th>\n",
" <th>open</th>\n",
" <th>opening</th>\n",
" <th>opening_theaters</th>\n",
" <th>ranking</th>\n",
" <th>title</th>\n",
" <th>total_theaters</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11/25</td>\n",
" <td>441226247</td>\n",
" <td>5/19</td>\n",
" <td>108037878</td>\n",
" <td>4163</td>\n",
" <td>1</td>\n",
" <td>Shrek 2</td>\n",
" <td>4223</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12/19</td>\n",
" <td>373585825</td>\n",
" <td>6/30</td>\n",
" <td>88156227</td>\n",
" <td>4152</td>\n",
" <td>2</td>\n",
" <td>Spider-Man 2</td>\n",
" <td>4166</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7/29</td>\n",
" <td>370274604</td>\n",
" <td>2/25</td>\n",
" <td>83848082</td>\n",
" <td>3043</td>\n",
" <td>3</td>\n",
" <td>The Passion of the Christ</td>\n",
" <td>3408</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6/16</td>\n",
" <td>279261160</td>\n",
" <td>12/22</td>\n",
" <td>46120980</td>\n",
" <td>3518</td>\n",
" <td>4</td>\n",
" <td>Meet the Fockers</td>\n",
" <td>3554</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/14</td>\n",
" <td>261441092</td>\n",
" <td>11/5</td>\n",
" <td>70467623</td>\n",
" <td>3933</td>\n",
" <td>5</td>\n",
" <td>The Incredibles</td>\n",
" <td>3933</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>12/19</td>\n",
" <td>249541069</td>\n",
" <td>6/4</td>\n",
" <td>93687367</td>\n",
" <td>3855</td>\n",
" <td>6</td>\n",
" <td>Harry Potter and the Prisoner of Azkaban</td>\n",
" <td>3855</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>11/4</td>\n",
" <td>186740799</td>\n",
" <td>5/28</td>\n",
" <td>68743584</td>\n",
" <td>3425</td>\n",
" <td>7</td>\n",
" <td>The Day After Tomorrow</td>\n",
" <td>3444</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>12/23</td>\n",
" <td>176241941</td>\n",
" <td>7/23</td>\n",
" <td>52521865</td>\n",
" <td>3165</td>\n",
" <td>8</td>\n",
" <td>The Bourne Supremacy</td>\n",
" <td>3304</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6/2</td>\n",
" <td>173008894</td>\n",
" <td>11/19</td>\n",
" <td>35142554</td>\n",
" <td>3017</td>\n",
" <td>9</td>\n",
" <td>National Treasure</td>\n",
" <td>3243</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3/10</td>\n",
" <td>162775358</td>\n",
" <td>11/10</td>\n",
" <td>23323463</td>\n",
" <td>3650</td>\n",
" <td>10</td>\n",
" <td>The Polar Express</td>\n",
" <td>3650</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1/6</td>\n",
" <td>160861908</td>\n",
" <td>10/1</td>\n",
" <td>47604606</td>\n",
" <td>4016</td>\n",
" <td>11</td>\n",
" <td>Shark Tale</td>\n",
" <td>4070</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12/30</td>\n",
" <td>144801023</td>\n",
" <td>7/16</td>\n",
" <td>52179887</td>\n",
" <td>3420</td>\n",
" <td>12</td>\n",
" <td>I, Robot</td>\n",
" <td>3494</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>9/30</td>\n",
" <td>133378256</td>\n",
" <td>5/14</td>\n",
" <td>46865412</td>\n",
" <td>3411</td>\n",
" <td>13</td>\n",
" <td>Troy</td>\n",
" <td>3411</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3/24</td>\n",
" <td>125544280</td>\n",
" <td>12/10</td>\n",
" <td>39153380</td>\n",
" <td>3290</td>\n",
" <td>14</td>\n",
" <td>Ocean's Twelve</td>\n",
" <td>3290</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>7/8</td>\n",
" <td>120908074</td>\n",
" <td>2/13</td>\n",
" <td>39852237</td>\n",
" <td>3591</td>\n",
" <td>15</td>\n",
" <td>50 First Dates</td>\n",
" <td>3612</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>8/26</td>\n",
" <td>120177084</td>\n",
" <td>5/7</td>\n",
" <td>51748040</td>\n",
" <td>3575</td>\n",
" <td>16</td>\n",
" <td>Van Helsing</td>\n",
" <td>3580</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>10/28</td>\n",
" <td>119194771</td>\n",
" <td>6/23</td>\n",
" <td>23920637</td>\n",
" <td>868</td>\n",
" <td>17</td>\n",
" <td>Fahrenheit 9/11</td>\n",
" <td>2011</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>4/28</td>\n",
" <td>118634549</td>\n",
" <td>12/17</td>\n",
" <td>30061756</td>\n",
" <td>3620</td>\n",
" <td>18</td>\n",
" <td>Lemony Snicket's A Series of Unfortunate Events</td>\n",
" <td>3623</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>11/4</td>\n",
" <td>114326736</td>\n",
" <td>6/18</td>\n",
" <td>30070196</td>\n",
" <td>2694</td>\n",
" <td>19</td>\n",
" <td>DodgeBall: A True Underdog Story</td>\n",
" <td>3020</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>12/2</td>\n",
" <td>114197520</td>\n",
" <td>7/30</td>\n",
" <td>50746142</td>\n",
" <td>3730</td>\n",
" <td>20</td>\n",
" <td>The Village</td>\n",
" <td>3733</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>12/30</td>\n",
" <td>110359362</td>\n",
" <td>10/22</td>\n",
" <td>39128715</td>\n",
" <td>3245</td>\n",
" <td>21</td>\n",
" <td>The Grudge</td>\n",
" <td>3348</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>6/2</td>\n",
" <td>102610330</td>\n",
" <td>12/17</td>\n",
" <td>858021</td>\n",
" <td>40</td>\n",
" <td>22</td>\n",
" <td>The Aviator</td>\n",
" <td>2530</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>11/25</td>\n",
" <td>101005703</td>\n",
" <td>8/6</td>\n",
" <td>24701458</td>\n",
" <td>3188</td>\n",
" <td>23</td>\n",
" <td>Collateral</td>\n",
" <td>3205</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>6/30</td>\n",
" <td>100492203</td>\n",
" <td>12/15</td>\n",
" <td>179953</td>\n",
" <td>8</td>\n",
" <td>24</td>\n",
" <td>Million Dollar Baby</td>\n",
" <td>2375</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>12/23</td>\n",
" <td>95170481</td>\n",
" <td>8/11</td>\n",
" <td>22956453</td>\n",
" <td>3472</td>\n",
" <td>25</td>\n",
" <td>The Princess Diaries 2: Royal Engagement</td>\n",
" <td>3490</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>7/15</td>\n",
" <td>88237754</td>\n",
" <td>3/5</td>\n",
" <td>28103367</td>\n",
" <td>3185</td>\n",
" <td>26</td>\n",
" <td>Starsky and Hutch</td>\n",
" <td>3185</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>6/10</td>\n",
" <td>88097164</td>\n",
" <td>1/16</td>\n",
" <td>27721185</td>\n",
" <td>2984</td>\n",
" <td>27</td>\n",
" <td>Along Came Polly</td>\n",
" <td>3052</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>9/9</td>\n",
" <td>86058055</td>\n",
" <td>4/30</td>\n",
" <td>24432195</td>\n",
" <td>2839</td>\n",
" <td>28</td>\n",
" <td>Mean Girls</td>\n",
" <td>3054</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>3/24</td>\n",
" <td>85417988</td>\n",
" <td>11/19</td>\n",
" <td>32018216</td>\n",
" <td>3212</td>\n",
" <td>29</td>\n",
" <td>The SpongeBob SquarePants Movie</td>\n",
" <td>3307</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>10/7</td>\n",
" <td>85288303</td>\n",
" <td>7/9</td>\n",
" <td>28416365</td>\n",
" <td>3091</td>\n",
" <td>30</td>\n",
" <td>Anchorman: The Legend of Ron Burgundy</td>\n",
" <td>3104</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>-</td>\n",
" <td>27285953</td>\n",
" <td>8/26</td>\n",
" <td>8111264</td>\n",
" <td>3355</td>\n",
" <td>71</td>\n",
" <td>No Escape</td>\n",
" <td>3415</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>-</td>\n",
" <td>26822658</td>\n",
" <td>10/23</td>\n",
" <td>10812861</td>\n",
" <td>3082</td>\n",
" <td>72</td>\n",
" <td>The Last Witch Hunter</td>\n",
" <td>3082</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>-</td>\n",
" <td>26822144</td>\n",
" <td>8/7</td>\n",
" <td>6610961</td>\n",
" <td>1603</td>\n",
" <td>73</td>\n",
" <td>Ricki and the Flash</td>\n",
" <td>2064</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>3/19</td>\n",
" <td>26501323</td>\n",
" <td>1/2</td>\n",
" <td>15027415</td>\n",
" <td>2602</td>\n",
" <td>74</td>\n",
" <td>The Woman in Black 2: Angel of Death</td>\n",
" <td>2602</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>5/7</td>\n",
" <td>26461644</td>\n",
" <td>3/13</td>\n",
" <td>11012305</td>\n",
" <td>3171</td>\n",
" <td>75</td>\n",
" <td>Run All Night</td>\n",
" <td>3171</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>6/11</td>\n",
" <td>25801047</td>\n",
" <td>2/27</td>\n",
" <td>10203437</td>\n",
" <td>2666</td>\n",
" <td>76</td>\n",
" <td>The Lazarus Effect</td>\n",
" <td>2666</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>9/3</td>\n",
" <td>25442958</td>\n",
" <td>4/10</td>\n",
" <td>237264</td>\n",
" <td>4</td>\n",
" <td>77</td>\n",
" <td>Ex Machina</td>\n",
" <td>2004</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>9/17</td>\n",
" <td>22764410</td>\n",
" <td>7/10</td>\n",
" <td>9808463</td>\n",
" <td>2720</td>\n",
" <td>78</td>\n",
" <td>The Gallows</td>\n",
" <td>2720</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>10/15</td>\n",
" <td>22467450</td>\n",
" <td>8/21</td>\n",
" <td>8326530</td>\n",
" <td>3261</td>\n",
" <td>79</td>\n",
" <td>Hitman: Agent 47</td>\n",
" <td>3273</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>3/26</td>\n",
" <td>22348241</td>\n",
" <td>1/30</td>\n",
" <td>8310252</td>\n",
" <td>2893</td>\n",
" <td>80</td>\n",
" <td>Project Almanac</td>\n",
" <td>2900</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>5/14</td>\n",
" <td>21571189</td>\n",
" <td>1/30</td>\n",
" <td>6213362</td>\n",
" <td>1823</td>\n",
" <td>81</td>\n",
" <td>Black or White</td>\n",
" <td>1823</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>7/30</td>\n",
" <td>21067116</td>\n",
" <td>5/29</td>\n",
" <td>9670235</td>\n",
" <td>2815</td>\n",
" <td>82</td>\n",
" <td>Aloha</td>\n",
" <td>2815</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>10/22</td>\n",
" <td>19375982</td>\n",
" <td>8/5</td>\n",
" <td>4038962</td>\n",
" <td>2320</td>\n",
" <td>83</td>\n",
" <td>Shaun the Sheep Movie</td>\n",
" <td>2360</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>5/21</td>\n",
" <td>18754371</td>\n",
" <td>1/16</td>\n",
" <td>197000</td>\n",
" <td>12</td>\n",
" <td>84</td>\n",
" <td>Still Alice</td>\n",
" <td>1318</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>-</td>\n",
" <td>18247445</td>\n",
" <td>10/23</td>\n",
" <td>8070493</td>\n",
" <td>1656</td>\n",
" <td>85</td>\n",
" <td>Paranormal Activity: The Ghost Dimension</td>\n",
" <td>1656</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>11/5</td>\n",
" <td>17737646</td>\n",
" <td>7/17</td>\n",
" <td>2434908</td>\n",
" <td>361</td>\n",
" <td>86</td>\n",
" <td>Mr. Holmes</td>\n",
" <td>898</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>-</td>\n",
" <td>17614323</td>\n",
" <td>10/9</td>\n",
" <td>521522</td>\n",
" <td>4</td>\n",
" <td>87</td>\n",
" <td>Steve Jobs</td>\n",
" <td>2493</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>9/17</td>\n",
" <td>17506470</td>\n",
" <td>6/19</td>\n",
" <td>6100010</td>\n",
" <td>2002</td>\n",
" <td>88</td>\n",
" <td>Dope</td>\n",
" <td>2002</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>3/19</td>\n",
" <td>17223265</td>\n",
" <td>2/6</td>\n",
" <td>7217640</td>\n",
" <td>2875</td>\n",
" <td>89</td>\n",
" <td>Seventh Son</td>\n",
" <td>2875</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>7/23</td>\n",
" <td>16432322</td>\n",
" <td>4/17</td>\n",
" <td>4577861</td>\n",
" <td>2012</td>\n",
" <td>90</td>\n",
" <td>Monkey Kingdom</td>\n",
" <td>2012</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>11/19</td>\n",
" <td>16029670</td>\n",
" <td>9/4</td>\n",
" <td>7355622</td>\n",
" <td>3434</td>\n",
" <td>91</td>\n",
" <td>The Transporter Refueled</td>\n",
" <td>3434</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>-</td>\n",
" <td>15128355</td>\n",
" <td>11/13</td>\n",
" <td>8317545</td>\n",
" <td>2603</td>\n",
" <td>92</td>\n",
" <td>Love the Coopers</td>\n",
" <td>2603</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>6/25</td>\n",
" <td>14674076</td>\n",
" <td>3/13</td>\n",
" <td>160089</td>\n",
" <td>4</td>\n",
" <td>93</td>\n",
" <td>It Follows</td>\n",
" <td>1655</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>10/8</td>\n",
" <td>14440985</td>\n",
" <td>8/21</td>\n",
" <td>5454284</td>\n",
" <td>2778</td>\n",
" <td>94</td>\n",
" <td>American Ultra</td>\n",
" <td>2778</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>-</td>\n",
" <td>14036500</td>\n",
" <td>10/16</td>\n",
" <td>4002226</td>\n",
" <td>1553</td>\n",
" <td>95</td>\n",
" <td>Woodlawn</td>\n",
" <td>1553</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>-</td>\n",
" <td>13443407</td>\n",
" <td>10/30</td>\n",
" <td>5002521</td>\n",
" <td>3003</td>\n",
" <td>96</td>\n",
" <td>Burnt</td>\n",
" <td>3003</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>6/25</td>\n",
" <td>12985600</td>\n",
" <td>3/20</td>\n",
" <td>3591282</td>\n",
" <td>1320</td>\n",
" <td>97</td>\n",
" <td>Do You Believe?</td>\n",
" <td>1356</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>10/1</td>\n",
" <td>12551031</td>\n",
" <td>6/5</td>\n",
" <td>2122177</td>\n",
" <td>481</td>\n",
" <td>98</td>\n",
" <td>Love & Mercy</td>\n",
" <td>791</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>4/16</td>\n",
" <td>12429583</td>\n",
" <td>1/23</td>\n",
" <td>5504441</td>\n",
" <td>3020</td>\n",
" <td>99</td>\n",
" <td>Strange Magic</td>\n",
" <td>3020</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>3/26</td>\n",
" <td>12314651</td>\n",
" <td>2/20</td>\n",
" <td>5963324</td>\n",
" <td>2880</td>\n",
" <td>100</td>\n",
" <td>Hot Tub Time Machine 2</td>\n",
" <td>2901</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1200 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" close gross open opening opening_theaters ranking title total_theaters year\n",
"0 11/25 441226247 5/19 108037878 4163 1 Shrek 2 4223 2004\n",
"1 12/19 373585825 6/30 88156227 4152 2 Spider-Man 2 4166 2004\n",
"2 7/29 370274604 2/25 83848082 3043 3 The Passion of the Christ 3408 2004\n",
"3 6/16 279261160 12/22 46120980 3518 4 Meet the Fockers 3554 2004\n",
"4 4/14 261441092 11/5 70467623 3933 5 The Incredibles 3933 2004\n",
"5 12/19 249541069 6/4 93687367 3855 6 Harry Potter and the Prisoner of Azkaban 3855 2004\n",
"6 11/4 186740799 5/28 68743584 3425 7 The Day After Tomorrow 3444 2004\n",
"7 12/23 176241941 7/23 52521865 3165 8 The Bourne Supremacy 3304 2004\n",
"8 6/2 173008894 11/19 35142554 3017 9 National Treasure 3243 2004\n",
"9 3/10 162775358 11/10 23323463 3650 10 The Polar Express 3650 2004\n",
"10 1/6 160861908 10/1 47604606 4016 11 Shark Tale 4070 2004\n",
"11 12/30 144801023 7/16 52179887 3420 12 I, Robot 3494 2004\n",
"12 9/30 133378256 5/14 46865412 3411 13 Troy 3411 2004\n",
"13 3/24 125544280 12/10 39153380 3290 14 Ocean's Twelve 3290 2004\n",
"14 7/8 120908074 2/13 39852237 3591 15 50 First Dates 3612 2004\n",
"15 8/26 120177084 5/7 51748040 3575 16 Van Helsing 3580 2004\n",
"16 10/28 119194771 6/23 23920637 868 17 Fahrenheit 9/11 2011 2004\n",
"17 4/28 118634549 12/17 30061756 3620 18 Lemony Snicket's A Series of Unfortunate Events 3623 2004\n",
"18 11/4 114326736 6/18 30070196 2694 19 DodgeBall: A True Underdog Story 3020 2004\n",
"19 12/2 114197520 7/30 50746142 3730 20 The Village 3733 2004\n",
"20 12/30 110359362 10/22 39128715 3245 21 The Grudge 3348 2004\n",
"21 6/2 102610330 12/17 858021 40 22 The Aviator 2530 2004\n",
"22 11/25 101005703 8/6 24701458 3188 23 Collateral 3205 2004\n",
"23 6/30 100492203 12/15 179953 8 24 Million Dollar Baby 2375 2004\n",
"24 12/23 95170481 8/11 22956453 3472 25 The Princess Diaries 2: Royal Engagement 3490 2004\n",
"25 7/15 88237754 3/5 28103367 3185 26 Starsky and Hutch 3185 2004\n",
"26 6/10 88097164 1/16 27721185 2984 27 Along Came Polly 3052 2004\n",
"27 9/9 86058055 4/30 24432195 2839 28 Mean Girls 3054 2004\n",
"28 3/24 85417988 11/19 32018216 3212 29 The SpongeBob SquarePants Movie 3307 2004\n",
"29 10/7 85288303 7/9 28416365 3091 30 Anchorman: The Legend of Ron Burgundy 3104 2004\n",
".. ... ... ... ... ... ... ... ... ...\n",
"70 - 27285953 8/26 8111264 3355 71 No Escape 3415 2015\n",
"71 - 26822658 10/23 10812861 3082 72 The Last Witch Hunter 3082 2015\n",
"72 - 26822144 8/7 6610961 1603 73 Ricki and the Flash 2064 2015\n",
"73 3/19 26501323 1/2 15027415 2602 74 The Woman in Black 2: Angel of Death 2602 2015\n",
"74 5/7 26461644 3/13 11012305 3171 75 Run All Night 3171 2015\n",
"75 6/11 25801047 2/27 10203437 2666 76 The Lazarus Effect 2666 2015\n",
"76 9/3 25442958 4/10 237264 4 77 Ex Machina 2004 2015\n",
"77 9/17 22764410 7/10 9808463 2720 78 The Gallows 2720 2015\n",
"78 10/15 22467450 8/21 8326530 3261 79 Hitman: Agent 47 3273 2015\n",
"79 3/26 22348241 1/30 8310252 2893 80 Project Almanac 2900 2015\n",
"80 5/14 21571189 1/30 6213362 1823 81 Black or White 1823 2015\n",
"81 7/30 21067116 5/29 9670235 2815 82 Aloha 2815 2015\n",
"82 10/22 19375982 8/5 4038962 2320 83 Shaun the Sheep Movie 2360 2015\n",
"83 5/21 18754371 1/16 197000 12 84 Still Alice 1318 2015\n",
"84 - 18247445 10/23 8070493 1656 85 Paranormal Activity: The Ghost Dimension 1656 2015\n",
"85 11/5 17737646 7/17 2434908 361 86 Mr. Holmes 898 2015\n",
"86 - 17614323 10/9 521522 4 87 Steve Jobs 2493 2015\n",
"87 9/17 17506470 6/19 6100010 2002 88 Dope 2002 2015\n",
"88 3/19 17223265 2/6 7217640 2875 89 Seventh Son 2875 2015\n",
"89 7/23 16432322 4/17 4577861 2012 90 Monkey Kingdom 2012 2015\n",
"90 11/19 16029670 9/4 7355622 3434 91 The Transporter Refueled 3434 2015\n",
"91 - 15128355 11/13 8317545 2603 92 Love the Coopers 2603 2015\n",
"92 6/25 14674076 3/13 160089 4 93 It Follows 1655 2015\n",
"93 10/8 14440985 8/21 5454284 2778 94 American Ultra 2778 2015\n",
"94 - 14036500 10/16 4002226 1553 95 Woodlawn 1553 2015\n",
"95 - 13443407 10/30 5002521 3003 96 Burnt 3003 2015\n",
"96 6/25 12985600 3/20 3591282 1320 97 Do You Believe? 1356 2015\n",
"97 10/1 12551031 6/5 2122177 481 98 Love & Mercy 791 2015\n",
"98 4/16 12429583 1/23 5504441 3020 99 Strange Magic 3020 2015\n",
"99 3/26 12314651 2/20 5963324 2880 100 Hot Tub Time Machine 2 2901 2015\n",
"\n",
"[1200 rows x 9 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movie_df"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save the movie Dictionaries corresponding to each row of the BoxOfficeMojo table.\n",
"import json # (dong)\n",
"\n",
"# Make a dictionary out of the dataset for storage in JSON format.\n",
"movieSaved = {feature: movie_df[feature].values.tolist() for feature in movie_df.columns.values}\n",
"\n",
"fp = open(\"allMovies.json\",\"w\")\n",
"json.dump(movieSaved, fp)\n",
"fp.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| notebook/builtins_module.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import builtins"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"print(len('abc'))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"print(builtins.len('abc'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<built-in function len>\n"
]
}
],
"source": [
"print(len)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<built-in function len>\n"
]
}
],
"source": [
"print(builtins.len)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(builtins.len is len)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"print(__builtins__.len('abc'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(__builtins__.len is len)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(__builtins__ is builtins)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| class4/homework/benzaquen_mercy_4_2.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate the correlation between the recycling rate and the median income. Discuss your findings in your PR."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied (use --upgrade to upgrade): xlrd in /usr/local/lib/python3.5/site-packages\n",
"Requirement already satisfied (use --upgrade to upgrade): matplotlib in /usr/local/lib/python3.5/site-packages\n",
"Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /usr/local/lib/python3.5/site-packages (from matplotlib)\n",
"Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /usr/local/lib/python3.5/site-packages (from matplotlib)\n",
"Requirement already satisfied (use --upgrade to upgrade): pytz in /usr/local/lib/python3.5/site-packages (from matplotlib)\n",
"Requirement already satisfied (use --upgrade to upgrade): pyparsing!=2.0.0,!=2.0.4,>=1.5.6 in /usr/local/lib/python3.5/site-packages (from matplotlib)\n",
"Requirement already satisfied (use --upgrade to upgrade): cycler in /usr/local/lib/python3.5/site-packages (from matplotlib)\n",
"Requirement already satisfied (use --upgrade to upgrade): six>=1.5 in /usr/local/lib/python3.5/site-packages (from python-dateutil->matplotlib)\n"
]
}
],
"source": [
"!pip install xlrd\n",
"!pip install matplotlib\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_excel(\"2013_NYC_CD_MedianIncome_Recycle.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CD_Name</th>\n",
" <th>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.286771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.264074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chinatown & Lower East Side</td>\n",
" <td>40919</td>\n",
" <td>0.156485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Chelsea, Clinton & Midtown Business Distric</td>\n",
" <td>92583</td>\n",
" <td>0.235125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Chelsea, Clinton & Midtown Business Distric</td>\n",
" <td>92583</td>\n",
" <td>0.246725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Murray Hill, Gramercy & Stuyvesant Town</td>\n",
" <td>101769</td>\n",
" <td>0.222046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Upper West Side & West Side</td>\n",
" <td>96009</td>\n",
" <td>0.256809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Upper East Side</td>\n",
" <td>104602</td>\n",
" <td>0.253719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Hamilton Heights, Manhattanville & West Harlem</td>\n",
" <td>41736</td>\n",
" <td>0.155888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Central Harlem</td>\n",
" <td>36468</td>\n",
" <td>0.133018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>East Harlem</td>\n",
" <td>30335</td>\n",
" <td>0.140438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Washington Heights, Inwood & Marble Hill</td>\n",
" <td>37685</td>\n",
" <td>0.149605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Hunts Point, Longwood & Melrose</td>\n",
" <td>21318</td>\n",
" <td>0.104569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Hunts Point, Longwood & Melrose</td>\n",
" <td>21318</td>\n",
" <td>0.103643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Belmont, Crotona Park East & East Tremont</td>\n",
" <td>22343</td>\n",
" <td>0.119219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Concourse, Highbridge & Mount Eden</td>\n",
" <td>25745</td>\n",
" <td>0.103573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Morris Heights, Fordham South & Mount Hope</td>\n",
" <td>24517</td>\n",
" <td>0.119646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Belmont, Crotona Park East & East Tremont</td>\n",
" <td>22343</td>\n",
" <td>0.110713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bedford Park, Fordham North & Norwood</td>\n",
" <td>30541</td>\n",
" <td>0.136455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Riverdale, Fieldston & Kingsbridge</td>\n",
" <td>56877</td>\n",
" <td>0.221890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Castle Hill, Clason Point & Parkchester</td>\n",
" <td>34779</td>\n",
" <td>0.105807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Co-op City, Pelham Bay & Schuylerville</td>\n",
" <td>54685</td>\n",
" <td>0.214509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Pelham Parkway, Morris Park & Laconia</td>\n",
" <td>43503</td>\n",
" <td>0.163576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Wakefield, Williamsbridge & Woodlawn</td>\n",
" <td>43541</td>\n",
" <td>0.182580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Greenpoint & Williamsburg</td>\n",
" <td>50778</td>\n",
" <td>0.141621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Brooklyn Heights & Fort Greene</td>\n",
" <td>73290</td>\n",
" <td>0.237205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Bedford-Stuyvesant</td>\n",
" <td>36528</td>\n",
" <td>0.125818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Bushwick</td>\n",
" <td>38274</td>\n",
" <td>0.132463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>East New York & Starrett City</td>\n",
" <td>33700</td>\n",
" <td>0.114030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Park Slope, Carroll Gardens & Red Hook</td>\n",
" <td>93969</td>\n",
" <td>0.302798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Sunset Park & Windsor Terrace</td>\n",
" <td>43351</td>\n",
" <td>0.197697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Crown Heights North & Prospect Heights</td>\n",
" <td>41075</td>\n",
" <td>0.156241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Crown Heights South, Prospect Lefferts & Wingate</td>\n",
" <td>41095</td>\n",
" <td>0.115119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Bay Ridge & Dyker Heights</td>\n",
" <td>57006</td>\n",
" <td>0.220855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Bensonhurst & Bath Beach</td>\n",
" <td>48252</td>\n",
" <td>0.183393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Borough Park, Kensington & Ocean Parkway</td>\n",
" <td>38215</td>\n",
" <td>0.156080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Brighton Beach & Coney Island</td>\n",
" <td>30159</td>\n",
" <td>0.134260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Flatbush & Midwood</td>\n",
" <td>41681</td>\n",
" <td>0.145995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Sheepshead Bay, Gerritsen Beach & Homecrest</td>\n",
" <td>49392</td>\n",
" <td>0.193802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Brownsville & Ocean Hill</td>\n",
" <td>27772</td>\n",
" <td>0.091464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>East Flatbush, Farragut & Rugby</td>\n",
" <td>45954</td>\n",
" <td>0.134002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Canarsie & Flatlands</td>\n",
" <td>63106</td>\n",
" <td>0.174876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Astoria & Long Island City</td>\n",
" <td>50716</td>\n",
" <td>0.215254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Sunnyside & Woodside</td>\n",
" <td>54136</td>\n",
" <td>0.198388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Jackson Heights & North Corona</td>\n",
" <td>47555</td>\n",
" <td>0.137919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Elmhurst & South Corona</td>\n",
" <td>45661</td>\n",
" <td>0.130604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Ridgewood, Glendale & Middle Village</td>\n",
" <td>54924</td>\n",
" <td>0.214185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Forest Hills & Rego Park</td>\n",
" <td>64372</td>\n",
" <td>0.210247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Flushing, Murray Hill & Whitestone</td>\n",
" <td>51251</td>\n",
" <td>0.192124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Briarwood, Fresh Meadows & Hillcrest</td>\n",
" <td>59124</td>\n",
" <td>0.194293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Richmond Hill & Woodhaven</td>\n",
" <td>58578</td>\n",
" <td>0.187987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Howard Beach & Ozone Park</td>\n",
" <td>60828</td>\n",
" <td>0.183898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Bayside, Douglaston & Little Neck</td>\n",
" <td>74960</td>\n",
" <td>0.253064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Jamaica, Hollis & St. Albans</td>\n",
" <td>51251</td>\n",
" <td>0.157345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Queens Village, Cambria Heights & Rosedale</td>\n",
" <td>76002</td>\n",
" <td>0.196679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Far Rockaway, Breezy Point & Broad Channel</td>\n",
" <td>46944</td>\n",
" <td>0.123351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Port Richmond, Stapleton & Mariner's Harbor</td>\n",
" <td>57975</td>\n",
" <td>0.196748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>New Springville & South Beach</td>\n",
" <td>71925</td>\n",
" <td>0.211485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Tottenville, Great Kills & Annadale</td>\n",
" <td>84670</td>\n",
" <td>0.210379</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CD_Name MdHHIncE RecycleRate\n",
"0 Battery Park City, Greenwich Village & Soho 119596 0.286771\n",
"1 Battery Park City, Greenwich Village & Soho 119596 0.264074\n",
"2 Chinatown & Lower East Side 40919 0.156485\n",
"3 Chelsea, Clinton & Midtown Business Distric 92583 0.235125\n",
"4 Chelsea, Clinton & Midtown Business Distric 92583 0.246725\n",
"5 Murray Hill, Gramercy & Stuyvesant Town 101769 0.222046\n",
"6 Upper West Side & West Side 96009 0.256809\n",
"7 Upper East Side 104602 0.253719\n",
"8 Hamilton Heights, Manhattanville & West Harlem 41736 0.155888\n",
"9 Central Harlem 36468 0.133018\n",
"10 East Harlem 30335 0.140438\n",
"11 Washington Heights, Inwood & Marble Hill 37685 0.149605\n",
"12 Hunts Point, Longwood & Melrose 21318 0.104569\n",
"13 Hunts Point, Longwood & Melrose 21318 0.103643\n",
"14 Belmont, Crotona Park East & East Tremont 22343 0.119219\n",
"15 Concourse, Highbridge & Mount Eden 25745 0.103573\n",
"16 Morris Heights, Fordham South & Mount Hope 24517 0.119646\n",
"17 Belmont, Crotona Park East & East Tremont 22343 0.110713\n",
"18 Bedford Park, Fordham North & Norwood 30541 0.136455\n",
"19 Riverdale, Fieldston & Kingsbridge 56877 0.221890\n",
"20 Castle Hill, Clason Point & Parkchester 34779 0.105807\n",
"21 Co-op City, Pelham Bay & Schuylerville 54685 0.214509\n",
"22 Pelham Parkway, Morris Park & Laconia 43503 0.163576\n",
"23 Wakefield, Williamsbridge & Woodlawn 43541 0.182580\n",
"24 Greenpoint & Williamsburg 50778 0.141621\n",
"25 Brooklyn Heights & Fort Greene 73290 0.237205\n",
"26 Bedford-Stuyvesant 36528 0.125818\n",
"27 Bushwick 38274 0.132463\n",
"28 East New York & Starrett City 33700 0.114030\n",
"29 Park Slope, Carroll Gardens & Red Hook 93969 0.302798\n",
"30 Sunset Park & Windsor Terrace 43351 0.197697\n",
"31 Crown Heights North & Prospect Heights 41075 0.156241\n",
"32 Crown Heights South, Prospect Lefferts & Wingate 41095 0.115119\n",
"33 Bay Ridge & Dyker Heights 57006 0.220855\n",
"34 Bensonhurst & Bath Beach 48252 0.183393\n",
"35 Borough Park, Kensington & Ocean Parkway 38215 0.156080\n",
"36 Brighton Beach & Coney Island 30159 0.134260\n",
"37 Flatbush & Midwood 41681 0.145995\n",
"38 Sheepshead Bay, Gerritsen Beach & Homecrest 49392 0.193802\n",
"39 Brownsville & Ocean Hill 27772 0.091464\n",
"40 East Flatbush, Farragut & Rugby 45954 0.134002\n",
"41 Canarsie & Flatlands 63106 0.174876\n",
"42 Astoria & Long Island City 50716 0.215254\n",
"43 Sunnyside & Woodside 54136 0.198388\n",
"44 Jackson Heights & North Corona 47555 0.137919\n",
"45 Elmhurst & South Corona 45661 0.130604\n",
"46 Ridgewood, Glendale & Middle Village 54924 0.214185\n",
"47 Forest Hills & Rego Park 64372 0.210247\n",
"48 Flushing, Murray Hill & Whitestone 51251 0.192124\n",
"49 Briarwood, Fresh Meadows & Hillcrest 59124 0.194293\n",
"50 Richmond Hill & Woodhaven 58578 0.187987\n",
"51 Howard Beach & Ozone Park 60828 0.183898\n",
"52 Bayside, Douglaston & Little Neck 74960 0.253064\n",
"53 Jamaica, Hollis & St. Albans 51251 0.157345\n",
"54 Queens Village, Cambria Heights & Rosedale 76002 0.196679\n",
"55 Far Rockaway, Breezy Point & Broad Channel 46944 0.123351\n",
"56 Port Richmond, Stapleton & Mariner's Harbor 57975 0.196748\n",
"57 New Springville & South Beach 71925 0.211485\n",
"58 Tottenville, Great Kills & Annadale 84670 0.210379"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.286771\n",
"1 0.264074\n",
"2 0.156485\n",
"3 0.235125\n",
"4 0.246725\n",
"Name: RecycleRate, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recycling_rate= df['RecycleRate']\n",
"recycling_rate.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 119596\n",
"1 119596\n",
"2 40919\n",
"3 92583\n",
"4 92583\n",
"Name: MdHHIncE, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"median_income= df['MdHHIncE']\n",
"median_income.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MdHHIncE</th>\n",
" <td>1.000000</td>\n",
" <td>0.884783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RecycleRate</th>\n",
" <td>0.884783</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MdHHIncE RecycleRate\n",
"MdHHIncE 1.000000 0.884783\n",
"RecycleRate 0.884783 1.000000"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.style.use('fivethirtyeight')"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-f6486e4619d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'RecycleRate'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'MdHHIncE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'orange'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.35\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m140000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The more people earn, the more they recycle\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Median Income\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"x= df.plot(kind='scatter', y='RecycleRate', x='MdHHIncE', color='orange', figsize= (7,5), )\n",
"x.set_ylim([0.05, 0.35])\n",
"x.set_xlim([10000, 140000])\n",
"x.set_title(\"The more people earn, the more they recycle\")\n",
"x.set_xlabel(\"Median Income\")\n",
"x.set_ylabel(\"Recycle Rate\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The correlation coeficient is 88%. I titled my graph 'The more people earn, the more they recycle', because that is what the data shows us but now that I am writing it for a second time it sounds too general and I am making it seem as if earning more money makes people recycle (causation). Recycling does not depend on how much money someone makes but as we can see, median income and recycling rates are correlated, so that could have something to do with what people earning more money know about recycling and their views on it."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
hypergravity/cham_hates_python | exercise/cham_teaches_python_06_aplpy_healpy.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://www.python.org/static/img/python-logo.png\">\n",
"\n",
"# Welcome to my lessons\n",
"\n",
"---\n",
"\n",
"**Bo Zhang** (NAOC, <mailto:[email protected]>) will have a few lessons on python.\n",
"\n",
"- These are **very useful knowledge, skills and code styles** when you use `python` to process astronomical data.\n",
"- All materials can be found on [**my github page**](https://github.com/hypergravity/cham_teaches_python).\n",
"- **jupyter notebook** (formerly named **ipython notebook**) is recommeded to use\n",
"\n",
"---\n",
"These lectures are organized as below:\n",
"1. install python\n",
"2. basic syntax\n",
"3. numerical computing\n",
"4. scientific computing\n",
"5. plotting\n",
"6. astronomical data processing\n",
"7. high performance computing\n",
"8. version control\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# APLpy basics\n",
"\n",
"[aplpyreaddocs](http://aplpy.readthedocs.io/en/stable/#)\n",
"\n",
"### - Introduction\n",
"\n",
" **APLpy : Astronomical Plotting Library in Python.**\n",
"\n",
" For convenient fits image or cubes plotting with astronomical coordinates\n",
"\n",
"### - Dependencies\n",
"\n",
" APLpy is based on **matplotlib**, so any matplotlib originated functions/classes can be easily adopted.\n",
"\n",
" For image reprojection or mosaicing purpose, APLpy relies on **Montage**, which is developed by IPAC team. To enable functions like making RGB cube, one should first install Montage package.\n",
"\n",
"[Montage package docs](http://montage.ipac.caltech.edu/docs/)\n",
"\n",
"### - Tips\n",
"\n",
" For plotting plenty of similar images, one could define a function to set up a set of standard properties (*i.e. def standard_setup( )*), such as axis and tick labels and use this user-defined function instead of the calling the APLpy functions time to time.\n",
"\n",
" Of course, a different setting by directly calling APLpy functions will overide the settings after calling the user-defined standard setup function.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# import matplotlib.pyplot as plt\n",
"# import numpy as np\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:Deleting work directory /tmp/tmp4VisvR\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Deleting work directory /tmp/tmp4VisvR [montage_wrapper.wrappers]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:Deleting work directory /tmp/tmpt09YEd\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": Deleting work directory /tmp/tmpt09YEd [montage_wrapper.wrappers]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:Deleting work directory /tmp/tmpzSLAIG\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": Deleting work directory /tmp/tmpzSLAIG [montage_wrapper.wrappers]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:Red:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": Red: [aplpy.rgb]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:vmin = 1.175e+01 (auto)\n",
"INFO:astropy:vmax = 2.671e+01 (auto)\n",
"INFO:astropy:Green:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": vmin = 1.175e+01 (auto) [aplpy.rgb]\n",
"INFO: vmax = 2.671e+01 (auto) [aplpy.rgb]\n",
"INFO: Green: [aplpy.rgb]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:vmin = 7.930e+02 (auto)\n",
"INFO:astropy:vmax = 8.299e+02 (auto)\n",
"INFO:astropy:Blue:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": vmin = 7.930e+02 (auto) [aplpy.rgb]\n",
"INFO: vmax = 8.299e+02 (auto) [aplpy.rgb]\n",
"INFO: Blue: [aplpy.rgb]\n",
"INFO"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:vmin = 2.530e+02 (auto)\n",
"INFO:astropy:vmax = 2.548e+02 (auto)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": vmin = 2.530e+02 (auto) [aplpy.rgb]\n",
"INFO: vmax = 2.548e+02 (auto) [aplpy.rgb]\n"
]
},
{
"ename": "AttributeError",
"evalue": "'WCS' object has no attribute 'naxis1'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-f94e21cb7114>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[0mpmax_b\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m98\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[0mstretch_b\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'linear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m vmid_b=None)\n\u001b[0m\u001b[0;32m 51\u001b[0m \u001b[0mfits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutpath\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34mr'wise234_RGB.fits'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'CTYPE3'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/aplpy/rgb.pyc\u001b[0m in \u001b[0;36mmake_rgb_image\u001b[1;34m(data, output, indices, vmin_r, vmax_r, pmin_r, pmax_r, stretch_r, vmid_r, exponent_r, vmin_g, vmax_g, pmin_g, pmax_g, stretch_g, vmid_g, exponent_g, vmin_b, vmax_b, pmin_b, pmax_b, stretch_b, vmid_b, exponent_b, make_nans_transparent, embed_avm_tags)\u001b[0m\n\u001b[0;32m 226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 227\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.jpg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.jpeg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 228\u001b[1;33m \u001b[0mavm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAVM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_header\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 229\u001b[0m \u001b[0mavm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 230\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pyavm/avm.pyc\u001b[0m in \u001b[0;36mfrom_header\u001b[1;34m(cls, header, include_full_header)\u001b[0m\n\u001b[0;32m 518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 519\u001b[0m \u001b[0mwcs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mWCS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 520\u001b[1;33m \u001b[0mself\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_wcs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 522\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minclude_full_header\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pyavm/avm.pyc\u001b[0m in \u001b[0;36mfrom_wcs\u001b[1;34m(cls, wcs)\u001b[0m\n\u001b[0;32m 549\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Projections do not agree: %s / %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mproj1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproj2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferenceDimension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnaxis1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnaxis2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 552\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferenceValue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrval\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferencePixel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrpix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'WCS' object has no attribute 'naxis1'"
]
}
],
"source": [
"import aplpy\n",
"from astropy.io import fits\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def standard_setup(fig):\n",
" fig.show_colorscale(cmap='gist_gray')\n",
" fig.tick_labels.set_font(size='large')\n",
" fig.frame.set_linewidth(1)\n",
" fig.frame.set_color('black')\n",
" fig.add_colorbar()\n",
" fig.colorbar.show()\n",
" fig.colorbar.set_location('top')\n",
" fig.colorbar.set_font(size='medium', weight='medium', \\\n",
" stretch='normal', family='sans-serif', \\\n",
" style='normal', variant='normal')\n",
" fig.list_layers()\n",
" #fig.axis_labels.hide_y()\n",
" #fig.tick_labels.hide_y()\n",
" #fig.axis_labels.hide_x()\n",
" #fig.tick_labels.hide_x()\n",
" \n",
"\n",
"\n",
"dirpath = r'./data/wise_image/'\n",
"outpath = r'./data/wise_image/'\n",
"\n",
"filepath1= dirpath+r'w4_cut.fits'\n",
"filepath2= dirpath+r'w3_cut.fits'\n",
"filepath3= dirpath+r'w2_cut.fits'\n",
"\n",
"aplpy.make_rgb_cube([filepath3,filepath2,filepath1], outpath+r'wise234_RGB.fits')\n",
"aplpy.make_rgb_image(outpath+r'wise234_RGB.fits',\n",
" outpath+r'rgb_image_wise234.png',\n",
" stretch_r='linear', \n",
" vmid_r=None,\n",
" pmin_r=10, \n",
" pmax_r=98,\n",
" exponent_r=2,\n",
" vmin_g=None,\n",
" vmax_g=None,\n",
" pmin_g=10,\n",
" pmax_g=98,\n",
" stretch_g='linear',\n",
" vmid_g=None,\n",
" exponent_g=2,\n",
" vmin_b=None,\n",
" vmax_b=None,\n",
" pmin_b=10,\n",
" pmax_b=98,\n",
" stretch_b='linear',\n",
" vmid_b=None)\n",
"fits.setval(outpath+r'wise234_RGB.fits','CTYPE3',value='RGB')\n",
"\n",
"\n",
"# Launch APLpy figure of 2D cube\n",
"img = aplpy.FITSFigure(outpath+r'wise234_RGB.fits',dimensions=[0,1],slices=[2],figsize=(10,9))\n",
"standard_setup(img)\n",
"img.recenter(084.78475,+30.09747,width=0.12,height=0.12)\n",
"img.show_rgb(outpath+r'rgb_image_wise234.png')\n",
"img.colorbar.hide()\n",
"img.add_label(0.9,0.9, 'WISE 2/3/4\\nRGB', color='black', relative=True, size='large',layer='source') \n",
"img.add_scalebar(0.05,\"1 pc\",color='white', corner='bottom left')\n",
"#add 850 um continuum contour\n",
"img.show_contour(r'./data/G178_final.850.fits',levels=np.linspace(0.08,0.23,5),colors='white')\n",
"# img.save(outpath+r'rgb_W3_contour.pdf')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Healpy \n",
"\n",
"[aplpyreaddocs](http://aplpy.readthedocs.io/en/stable/#)\n",
"\n",
"### - Introduction\n",
"\n",
" To take care of fits files of Healpix pixelization schemes (http://healpix.sourceforge.net/downloads.php), including fitsfile io and transformation to standard coordinate systems. \n",
"\n",
" This is particularly useful for all-sky survey such as Planck data, or SCUBA2 data release 1.\n",
"\n",
"### - "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno 2] No such file or directory: 'HFI_CompMap_CO-Type3_2048_R1.10.fits'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-9de64c481a11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;31m#read the fitsfile\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplankCO\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'HFI_CompMap_CO-Type3_2048_R1.10.fits'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#standard planck survey data from online archive\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mpixNum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8192\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/healpy/fitsfunc.pyc\u001b[0m in \u001b[0;36mread_map\u001b[1;34m(filename, field, dtype, nest, hdu, h, verbose, memmap)\u001b[0m\n\u001b[0;32m 252\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m \u001b[0mread\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mheader\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m*\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 253\u001b[0m \"\"\"\n\u001b[1;32m--> 254\u001b[1;33m \u001b[0mhdulist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 255\u001b[0m \u001b[1;31m#print hdulist[1].header\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 256\u001b[0m \u001b[0mnside\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhdulist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhdu\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'NSIDE'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfitsopen\u001b[1;34m(name, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Empty filename: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mHDUList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_backup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfromfile\u001b[1;34m(cls, fileobj, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 279\u001b[0m return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,\n\u001b[1;32m--> 280\u001b[1;33m save_backup=save_backup, cache=cache, **kwargs)\n\u001b[0m\u001b[0;32m 281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36m_readfrom\u001b[1;34m(cls, fileobj, data, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m 799\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_File\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 800\u001b[0m \u001b[1;31m# instantiate a FITS file object (ffo)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 801\u001b[1;33m \u001b[0mffo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_File\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 802\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 803\u001b[0m \u001b[0mffo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fileobj, mode, memmap, clobber, cache)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_fileobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_filename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 142\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_filelike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m_open_filename\u001b[1;34m(self, filename, mode, clobber)\u001b[0m\n\u001b[0;32m 491\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbzip2_mode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 492\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileobj_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPYFITS_MODES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[1;31m# Make certain we're back at the beginning of the file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/astropy/io/fits/util.pyc\u001b[0m in \u001b[0;36mfileobj_open\u001b[1;34m(filename, mode)\u001b[0m\n\u001b[0;32m 413\u001b[0m \"\"\"\n\u001b[0;32m 414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 415\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 417\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'HFI_CompMap_CO-Type3_2048_R1.10.fits'"
]
}
],
"source": [
"import os\n",
"import healpy as hp\n",
"from astropy.io import fits\n",
"import montage_wrapper as mt\n",
"\n",
"#read the fitsfile\n",
"plankCO = hp.read_map('HFI_CompMap_CO-Type3_2048_R1.10.fits')#standard planck survey data from online archive\n",
"pixNum = 8192\n",
"\n",
"#view the fitsfile in Cartesian projection\n",
"imageData = hp.cartview(plankCO, title='Histogram equalized Ecliptic', \n",
" unit='K km/s', min=1,max=500, cmap = 'gist_heat_r', \n",
" norm = 'log', cbar= False, return_projected_map = True, \n",
" xsize = pixNum)\n",
"\n",
"#fits header editing\n",
"hdu = fits.PrimaryHDU(imageData.data)\n",
"hdu.header['CTYPE1'] = 'GLON-CAR' \n",
"hdu.header['CRVAL1'] = 0.0 \n",
"hdu.header['CDELT1'] = -360.0/pixNum \n",
"hdu.header['CRPIX1'] = pixNum/2.0+0.5 \n",
"hdu.header['CTYPE2'] = 'GLAT-CAR' \n",
"hdu.header['CRVAL2'] = 0.0 \n",
"hdu.header['CDELT2'] = 360.0/pixNum \n",
"hdu.header['CRPIX2'] = pixNum/4.0+0.5\n",
"hdu.header['BUNIT'] = 'K*km/s'\n",
"hdu.header['TELESCOP'] = 'Planck'\n",
"hdu.writeto('PlanckCO_Type3_car.fits', clobber= True)\n",
"\n",
"#target fitsheader editing\n",
"hdrTAN = hdu.header.copy()\n",
"hdrTAN['CTYPE1'] = 'GLON-TAN'\n",
"hdrTAN['CTYPE2'] = 'GLAT-TAN'\n",
"\n",
"#fitsheader and file output\n",
"#use mongtage as the reprojection engine, one could also implement other reprojection packages\n",
"if os.path.isfile('TAN.hdr'):\n",
" os.remove('TAN.hdr')\n",
"\n",
"hdrTAN.totextfile('TAN.hdr')\n",
"\n",
"if os.path.isfile('PlanckCO_Type3_tan.fits'):\n",
" os.remove('PlanckCO_Type3_tan.fits')\n",
"\n",
"mt.reproject('PlanckCO_Type3_car.fits', \n",
" 'PlanckCO_Type3_tan.fits', \n",
" header = 'TAN.hdr')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| UserStudy/evaluation_pre_study.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-Processing"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def load_csvs(subject_id):\n",
" temp_events = \"study_data/s%d_events%s.csv\"\n",
" temp_gamestatus = \"study_data/s%d_gamestatus%s.csv\"\n",
" \n",
" events = []\n",
" gamestatus = []\n",
" split_counter = 0\n",
" split = \"\"\n",
"\n",
" while True:\n",
" if split_counter > 0:\n",
" split = \"_%d\" % split_counter\n",
" try:\n",
" ev_tmp = pd.read_csv(temp_events % (subject_id, split), sep=';')\n",
" gs_tmp = pd.read_csv(temp_gamestatus % (subject_id, split), sep=';')\n",
" ev_tmp['SubjectId'] = subject_id\n",
" gs_tmp['SubjectId'] = subject_id\n",
" ev_tmp['SplitCount'] = split_counter\n",
" gs_tmp['SplitCount'] = split_counter\n",
" events.append(ev_tmp)\n",
" gamestatus.append(gs_tmp)\n",
" \n",
" except FileNotFoundError:\n",
" break\n",
" \n",
" split_counter += 1\n",
" \n",
" return pd.concat(events).reset_index(), pd.concat(gamestatus).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def move_custom_data(re, rg):\n",
" rg[\"CamContXPos\"] = pd.Series(np.array(np.full(rg.size, 999), dtype='int16'))\n",
" rg[\"CamContYPos\"] = pd.Series(np.array(np.full(rg.size, 999), dtype='int16'))\n",
" rg[\"CamContZPos\"] = pd.Series(np.array(np.full(rg.size, 999), dtype='int16'))\n",
" \n",
" for index, re_row in re.copy().iterrows():\n",
" if re_row.TaskType == 'Custom':\n",
" rg_split_idx = rg.index[rg.SplitCount == re_row.SplitCount]\n",
" cam_cont_pos = re_row.TaskPos.strip('() ').split(',')\n",
" rg.loc[rg_split_idx, 'CamContXPos'] = cam_cont_pos[0]\n",
" rg.loc[rg_split_idx, 'CamContYPos'] = cam_cont_pos[1]\n",
" rg.loc[rg_split_idx, 'CamContZPos'] = cam_cont_pos[2]\n",
" re.drop(index, inplace=True)\n",
" continue\n",
" \n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def add_rc_count_to_events(re, rg):\n",
" re['RepeatCount'] = pd.Series(np.array(np.zeros(re.size), dtype='uint8'))\n",
" \n",
" last_time = 0\n",
" last_round_type = re.iloc[0].RoundType\n",
" last_split_count = 0\n",
" repeat_count = 0\n",
" start_index = 0\n",
" \n",
" for index, re_row in re.iterrows():\n",
" if (re_row.SplitCount != last_split_count) | (re_row.RoundType != last_round_type) | (index == re.index[-1]):\n",
" re.loc[start_index : index, 'RepeatCount'] = repeat_count\n",
" \n",
" sc_rt_rg_mask = (rg.SplitCount == last_split_count) & (rg.RoundType == last_round_type)\n",
" sc_rt_re_mask = (re.SplitCount == last_split_count) & (re.RoundType == last_round_type)\n",
"\n",
" last_rc_in_rg = rg.loc[(rg.Timestamp <= last_time) & sc_rt_rg_mask].RepeatCount.max()\n",
" # print (last_round_type, \"sc\", last_split_count, \"time\", last_time, \"rc\", repeat_count, \"lrc\", last_rc_in_rg)\n",
"\n",
" if repeat_count < last_rc_in_rg:\n",
" for rc in range(repeat_count+1, last_rc_in_rg+1):\n",
" rc_round = rg.loc[(rg.RepeatCount == rc) & sc_rt_rg_mask]\n",
" rc_start = rc_round.iloc[0].Timestamp\n",
" rc_end = rc_round.iloc[-1].Timestamp\n",
" \n",
" # print (last_round_type, repeat_count, last_rc_in_rg, rc_start, rc_end)\n",
" re.loc[(re.Timestamp >= rc_start) & (re.Timestamp <= rc_end) & sc_rt_re_mask, 'RepeatCount'] = rc\n",
" \n",
" start_index = index\n",
" repeat_count = 0\n",
" last_round_type = re_row.RoundType\n",
" last_split_count = re_row.SplitCount\n",
" \n",
" elif (re_row.Timestamp < last_time):\n",
" re.loc[start_index : index, 'RepeatCount'] = repeat_count\n",
" start_index = index\n",
" repeat_count += 1\n",
" \n",
" last_time = re_row.Timestamp\n",
" \n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"def create_rounds_df(re, rg):\n",
" round_columns = ['SubjectId', 'Round', 'RoundType', 'Trial', 'RepeatCount', 'SplitCount', 'ValidTrial', 'StartTime', 'EndTime', 'Duration']\n",
" round_data = []\n",
" \n",
" re[\"Trial\"] = pd.Series(np.array(np.zeros(re.size), dtype='uint8'))\n",
" re[\"ValidTrial\"] = pd.Series([], dtype=bool)\n",
" rg[\"Trial\"] = pd.Series(np.array(np.empty(re.size), dtype='uint8'))\n",
" rg[\"ValidTrial\"] = pd.Series([], dtype=bool)\n",
" \n",
" # iterate over all available rounds\n",
" for r in rg.Round.unique():\n",
" trialNum = 0\n",
" rg_round = rg.loc[rg.Round == r]\n",
" round_type = rg_round.iloc[0].RoundType\n",
" \n",
" re_round_idx = re.index[(re.RoundType == round_type)]\n",
" re.loc[re_round_idx, 'RoundType'] = round_type\n",
" \n",
" for sc in rg_round.SplitCount.unique():\n",
" rg_split = rg_round.loc[rg_round.SplitCount == sc]\n",
" \n",
" for rc in rg_split.RepeatCount.unique():\n",
" rg_trial = rg_split.loc[rg_split.RepeatCount == rc]\n",
" \n",
" trial_start = rg_trial.iloc[0].Timestamp\n",
" trial_end = rg_trial.iloc[-1].Timestamp\n",
" trial_dur = trial_end - trial_start\n",
" \n",
" rg_trial_idx = rg.index[(rg.Round == r) & (rg.SplitCount == sc) & (rg.RepeatCount == rc)]\n",
" re_trial_idx = re.index[(re.RoundType == round_type) & (re.SplitCount == sc) & (re.RepeatCount == rc)]\n",
" \n",
" if trial_dur <= 15:\n",
" rg.drop(rg_trial_idx, inplace=True)\n",
" re.drop(re_trial_idx, inplace=True)\n",
" continue\n",
" \n",
" re_trial = re.loc[re_trial_idx]\n",
" rings = re_trial.loc[(re_trial.TaskType == 'Ring') & (re_trial.TaskStatus != 'visible')].EventId.unique().size\n",
" povs = re_trial.loc[(re_trial.TaskType == 'POV') & (re_trial.TaskStatus != 'visible')].EventId.unique().size\n",
" \n",
" valid_trial = False\n",
"\n",
" if (round_type == 'Training_Ring_Only') & (rings == 20):\n",
" valid_trial = True\n",
" elif (round_type != 'Training_Ring_Only') & (povs == 9):\n",
" valid_trial = True\n",
" \n",
" round_data.append({\n",
" 'SubjectId': rg_round.iloc[0].SubjectId, 'Round': r, 'RoundType': round_type,\n",
" 'Trial': trialNum, 'RepeatCount': rc, 'SplitCount': sc,\n",
" 'ValidTrial': valid_trial, 'Duration': trial_dur})\n",
" \n",
" rg.loc[rg_trial_idx, 'Trial'] = trialNum\n",
" rg.loc[rg_trial_idx, 'ValidTrial'] = valid_trial\n",
" \n",
" re.loc[re_trial_idx, 'Trial'] = trialNum\n",
" re.loc[re_trial_idx, 'ValidTrial'] = valid_trial\n",
" \n",
" trialNum += 1\n",
" \n",
" return pd.DataFrame(data=round_data, columns=round_columns), re, rg"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def process_events(re, rg):\n",
" re[\"EndTime\"] = pd.Series([], dtype=float)\n",
" re[\"Duration\"] = pd.Series([], dtype=float)\n",
" re[\"Round\"] = pd.Series(np.array(np.zeros(re.size), dtype=\"uint8\"))\n",
"\n",
" for index, re_row in re.copy().iterrows():\n",
" if re_row.TaskStatus == 'visible':\n",
" started = re_row.Timestamp\n",
" rg_info = rg.loc[(rg.RoundType == re_row.RoundType) & (rg.Trial == re_row.Trial)].iloc[0]\n",
" re.loc[index,'Round'] = rg_info.Round\n",
"\n",
" is_corresponding_event = (re.EventId == re_row.EventId) & (re.TaskStatus != 'visible')\n",
" ce_idx = re.index[is_corresponding_event]\n",
" \n",
" if ce_idx.size > 0:\n",
" corresponding_event = re.loc[ce_idx].iloc[0]\n",
" finished = corresponding_event.Timestamp\n",
" duration = finished - started\n",
" status = corresponding_event.TaskStatus\n",
" re.drop(ce_idx, inplace=True)\n",
" else:\n",
" print('unfinshed event') \n",
" finished = np.nan\n",
" duration = np.nan\n",
" status = 'unfinished'\n",
" \n",
" re.loc[index,'EndTime'] = finished\n",
" re.loc[index, 'Duration'] = duration\n",
" re.loc[index, 'TaskStatus'] = status\n",
" \n",
" re = re.rename(columns = {'Timestamp': 'StartTime'})\n",
"\n",
" return re, rg"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"def process_gamestatus(rg):\n",
" # round pos and rot cols\n",
" rcols = ['PlayerXPos', 'PlayerYPos', 'PlayerZPos', 'MainCamXPos', 'MainCamYPos', 'MainCamZPos', 'PlayerXRot', 'PlayerYRot', 'PlayerZRot', 'MainCamXRot', 'MainCamYRot', 'MainCamZRot']\n",
" rg[rcols] = rg[rcols].round(3)\n",
" \n",
" rg['MainCamXRotRel'] = rg['MainCamXRot'] - rg['PlayerXRot']\n",
" rg['MainCamYRotRel'] = rg['MainCamYRot'] - rg['PlayerYRot']\n",
" rg['MainCamZRotRel'] = rg['MainCamZRot'] - rg['PlayerZRot']\n",
" \n",
" rg['MainCamXRotRelNorm'] = rg.apply(lambda row: ((row.MainCamXRotRel - 180) % 360 - 180), axis=1)\n",
" rg['MainCamYRotRelNorm'] = rg.apply(lambda row: ((row.MainCamYRotRel - 180) % 360 - 180), axis=1)\n",
" rg['MainCamZRotRelNorm'] = rg.apply(lambda row: ((row.MainCamZRotRel - 180) % 360 - 180), axis=1)\n",
" \n",
" return rg"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def fix_timestamps(ev, ga, ro):\n",
" total_dur = 0\n",
" for r in range(ro.Round.max() + 1):\n",
" curr_round = ro.loc[ro.Round == r]\n",
" trials = curr_round.Trial.max() + 1\n",
" \n",
" for t in range(trials):\n",
" trial_dur = curr_round.loc[curr_round.Trial == t].iloc[0].Duration\n",
" trial_mask = (ro.Round == r) & (ro.Trial == t)\n",
" ro.loc[trial_mask, 'StartTime'] = total_dur\n",
" ro.loc[trial_mask, 'EndTime'] = total_dur + trial_dur\n",
" \n",
" ga_trial_mask = (ga.Round == r) & (ga.Trial == t)\n",
" ev_trial_mask = (ev.Round == r) & (ev.Trial == t)\n",
" \n",
" ga_trial_start = ga.loc[ga_trial_mask].iloc[0].Timestamp\n",
" trial_time_offset = total_dur - ga_trial_start\n",
" \n",
" ga.loc[ga_trial_mask, 'Timestamp'] = ga.loc[ga_trial_mask, 'Timestamp'] + trial_time_offset\n",
" ev.loc[ev_trial_mask, 'StartTime'] = ev.loc[ev_trial_mask, 'StartTime'] + trial_time_offset\n",
" ev.loc[ev_trial_mask, 'EndTime'] = ev.loc[ev_trial_mask, 'EndTime'] + trial_time_offset\n",
" \n",
" total_dur += trial_dur + 0.001\n",
" return ro, ev, ga"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def preprocess(re, rg):\n",
" re = re.rename(columns = {\"EventInfo\" : \"TaskPos\", \"EventType\": \"TaskType\", \"EventStatus\": 'TaskStatus'})\n",
" \n",
" re, rg = move_custom_data(re, rg)\n",
" re, rg = add_rc_count_to_events(re, rg)\n",
" ro, re, rg = create_rounds_df(re, rg)\n",
" re, rg = process_events(re, rg)\n",
" rg = process_gamestatus(rg)\n",
" ro, re, rg = fix_timestamps(re, rg, ro)\n",
" \n",
" re = re[['SubjectId', 'EventId', 'Round', 'RoundType', 'Trial', 'ValidTrial',\n",
" 'TaskType', 'TaskStatus', 'TaskPos', 'Duration', 'StartTime', 'EndTime']]\n",
" \n",
" rg = rg[['SubjectId', 'Timestamp', 'Round', 'Trial',\n",
" 'PlayerXPos', 'PlayerYPos', 'PlayerZPos',\n",
" 'MainCamXPos', 'MainCamYPos', 'MainCamZPos',\n",
" 'CamContXPos', 'CamContYPos', 'CamContZPos',\n",
" 'PlayerXRot', 'PlayerYRot', 'PlayerZRot',\n",
" 'MainCamXRot', 'MainCamYRot', 'MainCamZRot',\n",
" 'MainCamXRotRel', 'MainCamYRotRel', 'MainCamZRotRel',\n",
" 'MainCamXRotRelNorm', 'MainCamYRotRelNorm', 'MainCamZRotRelNorm']]\n",
" \n",
" return ro, re, rg"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def create_final_csvs(write_csvs=False):\n",
" study_dict = { 'rounds': [], 'events': [], 'gamestatus': [] }\n",
" \n",
" for subject in range(4):\n",
" # check if data for the subject available\n",
" if len(glob.glob('study_data/s%d_*.csv' % subject)) < 2:\n",
" continue\n",
" \n",
" print('Subject #%d' % (subject))\n",
" \n",
" # load all csvs and concatenate splits if available\n",
" raw_events, raw_gamestatus = load_csvs(subject)\n",
" \n",
" # preprocess data\n",
" ro, ev, ga = preprocess(raw_events, raw_gamestatus)\n",
" \n",
" # add to data dict\n",
" study_dict['rounds'].append(ro)\n",
" study_dict['events'].append(ev)\n",
" study_dict['gamestatus'].append(ga)\n",
" \n",
" # clean index\n",
" ro_total = pd.concat(study_dict['rounds']).reset_index()\n",
" ev_total = pd.concat(study_dict['events']).reset_index()\n",
" gs_total = pd.concat(study_dict['gamestatus']).reset_index()\n",
" \n",
" print(gs_total)\n",
" \n",
" if write_csvs:\n",
" ro_total.to_excel('ro_all.xlsx')\n",
" ev_total.to_excel('ev_all.xlsx')\n",
" gs_total.to_excel('gs_all.xlsx')\n",
" \n",
" print('finished')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subject #3\n",
"unfinshed event\n",
" index SubjectId Timestamp Round Trial PlayerXPos PlayerYPos \\\n",
"0 0 3 0.000 0 0 5.483 3.150 \n",
"1 1 3 0.133 0 0 8.283 3.150 \n",
"2 2 3 0.268 0 0 11.224 3.150 \n",
"3 3 3 0.401 0 0 14.231 3.150 \n",
"4 4 3 0.536 0 0 17.309 3.150 \n",
"5 5 3 0.669 0 0 20.390 3.150 \n",
"6 6 3 0.805 0 0 23.501 3.150 \n",
"7 7 3 0.950 0 0 26.817 3.150 \n",
"8 8 3 1.094 0 0 30.064 3.150 \n",
"9 9 3 1.228 0 0 33.015 3.150 \n",
"10 10 3 1.363 0 0 35.927 3.150 \n",
"11 11 3 1.496 0 0 38.746 3.150 \n",
"12 12 3 1.632 0 0 41.591 3.150 \n",
"13 13 3 1.764 0 0 44.335 3.150 \n",
"14 14 3 1.900 0 0 47.111 3.150 \n",
"15 15 3 2.033 0 0 49.778 3.150 \n",
"16 16 3 2.166 0 0 52.437 3.150 \n",
"17 17 3 2.314 0 0 55.378 3.150 \n",
"18 18 3 2.457 0 0 58.299 3.150 \n",
"19 19 3 2.593 0 0 61.139 3.150 \n",
"20 20 3 2.724 0 0 63.965 3.150 \n",
"21 21 3 2.859 0 0 66.896 3.150 \n",
"22 22 3 2.993 0 0 69.835 3.150 \n",
"23 23 3 3.127 0 0 72.788 3.150 \n",
"24 24 3 3.264 0 0 75.818 3.150 \n",
"25 25 3 3.397 0 0 78.766 3.150 \n",
"26 26 3 3.529 0 0 81.713 3.150 \n",
"27 27 3 3.643 0 0 84.258 3.150 \n",
"28 28 3 3.775 0 0 87.198 3.150 \n",
"29 29 3 3.908 0 0 90.168 3.150 \n",
"... ... ... ... ... ... ... ... \n",
"6917 6917 3 931.467 5 0 5844.582 3.174 \n",
"6918 6918 3 931.611 5 0 5842.312 3.174 \n",
"6919 6919 3 931.745 5 0 5840.187 3.174 \n",
"6920 6920 3 931.879 5 0 5838.049 3.174 \n",
"6921 6921 3 932.013 5 0 5835.883 3.174 \n",
"6922 6922 3 932.147 5 0 5833.688 3.174 \n",
"6923 6923 3 932.281 5 0 5831.468 3.174 \n",
"6924 6924 3 932.415 5 0 5829.216 3.174 \n",
"6925 6925 3 932.550 5 0 5826.915 3.174 \n",
"6926 6926 3 932.683 5 0 5824.612 3.174 \n",
"6927 6927 3 932.817 5 0 5822.273 3.174 \n",
"6928 6928 3 932.951 5 0 5819.901 3.174 \n",
"6929 6929 3 933.085 5 0 5817.506 3.174 \n",
"6930 6930 3 933.219 5 0 5815.087 3.174 \n",
"6931 6931 3 933.355 5 0 5812.617 3.174 \n",
"6932 6932 3 933.488 5 0 5810.140 3.174 \n",
"6933 6933 3 933.614 5 0 5807.786 3.174 \n",
"6934 6934 3 933.756 5 0 5805.102 3.174 \n",
"6935 6935 3 933.890 5 0 5802.544 3.174 \n",
"6936 6936 3 934.024 5 0 5799.967 3.174 \n",
"6937 6937 3 934.157 5 0 5797.360 3.174 \n",
"6938 6938 3 934.292 5 0 5794.719 3.174 \n",
"6939 6939 3 934.426 5 0 5792.047 3.174 \n",
"6940 6940 3 934.550 5 0 5789.562 3.174 \n",
"6941 6941 3 934.694 5 0 5786.646 3.174 \n",
"6942 6942 3 934.817 5 0 5784.132 3.174 \n",
"6943 6943 3 934.951 5 0 5781.359 3.174 \n",
"6944 6944 3 935.075 5 0 5778.790 3.174 \n",
"6945 6945 3 935.200 5 0 5776.145 3.174 \n",
"6946 6946 3 935.348 5 0 5773.058 3.174 \n",
"\n",
" PlayerZPos MainCamXPos MainCamYPos ... PlayerZRot \\\n",
"0 99.500 5.654 3.724 ... 0.0 \n",
"1 101.334 8.462 3.724 ... 0.0 \n",
"2 102.959 11.407 3.724 ... -0.0 \n",
"3 104.409 14.417 3.724 ... -0.0 \n",
"4 105.768 17.494 3.723 ... -0.0 \n",
"5 107.072 20.573 3.723 ... -0.0 \n",
"6 108.388 23.681 3.723 ... 0.0 \n",
"7 109.849 26.991 3.722 ... -0.0 \n",
"8 111.415 30.232 3.721 ... -0.0 \n",
"9 113.000 33.178 3.721 ... 0.0 \n",
"10 114.714 36.096 3.720 ... 0.0 \n",
"11 116.476 38.930 3.716 ... 0.0 \n",
"12 118.328 41.790 3.712 ... 0.0 \n",
"13 120.181 44.542 3.708 ... 0.0 \n",
"14 122.140 47.318 3.708 ... 0.0 \n",
"15 124.111 49.982 3.710 ... 0.0 \n",
"16 126.130 52.641 3.711 ... 0.0 \n",
"17 128.337 55.589 3.712 ... 0.0 \n",
"18 130.409 58.519 3.711 ... 0.0 \n",
"19 132.270 61.364 3.711 ... 0.0 \n",
"20 133.980 64.189 3.713 ... 0.0 \n",
"21 135.645 67.115 3.714 ... 0.0 \n",
"22 137.240 70.050 3.714 ... 0.0 \n",
"23 138.807 72.997 3.715 ... -0.0 \n",
"24 140.406 76.022 3.716 ... 0.0 \n",
"25 141.945 78.968 3.717 ... 0.0 \n",
"26 143.458 81.912 3.717 ... 0.0 \n",
"27 144.748 84.456 3.718 ... -0.0 \n",
"28 146.225 87.394 3.718 ... 0.0 \n",
"29 147.727 90.361 3.718 ... -0.0 \n",
"... ... ... ... ... ... \n",
"6917 -663.633 5844.542 4.163 ... 0.0 \n",
"6918 -666.433 5842.267 4.163 ... 0.0 \n",
"6919 -669.027 5840.139 4.162 ... 0.0 \n",
"6920 -671.593 5837.999 4.162 ... 0.0 \n",
"6921 -674.145 5835.831 4.162 ... 0.0 \n",
"6922 -676.684 5833.638 4.161 ... 0.0 \n",
"6923 -679.193 5831.419 4.161 ... 0.0 \n",
"6924 -681.671 5829.171 4.160 ... 0.0 \n",
"6925 -684.139 5826.871 4.159 ... 0.0 \n",
"6926 -686.549 5824.566 4.158 ... 0.0 \n",
"6927 -688.941 5822.226 4.158 ... 0.0 \n",
"6928 -691.316 5819.851 4.157 ... 0.0 \n",
"6929 -693.662 5817.454 4.157 ... 0.0 \n",
"6930 -695.976 5815.033 4.156 ... 0.0 \n",
"6931 -698.280 5812.562 4.157 ... 0.0 \n",
"6932 -700.531 5810.083 4.156 ... 0.0 \n",
"6933 -702.619 5807.728 4.156 ... 0.0 \n",
"6934 -704.943 5805.043 4.156 ... 0.0 \n",
"6935 -707.104 5802.484 4.156 ... 0.0 \n",
"6936 -709.230 5799.906 4.156 ... 0.0 \n",
"6937 -711.328 5797.296 4.156 ... 0.0 \n",
"6938 -713.396 5794.650 4.156 ... 0.0 \n",
"6939 -715.431 5791.970 4.155 ... 0.0 \n",
"6940 -717.278 5789.477 4.154 ... 0.0 \n",
"6941 -719.386 5786.554 4.153 ... 0.0 \n",
"6942 -721.150 5784.035 4.153 ... 0.0 \n",
"6943 -723.038 5781.260 4.152 ... 0.0 \n",
"6944 -724.743 5778.693 4.150 ... 0.0 \n",
"6945 -726.465 5776.054 4.147 ... 0.0 \n",
"6946 -728.452 5772.973 4.146 ... 0.0 \n",
"\n",
" MainCamXRot MainCamYRot MainCamZRot MainCamXRotRel MainCamYRotRel \\\n",
"0 356.882 69.661 359.885 356.882 14.906 \n",
"1 356.571 72.828 359.870 356.571 13.383 \n",
"2 356.467 74.320 359.827 356.467 11.182 \n",
"3 356.658 74.779 359.626 356.658 9.214 \n",
"4 356.922 74.438 359.542 356.922 7.595 \n",
"5 357.014 73.222 359.701 357.014 6.023 \n",
"6 357.389 70.864 359.865 357.389 4.110 \n",
"7 358.036 67.340 359.890 358.036 2.101 \n",
"8 358.411 63.373 0.175 358.411 0.583 \n",
"9 358.500 60.856 0.428 358.500 0.483 \n",
"10 358.777 59.849 0.635 358.777 1.354 \n",
"11 0.441 59.512 0.296 0.441 2.164 \n",
"12 2.391 59.219 0.211 2.391 2.841 \n",
"13 4.031 58.216 359.896 4.031 2.887 \n",
"14 4.143 57.117 359.738 4.143 3.091 \n",
"15 3.631 56.950 359.632 3.631 3.996 \n",
"16 3.188 58.052 359.445 3.188 5.274 \n",
"17 2.892 60.626 359.524 2.892 6.786 \n",
"18 3.140 63.926 359.339 3.140 8.066 \n",
"19 3.006 66.259 358.995 3.006 8.227 \n",
"20 2.486 67.495 358.734 2.486 7.681 \n",
"21 2.129 67.655 359.136 2.129 6.513 \n",
"22 1.917 66.985 359.561 1.917 5.065 \n",
"23 1.637 66.817 359.794 1.637 4.684 \n",
"24 1.310 66.681 359.949 1.310 4.379 \n",
"25 0.910 67.216 0.067 0.910 4.571 \n",
"26 0.759 67.345 0.173 0.759 4.312 \n",
"27 0.372 67.400 0.190 0.372 4.122 \n",
"28 0.153 66.598 0.002 0.153 3.291 \n",
"29 0.133 66.836 359.821 0.133 3.893 \n",
"... ... ... ... ... ... \n",
"6917 355.577 226.070 358.637 355.577 7.038 \n",
"6918 356.058 228.374 358.505 356.058 9.233 \n",
"6919 356.754 229.342 358.757 356.754 9.756 \n",
"6920 357.090 229.695 359.166 357.090 9.595 \n",
"6921 357.465 229.434 359.966 357.465 8.823 \n",
"6922 357.814 227.861 1.559 357.814 6.664 \n",
"6923 358.069 226.228 2.761 358.069 4.290 \n",
"6924 358.590 224.722 4.087 358.590 2.018 \n",
"6925 358.810 224.563 4.811 358.810 1.139 \n",
"6926 359.031 225.597 4.998 359.031 1.511 \n",
"6927 359.320 226.904 5.432 359.320 2.193 \n",
"6928 359.710 228.047 5.736 359.710 2.719 \n",
"6929 0.118 228.861 5.904 0.118 2.888 \n",
"6930 0.242 229.644 5.940 0.242 2.962 \n",
"6931 0.177 230.498 5.946 0.177 3.068 \n",
"6932 0.107 231.050 5.982 0.107 2.896 \n",
"6933 0.196 231.496 6.150 0.196 2.699 \n",
"6934 0.161 231.810 6.267 0.161 2.287 \n",
"6935 359.706 232.360 6.255 359.706 2.157 \n",
"6936 359.650 233.022 6.359 359.650 2.137 \n",
"6937 359.701 233.839 6.446 359.701 2.225 \n",
"6938 359.496 237.636 6.829 359.496 5.260 \n",
"6939 359.823 246.514 7.749 359.823 13.382 \n",
"6940 359.922 253.160 8.462 359.922 19.372 \n",
"6941 359.752 258.419 8.861 359.752 23.783 \n",
"6942 359.726 259.509 8.589 359.726 24.094 \n",
"6943 0.653 255.099 7.522 0.653 18.915 \n",
"6944 2.432 245.570 6.173 2.432 8.820 \n",
"6945 4.226 237.167 5.117 4.226 0.039 \n",
"6946 6.180 231.854 4.755 6.180 -5.452 \n",
"\n",
" MainCamZRotRel MainCamXRotRelNorm MainCamYRotRelNorm \\\n",
"0 359.885 -3.118 14.906 \n",
"1 359.870 -3.429 13.383 \n",
"2 359.827 -3.533 11.182 \n",
"3 359.626 -3.342 9.214 \n",
"4 359.542 -3.078 7.595 \n",
"5 359.701 -2.986 6.023 \n",
"6 359.865 -2.611 4.110 \n",
"7 359.890 -1.964 2.101 \n",
"8 0.175 -1.589 0.583 \n",
"9 0.428 -1.500 0.483 \n",
"10 0.635 -1.223 1.354 \n",
"11 0.296 0.441 2.164 \n",
"12 0.211 2.391 2.841 \n",
"13 359.896 4.031 2.887 \n",
"14 359.738 4.143 3.091 \n",
"15 359.632 3.631 3.996 \n",
"16 359.445 3.188 5.274 \n",
"17 359.524 2.892 6.786 \n",
"18 359.339 3.140 8.066 \n",
"19 358.995 3.006 8.227 \n",
"20 358.734 2.486 7.681 \n",
"21 359.136 2.129 6.513 \n",
"22 359.561 1.917 5.065 \n",
"23 359.794 1.637 4.684 \n",
"24 359.949 1.310 4.379 \n",
"25 0.067 0.910 4.571 \n",
"26 0.173 0.759 4.312 \n",
"27 0.190 0.372 4.122 \n",
"28 0.002 0.153 3.291 \n",
"29 359.821 0.133 3.893 \n",
"... ... ... ... \n",
"6917 358.637 -4.423 7.038 \n",
"6918 358.505 -3.942 9.233 \n",
"6919 358.757 -3.246 9.756 \n",
"6920 359.166 -2.910 9.595 \n",
"6921 359.966 -2.535 8.823 \n",
"6922 1.559 -2.186 6.664 \n",
"6923 2.761 -1.931 4.290 \n",
"6924 4.087 -1.410 2.018 \n",
"6925 4.811 -1.190 1.139 \n",
"6926 4.998 -0.969 1.511 \n",
"6927 5.432 -0.680 2.193 \n",
"6928 5.736 -0.290 2.719 \n",
"6929 5.904 0.118 2.888 \n",
"6930 5.940 0.242 2.962 \n",
"6931 5.946 0.177 3.068 \n",
"6932 5.982 0.107 2.896 \n",
"6933 6.150 0.196 2.699 \n",
"6934 6.267 0.161 2.287 \n",
"6935 6.255 -0.294 2.157 \n",
"6936 6.359 -0.350 2.137 \n",
"6937 6.446 -0.299 2.225 \n",
"6938 6.829 -0.504 5.260 \n",
"6939 7.749 -0.177 13.382 \n",
"6940 8.462 -0.078 19.372 \n",
"6941 8.861 -0.248 23.783 \n",
"6942 8.589 -0.274 24.094 \n",
"6943 7.522 0.653 18.915 \n",
"6944 6.173 2.432 8.820 \n",
"6945 5.117 4.226 0.039 \n",
"6946 4.755 6.180 -5.452 \n",
"\n",
" MainCamZRotRelNorm \n",
"0 -0.115 \n",
"1 -0.130 \n",
"2 -0.173 \n",
"3 -0.374 \n",
"4 -0.458 \n",
"5 -0.299 \n",
"6 -0.135 \n",
"7 -0.110 \n",
"8 0.175 \n",
"9 0.428 \n",
"10 0.635 \n",
"11 0.296 \n",
"12 0.211 \n",
"13 -0.104 \n",
"14 -0.262 \n",
"15 -0.368 \n",
"16 -0.555 \n",
"17 -0.476 \n",
"18 -0.661 \n",
"19 -1.005 \n",
"20 -1.266 \n",
"21 -0.864 \n",
"22 -0.439 \n",
"23 -0.206 \n",
"24 -0.051 \n",
"25 0.067 \n",
"26 0.173 \n",
"27 0.190 \n",
"28 0.002 \n",
"29 -0.179 \n",
"... ... \n",
"6917 -1.363 \n",
"6918 -1.495 \n",
"6919 -1.243 \n",
"6920 -0.834 \n",
"6921 -0.034 \n",
"6922 1.559 \n",
"6923 2.761 \n",
"6924 4.087 \n",
"6925 4.811 \n",
"6926 4.998 \n",
"6927 5.432 \n",
"6928 5.736 \n",
"6929 5.904 \n",
"6930 5.940 \n",
"6931 5.946 \n",
"6932 5.982 \n",
"6933 6.150 \n",
"6934 6.267 \n",
"6935 6.255 \n",
"6936 6.359 \n",
"6937 6.446 \n",
"6938 6.829 \n",
"6939 7.749 \n",
"6940 8.462 \n",
"6941 8.861 \n",
"6942 8.589 \n",
"6943 7.522 \n",
"6944 6.173 \n",
"6945 5.117 \n",
"6946 4.755 \n",
"\n",
"[6947 rows x 26 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"finished\n"
]
}
],
"source": [
"create_final_csvs(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data-Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ro_tot = pd.read_excel('rounds_total.xlsx')\n",
"ev_tot = pd.read_excel('events_total.xlsx')\n",
"gs_tot = pd.read_excel('gamestatus_total.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def get_motion_data(gs, started, finished):\n",
" gidx = gs.index[(gs.Timestamp >= started) & (gs.Timestamp <= finished)]\n",
" md = gs.loc[gidx, ['PlayerYRot', 'PlayerXRot', 'MainCamYRotRelNorm', 'MainCamXRotRelNorm']]\n",
" return md"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gewinnspiel Auswertung"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"columns = ['SubjectId', 'RoundType', 'RoundScore', 'PovSucc', 'PovTotal', 'RingSucc', 'RingTotal']\n",
"data = []\n",
"\n",
"def filter_ev(group):\n",
" return ((group.Trial == group.Trial.max())\n",
" & group.ValidTrial \n",
" & (~group.RoundType.isin(['Training_Ring_Only', 'Training_Complete']))).any()\n",
"\n",
"def round_score(x):\n",
" pov_succ = x[(x.TaskType == 'POV') & (x.TaskStatus == 'success')].EventId.size\n",
" pov_tot = x[(x.TaskType == 'POV')].EventId.size\n",
" ring_succ = x[(x.TaskType == 'Ring') & (x.TaskStatus == 'success')].EventId.size\n",
" ring_tot = x[(x.TaskType == 'Ring')].EventId.size\n",
" round_score = pov_succ * (ring_succ / ring_tot)\n",
" data.append({'SubjectId': x.iloc[0].SubjectId, 'RoundType': x.iloc[0].RoundType, \n",
" 'RoundScore': round_score, 'PovSucc': pov_succ, 'PovTotal': pov_tot,\n",
" 'RingSucc': ring_succ, 'RingTotal': ring_tot})\n",
" return round_score\n",
"\n",
"grouped = ev_tot.groupby(['SubjectId', 'RoundType', 'Trial'])\n",
"ev_red = grouped.filter(filter_ev)\n",
"\n",
"#print(ev_red[(ev_red.SubjectId == 3) & (ev_red.TaskType == 'POV')])\n",
"\n",
"round_score_group = ev_red.groupby(['SubjectId', 'RoundType']).apply(round_score)\n",
"total_score = round_score_group.groupby(['SubjectId']).agg({'Sum': 'sum'}).sort_values(by=\"SubjectId\", ascending=True)\n",
"print(total_score)\n",
"\n",
"#df = pd.DataFrame(data=data, columns=columns)\n",
"#df.to_excel('scores.xlsx')\n",
"\n",
"#print(filtered_ev_tot.groupby(['SubjectId', 'RoundType', 'TaskType', 'TaskStatus'])['EventId'].agg({\"Count\": 'count'}).to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### POV Selection Times by Round Type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pov = ev_tot[(ev_tot.RoundType != 'Training_Complete') &(ev_tot.TaskType == 'POV') & (ev_tot.TaskStatus == 'success')]\n",
"# Fehlerhafte EInträge entfernen\n",
"pov = pov.drop(pov[(pov.RoundType == 'Audio') & (pov.Duration > 10)].index)\n",
"plot = pov.boxplot(column=['Duration'], by='RoundType', figsize=(20,10))\n",
"fig = plot.get_figure()\n",
"fig.savefig(\"output.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feedback Reaction Times"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for rt in ev_tot.RoundType.unique():\n",
" round_ev = ev_tot[ev_tot.RoundType == rt]\n",
" round_pov = events[events.TaskType == 'POV']\n",
" #pov_succ = pov_events.loc[events.Status == 'success']\n",
" \n",
" #ring_events = events.loc[(eve nts.TaskType == 'Ring')]\n",
" #ring_succ = ring_events.loc[events.Status == 'success'].shape[0]\n",
" #ring_fail = ring_events.loc[events.Status == 'timeout'].shape[0]\n",
" #pov_fail = pov_events.loc[events.Status == 'timeout'].shape[0]\n",
"\n",
" for pos in round_pov.Position.unique():\n",
" print(\"Plot for %s, %s\" % (rt, pos))\n",
" round_pov_pos = round_pov[round_pov.Position == pos]\n",
" round_pov_pos.plot(kind=\"scatter\", x=\"MainCamYRot\", y=\"MainCamXRot\", xlim=(-360,360), ylim=(180,-180))\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
mayankjohri/LetsExplorePython | Section 2 - Advance Python/Chapter S2.89: Code Review/02. Tools.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tools"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main =[]\n",
"main.append(sorted(['B','C', 'A' ,'F', 'H']))\n",
"main.append(sorted(['F', 'E', 'C', 'H']))\n",
"main.append(sorted(['E' ,'D', 'B']))\n",
"main.append(sorted(['A', 'C' ,'H', 'F']))\n",
"main.append(sorted(['E', 'F' ,'A']))\n",
"main.append(sorted(['D', 'H' ,'B']))\n",
"main.append(sorted(['E', 'C' ,'F', 'B', 'D']))\n",
"main.append(sorted(['A', 'H' ,'C', 'E']))\n",
"main.append(sorted(['G', 'A', 'E']))\n",
"main.append(sorted(['B', 'H', 'E']))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lst={}\n",
"def addelement(element):\n",
" if element not in lst.keys():\n",
" lst[element]=1\n",
" else:\n",
" lst[element]+=1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in main:\n",
" for j in i:\n",
" addelement(j)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n",
"A 5\n",
"C 5\n",
"B 5\n",
"E 7\n",
"D 3\n",
"G 1\n",
"F 5\n",
"H 6\n",
"------ after cleaning--------\n",
"A 5\n",
"C 5\n",
"B 5\n",
"E 7\n",
"D 3\n",
"G 1\n",
"F 5\n",
"H 6\n",
"8\n"
]
}
],
"source": [
"print len(lst.keys())\n",
"for key in lst.keys():\n",
" print key,lst[key]\n",
" #if lst[key]<3:\n",
" # lst.pop(key,None)\n",
"print '------ after cleaning--------'\n",
"for key in lst.keys():\n",
" print key,lst[key]\n",
"print len(lst.keys())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lst2items={}\n",
"def addelement2(el):\n",
" el = ''.join(map(str.strip,el))\n",
" if el not in lst2items.keys():\n",
" lst2items[el]=1\n",
" else:\n",
" lst2items[el]+=1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def checkcontain(myelements,thelist):\n",
" for i in myelements:\n",
" if i.strip() not in thelist:\n",
" return False\n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"newcomb = itertools.combinations(lst.keys(), 2)\n",
"\n",
"for i in newcomb:\n",
" for j in main:\n",
" if checkcontain(i,j):\n",
" addelement2(i)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BD 3\n",
"BE 3\n",
"BF 2\n",
"DH 1\n",
"DF 1\n",
"BH 3\n",
"CH 4\n",
"FH 3\n",
"ED 2\n",
"CB 2\n",
"AC 3\n",
"AB 1\n",
"AE 3\n",
"EH 3\n",
"AG 1\n",
"AF 3\n",
"AH 3\n",
"EG 1\n",
"EF 3\n",
"CF 4\n",
"CE 3\n",
"CD 1\n",
"------ after cleaning--------\n",
"BD 3\n",
"BE 3\n",
"BH 3\n",
"CH 4\n",
"FH 3\n",
"AC 3\n",
"AE 3\n",
"EH 3\n",
"AF 3\n",
"AH 3\n",
"EF 3\n",
"CF 4\n",
"CE 3\n"
]
}
],
"source": [
"for key in lst2items.keys():\n",
" print key,lst2items[key]\n",
" if lst2items[key]<3:\n",
" lst2items.pop(key,None)\n",
"print '------ after cleaning--------'\n",
"for key in lst2items.keys():\n",
" print key,lst2items[key]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
Nilesh4145/Deep-Learning-Modules |
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Polynomial Regression using SciKit Learn"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Date Created: 28/03/2017\n",
"\n",
"Author: Nilesh Chaturvedi\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#import necessary libraries\n",
"from sklearn import linear_model\n",
"from sklearn.preprocessing import normalize, PolynomialFeatures\n",
"import matplotlib.pyplot as plt\n",
"import csv\n",
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#Load Data\n",
"def load_data(filename):\n",
" \n",
" file_data = csv.reader(open(filename, \"r\"), delimiter = \",\")\n",
" training_data = []\n",
" testing_data = []\n",
" for training_example in list(file_data)[2:]:\n",
" if training_example[5]!=\"NaN\":\n",
" training_data.append([float(feature) for feature in training_example[:6]])\n",
" else:\n",
" testing_data.append([float(feature) for feature in training_example[:5]])\n",
" \n",
" return numpy.array(training_data), numpy.array(testing_data) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def polynomial_regression_model(training):\n",
" \n",
" # Extract the features from training data.\n",
" training_x = training[:,:5]\n",
" \n",
" # Extract values corresponding to every training example.\n",
" training_y = (training[:,5])[:,numpy.newaxis]\n",
" \n",
" #normalize the data\n",
" normalized_x = normalize(training_x, norm='l1', axis=0)\n",
" normalized_y = normalize(training_y, norm='l1', axis=0)\n",
" \n",
" # Make a polynomial transform of the data.\n",
" feature_transform = PolynomialFeatures(degree=2)\n",
" polynomial_x = feature_transform.fit_transform(normalized_x)\n",
" \n",
" #Fit linear model to transformed data\n",
" polynomial = linear_model.LinearRegression()\n",
" polynomial.fit(polynomial_x, normalized_y)\n",
" \n",
" return polynomial"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 0.25179856 0.09090909 0.07078233 0.12459372 0.13651425\n",
" 0.06340252 0.02289078 0.01782289 0.03137252 0.03437409 0.00826446\n",
" 0.00643476 0.0113267 0.01241039 0.00501014 0.00881903 0.0096628\n",
" 0.01552359 0.01700882 0.01863614] : [ 0.80778098]\n",
"\n",
"\n",
"[ 1. 0.16546763 0.18181818 0.18626929 0.1787649 0.18299094\n",
" 0.02737954 0.03008502 0.03082154 0.0295798 0.03027908 0.03305785\n",
" 0.03386714 0.03250271 0.03327108 0.03469625 0.03329841 0.03408559\n",
" 0.03195689 0.03271236 0.03348569] : [ 1.0525165]\n",
"\n",
"\n",
"[ 1. 0.15827338 0.18181818 0.18680149 0.16576381 0.17821957\n",
" 0.02505046 0.02877698 0.0295657 0.026236 0.02820741 0.03305785\n",
" 0.03396391 0.03013888 0.03240356 0.0348948 0.03096493 0.03329168\n",
" 0.02747764 0.02954236 0.03176222] : [ 0.82236822]\n",
"\n",
"\n",
"[ 1. 0.15107914 0.18181818 0.20383183 0.18959913 0.18405125\n",
" 0.02282491 0.02746893 0.03079474 0.02864447 0.0278063 0.03305785\n",
" 0.03706033 0.03447257 0.03346386 0.04154741 0.03864634 0.0375155\n",
" 0.03594783 0.03489596 0.03387486] : [ 1.38945432]\n",
"\n",
"\n",
"[ 1. 0.15827338 0.18181818 0.19159127 0.18959913 0.17009057\n",
" 0.02505046 0.02877698 0.0303238 0.0300085 0.02692081 0.03305785\n",
" 0.03483478 0.03447257 0.03092556 0.03670722 0.03632554 0.03258787\n",
" 0.03594783 0.03224902 0.0289308 ] : [ 1.20438739]\n",
"\n",
"\n",
"[ 1. 0.11510791 0.18181818 0.16072379 0.15167931 0.14813342\n",
" 0.01324983 0.02092871 0.01850058 0.01745949 0.01705133 0.03305785\n",
" 0.02922251 0.02757806 0.02693335 0.02583214 0.02437847 0.02380856\n",
" 0.02300661 0.02246877 0.02194351] : [ 0.41178383]\n",
"\n",
"\n",
"Polynomial Model Statistics \n",
"\n",
"Weights : [[ 0.00000000e+00 -1.54965346e-01 3.26930705e-01 -3.24509783e-01\n",
" -1.62920171e+00 -6.54069097e-01 5.58070010e+01 -3.31443610e+02\n",
" 2.19521593e+02 2.08293138e+01 -1.98514294e+01 -1.38209366e+02\n",
" -1.87109519e+02 -3.64895489e+01 7.27329160e+02 8.94498322e+01\n",
" 8.52965878e+01 -2.31500988e+02 -5.95603377e+01 1.51761024e+02\n",
" -3.01634261e+02]] \n",
"Bias : [ 0.0316951]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd1eea365f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if __name__ == \"__main__\":\n",
" \n",
" input_data = load_data(\"data_carsmall.csv\")\n",
" training_data = input_data[0]\n",
" \n",
" normalized_test = normalize(input_data[1], norm = 'l1', axis = 0)\n",
" feature_transform = PolynomialFeatures(degree=2)\n",
" to_be_predicted = feature_transform.fit_transform(normalized_test)\n",
" \n",
" #Estimate using polynomial model\n",
" polynomial_model = polynomial_regression_model(training_data)\n",
" polynomial_model_output = polynomial_model.predict(to_be_predicted )\n",
" \n",
" for point in range(len(to_be_predicted)):\n",
" print(str(to_be_predicted[point]) + \" : \" + str(polynomial_model_output[point]) + \"\\n\\n\")\n",
" \n",
" print(\"Polynomial Model Statistics \\n\\nWeights : {} \\nBias : {}\".format(polynomial_model.coef_, polynomial_model.intercept_))\n",
" \n",
" plt.scatter(to_be_predicted[:,0], polynomial_model_output, label = \"Feature 1\")\n",
" plt.scatter(to_be_predicted[:,1], polynomial_model_output, label = \"Feature 2\")\n",
" plt.scatter(to_be_predicted[:,2], polynomial_model_output, label = \"Feature 3\")\n",
" plt.scatter(to_be_predicted[:,3], polynomial_model_output, label = \"Feature 4\")\n",
" plt.scatter(to_be_predicted[:,4], polynomial_model_output, label = \"Feature 5\")\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2+"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
|
|
|
| {
"metadata": {
"name": "tpl_maps"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mapping utilities and options\n",
"==\n",
"***\n",
"\n",
"This notebook illustrate how to map SuperDARN radars and FoVs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"############################################\n",
"# This code adds davitpy to your python path\n",
"# Eventually, this won't be necessary\n",
"import sys\n",
"sys.path.append('/davitpy')\n",
"############################################"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pydarn.radar import *\n",
"from pydarn.plotting import *\n",
"from utils import *\n",
"import datetime as dt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot all radars in AACGM coordinates\n",
"Be patient, this takes a few seconds (so many radars, not to mention the coordinate calculatsions)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(15,10))\n",
"# Plot map\n",
"subplot(121)\n",
"m1 = plotUtils.mapObj(boundinglat=30., gridLabels=True, coords='mag')\n",
"overlayRadar(m1, fontSize=8, all=True, markerSize=5)\n",
"subplot(122)\n",
"m2 = plotUtils.mapObj(boundinglat=-30., gridLabels=True, coords='mag')\n",
"overlayRadar(m2, fontSize=8, all=True, markerSize=5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAGWCAYAAAAnoVHSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot all radars in geographic coordinates\n",
"This is a bit faster (but there are still lots of radars)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(15,10))\n",
"# Plot map\n",
"subplot(121)\n",
"m1 = plotUtils.mapObj(boundinglat=30., gridLabels=False)\n",
"overlayRadar(m1, fontSize=8, all=True, markerSize=5)\n",
"subplot(122)\n",
"m2 = plotUtils.mapObj(boundinglat=-30., gridLabels=False)\n",
"overlayRadar(m2, fontSize=8, all=True, markerSize=5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA1wAAAGKCAYAAAAYHUDfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot a single radar, highlight beams\n",
"Still a bit slow due to aacgm coordinates"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set map\n",
"figure(figsize=(10,10))\n",
"width = 111e3*40\n",
"m = plotUtils.mapObj(width=width, height=width, lat_0=60., lon_0=-30, coords='mag')\n",
"code = 'bks'\n",
"# Plotting some radars\n",
"overlayRadar(m, fontSize=12, codes=code)\n",
"# Plot radar fov\n",
"overlayFov(m, codes=code, maxGate=75, beams=[0,4,7,8,23])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAJJCAYAAADm/zAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot a nice view of the mid-latitude radars"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set map\n",
"fig = figure(figsize=(10,10))\n",
"m = plotUtils.mapObj(lat_0=70., lon_0=-60, width=111e3*120, height=111e3*55, coords='mag')\n",
"codes = ['wal','fhe','fhw','cve','cvw','hok','ade','adw','bks']\n",
"# Plotting some radars\n",
"overlayRadar(m, fontSize=12, codes=codes)\n",
"# Plot radar fov\n",
"overlayFov(m, codes=codes[:-1], maxGate=70)#, fovColor=(.8,.9,.9))\n",
"overlayFov(m, codes=codes[-1], maxGate=70, fovColor=(.8,.7,.8), fovAlpha=.5)\n",
"fig.tight_layout(pad=2)\n",
"rcParams.update({'font.size': 12})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAFVCAYAAABW0aGlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the RBSP mode\n",
"This is sloooooooow..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set map\n",
"figure(figsize=(8,8))\n",
"lon_0 = -70.\n",
"m = plotUtils.mapObj(boundinglat=35., lon_0=lon_0)\n",
"\n",
"# Go through each radar\n",
"codes = ['gbr','kap','sas','pgr', \\\n",
" 'kod','sto','pyk','han', \\\n",
" 'ksr','cve','cvw','wal', \\\n",
" 'bks','hok','fhw','fhe', \\\n",
" 'inv','rkn']\n",
"beams = [[3,4,6],[10,11,13],[2,3,5],[12,13,15], \\\n",
" [2,3,5],[12,13,15],[0,1,3],[5,6,8], \\\n",
" [12,13,15],[0,1,3],[19,20,22],[0,1,3], \\\n",
" [12,13,15],[0,1,3],[18,19,21],[0,1,3],\\\n",
" [6,7,9],[6,7,9]]\n",
"for i,rad in enumerate(codes):\n",
" # Plot radar\n",
" overlayRadar(m, fontSize=12, codes=rad)\n",
" # Plot radar fov\n",
" overlayFov(m, codes=rad, maxGate=75, beams=beams[i])\n",
"#savefig('rbsp_beams.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAgEAAAHaCAYAAACZ95oQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxYsAxhhjzEfxIoAxxhjzUb8A9rurDcmNUVIAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
}
],
"metadata": {}
}
]
} |
|
tchakravarty/PythonExamples | Code/PySpark/Chapter 2 - Downloading Spark and Getting Started.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook follows along with the code in Chapter 2 of Learning Spark. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "Py4JJavaError",
"evalue": "An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.\n: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/tirthankar/programming/python/PythonExamples/Code/PySpark/README.md\n\tat org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)\n\tat org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)\n\tat org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)\n\tat org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:207)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:32)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:57)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1707)\n\tat org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:885)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:138)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:99)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:286)\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:884)\n\tat org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:375)\n\tat org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:497)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n\tat py4j.Gateway.invoke(Gateway.java:259)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n\tat java.lang.Thread.run(Thread.java:745)\n",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mPy4JJavaError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-d9f0b10f26d3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtextFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'README.md'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/pyspark/rdd.py\u001b[0m in \u001b[0;36mcount\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 962\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 963\u001b[0m \"\"\"\n\u001b[1;32m--> 964\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapPartitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/pyspark/rdd.py\u001b[0m in \u001b[0;36msum\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 953\u001b[0m \u001b[1;36m6.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 954\u001b[0m \"\"\"\n\u001b[1;32m--> 955\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapPartitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 957\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/pyspark/rdd.py\u001b[0m in \u001b[0;36mreduce\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m 769\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 771\u001b[1;33m \u001b[0mvals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapPartitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 772\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 773\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/pyspark/rdd.py\u001b[0m in \u001b[0;36mcollect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 743\u001b[0m \"\"\"\n\u001b[0;32m 744\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mSCCallSiteSync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcss\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 745\u001b[1;33m \u001b[0mport\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPythonRDD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollectAndServe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jrdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrdd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 746\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_load_from_socket\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jrdd_deserializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 747\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 536\u001b[0m \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 537\u001b[0m return_value = get_return_value(answer, self.gateway_client,\n\u001b[1;32m--> 538\u001b[1;33m self.target_id, self.name)\n\u001b[0m\u001b[0;32m 539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 540\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/tirthankar/programming/python/PythonExamples/Dependencies/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m 298\u001b[0m raise Py4JJavaError(\n\u001b[0;32m 299\u001b[0m \u001b[1;34m'An error occurred while calling {0}{1}{2}.\\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m format(target_id, '.', name), value)\n\u001b[0m\u001b[0;32m 301\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 302\u001b[0m raise Py4JError(\n",
"\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.\n: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/tirthankar/programming/python/PythonExamples/Code/PySpark/README.md\n\tat org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)\n\tat org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)\n\tat org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)\n\tat org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:207)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:32)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:57)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:217)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1707)\n\tat org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:885)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:138)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:99)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:286)\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:884)\n\tat org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:375)\n\tat org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:497)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n\tat py4j.Gateway.invoke(Gateway.java:259)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n\tat java.lang.Thread.run(Thread.java:745)\n"
]
}
],
"source": [
"lines = sc.textFile('README.md')\n",
"lines.count()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lines' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-ba36659a0772>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'lines' is not defined"
]
}
],
"source": [
"lines.first()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that in the output above, unlike in the book, the output is not encoded 'UTF-8'. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'high-level APIs in Scala, Java, and Python, and an optimized engine that'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"python_lines = lines.filter(lambda line: 'Python' in line)\n",
"python_lines.first()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
tlkh/Generating-Inference-from-3D-Printing-Jobs |
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression\n",
"This is a test to use the scikit-learn's LinearRegression to model the amount of filament used per minute of the cohort class Edison+ 3D Printer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Dependencies\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn import linear_model\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"import csv\n",
"%run 'preprocessor.ipynb' #our own preprocessor functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of samples: 176\n"
]
}
],
"source": [
"with open('data_w1w4.csv', 'r') as f:\n",
" reader = csv.reader(f)\n",
" data = list(reader)\n",
" \n",
"matrix = obtain_data_matrix(data)\n",
"samples = len(matrix)\n",
"print(\"Number of samples: \" + str(samples))\n",
"\n",
"Y = matrix[:,[8]]\n",
"X = matrix[:,[9]]\n",
"S = matrix[:,[11]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the model (LinearRegression)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Create linear regression object\n",
"regr = linear_model.LinearRegression()\n",
"\n",
"# Train the model using the training sets\n",
"regr.fit(X, Y)\n",
"\n",
"# Make predictions using the testing set\n",
"Y_pred = regr.predict(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[ 0.08229866]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAADuCAYAAAA6JAOCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHi3gAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f87ca024e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean squared error: 122.52\n",
"Variance score: 0.71\n"
]
}
],
"source": [
"fig = plt.figure(1, figsize=(10, 4))\n",
"plt.scatter([X], [Y], color='blue', edgecolor='k')\n",
"plt.plot(X, Y_pred, color='red', linewidth=1)\n",
"\n",
"plt.xticks(())\n",
"plt.yticks(())\n",
"\n",
"print('Coefficients: ', regr.coef_)\n",
"\n",
"plt.show()\n",
"\n",
"# The mean squared error\n",
"print(\"Mean squared error: %.2f\"\n",
" % mean_squared_error(Y, Y_pred))\n",
"# Explained variance score: 1 is perfect prediction\n",
"print('Variance score: %.2f' % r2_score(Y, Y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bootstrap to find parameter confidence intervals"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.utils import resample\n",
"\n",
"bootstrap_resamples = 5000\n",
"intercepts = []\n",
"coefs = []\n",
"for k in range(bootstrap_resamples):\n",
" #resample population with replacement\n",
" samples_resampled = resample(X,Y,replace=True,n_samples=len(X))\n",
" \n",
" ## Fit model to resampled data\n",
" # Create linear regression object\n",
" regr = linear_model.LinearRegression()\n",
"\n",
" # Train the model using the training sets\n",
" regr.fit(samples_resampled[0], samples_resampled[1])\n",
" \n",
" coefs.append(regr.coef_[0][0])\n",
" intercepts.append(regr.intercept_[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculate confidence interval"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefs 95% CI = 0.06371 - 0.10284\n",
"Intercepts 95% CI = -2.82305 - 1.91128\n"
]
}
],
"source": [
"alpha = 0.95\n",
"p_lower = ((1-alpha)/2.0) * 100\n",
"p_upper = (alpha + ((1-alpha)/2.0)) * 100\n",
"coefs_lower = np.percentile(coefs,p_lower)\n",
"coefs_upper = np.percentile(coefs,p_upper)\n",
"intercepts_lower = np.percentile(intercepts,p_lower)\n",
"intercepts_upper = np.percentile(intercepts,p_upper)\n",
"print('Coefs %.0f%% CI = %.5f - %.5f' % (alpha*100,coefs_lower,coefs_upper))\n",
"print('Intercepts %.0f%% CI = %.5f - %.5f' % (alpha*100,intercepts_lower,intercepts_upper))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualize frequency distributions of bootstrapped parameters "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEWCAYAAABv+EDhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f87c7ff92b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEWCAYAAABv+EDhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNeFxWd4IhQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f87ca04ecf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(coefs)\n",
"plt.xlabel('Coefficient X0')\n",
"plt.title('Frquency Distribution of Coefficient X0')\n",
"plt.show()\n",
"\n",
"plt.hist(intercepts)\n",
"plt.xlabel('Intercept')\n",
"plt.title('Frquency Distribution of Intercepts')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
CompPhysics/ComputationalPhysics2 | doc/Projects/2019/Project1/ipynb/Project1.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Project 1, deadline March 22 -->\n",
"# Project 1, deadline March 22 \n",
"<!-- dom:AUTHOR: [Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html) at Department of Physics, University of Oslo, Norway -->\n",
"<!-- Author: --> \n",
"**[Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
"Date: **Jan 25, 2019**\n",
"\n",
"Copyright 1999-2019, [Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
"## Introduction\n",
"\n",
"\n",
"\n",
"\n",
" The spectacular demonstration of Bose-Einstein condensation (BEC) in gases of\n",
" alkali atoms $^{87}$Rb, $^{23}$Na, $^7$Li confined in magnetic\n",
" traps has led to an explosion of interest in\n",
" confined Bose systems. Of interest is the fraction of condensed atoms, the\n",
" nature of the condensate, the excitations above the condensate, the atomic\n",
" density in the trap as a function of Temperature and the critical temperature of BEC,\n",
" $T_c$. \n",
"\n",
" A key feature of the trapped alkali and atomic hydrogen systems is that they are\n",
" dilute. The characteristic dimensions of a typical trap for $^{87}$Rb is\n",
" $a_{h0}=\\left( {\\hbar}/{m\\omega_\\perp}\\right)^\\frac{1}{2}=1-2 \\times 10^4$\n",
" \\AA\\ . The interaction between $^{87}$Rb atoms can be well represented\n",
" by its s-wave scattering length, $a_{Rb}$. This scattering length lies in the\n",
" range $85 < a_{Rb} < 140 a_0$ where $a_0 = 0.5292$ \\AA\\ is the Bohr radius.\n",
" The definite value $a_{Rb} = 100 a_0$ is usually selected and\n",
" for calculations the definite ratio of atom size to trap size \n",
" $a_{Rb}/a_{h0} = 4.33 \\times 10^{-3}$ \n",
" is usually chosen. A typical $^{87}$Rb atom\n",
" density in the trap is $n \\simeq 10^{12}- 10^{14}$ atoms per cubic cm, giving an\n",
" inter-atom spacing $\\ell \\simeq 10^4$ \\AA. Thus the effective atom size is small\n",
" compared to both the trap size and the inter-atom spacing, the condition\n",
" for diluteness ($na^3_{Rb} \\simeq 10^{-6}$ where $n = N/V$ is the number\n",
" density). \n",
"\n",
"Many theoretical studies of Bose-Einstein condensates (BEC) in gases\n",
"of alkali atoms confined in magnetic or optical traps have been\n",
"conducted in the framework of the Gross-Pitaevskii (GP) equation. The\n",
"key point for the validity of this description is the dilute condition\n",
"of these systems, that is, the average distance between the atoms is\n",
"much larger than the range of the inter-atomic interaction. In this\n",
"situation the physics is dominated by two-body collisions, well\n",
"described in terms of the $s$-wave scattering length $a$. The crucial\n",
"parameter defining the condition for diluteness is the gas parameter\n",
"$x(\\mathbf{r})= n(\\mathbf{r}) a^3$, where $n(\\mathbf{r})$ is the local density\n",
"of the system. For low values of the average gas parameter $x_{av}\\le 10^{-3}$, the mean field Gross-Pitaevskii equation does an excellent\n",
"job. However,\n",
"in recent experiments, the local gas parameter may well exceed this\n",
"value due to the possibility of tuning the scattering length in the\n",
"presence of a so-called Feshbach resonance.\n",
"\n",
"\n",
"\n",
"Thus, improved many-body methods like Monte Carlo calculations may be\n",
"needed. The aim of this project is to use the Variational Monte Carlo\n",
"(VMC) method and evaluate the ground state energy of a trapped, hard\n",
"sphere Bose gas for different numbers of particles with a specific\n",
"trial wave function.\n",
"\n",
" This trial wave function is used to study the sensitivity of\n",
" condensate and non-condensate properties to the hard sphere radius\n",
" and the number of particles. The trap we will use is a spherical (S)\n",
" or an elliptical (E) harmonic trap in one, two and finally three\n",
" dimensions, with the latter given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"trap_eqn\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" V_{ext}(\\mathbf{r}) = \n",
" \\Bigg\\{\n",
" \\begin{array}{ll}\n",
"\t \\frac{1}{2}m\\omega_{ho}^2r^2 & (S)\\\\\n",
" \\strut\n",
"\t \\frac{1}{2}m[\\omega_{ho}^2(x^2+y^2) + \\omega_z^2z^2] & (E)\n",
"\\label{trap_eqn} \\tag{1}\n",
" \\end{array}\n",
" \\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where (S) stands for symmetric and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = \\sum_i^N \\left(\\frac{-\\hbar^2}{2m}{\\bigtriangledown }_{i}^2 +V_{ext}({\\mathbf{r}}_i)\\right) +\n",
"\t \\sum_{i<j}^{N} V_{int}({\\mathbf{r}}_i,{\\mathbf{r}}_j),\n",
"\\label{_auto1} \\tag{2}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as the two-body Hamiltonian of the system. Here $\\omega_{ho}^2$\n",
" defines the trap potential strength. In the case of the elliptical\n",
" trap, $V_{ext}(x,y,z)$, $\\omega_{ho}=\\omega_{\\perp}$ is the trap\n",
" frequency in the perpendicular or $xy$ plane and $\\omega_z$ the\n",
" frequency in the $z$ direction. The mean square vibrational\n",
" amplitude of a single boson at $T=0K$ in the trap ([1](#trap_eqn)) is\n",
" $\\langle x^2\\rangle=(\\hbar/2m\\omega_{ho})$ so that $a_{ho} \\equiv\n",
" (\\hbar/m\\omega_{ho})^{\\frac{1}{2}}$ defines the characteristic length\n",
" of the trap. The ratio of the frequencies is denoted\n",
" $\\lambda=\\omega_z/\\omega_{\\perp}$ leading to a ratio of the trap\n",
" lengths $(a_{\\perp}/a_z)=(\\omega_z/\\omega_{\\perp})^{\\frac{1}{2}} =\n",
" \\sqrt{\\lambda}$. Note that we use the shorthand notation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto2\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\sum_{i < j}^{N} V_{ij} \\equiv \\sum_{i = 1}^{N}\\sum_{j = i + 1}^{N} V_{ij},\n",
"\\label{_auto2} \\tag{3}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that is, the notation $i < j$ under the summation sign signifies a double sum\n",
" running over all pairwise interactions once.\n",
"\n",
" We will represent the inter-boson interaction by a pairwise,\n",
" repulsive potential"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto3\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" V_{int}(|\\mathbf{r}_i-\\mathbf{r}_j|) = \\Bigg\\{\n",
" \\begin{array}{ll}\n",
"\t \\infty & {|\\mathbf{r}_i-\\mathbf{r}_j|} \\leq {a}\\\\\n",
"\t 0 & {|\\mathbf{r}_i-\\mathbf{r}_j|} > {a}\n",
" \\end{array}\n",
"\\label{_auto3} \\tag{4}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $a$ is the so-called hard-core diameter of the bosons.\n",
" Clearly, $V_{int}(|\\mathbf{r}_i-\\mathbf{r}_j|)$ is zero if the bosons are\n",
" separated by a distance $|\\mathbf{r}_i-\\mathbf{r}_j|$ greater than $a$ but\n",
" infinite if they attempt to come within a distance $|\\mathbf{r}_i-\\mathbf{r}_j| \\leq a$.\n",
"\n",
" Our trial wave function for the ground state with $N$ atoms is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:trialwf\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\Psi_T(\\mathbf{r})=\\Psi_T(\\mathbf{r}_1, \\mathbf{r}_2, \\dots \\mathbf{r}_N,\\alpha,\\beta)\n",
" =\\left[\n",
" \\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\n",
" \\right]\n",
" \\left[\n",
" \\prod_{j<k}f(a,|\\mathbf{r}_j-\\mathbf{r}_k|)\n",
" \\right],\n",
"\\label{eq:trialwf} \\tag{5}\n",
" \\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\alpha$ and $\\beta$ are variational parameters. The\n",
" single-particle wave function is proportional to the harmonic\n",
" oscillator function for the ground state, i.e.,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto4\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" g(\\alpha,\\beta,\\mathbf{r}_i)= \\exp{[-\\alpha(x_i^2+y_i^2+\\beta z_i^2)]}.\n",
"\\label{_auto4} \\tag{6}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For spherical traps we have $\\beta = 1$ and for non-interacting\n",
" bosons ($a=0$) we have $\\alpha = 1/2a_{ho}^2$. The correlation wave\n",
" function is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto5\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" f(a,|\\mathbf{r}_i-\\mathbf{r}_j|)=\\Bigg\\{\n",
" \\begin{array}{ll}\n",
"\t 0 & {|\\mathbf{r}_i-\\mathbf{r}_j|} \\leq {a}\\\\\n",
"\t (1-\\frac{a}{|\\mathbf{r}_i-\\mathbf{r}_j|}) & {|\\mathbf{r}_i-\\mathbf{r}_j|} > {a}.\n",
" \\end{array}\n",
"\\label{_auto5} \\tag{7}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Project 1 a): Local energy\n",
"\n",
"Find the analytic expressions for the local energy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:locale\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" E_L(\\mathbf{r})=\\frac{1}{\\Psi_T(\\mathbf{r})}H\\Psi_T(\\mathbf{r}),\n",
"\\label{eq:locale} \\tag{8}\n",
" \\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for the above \n",
" trial wave function of Eq. ([5](#eq:trialwf)). \n",
"Find first the local energy the case with only the harmonic oscillator potential, that is we set $a=0$.\n",
"Use first that $\\beta =1$ and find the relevant local energies in one, two and three dimensions for one and\n",
"$N$ particles with the same mass. \n",
"\n",
" Compute also the analytic expression for the drift force to be used in importance sampling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto6\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" F = \\frac{2\\nabla \\Psi_T}{\\Psi_T}.\n",
"\\label{_auto6} \\tag{9}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find first the equivalent expressions for the just the harmonic oscillator part in one, two and three dimensions\n",
"with $\\beta=1$. \n",
"\n",
"Next, we will find the local energy for the full problem in three dimensions.\n",
"The tricky part is to find an analytic expressions for the derivative of the trial wave function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{1}{\\Psi_T(\\mathbf{r})}\\sum_i^{N}\\nabla_i^2\\Psi_T(\\mathbf{r}),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with the above \n",
"trial wave function of Eq. ([5](#eq:trialwf)).\n",
"We rewrite (and we can use the same general expressions for projects 2 and 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi_T(\\mathbf{r})=\\Psi_T(\\mathbf{r}_1, \\mathbf{r}_2, \\dots \\mathbf{r}_N,\\alpha,\\beta)\n",
"=\\left[\n",
" \\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\n",
"\\right]\n",
"\\left[\n",
" \\prod_{j<k}f(a,|\\mathbf{r}_j-\\mathbf{r}_k|)\n",
"\\right],\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi_T(\\mathbf{r})=\\left[\n",
" \\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\n",
"\\right]\n",
"\\exp{\\left(\\sum_{j<k}u(r_{jk})\\right)}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we have defined $r_{ij}=|\\mathbf{r}_i-\\mathbf{r}_j|$\n",
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"f(r_{ij})= \\exp{\\left(\\sum_{i<j}u(r_{ij})\\right)},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $u(r_{ij})=\\ln{f(r_{ij})}$.\n",
"We have also"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"g(\\alpha,\\beta,\\mathbf{r}_i) = \\exp{\\left[-\\alpha(x_i^2+y_i^2+\\beta\n",
" z_i^2)\\right]}= \\phi(\\mathbf{r}_i).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show that the first derivative for particle $k$ is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
" \\nabla_k\\Psi_T(\\mathbf{r}) &= \\nabla_k\\phi(\\mathbf{r}_k)\\left[\\prod_{i\\ne k}\\phi(\\mathbf{r}_i)\\right]\\exp{\\left(\\sum_{j<m}u(r_{jm})\\right)}\n",
" \\\\\n",
" &\\qquad\n",
" + \\left[\\prod_i\\phi(\\mathbf{r}_i)\\right]\n",
" \\exp{\\left(\\sum_{j<m}u(r_{jm})\\right)}\\sum_{l\\ne k}\\nabla_k u(r_{kl}),\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and find the final expression for our specific trial function.\n",
"The expression for the second derivative is (show this)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
" \\frac{1}{\\Psi_T(\\mathbf{r})}\\nabla_k^2\\Psi_T(\\mathbf{r})\n",
" &= \\frac{\\nabla_k^2\\phi(\\mathbf{r}_k)}{\\phi(\\mathbf{r}_k)}\n",
" + 2\\frac{\\nabla_k\\phi(\\mathbf{r}_k)}{\\phi(\\mathbf{r}_k)}\n",
" \\left(\\sum_{j\\ne k}\\frac{(\\mathbf{r}_k-\\mathbf{r}_j)}{r_{kj}}u'(r_{ij})\\right)\n",
" \\\\\n",
" &\\qquad\n",
" + \\sum_{i\\ne k}\\sum_{j \\ne k}\\frac{(\\mathbf{r}_k-\\mathbf{r}_i)(\\mathbf{r}_k-\\mathbf{r}_j)}{r_{ki}r_{kj}}u'(r_{ki})u'(r_{kj})\n",
" \\\\\n",
" &\\qquad\n",
" + \\sum_{j\\ne k}\\left( u''(r_{kj})+\\frac{2}{r_{kj}}u'(r_{kj})\\right).\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this expression to find the final second derivative entering the definition of the local energy. \n",
"You need to get the analytic expression for this expression using the harmonic oscillator wave functions\n",
"and the correlation term defined in the project.\n",
"\n",
"\n",
"### Project 1 b): Developing the code\n",
"\n",
"Write a Variational Monte Carlo program which uses standard\n",
" Metropolis sampling and compute the ground state energy of a\n",
" spherical harmonic oscillator ($\\beta = 1$) with no interaction and\n",
" one dimension. Use natural units and make an analysis of your\n",
" calculations using both the analytic expression for the local\n",
" energy and a numerical calculation of the kinetic energy using\n",
" numerical derivation. Compare the CPU time difference. The only\n",
" variational parameter is $\\alpha$. Perform these calculations for\n",
" $N=1$, $N=10$, $100$ and $500$ atoms. Compare your results with the\n",
" exact answer. Extend then your results to two and three dimensions\n",
" and compare with the analytical results.\n",
"\n",
"### Project 1 c): Adding importance sampling\n",
"\n",
"We repeat part b), but now we replace the brute force Metropolis algorithm with\n",
"importance sampling based on the Fokker-Planck and the Langevin equations. \n",
"Discuss your results and comment on eventual differences between importance sampling and brute force sampling.\n",
"Run the calculations for the one, two and three-dimensional systems only and without the repulsive potential. \n",
"Study the dependence of the results as a function of the time step $\\delta t$. \n",
"Compare the results with those obtained under b) and comment eventual differences.\n",
"\n",
"### Project 1 d): A better statistical analysis\n",
"\n",
"In performing the Monte Carlo analysis we will use the \n",
" blocking and bootstrap techniques to make the statistical analysis of the\n",
" numerical data. Present your results with a proper evaluation of the\n",
" statistical errors. Repeat the calculations from exercise c) and\n",
" include a proper error analysis. Limit yourself to the\n",
" three-dimensional case only.\n",
"\n",
"### Project 1 e): The repulsive interaction\n",
"\n",
" * [e)] We turn now to the elliptic trap with a repulsive\n",
"\n",
" interaction. We fix, as in Refs. [1,2] below,\n",
" $a/a_{ho}=0.0043$. Introduce lengths in units of $a_{ho}$,\n",
" $r\\rightarrow r/a_{ho}$ and energy in units of $\\hbar\\omega_{ho}$.\n",
" Show then that the original Hamiltonian can be rewritten as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"H=\\sum_{i=1}^N\\frac{1}{2}\\left(-\\nabla^2_i+x_i^2+y_i^2+\\gamma^2z_i^2\\right)+\\sum_{i<j}V_{int}(|\\mathbf{r}_i-\\mathbf{r}_j|).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is the expression for $\\gamma$? Choose the initial value for\n",
" $\\beta=\\gamma = 2.82843$ and set up a VMC program which computes the\n",
" ground state energy using the trial wave function of\n",
" Eq. ([5](#eq:trialwf)) using only $\\alpha$ as variational\n",
" parameter. Use standard Metropolis sampling and vary the parameter\n",
" $\\alpha$ in order to find a minimum. Perform the calculations for\n",
" $N=10,50$ and $N=100$ and compare your results to those from the\n",
" ideal case in the previous exercises. In actual calculations\n",
" employing the Metropolis algorithm, all moves are recast into the\n",
" chosen simulation cell with periodic boundary conditions. To carry\n",
" out consistently the Metropolis moves, it has to be assumed that the\n",
" correlation function has a range shorter than $L/2$. Then, to decide\n",
" if a move of a single particle is accepted or not, only the set of\n",
" particles contained in a sphere of radius $L/2$ centered at the\n",
" referred particle have to be considered. Benchmark your results with\n",
" those of Refs. [1,2].\n",
"\n",
"### Project 1 f): Finding the best parameter\n",
"\n",
"Repeat the previous calculations by varying the energy using the\n",
"conjugate gradient method or similar methods to obtain the best possible parameter\n",
"$\\alpha$. \n",
"\n",
"### Project 1 g): Onebody densities\n",
"\n",
"With the optimal parameters for the ground state wave function, compute again the onebody density with and without the Jastrow\n",
"factor. How important are the correlations induced by the Jastrow factor?\n",
"\n",
"\n",
"\n",
"\n",
"# Literature\n",
"\n",
"1. J. L. DuBois and H. R. Glyde, H. R., *Bose-Einstein condensation in trapped bosons: A variational Monte Carlo analysis*, Phys. Rev. A **63**, 023602 (2001).\n",
"\n",
"2. J. K. Nilsen, J. Mur-Petit, M. Guilleumas, M. Hjorth-Jensen, and A. Polls, *Vortices in atomic Bose-Einstein condensates in the large-gas-parameter region*, Phys. Rev. A **71**, 053610 (2005).\n",
"\n",
"## Introduction to numerical projects\n",
"\n",
"Here follows a brief recipe and recommendation on how to write a report for each\n",
"project.\n",
"\n",
" * Give a short description of the nature of the problem and the eventual numerical methods you have used.\n",
"\n",
" * Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.\n",
"\n",
" * Include the source code of your program. Comment your program properly.\n",
"\n",
" * If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.\n",
"\n",
" * Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.\n",
"\n",
" * Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.\n",
"\n",
" * Try to give an interpretation of you results in your answers to the problems.\n",
"\n",
" * Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.\n",
"\n",
" * Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.\n",
"\n",
"## Format for electronic delivery of report and programs\n",
"\n",
"The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:\n",
"\n",
" * Use Devilry to hand in your projects, log in at <http://devilry.ifi.uio.no> with your normal UiO username and password.\n",
"\n",
" * Upload **only** the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. The full version of the codes should be in your github repository.\n",
"\n",
" * In your github repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.\n",
"\n",
" * Still in your github make a folder where you place your codes. \n",
"\n",
" * In this and all later projects, you should include tests (for example unit tests) of your code(s).\n",
"\n",
" * Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.\n",
"\n",
"Finally, \n",
"we encourage you to work two and two together. Optimal working groups consist of \n",
"2-3 students. You can then hand in a common report."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"import requests\n",
"import pandas as pd \n",
"import time\n",
"\n",
"from bs4 import BeautifulSoup \n",
"import bs4 "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def gen_homeURL(start_index):\n",
" homeURL = ('http://bbs.nju.edu.cn/bbsdoc?board=NJU_HOME&start={}&type=doc').format(start_index)\n",
" return homeURL"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def check_link(url): \n",
" try: \n",
" r = requests.get(url) \n",
" r.raise_for_status() \n",
" r.encoding = r.apparent_encoding \n",
" return r.text \n",
" except: \n",
" print('无法链接服务器！！！') "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def get_map(start_index):\n",
" home_url = gen_homeURL(start_index)\n",
" rs = check_link(home_url) \n",
"\n",
" soup = BeautifulSoup(rs,'lxml') \n",
" trs = soup.find_all('a')[4:]\n",
"\n",
" user_list_raw = trs[0:40:2]\n",
" text_list = trs[1:41:2]\n",
"\n",
" user_list = [x.string for x in user_list_raw]\n",
" title_list = [x.string for x in text_list]\n",
" href_list = [x.get('href') for x in text_list]\n",
" url_list = ['http://bbs.nju.edu.cn/' + x for x in href_list]\n",
" return user_list, title_list, url_list"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_article(url):\n",
" raw_text = requests.get(url, verify=False)\n",
" soup = BeautifulSoup(raw_text.text, 'lxml')\n",
" articles = soup.find_all('textarea')\n",
" result = articles[0].string\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fetching url list, 10 of 90 finished!, size = 200\n",
"fetching url list, 20 of 90 finished!, size = 400\n",
"fetching url list, 30 of 90 finished!, size = 600\n",
"fetching url list, 40 of 90 finished!, size = 800\n",
"fetching url list, 50 of 90 finished!, size = 1000\n",
"fetching url list, 60 of 90 finished!, size = 1200\n",
"fetching url list, 70 of 90 finished!, size = 1400\n",
"fetching url list, 80 of 90 finished!, size = 1600\n",
"fetching url list, 90 of 90 finished!, size = 1800\n",
"fetching articles, 100 of 1800 finished!, size = 100\n",
"fetching articles, 200 of 1800 finished!, size = 200\n",
"fetching articles, 300 of 1800 finished!, size = 300\n",
"fetching articles, 400 of 1800 finished!, size = 400\n",
"fetching articles, 500 of 1800 finished!, size = 500\n",
"fetching articles, 600 of 1800 finished!, size = 600\n",
"fetching articles, 700 of 1800 finished!, size = 700\n",
"fetching articles, 800 of 1800 finished!, size = 800\n",
"fetching articles, 900 of 1800 finished!, size = 900\n",
"fetching articles, 1000 of 1800 finished!, size = 1000\n",
"fetching articles, 1100 of 1800 finished!, size = 1100\n",
"fetching articles, 1200 of 1800 finished!, size = 1200\n",
"fetching articles, 1300 of 1800 finished!, size = 1300\n",
"fetching articles, 1400 of 1800 finished!, size = 1400\n",
"fetching articles, 1500 of 1800 finished!, size = 1500\n",
"fetching articles, 1600 of 1800 finished!, size = 1600\n",
"fetching articles, 1700 of 1800 finished!, size = 1700\n",
"fetching articles, 1800 of 1800 finished!, size = 1800\n"
]
}
],
"source": [
"# def main():\n",
"start_num = range(20, 1801, 20)\n",
"user_list = []\n",
"title_list = []\n",
"url_list = []\n",
"content_list = []\n",
"for i,start_index in enumerate(start_num):\n",
" users, titles, urls = get_map(start_index)\n",
" user_list.extend(users)\n",
" title_list.extend(titles)\n",
" url_list.extend(urls)\n",
" if (i+1) % 10 == 0:\n",
" print('fetching url list, %d of %d finished!, size = %d' % (i+1, len(start_num), len(user_list)))\n",
" time.sleep(0.5)\n",
"\n",
"for i,url in enumerate(url_list):\n",
" try:\n",
" text = get_article(url)\n",
" except:\n",
" text = '空'\n",
" content_list.append(text)\n",
" if (i+1) % 100 == 0:\n",
" print('fetching articles, %d of %d finished!, size = %d' % (i+1, len(url_list), len(content_list)))\n",
" time.sleep(0.5)\n",
"result = pd.DataFrame({'title':title_list, 'user':user_list, 'url':url_list, 'content':content_list}, \n",
" columns = ['title', 'user', 'url', 'content'])\n",
"# main()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"save result to excel...\n",
"task done.\n"
]
}
],
"source": [
"print('save result to excel...')\n",
"writer = pd.ExcelWriter('fetch_data.xlsx')\n",
"result.to_excel(writer, sheet_name='bbs', encoding = 'utf-8', index = False)\n",
"writer.save()\n",
"print('task done.')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"发信人dreamfly宁静致远信区NJU_HOME本篇人气772标题南大和园仙林三室两厅毛坯房出租发信站南京大学小百合站TueMar11640122011南大和园仙林三室两厅110平米毛坯房出租有意者请站内联系谢谢134m来源南京大学小百合站httpbbsnjueducnFROM202119569m\n"
]
},
{
"data": {
"text/plain": [
"'发信人: dreamfly (宁静致远), 信区: NJU_HOME. 本篇人气: 772\\n标 题: 南大和园（仙林）三室两厅毛坯房出租\\n发信站: 南京大学小百合站 (Tue Mar 1 16:40:12 2011)\\n\\n\\r\\n 南大和园（仙林）三室两厅（110平米）毛坯房出租，有意者请站内联系，谢谢。\\r\\n\\r\\n\\n--\\n\\n[1;34m※ 来源:．南京大学小百合站 http://bbs.nju.edu.cn [FROM: 202.119.56.9][m\\n'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(re.sub('\\W', '', result.content[0]))\n",
"result.content[0]"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:spyder]",
"language": "python",
"name": "conda-env-spyder-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "title:migration,new"
},
"source": [
"# Vertex AI: Vertex AI Migration: AutoML Image Classification\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ1%20Vertex%20SDK%20AutoML%20Image%20Classification.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ1%20Vertex%20SDK%20AutoML%20Image%20Classification.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:flowers,icn"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "costs"
},
"source": [
"### Costs\n",
"\n",
"This tutorial uses billable components of Google Cloud:\n",
"\n",
"* Vertex AI\n",
"* Cloud Storage\n",
"\n",
"Learn about [Vertex AI\n",
"pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
"pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
"Calculator](https://cloud.google.com/products/calculator/)\n",
"to generate a cost estimate based on your projected usage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_local"
},
"source": [
"### Set up your local development environment\n",
"\n",
"If you are using Colab or Google Cloud Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
"\n",
"Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
"\n",
"- The Cloud Storage SDK\n",
"- Git\n",
"- Python 3\n",
"- virtualenv\n",
"- Jupyter notebook running in a virtual environment with Python 3\n",
"\n",
"The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
"\n",
"1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
"\n",
"2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
"\n",
"3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
"\n",
"4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.\n",
"\n",
"5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
"\n",
"6. Open this notebook in the Jupyter Notebook Dashboard.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_aip:mbsdk"
},
"source": [
"## Installation\n",
"\n",
"Install the latest version of Vertex SDK for Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_aip:mbsdk"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Google Cloud Notebook\n",
"if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" USER_FLAG = \"--user\"\n",
"else:\n",
" USER_FLAG = \"\"\n",
"\n",
"! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_storage"
},
"source": [
"Install the latest GA version of *google-cloud-storage* library as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_storage"
},
"outputs": [],
"source": [
"! pip3 install -U google-cloud-storage $USER_FLAG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_tensorflow"
},
"outputs": [],
"source": [
"if os.getenv(\"IS_TESTING\"):\n",
" ! pip3 install --upgrade tensorflow $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if not os.getenv(\"IS_TESTING\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "before_you_begin:nogpu"
},
"source": [
"## Before you begin\n",
"\n",
"### GPU runtime\n",
"\n",
"This tutorial does not require a GPU runtime.\n",
"\n",
"### Set up your Google Cloud project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
"\n",
"3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
"\n",
"4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
"\n",
"5. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
"\n",
"Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcp_authenticate"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
"\n",
"**Click Create service account**.\n",
"\n",
"In the **Service account name** field, enter a name, and click **Create**.\n",
"\n",
"In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"Click Create. A JSON file that contains your key downloads to your local environment.\n",
"\n",
"Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gcp_authenticate"
},
"outputs": [],
"source": [
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This provides access to your\n",
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"import os\n",
"import sys\n",
"\n",
"# If on Google Cloud Notebook, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this notebook locally, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:mbsdk"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
"\n",
"Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
" BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_bucket"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "validate_bucket"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:mbsdk"
},
"outputs": [],
"source": [
"import google.cloud.aiplatform as aip"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "init_aip:mbsdk"
},
"source": [
"## Initialize Vertex SDK for Python\n",
"\n",
"Initialize the Vertex SDK for Python for your project and corresponding bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "init_aip:mbsdk"
},
"outputs": [],
"source": [
"aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_file:u_dataset,csv"
},
"source": [
"#### Location of Cloud Storage training data.\n",
"\n",
"Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_file:flowers,csv,icn"
},
"outputs": [],
"source": [
"IMPORT_FILE = (\n",
" \"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "quick_peek:csv"
},
"source": [
"#### Quick peek at your data\n",
"\n",
"This tutorial uses a version of the Flowers dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
"\n",
"Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (`wc -l`) and then peek at the first few rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "quick_peek:csv"
},
"outputs": [],
"source": [
"if \"IMPORT_FILES\" in globals():\n",
" FILE = IMPORT_FILES[0]\n",
"else:\n",
" FILE = IMPORT_FILE\n",
"\n",
"count = ! gsutil cat $FILE | wc -l\n",
"print(\"Number of Examples\", int(count[0]))\n",
"\n",
"print(\"First 10 rows\")\n",
"! gsutil cat $FILE | head"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_a_dataset:migration"
},
"source": [
"## Create a dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "datasets_create:migration,new,mbsdk"
},
"source": [
"### [datasets.create-dataset-api](https://cloud.google.com/vertex-ai/docs/datasets/create-dataset-api)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_dataset:image,icn"
},
"source": [
"### Create the Dataset\n",
"\n",
"Next, create the `Dataset` resource using the `create` method for the `ImageDataset` class, which takes the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `Dataset` resource.\n",
"- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
"- `import_schema_uri`: The data labeling schema for the data items.\n",
"\n",
"This operation may take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_dataset:image,icn"
},
"outputs": [],
"source": [
"dataset = aip.ImageDataset.create(\n",
" display_name=\"Flowers\" + \"_\" + TIMESTAMP,\n",
" gcs_source=[IMPORT_FILE],\n",
" import_schema_uri=aip.schema.dataset.ioformat.image.single_label_classification,\n",
")\n",
"\n",
"print(dataset.resource_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_dataset:image,icn"
},
"source": [
"*Example Output:*\n",
"\n",
" INFO:google.cloud.aiplatform.datasets.dataset:Creating ImageDataset\n",
" INFO:google.cloud.aiplatform.datasets.dataset:Create ImageDataset backing LRO: projects/759209241365/locations/us-central1/datasets/2940964905882222592/operations/1941426647739662336\n",
" INFO:google.cloud.aiplatform.datasets.dataset:ImageDataset created. Resource name: projects/759209241365/locations/us-central1/datasets/2940964905882222592\n",
" INFO:google.cloud.aiplatform.datasets.dataset:To use this ImageDataset in another session:\n",
" INFO:google.cloud.aiplatform.datasets.dataset:ds = aiplatform.ImageDataset('projects/759209241365/locations/us-central1/datasets/2940964905882222592')\n",
" INFO:google.cloud.aiplatform.datasets.dataset:Importing ImageDataset data: projects/759209241365/locations/us-central1/datasets/2940964905882222592\n",
" INFO:google.cloud.aiplatform.datasets.dataset:Import ImageDataset data backing LRO: projects/759209241365/locations/us-central1/datasets/2940964905882222592/operations/8100099138168815616\n",
" INFO:google.cloud.aiplatform.datasets.dataset:ImageDataset data imported. Resource name: projects/759209241365/locations/us-central1/datasets/2940964905882222592\n",
" projects/759209241365/locations/us-central1/datasets/2940964905882222592"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_a_model:migration"
},
"source": [
"## Train a model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "trainingpipelines_create:migration,new,mbsdk"
},
"source": [
"### [training.automl-api](https://cloud.google.com/vertex-ai/docs/training/automl-api)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_automl_pipeline:image,icn"
},
"source": [
"### Create and run training pipeline\n",
"\n",
"To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
"\n",
"#### Create training pipeline\n",
"\n",
"An AutoML training pipeline is created with the `AutoMLImageTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `TrainingJob` resource.\n",
"- `prediction_type`: The type task to train the model for.\n",
" - `classification`: An image classification model.\n",
" - `object_detection`: An image object detection model.\n",
"- `multi_label`: If a classification task, whether single (`False`) or multi-labeled (`True`).\n",
"- `model_type`: The type of model for deployment.\n",
" - `CLOUD`: Deployment on Google Cloud\n",
" - `CLOUD_HIGH_ACCURACY_1`: Optimized for accuracy over latency for deployment on Google Cloud.\n",
" - `CLOUD_LOW_LATENCY_`: Optimized for latency over accuracy for deployment on Google Cloud.\n",
" - `MOBILE_TF_VERSATILE_1`: Deployment on an edge device.\n",
" - `MOBILE_TF_HIGH_ACCURACY_1`:Optimized for accuracy over latency for deployment on an edge device.\n",
" - `MOBILE_TF_LOW_LATENCY_1`: Optimized for latency over accuracy for deployment on an edge device.\n",
"- `base_model`: (optional) Transfer learning from existing `Model` resource -- supported for image classification only.\n",
"\n",
"The instantiated object is the DAG (directed acyclic graph) for the training job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_automl_pipeline:image,icn"
},
"outputs": [],
"source": [
"dag = aip.AutoMLImageTrainingJob(\n",
" display_name=\"flowers_\" + TIMESTAMP,\n",
" prediction_type=\"classification\",\n",
" multi_label=False,\n",
" model_type=\"CLOUD\",\n",
" base_model=None,\n",
")\n",
"\n",
"print(dag)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_automl_pipeline:image,icn"
},
"source": [
"*Example output:*\n",
"\n",
" <google.cloud.aiplatform.training_jobs.AutoMLImageTrainingJob object at 0x7f806a6116d0>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_automl_pipeline:image"
},
"source": [
"#### Run the training pipeline\n",
"\n",
"Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
"\n",
"- `dataset`: The `Dataset` resource to train the model.\n",
"- `model_display_name`: The human readable name for the trained model.\n",
"- `training_fraction_split`: The percentage of the dataset to use for training.\n",
"- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
"- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
"- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
"- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
"\n",
"The `run` method when completed returns the `Model` resource.\n",
"\n",
"The execution of the training pipeline will take upto 20 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_automl_pipeline:image"
},
"outputs": [],
"source": [
"model = dag.run(\n",
" dataset=dataset,\n",
" model_display_name=\"flowers_\" + TIMESTAMP,\n",
" training_fraction_split=0.8,\n",
" validation_fraction_split=0.1,\n",
" test_fraction_split=0.1,\n",
" budget_milli_node_hours=8000,\n",
" disable_early_stopping=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_automl_pipeline:image"
},
"source": [
"*Example output:*\n",
"\n",
" INFO:google.cloud.aiplatform.training_jobs:View Training:\n",
" https://console.cloud.google.com/ai/platform/locations/us-central1/training/2109316300865011712?project=759209241365\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712 current state:\n",
" PipelineState.PIPELINE_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712 current state:\n",
" PipelineState.PIPELINE_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712 current state:\n",
" PipelineState.PIPELINE_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712 current state:\n",
" PipelineState.PIPELINE_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712 current state:\n",
" PipelineState.PIPELINE_STATE_RUNNING\n",
" ...\n",
" INFO:google.cloud.aiplatform.training_jobs:AutoMLImageTrainingJob run completed. Resource name: projects/759209241365/locations/us-central1/trainingPipelines/2109316300865011712\n",
" INFO:google.cloud.aiplatform.training_jobs:Model available at projects/759209241365/locations/us-central1/models/1284590221056278528"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:migration"
},
"source": [
"## Evaluate the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "models_evaluations_list:migration,new"
},
"source": [
"### [projects.locations.models.evaluations.list](https://cloud.devsite.corp.google.com/ai-platform-unified/docs/reference/rest/v1beta1/projects.locations.models.evaluations/list)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"source": [
"## Review model evaluation scores\n",
"After your model has finished training, you can review the evaluation scores for it.\n",
"\n",
"First, you need to get a reference to the new model. As with datasets, you can either use the reference to the model variable you created when you deployed the model or you can list all of the models in your project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"outputs": [],
"source": [
"# Get model resource ID\n",
"models = aip.Model.list(filter=\"display_name=flowers_\" + TIMESTAMP)\n",
"\n",
"# Get a reference to the Model Service client\n",
"client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
"model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
"\n",
"model_evaluations = model_service_client.list_model_evaluations(\n",
" parent=models[0].resource_name\n",
")\n",
"model_evaluation = list(model_evaluations)[0]\n",
"print(model_evaluation)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"source": [
"*Example output:*\n",
"\n",
" name: \"projects/759209241365/locations/us-central1/models/623915674158235648/evaluations/4280507618583117824\"\n",
" metrics_schema_uri: \"gs://google-cloud-aiplatform/schema/modelevaluation/classification_metrics_1.0.0.yaml\"\n",
" metrics {\n",
" struct_value {\n",
" fields {\n",
" key: \"auPrc\"\n",
" value {\n",
" number_value: 0.9891107\n",
" }\n",
" }\n",
" fields {\n",
" key: \"confidenceMetrics\"\n",
" value {\n",
" list_value {\n",
" values {\n",
" struct_value {\n",
" fields {\n",
" key: \"precision\"\n",
" value {\n",
" number_value: 0.2\n",
" }\n",
" }\n",
" fields {\n",
" key: \"recall\"\n",
" value {\n",
" number_value: 1.0\n",
" }\n",
" }\n",
" }\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "make_batch_predictions:migration"
},
"source": [
"## Make batch predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "batchpredictionjobs_create:migration,new,mbsdk"
},
"source": [
"### [predictions.batch-prediction](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_test_items:batch_prediction"
},
"source": [
"### Get test item(s)\n",
"\n",
"Now do a batch prediction to your Vertex model. You will use arbitrary examples out of the dataset as a test items. Don't be concerned that the examples were likely used in training the model -- we just want to demonstrate how to make a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_test_items:automl,icn,csv"
},
"outputs": [],
"source": [
"test_items = !gsutil cat $IMPORT_FILE | head -n2\n",
"if len(str(test_items[0]).split(\",\")) == 3:\n",
" _, test_item_1, test_label_1 = str(test_items[0]).split(\",\")\n",
" _, test_item_2, test_label_2 = str(test_items[1]).split(\",\")\n",
"else:\n",
" test_item_1, test_label_1 = str(test_items[0]).split(\",\")\n",
" test_item_2, test_label_2 = str(test_items[1]).split(\",\")\n",
"\n",
"print(test_item_1, test_label_1)\n",
"print(test_item_2, test_label_2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "copy_test_items:batch_prediction"
},
"source": [
"### Copy test item(s)\n",
"\n",
"For the batch prediction, copy the test items over to your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copy_test_items:batch_prediction"
},
"outputs": [],
"source": [
"file_1 = test_item_1.split(\"/\")[-1]\n",
"file_2 = test_item_2.split(\"/\")[-1]\n",
"\n",
"! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
"! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
"\n",
"test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
"test_item_2 = BUCKET_NAME + \"/\" + file_2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "make_batch_file:automl,image"
},
"source": [
"### Make the batch input file\n",
"\n",
"Now make a batch input file, which you will store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You will use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each data item (instance). The dictionary contains the key/value pairs:\n",
"\n",
"- `content`: The Cloud Storage path to the image.\n",
"- `mime_type`: The content type. In our example, it is a `jpeg` file.\n",
"\n",
"For example:\n",
"\n",
" {'content': '[your-bucket]/file1.jpg', 'mime_type': 'jpeg'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "make_batch_file:automl,image"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"import tensorflow as tf\n",
"\n",
"gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
"with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
" data = {\"content\": test_item_1, \"mime_type\": \"image/jpeg\"}\n",
" f.write(json.dumps(data) + \"\\n\")\n",
" data = {\"content\": test_item_2, \"mime_type\": \"image/jpeg\"}\n",
" f.write(json.dumps(data) + \"\\n\")\n",
"\n",
"print(gcs_input_uri)\n",
"! gsutil cat $gcs_input_uri"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "batch_request:mbsdk"
},
"source": [
"### Make the batch prediction request\n",
"\n",
"Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
"\n",
"- `job_display_name`: The human readable name for the batch prediction job.\n",
"- `gcs_source`: A list of one or more batch request input files.\n",
"- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
"- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "batch_request:mbsdk"
},
"outputs": [],
"source": [
"batch_predict_job = model.batch_predict(\n",
" job_display_name=\"flowers_\" + TIMESTAMP,\n",
" gcs_source=gcs_input_uri,\n",
" gcs_destination_prefix=BUCKET_NAME,\n",
" sync=False,\n",
")\n",
"\n",
"print(batch_predict_job)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "batch_request:mbsdk"
},
"source": [
"*Example output:*\n",
"\n",
" INFO:google.cloud.aiplatform.jobs:Creating BatchPredictionJob\n",
" <google.cloud.aiplatform.jobs.BatchPredictionJob object at 0x7f806a6112d0> is waiting for upstream dependencies to complete.\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296\n",
" INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
" INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296')\n",
" INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
" https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/5110965452507447296?project=759209241365\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296 current state:\n",
" JobState.JOB_STATE_RUNNING"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "batch_request_wait:mbsdk"
},
"source": [
"### Wait for completion of batch prediction job\n",
"\n",
"Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "batch_request_wait:mbsdk"
},
"outputs": [],
"source": [
"batch_predict_job.wait()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "batch_request_wait:mbsdk"
},
"source": [
"*Example Output:*\n",
"\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328\n",
" INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
" INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328')\n",
" INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
" https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/181835033978339328?project=759209241365\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_RUNNING\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
" JobState.JOB_STATE_SUCCEEDED\n",
" INFO:google.cloud.aiplatform.jobs:BatchPredictionJob run completed. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_batch_prediction:mbsdk,icn"
},
"source": [
"### Get the predictions\n",
"\n",
"Next, get the results from the completed batch prediction job.\n",
"\n",
"The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
"\n",
"- `content`: The prediction request.\n",
"- `prediction`: The prediction response.\n",
" - `ids`: The internal assigned unique identifiers for each prediction request.\n",
" - `displayNames`: The class names for each class label.\n",
" - `confidences`: The predicted confidence, between 0 and 1, per class label."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_batch_prediction:mbsdk,icn"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"import tensorflow as tf\n",
"\n",
"bp_iter_outputs = batch_predict_job.iter_outputs()\n",
"\n",
"prediction_results = list()\n",
"for blob in bp_iter_outputs:\n",
" if blob.name.split(\"/\")[-1].startswith(\"prediction\"):\n",
" prediction_results.append(blob.name)\n",
"\n",
"tags = list()\n",
"for prediction_result in prediction_results:\n",
" gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{prediction_result}\"\n",
" with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
" for line in gfile.readlines():\n",
" line = json.loads(line)\n",
" print(line)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_batch_prediction:mbsdk,icn"
},
"source": [
"*Example Output:*\n",
"\n",
" {'instance': {'content': 'gs://andy-1234-221921aip-20210802180634/100080576_f52e8ee070_n.jpg', 'mimeType': 'image/jpeg'}, 'prediction': {'ids': ['3195476558944927744', '1636105187967893504', '7400712711002128384', '2789026692574740480', '5501319568158621696'], 'displayNames': ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'], 'confidences': [0.99998736, 8.222247e-06, 3.6782617e-06, 5.3231275e-07, 2.6960555e-07]}}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "make_online_predictions:migration"
},
"source": [
"## Make online predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "deploy_model:migration,new,mbsdk"
},
"source": [
"### [predictions.deploy-model-api](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "deploy_model:mbsdk,automatic"
},
"source": [
"## Deploy the model\n",
"\n",
"Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "deploy_model:mbsdk,automatic"
},
"outputs": [],
"source": [
"endpoint = model.deploy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "deploy_model:mbsdk,automatic"
},
"source": [
"*Example output:*\n",
"\n",
" INFO:google.cloud.aiplatform.models:Creating Endpoint\n",
" INFO:google.cloud.aiplatform.models:Create Endpoint backing LRO: projects/759209241365/locations/us-central1/endpoints/4867177336350441472/operations/4087251132693348352\n",
" INFO:google.cloud.aiplatform.models:Endpoint created. Resource name: projects/759209241365/locations/us-central1/endpoints/4867177336350441472\n",
" INFO:google.cloud.aiplatform.models:To use this Endpoint in another session:\n",
" INFO:google.cloud.aiplatform.models:endpoint = aiplatform.Endpoint('projects/759209241365/locations/us-central1/endpoints/4867177336350441472')\n",
" INFO:google.cloud.aiplatform.models:Deploying model to Endpoint : projects/759209241365/locations/us-central1/endpoints/4867177336350441472\n",
" INFO:google.cloud.aiplatform.models:Deploy Endpoint model backing LRO: projects/759209241365/locations/us-central1/endpoints/4867177336350441472/operations/1691336130932244480\n",
" INFO:google.cloud.aiplatform.models:Endpoint model deployed. Resource name: projects/759209241365/locations/us-central1/endpoints/4867177336350441472"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "endpoints_predict:migration,new,mbsdk"
},
"source": [
"### [predictions.online-prediction-automl](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-automl)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_test_item"
},
"source": [
"### Get test item\n",
"\n",
"You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_test_item:automl,icn,csv"
},
"outputs": [],
"source": [
"test_item = !gsutil cat $IMPORT_FILE | head -n1\n",
"if len(str(test_item[0]).split(\",\")) == 3:\n",
" _, test_item, test_label = str(test_item[0]).split(\",\")\n",
"else:\n",
" test_item, test_label = str(test_item[0]).split(\",\")\n",
"\n",
"print(test_item, test_label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "predict_request:mbsdk,icn"
},
"source": [
"### Make the prediction\n",
"\n",
"Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the Endpoint resource.\n",
"\n",
"#### Request\n",
"\n",
"Since in this example your test item is in a Cloud Storage bucket, you open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
"\n",
"The format of each instance is:\n",
"\n",
" { 'content': { 'b64': base64_encoded_bytes } }\n",
"\n",
"Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
"\n",
"#### Response\n",
"\n",
"The response from the `predict()` call is a Python dictionary with the following entries:\n",
"\n",
"- `ids`: The internal assigned unique identifiers for each prediction request.\n",
"- `displayNames`: The class names for each class label.\n",
"- `confidences`: The predicted confidence, between 0 and 1, per class label.\n",
"- `deployed_model_id`: The Vertex AI identifier for the deployed Model resource which did the predictions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "predict_request:mbsdk,icn"
},
"outputs": [],
"source": [
"import base64\n",
"\n",
"import tensorflow as tf\n",
"\n",
"with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
" content = f.read()\n",
"\n",
"# The format of each instance should conform to the deployed model's prediction input schema.\n",
"instances = [{\"content\": base64.b64encode(content).decode(\"utf-8\")}]\n",
"\n",
"prediction = endpoint.predict(instances=instances)\n",
"\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "predict_request:mbsdk,icn"
},
"source": [
"*Example output:*\n",
"\n",
" Prediction(predictions=[{'ids': ['3195476558944927744', '5501319568158621696', '1636105187967893504', '2789026692574740480', '7400712711002128384'], 'displayNames': ['daisy', 'tulips', 'dandelion', 'sunflowers', 'roses'], 'confidences': [0.999987364, 2.69604527e-07, 8.2222e-06, 5.32310196e-07, 3.6782335e-06]}], deployed_model_id='5949545378826158080', explanations=None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "undeploy_model:mbsdk"
},
"source": [
"## Undeploy the model\n",
"\n",
"When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "undeploy_model:mbsdk"
},
"outputs": [],
"source": [
"endpoint.undeploy_all()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:mbsdk"
},
"source": [
"# Cleaning up\n",
"\n",
"To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Dataset\n",
"- Pipeline\n",
"- Model\n",
"- Endpoint\n",
"- AutoML Training Job\n",
"- Batch Job\n",
"- Custom Job\n",
"- Hyperparameter Tuning Job\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup:mbsdk"
},
"outputs": [],
"source": [
"delete_all = True\n",
"\n",
"if delete_all:\n",
" # Delete the dataset using the Vertex dataset object\n",
" try:\n",
" if \"dataset\" in globals():\n",
" dataset.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the model using the Vertex model object\n",
" try:\n",
" if \"model\" in globals():\n",
" model.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the endpoint using the Vertex endpoint object\n",
" try:\n",
" if \"endpoint\" in globals():\n",
" endpoint.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the AutoML or Pipeline trainig job\n",
" try:\n",
" if \"dag\" in globals():\n",
" dag.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the custom trainig job\n",
" try:\n",
" if \"job\" in globals():\n",
" job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the batch prediction job using the Vertex batch prediction object\n",
" try:\n",
" if \"batch_predict_job\" in globals():\n",
" batch_predict_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
" try:\n",
" if \"hpt_job\" in globals():\n",
" hpt_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" if \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r $BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "UJ1 Vertex SDK AutoML Image Classification.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| tutorials/delta_extraction/adding_new_corrections.ipynb |
|
|
|
|
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/statespace_arma_0.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoregressive Moving Average (ARMA): Sunspots data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook replicates the existing ARMA notebook using the `statsmodels.tsa.statespace.SARIMAX` class rather than the `statsmodels.tsa.ARMA` class."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import stats\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import qqplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sunspots Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 309 (Annual 1700 - 2008)\n",
" Number of Variables - 1\n",
" Variable name definitions::\n",
"\n",
" SUNACTIVITY - Number of sunspots for each year\n",
"\n",
" The data file contains a 'YEAR' variable that is not returned by load.\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.sunspots.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.sunspots.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta.index = pd.Index(pd.date_range(\"1700\", end=\"2009\", freq=\"A-DEC\"))\n",
"del dta[\"YEAR\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dta.plot(figsize=(12,4));"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAHiCAYAAAAuz5CZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdf5jddX3n/ed7ZjIhP4jhRxIJBEKVzQK9SqQp1Nt2m5a1BVuL67XbQruAveKm9C6u9fa6F2tdtbtd695dW6W6ZbkVRaxSW62lLv4q3lmrK5QEgxpoJI1AQpCEkDHkBzk557zvP873JGdmzkzmZM7JOWfm+biuueZ8f535zHe+M/M6n/P+fL6RmUiSJEmauoFuN0CSJEnqN4ZoSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFhmhJUlMR8YaI+Po0jv9CRNzUzjZJUq8wREvSNETEhojYFxFzWzgmI+LlnWzXqRYR746ITzSuy8xrMvOubrVJkjrJEC1JJykiVgI/DSTwy11tzAlExNBU1kmSpsYQLUkn70bgAeBjwLGyhaJ3+o0Ny8fKIiLia8XqRyLiQET8arH+30XEtoh4PiLujYjlDcdfGhFfKbY9GxFvL9bPjYj3R8Su4uP99R7xiFgbETsj4taI+AHw0aK3+K8i4hMRsR94Q0S8JCI+EhHPRMTTEfEHETHY7JuNiA9ExI6I2B8RmyLip4v1VwNvB361+J4eGXseImIgIt4REU9GxO6I+HhEvKTYtrLonb8pIp6KiOci4vem/dORpA4yREvSybsR+PPi4xciYtmJDsjMf1E8vCwzF2bmX0TEzwF/CPwKcA7wJHAPQEScDvwd8EVgOfBy4P7iOX4P+ElgNXAZcAXwjoYv91LgTOACYH2x7lrgr4DFRbvvAsrF874C+HngjTT3UPG1zgQ+CfxlRJyWmV8E3gP8RfE9Xdbk2DcUHz8L/AiwEPjgmH1+ClgFXAW8MyIunqAdktR1hmhJOgkR8VPUwumnM3MT8E/Ar53k0/06cGdmPpyZR4DfBV5ZlIv8EvCDzHxfZr6YmS9k5oMNx/2nzNydmXuA3wduaHjeKvCuzDySmYeLdd/MzM9lZhVYBFwD/E5mHszM3cCfANc1a2RmfiIz92ZmOTPfB8ylFnqn+j3+cWZuz8wDxfd43ZiSkt/PzMOZ+QjwCLUXBpLUkwzRknRybgK+nJnPFcufpKGko0XLqfU+A1CEzL3AucAKagH9hMcVj5c3LO/JzBfHHLOj4fEFwBzgmYgYiYgR4H8AS5t9sYh4a0Q8FhE/LPZ9CXD2ib65Sdo6BDT23v+g4fEhar3VktSTHFQiSS2KiHnUSi8Gi3pjqPXKLo6Iy4CDwPyGQ156gqfcRS3Q1p9/AXAW8DS10Hv9CY7bUiyfX6yryybHNK7bARwBzs7M8mQNLOqfb6VWarElM6sRsQ+ISb5Ws7bWnU+tjORZ4LwTHCtJPceeaElq3euACnAJtRrh1cDFwN9Tq5PeDLw+IuYXU9mtG3P8s9Tqgus+CfxGRKwuBga+B3gwM58APg+8NCJ+pxhIeHpEXFkc9yngHRGxJCLOBt4JjJpmbjKZ+QzwZeB9EbGoGPz3soj4mSa7n04t9O4BhiLindTKQRq/p5URMdH/lU8Bb4mICyNiIcdrqCcN75LUqwzRktS6m4CPZuZTmfmD+ge1gXK/Tq2uuEQtWN5FbQBfo3cDdxUlFL+SmfcD/xH4DPAM8DKKuuTMfAF4NfBaauUOj1MbnAfwB8BG4NvAd4CHi3WtuBEYBh4F9lEbdHhOk/2+BHwB+B61UowXGV0a8pfF570R8XCT4+8E7ga+Bny/OP5NLbZVknpGZJ7oHThJkiRJjeyJliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJa1Jc3Wzn77LNz5cqV3W6GJEmSZrhNmzY9l5lLxq7vyxC9cuVKNm7c2O1mSJIkaYaLiCebrbecQ5IkSWqRIVqSJElqkSFakiRJalFHQ3RE3BkRuyPiuxNsj4i4LSK2RcS3I+LyTrZHkiRJaodO90R/DLh6ku3XABcVH+uBP+twe05KpZrc/9iz3Hb/49z/2LNUqtntJkmSJKmLOjo7R2Z+LSJWTrLLtcDHMzOBByJicUSck5nPdLJdrahUkxs+8iCbd4xwuFRh3vAgq1cs5u51VzI4EN1uniRJkrqg2zXR5wI7GpZ3Fut6xoatu9m8Y4RDpQoJHCpV2LxjhA1bd3e7aZIkSeqSbofoZl25TWslImJ9RGyMiI179uzpcLOO27JrP4dLlVHrDpcqPLpr/ylrgyRJknpLt0P0TmBFw/J5wK5mO2bmHZm5JjPXLFky7qYxHXPp8kXMGx4ctW7e8CCXLF90ytogSZKk3tLtEH0vcGMxS8dPAj/spXpogLWrlrJ6xWKiUoKsMr+oiV67amm3myZJkqQu6ejAwoj4FLAWODsidgLvAuYAZObtwH3Aa4BtwCHgNzrZnpMxOBDcve5KXvn6dZQWLOV973gLa1ctdVChJEnSLNbp2TmuP8H2BH67k21oh8GBYP7IduaPbOeqi5d1uzmSJEnqsm6Xc0iSJEl9xxAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLVoqNsNmOkq1WTD1t1s2bWfS5cvYu2qpQwORLebJUmSpGnoeIiOiKuBDwCDwIcz871jtr8E+ARwftGe/5aZH+10u06FSjW54SMPsnnHCIdLFeYND7J6xWLuXnelQVqSJKmPdbScIyIGgQ8B1wCXANdHxCVjdvtt4NHMvAxYC7wvIoY72a5TZcPW3WzeMcKhUoUEDpUqbN4xwoatu7vdNEmSJE1Dp2uirwC2Zeb2zCwB9wDXjtkngdMjIoCFwPNAucPtOiW27NrP4VJl1LrDpQqP7trfpRZJkiSpHTodos8FdjQs7yzWNfogcDGwC/gO8ObMrHa4XafEpcsXMW94cNS6ecODXLJ8UZdaJEmSpHbodIhuVvibY5Z/AdgMLAdWAx+MiHEpMyLWR8TGiNi4Z8+e9re0A9auWsrqFYuJSgmyyvyiJnrtqqXdbpokSZKmodMheiewomH5PGo9zo1+A/hs1mwDvg/887FPlJl3ZOaazFyzZMmSjjW4nQYHgrvXXcmSx/+WxTu/wZ9e/woHFUqSJM0AnQ7RDwEXRcSFxWDB64B7x+zzFHAVQEQsA1YB2zvcrlNmcCCYP7KdxU8/wFUXLzNAS5IkzQAdneIuM8sRcQvwJWpT3N2ZmVsi4uZi++3AfwY+FhHfoVb+cWtmPtfJdkmSJEnT0fF5ojPzPuC+Metub3i8C/j5TrdDkiRJahdv+y1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1yBAtSZIktcgQLUmSJLXIEC1JkiS1qOO3/ZY0sUo12bB1N1t27efS5YtYu2opgwPR7WZJkqQTMERLXVKpJjd85EE27xjhcKnCvOFBVq9YzN3rrpxSkDaAS5LUPYZoqUs2bN3N5h0jHCpVADhUqrB5xwgbtu7mqouXTXrsdAO4JEmaHmuipS7Zsms/h4sAXXe4VOHRXftPeGxjAE9GB3BJktR5hmipSy5dvoh5w4Oj1s0bHuSS5YtOeOx0ArgkSZo+Q7TUJWtXLWX1isVEpQRZZX5RkrF21dITHjudAC5JkqbPEC11yeBAcPe6K1ny+N+yeOc3+NPrXzHlmubpBHBJkjR9HQ/REXF1RGyNiG0R8bYJ9lkbEZsjYktE/K9Ot0nqFYMDwfyR7Sx++gGuunjZlAcFTieAS5Kk6evo7BwRMQh8CHg1sBN4KCLuzcxHG/ZZDPx34OrMfCoi7EqTpqAewOePbD/hbB6SJKm9Ot0TfQWwLTO3Z2YJuAe4dsw+vwZ8NjOfAshMpxeQJElST+t0iD4X2NGwvLNY1+ifAWdExIaI2BQRNzZ7oohYHxEbI2Ljnj17OtRcSZIk6cQ6HaKbFWjmmOUh4MeBXwR+AfiPEfHPxh2UeUdmrsnMNUuWLGl/SyVJkqQp6vQdC3cCKxqWzwN2Ndnnucw8CByMiK8BlwHf63DbJEmSpJPS6Z7oh4CLIuLCiBgGrgPuHbPP3wA/HRFDETEfuBJ4rMPtkiRJkk5aR3uiM7McEbcAXwIGgTszc0tE3Fxsvz0zH4uILwLfBqrAhzPzu51slyRJkjQdnS7nIDPvA+4bs+72Mct/BPxRp9siSZIktYN3LJQkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJa1PF5oqWZrlJNNmzdzZZd+7l0+SLWrlrK4EB0u1mSJKmDDNHSNFSqyQ0feZDNO0Y4XKowb3iQ1SsWc/e6Kw3SkiTNYJZzSNOwYetuNu8Y4VCpQgKHShU27xhhw9bd3W7apCrV5P7HnuW2+x/n/seepVLNbjdJkqS+Yk90D7NMoPdt2bWfw6XKqHWHSxUe3bWfqy5e1qVWTc7ec0mSps8Q3aMMOv3h0uWLmDc8yKGGID1veJBLli/qYqsm19h7DqN7z3s1+EuS1Gss5+hR/VomMNusXbWU1SsWE5USZJX5xYudtauWdrtpE5qs91ySJE2NIbpHGXT6w+BAcPe6K1ny+N+yeOc3+NPrX9Hz7xbUe88b9XrvuSRJvcYQ3aMMOv1jcCCYP7KdxU8/wFUXL+vpAA392XuuU8dBp5I0NdZE96h60Pnm954hB4aYP3eOQUdtUe89f+Xr11FasJT3veMtDloVMP2xGA6GljSbdDxER8TVwAeAQeDDmfneCfb7CeAB4Fcz86863a5eZ9BRJ9V7z+ePbHcwoY6ZzqBTB0NLmm06Ws4REYPAh4BrgEuA6yPikgn2+6/AlzrZnn7Tb2UCkvrbdMZiOBha0mzT6ZroK4Btmbk9M0vAPcC1TfZ7E/AZwL+2ktQl0xmL4WBoSbNNp0P0ucCOhuWdxbpjIuJc4F8Bt3e4LZKkSUxn0KmDoSXNNp0O0c3qD8YO9X4/cGtmVprse/yJItZHxMaI2Lhnz562NVCSVDOdKRud9UXSbNPpgYU7gRUNy+cBu8bsswa4JyIAzgZeExHlzPxc406ZeQdwB8CaNWucc0mSOuBkB506GFrSbNPpEP0QcFFEXAg8DVwH/FrjDpl5Yf1xRHwM+PzYAC1J6n3O+iJpNuloiM7MckTcQm3WjUHgzszcEhE3F9utg5YkSVLf6fg80Zl5H3DfmHVNw3NmvqHT7ZEkSZKmyzsWSpIk6aRkJtWEaiaZkMX8Edlk9Fp9XTbMMXF83ejnHLtu4fAQAz02xsIQLUmSelY/304+myXJFlSz9hwJxwLqsdDZsJyM3o8i1FaLgFvfVl+uVmvHTbhPtfaZhmNy7H7Vhq93Clx+wWLmDgyeeMdTyBAtSZJ6UrPbyf/YeS/hwzf+BBGjA16lOjrs1QPi8bDY0FvaEBhz1Pri8bFtOa6ntDE0NobkZts1sxmiJUnqYfWAWCkCXf3x6N7E472SEwXDejhsFgxrX2fM123ylvtEyxMdN/57Of45Gd2zmU0eb3xiH5ue3MeRchWo3U7+W0+NcNf/foLLLzhj0vMmdZohWpJmmH5++7vXjQ2gzXo0G3tEx/aONobfSv2t9CIUV6rjQ3L9+Nnqib0HKRUBuq5UrvLE3oOGaHWdIVqSZpBmb3+vXrF4ynce7HTbytUq1SqUq9VaWCw+yscC4+jQybHeyiY9rIzf92SNDsb1dTlunU6tlWctYHho4FhPNMDw0AArz1rQxVZJNYZoSZpBNmzdzeYdIxwqVYDa29+bd4ywYevuad0ApVkALjcG4Ept++FShQS++/QPj/Wo1oOyQVStWr1iMS9fupAtTz0Hg0PMnTPEy5cuZPWKxVM6vlpNNu8Y4Ym9B1l51gJWr1jcczM8qH8ZoiWpjZoNamo27dOJpnM6FjgnCJ7N6k4zYeMT+zhcBOi6w6UK//D957lk+aJJR+bX232wVIaEbz2171hInmoAPlqp9Ri+8GJ5agdIkxgYCN5+zcX85pvfSmXhMm65ef2Ug3C1mrznC4+xbfcBSuUqw0MDvHzpQt5+zcUGabWFIVrSrNNYg1rNWkisNpQWHNtW9LrWg2Z9//Gj/Y9v73Zv68K5Q03f/l502hyeeO7QlJ6jUql9Ey8erZ5gz/axx1ATGRgIhvdug73buPyCW6d83OYdI2zbfeDY78KRcpVtuw+weceI9dRqC0O0pJZkPXTm6MB4vId1/MCr+uPG/WjYf+xzNO5f/5p11TE9qNWGOtpaiB0deBvD7mwYqDXdt7+7wR5DdYKDEtVphmhplqtUk6OVKkcrVcqV5Gi19rnx8dFKlXI1KRefu93bqolN5+3vbrHHUJ3goER1miFamgWq1eTw0QqHShUOlyocOlrmwJEy1Uz+4fvPd7t5arOTffu7W+wxnPm6Ua7Tj+/KqL8YoqUZJHN0WK4/fvFoZVzvcXUm1zSor9hjOLN1q1ynH9+VUX8xREt96ki5QrlSGwT3+LMvHAvLZmP1G3sMZ7Zuluv027sy6i+GaKnHZSYvHq1y4EiZQ6UyB49UOFQqc7SSHCrVphF77kCpy62UTp49hjOb5TqaqQzRUg+pVpNDRyscOlIuQnOtHKNi97JmOHsMZy7LdTRTdTxER8TVwAeAQeDDmfneMdt/Haj/xTwA/FZmPtLpdkndUq0mpUqVI+UqpXLtczWTR3aMcLhJ7bIk9TPLdTRTdTRER8Qg8CHg1cBO4KGIuDczH23Y7fvAz2Tmvoi4BrgDuLKT7ZI65WilFoxL5dqUcUfKVUpj1h2tjE7JR44evz2zJM00lutopup0T/QVwLbM3A4QEfcA1wLHQnRm/u+G/R8Azutwm9Tn6jf7qM9vXPuclIs5jWv7HL/pRzYcl9Rv6NG43/gbhTS9TfOYdY03EnnhxTJJsvGJfR3//iWp31iuo5mo0yH6XGBHw/JOJu9lXgd8oaMtUs+ph+FSpcrRcq2ntn5zj0q1tlzfpx6ee63kIXutQZIkqaM6HaKbvVfTNG1ExM9SC9E/NcH29cB6gPPPP79d7VMH1e+Cd7ScHKlUauG4KGkoVRrCcsUAKkmS+kunQ/ROYEXD8nnArrE7RcSPAR8GrsnMvc2eKDPvoFYvzZo1a0xdJ1AbsFYLrtWT7CWtlTXU5iGu9/5WqrXlzKRSrS1Xs/Zx/DE92VssSZLULp0O0Q8BF0XEhcDTwHXArzXuEBHnA58FbsjM73W4PTPCsdkdjtaC8pFy40wPFUrlqjfcaFE3bknbrzxXkiR1OERnZjkibgG+RG2Kuzszc0tE3Fxsvx14J3AW8N8jAqCcmWs62a5+Uqkm5UryvWdfaAjJJuR26tYtafuR50qSpJqOzxOdmfcB941Zd3vD4zcCb+x0O/rJ/heP8vyBEnsPljh4pHZHur3eka5junlL2n7juZIkqcY7FvaAzOSFI2X2Hijx/MHSuNujqrO8Je3Uea4kzRaWrulEDNFdkpnsf7HM8wdLPH/wiCUaXeQtaafOcyVpNrB0TVMx0O0GzCaZyQ8PH+X7zx3k4af28eiu/fzghy8aoLusfktayiXIKnOLP5beknY8z5Wk2aCxdC0ZXbom1RmiO6x+d70Xj1YMzj2qfkvahY9+jnnf/3v+/c9dZG/DBDxXkmaDyUrXpDpDdAdkJj88dJTtew7w8FP7OHSkTKlcNTj3sPotaec9+Q0uv+AMQ+EkPFeSZrp66VojS9c0ljXRbZKZ7D9cZu/BIzx/sMRR78InaRoc1CR1T710bctTz8HgEHPnDFm6pnEM0W1ytJI8+sz+bjdD0gzgoCapu+qla7/55rdSWbiMW25e7wtZjWM5hyT1GAc1qRdVq8nDT+7jsw/v5OEn91Gd4bfGtXRNJ2JPtCT1GOfjVq/x3RFpPHuiJanHOKhJvcZ3R6TxDNGS1GOcj1u9xinfpPEM0ZLUY5yPuzWzrVa3G3x3RBrPmugZyumxpP5WH9TE3m1cfsGt3W5Oz7JW99RwyjdpPEP0DOQ/FUmzRWOtLoyu1XUQZvs45Zs0nuUcM5ADQCTNFtbqnjpO+SaNZoiegfynImm2sFZXUrd0PERHxNURsTUitkXE25psj4i4rdj+7Yi4vNNtmun8pyJptnAmE0nd0tEQHRGDwIeAa4BLgOsj4pIxu10DXFR8rAf+rJNtmg38pyJptnAmE0nd0ume6CuAbZm5PTNLwD3AtWP2uRb4eNY8ACyOiHM63K4ZzX8qkmYTa3UldUNkdm4+zYj418DVmfnGYvkG4MrMvKVhn88D783MrxfL9wO3ZubGiZ73zAsuzle//c6OtbuZzY9sBmD1Zaubbs+EF44cbbrt8Ue/C8BFl/xoy1+3W8fORv34c+rHYzV1/fjz7cdjNXX9+PP1uup9mcmBIxVePFrhtDmDLJw7SMToF8Onzx0at+5U+fTN/8emzFwzdn2nQ/S/AX5hTIi+IjPf1LDP/wT+cEyI/g+ZuWnMc62nVu7BwnNe9uOvedfdHWv3yZgsRPerfvyj5R+83teP14bX5MzWj9eG12Tv6+bPqJ+urczkqecPc+jIUSCIgWDenEHOP3PeqNA8G0P0K4F3Z+YvFMu/C5CZf9iwz/8ANmTmp4rlrcDazHxmoudds2ZNbtw4YUd1V5TKVTY9ua/bzWir3/61XwbgQ5+8d1Ycq1OjH68Nr8mZrR+vDa/J3tfNn1E/XVsPP7mP2776+LG53gHmDg3w73/uolFzvV9+wWLmDg223KZ2iIimIbrTNdEPARdFxIURMQxcB4w9s/cCNxazdPwk8MPJArQkSZJmhn6elrejdyzMzHJE3AJ8CRgE7szMLRFxc7H9duA+4DXANuAQ8BudbJMkSZJ6Q31a3sae6H6Zlrfjt/3OzPuoBeXGdbc3PE7gtzvdDkmSpFOhWk1KZ72cysJlPPzkPm+RPon6tLzbdh+gVK4y3EfT8nY8REuSJM0W1Wryni88xoFLXgeDQ9z21cd5+dKFTjU7gfq0vJt3jPDE3oOsPGtB37zoMERLkiS1yeYdI2zbfQCGhgE4Uq6ybfcBNu8YGTVQTscNDASXX3BG352fjt/2W5Ikabbo54Fyao0hWpIkqU3qA+Ua9ctAObXGEC1JktQm9YFyc4cGCGpzHvfLQDm1xppoSZKkNunngXJqjSFakiSpjfp1oJxaYzmHpFOmPnfq4QtexcNP7qNazW43SZpV/B2U2seeaEmnhHOnSt3l76DUXvZEt8nw0ACXLF/ES19yGsND/jGSxho1d2oMjJo7daay10+9ZDb+DkqdZIhuo5fMm8OFZy/g8vPP4NJz64HaUyzB7Js7tbHX7/CFP81tX32c93zhMYO0uma2/Q5KnWbC64CIYNFptUD94xfUAvU5BmrNcrNt7lR7/dRrZtvvoNRpprpTYNFpc1hpoO443zrvbbNt7lR7/dRrZtvvoNRpDiw8xRadNudYqH7hxaPsPVDi+UMljhytnvhgTcgBM71vts2dWu/1O9IQpO31UzfNtt9BqdMM0V10+mlzOP20OaykFqifP1hi70ED9ckY9dY5jHrr3Hk6e8dsmju13uu3bfcBSuUqw/b69aT6O1iVhct4+Ml9Mz5UzqbfQanTDNE9oh6oLzjLQH0yJnvr3H8W6gZ7/Xqf72BJ7TPbXpBCB0N0RJwJ/AWwEngC+JXM3DdmnxXAx4GXAlXgjsz8QKfa1C8aA/WBI2X2HSxxpFzhxaNVjpSrHK1USct9R/Gtc/Uie/16m+9gSe0xW1+QdrIn+m3A/Zn53oh4W7F865h9ysBbM/PhiDgd2BQRX8nMRzvYrr6ycO4QC+eO/jFlJkfKtUBdKlc5Uq4Un4+vq8yyQXW+dS6pVb6DJbXHbH1B2skQfS2wtnh8F7CBMSE6M58BnikevxARjwHnAoboSUQEp80Z5LQ5gxPuc7RS9FqXqzSL0zmFrux5w7Xn/5ElC6hUk0o1yYRK1h5Xs/ZRqSbVKrXHmbVZMQKafuEO8a1zSa3yHSypPWbrC9JOhuhlRUgmM5+JiKWT7RwRK4FXAA92sE2zxpzBAeYMDsDc6T0HwLJFp7V87KLT5pBZ6yEuVWolKEcrVY6Wk1KlQqmcx9dV2pO2fetcUit8B0tqj9n6gnRaIToi/o5aPfNYv9fi8ywEPgP8Tmbun2Cf9cB6gPPPP7/FlqobImq92fOYuMccar3ipSJMl8pVThseJBOWLz6No5VaT/fRSpVyNalUa/tZEy5punwHS2qP2fqCdFohOjP/5UTbIuLZiDin6IU+B9g9wX5zqAXoP8/Mz07yte4A7gBYs2aNEWoGiQjmDg0ydwiYC8NFD/gFk7yCHRWsK8nRavXYunrAPvaZWuhOjpex5LHteWzfPPa5YV0eP75u3D7F89YDv6T+4TtY6jX9OMvFbH1B2slyjnuBm4D3Fp//ZuwOERHAR4DHMvOPO9gWzTCDA8HgwOQ93N1QqdZ600vlKkcqtQGf9R72UrlKqVKxJ12S1FQ/z3IxG1+QdjJEvxf4dESsA54C/g1ARCwHPpyZrwFeBdwAfCciNhfHvT0z7+tgu6SOGRyIWgnL8CAwp+k+9fKV48G6yqFShUNHKhwqlbEzW5Jmp9k6y0W/6liIzsy9wFVN1u8CXlM8/jq1eRykWeN4+cr4nvTM5FCpwsFSmUNHis+lCuU2Db6UJPWu2TrLRb/yjoVSD4kIFswdYsHcITj9+PoXj1Y4eKQWqA8cKXOoVKZUNlhL0kwyW2e56FeGaKkP1OcFP6thXalc5VDRU32oVOFwyXIQSepns3WWi35liFZTlWpyaPGPUFqwjPsfe5a1q5Yy2OODGmab4aEBhoeGWTz/+Lr63SwPFYH6cBGwXzxaMVxLUo+brbNc9CtDtMapVJMbPvIgey56LTkwxJs+9S1Wr1jM3euuNEj3uMa7WZ65YPjY+szk8NHjPdb1x7PxFvFSv+vHKdA0da9c7IQAACAASURBVLNxlot+ZYjWOBu27mbzjhFysBbCDpUqbN4xwoatu7nq4mVdbp1ORkQwf3iI+cPjf+Wr1do82+VKUq4cf1yfh7t+Z8lyJSl7sxupq/p5CjT1Pl+gtcYQrXG27NrP4VJl1LrDpQqP7tpviJ6BBgaCuQPFzW6mqFypUh1z05m6sTe5qT2ub8uGxw1P2Hh8w8Ko5wXmDw+RJC9buoDM2rsm1UyqVWqfj33UlivVWhuqmQwMBJm1aQjtfVe/cgo0dYov0FpniNY4ly5fxLzhQQ41BOl5w4NcsnxRF1ulXjJU3FXy1H/dAIKlp5/W8rELi1cJV1x4JlkE7XK1SrUKlazd+bJSBO9qJuVq1nplylWqSy/i6IJlfO/ZF/jxC84ggtEhPjn2nNW0p16d4xRo6hRfoLXOEK1x1q5ayuoVi9m8Y4TDpQrzhgdZvWIxa1ct7XbTNIu1c7BrRDAYnPCul/XxAT/859eSA0P81y/+45TGB+SxMF4L1cfXF5+b9LY39tYf2zbueZt9sWarjr9D0Czk14N+dVTwb9hePd6TX67WPtt73xucAk0ncrIlGb5Aa50hWuMMDgR3r7uSDVt38+iu/VyyfJGzc6irujXY9WTHB0RE0Ws+c2QeD9Tlaq3nvlytHuu9L1fGh+7acrXWs19JZ4hpA6dA02SmU5LhC7TWGaLV1OBAcNXFy6yBVk/o1mBXxwccFxHMGQzmTN55P6lq9XjJTHls0K4eL69JRpfG1GvpG3vQa+tH7wsT9NZP2fHnaKzdr787kGOevxuDsJwCTZOZTkmGL9BaZ4iW1PO6FWYdH9BeAwPBANML4r2iXKly453/wMFLX0cODPGhDdv4sfNewodv/AkianX2WS+LGVNCUx/wOjxUG1uw5PRhKg0lNKM/j6+zdwo0TWQ6JRm+QGudIVpSz+tWmHV8gCbyv763Z9y7I9/e+UMe/P7eKb+wO614NfHypaefcN/GXvzjIft4rXvj53qv/bHlbKyPz6JHvfksODC6zH78tuZd/ZO9A9DsORrfSTj27sKxfettP/79OFh3aqZbkuELtNYYoiX1vG6FWccHaCKn+t2RmdSLfzLqLwYaB8OOGhhbHb/u2OPq6KDeWCZUn6qzXkbUWC402QuOiV5oTDYwuNn+7WZJxqlliJbU87oZZh0foGYs9Tm1IoIIGGB2vYA99k4Co3vlm/Xm1/f5xBuv5O8ff46tz+znny07nVe+7CwiYtIZesa+ozFu/EGTcQqN+89Whmj1lHZOY6aZZTph1utK7Tbdd0e8JjUV9RcPxdKUj/vly5bDZcs70qZmqtXjvfww+XSejVqZ9nPOQHfuTzCZyE6+r9Aha9asyY0bN3a7GZrE2rVrAdiwYcOUj6lPY/bN7z1DDgwxf+6cUzKNmWY2ryt1SqWaJ/XuiNek1F8iYlNmrhm7vvdivWatUdOYxcCoacykk+V1pU6pvzvypqsu4qqLl005AHtNSjNDx0J0RJwZEV+JiMeLzxMO9YyIwYj4VkR8vlPtUe+bbKCOdLK8rtRrvCalmaGTPdFvA+7PzIuA+4vlibwZeKyDbVEfqA/UaeRAHU2X15V6jdekNDN0MkRfC9xVPL4LeF2znSLiPOAXgQ93sC3qA/WBOvOHBwlgvnPyqg28rtRrvCalmaGTs3Msy8xnADLzmYiY6K/D+4H/AEw623xErAfWA5x//vntbKd6hHPyqhO8rtRrvCalmWFaIToi/g54aZNNvzfF438J2J2ZmyJi7WT7ZuYdwB1Qm52jxabqFJrO1E3OyatO8LpSr/GalPrftEJ0Zv7LibZFxLMRcU7RC30O0GzY8auAX46I1wCnAYsi4hOZ+W+n0y51T33qpj0XvZYcGOJNn/qWUzdJkqQZp5M10fcCNxWPbwL+ZuwOmfm7mXleZq4ErgO+aoDub07dJEmSZoNOhuj3Aq+OiMeBVxfLRMTyiLivg19XXeTUTZIkaTbo2MDCzNwLXNVk/S7gNU3WbwA2dKo9OjXqUzcdagjSTt0kSZJmGu9YqLZy6iZJkjQbdHKKO81CTt0kSZJmA0O02s6pmyRJ0kxnOYckSZLUIkO0JEmS1CJDtCRJktQiQ7QkSZLUosjMbrehZRGxB3iyC1/6bOC5LnzdfuX5mjrP1dR5rqbOczV1nqup81xNneeqNb16vi7IzCVjV/ZliO6WiNiYmWu63Y5+4fmaOs/V1Hmups5zNXWeq6nzXE2d56o1/Xa+LOeQJEmSWmSIliRJklpkiG7NHd1uQJ/xfE2d52rqPFdT57maOs/V1Hmups5z1Zq+Ol/WREuSJEktsidakiRJapEheooi4uqI2BoR2yLibd1uTy+LiCci4jsRsTkiNna7Pb0kIu6MiN0R8d2GdWdGxFci4vHi8xndbGMvmeB8vTsini6ur80R8ZputrEXRMSKiPj/IuKxiNgSEW8u1nttNTHJ+fLaGiMiTouIf4iIR4pz9fvFeq+tMSY5V15XE4iIwYj4VkR8vljuq+vKco4piIhB4HvAq4GdwEPA9Zn5aFcb1qMi4glgTWb24lyPXRUR/wI4AHw8M3+0WPf/AM9n5nuLF2hnZOat3Wxnr5jgfL0bOJCZ/62bbeslEXEOcE5mPhwRpwObgNcBb8Bra5xJztev4LU1SkQEsCAzD0TEHODrwJuB1+O1Ncok5+pqvK6aioj/C1gDLMrMX+q3/4f2RE/NFcC2zNyemSXgHuDaLrdJfSgzvwY8P2b1tcBdxeO7qP0zFxOeL42Rmc9k5sPF4xeAx4Bz8dpqapLzpTGy5kCxOKf4SLy2xpnkXKmJiDgP+EXgww2r++q6MkRPzbnAjoblnfgHdzIJfDkiNkXE+m43pg8sy8xnoPbPHVja5fb0g1si4ttFuUdPv913qkXESuAVwIN4bZ3QmPMFXlvjFG+5bwZ2A1/JTK+tCUxwrsDrqpn3A/8BqDas66vryhA9NdFkna8uJ/aqzLwcuAb47eIteald/gx4GbAaeAZ4X3eb0zsiYiHwGeB3MnN/t9vT65qcL6+tJjKzkpmrgfOAKyLiR7vdpl41wbnyuhojIn4J2J2Zm7rdlukwRE/NTmBFw/J5wK4utaXnZeau4vNu4K+plcNoYs8WNZr1Ws3dXW5PT8vMZ4t/VFXg/8XrC4CiBvMzwJ9n5meL1V5bE2h2vry2JpeZI8AGajW+XluTaDxXXldNvQr45WIM1T3Az0XEJ+iz68oQPTUPARdFxIURMQxcB9zb5Tb1pIhYUAzUISIWAD8PfHfyo2a9e4Gbisc3AX/Txbb0vPof2MK/wuurPqDpI8BjmfnHDZu8tpqY6Hx5bY0XEUsiYnHxeB7wL4F/xGtrnInOldfVeJn5u5l5XmaupJapvpqZ/5Y+u66Gut2AfpCZ5Yi4BfgSMAjcmZlbutysXrUM+Ova/yiGgE9m5he726TeERGfAtYCZ0fETuBdwHuBT0fEOuAp4N90r4W9ZYLztTYiVlMrqXoC+M2uNbB3vAq4AfhOUY8J8Ha8tiYy0fm63mtrnHOAu4pZqgaAT2fm5yPim3htjTXRubrb62rK+upvllPcSZIkSS2ynEOSJElqkSFakiRJapEhWpIkSWqRIVqSJElqkSFakiRJapEhWpIkSWqRIVqSJElqkSFakqYpIg5ExI9MYb+VEZERMatvdBURb4iIr0/j+C9ExE0n3lOSOscQLWnGi4gnIuJwEXafjYiPRsTCk3yuDRHxxsZ1mbkwM7e3p7XHvsa+iJjb4nEZES9vVzt6QUS8OyI+0bguM6/JzLu61SZJAkO0pNnjtZm5ELgc+AngHa0cHDUd/5sZESuBn6Z2i+Bf7vTXm65mveqzvadd0uxgiJY0q2Tm08AXgB+NiDMi4vMRsafo+f18RJxX37foEf4vEfEN4BBwN7WA+8GiV/uDxX7HeoAj4hcj4lsRsT8idkTEu1ts4o3AA8DHgFElC2N7wRvLIiLia8XqR4q2/Wqx/t9FxLaIeD4i7o2I5Q3HXxoRXym2PRsRby/Wz42I90fEruLj/fVe8YhYGxE7I+LWiPgB8NGit/ivIuITEbEfeENEvCQiPhIRz0TE0xHxBxEx2OwbjogPFOdqf0RsioifLtZfDbwd+NXie3pk7HmIiIGIeEdEPBkRuyPi4xHxkmJbvXzmpoh4KiKei4jfa/HnIUlNGaIlzSoRsQJ4DfAtan8DPwpcAJwPHAY+OOaQG4D1wOnAG4C/B24pSjhuafIlDlILwouBXwR+KyJe10ITbwT+vPj4hYhYNpWDMvNfFA8vK9r2FxHxc8AfAr8CnAM8CdwDEBGnA38HfBFYDrwcuL94jt8DfhJYDVwGXMHonvuXAmdSO2/ri3XXAn9VfN9/DtwFlIvnfQXw88CoMpgGDxVf60zgk8BfRsRpmflF4D3AXxTf02VNjn1D8fGzwI8ACxn/M/wpYBVwFfDOiLh4gnZI0pQZoiXNFp+LiBHg68D/At6TmXsz8zOZeSgzXwD+C/AzY477WGZuycxyZh490RfJzA2Z+Z3MrGbmt4FPNXnOpiLip6gF009n5ibgn4Bfm/q3OM6vA3dm5sOZeQT4XeCVRcnILwE/yMz3ZeaLmflCZj7YcNx/yszdmbkH+H1qLybqqsC7MvNIZh4u1n0zMz+XmVVgEXAN8DuZeTAzdwN/AlzXrJGZ+YniZ1HOzPcBc6mF3ql+j3+cmdsz80DxPV43pqTk9zPzcGY+AjxC7YWBJE2LdWuSZovXZebfNa6IiPnUwt3VwBnF6tMjYjAzK8Xyjla+SERcCbwX+FFgmFog/MspHn4T8OXMfK5Y/mSx7k9aaUOD5cDD9YXMPBARe4FzgRXUQvpExz3ZsPxksa5uT2a+OOaYxvN0ATAHeCYi6usGmOBcRsRbqfVSL6dWC74IOHvC7+rEbR0CGnvwf9Dw+BC13mpJmhZ7oiXNZm+l1uN5ZWYuAuolEdGwT445ZuzyWJ8E7gVWZOZLgNvHPF9TETGPWtnFz0TED4p647cAl0VEvef0IDC/4bCXnuBpd1ELtPWvsQA4C3iaWqB92VSOo1bqsqthudk5aFy3AzgCnJ2Zi4uPRZl56diDivrnW6l972dk5mLghxw/Zyc6383aWgaePcFxkjQthmhJs9np1OqgRyLiTOBdUzjmWWq1t5M95/OZ+WJEXMHUyzFeB1SAS6jVB68GLqZWg31jsc9m4PURMb8YyLjuBG37JPAbEbG6GBj4HuDBzHwC+Dzw0oj4nWIg4elFLzrUSlDeERFLIuJs4J3AqGnmJpOZzwBfBt4XEYuKwX8vi4hmZS2nUwu9e4ChiHgntZ7oxu9p5SQzo3wKeEtEXBi1aQvrNdTlqbZXkk6GIVrSbPZ+YB7wHLUZMb44hWM+APzrYjaP25ps/z+B/xQRL1ALn5+eYltuAj6amU9l5g/qH9QGyf16UeP7J0CJWrC8i9oAvkbvBu6KiJGI+JXMvB/4j8BngGeo9TxfB1DUgL8aeC21cofHqQ3OA/gDYCPwbeA71EpC/mCK30fdjdTKWR4F9lEbdHhOk/2+RG22lO9RK8V4kdFlH/VSmL0R8TDj3Ult1pSvAd8vjn9Ti22VpJZF5oneKZMkSZLUyJ5oSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFfXnHwrPPPjtXrlzZ7WZIkiRphtu0adNzmblk7Pq+DNErV65k48aN3W6GJEmSZriIeLLZess5JEmSpBYZoiVJkqQWGaIlSZKkFrUlREfEnRGxOyK+O8H2iIjbImJbRHw7Ii5v2HZ1RGwttr2tHe2RJEmSOqldPdEfA66eZPs1wEXFx3rgzwAiYhD4ULH9EuD6iLikTW1qm0o1uf+xZ7nt/se5/7FnqVSz202SJElSF7Vldo7M/FpErJxkl2uBj2dmAg9ExOKIOAdYCWzLzO0AEXFPse+j7WhXO1SqyQ0feZDNO0Y4XKowb3iQ1SsWc/e6KxkciG43T5IkSV1wqmqizwV2NCzvLNZNtL5nbNi6m807RjhUqpDAoVKFzTtG2LB1d7ebJkmSpC45VSG6WZdtTrJ+/BNErI+IjRGxcc+ePW1t3GS27NrP4VJl1LrDpQqP7tp/ytogSZKk3nKqQvROYEXD8nnArknWj5OZd2Tmmsxcs2TJuJvGdMylyxcxb3hw1Lp5w4NcsnzRKWuDJEmSesupCtH3AjcWs3T8JPDDzHwGeAi4KCIujIhh4Lpi356xdtVSVq9YTFRKkFXmFzXRa1ct7XbTJEmS1CVtGVgYEZ8C1gJnR8RO4F3AHIDMvB24D3gNsA04BPxGsa0cEbcAXwIGgTszc0s72tQugwPB3euu5JWvX0dpwVLe9463sHbVUgcVSpIkzWLtmp3j+hNsT+C3J9h2H7WQ3bMGB4L5I9uZP7Kdqy5e1u3mSJIkqcu8Y6EkSZLUIkO0JEmS1CJDtCRJktQiQ7QkSZLUIkO0JEmS1CJDtCRJktQiQ7QkSZLUIkO0JEmS1CJDtCRJktQiQ7QkSZLUIkO0JEmS1KKhbjdgpqtUkw1bd7Nl134uXb6ItauWMjgQ3W6WJEmSpsEQ3UGVanLDRx5k844RDpcqzBseZPWKxdy97kqDtCRJUh+znKODNmzdzeYdIxwqVUjgUKnC5h0jbNi6u9tNkyRJ0jQYojtoy679HC5VRq07XKrw6K79XWqRJEmS2sEQ3UGXLl/EvOHBUevmDQ9yyfJFXWqRJEmS2sEQ3UFrVy1l9YrFRKUEWWV+URO9dtXSbjdNkiRJ02CI7qDBgeDudVey5PG/ZfHOb/Cn17/CQYWSJEkzQFtCdERcHRFbI2JbRLytyfb/OyI2Fx/fjYhKRJxZbHsiIr5TbNvYjvb0ksGBYP7IdhY//QBXXbzMAC1JkjQDTHuKu4gYBD4EvBrYCTwUEfdm5qP1fTLzj4A/KvZ/LfCWzHy+4Wl+NjOfm25bJEmSpFOhHT3RVwDbMnN7ZpaAe4BrJ9n/euBTbfi6kiRJUle0I0SfC+xoWN5ZrBsnIuYDVwOfaVidwJcjYlNErG9DeyRJkqSOascdC5sV+eYE+74W+MaYUo5XZeauiFgKfCUi/jEzvzbui9QC9nqA888/f7ptliRJkk5aO3qidwIrGpbPA3ZNsO91jCnlyMxdxefdwF9TKw8ZJzPvyMw1mblmyZIl0260JEmSdLLaEaIfAi6KiAsjYphaUL537E4R8RLgZ4C/aVi3ICJOrz8Gfh74bhvaJEmSJHXMtMs5MrMcEbcAXwIGgTszc0tE3Fxsv73Y9V8BX87Mgw2HLwP+OiLqbflkZn5xum2SJEmSOqkdNdFk5n3AfWPW3T5m+WPAx8as2w5c1o42SJIkSaeKdyyUJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJaZIiWJEmSWmSIliRJklpkiJYkSZJa1JYQHRFXR8TWiNgWEW9rsn1tRPwwIjYXH++c6rGSJElSrxma7hNExCDwIeDVwE7goYi4NzMfHbPr32fmL53ksZIkSVLPaEdP9BXAtszcnpkl4B7g2lNwrCRJktQV7QjR5wI7GpZ3FuvGemVEPBIRX4iIS1s8lohYHxEbI2Ljnj172tBsSZIk6eS0I0RHk3U5Zvlh4ILMvAz4U+BzLRxbW5l5R2auycw1S5YsOenGSpIkSdPVjhC9E1jRsHwesKtxh8zcn5kHisf3AXMi4uypHCtJkiT1mnaE6IeAiyLiwogYBq4D7m3cISJeGhFRPL6i+Lp7p3KsJEmS1GumPTtHZpYj4hbgS8AgcGdmbomIm4vttwP/GvitiCgDh4HrMjOBpsdOt02SJElSJ007RMOxEo37xqy7veHxB4EPTvVYSZIkqZd5x0JJkiSpRYZoSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFhmhJkiSpRYZoSZIkqUWGaEmSJKlFhmhJkiSpRUPdboDU7yrVZMPW3WzZtZ9Lly9i7aqlDA5Et5slSZI6qC0hOiKuBj4ADAIfzsz3jtn+68CtxeIB4Lcy85Fi2xPAC0AFKGfmmna0SToVKtXkho88yOYdIxwuVZg3PMjqFYu5e92VBmlJkmawaZdzRMQg8CHgGuAS4PqIuGTMbt8HfiYzfwz4z8AdY7b/bGauNkCr32zYupvNO0Y4VKqQwKFShc07RtiwdXe3myZJkjqoHTXRVwDbMnN7ZpaAe4BrG3fIzP+dmfuKxQeA89rwdaWu27JrP4dLlVHrDpcqPLprf5daJEmSToV2hOhzgR0NyzuLdRNZB3yhYTmBL0fEpohY34b2SC2rVJP7H3uW2+5/nPsfe5ZKNad03KXLFzFveHDUunnDg1yyfFEnmilJknpEO2qimxV+Nk0gEfGz1EL0TzWsflVm7oqIpcBXIuIfM/NrTY5dD6wHOP/886ffas04JzvAbzp1zWtXLWX1isV883vPkANDzJ87h9UrFrN21dJ2fVuSJKkHtSNE7wRWNCyfB+wau1NE/BjwYeCazNxbX5+Zu4rPuyPir6mVh4wL0Zl5B0Ut9Zo1a6bWTahZYzpBuLGuGUbXNV918bJJjx0cCO5edyWvfP06SguW8r53vMXZOSRJmgXaUc7xEHBRRFwYEcPAdcC9jTtExPnAZ4EbMvN7DesXRMTp9cfAzwPfbUObNMtMZ4DfdOuaBweC+SPbWfz0A1x18TIDtCRJs8C0Q3RmloFbgC8BjwGfzswtEXFzRNxc7PZO4Czgv0fE5ojYWKxfBnw9Ih4B/gH4n5n5xem2SbPPdIKwdc2SJKlVbZknOjPvA+4bs+72hsdvBN7Y5LjtwGXtaINmhpOta64H4UMNQXqqQdi6ZkmS1CrvWKie0a0BftY1S5KkVrWjJlpqi+nUNdeD8JLH/5bFO7/Bn17/ipbuGmhdsyRJaoUhWj3DAX6SJKlfGKLVMxzgJ0mS+oUhWj2jXtcclRJklflFTbQD/CRJUq8xRKtnTLeuWZIk6VRxdg71lHpd8/yR7Se8W6Ck9jvZaSYlabYxREuSgOlNM6lTxxc6Um8wREuSgNHTTMLoaSZ9Z6i9TjYI+0JH6h2GaEkSMPk0k4bo9plOEPaFjtQ7DNFt9M1/2tt0/f7DRyfdrtGmc7768VipVwxGMDw0wJFy9di64aEBBiK8ttvo4Sf3senJfcfO86FShU1P7uP2Df/E5RecMemxX/zuD5q+0Pnid3/A/GH/pWvmeuXLzup2E8Zxdg5JEgCrVyzm5UsXQrk2zeTcoQFevnQhq1cs7nbTZpQn9h6k1PBCBaBUrvLE3oMnPHblWQsYHhr9r3t4aICVZy1oaxvVv6rV5OEn9/HZh3fy8JP7qFaz202asXzZqqaq1WTzjhGe2HuQlWctYPWKxQxYb6dp8rrqbQMDwduvuZjffPNbqSxcxi03r/dn1AH1IDy2x38qQbj+QmfLU8/B4BBz5wz5QkfHVKvJe77wGNt2H6BUrjJcvBB++zUX+3vcAYZojeMvoTrB66o/DAwEw3u3wd5tXH7Brd1uzow0nSDsCx1NZvOOEbbtPnDsBdqRcpVtuw+wecfICUuFuqlfO1gM0RqnX38J1du8rlrTr/9UdGLTDcK+0NFEJisV6vTf2ZP9m9XPHSyGaI3TzV9CzVxeV1PXz/9UNDUGYXXCdEqFpmM6f7P6uYPFgYUax4Er6gSvq6lr/KeSjP6novZyENbUea6mrlvnqluDg6fzN2s6A227zZ5ojePAFXWC19XU2Wt/atjjP3Wz8Vz1Y3lCt2rmp/M3q1u95+3Qlp7oiLg6IrZGxLaIeFuT7RERtxXbvx0Rl0/1WJ169V/ChY9+jnnf/3v+/c9d1NIvv70Vama619VsMt1ee38Hp8Ye/6nr13N1sr8L9SB821cf56827eS2rz7Oe77w2JSO7/a5qpcKzXvyG1x+wRmn5G/sdP5m9fPUmtPuiY6IQeBDwKuBncBDEXFvZj7asNs1wEXFx5X/f3v3H2NXWSZw/PvMbQcKpWlBW/uLX7VpQLKOZAKL7G6qgKusCpq40c2SuiGpJGB0dxNFN9ll/1pi1N2YbHDrQtJlFZddNTRIVEQb48agpVQo1Fo0YEubThVr6bYwMH32jzljLtN7Z+6ZMzPn3pnvJ2nuueeed87b977nvc9973POAe4CruywrGow1Xy9+ThbUZe6Tjyrsl/zQDtTZda+zmOw106GdMa/c73YVnXl6fZiW1U1X684E5nVZigi4irgjsz80+L5pwAy85+atvk3YHtm3lc83wtsBC6crGwr515wSV736Xsq1busXT/dBcDAmwfabnPspVdart/39G4A1l962fRXbAZNpd4vvvQqzx89SXO3ioDVSxdxzpmdfWer0l69VjYzOf7yCC+9MsKZCxssPqNBxOQDR2byqxdOcvKVETJH23jRwgbnn7uo4/J17Bd693iYbZnJz5/5JTT6WbVqZcfvUV3H4HT0jdk2HW1VRV3j1VTU2VZTHa+q1PnIiy/z6+PDp61//eJ+XnfOGTO23+lSxzg71TFrzGR1XnLmwmmp51Tcf8tbH8vMwfHrp+PdXA3sb3p+gNHZ5sm2Wd1hWQAiYjOwGWDxynXVajwFEwXPk6nSiescZKdS7qXiA7RZJrz8ykjHg0eV9uqlsmNBx4mXXwGC6IuOg47jL4/8PlgZ/Vtw8pURjr88eTvXtd8xvXg81FE2ItiwvvxYV9cxOB19Y7bbefEZDRYtbJwW+C8+ozGj+x1T13jVS21VZbyqciycubBBBKcFwmcsnPz/W3e/qlKuyr6nyCb0LQAADuhJREFUOmaN6cWJlekIolv14vHT2+226aTs6MrMLcAWgMHBwfyvj1xVpo6z4ke/+M20/81bv/IpAP7+E9tmtexU7Xzut3zhe/tec4LAGQv6+PBbL5qzP2NN1VhbEaN5ZJkwcip5zx+snrStvr7zAP/z2IHXrky46uLzeP/la7pyv9OhruOhl47Duo7B6egbdbRz1RSUOsbZqnqpraqMV1WOhappUb3cr7q1T1+17rza9n3/La3XT0cQfQBY2/R8DXCww236OyirHjKWFzV+4OmFEwRmW11nM8/Xs6jni7qOwV7tG319weUXLPNLfgeqtNWpU8nweW9kZPEKdj73246DyirjVZVjYSxPt8r5H73Yr6b6Ps1X0xFE/wRYHxEXAc8DHwT+Ytw224DbIuKrjKZr/C4zD0XEkQ7KqodUHXjmkypBR5UPh7r2q9lR1zFYZ9/wg7+7jc3qHr/0Rmgs4Avf29fxrG6V8apXA+G6+nOV92m+qhxEZ+arEXEb8G2gAdyTmU9FxC3F618EHgKuB54BTgB/NVHZqnVSvXr1G/hsq2uWpM7ZGc2OOo7BuvpGnR/8Bu+dGbvSBQv6gXJXuqj65azXPo/q7M9V3qf5alpOE83MhxgNlJvXfbFpOYFbOy0rzQd1zZL06uyMul8dfaOuD35n7TpXJSVjvn1xrzOQnY+X5qvKOxZKNaorIO3FQLjKrJ8zhnNXXR/8ztp1rmq+fC+OV1NVZyDbq+c11Gla7lgoSTOpedbv5EV/XOruYVXKTke9h897IycvuNo7B86Qqnd3nKqJgh291lhKxhkL+gjoqTvSzba6+jP4Pk2FM9GSul6VWT9/7u8NU/21wKuRdL/5lpJRRZ0n6Po+lWcQLanrVfmJ05/7u1+VLxzz8WokvWg+pWRUUXcg6/tUjkG0pK5XZdavrhlDT9LpXNUvHPPpaiSa+wxke4c50ZK6XpVcvbry/OrMbew1vZpfPBbsvP/yNVx+wbKeCKDN05emjzPR02gmbkm5ZNHCKf3tkVPJgpUbGD57BSeGX2XjhuU0emCAl9rZtu6P2L53iKcPHuPSVUtK9ekqZWFqx+EVF53L//7i1+zaf5STwyMs6m8wsHYpt2xc57E4zonhV/nmk4c4MTzy+3WL+hu887I31Hqr37lm5FRy092P8n9vupHsW8C/bn+GgbVLuffmK+2T0hQYRM9BYwPlkfXvIfsW8NH7HnegVM9r9AXXXLKCay5ZMatlp6rRF9x785WVgvf5YuOG5QysXXraF46NG5bXXbU5ZfveIXbtP0o2RtNmTgyPsGv/UbbvHZrVY0OaKwyi5yAHSqk71BG89yK/cMyOpw4e42TTbD/AyeERnj54zD4qTYFB9BzkQClNn5FTyYmlFzN89goe2XPY4G6G+IVj5r1p1RIW9TdOS5u5dNWSGmsl9S5PLJyDxgbKZg6UUnnNqVFH17yVj973ODfd/SgjnoylHjSWNnNWf4MAzjJtRqrEmeg5yPxCaXqYGqW5xLQZaXoZRM9BDpTS9DA1SnONaTPS9DGInqMcKKXqzCGVJLVjTrQktWEOqSSpHWeiJakNU6MkSe0YREvSBEyNkiS1UimdIyLOjYiHI2Jf8bisxTZrI+L7EbEnIp6KiI81vXZHRDwfEbuKf9dXqY8kSZI0G6rmRN8OPJKZ64FHiufjvQr8bWZeAvwhcGtEXNr0+j9n5kDx76GK9ZEkSZJmXNUg+gZga7G8Fbhx/AaZeSgzdxbLLwJ7gNUV9ytJkiTVpmoQvSIzD8FosAxMeMp6RFwIvAV4tGn1bRHxRETc0yodRJIkSeo2kwbREfHdiNjd4t8NZXYUEYuBrwEfz8xjxeq7gHXAAHAI+NwE5TdHxI6I2HHkyJEyu5YkSZKm1aRX58jMa9u9FhGHI2JlZh6KiJXAUJvtFjIaQH85M7/e9LcPN23zJeDBCeqxBdgCMDg4mJPVW5IkSZopVdM5tgGbiuVNwAPjN4iIAO4G9mTm58e9trLp6fuA3RXrI0mSJM24qkH0ncB1EbEPuK54TkSsioixK21cDdwEvL3Fpew+ExFPRsQTwNuAv65YH0mSJGnGVbrZSmb+BrimxfqDwPXF8g+Blrf3ysybquxfkiRJqkPVmWhJkiRp3jGIliRJkkoyiJYkSZJKMoiWJEmSSjKIliRJkkoyiJYkSZJKMoiWJEmSSjKIliRJkkoyiJYkSZJKMoiWJEmSSjKIliRJkkoyiJYkSZJKMoiWJEmSSjKIliRJkkoyiJYkSZJKMoiWJEmSSjKIliRJkkoyiJYkSZJKqhRER8S5EfFwROwrHpe12e7ZiHgyInZFxI6y5SVJkqRuUnUm+nbgkcxcDzxSPG/nbZk5kJmDUywvSZIkdYWqQfQNwNZieStw4yyXlyRJkmZd1SB6RWYeAigel7fZLoHvRMRjEbF5CuUlSZKkrrFgsg0i4rvAG1q89Hcl9nN1Zh6MiOXAwxHxs8z8QYnyFMH3ZoDzzz+/TFFJkiRpWk0aRGfmte1ei4jDEbEyMw9FxEpgqM3fOFg8DkXEN4ArgB8AHZUvym4BtgAMDg7mZPWWJEmSZkrVdI5twKZieRPwwPgNIuLsiDhnbBl4B7C70/KSJElSt6kaRN8JXBcR+4DriudExKqIeKjYZgXww4j4KfBj4JuZ+a2JykuSJEndbNJ0jolk5m+Aa1qsPwhcXyz/EnhzmfKSJElSN/OOhZIkSVJJBtGSJElSSQbRkiRJUkkG0ZIkSVJJBtGSJElSSQbRkiRJUkkG0ZIkSVJJBtGSJElSSQbRkiRJUkkG0ZIkSVJJBtGSJElSSQbRkiRJUkkG0V1s5FRyYunFHF19FY/sOczIqay7SpIkSQIW1F0BtTZyKrnp7kc5sv49ZN8CPnrf4wysXcq9N19Joy/qrp4kSdK85kx0l9q+d4hd+4+SjX6IPk4Mj7Br/1G27x2qu2qSJEnznkF0l3rq4DFODo+8Zt3J4RGePnisphpJkiRpjEF0l3rTqiUs6m+8Zt2i/gaXrlpSU40kSZI0plIQHRHnRsTDEbGveFzWYpsNEbGr6d+xiPh48dodEfF802vXV6nPXLJxw3IG1i7lrP4GAZzV32Bg7VI2blhed9UkSZLmvcic+hUfIuIzwAuZeWdE3A4sy8xPTrB9A3geuDIzn4uIO4DjmfnZMvsdHBzMHTt2TLnevWLkVLJ97xBPHzzGpauWsHHDck8qlCRJmkUR8VhmDo5fX/XqHDcAG4vlrcB2oG0QDVwD/CIzn6u433mh0Rdcc8kKrrlkRd1VkSRJUpOqOdErMvMQQPE4Wa7BB4H7xq27LSKeiIh7WqWDSJIkSd1m0iA6Ir4bEbtb/LuhzI4ioh94L/DfTavvAtYBA8Ah4HMTlN8cETsiYseRI0fK7FqSJEmaVpOmc2Tmte1ei4jDEbEyMw9FxEpgoosYvwvYmZmHm/7275cj4kvAgxPUYwuwBUZzoiertyRJkjRTqqZzbAM2FcubgAcm2PZDjEvlKALvMe8DdlesjyRJkjTjql6d4zzgfuB84FfABzLzhYhYBfx7Zl5fbHcWsB+4ODN/11T+XkZTORJ4FvjIWI71JPs9AtRxcuLrgF/XsN9eZXt1zrbqnG3VOduqc7ZV52yrztlW5XRre12Qma8fv7JSED3fRMSOVpc4UWu2V+dsq87ZVp2zrTpnW3XOtuqcbVVOr7WXdyyUJEmSSjKIliRJkkoyiC5nS90V6DG2V+dsq87ZVp2zrTpnW3XOtuqcbVVOT7WXOdGSJElSSc5ES5IkSSUZRHcoIt4ZEXsj4pmIuL3u+nSziHg2Ip6MiF0RsaPu+nST4vb2QxGxu2nduRHxcETsKx6X1VnHbtKmve6IiOeL/rUrIq6vs47dICLWRsT3I2JPRDwVER8r1tu3Wpigvexb40TEmRHx44j4adFW/1ist2+NM0Fb2a/aiIhGRDweEQ8Wz3uqX5nO0YGIaAA/B64DDgA/AT6UmU/XWrEuFRHPAoOZ2Y3XeqxVRPwJcBz4j8y8rFj3GeCFzLyz+IK2LDM/WWc9u0Wb9roDOJ6Zn62zbt2kuHHVyszcGRHnAI8BNwIfxr51mgna68+xb71GRARwdmYej4iFwA+BjwHvx771GhO01TuxX7UUEX8DDAJLMvPdvfZ56Ex0Z64AnsnMX2bmMPBV4Iaa66QelJk/AF4Yt/oGYGuxvJXRD3PRtr00TmYeysydxfKLwB5gNfatliZoL42To44XTxcW/xL71mkmaCu1EBFrgD8D/r1pdU/1K4Pozqxm9I6LYw7ggDuRBL4TEY9FxOa6K9MDVozdqbN4XF5zfXrBbRHxRJHu0dU/9822iLgQeAvwKPatSY1rL7Bvnab4yX0XMAQ8nJn2rTbatBXYr1r5F+ATwKmmdT3VrwyiOxMt1vntsr2rM/Ny4F3ArcVP8tJ0uQtYBwwAh4DP1Vud7hERi4GvAR/PzGN116fbtWgv+1YLmTmSmQPAGuCKiLis7jp1qzZtZb8aJyLeDQxl5mN116UKg+jOHADWNj1fAxysqS5dLzMPFo9DwDcYTYdRe4eLHM2xXM2hmuvT1TLzcPFBdQr4EvYvAIoczK8BX87Mrxer7VtttGov+9bEMvMosJ3RHF/71gSa28p+1dLVwHuLc6i+Crw9Iv6THutXBtGd+QmwPiIuioh+4IPAtprr1JUi4uziRB0i4mzgHcDuiUvNe9uATcXyJuCBGuvS9cYG2ML7sH+NndB0N7AnMz/f9JJ9q4V27WXfOl1EvD4ilhbLi4BrgZ9h3zpNu7ayX50uMz+VmWsy80JGY6rvZeZf0mP9akHdFegFmflqRNwGfBtoAPdk5lM1V6tbrQC+MfoZxQLgK5n5rXqr1D0i4j5gI/C6iDgA/ANwJ3B/RNwM/Ar4QH017C5t2mtjRAwwmlL1LPCR2irYPa4GbgKeLPIxAT6Nfauddu31IfvWaVYCW4urVPUB92fmgxHxI+xb47Vrq3vtVx3rqTHLS9xJkiRJJZnOIUmSJJVkEC1JkiSVZBAtSZIklWQQLUmSJJVkEC1JkiSVZBAtSZIklWQQLUmSJJVkEC1JkiSV9P/TT3eAEy3mJwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept 14.794147\n",
"ar.L1 1.390651\n",
"ar.L2 -0.688567\n",
"sigma2 274.760205\n",
"dtype: float64\n"
]
}
],
"source": [
"arma_mod20 = sm.tsa.statespace.SARIMAX(dta, order=(2,0,0), trend='c').fit(disp=False)\n",
"print(arma_mod20.params)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arma_mod30 = sm.tsa.statespace.SARIMAX(dta, order=(3,0,0), trend='c').fit(disp=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2622.636338109266 2637.569703216857 2628.606725954512\n"
]
}
],
"source": [
"print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept 16.762589\n",
"ar.L1 1.300797\n",
"ar.L2 -0.508115\n",
"ar.L3 -0.129615\n",
"sigma2 270.103138\n",
"dtype: float64\n"
]
}
],
"source": [
"print(arma_mod30.params)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2619.4036300024923 2638.070336386981 2626.8666148090497\n"
]
}
],
"source": [
"print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Does our model obey the theory?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.9564587300797276"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.stats.durbin_watson(arma_mod30.resid)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,4))\n",
"ax = fig.add_subplot(111)\n",
"ax = plt.plot(arma_mod30.resid)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"resid = arma_mod30.resid"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"NormaltestResult(statistic=49.848185187970486, pvalue=1.4983184245320314e-11)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.normaltest(resid)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,4))\n",
"ax = fig.add_subplot(111)\n",
"fig = qqplot(resid, line='q', ax=ax, fit=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(resid, lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AC Q Prob(>Q)\n",
"lag \n",
"1.0 0.009189 0.026347 8.710555e-01\n",
"2.0 0.041829 0.574050 7.504930e-01\n",
"3.0 -0.001332 0.574607 9.022201e-01\n",
"4.0 0.136070 6.408305 1.706604e-01\n",
"5.0 0.092437 9.109404 1.047792e-01\n",
"6.0 0.091922 11.789275 6.683835e-02\n",
"7.0 0.068738 13.292786 6.528800e-02\n",
"8.0 -0.015015 13.364769 9.990018e-02\n",
"9.0 0.187603 24.638782 3.397846e-03\n",
"10.0 0.213729 39.320451 2.230864e-05\n",
"11.0 0.201097 52.361568 2.344528e-07\n",
"12.0 0.117198 56.805868 8.568280e-08\n",
"13.0 -0.014045 56.869910 1.892680e-07\n",
"14.0 0.015397 56.947140 3.995146e-07\n",
"15.0 -0.024984 57.151166 7.735883e-07\n",
"16.0 0.080891 59.297286 6.870790e-07\n",
"17.0 0.041119 59.853717 1.110952e-06\n",
"18.0 -0.052029 60.747662 1.548297e-06\n",
"19.0 0.062500 62.042115 1.831357e-06\n",
"20.0 -0.010289 62.077319 3.380828e-06\n",
"21.0 0.074469 63.927788 3.192292e-06\n",
"22.0 0.124964 69.156672 8.972199e-07\n",
"23.0 0.093171 72.073518 5.794606e-07\n",
"24.0 -0.082147 74.348904 4.709285e-07\n",
"25.0 0.015689 74.432192 8.282791e-07\n",
"26.0 -0.025050 74.645278 1.366163e-06\n",
"27.0 -0.125876 80.044798 3.717826e-07\n",
"28.0 0.053212 81.013142 4.711139e-07\n",
"29.0 -0.038700 81.527166 6.908738e-07\n",
"30.0 -0.016896 81.625494 1.150403e-06\n",
"31.0 -0.019285 81.754060 1.866849e-06\n",
"32.0 0.105000 85.578967 8.916449e-07\n",
"33.0 0.040095 86.138707 1.245826e-06\n",
"34.0 0.008834 86.165979 2.045090e-06\n",
"35.0 0.014588 86.240617 3.259520e-06\n",
"36.0 -0.119334 91.253467 1.082871e-06\n",
"37.0 -0.036675 91.728670 1.519618e-06\n",
"38.0 -0.046206 92.485743 1.935578e-06\n",
"39.0 -0.017776 92.598214 2.985793e-06\n",
"40.0 -0.006220 92.612036 4.689438e-06\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/stattools.py:657: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs - 1) after 0.12is released. Set the number of lags to an integer to silence this warning.\n",
" FutureWarning,\n"
]
}
],
"source": [
"r,q,p = sm.tsa.acf(resid, fft=True, qstat=True)\n",
"data = np.c_[range(1,41), r[1:], q, p]\n",
"table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
"print(table.set_index('lag'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* This indicates a lack of fit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* In-sample dynamic prediction. How good does our model do?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predict_sunspots = arma_mod30.predict(start='1990', end='2012', dynamic=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAHSCAYAAADvxw2lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXSb53km/OvBThAEQIIU90WWbdlaKZl2Vk+cfamXxO7EdpImadPJl447mS/NOZ12pmncdJyZpm7aOZ40/TJNak+TKG6dzbXdpLabOE2cOJGszdpsy9pIgiQAkthIvNie748XLwTRlASCAN4F1+8cH8lcgCehRV28cT/3LaSUICIiIiJqdTa9D0BEREREZAQMxkREREREYDAmIiIiIgLAYExEREREBIDBmIiIiIgIAIMxEREREREAwKH3AQCgu7tbjo2N6X0MIiIiIrK4ffv2RaWUPau9zxDBeGxsDHv37tX7GERERERkcUKIMxd7H1spiIiIiIjAYExEREREBIDBmIiIiIgIgEF6jImIiIjMLpfLYXJyEplMRu+jEACPx4OhoSE4nc6qP4fBmIiIiKgOJicn0dHRgbGxMQgh9D5OS5NSIhaLYXJyEhs3bqz689hKQURERFQHmUwGoVCIodgAhBAIhUJrrt4zGBMRERHVCUOxcdTytWAwJiIiIrKI++67D1u3bsWOHTswPj6O5557DmNjY4hGo+WP+fGPf4ybb74ZAPDggw/CZrPh0KFD5fdv27YNp0+fLv/7/v37IYTAD3/4wwue68UXX8R73vMeXHnllbj22mvx/ve/Hw8//DDGx8cxPj4On8+HzZs3Y3x8HB/+8IfLz3v69GkMDQ2hWCxe8Hjj4+P45S9/iXvvvRf3338/7rnnHoyPj2PLli1oa2srP+4XvvAF3HnnneXPSyQS2LRpE06dOrXu///YY0xERERkAT//+c/x2GOP4fnnn4fb7UY0GkU2m73s5w0NDeG+++7Dww8/vOr79+zZgze+8Y3Ys2cP3vnOdwJQ20Z+7dd+DV/84hdxyy23AAB+9KMfoaenBwcOHAAA3HTTTbj//vsxMTEBQA3kgLrYbXh4GP/2b/+GN73pTQCA48ePI5lM4oYbbsATTzwBAPjSl74EADh9+jRuvvnm8uNKKfHGN74RTz31FN72trfhj//4j/Fbv/Vba+olvhgGYyIiIiILCIfD6O7uhtvtBgB0d3dX9Xk333wzfvKTn+DEiRPYvHnzBe+TUuKRRx7Bk08+iRtvvBGZTAYejwff/OY38brXva4cigHgzW9+c9Vnvfvuu/Gtb32rHIy/9a1v4e67767qc4UQ+PKXv4wPfOADePDBB/H0009j3759VT/3pTAYExEREdXZn/zTERydTtT1MbcM+PHZW7Ze9P3veMc78LnPfQ5XX3013va2t+HOO+8sB89Lsdls+P3f/318/vOfx0MPPXTB+372s59h48aN2LRpE2666SY88cQTuP322/HCCy/guuuuq/l/y/vf/37s2rULDzzwABwOBx5++GH84z/+Y9Wfv2PHDrzzne/EW9/6Vnzve9+Dy+Wq+SyV2GNMREREZAE+nw/79u3DV77yFfT09ODOO+/Egw8+uOoltJVv+8AHPoBf/OIXr+rT3bNnD+666y4AwF133YU9e/bU5ax9fX3YunUrnn76aRw4cABOpxPbtm1b02Pcc889GBwcXFOl+nJYMSYiIiKqs0tVdhvJbrfjpptuwk033YTt27fjoYceQigUwsLCQrm1Yn5+/lVtFg6HA5/+9KfxZ3/2Z+W3FQoFfPvb38ajjz6K++67rzwbOJlMYuvWrXjmmWfWdVatnaK3t7fqNopKNpsNNlt9a7ysGBMRERFZwIkTJ/DSSy+V//3AgQMYHR3FTTfdhL//+78HoIbdr3/966tWWT/60Y/iqaeeQiQSAQA89dRT2LlzJ86dO4fTp0/jzJkzuOOOO/C9730PH/jAB/Dss8/i8ccfL3/+D37wAxw+fLjq895xxx144okn8PDDD5er0npjMCYiIiKygFQqhY985CPYsmULduzYgaNHj+Lee+/FZz7zGbz88svYuXMndu3ahSuvvBIf+tCHXvX5LpcLn/zkJzE3NwdAbaN43/ved8HH3HHHHfjmN7+JtrY2PPbYY3jggQdw1VVXYcuWLXjwwQexYcOGqs8bDAbx2te+Fr29vXWZKFEPQkqp9xkwMTEh9+7dq/cxiIiIiGp27NgxXHvttXofgyqs9jURQuyTUk6s9vGsGJtMoShhhB9miIiIiKyGwdhkfvPBX+GDf/sclrJ5vY9CREREZCkMxiZzeHIRz56M4WMP7sVytqD3cYiIiIgsg8HYRDK5AhaWctg9EsRzp2L47f/7K2RyDMdERERGwXZH46jla8FgbCIz8QwA4AOvGcWf//pOPHsyhv/wf/cyHBMRERmAx+NBLBZjODYAbeayx+NZ0+dxwYeJhEvBuD/gwRuu7EZBSvyXbx/CJ76+D//fb1wHt8Ou8wmJiIha19DQECYnJ8tzgElfHo8HQ0NDa/ocBmMTmUksAwD6AupPP++fGEaxKPEH3zmM3/n68/jyh3YzHBMREenE6XQaZh4v1YatFCZSWTHW3HXDCO573zb86/E53PON/cjmi3odj4iIiMjUGIxNZCaeQaDNCa/rwkL/B18zij+9bSueOjaL/7TneeQKDMdEREREa8VgbCLheOaCanGl33jdGD57yxb88Mgs/vO39iPPcExERES0JuwxNpGZeKbcX7ya33zDRhSKEv/98WOwiQP4qzvH4bDzZx8iIiKiajAYm0g4voxtg/5Lfsxv33gFilLi808ch90m8MX3j8NuE006IREREZF5MRibhJIvIJrKos/fdtmP/fi/24R8UeILPziB3SOd+Mjrxxp/QCIiIiKT4+vsJjGXUADgoj3GK/3Hm67EcFcbnjsVa+SxiIiIiCyDwdgktFFtl+oxXmnHUBAHz8UbdSQiIiIiS2EwNolwXF3uUW3FGAB2DgUwtbiMWEpp1LGIiIiILIPB2CRmtOUewcv3GGt2DAUBAIcmWTUmIiIiuhwGY5MIxzPocDvgc1d/X3LbYABCAAcnFxt4MiIiIiJrYDA2icvNMF6Nz+3Aph4fDrNiTERERHRZDMYmEU6sPRgDwI6hAA5OxiGlbMCpiIiIiKyDwdgkwovLa7p4p9k5FEQ0pZSnWhARERHR6hiMTSBXKCKSUtAXqP7inWbHUAAAcIh9xkRERESXxGBsAnNJBVKubVSb5tp+Pxw2gYPsMyYiIiK6JAZjE5gpzTCupcfY47Rjc18HK8ZEREREl8FgbAJaf3AtFWNAnWd8iBfwiIiIiC6JwdgEyss9augxBtQNeMlMHqdjS/U8FhEREZGlMBibQDiegddlh99T/XKPSuc34LGdgoiIiOhiGIxNQFvuIYSo6fOv7vXB47Th4DlewCMiIiK6GAZjEwjHa5thrHHYbdg6EGDFmIiIiOgSLhuMhRBfE0LMCSFeqHjbw0KIA6V/TgshDpTePiaEWK5439808vCtYiaeQZ+/tv5izY6hAI5MJ5AvFOt0KiIiIiJrqaZi/CCAd1W+QUp5p5RyXEo5DuDbAL5T8e6T2vuklJ+o31FbU6EoMZtU1lUxBtRgvJwr4OVIqk4nIyIiIrKWywZjKeVPAMyv9j6hNr2+H8CeOp+LSiJJBYWirGmGcaXyBTz2GRMRERGtar09xjcCmJVSvlTxto1CiP1CiGeEEDeu8/FbXri03GO9FeONoXZ0uB04yD5jIiIiolXVNv/rvLtxYbU4DGBEShkTQlwH4HtCiK1SysTKTxRCfBzAxwFgZGRkncewLm2G8XorxjabwPahAA5xNTQRERHRqmquGAshHABuB/Cw9jYppSKljJV+vw/ASQBXr/b5UsqvSCknpJQTPT09tR7D8rStdwM1LveotGMoiOMzCSj5wrofi4iIiMhq1tNK8TYAx6WUk9obhBA9Qgh76fdXALgKwCvrO2Jrm0lk4HbYEPQ61/1YO4cCyBUkjoeTdTgZERERkbVUM65tD4CfA9gshJgUQnys9K678OpLd/8OwCEhxEEAjwD4hJRy1Yt7VJ1wPIP+dSz3qLR9KACAG/CIiIiIVnPZHmMp5d0XeftHV3nbt6GOb6M6mYkvr7u/WDMYbEOo3YWDk3H8Rl0ekYiIiMg6uPnO4NSK8fr7iwFACIEdQ9yAR0RERLQaBmMDKxYlZhOZulWMAfUC3stzKaSVfN0ek4iIiMgKGIwNLJpWkCvIdc8wrrRzOICiBI5Mv2qCHhEREVFLYzA2sPIMY3/9gvH2wdIGPLZTEBEREV2AwdjAtBnG9eoxBoCeDjcGAh4c5KIPIiIiogswGBuYVjHuD9avYgyofcasGBMRERFdiMHYwMLxDFx2G7q8rro+7o7hAM7ElrC4lK3r4xIRERGZGYOxgc3El9EbcMNmW/9yj0o7h9Q+48NTbKcgIiIi0jAYG1g4nkG/v379xZptg9oGPAZjIiIiIg2DsYHN1HmGsSbQ5sQV3e04eI59xkRENSsWgVde0fsURFRHDMYGJaUsbb2rfzAGgO1DAVaMiYjW4/77gU2bgIUFvU9CRHXCYGxQC0s5ZPPFhlSMAXUyxUwig7lEpiGPT0Rkebt2qb8+/7y+5yCiumEwNqjpxWUAaFjFeOeQ2mfMecZERDW67jr111/9St9zEFHdMBgb1EwDlntU2joQgN0mcJjzjImIatPVpbZS7N2r90mIqE4YjA0qnNCCcWMqxm0uO67a4GPFmIhoPSYmGIyJLITB2KBm4stw2ARCPnfDnmNnaQOelLJhz0FEZGkTE8CZM0AkovdJiKgOGIwNKhzPoNfvgb3Oyz0q7RgOYGEph8mF5YY9BxGRpV1/vforq8ZElsBgbFAz8cbMMK60Y1DdgHeQfcZERLXZtQsQgsGYyCIYjA2qGcF4c18HXHYbDrPPmIioNn4/sHkzgzGRRTAYG1B5uYe/scHY5bDh2gE/K8ZEROtx/fUc2UZkEQzGBpRYzmM5V2h4xRhQ5xm/MJVAscgLeERENZmYAMJhYHpa75MQ0ToxGBtQOKEt92jMDONKO4aCSCl5vBJNNfy5iIgsaWJC/ZXtFESmx2BsQOHF0gzjYHMqxgBw8Bz7jImIajI+DtjtbKcgsgAGYwMKxxu73KPSFT0+eF12HGKfMRFRbbxeYOtWVoyJLIDB2IBm4suwCaCngcs9NHabwLbBAA5NsWJMRFQzbQMeFyYRmRqDsQGF4xls6PDAYW/Ol2fnUABHpxPIFYpNeT4iIsuZmACiUXULHhGZFoOxAc0kGj/DuNKOoSCUfBEnZpJNe04iIkvhBjwiS2AwNqBwPNOU/mLN1b0dAIBT0XTTnpOIyFK2bwecTgZjIpNjMDagZmy9q6RNvwjHl5v2nEREluJ2Azt2cDIFkckxGBtMMpNDSsk3tWLs9zjhczswXRoTR0TUqmbiGbzjL5/BS7M1tJZdfz2wbx9Q5H0NIrNiMDaYmdKotr4mLPeoNBD0sGJMRC3vyHQcL86m8I3nzq79kycmgHgcOHmy/gcjoqZgMDYYbYbxQBMrxoC6ZY8VYyJqddGUAgB47FAY+bVO6uEGPCLTYzA2GK1q28weY4AVYyIiAIgk1WAcTSl49mRsbZ+8ZQvg8bDPmMjEGIwNJhzPQAhgQ0fzK8bRVBZKvtDU5yWyunyhiK//4gwePTiNF6biSCt5vY9ElxBJKmh32dHhceB7B6bW9slOJ7BrFyvGRCbm0PsAdKGZeAbdPjdcjub+zKJd9puJZzAaam/qcxNZ2b4zC/ij771wwdv6/B5s7G7HFT3t5V+v6PZhqLOtaYt9aHWRlIK+gAcTo1147NA0lt9bQJvLXv0DTEwAX/saUCgA9jV8HhEZAoOxwTR7hrFmMKhe9pteZDAmqqdIqWf1yx/cDQl1XvgrkTReiabw2KEw4su58sc67QI3bOzC3374+rWFMaqbaDKLbp8bt+0awMN7z+GpY7O4ZedA9Q8wMQE88ABw/DiwdWvjDkpEDcFgbDBqxdbb9OftLwVj9hkT1dd8OgsAuH5jF7p97lXffyqawslIGgfPLeIbz53FvxydwW3jg80+KkH9QWbrgB+v2RhCn9+D7x+YWlswrtyAx2BMZDp8zc5gwvFlXSrG2nNOLzIYE9VTNJWFEECn17Xq+7vaXbhutAvvnxjGn962DUOdbXhk32STT0maSFJBT4cbdpvALTv78eMTESyUfripytVXAz4f+4yJTIrB2EDSSh6JTL7pM4wBwOO0o6vdhek4R7YR1VMspaDT64LdJi77sTabwB27h/DTl6P8IVUHy9kCUkoePR1qZf+28UHkixKPHw5X/yB2O7B7NydTEJkUg7GBzCTUUKpHxVh73jD/Miaqq/l0FqH21avFq7lj9xCkBL67f40TEWjdtBnGWsvL1gE/rtzgw/fXOp3i+uuBAweAXO7yH0tEhsJgbCDa1jv9gnFbecEIEdVHLJVFyFd9MB4JefGajV14ZN8kpJQNPBmtNFeaYaxVjIUQeO/4AH51egGTC0vVP9DEBKAowJEjjTgmETUQg7GBhMvBuPmtFAAwGPTw5VuiOoumFYTaX33p7lJ+/bohnIqm8fzZhQadilajLffoqbgkqV2CfPTgdPUPpG3AYzsFkelcNhgLIb4mhJgTQrxQ8bZ7hRBTQogDpX/eU/G+PxRCvCyEOCGEeGejDm5FWhvDBv/a/hKtl/5gGxKZPFJcQEBUN/PptVWMAeA92/vhddl5Ca/JtNF6GzrOfw8e7vLiutFOfH//GoLxpk1AMMgLeEQmVE3F+EEA71rl7X8ppRwv/fMEAAghtgC4C8DW0uf8tRCCwzirFE5kEGp3wePU5/8yrYWDfcZE9ZErFLG4lFtzxbjd7cC7t/XjsYNhLGe5jbJZokkFQqiTQiq9d3wAJ2aTOBZOVPdAQqhVYwZjItO5bDCWUv4EwHyVj3cbgG9JKRUp5SkALwO4YR3naykz8Qz6dOovBoABbckH+4yJ6kIb89W1xooxoLZTJJU8/uXoTL2PRRcRSSno8rpetX3w13YMwGETa1sRPTEBHDoEZPj9lMhM1tNj/LtCiEOlVovO0tsGAZyr+JjJ0tteRQjxcSHEXiHE3kgkso5jWIdeW+80rBgT1VesFIy71zCVQvOajV2cadxk2gzjlbraXbjxqm7804FpFItVXoicmADyeTUcE5Fp1BqMvwxgE4BxAGEAf1F6+2qDOlf9LiKl/IqUckJKOdHT01PjMaxlJr6sa8W41++BTbBiTFQvsZQajEOrbLy7HM40br6LBWMAeO+uQUzHM/jl6SpfQK3cgEdEplFTMJZSzkopC1LKIoD/g/PtEpMAhis+dAjAGm4stK5MroCFpZxuEykAwGm3YUMHJ1MQ1UssrV7mWtmzWi3ONG6uaEpZdW03ALx9Sy+8Lnv1M42Hh4GeHk6mIDKZmoKxEKK/4l/fB0CbWPEogLuEEG4hxEYAVwH45fqO2Br0nmGs6Q96EI4zGBPVg1Yx7q6hxxhQZxrfwJnGTSGlvGTF2Oty4B1bevH4oTCUfBUXInkBj8iUqhnXtgfAzwFsFkJMCiE+BuALQojDQohDAN4M4FMAIKU8AuAfABwF8AMA90gpeaW6CtoMYz1bKQBgINCG8CJbKYjqIZZW4LAJ+D3Omh+DM42bI6nkoeSLF8wwXum2XYNIZPJ45kSV92Kuvx44ehRIp+t0SiJqtGqmUtwtpeyXUjqllENSyq9KKX9DSrldSrlDSnmrlDJc8fH3SSk3SSk3Syn/ubHHt46ZhFql1bOVQn1+D6bjy6xOEdVBLJVFZ7sLNttq1y+q857t/WhzcqZxo0VWbL1bzY1XdiPU7sL3D1TZITgxARSL6npoIjIFbr4ziHLF2K93K0UbMjl19ioRrU8snUWoxv5ijc/twLu393GmcYNFS8H4Yj3GAOCw23Dzjn48dWwWyUwV3yOvu079lX3GRKbBYGwQ4cUMgl4n2lz67kMZDKrBfIoX8IjWLXaJy1xrwZnGjadtvbtUxRhQ2ymUfBE/eKGKr8XAgPoP+4yJTIPB2CDC8Yzu1WLgfCtHmCPbiNYtls7WPJGi0ms3hjAY5EzjRqqmlQIAdg0HMdLlrb6d4vrrUfjVr/A/njiGL/7LifUek4gajMHYIGYSy7pPpADUqRQAOJmCqA7mU1mEapxIUclmE7jjOs40bqRIUr0oGWy79EVJIQRuGx/AsyejmEtcuoBQKEoc7L0S9hdfxDefPIwHnz1dxxMTUSMwGBuEug5a34t3ANDd7obTLjDNyRRE65LJFZBU8nVppQCAO3YPcqZxA0VTCkK+6i5K3jY+iKIEHj148arxr07P49b//VN8ccEPAPidjjgSmTz7xIkMjsHYAJR8AdFU1hAVY5tNoC/AWcZE6zVfWgddj1YKABgNtXOmcQNdaobxSldu8GHboH/VdopwfBmf3LMf//5vfo75dBZ3/8fbAQA3xs8AAGYvU2UmIn0xGBvAXELtbTNCMAY4y5ioHrRgvN6pFJU407hxIinlkjOMV3rv+CAOT8VxMpICoL5C8MDTL+Et9z+DHx6ZwSffciWe/vSb8K6btgFjY+h/6TAAYIbBmMjQGIwNIFzeeqd/KwUADATbOJWCaJ2ipSkHoTq1UgCcadxIa6kYA8AtOwcgBPD9/VP4wQthvO2Lz+AvnnwRN23uwVO/9yb83js2w+tyqB88MYHAkUMAWDEmMjqH3geg8xfd9N56p+kPeDCbyKBQlLCvYzEBUSvT1kHXs2JcOdP4j2/eqvt4R6soFiViqeya+sF7/R68flMIX/rxSRSKEpt7O/DN//AavH5T96s/+Prr4XzkEQSXEwzGRAbHirEBzBhkHbSmP9iGfFGWK15EtHblVoo6TKWoxJnG9be4nEO+KNdUMQaAj75+I/r8Hnzutq14/JNvXD0UA+oGPADXR1/BTJzfV4mMjBVjAwjHM+jwOOBzG+PLMVAK6NOLy+g1wGxlIjOKphW4HLa6/7munGl82/hgXR+7VVU7w3ilt2/pxdu39F7+A3fvBgC8dv4Unk+yYkxkZKwYG8B8em0v4TUal3wQrV8spa6DFqK+7UicaVx/kSrWQa9LMAhcfTW2z7yEWX5fJTI0BmMDSCl5tLuN0ys4GFSDMf/SJardfLo+yz1Ww5nG9RWtch30uuzejSsnX8IsK8ZEhsZgbAApJY92lzHaKADA3+aA12Xnkg+idYilFITaGxO0tJnG32MwrotaWynW5IorEJyfQ2RxiXOoiQyMwdgA0kreMP3FgLrytJ9LPojWJVpqpWiUN13dg5fmUlgoXfKj2kVSCtwOGzoa+X14ZAS2YgHBxRgWl3KNex4iWhcGYwNIK3n4PMYJxoA6y3iavXBENWtkKwUA7BoJAgAOTC427DlaRSSpoNvnrns/+AWGhwEAA4kIl3wQGRiDsQGklALaDVQxBtRZxmH2GBPVZCmbx3KuUNflHivtGApCCODAWQbj9Yqm1rbcoyYjIwCAgWSEs4yJDIzB2ACM1koBqBXjSEpBNl/U+yhEpqMt9+hqYCuFz+3A5t4O7D/HYLxea916V5OKijGDMZFxMRjrLF8oYjlXMNTlOwAYCLRBSq4vJapFrNT3293AVgpAbac4cHYBxSIvc61HU4JxIADp96M/GcVsgks+iIyKwVhn6WwBAAw1rg0A+oPnl3wQ0drESuO/GjWVQjM+HEQik8epWLqhz2Nl+UIR80vNmSUvhocxlo6yx5jIwBiMdZZW8gBguFYKLvkgql0zWikAYNdIJwBgP/uMazafzkLKBo9q04yMYCgVwxyDMZFhMRjrTAvGRrt8N6BVjDmyjWjNtFaKRk6lAIAre3zocDuw/+xCQ5/Hyua0GcbN2D46MoLexTlWjIkMjMFYZymtYmywcW1elwOBNifCXPJBtGaxlAKvyw5vg+8O2GwCO4YDOMALeDWLNGPrnWZ4GP7UIhaj8cY/FxHVhMFYZ2lF7TE2WisFUJplzB5jojWLpbMNb6PQ7BruxPGZJJZL9xVobSJNrhgDgGtmGrkCJ/4QGRGDsc60irHRplIAwEDAwyUfRDWIpbMNnWFcaddIEIWixOEpViFrES1VjLs7mvCDTGlkW388Un5eIjIWBmOdpQx6+Q5QJ1NwLTTR2sVSCrqbVDEeH1Y34LHPuDaRpAKf29HwthcA55d8JCKYYdGByJAYjHV2/vKdsca1AepkisWlHF+iJVqjWKp5rRQhnxsjXV5OpqhRU2YYawYHAWhLPlgxJjIiBmOdpQw6lQLgZAqiWkgpEUsrTWulAEqLPngBryaRpNLwRSxlbjeKvX2lJR+sGBMZEYOxztJKHg6bgNthvC/FQGmWMS/gEVUvqeSRK8jmhS2o7RQziQxbn2oQTTWxYgxAjAxjKMm10ERGZbw01mLSSh4+jwNCCL2P8ioDwdKSD45sI6pas5Z7VNIWfRxgO8WaRZJKcyZSlIjSkg/OMiYyJgZjnaWUgiEnUgBAr98DIdhKQbQW5XXQTQxb1/Z3wGW3YT/bKdYkkysgkck3tWKM4WH0xecwx8t3RIbEYKyztJI35EQKAHA5bOj2uVkxJlqD8ta7JlaM3Q47tg76OZlijcqj2pr4QwxGRuDJZpCejTTvOYmoagzGOkspeUNOpNCos4xZMSaqltZK0eh10CvtGu7E4ak4F0esQbT0tWpqxbg0ss0+ea55z0lEVWMw1pkajI1ZMQbUkW28fEdUPa2Vopk9xgAwPhJEJlfEiZlkU5/XzMpb75rcSgEAwcgMlrL55j0vEVWFwVhnRm6lANQLeOF4BlJKvY9CZAqxdBYdHgfcjua+ErSLiz7WTJdgXKoY9yc5y5jIiBiMdZY2eMV4IOjBUraAxDIrG0TViKWzTe0v1gx1tqHb5+YFvDXQgnGovYnBeMMGFJ1ODHL7HZEhMRjrLGXwinG/NsuYfcZEVYmlmrvcQyOEwPhwkCPb1iCaUhD0OuFq5hx5mw2FgSH0J6KYSzIYExkNg7GOpJRIZwvGDsal7XdcHEBUnXmdKsaAugHvlWgai0tZXYNNnncAACAASURBVJ7fbJo9w1hjGxnGACvGRIbEYKwjJV9EoSiN3UpR3n7Hb+BE1Yimsk2fSKHZNaL2GXM9dHUiTd56p7GPjWIwFWWPMZEBMRjrKJlR+3Z9Bh7X1tPhhsMmOJmCqArFosTCUra5PasVdgwFIQSwn+0UVYkklebOMNYMD6M3GcXcYrr5z01El8RgrKO0ogZjI1eM7TaBXr8HYb7kR3RZ8eUcCkWpW8XY53Zgc28HK8ZViupUMcbICOzFIrLnppr/3ER0SZcNxkKIrwkh5oQQL1S87c+FEMeFEIeEEN8VQgRLbx8TQiwLIQ6U/vmbRh7e7FImCMaAOpmCFWOiy4ul9ZlhXGnXSBAHzi2iWOSIxUtJK3ksZQu6BWMAsHHJB5HhVFMxfhDAu1a87UkA26SUOwC8COAPK953Uko5XvrnE/U5pjVpFWMjX74D1MkUrBgTXZ62SU2Xl+dLxoeDiC/ncCrGl+kvpTzDWKdWCgBom57ijHgig7lsMJZS/gTA/Iq3/YuUUhts+wsAQw04m+WlsyYJxkEPZuIZVqCILmM+rc866Eq7RjoBgGPbLiNS2lDYrWPFuCc+h4WlXPOfn4guqh49xr8F4J8r/n2jEGK/EOIZIcSNdXh8y0opBQAmaKUItCFbKCKa5g1qokvRax10pU09PvjcDuw/xw14lxLVs2Ls9yPn6+DINiIDWlcwFkL8NwB5AN8ovSkMYERKuQvA7wH4phDCf5HP/bgQYq8QYm8kElnPMUzLLK0UA0F1ZFuYI9uILklrpejy6heM7TaBncMBXsC7DK1irEuPMYD84BAGklHMcskHkaHUHIyFEB8BcDOAD8pSk5SUUpFSxkq/3wfgJICrV/t8KeVXpJQTUsqJnp6eWo9haqmMdvnOuOPaAKA/wCUfRNWYT2fR6XXCYdd34M+u4U4cCyexnC3oeg4jiyQV2ISO1f1hdcnHLCvGRIZS03dvIcS7APwXALdKKZcq3t4jhLCXfn8FgKsAvFKPg1pReSqFyxwVYy75ILq0WFrRtY1CMz4cRKEocXgqrvdRDCuSVNDV7obdJnR5ftfGMfQnIlzyQWQw1Yxr2wPg5wA2CyEmhRAfA/C/AXQAeHLFWLZ/B+CQEOIggEcAfEJKOb/qAxPSSh5elx02nb4xV6vT64TbYWPFmOgy1K13+k2k0IyXN+Cxz/hidJthXGIfHUFoOYFYhF8jIiO5bKlSSnn3Km/+6kU+9tsAvr3eQ7WKdDZv+It3ACCEwECwjRVjosuYT2dxda9P72Og2+fGSJeXG/AuIZLUNxhrkynyZ84CuEG/cxDRBbj5TkcppYAOEwRjoLTkgxVjokuKpYzRSgGo7RQMxhcXSSr6TKTQlGYZi3Nc8kFkJAzGOkor5qgYA6UlH6wYE11UvlDEwlIOoXb9WykAdQPeTCLDFqhVSCkRTWXR3aHjDzGlirErzLXQREbCYKyjVCZv+IkUmoGAB3PJDPKFot5HITIkbVFDt47LPSpx0cfFJZbzyBaK+laMBwchhYA/EkaO31eJDIPBWEcpJW/4Gcaa/mAbihKYTfIGNdFqYmltuYcxKsbX9nfAZbdxnvEqIin11S9de4zdbmS6ujEQj5TXUxOR/hiMdWSWy3dAxSzjRb4sS7SaWEr/ddCV3A47tg762We8irmkvss9NNnBIfQno5hJsE2NyCgYjHVkph5jbZbxFIMx0apiaTUYG6WVAlAXfRyaWuRL9StoGwp1baUAgKFhDCYimGMwbopfnZ7Hh/72OcSXc3ofhQyMwVhHpmqlKG+/4zdwotXEUsZqpQDUecaZXBEnZpJ6H8VQIgapGLs2jqE/GcEMCw5N8fihMH76chT3//CE3kchA2Mw1km+UEQmVzRNMO7wONHhcbCVgugiYqksbAIItjn1PkrZrmF10cd+9hlfIJJU4LQLBHT+WrmvGIU3pyA+PavrOVqFtgny68+dYe89XRSDsU7S2QIAmKaVAgAGAm2YZsWYaFWxdBZd7W5DbbIc6mxDt8+F/We5Xa2SNsNYCH2/VrbRUQBA7tRZXc/RCvKFIo5Mx3HnxDB6Ozz4r985zClLtCoGY52klDwAwGeScW0A0B/0cCYq0UXEUgpCBlnuoRFCYHy4k9WxFaIpBd06t1EAqFjywWDcaC9HUsjkinjdphA+e8sWHA0n8OCzp/U+FhkQg7FO0qVgbKaKcX+Aa6GJLiaWzhpmIkWlXSNBvBJJI77EC0ca3bfeaUpLPpzTkzofxPoOT6ptFNuHAnjXtj685ZoN+OKTL2Ka7YG0AoOxTlImDMaDQQ/m01lkcgW9j0JkOPPpLEJGCFsraH3GByZZNdZEUoruF+8AABs2oOBwwDcb1vsklnd4Kg6f24GNoXYIIfAnt25FUUr8yT8d0ftoZDAMxjpJl1spzBOM+wPqyDZOpiB6tagBWykAYMdwEEKAfcYlhaJEzCjB2GZDakM/Qgtz5b8TqDEOTcaxdcBfvgMw3OXFf37r1fjhkVk8dZSXH+k8BmOdlFspXCYKxkEu+SBajZIvIJnJGzIY+9wObOrx4ch0Qu+jGMLCUhZFCXQbpLqfHRjCQCKCWc4ybphcoYij4QR2DAUuePtv37gRV/f68NlHj2Apyx9MSMVgrJOUorYjdHjME4wHShVjTqYgutBCWu3fNWIrBaBOp+DFWZVRZhhr5NAwBhIRbr9roBdnk8jmi9g+FLzg7U67Dfe9bzumFpfxv55+SafTkdEwGOvEjJfv+kpLPnhZgehC0fJyD+NVjAGgz+/BTFzR+xiGYLRg7Nw4it5UDJH5tN5HsawXSvOLdwwGXvW+68e6cOfEML76b6dwfIavqhCDsW7OX74zz7g2j9OOULuLlSeiFYy4DrpSX8CDaEpBNs+5reVgbJDqftsVY3DIIhKnObKtUQ5NxtHhcWA05F31/X/w7mvgb3Piv333BRSLssmnI6NhMNZJSsnDaRdwO8wTjAFgIMiRbUQrzafVsGXUVgptpftckn92teq+IeYYA/Bs2ggAyJ46o/NJrOvwVBzbBwMXXejS2e7Cf33Ptdh3ZgEP7z3X5NOR0TAY6ySt5E3VRqHpD3DJB9FKsZRaMTZqK0WvXw3GM7wfgEhSQZvTjnaXQYoSpSUfOMuKcSMo+QKOhRPYPvTqNopKd+wexGs2duF//vPx8g9P1JoYjHWSUvKmmkihGQi2IcyKMdEFoqksnHYBv0Ev03LU4nnaDGO910GXlZd8TOl8EGt6cSaFXEFix2Dwkh8nhMB979uOpWwen3/8WJNOR0bEYKyTtJI31QxjTX/Ag6SSRzLDLVpEmvm0glC7gcLWCtrFWY4EK229M0gbBQDA78dymw/emWm9T2JJh6bUxTYrR7Wt5soNPnziTZvwnf1TePZktNFHI4NiMNZJWinAZ9Dq0qVs8Kt/oWgXWIhIbaUwahsFAPg9DrQ57awYQ+0xNtolyeSGfgRiM5CSF7/q7YWpOIJeJ4Y626r6+HvefCVGQ1780XdfgJLnltdWxGCsk5RJe4x7fGrlicGY6LxoOouQwcJWJSEE+gMe9hjDgBVjAMrAIPrjEcyXpptQ/RyavPTFu5U8Tjs+d9s2vBJN429+/EqDT0dGxGCsk5SSh89Eo9o03R3qX/7RFL+BE2nm04phNqldTF/A0/JLJLL5IhaWcuUf8I1CW/Ixm2DBoZ4yuQJOzCSxfZX5xZfypqt7cMvOATzwry/hea5SbzkMxjpJm/TynfaXP2/tEp1n9FYKQFvy0drBOJY21nIPjX1sFF3LCURm5/U+iqUcn0kiX5RV9Rev9N9v24a+gAe/+43nscBKfkthMNaJWVspOr0u2G2CrRREJcvZApayBUO3UgBqxXg2kWnpBQbRpDEXsbRdMQYASL58St+DWMzhSfXi3cpV0NUIeJ348gevQzSVxaf+4UBL/7lpNQzGOpBSmnYqhd0m0NXuYsWYqESrQna3G6sKuVJfwIN8USKabt0/u5GUWjE3WsXYf/UVAACFSz7q6tBkHKF2FwYCtbXObB8K4DO3bMGPT0Tw5WdO1vl0ZFQMxjrI5IooSpiyYgyo7RQMxkQqoy/30PRxycf5ddAGC8aOsVEAgDzDYFxPh6fi2D5U/cW71XzoNSO4decA/uJfTnCEW4tgMNZBSskDgCnHtQHqXypspSBSxcrroI0djLnk43wwNtxFycFBFIWAY4pLPuplOVvAS3OpNV+8W0kIgc/fvh1j3e345J4DmGvxC6ytgMFYB2ktGJtwKgWg9udxKgWRSqsYGy5srdAbUM/Xyks+oqksOjwOeJwG+97rdiPh70LbDINxvRwNJ1AoynUHYwDwuR348gevQ0rJ4T/t2Y98oViHE5JRMRjrQKsYm3EqBQD0+NyIpBQOoycCEEubo5Wiu90Nh020fMXYaG0UmuSGfgSiM3ofwzK0i3c7arh4t5rNfR24773b8dypefzlUy/W5THJmBiMdVBupTBpj3FPhxvZfBGJTF7voxDpLpZS4HHa4HUZrAq5gs0m0Ov3YLbVg7FBK/uZvkH0LMwhm2c1sh4OTcXR0+FGr79+X+87rhvCXdcP40s/OokfHZ+r2+OSsTAY60BrpTDz5TuAs4zrTckX+JeiCcXSWYTa3eu64NMsfQFPa1eMUwq6DVoxLgwNoT8ZQSTZul+fejo8GceONWy8q9a9t27Ftf1+fOofDmBqcbmuj03GYM5kZnIpiwTjSFLBph6fzqexhvhSDjfd/yMsLOXQ7rKjs92FTq8LQa8TnV4XutrP/z7odaLb58ZVvT5s6DDWBq9WFEsZex10pb6AB0enE3ofQzdRA1eM7aOj8OYUvHgmjMHOTXofx9TSSh4nIyn82o7+uj+2x2nHX39wN2554Ke45xvP4x/+n9fB5WCN0UrMmcxMLq0UAJi7lQJgxbie9vzqLBaWcvjEmzYhmy9icSmLhaUs5pdyODu/hIV0dtXWlW6fG1sG/NjS7y//urG7HXab8auXVhFLGzdsrdTv9+Bfj81BSmmKCnc9LWcLSCp5w/YYe0pLPtIvngTGGYzX42g4gaJEXS7erWZjdzv+/Nd34He+8Tz+xz8fw2dv2dqQ5yF9mDOZmVza5OPatK1RUY5sq4tcoYiHnj2N110Rwh+8+5qLfly+UMTicg6LS1nMJRWcmEni6HQCR8MJfPWnryBXUC9Depw2XNN3PihfP9aFzX0dzfqf03JiqSyu6fPrfYyq9AU8WM4VkFjOI+B16n2cptJ+kDdqMO4oLflY5pKPdTs0GQfQuGAMAO/e3o/ffMMY/u5np3H9WBfes73+1WnShzmTmclprRReo40MqlJ5LTQrxnXxgxdmEI5n8Ke3bbvkxznsNnT73Oj2uXHlhg68flN3+X3ZfBEvz6VwNJwoheU4Hjs4jW8+dxZ2m8C+P3obgl5zvNxvJlLKUo+xOf6/7SttAAsnllsuGM9pyz0MWt0PlIJx8cxZnU9ifocnF9Hn92CDv7GtZn/47mux/+wifv+RQ7i29GodmR8bY3SQUvJod9lhM+nL3TabQKjdhWiSs4zr4as/PYWxkBdvuWZDzY/hctiwZcCPX79uCH98yxZ86+Ovw8HPvgN/dec4CkWJV6LpOp6YNCklj2y+aJoe4/5A626/M3rFWPT2Imt3wj45qfdRTO9QaeNdo7kcNnzpg7thE8AXn+QIN6tgMNZBWsmb9uKdhmuh62PfmQUcOLeI33zDxrr/oCSEwLbSS4lnYgzGjaAt9wi1GzNsrdTbwmuhjboOusxmw3xnD5d8rFMyk8OpaBo7GthGUWkw2IbrRjtxci7VlOejxmMw1kFKyZv24p2mu8PNVoo6+NrPTsHvceDXrxtqyOMPd7VBCOB0dKkhj9/qyss9TFIx3tDhgRCtuRY6klQghLEXsSR6+tERCet9DFM7Mp2AlMC2JlSMNSNdXpybX+LSK4tgMNaBFSrGPT43L9+t09TiMn7wwgzuvmGkYf89uB12DATaWDFukFjph8Nuk1SMXQ61T70V10JHUgo6vS447cb9a2+5bwDd87N6H8PUDjfh4t1Kw11eJJU84su5pj0nNY5xv0NYWFopoN1tzot3mu4OF6KpLH9CXoeHnj0NAPjw68ca+jxj3V6cjrFi3AhaxdgsPcYA0OdvzSUfRp5hrMkPDqMnGUMq3Xpfn3o5NBXHYLCtPG+/GUa6vACAs/P8PmsFlw3GQoivCSHmhBAvVLytSwjxpBDipdKvnRXv+0MhxMtCiBNCiHc26uBmprZSmPtGeI/PjWyhiMQy10LXIq3kseeXZ/GubX0YDLY19LlGQ+2sGDfIvNZKYeCX51fqC3has8c4pRi3v7jENjoChywi+tJpvY9iWocnF5taLQaAkRCDsZVUUzF+EMC7VrztDwA8LaW8CsDTpX+HEGILgLsAbC19zl8LIcxdGm0ANRib+/8W7S8Y9hnX5pF9k0hm8vjYGzc2/LnGQl4sLOUQX+LLfPUWTSnwuR3wmGj0Yn/Ag5lWbKVIGj8Ya0s+ki++ou9BTCq+nMPp2FJTJlJUGu5kMLaSywZjKeVPAMyvePNtAB4q/f4hAO+tePu3pJSKlPIUgJcB3FCns1qGFXqMtZepOJli7YpFib/72SmMDwexe6Tz8p+wTqMhdbbmmXlWjevNTOugNb1+D+LLOSxlW+fVHiklIkmlvJzIqHxXlZZ8vHJK55OY05Eptb94R5ODcbvbgVC7C+cYjC2h1h7jXillGABKv2oDWAcBnKv4uMnS26iCJaZSlIJxhBfw1uxfj8/hdGypKdViABjTgjH7jOtuPp01VRsF0JqzjNPZApR8ESGD9xh3XaOugi6c4pKPWhwqBeNtA80NxoB6AY8VY2uo9+W71Qaxrno7SwjxcSHEXiHE3kgkUudjGFe+UISSL5q+Yqy9JMmK8dp99aenMBDw4N3b+pryfNrFEPYZ1180pZhmhrFG237XSu0UC1ovuMG3P7b3dCHhbodtiks+anF4Mo7hrjZ06vDDqjqybbnpz0v1V2swnhVC9ANA6de50tsnAQxXfNwQgOnVHkBK+RUp5YSUcqKnp6fGY5hPWikAgOmDcbDNCbtNMBiv0ZHpOH7+Sgwffv0YHE0aG9XmsqPP7+FkigaIpbOGf3l+pb4WXPKxsKQGYz0C01rFOjfAE2YwrsWhqUXsGAzq8twjXV5MLS4jXyjq8vxUP7X+zfwogI+Ufv8RAN+vePtdQgi3EGIjgKsA/HJ9R7SWVKmvz+yX72w2gW6fi60Ua/R3PzuNNqcdd18/0tTnHQ15WTGus2JRYsGErRQtWTEuXTzt9Bp/GtBiTz86IjN6H8N0FtJZnJtfbvrFO81wVxsKRdmSoxCtpppxbXsA/BzAZiHEpBDiYwD+J4C3CyFeAvD20r9DSnkEwD8AOArgBwDukVIWGnV4M0orWjA2/jfoy1HXQmf1PoZpzCUzePTANP79xBACTf4LeizUzopxnSUyOeSL0vB9qyt5XQ74PY6WqhgvlirGQYO3UgDAcm8/umIMxmt1WLt41+RRbZphzjK2jMu+ni+lvPsi73rrRT7+PgD3redQVpbMqMHY7As+AC0Ys2Jcra//4iyyhSI+2uCFHqsZ7fYislexxEQUo9B+KDRbKwUA9AfaWqqypfUYm6FinBscRnApgWIqDZuvXe/jmIYWjLfqFIwrl3y8QZcTUL1w812Tna8Ymz+c9HS42UpRpUyugG/84gzees0GXNHja/rzczJF/ZlxuYemL+BpqbXQ80s5CAEE2owfjG0j6jWdOGcZr8nhyTjGQl7dvsb9gTY4bIIVYwtgMG4yLRhboWrX7XMjxrXQVXn0wDRi6WzTRrStNBriZIp6i5VeLTHbVAqg9dZCLy5l4fc4m3bhdT1cG8cAMBiv1eGpOLYP6XPxDgDsNoGhzjbOMrYA43+XsJiUhSrG3T4X10JXQUqJr/3sFK7p68DrNoV0OYO25IN9xvUTTZu3laIv4EE0pSDXIjfoF5ZypmijACqWfJxkMK5WNKVganFZt/5izXCXl8HYAhiMm8xKFePza6Fbp/JUi2dPxnB8JonfeuNGCLHaqO/G87kd6Pa5WDGuo/mUeUaArdQf8EBKYK5FWqEWl7KmuHgHAJ1Xb0QRAvnTXPJRLa2/WK+JFBou+bAGBuMmS2e1Ocbmv3zXU95+x8kUl/LVn55Ct8+FW3cO6HqO0VA7TjMY100srSDQ5oTTBC/Pr9Rb3n7XGgsJzLShsCfUgYivE+Icg3G1Dk/GIQSwdcCv6zlGurxYWMohkcnpeg5aH/N9Rze5lJKHy26D22H+YNzN7XeXdTKSwr8en8MHXzMKj1Pfr7k6y5jVjHqJpbIImbCNAji/FrpV+owXl3IImqSVwmm3IRLcAPfMqruxaBWHp+LY2N2ODo++X2NtMgXbKcyNwbjJUpm8JarFQGXFmMH4Yh782Wm47DZ86LWjeh8FY6F2hOMZZHIcLV4PsbSCkEmqkCv1+9sAtM72u4WlLDpN0koBAIlQL7xznGVcrcOTcd37iwEGY6tgMG4yK82RDbQ54eBa6Et65sUI3nLNhnI/tp60yRTsgauPWCpryokUAOBvc8DjtLVEMM7kCljKFkxz+Q4AMv0D6JyfBTjx57LmEhnMJDK6TqTQDJeDcWu0KFkVg3GTpZS8JSZSAOpa6JDPxWB8EcWiRDi+jLFuYwzp12YZn46yz7geYmnztlIIIdQlHy0wy3hRWwdtouq+GByCV1lGYTGu91EM78h0AgCwTef+YkAtFgXanCw+mByDcZOls9apGANc8nEp0bSCXEFiIOjR+ygAuOSjngpFiYWlrGlbKQB1lvFsC1SMF5a0rXfm+Vq5xkYAAJGjL+l8EuM7GlaD8bUGCMYAMNzVxmBscgzGTZZSCpYKxupaaE6lWE14UQ0d/YE2nU+iCnidCHqdnExRBwtLWUhpzq13mr5Aayz50IKxWS7fAYD/KnUR0PwJzjK+nGPhBIY62+DX+eKdZoSzjE2PwbjJ0koePotcvgO0YMyK8WrCpVFY2gQAIxgNtbNiXAflddA+c/YYA+fXQheL1u5jLbdSmKhiHLpmEwAgdfK0vgcxgWPhBK7tN0a1GFD7jCcXli3/58rKGIybLG2hHmNAbaWIphSuhV7FdKliPBA0RsUYAMZCXlaM6yBWepXEzK0U/QEP8kWJWNrar/iUf4gx0ddqw2Z1yUeOSz4uKZMr4FQ0bahgPNLlRbZQxGzS+q/GWBWDcZOp49qsE4y7fW7kChLxZQ40XykcX4bbYTPUbfjRUDumF5eh5DmybT3MGLZW6vVrSz6s/Rf4oglbKRweN+Y7uiCmpvQ+iqGdmEmiKIEt/R16H6VMG9l2lq/MmRaDcRNJKZHOWqti3F26lc92ilebjmcwEGzTbQ30asZCXhQlMLnAcULrMZ9W/3s3e8UYAGYsPpliYSkHr8tuuqVK8VAv2ma55ONSjmkX7wxUMR7u5FhMs2MwbqLlXAFFCUtVjLX5vHOcTPEq04vLhplIoRktT6ZgO8V6aO0HZhoBtlJfi6yFNttyD81yXz/8sVm9j2Fox8IJtLvs5TBqBAPBNtgEl3yYGYNxE6WUPACLBWOfthba2n2KtQgvZgwzkUIzVlrycTrKb9rrMZ/Owu9xwGk377fQ7nY3HDZh+ckUC+ksOtvN00ahkYND2BCPsE3tEo6Fk7im3w+bzTivyrkcNvQHOLLNzMz7Xd2E0ora12m1qRQAEGXF+AL5QhFzyQwGDDSRAlB7YjvcDlaM10ld7mHeiRSAuqCn1+9piVYKM1aMnWMj6MguY+p0WO+jGJKUEsdmErjWQP3FmpEuL4OxiTEYN1Faqxi7rFMxDrQ54bQLRNhjfIHZpIKiBPoNNJECUDeejXZ7cZoXQ9ZlPpU19cU7TV/A0xKX74ImDMbtm8YAANHjnGW8msmFZSQzeUP1F2tGurw4x3scpsVg3ERaK4XPY51gbLMJhNrdrBivEF403gxjjTrLmBXj9VhYskgw9ls/GKsVY/O1UoSuvgIAkHyJwXg1Rrx4pxkJeRFJKljOcvqPGTEYN1EqUwrGFuoxBoDuDhenUqwwHTfeDGPNWEgdQJ8rFPU+imnF0uZeB63pC6itFFadQ54vFBFfNmcrhfdKdftdhrOMV3UsnIQQwDV9xmulGOpUv++fW+Arc2bEYNxE6az1Lt8B6gU8tlJcyOgV43xRYnqRL/XVQkqJhbR1KsZL2QISpR/arUa7uGbGijEGBlAUAmJyUu+TGNKxcAJjoXZ4DdiayFnG5sZg3ETlVgqLBeNunxvRJKdSVArHM+hwO9DhMd5fyGOlkW3sM65NYjmPfFFaIxgHrL3kY0FbB23Gr5XTiWQgBHeYs4xXY9SLd0BFMOYFPFNiMG6itAXHtQFAd4cbsbTC3fAVphaXDdlGAZwf2cY+49rEtOUePhOGrRWsvuTj/NY7c36tljb0oyM2y7anFVJKHmdiS7i2z3j9xYA6/afdZWcwNikG4yZKlca1eZ3WGdcGqK0UXAt9oXB8Gf0GW+6h6elwo81p5yzjGp1fB23ucW1A5Vpoa7bVlL9WJg3GhYFB9CciCC9a8weXWp2YMe7FO0Cd/jPc5cUke4xNicG4idJKHu0uu6GGkddDd4e25IN9xhojLvfQCCEwGvKyYlwjbeudFS7facHYqks+FkutFEEz9hgDsI+OoC8ZZeVxhaPhJADg2gFjBmOAs4zNzBDBeC6pWPZWdKVUJm+pUW2a7tJLyryAp8rkCoils4Zb7lFpLNSOM/ymXZPzFWPzB2OXw4ZunxuzFm2lWFgy9+pu7xWj6pKPczN6H8VQjoUT8Hschv4eO1wKxq2QbazGEMF4NpHBkemE3sdouFQ2b7n+YgDYUKoYRzjLGMD5i0xGW+5RabTbi7OxJRTYF75mVgrGANAXcFu2YrywlIPTLtDuMmf7X25p7gAAIABJREFUWkdpZFviRc4yrnQsnMC1/X4IYdxXX0e6vMjkiiwYmZAhgrEA8O3nrT+SJq3kLTeRAqhYC53iZAoAmC71axq5mjEWake2ULTspatGiqWyaHfZ4bHIXYE+f5t1p1Kks+j0ugwdoC7FNjIMAMic4ixjTbEocWImadj+Yo02meIcX5kzHUMEY3+bE48emLb8zVu1x9h6wbi8FpoVYwAoX5QxdMVYm0wRZZ/xWs2nFXRZYCKFpr+05MOKFpayplzuUTY0BAAonj2n80GM48z8EpayBWwxeDAe5sg20zJEMA56nYils/jJixG9j9JQKaVgyVYKIYQ6y5gvGQFQJ1IAxlzuoeEs49rF0llLTKTQ9AU8WFzKIZOz3vraxaWcaS/eASgv+XBOT7FXtcTIq6ArlbffzVtz4ouVGSIYd3ic6Gp34TvPT+l9lIZSWyms8fLrSgzG500tZhBqdxn6pfY+vwcuh42TKWowb5F10Jo+v3WXfMybvWLsciHT2Y2uxUh5wkarOxZOwG4TuKrXp/dRLsnjtKPX72bF2IQMEYwFgFt3DuDJY7OIW/gPf1qx5uU7QJ2Ny1YKlZFnGGtsNoHRLi9OMxiv2bxF1kFrtFc2rHgBb3Epa9qJFJrcwCAGEhEGrJJj4QSu6G43dOFBw5Ft5mSIYAwAd+weQjZfxOOHw3ofpWGSijXHtQHqyDZWjFVGnmFcaTTUjjNspVgTKSViFqsY95a331nrJV8pJRaXcug0cysFANvwEPqSMQaskmNh41+80wx3eXn5zoQME4y3Dfpx1QYfvmPR6RS5QhHZfBE+C16+A9RWilgqy7XQUKdSGHkihWYspFaM2btYvXS2gGy+aKmK8flWCmv9YJtU8sgXpblbKQB4No6iP8mKMQDEl3KYWlw2TTAe6fJiJpGxZP++lRkmGAshcPvuIew9s2DJvse0kgcAS7dS5IsSiy2+Fjql5JHM5A09kUIz2t2OTK6IObbAVG0+Za0ZxoD6PcnvcVhuLfRCad60qS/fAXCOqUs+Zs/N6X0U3R0rr4Lu0Pkk1Rnp8kJKYGrRWn+2rM4wwRgA3rtrAELAkpfwUqVgbMU5xkDlLOPWDlnhReNPpNCMlUa2nebItqrF0up/3yELjWsD1MkUVusxXijdVzH9DzGlkW1Lr5zR+SD60yZSGH1Um4azjM3JUMG4P9CGN2zqxnf2T1ru5d20or6UYtWKcTkYt3j1cboULgbMUDHuUke2sc+4etrWO7O/PL9SX6DNcmuhtXXQQbN/rYbVJR+Fs1zycSycQKjdhZ4Oc4xLHGYwNiVDBWMAuH33IM7NL2PvmQW9j1JXqXIrhfFv0tZC+0bV6usvzVQxHgh64LAJTqZYg1gpGIcsNMcYAPr91qsYLy5pP8SYu5VCqxi7ZqaRzVt7CdblaBfvzLLJsMfnhtthY3+4yRguGL9zax+8LrvlLuGlLd5K0VOqGLf6yLbpxWXYBNDrN34wdthtGO7ysmK8BlrF2Eqb7wB1MkUkpVhq++h8Wm2lMH11f2AAUgj0JaIt3auaLxRxYjZpmv5iQB2LOcyRbaZjuGDc7nbgXdv68NihsKVucpZ7jC06rs3f5oDLbkO0dDmpVU3HM9jQ4YHTbrg/WqsaDXGW8VrMp7NwOWxod1nrlZ/+gAdSWusH28WlLGwC8LeZvGLsciHXvQH9yaglL6ZX61Q0jWy+aJqJFBp1lnHr/kBjRob82/uO3UNIZvJ46tis3kepm3IrhUXHtalroV2W+ou1FmZY7lFprDTL2Go9/Y0SS6kzjM3yUm61+iy45GNhKYtAmxN2m/m/VmJoEP3JaEv3qh41ySrolUZKs4z5PdY8ag7GQojNQogDFf8khBD/rxDiXiHEVMXb37PWx37tFSH0BzyWmk5h9VYKAOju4Fro8GIGAyZY7qEZDXmRUvLl3lm6tPm0Yv4pB6uw4lrohaWc+dsoShyjoxhIRlv6Jflj4SScdoFNPcZeBb3ScJf6PZYrvc2j5mAspTwhpRyXUo4DuA7AEoDvlt79l9r7pJRPrPWx7TaB9+4axDMvRixTgbT6HGNAnUzRysFYSonp+LIpLt5pxkLaZIrWfYl2Lay2DlrTX95+Z6FgnDb/OmiNGB7CQCrW0vcBjoUTuHJDB1yOJr3Qnc0CyeS6H2a4Uy2UtPIPNWZTr//C3grgpJSyboMWb981iEJR4tGD0/V6SF2llAJcdlvz/lDroMfntswPMrVYXMohkyuaYrmHZrQ8y5jftKthtXXQmkCbE26HzVJLPhYssA66bGgI7Zk0otMRvU+im2PhRPMu3v3TPwGbNwNXXAHs3buuhxopfY9lMDaPeqW0uwDsqfj33xVCHBJCfE0I0bnaJwghPi6E2CuE2BuJvPoP+1W9HdgxFLDMdIq0krfsqDZNd4cLsXTrroWeLoUKM6yD1gx1emETrBhXS60YW2tUG6DeEegPeDCTsM4PtotLWfPPMNaUZhnnz5xtyV7VWErBXFJp/GKPV14BbrkFuPVWwOsFfD7gzW8GfvSjmh9yuJPB2GzWHYyFEC4AtwL4x9KbvgxgE4BxAGEAf7Ha50kpvyKlnJBSTvT09Kz62HfsHsKR6QSOl9ZAmpkajK3bRgGorRSFFl4LHV5UX4Y2U8XY5bBhsLMNp1v4JdpqZXIFLGULltt6p+kLeCxWMc5aqmIMAIH5uZa8D3AsrLY0NOzi3fIy8Cd/AmzZAvz4x8D99wMHDgA//SkwOgq8+93A979f00O3ux3o9rla+uKk2dSjYvxuAM9LKWcBQEo5K6UsSCmLAP4PgBtqfeBbdg7AYRP4rgUu4SWVvKUv3gEVSz5atJ2iXDE20VQKQJtMwYrx5WiBxIo9xoB6Ac8qUymWswVkckXL9BhrFeP+RGtewDvWyIkUjz8ObNsG3Hsv8L73AcePA5/+NOB0AoODwDPPADt3AnfcATz0UE1PMdzlxbmF1vu6mVU9gvHdqGijEEL0V7zvfQBeqPWBu9pdePM1G/Dd/VMomPzl+XQLBOPyWugWvYA3vZiB0y7QbbKX2tVZxvymfTkLVg/GgTbMJRRLtEItLFlsdffAAAC07Mi2Y+EEev3u+v7ZO3UKuO024OabAbcbePppYM8eNQxXCoXU9910E/DRjwJ/9VdrfqrhTi75MJN1BWMhhBfA2wF8p+LNXxBCHBZCHALwZgCfWs9z3LF7EHNJBT97Obqeh9Fdq7RSAK0bjMPxZfQFPLCZbG7qWKgd8eVceYUure78OmiLhK0V+gMeZAtFzFvgv4MFq6yD1rhckL296EtGW3IyxdFwon7V4kwG+Nzn1LaJp58GvvAFtW3iLW+5+Of4fGpl+fbbgU99CvjMZ4A19HqPdHkxvZix1GZJK1tXMJZSLkkpQ1LKeMXbfkNKuV1KuUNKeauUMrye53jzNRsQaHOa/hJeqgUqxq3eShFezKDfRDOMNaOlkW2sGl/afFr979qqFeNeC80y1mbGWubyHQDx/7P33vFt1Wf7//tIsrzkbXmv2LEdZ5BJyAACJOxNW6Clu3RAB4VCS8vzo+3zlLZPW9pvC7SFrqctXVBGwggFEggEEkIm2XYcYscjnvKQZGue3x9Hcpzg2Jatdc75vF+vvJTIts4NsqTr3Oe6r7u0lIqhXt11Ht1eP41d9vAI40OHFNvEd7+rDNgdOgT33APmSfyeJCbCv/4Fn/sc/OAH8JWvgH9yQrcsOwWfXx6ZQxHEN3GfHZZoMnL1/EJe2n9iZHucGnG4fJpPpUhPUtZCd+m0Y9zWP6SqRIogFYE4IeEzHp8ee7BjrC6rzGQZyTLWgDDu1aLtpaSEYnuP7oTxkU47Hp8cHmH83e9Cdze88ooicgNDjZPGZILf/Q7uvht+/Wv4xCfAM/GweWm2SKZQE3EvjAFuWFTCsMfP+r3Taj7HFD1YKYJrobsH1X8pNlT8fpmOgWFVJVIEKc1OQZJElvFE9DrcmAwS6cnafB0HhXG7BpZ8BG1BmVqxUgCUlmLt66JZZ1d2goN3s6ebYdzeDk8/rXR816yZ+uNIkmK/+NGP4O9/Vwb2nOM/JyLLWF2oQhgvLM1kRm6qaldEy7KM3a19KwUodgo9doy77S48PlmVHeOkBCOF6UmiYzwBvYFNapKkLg/5ZMmxJGI0SJqIbLMFrRTJ2uoYJw/ZsXf1MuzxxbqaqHGwfYBEk2FkS+eUeewx8HrhttumX5Qkwb33wm9/Cy++CJdeCn19Z/z2gvQkEoySSKZQCaoQxpIkccPCYrYc7aFFhb9YTrcPWUYXwjjXkki3Dj3GbYHLz2r0GIPiMz4mhPG4aHXrXRCjQSI/LZET/ep//dqcbiyJJm1tGg1c9s8f7KHFpv6Tl8ly8MQAtQVpmIzTeC49Hnj0USWPeObM8BX3xS8qSRbvvAO33HLGbzMaJIozk0XHWCWo5l3juoVKhMra3epbEe0IeKO1bqWAgDDWYce4rS+YYaxOYVyRm6LLafdQULbeaVcYQ2DJx4D6RZfN4SYrVUM2ChjJMi4a7KK5Vx8nsbIsc7B9kLqCafqLn3lGsVJ8+cvhKWw0N90EDzygdI43bDjjt5Vmp+gyak+NqEYYl2ansHRGNs/tUZ8wDg4N6qFjbE1L1OVa6JPCWH1WClA6xj0ONwPD+txaOBn0Ioy1sOTD5vRoJ8M4SKBjXDDYrRufceegi16Hm7rp+osfeQRmzIDLLgtPYafz1a8qG/LuueeMSRVl2SLLWC2oRhgDLCnP4kinHa/KsgAdLsUPpo+OsRmfXx7JEdUL7f3DJCcYyUhWZ5cqmEwx0Qduj93FwxsbWPnjjfzoxYPRKC1u6LG7NG2lAChIT+ZE/zByCBmt8Uif062pqDZgZPFEuaOX5l71d/Unw4FwbLzbuxfeeEPxFhsjlAyVlKR0jXftUgbyxqAsO4U+p4f+IdF8iHdUJYwrclLx+mXaVJYFaB+xUmg7rg3AmqZ0TLvtehPGQxRmJql2MCuYZXwmO8X+tn7ueXIPy3+8kZ+9XE+f082m+q5olhhTPD4/A8NesjUa1RakMCMJp9vHoIqjMSHYMVbnSeoZMZshP58qt003VoqRVdBF0xDGjzyiCNfPfjZMVZ2Bj34UFi2C++5TloicRlkgsk3YKeIfVQnj8kBXS21DQg4dWSlyLUqXRm9LPtr6hilS6eAdjP3a8vqUiMQbH93Clb/azPPvtXPjkhJevet8PrG8gsYuu242OY2sg7ZorAt5GvkayTK2Odzas1IAlJRQ4tDPko+D7YOUZCWTnjTFk5z+fnj8cbj5ZmW1cyQxGOCnP4XmZnjooQ98OZhlrMYAAb2hKqVWkRvsajkAa2yLCQGHW0fDd2n6XAvd3j/E+dXq+Z08nRSziby0RJp6HPQ53fzz3eP8dUsTrX1DlGQlc98Vddx4dumIVaS2wILHJ3Os20F1/jT9fypA6+ugg4xe8lGj0ufV4/Mz6PJqUxiXlpK/cx/NvU5kWVbtFarJcnC6q6D//GdwOJQtddHgoouU5IsHHlA61KPEuFjyoR5UpdTy0hJJSjCobnXt4LAijNN0IIytOhTGHp+fzkGXKpd7jKYiJ5WXD3Swbk8bwx4/yytzuP/q2aypy8doOPUDOCiaDncM6kIYa3KT2hgUaGAtdHAdtOZSKQBKSsh8dQPDHj9dgy7y0tU57DsZhj0+jnbZuWJe4dQeQJaV7XTnnAOLF4e3uPH4yU9g/nz44Q/hwQdH7s5ITiAjOUEIYxWgKqUmSRIVOamqW0Sgp7i2tEB2qJ6sFB0Dw8gyqlzuMZqzSjLY09LHdQuK+fTKinE7NVVWCwYJ6k8MwllRLDJG6KVjnJ+ehMkg0dhtj3UpU+bk1jsNPlelpZjtg6S6nDT3OjUtjOs7BvHL09h4t2EDHD4Mf/lLeAubiLlz4TOfgYcfVjrVM2aMfElJptDH4KSaUZXHGBQvpNo6xg6XF0mCFLP2h+8kScJq0df2u+AwqFozjIPce/ks9nz3Ev73w2dNePkyKcFIRW4qhzsGo1RdbOkN/D5rvWNsNhlYUJrJ1qO9sS5lyox097UojEci23o033k8ON1Eiocfhtxc+MhHwljVJPn+95UEjPvuO+XuMpFlrApUJ4wrclJp7nWqKifX7vKRajZp3g8WJDctUVepFO396s4wDmIyGkhKmPzJW21+GvUd6u0shkKvw40kabQLeRorqnLY29Kn2kzrkXXQWkulgBFhXGTv1vxCnoPtg6SajZRmpYT+w83N8NxzcOutSiJFtCkuhm98Q9mKt337yN2l2Sm02Jz4VKRf9IjqhHF5Tipur58TA+rxwDlcXl1EtQWxWsy6slIEO8ZqXQc9VWry0zjW42DY44t1KRGnJ5BycLrXWossr8rFL8O776uzaxy0UmRpsbsf2H43y9On+c7jgfYBZhWmY5jKa+63v1Vuv/Sl8BYVCvfcA1Yr3H234ncGSrOT8fhkOlSkX/SI6oRxhQoj2+xury78xUH0tha6vX+I9CSTrp5jgFkFacgyHOnUftdYD1vvgiwsyyTRZODtxp5YlzIlgh1jzeUYAxQVAVDt6de0lUKWZQ61D0xt453LBb//PVx9tbKNLlakp8P3vgebNsELLwAns4y1/NxpAdUJ4/Lc8RcRxCP2Ya8uMoyDWNMS6XW4dXO5qK1vWPX+4qlQU6B8aB06oX2fcY+OhHFSgpElFVkqFsZuEk0GkkOwBamGxETIz6fc2UOThsWVsp7ey0yrJfQffvJJ6OqCL385/IWFyuc/DzU18K1vgdcrhLFKUJ0wLkxPwmwyqKpj7HDpSxjnWhJ1tRa6vX9oJP9VT5Rnp2A2GajXwQBer8Ot+USK0ayoyuVg+8DIIJuaCC730OxMR0kJBQPddA26GHJr08bUYlPmNoLZvyHx8MOKGF29OsxVTYGEBPjxj+HAAfjTnyjKTMZsNChpPoK4RXXC2GCQKMtOoalbPWdcdpf+rBSgnyzj9v5h1WcYTwWT0cBMq4XDOniT15OVAmBZpbKYYOtR9XWNbU6PNgfvgpSUkGXrBLTbeQxuhysJdfBuxw545x24/XZlE108cN11sHIl3H8/CcNDzCvJYGezLdZVCcYhTn5zQqMiJ0VdHWO3vjrGI0s+BtXXbQqVYY+PXodb9RnGU6W2IE3zHePg1Q89dYzPKskg1Wzk7cbuWJcSMn1Oja6DDlJaSmpnO6BlYax0jIuzQmw4PPIIpKbCpz4VgaqmiCQpq6JPnIAHH2RJeRb7Wgd0MbSsVlQpjMtzUmnqUVZiqgGHy6erVIpci/Kh1GXX/uRtW18wqk1/HWNQkina+4fpH1JntNdk6HO6kWXtZxiPJsFoYOmMbLao0Gfc69R4d7+kBOPAwMiSDy3SYnOSmZIQWkOpp0eJR/v4xyEzM3LFTYXly+FDH4Kf/IRlqV7cPj/7WvtjXZXgDKhSGFfkpDDk8akmEkx3VgoddYzb+/UZ1RaktkAZjmnQcNd4ZGFEwCKkF1ZU5dLY5VBdtFSf1q0Ugci2KlcfzSq6choKLbYhSkLtFv/xjzA8HB9Dd2Pxox+By8Wyvz4EwPYmYaeIV1QpjMtzlGQKNWzAc3v9uL1+LGb9COO0RBOJJoMuPMYnO8b6tFLU5CvJFFregKeXddCns7xK8RmrqWvs98vat1IElnzMY1DDHeMhSjJD8Bf7fPCb38B558G8eZErbDpUV8OXvkTy//2R833d7BDCOG5RpTCuGBHG8X+27HB5AXTVMZYkiVxLomo6+tMh2DEu0KnHuDgzGUuiSdNT1sGOsabF1hjMLkwnIzlBVT7jwWEvflmjW++CBIRxjUazjGVZpjXUjvFLL8H778dvtzjI/fdDSgp3bf4bO5tsqrGD6g1VCuOizCRMBokmFQhje0AY62n4DhQ7RZcOOsbt/UPkWswkmvTjIR+NJEnU5Fs0nWU80jG26EsYGwwSyytzVJVn3BuIiNS0x7i4GIAZQ70ctw3h11hefK/DzZDHF5owfuQRKCiA66+PXGHhwGqFO+5gwdZXyD1Wr4qr3npElcLYZDRQmp2iil8qhzsgjJP0JYytOukYt/UN69ZfHCSYTKHV7kevXZ8dY1DsFC22IdWsHw5mp2v6uUpMhLw8Cu09uL1+OgbV5QGfiGAixaSj2o4dUzrGX/wimFXwvN91F760dL6++e9sP6bOtetaR5XCGKA8J0UVHWM9WikArGlmuu16GL7T53KP0dTkp2FzejR7hcDmdJOWZMJsUu3b5ZRZEfAZq8VO0RcQxpq2UgCUlpLT2wFAswoaRKEQclTb+vUgy3DLLRGsKoxkZWG48+tcXv827Zu2xLoawRio9p2+IieVpu74j2yzu5SsQouO4tpAWfLR63Bpfi10u07XQY+mNjCAV3/CHuNKIkOPzrbejWZmnoVcS6JqBvBsDiU2UNMdY4CSEizdJwA0txo6uNxj0sJ4wwYoK4OZMyNYVXiR7rwTZ7KFxX/6VaxLEYyBaoVxeU4Kgy5v3K8s1W/HOBG/TNw/P9NhYNjDoMur20SKIDUF2k6m6HW4tO1ZHQdJklhRpfiM470JAaOsFFp/vkpKSGhvwyChGpvLZGmxDZGRnEB60iS6/n4/vPYaXHSRskhDLWRm8t5Nn2Pl/rcYfOudWFcjOA3VCuMKlUS2BYfvUnUU1wb6WAvd3qfvDOMguZZEclLNmk2m6LG7yU7VV4bxaFZU5dA56KKxK/6tazanG6NBIl3rMx2lpUh9fVQla2/7XYvNOfnBu927obcXVq+ObFERQLrjDvqSLLj+6/+LdSmC01CtMC7PUYz58e4ztg/rNJVCB8K4rV/fGcajqclP03DHWL9WClAWfQBsUYHP2Ob0kJmcgKSm7uFUCES2zWeQpjhvDoVKa18IUW0bNyq3F10UuYIixLzZpfz+nBvIff0VePfdWJcjGIVqhXFJVgoGKf47xnq2UgCaTqYQHeOT1Bak0dAxqLnoKFmWsTndZOssqm00pdnJFGcmqyK2rc/p1v7gHYxsv6vz9WvKSiHLcmDr3SQTKTZsgLo6KCqKbGERIMVsYtuVtzCQmgHf/W6syxGMQrXC2GwyUJSZHP8dY7cXs8mgu4n23ICQ0HLHuL1/CIMEeWn6vcwepCY/DYfbR2tgE6BWGBj24vHJuu4YS5LE8qocthztifsTn16HWx9+8EDHeMawjR6He8Syp3ZsTg9O9yQzjN1ueOMNVXaLg8yuLeZ359ygJGts3RrrcgQBVK3WKnJSVdEx1puNAhTriLIWWrvDd219w+SnJ2EyqvplFBZqgwN4GvMZB4dHdSG2xmFFVQ59Tg8HTwzEupRx6XN6yNR6IgWMLPkotitdfK1Eto0kUkwm6eedd8DpVKW/OMji8iz+MP8KvNk5omscDbq74fXX4eGHx/02VX+iqyHL2OHykaqzqDZQukzWNG0v+RAZxiepybcA2kum6HUov796F8bLA3nG8R7bZnO6ydKDlSKw5MPa1wVoZwAvpOUeGzeCwQAXXBDZoiLIkoosnOZkdt38BXj5ZXjrrViXpA3sduXE6Q9/gDvvhIsvVjYjWq1w4YXw1a+O++OqFsYVOan0OT0joe7xiN3l1V0iRZBcS6LGrRTDFOo8wzhIWlICxZnJ1GtMGPcErnjk6DiVAhQffWVualwLY8UP7tF+hnGQkhLSupUlH8fivEE0WULKMN6wARYtgqysCFcVOQozkinKSOLvi6+EvDzRNZ4qPh/83//BNddAZSWkpcGyZXDrrfDoo9DXB1dcAQ8+CP/5D7S2jvtwqlZsJ5MpnHF7+UyvVgpQhHHwjU5ryLJMW98QF8/Oj3UpcUNNvkW7VgodD98FWV6Vw9rdbXh9/ri0DzndPtxev/YzjIOUlpLQ2Ejxhcnsbe2PdTVhocU2RHqSiYzkCbr+Dofiyb3zzugUFkEWV2Sz9Vgv3Hsv3HWX4ps+//xYl6UeXn0V7r4b9uxRlrwsXQqf/SzMmwdz58KMGcqVhRCIv3e3EKjIDWYZx+/Zst3l1V0iRZBKaypHuxwMDntiXUrY6XW4cXn9wkoxipqCNI52OfD4/LEuJWz0OIIdY52IrXFYUZWL3eWNWxE2stxDD1YKUAbwWlpYUJrJ7ua+WFcTFlonm0jx5pvg8ajaXxxkcVkm7f3DtN78KeVyv+gaT46DB+GqqxSbRH8//POfUF+v3P7Xf8G110JVVciiGFQujMuyT3aM4xW7jjvGl87Jx+3zs/FQZ6xLCTvt/SKq7XRq89Nw+/xx7/sPhV6HmxSzkaQE/c0JnM6yymyAuI1t63MqJ+DxevUw7JSUQF8fS3ISaO0b0sQ8hxLVNkkbhdkM554b+aIizJIK5XW1o3MYvv1tZTjstddiW1Q809kJt9+udITffBN+8hNFJN90U9i2H6paGCclGCnMSIrrjrGerRQLS7PIT0/kxb3tsS4l7LT1ieUep1OTH0ymsMe4kvChm/ivSZBjSWRWQVrc+oxPdox18nwFsoyXJCiff7uPq7trrGQYOyfnL964EZYvh5RJ5h3HMbMK0kgxG9lxrBe+8AUlk/m73wUVrGCPKkND8OMfK3aJxx6D226Dxka45x5ICu/nsKqFMQSTKeK3Y6ykUuhTGBsMEpfNKeD1w10ji060gugYf5CZeRYMkraSKXp0vvXudJZX5fDusV5cXl+sS/kAJ6P1dGSlAGrc/RgNEruP22Jc0PToc3pwuH0TWyl6e2HXLlXnF4/GZDSwoDSTHc02ReB95ztKJzS41U/v+P3w97/DrFlKR/3CC2H/fnjoIcjNjcghpyWMJUk6JknSXkmSdkuStD1wX7YkSa9IktQQuI3oyGhFTmrcXrqVZRmH24tFh3FtQS6fV4jL6+f1w12xLiWstPUPYTYahGgaRVKCkYqcVA7HedZtKPQ6XKJjPIoVVbm4vP649LTqzkoR6BgnnmijrjCNXXGcc0tcAAAgAElEQVT4nITCyai2CZoNr72mdFM14C8Osrg8i4Ptg0oD6dZblZOe++8XXeN9+5R0iVtuUUTwxo2wdi3U1kb0sOHoGF8oy/ICWZaXBP59L7BBluVqYEPg3xGjPCeVbrs7Lge8nG4fsqy/ddCjObsim1yLmRf3actO0d43TEFGEgZDeDxNWqEmP436Dg1ZKexusnUe1TaapTOyMUjx6TMOWikyJ0o00ArBNciBAbz3WvrxxflmwvEIJhhNKIw3bACLRUkf0AiLy7Pw+WX2HO9TMqrvuw/eflvJNtYrL70EK1ZAczP8+c/w7rtKtzgKRMJKcS3w58Df/wxcF4FjjFCRE78DeEH7gJ6FsdEgccmcAl471MmwJ/4uv04VsdxjbGoL0jjW49DEcy3LsmKlEFFtI2QkJzCvOCMufcZ9Tg/pSaa4jJKLCElJysKClhYWlGZhd3lp7FLvSemkl3ts3KjEmSVo5wRoYVkWkgTbmwJ2mM9+FsrK9Os1/s1vlMSJqirYvh0++ckppUtMlekeSQZeliRphyRJXwjcly/LcjtA4DZvmscYl/IcJbItHoXxYEAY63X4LsgVcwtxun1sqteOnaKtb3hya0t1Rm1BGrIMRzrV+wEdxOn24fL6hZXiNJZV5bDruA2nO77mBnodbv1kGAcpLYXjx1lQmgkQlxaXydLaN0TaRBnGra1w+LCmbBSgnHDW5KWxIyiMzWYlcuydd+Cpp2JbXDTx+ZQs59tvh8svV7zWAS99NJmuMF4py/Ii4HLgy5IkTTqVWpKkL0iStF2SpO1dXVMXTMElH/GYTCE6xgrnVGaTmZLAeo2kU/j8MicGhikUiRQf4GQyhfoH8E4Oc+lMbE3AiqpcPD6Z7cfia9jL5nTrx18cJJBlXJmbSlqSiV0qTqZosTkn7hZv2KDcamTwbjSLyrPY2WzDH7TDfOYzyma/r3wFeuLvCk3YsdvhhhvgF7+Ar30Nnn1WsczEgGkJY1mW2wK3ncAzwFKgQ5KkQoDA7ZghtrIsPybL8hJZlpdYrdYp15CaaMKalhiXA3h20TEGIMFo4JLZ+Ww42BmX0+yh0jXowueXRSLFGFTkpGA2GjSxGlos9xibsyuyMBmkuPMZ9zk9+lnuEaS0FFpaMBgkFpRmsqs5vk5WQqHFNjTxVbgNG5QhrLPOik5RUWRJeRaDw14aglfbTCb44x8VUayBDX/j0tam2GOef15Jm/jlL8EYu9CCKQtjSZJSJUlKC/4duATYB6wDPhX4tk8Ba6db5ERUxGlkm8OliEC9C2NQ0ikGXV7eOtId61KmTVu/yDA+Eyajgao8iyYi23odysIE0TE+lRSziYVlmWw5Gl/C2OZ0k63HjrHNBg4HC0szqe8YVGU0ppJhPMFyD1lW/MUXXhhVv2m0WFyuBHhtb+o9eef8+UpE2V//Ci++GKPKIsyePXDOOdDQAOvWKR3yGDOd3658YLMkSXuAbcALsiy/BPwYuFiSpAbg4sC/I0p5TmqcCuOglUK/cW1BVlblkpZk4sW9J2JdyrRp7xMZxuNRm2+hXgNWih57sGMsUilOZ3lVLntb+hiIozQgm0OnVgpQBvDKMvHLxO3K7vHoH/Jgd3nHF8YNDdDSojl/cZDynBRyLeaTPuMg990Hc+bAF7+orD7WEi+8cHJ74ebNcOWVsa0nwJSFsSzLR2VZnh/4M0eW5QcC9/fIsrxaluXqwG3vRI81XSpyUjgxMMyQO74u0wsrxUnMJgMX1+XzyoEOPD5/rMuZFu3BjrEQxmNSU5BGW/9wXImmqTDiMRapFB9gRVUOfhm2HY342/ukcHv9ONw+fVopAI4fZ35JYABPhT7jSSVSaNhfDCBJEovKsj4ojBMTFUtFWxt885uxKS4SPPQQXHMN1NQoQ4bz58e6ohE0cT0imEzR3BtfXWO7GL47hcvnFdI/5InLqKdQaOsbJsVsJD1ZPK9jURsYwFN717jX4cZsMpBqFld8TmdhWSaJJkPc+Iz7ghnGerO9jOoY51gSKctOUWUyxaQyjDdsUE4EZs6MUlXRZ0lFFk09TroGXad+YelS+MY3lFXIwRMEteLzwR13KAN2V10Fb7xxMpM7TtCEMK4ICON4S6ZwuLxIEqSID1YAzqvOJdVsZL3Kl3009TgozUpBksRyj7EYSaZQuc84uA5aPM8fJNFkZElFFm83xsfMgC2w9U6XHmNJgqYmQDlhUXPHuPRMHWO/X9l4t3q18t+rUYI+4w90jQG+/32orobPf15JcFAjLhfcfDP86lfKQOHTT0Nqaqyr+gCaEMZlI0s+4ksY211eUs0m8cEaICnByEV1+by8vwOviu0UDZ12qvNjEyOjBkqykkk1GzXRMRaDd2dmRVUuh04M0mN3TfzNESZoe9GdlSIxUVkE0dAAwILSTE4MDI/YvdRCi22ItETTma/C7dkDvb2a9RcHmVucgdloYOdY6SLJyYql4tgxxXesNgYHFQ/xv/8NDz4IP/95TJMnxkMTwjgjOYHsVDPH4mwAz+HyCn/xaVwxt4Aeh5ttx+LDmxgqQ24fx21OqvPSYl1K3CJJEjUFaZroGAthfGaWVWYDo7Z1xZARK4XeOsageDQPHwZQ7aKPFtsQxVnJZ24iadxfHCTRZGReSQbbz/T5eO658OUvK/7ct96KbnHTobtbOal5/XVlvfNdd8W6onHRhDAGZaIz3jrGDpdPJFKcxqpaK0kJBtarNJ3iSKcdWYYa0TEel9r8NA6fGERW8TpTW8BKIRib2YUZGCTY3zYQ61JGrBRZqTrrGIMijOvrQZaZXZSO2WhQnZ1CWe4xgb941qy486JGgiXlWexrHWDYc4YwgR/9SLlK8NnPwpAKrgwcPw7nnQd798IzzyjrneMczQjjipxUjnXHV8fYLjrGHyDFbOLC2jxe2n/i5IYfFdHQqXRBq/NFx3g8avLTsDk9dAciz9SIYqUQUW1nItlspNJq4UBcCOOglUKHJzI1NTAwAJ2dJJqM1BWlq2oDnizLtNqGzpxI4XYrq4E1bqMIsqg8C7fPz74zxe5ZLPD73ysnQ9//fnSLC5VDh2DlSiVR4z//gauvjnVFk0Izwrg8J4W2/qG42qzmcHlFIsUYXDa3gK5BFztUuKWpvsNOglGiImeC1aU6p7YgkEyhUjuFy+vD7vKSI6LaxmVOUToH2mKfrWpzuElOMJKUoMMrdLW1ym19PQALSzPZ29KvmjmOgSEvg+NlGG/bBg6HboTxuAN4QdasgVtvhZ/+FN59N0qVhci77yrWD5cLNm1SNtupBM0I44qcVGQZjvfGz6UFuxDGY3LRrDzMJgMv7lVfOkVDxyCVuRZMRs28dCLCSDKFSgfwRjKMhZViXGYXptPWP4zNEdsrAzY9roMOUlOj3AZ8xgvLMhny+KjvUEdywfGJoto2bFCSKFatimJVsSPXkkhFTsrE3v2f/QwKCxVLhTvOrsxt2KD4wdPSFC/0ggWxrigkNPPpXh6HyRTCSjE2aUkJnF9t5aV96rNT1HcOikSKSZBrMZOdalatMA5uvRPCeHzmFGUAcKA9tnaKPqcOt94FKStT0ikCHePgAN6u4+q4ItfaN8Fyjw0bYNEiyM6OYlWxZXF5NjubbOPPaGRkwG9/C/v2wQ9/GL3iJuKpp+CKK6CiQhHFKsyd1owwPpllHD8+Y8VKocNLe5Pg8rkFtPcPs6dFPV44p9tLi21opBsqODOSJFGTb1FtMkWwYyyG78ZndlE6APtjbKewOXWcIGI0KuIjIIzLslPITjWrJpkimGFcnDlGx9jhgK1bdWOjCLK4PIseh3tiPXPVVXDLLfDAA/Dee9Epbjx+9zu48UZYsiQuF3dMFs0I48yUBNKTTHHVMXa4fFgSdXp5bwLW1OWTYJRYv0896RSNnQ5kGarzRMd4MtTmp9HQMai6qwIwKhdXr2JrkmSnminMSIp5MoXN6SFTr1YKOCWyTZIk5pdkqCaZosXmJNVsHPv527wZPB7dCeMlFZPwGQf55S+VbvqnPw22GF0l8PuVrvUXvgCXXgovvwxZWbGpJQxoRhhLkkR5TmrcdIzdXj9unx+L6BiPSUZKAitn5rJ+X7tqIr2Cg2QikWJy1Bak43D7Ri6Vqoke0TGeNMoAXqyFsVufiRRBamqgsRG8XgAWlmVxpMvO4LAnxoVNTEsgkWLMDOMNGyAhQUk20BEzrRbSk0zsaJpE3n9OjtKp3bsXFi6Ed96JfIGjOXpUGQa87z742Mdg7dq43GYXCpoRxhBfWcYOl/IGJYbvzszlcws43jsU827TZKnvHMRsNIhEiklSW6B01tWYTNHrcGE0SKQn6bgLOUlmF2XQ2GVnyB2bRCCfX6Z/SMfDd6AIY49nZDX0gtJMZBnea4l9YshEKMJ4nMG75ctVL7RCxWCQWFSeNbmOMcA11yh+XklSkiAefFDp4kYSv19Z7TxvHuzYoYjzxx9XTmRUjqaEcUVOKi22ITxxEFNjF8J4Qi6eXYDRIKkmneJIh51Ka6pIpJgkwc66Gn3GvQ6lA2kwiHXuEzG7MB2/DIdOxOYEd2DIgyzr3PZyWmTb/OAAngoiMc+43KO3F3bt0p2NIsjisizqO+z0OyfZ9V+6VPn/dc01cPfdym1PT2SKq69X4tfuuAMuuAD271fi4860uVBlaOoTvjwnBZ9fCQuPNUFhLFIpzkx2qpllldms33dCFXaK+s5BZgp/8aRJT0qgKCOJehUmU/TYxda7yTInMIAXq2SKXj0v9whyWmRbRnICldbUuPcZ9w95GBz2jp1I8frrIMu6FcZnz1BSODYe7pj8D2Vmwr//rayMfuUVJSZt8+bwFeXzKTFx8+crYvjPf4bnn4eSkvAdIw7QlDCuyA0mU8TeTiGsFJPj8rmFvN/tiPuuotPt5XivSKQIlZqCNA6rJE91NMrWOx0LrRAoyUomPckUM0tUX0AY63r4LjdXEUWBjjEodordx/viuukQbGKN2THesEGxUJx9dpSrig+WVmRTm5/GwxuP4AtlgFmS4CtfgS1blBi/Cy5Q1khP11px4ACsWAH33KMM2B04oKx31kiXeDSaEsYns4xjP4B3smMshu/G49I5BUgSrN8b3+kUjZ3KyVaNyDAOidr8NBo77XFhbwqFXoebbLH1blJIksTsovSYCWObQ7nUrOuOsSQpdopRwnhhWRbddvdIHFo80hJY7lF8JmF8/vlg1ufzajBI3LGmmsYuB8+/1xb6AyxaBDt3woc/DN/5Dlx2GXSE0H0O4vEoiRMLFyoDnv/4BzzzjLJcRKNoShhbLYmkmI1x0jFWBlFEXNv4WNMSObsim/X74ttnLBIppkZNfhpunz9uhmInS49DWClCYU5RBofaB2KyhtjmFMtYgFMi20BZDQ3EtZ2ixXaG5R4nTij/LRddFIOq4ofL5hQwqyCNX25oCK1rHCQ9XRGyjz0Gb76pWCs2bjzz9/t8ire7sVEZqHv+eVi2TEmcuPZapUt8882a7BKPRlPCOBjZFg8d45NWCtExnogr5hZQ32HnSGf8XnIPJlKUZ4tEilCoLVBOJA6pyGfs8fnpH/IIoRUCc4rScXn9vN8d/RMgm7BSKNTUQEuLshQD5bWXaDLEvTBOMRs/mCgS9MWed170i4ojDAaJO1ZXc7TLwXN7ptA1BkXEfv7zsG2bYrdZs0bpIl93nWKzmD8fysuVTXomkxL/NnOmsqTj6quV36l//xueeALy8sL63xevaM4AW5GTEhd+1eCbdXqyzt+sJ8Flcwv53nMHWL+3na+uro51OWPSIBIppsTMPCWP87tr9wNw5bzCsfNK44jga1d0jCfPyQ14A1G/qmJzejAZJDHoHBzAO3IE5s8nwWhgXnFGXCdTBBMpPvCe8NZbkJSkXL7XOZcGusa/2tDAVWcVTv0zaN48ePdduPNOZQFHZqbyZ8YM5TYr6+R9o/++YIHSedYRmnsnKc9J5dWDHfj8MsYYRi01dtnJtZhFDuokKMhIYlllNn/d2sTnzptBijn+fi0bOgdZUKreTT6xIinByJNfWsE9/97DV/6+i+fmtPE/180lLy0p1qWdkeDWu+zUxBhXoh6qrBbMJgMH2ge4bmFxVI/d53STmWKO+xOuiDM6sm3+fEAZwPvL1ibcXj9mU/yd1AeXe3yAzZvhnHN06y8ejcEg8fU1NXzp8R2s29PGDYumkQBhsSh5w4Jxib9XyjSpyEnB45Npi/G2rcYuB1VWMag1We6+pJbOQRe/f/P9WJfyAYKJFGIV9NSoLUjj6dtWcO/ls3jtcBcX//wNnt7ZErfT8r124VkNlQSjgVkFaexvi/5CCZvDQ3aqaEAwc6ZyO9pnXJaF2+uPWcb0RIyZYWy3K3m8554bm6LikEtm51NXmM5DG4/ExMevNzQnjMtzlMi2WPqMZVnmSKedKiGkJs2SimwunZPPo5sa6Rp0xbqcUwh6n0UixdQxGQ18aVUV6+84j5l5Fu56Yg+f+/N22vvjb2J+ZB20SKUIiTmBZIpon/D0BjrGuic1VcmTHR3ZVha/A3gDwx4Ghr0UZ54mjLdtU4bAhDAeQekaV/N+t4O1u6foNRZMGs0J44pc5bJMLJMpehxu+oc8zBQd45D41mWzcHn9/HJD/cTfHEXqAzm8IpFi+lRZLTzxxeXcf9Vs3m7s5pKfv8E/tzXHVff4pJVCiK1QmF2YTp/TQ3v/cFSP2+d063sd9GhOi2wrykjCmpbI7ub4E8atZ0qk2LxZGRhbvjwGVcUvl8zOZ3ZhOg9tbBBd4wijOWGcn5ZEoskQ03ioYIdRdIxDo9Jq4WPnlPGPbcfjKqGiQSRShBWjQeKz587gP18/nznF6dz79F4+8YdtHO+NfZoMKCe2kqTzXNwpMLsoAyDqecY2p0c8V0GCkW2BE01JklhQmsmuOOwYt5xpucfmzcqgWEZGDKqKXyRJ6Rof63HyrOgaRxTNCWODQaI8J4VjMbRSBEWdWB8cOl9bXU1ygpGfvHQo1qWMIBIpIkN5Tip/v3UZP7huLruabVz6/97gL1uOxbx73OtwkZmcENPhXTVSV5iGJBFVn7Esy0rHWHT3FWpqoK8PurtH7lpQmsn73Y6RDYHxQnC5xynC2OtVNrYJG8WYXDw7nzlFomscaTT5Sa9kGceuY9zYZSfFbKQwPX4n7+OVXEsiX1pVycsHOtj2fm+sywGU5R7CRhEZDAaJjy8r5+W7VrGkIpv71+7n9fqumNYk1kFPjRSziRm5qVHtGNtdXjw+WVgpggQj207ZgBefPuMW2xDJCcZTX2t79yrDdytXxq6wOEbpGtfQ1OPk6V2tsS5Hs2hSGFfkpNDU48Q/lU0xYeBIp9JhNIiO05T43LmV5Kcn8sMXD8a8e+hweWmxDVEjuv8RpTgzmd9/cgkpZiMbD3bGtJYeu5scEdU2JeYUZXAgisK4z6msgxbDdwFGR7YFOKskE0mKR2E8RoZxcLGH6BifkTV1ecwtTufhjUfwiK5xRNCkMC7PScXl9dMxGN0hkCBHuxxi8G4aJJuNfOPiWnYf7+PFvSdiWktjV3DwTjyfkcZsMrCiKofX6ztjekIkOsZTZ05ROq19Q1G7bB9cxiI8xgHKyyEh4ZTINkuiiZq8tDgUxkNj+4tLS6GsLDZFqQBJkvj66hqae508s1N0jSOBJoVxRSCy7Vh39H3GDpeX1r4hkWE8TT60uITa/DR+8p9DuL2xOysWiRTRZVWNleO9QzGdEeh1uMkWUW1TYnahsiErWl1jW6BjLHKMA5hMUFV1SscYFJ/x7uN9Mb8CN5rWviGKRwtjWVaEsegWT8jqujzOKsngodcaRNc4AmhSGJfnKOkBsfAZv9+tHFMM3k0Po0Hi3itm0dTj5G/vNMWsjoYOkUgRTVbV5AHwRox8xn6/jM3pFuugp8icwGroA+1REsaBaD1hpRjFaZFtoOQZ9zk9MT3hHM3gsIc+p+fUqLamJmhrE/7iSRBMqDjeO8TTO1tiXY7m0KQwLspMJsEo0RSD+CcR1RY+LqixsnJmDr/a0MDAsCcmNTR0ikSKaFKWk0JFTgqbYiSM+4Y8+GWRYTxVciyJFKQnRW0AT1gpxqCmBo4cUZZkBDg5gGeLVVWn0No3RlSb8BeHxIW1ecwvyeChjUdielVVi2jy095okCjLTqH+xGDUj32k047RII3YOQRTR5Ikvn15HTanh9+83hiTGkQiRfRZVWNlS2MPwx7fxN8cZnodytZFIYynzuyi9KhFttmcHiQJMpKFlWKEmhpwuaC5eeSu6rw0Us3GuFn00dI7xnKPt96C9HSYOzdGVamLYEJFi010jcONJoUxwIqqXN6OwYdrY5ed8uwUzCbN/q+NKnOLM7h+YTF/3Pw+bX3RXR8sEiliw6paK0MeH9uPRb+71etQrkyIVIqpM6concYuR1Tee/ucbjJE5vSpjBHZZjRIzCvJiJsBvDEzjDdvVrbdGY0xqkp9XFBrZX5ppugahxnNqrfVdXkMeXxsaeyJ6nGVqDYhpMLJNy6pQQYefDm6q6KDthjRMY4uyypzMBsNvNEQfTuF6BhPnzlF6fj8MoejcMWu1+EWNorTGSOyDWBBaRYH2gdiciXmdFpsQyQlGE56+W022LdP2ChCJOg1bu0b4t87RNc4XGhWGC+vyiHVbOTVgx1RO6bX5+dYj0MM3oWZkqwUPrOigqd3tUQ1I7WhU0S1xYIUs4mzZ2Sx6XD0hXFPYJgrR6RSTJnZhdFbDd3n9JAplnucSl6eYkkYFdkGSjKFxyfzXkv0NhOeiRbbEMWZozKMt2xRboUwDpkLaqwsKM3kkddErnG40KwwTjQZOa/ayoaD0ctEbe514vHJVFmFvzjc3H7BTNKTEvjR+oNRO6ZIpIgdq2qsHO4YpL0/uvaZXrsY5poupdnJpCWZONAeeQHWbXeJ5+p0JEmxU5zWMV45U2kW/fPd5jP8YPRo7Rs61V+8ebMSNbd0aeyKUimSJHHbBVW09g3FLM1Ha2hWGINipzgxMBy1CenGLhHVFikyUhL46kUzebOhO2ov/vqOQZFIESNiFdvW43CTlmQSMwLTQJIkZhemR/x9t3NgmMMdg5xVkhHR46iSMSLb0pIS+PDiEp7f007XoCtGhSkEt96NsHkzLFoEKaIJMRUurM0jO9Us1kSHCU2/+180Kw9JglcORMdOIaLaIssnlpdTmp3Mj9YfwheFdd/1HXZqhL84JtTkWyhIT+KN+u6oHrfXITKMw8GcogwOtQ9G9HW6ft8JZBmunFcYsWOolpoaJZVi6NQrLp9cUYHb5+cf22LXNba7vNhGZxi7XLBtm7BRTAOzycDVZxXyyoEO+odiE22qJTQtjHMsiSwqy2LDoegJ47y0RNKThOctEiSajNxz6SwOtg/w/HttET1WcINhtTjJiQmSJHF+TS5vNnThjaJvTqyDDg+zi9IZ8vhGFh5Fghf2tlOdZxHDsWNRU6Nskjty5JS7q6wWVtVYeXxrU8xSDFptp2UY79ypiGMhjKfF9YtKcHv9rN/bHutSVI+mhTHAmrp89rUORMWr2NhlFzaKCHP1WYUUZybz3J7IvvhFIkXsWVWTx8Cwlz0t0YuYausfIsciotqmS3ADXqTyjDsHhnn3WC9XniW6xWMyRmRbkE+vrKBz0MX6fbERUB+Iagsu9lixIib1aIX5JRlUWlOFnSIM6EAYK17FDQc7I3ocWZZp7LRTJaLaIookSaypy2Pzka6Ixg7VdyhRUzUikSJmnDszF4NE1NIpGjoGOdrlYHllTlSOp2Vm5lkwGw0RS5ERNooJGEcYr6q2MiM3lT+/fSy6NQVosZ223GPzZqiuhvz8mNSjFSRJ4oaFxWx7v5fjMdj6qyWmLIwlSSqVJOk1SZIOSpK0X5KkOwL3f0+SpFZJknYH/lwRvnJDZ2aehfKcFDZEOLata9DFoMsrOsZRYHVdPsMeP283Rs5/eqTTjtlkoEwkUsSMjJQEFpRmsqkhOj7jZ3e3YpDgqvlCbE2XBKOBmgILB9ojI4yFjWICLBYoKhpTGBsMEp9aXs7O5j72xGDhR4vNSaLJQK7FrNg93npL2CjCxLULigF4VnSNp8V0OsZe4BuyLNcBy4AvS5I0O/C1X8iyvCDw58VpVzkNJEli9ax83mrswen2Ruw4I4N3omMccc6pzA5kVEfuKkB9xyCVuSKRItasqsnjvZY+egP5wpFClmXW7m7j3GoreWlJET2WXphTmMH+toGwx2UGbRRXiG7x+NTUfCDLOMiHFpdgSTTFpGvc2jdEcVYgw/jwYejpgZUro16HFinNTuGcGdk8s6s1ajG1WmTKn/qyLLfLsrwz8PdB4CBQHK7Cwsma2Xm4vX7ejGDnqbFLEcaiYxx5Ek1Gzq+xsjGCGdUikSI+WFVrRZbhzQhvwdvRZKPFNsR1C4oiehw9Mac4nV6HmxMDw2F93Jf2B2wUwl88PmNEtgUJRrc9914bnYPhfX4mosU2dKqNAkTHOIzcsKiYo90O9sTBIhe1EpZ2mCRJFcBC4J3AXV+RJOk9SZL+KElSVjiOMR3OrsgmLcnEqxGMbTvSaceSaCI/XQzuRIPVdfkRy6gOJlIIf3HsmVecQVZKApsinGf8zK5WkhOMXDqnIKLH0ROzC5UBvHD7jF94T7FRiBPXCaipUbqxPT1jfvmTy8vx+GT+8c7xqJalCOPA4N1bb0Fu7klPtGDaXD6vkESTgad3ihXRU2XawliSJAvwFPB1WZYHgN8AVcACoB148Aw/9wVJkrZLkrS9qyuyH3oJRgMX1uax8VBnxHI1j3TZqbKmnlxxKYgoF9ZakSQisvI7aIuZmSc+eGON0SBxbrWVN+q78Ufotev2+nlhbzsXz84nNdEUkWPokbrCdCQpvKuhOweG2SZsFJNjnAE8gEqrhQtqrTz+TvSi2xwuL70O96mJFCtXKtv6BGEhPVcBmAAAAB5gSURBVCmBi2fn89yetphF8qmdaQljSZISUETx32RZfhpAluUOWZZ9siz7gd8BY+54lGX5MVmWl8iyvMRqtU6njEmxui6PHoeb3REaNmjsdIjFHlFkJKM6Aj5jkUgRX6yqsdJtd3HwRGQGuTbVd9Hn9HD9wrh0gqmW1EQTM3JSwxrZJmwUITCBMAb49IoKuqIY3dbaNyqR4sQJJWdZ2CjCzg2LirE5PRG/0qZVppNKIQF/AA7KsvzzUfePfse6Htg39fLCxwU1eRgNUkTSKQaHPZwYGBaDd1FmdV0ee1v76Qizh7EhkEhRnpMa1scVTI3zq3MBIvYm/+zuVrJTzZwbOI4gfNQVpYc1meKF99qZKWwUk2PGDDCZxhXG51dbqcxN5U9vHYtKScEM4+LMZMVGAUIYR4Dzqq3kWszCTjFFptMxXgl8ArjotGi2n0iStFeSpPeAC4E7w1HodMlISWBpRXZEOoxHu5TtTmLwLrqsqVNyL8P9nNZ3DFJltWA0iMt78UBeehJ1hekRyTMeHPbw6oEOrj6rkASRQBJ25hSlc7x3KCxrajsHhY0iJBISoLJyXGFsMEh8akUFu4/3savZFvGSghnGpVkBYZyUBIsWRfy4eiPBaODq+UVsONhJv1OsiA6V6aRSbJZlWZJl+azR0WyyLH9CluV5gfuvkWU5bvYTrq7L43DHYNjDr0VUW2yozrNQmp0c9qsADR12sQo6zlhVY2VHkw27K7yRiy/tO4HL6+daYaOICHOKMoDwDOD9J7DU4ypho5g840S2BYlmdFurbQizyUCuJVHxFy9dCmaxgj0S3LCwBLfPz/N722JdiurQVYvk4tlKhzHcA1uNXXZMBonyHLEMIpoEM6o3H+lmyB2eLXgikSI+WVVjxeuXeftIeCMXn93dSnlOCgtLM8P6uAKFkWSKMNgpnhc2itCprYWGBvCfeQjLkmjiI0tKeGFvO51htqWdTottiJLMZAxDTti5U9goIsjc4nSq8yw8s1Ms+wgVXQnj8pxUZuZZwi6Mj3TaKc9JEZdiY8CaunxcXj9vhUkwNQS6/2KjVnyxuDyLVLMxrD7jjoFh3m7s4doFxSJNJkJY0xLJS0uc9gCesFFMkZoaGB6GlvG9pp9aXoHXL/O3d5ojWs6xHgfFWcmwbRv4fEIYRxBJkrh+UTHbm2w09ThiXY6q0J2SW1OXzztHexkYDp/v5kiXXfiLY8TSGdmkJZrYcCg8JzsNgUQKYaWIL8wmA8urctlU3xW2pS7rdrchy4ilHhFmTlH6tK0UQRvFlUIYh0YwmWICO0VFbioX1ubxt3eacXnDc/XtdI50DrK/bYBllTmKjUKSYPnyiBxLoHDdgmIkSclpF0weHQrjPLx+OWyDPB6fn+YepxDGMcJsMnB+jZUNBzvDknMrEinil1W1VlpsQ7zfHZ7ux7O7W5lfkkGlmA2IKHOKMmjotDPsmbrgemFvO1XWVGFxCpVJRLYF+fSKCrrtLl7cG5mxoMe3NpNglLjp7FJFGM+dC5nCwhRJijKTWV6ZI1ZEh4juhPHCsiyyU81hG9hq6nHg9cti8C6GrK7Lo3PQxb4w5KWKRIr4ZVW1knceDjtFQ4fSvbpODN1FnNlF6fj8Mg0d9in9fNegi23v93LlWUXC8hIqhYVgsUxKGJ9XnUuVVYluC7eIcrq9PLWzhcvnFpKbbIItW4SNIkpcv7CYph4nO5sjs8NBi+hOGBsNEhfW5vHa4S68vulvhTnSKaLaYs2FtXkYJMKy8ruhwy66UnFKWU4KlbmpYRHGz+5uxWiQuOosYaOINHOKlAG8qfqMX9p/Ar+wUUwNSVK6xpMQxpIk8ekVFbzX0s+uMC/CWre7jcFhL59YXg5798LgoBDGUeLyeYUkJYgV0aGgO2EMcPHsPPqHPGxvmn5uY2OX0gURl2NjR1aqmcXlWbw6zTxjeyCRQviL45fza6xsPdozrcvyfr/Ms7vaOHdmLta0xDBWJxiL0qwU0hJNU14N/cJ7bcJGMR0mEdkW5IZFJaQlmvi/MC78kGWZv25tYlZBGkvKsxQbBSiroAURx5Jo4tI5BTz/XnvE/ONaQ5fC+LxqK2ajISx2iiOddgozkrAkmsJQmWCqrK7L50D7AG2BlaNT4YhIpIh7VtVYGfb4efdY75QfY0ezjda+Ia5bKLrF0cBgkKgrnNoGvBEbxbxCYaOYKrW1cOwYuFwTfmtqookbzy7lxb3tYdsouvt4H/vbBrhlWbnyHG7eDCUlUFYWlscXTMz1C4vpH/Lw2iGxInoy6FIYpyaaWFaVM+0OIygdY2GjiD1r6vIA2HBo6s9pfSCRQuSkxi/nVGZjNhmmNTz7zK5WkhOMXDK7IIyVCcZjdlE6B9sHQu70B20UV4ilHlOnpgZkGRobJ/Xtn1xejk+W+dvWprAc/q9bm0g1G7l+YbFSx+bNio1CnOhEjeDVMWGnmBy6FMYAF9fl8X63Y8QKMRVkWaax0y4G7+KAKquF8pyUaV0FaOgYxGwyUJYtFrXEKylmE0srsqfsM3Z7/bzwXjuXzMknVVzliRqr6/IY8vi46bGtdA5OvhP54ntKGkWtOFmdOiEkU4CS939RbR5/39Y87cVJNoeb599r54ZFynY9mpuhtVXYKKKMyWjg2vlFvHa4E5vDHety4h7dCuOL6gJb8KYxsHViYBiH20eV6BjHnOAWvLcbe3C6p7Y2uCFwkiMSKeKbVTVWGjrtU7LNvH64k/4hj0ijiDLnVVt59OOLqT8xyHUPvzWpXOOuQRfvvN8jbBTTZZJZxqP50gVVdNvd/Pr1I9M69JM7juP2+vn4snLljqC/WAzeRZ0bFpXg8ck8H6E4Pi2hW2FcnJnM7MJ0NkzDThH0pFZZReZtPLCmLg+318+bDVPbgicSKdTBqloltu2NKXSN1+5uIyfVzHkzc8NdlmACLplTwJNfWo5fho/89u0Jr+4IG0WYSE+HgoJJd4wBzq7I5toFRTy66SjHppgb7vfLPL61maUV2dQWBDr+b70FaWkwb96UHlMwdWYXpTOrIE3YKSaBboUxKEJqe1PvlC8tNAaEsfAYxwdnz8gmLck0JTtFMJFC+Ivjn+o8C4UZSSHbKQaGPbxysIOr5xdhEuvbY8Lc4gzWfmUllVYLt/5lO79/8+gZM3NffK+dSmGjCA+TjGwbzXeuqCPBKPH95/ZPKdf4jYYumnudfHx5+ck7N21Stt0ZjSE/nmD6XL+wmF3NfWFbkqRVdP3psGZ2Pn4ZXjs8ta7xkS47aUkmrBYR+RQPJBgNrKqxsvFQV8hb8I6IkxzVIEkS51db2dzQzb7WyWfjvrTvBG6vn2vFCuiYkp+exBNfXM6lswv4wQsH+c4z+/CclinfbRc2irASQmRbkPz0JO68uIbXDndNaVD98a3N5FrMXDYnMOTa2AgHDsDll4f8WILwcN3CYgxiRfSE6FoYzy3KIC8tccp2iiOdSiKFeOOOH9bU5dNtd7GnJbSA+o2BLrPoGKuDW5aVYTRKXPXQZu78125abM4Jf+bZXa1U5KSwoFSsoY01yWYjv75lEbdfUMU/tjXz6T9to9/pGfn6S/sCSz2EjSI81NZCVxfYQsvu/9SKCmryLXz/uf0hJYq02JxsPNTBTWeXYjYFZMa6dcrtNdeEVIMgfOSnJ7FyZi7/ereZgWHPxD+gU3QtjA0GidV1+Wyq78LtDX0LXmOXg5kikSKuuKDWitEgTfpkR5ZlfvFKPb/aeITL5hRQkSMSKdTAWSWZbLrnQm67oIoX97Zz0c828cALB+hzjm2LOtE/zJajPVy7oFicyMYJBoPENy+bxc8+Mp9t7/dyw2/eoqlHucT74l5howgrwQG8hoaQfizBaOD718ylxTbEr1+fXNwbwD+2NQPw0aWjsorXrYO5c6GyMqQaBOHlG5fU0m138711+2NdStyia2EMis/Y7vKy9WhPSD/XP+Sha9AlEinijMyU4Ba8iX3Gfr/Md9ft55cbGvjw4hIe/thCIZpUREZyAt+6bBav33MB1y4o4veb3+f8n7zGo5saP9DdWrenFVlGpFHEIR9eXMLjnzuHHoeb6x55ixf3trP1qLBRhJUQI9tGs7wqh6vnF/HbTY0jJy7j4fb6+de7x7loVj4lWYFGQ28vvPkmXHttyMcXhJcFpZl8+YIqnt7Zykv7RELFWOheGK+cmUtakok/vfV+SD8XzD8WHeP4Y01dHodODI57ed3t9fP1f+3mL1ua+ML5lfz0w2eJgSyVUpiRzE8/Mp/1d5zH4vIsfrT+EBf97HWe2tGCL+A1f3ZXG/NLM5mRKxJk4pFzKnN49vaVZKWauf1vO5U0innCRhE2KiuVgbcQfcZB7ruijgSDxH8/d2DC731p/wm67W4+vmxUt/jFF8HnEzaKOOGrq6uZV5zBt5/eG1KuuF7QvRJISjDy1Ytm8trhLt5smPyU+0hUm+gYxx2rAxnVG8+wBc/p9vL5v2xn3Z427r18Ft+5ok50pjTArIJ0/vSZpfzj88vITUvkG0/u4cpfvclfthzjQPsA14uhu7imIjeVZ25byYW1Vs6uyGJWgbBRhA2zGWbMmFLHGKAgI4mvra5mw6HOCVN/Ht/SRFl2CudXW0/euXYtFBbCkiVTOr4gvCQYDfzipvk43T7ufWrvlFJHtIzuhTEoAwal2ck88MLBkQ7TRDR22TEbDZRmJUe4OkGoVFktzMhNHXOSus/p5uO/f4c3G7r43w/N40urqmJQoSCSLK9Suo8PfXQhTreP+9fux2iQuGq+EMbxTkZKAn/6zFKe+OJycbIabqYQ2Taaz6ycwcw8C99/7sAZB/EOnxhk27FePr6sDENwUZLLBS+9BFdfDQYhOeKFmXlpfOuyWWw81Mk/3z0e63LiCvFbCiSajNx7WR2HTgzy7x2T+wVp7LRTkZsiLr/HKatn5bG1sQe76+QWvBP9w9z46Bb2tQ7w61sWc9PZZeM8gkDNGAwSV88v4tW7VvHf187he1fPJlfEKqoGIYojQFAY+0MfNAcwmwz89zVzaO518uimo2N+z+NbmzCbDHxkcenJO197Dex24S+OQz69ooKVM3P4n+cPTMo/rheEqgtwxbwCFpdn8bOX63G4Jl4pHIxqE8Qnq+vycfv8bA7YY97vdvCh37xNW98w//fZs7lsbkGMKxREA7PJwCeXV/CJ5RWxLkUgiC21teB0QlvblB9ixcxcrjyrkF+/foTjvafOcNhdXp7e2cJVZxWSlWo++YV16yA1FS66aMrHFUQGg0Hipx+ej9EgcdcTeyZ9xVzrCGEcQJIk7ruyjq5BF49uGj+WxuX10dzrFIN3ccySiizSk0y8erCTfa39fPg3bzPs8fGPzy9jRZVYBywQCHRGMJli795pPcx/XVmH0SDx38+fOoj37K5WHG4fn1g2atOdLCvC+NJLISlpWscVRIaizGT++9o57Giy8dsJtI9eEMJ4FIvKsrh6fhGPvXmU9v6hM37fsW4nflkM3sUzCUYDF9Tm8Z/9J7j5sa0kJRh58kvLmVeSEevSBAKBIPosWwYpKcog3DQozEjmqxdV88qBDl4LDDjLsszjW5uYU5R+6gKdnTuhtVWkUcQ51y0o5op5Bfy/V+vZ3zb5baJaRQjj0/jmpbX4Zfjpf84caxOMaqsSHeO4Zs3sfAaHvRRmJPHv25ZTKZ4vgUCgV1JSlAG4p54C78R2wfH43LkzqLSm8r3ARrztTTYOnRjkE8vKT/WHr12rDNxdeeU0ixdEEkmSeOC6eWSlmLnzX7tD2nKoRYQwPo3S7BQ+u3IGT+9sZW/L2GdOwai2SqvIRI1nLp9bwA+vn8cTX1xOYYZIDxEIBDrnIx+B7m7YtGlaD6MM4s2lqcfJ7944yuNbm0hLMnHN6ZGI69bBypWQK+xr8U5Wqpn//fBZ1HfYefDlqeVdawUhjMfg9guryEk184MXDoyZ79fYZac4M5kUsykG1QkmS4LRwMfOKTt1EEQgEAj0yuWXK4NwTz457Yc6tzqXK+YV8MjrR3hxbzsfWlRy6mfisWOwZ49Io1ARF9bmccs5Zfx+8/shbwPWEkIYj0F6UgJfv7iGd97v5eUDHwwzP9JpF/5igUAgEKiLlBS46qqw2CkA/uvK2UhIeHwyHx89dAfw3HPKrfAXq4r7rqyjPDuFbzyxh8FhT6zLiQlCGJ+Bj55dysw8Cz9efwi392Tuo98v09hlF4kUAoFAIFAfYbJTgJJo8D/XzeWLqyo/GF+6di3U1UF19bSPI4geKWYTP79pAe39Q3x/EivAtYgQxmfAZDRw3xV1vN/t4PGtTSP3t/UPMezxiwxjgUAgEKiPoJ3iiSfC8nAfXlzCty+vO/XOvj5FeItusSpZVJbF7RfM5N87Wnhxb3usy4k6QhiPwwW1Vs6rzuVXGxvodyqXFIKDd1Vi8E4gEAgEaiNop3j66bDYKcZk/XrlsYW/WLV8bXU180sy+No/dvHkdn2tjBbCeBwkSeI7V9TRP+ThoY0NADR2KWsTRcdYIBAIBKrkxhsVO8Xrr0fm8deuhbw8WLo0Mo8viDhmk4G/fO4cllXmcM+/3+PBlw+PGUagRYQwnoC6wnRuWlLKn7cc41i3gyOddjJTEsgWSQcCgUAgUCNhTKf4AG630jG++mowGsP/+IKokZGcwJ8+czY3LinhoY1HuPNfu3F5tZ9xLITxJLjrkhoSjAb+96VDI4N3p4SYCwQCgUCgFpKTFeEaCTvFpk0wMCD8xRohwWjgfz90FndfUsOzu9v4xB+20ed0x7qsiCKE8STIS0vitlVVrN93gt3NfWLjnUAgEAjUTTCdItx2inXrFOG9Zk14H1cQMyRJ4isXVfPLmxewu7mPG37zNk09jliXFTGEMJ4kt55XSUF6Em6fSKQQCAQCgcqJhJ1ClhV/8SWXKEN+Ak1x7YJiHr/1HHodbq7/9dvsaLLFuqSIIITxJEk2G/nmZbWA4jsWCAQCgUC1RMJOsWcPHD8ubBQaZumMbJ6+bQVpSSY+9rutmoxzE8I4BK5fWMzzXz2XlTNzYl2KQCAQCATTI9x2irVrQZKUODiBZqm0Wnjm9pXMLc7g9r/t5NFNjZpKrBDCOAQkSWJucYYYvBMIBAKB+gm3nWLdOli+XIlqE2ia7FQzf7v1HK46q5AfrT/Efc/uw+vzT/yDKkAIY4FAIBAI9Eg47RTHj8POnWKph45ISjDyq5sXctsFVfz9nWYefKU+1iWFBSGMBQKBQCDQK+Fa9vHcc8qt8BfrCoNB4luXzeKXNy/g8+dVxrqcsCCEsUAgEAgEeuWyy8BigSeemN7jrF0LNTUwa1Z46hKoimsXFGtm8VnEhLEkSZdJknRYkqQjkiTdG6njCAQCgUAgmCLJycqw3HTsFAMD8Nprolss0AQREcaSJBmBR4DLgdnARyVJmh2JYwkEAoFAIJgGN94IPT2KuJ0KL70EHo/wFws0QaQ6xkuBI7IsH5Vl2Q38ExCvGIFAIBAI4o2gnWKq6RTr1kFurpJIIRConEgJ42Lg+Kh/twTuG0GSpC9IkrRdkqTtXV1dESpDIBAIBALBuEwnncLjgRdeUOwYRmNk6hMIokikhPFYQb+npD/LsvyYLMtLZFleYrVaI1SGQCAQCASCCfnIR6Zmp3jzTejrE/5igWaIlDBuAUpH/bsEaIvQsQQCgUAgEEyHqdgpOjvhgQcgMREuuSRytQkEUSRSwvhdoFqSpBmSJJmBm4F1ETqWQCAQCASC6TDaTuHxTPz9Tz4Jc+bA5s3wi18oG/QEAg0QEWEsy7IX+ArwH+Ag8IQsy/sjcSyBQCAQCARhIGinGG/ZR1eXkmJx441QUaFsu7vttmhVKBBEnIjlGMuy/KIsyzWyLFfJsvxApI4jEAgEAoEgDEy07OPJJ2H2bGWZxw9/CFu2KF1jgUBDiM13AoFAIBAITtopnnnmVDtFVxfcdJPSJS4vhx074NvfBpMpdrUKBBFCCGOBQCAQCAQKwWUfQTvFU08pXeFnnlEG7bZuhblzY1qiQBBJxOmeQCAQCAQChUsvVewUjz0Gf/gD/OtfsHgxbNwoBLFAFwhhLBAIBAKBQCFop/jHPyAhAX7wA/jmN5W/CwQ6QAhjgUAgEAgEJ7n7bsVjfP/9MG9erKsRCKKKEMYCgeD/b+9+QywrCziOf386Zu2akaaxqTkraLCQ5rqYgaUUFRW1hUimkWAQgS/sD4VS73pTEhGxL0RI2agU+gMpVGJLaKVl7ra77bqOuiW2NLSYlavV6trTi/MMcxvmDl2bc8497fcDlznznHvOHua7Z++z9547V5IWbdw42Qd9SP9HfPOdJEmShBNjSZIkCXBiLEmSJAFOjCVJkiTAibEkSZIEODGWJEmSACfGkiRJEuDEWJIkSQKcGEuSJEmAE2NJkiQJcGIsSZIkAU6MJUmSJMCJsSRJkgQ4MZYkSZIAJ8aSJEkS4MRYkiRJApwYS5IkSYATY0mSJAmAlFL6PgaSHALm+j6OFrwC+FvfB9GCVwFP9n0QLbDXsNhrWOw1LPYaFntN5sxSyinLrZhp4Q97MeZKKZv6PojVluTmUsrH+j6O1ZbkQXsNh72GxV7DYq9hsdew9NHLSynadWffB6CJ2GtY7DUs9hoWew2LvVaJE+MWlVL8izog9hoWew2LvYbFXsNir9UzLRPjm/s+AE3EXsNir2Gx17DYa1jsNSyd95qKN99JkiRJfZuWZ4wlSZKkXrU2MU5yS5KDSfaMjJ2X5P4kv01yZ5IT6/hskn8k2VlvN41sc0G9/2NJvpYkbR3z0WySXnXduXXd3rr+pXXcXh2Y8Py6auTc2pnkX0neUNfZqwMT9jouydY6vi/JDSPb2KsDE/Z6SZJb6/iuJJeObGOvliU5I8lP67myN8l1dfykJHcnebR+feXINjfUJnNJ3jkybq+WTdorycn1/s8k2bJkX+30KqW0cgPeAmwE9oyM/Rq4pC5fA3yhLs+O3m/Jfh4A3gQE+BHwrraO+Wi+TdhrBtgNnFe/Pxk41l7T2WvJdq8Hfjfyvb2mrBdwJXB7XV4DPA7M2mtqe10L3FqXTwW2A8fYq7NW64CNdfnlwCPABuBG4Po6fj3wpbq8AdgFHA+sB/b7+DXVvdYCFwMfB7Ys2VcrvVp7xriUci/w1JLh1wH31uW7gctW2keSdcCJpZT7S/NT+Abw/tU+Vk3c6x3A7lLKrrrtn0spL9irO//D+fUh4Dbw/OrShL0KsDbJDPAy4DngaXt1Z8JeG4BtdbuDwF+BTfbqRillvpSyoy4fAvYBpwGbga31bltZ/NlvpvmP5+FSyu+Bx4AL7dWNSXuVUp4tpfwc+Ofoftrs1fU1xnuA99Xly4EzRtatT/KbJPckeXMdOw04MHKfA3VM3RjX6xygJLkryY4kn63j9urXSufXgg9SJ8bYq2/jen0XeBaYB54AvlxKeQp79W1cr13A5iQzSdYDF9R19upYklngfOBXwKtLKfPQTMZons2HpsEfRjZb6GKvjv2XvcZprVfXE+NrgGuTbKd5Cv25Oj4PvLaUcj7wKeDb9fqt5a4X8ddodGdcrxmalzauql8/kORt2Ktv43oBkOSNwN9LKQvXTdqrX+N6XQi8ALyG5qXeTyc5C3v1bVyvW2gelB8EvgrcBxzBXp1KcgLwPeATpZSnV7rrMmNlhXG1YIJeY3exzNiq9Or0I6FLKQ/TvAxPknOA99Txw8Dhurw9yX6aZyUPAKeP7OJ04I9dHvPRbFwvmi73lFKerOt+SHM93jexV29W6LXgChafLQbPr16t0OtK4MellOeBg0l+AWwCfoa9erPC49cR4JML90tyH/Ao8Bfs1Ykkx9FMsr5VSvl+Hf5TknWllPn6svvBOn6A/3w1baGL/x52ZMJe47TWq9NnjJOcWr8eA3weuKl+f0qSY+vyWcDZNG8QmgcOJbmovtvwI8APujzmo9m4XsBdwLlJ1tTrIC8BHrJXv1botTB2OXD7wpi9+rVCryeAt6axFrgIeNhe/Vrh8WtN7USStwNHSin+e9iR+rP9OrCvlPKVkVV3AFfX5atZ/NnfAVyR5Ph66cvZwAP26saL6LWsVnu1+M7D22gukXieZmb/UeA6mncgPgJ8kcUPGLkM2EtzrdYO4L0j+9lEc23XfmDLwjbe+utV7//h2mwPcKO9pr7XpcAvl9mPvaasF3AC8J16fj0EfMZeU91rFpijeRPRT4Az7dVpq4tpXkLfDeyst3fT/LakbTTP3m8DThrZ5nO1yRwjv8nAXlPb63GaN8M+U8/HDW328pPvJEmSJPzkO0mSJAlwYixJkiQBTowlSZIkwImxJEmSBDgxliRJkgAnxpIkSRLgxFiSJEkCnBhLkiRJAPwbgZRZEZ/Dvn0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"dta.loc['1950':].plot(ax=ax)\n",
"predict_sunspots.plot(ax=ax, style='r');"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def mean_forecast_err(y, yhat):\n",
" return y.sub(yhat).mean()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.636033253711686"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| doc/tutorial/aesthetics.ipynb |
|
| {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _aesthetics_tutorial:\n",
"\n",
".. currentmodule:: seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Controlling figure aesthetics"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Drawing attractive figures is important. When making figures for yourself, as you explore a dataset, it's nice to have plots that are pleasant to look at. Visualizations are also central to communicating quantitative insights to an audience, and in that setting it's even more necessary to have figures that catch the attention and draw a viewer in.\n",
"\n",
"Matplotlib is highly customizable, but it can be hard to know what settings to tweak to achieve an attractive plot. Seaborn comes with a number of customized themes and a high-level interface for controlling the look of matplotlib figures."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"np.random.seed(sum(map(ord, \"aesthetics\")))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Let's define a simple function to plot some offset sine waves, which will help us see the different stylistic parameters we can tweak."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sinplot(flip=1):\n",
" x = np.linspace(0, 14, 100)\n",
" for i in range(1, 7):\n",
" plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"This is what the plot looks like with matplotlib defaults:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"To switch to seaborn defaults, simply import the package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The seaborn defaults break from the MATLAB inspired aesthetic of matplotlib to plot in more muted colors over a light gray background with white grid lines. We find that the grid aids in the use of figures for conveying quantitative information – in almost all cases, figures should be preferred to tables. The white-on-gray grid that is used by default avoids being obtrusive. The grid is particularly useful in giving structure to figures with multiple facets, which is central to some of the more complex tools in the library.\n",
"\n",
"Seaborn splits matplotlib parameters into two independent groups. The first group sets the aesthetic style of the plot, and the second scales various elements of the figure so that it can be easily incorporated into different contexts.\n",
"\n",
"The interface for manipulating these parameters are two pairs of functions. To control the style, use the :func:`axes_style` and :func:`set_style` functions. To scale the plot, use the :func:`plotting_context` and :func:`set_context` functions. In both cases, the first function returns a dictionary of parameters and the second sets the matplotlib defaults.\n",
"\n",
".. _axes_style:\n",
"\n",
"Styling figures with :func:`axes_style` and :func:`set_style`\n",
"-------------------------------------------------------------\n",
"\n",
"There are five preset seaborn themes: ``darkgrid``, ``whitegrid``, ``dark``, ``white``, and ``ticks``. They are each suited to different applications and personal preferences. The default theme is ``darkgrid``. As mentioned above, the grid helps the plot serve as a lookup table for quantitative information, and the white-on grey helps to keep the grid from competing with lines that represent data. The ``whitegrid`` theme is similar, but it is better suited to plots with heavy data elements:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"whitegrid\")\n",
"data = np.random.normal(size=(20, 6)) + np.arange(6) / 2\n",
"sns.boxplot(data=data);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"For many plots, (especially for settings like talks, where you primarily want to use figures to provide impressions of patterns in the data), the grid is less necessary."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"dark\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sinplot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"white\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Sometimes you might want to give a little extra structure to the plots, which is where ticks come in handy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"ticks\")\n",
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _remove_spines:\n",
"\n",
"Removing spines with :func:`despine`\n",
"------------------------------------\n",
"\n",
"Both the ``white`` and ``ticks`` styles can benefit from removing the top and right axes spines, which are not needed. It's impossible to do this through the matplotlib parameters, but you can call the seaborn function :func:`despine` to remove them:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sinplot()\n",
"sns.despine()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Some plots benefit from offsetting the spines away from the data, which can also be done when calling :func:`despine`. When the ticks don't cover the whole range of the axis, the ``trim`` parameter will limit the range of the surviving spines."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f, ax = plt.subplots()\n",
"sns.violinplot(data)\n",
"sns.despine(offset=10, trim=True);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You can also control which spines are removed with additional arguments to :func:`despine`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"whitegrid\")\n",
"sns.boxplot(data=data, color=\"deep\")\n",
"sns.despine(left=True)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Temporarily setting figure style\n",
"--------------------------------\n",
"\n",
"Although it's easy to switch back and forth, you can also use the :func:`axes_style` function in a ``with`` statement to temporarily set plot parameters. This also allows you to make figures with differently-styled axes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with sns.axes_style(\"darkgrid\"):\n",
" plt.subplot(211)\n",
" sinplot()\n",
"plt.subplot(212)\n",
"sinplot(-1)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Overriding elements of the seaborn styles\n",
"-----------------------------------------\n",
"\n",
"If you want to customize the seaborn styles, you can pass a dictionary of parameters to the ``rc`` argument of :func:`axes_style` and :func:`set_style`. Note that you can only override the parameters that are part of the style definition through this method. (However, the higher-level :func:`set` function takes a dictionary of any matplotlib parameters).\n",
"\n",
"If you want to see what parameters are included, you can just call the function with no arguments, which will return the current settings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.axes_style()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You can then set different versions of these parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_style(\"darkgrid\", {\"grid.linewidth\": .5, \"axes.facecolor\": \".9\"})\n",
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _plotting_context:\n",
"\n",
"Scaling plot elements with :func:`plotting_context` and :func:`set_context`\n",
"---------------------------------------------------------------------------\n",
"\n",
"A separate set of parameters control the scale of plot elements, which should let you use the same code to make plots that are suited for use in settings where larger or smaller plots are appropriate.\n",
"\n",
"First let's reset the default parameters by calling :func:`set`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The four preset contexts, in order of relative size, are ``paper``, ``notebook``, ``talk``, and ``poster``. The ``notebook`` style is the default, and was used in the plots above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_context(\"paper\")\n",
"plt.figure(figsize=(8, 6))\n",
"sinplot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_context(\"talk\")\n",
"plt.figure(figsize=(8, 6))\n",
"sinplot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_context(\"poster\")\n",
"plt.figure(figsize=(8, 6))\n",
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Most of what you now know about the style functions should transfer to the context functions.\n",
"\n",
"You can call :func:`set_context` with one of these names to set the parameters, and you can override the parameters by providing a dictionary of parameter values.\n",
"\n",
"You can also independently scale the size of the font elements when changing the context. (This option is also available through the top-level :func:`set` function)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
"sinplot()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Similarly (although it might be less useful), you can temporarily control the scale of figures nested under a ``with`` statement.\n",
"\n",
"Both the style and the context can be quickly configured with the :func:`set` function. This function also sets the default color palette, but that will be covered in more detail in the :ref:`next section <palette_tutorial>` of the tutorial."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| image-classification/dlnd_image_classification.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Image Classification\n",
"In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.\n",
"## Get the Data\n",
"Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"CIFAR-10 Dataset: 171MB [00:14, 12.1MB/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All files found!\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"from urllib.request import urlretrieve\n",
"from os.path import isfile, isdir\n",
"from tqdm import tqdm\n",
"import problem_unittests as tests\n",
"import tarfile\n",
"\n",
"cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
"\n",
"# Use Floyd's cifar-10 dataset if present\n",
"floyd_cifar10_location = '/input/cifar-10-python.tar.gz'\n",
"if isfile(floyd_cifar10_location):\n",
" tar_gz_path = floyd_cifar10_location\n",
"else:\n",
" tar_gz_path = 'cifar-10-python.tar.gz'\n",
"\n",
"class DLProgress(tqdm):\n",
" last_block = 0\n",
"\n",
" def hook(self, block_num=1, block_size=1, total_size=None):\n",
" self.total = total_size\n",
" self.update((block_num - self.last_block) * block_size)\n",
" self.last_block = block_num\n",
"\n",
"if not isfile(tar_gz_path):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
" urlretrieve(\n",
" 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
" tar_gz_path,\n",
" pbar.hook)\n",
"\n",
"if not isdir(cifar10_dataset_folder_path):\n",
" with tarfile.open(tar_gz_path) as tar:\n",
" tar.extractall()\n",
" tar.close()\n",
"\n",
"\n",
"tests.test_folder_path(cifar10_dataset_folder_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data\n",
"The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n",
"* airplane\n",
"* automobile\n",
"* bird\n",
"* cat\n",
"* deer\n",
"* dog\n",
"* frog\n",
"* horse\n",
"* ship\n",
"* truck\n",
"\n",
"Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n",
"\n",
"Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\". Answers to questions like these will help you preprocess the data and end up with better predictions."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Stats of batch 1:\n",
"Samples: 10000\n",
"Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
"First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
"\n",
"Example of Image 2:\n",
"Image - Min Value: 20 Max Value: 255\n",
"Image - Shape: (32, 32, 3)\n",
"Label - Label Id: 9 Name: truck\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCXoAKEzQA0Bhgh4AChP0AFDY/wb1gCgl/MrBSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f373887a8d0>"
]
},
"metadata": {
"image/png": {
"height": 250,
"width": 253
}
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import helper\n",
"import numpy as np\n",
"\n",
"# Explore the dataset\n",
"batch_id = 1\n",
"sample_id = 2\n",
"helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement Preprocess Functions\n",
"### Normalize\n",
"In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def normalize(x):\n",
" \"\"\"\n",
" Normalize a list of sample image data in the range of 0 to 1\n",
" : x: List of image data. The image shape is (32, 32, 3)\n",
" : return: Numpy array of normalize data\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return x / 255.0\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_normalize(normalize)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One-hot encode\n",
"Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.\n",
"\n",
"Hint: Don't reinvent the wheel."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def one_hot_encode(x):\n",
" \"\"\"\n",
" One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
" : x: List of sample Labels\n",
" : return: Numpy array of one-hot encoded labels\n",
" \"\"\"\n",
" x = np.asarray(x)\n",
" result = np.zeros((x.shape[0], 10))\n",
" result[np.arange(x.shape[0]), x] = 1\n",
" return result\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_one_hot_encode(one_hot_encode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Randomize Data\n",
"As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess all the data and save it\n",
"Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"# Preprocess Training, Validation, and Testing Data\n",
"helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Point\n",
"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import pickle\n",
"import problem_unittests as tests\n",
"import helper\n",
"\n",
"# Load the Preprocessed Validation data\n",
"valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the network\n",
"For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n",
"\n",
">**Note:** If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages to build each layer, except the layers you build in the \"Convolutional and Max Pooling Layer\" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n",
"\n",
">However, if you would like to get the most out of this course, try to solve all the problems _without_ using anything from the TF Layers packages. You **can** still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the `conv2d` class, [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d), you would want to use the TF Neural Network version of `conv2d`, [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d). \n",
"\n",
"Let's begin!\n",
"\n",
"### Input\n",
"The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n",
"* Implement `neural_net_image_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
" * Set the shape using `image_shape` with batch size set to `None`.\n",
" * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"* Implement `neural_net_label_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
" * Set the shape using `n_classes` with batch size set to `None`.\n",
" * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"* Implement `neural_net_keep_prob_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n",
" * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"\n",
"These names will be used at the end of the project to load your saved model.\n",
"\n",
"Note: `None` for shapes in TensorFlow allow for a dynamic size."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image Input Tests Passed.\n",
"Label Input Tests Passed.\n",
"Keep Prob Tests Passed.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"def neural_net_image_input(image_shape):\n",
" \"\"\"\n",
" Return a Tensor for a batch of image input\n",
" : image_shape: Shape of the images\n",
" : return: Tensor for image input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return tf.placeholder(tf.float32, shape=(None, ) + image_shape, name=\"x\")\n",
"\n",
"\n",
"def neural_net_label_input(n_classes):\n",
" \"\"\"\n",
" Return a Tensor for a batch of label input\n",
" : n_classes: Number of classes\n",
" : return: Tensor for label input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return tf.placeholder(tf.uint8, shape=(None, n_classes), name=\"y\")\n",
"\n",
"\n",
"def neural_net_keep_prob_input():\n",
" \"\"\"\n",
" Return a Tensor for keep probability\n",
" : return: Tensor for keep probability.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return tf.placeholder(tf.float32, name=\"keep_prob\")\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tf.reset_default_graph()\n",
"tests.test_nn_image_inputs(neural_net_image_input)\n",
"tests.test_nn_label_inputs(neural_net_label_input)\n",
"tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convolution and Max Pooling Layer\n",
"Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n",
"* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n",
"* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n",
" * We recommend you use same padding, but you're welcome to use any padding.\n",
"* Add bias\n",
"* Add a nonlinear activation to the convolution.\n",
"* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n",
" * We recommend you use same padding, but you're welcome to use any padding.\n",
"\n",
"**Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
" \"\"\"\n",
" Apply convolution then max pooling to x_tensor\n",
" :param x_tensor: TensorFlow Tensor\n",
" :param conv_num_outputs: Number of outputs for the convolutional layer\n",
" :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
" :param conv_strides: Stride 2-D Tuple for convolution\n",
" :param pool_ksize: kernal size 2-D Tuple for pool\n",
" :param pool_strides: Stride 2-D Tuple for pool\n",
" : return: A tensor that represents convolution and max pooling of x_tensor\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" input_depth = x_tensor.get_shape().as_list()[-1]\n",
"\n",
" conv_strides = (1,) + conv_strides + (1, )\n",
" pool_ksize = (1,) + pool_ksize + (1, )\n",
" pool_strides = (1,) + pool_strides + (1, )\n",
"\n",
" \n",
" weights = tf.Variable(tf.random_normal(list(conv_ksize) + [input_depth, conv_num_outputs]))\n",
" bias = tf.Variable(tf.zeros([conv_num_outputs])) \n",
" x = tf.nn.conv2d(x_tensor, weights, conv_strides, 'SAME')\n",
" x = tf.nn.bias_add(x, bias)\n",
" x = tf.nn.relu(x) \n",
" x = tf.nn.max_pool(x, pool_ksize, pool_strides, 'SAME')\n",
" \n",
" return x\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_con_pool(conv2d_maxpool)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Flatten Layer\n",
"Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"from tensorflow.contrib.layers.python import layers\n",
"\n",
"def flatten(x_tensor):\n",
" \"\"\"\n",
" Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
" : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
" : return: A tensor of size (Batch Size, Flattened Image Size).\n",
" \"\"\"\n",
" return layers.flatten(x_tensor)\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_flatten(flatten)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully-Connected Layer\n",
"Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"from tensorflow.contrib.layers.python import layers\n",
"\n",
"def fully_conn(x_tensor, num_outputs):\n",
" \"\"\"\n",
" Apply a fully connected layer to x_tensor using weight and bias\n",
" : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
" : num_outputs: The number of output that the new tensor should be.\n",
" : return: A 2-D tensor where the second dimension is num_outputs.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" \n",
" x = layers.fully_connected(x_tensor, num_outputs)\n",
" return x\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_fully_conn(fully_conn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Output Layer\n",
"Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.\n",
"\n",
"**Note:** Activation, softmax, or cross entropy should **not** be applied to this."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def output(x_tensor, num_outputs):\n",
" \"\"\"\n",
" Apply a output layer to x_tensor using weight and bias\n",
" : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
" : num_outputs: The number of output that the new tensor should be.\n",
" : return: A 2-D tensor where the second dimension is num_outputs.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" return layers.fully_connected(x_tensor, num_outputs, activation_fn=None)\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_output(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Convolutional Model\n",
"Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:\n",
"\n",
"* Apply 1, 2, or 3 Convolution and Max Pool layers\n",
"* Apply a Flatten Layer\n",
"* Apply 1, 2, or 3 Fully Connected Layers\n",
"* Apply an Output Layer\n",
"* Return the output\n",
"* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Neural Network Built!\n"
]
}
],
"source": [
"def conv_net(x, keep_prob):\n",
" \"\"\"\n",
" Create a convolutional neural network model\n",
" : x: Placeholder tensor that holds image data.\n",
" : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
" : return: Tensor that represents logits\n",
" \"\"\"\n",
" # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
" # Play around with different number of outputs, kernel size and stride\n",
" # Function Definition from Above:\n",
" # conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
" conv_ksize = (2, 2)\n",
" conv_strides = (2, 2)\n",
" pool_ksize = (2, 2)\n",
" pool_strides = (2, 2) \n",
" conv_output = 32\n",
" x = conv2d_maxpool(x, conv_output, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
"# x = conv2d_maxpool(x, conv_output, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
"# x = conv2d_maxpool(x, conv_output, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
"\n",
" # TODO: Apply a Flatten Layer\n",
" # Function Definition from Above:\n",
" # flatten(x_tensor)\n",
" x = flatten(x)\n",
" \n",
"\n",
" # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
" # Play around with different number of outputs\n",
" # Function Definition from Above:\n",
" # fully_conn(x_tensor, num_outputs)\n",
" x = fully_conn(x, 4096)\n",
"# x = tf.nn.relu(x)\n",
" x = tf.nn.dropout(x, keep_prob)\n",
"\n",
" # TODO: Apply an Output Layer\n",
" # Set this to the number of classes\n",
" # Function Definition from Above:\n",
" # output(x_tensor, num_outputs)\n",
" num_outputs = 10\n",
" x = output(x, num_outputs)\n",
" return x\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"\n",
"##############################\n",
"## Build the Neural Network ##\n",
"##############################\n",
"\n",
"# Remove previous weights, bias, inputs, etc..\n",
"tf.reset_default_graph()\n",
"\n",
"# Inputs\n",
"x = neural_net_image_input((32, 32, 3))\n",
"y = neural_net_label_input(10)\n",
"keep_prob = neural_net_keep_prob_input()\n",
"\n",
"# Model\n",
"logits = conv_net(x, keep_prob)\n",
"\n",
"# Name logits Tensor, so that is can be loaded from disk after training\n",
"logits = tf.identity(logits, name='logits')\n",
"\n",
"# Loss and Optimizer\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
"optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
"\n",
"# Accuracy\n",
"correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
"\n",
"tests.test_conv_net(conv_net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Neural Network\n",
"### Single Optimization\n",
"Implement the function `train_neural_network` to do a single optimization. The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n",
"* `x` for image input\n",
"* `y` for labels\n",
"* `keep_prob` for keep probability for dropout\n",
"\n",
"This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n",
"\n",
"Note: Nothing needs to be returned. This function is only optimizing the neural network."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
" \"\"\"\n",
" Optimize the session on a batch of images and labels\n",
" : session: Current TensorFlow session\n",
" : optimizer: TensorFlow optimizer function\n",
" : keep_probability: keep probability\n",
" : feature_batch: Batch of Numpy image data\n",
" : label_batch: Batch of Numpy label data\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability}) \n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_train_nn(train_neural_network)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Stats\n",
"Implement the function `print_stats` to print loss and validation accuracy. Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy. Use a keep probability of `1.0` to calculate the loss and validation accuracy."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
" \"\"\"\n",
" Print information about loss and validation accuracy\n",
" : session: Current TensorFlow session\n",
" : feature_batch: Batch of Numpy image data\n",
" : label_batch: Batch of Numpy label data\n",
" : cost: TensorFlow cost function\n",
" : accuracy: TensorFlow accuracy function\n",
" \"\"\"\n",
" cost_val = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\n",
" accuracy_val = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\n",
" print('Cost: %f, Accuracy: %.2f%%' % (cost_val, accuracy_val * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters\n",
"Tune the following parameters:\n",
"* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n",
"* Set `batch_size` to the highest number that your machine has memory for. Most people set them to common sizes of memory:\n",
" * 64\n",
" * 128\n",
" * 256\n",
" * ...\n",
"* Set `keep_probability` to the probability of keeping a node using dropout"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO: Tune Parameters\n",
"epochs = 10\n",
"batch_size = 256\n",
"keep_probability = 0.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train on a Single CIFAR-10 Batch\n",
"Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking the Training on a Single Batch...\n",
"Epoch 1, CIFAR-10 Batch 1: Cost: 2.092699, Accuracy: 26.54%\n",
"Epoch 2, CIFAR-10 Batch 1: Cost: 1.954140, Accuracy: 34.86%\n",
"Epoch 3, CIFAR-10 Batch 1: Cost: 1.761526, Accuracy: 39.66%\n",
"Epoch 4, CIFAR-10 Batch 1: Cost: 1.601536, Accuracy: 43.00%\n",
"Epoch 5, CIFAR-10 Batch 1: Cost: 1.469287, Accuracy: 44.72%\n",
"Epoch 6, CIFAR-10 Batch 1: Cost: 1.329807, Accuracy: 47.28%\n",
"Epoch 7, CIFAR-10 Batch 1: Cost: 1.180224, Accuracy: 49.04%\n",
"Epoch 8, CIFAR-10 Batch 1: Cost: 1.095533, Accuracy: 49.24%\n",
"Epoch 9, CIFAR-10 Batch 1: Cost: 1.039158, Accuracy: 50.98%\n",
"Epoch 10, CIFAR-10 Batch 1: Cost: 0.965202, Accuracy: 50.96%\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"print('Checking the Training on a Single Batch...')\n",
"with tf.Session() as sess:\n",
" # Initializing the variables\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # Training cycle\n",
" for epoch in range(epochs):\n",
" batch_i = 1\n",
" for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
" train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
" print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n",
" print_stats(sess, batch_features, batch_labels, cost, accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully Train the Model\n",
"Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training...\n",
"Epoch 1, CIFAR-10 Batch 5: Cost: 1.427273, Accuracy: 48.40%\n",
"Epoch 2, CIFAR-10 Batch 5: Cost: 1.156284, Accuracy: 53.38%\n",
"Epoch 3, CIFAR-10 Batch 5: Cost: 1.069375, Accuracy: 55.12%\n",
"Epoch 4, CIFAR-10 Batch 5: Cost: 0.959119, Accuracy: 57.22%\n",
"Epoch 5, CIFAR-10 Batch 5: Cost: 0.879487, Accuracy: 58.14%\n",
"Epoch 6, CIFAR-10 Batch 5: Cost: 0.779166, Accuracy: 58.98%\n",
"Epoch 7, CIFAR-10 Batch 5: Cost: 0.744247, Accuracy: 59.58%\n",
"Epoch 8, CIFAR-10 Batch 5: Cost: 0.662269, Accuracy: 60.22%\n",
"Epoch 9, CIFAR-10 Batch 5: Cost: 0.609130, Accuracy: 60.60%\n",
"Epoch 10, CIFAR-10 Batch 5: Cost: 0.518126, Accuracy: 60.84%\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"save_model_path = './image_classification'\n",
"\n",
"print('Training...')\n",
"with tf.Session() as sess:\n",
" # Initializing the variables\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # Training cycle\n",
" for epoch in range(epochs):\n",
" # Loop over all batches\n",
" n_batches = 5\n",
" for batch_i in range(1, n_batches + 1):\n",
" for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
" train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
" print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n",
" print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
" \n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" save_path = saver.save(sess, save_model_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint\n",
"The model has been saved to disk.\n",
"## Test Model\n",
"Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from ./image_classification\n",
"Testing Accuracy: 0.6142578125\n",
"\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAJ/CAYAAACUb342AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKDgWEREREQn+P6eS/MWA7boMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3733ac5cf8>"
]
},
"metadata": {
"image/png": {
"height": 319,
"width": 355
}
},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import tensorflow as tf\n",
"import pickle\n",
"import helper\n",
"import random\n",
"\n",
"# Set batch size if not already set\n",
"try:\n",
" if batch_size:\n",
" pass\n",
"except NameError:\n",
" batch_size = 64\n",
"\n",
"save_model_path = './image_classification'\n",
"n_samples = 4\n",
"top_n_predictions = 3\n",
"\n",
"def test_model():\n",
" \"\"\"\n",
" Test the saved model against the test dataset\n",
" \"\"\"\n",
"\n",
" test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
" loaded_graph = tf.Graph()\n",
"\n",
" with tf.Session(graph=loaded_graph) as sess:\n",
" # Load model\n",
" loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
" loader.restore(sess, save_model_path)\n",
"\n",
" # Get Tensors from loaded model\n",
" loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
" loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
" loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
" loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
" loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
" \n",
" # Get accuracy in batches for memory limitations\n",
" test_batch_acc_total = 0\n",
" test_batch_count = 0\n",
" \n",
" for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
" test_batch_acc_total += sess.run(\n",
" loaded_acc,\n",
" feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
" test_batch_count += 1\n",
"\n",
" print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
"\n",
" # Print Random Samples\n",
" random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
" random_test_predictions = sess.run(\n",
" tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
" feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
" helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
"\n",
"\n",
"test_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Why 50-80% Accuracy?\n",
"You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130). That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n",
"## Submitting This Project\n",
"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| notebooks/dataset_degree_distribution.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"from almc.bayesian_rescal import PFBayesianRescal\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def load_dataset(dataset):\n",
"\n",
" if dataset == 'umls':\n",
" mat = loadmat('../data/%s/uml.mat' % (dataset))\n",
" T = np.array(mat['Rs'], np.float32)\n",
" elif dataset == 'nation':\n",
" mat = loadmat('../data/%s/dnations.mat' % (dataset))\n",
" T = np.array(mat['R'], np.float32)\n",
" elif dataset == 'kinship':\n",
" mat = loadmat('../data/%s/alyawarradata.mat' % (dataset))\n",
" T = np.array(mat['Rs'], np.float32)\n",
" elif dataset == 'wordnet':\n",
" T = pickle.load(open('../data/%s/reduced_wordnet.pkl' % (dataset), 'rb'))\n",
"\n",
" if dataset != 'wordnet':\n",
" T = np.swapaxes(T, 1, 2)\n",
" T = np.swapaxes(T, 0, 1) # [relation, entity, entity]\n",
" T[np.isnan(T)] = 0\n",
" return T"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataset = 'umls'\n",
"T = load_dataset(dataset)\n",
"n_relation, n_entity, _ = T.shape"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"49"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_relation"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"135"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_entity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of triples per relation"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 49 artists>"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16a60e128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_relation), sorted(np.sum(np.sum(T,1),1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Outgoing degree distribution"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 135 artists>"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRUz6ktQiJn1JapH/D2d3ORZZacDxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16bea2ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_entity), sorted(np.sum(np.sum(T,0),1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Incoming degree distribution"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 135 artists>"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16a6b7358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_entity), sorted(np.sum(np.sum(T,0),0)))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"T = pickle.load(open('../data/wordnet/wordnet_csr.pkl','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_relation = len(T)\n",
"n_entity = T[0].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 11 artists>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEACAYAAABYq7oeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npBbDQZLUYjhIkloMB0lSy/8HK12OJ1P5mo4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115a24f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_relation), sorted([R.nnz for R in T]))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sumT = T[0].copy()\n",
"for k in range(1, n_relation):\n",
" sumT += T[k]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 38696 artists>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEhRJREFUeJzt3X+s3XV9x/HnCyoIOruqa7u0KBgYlsUMmZQlzHkyXBVN\nCtkyhokORLNkzGE252hNll7/UTBZHMnGks0f6RyuqS5K3RQKqyfLlkF1lB+jpbuLK9aOXpi/FqYz\nFN7743xaTy+X3lN6zj3n0ucjac73fO7n+/2+7+ee+33dz/f7PaepKiRJOmXcBUiSJoOBIEkCDARJ\nUmMgSJIAA0GS1BgIkiRgwEBIsjTJ55LsSfJwkkuSLEuyPcneJHcmWdrXf2OS6dZ/3ejKlyQNy6Az\nhFuAL1fVGuDngEeADcDdVXU+sAPYCJDkAuAqYA1wOXBrkgy7cEnScM0bCEleBryxqj4NUFWHqur7\nwBXA5tZtM3BlW14PbGn99gHTwNphFy5JGq5BZgjnAP+d5NNJ7kvyF0nOBFZU1QxAVR0Elrf+q4D9\nfesfaG2SpAk2SCAsAS4C/qyqLgL+l97potmfeeFnYEjSIrZkgD7fAvZX1dfb87+lFwgzSVZU1UyS\nlcDj7esHgLP61l/d2o6SxACRpOehqkZyXXbeGUI7LbQ/yc+0psuAh4FtwLWt7Rrg9ra8Dbg6yWlJ\nzgHOBXY+x7Yn/t+mTZvGXoN1WudirnMx1LiY6hylQWYIADcAtyV5EfAN4N3AqcDWJNcBj9K7s4iq\n2p1kK7AbeAq4vkb9XUiSTthAgVBVDwAXz/GlNz9H/48CHz2BuiRJC8x3Ks+j0+mMu4SBWOdwWefw\nLIYaYfHUOUoZ19mcJJ5JkqTjlIQa10VlSdLJwUCQJAEGgiSpMRAkSYCBIElqDARJGrOpqalxlwB4\n26kkjV27lfR4+nrbqSRpdAwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAg\nSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkpqBAiHJviQPJNmVZGdrW5Zke5K9\nSe5MsrSv/8Yk00n2JFk3quIlScMz6AzhGaBTVa+vqrWtbQNwd1WdD+wANgIkuQC4ClgDXA7cmmQk\n/yG0JGl4Bg2EzNH3CmBzW94MXNmW1wNbqupQVe0DpoG1SJIm2qCBUMBdSb6W5L2tbUVVzQBU1UFg\neWtfBezvW/dAa5MkTbAlA/a7tKoeS/JTwPYke+mFRL/ZzyVJi8hAgVBVj7XHJ5J8kd4poJkkK6pq\nJslK4PHW/QBwVt/qq1vbs0xNTR1Z7nQ6dDqd461fkl7Qut0u3W53QfaVqmP/YZ/kTOCUqnoyyUuA\n7cCHgcuA71TVzUluBJZV1YZ2Ufk24BJ6p4ruAs6rWTtKMrtJkk5KSRj0eNj6juRGnUFmCCuALySp\n1v+2qtqe5OvA1iTXAY/Su7OIqtqdZCuwG3gKuN4jvyRNvnlnCCPbsTMESQImZ4bgO5UlSYCBIElq\nDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQB\nBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIa\nA0GSBBxHICQ5Jcl9Sba158uSbE+yN8mdSZb29d2YZDrJniTrRlG4JGm4jmeG8H5gd9/zDcDdVXU+\nsAPYCJDkAuAqYA1wOXBrkgynXEnSqAwUCElWA28DPtHXfAWwuS1vBq5sy+uBLVV1qKr2AdPA2qFU\nK0kamUFnCB8HPghUX9uKqpoBqKqDwPLWvgrY39fvQGuTJE2wJfN1SPJ2YKaq7k/SOUbXOsbX5jQ1\nNXVkudPp0Okca/OSdPLpdrt0u90F2Veqjn0cT/IR4J3AIeAM4CeALwBvADpVNZNkJfDVqlqTZANQ\nVXVzW/8OYFNV3TtruzXfviXpZJCEQY+Hre9IrsvOe8qoqj5UVa+qqtcAVwM7qupdwJeAa1u3a4Db\n2/I24OokpyU5BzgX2Dn0yiVJQzXvKaNjuAnYmuQ64FF6dxZRVbuTbKV3R9JTwPVOBSRp8s17ymhk\nO/aUkSQBi+iUkSTp5GAgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLU\nGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkC\nDARJUmMgSJIAA0GS1BgIkiRggEBIcnqSe5PsSvJwko+09mVJtifZm+TOJEv71tmYZDrJniTrRvkN\nSJKGI1U1f6fkzKr6QZJTgX8GPgCsB75dVR9LciOwrKo2JLkAuA24GFgN3A2cV7N2lGR2kySdlJIw\n6PGw9c0o6hjolFFV/aAtnt7W+S5wBbC5tW8GrmzL64EtVXWoqvYB08DaYRUsSRqNgQIhySlJdgEH\ngW5V7QZWVNUMQFUdBJa37quA/X2rH2htkqQJtmSQTlX1DPD6JC8D7kzSAWbPb477/M/U1NSR5U6n\nQ6fTOd5NSNILWrfbpdvtLsi+BrqGcNQKyR8BPwTeA3SqaibJSuCrVbUmyQagqurm1v8OYFNV3Ttr\nO15DkCQW0TWEJK88fAdRkjOAXwF2AduAa1u3a4Db2/I24OokpyU5BzgX2DnkuiVJQzbIKaOfBjYn\nCb0A+UxV/UO7prA1yXXAo8BVAFW1O8lWYDfwFHC9UwFJmnzHfcpoaDv2lJEkAYvolJEk6eRgIEiS\nAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJ\njYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIk\nYIBASLI6yY4kDyd5KMkNrX1Zku1J9ia5M8nSvnU2JplOsifJulF+A5Kk4UhVHbtDshJYWVX3J3kp\n8K/AFcC7gW9X1ceS3Agsq6oNSS4AbgMuBlYDdwPn1awdJZndJEknpSQMejxsfTOKOuadIVTVwaq6\nvy0/Ceyhd6C/Atjcum0GrmzL64EtVXWoqvYB08DaIdctSRqy47qGkORs4ELgHmBFVc1ALzSA5a3b\nKmB/32oHWpskaYItGbRjO130eeD9VfVkktnzm+M+/zM1NXVkudPp0Ol0jncTkvSC1u126Xa7C7Kv\nea8hACRZAvwd8JWquqW17QE6VTXTrjN8tarWJNkAVFXd3PrdAWyqqntnbdNrCJLEIrqG0HwK2H04\nDJptwLVt+Rrg9r72q5OcluQc4Fxg5xBqlSSN0CB3GV0K/CPwEL3TQgV8iN5BfitwFvAocFVVfa+t\nsxF4D/AUvVNM2+fYrjMESWJyZggDnTIayY4NBEkCJicQfKeyJAkwECRJjYEgSQIMBElSYyBIkgAD\nQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2B\nIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSM28gJPlkkpkkD/a1LUuy\nPcneJHcmWdr3tY1JppPsSbJuVIVLkoZrkBnCp4G3zGrbANxdVecDO4CNAEkuAK4C1gCXA7cmyfDK\nlSSNyryBUFX/BHx3VvMVwOa2vBm4si2vB7ZU1aGq2gdMA2uHU6okaZSe7zWE5VU1A1BVB4HlrX0V\nsL+v34HWJkmacMO6qFxD2o4kaUyWPM/1ZpKsqKqZJCuBx1v7AeCsvn6rW9ucpqamjix3Oh06nc7z\nLEeSXpi63S7dbndB9pWq+f+4T3I28KWqel17fjPwnaq6OcmNwLKq2tAuKt8GXELvVNFdwHk1x06S\nzNUsSSedJAx6PGx9R3KzzrwzhCSfBTrAK5J8E9gE3AR8Lsl1wKP07iyiqnYn2QrsBp4CrveoL0mL\nw0AzhJHs2BmCJAGTM0PwncqSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQ\nJDUGgiQJMBAkSY2BIEljdOqpLxl3CUcYCJI0Rs8884Nxl3CEgSBJAgwESVJjIEiSAANBktQYCJIk\nwECQJDUGgiQJMBAkaWySM8ZdwlEMBEkam/8bdwFHMRAkSYCBIEljsXLl2eMu4VlSVePZcVLj2rck\njVuSo54PejxMQlVl/p7HzxmCJC2w5MXjLmFOBoIkLaBeGPxo3GXMaWSBkOStSR5J8u9JbhzVfiRp\n0p1yypkkL57oMIARBUKSU4A/Bd4C/CzwjiSvHcW+Rq3b7Y67hIFY53BZ5/AshhphuHUmaQHQe6z6\nIb0gmNwwgNHNENYC01X1aFU9BWwBrhjRvkbqZHwxj5J1DtdiqHMx1AjHX+fhv/j7D/yHH3t+NOtx\n8i0Z0XZXAfv7nn+LXkhI0vPWuzPndHoH2bkej8ezt/HhD990HNuafaBffAEw20huO03ya8Bbquq3\n2vN3Amur6oa+Ps+67fT4r7w/14tiOC+OydjWoNs8xOD5Ps7v9Vh1LuS4zWeuOifxNXIIeHoC6xrV\nNhfvwXY+k3Db6ahmCAeAV/U9X93ajjL7PtznZ3Yqn0hKT+q2Bt3m00Pc1jDrmu1YdS7kuM1nrjon\n9TUyqdsa5TZfWIZzPDzBGkY0QzgV2AtcBjwG7ATeUVV7hr4zSdJQjGSGUFVPJ3kfsJ3ehetPGgaS\nNNnG9tEVkqTJMpZ3Ko/7TWtJ9iV5IMmuJDtb27Ik25PsTXJnkqV9/TcmmU6yJ8m6vvaLkjzYvo8/\nGUJdn0wyk+TBvrah1ZXktCRb2jr/kqT/Os+J1rkpybeS3Nf+vXUC6lydZEeSh5M8lOSG1j4xYzpH\njb/b2idqPJOcnuTe9jvzcJKPTNpYzlPnRI1n37ZOafVsa8/HO55VtaD/6IXQfwCvBl4E3A+8doFr\n+AawbFbbzcAftuUbgZva8gXALnqn185utR+eWd0LXNyWv0zvzqoTqesXgQuBB0dRF/DbwK1t+TeA\nLUOscxPw+3P0XTPGOlcCF7bll9K7rvXaSRrTY9Q4ieN5Zns8FbgHuHSSxnKeOiduPNv6vwf8NbBt\nEn7fR3rgfY4B+AXgK33PNwA3LnAN/wm8YlbbI8CKtrwSeGSu+oCvAJe0Prv72q8G/nwItb2aow+0\nQ6sLuAO4pC2fCjwxxDo3AR+Yo99Y65xVyxeBN0/qmPbVeNkkjydwJr0bRS6Y8LHsr3PixpPe3Zd3\nAR1+HAhjHc9xnDKa601rqxa4hgLuSvK1JO9tbSuqagagqg4Cy1v77HoPtLZV9Go/bFTfx/Ih1nVk\nnap6GvhekpcPsdb3Jbk/ySf6proTUWeSs+nNau5huD/rodXaV+O9rWmixrOd3tgFHAS6VbWbCRzL\n56gTJmw8gY8DH6R3PDpsrON5sn7a6aVVdRHwNuB3kryRo38ozPF8UgyzrmHe+Hwr8JqqupDeL+If\nD3HbJ1RnkpcCnwfeX1VPMtqf9fOqdY4aJ248q+qZqno9vb9s35ikwwSO5aw6fynJm5iw8UzydmCm\nqu6fZ/0FHc9xBMJAb1obpap6rD0+QW+KvhaYSbICIMlK4PHW/QBwVt/qh+t9rvZhG2ZdR76W3ntF\nXlZV3xlGkVX1RLW5KfCX/PijSsZaZ5Il9A60n6mq21vzRI3pXDVO6ni22v6H3rnqNzBhYzlHnX8P\nvGECx/NSYH2SbwB/A/xyks8AB8c5nuMIhK8B5yZ5dZLT6J3z2rZQO09yZvtrjCQvAdYBD7Uarm3d\nrgEOHzy2AVe3K/bnAOcCO9t07vtJ1iYJ8Jt965xQiRyd5MOsa1vbBsCvAzuGVWd78R72q8C/TUid\nn6J3jvWWvrZJG9Nn1Thp45nklYdPsyQ5A/gVehc5J2osn6PO+ydtPKvqQ1X1qqp6Db1j4I6qehfw\nJcY5nidy0eb5/gPeSu9uimlgwwLv+xx6dzbtohcEG1r7y4G7W13bgZ/sW2cjvav6e4B1fe0/37Yx\nDdwyhNo+C/wXvffzfxN4N7BsWHXR++CYra39HuDsIdb5V8CDbWy/SLswNuY6L6X3+ROHf973tdfe\n0H7WJ1rrMWqcqPEEXtdq2wU8APzBsH9vRlznRI3nrJrfxI8vKo91PH1jmiQJOHkvKkuSZjEQJEmA\ngSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAHw/5YORLLRRuwxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115a36b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_entity), sorted(sumT.sum(1)))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 38696 artists>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119f0bac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"A = np.squeeze(np.asarray(sumT.sum(0)))\n",
"plt.bar(range(n_entity), sorted(A))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_entity = 100\n",
"e = np.random.normal(loc = np.random.normal(size=n_entity), size=n_entity)\n",
"r = np.random.normal(loc = np.random.normal(size=n_entity), size=n_entity)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.zeros([len(r), n_entity, n_entity])\n",
"\n",
"import itertools\n",
"for i, j, k in itertools.product(range(n_entity), range(n_entity), range(len(r))):\n",
" x[k,i,j]= e[i]*e[j]*r[k]\n",
" \n",
"x = 1./(1.+np.exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.random.binomial(1, x)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 100 artists>"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15c9ae6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(range(n_entity), sorted(np.sum(np.sum(x,0),1)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| docs/notebooks/different_methods.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trying different Matrix Factorzation Methods\n",
"\n",
"In this notebook we will try out several MF methods supported by SMURFF.\n",
"\n",
"### Downloading the files\n",
"\n",
"As in the previous example we download the ChemBL dataset. The resulting IC50 matrix is a compound x protein matrix, split into train and test. The ECFP matrix has features as side information on the compounds."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import smurff\n",
"import logging\n",
"\n",
"logging.basicConfig(level = logging.INFO)\n",
"\n",
"ic50_train, ic50_test, ecfp = smurff.load_chembl()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Matrix Factorization without Side Information (BPMF)\n",
"\n",
"As a first example we can run SMURFF without side information. The method used here is BPMF.\n",
"\n",
"Input matrix for `Y` is a sparse scipy matrix (either coo_matrix, csr_matrix or csc_matrix). The test matrix\n",
"`Ytest` also needs to ne sparse matrix of the same size as `Y`. Here we have used burn-in of 20 samples for the Gibbs sampler and then collected 80 samples from the model. We use 16 latent dimensions in the model.\n",
"\n",
"For good results you will need to run more sampling and burnin iterations (>= 1000) and maybe more latent dimensions.\n",
"\n",
"We create a trainSession, and the `run` method returns the predictions of the `Ytest` matrix. `predictions` is a list of of type `Prediction`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"trainSession = smurff.BPMFSession(\n",
" Ytrain = ic50_train,\n",
" Ytest = ic50_test,\n",
" num_latent = 16,\n",
" burnin = 20,\n",
" nsamples = 80,\n",
" verbose = 0,)\n",
"\n",
"predictions = trainSession.run()\n",
"print(\"First prediction element: \", predictions[0])\n",
"\n",
"rmse = smurff.calc_rmse(predictions)\n",
"print(\"RMSE =\", rmse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Matrix Factorization with Side Information (Macau)\n",
"\n",
"If we want to use the compound features we can use the Macau algorithm.\n",
"\n",
"The parameter `side_info = [ecfp, None]` sets the side information for rows and columns, respectively. In this example we only use side information for the compounds (rows of the matrix).\n",
"\n",
"Since the `ecfp` sideinfo is sparse and large, we use the CG solver from Macau to reduce the memory footprint and speedup the computation.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions = smurff.MacauSession(\n",
" Ytrain = ic50_train,\n",
" Ytest = ic50_test,\n",
" side_info = [ecfp, None],\n",
" direct = False, # use CG solver instead of Cholesky decomposition\n",
" num_latent = 16,\n",
" burnin = 40,\n",
" nsamples = 100).run()\n",
"\n",
"smurff.calc_rmse(predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Macau univariate sampler\n",
"\n",
"SMURFF also includes an option to use a *very fast* univariate sampler, i.e., instead of sampling blocks of variables jointly it samples each individually. An example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions = smurff.MacauSession(\n",
" Ytrain = ic50_train,\n",
" Ytest = ic50_test,\n",
" side_info = [ecfp, None],\n",
" direct = True,\n",
" univariate = True,\n",
" num_latent = 32,\n",
" burnin = 500,\n",
" nsamples = 3500,\n",
" verbose = 0,).run()\n",
"smurff.calc_rmse(predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using it we recommend using larger values for `burnin` and `nsamples`, because the univariate sampler mixes slower than the blocked sampler."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| notebooks/subgroup-lattice.ipynb |
|
| {
"cells": [{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Subgroup Lattice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a draft version of the Subgroup Lattice module, inspired by the XGAP version, and implements few features. At the moment is possible to explore the lattice by right clicking a node and explore its properties, and by generating all subgroups lattice."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [{
"data": {
"text/plain": [
"true"
]
},
"execution_count": 1,
"metadata": {
"text/plain": ""
},
"output_type": "execute_result"
}],
"source": [
"LoadPackage(\"subgrouplattice\");"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [{
"data": {
"text/plain": [
"Sym( [ 1 .. 4 ] )"
]
},
"execution_count": 2,
"metadata": {
"text/plain": ""
},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F1\",\"height\" : 600,\"title\" : \"GraphicSubgroupLattice\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {\"F43\" : {\"id\" : \"F43\",\"title\" : \"Subgroup Lattice\",\"callback\" : {},\"menus\" : {\"F45\" : {\"id\" : \"F45\",\"title\" : \"All Subgroups\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F44\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"SymmetricGroup( [ 1 .. 4 ] )\"],\"requiredArgs\" : {}},\"menus\" : {}}}}},\"graph\" : {\"type\" : \"directed\",\"id\" : \"F3\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F4\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"diamond\",\"id\" : \"F4\",\"size\" : 10,\"title\" : \"G\",\"layer\" : -4,\"color\" : \"\",\"parent\" : \"\",\"selected\" : true,\"menus\" : {\"F6\" : {\"id\" : \"F6\",\"title\" : \"Size\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F5\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F8\" : {\"id\" : \"F8\",\"title\" : \"IsAbelian\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F7\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F10\" : {\"id\" : \"F10\",\"title\" : \"IsCyclic\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F9\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F12\" : {\"id\" : \"F12\",\"title\" : \"IsNilpotent\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F11\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F14\" : {\"id\" : \"F14\",\"title\" : \"IsNormal\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F13\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\",\"SymmetricGroup( [ 1 .. 4 ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F16\" : {\"id\" : \"F16\",\"title\" : \"IsPerfect\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F15\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F18\" : {\"id\" : \"F18\",\"title\" : \"IsSimple\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F17\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F20\" : {\"id\" : \"F20\",\"title\" : \"IsSolvable\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F19\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F22\" : {\"id\" : \"F22\",\"title\" : \"Isomorphism\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F21\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( [ (1,4)(2,3), (1,3)(2,4), (2,4,3), (3,4) ] )\"],\"requiredArgs\" : {}},\"menus\" : {}}},\"messages\" : {},\"callbacks\" : {}},\"F23\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"diamond\",\"id\" : \"F23\",\"size\" : 10,\"title\" : \"1\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {\"F25\" : {\"id\" : \"F25\",\"title\" : \"Size\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F24\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F27\" : {\"id\" : \"F27\",\"title\" : \"IsAbelian\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F26\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F29\" : {\"id\" : \"F29\",\"title\" : \"IsCyclic\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F28\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F31\" : {\"id\" : \"F31\",\"title\" : \"IsNilpotent\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F30\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F33\" : {\"id\" : \"F33\",\"title\" : \"IsNormal\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F32\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\",\"SymmetricGroup( [ 1 .. 4 ] )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F35\" : {\"id\" : \"F35\",\"title\" : \"IsPerfect\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F34\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F37\" : {\"id\" : \"F37\",\"title\" : \"IsSimple\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F36\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F39\" : {\"id\" : \"F39\",\"title\" : \"IsSolvable\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F38\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}},\"F41\" : {\"id\" : \"F41\",\"title\" : \"Isomorphism\",\"callback\" : {\"func\" : \"unknown\",\"id\" : \"F40\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"Group( () )\"],\"requiredArgs\" : {}},\"menus\" : {}}},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F42\" : {\"id\" : \"F42\",\"source\" : \"F23\",\"length\" : 0,\"weight\" : 0,\"color\" : \"\",\"invisible\" : false,\"target\" : \"F4\"}}},\"messages\" : {\"F2\" : {\"type\" : \"default\",\"id\" : \"F2\",\"text\" : \"There are 8 levels in this Group.\",\"title\" : \"\"}},\"latticeType\" : {\"menus\" : [\"rec( func := function ( poset, G ) local L, cls, len, sz, graphHasse, max, rep, z, t, i, j, k, nodes, last, knownArgs, m, cb; L := LatticeSubgroups( G ); cls := ConjugacyClassesSubgroups( L ); len := [ ]; sz := [ ]; for i in cls do Add( len, Size( i ) ); AddSet( sz, Size( Representative( i ) ) ); od; graphHasse := poset!.graph; graphHasse!.nodes := rec( ); graphHasse!.links := rec( ); nodes := [ ]; sz := Reversed( sz ); for i in [ 1 .. Length( cls ) ] do nodes[i] := [ ]; for j in [ 1 .. len[i] ] do if len[i] = 1 then nodes[i][j] := Shape( ShapeType.DIAMOND, String( i ) ); else nodes[i][j] := Shape( ShapeType.CIRCLE, String( i ) ); fi; SetLayer( nodes[i][j], - Size( Representative( cls[i] ) ) ); for m in poset!.latticeType.contextMenus do knownArgs := [ poset, Representative( cls[i] ) ]; if m.group = true then Add( knownArgs, G ); fi; cb := Callback( m.func, knownArgs ); Add( nodes[i][j], Menu( m.name, cb ) ); od; if i = Length( cls ) and j = len[i] then SetTitle( nodes[i][j], \\\"G\\\" ); fi; Add( graphHasse, nodes[i][j] ); od; od; last := rec( o := 0, n := 0 ); for i in [ 1 .. Length( cls ) ] do for j in [ 1 .. len[i] ] do if Layer( nodes[i][j] ) <> last.o then last.o := Layer( nodes[i][j] ); last.n := last.n - 2; fi; SetLayer( nodes[i][j], last.n ); od; od; max := MaximalSubgroupsLattice( L ); for i in [ 1 .. Length( cls ) ] do for j in max[i] do rep := ClassElementLattice( cls[i], 1 ); for k in [ 1 .. len[i] ] do if k = 1 then z := j[2]; else t := cls[i]!.normalizerTransversal[k]; z := ClassElementLattice( cls[j[1]], 1 ); z := cls[j[1]]!.normalizerTransversal[j[2]] * t; z := PositionCanonical( cls[j[1]]!.normalizerTransversal, z ); fi; Add( graphHasse, Link( nodes[j[1]][z], nodes[i][k] ) ); od; od; od; return Draw( poset ); end, group := true, multiple := false, name := \\\"All Subgroups\\\" )\"],\"contextMenus\" : [\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Size\\\", String( Size( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"Size\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is commutative?\\\", String( IsCommutative( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsAbelian\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is cyclic?\\\", String( IsCyclic( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsCyclic\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is NilpotentGroup?\\\", String( IsNilpotentGroup( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsNilpotent\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is normal?\\\", String( IsNormal( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := true, name := \\\"IsNormal\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is perfect group?\\\", String( IsPerfectGroup( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsPerfect\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is simple group?\\\", String( IsSimpleGroup( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsSimple\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Is solvable group?\\\", String( IsSolvableGroup( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"IsSolvable\\\" )\",\"rec( func := function ( poset, n ) local message; message := FrancyMessage( FrancyMessageType.INFO, \\\"Isomorphism\\\", String( IdGroup( n ) ) ); Add( poset, message ); return Draw( poset ); end, group := false, name := \\\"Isomorphism\\\" )\"],\"knowsLevels\" : true,\"trivial\" : true,\"hasse\" : true,\"canCompare\" : true}}}"
},
"execution_count": 3,
"metadata": {
"application/vnd.francy+json": {}
},
"output_type": "execute_result"
}
],
"source": [
"G := SymmetricGroup(4); #G := PSL(2, 7); #G := DihedralGroup(44);\n",
"GraphicSubgroupLattice(G);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "GAP 4",
"language": "gap",
"name": "gap-4"
},
"language_info": {
"codemirror_mode": "gap",
"file_extension": ".g",
"mimetype": "text/x-gap",
"name": "GAP 4",
"nbconvert_exporter": "",
"pygments_lexer": "gap",
"version": "4.dev"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
nholtz/structural-analysis |
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class Node"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import salib as sl\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Node(object):\n",
" \n",
" DIRECTIONS = {'FX':0, 'FY':1, 'MZ':2}\n",
" \n",
" def __init__(self,ident,x,y):\n",
" self.id = ident\n",
" self.x = x\n",
" self.y = y\n",
" self.constraints = set()\n",
" self.dofnums = [None] * 3\n",
" \n",
" def add_constraint(self,cname):\n",
" c = cname.upper()\n",
" if c not in self.DIRECTIONS:\n",
" raise Exception('Invalid constraint name: {}'.format(cname))\n",
" self.constraints.add(c)\n",
" \n",
" def to(self,other):\n",
" \"\"\"Return the directional cosines and distance to the other node.\"\"\"\n",
" dx = other.x-self.x\n",
" dy = other.y-self.y\n",
" L = math.sqrt(dx*dx + dy*dy)\n",
" return dx/L,dy/L,L\n",
" \n",
" def __repr__(self):\n",
" return '{}(\"{}\",{},{})'.format(self.__class__.__name__,self.id,self.x,self.y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"widgets": {
"state": {},
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| ml/ex01/template/taskC.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data Generation\n",
"==="
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from numpy.random import rand, randn"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"n, d, k = 100, 2, 2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array([0.69872366, 0.75176984]), array([0.25997411, 0.14504062])]\n",
"[array([[0.01764816, 0. ],\n",
" [0. , 0.06360523]]), array([[0.01764816, 0. ],\n",
" [0. , 0.06360523]])]\n",
"8.824977827076287\n"
]
},
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(20)\n",
"X = rand(n, d)\n",
"\n",
"# means = [rand(d) for _ in range(k)] # works for any k\n",
"means = [rand(d) * 0.5 + 0.5 , - rand(d) * 0.5 + 0.5] # for better plotting when k = 2\n",
"\n",
"S = np.diag(rand(d))\n",
"\n",
"sigmas = [S]*k # we'll use the same Sigma for all clusters for better visual results\n",
"\n",
"print(means)\n",
"print(sigmas)\n",
"print(2**np.pi)\n",
"np.linalg.det(np.eye(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solution\n",
"==="
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def compute_log_p(X, mean, sigma):\n",
" dxm = X - mean\n",
" exponent = -0.5 * np.sum(dxm * np.dot(dxm, np.linalg.inv(sigma)), axis=1)\n",
" return exponent - np.log(2 * np.pi) * (d / 2) - 0.5 * np.log(np.linalg.det(sigma))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"log_ps = [compute_log_p(X, m, s) for m, s in zip(means, sigmas)] # exercise: try to do this without looping"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0\n",
" 1 0 1 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1\n",
" 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0]\n"
]
}
],
"source": [
"assignments = np.argmax(log_ps, axis=0)\n",
"print(assignments)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors = np.array(['red', 'green'])[assignments]\n",
"plt.scatter(X[:, 0], X[:, 1], c=colors, s=100)\n",
"plt.scatter(np.array(means)[:, 0], np.array(means)[:, 1], marker='*', s=200)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| Trajectory_Simulator.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"import numpy as np\n",
"import beadpy\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 483,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 664,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def trajectory_simulator(pre_duration = 250, #Mean event start time\n",
" pre_sigma = 50, #Sigma of event start time distribution\n",
" post_duration = 250, #The bead stays on for this long at the end of the trajectory\n",
" mean_duration = 100, #Mean event duration\n",
" min_duration = 10, #Minimum event duration\n",
" mean_rate = 500, #Mean rate (distance units/timestep)\n",
" rate_sigma = 50, #Sigma of the rate distribution\n",
" noise_sigma = 500, #Mean sigma for the bead movement\n",
" noise_sigma_sigma = 100, #Sigma of the noise sigma distribution\n",
" pause_prob = 0.001, #Probability of entering a pause in a given timestep\n",
" pause_duration_prob = 0.2, #Probability of remaining paused in a given timestep once a pause has begun.\n",
" rate_change_prob = 0.1, #Probablity that the rate will change in a given timestep\n",
" DNA_length = 15000, #Length of the DNA - a hard limit on the event length\n",
" trajectory_number = 0):\n",
" length = int(np.random.exponential(mean_duration)) #Length is drawn from an exponential distribution.\n",
" while length < min_duration:\n",
" length = int(np.random.exponential(mean_duration)) #The length should be at least a certain value.\n",
" \n",
" current_rate = 0\n",
" pre = int(np.random.normal(loc=pre_duration, scale = pre_sigma))\n",
" post = post_duration\n",
" rate = 0\n",
" ratesequence = [0]*pre\n",
" noise_sigmaval = int(np.random.normal(loc=noise_sigma, scale = noise_sigma_sigma))\n",
" position = [0]*pre\n",
" nucleotides = []\n",
" current_position = 0\n",
" for i in range(0,pre):\n",
" nucleotides.append(float(position[i]+np.random.normal(loc=0.0, scale = noise_sigmaval)))\n",
" for i in range(0,length):\n",
" randomnumber = random.random() #generate a random float between 0 and 1\n",
" if i == 0: #Start the event\n",
" rate = np.random.normal(loc=mean_rate, scale = rate_sigma) \n",
" elif not rate == 0: #When during an event/no pause.\n",
" if (randomnumber <= pause_prob): #Start a pause.\n",
" rate = 0\n",
" elif (randomnumber > pause_prob) & (randomnumber <= (pause_prob + rate_change_prob)): #Change the rate\n",
" rate = np.random.normal(loc=mean_rate, scale = rate_sigma)\n",
" else: #No rate change\n",
" rate = rate #just FYI! \n",
" elif (rate == 0) & (not i ==0): #When in a pause.\n",
" if (randomnumber < (1- pause_duration_prob)): #End the pause.\n",
" rate = np.random.normal(loc=mean_rate, scale = rate_sigma)\n",
" else:\n",
" rate = 0 #Continue the pause.\n",
" ratesequence.append(rate)\n",
" current_position = current_position + rate\n",
" position.append(current_position)\n",
" nucleotides.append(float(current_position+np.random.normal(loc=0.0, scale = noise_sigmaval)))\n",
" if current_position > DNA_length:\n",
" length = i\n",
" break\n",
" for i in range(0,post):\n",
" ratesequence.append(0)\n",
" position.append(current_position)\n",
" nucleotides.append(float(current_position+np.random.normal(loc=0.0, scale = noise_sigmaval)))\n",
" time = range(0,len(nucleotides))\n",
" results = pd.DataFrame({'time' : time,\n",
" 'nucleotides' : nucleotides,\n",
" 'rate' : ratesequence,\n",
" 'position' : position})\n",
" results['trajectory'] = trajectory_number\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 733,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test = trajectory_simulator(pre_duration = 300,\n",
" pre_sigma = 5,\n",
" post_duration = 250, \n",
" mean_duration = 100,\n",
" min_duration = 10,\n",
" mean_rate = 80,\n",
" rate_sigma = 30,\n",
" noise_sigma = 100,\n",
" noise_sigma_sigma = 20,\n",
" pause_prob = 0.1,\n",
" pause_duration_prob = 0.9,\n",
" rate_change_prob = 0.01,\n",
" DNA_length = 15000,\n",
" trajectory_number = 0)\n",
"exampletrajseg = beadpy.trajectory_plotter(test, 0, method = ('auto', 'whole'), \n",
" sigma_start = 10, sigma_end = 250, \n",
" eventregion = (200,500), \n",
" segmenttable = 0)"
]
},
{
"cell_type": "code",
"execution_count": 734,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rate</th>\n",
" <th>intercept</th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>y1</th>\n",
" <th>y2</th>\n",
" <th>displacement</th>\n",
" <th>duration</th>\n",
" <th>trajectory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>-7.0</td>\n",
" <td>0.0</td>\n",
" <td>308.0</td>\n",
" <td>-7.0</td>\n",
" <td>-7.1</td>\n",
" <td>-0.1</td>\n",
" <td>308.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>36.9</td>\n",
" <td>-11310.0</td>\n",
" <td>308.0</td>\n",
" <td>320.0</td>\n",
" <td>59.8</td>\n",
" <td>502.7</td>\n",
" <td>443.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.9</td>\n",
" <td>-2234.0</td>\n",
" <td>320.0</td>\n",
" <td>360.0</td>\n",
" <td>938.2</td>\n",
" <td>1334.7</td>\n",
" <td>396.5</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.3</td>\n",
" <td>1041.0</td>\n",
" <td>360.0</td>\n",
" <td>400.0</td>\n",
" <td>1525.0</td>\n",
" <td>1578.8</td>\n",
" <td>53.8</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>24.3</td>\n",
" <td>-7998.0</td>\n",
" <td>400.0</td>\n",
" <td>418.0</td>\n",
" <td>1725.7</td>\n",
" <td>2163.3</td>\n",
" <td>437.6</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20.4</td>\n",
" <td>-6515.0</td>\n",
" <td>418.0</td>\n",
" <td>439.0</td>\n",
" <td>1994.7</td>\n",
" <td>2422.3</td>\n",
" <td>427.5</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>93.9</td>\n",
" <td>-38750.0</td>\n",
" <td>439.0</td>\n",
" <td>445.0</td>\n",
" <td>2486.8</td>\n",
" <td>3050.4</td>\n",
" <td>563.6</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>31.2</td>\n",
" <td>-10685.0</td>\n",
" <td>445.0</td>\n",
" <td>461.0</td>\n",
" <td>3181.9</td>\n",
" <td>3680.5</td>\n",
" <td>498.6</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>150.1</td>\n",
" <td>-65695.0</td>\n",
" <td>461.0</td>\n",
" <td>465.0</td>\n",
" <td>3488.7</td>\n",
" <td>4089.0</td>\n",
" <td>600.3</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>6.3</td>\n",
" <td>1119.0</td>\n",
" <td>465.0</td>\n",
" <td>500.0</td>\n",
" <td>4033.3</td>\n",
" <td>4252.6</td>\n",
" <td>219.3</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>58.4</td>\n",
" <td>-24929.0</td>\n",
" <td>500.0</td>\n",
" <td>514.0</td>\n",
" <td>4265.2</td>\n",
" <td>5082.7</td>\n",
" <td>817.4</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12.5</td>\n",
" <td>-1513.0</td>\n",
" <td>514.0</td>\n",
" <td>519.0</td>\n",
" <td>4914.7</td>\n",
" <td>4977.2</td>\n",
" <td>62.5</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>106.8</td>\n",
" <td>-50394.0</td>\n",
" <td>519.0</td>\n",
" <td>533.0</td>\n",
" <td>5025.8</td>\n",
" <td>6520.7</td>\n",
" <td>1495.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.0</td>\n",
" <td>6590.0</td>\n",
" <td>533.0</td>\n",
" <td>797.0</td>\n",
" <td>6613.2</td>\n",
" <td>6624.6</td>\n",
" <td>11.4</td>\n",
" <td>264.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rate intercept x1 x2 y1 y2 displacement duration \\\n",
"0 0.0 -7.0 0.0 308.0 -7.0 -7.1 -0.1 308.0 \n",
"1 36.9 -11310.0 308.0 320.0 59.8 502.7 443.0 12.0 \n",
"2 9.9 -2234.0 320.0 360.0 938.2 1334.7 396.5 40.0 \n",
"3 1.3 1041.0 360.0 400.0 1525.0 1578.8 53.8 40.0 \n",
"4 24.3 -7998.0 400.0 418.0 1725.7 2163.3 437.6 18.0 \n",
"5 20.4 -6515.0 418.0 439.0 1994.7 2422.3 427.5 21.0 \n",
"6 93.9 -38750.0 439.0 445.0 2486.8 3050.4 563.6 6.0 \n",
"7 31.2 -10685.0 445.0 461.0 3181.9 3680.5 498.6 16.0 \n",
"8 150.1 -65695.0 461.0 465.0 3488.7 4089.0 600.3 4.0 \n",
"9 6.3 1119.0 465.0 500.0 4033.3 4252.6 219.3 35.0 \n",
"10 58.4 -24929.0 500.0 514.0 4265.2 5082.7 817.4 14.0 \n",
"11 12.5 -1513.0 514.0 519.0 4914.7 4977.2 62.5 5.0 \n",
"12 106.8 -50394.0 519.0 533.0 5025.8 6520.7 1495.0 14.0 \n",
"13 0.0 6590.0 533.0 797.0 6613.2 6624.6 11.4 264.0 \n",
"\n",
" trajectory \n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 0.0 \n",
"8 0.0 \n",
"9 0.0 \n",
"10 0.0 \n",
"11 0.0 \n",
"12 0.0 \n",
"13 0.0 "
]
},
"execution_count": 734,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exampletrajseg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the table of actual changepoints below (ignoring rate changes of less than 10) with the above table of the discovered changepoints above."
]
},
{
"cell_type": "code",
"execution_count": 735,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>changepoint</th>\n",
" <th>duration</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>306</td>\n",
" <td>3</td>\n",
" <td>70.277313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>309</td>\n",
" <td>5</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>314</td>\n",
" <td>12</td>\n",
" <td>76.769973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>326</td>\n",
" <td>23</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>349</td>\n",
" <td>1</td>\n",
" <td>127.439888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>350</td>\n",
" <td>8</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>358</td>\n",
" <td>8</td>\n",
" <td>39.171484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>366</td>\n",
" <td>34</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>400</td>\n",
" <td>7</td>\n",
" <td>59.906436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>407</td>\n",
" <td>15</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>422</td>\n",
" <td>10</td>\n",
" <td>33.587825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>432</td>\n",
" <td>6</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>438</td>\n",
" <td>14</td>\n",
" <td>84.763355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>452</td>\n",
" <td>10</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>462</td>\n",
" <td>6</td>\n",
" <td>97.203649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>468</td>\n",
" <td>26</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>494</td>\n",
" <td>4</td>\n",
" <td>69.152155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>498</td>\n",
" <td>6</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>504</td>\n",
" <td>8</td>\n",
" <td>77.151217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>512</td>\n",
" <td>8</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>520</td>\n",
" <td>15</td>\n",
" <td>108.943335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>535</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" changepoint duration rate\n",
"0 306 3 70.277313\n",
"1 309 5 0.000000\n",
"2 314 12 76.769973\n",
"3 326 23 0.000000\n",
"4 349 1 127.439888\n",
"5 350 8 0.000000\n",
"6 358 8 39.171484\n",
"7 366 34 0.000000\n",
"8 400 7 59.906436\n",
"9 407 15 0.000000\n",
"10 422 10 33.587825\n",
"11 432 6 0.000000\n",
"12 438 14 84.763355\n",
"13 452 10 0.000000\n",
"14 462 6 97.203649\n",
"15 468 26 0.000000\n",
"16 494 4 69.152155\n",
"17 498 6 0.000000\n",
"18 504 8 77.151217\n",
"19 512 8 0.000000\n",
"20 520 15 108.943335\n",
"21 535 0 0.000000"
]
},
"execution_count": 735,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"changepoints = []\n",
"changepoint_values = []\n",
"duration = []\n",
"for i in test.time[1:]:\n",
" if abs(test.rate[i] - test.rate[i-1]) > 10:\n",
" changepoints.append(i)\n",
" changepoint_values.append(test.rate[i])\n",
"for i in range(0, (len(changepoints) - 1)):\n",
" duration.append(changepoints[i+1]-changepoints[i])\n",
"duration.append(0)\n",
"cpts = pd.DataFrame({'changepoint' : changepoints,\n",
" 'rate' : changepoint_values,\n",
" 'duration' : duration})\n",
"cpts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation of a combined phi29 + E. coli experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate a large results table."
]
},
{
"cell_type": "code",
"execution_count": 700,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"phi29results = pd.DataFrame()\n",
"for j in range(0,1000):\n",
" temp = trajectory_simulator(pre_duration = 300,\n",
" pre_sigma = 20,\n",
" post_duration = 250, \n",
" mean_duration = 100,\n",
" min_duration = 10,\n",
" mean_rate = 80,\n",
" rate_sigma = 40,\n",
" noise_sigma = 100,\n",
" noise_sigma_sigma = 20,\n",
" pause_prob = 0.1,\n",
" pause_duration_prob = 0.5,\n",
" rate_change_prob = 0.01,\n",
" DNA_length = 15000,\n",
" trajectory_number = j)\n",
" phi29results = phi29results.append(temp)"
]
},
{
"cell_type": "code",
"execution_count": 699,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"coliresults = pd.DataFrame()\n",
"for j in range(1001,2000):\n",
" temp = trajectory_simulator(pre_duration = 300,\n",
" pre_sigma = 20,\n",
" post_duration = 250, \n",
" mean_duration = 150,\n",
" min_duration = 10,\n",
" mean_rate = 442,\n",
" rate_sigma = 198,\n",
" noise_sigma = 100,\n",
" noise_sigma_sigma = 20,\n",
" pause_prob = 0.1,\n",
" pause_duration_prob = 0.7,\n",
" rate_change_prob = 0.05,\n",
" DNA_length = 15000,\n",
" trajectory_number = j)\n",
" coliresults = coliresults.append(temp)"
]
},
{
"cell_type": "code",
"execution_count": 701,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"results = pd.concat([phi29results,coliresults])"
]
},
{
"cell_type": "code",
"execution_count": 702,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"combosegments = beadpy.segment_finder(results, method = 'auto', sigma_start=10, sigma_end=200)"
]
},
{
"cell_type": "code",
"execution_count": 703,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc24d1390>"
]
},
"execution_count": 703,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beadpy.segmentplotter(combosegments,1000,-2000,15000, 2, 10)"
]
},
{
"cell_type": "code",
"execution_count": 719,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"beadpy.ratehist(combosegments,0, 1100, 40, 'displacement')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
mmadsen/experiment-seriationct | notebooks/.ipynb_checkpoints/spatial-distance-network-2-checkpoint.ipynb |
|
| {
"metadata": {
"name": "",
"signature": "sha256:e90c4760e91d5109b3491ff3476162d92a42025d33b0c616362a01d27a4df96c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import networkx as nx\n",
"import numpy as np\n",
"import logging as log\n",
"import itertools\n",
"import random\n",
"import math\n",
"import pprint as pp\n",
"import matplotlib.pyplot as plt\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reload(log)\n",
"log.basicConfig(level=log.DEBUG, format='%(asctime)s %(levelname)s: %(message)s')\n",
"slattice = nx.grid_2d_graph(10, 10, periodic=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw_graphviz(slattice, prog=\"neato\", node_size=100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFBCAYAAACmf9ykAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngf8DHLwTq006Zq8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10bc81bd0>"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for n,d in slattice.nodes_iter(data=True):\n",
" slattice.node[n]['xcoord'] = n[0]\n",
" slattice.node[n]['ycoord'] = n[1]\n",
" lab = \"assemblage-\"\n",
" lab += str(n[0])\n",
" lab += \"-\"\n",
" lab += str(n[1])\n",
" slattice.node[n]['label'] = lab\n",
" slattice.node[n]['level'] = \"None\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g = nx.convert_node_labels_to_integers(slattice, first_label=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def print_gml(g):\n",
" for t in nx.generate_gml(g):\n",
" print t"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sanity_check_params(lattice_size, slice_size, num_slices):\n",
" \"\"\" \n",
" Checks to ensure that the initial lattice is large enough for \n",
" sampling N slices of M nodes each, without replacement.\n",
" \"\"\"\n",
" total = lattice_size[0] * lattice_size[1]\n",
" needed = slice_size * num_slices\n",
" if needed <= total:\n",
" return True\n",
" else:\n",
" return False"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sanity_check_params((10,10),5,10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"True"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_random_slice_indices_from_lattice(g, lattice_size, slice_size, num_slices):\n",
" \"\"\"\n",
" Create non-overlapping random samples of indices for each slice, sampled from\n",
" a master lattice with lattice_size (tuple) nodes. Creates num_slices lists of \n",
" slice_size each nodes, after sanity checking that the master lattice is large enough.\n",
" \"\"\"\n",
" if sanity_check_params(lattice_size, slice_size, num_slices) == False:\n",
" log.error(\"Master lattice of size %s insufficient for %s slices of %s nodes\",\n",
" lattice_size, num_slices, slice_size)\n",
" return None\n",
" \n",
" slice_list = []\n",
" node_list = range(0, g.number_of_nodes())\n",
" sample_size = slice_size * num_slices\n",
" node_sample = random.sample(node_list, sample_size)\n",
" start = 0\n",
" for i in range(0, num_slices):\n",
" end = start + slice_size\n",
" slice_indices = node_sample[start:end]\n",
" start = end\n",
" slice_list.append(slice_indices)\n",
" \n",
" return slice_list\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slice_list = create_random_slice_indices_from_lattice(g, (10,10), 5, 10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gt = nx.grid_2d_graph(10, 10, periodic=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gt = nx.convert_node_labels_to_integers(gt, first_label=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw_graphviz(gt, prog=\"neato\", node_size=300,with_labels=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFBCAYAAACmf9ykAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJAAUkEVERALgfwFoRg2w8KLJqAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10af61050>"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def filter_mirror_tuples(tuplist):\n",
" filtered = []\n",
" for tup in tuplist:\n",
" found = False\n",
" for testtup in filtered:\n",
" mirrortup = (testtup[1],testtup[0])\n",
" if tup == testtup or tup == mirrortup:\n",
" found = True\n",
" if found == False:\n",
" filtered.append(tup)\n",
" \n",
" return filtered\n",
"\n",
"def random_interconnect_clusters(full_g, clusters_to_connect, cluster_id_ranges, num_interconnects):\n",
" \"\"\"\n",
" Given a union graph of the clusters, and a tuple of two clusters to connect (given simply as integers), \n",
" and a number of interconnects to make, randomly sample pairs of node IDs from the two clusters, and \n",
" create an edge in the full union graph. \n",
" \"\"\"\n",
" c1_ids = cluster_id_ranges[clusters_to_connect[0]]\n",
" c2_ids = cluster_id_ranges[clusters_to_connect[1]]\n",
" c1_nodes_chosen = random.sample(c1_ids, num_interconnects)\n",
" c2_nodes_chosen = random.sample(c2_ids, num_interconnects)\n",
" edge_list = zip(c1_nodes_chosen, c2_nodes_chosen)\n",
" log.debug(\"Edges to construct: %s\", edge_list)\n",
" for new_edge in edge_list:\n",
" full_g.add_edge(*new_edge) # *new_edge unpacks the tuple\n",
"\n",
"def assign_spatial_locations_to_cluster(full_g, cluster_ids, x_centroid, y_centroid, sd_dist, cluster_id):\n",
" \"\"\"\n",
" Assigns xcoord and ycoord, and spatial node label, to nodes in a cluster based upon a random normal\n",
" spread around a centroid. Thus, we can achieve complete spatial overlap in clusters (and thus a spatial null model),\n",
" by assigning the same centroid. \n",
" \"\"\"\n",
" for id in cluster_ids:\n",
" xcoord = abs(int(math.ceil(random.normalvariate(x_centroid, sd_dist))))\n",
" ycoord = abs(int(math.ceil(random.normalvariate(y_centroid, sd_dist))))\n",
" #log.debug(\"node %s at %s,%s\",id,xcoord,ycoord)\n",
" full_g.node[id]['xcoord'] = str(xcoord)\n",
" full_g.node[id]['ycoord'] = str(ycoord)\n",
" lab = \"assemblage-\"\n",
" lab += str(xcoord)\n",
" lab += \"-\"\n",
" lab += str(ycoord)\n",
" full_g.node[id]['label'] = lab\n",
" full_g.node[id]['level'] = \"None\"\n",
" full_g.node[id]['cluster_id'] = str(cluster_id)\n",
" \n",
"\n",
"def assign_uniform_intracluster_weights(g, weight):\n",
" \"\"\"\n",
" Assigns a uniform weight to the edges in a cluster. \n",
" \n",
" \"\"\"\n",
" for a,b,d in g.edges(data=True):\n",
" d['weight'] = weight\n",
" d['unnormalized_weight'] = weight\n",
" d['normalized_weight'] = weight\n",
" \n",
" \n",
"def assign_node_distances(g):\n",
" \"\"\"\n",
" Once spatial coordinates have been assigned, we can assign the distance attribute to edges. We use the \n",
" Euclidean distance given X,Y coordinates.\n",
" \"\"\"\n",
" for a,b,d in g.edges(data=True):\n",
" ax = int(g.node[a]['xcoord'])\n",
" bx = int(g.node[b]['xcoord'])\n",
" ay = int(g.node[a]['ycoord'])\n",
" by = int(g.node[b]['ycoord'])\n",
" dist = math.sqrt(abs(by - ay) + abs(bx - ax))\n",
" d['distance'] = dist\n",
" \n",
"def generate_random_complete_clusters_with_interconnect(num_clusters, num_nodes_cluster, \n",
" density_interconnect, centroid_range_tuple, cluster_spread):\n",
" \"\"\"\n",
" Generates a random graph with M clusters, each of which is the complete graph of N nodes, with a fraction of nodes\n",
" randomly interconnected between clusters. This base graph will serve as slice 1 in a temporal network, and be \n",
" evolved from this point. Given a tuple of integers for the range of possible centroid X and Y coordinates, the \n",
" clusters are distributed around randomly chosen centroids, with a spread factor given. \n",
" \"\"\"\n",
" clusters = []\n",
" cluster_id_ranges = []\n",
" starting_id = 0\n",
" for i in range(0,num_clusters):\n",
" g = nx.complete_graph(num_nodes_cluster)\n",
" g = nx.convert_node_labels_to_integers(g, first_label=starting_id)\n",
" \n",
" assign_uniform_intracluster_weights(g, 0.5)\n",
" \n",
" clusters.append(g)\n",
" cluster_ids = range(starting_id, starting_id + num_nodes_cluster)\n",
" cluster_id_ranges.append(cluster_ids)\n",
" starting_id += num_nodes_cluster\n",
" full_g = nx.union_all(clusters)\n",
" log.debug(\"range of cluster ids per cluster: %s\", cluster_id_ranges)\n",
" \n",
" \n",
" # now, we interconnect random nodes in the formerly independent clusters, given\n",
" # the known range of \n",
" num_interconnects = int(math.ceil(density_interconnect * num_nodes_cluster))\n",
" log.debug(\"interconnecting %s random nodes between each cluster\", num_interconnects)\n",
" cluster_ids = range(0, num_clusters)\n",
" paired_clusters = list(itertools.product(cluster_ids,cluster_ids))\n",
" non_self_pairs = [tup for tup in paired_clusters if tup[0] != tup[1] ]\n",
" unique_pairs = filter_mirror_tuples(non_self_pairs)\n",
" log.debug(\"num cluster pairs without self-pairing: %s\", len(unique_pairs))\n",
" log.debug(\"cluster pairs: %s\", unique_pairs)\n",
" \n",
" for pair in unique_pairs:\n",
" random_interconnect_clusters(full_g,pair,cluster_id_ranges,num_interconnects)\n",
" \n",
" xcentroids = np.random.random_integers(centroid_range_tuple[0], centroid_range_tuple[1], num_clusters)\n",
" ycentroids = np.random.random_integers(centroid_range_tuple[0], centroid_range_tuple[1], num_clusters)\n",
" centroids = zip(xcentroids, ycentroids)\n",
" \n",
" for cluster in range(0, num_clusters):\n",
" ids = cluster_id_ranges[cluster]\n",
" centroid = centroids[cluster]\n",
" log.debug(\"cluster %s has centroid at: %s\", cluster, centroid)\n",
" assign_spatial_locations_to_cluster(full_g, ids, centroid[0], centroid[1], cluster_spread, cluster)\n",
" \n",
" # now, given spatial coordinates, assign the distance value to each edge\n",
" assign_node_distances(full_g)\n",
" \n",
" return full_g"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g2 = generate_random_complete_clusters_with_interconnect(4, 10, 0.3, (50,500), 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,922 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,922 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,922 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,923 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,923 DEBUG: Edges to construct: [(9, 17), (2, 18), (3, 16)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,924 DEBUG: Edges to construct: [(9, 29), (6, 27), (1, 25)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,924 DEBUG: Edges to construct: [(6, 38), (1, 31), (9, 33)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,924 DEBUG: Edges to construct: [(18, 22), (15, 25), (12, 23)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,924 DEBUG: Edges to construct: [(15, 34), (14, 36), (19, 32)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,925 DEBUG: Edges to construct: [(21, 31), (29, 39), (26, 37)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,925 DEBUG: cluster 0 has centroid at: (112, 139)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,926 DEBUG: cluster 1 has centroid at: (100, 325)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,926 DEBUG: cluster 2 has centroid at: (304, 443)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 12:48:05,927 DEBUG: cluster 3 has centroid at: (474, 413)\n"
]
}
],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw_graphviz(g2, prog=\"neato\", node_size = 300, with_labels = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFBCAYAAACmf9ykAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSCALIYQQJuD/AxyX25WeICjXAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10cd9ec10>"
]
}
],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def assign_to_slice(g, slice):\n",
" \"\"\"\n",
" Annotate each node with an \"appears_in_slice\" attribute. If the slice is the first, \n",
" also assign a \"parent_node\" attribute of \"initial\" since we will not be assigning\n",
" specific parents in this slice.\n",
" \"\"\"\n",
" for n,d in g.nodes_iter(data=True):\n",
" g.node[n]['appears_in_slice'] = str(slice)\n",
" if slice == 1:\n",
" g.node[n]['parent_node'] = 'initial'\n",
" \n",
"\n",
"def get_node_labels_for_cluster(g, cluster_id):\n",
" \"\"\"\n",
" Given a graph whose nodes are labeled by \"cluster_id\", return the \"label\" attribute\n",
" of those nodes matching the passed cluster ID. \n",
" \"\"\"\n",
" labels = []\n",
" for n,d in g.nodes_iter(data=True):\n",
" if g.node[n]['cluster_id'] == str(cluster_id):\n",
" labels.append(g.node[n]['label'])\n",
" \n",
" return labels\n",
" \n",
"def assign_random_parent_from_previous(s_g, prev_g, num_clusters):\n",
" \"\"\"\n",
" Given a slice, and the previous slice, go through nodes in the current slice, \n",
" and choose a random parent from nodes in the same cluster. \n",
" \"\"\"\n",
" # cache the previous slice assemblages by label so we don't ask every time\n",
" cluster_label_map = dict()\n",
" for i in range(0,num_clusters):\n",
" cluster_label_map[i] = get_node_labels_for_cluster(prev_g, i)\n",
" \n",
" for n,d in s_g.nodes_iter(data=True):\n",
" cluster = int(s_g.node[n]['cluster_id'])\n",
" random_parent = random.choice(cluster_label_map[cluster])\n",
" s_g.node[n]['parent_node'] = random_parent\n",
"\n",
"def generate_sequential_slices(num_slices, num_clusters, num_nodes_cluster, density_interconnect, centroid_range_tuple, cluster_spread):\n",
" \"\"\"\n",
" Using generate_random_complete_clusters_with_interconnect, create num_slices graphs, designating one the initial slice\n",
" \"\"\"\n",
" slice_map = dict()\n",
" for slice_id in range(1, num_slices+1):\n",
" log.debug(\"creating slice %s\", slice_id)\n",
" s_g = generate_random_complete_clusters_with_interconnect(num_clusters, num_nodes_cluster, \n",
" density_interconnect, centroid_range_tuple, cluster_spread)\n",
" assign_to_slice(s_g,slice_id)\n",
" slice_map[slice_id] = s_g\n",
" \n",
" log.debug(\"slice_map: %s\", slice_map)\n",
" \n",
" # wire parents for slices after the initial slice\n",
" for slice_id in range(2, num_slices+1):\n",
" prev_id = slice_id - 1\n",
" assign_random_parent_from_previous(slice_map[slice_id], slice_map[prev_id], num_clusters)\n",
" \n",
" return slice_map\n",
" \n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slicemap = generate_sequential_slices(5, 4, 10, 0.3, (50,500), 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,804 DEBUG: creating slice 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,808 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,808 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,809 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,809 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,810 DEBUG: Edges to construct: [(3, 12), (4, 14), (8, 15)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,810 DEBUG: Edges to construct: [(6, 26), (0, 24), (7, 22)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,811 DEBUG: Edges to construct: [(0, 39), (3, 37), (9, 34)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,811 DEBUG: Edges to construct: [(12, 27), (10, 29), (18, 22)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,812 DEBUG: Edges to construct: [(10, 37), (16, 35), (13, 30)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,812 DEBUG: Edges to construct: [(28, 35), (24, 34), (23, 31)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,812 DEBUG: cluster 0 has centroid at: (298, 314)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,813 DEBUG: cluster 1 has centroid at: (220, 470)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,814 DEBUG: cluster 2 has centroid at: (55, 213)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,814 DEBUG: cluster 3 has centroid at: (274, 112)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,816 DEBUG: creating slice 2\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,818 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,819 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,819 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,820 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,820 DEBUG: Edges to construct: [(6, 13), (8, 12), (1, 18)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,820 DEBUG: Edges to construct: [(9, 29), (7, 27), (0, 20)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,821 DEBUG: Edges to construct: [(3, 39), (9, 37), (1, 34)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,822 DEBUG: Edges to construct: [(13, 29), (15, 28), (19, 23)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,822 DEBUG: Edges to construct: [(17, 39), (19, 31), (18, 33)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,823 DEBUG: Edges to construct: [(20, 34), (27, 30), (24, 38)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,823 DEBUG: cluster 0 has centroid at: (211, 230)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,824 DEBUG: cluster 1 has centroid at: (90, 163)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,825 DEBUG: cluster 2 has centroid at: (183, 218)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,825 DEBUG: cluster 3 has centroid at: (131, 145)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,827 DEBUG: creating slice 3\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,830 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,830 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,830 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,831 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,831 DEBUG: Edges to construct: [(7, 18), (0, 15), (8, 17)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,831 DEBUG: Edges to construct: [(2, 21), (8, 24), (1, 20)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,832 DEBUG: Edges to construct: [(9, 38), (3, 34), (7, 39)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,832 DEBUG: Edges to construct: [(19, 29), (11, 27), (14, 23)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,833 DEBUG: Edges to construct: [(12, 36), (14, 33), (17, 31)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,833 DEBUG: Edges to construct: [(22, 35), (24, 31), (25, 37)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,833 DEBUG: cluster 0 has centroid at: (420, 252)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,834 DEBUG: cluster 1 has centroid at: (374, 416)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,835 DEBUG: cluster 2 has centroid at: (120, 318)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,836 DEBUG: cluster 3 has centroid at: (382, 456)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,839 DEBUG: creating slice 4\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,842 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,843 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,843 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,844 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,844 DEBUG: Edges to construct: [(1, 13), (2, 11), (6, 14)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,845 DEBUG: Edges to construct: [(7, 27), (4, 28), (9, 23)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,846 DEBUG: Edges to construct: [(3, 39), (7, 32), (5, 38)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,846 DEBUG: Edges to construct: [(17, 22), (12, 26), (19, 27)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,847 DEBUG: Edges to construct: [(13, 36), (19, 32), (14, 37)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,847 DEBUG: Edges to construct: [(28, 31), (24, 37), (27, 39)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,848 DEBUG: cluster 0 has centroid at: (323, 352)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,849 DEBUG: cluster 1 has centroid at: (256, 167)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,850 DEBUG: cluster 2 has centroid at: (151, 285)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,850 DEBUG: cluster 3 has centroid at: (77, 421)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,852 DEBUG: creating slice 5\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,856 DEBUG: range of cluster ids per cluster: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,857 DEBUG: interconnecting 3 random nodes between each cluster\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,857 DEBUG: num cluster pairs without self-pairing: 6\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,858 DEBUG: cluster pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,858 DEBUG: Edges to construct: [(9, 13), (3, 16), (0, 12)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,859 DEBUG: Edges to construct: [(4, 26), (1, 27), (2, 25)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,859 DEBUG: Edges to construct: [(7, 38), (6, 35), (5, 36)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,860 DEBUG: Edges to construct: [(14, 22), (19, 25), (15, 28)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,860 DEBUG: Edges to construct: [(10, 30), (18, 35), (17, 31)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,861 DEBUG: Edges to construct: [(20, 39), (27, 37), (23, 31)]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,861 DEBUG: cluster 0 has centroid at: (158, 471)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,862 DEBUG: cluster 1 has centroid at: (330, 260)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,862 DEBUG: cluster 2 has centroid at: (189, 386)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,863 DEBUG: cluster 3 has centroid at: (280, 393)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"2015-03-30 13:13:30,865 DEBUG: slice_map: {1: <networkx.classes.graph.Graph object at 0x10af44350>, 2: <networkx.classes.graph.Graph object at 0x103a82250>, 3: <networkx.classes.graph.Graph object at 0x10af44dd0>, 4: <networkx.classes.graph.Graph object at 0x10af44210>, 5: <networkx.classes.graph.Graph object at 0x10af44090>}\n"
]
}
],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slicemap"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 167,
"text": [
"{1: <networkx.classes.graph.Graph at 0x10af44350>,\n",
" 2: <networkx.classes.graph.Graph at 0x103a82250>,\n",
" 3: <networkx.classes.graph.Graph at 0x10af44dd0>,\n",
" 4: <networkx.classes.graph.Graph at 0x10af44210>,\n",
" 5: <networkx.classes.graph.Graph at 0x10af44090>}"
]
}
],
"prompt_number": 167
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print_gml(slicemap[4])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"graph [\n",
" name \"(complete_graph(10))_with_int_labels\"\n",
" node [\n",
" id 0\n",
" label \"assemblage-319-343\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"343\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"319\"\n",
" parent_node \"assemblage-423-230\"\n",
" ]\n",
" node [\n",
" id 1\n",
" label \"assemblage-327-341\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"341\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"327\"\n",
" parent_node \"assemblage-408-249\"\n",
" ]\n",
" node [\n",
" id 2\n",
" label \"assemblage-310-368\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"368\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"310\"\n",
" parent_node \"assemblage-420-236\"\n",
" ]\n",
" node [\n",
" id 3\n",
" label \"assemblage-332-355\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"355\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"332\"\n",
" parent_node \"assemblage-430-246\"\n",
" ]\n",
" node [\n",
" id 4\n",
" label \"assemblage-329-347\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"347\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"329\"\n",
" parent_node \"assemblage-425-257\"\n",
" ]\n",
" node [\n",
" id 5\n",
" label \"assemblage-316-362\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"362\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"316\"\n",
" parent_node \"assemblage-407-267\"\n",
" ]\n",
" node [\n",
" id 6\n",
" label \"assemblage-348-369\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"369\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"348\"\n",
" parent_node \"assemblage-420-236\"\n",
" ]\n",
" node [\n",
" id 7\n",
" label \"assemblage-354-358\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"358\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"354\"\n",
" parent_node \"assemblage-407-267\"\n",
" ]\n",
" node [\n",
" id 8\n",
" label \"assemblage-326-346\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"346\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"326\"\n",
" parent_node \"assemblage-420-236\"\n",
" ]\n",
" node [\n",
" id 9\n",
" label \"assemblage-316-348\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"348\"\n",
" level \"None\"\n",
" cluster_id \"0\"\n",
" xcoord \"316\"\n",
" parent_node \"assemblage-430-246\"\n",
" ]\n",
" node [\n",
" id 10\n",
" label \"assemblage-246-147\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"147\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"246\"\n",
" parent_node \"assemblage-372-412\"\n",
" ]\n",
" node [\n",
" id 11\n",
" label \"assemblage-237-168\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"168\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"237\"\n",
" parent_node \"assemblage-386-428\"\n",
" ]\n",
" node [\n",
" id 12\n",
" label \"assemblage-254-166\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"166\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"254\"\n",
" parent_node \"assemblage-375-416\"\n",
" ]\n",
" node [\n",
" id 13\n",
" label \"assemblage-266-172\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"172\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"266\"\n",
" parent_node \"assemblage-394-428\"\n",
" ]\n",
" node [\n",
" id 14\n",
" label \"assemblage-255-160\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"160\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"255\"\n",
" parent_node \"assemblage-377-403\"\n",
" ]\n",
" node [\n",
" id 15\n",
" label \"assemblage-267-182\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"182\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"267\"\n",
" parent_node \"assemblage-368-422\"\n",
" ]\n",
" node [\n",
" id 16\n",
" label \"assemblage-270-164\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"164\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"270\"\n",
" parent_node \"assemblage-375-416\"\n",
" ]\n",
" node [\n",
" id 17\n",
" label \"assemblage-262-187\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"187\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"262\"\n",
" parent_node \"assemblage-375-416\"\n",
" ]\n",
" node [\n",
" id 18\n",
" label \"assemblage-270-178\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"178\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"270\"\n",
" parent_node \"assemblage-360-419\"\n",
" ]\n",
" node [\n",
" id 19\n",
" label \"assemblage-273-158\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"158\"\n",
" level \"None\"\n",
" cluster_id \"1\"\n",
" xcoord \"273\"\n",
" parent_node \"assemblage-386-428\"\n",
" ]\n",
" node [\n",
" id 20\n",
" label \"assemblage-147-298\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"298\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"147\"\n",
" parent_node \"assemblage-127-307\"\n",
" ]\n",
" node [\n",
" id 21\n",
" label \"assemblage-156-287\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"287\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"156\"\n",
" parent_node \"assemblage-126-327\"\n",
" ]\n",
" node [\n",
" id 22\n",
" label \"assemblage-149-281\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"281\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"149\"\n",
" parent_node \"assemblage-119-319\"\n",
" ]\n",
" node [\n",
" id 23\n",
" label \"assemblage-148-279\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"279\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"148\"\n",
" parent_node \"assemblage-125-308\"\n",
" ]\n",
" node [\n",
" id 24\n",
" label \"assemblage-161-288\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"288\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"161\"\n",
" parent_node \"assemblage-119-327\"\n",
" ]\n",
" node [\n",
" id 25\n",
" label \"assemblage-154-294\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"294\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"154\"\n",
" parent_node \"assemblage-119-327\"\n",
" ]\n",
" node [\n",
" id 26\n",
" label \"assemblage-177-301\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"301\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"177\"\n",
" parent_node \"assemblage-117-306\"\n",
" ]\n",
" node [\n",
" id 27\n",
" label \"assemblage-150-285\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"285\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"150\"\n",
" parent_node \"assemblage-119-319\"\n",
" ]\n",
" node [\n",
" id 28\n",
" label \"assemblage-150-282\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"282\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"150\"\n",
" parent_node \"assemblage-126-312\"\n",
" ]\n",
" node [\n",
" id 29\n",
" label \"assemblage-163-284\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"284\"\n",
" level \"None\"\n",
" cluster_id \"2\"\n",
" xcoord \"163\"\n",
" parent_node \"assemblage-121-290\"\n",
" ]\n",
" node [\n",
" id 30\n",
" label \"assemblage-78-423\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"423\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"78\"\n",
" parent_node \"assemblage-367-457\"\n",
" ]\n",
" node [\n",
" id 31\n",
" label \"assemblage-101-412\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"412\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"101\"\n",
" parent_node \"assemblage-365-456\"\n",
" ]\n",
" node [\n",
" id 32\n",
" label \"assemblage-70-417\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"417\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"70\"\n",
" parent_node \"assemblage-388-443\"\n",
" ]\n",
" node [\n",
" id 33\n",
" label \"assemblage-88-439\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"439\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"88\"\n",
" parent_node \"assemblage-381-458\"\n",
" ]\n",
" node [\n",
" id 34\n",
" label \"assemblage-81-434\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"434\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"81\"\n",
" parent_node \"assemblage-367-457\"\n",
" ]\n",
" node [\n",
" id 35\n",
" label \"assemblage-80-418\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"418\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"80\"\n",
" parent_node \"assemblage-367-457\"\n",
" ]\n",
" node [\n",
" id 36\n",
" label \"assemblage-87-419\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"419\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"87\"\n",
" parent_node \"assemblage-388-443\"\n",
" ]\n",
" node [\n",
" id 37\n",
" label \"assemblage-94-416\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"416\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"94\"\n",
" parent_node \"assemblage-386-446\"\n",
" ]\n",
" node [\n",
" id 38\n",
" label \"assemblage-85-429\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"429\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"85\"\n",
" parent_node \"assemblage-391-459\"\n",
" ]\n",
" node [\n",
" id 39\n",
" label \"assemblage-69-406\"\n",
" appears_in_slice \"4\"\n",
" ycoord \"406\"\n",
" level \"None\"\n",
" cluster_id \"3\"\n",
" xcoord \"69\"\n",
" parent_node \"assemblage-393-473\"\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 1\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 2\n",
" distance 5.83095189485\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 3\n",
" distance 5.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 4\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 5\n",
" distance 4.69041575982\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 6\n",
" distance 7.4161984871\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 7\n",
" distance 7.07106781187\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 8\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 0\n",
" target 9\n",
" distance 2.82842712475\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 2\n",
" distance 6.63324958071\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 3\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 4\n",
" distance 2.82842712475\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 5\n",
" distance 5.65685424949\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 6\n",
" distance 7.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 7\n",
" distance 6.63324958071\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 8\n",
" distance 2.44948974278\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 9\n",
" distance 4.24264068712\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 1\n",
" target 13\n",
" distance 15.1657508881\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 3\n",
" distance 5.9160797831\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 4\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 5\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 6\n",
" distance 6.2449979984\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 7\n",
" distance 7.34846922835\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 8\n",
" distance 6.16441400297\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 9\n",
" distance 5.09901951359\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 2\n",
" target 11\n",
" distance 16.5227116419\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 4\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 5\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 6\n",
" distance 5.47722557505\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 7\n",
" distance 5.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 8\n",
" distance 3.87298334621\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 9\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 3\n",
" target 39\n",
" distance 17.7200451467\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 5\n",
" distance 5.29150262213\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 6\n",
" distance 6.40312423743\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 7\n",
" distance 6.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 8\n",
" distance 2.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 9\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 4\n",
" target 28\n",
" distance 15.6204993518\n",
" ]\n",
" edge [\n",
" source 5\n",
" target 38\n",
" distance 17.2626765016\n",
" ]\n",
" edge [\n",
" source 5\n",
" target 6\n",
" distance 6.2449979984\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 5\n",
" target 7\n",
" distance 6.48074069841\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 5\n",
" target 8\n",
" distance 5.09901951359\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 5\n",
" target 9\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 6\n",
" target 7\n",
" distance 4.12310562562\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 6\n",
" target 8\n",
" distance 6.7082039325\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 6\n",
" target 9\n",
" distance 7.28010988928\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 6\n",
" target 14\n",
" distance 17.378147197\n",
" ]\n",
" edge [\n",
" source 7\n",
" target 32\n",
" distance 18.5202591775\n",
" ]\n",
" edge [\n",
" source 7\n",
" target 8\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 7\n",
" target 9\n",
" distance 6.92820323028\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 7\n",
" target 27\n",
" distance 16.6433169771\n",
" ]\n",
" edge [\n",
" source 8\n",
" target 9\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 9\n",
" target 23\n",
" distance 15.3948043183\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 11\n",
" distance 5.47722557505\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 12\n",
" distance 5.19615242271\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 13\n",
" distance 6.7082039325\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 14\n",
" distance 4.69041575982\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 15\n",
" distance 7.48331477355\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 16\n",
" distance 6.40312423743\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 17\n",
" distance 7.48331477355\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 18\n",
" distance 7.4161984871\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 10\n",
" target 19\n",
" distance 6.16441400297\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 12\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 13\n",
" distance 5.74456264654\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 14\n",
" distance 5.09901951359\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 15\n",
" distance 6.63324958071\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 16\n",
" distance 6.0827625303\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 17\n",
" distance 6.63324958071\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 18\n",
" distance 6.5574385243\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 11\n",
" target 19\n",
" distance 6.78232998313\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 13\n",
" distance 4.24264068712\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 14\n",
" distance 2.64575131106\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 15\n",
" distance 5.38516480713\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 16\n",
" distance 4.24264068712\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 17\n",
" distance 5.38516480713\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 18\n",
" distance 5.29150262213\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 19\n",
" distance 5.19615242271\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 12\n",
" target 26\n",
" distance 14.5602197786\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 36\n",
" distance 20.6397674406\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 14\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 15\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 16\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 17\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 18\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 13\n",
" target 19\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 37\n",
" distance 20.4205778567\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 15\n",
" distance 5.83095189485\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 16\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 17\n",
" distance 5.83095189485\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 18\n",
" distance 5.74456264654\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 14\n",
" target 19\n",
" distance 4.472135955\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 15\n",
" target 16\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 15\n",
" target 17\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 15\n",
" target 18\n",
" distance 2.64575131106\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 15\n",
" target 19\n",
" distance 5.47722557505\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 16\n",
" target 17\n",
" distance 5.56776436283\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 16\n",
" target 18\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 16\n",
" target 19\n",
" distance 3.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 17\n",
" target 18\n",
" distance 4.12310562562\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 17\n",
" target 19\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 17\n",
" target 22\n",
" distance 14.3874945699\n",
" ]\n",
" edge [\n",
" source 18\n",
" target 19\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 19\n",
" target 32\n",
" distance 21.4941852602\n",
" ]\n",
" edge [\n",
" source 19\n",
" target 27\n",
" distance 15.8113883008\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 21\n",
" distance 4.472135955\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 22\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 23\n",
" distance 4.472135955\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 24\n",
" distance 4.89897948557\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 25\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 26\n",
" distance 5.74456264654\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 27\n",
" distance 4.0\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 28\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 20\n",
" target 29\n",
" distance 5.47722557505\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 22\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 23\n",
" distance 4.0\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 24\n",
" distance 2.44948974278\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 25\n",
" distance 3.0\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 26\n",
" distance 5.9160797831\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 27\n",
" distance 2.82842712475\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 28\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 21\n",
" target 29\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 23\n",
" distance 1.73205080757\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 24\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 25\n",
" distance 4.24264068712\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 26\n",
" distance 6.92820323028\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 27\n",
" distance 2.2360679775\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 28\n",
" distance 1.41421356237\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 22\n",
" target 29\n",
" distance 4.12310562562\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 24\n",
" distance 4.69041575982\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 25\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 26\n",
" distance 7.14142842854\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 27\n",
" distance 2.82842712475\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 28\n",
" distance 2.2360679775\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 23\n",
" target 29\n",
" distance 4.472135955\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 37\n",
" distance 13.9642400438\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 25\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 26\n",
" distance 5.38516480713\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 27\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 28\n",
" distance 4.12310562562\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 24\n",
" target 29\n",
" distance 2.44948974278\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 25\n",
" target 26\n",
" distance 5.47722557505\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 25\n",
" target 27\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 25\n",
" target 28\n",
" distance 4.0\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 25\n",
" target 29\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 26\n",
" target 27\n",
" distance 6.5574385243\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 26\n",
" target 28\n",
" distance 6.78232998313\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 26\n",
" target 29\n",
" distance 5.56776436283\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 27\n",
" target 39\n",
" distance 14.2126704036\n",
" ]\n",
" edge [\n",
" source 27\n",
" target 28\n",
" distance 1.73205080757\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 27\n",
" target 29\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 28\n",
" target 29\n",
" distance 3.87298334621\n",
" normalized_weight 0.5\n",
" unnormalized_weight 0.5\n",
" weight 0.5\n",
" ]\n",
" edge [\n",
" source 28\n",
" target 31\n",
" distance 13.3790881603\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 32\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 33\n",
" distance 5.09901951359\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 34\n",
" distance 3.74165738677\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 35\n",
" distance 2.64575131106\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 36\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 37\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 38\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 39\n",
" distance 5.09901951359\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 30\n",
" target 31\n",
" distance 5.83095189485\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 32\n",
" distance 6.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 33\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 34\n",
" distance 6.48074069841\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 35\n",
" distance 5.19615242271\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 36\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 37\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 38\n",
" distance 5.74456264654\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 31\n",
" target 39\n",
" distance 6.16441400297\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 33\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 34\n",
" distance 5.29150262213\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 35\n",
" distance 3.31662479036\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 36\n",
" distance 4.35889894354\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 37\n",
" distance 5.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 38\n",
" distance 5.19615242271\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 32\n",
" target 39\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 34\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 35\n",
" distance 5.38516480713\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 36\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 37\n",
" distance 5.38516480713\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 38\n",
" distance 3.60555127546\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 33\n",
" target 39\n",
" distance 7.21110255093\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 34\n",
" target 35\n",
" distance 4.12310562562\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 34\n",
" target 36\n",
" distance 4.58257569496\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 34\n",
" target 37\n",
" distance 5.56776436283\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 34\n",
" target 38\n",
" distance 3.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 34\n",
" target 39\n",
" distance 6.32455532034\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 35\n",
" target 36\n",
" distance 2.82842712475\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 35\n",
" target 37\n",
" distance 4.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 35\n",
" target 38\n",
" distance 4.0\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 35\n",
" target 39\n",
" distance 4.79583152331\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 36\n",
" target 37\n",
" distance 3.16227766017\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 36\n",
" target 38\n",
" distance 3.46410161514\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 36\n",
" target 39\n",
" distance 5.56776436283\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 37\n",
" target 38\n",
" distance 4.69041575982\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 37\n",
" target 39\n",
" distance 5.9160797831\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
" edge [\n",
" source 38\n",
" target 39\n",
" distance 6.2449979984\n",
" normalized_weight 0.5\n",
" weight 0.5\n",
" unnormalized_weight 0.5\n",
" ]\n",
"]\n"
]
}
],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
Startupsci/data-science-notebooks | .ipynb_checkpoints/python-data-files-checkpoint.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Data Files\n",
"\n",
"File operations using Python and libraries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CSV files in native Python\n",
"\n",
"Python provides a native module to perform CSV file operations.\n",
"\n",
"[Official documentation: CSV module in Python 2.7](https://docs.python.org/2/library/csv.html)\n",
"\n",
"### List to CSV"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"game-scores.csv\n",
"vehicles.csv\n",
"\n"
]
}
],
"source": [
"# For reading/writing CSV files\n",
"import csv\n",
"# For listing system file folders\n",
"from subprocess import check_output\n",
"\n",
"# Use with open to ensure file is closed when block ends\n",
"# The wb flag opens file for writing\n",
"with open('data/fileops/vehicles.csv', 'wb') as csv_file:\n",
" # Prepare csv writer\n",
" wtr = csv.writer(csv_file, delimiter=',', quotechar='\"',\n",
" quoting=csv.QUOTE_MINIMAL)\n",
" # Write CSV header row\n",
" wtr.writerow(['type', 'wheels', 'speed', 'weight', 'invented'])\n",
" # Write CSV data rows\n",
" wtr.writerow(['Scooter', 2, 150, 109.78, 1817])\n",
" wtr.writerow(['Car', 4, 250, 1818.45, 1885]) \n",
" wtr.writerow(['Plane', 10, 850, 270000, 1903])\n",
"\n",
"# Check file created\n",
"print(check_output([\"ls\", \"data/fileops\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CSV to List"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"type\twheels\tspeed\tweight\tinvented\n",
"Scooter\t2\t150\t109.78\t1817\n",
"Car\t4\t250\t1818.45\t1885\n",
"Plane\t10\t850\t270000\t1903\n"
]
}
],
"source": [
"# The rb flag opens file for reading\n",
"with open('data/fileops/vehicles.csv', 'rb') as csv_file:\n",
" rdr = csv.reader(csv_file, delimiter=',', quotechar='\"')\n",
" for row in rdr:\n",
" print '\\t'.join(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dictionary to CSV"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"game-scores.csv\n",
"vehicles.csv\n",
"\n"
]
}
],
"source": [
"# Dictionary data structures can be used to represent rows\n",
"game1_scores = {'Game':'Quarter', 'Team A': 45, 'Team B': 90}\n",
"game2_scores = {'Game':'Semi', 'Team A': 80, 'Team B': 32}\n",
"game3_scores = {'Game':'Final', 'Team A': 70, 'Team B': 68}\n",
"\n",
"headers = ['Game', 'Team A', 'Team B']\n",
"\n",
"# Create CSV from dictionaries\n",
"with open('data/fileops/game-scores.csv', 'wb') as df:\n",
" dict_wtr = csv.DictWriter(df, fieldnames=headers)\n",
" dict_wtr.writeheader()\n",
" dict_wtr.writerow(game1_scores)\n",
" dict_wtr.writerow(game2_scores)\n",
" dict_wtr.writerow(game3_scores)\n",
"\n",
"print(check_output([\"ls\", \"data/fileops\"]).decode(\"utf8\")) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CSV to Dictionary"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quarter\t45\t90\n",
"Semi\t80\t32\n",
"Final\t70\t68\n",
"Game\tTeam A\tTeam B\n"
]
}
],
"source": [
"# Read CSV into dictionary data structure\n",
"with open('data/fileops/game-scores.csv', 'rb') as df:\n",
" dict_rdr = csv.DictReader(df)\n",
" for row in dict_rdr:\n",
" print('\\t'.join([row['Game'], row['Team A'], row['Team B']]))\n",
" print('\\t'.join(row.keys()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pandas for CSV file operations\n",
"\n",
"Pandas goal is to become the most powerful and flexible open source data analysis / manipulation tool available in any language. Pandas includes file operations capabilities for CSV, among other formats.\n",
"\n",
"CSV operations in Pandas are much faster than in native Python.\n",
"\n",
"### DataFrame to CSV"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Product</th>\n",
" <th>Region</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Josh</td>\n",
" <td>PC</td>\n",
" <td>North</td>\n",
" <td>34.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Eli</td>\n",
" <td>Phone</td>\n",
" <td>South</td>\n",
" <td>12.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ram</td>\n",
" <td>SW</td>\n",
" <td>West</td>\n",
" <td>4.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bil</td>\n",
" <td>Cloud</td>\n",
" <td>East</td>\n",
" <td>31.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Product Region Sales\n",
"0 Josh PC North 34.32\n",
"1 Eli Phone South 12.10\n",
"2 Ram SW West 4.77\n",
"3 Bil Cloud East 31.63"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Create a DataFrame\n",
"df = pd.DataFrame({\n",
" 'Name' : ['Josh', 'Eli', 'Ram', 'Bil'],\n",
" 'Sales' : [34.32, 12.1, 4.77, 31.63],\n",
" 'Region' : ['North', 'South', 'West', 'East'],\n",
" 'Product' : ['PC', 'Phone', 'SW', 'Cloud']})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"game-scores.csv\n",
"sales.csv\n",
"sales.xlsx\n",
"vehicles.csv\n",
"\n"
]
}
],
"source": [
"# DataFrame to CSV\n",
"df.to_csv('data/fileops/sales.csv', index=False)\n",
"\n",
"print(check_output([\"ls\", \"data/fileops\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CSV to DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Product</th>\n",
" <th>Region</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Josh</td>\n",
" <td>PC</td>\n",
" <td>North</td>\n",
" <td>34.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Eli</td>\n",
" <td>Phone</td>\n",
" <td>South</td>\n",
" <td>12.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ram</td>\n",
" <td>SW</td>\n",
" <td>West</td>\n",
" <td>4.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bil</td>\n",
" <td>Cloud</td>\n",
" <td>East</td>\n",
" <td>31.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Product Region Sales\n",
"0 Josh PC North 34.32\n",
"1 Eli Phone South 12.10\n",
"2 Ram SW West 4.77\n",
"3 Bil Cloud East 31.63"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CSV to DataFrame\n",
"df2 = pd.read_csv('data/fileops/sales.csv')\n",
"\n",
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame to Excel"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"game-scores.csv\n",
"sales.csv\n",
"sales.xlsx\n",
"vehicles.csv\n",
"\n"
]
}
],
"source": [
"# DataFrame to XLSX Excel file\n",
"df.to_excel('data/fileops/sales.xlsx', index=False)\n",
"\n",
"print(check_output([\"ls\", \"data/fileops\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Excel to DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Product</th>\n",
" <th>Region</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Josh</td>\n",
" <td>PC</td>\n",
" <td>North</td>\n",
" <td>34.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Eli</td>\n",
" <td>Phone</td>\n",
" <td>South</td>\n",
" <td>12.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ram</td>\n",
" <td>SW</td>\n",
" <td>West</td>\n",
" <td>4.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bil</td>\n",
" <td>Cloud</td>\n",
" <td>East</td>\n",
" <td>31.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Product Region Sales\n",
"0 Josh PC North 34.32\n",
"1 Eli Phone South 12.10\n",
"2 Ram SW West 4.77\n",
"3 Bil Cloud East 31.63"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Excel to DataFrame\n",
"df3 = pd.read_excel('data/fileops/sales.xlsx')\n",
"\n",
"df3"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
openp2pdesign/Labs-Survey---Analysis |
|
|
| {
"metadata": {
"name": "",
"signature": "sha256:f0af035e42f58837c5b2ddd04d60fae2d5fc241c443eb6f2ad823391504ce0ae"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Q016 - Da dove provengono le risorse che hanno permesso la nascita del laboratorio?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# -*- coding: UTF-8 -*-\n",
"\n",
"# Render our plots inline\n",
"%matplotlib inline \n",
"\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn\n",
"import shutil\n",
"\n",
"pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn\n",
"pd.set_option('display.max_columns', None) # Display all the columns\n",
"plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs\n",
"\n",
"# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html\n",
"\n",
"# Change the font\n",
"matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Load csv file first\n",
"data = pd.read_csv(\"data/lab-survey.csv\", encoding=\"utf-8\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Check data\n",
"#data[0:4] # Equals to data.head()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Range: D16[SQ001] - D16[SQ011] - D16[other]\n",
"\n",
"funding_columns = [\"D16[SQ001]\",\"D16[SQ002]\",\"D16[SQ003]\",\"D16[SQ004]\",\n",
" \"D16[SQ005]\",\"D16[SQ006]\",\"D16[SQ007]\",\"D16[SQ008]\",\n",
" \"D16[SQ009]\",\"D16[SQ010]\",\"D16[SQ011]\",\"D16[SQ012]\",\"D16[SQ013]\"]\n",
"funding_options = ['Singolo individuo privato',\n",
" 'Gruppo di individui privati',\n",
" 'Scuola primaria (scuola elementare)',\n",
" u'Universita\u0301',\n",
" 'Museo',\n",
" 'Centro di ricerca',\n",
" \"Incubatore o acceleratore d'impresa\",\n",
" 'Coworking',\n",
" 'Impresa privata',\n",
" 'Fondazione',\n",
" 'Partecipazione a bando pubblico',\n",
" 'Scuola secondaria di primo grado (scuola media)',\n",
" 'Scuola secondaria di secondo grado (scuola superiore)']\n",
"funding = data[funding_columns]\n",
"funding.replace(u'S\u00ec', 'Si', inplace=True) # Get rid of accented characters \n",
"funding_other = data['D16[other]'].str.lower().value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:20: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#funding[0:4]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Combinations..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create all the possible combinations from the main options\n",
"# See http://stackoverflow.com/questions/17176887/python-get-all-permutation-of-a-list-w-o-repetitions\n",
"\n",
"import itertools \n",
"\n",
"all_combinations = {}\n",
"all_combinations_columns = []\n",
"\n",
"for i in range(1, len(funding_columns)+1):\n",
" comb = list(itertools.combinations(funding_columns, i))\n",
" for k in comb:\n",
" # Each combination\n",
" all_combinations[k] = {}\n",
" all_combinations[k][\"col_list\"] = list(k)\n",
" # Build the string and boolean list of each combination\n",
" comb_list = []\n",
" comb_bool_list = []\n",
" # Put default False value\n",
" for l in funding_columns:\n",
" comb_bool_list.append(False)\n",
" for j in k:\n",
" pos = funding_columns.index(j) # Get position\n",
" comb_list.append(funding_options[pos])\n",
" comb_bool_list[pos] = True\n",
" all_combinations[k][\"list\"] = comb_list\n",
" all_combinations[k][\"bool_list\"] = comb_bool_list\n",
" all_combinations[k][\"str\"] = \", \".join(comb_list)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Check which combinations correspond each row\n",
"str_values = []\n",
"for i in funding.index:\n",
" current_bool_list = list(funding.ix[i].isin([\"Si\"]))\n",
" for i in all_combinations:\n",
" if current_bool_list == all_combinations[i][\"bool_list\"]:\n",
" str_values.append(all_combinations[i][\"str\"])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Add combination column\n",
"funding[\"Combination\"] = pd.Series(str_values)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Gather data\n",
"resulting_combinations = funding[\"Combination\"].value_counts()\n",
"resulting_combinations_percentage = funding[\"Combination\"].value_counts(normalize=True)*100"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Plotting the first 10 values of the most popular combinations\n",
"resulting_combinations[0:10].plot(kind='bar',figsize=(20,10),rot=90)\n",
"plt.title(\"Da dove provengono le risorse che hanno permesso la nascita del laboratorio? Combinazioni\", fontsize=18, y=1.02)\n",
"plt.ylabel(\"Lab\", fontsize=16)\n",
"plt.xlabel(\"Combinazioni\", fontsize=16)\n",
"plt.savefig(\"svg/Q016-Combinazioni.svg\")\n",
"plt.savefig(\"png/Q016-Combinazioni.png\")\n",
"plt.savefig(\"pdf/Q016-Combinazioni.pdf\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABIwAAANxCAYAAACG/SLmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x10e7c8910>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%capture output\n",
"\n",
"# Save the output as a variable that can be saved to a file\n",
"# Data of the combinations\n",
"print \"Data:\"\n",
"print resulting_combinations\n",
"print\n",
"# Data of the combinations: percentage\n",
"print \"Data %:\"\n",
"print resulting_combinations_percentage"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Save+show the output to a text file\n",
"%save Q016-Combinazioni.py str(output)\n",
"shutil.move(\"Q016-Combinazioni.py\", \"text/Q016-Combinazioni.txt\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The following commands were written to file `Q016-Combinazioni.py`:\n",
"Data:\n",
"Gruppo di individui privati 26\n",
"Gruppo di individui privati, Impresa privata 6\n",
"Singolo individuo privato 6\n",
"Partecipazione a bando pubblico 5\n",
"Singolo individuo privato, Gruppo di individui privati 2\n",
"Gruppo di individui privati, Partecipazione a bando pubblico 2\n",
"Gruppo di individui privati, Fondazione 2\n",
"Scuola primaria (scuola elementare), Centro di ricerca 2\n",
"Impresa privata, Partecipazione a bando pubblico 2\n",
"Impresa privata 1\n",
"Gruppo di individui privati, Centro di ricerca, Impresa privata 1\n",
"Singolo individuo privato, Gruppo di individui privati, Impresa privata 1\n",
"Impresa privata, Fondazione 1\n",
"Centro di ricerca 1\n",
"Gruppo di individui privati, Impresa privata, Fondazione 1\n",
"Gruppo di individui privati, Museo 1\n",
"Gruppo di individui privati, Coworking 1\n",
"Gruppo di individui privati, Coworking, Impresa privata 1\n",
"Incubatore o acceleratore d'impresa 1\n",
"Fondazione 1\n",
"Gruppo di individui privati, Scuola secondaria di secondo grado (scuola superiore) 1\n",
"Singolo individuo privato, Partecipazione a bando pubblico 1\n",
"Scuola secondaria di secondo grado (scuola superiore) 1\n",
"dtype: int64\n",
"\n",
"Data %:\n",
"Gruppo di individui privati 37.142857\n",
"Gruppo di individui privati, Impresa privata 8.571429\n",
"Singolo individuo privato 8.571429\n",
"Partecipazione a bando pubblico 7.142857\n",
"Singolo individuo privato, Gruppo di individui privati 2.857143\n",
"Gruppo di individui privati, Partecipazione a bando pubblico 2.857143\n",
"Gruppo di individui privati, Fondazione 2.857143\n",
"Scuola primaria (scuola elementare), Centro di ricerca 2.857143\n",
"Impresa privata, Partecipazione a bando pubblico 2.857143\n",
"Impresa privata 1.428571\n",
"Gruppo di individui privati, Centro di ricerca, Impresa privata 1.428571\n",
"Singolo individuo privato, Gruppo di individui privati, Impresa privata 1.428571\n",
"Impresa privata, Fondazione 1.428571\n",
"Centro di ricerca 1.428571\n",
"Gruppo di individui privati, Impresa privata, Fondazione 1.428571\n",
"Gruppo di individui privati, Museo 1.428571\n",
"Gruppo di individui privati, Coworking 1.428571\n",
"Gruppo di individui privati, Coworking, Impresa privata 1.428571\n",
"Incubatore o acceleratore d'impresa 1.428571\n",
"Fondazione 1.428571\n",
"Gruppo di individui privati, Scuola secondaria di secondo grado (scuola superiore) 1.428571\n",
"Singolo individuo privato, Partecipazione a bando pubblico 1.428571\n",
"Scuola secondaria di secondo grado (scuola superiore) 1.428571\n",
"dtype: float64\n",
"\n"
]
}
],
"prompt_number": 12
}
],
"metadata": {}
}
]
} |
|
|
|
|
|
| {
"metadata": {
"name": "",
"signature": "sha256:37248f7f7a41e3eb04cf5b67f060c059f813e2666c0788a754510e5ee5029075"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import "
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_pandas-summarize.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Pandas 5: Summarizing data \n",
"\n",
"Another in a series of notebooks that describe Pandas' powerful data management tools. In this one we summarize our data in a variety of ways. Which is more interesting than it sounds. \n",
"\n",
"Outline: \n",
"\n",
"* [WEO government debt data](#weo). Something to work with. How does Argentina's government debt compare to the debt of other countries? How did it compare when it defaulted in 2001? \n",
"* [Describing numerical data](#describe). Descriptive statistics: numbers of non-missing values, mean, median, quantiles. \n",
"* [Describing catgorical data](#value-counts). The excellent `value_counts` method. \n",
"* [Grouping data](#groupby). An incredibly useful collection of tools based on grouping data based on a variable: men and woman, grads and undergrads, and so on. \n",
"\n",
"**Note: requires internet access to run.** \n",
"\n",
"This Jupyter notebook was created by Dave Backus, Chase Coleman, and Spencer Lyon for the NYU Stern course [Data Bootcamp](http://databootcamp.nyuecon.com/). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=prelims></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries \n",
"\n",
"Import packages, etc. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys # system module \n",
"import pandas as pd # data package\n",
"import matplotlib.pyplot as plt # graphics module \n",
"import datetime as dt # date and time module\n",
"import numpy as np # foundation for Pandas \n",
"\n",
"%matplotlib inline \n",
"\n",
"# check versions (overkill, but why not?)\n",
"print('Python version:', sys.version)\n",
"print('Pandas version: ', pd.__version__)\n",
"print('Today: ', dt.date.today())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=weo></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WEO data on government debt \n",
"\n",
"We use the IMF's data on government debt again, specifically its [World Economic Outlook database](https://www.imf.org/external/ns/cs.aspx?id=28), commonly referred to as the WEO. We focus on government debt expressed as a percentage of GDP, variable code `GGXWDG_NGDP`. \n",
"\n",
"The **central question** here is how the debt of Argentina, which defaulted in 2001, compared to other countries. Was it a matter of too much debt or something else? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load data\n",
"\n",
"First step: load the data and extract a single variable: government debt (code `GGXWDG_NGDP`) expressed as a percentage of GDP. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url1 = \"http://www.imf.org/external/pubs/ft/weo/2016/02/weodata/\"\n",
"url2 = \"WEOOct2016all.xls\"\n",
"url = url1 + url2 \n",
"weo = pd.read_csv(url, sep='\\t', \n",
" usecols=[1,2] + list(range(19,46)), \n",
" thousands=',', \n",
" na_values=['n/a', '--']) \n",
"print('Variable dtypes:\\n', weo.dtypes.head(6), sep='')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean and shape \n",
"\n",
"Second step: select the variable we want and generate the two dataframes. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# select debt variable \n",
"variables = ['GGXWDG_NGDP']\n",
"db = weo[weo['WEO Subject Code'].isin(variables)]\n",
"\n",
"# drop variable code column (they're all the same) \n",
"db = db.drop('WEO Subject Code', axis=1)\n",
"\n",
"# set index to country code \n",
"db = db.set_index('ISO')\n",
"\n",
"# name columns \n",
"db.columns.name = 'Year'\n",
"\n",
"# transpose \n",
"dbt = db.T\n",
"\n",
"# see what we have \n",
"dbt.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example.** Let's try a simple graph of the dataframe `dbt`. The goal is to put Argentina in perspective by plotting it along with many other countries. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"dbt.plot(ax=ax, \n",
" legend=False, color='blue', alpha=0.3, \n",
" ylim=(0,150)\n",
" )\n",
"ax.set_ylabel('Percent of GDP')\n",
"ax.set_xlabel('')\n",
"ax.set_title('Government debt', fontsize=14, loc='left')\n",
"dbt['ARG'].plot(ax=ax, color='black', linewidth=1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise.** \n",
"\n",
"* What do you take away from this graph? \n",
"* What would you change to make it look better?\n",
"* To make it mnore informative?\n",
"* To put Argentina's debt in context? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise.** Do the same graph with Greece (GRC) as the country of interest. How does it differ? Why do you think that is? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=describe></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Describing numerical data \n",
"\n",
"Let's step back a minute. What we're trying to do is compare Argentina to other countries. What's the best way to do that? This isn't a question with an obvious best answer, but we can try some things, see how they look. One thing we could do is compare Argentina to the mean or median. Or to some other feature of the distribution. \n",
"\n",
"We work up to this by looking first at some features of the distribution of government debt numbers across countries. Some of this we've seen, some is new. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What's (not) there?\n",
"\n",
"Let's check out the data first. How many non-missing values do we have at each date? We can do that with the `count` method. The argument `axis=1` says to do this by date, counting across columns (axis number 1). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dbt.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# count non-missing values \n",
"dbt.count(axis=1).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Describing series \n",
"\n",
"Let's take the data for 2001 -- the year of Argentina's default -- and see what how Argentina compares. Was its debt high compare to other countries? \n",
"\n",
"which leads to more questions. How would we compare? Compare Argentina to the mean or median? Something else? \n",
"\n",
"Let's see how that works. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 2001 data \n",
"db01 = db['2001'] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db01['ARG']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db01.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db01.median()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db01.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db01.quantile(q=[0.25, 0.5, 0.75])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Comment.** If we add enough quantiles, we might as well plot the whole distribution. The easiest way to do this is with a histogram. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"db01.hist(bins=15, ax=ax, alpha=0.35)\n",
"ax.set_xlabel('Government Debt (Percent of GDP)')\n",
"ax.set_ylabel('Number of Countries')\n",
"\n",
"ymin, ymax = ax.get_ylim()\n",
"ax.vlines(db01['ARG'], ymin, ymax, color='blue', lw=2) "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Comment** Compared to the whole sample of countries in 2001, it doesn't seem that Argentina had particularly high debt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Describing dataframes\n",
"\n",
"We can compute the same statistics for dataframes. Here we hve a choice: we can compute (say) the mean down rows (`axis=0`) or across columns (`axis=1`). If we use the dataframe `dbt`, computing the mean across countries (columns) calls for `axis=1`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# here we compute the mean across countries at every date\n",
"dbt.mean(axis=1).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# or we could do the median\n",
"dbt.median(axis=1).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# or a bunch of stats at once \n",
"# NB: db not dbt (there's no axix argument here)\n",
"db.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# the other way \n",
"dbt.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Example.** Let's add the mean to our graph. We make it a dashed line with `linestyle='dashed'`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"dbt.plot(ax=ax, \n",
" legend=False, color='blue', alpha=0.2, \n",
" ylim=(0,200)\n",
" )\n",
"dbt['ARG'].plot(ax=ax, color='black', linewidth=1.5)\n",
"ax.set_ylabel('Percent of GDP')\n",
"ax.set_xlabel('')\n",
"ax.set_title('Government debt', fontsize=14, loc='left')\n",
"dbt.mean(axis=1).plot(ax=ax, color='black', linewidth=2, linestyle='dashed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question.** Do you think this looks better when the mean varies with time, or when we use a constant mean? Let's try it and see. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dbar = dbt.mean().mean()\n",
"dbar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"dbt.plot(ax=ax, \n",
" legend=False, color='blue', alpha=0.3, \n",
" ylim=(0,150)\n",
" )\n",
"dbt['ARG'].plot(ax=ax, color='black', linewidth=1.5)\n",
"ax.set_ylabel('Percent of GDP')\n",
"ax.set_xlabel('')\n",
"ax.set_title('Government debt', fontsize=14, loc='left') \n",
"xmin, xmax = ax.get_xlim()\n",
"ax.hlines(dbar, xmin, xmax, linewidth=2, linestyle='dashed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise.** Which do we like better?\n",
"\n",
"**Exercise.** Replace the (constant) mean with the (constant) median? Which do you prefer? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<a id=value-counts></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Describing categorical data \n",
"\n",
"A **categorical variable** is one that takes on a small number of values. States take on one of fifty values. University students are either grad or undergrad. Students select majors and concentrations. \n",
"\n",
"We're going to do two things with categorical data: \n",
"\n",
"* In this section, we count the number of observations in each category using the `value_counts` method. This is a series method, we apply it to one series/variable at a time. \n",
"* In the next section, we go on to describe how other variables differ across catagories. How do students who major in finance differ from those who major in English? And so on. \n",
"\n",
"We start with the combined MovieLens data we constructed in the previous notebook. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = 'http://pages.stern.nyu.edu/~dbackus/Data/mlcombined.csv'\n",
"ml = pd.read_csv(url, index_col=0,encoding = \"ISO-8859-1\")\n",
"print('Dimensions:', ml.shape)\n",
"\n",
"# fix up the dates\n",
"ml[\"timestamp\"] = pd.to_datetime(ml[\"timestamp\"], unit=\"s\")\n",
"ml.head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# which movies have the most ratings? \n",
"ml['title'].value_counts().head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ml['title'].value_counts().head(10).plot.barh(alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# which people have rated the most movies?\n",
"ml['userId'].value_counts().head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<a id=groupby></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Grouping data \n",
"\n",
"Next up: group data by some variable. As an example, how would we compute the average rating of each movie? If you think for a minute, you might think of these steps:\n",
"\n",
"* Group the data by movie: Put all the \"Pulp Fiction\" ratings in one bin, all the \"Shawshank\" ratings in another. We do that with the `groupby` method. \n",
"* Compute a statistic (the mean, for example) for each group. \n",
"\n",
"Pandas has tools that make that relatively easy. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# group \n",
"g = ml[['title', 'rating']].groupby('title')\n",
"type(g)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have a groupby object, what can we do with it? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# the number in each category\n",
"g.count().head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# what type of object have we created?\n",
"type(g.count())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Comment.** Note that the combination of `groupby` and `count` created a dataframe with\n",
"\n",
"* Its index is the variable we grouped by. If we group by more than one, we get a multi-index.\n",
"* Its columns are the other variables. \n",
"\n",
"**Exercise.** Take the code \n",
"\n",
"```python\n",
"counts = ml.groupby(['title', 'movieId'])\n",
"```\n",
"\n",
"Without running it, what is the index of `counts`? What are its columns? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"counts = ml.groupby(['title', 'movieId']).count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gm = g.mean()\n",
"gm.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# we can put them together \n",
"grouped = g.count()\n",
"grouped = grouped.rename(columns={'rating': 'Number'})\n",
"grouped['Mean'] = g.mean()\n",
"grouped.head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grouped.plot.scatter(x='Number', y='Mean')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise.** Compute the median and add it to the dataframe. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"\n",
"The [Brandon Rhodes video](https://youtu.be/5JnMutdy6Fw) covers most of this, too. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| introduction_to_data_science/lesson_3/lecture/Calculating R^2.ipynb |
|
| {
"metadata": {
"name": "",
"signature": "sha256:60a8082dd301553f0535010e5edde68e6a15581799cb431a05fae044fd3fe9de"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"def compute_r_squared(data, predictions):\n",
" # Write a function that, given two input numpy arrays, 'data', and 'predictions,'\n",
" # returns the coefficient of determination, R^2, for the model that produced \n",
" # predictions.\n",
" # \n",
" # Numpy has a couple of functions -- np.mean() and np.sum() --\n",
" # that you might find useful, but you don't have to use them.\n",
"\n",
" # YOUR CODE GOES HERE\n",
" # Calculate denominator\n",
" SST = ((data - np.mean(data)) **2).sum()\n",
" # Calculate numerator\n",
" SSReg = ((predictions-data)**2).sum()\n",
" r_squared = 1 - SSReg / SST\n",
"\n",
" return r_squared"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
}
],
"metadata": {}
}
]
} |
|
|
| examples/notebooks/16_uncertainty.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"id": "0b396ae5",
"metadata": {},
"source": [
"# Uncertainty quantification\n",
"*R.A. Collenteur, University of Graz, WIP (May-2021)*\n",
"\n",
"In this notebook it is shown how to compute the uncertainty of the model simulation using the built-in uncertainty quantification options of Pastas. \n",
"\n",
"- Confidence interval of simulation\n",
"- Prediction interval of simulation\n",
"- Confidence interval of step response\n",
"- Confidence interval of block response\n",
"- Confidence interval of contribution\n",
"- Custom confidence intervals\n",
"\n",
"The compute the confidence intervals, parameters sets are drawn from a multivariate normal distribution based on the jacobian matrix obtained during parameter optimization. This method to quantify uncertainties has some underlying assumptions on the model residuals (or noise) that should be checked. This notebook only deals with parameter uncertainties and not with model structure uncertainties."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ff7a826d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python version: 3.8.2 (default, Mar 25 2020, 11:22:43) \n",
"[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
"Numpy version: 1.21.2\n",
"Scipy version: 1.7.1\n",
"Pandas version: 1.3.3\n",
"Pastas version: 0.19.0b\n",
"Matplotlib version: 3.4.3\n"
]
}
],
"source": [
"import pandas as pd\n",
"import pastas as ps\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ps.set_log_level(\"ERROR\")\n",
"ps.show_versions()"
]
},
{
"cell_type": "markdown",
"id": "73c48782",
"metadata": {},
"source": [
"## Create a model\n",
"\n",
"We first create a toy model to simulate the groundwater levels in southeastern Austria. We will use this model to illustrate how the different methods for uncertainty quantification can be used. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "77ba7a88",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit report GWL Fit Statistics\n",
"====================================================\n",
"nfev 34 EVP 74.66\n",
"nobs 365 R2 0.75\n",
"noise True RMSE 0.19\n",
"tmin 2007-01-01 00:00:00 AIC -2051.35\n",
"tmax 2016-12-31 00:00:00 BIC -2020.16\n",
"freq D Obj 0.63\n",
"warmup 3650 days 00:00:00 ___ \n",
"solver LeastSquares Interp. No\n",
"\n",
"Parameters (8 optimized)\n",
"====================================================\n",
" optimal stderr initial vary\n",
"rch_A 0.523738 ±9.98% 0.524588 True\n",
"rch_a 63.697197 ±13.24% 10.000000 True\n",
"rch_srmax 439.818445 ±42.59% 250.000000 True\n",
"rch_lp 0.250000 ±nan% 0.250000 False\n",
"rch_ks 917.004525 ±212.71% 100.000000 True\n",
"rch_gamma 4.937956 ±20.85% 2.000000 True\n",
"rch_kv 1.000000 ±nan% 1.000000 False\n",
"rch_simax 2.000000 ±nan% 2.000000 False\n",
"constant_d 262.645495 ±0.03% 263.166264 True\n",
"noise_alpha 122.549303 ±24.78% 10.000000 True\n",
"noise_beta 8.446770 ±12.38% 10.000000 True\n"
]
}
],
"source": [
"gwl = pd.read_csv(\"data_wagna/head_wagna.csv\", index_col=0, parse_dates=True, \n",
" squeeze=True, skiprows=2).loc[\"2006\":].iloc[0::10]\n",
"evap = pd.read_csv(\"data_wagna/evap_wagna.csv\", index_col=0, parse_dates=True, \n",
" squeeze=True, skiprows=2)\n",
"prec = pd.read_csv(\"data_wagna/rain_wagna.csv\", index_col=0, parse_dates=True, \n",
" squeeze=True, skiprows=2)\n",
"\n",
"# Model settings\n",
"tmin = pd.Timestamp(\"2007-01-01\") # Needs warmup\n",
"tmax = pd.Timestamp(\"2016-12-31\")\n",
"\n",
"ml = ps.Model(gwl)\n",
"sm = ps.RechargeModel(prec, evap, recharge=ps.rch.FlexModel(), \n",
" rfunc=ps.Exponential, name=\"rch\")\n",
"ml.add_stressmodel(sm)\n",
"\n",
"# Add the ARMA(1,1) noise model and solve the Pastas model\n",
"ml.add_noisemodel(ps.ArmaModel())\n",
"ml.solve(tmin=tmin, tmax=tmax, noise=True)"
]
},
{
"cell_type": "markdown",
"id": "6913529b",
"metadata": {},
"source": [
"## Diagnostic Checks\n",
"\n",
"Before we perform the uncertainty quantification, we should check if the underlying statistical assumptions are met. We refer to the notebook on Diagnostic checking for more details on this."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "75cd6a5f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ml.plots.diagnostics();"
]
},
{
"cell_type": "markdown",
"id": "6f0341d4",
"metadata": {},
"source": [
"## Confidence intervals\n",
"\n",
"After the model is calibrated, a `fit` attribute is added to the Pastas `Model` object (`ml.fit`). This object contains information about the optimizations (e.g., the jacobian matrix) and a number of methods that can be used to quantify uncertainties."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d60d9f94",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe781e0dee0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ci = ml.fit.ci_simulation(alpha=0.05, n=1000)\n",
"ax = ml.plot(figsize=(10,3));\n",
"ax.fill_between(ci.index, ci.iloc[:,0], ci.iloc[:,1], color=\"lightgray\")\n",
"ax.legend([\"Observations\", \"Simulation\", \"95% Confidence interval\"], ncol=3, loc=2)"
]
},
{
"cell_type": "markdown",
"id": "436a7029",
"metadata": {},
"source": [
"## Prediction interval"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "484e013e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe781d68e80>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ci = ml.fit.prediction_interval(n=1000)\n",
"ax = ml.plot(figsize=(10,3));\n",
"ax.fill_between(ci.index, ci.iloc[:,0], ci.iloc[:,1], color=\"lightgray\")\n",
"ax.legend([\"Observations\", \"Simulation\", \"95% Prediction interval\"], ncol=3, loc=2)"
]
},
{
"cell_type": "markdown",
"id": "cdc2d08d",
"metadata": {},
"source": [
"## Uncertainty of step response "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3bbeffab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe781c6ac10>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ci = ml.fit.ci_step_response(\"rch\")\n",
"ax = ml.plots.step_response(figsize=(6,2))\n",
"ax.fill_between(ci.index, ci.iloc[:,0], ci.iloc[:,1], color=\"lightgray\")\n",
"ax.legend([\"Simulation\", \"95% Prediction interval\"], ncol=3, loc=4)"
]
},
{
"cell_type": "markdown",
"id": "933f046d",
"metadata": {},
"source": [
"## Uncertainty of block response"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d1003124",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe781fa8160>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ci = ml.fit.ci_block_response(\"rch\")\n",
"ax = ml.plots.block_response(figsize=(6,2))\n",
"ax.fill_between(ci.index, ci.iloc[:,0], ci.iloc[:,1], color=\"lightgray\")\n",
"ax.legend([\"Simulation\", \"95% Prediction interval\"], ncol=3, loc=1)"
]
},
{
"cell_type": "markdown",
"id": "001df91f",
"metadata": {},
"source": [
"## Uncertainty of the contributions"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7edb337a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAADQCAYAAAAasZepAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACqwUlEQVR4nOydd5gcV5X231vVOUxPziON0igHy7JsY2PLASeiMazxZ+ICJsOyfCwGlmhM2IWPZBYTlrCsMSbYxhhjHHC2HCRbkqVRHE3O3TOdU3XV/f6oruqq7uqe7pnuSbq/59EjqWN1d9W95577nvcQSikYDAaDwWAwGAyGDLfQB8BgMBgMBoPBYCwmWIDMYDAYDAaDwWBoYAEyg8FgMBgMBoOhgQXIDAaDwWAwGAyGBhYgMxgMBoPBYDAYGkwLfQBG1NfX087OzoU+DAaDwWAwGAzGMmb//v1eSmlD9u2LMkDu7OzEvn37FvowGAwGg8FgMBjLGEJIv9HtTGLBYDAYDAaDwWBoYAEyg8FgMBgMBoOhgQXIDAaDwWAwGAyGhkWpQWYwGAwGo5IIgoChoSHE4/GFPhQGgzEP2Gw2tLe3w2w2F/V4FiAzGAwG44xjaGgIbrcbnZ2dIIQs9OEwGIwKQimFz+fD0NAQVq1aVdRzmMSCwWAwGGcc8XgcdXV1LDhmMM4ACCGoq6sraceIBcgMBoPBOCNhwfHiRxAESJK00IfBWAaUer2zAJnBYDAYDMaiQ5IkpFIpJBKJhT4UxhkIC5AZDAaDwVggbr31VmzevBnbtm3Djh078Pzzz+N973sfuru7y/L6nZ2d8Hq9BR/z9a9/Xff/V73qVWV577ly/Phx9PT0oKenB4cPH57zn6NHj874nt///vexZcsWbN68Gd/73vfU27/85S+jra0NO3bswI4dO/DAAw8AAJ555hls27YN55xzDk6dOgUA8Pv9uPLKK0EpNXyPPXv2YP369di+fTsuuOACHD9+fNbf0a9+9St89KMfBQDcfvvt+J//+Z+8j+3r68Nvf/tb9f/79u3Dxz/+8Vm/t5ZrrrkGfr9/xmMdGRkpy/sV4stf/jK+/e1vz/l1WIDMYDAYDMYCsHfvXtx///146aWXcOjQITzyyCPo6OjAz3/+c2zatGnejiM7QH722Wfn7b0LIYrivL7e4cOH8bOf/QwvvPACDh48iPvvvx8nT55U7//kJz+JAwcO4MCBA7jmmmsAAN/5znfwpz/9CV//+tfx4x//GABwyy234HOf+1zBLf077rgDBw8exLve9S58+tOfLvlYjfjgBz+Id77znXnvzw6Qd+3ahR/84Aclv48RDzzwAKqrqws+ZjYBciqVmsNRzQ0WIDMYDAaDsQCMjo6ivr4eVqsVAFBfX4/W1lbs2bMH+/btAwC4XC585jOfwdlnn43LL78cL7zwAvbs2YPVq1fjvvvuA6DPIgLA6173Ojz++OM57/emN70JZ599NjZv3oyf/vSnAICbb74ZsVgMO3bswI033qi+JyBX/n/605/Gli1bsHXrVtx1110AgMcffxx79uzBW97yFmzYsAE33nhj3mzpUuLo0aM477zz4HA4YDKZcPHFF+Oee+4p+Byz2YxYLIZoNAqz2Yyenh4MDw/j4osvLuo9L7roIjXz7HK58MUvfhHnnnsu9u7di//93//F7t27sWPHDnzgAx9Qg+Zf/vKX6OrqwsUXX4xnnnlGfS1t5vTUqVO4/PLLsX37duzcuRM9PT24+eab8dRTT2HHjh347ne/i8cffxyve93rAABTU1N405vehG3btuG8887DoUOH1Nf853/+Z/WcyxdQKzsVfX192LhxI97//vdj8+bNuOKKKxCLxfDHP/4R+/btw4033ogdO3YgFoth//79uPjii3H22WfjyiuvxOjoKAA5w/65z30OF198MW699VZ0dnaqOvRoNIqOjg4IgoCf/exnOOecc7B9+3Zcd911iEajRX3nxcJs3hgMBoNxRvOVvxxB90iwrK+5qbUKX3r95oKPueKKK/DVr34VXV1duPzyy3H99dfnBFaRSAR79uzBt771LVx77bX493//dzz88MPo7u7Gu971LrzhDW8o+ph+8YtfoLa2FrFYDOeccw6uu+46fPOb38Rtt92GAwcO5Dz+7rvvxoEDB3Dw4EF4vV6cc845uOiiiwAAL7/8Mo4cOYLW1lZccMEFeOaZZ3DhhRcWfSyLkS1btuDzn/88fD4f7HY7HnjgAezatUu9/7bbbsP//M//YNeuXfjOd76DmpoafPazn8VNN90Eu92O3/zmN/i///f/4pZbbin6Pf/yl79g69atAOTfesuWLfjqV7+Ko0eP4lvf+haeeeYZmM1mfPjDH8Ydd9yB17zmNfjSl76E/fv3w+Px4JJLLsFZZ52V87o33ngjbr75Zlx77bWIx+OQJAnf/OY38e1vfxv3338/AOgWUV/60pdw1lln4d5778U//vEPvPOd71TPiWPHjuGxxx5DKBTC+vXr8aEPfaigl/DJkydx55134mc/+xn+6Z/+CX/605/w9re/Hbfddhu+/e1vY9euXRAEAR/72Mfw5z//GQ0NDbjrrrvw+c9/Hr/4xS8AyDKVJ554AgDw0ksv4YknnsAll1yCv/zlL7jyyithNpvx5je/Ge9///sBAP/+7/+O//7v/8bHPvaxor/7mZgxQCaE/ALA6wBMUEq3GNz/aQA3al5vI4AGSukUIaQPQAiACCBFKd2V/XwGg8FgMM5EXC4X9u/fj6eeegqPPfYYrr/+enzzm9/UPcZiseCqq64CAGzduhVWqxVmsxlbt25FX19fSe/3gx/8QM2IDg4O4uTJk6irq8v7+Keffho33HADeJ5HU1MTLr74Yrz44ouoqqrC7t270d7eDgDYsWMH+vr6lnyAvHHjRnzmM5/Ba17zGrhcLmzfvh0mkxwmfehDH8IXvvAFEELwhS98AZ/61Kfwi1/8Ajt27MBzzz0HAHjyySfR2toKSimuv/56mM1mfOc730FTU1POe914442w2+3o7OzED3/4QwAAz/O47rrrAACPPvoo9u/fj3POOQcAEIvF0NjYiOeffx579uxBQ0MDAOD666/HiRMndK8dCoUwPDyMa6+9FoDcIGMmnn76afzpT38CAFx66aXw+XwIBAIAgNe+9rWwWq2wWq1obGzE+Pi4+tsbsWrVKuzYsQMAcPbZZxuep8ePH8fhw4fxmte8BoAsKWlpaVHvv/7663X/vuuuu3DJJZfgd7/7HT784Q8DkCUx//7v/w6/349wOIwrr7xyxs9ZCsVkkH8F4DYAhspvSul/AvhPACCEvB7AJymlU5qHXEIpLVwhwGAwGAzGAjFTpreS8DyPPXv2YM+ePdi6dSt+/etf6+43m82qlpXjOFWOwXGcqs80mUw6KzQjr9fHH38cjzzyCPbu3QuHw4E9e/bM6AlbSDahHIfyGRZSK1pO3vve9+K9730vAOBzn/ucGghqg9z3v//9qjRBgVKKr33ta7jrrrvw0Y9+FF/5ylfQ19eHH/zgB7j11ltz3ueOO+7QZacBOZDleV59vXe96134xje+oXvMvffeO6Nd2WzkLkbPUd6n1N86+/GxWMzw/TZv3oy9e/cavobT6VT//YY3vAGf/exnMTU1hf379+PSSy8FALz73e/Gvffei+3bt+NXv/qVoaxoLsyoQaaUPglgaqbHpbkBwJ1zOiIGg8FgMM4Ajh8/risCO3DgAFauXFny63R2duLAgQOQJAmDg4N44YUXch4TCARQU1MDh8OBY8eOqVlPQA7CBUHIec5FF12Eu+66C6IoYnJyEk8++SR2795d8vEtJSYmJgAAAwMDuPvuu3HDDTcAgKqPBYB77rkHW7boN9R//etf47WvfS1qamoQjUbBcRw4jpu1Lvayyy7DH//4R/V4pqam0N/fj3PPPRePP/44fD4fBEHAH/7wh5znVlVVob29Hffeey8AIJFIIBqNwu12IxQKGb7fRRddhDvuuAOAvJiqr69HVVXVrI49H9r3X79+PSYnJ9UAWRAEHDlyxPB5LpcLu3fvxic+8Qm87nWvUxcRoVAILS0tEARBPfZyUjYNMiHEAeAqAB/V3EwBPEQIoQB+Qin9abnej8FgMBiMpUw4HMbHPvYx+P1+mEwmrF27Fj/96U/xlre8paTXueCCC7Bq1Sps3boVW7Zswc6dO3Mec9VVV+H222/Htm3bsH79epx33nnqfTfddBO2bduGnTt36gKNa6+9Fnv37sX27dtBCMF//Md/oLm5GceOHZv9hy4BnufL6mShBFaFuO666+Dz+WA2m/GjH/0INTU1AIB/+7d/w4EDB0AIQWdnJ37yk5+oz4lGo/j1r3+Nhx56CADwr//6r7juuutgsVhw552zyxlu2rQJX/va13DFFVdAkiT1eM477zx8+ctfxvnnn4+Wlhbs3LnT8Dv6zW9+gw984AP44he/CLPZjD/84Q/Ytm0bTCYTtm/fjne/+9067fKXv/xlvOc978G2bdvgcDhydjLKwbvf/W588IMfhN1ux969e/HHP/4RH//4xxEIBJBKpfAv//Iv2LzZeDfn+uuvx1vf+lZdlviWW27Bueeei5UrV2Lr1q15g//ZQopJxRNCOgHcb6RB1jzmegBvp5S+XnNbK6V0hBDSCOBhAB9LZ6SNnn8TgJsAYMWKFWf39/eX9EEYDAaDwSiWo0ePYuPGjQt9GIwCZG/NWyyWooJcBiMfRtc9IWS/UY1cOW3e3oYseQWldCT99wSAewDk3ZuhlP6UUrqLUrpLEZ8zGAwGg8E4M/FGU+j3JyGlE3nLwUqOsXQoS4BMCPEAuBjAnzW3OQkhbuXfAK4AcLgc78dgMBgMBmP5IkoSpmMikiJFJCkXIGoLERmMSlOMzdudAPYAqCeEDAH4EgAzAFBKb08/7FoAD1FKI5qnNgG4J10FaQLwW0rpg+U7dAaDwWAwZg+ldEZHAMbCIIiZbHFMoHBbWQaZMTdKPX9mDJAppTcU8ZhfQbaD0952GsD2ko6GwWAwGIx5wGazwefzoa6ujgXJi5CUlAlmkiKTWDDmBqUUPp+vKE9oBdZJj8FgMBhnHO3t7RgaGsLk5ORCHwrDgFhShC+ShJkDRApEHTwIIQU7uDEYhbDZbAUbnGTDAmQGg8FgnHGYzWasWrVqoQ+DkYffPt+Pz913GG/c4Mafj4Vw51vb4bZy2LBhA8v4M+aFcrpYMBgMBoPBYMyZ6WgSALChQe7KNhRMgRBi2NCEwagELEBmMBgMBoOxqIgmRBAAq2ssAICRoABCCJLJ5MIeGOOMgQXIDAaDwWAwFhWJlAgLT9DoNIEAmIikAIBlkBnzBguQGQwGg8FgLCoSgggzT2DmCaptPCajIiRJQiqVWuhDY5whsACZwWAwGAzGoiKekmDh5WK8eiePyXQGOZFILORhMc4gWIDMYDAYDAZjUZFMSywAoMFhgjcqAmASC8b8wQJkBoPBYDAYi4qEIMGsySB7IylQSlmAzJg3mA8yg8FgMBiMRUVCI7FocJgQS1FEkhJ4nmmQGfMDyyAzGAwGg8FYVCS0EgsnDwBqoZ4kSQt5aIwzBBYgMxgMBoPBWFQkUhqJhUPe7PZGWLMQxvzBAmQGg8FgMBiLimSWiwUgZ5BZgMyYL1iAzGAwGAwGY1Gh1SDX2HjwBKrVGwuQGfPBjAEyIeQXhJAJQsjhPPfvIYQECCEH0n++qLnvKkLIcULIKULIzeU8cAaDwWAwGMuTREqCmZMDZJ4jqHPw8KY1yCxAZswHxWSQfwXgqhke8xSldEf6z1cBgBDCA/gRgKsBbAJwAyFk01wOlsFgMBgMxvJHlCj4dIAMyDpk1iyEMZ/MGCBTSp8EMDWL194N4BSl9DSlNAngdwDeOIvXYTAYDAaDcQaRkij4THyMBqfcbhoAksnkAh0V40yiXBrk8wkhBwkhfyOEbE7f1gZgUPOYofRthhBCbiKE7COE7JucnCzTYTEYDAaDwVhqiBKFJoGMeocJvmgKEmsWwpgnyhEgvwRgJaV0O4AfArg3fTsxeCzN9yKU0p9SSndRSnc1NDSU4bAYDAaDwWAsRVLZEgsnj5QEBOISRFEEpXnDCQajLMw5QKaUBiml4fS/HwBgJoTUQ84Yd2ge2g5gZK7vx2AwGAwGY3kjSRQ8yQTIDYoXclTWIYuiuCDHxThzmHOATAhpJkQ+iwkhu9Ov6QPwIoB1hJBVhBALgLcBuG+u78dgMBgMBmN5I1IKXhOh1DvSXsgR5oXMmB9MMz2AEHIngD0A6gkhQwC+BMAMAJTS2wG8BcCHCCEpADEAb6Py3keKEPJRAH8HwAP4BaX0SEU+BYPBYDAYjGVDKiuDXO/UZ5AFQYDdbl+QY2OcGcwYIFNKb5jh/tsA3JbnvgcAPDC7Q2MwGAwGg3EmIkoUhGR0xh4rBzMHeCOy/phlkBmVhnXSYzAYDAaDsagQszLIhBDUO0zwRlOglDIvZEbFYQEyg8FgMBiMRQOlFBKFqkFOlzmh3il30wOAeDy+UIfHOENgATKDwWAwGIxFgyjJ0gqeEBBCVEu3eocJk2kNciwWY04WjIrCAmQGg8FgLBkkSUIgEKi4D64kSRgaGmJd2xaAVDpA5jg5e2yxWADITha+qKgG0NPT0wt2jIzlDwuQGQwGg7FkmJycxODgIHw+X0XfJxQKwe/3w+v1VvR9GLkoATAHeaFSU1MDQgganCZIFJiOy4V6LEBmVBIWIDMYZeT06dMIhUILfRgMxrJFub68Xm9Fs8iRSASAnKWUJKli78PIRckg8xwBz/Ow2WzpIj3ZC9mX1iEnk0mkUqkFO07G8oYFyAxGmRAEAdFoFJOTkwt9KAzGskWRPIiiWFEng1gsBkDe4lf+zZgfJFWDDJjNZlitVlBKVS/kyYgcFBNCEI1GF+w4GcsbFiAzGGVCEAR1Ms3ObPn9foyNjS3QkTEYywNRFHXZ3Eru1ig+u5RS5pgwz2gzyGazWdUgN6QzyIqThSRJaqafwSg3LEBmMMpEIpFQK66VLLIkSZAkCWNjY/B6vazgh8GYA7FYDBwnT1uU0ooGyIpDAqWUZZDnGVWDTACTSc4am81muCwcrDxRM8gAWAaZUTFYgMxglIFUKoXh4WE1u6UUEA0ODqK7uxupVAqEEJbtYDDmQCKR0O3OGO3WlANJknSveyYEYaIoLho9byo9jnKEqAGyVoesZJCB3HOCwSgXLEBmMMpAJBJRM1uAPMEKgqALiCmlCIfDC3F4DMayIDsYIoRURP4giqLanAKQ5RbLPQjr6+vD8ePHF0WQLKTkAJjnAJ6XZRVWqxUAUO+Uu+kpUEoXxTEzlh8sQGYwykAymdRpIwkhCIVCOdXvZ0ImisGoFIFoHJ95aAy/e8UPoHKLTmXHR8tylkdpZSRTU1MLfDRAIinrv00cURMPFosFHMehISuDzIooGZWCBcgMRhlQCnoUJElCMBjUZZWVxzHLKAZjdjx0bBpHJhL434MBeKMpUEoRDAbL/j7ZwTAhZFkHyMlkUq2f8Pl8C54tVwJkXhMgazPI07FMsxBJklgRJaMizBggE0J+QQiZIIQcznP/jYSQQ+k/zxJCtmvu6yOEvEIIOUAI2VfOA2cwFhNGW3xGmS2O4ypqTcVgLGeOTWR2YJ7ul/8dj8fLnkGMx+O6hSyldFlft4oDDyAHnAv9WROCPJ7yJBMgm81m2erNwUOigC+WySIHg8EFD+oZy49iMsi/AnBVgft7AVxMKd0G4BYAP826/xJK6Q5K6a7ZHSKDsfhRKt6LYaEnHwZjqXJ0Io4dzTZ0eMx4eVQOiimlOH369KyziJRSdQdI8VbOLqZd7lZv2Qv8hZYsJNMaZI5ADZBNJhMopWhIeyF7NU4WiUSCddVjlJ0ZA2RK6ZMA8oqSKKXPUkqVM/M5AO1lOjYGY8kgiiKGgwKe6NVPrNlyCrYdyGDMjkgihT5/ElubbNjSaEX3ZELdZqeUYmRkZFavOz4+juPHjyMYDKK3txcnT540rBVYztdtKpVSxypK6YK77QhKBpkjamabpB0t6rK8kAH5mMfGxuD3+1kmmVE2yq1Bfi+Av2n+TwE8RAjZTwi5qdATCSE3EUL2EUL2sU5kjKWGJEn42hOT+M9nvOibLqxVXOjsDIOxFOmZlCVLHR4ztjTaEBMoTmuutVgsNqvdGcVLeWBgoGAQnF1nsJzI/mwLXUwcTsgBstVEdHUcJpMJDY50Bjmqz3pLkoShoSHdQmlwcBA9PT0l7fAxGAplC5AJIZdADpA/o7n5AkrpTgBXA/gIIeSifM+nlP6UUrqLUrqroaGhXIfFYMwLlFIMBuRJ5sBY4UzTcs5EMRiVYmBKDtpaq0zY3CgXbB2d1AfEfr+/5NfNV3z320N+/PgFn/r/7C5+y4nsAHmhbe2mI/Lv6rHyOjcRi8UCp4WD3UwwETEOev1+P1KpFARBQDAYRDwex8TExLwcN2N5UZYAmRCyDcDPAbyRUqqOKJTSkfTfEwDuAbC7HO/HYCw2osnMYH3KVziDvJwnWgajUvij8nXlsfKod5rQ4OB1AfJsHC2yG4IoTMdE/PZQAH89EUafX37f5Vxga1RkvJCuHRMh+b2rbbwug6y0nF5XZ8WB0fxNYoLBICKRiOrMMTU1xcZcRsnMOUAmhKwAcDeAd1BKT2hudxJC3Mq/AVwBwNAJg8FY6nzuocy23uN9EQhi/uzLcp5oGYxKMR2RgyaXRZ62NjRYccyrv44SiURJ2+mCIORYMQLACc3rPtWXkRss1+t2MhTHT/dNwZeWLVSqAUux9Phi8Fg5VFlJTgaZEIJXr3BgKJhCfyBX9kIpRSAQQCwWU4Ni1sWUMRuKsXm7E8BeAOsJIUOEkPcSQj5ICPlg+iFfBFAH4L+y7NyaADxNCDkI4AUAf6WUPliBz8BgLCjhRErNZL1hgxsA8GRfZjCWKEX3RBySJtvBZBazQxTFZa0FZeTHH03CyhOYeTlg2tBgxWRE1LkZcBxXkn42Xwe2waB8jnV4zDg0Ll+rkiQtWy/k3x2cwn3HQvj5frnefqGLiU9541hVIwfD2gDZZDKBEILd7XYAwL5h43qOaDSqW8wovvQMRimYZnoApfSGGe5/H4D3Gdx+GsD23GcwGMuLAZ88IX/2onq8qsOBp/uj2D8Sw55VTvT5BfROJ/G9vT68Y3s1rt/qWRQ+o0sRSil6enogCAJWrVoFh8Ox0IfEmEcCMQEuayans7Fe1iF3TyZwUdr6S5IkRCIRuN3uol4zX7Z5LJSCx8rhnFY77jsehChR8NzCZlUryZEJ+XM9PxRDTJBgN3MLWkw8GEjiirUuUEp1GX6z2QwAqHOYsLrGjJdG43jLZk/O8wkhOT70SjEmg1EsrJMegzFHptPayKp0QUlXvQWnppJ48GQYn3hgFN/bK8vyH9dklZmTRelEo1GkUnL3tN7eXpZJPsMIRAVVXgEAa2otqLZxeGZAnzEupfW0KIqGOlZ/QkS1ncfKajNSEjAakjPNy3FhSynFcCCJFR4zkiLF/hF5bFqoz5pMSYinKKrSiyFtBllpFgIAGxusOOlL6HbmFCRJymkVLooiG3cZJcECZAZjjkxFlABZvpzW1FgwEkrhpVH9YDwYEBBOypq45bpVW0m0mkJKKfr7+5l90xlEPCXCZspMWTxH8OqVTrwwFEUokTkPEolE0QVZ+c6fYFyCx8qhwyNnLAfSWtfluCgLxAREBIrLVjvhNBMcGJWzyQvlZBGKy9+x05wbIPM8rx5TV50VMYFiyECHDCDn2JU22gxGsbAAmcGYI1NpS6Iqq2xgv7pWrrR+fiiGJpcJN+2qwecvlq0LleKfhbZRWopkZ3/i8TgGBwcX6GgY801CkFT9scKV61xIScBP92W6qJVSkJXPxSIQF+Gx8WqA3J92spAkadktyiYC8nVV6+CxoUFuwAIsXDFxMCYHvA5LbnhCCAHPy+NsV1pic3wG1yDda7OW1IwSYAEyY1lAKV2wiUuprndrMsgK25qseMOGKtW3tc8vD/6EkGWZjaokRln3SCSyLLe9GbkkUhIsnD5A7qy24C2bq/BYb0Rt0CNJEgKBQFGvmW/M8CckVFl52M0cmpy86nHOcdyy2/2ZDMkZY4+Vx4Z6KwYCAqLpna6FuLYUOz+XmcuRSQByoR4AtFWZYDcR9EyV9nssdBMUxtKBBciMZcHk5CSOHj06r4Pf8PAwQqEQYoIIEweY0pN3nYNX5RbOdBakysrDY+XU7cCFtlFaihgtKCilmJ6eNng0Y7mRSEmw8LkB05s2VoEjwJP9maxxsTpkURQx4E8inspIMgJxEZGkhFa3EoiZMRzKuF0stwWZNx0gV9t4rE4v7gcCwoIVEwei8ns6LIUDZI4QrKqx4HQJAXIpiycGgwXIjGWB0kFrdHR0Xt5PFEVMT0+jv78fCUGEWZPZIoRgRXprVltU1OExq1pGSZJYwUiJ5M32zaJ7GmPpkUiJORILAPDYeGxtsmHvYOZ6KtYO8Oh4BB++fxS3PjGp3qZki9vT13BrlRkjIVkSpbhkjIyMLLrrV5IkDA4Oluz3qwTIHhuHldV6SclCLOIViYUzT4CsNAsBZDlb73TSsFAv7+szuzdGkbAAmbHk0fqTxuPxim+BUkpx9OhR9f+hSFTNHis0u+Qsh92kD5AHgxntMbMdKh5KaV7toCiKy27bm5FLUszVICvsarVjMCBgIpxpdFFMoPhUjx8A8PJoHE/1R/Dhv4yowbKyyG1zmxATKPxxOcs8PT2NqampeVuMF0sgEEAgEMDAwEBJOltvJOPC0+gywcoTtQHHgmSQY/LxKEV62WgD5DW1FsRSVHUZKQbmpc4oFhYgM5Y82d2wKp1RjMViusxGJJbImbib0gGyL5bJenZ4zIgkJXWiLaXafrni9XoxPj4+4+NSqZRhNkmhFGsvxtIkIUjIEzNhV5vcOEKxKJMkqahzon86AQtPYOKAbz3lxUBAQCgpYVWNGfUOuRistUoOlIdD+qBK66qyGFAyo5TSkrLIU5EknBYOZp6AIwQrqs3oT9dKJJPJeS9qUzPIZmJ4zZvNZnW8V+o9TpUgs2Bd9RjFwgJkxpJHmw1Q2oxWkkQioRu4BVGCKetKumy1C04Lh0tXOdXblIzUINMhA5Az6OPj45icnJwxAyyKIiYiKfz3/mmMZQUqlFK2bXoGkE+DDADtVSbU2nkcnshcT8UEyCPBJLY0WvGRc+tg5oCzW224cIUDX7usSb3G29Ja5JGg/rwzakaxkCiSD0mSSkoSBON6f+mV1WZVYgHMv7VdIF2kZ89TpKc0CwHkpIOFJ3i0J1x0IC9JEtu9YxQFC5AZS57sdrHJZLKijhapVEqXORJEmiOxaHSZcNc/daBT42jRnuWpWmyWa7kSCARAKQUhZMYJXRRFfPcZL+45GsQ3nvLmaA6j0Sizb1rmFJJYEEKwSWNRBsjnTL5W0gqhhGzn9po1Ltzzf1biK5c24eaLGuCx8epjGpwmmDhgJGsbX9EjLwYkSdJ91lLszCIJEQ5z5ntd4THDH5cQSogLsogPRJOwmwl4Ln8GWflsZp7gXTuq8dJoHPefCEGUivvM4XDxATXjzIUFyIwlT3bASgipqJtFdkYlJSEnQDaizs7DaSYYCGSyM2dygKxkvIrJ+k+F4zg0HkeLy4SeqSQe6dF/b5RSpkNexlBKkUzRvBlkANjYaMVkRIQ3ktEhFxoHKKWIJCU48uk20vAcQbPLhOFgbiZ1sQTI2RIkSmnR+uFQPKXT+3akJSVDQdnJYj6dgUKhEHzBiGGTEAWTyaQLbt+wwY2zWmz4yYvT+Ke7BlUd+otDUbzr7iE81ps7xi6UQwdjacECZMaSJxBJ4LbnfTg0Jmc6Kp3Zyc5KCRLNm9nSQgjBymqLqu8DFqZKfLGgXWjMlPU/OCQH0B85txabG6345Ut+xAS9/nOxBCuM8pOSKCigc4vJZnOD7DWuZJFnGgcEQUBEkOC0zHzttrrNORlkQJZbLYZMZCqVQiAuwR/PXEPFXg/hRAp2s76YGMhIweYzQJ6cnEz/JvlDE47jdDUnhBD866vqsa7WgoRI8adueaz4w5EgfFER39/rw2Qk97djMgvGTLAAmbHk+f3LY3jwZBhfeWxCDZoqmZnNDuRSEs3RIOdDKYBRJlVK6RlZUS1JUk7Wf2pqCtPT0xgbG8t5/Mi0PEm3us1491nVCCUl/PjFKd33eCZn45c7cUG+5gotRFfVWGAzEZ3MolAQNDLhg0QxYwYZkL2QR0MpQzuxxbBzMTwdwfvuHcRH7x9BMCGCUlp0ABhOiLoMcoPTBDMHDAbkoHI+F/HxeByRpFQwgwxkvJAVauw8vntNCy5Z5cTjvREIIkV/QMCuVjskCtx/XP9dlPL9MM5cZhwZCCG/IIRMEEIO57mfEEJ+QAg5RQg5RAjZqbnvKkLI8fR9N5fzwBkMhUMj8kCXECn+cVrOmiQSiYrpkEVRxN7BKD778BgCcREpiRbMbGlZWW1GOClhKu1uQQhZdH6q80EqlcJgQMBjp8NIirK/7MTEBIaHh+Hz+XKC3Ym0V2uNXe72ddU6F/5xOoKXRjOTN8sgL1+8YTkIrbbln7J4jmBDvRWHxzPnRDKZNHSaiMfj6B+dAJDfTkxLq9uEpEjhjerHlErLuYrlseNeRNNWdA+fkq+dYnX5kaQIhyaLznMEbVVmNYNMKZ1Ry10ORFEO7CMCVTPI+QJkbaGelgtXOBARKJ7sjyCSlHB2mw1ntdjw3GDubxSLxRZF9p+xeCkm7/UrAFcVuP9qAOvSf24C8GMAIITwAH6Uvn8TgBsIIZvmcrAMRjaUUpyeSuDVKx1oc5vw3FBmIJyamqrIe/Z4Y7j1iUm8Mp7A/cdDSEmFM1taVnrkoj1FZjHfGr/FwkQwhk88MIrvPOvDf++XO+EpBXuUUni9Xv3jQwm401ZUhBB8YFctamy8LjOk9cNmLC+UHYR6h6ng47Y0WdHnFxBMyIEsx3GG11cwGEQk/RhHge181ckircvNdrJYLIW2rwwHUW3jsLHBisf75IViMbtTkiQhKuTqsDs8ZgwF59dtJx6PywuOpKQWDWqlFFq0XshadrTYYOUJfv+KLLNY4THjnDYHhkOpHA35ckpOKM4cLOAvLzMGyJTSJwEUijTeCOB/qMxzAKoJIS0AdgM4RSk9TSlNAvhd+rEMRtkQhBQmIyk0u0zY3e7AK+NxJFJSRVsQf/nREfXf9xwNIpQQiyrSA6DpVJUZrM9Ei7JHjk4gKVI0OHg83htGSspIJQA5G6zN/HnDAmrsGWcBM0+wZ5UTL4/GEE3Kj1ss2TxG+Rnxy79rnYMv+LgtTTYAwNGJjA7ZqAA0EokgIsjnmqtAgKws2pS200Y65MWwczEcSKDVbca57Xb0TgvwRlNF+f3GEgJSUq7MpMNjxng4pY6l8xEgK57LWg1yvgxyvgDZauJwdptdbQ2+wmPB7rRH9gtD+mC4VL/oxczExAT6+/sxOTk584MZRVMODXIbgEHN/4fSt+W73RBCyE2EkH2EkH3sR2YUizcUhUTlrfdtTVakJOC4V84iCoJQ9oxiIiWqXZtu2OpBPEUxFEwVrUH22HhU2zidk4UgCGqm50xxY+iZjMDKE7x/Vy0iAsWRCf0EnB3sTsVSugAZAM5ttyMlAS+NZvxfz8TFxpmANyQHvDW2wgFyV50VJg44otEh+/1+jI2N6SQH8Xgc0XS9QiENcl1dHQCg1sHDyhNDJ4ti7OQqzVgwgQYnn2mYMhwrqljZH5G/p2yZSXuVGRTAcCgFSum8ZFoVOUwxGmRts5BsXr3Sof672sah0WXCCo8Z+0ZyA+TFkP0vB8oi0Ov1LqrmNUudcgTIRmcwLXC7IZTSn1JKd1FKdzU0NJThsBhnAuMBedCrtvHY1GgDR4BX0hrESnRMGvXLr/3J8+tw/VYPrGlpRbEZZAA5ThZApphoeHgYJ06cWDYDdz56vRG0VplwVosNPJFb/WqRJAljY2Nq9mo6KqI2K0De2GCF00x0OmS2zbg8CcaSIABsGr9eo+DJwhN01Vl1Cy5KKXw+H06fPo3x8XFMTExAFEWE0zsPTrPxtctxHNxuN0wmEzhC0OI2GWaQF3rnglKKibCABqcJKz1mVNs4HE5n0Gfy+/VH5e/JkfUdZDtZzEcGWRAEJEQKkWLGDLLdbs/7uS5Y4cBHdtfiM6+uV59/Xocdr4zHMRXV/37LwT89W0qzXLLii4FyBMhDADo0/28HMFLgdgajbEwE5YG72sbDaeGwqsaidtOqRMekEb8ckMvNAwjWp62lSguQzRgICGpFvNIJTrsdPDo6WtbjXmx4IwLqHCbYzRzW11txcDR3Ak4kEgiHwxgZGcF0XMzJHvIcwaZGG7o1wRBrI7s8CcYFOMwcuHTAw/M8XC6XYQC1qdGKU74k4qlMJk0Jgrxer7oNrUhz8mmQKaWwWq2w2WTZRluVOUeDDMjjzNDQ0IIFyTFBLhR2W+TOc9prQhTFgrsqwaj8eRxZAWlblRkcAYYC89dyOplMwp8uXq5OX+uFNMj5gmeOEFzd5carV2a6mF622gWJAo+e1o8Ny0GHLAiC+l3kkxQxZkc5AuT7ALwz7WZxHoAApXQUwIsA1hFCVhFCLADeln4sg1E2hqbkSanBKQ+oWxqtOO5NQhAzWtZy4k+3QXVb5UtH8V4thZUeM+IpigmNN2c0GkUsFlMHumQyuaw9koPxFNzpSXlHiw2nppIIJfQOAZRSTExMYDIgO11kSywA+fceCqYwnZ5Y2QSxPAnGUjqnBafTiZaWFsPHbm6wQqTACa+xVEnVuQtKBjl/gGwymeBwyFv2rW4TxsIpw25tkiRhcHBwQbKRwZg8jihZ180NVoynG6ZQSjE0NJS3KYY/Kt/uMMvewjzPgxACC0/Q5DSphXpAZVtOKzIOJfOt7BblC4IJIXC73UW/fluVGevrLXg2y81iOYwX2U1ilvvu43xSjM3bnQD2AlhPCBkihLyXEPJBQsgH0w95AMBpAKcA/AzAhwGAUpoC8FEAfwdwFMDvKaVHKvAZGGcwfVNRmDig0SkX0WxpsiEpUpyayhTplHNgD8XTGZf0pLoxHSCPhAq/ByGZtqkrq/VOFoA8Qfj9fp2vb6WKDBcDwXhKLY46p80OCuCuwwH8+IUpjGm+y1gsplriGQbI6aIs7ZY68zddfoQTgi6QdTgcsFgscLlcOY/d2GgDAXJ07dlEBQkcAWym/BILQghsNhs4jkNblRkiBUZDKd05qpBKpRakO1swPSYp19PmRnlMUnTYlFL09PQYZkr9Efk7cpo5SJKE1atXq/e1ezJWb5V2skgmkxgKCPjeXh+AmQNkAPB4PHkzzEac02bHSV9SXUwrLPW6hWz9uyiKZ6S3fiUoxsXiBkppC6XUTCltp5T+N6X0dkrp7en7KaX0I5TSNZTSrZTSfZrnPkAp7Urfd2slPwjjzGRgKo5mlwl8WuKgBKzd6UxEufWBmQBZfr/VtXKwuzUdqOWDUqoGvys8uU4WADA9Pa3LQAUCgSWvjzNClCjCCVGd0NfVWXFWiw33Hg3hrydC+G56klSYTnvPZmuQAWBNrdwc4pXxTGAiiiJrI7vMCCVEdVHKcRysVvk6b2lpyQmiXBYOK6rNOOYtfA74YxI8Vj5vEMbz8vlmtVpBKVWdLD74lxG8788jODqZ+/oLsTgLxuRxRMkgqw1TJjLHJ0kS+vr6dAVclFL0jsvZ03onD57nYbFY1O+2o8qM4aAAUZJ9yisZIAuCgBeHMwF8o0v+rpXfwAin01lSQdruNnknILtYL5VKLendumg8ie88PYHfveIHsPCa+OUE66THWNIM+uNodWdM46ttPNrcpqLbzZZKMCZv2yqtWattPH715ja8Y3s1gEzGI/tvq9WK6mr5MQ4LhyYnj97pzBawUSAsiuKSHrjzoUzoikwFAN621aP++8hEQvfdFMogmziC9fVWHJ3Uf09LPSvE0BOOp3RuE4rNl8ViQVNTU87jN9ZbcWwyYdj5TsEXS6G2gG2cEiiazWZQSlUvZIVHT+u3spVagvkmmLWrpTRMyc6gU0p1coJgMIjRUApOM4Hbwqnd6ZxOWbvb7jFDkKBKwSoZdAmCgJO+BOodPO65YQUcZjl7n90xTwvP83kbhhixqsaMWjuPlwzcLIaGhpZsMuLvRyfx6OkI/vdgAIcn4ovGm3s5wAJkxpJmMiKo+mOFTY1WdE8kVK1gOQPkUEyAhSe6orx6RyaDDQCbNm3Sbf11dHRg1apVcLlc6u1d6Qm8EEr1/XIjnJAnXG3As7nRhq9c2ojbX98KngCP92Z+s+kCAbL8XCt6pwVE0kVXCxWoMCpHOJGC05IpRNIGTvX19WhtbdVdcxsbrYgIFAOB/FvNUzERdXnOKQCq9pgQAp7nUaVZ0G1rtuk69inE4/F5t9kKxY2uJyv6/YLq1AHI35t2PJmensZ42v2CEKIGmw6HAxzH5ThZVLKYTRRF9EwlsLbWojZdmilABjLBfDEQQrCjxYaDY/GchVMikViyY+2BoRAsPIHNRPBYugiRBcjlgQXIjCVLIiUilJDUimeFna12hJKSusWaSCTKNmn5I/EcSyQtJpMJHMep2khCCDwej1rso2QpNjVYMRkVMRkp7J8aCAQW3GO13MQEOeC1Zmk/z261o91jxvZmG54eyNgvTcdEWHiS145rU4MVFNBtqS9EoMKoHOFkRmKhBKxaPB6PLgOoSK2OThgvQsMJEYOB3MW1Fm3wpbgmvH9XDd59VjXObrHpikMV5qvrnJZE2q3DounmuakxfU1kLcITiYTqSBGNRhFIiOr4qQTINpsNlFJ0VMnBqRIgi6IIUdR/3nIRjicxHEypkjWFfA1BFBwOR0GdcjZntdgQTEg4PaUv4KSUYnx8fElKs3q8UaytteDcdjueG4yqtm+V+q3OJFiAzFiyqM0DsrJAZ7fYYeKAvemKZY7jyjZp+SMJ2At0BVEGdEVOoc2AaLcDVa30DFlkAEs2s5GPeDpAtuRpz33hSgfGwymcSk9iUzERNfb8WtH19VZwBDl2b0yHt3yIJDPd1Yyyiortm0KLy4RqG4ejeXTIX3/Ki5QkL8o4jkNtba16n9vtRmtrqy5AVqze3rihCm/Z7DEsDgWgBp7zSTIdIGvNONbXW8EToHsyd9ybnp6GIAjyTktCUjPjyvikjGEuq9zUaDDtZMFxXMWyyGOBOCiAZlfmt5UkacYA2W63lxQg72iWG6m8ZGArqQTJS42xUBItbhN2tNgRSEgYDAgV+60kSVrytnilwAJkxpJF8UDODpAdFg5bGm04kB4EyzlphROpvL6pQGZyUSZXbVaLEKLerxTSFCOzmJqaWlbZ0HjaXsuaJ0A+r8MBngBP9cm/2UQkVbCDmt3MYU2NBUeyipLYNuPyIJESIYhUzSDn053W1taqMgtCCDY0WA0zyN5oCofG4rh+iwe72+VdnYaGBqxevRqbNm3CypUrdQEzIOuRtYHYmloLrDzRnXPAwnRnUwNkzfVkM3FYU2vRFa8qx+fz+VSHnFBCQpWN18kZtHKLDo2TRSWDo4mQ3t5NOY5CRXqA/LuUMjbW2Hl0VptxYNT4c4RCoSWVeaWUYjqWgsfGY2uTnHR5ZTxR9tobhbGxMfT09Cw5pyBKKYaHhzE6OlqS1pwFyIwly1hADqCMgidFgxdJSmWbtDJtUPPbQlVVVan/BnK9Q+12OYPBcwRd9daiMshKV7lsKKXwer3w+/2lfIwFJ5/EQqHKyuOcdjseOR3G0ckEjk4msL25sEvIpkYrTvgy/tcAs3tbLmRrbPMFyFoJEyBLb0bDuTKInvTOxDnptsxav+N8tmHZAbLSJMjo+p3vDFtSzA2QAXmheXQyoX5eBWXcSIkSwkk5g5yt91XGqQ6PGUPBVMY7ukJNeCYNdgNn0h8D8jhbSqEeAJzVYkf3ZELXSEaBELKk6hciSXnx6LFyaHKa0ODkcSitjS/3Qk2xIgWgNttZKoRCIfj9fkxNTZVkn8oCZMaSZVLpomdQaKNq8NJbrOWYtARBQESQCnbeUgp7ADmjpQTMCtotwU0NVvROJxETCmdAFE/kbH3c1NQUxsfHMTw8jImJidl8pAUhnpQDHguff/h5XZcbwYSEmx8ag8NM8KaNhZsCbG5U/K8zwUAikVhS2SCGMUqArCxM8wVEPM/rrj9Vh5wVxCoZUaUITWmOUQijrf6uOgv6ppNIivqMlCRJ81o3kJFY6D/DNV1uVFk53P7ilG7hoFhOhtIFfIqbTHaATAhBR5UZkaQEf1x+bKWC/75peSyvKzFABjLBfLGc1WJDSkJO9h+Qf7ul5D/vC8ufoTq9C7Cl0YYjE3FQShGPx8vqzJFIJNTXi8ViS6o2RrFMVWQ0xX4vLEBmLFmUrIO2SE+Z6BRd6hFN2+m5mqcLgoCoIOXtvKV0olJobW3FihUrdI/RZqI2NlghUeCEb+YsMqU0J4vs8/nUi35ycnLJWMJF4nIQm09iAcguAU1OHiKVdaJua+HGARn/68x3wHEc0yEvA1Tv8XQr5UIZw6qqKvUcWVtrhd1M8N8vTeMPhwPo98vn3WBAQK2dL6hpzsZiseRs5XfVyR37sgu+5rt9cTIlNzzhswJkl4XDO3dU4+hkAn84kpsVDcblxWNV+trSjl3KOJXtZFGp4P/IWAxrai1wWUsPkJ1OZ0k65M2NVpg54OU8MotYLLZkJG3TUb1l5sYGK/xxCWPhVNnPw+zdg6W0Q6c99lJ2lFmAzFiyeMMJOM2cWuylzdbaTBxW11hwLN1uthxFW6IoIpqkOjslLUohTyGUCnEA2FBvBQF0hv6FCIfDGBkZwdGjRzEwMKAL+CmlGBkZWRJenpGEfNyWPBILAOAIwdu3V8Nj5XDBykxWkBCC9vZ22Gw23aRYY5f9rw9prLeYDnl5kMkgczPqUrXBkpkn+MyFDRAlil8f8OPjfx3FHQf9ePR0BKtrMhnhYgIxI8uxrjr5NU5mLXDnu5ApkRJzsscKl612YX29Bf9zwK8WLSsMBeXvtcbGg1Kq+16Vcaq9Sh8gFwq65jL2nJ5KYHWNfuFTbIBcSstpALCaOGxqtOFlg0I9QP6MR48eRW9v76IPlKNpy0zFl3+TZtdEkqSyykVisZiu0+tSadGdvaiTJAlTU1NFPZcFyIwlizecRLU9012rtrZWN1iur7fgpC+hdoIaGxub04AXT6YzyHkkFsUEyCaTSZ3AnRYOK6vN6hZwNCnhlscn8OuXpw0nG6VgTxRFw4EvFoshFAphamoK3d3d6OnpQTKZzHncQqMM6oUyyABwyWoX7nhrBy5YkXETcDgcqK6uxpo1a1BfX68Lkne22nF4PKHb8l5KWQ6GMUPjXgCaRhgFAmSl653CrjY7vn9NCz52bi1q7DzufEWe1K9Ym3G8KDYQy85c1zl41Np5nPDlXmOV0uoakUxJMOW5lsw8wbeuaMYKjxm/OeDXfTd/PxVCrZ2X5WhZAbLyndQ5eNjNRHWykCTJMNHg8/nQ3d09K0lTMiXBHxfR5NL/DsX+LhaLBXV1dSXbvfX7BUxFc7PhkiSphd2L3dUimpS/b3s62dDhMcNhJqo2vpwBcvYOZSQSWRIJmWQymVNbEA6Hizp2FiAzliy+SFKVV0iSBLvdjpqaGvViWF9vRUyg6uCeSqXmVNB2fDQICqDFbTxwF1ssotUzbm6UC31SEsUDJ0N4fiiGPxwJ6tqu5iP7Alf0VaOjo2oWazFmQWJpDXK+Ir1CaJs3GAXICZHqGjgkk0mmQ17CCIKAUZ8fAOBINwopFCATQnQ6ZADw2Hhcuc6Nb1/VjA/vrsVv39qOV63IPKbYQCx7AUwIQVedBScMrOTmP4Oc/34TJ2v4BwKCuqMWTIh4aSSO16xxwsQREEJ015IiZSGEYKVH1lorGO3K+P1+VepVKv6o/NpVVv3vOpODhZbGxkbD3zH7cymc1SLrlg+M5ZelaRMSi5WIOpbquygqSRdBEMomiZkKx3HrExP48zE56J5vKdFsMTpGQkhRyRMWIDOWLMF4Ci5LJoPM8zycTqcaOK6vl7eblAlMqd6eLS/0y9mnHQaOCsVYEiloJ9rtzXbEUxTHvAnsG46hs9qMBiePPx+bXeZTaQKgkEqlFp2P8mggDpuJ5PVBzgfHcWr7X0CeQLV2XFubZG3hfk0rWaZDXtqEw2FEBfl8VrT/+ZwmFLQ6ZC31DlO6cG12gVi2rAeQO2IOh1K485A+O0spnbfdm2RKynGwyObVK52wmQge6ZGD20Njsu/wrrSTh9F3oIxTq2vN6J3OjCvZxV9KQRiAWVlSeoPy9enS7MyVMp4C8jmxcuVK1NTU5PxGzc3NOefMqhozPFYur8xCexyL2dUilpVBBmQd8oCmi2I5dtEopfj9K9PYOxjDz/ZNwxtJLRkJWzwex7GJGO485Ic/rbuXJKmoZBkLkBlLlnAi011LyR5wHKdWNbe6TXBZOBz3ZiYqQRBmXax32hdDtY1DncM4U1HsgK51stjWbANHgAOjcfT5BWxosOLy1S4cGovn2FMVg1FWeXJyclFlkXt9MazwmMGVsCWqkO0mUFVVpU5+NhOHLU02XROApTKIM4yJxWKIJOTrwGHmcqQARmgbhhTDTAG3gtJNT8s5bXJTojsOBXTt0eezUY0gSnk1yAp2M4fzOxx4eiAKQaQ4MCp3BO2qkxechQLkVdUWRASKiYj8O2R/tuwMa6kB2cC4rAd1W2cfICvH29raqtvJczgcqK2tzfnduHTb6QOjhZ0eFrurRSQtV7NpAmRtF8VyaYUlScKzA1G0pndPn+yXz/WlIGHzh2P4wj8mcMehAG59YlJtM16MzKKokYEQchUh5Dgh5BQh5GaD+z9NCDmQ/nOYECISQmrT9/URQl5J37ev9I/HYMg2LUNDQ7qsTCQpqnpg7aCobfPcVWfB8awt0NkGTAP+uFq0YkSxA7rWycJl4bCuzoJHe8IIJyWsrDbjwpUOUADPDJRnglW2ChcLY6EEGpzFbWtroZTmyFiyO2ntbLFjMCBgQtPCeykM4gxjEokEIoIEm4mA5wgopTMGtFarteigt5RAzGKx5Eyoq2osuPuGFejwmPG3k5lxZT4XZokiMsgAsGeVE5GkhBeHY3h5LI5tTTbV+SJfgMxxnNr+uTcts8gOGhOJhHoNllIApTx+xCu/ljurtqPY31ALIUQXECutqD0eT85jd7TYMR0X0e8vnDBZzK4WSoBs1XR3VRycjqa7KJZDK9w7GcJIKIXXb3Bjba0FewflXbp4PL5ovxuFZ3unEUlKuGSVE0cnE9ivkS/OJBGZ8QwkhPAAfgTgagCbANxACNmkfQyl9D8ppTsopTsAfBbAE5RS7VVySfr+XcV+KAZDIZlMYmhoCH6/H319faq1WSQpGnqjOp1OnQ55ICCoXsOU0llvmfkiKdQ58k+mxQ7oWicLQA7qJqNyFmalx4KV1Ras8JjxVH95Cn0opZiYmFgUNnCUUkyEBNQX+B4LPTd7IieE6NxLdrTIWa9XNNrCcurwGPOLbK2od44p5jrL1iHnY64BMiBnIy9c4cAxbwKBeCabOl+FesVILABZGlZt4/D1JycxHk5hZ2vGP9joO1B2a1ZWm0EAnNbokIPBoBoYZcu6otFo0UFTPB5HJC0FyC5+LlYbno0SDBNCVEmWx+PJOW/OSkvlipFZzGfRZSlEEimYuNwuiqtrLGqhXjl2M3on5STDqhoLzm614bg3gUhSWhI65O6xGEwc8JFza1Ft4/DgKXnhKknSjNn1Ymb03QBOUUpPU0qTAH4H4I0FHn8DgDuLO3QGY2aUAhBAnjCnp6cRTYqQaKayXbv1brfbdTpkiQInNZXms1lRU0rhi6ZQZ88/aBcbIGudLICMDhDI+Pm+eqUD3RMJeA2qrGeDJEkYHBxc8KrjaFJEPCXltAcvhnwNHVwul/rdr6w2o8rK4aCmUK/YggzG4kAQBNXYfzQQx0Onwuq2aL6iq2zcbnfRrgalZJvzBW3ntNkhUeBg1sJsPgq8Ctm8aeE5gktXy7trTjPBBTMUKirezzYTh1a3SRcgA1CDC20DCaC0gNLr9aoBsnYRVIyUJh9ms1lNQijzgpJJ1lLvNKG9yoQDY4UDvGICqYUimkzBZso9fzc2WHHCm0Qq7eA0Vx31WCDTyGVHi3yuvzIez+tqsliglOLkVAKrqi2wmThcttqFF4djCKZlW8o4k49iRoY2AIOa/w+lb8uBEOIAcBWAP2mPEcBDhJD9hJCbing/BkOH9uJWnBp8IfmidJhzmwdwHKfavXXVywOkthnHbApogrEUkiLNG9iVOqBrA/quOgtu2lWDb13RpGYCLup0ggL4x+nyZS4EQVjwTEg4oW8bXAr5vl9ti2GOEGxrsslFSOnbyu0HyqgclFL09vZicHAQk5OT+NaTcodIRStbbNDrdDpnflCaUjKVRh31AGBNrQVOM8GhsflvVFNsBhkA3rmjGr+4tg2/vq4dHk2DJaNri+M49btZXWtB73RGiiBJEkZGRpBIJHI6fBYbUCrXZVQob4AMAO3t7WhsbFR11IQQNDY25iyGdrc58PJoXFfYa0QoFFrw5IIRkURKpz9W2NhgRUKkahObuSYIxtNda2vtPDbUW2EzEdUBZDHXeIiiiAF/Ep1pj+0LVjggUaguUaIoFowFipmljK68fGfK6wE8kyWvuIBSuhOyROMjhJCLDN+EkJsIIfsIIfuWWp9vRuWglOYMwKIo4vCxUwAy3bWyJ67a2lpwHIcqK48Wt0ltOa1QaqDoj6WtiGz520yX4sOpncAJIXjDhipsbsy4W7RVmXF2qw33Hg0injLeroynJBz3yj7PxVCqPrAShFVj+9IL9PJNmGazWXfftmYbvFERI6FM9r1Y30vGwhKJRFQ5zImBUXRPJnDFWhc+fWE9gOIDZKOCOiMkSSranhGAzkVFC88RbGmy6TLI85VdS6RmLtJTMHEEjU5TTtYx3yJBLdSrsWA8nFKdEYBMcyKjoudiArJ4PC4vIgQJ5iyZADA7DbKC1WrNCYhrampyxoAbtnmwstqMbz01qXZaNEKSpMXpKZ8UDTPIm7LarM9VZjYeiMNl4WA1cTDzBOvrrTiabnClbSCy2JgIxOCPS1hZLccH6+osqHPwuqY5hZInxZyBQwA6NP9vBzCS57FvQ5a8glI6kv57AsA9kCUbOVBKf0op3UUp3dXQ0FDEYTHOBPI5TmitnyilOROXNqu4oV7ebtJ2ASp1Ra1087IbDEYKpQzoWp10Pq7f4kEwIeGBE7krdFGi+OzD4/jUg2P4zEPjuomrEKFQaEGLKpSiEkeB7zEf+bJ3gH7BsT2tLdRm85aCVo4hB8jK+TmU9i+/cIVD7RRWbIBs5Iec73GlZCqzi0K1bGuyYTScwmREvzCrNElBxCwuJxWO4/IuEhRHoHXproGnsroGRqPRnAQGUFxAqdwfSUo5O0pzCY7zwXFcjsOJ3czhi3saYeU5fPWxCTWbbcRizJRGkynYDJIN9U4TGpy8GiATQuZ0/OOhBGo1u6cbG6zo9ScRE6R5tTQslaOj8k5GZ7V8fhNCcF67Ay+PxBFPSTO6fBRzFr4IYB0hZBUhxAI5CL4v+0GEEA+AiwH8WXObkxDiVv4N4AoAh4v8bMuSUCiEo0ePFvSmVbxrjQaeM41kMonxcAo/eXFKtw0WEZTCDuMqbI7j1OxHV70FUzER3mhGDxiNRkta9aqBXYHMZykZZG0An49NjTac1WLDXYcDGAnpFwqvjMdx0pfExZ0OnPQl8MPnivM6XuiCk3BWa9RSKBQgu1wu9ftvdZtQ5+Bz2k4zHfLiR1tI6kvbimkdT0oJnIrRIRc6p4zQdurLfu1NjfqsHZDrGVwJitUgFyJfZlxxsliXlrhoLTMVjD5fMYVhijtEVMgNkOciryhEdXV1zjnU4DThM6+ux3hExN9OGI8RcynuriT5MsiAnEXuTlu9zVVmNhlO6grUN2TV9iy0dC8fvV75uNo9mQXg7naloZR8nSYSCfB5TrgZRxtKaQrARwH8HcBRAL+nlB4hhHyQEPJBzUOvBfAQpVT7TTUBeJoQchDACwD+Sil9sITPt+wYGRmBKIoYGxszPGEVDd7Y2Bh6enoWhfPAQpJIJvHVx8bxl+Mh3PL4hJrB0OrWsoveFJRswYZ0w5CXs/xxS/FDnimwKyU4BuQtzWImgY+cKzfCuO35Kd1EdGg8Dp4AHz23Djdur8YzA1F1ATEdE+HLU9xXrEF6pVA8bUuVWBjJaLRkS1YUHbKk+c4W4wTH0KPNRCkFqtqJudRFaKHHE0LQ0tJS0vGpzTNWr865b1WNBVae6ORc87FzUYoG2QhJkvJeW0rg7LJwaK8y6Wo5AKiOQgdGYzoHj2IWpEoAHRVoznhQqQDZ5XIZBvRbmmzY0mjF30/ll2KVmlSZD6JJUdckRMumBhumYhmp2VxkZr6IoMsgb0jX9hxNB+CLNfkwFoyDI0CNRm+/pdEKC0/U+ZIQArfbXWX0/KKW45TSByilXZTSNZTSW9O33U4pvV3zmF9RSt+W9bzTlNLt6T+bleeeKUxNTeHYsWPq6iqZTKo6IEophoeHc7a7Y7EYBEFQV30DAwOL7qKcT17un0afX8D7z66B08zhly/7AUBX+Zxve9DhcIDjOKyptWBNjQV3HvIjkdbzlppJDcUF9f2MKDVAVo5vJppdZrxzezUOjcXxeF8E9x4N4kv/GMcfjwSxrs4Ku5nDtRur0OTkccdBP8ZCAt537zDedfcwvvrYBAQx99xZyIITJRNfSKpixEwBssVi0WWGtjfbEEhIOo9T1nZ68ROOJ3HHQT+OTibgjYpwmEnJFm8K2XaK2bhcrpKbivA8jy1btsDhcOSMOyaOYF2dRZdBno8C0WJ9kPPBcVze79VqtapzVFe9Fce9iZzv9OhkAv/+6AQ+98i47r6ZxldlMeSLplCb5Q5Uii68FHiez2kZrnDpaidGQimc8BnLBeaz+UuxxARR54Gs5axW+XO+NFq8768RkkTTv1EmyHRZeXR4zOpisBxey5VgPChLQ3jNDovVxGFrk1UNkCVJgtPpdBs9n3XSqxCCIGB0dBSpVApDQ0Oyb28kogukJElCf3+/LkgOBAK6/wuCsOhaBc8nz/f7Acgm92/d4sHBsTgOT8TVDLJzhgCZUgqOELxnZzUmoyKe7pcHuFInrnA6QM63Wp9NgKyVBRTiqnUurK+34Ad7ffj5/mnsH4lDosC2Zjm7Y+YJ3rrFgxO+JL76+CQSIsWVa114YTiGO1/xG77mQu1MRNOtUY10c4UwahKSjXbBsU3RIWfZvS1GHSFDhlKKHz/vxZ2vBPDvj4zjhaEo6rO6VpYSIBNC8gZDhJCSg+NstP7bChsarDg9lVQX4kBldy5SqVRaojD7ALlQtlbrENRVZ4U/Lqme7QqK326/X9AtDkRRzFsYJkkSRFFEIN2oY0W1/trOJ/koB/lakb9qhRNmDnisN4LuibhqBaY95sU2F8sSC+PfvtVtRovLhJdG5DFwtos1XyQJkQK1Wd71G+utarc+o2L6xcBkOGnoPLWr1Y6RUAqjaekix3Gzk1gwZod2IhZFEcFgEH6/Xxf8UkoRjUbR39+vDiTZgvHF2Cp4Pjk4HEJntRkeG4+r1rlQbePwvwf8OOlLwm4isJlJ3sCJ53n1vu3NNjQ4eTyt6U5XyqpXDZDzZJBn8/sUKvjRwnMEX9jTiFU1FqyqMePnb2rF1etcuGJtZoK/bLULDQ4eAwEBu9vs+Nh5dbh8jRN/PBLM8S9dSD2uIMrfU6mayWICZO3E1+g0odll0jUMYXZvi5tAOILHToexvdkGp4XDeESENSszWmpm0ePxGF5jhYLnYjEqtN1Yb4VIgZNT+vb2lWpUE4nFEU9ROGeh6VeY6TtVgtX1imVmliNQ73QSVVYOdjNRmzAAhRekgUAAhBA8ejoMkQIXrXTonleqNrwUFAvQbFwWDrvbHbj/eAj/9tA4Pnr/KKZjuW20F9MuVEzIHyADwM5WWWqm7CTOZvxTLN6yewBsaLAilJQwHJTP7cWoQ56OCvBYc2NfpUnO/pHCiSIWIFcIrfWJJEkYHx833J5RMsunT59GIpEwvPjK1U+9XASDQZw4caIkDe9sGQ4k0ZEW2NtMHN6+vRqHJxJ4qj+K1653gy9QgQ1ksoqEEJzb7tANFkDxmdRwPLfnPSBPkh6PZ1bbSzNtAWuptvH4zlXN+O7VLWh2mfGRc+vQ7Mp8bjNPcOtrmnDlWhfeuaMaAPC+nTWw8gT3dOcOigt1PikBsqnEALmYbHv2xLet2YZXxuM6Gzxm97Z4OTw4BUECrl7nwhf2yE5G2VmrUgNko6IsQB6T5xogGxXabkjbax2b1OuQK7Vz4Q3Kc4rLOvupfCYfaGUM7ay2wMwBJ7IK9cbCKayqsWBPpxNP90cRTcvf8rXbHhkZwfDwMCilePR0BF11Fqyp1QfE+YLYcmC1WvNmzS9bLdcyVFk5BOIifn84d5xcTIvspEhhKSCv2dkqF6QdmZDnOUEQSp63x4OyFCE7E6s0tVIKARfT96IQTqTUQn4tbVVydn0m/2sWIFeI7O2GZDIJUaK4uzuIvxwL6oqHAPnEPXnypOHkrQTYi2Vin5ycRDKZRKX9qkWJYjwsoMmVGcCvXOtSA+ZrN1blNAnJRluos63JhoRI1UKTUjokheICbCai0zJxHIempia0t7djw4YNJX8+bSvUYh9fKLBsdZvxsfPq0FkjTzYuK4/L17jwVH8EU1lFe8lkcl4WONkoixO+hJGHEIKmpqYZH2cymXTf5/YmGyIC1WXQJUlalFuBDODYiGLJZMG6Oiu+d3UzPnpunXr/TNe6ESaTCV1dXaiqqoLNZlM78RUKkoqF5/mc1/DY0r7r86RD7p2UA9AW1+w1uzN9p4qThZknWFtnxSsT+qTCZEREo5PHxaucSIoU+zRBR3aAnEwmVS/2qCDXCJzVot9Jc7vdFdMgA/J5pLSjzmZXmx037arBf17ZjIs6nXikJ6yTy1BKF9xLXosg0oJzwrYmG0wc8JKmQL3U3cPJgLwIq8pahLVVmVBl5dA9Kb/2YixijCTEvHVDO1vtODQWR9KgTkeBBcgVQhAE7B+JoUez1fZUXwS/eGkaP9k3jT8e0Q+YWo/eR3vC+MuxoC7TmW81vhAoAcb09HRFpR9jwThSEtCsCZAJIfiPK5rwkze0qp2gCg2mWhnD1iYrCPT+uMVOXOFEKkdeoWShCrWgnYm56iBn4vUb3BAl4DvP+nIGgoWQWaRmyCBzHIf29nZ0dXWpWSStZd9MVFdXZxZEBn7IwMJ8bsbMjAbkwKoxfb2vrbPqslazvc54nseKFSuwZs0a1NTUoLGx0dCFYjYoPsFaNjZY1ep+hUrtXJxOB8htVbMbf4y8gbPRWtud02bHSV9SdRgRRIqpmIgGpwkb662otnG6JgyiKOoW4uFwWL0+f/eKvCBSpBvK8VQye6xQVVVluLPApZs2tVWZcdlqJ2IpfcAPyLuOxUhmEokEent7KyavkSQKUaIFPbDtZg6bGm14Kf0ZKKWYnp4u6X2mQvJzXZYsr2pCsKnBiiMTGa/l7CLAhZajhJMinBbjL2hXq02XXTeCBcgV4s9HpvGlf0zgEw+Mqh16nhqIoM7B44IVDtxx0I+TvtxM1v0nQvjuXh9+sm8a337Gq5NpeL3eef0MRkiSpAbFhJCKBhuDU/JAq80gA4DbyqOtSg6KKaUF9WpaGYPbymN1rUVXuFXsllMkkcop0ON5fs6G9vkG6nLR6jbj/btqcHAsjj8dyWTLFyoTIqTlDvl2BSVJgtvthsViQUdHB1wuFxobG4sugtROrjV2udJa+3sv1q1AhlxQ47JwBbeM55L1JYSgtbUVDQ0NZbMRM5L+bKy3IpCQMBbOBEaVKmLq9cVg4mTN/WwwmUwzBsgWi0UdQ8/vkOUWz6ZrOZRAudFpAs/JMrZ9wzHdYlyb2AkEAuprHRyNo7PajHPa9IsMo0VHuSnGh35rkw3VNg5P9eVKI4sZQ7xeLyKRSMV2WgWpOLnazhYb+vyC+lvF4/GSAldvUNYWGwWamxttGA2lMBVN6ZJ4yWQS4XAYR48excTERNHvVU7iyRRSUn7nqa3NNpg5YN9wfpkFC5ArgChKuOPgFDo8ZthNBL856EdMkPDSSByv6nDgo+fWwmPj8cPnfDp9JKUU9x0NYXOjFe/cIXvbPtWfuTij0eiCd6xJJpNqQCdJUsmr0VLwheUJpdpWeDKbqQpbu+2+rcmGo5MJ3bZZMZ8hkszdqilHpbXRZDAbR4xCvGFDFc5tt+O+YyHdxJVIJOZdZiGIEkxc/s9osVjU35PjOHR2dqKurs7wsUZkb51va7Lh8HgiR3d+pha9Lma8Eb2VlBGz3ampFE6nM+dc3tCQ2zAEqMzOxcB0HC1us076VSwcxxW1+NQWzbVXmbCm1oIHT4aRkigmIpkAGQDOa7cjlqLoTmfltAtSSqmaYUxJFH3+JM5p08srCnkylxNCiM473QieIzivw4H9IzHd+FFsFlYJFqenpyuye5BMFRcgn50uSFP6AJSiiaeUYiqcgM2kl/cpv9nmdHOcI+lzPRQKIR6P48SJE+jr6wMgSzIXYvd7Kiyfa/kCZJuJw5Ymm05+kg0LkCtAny8Mf1zCtRvdeP0GN54fjOGvJ+Tg5IIVDritPN6/qwanpwX8Ku3rCwCnpwWMhlO4bLUT122qwqoaM+446FeDaErpgtvMZAdUkUhEDdqj0WhZLwR/VH7d7K0dLcVkgrQD4bYmK1ISVP9GZbCbaQCLJFI6M3uO40oK3PJBCNHZRXEch/r6+rIHydd0uRFKSjmr5fnOpqZECXyez6bIK+aKNou8q00uUnkly+5tMVZcn8lQStE7ndDJqYweU6kGErNFKz9QWOExw27WNwyp1M7FYCCBNrf+OyOEoKamRk1kKLrr7DFF2a0pBkXiRAjBP22pwkBAwI+e92Ei3e2w0Sn/LluabOAJcEAja4pEIvB6veju7lYXpmOhFESq73AGlGdXrlg8Hs+M73VOmxzwZ2/DzySzSKVSuvsrMd4oQftMBiad1WbU2nlVZlFKs6h4PI5IUtLNwfX19aqscU2tBTYTUbvSxeNxQyeukZGRot6vnCjSEGcBC8SzW+0YDORPErEAuQJ0p4tNVtVYcPU6NwgBfvWyH9U2Tq38vHCFA69b78Y9R4P4xf5pSJSqFZXntDnAcwRv2+rBcCilsyar1Gq0WAKROL72+Dge0LTkVI6pr68PfX19ZfPYnY7IF12hALmYjJLSMASQt4Q4Ahway0xeqVRqxu3PSFLUaZAppTNmIIolW2bR2NhYdh/QHc02eKwcnuzLDNSz0aPNFUGkMOXZQm9rayuqecpMaL/PbU1WWHmCFzULA2b3tvgYmwphNJTClqb85/1iDJAJITm7QDxHsL7OmpNBLvfOhSRRjAQFtLr1QabFYkFrayu6urrgcDhQV1eHrq4udHZ26oJks9lc9PepzZRfsMKJf9pShYd7IvjLsSAIgLq0X7XdzGF9vRUHx/SyprGxMVBK1dcYDspBSXuV/tgrWZyXTb6uelq2p7fhXzTYhi+0I5BIJNTPWqnxplhHIEIIdrba8PJoxtEnHA4XdS7G43F4oym1Ex3HcaiqqkJdXR0IkYvWNzZY1QUEIcRQCioIQsU7SmYz6ZeTdY4C8cPWpsK1LSxArgC9Y7K2s9FpQoPTpG5xnNfhULfCCCG4aVcNrlrnwt1Hg7jrlQBeHo1hdY1ZLUw5v8OBDo8Z92bZdC3k5P7YCS+eHYjiv16Ygi+aUrPa2sFifHy8LO/ljyTBk1xrNS3FBMh2u10dCB0WDmtrLbqMIqV0xhV1JKulJ8dxZdvudTqd6vEpRYUNDQ1lzaTwHMGFKx14cTiGmJAZGBOJxLy6OqQkCdk/JyEEq1evzltZXira79Nq4rC92YYXhmO6yTC7IQ9jYTk87AcgJxXyYZQFXQw0NTXlLGg3NljR7xfUhkZA+XcupiIJpCSg3pkJcjmOQ0NDg1rQuHr1ajQ3N8NsNsPpdKK6ulo9ltra2qLfK9uz/f9sq8YKjxmnpwXU2HldJ78dLTac8iURTjfa0F53yr+H0gFyW9X8NQjJxmw2z/h+NhOHbc22nAB5puRC9k5rJeZsVWJRRBfFnS12hJMSTqa7BBZ7LsbjcQwGBHRUZ2p+7Ha7btdzc6N8rocSIiRJMlx0zHcyxu/3Y3hSjsMKeYR3VpvxsXPzXwcsQC4zlFKMToXBkUzm84atHqyqMeN1XfrtLI4QfGR3LS7udOCuwwEcmUiowbRy/2u73Dg5lVQL+ha6m8/+gcz2yaOn5QtMaYmtEA6Hy1K96o8m4LJyBSfFYjIOZrNZ9xrbmm047k0grtEhz2T3Fs3KIJdTJ8fzvDpQK1lpoy5dc+XClU4kRKob7JVGNPOFkKI5ekmXy1WWzLECx3G6rN45bXaMh1O6rbTFZtd0pjM0JY8lhSQW87X1XipOpzMnO7uhwQqJ6ptqlDuTOJ52/dDqtimlBWUTra2t8Hg8aG9vR319fdHvZbPZdAtKE0fwhg3y+0xlNdPY1mwDBXBoPP/CeziYQrWNy9kdnA/9sZa6uroZz6tdbXLXNSXrrRCLxfLOc6lUSvd9iaJY9u6lmQzyzI/d0SLvnO4vUWYxFYrCGxXRkV7IWCwW1W5Rkd1saZR/7+7JwokWbYFmpZmYmEAwLv82BSWaHMGV6/JfL4tzxFnCJJNJ+GMpVFk5NRDoqrfih69tVf1ptRBC8L6za2E1cVhZbcZbt+izaJeucsJmIjpJQywWWxAPWwAY8sexrtaCTQ1WPN6rX4Fmt8yeK/6oANcMAqtisrjZXbO2NdkgUqj2NMDMMotIUoLDVJkAGYCazdFmeLSWZfkoJau2qcGKGpu+myAg/1bzZccjiJJOM5etwS4X2g5qSpX8CwYLg8Xm23mmMhaQgwejtrAKi01eocVsNusWZevrreAI8Mp4bqFeuc65sUBuAwcjb2YthBB0dHTk7TCYD86gIdOeVfJi/uJO/eJ2fZ0sazo4lj8gHAjkSkMq3UHPiKqqqhl/D3X8GNJnkQsVu2XPz5VYkGc0yDP/jlVWHuvqLHhpNPMZgsHgjJ/9+Lj8+ZRW4NpEhjI/ddVbYeL086kRlNKyLxKMUKwFx9MuMo0FFt0zwQLkMpNIJOCPS6pHbzHU2Hn8+HUt+M8rm3MqLh0WDns6nXiyL6puWQFYsCzySDCJZrcJF3c6MRAQ0Ded66qhDAb5tluKJRBLztghqthJUxtsbmqUL2itzALIH9SLEkUiJemK9Mo9kNfU1GDjxo26121qapoxu+FyubB69eqisms8R/CqFbINk1ZmARTn5FEOBFHSZZCNNJzlQGtdVe80YXWNOadAkVK6aLzFz3Qmwwk4zAS2AumwxeZgkU1NTY06xrgsHNbVWfDyaK4vbLmciJQOZ9oM8ly7AxYie5fHZuLwm+va8Ynz9ZloM0+wpcmKg2PGmtPj3gSOeRM4q0V/rKU2TioHPM+jqamp4GKh2WVGZ7UZzw3pEwuFXJyMCvj8fn9ZZV1KBrmYABmQC9JOeJMIpDOrxbhZvDgo74ZvbrTljNXKGGvhCbrqrAX9hIHy7X7H43EkErLPuFF8EYvFQAjBWDgFj5XTxVTK71zs4pAFyGVGEAT442JJATIA1DpMeSeHa7rcSIhUlTQoAeh8m3BTSjERFtDgNOHClQ5wBHi8z1jHFI/H0d3djdOnT0MURbWldinHHIynCm6PEEKKDpBramrUCdZm4tBVZ9U1kFC01EYXnKL1UnR2c2kMkg+jz8LzPFpaWvJezIrjhd1uR0dHR1EX/atXOpA0kFnMl9wgJUm6ohJKaUUmRYvFols0nNPmQPdkAiHNInOxeIszgPFgQi30ysdiD5CzpQ07W+SmGsGEfswrl8xiPJibda+kh7CRpV2NnTf0rd7ebMNQMAVfVgfP5wej+NSDYwCAN27U7xyVo/33bKivr0d7e3vBJMP5HQ50TyQwnSUnyTenCakUvvesF785kAmgKaXo7u4uWxY1KRavQQaA3W12UGR8f4uxaT0yEcfqGgtcFi4nw6/ILQBgS5MVp3xJneb+0FgcT/fr44O51n6Iooienh6cPHkS3d3dOHXqVM7rKcHzWDiF5iyHF7vdjq6urqLHkqICZELIVYSQ44SQU4SQmw3u30MICRBCDqT/fLHY5y43kskk/HER1TNkPkthda0F6+st+NvJzPbcQmgofWG5KKTOwcNj43F2qx3/OB1BKl0Zq/UWVojH4zh+/Dh6e3vR19eHkydPFt1ZyB9LwW0pvF1YbIBMCNF5fu5oseHUVFI34FFKDSuTjQLk+dru9Xg8cLvdhsEvpVTN6rjdbrXIjRCCdevWoaurKyfTvbHBilo7j6f79dkQQRDmZfsrmdIHyFartSKFV9k+p7vb7ZAo8NKI/jNGo9GKdbpiFM9JbxyrqgvXE8ynw8FsMJlMumPc2SoHJC+PlFYQXCyToQTsmqw7x3EVzcBmF+oVYluTcRfLuw7Lu3Rrai05u6Umk2nBdOYej6dgkuGiTicogEd69BnXfM2yjo9H8cjpCO46HMQpX8ZSFAD6+vrKkkkWUsVrkAH5O6938NiryYSHQqG8x5JKiTjpS6Ar3ekwuymXdozd0WyHSOXfe99wDF9/YhKfe2Qc33zKi7+f1H8/c4lbIpGI+htRSpFMJnNqaOLxeCZAzurC29LSAovFgpaWlqLOtRkfQQjhAfwIwNUANgG4gRCyyeChT1FKd6T/fLXE5y4bDgwGMBpKocNT3sH8mi43hoIptSuYoqGcz0r8kWn5wqpLZyyuWuvCVEzEyyMx/ON0GG+9axB/OZbbQluSJLVPeyqVwvDw8IzvRSmFPyYW1CTO1EUvG63W9YIVDkg00xEKyL8FlDTYypqvAFnRDGonPmWAyC4+bGxsBCBPNFarVe1Gp32MKrMY0css5mvBpTQKUahkxki7sFhXZ4HHyuGF4dyuWJWWlyg7KAxjwokUJiKpgg4WwOLPIAN6aU9X+px7PmtrPplMlmVRNhlK5jRWqeQiQtuVdCZW11rgtnA6HXIwIeKkL4k3bnDjG5c35TxnvuUV2bhcrrzzSYfHjG1NVjx4MqRr7pUvC/v8YCYoVHZ+tc8pR42OokGeyeZNgRC58cnLI/GiCtT7vCHEBIq1tfLvQinNOb+UMXZjgxV2E8EzA1F866lJPDsYBUfk7+32F6fUAkdKKSYmJtTzv9RxMRaL6WIeZedXe1skIiftJiP6AJnjOHW+mal7pPqcIh6zG8ApSulpSmkSwO8AvLGoV5/bc5ckfzw0CbeFwxs2lLfw6NUrnXBbODxwQr+CLceFViyj/nSAnN4K3dlqh9NMcNfhIO7uDkKist9z9jZUNuFwGNFobqCie0wihYRIUW3T64e0RWlOp7OkAEvrFrGy2oIVHjOeytoCMupWmDTwm5zPgiFCCFauXKkGCE1NTVi9ejXa2tp0j7NYLNi8eTPWrl2r3ma323O2XS9MyyxeyNLk+v3+igdyKVHvYlHJAFmbQeYIwTltduwfied0r5yamqrY506lUjh27Bh6e3tZkJwHRSrQUKBd8nzu2swFp9OpZqZ4jmB3ux37sjqxAeXpqjcVTeq6jBoFMOUku9i5EBwh2Npsw4GxuHreHxqLg0J20zHypl3oAFnZZcyXWbymy43xiKhrggLIc0a2zOLl4SjW1FhwwQoHnuyL5ATV5dDiFuuDrOW8drlx0oHRTKJtdHTUsEB9MMtZhuNyHaUUWZGZJ9jWbMNjvRHEUhTv3FGNH7++FbdeJu/a3qdJnEmShOPHj+PEiRM4cuRISdaHRnGD8hkAebwVBAHHvQlIFGhyZa4HrURIafQ1045IMQFyG4BBzf+H0rdlcz4h5CAh5G+EkM0lPheEkJsIIfsIIfvm03aqnIiiiCNjUWxtthn2LZ8LFp7g8jUuPDcYxVRa1yVJEiYmJuZt4h31ywFVnUMelM08wXWbPTjmTaDPL2BXq3zxZW9DZaMYxxdi3KBC22q1YtOmTVi3bh04jkNTU24WYia01dsXrnTgyERC/T6VY8vuHa9sZWl3BOd7sjabzejq6sKKFStQV1cHh8Nh2KjEKJBQfFEVNqkyi9yBqdId5pKipGbijSrjy4m2bTUAnNMue4Fm2xGlUqmKyUuUQCgWi83rYnYpoQTIhdpML5UA2eFw6Mbj8zociAo0x3e9HLsWkYSokylQSiueZS+28x4A7Gq1wxsV0TstZw+PTiZg4QnW1RlnaReDhEb7+Xie142b57Y74LJweCwrI2wksxgMJLGqxoyLO50IJCR151chkUjMWYueSMlBuTmr6LlQ0LelSY5NnhvU75z29/fn7EYPp+d7pUui0fWnbTazsyWTiLlyrQttVWbUOkw4v8OOx3sjOgmmIo8AgIGBgaJ3wo3GaUW2FAgE5G6+SQnfeHISZk6e6wDj1uLFdMItJooz+razI7KXAKyklG4H8EMA95bwXPlGSn9KKd1FKd3V0NBQxGEtPoZGxzEWTs2opZstV61zQaTA309lAlBRFOetEn8sEAOBfiL7py0etTvgue12bGqw4tHT4RmD9lgsVrCzjhIgV2s6+CjVxhaLBZs2bZpVQYrL5coEyCscoACeybI98/v9OHz4sKptUiUWfEb7tBBaOaWLUamaXbfbjdbW1szqmRBcsMKB/SNxXVFFMUUbcyUpiOA1X12lJ0XtVtrOFjssPNHJaoDKtXBXBm6l2no+F7NLifHAzAEysHh9kLVky552NNtg5UmOzKKQh26xhBMiHGZ9cFTp70jblTSb7HHpnDY7CICn+iP40fM+/PlYCGtqLYYZz0oUPs8GjuNQXV0Nk8mEdevW6e4z8wQXrnBg72BUJ0/LzghHkylMxUS0uM04u9UGu5ngqb7cMWdwcHBOC/OYIJ8/VlOmNqa2thbNzc155wgTR7C7zY4XhmO6rLYgCDkFyyPTMV2XxHxjtTLG7mrLzMdak4LXrHEhIlA8P2Q83xfbhr1QgR+lFENDQxgYGMAjPSH44xK+8ZpmtZW50e6HyWSaccFXzNU0BKBD8/92ALrG2pTSIKU0nP73AwDMhJD6Yp5bCURRxPT09Ly7PAxNyhkiJcNabtqqzDirxYa/nwqrJ7eSRZ4PxoJxeGxczgD3yVfVYUujFbvb7bh8jQtDwRSOeWf2RCzUcW9sWg76tRnkYnVDhdDq6FZUW9BZbcZjvcZZ04mJCYRCoUzHIi4TIC+FbJaWmpoa3QChyCxezBq0gsFgRXXtsaSgVr1XeksYkBcHyoRuN3M4u9WGZwaikLIC1dlWVyuaeiNf8lgsptsSnK9CyKXGVLqlfJWt8HS0VK457cLdauJwVqsNzw3FchZHc80gRrKaF83H95OdIVcghOTcXmPnsb7eij8cCeJvJ+XxfE2tcfZ4Me0QtLa2Yv369TCZTKipqdHdt2e13Gwp2/ItHo+r1/ZoesHX6OJhNXE4r92BZwajOTIbrTRgNsSS8nhl0wTINTU1qKurM9xdVDivw45gQtL5Fis1TdpxbNgfR7WmS2I+fbbL5ZITWC4TLljhwI3b9L0ctjXb0ODg8Y/TxvOs4iY0U/vrmaRwyn0HRuNorzJhQ4NVd5+RhEf5ffM6ReV9twwvAlhHCFlFCLEAeBuA+7QPIIQ0k/Q7EEJ2p1/XV8xzyw2lFP39/RgZGUFfX9+8ZmxU4/YSLd6MUATl2T/c1evc8EZFnU2X9uKsJPmsmFrdZnzzimbUOWT7N7uJ4I9HguidThb8/iORiGH2O5lM4tSwnL1VMshGFkOzgRCi8/O8bLULJ3xJDPiN/ZyHhoaQEGQJhknTJnwpZLOy0bavVtwssjXYhJCKSQGSySTigqgLkCudNXK5XLpz8MIVTkzFRBzLklnM9nP7fD5MT09jcHAw51wfGxvLabM7n+1WlwqhuHx9ZbsaZLNYAqiZyF7In9/ugC8q4tRUZowpR1FsJKmXWMzH95PdpVJBKSbOHqOvXJf5Lt59VjWuz2qEpWUxZJAVlM+RrVPd1GBFg5PPaZKl7Ujqj8pBZpVV/j1evdKBSFLSNelQiEajs567o0n5urGmq561QaB2xzCbs1vtsJkInuzL/Qxa6aMvkkStJpbJFyA7nU51nPvsRQ24YVu17n6OEOxZ5cRLo7G89UnxeBx9fX0FY7Ziv6eTvoQuOFYwuj6Ua9Vutxumkmec5SmlKQAfBfB3AEcB/J5SeoQQ8kFCyAfTD3sLgMOEkIMAfgDgbVTG8LkzfsI5oGzdU0rLovMpFlEUMR2TT9hCzgvFoBRlrVmzJmdb4Nx2O+ocvK6zHqV0XvxcJ8K5VdPZOMwcLl3txPNDMXzsr6P434N+AEBMkHSNToDMNlMwGMTY2Bi8Xi9EUcTw8DCmoilwBKhKt5ouRfs2E1qZwp5VTnAEeKTA6nZySv4M5vRHXyoTdTbaYJEjBJescuKF4RgmwhkNdiW9gaenp5EUqW6hUQmLNy08z+vbTrfbYeaAp7Js7mb7ubUaY60OMR6PG0qI5qMQcqkRiguw8qRgsZEkSYsqgCqEtlAPkLeeOQI8N6g/H+Lx+KzdLJIpCUmRwjnPATIgZ92yEwSSJKGqqipnnN7T6URntRkf2FWDt2z25J0b52OxPBusVqsu88gRgos7nXh5NA5/PNffWhRF+KPyQkjx8D+rxY5qG4cHT+Ymg+Yyd8eUADmdcNAm1CwWS07tiYItndV+eiA3qx0MBtVANBgT4LRmLATz7fZpdcj5uHS1CxJFzsIi5zPFYoYLx4mJCTk2CAr45UvT6PcncWQinrMTGE5K8McltTW2gtazWYvS/ITjOMOTr6g0GKX0AUppF6V0DaX01vRtt1NKb0//+zZK6WZK6XZK6XmU0mcLPbeSaNsnSpKE8fHxeZmQBEHAdFzeHqieY4BstVrVjGlHR4fu5OM5gqvWuvDSaFyX9ZyPdsG+iFCUfORtW6tx7cYqnNViw12Hg/jsw2N4991DeMefhnL0n5IkYWhoCF6vF+Pj4zh27Bii0Sj8cVFt152d9Z0r2mx0jZ3HOW12PHZaX2msQCmF1ydn/ZQJfDEO5MXAcZzqlQwAr1svT2Z/Oa4vMEkmk2XfkaCUIhAIQBCpmkGezwld+b0dZg4XrHDioVNhTEb0wUkymSyoi89GWYQr/9ZKhpRix3hKwilfQjcGse59eoLRxIzZY6vVumQWptl2aB4bj00N1hwdMjB7i8FQXM5SajXI8zUuGbVnVgKQ+vp6XSBi5glue10rXj+Dq9NiDZABoLa2VveZ9qxyQqLAUwZNsqanpzGdlgy508GlmSe4ep0bLw7H8KkHR3P6Bcx27o4mRfAkUxuTPUc2NDTk9dDfs8qJcFLC/pHc7qIjIyOyLjiegktzXRaSwxWSdACy3du6OsuM9UnKOKpdOCYSCTU7//29PvypO4iP3D+Kzzw0jttf1AfTip1ce1aAXMh9pbOzE6dOnXrF6L6lt088A5FIRPcDCIJQ0qQ3WwRBUD0fq+cgsSCEoLq6Wv2/xWJBa2urbsV+TZcbVp7g7qP67Hgltm8VH+NESoQ/LqoeyIWosfN479k1+Mqljbh+SxVeGU8gIlA0OE34f8964Y3oXSMU3ZFSzEQpxbTGA1mSpLJaAGW/1uVrXJiOi3hpxPg8yW7pWcluVZWmtrZWPZcanCZcuMKBv58K6Yr1KlG0FovFkEqlkNQEyPMlU9H6XwPAO3dUQ6IUdxzy624vNZuTbY2kDbCVXayvPj6Jf/nbmLoIYd37cvEFo7o27tnM1rFmodD6rSqc1+FAn1/AWCij8SzVYjAQCKC3txfRaBS+oBycaRcW8xVg8jyvW3QCmQDEbrfPaiHD8/yila1ljx+d1RasqjHjHwYyC6/Xq0os3Bonq9eud2N9vQXHvUn8Jr2rqmU2cptYMqUW6BlJXwghaG1tNXzujhYbPFbOMKMbi8UwPT2NUELfybbQ+VVVVTXj73flWhf6/EKOTV42StJMQUkoJFISjnkT2FBvRZWVwwqPGQ+cCOua0QylA+TWrAC50JzN8zwEoyISLMMA+ZWRIN7xpyH829/HEBUknTaokvRMBPHsQATntttL8iU0QhsgA8hxLvDYeLxmrQuP90bUYFP5nOXMlkuShN7eXhw9ehTH+2VtUikFiBwheMeOGrx1cxVu2OrBVy9rhESB/35p5kDeHxfVhUZ2Zfhcyc5I72q1o8rK4eE8MgsldjTxsva4HMWCC0V2N6w3bqxCVKB4+JQ+q+n3+3P8oOdCIBDAZDiJcFJSZTrzlRHMllk0uky4Yq0bj/dGcjRxyjZpMWRb4mkXFpFIBIG4qA7evzngRyC9JRuNRg2L+s5EKKUIxYWCGWRKaVklVvNB9ph9Xrt8/j2XVRSbSqWKSuAIgoChoSFEIhH09vaiZ0ButjTfRXoKTU1NuvdTxlNCSFH+stlkB6GLCZPJlBNgXbrKhZO+JAYD+utYkiRMBuSxVHtOV9t4fOeqFlyxxoX7j4dyOrhOTEyUXAMRTYqq/hgw1gibTCa0tbXl/B4mTtYFPzcUzRkDleLBcEJS7WpnyvBrdcj5uGy1C/UOHr96yY+URDERSekalmiJRCJqlz9lEdk7LUCiwHWbq/C/b2nHd69uRqOTx0/3Tam7vy+PxOG2cGjVtJjmOK6khmJallWALEkSfv6iD8GE7HX6y3QgFg6HKz4hPX1qChIFbtpVO6fXsVqtOSeishLUnuTXbqyCRIH7jum1yOXKIlNKcfLkSTUTdmJQDpBr7aVnKd51Vg1u3F6NZpcZb9lchaf6ozktSLOZjmcyyJUwkNf6IZv5tB53KKoGMVqE9MVn5uRK7aWcQSaE6Caj9fVWbGyw4v4TIZ2eS9lqKwfKeanIa85NBwvzOaFrM+cA8IYNbogSdFp+hWKzOcq1oSUQCEAQBAiCgIfSi46Pn1eHeIrifo2UpRK2cosZn8+HEydO5GTdU6kUooKkkwpkY1SsvNjR2kkCQLPbjJXVZp3/LFD8bo02+UEphT+9ja/93ubzeuJ5Hu3t7WrBsjYA0cq4ikFxX1jMZGfMldqVf5zWJxYkSYLPH4KVJ7qGSApv3lSFlAQ8nNUrQCkILyWTHEumVP1xIUeg6upqw0XL1evcoBT4xpOTOYFqMiUhIVI4LcVJeEwm04w7GGae4P27atAzncSbfjuAf75nGP/ywKhhkEwpxcDAAE6dOqUmak5Oyef82loLOEJgNXH455016PML+MORACilODgWx9mt9pzvfrZuScsqQO6bDOLwRALv3FGN13a58NCpsFqAVOk2ukdGQ6i186qpdqkok3d7e7vh/R6PR5f1bHLJ2+N/OxVCJG33omiuy2HTFQ6HkUql1EE5mA4cq6xzO2Wu21SFRieP/94/nSOwV4gmJfiiIhrTnbUqESBnZ4GvWudGSoIuiFFIiUqALA/mi8HQfi5kb4dd0+XCaCildldSiEQiZWkckkgkIFGKvxwPYW2tRW3DPp8Tutvt1gWzbVVmnNNmx19PhHIM7L1eb1E7MbF4Al95bAJfeWxCN8iPjIwgmqL445EANjdacdlqJ3a22vBQj2zPqGytz2eb+IVEqY5PJpPo7+/PkcBFBVowgzyTvnExYqR5PK/dge7JRM4ifKZdC0EQ4Pf7dbcpkiglw7cQNmlKa+ZsCZzFYilpjLRYLGWtMakE2TsYNXYeO1vseKw3kjOPxQRJlZFl055uWX13dxCjIX3STrlOit3BigmiavE2U4bXqGCv3WPGpy+sxzFvAj96Xh8fKeeXyyKfU8V0UCxml+dVHQ5csipzPQ8FU/jdK/kz58lkxgnrhDcJj5VDvWYX+4IVDlzc6cD/HgzgNwf9mI6LWN+gzxZTSlkGGQCe65FX4rta7XjLZnkV++djwZK1XrNhIpREo9NUcqaDEAKr1Yr29nZ0dXUVPBGzDcDfvKkKMYHi76cyQV252lhGo1Hd9xVKyBeMe44BstXE4R07qtEzncQTBkUOAPD7IwFIFDi71aZ+P+XGbDbrBvEOjxnntttx//FQzopWySCbeJK3GnYpkb04uHCFEx4rl5NNVbba5nrdxONxnPAmMBJK4fUbMkUj853xyg603rSxCsGEZGjZVEwr4COjYbw4HMOLwzH89/5p3XP/3B1ERKD4wDm1cmHtOjd8URH70vaMStHimUA8Hld/c0EQdOOTHCBLOqmAFiM971LAqHPXBSsckCjwdH9usV6+BI4gCDh16lTONagEMMrCYqF8hBsbGwHkbu8XK5ngOA61tXPbdZ0PTCZTThB/6WonvFExZzc0KUqwmPLPEddv9SCRkvDFf0zoGo4ApdVBxDQSC6M20Fp4njfMIl+40om3bfXgsd6Ibj4OJ5UFmPz4YhapxeiQCSH41AX1+PP/WYHfvbUdF3c68NfjmSSfFu05742m8FR/BFub9LtJhBB84vx6bGuy4veH5Zqs9XW58cJs9e3LKkDumQyDJ3Kw0+CUPXkf6gkjmpSK7tYyW3zRFGrsxX+dhBB4PB6sXr0a69atQ1VV1YyrHLvdrpss1tZZsa3Jij8fC6l2LYqeabb2QQpKgwPldUPpE9hdhhbaF3c6sabGgt8c8COZZTMz4E/inu4gLl/txMYGW0UzttnbZm/d7EEoKeEtvxvE//nDIPamt0MT6WO0pgPkpY5y7imYeYIr1rpky7csZ4dEIjHnLHIsFsPT/RFwBNit6bY031Xr2fZUW5usWFNjwb3HQrrBWJKkHA/jbCRJwoEReZv04k4HHjwZVl1lKKV4pCeEnS02rK6Rz5fdbXbU2nk8cDKkPqaYznrhcBgnT55Ef39/jjxhqaB1RFE+t5Ili8aT8MfEghnkpXrNeTwe3fm2qsaMzmoz/tGbu70+OTlpmDlUrC+zz5OYIP9f+70tRJGbx+PBpk2bcgKvYoIlQP7sSyFABnITVOd1OOC25Nq3JUWqSh+M2N5sx1cva8JoKKWTSAIZyU0kEpkxmyxrkItPNuT7nq/f4sG6Ogt+tm9KDYyVv11mDhzHFZUdztdExgieI3BZeVy7sQqxFMVtz/vw/FA07/P3DkaRkoAbt1fn3GfhCb52eRPevt2Dc9vtOc1oTKbSE5cKyypAHpqOocFpUvUnb9ooZ1gf7glXvOPcVFQo2v9YsW/r6OgoWc/a0tKSlUX2wBcVdQ0fKKUYHh4u6XW1UEoRi8XwWG8Yb71rALc8PoHxsOxL7ChDgMwRgn8+uxoTERE3PzSG7zzjxdv/OIjPPzKOrz0xCbuZw3t2ZjRplQqksrVyGxqsuGqdCzwBRAp85xkvRkMCEin5orXwlclmLwTZ2QRFj/bgSeMs8lwsBIPhCB7pCWN3ux1ua+Yame8JPVtmQQjBmza6MRgQ8FKWvMRoW1vL8PAwDo8n0F5lwk27amEzEXWrcDySwnhExLntmYwTzxFcudaFl0biqpPBTG3iU6kUBgYGkEgkEAqFcOrUqSWZdVaKbRS0TTLuOziKhEixs9U4S1xIW7nYyW5SQwjBpaudOO5NqnZUCtk2gQr5kjqjoRR4kpFYAAvXitvofYud1xwOx5LZkbPb7brPZeEJLlvjxN5BfaFbIkXzSiwUtjbZsKvVjvuOBXN2LCml6O3thdfrxenTpyFJEoLBYE4dVUwQ1UC8mDnSbDYbLjZ5juAju2sRiEu4I+2w4Y3KiZI6hwmU0qIkMBzH5Txuph3XtXVWtFeZ8FR/FLc8Pol7jhqf7z1TSVTbOFWel/PehOBtW6vxhT2NOfrjucQPyypAHpyOodmV+TLW1VmxqcGK+46HIEoUyWSyIh6kgighmJCK7qDncrlmXbXrcDh0E8bZrTas8Jhxd3dQNxiHw2H09/cXnOTzIQgC/nYiiO8844OJI3hxOIa/HA9BovKJWA62N9txTZfcxe6FoSg2NlgxGZF72H/k3Fq1l3sl/TEtFkvOa394dy3ueEs7bnttC3gC/PA5n5rltppmXw272LDZbLpgv9Flwjltdjx0KpxjHp9MJnHixIlZBcmSJOHpU1MIJiS8ZnVG2rEQ3Qg5jsvZKrxwpRO1dh73Zg3MysLAqLhXkiRM+wPonoxjS5MNHhuPq9e58fRAFOPhFE5Pyc9ZW6c/V65c5wIhwAPpjNNMPu3ZsjClkGe+mh+Vi2g0it7ppKq91eq8n+2dRr2D1+0saFnM/rgzYTKZcsaLizvl4q7s9vZKIaviaDE5OYnu7m71/KOU4uhkQg2sXxqNYUuTTReILSabtOxdqnwsNUegxsZG3fd81Vo3RKovukuIVM3sFuItW6oQSEh4pCf3XFBIJpPo7u7G4OAgTp48qduNiSVTqga52GskOwuusLbOimu6XPjriRCOexMYD8vXaqPLVFKRrJKlXrlyJVasWIG1a9fm7NRm88lX1eOKtS7saLbh1y/7cdKn3ynbOxjFIz0R1Bt08S2GuSywF88VpSEWi81qO3EkkECTS/8lvnGjG+PhFJ4fiqkTTLmLY3xheWu1GP9jjuNQX18/p/fTnuSEEFy7qQp9fgEvj+q3MkOhEIaHh0vOnMfjcfzPgWlYeIL/el0rvnaZ7EFabSvv6fL+s2vxlUsb8Zu3dODzFzfiJ29oxe+v78CrV2aCmEpPkNnG9hyRt34anCa866waHBpP4NHTYVjSFm9LpVlBMWRnkV+73g1/XMpp5qJ4VZe6KxGPx9Hd3Y3nh2NwWzicrQmCFkozme1mYeYJXr/ejZdH4+ib1tvaSZJk6OQRjUYxGEwhKlBsbpQXGW/Y4AYBcO/RIA6Nx2HlCVbV6IOjeocJ53U48PCpsFoYmEgkDN8jX90ETXefnI/W8uWAUooHjvnxsb+O4iP3j6iZKWV8GvQn0VmdP8u0FB0stGQHB3UOE7Y32/BoumBTi1K57/f7MTExoZun7jkawqf/PoYP3jeCH+z1od8vYEezTffcxRQgA7kSk2yMMo6Lnewuie0eM7Y12/DAiYzMUev1XojNDVZsqLfinu6AYZMqQP5dCSHqGNzf349AIICenh5ENS4Wxc6Rbrc77y7oO3fUoMbO43t7feiZSsBpJnBZSrM1dbvdaGpqUpOAHMflDcoV1tdb8fHz6nDzq+tRbefx3WczSSlKKX78grzbdE3X7BZTyy5ABiCfANHcYoZ8RBIp+ONiToB8XrsDTU4efz4mZ12UVsblLNgbD8qr/mIlFnMdFNxut26rZ0+nnAUz2p5Q9G2lZM67h/0IxCV8aHctGl0mbGu24SdvaMW3r2qe03FnY+YJzm61q4MJISQnQz1T8cFcKWQvdOVaF9bVWjAZEeFJLw6WajbLiOwJ7KwWG1pcJvzVwPpMCWiyizcLoWwZn/Am0FVvyfEHX4gJPXvbGwCuWueClSc5WWRA3onJHofi8ThGgnIwrbQ0rXeasGeV3KFv/0gM6+oshpPka7tcCCUlPJ1ehFBK4ff7c+wZ4/F43oy9sgWr1BkoXqGTk5Ml+TjPB4Ig4KGeMOxmgphA8b1nfepkPzIygrGwgGa38TVlMpnQ2dk5vwdcZoyyqFevc2MyKuKF4Vz/41QqpWuSAMiuPnce8mN7sw2vWiHX1RAAu9v1WffFFiDP5I27FC0zjboFvnVzFbxREY+eVhpaUFj5mX8LQgiu21yF8Yio1iYYof0OU6kUBgcH5SRiKpOpLmVeqq+vNzxXnBYO//qqOgwHBTzRF8WGBmvJRbIcx+U4ZnAcV5SNn8vK4+Pn1WEgIODXL8vj4UhI3lX++Hm1uGLt7LzQl2WALEkS+vr61C0nURQLZn4HfPI2RXaAzHMEr1tfhSMTCbXlazAYxNDQUM725WyD5jG/PNkVEyCXQ3NFCFE9KAF9Fuz0VG5zByUzUWzh3nOn5Srz7U2ZC6Otyoxm1/xrASsdkHIcl1fuwnMEN1/UgC2NVnzivDpQSpdVBpnjOJ2mnSMEV3e50T2ZQO+08Xl0+vRpdHd3F+W3HYlEEBMkDAQEdBlUFi+EttSo0YvbyuPyNS483hfBVFR/jSh6fu3YEI/H1TbVDc7M+XntpiokRIrRUCqnUERhW5MNHR4z/qIpDDSScyhFgqIkd5XMHptEUcTp06dVl4PR0VGMj49jaGgIx44dK3vToNkSjMZxypfAa7vceM/OGhwYi+PZdPHrVCSBqEB1sjgFQghaWlqW/ILUbDbnZOzObbejwcEbWkpqzwmFZwejiKUo3rG9Gje/uh7fv6YFP3htC1ZWW3TPW2wBcrbnejbZcoWlQnYjrx3NNqyvt+D3hwNISVTOIBchsQDkc+HsVht+8uI0fvnS9IzXrPZ+JRAnhJR0nRT6TbY32/HZixpw4QoHPnJuHYDy2KwW2zzm7FY7Xtvlxn3HQuieiOOPR+Sai40Ns3OyMeowWNLzZ/3MeUDp5NbX14djx47h6NGjGB4eRn9/PwYHB9VsliAIOD4sW6MYDbZXrHXBbiL487FMBXkwGFQzXKIooqenB0ePHi0pa60w5peD85k0yMXqsorBYrHoXuvqLjecZoJfvmx8kUmShIGBgaImzcOjYdnT2eC7nG/mw+Ipe9tdS5PLhG9e0YwdLfYlXTCUD4/Howv6X7PGCZuJ4Fd5ziMg00Sk0K6EsqDtmUpCokBXlh5XkqQF03MbaeLeuFFuHPIXg6AlmUzqZErJZBKTEblARusL3lltUcef1TXGn40QgjducOPUVFLXclWRsIRCITVTPxgQ8L4/D+MdfxrCJx4YxemsRYsgCDh+/LjOK1SSJNUpYmBgYMG9ll/qm0JKkreTr17nwgqPGb9+We6kNRqSFxnZSQ1AnpQXc3e1UqitrdWdbzxHcE2XGwfH4jmd2LIRJYq/HAuhxWXC+npZirKm1pIj3wEWXwYZkIPJ7OOyWCxoaGiYs9RwoTCZTLpFNkkXiE1ERPzjtCyfKuRioYUjBJ+9qAFXrHXhT91BPJqnm2s2EqWq1rlUuZoi88wXsJ7f4cDNFzWg0WmCJEllmfMsFkvRc/m7z6pGvZPHvz00jod7Inj9enfe4ryZkCRp+QbIgPwBI5GImuH1+/0IhUIIBALo6+tDd3c3jh8/ju5+OdhtdOYOtk4Lh9esdeHJvgh8Gg2cz+eDz+fD+Pi43MwgrfEpdYtyPChPdNVFZJDLOeg3NTWpJ7nLwuHt26vx8mgmQ5NNLBYrqu32aV8cq2oWRyA4HwFysVl9juMW5SQ0FwghukyO28rjnTuqsX8kXnCwVrSw+a4VQRDAcRxOpAsu1tXrsxAL+V263e6c37zVbcaFKx24/3gop5GDUlSm6H6TgoAHToRQ6+BzzpubX12PjQ1WnNOef1C+bLULtXYefzisd6QIh8MYGBhQm2n8v2e9EESKd+6ohj8u4dMPjuHl0cy2/Ezb10qh7kJmkveemgABsLHRBp4jeNdZ1RgJpfDQqTCOe9PnRla2neO4WbUrXqwYJUWuWOuChSe4u7uwK8kvXppGz3QS122uKvh9LNbvyuVy6a5zZfdTO3ctRbJlCrtabVhXZ8GdhwIIJCTY0x0OixnjbCYOHz23Fhvqrfj1y/687Ze1KM5KisSi1LG0UICspZy1Ik1NTUUdp93M4VMX1GNdrQVv3lSF9549+y6LJpNpTvNMUc8khFxFCDlOCDlFCLnZ4P4bCSGH0n+eJYRs19zXRwh5hRBygBCybzYHmS2FUFCyJQAwHpYF6/kKyd6w3g0K4Fcv+3WvNT4+riuGUSrLS2EiEIPTTGYU5ttstrJu0ZvNZp2255ouN1bXmPGzfdM5BuRARo9cqJFIPClgIJDUbd8tFPPVJKDYVqdLfbs3H9lbhq9b78aWRit+um8K3kh+WU6ha0UQBPhjIh7vjaDJyecUsC6kVIUQghUrVuRMEP9nWzUSIsWfuo21/AMDA4jFYri/exoJkWJTQ+7W49o6K/7zymZUWfN/PjNPcO3GKhwaT6hBovZ9AODwRBwnfUm8Y3s1/mmLB9+/pgUtbhO+8tgEXhgqbpeLUopoNFr0zlG5SSaT6PFG0eQywZW2I9vdZsemBivuPBTACW8SVVYOdQ79d0UpXTbZY0A+17NlPR4bjyvXuvCP0xGMhY2zyL5oCn89EcKVa124al1h/eViXbgTQtDUJBd5Kx32llphnhEOhyMn8H/PWTWYjIqIp+TOkIp8r6jkCyF479nVmI6L+NORIB48GcL+kdxW9grZfQlKHU9nyiIrlHOcdjqdRWejtzTa8N1rWvDPO2tyaldKYa4a9xmvKkIID+BHAK4GsAnADYSQTVkP6wVwMaV0G4BbAPw06/5LKKU7KKW7ijkob1Q07KxSiPFICk2u/IbQzW4zrt8id4x5WuMZnL0FmW23MxOUUowFYgX1xzzPw+PxoLW1tajXLAXtSc5zBB/aXQdfVMTH/zqK3xyYztnCo1RuZ5lve7xnPIiUBKyqXhwZ5PnyHS5msFiuAXJ2xo4jBP9yfh0kCvzgOV9BqcX09LSho0IgHMMn/jqM09MCLliZ24VpoaUqPM+jublZN8l1eMy4uNOJvx4P6XxNFZLJJHp6evBYbxid1Wb8y/l1s37/q9a54LZw+MmLU4aL2ecHYzBxwKs75e+uxs7jG69pwqoaC77+5CT2j2TGp5O+BH718jR+/fI0Hu4J6zLgSia5t7e3Ys4XykJpampKt6MwNTWF0ZCAFk0RHiEE79kpBwKP90XQ4THnXHcWi2VZaf2B3CY1ANJZYeAPh41t+/aPxJCSZIeUmVjMY1NNTQ02bdqEtWvXYu3atQt9OGWBEJKzy7Gt2Ybz0jtHJk52nmhpacmR2ORjY4MN53fYcecrAdz2/BS+9I8J/PJlP+49GsS3n/bq6kKC6Wtc8ZWfzQKpoaFhxvm1nOM0IQStra3ztnNACEFDQ8OcXqOYb3U3gFOU0tOU0iSA3wF4o/YBlNJnKaVK1c5zANrnclD+uIhP/m0UQzPos7SMh1OGWjYt12/1YH29Bd991qe2fDWCUqp2rhodHS2otUwmk+kuesYDOiEEq1atmlVTkGLI1iJvbLDiHTuqMRpO4a7DQfzLA6M4PK6fGJVsWDKZW4jVPSJv+a3Mo6FUMPKxLbe37XzqVE0m04zbuot5Epor2V2Wmt1mvOesGrw0GsefszrNaVGsE7Pv//vhUUxGRXzy/Dq856zqnOcthu/SKGi5YZsHgkTxm7RhfjaCSHFsMoGzW+1zGujtZg4fP78Op6aS+PqTkzqbJ4lSPDcYxdYmm65TmtvK45ZLG7HCY8atT0zi0FgcT/ZF8KkHx3BPdxB3dwfx/b0+vP/PwzpfViWT3NPTgxMnTmB8fLysThc+nw9erxejo6M4duwYfD6f6noyFkrl1IVsbLDhnLTlX63BuFlMW9ulhpF7Sr3DhCvXuvFITzgni+yNpPDo6Qg8Vg4ritBfLvYGRoqkarFmumeDUWe6j59fh/M77NjVald3jBsbG4seK96/qxYtLhOu3+LBVetcuLs7iJ/vn8bjfRH820NjOO5N4MhEHL9M74SvrjHPunicEILOzk54PJ68x1fucdrpdBa1WztXFLeRue5WFHO2tgEY1Px/KH1bPt4L4G+a/1MADxFC9hNCbsr3JELITYSQfYSQfR4SR1SQ8Jn0CXHbcz58+C8j+OFzPvzXCz7829/HcNtzcpDbM5XEbc/50DstoMFZ+CQxcQRf2NOIdo8ZX39yEkcn5e3NvYNRfOahMdx3LLOSF0URJ0+ehM/nQ39/f94g2efzYSQooMVtPIi5XK6KywSyL8C3bq7CT97Qil+/uQ0NTh5feXwCff5cj9e+vr6cDHrPpGwh1F5VeFC22+1obW3VXZjKCrFcgyDP52o8K8lMRSOLIairFGazOWcb+JouF85ps+Pn+6fxH09783p1JhIJnWwnlUrhxQE/nBYOe1Y5DX/Dhc4gA/L5mu3R2eo249qNVXjoVBhP9uVqsCejKYgURQUtM3F+hwMf3l2Ll0fj+OORzNjz3GAMo+EULl+T6/vpsvK45bImNLtkucX39/qwscGKO9/agbtvWIHvX9OCNbUWfH+vDz/bN6X7zSiVmyV5vV6cOHGibBllv9+v1ogoO1RDQ0PwhmIIJSXDwul3n1UNMyfrsbUsRW/cYuA4zlCL/NbNVbDwBN971qf66B4YjeG99w7jyEQC25qL84FeanZpywGe53PkaVVWHp+/uBEbGqzqeMrzvM51qhCNThN+9qY2vGNHNT6wqxZddRZ0eMz4+ZtaUWXl8akHx/CZh8ZxcCyOLY1WNLtnHyAD8pzW0dGBlStXGs7blUhQNTc3z0uBZjneo5hIxuhXNZwpCSGXQA6QP6O5+QJK6U7IEo2PEEIuMnoupfSnlNJdlNJdDTVV+NYVzeA5gk89OIYHT8muCo/1RvDAiTASKYon+iL48mMT+MQDo3jwlBy8dmp0s4QQwxOy2sbjq5c2os7B45bHJ/D8YBTfekoOln+6bxqPpjMv2brngYGBnK5a8XgcQ+M+BBISWg28PJWt60pjsVh0vdIJIWirMqPOYcItlzXBxnO45bEJ+LOKjwRByGn+MDgdQ72DL6inVrwOq6ur0dXVhdraWnWwqK6uVvWd2RdcKRmEmSyCKoHJZCq4HbacA2QAOf6VhBB87qIGvG2rB0/1R/HbQ35IlBq2Rh0fH1cDLsWBobPanNP2U2GxfJdGzQzevr0amxqs+O6zXpzI0ggb2bvNhSvXunDRSgd+e8iP494EBvxJ/P1UCLV2HheuMA4UPTYet17ehHoHD0KAT11QD4eFA8/JDgdfu6wJr1/vxp+PhfCNpybVpiQKlFLVJq5YKVk+kslkzk6U4hKkjKXbm3MTBCurLfjTDSuwy6CD3mLPhs4Wox2LeqcJH95di8MTCfzXC3Lm/Y5DAdQ5eHx4dy3ec9bM2bb5qtVg5JI9ZipwHKc7j6uqqnKC6Zkw8wT/eWUzfvjaFjS7zPjyJY2osnK4ZJUTX7qkEZ95dUY+MNeklMvlwurVq3PG/0pci0rzkHXr1hnWgpQDl8tVFplWMaP8EIAOzf/bAeS0fiKEbAPwcwBXU0rVdBKldCT99wQh5B7Iko0nZ3rTtiozvvGaJtz+4hRe2+XGeR0OTMdEDAYEbGu2IZ6ScGwygaf7o7hqnQs8R1QrEEXWAMAwS+qx8fjKpY34vw+O4ZYnZFeHX7ypDf/vWS9uf3EKGxutaM3KCCuWcytWrFAHo1AohOF0w4DsxyvM13ZhU1MTQqHcrfAGpwlf2NOAzz48jq88NoGvX94Ee3rbVpnIpqen1W2Poen4jFIVSZLUz8XzPFpbW9Ha2qq+t8vlwsqVKyGKIqLRqNpHvrGxESaTCaOjozNu8VqtVrS0tMzqu5gLjY2NajZU6WCksJy2B41wOBywWq26zKKZJ3j79mpMRUXcdTiIh3siCCVE3LSrFld3ZRZliixp7dq1iEQiGAmmsKvNeNIu1bezkihZ5NHRUXWcMPMEn7+4AZ/82yi+8eQkvndNi9r6fCKSbsE6w25VKe//od21eGU8gU89OKbefu3GqryLC0DWJP+/q1sQSog5zj08R/CBc2rR4jbhZ/um8YVHJ/CFPQ2qXlFBGdPWrFkzq4kwkUjg9OnTeLgnhCPjcVy7sQor0kmKmCDij0cC2NZkxVoDD2zAuG39Qtr/VRqlsCt7PrpktQtDwRTuOhzAc4Ny1v3dZ1Xjmq7iGiNQSlmAvEBYLBY4nU7DHebs87i1tRVVVVUlFc1qx4AOjxm/fnM7TJzetaRczbRsNhtaWlowOjqqdvCr5HlltVpVO8dAoLCbSykQQnQJw7lQzIz/IoB1hJBVhBALgLcBuC/rgFYAuBvAOyilJzS3OwkhbuXfAK4AcLjYg2urMuOWy5pwXoecSamx89iWzkbYTBx2tNjx0fPqsLbOilU1mU5dDodD/ZNva6PVbcZtr23BWzdX4ZOvqkOjy4RPXVAPniP4z6e86naXFqVIZ2RkBMlkEqFQCMfSGaZOA1s0l8s1bxIBq9Wat+d5V70V/3ZhPXqmkvjKYxMIJfRFPCMjIxgZGZG7WwXihluiWsxms2GwqH1vl8sFj8eDlpYWdHV1oaurC/X19aiursaaNWsKru44jsu75VNpeJ7Hpk2bsHHjRjidGXlAufXVixVt4xAtHzinBq9a4UBXvQXr6qz40QtTOS2pU6kUBgYG4PWHMB0X8y4ay2kdVA6qq6tzjsdj4/G5ixrgj4v4T428ZDKSAoHcMrhcuK08PnVBHbY32/CR3bV400Y3rts88+6J08KhOc93DABv2FCFz7y6Hid8CXzgPlmi9uxAFGFNAbQkSTh9+nTRTYQUlAz0wHQc33vWh4d7Ivjw/aP44qPjmI6J2Dccgz8u4Z+2lOb7bjabF9W5UU6MurAp3Ljdg12tdoSSEna22HDV2uLb6nIctygkS2cqRq2UjTzzlcBtLoVqZj53Z7yc10ttba2aFZ9rk41iaW6WO/TO5jvJ95xy7T7POMpTSlOEkI8C+DsAHsAvKKVHCCEfTN9/O4AvAqgD8F/pA06lHSuaANyTvs0E4LeU0gfLcuR5yJY1VFVVwe12IxjMrRSudZjwLs0WVoPThI+fV4evPzmJXx+YxmWrXeibTuKiTqe6klMq96en5UYKzw3G0F5lygkGCCEl9TAvB01NTQgEAobZ2XM7HPjXV9Xje3u9+OI/JnDr5U1qAZDymYLhKCYjKTS6Cme9S82KE0J0q2mLxYI1a9bo2uXyPI+qqipYLBZUVVUt6ICvBMIrV65ET08P4vH4osp6VhKn05mTRQYAq4nD5y6St/SSIsXND43hu8960eFpUXdulGKwU175uW0FdOyLKQgi6a5tQ0NDuuze2jorPrS7Fj94bgr/c8CP9+yswVhYLsg1F9kIoFh2tNixo6X8k9GFK51odJpw79EgnuyP4O+nwrDwBG/f7sG1G2ULKlEU1UxysYtAZUH9RF8EBMB3rmrG3sEo7jsWwsf/OooqGwenhcPWptIyUPM9Zs43tbW1mJiYyMkgyg0j6tHvF9BVX1o2fzlqtpcSNpstJwtaSBdcXV2tOmWVw36x3ImbxsZGdQ6ejwSf2WzGypUrkUwm1Q6ihVB2dk0mE2pqauD1enXPcTgcZZuri3oVSukDAB7Iuu12zb/fB+B9Bs87DWB79u2VRJKknEG2tbUV4XC4qK5Sr1rhwDVdLtx7NIR7j8pdtZ7sj+KzFzWoulzlxxgLC3hlPI43b8pdrVR6e8IInufR0dGRtznAnlVO2M0Etz4xia89MYmvXNKoTvSUUrUjoFFluQIhpCwDssViwbp16+D3+9XgeLEZxyt+uadOnYIkSYsqqKskra2t6O3tzTtQWXhZm/yJB0Zx6xMT+H9XtcBhySy2nklnltfmabcMLD65itvthsViyVkYXLFW7nr3p+4gggkRzw1GsdHA/3gx01Vvxb+9ugEpieKEN4G7u4P4xUt+DAYEfPTcOvAcQTKZRH9/Pzo7O2e8DlOpFILBIMZCAu4/HsKWJiu66uU/F3U68fUnJtHvF7C7zV5QJpJNucaWxYySwMme1AF5EVpqcFzO7WTG7GlubkYwGCxKkqdIQIeGhnKeMxsqMS/Nd+yinMM8z2N4eLjgd+J2u9HeLhulKcWvU1NTiEajkCQJjY2NZTuuxTVLlQGLxZJzYppMppKsVj6wqxY3bvPgnDY7btzmwYvDMdz6xASSWbKL7z3rg5kjOZXYgByoL0Sxicvlyiu1AIBz2x34l/PrcGgsjm8/o3cmmEp3GSzk6UwIKdu2C8dxqK2tLWgzs9BYLBasXLkSVVVVZ4zOz+Fw5C0+Uah3mvCZVzdgJJTCd/dmJntKKZ7ojeDcdnveVuVzqbquFIQQtLW1GX7mm3bV4rwOOx7uiSAmUFy7cWk2sTBxBJsabfj8xQ24YasHD/dE8M2nJiGIVM3+Dw4Ozjhhh0Ih9PuT+PTfx5BIUbxbswu3qsaC/7iyGa9f78abi5CJaFmIpMJCUGwXs2JZjrZ4Sw2z2az7XWdKAJB0R8FyZDqX085mdXX1jHOP0vlV+Y5tNhtaW1uxdu1adHV1lfV6WHYBcr4MRF1dHez24rxLeY7ghm3V+NIljbhhWzU+em4t9o/E/397Zx7cxn3d8c8DSIIXRFIiBVGkJIqiDrNx4kPxVTd27YmdeJy6STtjezyN2zR100wnTdPONEmPccaumzhJJ4fTuJ4cU08b56ibVk6buIlrxbHHTiRrfFuyKEsWKVLiIdo8QBAg8OsfuwtAJEiCxAK7AN9nZgfgYven3/tqj/e73uPu/cPpBAJnpuZ4aXiWm89vypknPBgMeuYEtLe309TURG1tLVVVC5OnXNPdyB9e1MJTJ6PcfyCTRdCxbake5LW4IKShoYGtW7f6zqkrJm1tbcvOoX/7plo+dFELT/fPpMOUjUwnGYkmuXCZ6QJ+60EGK1RWrsZaVUD4m6s28uAHOrjvxvYVT4Xw2/x1EeG2dzRzx17r/+6eJ0aI207y5OQkJ0+eXHK07fDAGH/1qLWg8PPXb2L3vF7Plrogf/zO9bxt48qeE2vl2RIMBl11kit1UWO50dbWln5H5HO/L9UoXwmV5CCDpeNinWYbN24s6TPCP09tF3BSO+ZCRNi2bduqLqb37AzzscvW89zpGB/+z1N869A4/2onEnhX10KHXERyBhEvFU7r1GlR5WoYvL93Hb/7a+v48dEp/vngOG/FLMcGYMMSDnJ9fb1ve3sV9xARtmzZsuxc8Jv2hHnXtnoefO5NfjUQ5bgdb3vHEtMr/NiD7JBrwY3D+vqqBSnYnbUGNTU1i54XCoXo7Oz0nc2/tWcdH71kPQdOzXD3/mFm51LpzHt9fX1EowtTWieTSb7w+ElSwOeui9C9xP/zSsleFFvptLa2utJoCoVCa0YzvxMIBNiyxQr4le+0icbGxoIbS5XmIDsNB8cRdkaWNm7cWHBmvJVSUcrmmn+cTTAYpLu7m76+vhVnkrquJ8zbIrV85/m3+OErExiwAnU35nYgShEIOx8CgQBdXV288cYbRKPRc27c2y9oZiaR4kdHJvlp3xTt4SpCQVkyK2CuYPdKZeJEEzl27Fg6CcR8RISPXb6Bwck57nliJB3dYdsSqcoXi1HuB6qqqujo6MiZHTAX1dXVbNmyBRHhxIkTCxbeBAIBOjs7qa2tJRQKcezYsbzWQpSKG3aFqQoIX31mjM/sH+Hvrm6jtipAPB7n+PHj6TBMTpbJAy+9xrODM9z29qZFkyOtlrU0VcC5LlYS8isXlT5nu9xoaGggEoms6B53pgzkWry5HJW6eFxE6O7uJpFIMDMzQ0NDgyd2+rYHeX5sP2eY0nm5zt+CwSDhcHjZVnl1dTU7d+5c1fzgzeFq/vLKVr79/g7+4tc38Omrcrdm6uvrfdVb5Dg683t/rRisG/inG9s5P1LLiTcTRBqrcuoeCAQwxmjGpjVGKBRi165dhMPhRZ3a2qoAd127kV0bQpyZslILZ6dIno/fQ1I1NTXl1asjdubIYDCYvsecld9OkP2urq50T0goFEo7037iup5G/vyKDbx0Jsad/zdMNGG93I0xxGIxRkZGOHz4MEeOHOHFQWvh8nJTaFbKWkx2EQ6HC1qcvFbmbJcbbW1tRCKRvI8XEdra2vJaIJvr3EqdYuPY1tTU5FkjQNwIM+I2e/fuNU8++STj4+OcPXsWsFpZdXV1xGIxGhsbCYVCJBKJdEDrlV4kTmij2dlZV0KtOIgIkUjENz3I2TgZz8bGxhbYnDKG/+2bYl0oyBVb69OaNjc3Ew6HSaVSxONxXy+oU4qHMSbdQwrk7CExxnDg1AytDVV0t+S+HwOBANu3by+LhtaZM2dyRhuATMSF+S81Jz7wUs7w2bNn08H4/cTPT0zzxadGiTRW8UcXt7C3o+6cZB4pY7jn5yMcHJzh+zdvXTLb5koREXbu3FmxL/vFSKVSHD16dEGW1nwQEXp6eio28+BaJBqNLhlBaD56DbiDiDxrhyY+d7/fHtJgOcgHDx4ErJBCxcqulEqlGB4eZnR0NP0iK1QPJ4SLn4e+JiYmll2tHgwG2bVrl696whVvSaVS6SkEg4OD6QZqvgQCAbZu3Vo2sW6NMQwNDaVjnjuISMHz4cbHxxkcHPSdk/zC6Rhf++UYpyatkYB3dtRxYXstF7bX8c1D4/zoyCTXdDfwiSvc7QAQEXp7e9dk4zsej6dDSebC0aSlpYWamhqmp6eZnZ2ltbXV07UuSnGIRqMMDQ0Ri8Xyej709vb6ahFwOVK2DnIpSKVSzM3NMTo6uuBluBKcxXlLLfTxC9FoNGcabsgsaCwXR0YpPalUKuec28UIBAJ0d3eX3ZCwMYaxsTHOnDmTtlNE2LFjR8G2TE9PpyNGFPocdqZABQKBdKhLZ9QnlUqd8zxyRt0Wc8gSScMv3pjmZ69Pc2RkltmkoaEmwHQ8xft2h7lj7+JhJJdisU4IEaGhoYGurq4Vl1kpzM7OnpM4yZnaZowhEolQV1fn604XxX0SiQT9/f1pRznXMyIYDHLeeed5ULvKQh3kPJmamlo2zNFiBAIB9uzZUzatuUQiwfHjxxf0BNbW1tLT0+NhzZRyIJVK0d/fz9TU1KIOnuOodXZ20tzcXNoKusjU1BQDAwPMzc2xbt061+YSp1IpRkdHGRkZWXQqR21tLXV1dRhjiMfjxGKx9PPJcYojkUjODJTOOYlEIu10BYNBYrEYo6OjyzZwEknDc6djPHFimrdiST59lbWIb6WICJs2bSKVSjE2NkYymUz/u866kHJ5bhaLubm59Mhea2sr69atSzdmlLWJE1VmdnaWs2fPLnhXh8Nhtm3b5mENKwN1kFdAIpFgYGAgHeYoX41aWlro6OgoZtVcJ5lMMjQ0dE6aTL/OoVb8hzEmPU1pQfrcQIDNmzdTXV1dMREKiuWwxGIxTp48STweTzuKxhi6urpyahePx5mYmGBmZoZIJLLqKWjRaJRTp04Rj8eLNt1j/pQUZ2rb2NgYgO+npCmKHzDGMDExweTkJLW1tczNzZ0Te1lZPeogr4KZmRmmp6cZGRlZdhjUCUtSDouPcpFMJjl9+jTj4+Ps3r3b95EGFH8xNTVFf39/umczGAzS2dmp03RWSDKZTKdMra+vL8l9aIzhzTffTC8cdPudEAqF6OnpWdCwcKZ/lNu0G0VRKgt1kAvAGRZ0eslyTb+or69n+/btZT8cpkN6ympJJpMMDw8zMTHBjh07KjI+ZyWTTCYZGRlJ9+zm825wnhXNzc0EAgGSySQzMzPE41bCmHA4THt7uza4FUXxLYs5yPoGy4NAIEBbWxutra1MT08zPj7O5OQkqVSK6upq6urqaG9vrwjHshJsULwhGAzS3t5Oe3u711VRVkEwGGTTpk20tbUxMTHB+Pg4MzMzCxb0OXGeGxsbaWhooL6+fsHIWSKRYG5urmxH1BRFUfJykEXkPcCXgSDwDWPMZ+f9LvbvNwBR4PeNMYfyObeccF4KjY2N6Z5knf+jKEolEQwGaWlpoaWlJZ0sZHJykmg0Sk1NDaFQiObm5iWffdXV1dprrChKWbOsgywiQeBrwLuBAeCAiOwzxrySddh7gZ32dinwdeDSPM8tS5zsfYqiKJWKiFBXV6c9wYqirDnyiatzCdBnjHndGBMHvgvcNO+Ym4AHjcUzQLOItOd5rqIoiqIoiqL4hnwc5A6gP+vvAXtfPsfkcy4AInKHiBwUkYMjIyN5VEtRFEVRFEVR3CcfBznXqq35y5sXOyafc62dxjxgjNlrjNlbSApXRVEURVEURSmEfBbpDQBbsv7uBAbzPKYmj3MVRVEURVEUxTfk04N8ANgpIttFpAa4Bdg375h9wAfF4jLgLWPMUJ7nKoqiKIqiKIpvWLYH2RgzJyJ/CjyKFartW8aYl0XkI/bv9wP/gxXirQ8rzNsfLHVuUSxRFEVRFEVRFBfwZSY9EZkEjhRQRBPwVoHV8EMZrcCox3XwSxmqRYZK0cKNOvhBCz9oCapFNqpFhkK18IsdqoW7ZagWGXYbY8IL9hpjfLcBBws8/wEX6uB5GYXq4Bc7VAvVooh18FwLP2ipWqgWxdLCR3aoFqpFSbXIZw5yOfJIBZVRKH6xQ7Vwt4xC8YMdftAB/GGHauFuGW6gWlj4xQ7Vwt0yCsUvdhRNC79OsThojNnrdT28RnXIoFpkUC0yqBYZVIsMqkUG1SKDapFBtciwmBZ+7UF+wOsK+ATVIYNqkUG1yKBaZFAtMqgWGVSLDKpFBtUiQ04tfNmDrCiKoiiKoihe4dceZEVRFEVRFEXxBHWQFUVRFEVRFCWLkjjIIrJFRB4XkVdF5GUR+TN7/3oR+amIHLU/W7LO+ZSI9InIERG53t4XFpHnsrZREflSKWxwA7d0sPffKiIvisgLIvITEWn1wqbV4rIWN9s6vCwi93phTyGsVAsR2WAfPyUi980r62L7uugTka+IiHhh02pxWYu/F5F+EZnywpZCcUsLEakXkf8WkcN2OZ/1yqbV4vJ18RMRed4u534RCXph02pxU4usMveJyEultMMNXL4u9tvvFse/2OiFTavFZS1qROQBEXnNfm78jhc2eU6h8ePyjDHXDlxkfw8DrwG9wL3AJ+39nwQ+Z3/vBZ4HQsB24BgQzFHus8C7SmGDn3TAyoA4DLTax90L3Om1fR5psQE4CbTZx/0LcK3X9hVZiwbgSuAjwH3zyvoVcDkgwI+B93ptn4daXGaXN+W1XV5qAdQDv2l/rwF+scavi3X2pwAPA7d4bZ9XWti/fwD4DvCS17Z5fF3sB/Z6bZNPtPgMcLf9PYDta6y1rSQ9yMaYIWPMIfv7JPAq0AHchOXQYH/+tv39JuC7xphZY8xxrBTWl2SXKSI7gY1YD/uywEUdxN4a7B7CdcBgqexwAxe16AZeM8aM2Mf9DCir1u5KtTDGTBtjngRi2eWISDvWy/9pYz3ZHiSjX1nglhb2b88YY4ZKUe9i4JYWxpioMeZx+3scOAR0lsIGt3D5upiwv1ZhNRjKaqW6m1qISCPwCeDu4tfcfdzUotxxWYsPAf9gH5cyxhSalbIsKfkcZBHpAi4EfglEnBeY/ekMaXQA/VmnDdj7srkV+J7tCJQdhehgjEkAfwK8iOUY9wLfLE3N3afAa6IP2CMiXSJShXXzbylNzd0nTy0WowNLF4dc903ZUKAWFYVbWohIM/A+4DH3a1ka3NBCRB7FGoWbBP69ODUtPi5ocRfwRSBarDqWCpfukW/b0yv+1u58KksK0cJ+RgDcJSKHROQHIhIpYnV9S0kdZLu1+jDw8axWfM5Dc+yb7wjfAjzkVt1KSaE6iEg1loN8IbAZeAH4lOsVLQGFamGMGcfS4ntYowkngDm361kKVqDFokXk2FeuDchCtagY3NLCbkA+BHzFGPO6W/UrJW5pYYy5HmtIOgRc41L1SkqhWojIBUCPMeaHbtet1Lh0XdxmjDkf+A17+z236ldKXNCiCmuE6SljzEXA08AXXKxi2VAyB9l26h4G/s0Y8x/27jP2sLAzPDxs7x/g3F7ATrKmEIjIO4AqY8yzRa+4y7ikwwUAxphjdg/694Eril97d3HrmjDGPGKMudQYczlwBDhaivq7yQq1WIwBzh06P+e+KRdc0qIicFmLB4CjxpgvuV7REuD2dWGMiQH7sIagywqXtLgcuFhETgBPArtEZH9xalw83LoujDGn7M9JrDnZlyx9hv9wSYsxrBEFp+H0A+CiIlTX95QqioVgTQF41Rjzj1k/7QNut7/fDvxX1v5bRCQkItuBnViLjxxupQx7j13U4RTQKyJt9nHvxppvVDa4eU04q43t1bkfBb5RfAvcYxVa5MQePpsUkcvsMj+43Dl+wy0tKgE3tRCRu4Em4OMuV7MkuKWFiDRmOQtVwA3AYfdrXDxcfF583Riz2RjThbVY6zVjzNXu17h4uHhdVIkdCcp2Mm8Eyiqqh4vXhQEeAa62d10LvOJqZcsFU5rVlVdiDfW+ADxnbzdgRSB4DKvH7zFgfdY5f40VqeAI81ZcA68De0pRd7/qgLXy9FW7rEeADV7b56EWD2HdwK9QZivSC9DiBHAWmMLqOe619+/FerAfA+7DzpZZLpvLWtxr/52yP+/02j4vtMAaSTD288Ip58Ne2+eRFhHggF3Oy8BXsUYjPbex1FrMK7OL8oxi4dZ10YAVFcu5Lr5MjshZft7cvC6AbcATdlmPAVu9ts+LTVNNK4qiKIqiKEoWmklPURRFURRFUbJQB1lRFEVRFEVRslAHWVEURVEURVGyUAdZURRFURRFUbJQB1lRFEVRFEVRslAHWVEURVEURVGyUAdZURRFURRFUbL4f4J80RB2Gz/KAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ci = ml.fit.ci_contribution(\"rch\")\n",
"r = ml.get_contribution(\"rch\")\n",
"ax = r.plot(figsize=(10,3))\n",
"ax.fill_between(ci.index, ci.iloc[:,0], ci.iloc[:,1], color=\"lightgray\")\n",
"ax.legend([\"Simulation\", \"95% Prediction interval\"], ncol=3, loc=1)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "7fc264ac",
"metadata": {},
"source": [
"## Custom Confidence intervals\n",
"It is also possible to compute the confidence intervals manually, for example to estimate the uncertainty in the recharge or statistics (e.g., SGI, NSE). We can call `ml.fit.get_parameter_sample` to obtain random parameter samples from a multivariate normal distribution using the optimal parameters and the covariance matrix. Next, we use the parameter sets to obtain multiple simulations of 'something', here the recharge."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "20ba64a2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = ml.fit.get_parameter_sample(n=1000, name=\"rch\")\n",
"data = {}\n",
"\n",
"# Here we run the model n times with different parameter samples\n",
"for i, param in enumerate(params):\n",
" data[i] = ml.stressmodels[\"rch\"].get_stress(p=param)\n",
"\n",
"df = pd.DataFrame.from_dict(data, orient=\"columns\").loc[tmin:tmax].resample(\"A\").sum()\n",
"ci = df.quantile([0.025, .975], axis=1).transpose()\n",
"\n",
"r = ml.get_stress(\"rch\").resample(\"A\").sum()\n",
"ax = r.plot.bar(figsize=(10,2), width=0.5, yerr=[r-ci.iloc[:,0], ci.iloc[:,1]-r])\n",
"ax.set_xticklabels(labels=r.index.year, rotation=0, ha='center')\n",
"ax.set_ylabel(\"Recharge [mm a$^{-1}$]\")\n",
"ax.legend(ncol=3);"
]
},
{
"cell_type": "markdown",
"id": "1e7497ce",
"metadata": {},
"source": [
"## Uncertainty of the NSE\n",
"The code pattern shown above can be used for many types of uncertainty analyses. Another example is provided below, where we compute the uncertainty of the Nash-Sutcliffe efficacy."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b095b82b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe7b2102490>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = ml.fit.get_parameter_sample(n=1000)\n",
"data = []\n",
"\n",
"# Here we run the model n times with different parameter samples\n",
"for i, param in enumerate(params):\n",
" sim = ml.simulate(p=param)\n",
" data.append(ps.stats.nse(obs=ml.observations(), sim=sim))\n",
"\n",
"fig, ax = plt.subplots(1,1, figsize=(4,3))\n",
"plt.hist(data, bins=50, density=True)\n",
"ax.axvline(ml.stats.nse(), linestyle=\"--\", color=\"k\")\n",
"ax.set_xlabel(\"NSE [-]\")\n",
"ax.set_ylabel(\"frequency [-]\")\n",
"\n",
"from scipy.stats import norm\n",
"import numpy as np\n",
"\n",
"mu, std = norm.fit(data)\n",
"\n",
"# Plot the PDF.\n",
"xmin, xmax = ax.set_xlim()\n",
"x = np.linspace(xmin, xmax, 100)\n",
"p = norm.pdf(x, mu, std)\n",
"ax.plot(x, p, 'k', linewidth=2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
|
|
| notebook/numpy_tril.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]]\n"
]
}
],
"source": [
"a = np.arange(16).reshape(4, 4)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]\n",
" [12 13 14 15]]\n"
]
}
],
"source": [
"print(np.tril(a))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 0]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]]\n"
]
}
],
"source": [
"print(np.tril(a, k=2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0]\n",
" [ 4 0 0 0]\n",
" [ 8 9 0 0]\n",
" [12 13 14 0]]\n"
]
}
],
"source": [
"print(np.tril(a, k=-1))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n"
]
}
],
"source": [
"a = np.arange(12).reshape(3, 4)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]]\n"
]
}
],
"source": [
"print(np.tril(a))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 0 0 0]\n",
" [4 0 0 0]\n",
" [8 9 0 0]]\n"
]
}
],
"source": [
"print(np.tril(a, k=-1))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]\n",
" [12 13 14 15]]\n",
"\n",
" [[16 0 0 0]\n",
" [20 21 0 0]\n",
" [24 25 26 0]\n",
" [28 29 30 31]]]\n"
]
}
],
"source": [
"print(np.tril(np.arange(32).reshape(2, 4, 4)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]\n",
" [12 13 14 15]]]]\n"
]
}
],
"source": [
"print(np.tril(np.arange(16).reshape(1, 1, 4, 4)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]\n",
" [12 13 14 15]]\n"
]
}
],
"source": [
"a_tril = np.tril(np.arange(16).reshape(4, 4))\n",
"print(a_tril)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 4 8 12]\n",
" [ 0 5 9 13]\n",
" [ 0 0 10 14]\n",
" [ 0 0 0 15]]\n"
]
}
],
"source": [
"print(a_tril.T)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0]\n",
" [ 4 5 0 0]\n",
" [ 8 9 10 0]\n",
" [12 13 14 15]]\n"
]
}
],
"source": [
"print(a_tril.T.T)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"of 51993\n",
"is 11480\n",
"at 14163\n",
"no 6844\n",
"cost 82\n",
"away 2052\n",
"license 76\n",
"wwwgutenbergnet 3\n",
"release 67\n",
"october 32\n",
"language 53\n",
"distributed 33\n",
"proofreading 2\n",
"transcribers 1\n",
"was 24892\n",
"penny 20\n",
"does 620\n",
"entire 32\n",
"has 2753\n",
"to 56146\n",
"table 636\n",
"presented 224\n",
"romance 26\n",
"merchants 21\n",
"all 8744\n",
"visitorthe 4\n",
"alarmthe 2\n",
"consequences 76\n",
"iiithe 1\n",
"madnessthe 2\n",
"fearful 115\n",
"suggestion 67\n",
"watchthe 3\n",
"proposalthe 2\n",
"familythe 3\n",
"appearance 335\n",
"resultthe 2\n",
"mystery 121\n",
"consternation 27\n",
"george 184\n",
"second 358\n",
"communications 10\n",
"loverthe 2\n",
"hearts 129\n",
"hollands 24\n",
"agreement 112\n",
"sudden 181\n",
"servantthe 2\n",
"xvithe 1\n",
"affecting 39\n",
"xviithe 1\n",
"results 66\n",
"advicethe 4\n",
"vampyrethe 12\n",
"servant 160\n",
"her 17613\n",
"xxthe 1\n",
"terrific 47\n",
"interview 121\n",
"xxithe 1\n",
"nephew 117\n",
"xxiithe 1\n",
"determination 63\n",
"leave 830\n",
"moonlight 70\n",
"window 617\n",
"loverher 2\n",
"xxviiimr 1\n",
"an 5393\n",
"gratingthe 2\n",
"lonely 78\n",
"offerthe 4\n",
"xxxiithe 1\n",
"thousand 328\n",
"poundsthe 2\n",
"strangers 64\n",
"precautions 17\n",
"chase 30\n",
"xxxivthe 1\n",
"threatits 2\n",
"explanationmarchdales 2\n",
"duel 63\n",
"consultation 33\n",
"morning 853\n",
"storm 84\n",
"fightthe 2\n",
"subterranean 15\n",
"xlvthe 1\n",
"dead 479\n",
"conduct 123\n",
"mr 7951\n",
"chillingworth 396\n",
"xlixthe 1\n",
"struggle 89\n",
"livthe 1\n",
"lvthe 1\n",
"mobthe 6\n",
"pringle 133\n",
"pringlemidnight 2\n",
"lixthe 1\n",
"plan 151\n",
"breakfast 198\n",
"strangerthe 2\n",
"particulars 42\n",
"chivalry 12\n",
"storythe 2\n",
"their 3326\n",
"dispersion 5\n",
"lxxivthe 1\n",
"explanations 18\n",
"dr 87\n",
"discovery 103\n",
"marchdale 485\n",
"murder 74\n",
"lxxxithe 1\n",
"flighthis 2\n",
"place 1527\n",
"fired 68\n",
"quality 48\n",
"pocket 193\n",
"marmaduke 32\n",
"lxxxviithe 1\n",
"housetopsthe 2\n",
"floravarney 2\n",
"xciithe 1\n",
"xciiithe 1\n",
"anderburythe 2\n",
"family 847\n",
"takes 114\n",
"than 2857\n",
"accepts 4\n",
"one 6367\n",
"sweden 4\n",
"hold 370\n",
"credulous 5\n",
"following 195\n",
"seemingly 12\n",
"authentic 7\n",
"readers 22\n",
"express 191\n",
"nothing 1820\n",
"unhappy 139\n",
"fact 539\n",
"prints 9\n",
"year 233\n",
"stamped 13\n",
"approbation 13\n",
"calculated 56\n",
"support 95\n",
"notices 7\n",
"grows 31\n",
"shrieks 15\n",
"clock 82\n",
"have 11328\n",
"announced 87\n",
"gather 28\n",
"strength 240\n",
"effort 199\n",
"off 1442\n",
"signal 35\n",
"winds 29\n",
"warring 1\n",
"swept 33\n",
"four 265\n",
"would 5714\n",
"century 19\n",
"phenomena 26\n",
"if 6949\n",
"blown 22\n",
"toy 17\n",
"town 372\n",
"before 2865\n",
"come 2868\n",
"trembled 85\n",
"thata 1\n",
"hailyes 1\n",
"dashed 47\n",
"lie 139\n",
"remarkable 123\n",
"invaded 5\n",
"millions 45\n",
"suspended 14\n",
"redoubled 3\n",
"direction 221\n",
"antique 18\n",
"west 61\n",
"latticed 4\n",
"send 179\n",
"purpose 367\n",
"series 61\n",
"eyes 2168\n",
"look 1657\n",
"on 11830\n",
"walnutwood 1\n",
"elaborate 13\n",
"owe 36\n",
"elizabethan 2\n",
"hung 121\n",
"furnishing 5\n",
"nodding 62\n",
"streams 15\n",
"coverings 3\n",
"head 2102\n",
"nearly 263\n",
"near 639\n",
"disclosed 17\n",
"moaned 12\n",
"moved 378\n",
"possess 44\n",
"turmoil 13\n",
"lapsed 9\n",
"pearly 1\n",
"fairer 9\n",
"transition 7\n",
"presents 38\n",
"us 1832\n",
"charms 28\n",
"matured 8\n",
"terrifying 9\n",
"dread 114\n",
"eternity 16\n",
"celestial 7\n",
"amid 75\n",
"next 614\n",
"forked 4\n",
"should 2681\n",
"murmurs 11\n",
"weeps 4\n",
"thinks 82\n",
"surely 154\n",
"flasha 1\n",
"instant 168\n",
"terrible 252\n",
"such 3195\n",
"never 2490\n",
"delusion 29\n",
"saw 1513\n",
"me 9329\n",
"thicklymoreover 1\n",
"utterwith 1\n",
"confines 12\n",
"waits 13\n",
"froze 4\n",
"nails 34\n",
"word 856\n",
"see 2740\n",
"feeling 707\n",
"creeps 6\n",
"brighter 42\n",
"building 84\n",
"mistake 133\n",
"choking 26\n",
"sensation 78\n",
"appears 114\n",
"intruder 19\n",
"while 1291\n",
"makes 154\n",
"utterly 60\n",
"onehalf 3\n",
"hinges 7\n",
"happiest 18\n",
"perfectly 222\n",
"back 1831\n",
"feature 24\n",
"teethprojecting 1\n",
"exposed 33\n",
"help 503\n",
"bent 189\n",
"horrible 101\n",
"protruding 6\n",
"object 399\n",
"want 806\n",
"earth 258\n",
"pauses 6\n",
"withdraw 37\n",
"glittering 48\n",
"eye 390\n",
"ceasedall 1\n",
"hushed 38\n",
"proclaims 1\n",
"hissing 12\n",
"advances 19\n",
"charm 58\n",
"bony 20\n",
"granted 37\n",
"bedshe 2\n",
"sucking 8\n",
"follows 52\n",
"swooned 3\n",
"repast 24\n",
"ii 70\n",
"flashed 26\n",
"among 857\n",
"directly 130\n",
"think 2196\n",
"ears 187\n",
"men 1196\n",
"female 57\n",
"said 12952\n",
"anything 1206\n",
"housesearch 1\n",
"stunned 23\n",
"supported 40\n",
"cried 1028\n",
"darted 25\n",
"bounded 15\n",
"resembles 8\n",
"mad 124\n",
"staircase 153\n",
"presently 129\n",
"powerful 72\n",
"mind 1419\n",
"something 1546\n",
"bar 128\n",
"true 500\n",
"rather 651\n",
"named 51\n",
"free 375\n",
"floraflora 5\n",
"violent 79\n",
"consumed 14\n",
"immediate 75\n",
"uncertain 48\n",
"two 1806\n",
"led 284\n",
"dabbled 2\n",
"ferocity 10\n",
"panic 14\n",
"mature 7\n",
"frightened 211\n",
"sooner 129\n",
"dont 2330\n",
"report 120\n",
"tremendous 52\n",
"trigger 10\n",
"clutch 8\n",
"procure 21\n",
"step 265\n",
"flushed 102\n",
"looked 2004\n",
"countenance 137\n",
"point 456\n",
"annihilated 2\n",
"ejaculated 12\n",
"nonodo 1\n",
"mei 17\n",
"affair 196\n",
"screamed 52\n",
"exclaiming 20\n",
"thence 11\n",
"approached 147\n",
"murdered 29\n",
"shadows 74\n",
"theretheretowards 1\n",
"heavily 89\n",
"pointed 138\n",
"vista 13\n",
"theredo 1\n",
"indicated 47\n",
"making 556\n",
"obstacle 15\n",
"watched 146\n",
"seize 38\n",
"clammy 8\n",
"efficacy 4\n",
"sprang 58\n",
"pulled 96\n",
"office 240\n",
"iii 42\n",
"worn 108\n",
"breathless 34\n",
"anxiety 103\n",
"exactly 228\n",
"lain 19\n",
"consequently 81\n",
"shudder 38\n",
"poor 786\n",
"blamed 23\n",
"anybody 252\n",
"clung 41\n",
"brothers 182\n",
"tremblingly 2\n",
"halfreclining 1\n",
"swoon 12\n",
"restore 26\n",
"satisfaction 224\n",
"surrounded 127\n",
"none 321\n",
"shuddered 22\n",
"distinctly 85\n",
"cup 57\n",
"die 196\n",
"nonono 4\n",
"composed 83\n",
"thoughts 369\n",
"cameit 1\n",
"wound 120\n",
"observable 12\n",
"halfadozen 29\n",
"sleepno 1\n",
"sigh 103\n",
"silence 348\n",
"former 279\n",
"runnagate 1\n",
"blow 147\n",
"ago 256\n",
"tis 71\n",
"whileninety 1\n",
"muse 3\n",
"nownot 1\n",
"ready 425\n",
"evilfated 1\n",
"mansion 53\n",
"observation 125\n",
"playmates 1\n",
"impressions 28\n",
"shadow 176\n",
"inhale 2\n",
"dawned 15\n",
"slumbering 10\n",
"distinguish 41\n",
"agitated 90\n",
"whenever 111\n",
"wisest 10\n",
"namely 41\n",
"instinct 22\n",
"executed 55\n",
"require 80\n",
"struck 283\n",
"choose 90\n",
"fasten 7\n",
"thus 365\n",
"waking 61\n",
"bring 336\n",
"usual 426\n",
"henryi 1\n",
"sea 230\n",
"weather 118\n",
"singing 92\n",
"spiritstirring 1\n",
"spirits 206\n",
"habit 148\n",
"attacked 51\n",
"inexplicable 18\n",
"disturbed 85\n",
"why 1641\n",
"superstitious 16\n",
"honour 334\n",
"countries 24\n",
"extremely 169\n",
"rife 7\n",
"pronounce 10\n",
"drinks 9\n",
"scat 1\n",
"anguish 15\n",
"rules 49\n",
"uphold 11\n",
"thoughtthe 1\n",
"mere 178\n",
"cling 23\n",
"sheetanchor 1\n",
"fertile 9\n",
"peace 251\n",
"tokens 12\n",
"accumulate 1\n",
"grief 115\n",
"aside 146\n",
"share 106\n",
"talking 325\n",
"beg 254\n",
"suggestive 7\n",
"likeness 51\n",
"henceforward 2\n",
"tip 62\n",
"answer 438\n",
"summons 14\n",
"betrayed 32\n",
"abundantly 10\n",
"repudiate 6\n",
"yetand 2\n",
"guard 65\n",
"gathered 92\n",
"matters 178\n",
"frail 8\n",
"reed 2\n",
"fancy 212\n",
"affection 178\n",
"therefore 366\n",
"cheerful 129\n",
"play 160\n",
"assured 88\n",
"midday 21\n",
"makings 1\n",
"confidant 7\n",
"opportunity 177\n",
"vampyrefor 1\n",
"oncewas 1\n",
"met 374\n",
"horseback 20\n",
"responded 26\n",
"interrupting 17\n",
"wheeling 4\n",
"untrue 8\n",
"foundation 131\n",
"ah 442\n",
"vexed 40\n",
"publicity 4\n",
"theme 47\n",
"till 348\n",
"closeted 4\n",
"particular 249\n",
"process 46\n",
"fool 118\n",
"youthat 5\n",
"avoid 110\n",
"ancestral 6\n",
"improvement 31\n",
"incoherently 9\n",
"serious 185\n",
"repaired 28\n",
"tenderly 64\n",
"travels 9\n",
"lent 21\n",
"sometimes 519\n",
"taste 71\n",
"vex 6\n",
"minister 56\n",
"diseased 7\n",
"yours 172\n",
"minei 2\n",
"indulge 17\n",
"ugly 56\n",
"extreme 61\n",
"pathos 3\n",
"reply 330\n",
"spasmodic 5\n",
"inflicted 23\n",
"flown 13\n",
"suggestions 16\n",
"quarrel 48\n",
"beckoned 23\n",
"because 1123\n",
"unable 184\n",
"closet 20\n",
"likes 43\n",
"subjects 69\n",
"bear 320\n",
"entertains 1\n",
"freely 99\n",
"wealth 73\n",
"though 1414\n",
"pulling 51\n",
"ghouls 1\n",
"frequently 73\n",
"getting 305\n",
"stretched 66\n",
"credence 2\n",
"powers 78\n",
"drop 67\n",
"addressed 175\n",
"consequence 166\n",
"affliction 37\n",
"loving 120\n",
"begs 12\n",
"counsel 34\n",
"estate 97\n",
"acquaintance 179\n",
"civil 42\n",
"properties 2\n",
"perceive 74\n",
"desire 212\n",
"allow 234\n",
"suffer 76\n",
"silvery 11\n",
"older 82\n",
"usage 13\n",
"confess 89\n",
"climbed 18\n",
"high 389\n",
"spacious 8\n",
"died 189\n",
"obtain 70\n",
"style 44\n",
"dreaded 41\n",
"right 1116\n",
"test 18\n",
"accuracy 12\n",
"willing 74\n",
"cloudless 4\n",
"worthy 118\n",
"buried 45\n",
"corroborative 2\n",
"shrink 47\n",
"concealed 49\n",
"wherewhere 3\n",
"climbing 13\n",
"fancied 75\n",
"hasty 37\n",
"sword 56\n",
"quicker 32\n",
"temper 85\n",
"run 286\n",
"prepared 173\n",
"secure 55\n",
"everything 753\n",
"survey 24\n",
"meadows 34\n",
"carried 274\n",
"pruning 1\n",
"exact 39\n",
"singly 10\n",
"amply 13\n",
"perplexities 6\n",
"relieve 38\n",
"happiness 283\n",
"lime 6\n",
"line 204\n",
"number 249\n",
"enveloped 23\n",
"extended 36\n",
"descend 24\n",
"unerring 3\n",
"deliberate 20\n",
"rises 26\n",
"pursued 224\n",
"intricacies 10\n",
"deepest 20\n",
"confounded 36\n",
"nothingnothing 5\n",
"vehemently 6\n",
"fashionable 18\n",
"hundred 277\n",
"suffering 119\n",
"prostration 5\n",
"contend 15\n",
"preternatural 13\n",
"proving 13\n",
"parcel 26\n",
"investigation 16\n",
"beyond 305\n",
"assuredly 26\n",
"strikes 18\n",
"henrybe 1\n",
"desirable 35\n",
"rouse 28\n",
"character 433\n",
"dawning 11\n",
"early 274\n",
"interested 112\n",
"peculiar 112\n",
"unfortunately 25\n",
"worst 96\n",
"specimen 14\n",
"procured 22\n",
"branches 42\n",
"extravagances 4\n",
"landed 14\n",
"proprietor 30\n",
"crimes 11\n",
"gamingtable 13\n",
"raising 116\n",
"firmly 79\n",
"england 115\n",
"selling 15\n",
"princes 54\n",
"land 101\n",
"written 194\n",
"law 144\n",
"regretted 35\n",
"generous 81\n",
"introduced 55\n",
"people 1269\n",
"honourable 89\n",
"claims 23\n",
"induced 69\n",
"eventually 11\n",
"liberal 32\n",
"conducted 64\n",
"fathers 259\n",
"occupation 85\n",
"minds 98\n",
"slipping 27\n",
"precipice 3\n",
"knowledge 260\n",
"preservation 12\n",
"effected 25\n",
"personal 167\n",
"risk 41\n",
"rock 21\n",
"abyss 8\n",
"rescued 23\n",
"saving 54\n",
"frankly 31\n",
"settled 190\n",
"acquiescence 15\n",
"situated 42\n",
"arrangements 78\n",
"supplied 17\n",
"trips 2\n",
"anticipated 27\n",
"continent 9\n",
"reluctant 22\n",
"retained 23\n",
"distant 131\n",
"admirers 10\n",
"chosen 77\n",
"treated 77\n",
"experience 151\n",
"infatuation 6\n",
"desired 85\n",
"pleasure 415\n",
"frank 35\n",
"demeanour 24\n",
"intellect 46\n",
"entertaining 24\n",
"companion 157\n",
"combined 43\n",
"expense 41\n",
"observe 123\n",
"brief 54\n",
"changes 77\n",
"calmly 56\n",
"put 1277\n",
"encouraging 17\n",
"abandoned 93\n",
"coffins 16\n",
"accompany 47\n",
"learned 118\n",
"grow 76\n",
"decided 163\n",
"belonging 73\n",
"clandestine 4\n",
"forego 8\n",
"key 76\n",
"decision 51\n",
"respect 210\n",
"anyin 1\n",
"disposed 108\n",
"ashamed 127\n",
"learn 94\n",
"defend 37\n",
"firearms 7\n",
"protect 32\n",
"deaths 15\n",
"less 528\n",
"sunset 30\n",
"consent 84\n",
"missing 36\n",
"starting 71\n",
"satisfying 7\n",
"alarmed 56\n",
"depressing 12\n",
"trial 43\n",
"resistance 20\n",
"possesses 18\n",
"forgot 81\n",
"front 355\n",
"score 40\n",
"chemical 7\n",
"mile 55\n",
"lane 39\n",
"glebe 1\n",
"situations 5\n",
"embedded 1\n",
"consistency 6\n",
"gothic 1\n",
"picturesque 19\n",
"welldeserved 2\n",
"flimsy 2\n",
"italianised 1\n",
"walesden 1\n",
"motives 31\n",
"inefficiently 1\n",
"robbed 22\n",
"pulpit 7\n",
"temptation 12\n",
"fastened 32\n",
"fiddlestick 3\n",
"inevitable 38\n",
"hardens 2\n",
"unpunished 3\n",
"trap 37\n",
"rust 6\n",
"irradiated 3\n",
"enabled 50\n",
"lock 70\n",
"convenience 21\n",
"vaultif 1\n",
"denounced 8\n",
"feasibly 1\n",
"beside 303\n",
"noisome 3\n",
"lit 70\n",
"influencing 4\n",
"destiny 36\n",
"metal 27\n",
"touched 196\n",
"inscriptions 5\n",
"plates 27\n",
"borne 69\n",
"ye 49\n",
"yeoman 2\n",
"soule 1\n",
"fond 151\n",
"lid 32\n",
"black 394\n",
"assisting 12\n",
"rubbing 82\n",
"rotting 2\n",
"suspense 16\n",
"interior 13\n",
"answered 470\n",
"damning 5\n",
"conceived 39\n",
"instead 272\n",
"obstinacy 16\n",
"prophets 3\n",
"coward 23\n",
"ascended 29\n",
"strenuously 3\n",
"astonishes 4\n",
"intellects 4\n",
"physically 24\n",
"coffinit 1\n",
"sane 6\n",
"thingupon 1\n",
"dispose 25\n",
"becomes 54\n",
"obstinate 20\n",
"derangement 1\n",
"summoning 6\n",
"agree 116\n",
"inconsistent 18\n",
"characters 47\n",
"religious 25\n",
"introduce 33\n",
"silences 2\n",
"beaten 62\n",
"promising 31\n",
"eradicated 2\n",
"showed 296\n",
"securely 9\n",
"vision 36\n",
"consistent 15\n",
"consisted 53\n",
"thoughti 2\n",
"darling 143\n",
"rung 21\n",
"scratching 16\n",
"food 76\n",
"invisible 34\n",
"count 723\n",
"exquisitelychiselled 1\n",
"eyesits 1\n",
"raised 282\n",
"mechanical 12\n",
"x 42\n",
"whistle 47\n",
"clue 19\n",
"relaxed 15\n",
"equalled 6\n",
"strangelooking 3\n",
"caused 141\n",
"welcomenone 1\n",
"potent 10\n",
"charlesit 1\n",
"cruelty 30\n",
"calls 50\n",
"constancy 23\n",
"sobbed 50\n",
"unkindest 1\n",
"farewell 67\n",
"charlesdear 2\n",
"admired 47\n",
"culture 21\n",
"dismay 33\n",
"wakingwell 1\n",
"lingering 32\n",
"fancying 7\n",
"strangeness 6\n",
"fondly 13\n",
"scarce 14\n",
"joyful 68\n",
"unreal 11\n",
"dictated 19\n",
"trifled 12\n",
"cherish 19\n",
"involved 33\n",
"profoundest 10\n",
"uniting 13\n",
"ocular 4\n",
"demonstration 32\n",
"butbut 4\n",
"isthere 3\n",
"tormented 47\n",
"generosity 38\n",
"insanity 13\n",
"spend 55\n",
"accommodate 6\n",
"contains 5\n",
"conversing 38\n",
"guarded 16\n",
"viewing 11\n",
"consists 15\n",
"curdle 1\n",
"coolest 4\n",
"waxes 1\n",
"partiality 10\n",
"oppressive 18\n",
"rapturously 19\n",
"chain 40\n",
"prize 13\n",
"impassable 5\n",
"thundering 12\n",
"denunciation 2\n",
"indicate 19\n",
"inspired 43\n",
"scrutiny 18\n",
"bit 186\n",
"hidden 86\n",
"demand 73\n",
"worked 104\n",
"disturbing 13\n",
"sent 406\n",
"occupant 7\n",
"listened 229\n",
"annoyed 16\n",
"yesyesyes 2\n",
"nobody 262\n",
"inefficient 6\n",
"finer 6\n",
"fashioned 7\n",
"reluctantly 25\n",
"holster 1\n",
"forfeit 10\n",
"triggera 1\n",
"putting 276\n",
"cursory 2\n",
"eventswhat 1\n",
"sufferance 2\n",
"proceeds 7\n",
"corresponding 26\n",
"qualities 67\n",
"wed 8\n",
"ties 15\n",
"sarcastically 8\n",
"producedmore 1\n",
"generously 11\n",
"bravely 3\n",
"husband 316\n",
"nook 13\n",
"construed 4\n",
"reflections 52\n",
"precautionary 1\n",
"carols 1\n",
"golden 92\n",
"luster 1\n",
"self 87\n",
"elsewhere 53\n",
"valley 32\n",
"vanish 11\n",
"muttered 144\n",
"cant 562\n",
"ghostesses 1\n",
"womanwhy 1\n",
"wampyre 9\n",
"sich 3\n",
"aggravating 8\n",
"addressing 131\n",
"distinct 55\n",
"accordant 3\n",
"views 59\n",
"reject 11\n",
"amity 2\n",
"esq 1\n",
"contemplation 44\n",
"strangeamazingly 1\n",
"condolence 5\n",
"estates 45\n",
"parting 87\n",
"lets 64\n",
"thissuppose 1\n",
"mock 10\n",
"painting 13\n",
"radiant 43\n",
"rapidity 38\n",
"impetus 11\n",
"associated 94\n",
"colloquy 3\n",
"compose 8\n",
"mellifluous 1\n",
"displayed 35\n",
"perfection 28\n",
"andand 11\n",
"shapes 30\n",
"ailment 1\n",
"losing 57\n",
"retire 48\n",
"regimen 2\n",
"momentarily 7\n",
"painter 15\n",
"complied 14\n",
"beggar 30\n",
"henryhenry 1\n",
"mortals 4\n",
"welcomewelcomemost 1\n",
"deal 298\n",
"stung 9\n",
"especial 10\n",
"consume 3\n",
"breaking 99\n",
"unenviable 3\n",
"xv 34\n",
"import 11\n",
"godsend 2\n",
"sapient 1\n",
"individualthe 1\n",
"gross 15\n",
"stagger 7\n",
"everywhere 96\n",
"superior 82\n",
"quietness 8\n",
"street 414\n",
"expensive 33\n",
"assortment 3\n",
"fifty 98\n",
"sailor 20\n",
"heartylooking 1\n",
"ahoy 9\n",
"lubber 22\n",
"shiver 25\n",
"shouted 328\n",
"dock 10\n",
"aye 50\n",
"politeness 58\n",
"reckonings 2\n",
"profusion 5\n",
"bedsgood 3\n",
"ribs 10\n",
"coursegod 1\n",
"obediently 6\n",
"crinkles 20\n",
"ha 191\n",
"rascal 48\n",
"desarves 1\n",
"seaman 25\n",
"pursers 2\n",
"wagabone 1\n",
"amdont 1\n",
"digs 1\n",
"aboard 30\n",
"hated 32\n",
"perseverance 22\n",
"jacki 2\n",
"goodbye 102\n",
"contradicted 12\n",
"discussion 39\n",
"praiseworthy 3\n",
"cooperation 4\n",
"hereby 1\n",
"marries 6\n",
"ps 5\n",
"immemorial 2\n",
"hardly 371\n",
"bellowed 4\n",
"youneversawit 1\n",
"forged 9\n",
"nobly 7\n",
"countrys 10\n",
"forgery 4\n",
"bottle 137\n",
"obey 38\n",
"stewards 12\n",
"whatsitsname 5\n",
"timbers 10\n",
"id 134\n",
"luck 44\n",
"furious 22\n",
"mermaid 7\n",
"bannersworth 1\n",
"describe 42\n",
"relationship 14\n",
"puzzles 2\n",
"sensible 115\n",
"wouldnt 262\n",
"physiology 6\n",
"yardarm 17\n",
"shortly 56\n",
"sisters 94\n",
"premise 1\n",
"voyage 43\n",
"moisture 5\n",
"quid 4\n",
"indulged 17\n",
"appointment 62\n",
"sink 31\n",
"sorrowwhen 1\n",
"nevernevernever 1\n",
"acutely 16\n",
"sharer 4\n",
"united 97\n",
"semicircular 4\n",
"directions 60\n",
"exclusively 12\n",
"fairest 3\n",
"soil 29\n",
"weeds 10\n",
"floral 3\n",
"flowergarden 4\n",
"blighted 21\n",
"dawn 45\n",
"taper 9\n",
"waist 40\n",
"woe 8\n",
"endearing 12\n",
"conquer 15\n",
"eloquence 19\n",
"majesty 80\n",
"redeeming 6\n",
"rescues 1\n",
"ere 40\n",
"yetthe 1\n",
"tremulous 17\n",
"mercyevery 1\n",
"maiden 26\n",
"confession 23\n",
"sorrow 139\n",
"grisly 6\n",
"hearthand 1\n",
"obscured 14\n",
"temporary 47\n",
"thunderstorm 2\n",
"sunshine 44\n",
"beam 20\n",
"shrine 10\n",
"behold 39\n",
"willi 4\n",
"minutesa 1\n",
"lipsa 1\n",
"elegantly 9\n",
"freeing 5\n",
"courtly 19\n",
"apologies 12\n",
"madam 146\n",
"shower 11\n",
"plausible 2\n",
"troubling 16\n",
"coincidence 22\n",
"insurmountable 4\n",
"rude 20\n",
"ownmy 2\n",
"innocent 138\n",
"topics 13\n",
"belied 3\n",
"compared 44\n",
"tends 3\n",
"creation 19\n",
"sexto 1\n",
"comparing 10\n",
"adapt 10\n",
"opera 18\n",
"composition 23\n",
"carving 8\n",
"ordered 167\n",
"meetings 12\n",
"bidding 17\n",
"grievously 13\n",
"insulted 22\n",
"persecuted 6\n",
"blooded 3\n",
"sneer 5\n",
"unnecessary 33\n",
"shoregoinglooking 1\n",
"madmen 5\n",
"chaplain 3\n",
"maimed 9\n",
"decline 30\n",
"angrily 105\n",
"motioned 11\n",
"horsemarine 4\n",
"enemies 47\n",
"scamp 6\n",
"surprising 39\n",
"shaking 263\n",
"eccentric 18\n",
"hates 2\n",
"goose 15\n",
"zealous 12\n",
"ages 32\n",
"inhabited 16\n",
"plunged 35\n",
"pleases 20\n",
"toothan 1\n",
"separatedhenry 1\n",
"respects 52\n",
"phrase 56\n",
"mecharles 1\n",
"joycharles 1\n",
"devotionnever 1\n",
"concentration 14\n",
"verole 5\n",
"hungary 4\n",
"paternal 13\n",
"reposed 12\n",
"splendid 93\n",
"torchlight 2\n",
"corruption 11\n",
"departed 54\n",
"assembled 63\n",
"thanked 83\n",
"interfering 9\n",
"stations 5\n",
"auxiliary 1\n",
"abstain 8\n",
"necromancer 1\n",
"methey 1\n",
"complaint 47\n",
"reproachfully 20\n",
"purse 52\n",
"mercury 5\n",
"vicious 18\n",
"credit 107\n",
"extortion 2\n",
"passes 30\n",
"inch 32\n",
"poisoned 12\n",
"enoughthere 2\n",
"inhuman 5\n",
"exactlyi 1\n",
"policy 17\n",
"scandal 7\n",
"properbut 2\n",
"split 10\n",
"employers 8\n",
"government 83\n",
"products 3\n",
"safest 3\n",
"hoard 1\n",
"thoughtfully 70\n",
"gauze 7\n",
"wire 6\n",
"undertaking 17\n",
"coolly 23\n",
"magnificence 14\n",
"splendour 10\n",
"haughty 45\n",
"amassed 4\n",
"tastes 15\n",
"revenues 1\n",
"cuirassiers 1\n",
"delayed 19\n",
"morvens 1\n",
"paramour 1\n",
"swiftnessa 1\n",
"coldness 26\n",
"statuary 2\n",
"onsay 1\n",
"covet 3\n",
"hides 10\n",
"surpassing 6\n",
"evanescent 1\n",
"ocean 37\n",
"lake 10\n",
"mandespite 1\n",
"striving 21\n",
"noi 4\n",
"thanksa 1\n",
"reserve 37\n",
"seatednonsense 1\n",
"morgan 1\n",
"comfortably 31\n",
"petulance 2\n",
"highest 87\n",
"wrecked 4\n",
"deserting 10\n",
"corporal 25\n",
"delicate 118\n",
"approve 29\n",
"couldnt 297\n",
"waves 53\n",
"trusts 6\n",
"argumentative 7\n",
"delicatewhere 1\n",
"sternway 1\n",
"deck 27\n",
"stuck 44\n",
"doorway 59\n",
"prevailed 31\n",
"excepting 13\n",
"shrinkyard 1\n",
"armgrapnel 1\n",
"replenishing 1\n",
"badly 61\n",
"imply 14\n",
"scenes 40\n",
"depressed 32\n",
"council 63\n",
"xxii 16\n",
"decisive 21\n",
"payment 19\n",
"explicit 10\n",
"lamentably 1\n",
"stuff 22\n",
"presumptuous 13\n",
"beforehand 46\n",
"especially 211\n",
"reconcile 20\n",
"sails 22\n",
"itexplicit 1\n",
"tonguetied 2\n",
"unfold 2\n",
"loading 5\n",
"jagged 6\n",
"injury 57\n",
"define 10\n",
"professionally 6\n",
"deduce 4\n",
"forbids 2\n",
"xxiii 12\n",
"representing 25\n",
"sparkled 10\n",
"knowsare 1\n",
"wantwhich 1\n",
"irony 24\n",
"bustled 4\n",
"youjack 1\n",
"dinner 455\n",
"cruise 8\n",
"obliterated 4\n",
"limit 20\n",
"togetherhow 1\n",
"glimpses 17\n",
"witnessed 29\n",
"anon 5\n",
"shaken 47\n",
"torn 54\n",
"reigned 15\n",
"deference 34\n",
"civilians 6\n",
"approval 42\n",
"enforced 15\n",
"prematurely 9\n",
"plans 83\n",
"green 165\n",
"transiently 1\n",
"tinge 6\n",
"converting 6\n",
"prospect 77\n",
"sirwith 1\n",
"wanting 49\n",
"compassion 51\n",
"settling 27\n",
"mooted 1\n",
"heightened 19\n",
"resenting 3\n",
"accede 6\n",
"immaterial 2\n",
"challenged 24\n",
"practice 31\n",
"censorious 1\n",
"pink 23\n",
"humane 10\n",
"worry 22\n",
"actor 4\n",
"converted 13\n",
"pretend 49\n",
"dudgeon 6\n",
"cooling 11\n",
"host 53\n",
"dance 56\n",
"halfacrown 12\n",
"mum 7\n",
"afore 67\n",
"plain 200\n",
"yourn 12\n",
"anymore 2\n",
"choosing 11\n",
"liberty 135\n",
"excels 1\n",
"select 28\n",
"dispatch 24\n",
"directed 114\n",
"target 5\n",
"persecutions 5\n",
"confide 23\n",
"permanent 11\n",
"deliberation 12\n",
"pinions 4\n",
"perplexity 34\n",
"suspect 43\n",
"oddestlooking 1\n",
"droll 7\n",
"chap 25\n",
"seventyfour 2\n",
"sung 16\n",
"bigger 10\n",
"spray 6\n",
"sailed 9\n",
"describing 17\n",
"captain 1405\n",
"menthey 1\n",
"stanch 1\n",
"sluggard 3\n",
"healthy 29\n",
"wink 28\n",
"wag 11\n",
"gooda 1\n",
"foreland 2\n",
"mate 8\n",
"mopish 1\n",
"pathless 2\n",
"wider 14\n",
"mary 627\n",
"chest 90\n",
"repulsiveness 1\n",
"singularlooking 2\n",
"shipmate 2\n",
"bumped 2\n",
"evillooking 1\n",
"jokes 18\n",
"queer 31\n",
"trading 7\n",
"dumb 34\n",
"broom 10\n",
"delicateness 1\n",
"scared 46\n",
"polar 3\n",
"hopping 6\n",
"trader 7\n",
"prayers 31\n",
"lot 68\n",
"biscuit 16\n",
"brandy 24\n",
"relish 35\n",
"wilderness 13\n",
"expanse 10\n",
"freshness 20\n",
"casks 14\n",
"outrageously 1\n",
"envy 29\n",
"heavensthe 1\n",
"drowned 36\n",
"herculean 3\n",
"irishman 1\n",
"tight 62\n",
"amhes 1\n",
"quartersthere 1\n",
"itwe 3\n",
"singoh 1\n",
"singingsuch 1\n",
"safely 31\n",
"xxvi 10\n",
"defeat 34\n",
"skirted 4\n",
"dressingtable 7\n",
"fulfilling 7\n",
"conveyances 1\n",
"gushed 6\n",
"unclehis 1\n",
"worldit 1\n",
"transaction 21\n",
"damme 22\n",
"youre 290\n",
"liar 7\n",
"worthlessness 5\n",
"condescend 14\n",
"dishonourable 6\n",
"tomorrowwait 1\n",
"rectitude 5\n",
"wicked 62\n",
"delinquency 2\n",
"foe 28\n",
"vexatious 6\n",
"shades 18\n",
"grounded 2\n",
"falsehood 16\n",
"solace 4\n",
"unsuspecting 1\n",
"receiving 101\n",
"documents 20\n",
"dissimulation 1\n",
"motherhush 1\n",
"letterswhere 1\n",
"neednt 54\n",
"contrasting 5\n",
"offended 60\n",
"alive 123\n",
"stillcharles 1\n",
"fleets 1\n",
"remotest 16\n",
"affectionate 96\n",
"odious 13\n",
"endeavours 12\n",
"infallible 10\n",
"authority 75\n",
"surmounted 4\n",
"ivy 17\n",
"handful 15\n",
"doubtsi 1\n",
"challenger 3\n",
"bereaved 8\n",
"overlooked 18\n",
"freed 11\n",
"realised 6\n",
"maddened 7\n",
"destroyed 76\n",
"tells 52\n",
"surelysurely 1\n",
"adhere 3\n",
"xxix 6\n",
"conjectured 3\n",
"arrogated 1\n",
"doctrines 2\n",
"stronghold 3\n",
"ecclesiastical 10\n",
"pitfalls 4\n",
"memories 30\n",
"gilding 3\n",
"refulgence 3\n",
"gleams 2\n",
"repaid 12\n",
"waste 48\n",
"envelopes 2\n",
"gestures 14\n",
"splashes 2\n",
"grating 15\n",
"dreary 54\n",
"tantalized 1\n",
"deepening 7\n",
"pallet 2\n",
"parchment 6\n",
"taller 7\n",
"responsible 27\n",
"reliance 22\n",
"dictates 4\n",
"bluntly 4\n",
"peeped 20\n",
"king 77\n",
"harbours 2\n",
"englands 5\n",
"pleaseits 1\n",
"siri 4\n",
"botherstuff 1\n",
"rascals 8\n",
"acquainting 2\n",
"xxxi 6\n",
"neglected 31\n",
"perplexing 5\n",
"costly 17\n",
"magnitude 9\n",
"devoutly 9\n",
"staid 11\n",
"keeps 35\n",
"fidgets 4\n",
"fates 4\n",
"reminiscence 5\n",
"complexions 1\n",
"playful 11\n",
"twins 19\n",
"omens 2\n",
"moresay 1\n",
"regulation 4\n",
"climax 9\n",
"besought 24\n",
"jewels 12\n",
"heaved 23\n",
"softly 174\n",
"haveperhaps 1\n",
"runaway 4\n",
"opposite 186\n",
"resolving 7\n",
"annual 10\n",
"torturefor 1\n",
"reunion 7\n",
"faintest 14\n",
"fluttering 27\n",
"imaginings 3\n",
"disappointing 4\n",
"boon 13\n",
"competence 2\n",
"nevernever 2\n",
"unto 20\n",
"forgetfulness 11\n",
"resplendent 4\n",
"sympathies 12\n",
"hovered 14\n",
"annually 2\n",
"unexpectedly 80\n",
"buoyancy 4\n",
"unhallowed 4\n",
"tract 8\n",
"pageantry 1\n",
"recall 71\n",
"cheaply 5\n",
"magicianlike 1\n",
"hedge 14\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"brood 9\n",
"irretrievably 3\n",
"lustrouslooking 1\n",
"scintillation 1\n",
"displace 3\n",
"holy 56\n",
"cattle 20\n",
"onward 16\n",
"evergreens 4\n",
"perfume 8\n",
"paradise 8\n",
"fragrant 6\n",
"essences 4\n",
"cowardheart 1\n",
"vanished 53\n",
"violated 3\n",
"ruthless 5\n",
"robe 6\n",
"declaration 32\n",
"wellknown 26\n",
"onwards 15\n",
"hem 14\n",
"terrifiedhe 1\n",
"wreak 1\n",
"rustic 7\n",
"spiritsa 1\n",
"ceded 1\n",
"defined 33\n",
"slave 28\n",
"lipservice 1\n",
"tottering 5\n",
"commences 1\n",
"beseeching 4\n",
"consisting 14\n",
"extending 23\n",
"fluid 2\n",
"pitied 30\n",
"hysterically 3\n",
"strips 11\n",
"maddening 4\n",
"empires 1\n",
"ambition 24\n",
"benign 2\n",
"thy 53\n",
"knees 98\n",
"motto 5\n",
"integrity 5\n",
"mourner 1\n",
"enamoured 4\n",
"paroxysm 11\n",
"wretch 39\n",
"pitiable 8\n",
"regretful 7\n",
"coexistent 1\n",
"attacks 19\n",
"oceans 3\n",
"catastrophe 18\n",
"welltoo 1\n",
"quick 158\n",
"unavailing 12\n",
"unhand 1\n",
"brightening 15\n",
"windowsill 6\n",
"absent 50\n",
"bushwood 1\n",
"wisely 4\n",
"fever 37\n",
"custom 58\n",
"arming 5\n",
"compliance 34\n",
"attracted 70\n",
"composure 55\n",
"weighing 12\n",
"thanksgiving 6\n",
"unproductive 1\n",
"crushed 57\n",
"recruit 5\n",
"enthusiastically 7\n",
"submit 44\n",
"battalions 25\n",
"economise 1\n",
"considerations 47\n",
"willingness 7\n",
"presumed 15\n",
"chin 121\n",
"protest 34\n",
"shipload 1\n",
"injustice 31\n",
"circle 91\n",
"huzza 2\n",
"cruizing 1\n",
"itname 1\n",
"meal 31\n",
"oblige 33\n",
"infringing 1\n",
"taunts 11\n",
"succeededand 1\n",
"unfairly 3\n",
"motions 13\n",
"sira 2\n",
"manyoung 1\n",
"irritating 5\n",
"unpick 1\n",
"licence 13\n",
"anyhow 19\n",
"urgent 17\n",
"speedily 15\n",
"bowing 48\n",
"affords 3\n",
"furnished 30\n",
"insist 21\n",
"fowl 19\n",
"himthats 3\n",
"accusation 9\n",
"smoothspoken 1\n",
"blandly 14\n",
"keelhaul 1\n",
"avenge 7\n",
"settledi 1\n",
"oneand 5\n",
"soin 1\n",
"soi 4\n",
"soit 1\n",
"unruffledand 1\n",
"spliced 1\n",
"isisa 1\n",
"newspapers 16\n",
"lubberahoy 1\n",
"swabbed 1\n",
"ducked 7\n",
"crockery 8\n",
"enemynone 1\n",
"fishone 1\n",
"charged 32\n",
"gravity 46\n",
"xxxviii 4\n",
"whatdyecallem 1\n",
"tom 46\n",
"dilemma 8\n",
"relinquish 15\n",
"anybodythe 1\n",
"vampyreif 1\n",
"transacting 1\n",
"whatsoname 1\n",
"backout 1\n",
"default 9\n",
"trumpcard 1\n",
"afeard 5\n",
"raving 3\n",
"arising 19\n",
"halfanhour 15\n",
"exploding 3\n",
"damage 17\n",
"reside 2\n",
"johnjack 1\n",
"scandalous 2\n",
"mebut 4\n",
"ihoweversince 1\n",
"horsemarinelooking 1\n",
"brush 14\n",
"unfortunatevery 1\n",
"fightyes 1\n",
"inevitably 23\n",
"resigning 7\n",
"efficiently 3\n",
"ammunition 15\n",
"bells 70\n",
"prolong 8\n",
"refusal 25\n",
"limits 12\n",
"dispensation 3\n",
"familys 15\n",
"cleverest 3\n",
"unconcerned 4\n",
"twisted 27\n",
"unused 14\n",
"causus 1\n",
"shooting 17\n",
"howall 1\n",
"partisanship 2\n",
"shabby 43\n",
"tardily 1\n",
"interfered 11\n",
"blowed 7\n",
"backbone 1\n",
"xxxix 4\n",
"wellwhat 1\n",
"fir 6\n",
"fanlike 1\n",
"interferes 1\n",
"combatants 3\n",
"reckless 20\n",
"evade 6\n",
"commonplace 15\n",
"reserved 45\n",
"perpendicular 6\n",
"forwardthe 1\n",
"dummyto 1\n",
"bannerworthhere 1\n",
"madnessworse 1\n",
"coronation 5\n",
"hurrah 105\n",
"penguins 1\n",
"festival 6\n",
"broadsides 3\n",
"accommodated 8\n",
"rove 1\n",
"downcast 29\n",
"resounded 16\n",
"exhort 2\n",
"unpopular 3\n",
"tumult 17\n",
"indiscretion 7\n",
"inculpating 1\n",
"multiplied 2\n",
"arithmetic 8\n",
"bustle 32\n",
"origin 16\n",
"amiss 20\n",
"accelerated 3\n",
"supplementary 4\n",
"vampyredown 1\n",
"disperse 21\n",
"lumpylooking 1\n",
"vampyreburn 1\n",
"folly 32\n",
"minority 7\n",
"lawless 9\n",
"struggled 27\n",
"onwardfirst 1\n",
"quell 5\n",
"proportion 21\n",
"rein 15\n",
"fathomless 1\n",
"ghoul 2\n",
"nations 45\n",
"gesticulated 3\n",
"knocker 16\n",
"warning 61\n",
"bitterest 5\n",
"nonsuccess 4\n",
"attics 4\n",
"neared 7\n",
"viewed 16\n",
"opportunely 8\n",
"eluding 2\n",
"population 18\n",
"outbreaks 1\n",
"splinters 5\n",
"bloods 1\n",
"weve 45\n",
"pour 18\n",
"tangent 1\n",
"coursing 3\n",
"mire 3\n",
"eels 1\n",
"begrimed 3\n",
"undergrowth 2\n",
"execration 8\n",
"passions 36\n",
"broadsideand 1\n",
"bodies 31\n",
"lee 6\n",
"subtle 25\n",
"humanityhe 1\n",
"perilous 5\n",
"confidencemight 1\n",
"arrow 9\n",
"structure 12\n",
"complimented 11\n",
"herewhy 1\n",
"famous 60\n",
"hilloastop 1\n",
"knotty 5\n",
"xliv 2\n",
"ingenuity 9\n",
"encrusted 1\n",
"restlessly 12\n",
"himselfhe 2\n",
"acute 13\n",
"noisea 1\n",
"totters 2\n",
"chains 16\n",
"returningthat 1\n",
"declined 34\n",
"prig 2\n",
"fitting 15\n",
"fari 1\n",
"hopeful 49\n",
"obdurate 17\n",
"notwhen 2\n",
"evading 1\n",
"misled 4\n",
"commencing 4\n",
"attentionhe 1\n",
"invalid 29\n",
"cash 7\n",
"imposing 18\n",
"causedoors 1\n",
"tampering 2\n",
"inconsistencies 5\n",
"practical 77\n",
"oratorical 4\n",
"bath 22\n",
"refined 25\n",
"illiterate 3\n",
"hamlet 6\n",
"moralise 4\n",
"doingtold 1\n",
"taxgatherers 1\n",
"habitations 2\n",
"official 80\n",
"en 10\n",
"sacked 4\n",
"offences 10\n",
"pairs 16\n",
"sepulture 2\n",
"hoisted 4\n",
"invaders 5\n",
"triumphant 41\n",
"flourished 14\n",
"clergyman 27\n",
"veneration 13\n",
"canted 1\n",
"diviner 1\n",
"decayingeven 1\n",
"brawny 3\n",
"lump 15\n",
"spadesful 1\n",
"spades 2\n",
"humid 2\n",
"excitementa 1\n",
"handles 2\n",
"accomplishment 19\n",
"imbedded 1\n",
"receptacle 8\n",
"backing 13\n",
"thrusting 14\n",
"spokes 1\n",
"audibly 11\n",
"backwards 36\n",
"scampering 2\n",
"knowed 10\n",
"dicks 18\n",
"andahalf 1\n",
"arter 24\n",
"coffinlid 2\n",
"stilldont 1\n",
"wontwe 2\n",
"ithush 1\n",
"throats 7\n",
"occupants 5\n",
"folks 22\n",
"thatll 23\n",
"generations 18\n",
"disturbances 6\n",
"tidings 24\n",
"notoriety 4\n",
"owes 3\n",
"xlvii 2\n",
"overburdened 2\n",
"commands 46\n",
"gingerbreadlooking 1\n",
"outlandish 3\n",
"demdry 1\n",
"greek 14\n",
"itgo 1\n",
"itnow 2\n",
"jacket 54\n",
"sleeves 39\n",
"pump 21\n",
"pumped 5\n",
"danced 37\n",
"grotesque 11\n",
"edged 5\n",
"kicked 9\n",
"shins 2\n",
"spout 4\n",
"hydropathic 1\n",
"saturated 5\n",
"fortitude 18\n",
"flurried 11\n",
"mop 8\n",
"masters 62\n",
"lieu 14\n",
"insured 2\n",
"screaming 11\n",
"fresher 7\n",
"torch 7\n",
"valueless 5\n",
"insidious 1\n",
"mar 3\n",
"lines 66\n",
"chambermaid 4\n",
"detailing 2\n",
"outraged 11\n",
"pretext 29\n",
"leaders 22\n",
"invests 1\n",
"compunction 7\n",
"adventurers 2\n",
"landing 27\n",
"nimbleness 2\n",
"arminarm 17\n",
"dimensions 11\n",
"captors 3\n",
"certainlydirectly 1\n",
"toothdrawing 1\n",
"concern 47\n",
"devolve 1\n",
"civilisation 2\n",
"tenement 5\n",
"distorted 23\n",
"drunken 26\n",
"constabulary 2\n",
"decency 3\n",
"backed 22\n",
"admirably 12\n",
"defended 16\n",
"carbines 3\n",
"threes 6\n",
"himburn 3\n",
"solve 8\n",
"accomplishing 7\n",
"starved 11\n",
"prostrate 17\n",
"soldier 233\n",
"handcuffs 1\n",
"gaol 5\n",
"lurid 7\n",
"conflagration 10\n",
"unequivocallythat 1\n",
"tapis 1\n",
"traitor 21\n",
"tramp 18\n",
"belligerents 1\n",
"mates 7\n",
"hodge 1\n",
"matethats 1\n",
"streetdoor 3\n",
"resolutely 27\n",
"blunderbuss 4\n",
"slugs 3\n",
"outwell 2\n",
"reconnaissance 2\n",
"timber 6\n",
"batteringramswhich 1\n",
"unnecessarily 4\n",
"cowards 4\n",
"insensibly 7\n",
"downdestroy 1\n",
"discomposed 14\n",
"skelter 1\n",
"precipitancy 1\n",
"blandest 3\n",
"awed 6\n",
"gleamed 25\n",
"thunderstricken 2\n",
"crowding 27\n",
"betters 5\n",
"beer 18\n",
"mens 36\n",
"cellar 19\n",
"inquiringly 40\n",
"flavour 18\n",
"shavings 3\n",
"mobhurra 1\n",
"indians 5\n",
"outhouses 3\n",
"heartiness 10\n",
"smallest 32\n",
"halted 33\n",
"sergeant 49\n",
"inextricably 2\n",
"liv 2\n",
"orderly 58\n",
"scott 3\n",
"singlehanded 8\n",
"accosted 9\n",
"asks 36\n",
"pooh 8\n",
"treetops 3\n",
"grandeur 19\n",
"extend 18\n",
"vicissitudes 8\n",
"penetrating 15\n",
"rheumatiz 1\n",
"therell 12\n",
"hunting 29\n",
"shoes 94\n",
"tormentor 2\n",
"tradesman 11\n",
"plastered 5\n",
"glove 15\n",
"fetching 10\n",
"smack 6\n",
"townbull 1\n",
"ayay 5\n",
"crafts 1\n",
"commander 178\n",
"implicitly 10\n",
"smoking 58\n",
"intelligible 20\n",
"threequarters 3\n",
"luxuries 8\n",
"reigns 4\n",
"unmitigated 3\n",
"leviathan 2\n",
"accessible 25\n",
"medically 1\n",
"unceasing 3\n",
"lesser 24\n",
"edgewaysde 1\n",
"mew 1\n",
"darkening 21\n",
"harkye 1\n",
"steadying 1\n",
"bruteis 1\n",
"sold 50\n",
"spithead 1\n",
"anynext 1\n",
"loosen 4\n",
"chillingworthare 1\n",
"studied 20\n",
"ideaa 1\n",
"hangings 7\n",
"distanced 2\n",
"blight 8\n",
"congratulated 19\n",
"trespasser 1\n",
"lightde 1\n",
"hiccups 1\n",
"herewhen 1\n",
"entanglement 4\n",
"yo 1\n",
"personages 21\n",
"persona 3\n",
"outa 2\n",
"wake 51\n",
"wakened 2\n",
"climb 11\n",
"sarvice 2\n",
"dawns 1\n",
"eminently 7\n",
"interim 2\n",
"plumply 2\n",
"cavil 1\n",
"parklike 1\n",
"obliging 22\n",
"district 26\n",
"pleaseafter 1\n",
"deborah 3\n",
"satiated 2\n",
"disgusting 6\n",
"unpretending 3\n",
"humbugged 3\n",
"scientificlooking 1\n",
"scythe 4\n",
"mowing 8\n",
"betraythat 1\n",
"merrier 5\n",
"lacked 12\n",
"answers 40\n",
"truer 10\n",
"suchlike 5\n",
"heat 49\n",
"fullest 7\n",
"merited 4\n",
"excesses 6\n",
"unreflecting 2\n",
"squandered 1\n",
"wits 26\n",
"flattered 21\n",
"ague 5\n",
"ensuing 12\n",
"prolific 3\n",
"angerstein 1\n",
"highway 9\n",
"purlite 1\n",
"inciting 1\n",
"clocks 18\n",
"floating 20\n",
"advancinggot 1\n",
"enshrouded 5\n",
"laboriously 14\n",
"offenders 5\n",
"rumours 8\n",
"councillor 1\n",
"thenbe 1\n",
"campaign 103\n",
"attained 35\n",
"invariably 21\n",
"stumblingblock 3\n",
"inaction 3\n",
"thanthan 2\n",
"threats 10\n",
"venturous 1\n",
"hairbrained 1\n",
"lxiii 2\n",
"abettor 1\n",
"prowling 15\n",
"ayear 8\n",
"mug 9\n",
"relit 4\n",
"gnawing 5\n",
"discontent 4\n",
"whit 5\n",
"bet 18\n",
"eccles 19\n",
"handkerchiefs 17\n",
"halfpast 20\n",
"illegitimate 14\n",
"illegitimates 1\n",
"valuables 7\n",
"secretary 14\n",
"toohis 1\n",
"solved 9\n",
"travellers 37\n",
"instantaneously 8\n",
"floorsuch 1\n",
"whitein 1\n",
"laughingstock 4\n",
"reminiscences 17\n",
"selfdeceit 1\n",
"bending 92\n",
"murmuring 23\n",
"knockdown 1\n",
"substance 22\n",
"overi 1\n",
"looklook 2\n",
"garmentthere 1\n",
"vampyregive 1\n",
"ramming 2\n",
"offyes 1\n",
"recovercertainly 1\n",
"mewell 2\n",
"repugnance 9\n",
"doi 3\n",
"indeedthen 1\n",
"gory 4\n",
"marchdalebeholdheaven 1\n",
"doubtyes 1\n",
"likeyou 2\n",
"nowhat 1\n",
"dungeonif 2\n",
"proscribed 1\n",
"objectbe 1\n",
"fruits 9\n",
"progressed 2\n",
"paramount 8\n",
"insure 1\n",
"capabilities 4\n",
"durance 3\n",
"dissemble 1\n",
"solemnity 28\n",
"appreciates 2\n",
"voluntary 14\n",
"bidden 6\n",
"physiologists 1\n",
"betom 1\n",
"therethen 1\n",
"frenchmans 6\n",
"tropicsvery 1\n",
"swell 18\n",
"blinkers 2\n",
"andandhold 1\n",
"planter 2\n",
"ravens 2\n",
"worshipped 7\n",
"heari 1\n",
"iiyes 1\n",
"goneso 1\n",
"breakindeed 1\n",
"madheaded 2\n",
"tragedyyes 1\n",
"clenched 25\n",
"inaudible 6\n",
"heartwhole 1\n",
"fox 21\n",
"keen 30\n",
"treacherous 10\n",
"thatan 1\n",
"enterprize 1\n",
"apace 5\n",
"dagger 15\n",
"viper 1\n",
"unrestrained 4\n",
"athletic 3\n",
"murders 8\n",
"anticipationsanticipations 1\n",
"discomfited 19\n",
"celllike 1\n",
"chainswhich 1\n",
"frantically 3\n",
"wickedness 15\n",
"shrouded 7\n",
"compositions 1\n",
"menatarms 3\n",
"avoided 49\n",
"blunted 3\n",
"melee 2\n",
"strangeri 1\n",
"wasand 6\n",
"colbert 1\n",
"welli 2\n",
"caucithat 1\n",
"guthrie 3\n",
"ungallant 1\n",
"endanger 3\n",
"knights 19\n",
"cauci 3\n",
"riches 29\n",
"browa 1\n",
"wars 34\n",
"uponno 1\n",
"handmaiden 1\n",
"device 13\n",
"doth 3\n",
"earshe 1\n",
"travelworn 1\n",
"accoutrements 3\n",
"encircled 9\n",
"endedi 1\n",
"bribing 1\n",
"tournamentand 1\n",
"divulge 1\n",
"theni 4\n",
"seats 24\n",
"tilted 5\n",
"jingled 4\n",
"thati 4\n",
"thingsoh 1\n",
"innin 1\n",
"stars 63\n",
"evernothing 1\n",
"undertaker 6\n",
"deathfollowing 1\n",
"masses 37\n",
"deathit 1\n",
"eventsso 1\n",
"wayyes 1\n",
"surly 5\n",
"eggs 15\n",
"taproom 1\n",
"professioni 2\n",
"ityou 4\n",
"gridironthats 1\n",
"pint 18\n",
"cheese 21\n",
"comethey 1\n",
"defunctoh 1\n",
"mourners 3\n",
"noand 1\n",
"lowered 34\n",
"shovelfull 1\n",
"disputes 10\n",
"application 28\n",
"oneyes 1\n",
"distractedwhat 1\n",
"multitudes 1\n",
"outflanked 2\n",
"lxxi 1\n",
"creditable 11\n",
"overt 1\n",
"condemned 22\n",
"successfuli 1\n",
"flowerbeds 3\n",
"wrest 1\n",
"stouter 9\n",
"belligerently 1\n",
"confronted 17\n",
"doubtcertainly 1\n",
"discoveryou 1\n",
"marketplace 4\n",
"naturei 1\n",
"youeven 2\n",
"beguile 2\n",
"dining 42\n",
"loathsome 6\n",
"effortit 1\n",
"themi 3\n",
"thenand 7\n",
"gamblingtable 2\n",
"versed 1\n",
"saloon 4\n",
"gambling 6\n",
"proves 6\n",
"himno 2\n",
"wellthe 1\n",
"shouldill 1\n",
"doorkeepers 1\n",
"appointments 13\n",
"bona 1\n",
"frosts 4\n",
"homeall 1\n",
"expectationyou 1\n",
"willand 2\n",
"wreck 22\n",
"exclaim 4\n",
"weeksdo 1\n",
"neverbut 1\n",
"dayday 1\n",
"ebb 6\n",
"mustered 7\n",
"wellyes 1\n",
"upshe 2\n",
"devour 1\n",
"illyes 1\n",
"alarmedi 1\n",
"hopeno 1\n",
"friendno 1\n",
"debauch 2\n",
"fluctuating 4\n",
"maddesperate 1\n",
"meof 1\n",
"bedthere 1\n",
"pulsation 2\n",
"surelysomething 1\n",
"boardingschool 3\n",
"wasanything 2\n",
"conduced 4\n",
"sufferers 5\n",
"violenceit 1\n",
"aggressions 1\n",
"resulti 1\n",
"insurgents 1\n",
"afraidwell 1\n",
"meclose 1\n",
"disagreed 4\n",
"links 20\n",
"townsmen 1\n",
"dismounted 33\n",
"pleasurable 3\n",
"solaced 2\n",
"divers 17\n",
"norwest 1\n",
"reform 11\n",
"adoration 10\n",
"modicum 2\n",
"testing 4\n",
"clogged 2\n",
"aright 2\n",
"impending 24\n",
"vibration 9\n",
"festering 4\n",
"blustrous 1\n",
"proficient 3\n",
"phraseology 2\n",
"rumandwater 3\n",
"accusing 3\n",
"bumplush 1\n",
"quart 8\n",
"knobber 1\n",
"flattering 23\n",
"refutation 2\n",
"beautifulmy 2\n",
"repays 2\n",
"florawhat 1\n",
"muchmuch 1\n",
"bemy 1\n",
"doneif 1\n",
"lxxv 1\n",
"purporting 2\n",
"clapped 19\n",
"acquire 11\n",
"mule 7\n",
"roast 24\n",
"bloom 30\n",
"revered 3\n",
"punishing 6\n",
"ahi 1\n",
"selfish 31\n",
"withholding 2\n",
"injunction 11\n",
"carpenter 9\n",
"carpentering 2\n",
"retribution 9\n",
"conceded 6\n",
"combatted 2\n",
"confederates 1\n",
"interstices 2\n",
"audacity 11\n",
"submerge 1\n",
"starts 14\n",
"hangedexecuted 1\n",
"schools 10\n",
"treatise 4\n",
"kin 4\n",
"monday 30\n",
"himfar 1\n",
"rites 2\n",
"influences 14\n",
"conveyance 18\n",
"gallows 4\n",
"imitating 8\n",
"officiously 2\n",
"conduction 2\n",
"manipulation 1\n",
"contraction 4\n",
"seefive 1\n",
"stealing 28\n",
"dimness 6\n",
"twig 2\n",
"inclining 7\n",
"placeto 1\n",
"secreted 8\n",
"savagely 7\n",
"lxxx 1\n",
"exerted 17\n",
"dominant 4\n",
"committedsuch 1\n",
"pleasures 16\n",
"huddled 8\n",
"altogethercome 1\n",
"foundrecollect 1\n",
"isdont 1\n",
"cinder 1\n",
"hector 1\n",
"pilethis 1\n",
"scraps 19\n",
"chips 5\n",
"vitals 2\n",
"himtheres 1\n",
"denouncing 1\n",
"impersonation 2\n",
"gauntlet 7\n",
"panting 36\n",
"toto 10\n",
"indeedindeed 2\n",
"thorough 9\n",
"furthest 3\n",
"solacing 2\n",
"pinch 19\n",
"snuff 25\n",
"shirked 2\n",
"knelt 12\n",
"reap 3\n",
"ankle 4\n",
"soonest 4\n",
"upthank 1\n",
"firearm 2\n",
"galloping 40\n",
"hurtful 1\n",
"cottagehouse 1\n",
"lxxxii 1\n",
"dimly 29\n",
"detour 2\n",
"preparatory 15\n",
"agent 24\n",
"floorthat 1\n",
"plots 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"dint 9\n",
"reins 23\n",
"mutter 7\n",
"hiddenpull 1\n",
"downmurder 1\n",
"scaffold 5\n",
"obtaining 20\n",
"rouses 1\n",
"gripe 1\n",
"intrusive 3\n",
"actionsfor 1\n",
"lovely 86\n",
"mistrustingly 1\n",
"feats 7\n",
"revelations 5\n",
"invocation 5\n",
"stormy 26\n",
"dispassionate 6\n",
"memorys 2\n",
"harshno 1\n",
"eternal 38\n",
"protestation 4\n",
"rejected 26\n",
"profligate 4\n",
"feeds 1\n",
"playedthe 1\n",
"palaces 9\n",
"craves 1\n",
"scorching 2\n",
"thoughtall 1\n",
"unsteady 15\n",
"refund 51\n",
"arousing 8\n",
"scar 16\n",
"appealing 25\n",
"seasons 14\n",
"placethere 1\n",
"vacancy 11\n",
"tellmost 1\n",
"rightsuch 1\n",
"heavenialthough 1\n",
"noii 1\n",
"coincidences 4\n",
"continuation 3\n",
"contemplating 16\n",
"stifled 14\n",
"concealments 1\n",
"varneywill 1\n",
"confidences 10\n",
"chocolate 3\n",
"claret 2\n",
"roots 7\n",
"lodging 57\n",
"ostler 5\n",
"fiddlestrings 1\n",
"biglooking 1\n",
"bucket 7\n",
"faculty 16\n",
"spouse 4\n",
"cleanest 4\n",
"milk 23\n",
"brickfield 1\n",
"peas 6\n",
"supposes 5\n",
"itecholl 1\n",
"youhis 1\n",
"therenone 1\n",
"cheapness 1\n",
"voted 7\n",
"plaguing 1\n",
"remnants 3\n",
"ford 5\n",
"goodthen 1\n",
"smith 10\n",
"shade 81\n",
"ripling 1\n",
"poetry 21\n",
"enchanted 28\n",
"gentlyso 1\n",
"approachingthe 1\n",
"heartit 1\n",
"expanding 5\n",
"objectall 1\n",
"shallow 12\n",
"minuteswhen 1\n",
"endued 2\n",
"swiftly 23\n",
"brake 1\n",
"jewellery 4\n",
"miscellaneous 9\n",
"initial 3\n",
"dispensations 3\n",
"memoir 1\n",
"seemem 1\n",
"robber 6\n",
"timemy 1\n",
"charlesdo 1\n",
"betraying 14\n",
"moodily 8\n",
"associate 12\n",
"resent 10\n",
"mitchell 3\n",
"instalment 2\n",
"dollop 1\n",
"memento 2\n",
"unheard 10\n",
"byto 1\n",
"responsive 10\n",
"bonfire 5\n",
"dearly 35\n",
"gallery 38\n",
"housenot 4\n",
"thicker 7\n",
"grappled 5\n",
"recoil 6\n",
"gashes 1\n",
"tumbling 27\n",
"housetops 5\n",
"coping 4\n",
"earthfor 1\n",
"fieldsfrom 1\n",
"murderthe 2\n",
"inadvertently 7\n",
"sprawled 2\n",
"pushes 3\n",
"snapped 15\n",
"pauseno 1\n",
"groans 16\n",
"himhold 1\n",
"lxxxviii 1\n",
"tedium 1\n",
"bruised 17\n",
"ox 1\n",
"abodemy 1\n",
"cautiousi 1\n",
"lair 2\n",
"luxuriously 4\n",
"heroine 3\n",
"kindest 15\n",
"magician 3\n",
"thingsthe 1\n",
"nighthis 1\n",
"tenure 2\n",
"graced 9\n",
"crossroad 1\n",
"liferecollect 1\n",
"lifedo 1\n",
"hearhear 1\n",
"exchanging 22\n",
"coating 1\n",
"chasm 3\n",
"toil 19\n",
"latei 2\n",
"forsook 2\n",
"drowning 14\n",
"infinitely 38\n",
"judges 10\n",
"narrowly 12\n",
"encompassed 5\n",
"solicitations 2\n",
"burden 45\n",
"hangmans 1\n",
"ripple 5\n",
"terrify 3\n",
"aver 2\n",
"debility 3\n",
"florain 1\n",
"knot 23\n",
"escapes 2\n",
"plays 9\n",
"allnot 1\n",
"razings 1\n",
"efface 4\n",
"divert 10\n",
"largeit 1\n",
"gig 3\n",
"tarried 1\n",
"civility 6\n",
"imperceptible 4\n",
"offencenot 1\n",
"tooit 2\n",
"sprained 5\n",
"pond 26\n",
"aquatic 2\n",
"upwell 1\n",
"skimmed 3\n",
"squirrel 1\n",
"meals 16\n",
"drawer 17\n",
"chief 216\n",
"ghastlyterrific 1\n",
"improvements 6\n",
"inclement 2\n",
"preclude 3\n",
"horsestealers 1\n",
"cabbagestump 1\n",
"prodigiously 1\n",
"mattersit 1\n",
"nowshovel 1\n",
"allwe 1\n",
"lxxxix 1\n",
"moonlightnow 1\n",
"ferryman 23\n",
"rambling 9\n",
"boats 26\n",
"amends 6\n",
"shivered 10\n",
"taxing 2\n",
"fatherhelp 1\n",
"iithat 1\n",
"lighta 2\n",
"xc 1\n",
"spya 1\n",
"muchthat 2\n",
"fathomed 3\n",
"elucidate 3\n",
"listenit 1\n",
"wrongto 1\n",
"conspiring 1\n",
"duties 56\n",
"navy 5\n",
"fishfag 1\n",
"sucks 2\n",
"discarded 3\n",
"american 1\n",
"bred 26\n",
"capers 4\n",
"measles 5\n",
"shove 9\n",
"answerable 4\n",
"reptiles 2\n",
"babies 14\n",
"gilded 9\n",
"buffetted 1\n",
"jackwe 1\n",
"thieflike 1\n",
"piratea 1\n",
"scurvily 1\n",
"fromthe 1\n",
"calcutta 2\n",
"yesits 1\n",
"smugglers 6\n",
"yesso 1\n",
"fainter 8\n",
"atelling 1\n",
"conveyed 31\n",
"hitch 4\n",
"aboutat 1\n",
"ould 4\n",
"troubleits 1\n",
"drinksome 1\n",
"bannerworthvery 1\n",
"halls 7\n",
"glee 7\n",
"handsomest 6\n",
"underneath 38\n",
"twentyseven 4\n",
"acres 6\n",
"saltzburgh 10\n",
"tumbler 21\n",
"disregard 9\n",
"mission 39\n",
"leek 41\n",
"enormously 8\n",
"uneasylooking 1\n",
"coffeeroom 11\n",
"sparingly 2\n",
"exposing 12\n",
"forever 37\n",
"sand 21\n",
"constituted 16\n",
"ambiguously 1\n",
"tamely 2\n",
"volubility 6\n",
"wily 2\n",
"cannotwill 1\n",
"igniting 2\n",
"cavernouslooking 1\n",
"slammed 9\n",
"speculations 14\n",
"pastthat 1\n",
"whobut 1\n",
"himhiding 1\n",
"crusade 1\n",
"titledeeds 2\n",
"turmoils 1\n",
"dwelt 15\n",
"townin 1\n",
"informing 24\n",
"pringlewhich 1\n",
"lawyerand 1\n",
"continuations 1\n",
"goodtempered 3\n",
"courseof 2\n",
"gills 212\n",
"stable 17\n",
"guarantee 7\n",
"humility 35\n",
"apologised 9\n",
"catholic 8\n",
"temperature 10\n",
"purling 2\n",
"spheres 5\n",
"gifted 9\n",
"rills 2\n",
"fishponds 1\n",
"redundant 1\n",
"entertainments 5\n",
"similarity 2\n",
"bothering 6\n",
"impute 4\n",
"artfully 3\n",
"slighted 16\n",
"investigations 2\n",
"magistrates 4\n",
"coroner 11\n",
"personate 1\n",
"tenth 15\n",
"jackinoffice 2\n",
"latest 21\n",
"corroborated 8\n",
"insane 11\n",
"juryi 1\n",
"verdict 2\n",
"forming 26\n",
"singleminded 1\n",
"files 11\n",
"renamed 5\n",
"distributing 37\n",
"gutenbergtm 280\n",
"registered 15\n",
"entity 15\n",
"literary 66\n",
"archive 65\n",
"located 21\n",
"performing 28\n",
"references 20\n",
"format 20\n",
"status 22\n",
"requirements 24\n",
"derive 13\n",
"tax 30\n",
"donations 76\n",
"volunteers 33\n",
"virus 5\n",
"equipment 26\n",
"disclaim 5\n",
"indirect 6\n",
"elect 6\n",
"problem 26\n",
"warranties 15\n",
"fitness 12\n",
"invalidity 6\n",
"synonymous 6\n",
"readable 10\n",
"widest 12\n",
"computers 10\n",
"non 9\n",
"organized 9\n",
"ein 6\n",
"businesspglaforg 4\n",
"charities 5\n",
"charitable 23\n",
"solicit 20\n",
"prohibition 7\n",
"volunteer 11\n",
"newsletter 5\n",
"tolstoy 9\n",
"tolstoytolstoi 1\n",
"infamies 1\n",
"anna 293\n",
"kuragin 57\n",
"petersburg 243\n",
"annette 9\n",
"virulent 4\n",
"disconcerted 28\n",
"complacently 12\n",
"discerned 7\n",
"wearisome 7\n",
"novosiltsevs 1\n",
"buonaparte 29\n",
"stale 9\n",
"overflowed 10\n",
"russia 165\n",
"role 23\n",
"hydra 1\n",
"selfabnegation 2\n",
"powerless 11\n",
"hardenburg 1\n",
"prussias 1\n",
"emigres 1\n",
"dowager 18\n",
"pavlovnas 39\n",
"console 14\n",
"paternity 2\n",
"prodigal 6\n",
"princess 944\n",
"meinen 1\n",
"attendez 1\n",
"petersbourg 1\n",
"hostess 20\n",
"gray 73\n",
"hierarchy 2\n",
"pierre 1786\n",
"murmured 52\n",
"perpetual 17\n",
"chimerical 1\n",
"selfconfident 16\n",
"hummed 11\n",
"groups 31\n",
"maitre 1\n",
"morsels 4\n",
"raconteur 1\n",
"bosomwhich 1\n",
"unquestionable 4\n",
"beamed 14\n",
"madame 115\n",
"lorgnette 4\n",
"anecdote 7\n",
"sticking 30\n",
"needle 29\n",
"maintenance 6\n",
"feminine 17\n",
"andrew 1040\n",
"clearcut 1\n",
"screwing 19\n",
"radiantly 5\n",
"influential 12\n",
"drubetskayas 1\n",
"adjutant 157\n",
"comedy 3\n",
"petitions 4\n",
"crazy 13\n",
"mi 1\n",
"conde 2\n",
"executions 6\n",
"royalist 1\n",
"regime 2\n",
"blushing 51\n",
"buonapartists 1\n",
"slapping 8\n",
"anarchy 1\n",
"abuses 3\n",
"regicide 3\n",
"ironical 12\n",
"emancipation 2\n",
"saviour 8\n",
"executing 11\n",
"imposture 3\n",
"africa 9\n",
"gasps 2\n",
"hippolytes 4\n",
"absentmindedness 8\n",
"chatter 8\n",
"eyeglass 40\n",
"joyfully 47\n",
"intentionally 4\n",
"princesse 2\n",
"pretense 7\n",
"everyones 7\n",
"buti 3\n",
"diplomatist 9\n",
"expects 9\n",
"briskly 31\n",
"embarrassment 29\n",
"restraining 12\n",
"dryly 7\n",
"indicates 4\n",
"feebly 26\n",
"talkas 1\n",
"determines 1\n",
"lackey 4\n",
"halfclosed 8\n",
"quivering 28\n",
"weighs 3\n",
"torments 8\n",
"treating 18\n",
"relations 130\n",
"reassuring 4\n",
"kuragins 12\n",
"il 13\n",
"cab 9\n",
"nullifying 1\n",
"dolokhov 284\n",
"linen 34\n",
"unfastened 6\n",
"englishmans 1\n",
"lightblue 5\n",
"swaggering 4\n",
"shortbegan 1\n",
"startle 3\n",
"emitting 3\n",
"fade 11\n",
"emptying 5\n",
"august 50\n",
"rostovsthe 1\n",
"daughterboth 1\n",
"nataly 5\n",
"eldest 65\n",
"fortyfive 5\n",
"childbearingshe 1\n",
"chere 8\n",
"cleanshaven 6\n",
"stroking 16\n",
"conservatory 18\n",
"manager 97\n",
"lvovna 1\n",
"callers 4\n",
"scraping 17\n",
"bezukhovs 19\n",
"cyrils 1\n",
"handsomer 4\n",
"borys 3\n",
"portly 8\n",
"smoothing 24\n",
"feigned 19\n",
"severity 30\n",
"ilya 32\n",
"doll 19\n",
"monstrosity 1\n",
"fifteenyearold 1\n",
"suppress 11\n",
"halfgrown 1\n",
"archives 2\n",
"schubert 2\n",
"gambols 1\n",
"winking 24\n",
"peered 5\n",
"ilynichna 2\n",
"lawsuit 2\n",
"andso 2\n",
"orlov 5\n",
"excellency 123\n",
"troddendown 3\n",
"query 1\n",
"pattern 28\n",
"olga 2\n",
"scowling 10\n",
"hills 97\n",
"mosquitoes 1\n",
"bees 26\n",
"politics 15\n",
"disconcerting 4\n",
"clumsy 13\n",
"calming 5\n",
"applying 19\n",
"spendthrift 1\n",
"dmitrievna 90\n",
"manifesto 11\n",
"egged 1\n",
"civilian 14\n",
"shinshin 26\n",
"irreproachably 2\n",
"teased 2\n",
"honorable 13\n",
"mixing 8\n",
"rentes 1\n",
"vacancies 1\n",
"prettily 7\n",
"forsome 1\n",
"doeuvres 2\n",
"naively 8\n",
"prank 3\n",
"oui 1\n",
"gay 126\n",
"pearshaped 1\n",
"earrings 12\n",
"reticule 19\n",
"saintsday 1\n",
"deathbed 4\n",
"knives 9\n",
"decanters 11\n",
"pineapples 4\n",
"ate 40\n",
"soups 2\n",
"patties 1\n",
"napkin 15\n",
"dessert 9\n",
"greediness 2\n",
"conscientious 17\n",
"forwarded 5\n",
"fiew 1\n",
"vreatening 1\n",
"empire 18\n",
"alliances 3\n",
"attainment 8\n",
"proverbe 1\n",
"puckering 11\n",
"suvorovs 6\n",
"vight 1\n",
"vill 3\n",
"discuss 22\n",
"zeres 1\n",
"flaming 9\n",
"russians 145\n",
"approvingly 14\n",
"contralto 1\n",
"doubled 11\n",
"incredible 10\n",
"pineapple 1\n",
"fanwise 1\n",
"instigation 1\n",
"crumpling 3\n",
"oo 4\n",
"wail 12\n",
"paws 6\n",
"crisp 11\n",
"bliss 19\n",
"unspoilt 2\n",
"verse 12\n",
"fan 64\n",
"fanning 7\n",
"pocketbooks 2\n",
"semen 1\n",
"cooper 5\n",
"broadened 1\n",
"onlookers 5\n",
"provocatively 2\n",
"otherwho 1\n",
"adroit 5\n",
"maneuvers 20\n",
"capered 1\n",
"livelier 8\n",
"oerpassed 1\n",
"catherine 13\n",
"dere 1\n",
"decorous 1\n",
"varnish 13\n",
"rigid 28\n",
"impudently 2\n",
"mamontov 1\n",
"effectively 2\n",
"sensuit 1\n",
"heiresses 6\n",
"transitions 2\n",
"blackest 1\n",
"housemaid 22\n",
"wheedled 1\n",
"themthat 1\n",
"worming 1\n",
"pails 1\n",
"boots 112\n",
"stocking 21\n",
"spilled 3\n",
"bust 5\n",
"monitress 2\n",
"quiltthe 1\n",
"characteristically 4\n",
"implying 9\n",
"andwith 1\n",
"congratulating 2\n",
"palms 11\n",
"significance 66\n",
"flitted 10\n",
"tenaciously 2\n",
"biting 20\n",
"coloring 2\n",
"bourienne 112\n",
"superintending 2\n",
"facilitating 1\n",
"retirement 31\n",
"province 21\n",
"antechamber 7\n",
"wig 24\n",
"bushy 10\n",
"aroundall 1\n",
"shod 3\n",
"tenacious 5\n",
"pedal 1\n",
"pouch 4\n",
"nail 11\n",
"heloise 2\n",
"patted 39\n",
"anyones 10\n",
"separation 56\n",
"separating 13\n",
"rebels 1\n",
"flatters 1\n",
"lightwere 1\n",
"frontier 17\n",
"inherited 7\n",
"finest 11\n",
"chronicles 1\n",
"marriages 9\n",
"gospels 4\n",
"conform 6\n",
"speedy 6\n",
"fieldwork 1\n",
"countryrumors 1\n",
"marches 10\n",
"merit 48\n",
"humor 11\n",
"snoring 10\n",
"passagestwenty 1\n",
"practicing 2\n",
"haired 4\n",
"frenchwoman 13\n",
"crybaby 1\n",
"incessantly 41\n",
"kitty 1\n",
"strategy 2\n",
"plaited 7\n",
"pomerania 2\n",
"vaten 1\n",
"quand 5\n",
"meditatively 3\n",
"genealogical 3\n",
"formality 6\n",
"courtiers 19\n",
"tsar 45\n",
"chattered 9\n",
"hofskriegswurstschnappsrath 1\n",
"spoon 21\n",
"minutely 9\n",
"bonapartist 3\n",
"harnessed 14\n",
"siege 6\n",
"ochakov 1\n",
"wifeperhaps 1\n",
"playfellow 5\n",
"satire 3\n",
"goodby 31\n",
"orgo 1\n",
"oldage 1\n",
"apron 56\n",
"accoucheur 1\n",
"disfavor 3\n",
"youas 1\n",
"wigless 1\n",
"inspect 10\n",
"regimental 50\n",
"twentymile 1\n",
"mending 10\n",
"beforepresented 1\n",
"button 18\n",
"austere 9\n",
"gibe 1\n",
"saddle 43\n",
"preening 1\n",
"plumage 8\n",
"caleche 29\n",
"ex 2\n",
"devoured 2\n",
"subordinate 9\n",
"commanderinchiefs 17\n",
"mimicked 6\n",
"recognizing 33\n",
"pleaseive 1\n",
"fedeshon 1\n",
"fifth 28\n",
"polesall 1\n",
"flank 96\n",
"pranced 3\n",
"fulfilled 19\n",
"prearranged 4\n",
"confirming 7\n",
"macks 3\n",
"jests 5\n",
"kozlovski 16\n",
"nostitz 2\n",
"acknowledged 29\n",
"notebook 21\n",
"circulated 7\n",
"disgraced 13\n",
"strauch 1\n",
"mille 1\n",
"individu 1\n",
"accenthaving 1\n",
"loathe 2\n",
"morgen 2\n",
"fleissig 1\n",
"hoch 4\n",
"platoon 4\n",
"crumpled 7\n",
"stubby 1\n",
"rat 2\n",
"dwink 3\n",
"piping 4\n",
"antipathy 13\n",
"rubbles 1\n",
"reporting 17\n",
"ugh 8\n",
"indolent 1\n",
"shoe 12\n",
"fwiend 4\n",
"childwen 1\n",
"scacwow 1\n",
"quahtehmasteh 1\n",
"miwacle 1\n",
"congested 1\n",
"cords 6\n",
"flay 4\n",
"cadets 5\n",
"munching 14\n",
"quiver 6\n",
"retraced 5\n",
"squadrons 6\n",
"disapproving 3\n",
"twue 1\n",
"guarding 5\n",
"redroofed 1\n",
"convent 20\n",
"rearguard 10\n",
"milkwhite 2\n",
"inflammable 4\n",
"campfires 31\n",
"deafening 14\n",
"railings 10\n",
"muskets 21\n",
"planks 15\n",
"fleck 1\n",
"chip 8\n",
"daddy 5\n",
"unseemly 7\n",
"restive 4\n",
"spurring 3\n",
"snorted 17\n",
"dwagging 1\n",
"fwo 1\n",
"dandy 2\n",
"perched 10\n",
"zikin 1\n",
"intangible 6\n",
"aligning 1\n",
"curtseying 6\n",
"snubnosed 4\n",
"range 37\n",
"bagration 134\n",
"thud 9\n",
"recognizable 2\n",
"reloaded 1\n",
"hew 1\n",
"powdah 1\n",
"lambach 1\n",
"amstetten 1\n",
"melk 1\n",
"hospitals 25\n",
"mortier 5\n",
"schmidt 7\n",
"dozed 8\n",
"victories 11\n",
"evenly 8\n",
"petitioners 5\n",
"reappeared 25\n",
"surroundings 15\n",
"twinkled 5\n",
"skittles 8\n",
"haute 1\n",
"javoue 1\n",
"victorieuses 1\n",
"retorted 98\n",
"subtleties 2\n",
"archdukes 1\n",
"schonbrunn 4\n",
"murat 38\n",
"tremblement 1\n",
"skirmishing 6\n",
"gunpowder 13\n",
"accentuating 2\n",
"innovation 2\n",
"provinces 21\n",
"pillagedthey 1\n",
"beaux 1\n",
"cannonading 4\n",
"don 9\n",
"atrocities 1\n",
"compagne 1\n",
"whomhe 1\n",
"admithe 1\n",
"secretest 1\n",
"brunns 1\n",
"attractions 19\n",
"archduchess 2\n",
"marechaux 1\n",
"lannes 5\n",
"incendiary 6\n",
"sommes 1\n",
"conclusiveness 1\n",
"olmutz 22\n",
"temple 35\n",
"insoluble 9\n",
"langleterre 1\n",
"transportee 1\n",
"whips 9\n",
"flayed 1\n",
"neighboring 10\n",
"sacks 6\n",
"checking 24\n",
"seventh 15\n",
"snub 7\n",
"worldto 1\n",
"deliverer 1\n",
"tormenting 19\n",
"znaim 12\n",
"mefit 1\n",
"kozlovskis 1\n",
"wornhe 1\n",
"disrespectfully 3\n",
"endeavor 4\n",
"buxhowden 12\n",
"forestalled 3\n",
"sixtysix 1\n",
"roadless 1\n",
"authorized 2\n",
"ratify 3\n",
"benches 8\n",
"tushin 58\n",
"entrenchment 5\n",
"noses 10\n",
"shelters 3\n",
"steaming 8\n",
"pockmarked 8\n",
"broadshouldered 2\n",
"licking 4\n",
"morningdespite 1\n",
"musketeer 2\n",
"fera 3\n",
"danser 1\n",
"suvara 1\n",
"nom 2\n",
"lukich 1\n",
"bonfires 4\n",
"wattle 15\n",
"dominated 4\n",
"sketched 6\n",
"concentrate 7\n",
"podolsk 3\n",
"snacks 2\n",
"ffflop 1\n",
"zakharchenko 1\n",
"reinforce 1\n",
"downhill 12\n",
"humming 19\n",
"smilehis 1\n",
"employing 2\n",
"entreating 11\n",
"persuasiveness 2\n",
"defection 1\n",
"longdrawn 6\n",
"occupations 18\n",
"sount 1\n",
"imperative 5\n",
"milldam 1\n",
"vish 1\n",
"foward 2\n",
"huraaaah 1\n",
"bayonet 5\n",
"leaping 2\n",
"seventypound 1\n",
"disobeying 2\n",
"timokhins 1\n",
"surrendering 2\n",
"ekonomov 3\n",
"munition 2\n",
"effrontery 1\n",
"munitionwagon 1\n",
"officerall 1\n",
"feverish 24\n",
"streak 5\n",
"matvevna 4\n",
"limbered 1\n",
"reprimanding 1\n",
"reproaching 4\n",
"whyrode 1\n",
"sprain 1\n",
"gruntersdorf 1\n",
"rumble 8\n",
"excruciating 4\n",
"swelling 28\n",
"squatted 4\n",
"quarreling 2\n",
"hoarsely 15\n",
"battalionand 1\n",
"redounded 2\n",
"embarrassed 35\n",
"examiner 1\n",
"thirds 3\n",
"soldierswounded 1\n",
"acheit 1\n",
"wellif 1\n",
"todayfrightful 1\n",
"snowflakes 1\n",
"fluffy 4\n",
"shaping 3\n",
"dolls 7\n",
"bonea 1\n",
"kinder 8\n",
"pitys 2\n",
"quitrent 5\n",
"peasants 124\n",
"dinners 32\n",
"halting 17\n",
"vinesse 1\n",
"miniaturist 2\n",
"tuft 5\n",
"refitting 1\n",
"hints 22\n",
"relishing 4\n",
"heror 1\n",
"paralyzed 2\n",
"liveries 5\n",
"sergey 15\n",
"etc 5\n",
"bashfulness 3\n",
"traveled 9\n",
"unbuttoning 2\n",
"moistened 6\n",
"hothouse 7\n",
"venerable 21\n",
"overpowering 7\n",
"pere 4\n",
"soup 23\n",
"genial 13\n",
"merci 3\n",
"heiress 11\n",
"bouriennes 14\n",
"unattractive 2\n",
"maroon 9\n",
"paleblue 3\n",
"shiningly 1\n",
"smelling 7\n",
"supercilious 4\n",
"womeneven 1\n",
"pearl 6\n",
"gentille 1\n",
"irritably 6\n",
"keensighted 2\n",
"seducer 2\n",
"ulterior 2\n",
"prompt 10\n",
"coarsest 3\n",
"etiquette 2\n",
"lifeand 3\n",
"dowry 9\n",
"sonsname 1\n",
"selfsacrifice 18\n",
"midwinter 2\n",
"teatime 9\n",
"adroitly 3\n",
"gleeful 4\n",
"blubberers 1\n",
"schelling 2\n",
"reread 4\n",
"imperceptibly 9\n",
"twelfth 4\n",
"emperorsthe 1\n",
"ismaylov 2\n",
"jews 5\n",
"restaurant 3\n",
"trip 13\n",
"chessmen 5\n",
"cushay 1\n",
"speck 13\n",
"gabriel 5\n",
"irascibility 5\n",
"irregularity 3\n",
"storythey 1\n",
"slashed 1\n",
"recounting 3\n",
"drubetskoy 18\n",
"campaigning 3\n",
"standards 16\n",
"peaks 2\n",
"blissful 20\n",
"vewy 8\n",
"guardsmans 3\n",
"subordination 4\n",
"code 12\n",
"tightlaced 1\n",
"unwritten 4\n",
"commanderin 7\n",
"retinues 1\n",
"confuted 1\n",
"maneuver 5\n",
"adroitness 3\n",
"shiftiness 1\n",
"highroad 23\n",
"sotnya 1\n",
"wischau 6\n",
"dragoon 8\n",
"alley 8\n",
"sire 31\n",
"outposts 13\n",
"dispatching 5\n",
"adjutantswas 1\n",
"bivouacs 3\n",
"cogwheels 3\n",
"chimes 3\n",
"axles 1\n",
"whirr 2\n",
"complicated 22\n",
"humiliations 3\n",
"enthusiasmwas 1\n",
"socalled 18\n",
"emperorsthat 1\n",
"dyou 4\n",
"temporize 1\n",
"map 22\n",
"demonstrate 5\n",
"herr 3\n",
"drowsy 15\n",
"environs 1\n",
"bulged 3\n",
"elsehe 1\n",
"whereby 5\n",
"thuerassa 2\n",
"outwards 2\n",
"stubbornly 3\n",
"twirled 9\n",
"rotary 1\n",
"latters 4\n",
"langerons 3\n",
"drone 5\n",
"foi 3\n",
"righthe 1\n",
"billeted 3\n",
"expresses 13\n",
"familyi 2\n",
"memoriesand 1\n",
"shaft 8\n",
"spotune 1\n",
"ahahah 1\n",
"rrrr 1\n",
"pawed 1\n",
"pricking 4\n",
"heralds 1\n",
"enclosures 2\n",
"dislocation 3\n",
"rhyme 3\n",
"intervalstrata 1\n",
"selfcomplacent 2\n",
"venturing 9\n",
"malevolent 9\n",
"obeys 3\n",
"haze 7\n",
"volkonsky 1\n",
"strogonov 1\n",
"wellgroomed 1\n",
"stuffy 3\n",
"cheerless 4\n",
"advent 3\n",
"ferons 1\n",
"cheery 7\n",
"cob 1\n",
"oneself 16\n",
"unceasingly 4\n",
"himat 1\n",
"distraught 9\n",
"andrewnot 1\n",
"volleys 1\n",
"caissons 2\n",
"uhlans 28\n",
"collide 1\n",
"warlike 8\n",
"foreboding 13\n",
"zum 1\n",
"caleches 3\n",
"ballstruck 1\n",
"woundedhad 1\n",
"wellkept 2\n",
"plowland 1\n",
"unfortunates 4\n",
"hind 19\n",
"fails 7\n",
"serf 18\n",
"millpool 1\n",
"unconsciousness 7\n",
"lacerating 2\n",
"paltry 2\n",
"larrey 3\n",
"amulet 1\n",
"jolt 3\n",
"unendurable 3\n",
"visions 14\n",
"tallow 5\n",
"kolya 1\n",
"unnoticed 19\n",
"blissfully 2\n",
"gwiskamy 1\n",
"husky 8\n",
"glued 5\n",
"cushions 17\n",
"duportthe 1\n",
"skirts 29\n",
"whodreading 1\n",
"trotter 3\n",
"stylish 1\n",
"laced 5\n",
"incarnate 2\n",
"clubs 6\n",
"asparagus 4\n",
"entree 1\n",
"ipatka 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ilyushka 2\n",
"businesslike 26\n",
"banquet 14\n",
"foregathered 1\n",
"conspiracy 14\n",
"rostopchin 103\n",
"othersilya 1\n",
"parodying 2\n",
"weathercock 5\n",
"kaftans 1\n",
"fashionably 2\n",
"naryshkin 3\n",
"uvarov 4\n",
"improper 10\n",
"bekleshev 2\n",
"committeemen 2\n",
"committeeman 1\n",
"majordomo 12\n",
"corks 5\n",
"choir 8\n",
"barriers 5\n",
"guarantees 1\n",
"unsolved 2\n",
"postboys 1\n",
"preoccupation 4\n",
"submissively 13\n",
"sokolniki 8\n",
"kostroma 3\n",
"guiltlessness 1\n",
"pwoceed 1\n",
"hunchback 1\n",
"itbecause 1\n",
"borrow 7\n",
"vulgarity 2\n",
"allezvous 1\n",
"canonized 1\n",
"molieres 1\n",
"diable 3\n",
"galley 3\n",
"crouch 1\n",
"amant 1\n",
"cleverer 5\n",
"amants 1\n",
"swooping 2\n",
"searches 2\n",
"joya 1\n",
"scraggy 2\n",
"russias 7\n",
"larks 4\n",
"latch 6\n",
"flapped 2\n",
"banister 2\n",
"soles 10\n",
"fedya 6\n",
"throttle 2\n",
"elevate 1\n",
"fitful 8\n",
"prediction 5\n",
"zest 2\n",
"dmitrich 11\n",
"misleading 2\n",
"wecollect 1\n",
"coax 4\n",
"clanking 5\n",
"punt 1\n",
"sunday 113\n",
"fateful 1\n",
"iou 4\n",
"ruffled 11\n",
"enchantress 8\n",
"lyre 1\n",
"untrained 2\n",
"mio 4\n",
"crudele 2\n",
"si 4\n",
"vibrated 3\n",
"donot 1\n",
"questionone 1\n",
"thread 26\n",
"traveler 11\n",
"grayish 1\n",
"unfinished 14\n",
"sugar 23\n",
"skulla 1\n",
"blasphemous 3\n",
"exulting 8\n",
"austerity 2\n",
"masons 12\n",
"believes 15\n",
"voicewhich 2\n",
"brokeor 1\n",
"hopelessnessat 1\n",
"regeneration 7\n",
"imbibe 1\n",
"purification 4\n",
"wholethe 1\n",
"wincing 5\n",
"postmasters 1\n",
"martinists 1\n",
"irreproachable 6\n",
"convictionsdo 1\n",
"welllit 1\n",
"numb 2\n",
"gospelit 1\n",
"stimulate 3\n",
"enlightenment 10\n",
"socially 5\n",
"atheist 2\n",
"posterity 12\n",
"purifying 2\n",
"sways 2\n",
"solomons 3\n",
"morality 5\n",
"cognizable 1\n",
"candor 1\n",
"youturn 1\n",
"unevenly 4\n",
"mallets 2\n",
"universe 18\n",
"knockings 2\n",
"aprons 4\n",
"mundi 1\n",
"angles 6\n",
"squared 1\n",
"password 4\n",
"goeth 1\n",
"falleth 1\n",
"shalt 2\n",
"congratulations 19\n",
"collector 3\n",
"values 3\n",
"blinked 2\n",
"attractively 1\n",
"odessa 2\n",
"essence 29\n",
"thermometer 2\n",
"voulu 2\n",
"daffaires 2\n",
"copenhagena 1\n",
"advantageously 4\n",
"despised 19\n",
"appraising 1\n",
"bases 3\n",
"danish 1\n",
"urope 1\n",
"glogau 2\n",
"tuesday 6\n",
"diplomatista 1\n",
"enrollment 4\n",
"recommenced 8\n",
"petrusha 2\n",
"bennigsen 68\n",
"germani 1\n",
"korchevoa 1\n",
"preussischeylau 3\n",
"selfcensure 1\n",
"twists 4\n",
"octogenarians 1\n",
"pultusk 6\n",
"ostermann 5\n",
"unfordable 1\n",
"buxhowdens 1\n",
"biscuits 10\n",
"sweat 3\n",
"convalescent 3\n",
"foremen 1\n",
"upkeep 1\n",
"alimony 1\n",
"landowner 7\n",
"confessed 17\n",
"lodgewere 1\n",
"reforming 3\n",
"raceand 1\n",
"concessions 4\n",
"buildingsschools 1\n",
"gladdening 2\n",
"benefits 6\n",
"instructing 2\n",
"manorial 3\n",
"threshing 4\n",
"anton 3\n",
"sparkle 9\n",
"enthusiasms 1\n",
"oldtime 1\n",
"constrained 15\n",
"badwrong 1\n",
"littlethough 1\n",
"pastime 5\n",
"crooking 4\n",
"thingwhat 1\n",
"simpler 4\n",
"grudged 2\n",
"laborerthats 1\n",
"qualifications 6\n",
"smolensk 101\n",
"heal 7\n",
"callous 3\n",
"shave 6\n",
"deitythe 1\n",
"herders 1\n",
"puddles 3\n",
"epoch 9\n",
"longhaired 2\n",
"disobeys 1\n",
"amie 2\n",
"devriez 1\n",
"garrulously 1\n",
"barefoot 8\n",
"trickles 1\n",
"appeased 7\n",
"ponder 7\n",
"visithe 1\n",
"dementyev 1\n",
"reassigned 1\n",
"orderedand 1\n",
"bartenstein 2\n",
"platovs 7\n",
"oudinots 1\n",
"fledif 1\n",
"pitiless 3\n",
"steamed 10\n",
"baked 7\n",
"roomas 1\n",
"tidied 4\n",
"reconnoitering 1\n",
"fwom 6\n",
"widden 1\n",
"fwashing 1\n",
"friedland 4\n",
"putrefaction 1\n",
"cigar 17\n",
"makar 19\n",
"pesthouse 1\n",
"wards 5\n",
"stiffly 11\n",
"graybearded 2\n",
"gowns 6\n",
"outsidegradually 1\n",
"impotence 5\n",
"pensive 16\n",
"pavilion 3\n",
"historic 36\n",
"henceforth 17\n",
"conventionality 2\n",
"obstinately 6\n",
"zhilinskis 1\n",
"regimentwhite 1\n",
"chamoisleather 1\n",
"borisran 1\n",
"respond 10\n",
"uncalledfor 2\n",
"meanwhilereal 1\n",
"passionswent 1\n",
"reconstruction 2\n",
"commuted 1\n",
"countryin 1\n",
"lilaccolored 1\n",
"deadlooking 1\n",
"youngyield 1\n",
"anewbut 1\n",
"bathe 2\n",
"slim 11\n",
"separateprobably 1\n",
"lush 1\n",
"stems 3\n",
"trunk 12\n",
"passagethe 1\n",
"stonestill 1\n",
"shiny 5\n",
"meeveryone 2\n",
"curled 21\n",
"inquisitively 5\n",
"irrational 6\n",
"reforms 3\n",
"kochubey 11\n",
"publicwere 1\n",
"anterooms 1\n",
"lipscame 1\n",
"greenishhazel 1\n",
"deviating 2\n",
"manages 5\n",
"reputationexpected 1\n",
"magnitski 10\n",
"noblesse 1\n",
"etre 1\n",
"profundity 6\n",
"rooted 13\n",
"beone 1\n",
"satirist 2\n",
"mentality 1\n",
"juridical 2\n",
"immoral 3\n",
"category 6\n",
"deviated 3\n",
"stammering 6\n",
"institutions 13\n",
"revolutions 3\n",
"repel 7\n",
"convictionaiming 1\n",
"preaching 2\n",
"monarchical 1\n",
"reproved 5\n",
"reunite 1\n",
"profligacy 3\n",
"manhe 1\n",
"diverting 4\n",
"meand 12\n",
"ligne 1\n",
"eightpage 1\n",
"diploma 1\n",
"superb 10\n",
"artificially 3\n",
"onlooker 1\n",
"interpretation 3\n",
"allembracing 1\n",
"oily 4\n",
"attracts 2\n",
"monetary 2\n",
"stalls 15\n",
"das 1\n",
"livonian 1\n",
"baltic 2\n",
"fiancee 4\n",
"longforgotten 3\n",
"prayerful 1\n",
"tonightbut 2\n",
"rides 7\n",
"quay 2\n",
"doffed 5\n",
"provincial 9\n",
"scrutinized 6\n",
"pins 9\n",
"doorspeaking 1\n",
"rumpled 4\n",
"indispensable 15\n",
"dutch 15\n",
"temps 3\n",
"everquickening 1\n",
"exquisitely 5\n",
"superabundance 1\n",
"anotherand 2\n",
"petersburglike 1\n",
"fiscal 1\n",
"ejaculating 2\n",
"highpitched 6\n",
"recitation 1\n",
"dron 52\n",
"rubber 5\n",
"methodical 6\n",
"sein 1\n",
"discerning 5\n",
"appraised 3\n",
"stifles 2\n",
"discomfort 5\n",
"dumped 1\n",
"philosophize 1\n",
"marveled 1\n",
"obstinacyas 1\n",
"fiancees 2\n",
"unbearable 2\n",
"butso 1\n",
"divined 10\n",
"lifenot 2\n",
"forgetthat 1\n",
"beneficent 6\n",
"befalls 1\n",
"incalculable 3\n",
"abouti 2\n",
"reduce 4\n",
"longi 1\n",
"stepmothers 1\n",
"toiling 18\n",
"visionary 7\n",
"wealthier 1\n",
"theodosia 4\n",
"akinfi 1\n",
"betted 3\n",
"trepak 2\n",
"expectationthe 1\n",
"uglier 5\n",
"carryings 1\n",
"promissory 3\n",
"sportsman 5\n",
"protodeacons 1\n",
"leash 11\n",
"arabchik 1\n",
"becausethats 1\n",
"standa 1\n",
"wellfed 5\n",
"outing 1\n",
"zharov 1\n",
"sidorych 2\n",
"daniels 4\n",
"sportsmen 3\n",
"heaving 12\n",
"simons 1\n",
"hindquarters 3\n",
"cocking 2\n",
"wolfs 7\n",
"lyubim 1\n",
"gag 3\n",
"booty 9\n",
"fallow 2\n",
"ryefield 5\n",
"tails 7\n",
"diana 1\n",
"spotted 6\n",
"gentlefolk 2\n",
"scenting 2\n",
"scut 2\n",
"hearken 1\n",
"pad 6\n",
"birchwood 2\n",
"anisya 15\n",
"buttermilk 2\n",
"mead 2\n",
"nutandhoney 1\n",
"fedorovnas 3\n",
"savor 1\n",
"elected 2\n",
"patter 1\n",
"thrumming 1\n",
"theatrical 4\n",
"ecstasies 4\n",
"vogel 2\n",
"wasthat 1\n",
"devotedly 6\n",
"celebration 4\n",
"grasshoppers 1\n",
"raisins 5\n",
"almonds 1\n",
"bookcase 8\n",
"comenothing 1\n",
"innocentthat 1\n",
"darkwe 1\n",
"corded 4\n",
"metempsychosis 1\n",
"skirtthis 1\n",
"squeaking 6\n",
"belted 2\n",
"clanging 2\n",
"dappled 1\n",
"fursso 1\n",
"distantin 1\n",
"jerks 5\n",
"somethingprobably 1\n",
"clowns 1\n",
"ladynicholasstarted 1\n",
"whohaving 2\n",
"sasha 2\n",
"frightening 8\n",
"reentering 5\n",
"alltransforming 1\n",
"receding 6\n",
"blink 4\n",
"windowpanes 2\n",
"admonish 3\n",
"trousseau 4\n",
"iberian 6\n",
"vrazhok 1\n",
"booksno 1\n",
"reconciled 16\n",
"sexe 1\n",
"spaniards 4\n",
"deserter 5\n",
"chatted 4\n",
"entrenched 6\n",
"toys 7\n",
"patriotic 20\n",
"scathing 2\n",
"corresponded 5\n",
"pointer 3\n",
"herknew 1\n",
"bouriennehad 1\n",
"demonstrations 12\n",
"querulousness 2\n",
"virulence 2\n",
"circlethough 1\n",
"devotions 2\n",
"depose 1\n",
"protested 13\n",
"duchy 3\n",
"impudent 9\n",
"sundays 19\n",
"honestly 13\n",
"disapprove 3\n",
"tribute 12\n",
"maxims 1\n",
"douleurs 1\n",
"tendre 2\n",
"consoler 2\n",
"douceur 1\n",
"disappointments 10\n",
"foo 1\n",
"bonjour 3\n",
"angrier 3\n",
"marywho 1\n",
"natashamademoiselle 1\n",
"theaters 4\n",
"alenina 1\n",
"prompters 1\n",
"queerly 2\n",
"belated 4\n",
"selfassured 7\n",
"nudging 5\n",
"scantily 4\n",
"boxher 1\n",
"timenor 1\n",
"scales 14\n",
"naryshkins 1\n",
"quieted 5\n",
"unwillingly 5\n",
"danilovs 1\n",
"entailed 2\n",
"bait 2\n",
"priding 2\n",
"festivity 3\n",
"georgei 1\n",
"pierrethat 1\n",
"grande 2\n",
"laughingly 16\n",
"sorting 6\n",
"pleading 7\n",
"deceiver 2\n",
"marriagekhvostikov 1\n",
"unbounded 17\n",
"rugs 3\n",
"hatched 1\n",
"condescendingly 8\n",
"cocottes 1\n",
"tproo 2\n",
"wrenching 2\n",
"bewail 2\n",
"trottinghorses 1\n",
"powdery 1\n",
"sage 15\n",
"everentered 1\n",
"paperweight 2\n",
"kidnap 1\n",
"cringing 2\n",
"reestablish 6\n",
"meshcherski 2\n",
"abasement 1\n",
"illlit 1\n",
"portend 2\n",
"inconceivable 5\n",
"concentrating 2\n",
"transporting 1\n",
"eastwards 2\n",
"burglaries 1\n",
"incendiarisms 1\n",
"metternich 3\n",
"oldenburgand 1\n",
"helena 4\n",
"bons 2\n",
"impotenceapart 1\n",
"predestined 10\n",
"consciously 3\n",
"verser 2\n",
"peuples 2\n",
"historywhatever 1\n",
"prussiaundertaken 1\n",
"inclinations 14\n",
"allurement 1\n",
"undermined 7\n",
"historian 16\n",
"louisewho 1\n",
"escort 22\n",
"ville 4\n",
"asia 1\n",
"gerard 6\n",
"kovno 1\n",
"telescope 8\n",
"mustached 1\n",
"berthier 13\n",
"impel 1\n",
"aidesde 1\n",
"eclipsed 1\n",
"balashev 95\n",
"envied 11\n",
"potocka 1\n",
"unfulfilled 2\n",
"transmit 2\n",
"injudicious 3\n",
"herd 16\n",
"trappings 1\n",
"bracelets 4\n",
"necklaces 3\n",
"re 4\n",
"overfat 1\n",
"davouts 4\n",
"alexanderthough 1\n",
"chivalrous 6\n",
"auditing 1\n",
"flaunt 1\n",
"duroc 2\n",
"corpulent 3\n",
"expended 12\n",
"wallachia 2\n",
"gulf 14\n",
"compassionately 10\n",
"madfor 1\n",
"grinned 6\n",
"dvina 1\n",
"himan 3\n",
"envoyespecially 1\n",
"welcoming 3\n",
"defeats 5\n",
"sevres 2\n",
"hearer 3\n",
"himwhich 4\n",
"towered 2\n",
"ravenous 1\n",
"drissa 22\n",
"stateliness 9\n",
"immutability 1\n",
"invasion 16\n",
"foreseenare 1\n",
"utilizing 1\n",
"tormasov 3\n",
"foreigners 20\n",
"assistants 5\n",
"influenced 27\n",
"lastly 14\n",
"ownthere 1\n",
"discouraged 3\n",
"frankness 25\n",
"diffidence 4\n",
"councils 4\n",
"peoplethis 1\n",
"ninth 3\n",
"capitalthat 1\n",
"chefdoeuvre 1\n",
"quartersa 1\n",
"rolledup 3\n",
"semicouncil 1\n",
"hips 2\n",
"glancednot 1\n",
"truthscience 1\n",
"deviations 5\n",
"eloquently 5\n",
"campi 1\n",
"expounded 4\n",
"wellfilled 1\n",
"drumming 5\n",
"mathematician 1\n",
"nursed 15\n",
"hurraha 1\n",
"attributeslove 1\n",
"understandable 2\n",
"billets 1\n",
"camped 1\n",
"appropriated 2\n",
"torrents 2\n",
"inattentively 1\n",
"grandiloquently 2\n",
"hendrikhovna 16\n",
"andnow 1\n",
"blonde 7\n",
"preside 5\n",
"fours 3\n",
"duncolored 1\n",
"vertically 2\n",
"sunbeams 2\n",
"detonators 1\n",
"platoons 1\n",
"spears 1\n",
"sportsmans 1\n",
"sevastyanych 1\n",
"intersect 1\n",
"elbowas 1\n",
"homelike 1\n",
"ostermanntolstoy 1\n",
"combinations 15\n",
"allopaths 1\n",
"kopeks 1\n",
"deriving 4\n",
"griefstricken 1\n",
"overlaid 1\n",
"theatersbut 1\n",
"coquet 1\n",
"carefree 2\n",
"agrafena 6\n",
"communing 6\n",
"againanother 1\n",
"harmand 1\n",
"stool 26\n",
"grandiloquent 1\n",
"uprightness 2\n",
"undertakings 8\n",
"gavest 2\n",
"goliath 5\n",
"loins 1\n",
"mercies 2\n",
"transgressions 2\n",
"smite 4\n",
"vanishednot 1\n",
"hath 5\n",
"threescore 2\n",
"q 5\n",
"besouhoff 1\n",
"incorrectly 1\n",
"leaflet 1\n",
"muscovites 3\n",
"donned 7\n",
"spyer 1\n",
"spire 8\n",
"dononsense 1\n",
"naturedly 1\n",
"brightens 1\n",
"emperorhe 1\n",
"presentable 5\n",
"smarten 3\n",
"cassockprobably 1\n",
"breathlessto 1\n",
"sheetvarious 1\n",
"embankment 2\n",
"noblemens 1\n",
"toothless 3\n",
"zinaida 1\n",
"statesgeneralevoked 1\n",
"graven 3\n",
"meekest 6\n",
"consonants 1\n",
"waising 1\n",
"enwich 1\n",
"bettah 1\n",
"wetun 1\n",
"gwudge 1\n",
"livesevewy 1\n",
"fo 1\n",
"senators 2\n",
"militiawe 1\n",
"orators 4\n",
"glinka 1\n",
"editor 1\n",
"thunderclaps 2\n",
"buffoons 1\n",
"expiation 6\n",
"therehe 2\n",
"mamonov 1\n",
"inexperienced 18\n",
"scythian 2\n",
"awareness 1\n",
"luring 4\n",
"fortuitously 1\n",
"bagrationbeing 1\n",
"inferior 24\n",
"hating 7\n",
"frenchhaving 1\n",
"neverovskis 1\n",
"devoting 6\n",
"plotted 2\n",
"frenchified 1\n",
"forecasts 1\n",
"knowunder 1\n",
"melts 1\n",
"nowmichael 1\n",
"notepaperdo 1\n",
"potemkin 1\n",
"serfswere 1\n",
"forties 1\n",
"booming 6\n",
"utensils 1\n",
"packages 5\n",
"selivanov 1\n",
"boom 10\n",
"blending 10\n",
"bombarded 1\n",
"marvels 4\n",
"tuckedup 1\n",
"twilightand 1\n",
"cakelike 1\n",
"unreaped 1\n",
"ankledeep 2\n",
"peopled 4\n",
"limes 3\n",
"carted 6\n",
"commandeered 1\n",
"bareheaded 15\n",
"towel 4\n",
"dilatory 1\n",
"latteras 1\n",
"lifewe 1\n",
"kazan 5\n",
"establishments 7\n",
"enthusiasmnews 1\n",
"ape 4\n",
"decrepit 4\n",
"dissensions 2\n",
"impolite 1\n",
"joconde 1\n",
"thiers 12\n",
"reciprocity 1\n",
"borodino 105\n",
"capitale 1\n",
"eglises 2\n",
"lelorgne 3\n",
"dinnerless 1\n",
"quest 9\n",
"barefaced 2\n",
"baser 1\n",
"pettiness 2\n",
"intimidate 1\n",
"boastful 4\n",
"interlocutor 1\n",
"immortally 2\n",
"paralysis 6\n",
"looted 6\n",
"mmmarateate 1\n",
"bouriennewho 1\n",
"itcoming 1\n",
"deadits 1\n",
"womenand 1\n",
"boorishness 3\n",
"literate 1\n",
"emigrate 3\n",
"eruption 2\n",
"karp 12\n",
"visloukhovo 2\n",
"dronushka 10\n",
"migration 6\n",
"wizards 1\n",
"subjectthe 1\n",
"weepers 1\n",
"monthly 2\n",
"pasture 2\n",
"pasther 1\n",
"alivehe 1\n",
"deadand 2\n",
"cooompany 1\n",
"peasantsasks 1\n",
"dissuasions 2\n",
"rioting 6\n",
"unconciously 1\n",
"exelder 1\n",
"decidedtheres 1\n",
"othera 2\n",
"ladssolid 1\n",
"evenhe 1\n",
"plighted 2\n",
"thwough 1\n",
"sewene 1\n",
"konovnitsyn 15\n",
"tsarevo 1\n",
"mattersomething 1\n",
"suffused 9\n",
"vient 1\n",
"doute 1\n",
"eventsthe 1\n",
"proverbs 3\n",
"fervor 2\n",
"frivolous 3\n",
"pubheard 1\n",
"pushkins 1\n",
"deported 2\n",
"nizhni 6\n",
"rentrez 1\n",
"entrez 3\n",
"nen 1\n",
"witticism 1\n",
"joan 1\n",
"amazon 2\n",
"novels 3\n",
"talkhow 1\n",
"saccuse 1\n",
"wittgenstein 4\n",
"stormcloud 2\n",
"idiotic 7\n",
"cajolery 1\n",
"bolotnoe 1\n",
"sauce 9\n",
"moscowthat 1\n",
"moscowwhich 1\n",
"utitsa 6\n",
"outpost 1\n",
"flankand 1\n",
"voyna 2\n",
"redoubtour 1\n",
"commandersnot 1\n",
"leftmaintained 1\n",
"peasantseven 1\n",
"amphitheater 2\n",
"riverswas 1\n",
"mounds 4\n",
"teemed 1\n",
"summit 12\n",
"soiled 1\n",
"narrowing 1\n",
"barns 1\n",
"fleches 20\n",
"lowgrowing 1\n",
"dove 3\n",
"stink 2\n",
"skilled 3\n",
"affording 5\n",
"undermine 4\n",
"raum 1\n",
"verlegt 1\n",
"preis 1\n",
"nur 1\n",
"eggshell 1\n",
"tomorrowthat 1\n",
"fabvier 5\n",
"compartment 1\n",
"aidedecamps 1\n",
"exterminate 2\n",
"beaussets 4\n",
"salamanca 2\n",
"luckily 1\n",
"curlyheaded 1\n",
"depicting 1\n",
"achievements 2\n",
"communicating 12\n",
"dictation 1\n",
"dessaixs 4\n",
"fouche 2\n",
"vicekings 2\n",
"gibrards 2\n",
"mannapoleonto 1\n",
"armyfrench 1\n",
"conquerors 6\n",
"wellplanned 1\n",
"baseless 3\n",
"courtesan 1\n",
"watchmaker 1\n",
"goldentinted 1\n",
"translucent 2\n",
"riverbanks 1\n",
"puffsuddenly 1\n",
"cloudlets 2\n",
"trakhtatatakh 1\n",
"trenches 4\n",
"earthwork 4\n",
"nonmilitary 1\n",
"college 33\n",
"bargees 1\n",
"humorist 1\n",
"foxes 5\n",
"shockfor 2\n",
"tighter 8\n",
"menand 2\n",
"themselvesin 1\n",
"givenalmost 1\n",
"retaken 2\n",
"itrepeatedly 1\n",
"adjutanta 1\n",
"lathering 1\n",
"negation 2\n",
"courte 2\n",
"marengo 1\n",
"centerdestroy 1\n",
"rag 4\n",
"serried 2\n",
"departurebefore 1\n",
"scherbinin 1\n",
"nonchalantly 1\n",
"judiciously 4\n",
"inspirited 1\n",
"overlay 1\n",
"calculating 5\n",
"everrecurring 1\n",
"welling 2\n",
"flaps 3\n",
"garbs 1\n",
"ooh 11\n",
"thighbone 1\n",
"lifereturned 1\n",
"rim 7\n",
"belauded 1\n",
"benefactions 2\n",
"neapolitans 2\n",
"thirtysecond 1\n",
"kalisch 1\n",
"acid 3\n",
"downfall 6\n",
"arbitrarily 6\n",
"discontinuous 2\n",
"ratio 4\n",
"postulates 1\n",
"equivalent 7\n",
"observationas 1\n",
"biography 1\n",
"hungerstricken 1\n",
"moscowand 2\n",
"licks 3\n",
"malo 2\n",
"yaroslavets 2\n",
"berezina 17\n",
"replenished 4\n",
"generalsas 1\n",
"eventthe 1\n",
"deceptions 2\n",
"commissary 6\n",
"diametrically 1\n",
"lowergrade 1\n",
"roomier 1\n",
"grunts 1\n",
"longcoat 2\n",
"sided 1\n",
"processions 2\n",
"manselfish 1\n",
"afterward 1\n",
"pope 3\n",
"dovelike 1\n",
"helenehaving 1\n",
"catholicism 1\n",
"proprieties 10\n",
"confessors 1\n",
"twofold 4\n",
"egg 12\n",
"remarriage 2\n",
"devoirs 1\n",
"officialsthe 1\n",
"sheaf 1\n",
"vereshchagin 23\n",
"investigate 2\n",
"hermight 1\n",
"elicited 8\n",
"escapinggoing 1\n",
"mats 4\n",
"rightive 1\n",
"flurrying 1\n",
"theseyes 1\n",
"saxony 1\n",
"repacking 1\n",
"packingpress 1\n",
"seminarists 1\n",
"cookshops 2\n",
"dissolution 7\n",
"theywhich 1\n",
"adequately 1\n",
"chiffonier 4\n",
"shamefacedly 1\n",
"tag 1\n",
"oratory 1\n",
"champing 1\n",
"kudrino 1\n",
"presnya 1\n",
"podnovinsk 1\n",
"pierreor 1\n",
"himin 2\n",
"quizzical 2\n",
"kindliness 3\n",
"herfilled 1\n",
"mastermay 1\n",
"baldheaded 1\n",
"manuscripts 5\n",
"enfin 1\n",
"etait 1\n",
"longfelt 1\n",
"clemency 7\n",
"domes 2\n",
"monuments 4\n",
"despotism 1\n",
"inscribe 2\n",
"kremlinyes 1\n",
"quon 1\n",
"emperorwithout 1\n",
"wordle 1\n",
"vying 2\n",
"kammerkollezski 2\n",
"bee 14\n",
"coup 1\n",
"anythingwe 1\n",
"spree 2\n",
"deathkilled 1\n",
"overalls 1\n",
"victoriously 1\n",
"superintendents 1\n",
"upand 2\n",
"leppichs 1\n",
"munitions 2\n",
"posters 1\n",
"consistory 7\n",
"suffragan 1\n",
"excellencyi 1\n",
"halfshaven 2\n",
"uncomprehended 2\n",
"doting 2\n",
"saffron 1\n",
"screens 6\n",
"dedicates 1\n",
"nikolski 1\n",
"kneading 1\n",
"baking 3\n",
"cooking 13\n",
"stabling 2\n",
"chalked 3\n",
"radiating 1\n",
"humanfor 1\n",
"nonhuman 1\n",
"voyons 2\n",
"vieux 1\n",
"contorting 1\n",
"brigand 9\n",
"ramballe 16\n",
"disillusion 1\n",
"parlorthe 1\n",
"perceiving 5\n",
"limonade 2\n",
"smolenskhe 1\n",
"cheekand 1\n",
"paristhe 1\n",
"unterkunft 4\n",
"incongruities 3\n",
"parisienne 1\n",
"coeur 3\n",
"winefollowed 1\n",
"concealedhis 1\n",
"dizzy 1\n",
"mytishchi 12\n",
"fractured 2\n",
"burningset 1\n",
"sushchevski 1\n",
"chilled 8\n",
"disbelieving 2\n",
"snore 2\n",
"cricket 4\n",
"turndown 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mortifying 1\n",
"iwhile 1\n",
"journeyas 1\n",
"povarskoy 6\n",
"ablaze 2\n",
"lanes 12\n",
"torturedas 1\n",
"palecolored 1\n",
"foxfur 1\n",
"ceilings 5\n",
"hiss 2\n",
"sheaves 2\n",
"animating 4\n",
"bitim 1\n",
"aimless 4\n",
"seminary 1\n",
"givewith 1\n",
"angina 5\n",
"cometh 1\n",
"encompass 3\n",
"sling 3\n",
"birthday 21\n",
"extolling 2\n",
"adjutantgeneral 1\n",
"gracieux 1\n",
"michauds 1\n",
"relevant 1\n",
"michaudquoique 1\n",
"warrants 2\n",
"itthat 10\n",
"warcan 1\n",
"camphe 1\n",
"brusquely 1\n",
"thursdaystoday 1\n",
"stallions 1\n",
"brightest 14\n",
"valses 2\n",
"poundwho 1\n",
"remount 1\n",
"conspiratorial 2\n",
"dianas 1\n",
"malvintseva 7\n",
"sonyaif 1\n",
"peacea 1\n",
"appropriately 4\n",
"inextricable 1\n",
"propertyand 1\n",
"saidthat 4\n",
"redbut 1\n",
"selfsacrificing 2\n",
"meditative 4\n",
"inculpate 2\n",
"protecting 5\n",
"stoves 1\n",
"hurryingnot 1\n",
"evergrowing 1\n",
"battens 1\n",
"vacantly 9\n",
"deft 2\n",
"potato 8\n",
"tt 1\n",
"surname 5\n",
"karataev 55\n",
"whichever 3\n",
"platons 2\n",
"shoeswas 1\n",
"semicircles 2\n",
"conversationof 1\n",
"stiffness 9\n",
"himselfand 3\n",
"saws 1\n",
"mannot 1\n",
"exhales 1\n",
"semiopen 1\n",
"herconsiderate 1\n",
"lovingnot 1\n",
"therenot 3\n",
"loveboundless 1\n",
"fester 1\n",
"divan 4\n",
"correspond 7\n",
"elsesomething 1\n",
"understandand 1\n",
"aloofness 2\n",
"eventwhich 1\n",
"tarutino 31\n",
"pakhra 4\n",
"eventfrom 1\n",
"lanskoy 2\n",
"coursea 1\n",
"koutouzov 2\n",
"armiesboth 1\n",
"guerrilla 11\n",
"chime 2\n",
"serpukhov 1\n",
"reeling 2\n",
"dividednot 1\n",
"bagovut 1\n",
"crosspurposes 1\n",
"battletarutino 1\n",
"austerlitztakes 1\n",
"freer 3\n",
"viewto 1\n",
"administration 8\n",
"clothingof 1\n",
"armyand 2\n",
"commissaries 3\n",
"maraude 1\n",
"husbandmen 1\n",
"razed 1\n",
"envoys 2\n",
"disorders 2\n",
"tresor 1\n",
"azor 1\n",
"bask 1\n",
"solidity 3\n",
"infested 1\n",
"nearlay 1\n",
"halfburned 2\n",
"flowered 1\n",
"louse 1\n",
"handiwork 2\n",
"occupationsuch 1\n",
"hardships 4\n",
"inhis 1\n",
"simplicityhere 1\n",
"youthe 1\n",
"unburned 1\n",
"heathens 1\n",
"beauharnais 2\n",
"settings 1\n",
"carriagessee 1\n",
"colic 2\n",
"worsening 1\n",
"limitless 2\n",
"dated 9\n",
"forminsk 8\n",
"broussier 3\n",
"undiscerning 1\n",
"prose 3\n",
"workings 3\n",
"figner 1\n",
"seslavin 2\n",
"raining 6\n",
"pleadingly 1\n",
"unexpressed 3\n",
"ripe 11\n",
"barthelemi 1\n",
"happenedthe 1\n",
"hourra 1\n",
"prowl 3\n",
"disruption 1\n",
"decrease 2\n",
"conquering 3\n",
"subjugated 1\n",
"expulsion 1\n",
"transportthere 1\n",
"rapier 5\n",
"quarte 1\n",
"infringes 1\n",
"momenta 1\n",
"product 7\n",
"threeline 1\n",
"armyis 1\n",
"captiveall 1\n",
"davydovs 1\n",
"unsaddling 1\n",
"whichas 1\n",
"partiesone 1\n",
"gwown 1\n",
"renowned 4\n",
"shamshevo 10\n",
"avalanche 3\n",
"straggler 2\n",
"shcherbaty 3\n",
"esaul 31\n",
"strewed 4\n",
"leaking 2\n",
"befitted 1\n",
"feoklitych 1\n",
"petyais 1\n",
"petyaabashed 1\n",
"shimmering 1\n",
"villageand 1\n",
"undertones 1\n",
"plastun 1\n",
"pokrovsk 3\n",
"gzhat 1\n",
"drudgery 4\n",
"slink 2\n",
"dayeverybody 1\n",
"dwy 1\n",
"floppy 1\n",
"scowl 5\n",
"twentyfirst 2\n",
"chopping 2\n",
"pities 1\n",
"bosse 2\n",
"wonderworker 1\n",
"stainingi 1\n",
"staining 1\n",
"lanciers 1\n",
"demandent 1\n",
"likhachevisnt 1\n",
"bl 1\n",
"blotch 3\n",
"cavern 2\n",
"snored 4\n",
"fuguethough 1\n",
"triumphal 3\n",
"watery 9\n",
"haunches 1\n",
"gallantlooking 1\n",
"palegreenish 1\n",
"reassembled 2\n",
"togetherthe 2\n",
"grayblue 1\n",
"bandylegged 1\n",
"nourishing 2\n",
"valve 1\n",
"halfdozen 9\n",
"godfearing 1\n",
"chastened 4\n",
"vos 1\n",
"disappears 2\n",
"neysran 1\n",
"stealth 6\n",
"ofeven 1\n",
"threads 9\n",
"quun 2\n",
"chichagov 10\n",
"lyrical 1\n",
"armyto 1\n",
"sacredness 1\n",
"subjectthis 2\n",
"whichwith 1\n",
"stupidly 2\n",
"soothed 17\n",
"lifelovewas 1\n",
"intermittently 1\n",
"styled 1\n",
"himselfwho 1\n",
"frostbitten 2\n",
"twentysix 2\n",
"dignitynapoleon 1\n",
"happeningwhat 1\n",
"verbally 3\n",
"aloneincurring 1\n",
"displeasuresaid 1\n",
"deviationwere 1\n",
"individualsfor 1\n",
"eveningafter 1\n",
"simplehearted 1\n",
"axes 4\n",
"oooooooo 1\n",
"timelacking 1\n",
"blazed 1\n",
"sharpnosed 1\n",
"gobble 1\n",
"poleon 1\n",
"swaggerer 1\n",
"wagwhom 1\n",
"henri 3\n",
"quatre 2\n",
"vivarika 1\n",
"detrava 1\n",
"petersburgfar 1\n",
"demandednamely 1\n",
"allcarried 1\n",
"vis 1\n",
"inertiae 1\n",
"servicecould 1\n",
"bootswas 1\n",
"greece 1\n",
"foamflecked 1\n",
"innermost 7\n",
"conforming 1\n",
"evermanifest 1\n",
"senselessness 1\n",
"himfelt 1\n",
"combative 1\n",
"fourths 1\n",
"larvae 1\n",
"plunderers 4\n",
"reoccupation 1\n",
"gangs 1\n",
"annex 1\n",
"triflingsuch 1\n",
"thinkingespecially 1\n",
"donedid 1\n",
"anywere 1\n",
"stepanych 1\n",
"iswere 1\n",
"burnedout 1\n",
"colosseum 2\n",
"cabmen 1\n",
"bathin 1\n",
"displacement 1\n",
"treaties 1\n",
"chateaubriand 5\n",
"mysticism 3\n",
"universities 2\n",
"governmentan 1\n",
"diffusion 2\n",
"herdsman 2\n",
"rulers 20\n",
"central 5\n",
"tenyear 1\n",
"westsimilar 1\n",
"perceives 1\n",
"dominion 3\n",
"disrobe 1\n",
"liberalism 1\n",
"fatherlandnow 1\n",
"peoplesat 1\n",
"poweralexander 1\n",
"ether 1\n",
"vrazhek 1\n",
"irredeemably 1\n",
"outhis 1\n",
"illhumored 1\n",
"otradnoethat 1\n",
"plows 1\n",
"unerringly 1\n",
"bailiff 1\n",
"harvested 1\n",
"vexations 4\n",
"fruitfulprobably 1\n",
"cameo 2\n",
"readinga 1\n",
"straitened 3\n",
"elections 2\n",
"bailiffs 2\n",
"pregnancies 2\n",
"crossness 1\n",
"bashfully 4\n",
"earnestand 1\n",
"accomplishments 9\n",
"inconveniencing 2\n",
"knowbut 4\n",
"themes 4\n",
"essentials 2\n",
"founders 1\n",
"threemonthold 1\n",
"afraidbut 1\n",
"fussed 1\n",
"servantsthe 1\n",
"entailing 2\n",
"admiringly 6\n",
"aimsonly 1\n",
"belovas 1\n",
"deafness 1\n",
"workmanship 3\n",
"idiocygossner 1\n",
"armswell 1\n",
"bwethwen 1\n",
"loosehed 1\n",
"schwatz 1\n",
"enticed 1\n",
"wotten 1\n",
"mayi 3\n",
"develops 1\n",
"disputingpierre 1\n",
"christianityand 1\n",
"surest 3\n",
"knack 2\n",
"slacken 2\n",
"elusivethe 1\n",
"deity 11\n",
"divinely 3\n",
"aimsthe 1\n",
"northwesterly 1\n",
"subsides 2\n",
"bequeathed 4\n",
"inherent 5\n",
"specialist 5\n",
"subjugation 1\n",
"schlosser 1\n",
"controls 2\n",
"ideais 1\n",
"refutes 1\n",
"incommensurate 1\n",
"circulationpaper 1\n",
"counters 1\n",
"wields 1\n",
"emergence 1\n",
"toin 5\n",
"inconveniencein 1\n",
"delegation 1\n",
"conceivable 7\n",
"beaumarchais 1\n",
"vagrants 2\n",
"jerusalemhad 1\n",
"minnesingers 3\n",
"reformer 1\n",
"newest 2\n",
"unconfirmed 1\n",
"verifies 1\n",
"participated 1\n",
"unexecuted 4\n",
"generalized 2\n",
"indefinitely 6\n",
"servicethe 1\n",
"justifications 5\n",
"parliaments 1\n",
"viewtheological 1\n",
"selfcognition 1\n",
"thinkers 2\n",
"allgood 1\n",
"naturalists 2\n",
"apes 2\n",
"surveys 1\n",
"itgood 1\n",
"drunkards 2\n",
"becan 1\n",
"caseas 1\n",
"unconditioned 2\n",
"differs 3\n",
"undefinable 4\n",
"astronomy 8\n",
"infinitesimal 2\n",
"integration 1\n",
"uninvited 2\n",
"httpgutenbergorglicense 3\n",
"dickens 13\n",
"steerforths 34\n",
"micawbers 91\n",
"emigrants 6\n",
"regretpleasure 2\n",
"companionsthat 2\n",
"wearying 2\n",
"parent 22\n",
"privileged 7\n",
"ghosts 13\n",
"sherry 22\n",
"impiety 4\n",
"meandering 3\n",
"meander 1\n",
"gravestone 1\n",
"werealmost 1\n",
"sometimesbolted 1\n",
"adage 2\n",
"doesfor 1\n",
"baboon 2\n",
"babooor 1\n",
"inflexible 13\n",
"elms 2\n",
"rookswhat 1\n",
"thoughtmr 1\n",
"nests 4\n",
"unkindly 4\n",
"peggotty 701\n",
"matchedand 1\n",
"mused 13\n",
"deathmy 1\n",
"sevens 4\n",
"teaboard 4\n",
"abruptnessas 1\n",
"arewe 2\n",
"yaaah 1\n",
"exclude 3\n",
"superabundant 1\n",
"affirmed 1\n",
"softest 5\n",
"quail 1\n",
"oration 6\n",
"roughened 4\n",
"nutmeggrater 2\n",
"groundfloor 7\n",
"dimlyburning 1\n",
"pepper 8\n",
"peggottyfor 1\n",
"bodgers 3\n",
"sweetsmelling 2\n",
"crocodiles 4\n",
"perspicuously 1\n",
"threadhow 1\n",
"dome 4\n",
"footstool 5\n",
"knowyou 2\n",
"maynt 6\n",
"monsters 5\n",
"wakefulness 2\n",
"motheryou 1\n",
"infer 11\n",
"parasol 12\n",
"fringe 1\n",
"explosives 1\n",
"parlourwindow 3\n",
"halfayear 1\n",
"telescopei 1\n",
"couldand 2\n",
"skylark 2\n",
"coaxingly 10\n",
"yarmouth 53\n",
"spongy 1\n",
"ropewalks 1\n",
"forges 2\n",
"yons 1\n",
"masr 112\n",
"shiplooking 1\n",
"sarah 7\n",
"jane 42\n",
"hooks 4\n",
"theoryand 1\n",
"seenin 1\n",
"whitewashed 2\n",
"pockethandkerchief 51\n",
"imparting 10\n",
"outhouse 3\n",
"dabs 4\n",
"lass 34\n",
"wi 3\n",
"kettleful 1\n",
"muck 1\n",
"allfours 4\n",
"drowndead 2\n",
"bacheldore 1\n",
"peggottyi 2\n",
"crib 2\n",
"hammocks 1\n",
"pebbles 6\n",
"skyblue 7\n",
"raiment 3\n",
"jetty 1\n",
"causeway 2\n",
"sincei 1\n",
"reverseand 1\n",
"fretful 5\n",
"waterboots 1\n",
"mawther 6\n",
"hammock 4\n",
"unemployed 3\n",
"obtains 1\n",
"comforter 5\n",
"azacklythat 1\n",
"howconnected 1\n",
"rambled 5\n",
"uncovering 2\n",
"pettish 2\n",
"edward 61\n",
"stillmissing 1\n",
"treadbut 1\n",
"hardihood 10\n",
"storecloset 2\n",
"coalcellar 1\n",
"tributary 6\n",
"brokenhearted 6\n",
"taint 9\n",
"responses 6\n",
"pokes 3\n",
"crocodilebook 2\n",
"orally 1\n",
"doublegloucester 1\n",
"fourpencehalfpenny 1\n",
"pore 1\n",
"verily 10\n",
"boottreesthe 1\n",
"churchsteeple 1\n",
"conceiving 3\n",
"gravelyi 1\n",
"justiceand 1\n",
"timeexcept 1\n",
"wrapper 9\n",
"catll 1\n",
"breadandbutter 7\n",
"keepsake 4\n",
"slouching 3\n",
"pastry 3\n",
"parsties 2\n",
"writin 2\n",
"willin 8\n",
"william 46\n",
"chops 15\n",
"bouncing 5\n",
"dexterity 9\n",
"topsawyerperhaps 1\n",
"speckled 3\n",
"topsawyer 1\n",
"teaspoon 4\n",
"ribstwo 1\n",
"ribsa 1\n",
"waslet 2\n",
"threepence 4\n",
"ihow 1\n",
"farthing 4\n",
"coalshere 1\n",
"waggon 6\n",
"boaconstrictor 1\n",
"midsummer 2\n",
"insides 2\n",
"slanted 3\n",
"impressment 1\n",
"broiling 2\n",
"rasher 3\n",
"tunes 10\n",
"fades 5\n",
"playingi 1\n",
"bullneck 1\n",
"copybooks 3\n",
"pasteboard 2\n",
"chirps 1\n",
"vacation 12\n",
"fiveandforty 2\n",
"flats 3\n",
"unlock 2\n",
"filed 2\n",
"interlaced 2\n",
"bloodhe 1\n",
"thiswhen 1\n",
"supped 1\n",
"almond 1\n",
"tungays 2\n",
"remonstrance 19\n",
"sevenandsixpenny 1\n",
"coalbill 1\n",
"stumped 1\n",
"launch 4\n",
"itmiserable 1\n",
"cloggy 1\n",
"ridge 3\n",
"offender 13\n",
"smarted 2\n",
"pluck 4\n",
"cemented 1\n",
"sultana 1\n",
"orange 3\n",
"traddlesi 1\n",
"dunce 1\n",
"commons 72\n",
"whatwhere 1\n",
"iicould 1\n",
"cheersi 1\n",
"cheers 10\n",
"contrition 5\n",
"itdo 2\n",
"schoollife 1\n",
"dooty 4\n",
"toebesure 1\n",
"sledgehammer 1\n",
"gravesend 7\n",
"swaggeringwhich 1\n",
"norfolk 4\n",
"genlmn 5\n",
"chuckling 9\n",
"heartiest 2\n",
"rulerings 1\n",
"agoin 1\n",
"natral 11\n",
"fund 8\n",
"waitin 1\n",
"bleak 14\n",
"brimfull 1\n",
"fondling 6\n",
"bounced 3\n",
"mumbly 1\n",
"coals 18\n",
"nonsensical 3\n",
"continuallyand 1\n",
"pantries 1\n",
"timesshe 1\n",
"scoop 10\n",
"interdicted 2\n",
"babyit 1\n",
"tempers 4\n",
"wenot 1\n",
"abets 2\n",
"ticking 17\n",
"schoola 1\n",
"ghostly 13\n",
"sputtering 3\n",
"wreathing 2\n",
"hamper 6\n",
"newspaper 48\n",
"mint 3\n",
"moped 4\n",
"backseeing 1\n",
"buriali 1\n",
"elsetill 1\n",
"dearso 1\n",
"condoled 2\n",
"speculated 6\n",
"moody 7\n",
"elseit 1\n",
"importancethat 1\n",
"sourness 1\n",
"protestations 3\n",
"prettys 1\n",
"cheerily 22\n",
"quotation 13\n",
"whimpering 8\n",
"whisker 13\n",
"backhander 1\n",
"cricketer 1\n",
"mantling 2\n",
"exceptit 1\n",
"trotters 1\n",
"philosophically 2\n",
"exponent 1\n",
"spiritless 2\n",
"keptto 1\n",
"languished 4\n",
"moping 4\n",
"corduroy 2\n",
"grinbys 11\n",
"waterside 5\n",
"scuffling 2\n",
"flawed 1\n",
"pasted 2\n",
"walker 8\n",
"mealys 1\n",
"waterman 1\n",
"theatres 4\n",
"pantomimes 1\n",
"arcana 1\n",
"yourselfi 1\n",
"itas 3\n",
"selfdependence 1\n",
"dirtyfaced 1\n",
"micawbercome 1\n",
"otherwiseto 1\n",
"halfprice 2\n",
"martins 3\n",
"churchwhich 1\n",
"venison 1\n",
"covent 18\n",
"shirtsleeves 3\n",
"tipp 3\n",
"uprose 2\n",
"rebelled 2\n",
"treatpartly 1\n",
"bowwindows 2\n",
"turnkey 74\n",
"loungingplace 1\n",
"balustrades 5\n",
"insolvent 10\n",
"bail 1\n",
"uncomfortableas 1\n",
"liabilities 8\n",
"methough 1\n",
"friendless 4\n",
"thatin 1\n",
"neer 2\n",
"shallin 1\n",
"awayto 1\n",
"stipend 6\n",
"tipps 1\n",
"deadwall 2\n",
"doorstep 10\n",
"snowdrift 4\n",
"leasttook 1\n",
"outcasts 3\n",
"colduntil 1\n",
"churchbells 2\n",
"footsore 3\n",
"chathamwhich 1\n",
"mastless 1\n",
"arkscrept 1\n",
"cots 1\n",
"oilskin 7\n",
"goroo 4\n",
"flights 4\n",
"mouthing 2\n",
"nelson 3\n",
"hoppickers 1\n",
"perspectives 2\n",
"fellowa 1\n",
"whitened 2\n",
"canterbury 46\n",
"halfclothed 1\n",
"halftide 1\n",
"calais 12\n",
"flydriver 1\n",
"damsel 4\n",
"trotwoods 4\n",
"sleptand 1\n",
"besidesmight 1\n",
"pleasantlooking 1\n",
"selfsupport 2\n",
"aniseed 1\n",
"anchovy 3\n",
"janet 34\n",
"boyhis 1\n",
"woolgathering 1\n",
"lancet 1\n",
"inflexibility 2\n",
"unbending 4\n",
"mobcap 1\n",
"ridinghabit 1\n",
"abjuration 1\n",
"kettleholder 2\n",
"inconvenienced 3\n",
"simpered 3\n",
"illuse 1\n",
"robin 14\n",
"isits 1\n",
"abound 2\n",
"proprietorship 5\n",
"loftier 3\n",
"earits 1\n",
"presuming 6\n",
"firsts 1\n",
"reverend 20\n",
"goodhumoured 17\n",
"advicebut 1\n",
"ogled 3\n",
"trespassed 1\n",
"feints 1\n",
"intractable 1\n",
"abetting 3\n",
"itupon 1\n",
"aunti 1\n",
"waysmirking 1\n",
"haughtily 15\n",
"memorials 1\n",
"pony 8\n",
"raps 1\n",
"vowed 3\n",
"hucksters 3\n",
"hairbreadth 1\n",
"latticewindows 2\n",
"unshaded 2\n",
"therepointing 1\n",
"sweeps 3\n",
"heeps 12\n",
"waysuch 1\n",
"rightabout 1\n",
"beamends 2\n",
"redbrick 3\n",
"hearthrug 13\n",
"maldon 55\n",
"needy 2\n",
"ripening 3\n",
"adams 10\n",
"suppersin 1\n",
"arewho 1\n",
"intrusionthat 1\n",
"waybut 2\n",
"themselvesthat 2\n",
"umble 68\n",
"stealthy 10\n",
"articled 7\n",
"shelfi 1\n",
"cogitating 4\n",
"sixtysecond 1\n",
"slyly 1\n",
"headboys 1\n",
"shearers 1\n",
"beggarwoman 1\n",
"markleham 37\n",
"originate 6\n",
"maldons 4\n",
"cherrycoloured 3\n",
"backgarden 1\n",
"outspoken 1\n",
"misrepresented 1\n",
"pipesa 1\n",
"duet 1\n",
"cousinthat 1\n",
"supperroom 3\n",
"merciless 1\n",
"interjectional 1\n",
"unseasonable 6\n",
"counterfeiting 1\n",
"probableand 1\n",
"illfated 2\n",
"marbles 1\n",
"birdcages 2\n",
"affront 7\n",
"umbleness 9\n",
"copperfieldlatin 1\n",
"attainments 1\n",
"umbly 10\n",
"vomiting 1\n",
"eatables 2\n",
"fathersinlaw 1\n",
"minebut 1\n",
"doorit 1\n",
"yearcame 1\n",
"humanin 1\n",
"genteelly 6\n",
"copperfieldsto 1\n",
"heepdoes 1\n",
"vegetationin 1\n",
"facers 1\n",
"cato 1\n",
"partitioned 3\n",
"sausagemeat 1\n",
"willie 1\n",
"castall 1\n",
"shepherd 23\n",
"nettingalls 4\n",
"sameis 1\n",
"laces 2\n",
"discompose 1\n",
"lifeas 1\n",
"agonies 5\n",
"oneandtwenty 1\n",
"believei 1\n",
"inestimable 8\n",
"papathe 1\n",
"hopgrower 1\n",
"resumption 10\n",
"oftenrepeated 2\n",
"navigation 7\n",
"knowand 7\n",
"knaves 5\n",
"burningglass 1\n",
"penitently 1\n",
"speechwhich 1\n",
"ohi 1\n",
"annieand 1\n",
"forthits 1\n",
"illegibly 1\n",
"mistrusted 6\n",
"inhabiting 3\n",
"wery 16\n",
"orse 1\n",
"bookkeeper 1\n",
"esquirewhich 1\n",
"leavings 1\n",
"taskmasters 1\n",
"linklighted 1\n",
"wellformed 3\n",
"inand 1\n",
"contortions 2\n",
"daisy 27\n",
"sackcloth 1\n",
"prosy 1\n",
"copperfieldand 2\n",
"hackneychariot 3\n",
"sightseeing 1\n",
"newlykindled 1\n",
"housewith 2\n",
"dartle 78\n",
"thoughi 2\n",
"landis 1\n",
"saysome 1\n",
"leadcoloured 3\n",
"babyhair 1\n",
"outstrip 1\n",
"easychairs 1\n",
"softfooted 1\n",
"noticedthe 1\n",
"selfcontained 2\n",
"guessand 1\n",
"undrew 1\n",
"greenest 1\n",
"asthma 4\n",
"lusty 1\n",
"olderlooking 1\n",
"measurement 1\n",
"dressmaking 1\n",
"yarmouthah 1\n",
"doespecially 1\n",
"seeand 4\n",
"spoiledand 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"shortbreathed 1\n",
"myselfbut 1\n",
"saynot 1\n",
"hermore 1\n",
"queerest 1\n",
"turnips 1\n",
"nestle 4\n",
"witch 6\n",
"gorm 1\n",
"horroar 1\n",
"porkypine 4\n",
"saltbut 1\n",
"longrun 2\n",
"townlights 2\n",
"irontrue 1\n",
"eveningas 1\n",
"anythink 1\n",
"upgentlmenlike 1\n",
"indescribably 3\n",
"bewitched 2\n",
"monopoly 2\n",
"castles 3\n",
"highnosed 2\n",
"weazen 3\n",
"homei 3\n",
"careering 3\n",
"placebe 1\n",
"unrecognized 1\n",
"grander 1\n",
"petrels 1\n",
"boatbuilder 5\n",
"workingdress 1\n",
"usevidently 1\n",
"flaunting 3\n",
"mowcher 56\n",
"dwarf 7\n",
"archly 4\n",
"fie 10\n",
"handkerchersand 1\n",
"mitherss 1\n",
"mithers 2\n",
"spinnage 1\n",
"shavers 1\n",
"moustachios 2\n",
"pyegrave 1\n",
"griffinwho 1\n",
"notnotnot 1\n",
"lipsalve 1\n",
"tuckeredging 1\n",
"onthick 1\n",
"jemmy 2\n",
"haberdashers 2\n",
"doesexceedingly 1\n",
"noddle 2\n",
"shortarmed 1\n",
"cupper 1\n",
"youfor 2\n",
"endell 1\n",
"sandswas 1\n",
"tick 1\n",
"aloudmarthas 1\n",
"tenderheart 1\n",
"ringlets 3\n",
"marthas 2\n",
"steerforthsof 1\n",
"eightandforty 5\n",
"equity 1\n",
"recurring 5\n",
"lengthen 3\n",
"upwhether 1\n",
"hackney 7\n",
"accompaniments 1\n",
"marketcarts 1\n",
"bellswe 1\n",
"writingtable 2\n",
"allegations 1\n",
"factbut 2\n",
"jorkinss 4\n",
"andugh 1\n",
"chimneys 23\n",
"summun 3\n",
"handy 5\n",
"mashed 3\n",
"vegetablesfrom 1\n",
"kidneysfrom 1\n",
"markham 6\n",
"youthfullooking 1\n",
"appetites 1\n",
"dinnerparty 8\n",
"graingers 1\n",
"elselooked 1\n",
"windowcurtains 2\n",
"amigoarawaysoo 1\n",
"furred 1\n",
"expiatemy 1\n",
"heldmy 1\n",
"brothbasin 1\n",
"meats 9\n",
"andin 3\n",
"foreknowledge 6\n",
"untilto 1\n",
"pugdog 1\n",
"gauntly 1\n",
"guestsall 1\n",
"briefs 1\n",
"selfabasement 2\n",
"lordi 1\n",
"gulpidges 1\n",
"unassuming 3\n",
"invention 21\n",
"breezes 2\n",
"splay 2\n",
"edwill 1\n",
"incorrigible 3\n",
"umbrella 16\n",
"detestable 4\n",
"sandwiches 4\n",
"carpetbag 2\n",
"stipendiary 5\n",
"dayabout 1\n",
"excommunicating 1\n",
"pavingrateand 1\n",
"starch 5\n",
"pickings 1\n",
"officeif 1\n",
"dinnerbell 1\n",
"variable 1\n",
"revive 6\n",
"sexwho 1\n",
"unfavourable 4\n",
"gipsy 11\n",
"lackadaisical 1\n",
"curlshow 1\n",
"depreciated 4\n",
"headand 2\n",
"mariner 11\n",
"unquiet 6\n",
"cardamums 1\n",
"complaintor 1\n",
"evin 1\n",
"laundressed 1\n",
"undercareful 1\n",
"gonethanked 1\n",
"floortraddles 1\n",
"blacking 3\n",
"shavingglass 2\n",
"itdraperclothmerchantand 1\n",
"houseyawler 1\n",
"publishing 1\n",
"encyclopaedia 1\n",
"paidthough 1\n",
"wasthough 1\n",
"curates 3\n",
"circumference 1\n",
"chaucer 1\n",
"terminga 1\n",
"slatternly 2\n",
"lave 1\n",
"expressionwhich 2\n",
"avocation 1\n",
"payand 1\n",
"ampial 1\n",
"arrangementone 1\n",
"celibacy 2\n",
"rates 4\n",
"frying 2\n",
"forasmuch 3\n",
"crust 11\n",
"doraif 1\n",
"theai 1\n",
"comestibles 1\n",
"gridiron 7\n",
"cayenne 2\n",
"ketchup 2\n",
"thinkbut 3\n",
"remorseful 6\n",
"twa 1\n",
"viewon 1\n",
"truman 1\n",
"bankinghouses 2\n",
"sagely 2\n",
"moneyon 1\n",
"piccadillywhich 1\n",
"unmindful 6\n",
"bacchanal 1\n",
"jovially 1\n",
"whatshisnames 1\n",
"apothecary 10\n",
"braving 2\n",
"penned 2\n",
"broker 18\n",
"lunar 1\n",
"rememberand 1\n",
"seefrom 1\n",
"suppositionthat 1\n",
"jaundiced 3\n",
"irish 7\n",
"trancesteerforth 1\n",
"smilefor 1\n",
"seatsmoke 1\n",
"prettieri 1\n",
"waywhat 1\n",
"fordont 2\n",
"surprises 5\n",
"birdits 1\n",
"moppet 1\n",
"roughweather 2\n",
"oughta 1\n",
"afeerd 6\n",
"fearsome 1\n",
"mecome 1\n",
"emilys 10\n",
"messages 8\n",
"streamer 1\n",
"randoms 1\n",
"wariety 1\n",
"forinnersbless 1\n",
"homein 1\n",
"mercys 3\n",
"chay 1\n",
"rightim 1\n",
"worrit 3\n",
"danlwhat 1\n",
"widder 2\n",
"stillthough 1\n",
"hamhe 1\n",
"foundered 3\n",
"kiender 7\n",
"beein 5\n",
"unsaid 2\n",
"lobsterpots 1\n",
"fagging 3\n",
"deploring 3\n",
"uponin 1\n",
"copperfieldall 1\n",
"wheedling 3\n",
"libertine 1\n",
"wayuntouched 1\n",
"birthto 1\n",
"compressible 1\n",
"deeplyinjured 1\n",
"beor 1\n",
"conundrum 1\n",
"romantically 1\n",
"industrial 2\n",
"proctorial 1\n",
"gardeners 3\n",
"dutyoffice 1\n",
"thoughtfor 1\n",
"rebel 3\n",
"generaland 1\n",
"droppedas 1\n",
"doingand 1\n",
"repealed 1\n",
"benjamin 3\n",
"anomalies 2\n",
"usfor 1\n",
"sinecurist 1\n",
"diocese 1\n",
"saunter 4\n",
"picnic 2\n",
"driveller 1\n",
"laceedged 1\n",
"yearsalmost 1\n",
"pouted 6\n",
"guitarcase 6\n",
"guildford 1\n",
"arabiannight 1\n",
"heather 3\n",
"sliced 1\n",
"pitted 2\n",
"baleful 2\n",
"flyi 1\n",
"sahara 7\n",
"oldand 1\n",
"grape 2\n",
"whilei 1\n",
"jeweller 1\n",
"exhorting 2\n",
"immoderate 1\n",
"corporeally 1\n",
"widders 1\n",
"colouring 4\n",
"sophys 11\n",
"ait 1\n",
"repurchase 2\n",
"tottenham 3\n",
"artfulness 3\n",
"sauntered 6\n",
"nowp 1\n",
"newlaid 3\n",
"itreturned 1\n",
"lengthened 5\n",
"compounding 1\n",
"spoonful 3\n",
"grudging 3\n",
"lightheaded 7\n",
"partysuppertable 1\n",
"overshadow 2\n",
"alwayscome 1\n",
"stillin 2\n",
"precedent 12\n",
"isbut 2\n",
"guardsand 1\n",
"trustfully 2\n",
"ruddiness 1\n",
"curtness 1\n",
"directedi 1\n",
"seraphic 2\n",
"pupilage 1\n",
"agrees 3\n",
"rhapsodical 1\n",
"doctorwhich 1\n",
"practicetraddles 1\n",
"drumor 1\n",
"penman 1\n",
"exhorted 2\n",
"usefully 1\n",
"peru 1\n",
"unfrequent 1\n",
"immure 1\n",
"extrication 1\n",
"ostentatious 2\n",
"headvoice 1\n",
"chorister 1\n",
"bumpers 3\n",
"consign 2\n",
"unliquidated 1\n",
"boylodger 1\n",
"graminivorous 1\n",
"whistclub 1\n",
"favouring 4\n",
"petticoator 1\n",
"decanterstand 1\n",
"muttonchop 2\n",
"laboursketching 1\n",
"damped 4\n",
"rosewater 1\n",
"restrict 1\n",
"chancing 1\n",
"dots 1\n",
"penandink 2\n",
"orations 2\n",
"sheridan 1\n",
"canning 1\n",
"overstarched 1\n",
"confining 2\n",
"himknowing 1\n",
"jewellerythat 1\n",
"nightof 1\n",
"unpalatable 2\n",
"resignedclearly 1\n",
"doori 1\n",
"wantley 1\n",
"ticketporters 1\n",
"officepapers 1\n",
"lease 5\n",
"subjoin 1\n",
"fondled 4\n",
"floodgates 1\n",
"dewdrops 1\n",
"undutiful 7\n",
"herwere 1\n",
"lures 1\n",
"probates 1\n",
"inveiglers 1\n",
"surrogate 2\n",
"ablebodied 1\n",
"gabled 1\n",
"plying 6\n",
"legallooking 1\n",
"burly 4\n",
"steppingstone 2\n",
"domiciliary 1\n",
"slightingly 1\n",
"disturbedwe 1\n",
"timeof 1\n",
"truthbut 1\n",
"artlessness 1\n",
"selfhaving 1\n",
"partnerand 1\n",
"villainous 7\n",
"chopsticks 1\n",
"ballad 6\n",
"charitably 1\n",
"wickfieldheaven 1\n",
"ketch 2\n",
"umblenessnot 1\n",
"moonlightthat 1\n",
"males 1\n",
"trotwoodexclaimed 1\n",
"estimated 3\n",
"clockpendulum 1\n",
"fidgeted 1\n",
"homeand 1\n",
"thowts 2\n",
"youfur 1\n",
"somewheers 1\n",
"whatall 1\n",
"ablowing 1\n",
"sech 1\n",
"atravelling 1\n",
"childrenparticular 1\n",
"yourselvesand 1\n",
"beenbefore 1\n",
"drawed 1\n",
"elseways 1\n",
"hearthbroomy 1\n",
"sisterthe 1\n",
"handso 2\n",
"oddand 1\n",
"pidger 4\n",
"sentimentsto 1\n",
"clarissa 36\n",
"disunion 1\n",
"fingersminuets 1\n",
"affectionor 1\n",
"lightinclinations 1\n",
"ladyone 1\n",
"devonshireand 1\n",
"vinegartraddles 1\n",
"exchanges 1\n",
"lookthe 1\n",
"lookand 2\n",
"rosebud 3\n",
"erratic 1\n",
"hardworking 1\n",
"jealousleastways 1\n",
"birdlike 3\n",
"pecking 3\n",
"timesand 1\n",
"ifor 2\n",
"intolerableat 1\n",
"howsever 1\n",
"congereel 1\n",
"unsay 2\n",
"aspersionsi 1\n",
"comesmay 1\n",
"wresting 1\n",
"chafe 1\n",
"weed 4\n",
"righti 1\n",
"postman 3\n",
"ticks 1\n",
"stenographic 1\n",
"mystify 1\n",
"wallow 1\n",
"brownpaper 1\n",
"meatscreen 1\n",
"facesnot 1\n",
"pleasantand 1\n",
"commends 1\n",
"houseour 1\n",
"housedoras 1\n",
"ribbondo 1\n",
"heralready 1\n",
"lavendercoloured 1\n",
"pewopener 9\n",
"infection 5\n",
"goodhumour 3\n",
"posthorses 2\n",
"moreall 1\n",
"rustno 1\n",
"sixwhich 1\n",
"meddled 1\n",
"annes 1\n",
"incapables 2\n",
"excise 2\n",
"pawning 1\n",
"quartern 1\n",
"disreputable 1\n",
"sixes 1\n",
"stitched 2\n",
"workfor 1\n",
"writeri 1\n",
"childwifes 3\n",
"embitterment 1\n",
"penwhich 1\n",
"babyhouse 1\n",
"unqualified 4\n",
"pokey 1\n",
"operas 1\n",
"mayday 1\n",
"saidfirst 1\n",
"craze 1\n",
"dickmad 1\n",
"davidin 1\n",
"humbling 2\n",
"husbandnot 1\n",
"surebut 1\n",
"apprehensionat 1\n",
"imperfections 1\n",
"endures 1\n",
"holly 2\n",
"coughgone 1\n",
"boatmens 1\n",
"coasting 4\n",
"sorryi 1\n",
"rememberhear 1\n",
"snew 2\n",
"thingthat 2\n",
"ventersome 1\n",
"sluggish 4\n",
"steamboilers 1\n",
"paddles 1\n",
"anchors 2\n",
"slush 1\n",
"hereabout 1\n",
"believewe 1\n",
"notfur 1\n",
"seafaringway 1\n",
"furdest 2\n",
"finelooking 2\n",
"foolto 1\n",
"trotless 1\n",
"session 5\n",
"function 6\n",
"whittington 10\n",
"uxbridge 1\n",
"potboy 4\n",
"sowhich 1\n",
"corrupting 1\n",
"pitfall 1\n",
"babysmile 1\n",
"momenti 1\n",
"sillyto 1\n",
"nestled 4\n",
"sportive 3\n",
"hues 3\n",
"unpropitious 2\n",
"maritime 4\n",
"farwhich 1\n",
"thunderbolt 6\n",
"avenging 7\n",
"dispelledthat 1\n",
"painfulness 1\n",
"tripartite 1\n",
"alienation 3\n",
"sweetmeathe 1\n",
"subscribes 2\n",
"esse 1\n",
"detainees 2\n",
"mazes 3\n",
"fellowman 4\n",
"lawfor 1\n",
"arrows 4\n",
"doublequick 2\n",
"relapses 1\n",
"overpower 2\n",
"cull 1\n",
"aliens 1\n",
"jogging 6\n",
"maammr 1\n",
"roofparticularly 1\n",
"punchwouldachoke 1\n",
"copperfieldno 1\n",
"wickfieldaredress 1\n",
"threeor 1\n",
"quickthen 1\n",
"giddily 1\n",
"mockmodesty 1\n",
"penance 3\n",
"ladymother 4\n",
"tendernesshe 1\n",
"notor 1\n",
"hatreds 1\n",
"heavnly 1\n",
"neezingby 1\n",
"birdsnestingthis 1\n",
"snake 2\n",
"himshe 2\n",
"suddenor 1\n",
"unnerstandthe 1\n",
"alooking 1\n",
"heerafter 1\n",
"seaand 1\n",
"atalking 1\n",
"thowtnow 1\n",
"arternoon 4\n",
"shellfor 1\n",
"towrds 2\n",
"frightning 1\n",
"sheas 1\n",
"onmartha 1\n",
"docks 15\n",
"wen 5\n",
"maamwen 1\n",
"tant 8\n",
"mightfind 1\n",
"moor 3\n",
"faithfullest 2\n",
"banknotesfifty 1\n",
"scholarship 1\n",
"sothat 2\n",
"mostlike 1\n",
"nightmr 1\n",
"enhancing 4\n",
"suffocated 5\n",
"serenest 1\n",
"counselled 2\n",
"hertoo 1\n",
"happyby 1\n",
"restanything 1\n",
"hopefully 9\n",
"disagreeableand 1\n",
"edgewise 1\n",
"darkest 5\n",
"wickedest 1\n",
"pretences 5\n",
"youand 4\n",
"foolscap 3\n",
"ream 1\n",
"bureauof 1\n",
"andheep 2\n",
"thatheepbegan 1\n",
"rounding 1\n",
"withoutto 1\n",
"unlaborious 1\n",
"askheepif 1\n",
"micawberif 1\n",
"abominate 1\n",
"tyrannize 1\n",
"attested 2\n",
"imitations 1\n",
"endangering 2\n",
"byheepon 1\n",
"quarterdays 1\n",
"perpetuated 1\n",
"peroration 2\n",
"substantiate 1\n",
"pyre 1\n",
"earlierand 1\n",
"pacified 2\n",
"sulkily 2\n",
"disgorge 1\n",
"securities 3\n",
"cur 4\n",
"bankers 12\n",
"publichouses 5\n",
"respectively 3\n",
"thoughtsbut 1\n",
"canoe 1\n",
"reconsidered 1\n",
"billsa 1\n",
"directionwhich 1\n",
"namesi 1\n",
"misappropriated 2\n",
"adverted 1\n",
"mereally 1\n",
"presentand 1\n",
"supportable 1\n",
"ibut 2\n",
"tumultuously 1\n",
"bylanes 1\n",
"awokeor 1\n",
"schooner 1\n",
"tattood 1\n",
"beatwhich 1\n",
"seaexactly 1\n",
"wrongedi 1\n",
"likeregarded 1\n",
"stunted 2\n",
"courtshipinto 1\n",
"faultsyou 1\n",
"penetrates 1\n",
"adaptation 5\n",
"guernsey 2\n",
"shaggiest 1\n",
"yeoheaveyeo 1\n",
"heyday 1\n",
"ingredients 3\n",
"onan 1\n",
"sapling 1\n",
"albion 3\n",
"positionam 1\n",
"hemisphere 1\n",
"partings 1\n",
"ringbolts 1\n",
"windsail 1\n",
"handkerchiefsand 1\n",
"remaineda 1\n",
"awayfrom 1\n",
"homereasons 1\n",
"heris 2\n",
"mineshe 1\n",
"housefronts 1\n",
"remarkedi 1\n",
"hasthat 1\n",
"fishstreet 1\n",
"timehonoured 1\n",
"potential 2\n",
"wainscoted 2\n",
"placewhich 1\n",
"overwere 1\n",
"barristers 1\n",
"halfclerk 1\n",
"rumpling 1\n",
"sophydown 1\n",
"sophywho 2\n",
"louisas 1\n",
"improvise 1\n",
"roofa 1\n",
"himselfthat 1\n",
"lawstationers 1\n",
"redtape 1\n",
"inkjars 1\n",
"feathery 3\n",
"coatpocket 1\n",
"tucks 1\n",
"phrenological 1\n",
"developments 1\n",
"saidin 1\n",
"aiding 1\n",
"respectsa 1\n",
"inchrule 1\n",
"harmoniously 2\n",
"jarred 7\n",
"dislikings 3\n",
"muchthough 1\n",
"sooty 1\n",
"offi 1\n",
"averring 1\n",
"engineturned 1\n",
"fishslices 1\n",
"theatrethe 1\n",
"oldrascal 1\n",
"unchallengeable 1\n",
"felonies 1\n",
"massively 1\n",
"paupers 1\n",
"allgoverning 1\n",
"vineyards 3\n",
"outwith 1\n",
"repentanceif 1\n",
"stronglyas 1\n",
"satisfactiongreater 1\n",
"felony 2\n",
"tailand 1\n",
"witnessbox 3\n",
"worshipful 6\n",
"marketvalue 1\n",
"hardfrozen 1\n",
"chairof 1\n",
"musically 3\n",
"workbasket 4\n",
"windowseat 15\n",
"doubtto 1\n",
"dorafondly 1\n",
"introductory 3\n",
"wedded 2\n",
"nameand 1\n",
"solitoode 1\n",
"oddtimes 1\n",
"reskied 1\n",
"fellowcolonist 1\n",
"exclusiveness 2\n",
"belllike 1\n",
"bettering 1\n",
"liquidate 1\n",
"manliest 1\n",
"unfriended 2\n",
"staywhich 1\n",
"monthand 1\n",
"journeying 7\n",
"shrivelled 11\n",
"scolds 1\n",
"beenwhich 1\n",
"neil 2\n",
"ted 3\n",
"analogous 2\n",
"notching 1\n",
"phosphorescently 1\n",
"inviolate 3\n",
"anno 2\n",
"boardingschools 1\n",
"firms 2\n",
"ladylike 1\n",
"rathernot 1\n",
"surequite 1\n",
"otherwisedoctor 1\n",
"deplore 4\n",
"dombeybut 1\n",
"trembly 3\n",
"linendrapers 1\n",
"aquiline 1\n",
"frills 3\n",
"propagate 2\n",
"mincing 4\n",
"clipping 3\n",
"chickmay 1\n",
"inconsiderately 1\n",
"boythe 1\n",
"thinkin 1\n",
"loo 2\n",
"loor 1\n",
"morethose 1\n",
"roundly 4\n",
"bickerings 1\n",
"acceptation 1\n",
"rosycheeked 2\n",
"matronexcellent 1\n",
"dexterously 3\n",
"stoker 3\n",
"ohh 1\n",
"testimonials 4\n",
"showerbaths 1\n",
"beingask 1\n",
"biler 19\n",
"wamt 4\n",
"gratings 1\n",
"glories 6\n",
"quiettempered 1\n",
"crookedeyed 1\n",
"umbrellamender 1\n",
"nightly 8\n",
"grimshe 1\n",
"tooshe 2\n",
"fostermother 1\n",
"lonelysobbed 1\n",
"bosomtook 1\n",
"tickerlerly 2\n",
"wrenchas 1\n",
"lork 2\n",
"adjured 2\n",
"disciple 2\n",
"verywideopen 1\n",
"toxes 2\n",
"coercion 2\n",
"drudging 2\n",
"unlearned 1\n",
"doorscame 1\n",
"misunderstood 1\n",
"dombeyrequested 1\n",
"gainsay 3\n",
"spectatornot 1\n",
"stuffs 2\n",
"woodenestof 1\n",
"welsh 7\n",
"chronometers 2\n",
"sextants 1\n",
"quadrants 1\n",
"narrowest 1\n",
"label 1\n",
"successively 4\n",
"coffeecolour 2\n",
"fairfaced 2\n",
"wally 23\n",
"carkers 33\n",
"madeiratheres 1\n",
"mayto 1\n",
"clinked 5\n",
"cetera 4\n",
"seveneighths 1\n",
"wallytherefore 1\n",
"pitches 1\n",
"norvals 1\n",
"balances 3\n",
"conversion 2\n",
"nowhaving 1\n",
"knighterrants 1\n",
"wildernesses 5\n",
"squints 1\n",
"likethe 1\n",
"mollified 2\n",
"blowinga 1\n",
"ado 3\n",
"fondle 3\n",
"lucretia 38\n",
"slabs 1\n",
"underpaid 1\n",
"herwent 1\n",
"gallons 2\n",
"doorsteps 9\n",
"collation 4\n",
"tailed 1\n",
"encased 2\n",
"staggss 18\n",
"camberling 1\n",
"tripods 1\n",
"beershop 1\n",
"cowhouses 1\n",
"dunghills 4\n",
"tumuli 1\n",
"fenders 1\n",
"chimneysweeper 2\n",
"pippins 1\n",
"besttemperedest 1\n",
"disclosures 2\n",
"breakwater 1\n",
"pauland 2\n",
"revilings 1\n",
"unfrequented 1\n",
"handsset 1\n",
"timeeven 1\n",
"substitutes 1\n",
"luxuriantly 1\n",
"ownbeyond 1\n",
"womanshe 1\n",
"tutored 2\n",
"clark 3\n",
"lilting 1\n",
"liveits 1\n",
"soltake 1\n",
"areheres 1\n",
"florencedinner 1\n",
"hungryand 1\n",
"reappearing 2\n",
"queerer 1\n",
"heretheres 1\n",
"congregated 2\n",
"disqualifications 1\n",
"waitfor 1\n",
"retributive 1\n",
"tomr 1\n",
"princesss 41\n",
"tinkling 3\n",
"effervescent 2\n",
"bagstock 100\n",
"jawbones 1\n",
"joehes 1\n",
"apostrophizing 4\n",
"timeso 2\n",
"majorpauls 1\n",
"distincter 2\n",
"bereft 6\n",
"steeplechase 1\n",
"thrush 1\n",
"tigercats 1\n",
"widowwhose 1\n",
"weaning 1\n",
"toxdared 1\n",
"superstructure 1\n",
"monstrously 4\n",
"puppetshow 1\n",
"gloss 4\n",
"maybut 1\n",
"brighton 35\n",
"historywho 1\n",
"peruvian 16\n",
"pipchins 40\n",
"relict 11\n",
"sweeten 1\n",
"frontgardens 1\n",
"cactus 1\n",
"lath 2\n",
"acidity 1\n",
"regaled 2\n",
"buckram 1\n",
"drapery 7\n",
"beennot 2\n",
"disrespectfullya 1\n",
"necromancy 1\n",
"multifarious 1\n",
"unburdened 1\n",
"illnesses 1\n",
"foryoull 1\n",
"melancholythat 1\n",
"cheapest 1\n",
"unwavering 1\n",
"awayfarther 1\n",
"partowner 1\n",
"repassing 3\n",
"unclei 1\n",
"cosy 2\n",
"wayhasnt 1\n",
"gymnastic 1\n",
"pokers 2\n",
"caius 1\n",
"flagstaffs 1\n",
"sledgehammers 1\n",
"uprearing 2\n",
"boatbuilding 1\n",
"placewere 1\n",
"forkand 1\n",
"atomies 2\n",
"unturnedand 1\n",
"pocketwith 1\n",
"coachoffice 3\n",
"toughness 2\n",
"weathereye 1\n",
"prawns 1\n",
"fellowsufferers 1\n",
"adulatory 1\n",
"bedford 5\n",
"oldsters 1\n",
"toxbut 1\n",
"kamschatka 1\n",
"dilated 6\n",
"joebroad 1\n",
"knobby 6\n",
"standby 2\n",
"breakfastcups 1\n",
"copiously 1\n",
"reproachfulness 1\n",
"newblown 1\n",
"purge 1\n",
"impeachment 3\n",
"rhyming 1\n",
"fleeeg 1\n",
"sweetbreads 1\n",
"unsympathetic 2\n",
"boundscan 1\n",
"blimbers 40\n",
"muchtoo 1\n",
"deadstone 1\n",
"verbs 3\n",
"fastand 1\n",
"tle 6\n",
"tolerance 1\n",
"cornelia 39\n",
"virgil 2\n",
"terence 2\n",
"omega 1\n",
"disrelish 1\n",
"textdown 1\n",
"tozer 25\n",
"immoveable 5\n",
"lobes 1\n",
"winey 1\n",
"timethe 2\n",
"downnothing 1\n",
"ephesians 1\n",
"thereon 2\n",
"ditto 1\n",
"spelt 3\n",
"haec 1\n",
"taurus 1\n",
"harassings 1\n",
"pencilled 1\n",
"feelingin 1\n",
"guttering 2\n",
"flusha 1\n",
"floorcloth 2\n",
"cranium 1\n",
"facemr 1\n",
"sexes 4\n",
"firebuckets 1\n",
"groundglass 2\n",
"sediment 2\n",
"bathroom 1\n",
"sultan 1\n",
"wayafter 1\n",
"quartettes 1\n",
"coolish 1\n",
"yesnot 1\n",
"newsi 1\n",
"seasonshasnt 1\n",
"headclerk 1\n",
"lurk 3\n",
"barbados 10\n",
"endorse 1\n",
"fiddleplaying 1\n",
"unspeakably 6\n",
"unpardonable 1\n",
"trumpeted 1\n",
"unwise 1\n",
"unsought 3\n",
"unquestioned 7\n",
"herestriking 1\n",
"breastmy 1\n",
"birthdayled 1\n",
"hisi 1\n",
"perchs 8\n",
"soontozer 1\n",
"bayswater 1\n",
"kensington 1\n",
"briggss 2\n",
"doughty 2\n",
"personale 1\n",
"simillimia 1\n",
"sketching 4\n",
"selfdefence 3\n",
"boxfull 1\n",
"cramming 1\n",
"blanks 1\n",
"copperplate 1\n",
"halfyearly 2\n",
"schoolmates 1\n",
"analysed 1\n",
"headbenignant 1\n",
"mercifulstood 1\n",
"gruffvoiced 1\n",
"skeins 1\n",
"partyday 1\n",
"hairdresser 1\n",
"overheating 1\n",
"waistcoatbuttoning 1\n",
"baps 13\n",
"skettles 61\n",
"cityvery 1\n",
"respectabledoctor 1\n",
"skettless 1\n",
"herwhispered 1\n",
"thrilling 7\n",
"itthough 2\n",
"gentlemenand 2\n",
"chesterfields 1\n",
"coachwindows 1\n",
"afterwardsnext 1\n",
"beenit 1\n",
"ministry 3\n",
"lifeyou 1\n",
"jumbling 2\n",
"ringbolt 1\n",
"bunsby 87\n",
"capen 35\n",
"spirithook 1\n",
"fares 2\n",
"darlingall 1\n",
"refusematter 1\n",
"waggonruts 1\n",
"steams 1\n",
"whilom 1\n",
"fermentation 1\n",
"birmingham 4\n",
"starsand 1\n",
"himselfpictured 1\n",
"sawthe 1\n",
"centred 1\n",
"blimbersexcept 1\n",
"bedsidepaul 1\n",
"instantlonger 1\n",
"goodbyeturning 1\n",
"lulling 1\n",
"dustbin 2\n",
"uncomplimentary 1\n",
"shakings 3\n",
"rubbings 1\n",
"hesand 1\n",
"persuasive 9\n",
"gum 2\n",
"thisis 1\n",
"hyena 1\n",
"indraught 1\n",
"andsaid 1\n",
"throes 1\n",
"timewill 1\n",
"selfglorification 2\n",
"strongroom 1\n",
"pledgehastily 1\n",
"upsay 1\n",
"assignations 1\n",
"jugglers 2\n",
"moneybox 3\n",
"salmoncoloured 1\n",
"walksoh 1\n",
"hastier 1\n",
"streetcorners 1\n",
"sounder 1\n",
"chic 3\n",
"paulthat 1\n",
"usedpaul 1\n",
"dearthat 1\n",
"mislaid 2\n",
"disinterestednessthere 1\n",
"ebbed 3\n",
"aboutwould 1\n",
"dispensary 1\n",
"di 22\n",
"gleaned 1\n",
"tolled 1\n",
"unresponsive 1\n",
"shoebuckles 1\n",
"anticipations 2\n",
"truck 1\n",
"impounded 2\n",
"modulation 1\n",
"figtree 1\n",
"bedraggled 1\n",
"illstarved 1\n",
"kcb 1\n",
"partand 1\n",
"muchin 1\n",
"permittedmay 1\n",
"uncontradicted 2\n",
"unsuspicious 2\n",
"apostrophising 2\n",
"irewhen 1\n",
"heartyto 1\n",
"washleather 2\n",
"rakish 1\n",
"carpetbags 1\n",
"stokin 1\n",
"denyin 2\n",
"quondam 2\n",
"midair 2\n",
"boasts 2\n",
"jealousies 2\n",
"spurning 4\n",
"lapping 1\n",
"penning 1\n",
"handsthat 2\n",
"staunchly 1\n",
"harrogate 1\n",
"whathisname 1\n",
"arcadian 2\n",
"feenix 101\n",
"handscreen 3\n",
"antony 3\n",
"reversing 2\n",
"parcelling 1\n",
"warily 2\n",
"pared 2\n",
"mouses 1\n",
"smalltalk 2\n",
"indiarubber 1\n",
"broadwise 1\n",
"unstrung 2\n",
"lipsmr 1\n",
"thieve 1\n",
"blubbering 2\n",
"coatcuff 8\n",
"overwatered 1\n",
"desperado 1\n",
"buffetings 1\n",
"badger 1\n",
"cornish 3\n",
"taxgatherer 2\n",
"bedwas 1\n",
"responds 2\n",
"picks 3\n",
"dicats 1\n",
"fantasy 2\n",
"pavementparticularly 1\n",
"hecatombs 1\n",
"pendant 3\n",
"laggard 1\n",
"paving 2\n",
"aweit 1\n",
"roundheaded 1\n",
"whoooop 2\n",
"slighting 3\n",
"ajust 1\n",
"pavingstone 6\n",
"rarer 1\n",
"walterwho 1\n",
"inard 1\n",
"blowoverhaul 1\n",
"shein 1\n",
"ratcliffe 1\n",
"expansive 2\n",
"halfamile 1\n",
"bulkheadhuman 1\n",
"largewith 1\n",
"dreadnought 2\n",
"succedaneum 1\n",
"weatherbut 1\n",
"rowingmatch 1\n",
"contemporaneous 1\n",
"pocketkerchief 1\n",
"sculpturing 1\n",
"ptolemy 1\n",
"lifehad 1\n",
"kate 6\n",
"orphaned 2\n",
"tinkered 1\n",
"shelving 1\n",
"caulk 1\n",
"favouringif 1\n",
"didthat 1\n",
"lastkindly 1\n",
"handcuff 1\n",
"placecautiously 1\n",
"thresholdand 1\n",
"sheering 1\n",
"bonehouse 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"shipboys 1\n",
"poisonto 1\n",
"instrumentbusiness 1\n",
"turnto 2\n",
"twinges 2\n",
"kidneypudding 1\n",
"frolics 1\n",
"sayif 1\n",
"attractivelooking 1\n",
"markthe 1\n",
"colossus 2\n",
"straddled 1\n",
"tropical 2\n",
"harlequin 1\n",
"bayonetted 1\n",
"drawl 1\n",
"acknowledgelet 1\n",
"openthat 1\n",
"prepossessed 4\n",
"primitiveness 1\n",
"slythe 1\n",
"expeditionthe 1\n",
"drill 1\n",
"dental 1\n",
"mingles 1\n",
"exuberance 1\n",
"worms 1\n",
"weaving 5\n",
"overlapped 1\n",
"appearedand 1\n",
"falsest 3\n",
"emphasised 3\n",
"whatshisname 9\n",
"koran 1\n",
"tomorrowi 1\n",
"proposedthe 1\n",
"boa 1\n",
"roador 1\n",
"landscapes 3\n",
"chieftainship 1\n",
"wayfor 1\n",
"himhere 1\n",
"interestingalmost 1\n",
"carriagedoor 4\n",
"skillespecially 1\n",
"surpassedthat 1\n",
"purifies 1\n",
"weakest 3\n",
"ofa 1\n",
"fellowpilgrims 1\n",
"lookersout 1\n",
"exclaimis 1\n",
"conseqi 1\n",
"homeat 1\n",
"herselfshe 1\n",
"scaffoldings 1\n",
"drawerand 1\n",
"birda 1\n",
"pictorial 4\n",
"effervescence 1\n",
"nodthe 1\n",
"seaport 1\n",
"rusticity 1\n",
"dandelionstalks 1\n",
"silkworm 1\n",
"clip 2\n",
"hopenothing 1\n",
"intellectthough 1\n",
"discoursenot 1\n",
"immediatelyof 1\n",
"sprinklings 2\n",
"culpable 1\n",
"formula 1\n",
"sunsetevidently 1\n",
"soonfor 1\n",
"upstarthad 1\n",
"nuptial 6\n",
"kitchenservants 1\n",
"wheeledchair 1\n",
"alwayswill 1\n",
"gushes 2\n",
"candelabra 2\n",
"florenceturned 1\n",
"milliner 11\n",
"florencehow 1\n",
"greyly 1\n",
"churchdoor 2\n",
"miff 42\n",
"altarcloth 1\n",
"soapandwater 1\n",
"miffs 2\n",
"cleavers 1\n",
"fawncoloured 3\n",
"retorts 3\n",
"dombeyat 1\n",
"concludes 2\n",
"sidea 1\n",
"commandments 1\n",
"badenbaden 5\n",
"saysgoodnatured 1\n",
"curtseyto 1\n",
"cherubims 1\n",
"seconding 1\n",
"devolves 2\n",
"whothat 1\n",
"neverin 1\n",
"cultivating 5\n",
"aand 1\n",
"languishes 1\n",
"tigerish 1\n",
"recedes 1\n",
"naps 1\n",
"taketh 2\n",
"horseroad 1\n",
"venturesome 1\n",
"cheekthe 2\n",
"shropshire 3\n",
"flatbrimmed 1\n",
"creationjob 1\n",
"eyesas 1\n",
"splash 1\n",
"waythe 1\n",
"longitude 1\n",
"afire 5\n",
"aholding 1\n",
"dombeyii 1\n",
"placeor 1\n",
"consolingly 1\n",
"officesare 1\n",
"sodawater 1\n",
"edard 14\n",
"jargon 2\n",
"eitherneither 1\n",
"slopselling 1\n",
"mourningone 1\n",
"morespecial 1\n",
"isthough 1\n",
"parrot 22\n",
"juno 2\n",
"nymphaccording 1\n",
"picturewhat 1\n",
"wayfarers 2\n",
"allpotent 1\n",
"backas 2\n",
"conniving 1\n",
"waitingwomen 2\n",
"contradicts 1\n",
"ioh 3\n",
"reparationwould 1\n",
"weatherdust 1\n",
"gravelclotted 1\n",
"unbinding 1\n",
"hardily 1\n",
"headgear 1\n",
"meagre 7\n",
"marwood 5\n",
"remorsefully 3\n",
"wretchand 1\n",
"unshaped 1\n",
"littlediving 1\n",
"deafen 1\n",
"woof 2\n",
"casts 1\n",
"whetherand 1\n",
"persuasively 6\n",
"thornless 1\n",
"wreath 4\n",
"gingerly 2\n",
"delightfullest 1\n",
"installation 1\n",
"houralone 1\n",
"yearned 1\n",
"stillupon 1\n",
"thenin 1\n",
"oppressively 1\n",
"thinkresolved 1\n",
"threadneedle 1\n",
"tiddlers 1\n",
"legsand 2\n",
"surmounting 1\n",
"jacklittle 1\n",
"speechman 1\n",
"amalgamation 1\n",
"dombeywatchful 1\n",
"apartnot 1\n",
"dazzle 1\n",
"frights 1\n",
"wellconsidered 1\n",
"syllablethe 1\n",
"butted 1\n",
"ishall 2\n",
"carkertrust 1\n",
"peachvelvet 2\n",
"scrawling 1\n",
"florencebut 1\n",
"himgrangerif 1\n",
"dressmore 1\n",
"illnessand 1\n",
"tolerating 1\n",
"secretlikehas 1\n",
"secretlike 1\n",
"wotever 2\n",
"wedges 2\n",
"coves 3\n",
"bisness 1\n",
"wonderfriends 1\n",
"richardsand 1\n",
"ironing 2\n",
"objectors 1\n",
"beerob 1\n",
"transmigrated 1\n",
"bettered 1\n",
"tarry 3\n",
"endorsements 1\n",
"obserwation 3\n",
"signmanual 1\n",
"guzzlings 2\n",
"forand 2\n",
"jollson 1\n",
"dooras 1\n",
"libertyand 1\n",
"bitters 1\n",
"fables 2\n",
"ownwould 1\n",
"selfinflicted 1\n",
"hardening 1\n",
"disliking 1\n",
"dependents 3\n",
"moneyor 1\n",
"gentlemenin 1\n",
"vermin 3\n",
"welllighted 1\n",
"confusions 2\n",
"sonsinlaw 1\n",
"flyaway 1\n",
"prom 1\n",
"begad 1\n",
"pettishly 1\n",
"quire 1\n",
"monosyllable 3\n",
"giggling 1\n",
"trueblue 1\n",
"decrepitudeedith 1\n",
"seabirds 2\n",
"montargis 1\n",
"pipchinsshows 1\n",
"dropsical 1\n",
"maymay 1\n",
"iithats 1\n",
"stammers 1\n",
"urges 2\n",
"billy 1\n",
"doubtman 1\n",
"anglonorman 1\n",
"dank 1\n",
"beenshut 1\n",
"cursorily 1\n",
"smalls 1\n",
"watcher 4\n",
"counterbalanced 1\n",
"firstcarker 1\n",
"misconceive 1\n",
"devotionfor 1\n",
"vultures 1\n",
"comforters 1\n",
"masinlaw 1\n",
"stairfoot 1\n",
"selfcontempt 2\n",
"callousness 1\n",
"selfthe 1\n",
"meethosalem 1\n",
"timessome 1\n",
"haveand 1\n",
"voluble 4\n",
"pipchinsesi 1\n",
"ireful 2\n",
"essex 2\n",
"cooows 1\n",
"acidulation 1\n",
"youllyoull 2\n",
"bluecoat 1\n",
"complication 3\n",
"timetoto 1\n",
"inflated 2\n",
"gobetween 1\n",
"contumacy 2\n",
"obtuse 2\n",
"madamheaven 1\n",
"hecan 1\n",
"lynxeyed 1\n",
"twentyfold 1\n",
"bottlemakers 3\n",
"lovey 3\n",
"shouldershes 1\n",
"tigercat 1\n",
"misinterpret 1\n",
"canting 1\n",
"beckoner 1\n",
"footprint 1\n",
"imperilled 1\n",
"foetid 1\n",
"heavenbut 1\n",
"convictships 1\n",
"maturity 1\n",
"figs 2\n",
"knowsi 1\n",
"welland 1\n",
"fascinator 1\n",
"inunthankfully 1\n",
"manifestedwith 1\n",
"fiercer 3\n",
"repressible 1\n",
"recurrence 2\n",
"groundsomeone 1\n",
"marketplaces 1\n",
"turbid 6\n",
"deadlight 1\n",
"dabbing 6\n",
"woice 5\n",
"almightys 1\n",
"halflulled 1\n",
"hilltops 1\n",
"suffusionwhen 1\n",
"dished 1\n",
"obleege 1\n",
"rejices 1\n",
"dooleastways 1\n",
"talkedand 1\n",
"overbalanced 1\n",
"homeno 1\n",
"wainscot 2\n",
"driftin 1\n",
"prettya 1\n",
"slings 1\n",
"hesitatingif 1\n",
"notyou 2\n",
"ownership 1\n",
"faction 1\n",
"drinkingplace 1\n",
"diwision 1\n",
"indiwidually 1\n",
"characterif 1\n",
"dombeynot 1\n",
"inconveniencei 1\n",
"gillsand 1\n",
"treatedand 1\n",
"youare 2\n",
"adjectives 1\n",
"youthough 1\n",
"feenixif 1\n",
"nightman 1\n",
"providential 2\n",
"childquelling 1\n",
"ushers 2\n",
"commemoration 1\n",
"lapses 1\n",
"hovel 2\n",
"glean 1\n",
"outscrewed 1\n",
"slily 1\n",
"insiniwated 1\n",
"combating 1\n",
"wayher 1\n",
"ferret 1\n",
"cryingfor 1\n",
"implacability 1\n",
"comprehensively 1\n",
"misdeed 1\n",
"havethough 1\n",
"worldor 1\n",
"waifs 3\n",
"johnthough 1\n",
"generalities 2\n",
"latterlyyou 1\n",
"wraith 1\n",
"mecame 1\n",
"nighti 1\n",
"roadi 1\n",
"frenchmanit 1\n",
"havre 1\n",
"beforeand 1\n",
"famethat 1\n",
"againstwhen 1\n",
"sicilian 1\n",
"cajoled 1\n",
"meresolved 1\n",
"falselythat 1\n",
"unquenchable 3\n",
"betrayers 1\n",
"treacherously 1\n",
"pocketknife 1\n",
"postyard 1\n",
"countryhe 1\n",
"postponing 4\n",
"groundlessly 1\n",
"postyards 2\n",
"houseroofs 1\n",
"wineshops 1\n",
"arcades 1\n",
"tolling 2\n",
"seabreeze 1\n",
"inland 1\n",
"teethhad 1\n",
"attractedor 1\n",
"comfortless 2\n",
"terrorfelt 1\n",
"shrieklooked 1\n",
"roundsaw 1\n",
"headthan 1\n",
"hison 1\n",
"ififi 1\n",
"steppedand 1\n",
"fraudulent 3\n",
"exhaustless 2\n",
"agobefore 1\n",
"seai 1\n",
"entirley 1\n",
"returnednot 1\n",
"halfsmothered 1\n",
"halfashamed 1\n",
"boltropes 1\n",
"minutesan 1\n",
"gaslamps 2\n",
"corrugation 1\n",
"wy 3\n",
"pave 2\n",
"espies 1\n",
"pewopening 1\n",
"drier 1\n",
"clasps 2\n",
"changedshe 2\n",
"goldsmiths 1\n",
"wormy 1\n",
"soundingboards 1\n",
"eyelethole 1\n",
"lubricates 1\n",
"chokes 1\n",
"hesitates 1\n",
"noised 1\n",
"characterhis 1\n",
"crabbed 1\n",
"riddled 1\n",
"selfdisrespect 1\n",
"yearsyou 1\n",
"obtrusion 1\n",
"melodiously 1\n",
"dolorouslooking 1\n",
"situationyoull 1\n",
"fingerposts 1\n",
"alices 1\n",
"bestliked 1\n",
"galonly 1\n",
"paintbut 1\n",
"forgives 2\n",
"onenot 1\n",
"tumblerfull 1\n",
"conclave 1\n",
"spokesman 1\n",
"indecencies 1\n",
"gigs 3\n",
"dinnerknives 1\n",
"kitchenrange 1\n",
"catalogues 2\n",
"fireescape 2\n",
"frenchpolished 1\n",
"telescopic 1\n",
"andhigh 1\n",
"springvans 1\n",
"screwdrivers 2\n",
"mewould 1\n",
"worryings 1\n",
"daintiesor 1\n",
"yetso 1\n",
"unforgiving 2\n",
"russet 3\n",
"leak 1\n",
"feltoh 1\n",
"hopehes 1\n",
"indi 1\n",
"illarranged 1\n",
"biffin 2\n",
"imprimis 1\n",
"gravis 1\n",
"doctus 1\n",
"orangeflowers 1\n",
"buck 3\n",
"asasas 1\n",
"corsair 2\n",
"lambent 1\n",
"sometimeswhich 1\n",
"feederwithout 1\n",
"metaphor 2\n",
"shipbiscuit 1\n",
"wontthrew 1\n",
"bokum 4\n",
"heathwas 1\n",
"saidshe 1\n",
"perilously 1\n",
"acquaintedcannot 1\n",
"alsoi 1\n",
"relativeand 1\n",
"donenot 1\n",
"lightens 1\n",
"barkerman 1\n",
"shakespeareman 1\n",
"throatfor 1\n",
"blisswhich 1\n",
"wifethat 1\n",
"brideas 1\n",
"situatedand 1\n",
"grandpapa 1\n",
"wwwgutenbergorgcontact 1\n",
"flintwinch 261\n",
"merdles 67\n",
"shoal 2\n",
"prunes 19\n",
"resigns 2\n",
"chelsea 2\n",
"mitigation 1\n",
"metamorphosed 1\n",
"marseilles 30\n",
"rarity 1\n",
"portuguese 1\n",
"cicala 1\n",
"whitehot 1\n",
"dreamily 3\n",
"brightpointed 1\n",
"definable 1\n",
"prisonkeeper 3\n",
"rigaud 121\n",
"againthis 1\n",
"pavements 4\n",
"melon 1\n",
"soberly 2\n",
"haha 8\n",
"barronneau 4\n",
"dieat 1\n",
"twoandtwenty 2\n",
"altroan 1\n",
"realising 3\n",
"madhouse 1\n",
"hattey 1\n",
"fig 2\n",
"couldespecially 1\n",
"everywherethis 1\n",
"ounce 3\n",
"disembodied 1\n",
"jets 1\n",
"congregationless 1\n",
"plagues 3\n",
"dimmer 3\n",
"luxuriousness 2\n",
"hoursi 1\n",
"jeremiah 103\n",
"afeared 2\n",
"tot 1\n",
"hardfavoured 1\n",
"enoughas 1\n",
"environment 1\n",
"welljeremiah 1\n",
"beena 1\n",
"youalmost 1\n",
"lifeso 1\n",
"flintwinchs 17\n",
"becameunlike 1\n",
"cabbageheaded 1\n",
"lunged 1\n",
"womanaffery 2\n",
"commissionmerchants 1\n",
"afflictedmother 1\n",
"overpoised 1\n",
"dryhe 1\n",
"junketing 1\n",
"dayor 1\n",
"plannot 1\n",
"dressit 1\n",
"neatwere 1\n",
"onesmrs 1\n",
"grouts 1\n",
"hearths 1\n",
"ledgers 2\n",
"consignment 1\n",
"partridges 1\n",
"spiked 3\n",
"defaulters 3\n",
"ironplated 1\n",
"clearlike 1\n",
"ackney 1\n",
"helplessest 1\n",
"faceup 1\n",
"hoarseness 2\n",
"puffier 1\n",
"bangham 25\n",
"gallipots 2\n",
"buryin 1\n",
"awayof 1\n",
"comatose 1\n",
"supersede 1\n",
"snooks 1\n",
"addwhat 1\n",
"marvelled 1\n",
"theresthe 1\n",
"dandelions 1\n",
"nnot 1\n",
"godchild 1\n",
"lowliest 2\n",
"betterthat 1\n",
"purposein 1\n",
"seamstress 7\n",
"workwoman 1\n",
"groupruined 1\n",
"certaintyon 1\n",
"cliffords 1\n",
"auctioneers 1\n",
"distillery 1\n",
"downatheel 1\n",
"slingo 1\n",
"graceless 2\n",
"recrossing 1\n",
"interrogator 1\n",
"alepot 1\n",
"coalmerchant 1\n",
"awellaits 1\n",
"halfrepressed 1\n",
"receivedhemtestimonials 1\n",
"testimonial 6\n",
"turnkeys 8\n",
"litigiously 1\n",
"sawdust 1\n",
"pipelights 1\n",
"prophetically 1\n",
"equinoctial 1\n",
"errandbearers 1\n",
"speculative 1\n",
"alcoholic 1\n",
"clarionetplayers 1\n",
"footway 1\n",
"unsightly 4\n",
"halffinished 2\n",
"pinches 2\n",
"cripplewayboo 1\n",
"plornish 168\n",
"frilling 2\n",
"gipsys 1\n",
"tealeaf 2\n",
"herselfwhich 1\n",
"overtop 1\n",
"grievances 3\n",
"gabbled 1\n",
"demagogues 1\n",
"fieldday 1\n",
"commendable 1\n",
"crammer 1\n",
"heavenborn 1\n",
"tornup 1\n",
"bamboozling 1\n",
"callow 1\n",
"coachmens 1\n",
"coops 1\n",
"monde 1\n",
"distillation 1\n",
"gunbarrel 3\n",
"hocussed 1\n",
"secretaryship 1\n",
"betterkeep 1\n",
"doyce 135\n",
"manslaughter 1\n",
"perfects 1\n",
"highlyconnected 2\n",
"newgate 1\n",
"ignorances 4\n",
"adoption 1\n",
"manys 2\n",
"saone 5\n",
"doorposts 2\n",
"cabaret 3\n",
"discussionespecially 1\n",
"flycatchers 1\n",
"landladys 1\n",
"patronised 4\n",
"madameor 1\n",
"negativing 1\n",
"madames 1\n",
"nobodywanted 1\n",
"prospered 5\n",
"menbleeding 1\n",
"tortuously 1\n",
"murderwhich 1\n",
"antiquaries 1\n",
"inquirers 1\n",
"plastering 1\n",
"welcomewhy 1\n",
"seewhyi 1\n",
"seventyfive 2\n",
"barbary 2\n",
"gloucestershire 1\n",
"snapperup 1\n",
"ekally 2\n",
"malefactors 1\n",
"irreverent 1\n",
"proverbial 2\n",
"knewit 1\n",
"unfruitful 1\n",
"airtight 1\n",
"songless 1\n",
"casbyrecognisable 1\n",
"wallfruit 2\n",
"latchkey 1\n",
"hairpins 2\n",
"innan 1\n",
"dogstealer 1\n",
"accessories 1\n",
"steamtug 3\n",
"mummer 1\n",
"tittered 2\n",
"arthuror 1\n",
"pagodian 1\n",
"arthurcannot 4\n",
"soare 1\n",
"interweave 1\n",
"fs 66\n",
"inane 1\n",
"henley 1\n",
"disenchanted 1\n",
"northwest 1\n",
"cropping 1\n",
"purposing 1\n",
"easel 5\n",
"movingly 1\n",
"misdirection 1\n",
"grimnessthe 1\n",
"basest 1\n",
"personwho 1\n",
"suppositious 2\n",
"cannever 1\n",
"knowsas 1\n",
"youngfar 1\n",
"workers 1\n",
"isntwhos 1\n",
"debilitated 1\n",
"kinda 1\n",
"temperi 1\n",
"yoogh 1\n",
"drains 1\n",
"nighta 1\n",
"pursuant 1\n",
"airier 1\n",
"widowed 2\n",
"clyde 1\n",
"saywhoever 1\n",
"expandthat 1\n",
"tesselated 1\n",
"spezzian 1\n",
"moorish 1\n",
"carrara 1\n",
"flytrap 1\n",
"parlourmaids 2\n",
"lucubrations 1\n",
"footpath 1\n",
"lounger 3\n",
"cuyps 1\n",
"decimuss 2\n",
"departmentwithout 1\n",
"pattered 1\n",
"unutterably 1\n",
"abeyance 2\n",
"horsemonger 5\n",
"fronted 1\n",
"tarrying 1\n",
"eightysix 1\n",
"fortyeight 1\n",
"businessbut 1\n",
"chiverys 7\n",
"swain 3\n",
"jumbo 1\n",
"kilt 1\n",
"mousecage 1\n",
"tollplate 1\n",
"lowlier 1\n",
"johni 1\n",
"backstreets 1\n",
"insolvents 1\n",
"edifyingthe 1\n",
"shuffle 4\n",
"unadmiringly 1\n",
"eccentricity 2\n",
"selfdepreciation 1\n",
"andandtestimonials 1\n",
"pockethad 1\n",
"captivesand 1\n",
"thisyoung 1\n",
"admiredthenot 1\n",
"hishis 1\n",
"knowmy 1\n",
"recreations 2\n",
"mindwhatever 1\n",
"smokeless 1\n",
"billiardmarker 2\n",
"upallnight 1\n",
"sixgo 3\n",
"onion 1\n",
"whiter 1\n",
"myselfmost 1\n",
"vastest 1\n",
"creaturesmy 1\n",
"withwhat 1\n",
"saya 1\n",
"nonprofessional 1\n",
"unfortunatelygood 1\n",
"unpowdered 1\n",
"hurtbut 1\n",
"scornfullest 1\n",
"checki 1\n",
"approachable 1\n",
"fireescapes 1\n",
"homewho 1\n",
"weightiest 1\n",
"facto 1\n",
"sparkler 174\n",
"frequenting 1\n",
"lounges 1\n",
"paired 1\n",
"amicus 1\n",
"humanly 1\n",
"recondite 2\n",
"tobacconists 1\n",
"havannah 1\n",
"fineflavoured 1\n",
"deploringly 1\n",
"furling 2\n",
"woebegone 1\n",
"himtoo 1\n",
"perquisitions 1\n",
"illuminate 1\n",
"likeand 1\n",
"stepladder 3\n",
"darkwould 1\n",
"papai 1\n",
"hazarding 1\n",
"clennamwanted 1\n",
"mogul 1\n",
"backslidings 1\n",
"rooting 2\n",
"dingier 1\n",
"patriarchwho 1\n",
"beliefshe 1\n",
"disavowal 1\n",
"bestnatured 1\n",
"tunbridge 1\n",
"behindshe 1\n",
"severeought 1\n",
"gentlerlooking 1\n",
"iamto 1\n",
"beautified 1\n",
"innerdoor 1\n",
"rugg 110\n",
"glasscases 1\n",
"missions 1\n",
"advanceand 1\n",
"pithy 1\n",
"pegsill 1\n",
"waggishness 1\n",
"recreate 1\n",
"yardwas 1\n",
"dragooned 1\n",
"assize 1\n",
"immoralities 1\n",
"baptistcoffeebiggin 1\n",
"blithest 2\n",
"duller 1\n",
"tothedog 1\n",
"gipsies 2\n",
"footboys 1\n",
"dreadfullyparticularly 1\n",
"neglectful 2\n",
"tudor 4\n",
"coalition 2\n",
"oracles 2\n",
"micklesmiggles 1\n",
"ecarte 1\n",
"portentously 1\n",
"fellowtraveller 1\n",
"wrongheaded 2\n",
"heraldry 1\n",
"currantjelly 1\n",
"pierglass 1\n",
"retracing 1\n",
"sisterwoman 1\n",
"notification 1\n",
"waterlilies 1\n",
"unthankful 1\n",
"donethat 1\n",
"hopesall 1\n",
"remissness 1\n",
"charactershall 1\n",
"businesshours 1\n",
"housedsaw 1\n",
"countinghouseits 1\n",
"wryest 1\n",
"blandois 155\n",
"duskfor 1\n",
"vineleaves 2\n",
"jeremiahs 3\n",
"mepardon 1\n",
"cyphers 1\n",
"freedoms 1\n",
"acquaintedyou 1\n",
"stolidly 2\n",
"acquaintancechance 1\n",
"fathera 1\n",
"samaritan 1\n",
"chirpings 1\n",
"alsostrephon 1\n",
"povertywas 1\n",
"pensioner 11\n",
"toddled 1\n",
"airgun 2\n",
"presenceturned 1\n",
"flouncing 3\n",
"prisonchild 1\n",
"heartno 1\n",
"streetssmiling 1\n",
"witho 1\n",
"sirits 1\n",
"pensioners 1\n",
"memespirit 1\n",
"ishaimmoral 1\n",
"ishumparricidal 1\n",
"stigmatise 1\n",
"interesti 1\n",
"bemay 1\n",
"afterwardsand 1\n",
"dingily 1\n",
"meeh 1\n",
"insatiate 2\n",
"roundeh 1\n",
"darlingeh 1\n",
"recoverer 1\n",
"altarrailing 1\n",
"shenot 1\n",
"hewho 1\n",
"pillbox 2\n",
"huckstering 1\n",
"toas 1\n",
"sayto 1\n",
"sympathised 1\n",
"doubleknocks 1\n",
"saturating 1\n",
"sustains 1\n",
"fellers 2\n",
"herat 1\n",
"ratherlike 1\n",
"directiondespatchboxing 1\n",
"choppingboards 1\n",
"rollingpins 1\n",
"doubleloaded 1\n",
"fetter 2\n",
"battlehorses 1\n",
"stalled 1\n",
"dorsetshire 2\n",
"boilingover 1\n",
"pleas 1\n",
"illustrated 1\n",
"differentto 1\n",
"prosingly 1\n",
"goodfortune 4\n",
"intermingling 1\n",
"munificently 1\n",
"themhumnot 1\n",
"mehumwith 1\n",
"carriagewhich 1\n",
"muchtrodden 2\n",
"eightpence 1\n",
"gatefees 1\n",
"mouthfrom 1\n",
"ridges 1\n",
"goitre 1\n",
"chalet 1\n",
"cancelling 1\n",
"barrenness 2\n",
"defences 2\n",
"packsaddles 1\n",
"piers 1\n",
"overfatigued 1\n",
"tohato 6\n",
"discoveredthrough 1\n",
"sirdetrimental 1\n",
"woodfire 1\n",
"familyof 1\n",
"perfections 1\n",
"chaperon 1\n",
"takento 1\n",
"disarranging 1\n",
"mindto 1\n",
"anylying 1\n",
"footthawed 1\n",
"ihawill 1\n",
"ishahighly 1\n",
"sentimentsthat 1\n",
"altogetherhum 1\n",
"onhamy 1\n",
"broadcloths 1\n",
"ablutions 1\n",
"familya 1\n",
"explanationbut 1\n",
"paralysing 1\n",
"wouldhumoverlook 1\n",
"asperities 1\n",
"hehahe 1\n",
"withhuma 1\n",
"simplon 1\n",
"dreamonly 1\n",
"induct 1\n",
"mindabsolutely 1\n",
"swarms 3\n",
"hopefor 1\n",
"respecti 1\n",
"meanto 1\n",
"capthe 1\n",
"bided 2\n",
"rialto 1\n",
"wellconducted 1\n",
"sayhumimpressively 1\n",
"iha 3\n",
"tohumcircumstances 1\n",
"youconstantly 1\n",
"worldahagentleman 1\n",
"merehumbaby 1\n",
"ihaam 2\n",
"thehumlap 1\n",
"shouldhasystematically 1\n",
"anxiousto 1\n",
"ahatruly 1\n",
"illadjusted 1\n",
"alarum 1\n",
"billiardmarking 1\n",
"picturegalleries 2\n",
"ihawhat 1\n",
"sayyou 2\n",
"ornamentally 1\n",
"ofhamrs 2\n",
"ofhaworldwide 1\n",
"ashumnational 1\n",
"lifebetter 1\n",
"ihaapprove 1\n",
"beha 1\n",
"hummuch 1\n",
"mighthumit 1\n",
"willhakeep 1\n",
"ishanot 1\n",
"requireshumgreat 1\n",
"ahumwreck 1\n",
"arithmeticians 1\n",
"splenetic 1\n",
"decry 1\n",
"pressingly 2\n",
"grotesquely 1\n",
"bilge 1\n",
"casebut 1\n",
"mancorpo 1\n",
"marco 1\n",
"tractable 1\n",
"himmade 1\n",
"jocosely 1\n",
"furled 1\n",
"squired 1\n",
"sparklers 15\n",
"loiterers 1\n",
"upstarts 1\n",
"journeymen 1\n",
"politest 4\n",
"amphitheatres 1\n",
"smallswords 1\n",
"beenhadisappointed 1\n",
"confiscating 1\n",
"irreconcilability 1\n",
"hurryfor 1\n",
"policeoffice 1\n",
"formfilling 1\n",
"diabolic 1\n",
"cleave 1\n",
"prevention 1\n",
"footways 1\n",
"schemer 1\n",
"championed 2\n",
"circumstancesthat 1\n",
"paysin 1\n",
"semigoring 1\n",
"misrepresent 1\n",
"seehows 1\n",
"freeas 1\n",
"sunflowers 1\n",
"fullblow 1\n",
"advancea 1\n",
"watermens 1\n",
"sandwich 1\n",
"maccaroni 1\n",
"meacquainted 1\n",
"construing 1\n",
"chaff 5\n",
"sirdemanded 1\n",
"tally 1\n",
"trustbetrayers 1\n",
"hawho 1\n",
"paints 1\n",
"divides 1\n",
"conceals 1\n",
"oftenoften 1\n",
"italysomewhere 1\n",
"lumpishly 1\n",
"winecooler 1\n",
"overhad 1\n",
"tense 2\n",
"lightcomedy 2\n",
"macheath 1\n",
"toehwell 1\n",
"peartree 3\n",
"dames 1\n",
"indigestive 1\n",
"fitfully 2\n",
"oatmeal 1\n",
"treacle 1\n",
"counterchecking 1\n",
"curioussomething 1\n",
"loadstone 1\n",
"cantto 1\n",
"freehold 2\n",
"surgical 1\n",
"liqueur 1\n",
"believers 1\n",
"communicable 1\n",
"defaulting 1\n",
"mouthif 1\n",
"hollyhock 1\n",
"uprooted 1\n",
"padrona 6\n",
"appen 1\n",
"intertwining 1\n",
"oiling 1\n",
"youspoke 1\n",
"sagaciously 1\n",
"exhalation 2\n",
"outstretching 1\n",
"sinecure 1\n",
"sweetesttempered 1\n",
"blindest 1\n",
"ungenteel 1\n",
"sonall 1\n",
"extollings 1\n",
"beautybut 1\n",
"sparklerand 1\n",
"preeminently 1\n",
"ishaapproved 1\n",
"enchantresses 1\n",
"announcehaformally 1\n",
"tohuma 1\n",
"projectsif 1\n",
"explaininghawhat 1\n",
"mehumthe 1\n",
"alongone 1\n",
"alongwith 1\n",
"thehumformation 1\n",
"brideelect 1\n",
"easter 1\n",
"flounced 2\n",
"concernedbut 1\n",
"customhouses 1\n",
"legions 2\n",
"viewers 1\n",
"breakfastinvitation 1\n",
"torlonia 1\n",
"consularity 1\n",
"murderousheaded 1\n",
"hideousness 1\n",
"erst 1\n",
"sometimesmight 1\n",
"robes 3\n",
"slough 2\n",
"platoonexercise 1\n",
"aharesponsibility 1\n",
"canhatake 1\n",
"thehablessing 1\n",
"eligibly 1\n",
"highways 1\n",
"itruins 1\n",
"competing 1\n",
"thehumsense 1\n",
"thehaobserved 1\n",
"saidhahe 1\n",
"andhaprivilege 1\n",
"ticklish 1\n",
"potentate 1\n",
"humrest 1\n",
"throve 1\n",
"imported 1\n",
"involvingha 1\n",
"thehafact 1\n",
"arthurfoolish 1\n",
"stationcommunicated 1\n",
"emboldens 1\n",
"tohastate 1\n",
"solusfor 1\n",
"elucidation 1\n",
"hawell 1\n",
"andhummy 1\n",
"withhamy 1\n",
"knowwas 1\n",
"antics 1\n",
"plumbed 1\n",
"lifeeven 1\n",
"sirbut 1\n",
"littlefathers 1\n",
"ihabeg 1\n",
"amongha 1\n",
"thathaonce 1\n",
"byhumlittle 1\n",
"unmerciful 1\n",
"tinbox 1\n",
"shopwindows 1\n",
"blocks 1\n",
"disembarked 1\n",
"felons 1\n",
"windowgap 1\n",
"stilllife 1\n",
"beenhaso 1\n",
"forhataking 1\n",
"ihaneed 1\n",
"morehumbroken 1\n",
"youhasee 1\n",
"capthough 1\n",
"ignominiously 1\n",
"expatiating 1\n",
"tohumthe 1\n",
"marryhawe 1\n",
"andhumelevate 1\n",
"andhai 1\n",
"thathastrong 1\n",
"sayhajealousy 1\n",
"nothumin 1\n",
"thehafather 1\n",
"sohahonourable 1\n",
"myhapersonal 1\n",
"solelyto 1\n",
"tonei 1\n",
"bobmany 1\n",
"reproduced 2\n",
"walltop 1\n",
"weatherworn 1\n",
"besieging 1\n",
"icesay 4\n",
"predispose 1\n",
"bondswoman 1\n",
"pleasurethe 1\n",
"feelingto 1\n",
"thrusts 1\n",
"venicewhich 1\n",
"ibeing 1\n",
"assertions 1\n",
"inadvertent 1\n",
"doesquite 1\n",
"mockeryi 1\n",
"probabilitiesrather 1\n",
"upof 1\n",
"sneezer 1\n",
"teaser 1\n",
"arthurssensible 1\n",
"airlessness 1\n",
"mindperhaps 1\n",
"sprig 2\n",
"quinbus 1\n",
"deathor 1\n",
"weakeneddown 1\n",
"whostop 1\n",
"whyno 1\n",
"keepsakes 1\n",
"masterspirit 1\n",
"agglomeration 1\n",
"banquos 1\n",
"titled 1\n",
"empanel 1\n",
"reposes 1\n",
"laudanumbottle 1\n",
"rapidbeen 1\n",
"dabbling 1\n",
"airwas 1\n",
"preparative 1\n",
"wilder 1\n",
"downlooking 1\n",
"sitter 1\n",
"himbrought 1\n",
"steamlike 1\n",
"crimeas 1\n",
"commonsense 1\n",
"exonerate 1\n",
"swindles 1\n",
"wroth 1\n",
"exoneration 1\n",
"caption 1\n",
"inmr 1\n",
"madder 1\n",
"ob 1\n",
"bithznithz 1\n",
"therethe 1\n",
"selfsubdual 1\n",
"clennamwhich 1\n",
"wasyou 1\n",
"prisonparapet 1\n",
"herbs 1\n",
"simoom 1\n",
"upas 1\n",
"garretwindow 1\n",
"certaintyas 1\n",
"answerno 1\n",
"sexs 1\n",
"bowlers 1\n",
"lamed 1\n",
"hadi 1\n",
"completelydid 1\n",
"coaching 1\n",
"companyi 1\n",
"consequentementallyit 1\n",
"englishfirst 1\n",
"londra 2\n",
"panco 6\n",
"havea 1\n",
"expensei 1\n",
"mothermight 1\n",
"undiscernible 1\n",
"contrabandist 3\n",
"chagrins 2\n",
"pollutes 1\n",
"pigdriver 1\n",
"nowso 1\n",
"recallingas 1\n",
"headsmans 1\n",
"itevery 1\n",
"yearsno 1\n",
"isbrought 1\n",
"prisonill 1\n",
"fellowif 1\n",
"coattail 1\n",
"tradebargains 1\n",
"derange 1\n",
"acquaintanceof 1\n",
"demandi 1\n",
"flintwinchwhen 1\n",
"richi 1\n",
"proofsi 1\n",
"rapacious 1\n",
"abases 1\n",
"madamea 1\n",
"duskthere 1\n",
"notwhat 1\n",
"impietystill 1\n",
"redemption 1\n",
"watchcase 1\n",
"stabbers 1\n",
"ruggedness 1\n",
"rewarding 1\n",
"prisonerjailcomrade 1\n",
"soulcame 1\n",
"bachelorapartmentfurnished 1\n",
"cognac 2\n",
"doggedness 1\n",
"lunatickeeper 1\n",
"sakeshe 1\n",
"flintwinchask 1\n",
"rustles 1\n",
"marseillesjail 1\n",
"saunterers 1\n",
"halfremembered 1\n",
"hater 1\n",
"covenant 1\n",
"mynheer 1\n",
"enlargement 1\n",
"perukemaker 1\n",
"moonshine 1\n",
"instep 1\n",
"slowgoing 1\n",
"hummingtop 1\n",
"capersauce 1\n",
"goggleeyed 1\n",
"disqualified 1\n",
"marketwarring 1\n",
"crane 1\n",
"opprobrious 1\n",
"peasantwoman 1\n",
"businessor 1\n",
"fellowspull 1\n",
"droopingbut 1\n",
"flaky 1\n",
"chaplet 1\n",
"chucking 1\n",
"sunbrowned 2\n",
"ishere 1\n",
"awriting 1\n",
"froward 1\n",
"gutenberg 149\n",
"by 8287\n",
"prest 3\n",
"anyone 313\n",
"and 69062\n",
"almost 655\n",
"restrictions 12\n",
"you 20613\n",
"copy 74\n",
"blood 239\n",
"january 10\n",
"english 114\n",
"start 109\n",
"debra 2\n",
"pg 7\n",
"note 136\n",
"edition 13\n",
"which 7707\n",
"were 7082\n",
"three 733\n",
"authorship 2\n",
"james 47\n",
"contents 66\n",
"exciting 11\n",
"art 104\n",
"spirit 220\n",
"salisburysquare 1\n",
"chapter 1132\n",
"hailstormthe 2\n",
"pursuit 79\n",
"offer 162\n",
"vthe 1\n",
"moonlightthe 5\n",
"bannerworths 67\n",
"unpleasant 66\n",
"circumstance 135\n",
"occurrences 20\n",
"pistolshot 2\n",
"return 381\n",
"alarm 128\n",
"hall 484\n",
"xithe 1\n",
"occurrence 33\n",
"varneythe 8\n",
"resemblancea 2\n",
"confusion 143\n",
"xixflora 1\n",
"manuscriptan 2\n",
"conference 29\n",
"uncle 538\n",
"xxvithe 1\n",
"spot 263\n",
"prisoner 128\n",
"asseveration 3\n",
"xxxiiithe 1\n",
"xxxvthe 1\n",
"xxxvithe 1\n",
"separate 51\n",
"again 2760\n",
"open 514\n",
"xlvithe 1\n",
"watch 245\n",
"stake 50\n",
"admission 36\n",
"housethe 2\n",
"wine 236\n",
"house 1886\n",
"firethe 3\n",
"abodejack 2\n",
"lviithe 1\n",
"hat 305\n",
"againthe 6\n",
"inn 119\n",
"lxivthe 1\n",
"lxviiimarchdales 1\n",
"result 168\n",
"funeral 55\n",
"commotion 22\n",
"mutual 43\n",
"ruse 7\n",
"refuge 69\n",
"hungarian 25\n",
"populacevarney 2\n",
"dangerhe 2\n",
"miraculous 5\n",
"lxxxviiithe 1\n",
"subdued 46\n",
"conflictthe 2\n",
"doctor 868\n",
"marriage 227\n",
"arranged 119\n",
"decides 6\n",
"entertainment 38\n",
"unprecedented 5\n",
"but 12371\n",
"say 2986\n",
"he 27693\n",
"possible 478\n",
"any 3456\n",
"do 5069\n",
"belief 109\n",
"spread 126\n",
"most 1448\n",
"credibility 1\n",
"subject 553\n",
"light 819\n",
"extraordinary 160\n",
"here 2536\n",
"work 733\n",
"other 2227\n",
"like 2852\n",
"deserving 20\n",
"how 3024\n",
"calm 216\n",
"faint 115\n",
"gun 51\n",
"battle 368\n",
"lethargy 5\n",
"producing 28\n",
"five 245\n",
"giant 18\n",
"blast 13\n",
"suddenly 668\n",
"awakened 90\n",
"chimera 1\n",
"stillstill 1\n",
"breaks 10\n",
"magic 22\n",
"pattering 6\n",
"million 16\n",
"fury 38\n",
"particles 8\n",
"exchanged 42\n",
"cry 255\n",
"gust 10\n",
"blew 43\n",
"moment 1328\n",
"hailstones 1\n",
"dash 13\n",
"done 1342\n",
"oh 1470\n",
"raged 10\n",
"chimneypiece 26\n",
"itself 501\n",
"ceiling 29\n",
"low 403\n",
"stained 15\n",
"yet 1386\n",
"when 6363\n",
"shines 12\n",
"apartment 127\n",
"panelled 12\n",
"young 1691\n",
"stately 62\n",
"expression 560\n",
"bed 575\n",
"carved 17\n",
"dashes 1\n",
"occasional 35\n",
"discharge 48\n",
"saves 6\n",
"creature 209\n",
"much 2853\n",
"side 787\n",
"providence 27\n",
"judge 133\n",
"fatigue 35\n",
"power 479\n",
"within 644\n",
"eyelashes 9\n",
"visiblewhiter 1\n",
"spotless 6\n",
"skin 31\n",
"child 772\n",
"years 646\n",
"vivid 19\n",
"mountains 31\n",
"effectually 23\n",
"continues 12\n",
"uproar 16\n",
"presses 10\n",
"heavens 82\n",
"concussion 6\n",
"prayer 50\n",
"dear 2068\n",
"prays 2\n",
"darkness 138\n",
"figure 368\n",
"tall 157\n",
"lull 8\n",
"delusionshe 1\n",
"awake 76\n",
"paralysed 11\n",
"limbs 48\n",
"darker 17\n",
"consuming 3\n",
"repeats 6\n",
"relief 105\n",
"flickering 9\n",
"lifelike 6\n",
"pane 7\n",
"introduces 4\n",
"fastening 11\n",
"swung 16\n",
"move 179\n",
"drawn 143\n",
"hang 40\n",
"ends 54\n",
"articulation 1\n",
"gone 827\n",
"herself 1039\n",
"towards 700\n",
"metalliclooking 2\n",
"gigantic 25\n",
"seemed 1527\n",
"places 188\n",
"walkcan 1\n",
"overturn 5\n",
"judgment 74\n",
"width 9\n",
"minuteoh 1\n",
"agony 63\n",
"minute 216\n",
"enough 742\n",
"foreseenwith 1\n",
"seized 144\n",
"entwined 6\n",
"lights 80\n",
"inhabitants 78\n",
"asked 1255\n",
"didwhere 1\n",
"dreaming 37\n",
"fromcan 1\n",
"mother 1181\n",
"joined 89\n",
"fainted 20\n",
"corridor 39\n",
"piercing 26\n",
"however 684\n",
"recover 62\n",
"follow 158\n",
"built 47\n",
"required 122\n",
"nearest 84\n",
"eating 75\n",
"weapon 38\n",
"am 3313\n",
"somehow 88\n",
"curdles 1\n",
"veins 35\n",
"difficulty 204\n",
"push 29\n",
"using 56\n",
"massive 23\n",
"resisted 10\n",
"loud 161\n",
"snapit 1\n",
"duration 25\n",
"swelled 21\n",
"crowbaranother 1\n",
"accurately 17\n",
"bound 190\n",
"kind 666\n",
"vicinity 21\n",
"sideface 1\n",
"fearfullooking 1\n",
"detain 25\n",
"likely 236\n",
"acted 60\n",
"uttered 150\n",
"bore 90\n",
"bullets 38\n",
"convinced 148\n",
"returning 113\n",
"nervous 47\n",
"recoiled 9\n",
"colourthe 1\n",
"lustre 18\n",
"rushing 37\n",
"impulse 65\n",
"shrieking 7\n",
"laugh 176\n",
"disappeared 96\n",
"stamp 14\n",
"agitation 96\n",
"let 1417\n",
"road 429\n",
"awfully 15\n",
"hesitated 41\n",
"insensible 31\n",
"approaching 107\n",
"plainly 122\n",
"wooded 9\n",
"bounds 22\n",
"thicket 4\n",
"terrified 58\n",
"human 341\n",
"therelook 1\n",
"twelve 84\n",
"efforts 106\n",
"possibly 124\n",
"replied 829\n",
"escape 142\n",
"thusthere 1\n",
"above 373\n",
"take 1566\n",
"try 290\n",
"idea 415\n",
"close 443\n",
"bullet 41\n",
"impossibility 32\n",
"steady 90\n",
"manner 784\n",
"headlong 34\n",
"soon 921\n",
"disregarded 5\n",
"probably 230\n",
"precise 28\n",
"parts 64\n",
"topple 2\n",
"wonderingly 10\n",
"others 534\n",
"faces 249\n",
"regard 177\n",
"hasten 18\n",
"home 1063\n",
"able 378\n",
"account 358\n",
"visitation 18\n",
"steps 266\n",
"occur 52\n",
"totally 17\n",
"remarked 212\n",
"torment 20\n",
"master 518\n",
"threshold 39\n",
"deari 1\n",
"domestics 9\n",
"greatest 157\n",
"render 72\n",
"evidently 477\n",
"happenedwhat 1\n",
"robust 9\n",
"recorded 19\n",
"active 80\n",
"adopted 44\n",
"persevering 13\n",
"consciousness 131\n",
"friendly 125\n",
"timidly 79\n",
"safe 127\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"recommended 22\n",
"stimulant 7\n",
"persuaded 56\n",
"cheeks 77\n",
"alone 787\n",
"endeavour 88\n",
"fly 87\n",
"feel 546\n",
"leaned 74\n",
"certain 483\n",
"herenot 3\n",
"exactexact 1\n",
"broad 117\n",
"action 332\n",
"calmness 28\n",
"really 829\n",
"busy 135\n",
"else 618\n",
"painful 119\n",
"position 553\n",
"knew 1190\n",
"wonderfully 39\n",
"imagination 114\n",
"downright 9\n",
"rays 56\n",
"modify 1\n",
"constitution 35\n",
"intrusion 15\n",
"indebted 31\n",
"creep 18\n",
"certainly 444\n",
"continually 120\n",
"uncomfortable 115\n",
"solution 22\n",
"wrapped 53\n",
"communings 1\n",
"features 74\n",
"gazed 123\n",
"case 478\n",
"tools 25\n",
"pity 162\n",
"likelihood 11\n",
"uninhabited 3\n",
"inhabit 5\n",
"miserable 122\n",
"horriblehorrible 1\n",
"soothe 15\n",
"evening 635\n",
"birds 74\n",
"pleasant 255\n",
"consideration 178\n",
"remain 247\n",
"proceeding 71\n",
"beings 40\n",
"drops 30\n",
"goreone 1\n",
"echo 21\n",
"thoroughly 74\n",
"deliver 27\n",
"beliefit 1\n",
"marvel 7\n",
"sure 784\n",
"justice 92\n",
"seriously 70\n",
"attachment 41\n",
"despite 109\n",
"mentally 30\n",
"wildest 11\n",
"suppositions 6\n",
"entertain 55\n",
"bitterly 32\n",
"abatedbe 1\n",
"torepudiateit 1\n",
"disbelieve 5\n",
"zealously 5\n",
"engage 14\n",
"hypocrite 13\n",
"rode 268\n",
"markettown 2\n",
"servants 162\n",
"talebearing 1\n",
"gossiping 3\n",
"excuse 148\n",
"dozen 59\n",
"bottom 120\n",
"reports 62\n",
"thats 804\n",
"yesi 3\n",
"obtained 59\n",
"drugs 2\n",
"hearing 180\n",
"omitting 6\n",
"remembrance 145\n",
"questions 162\n",
"care 412\n",
"understanding 139\n",
"ride 100\n",
"ordeal 5\n",
"received 468\n",
"recollection 82\n",
"preserve 40\n",
"harm 111\n",
"read 435\n",
"groaned 25\n",
"relieved 91\n",
"tomb 41\n",
"housebreakerit 1\n",
"diningroom 36\n",
"dreamwould 1\n",
"trifling 33\n",
"prompts 3\n",
"ought 361\n",
"promised 128\n",
"requested 39\n",
"oldfashioned 46\n",
"contrary 208\n",
"narcotic 2\n",
"languor 11\n",
"confident 38\n",
"besides 281\n",
"worth 172\n",
"lead 133\n",
"liked 192\n",
"impossible 270\n",
"development 13\n",
"palpable 7\n",
"medicines 4\n",
"depend 73\n",
"particularly 235\n",
"wastes 4\n",
"presenting 46\n",
"decidedly 44\n",
"random 19\n",
"binding 16\n",
"lying 266\n",
"quarter 118\n",
"roused 75\n",
"seal 26\n",
"genuine 40\n",
"neighbour 39\n",
"proffer 2\n",
"poorer 16\n",
"proximity 12\n",
"watchfulness 7\n",
"argument 47\n",
"clearness 19\n",
"considerably 21\n",
"grapple 5\n",
"tore 35\n",
"herewhat 1\n",
"lapel 3\n",
"rotten 16\n",
"smell 98\n",
"relic 4\n",
"proofs 14\n",
"brightness 24\n",
"shown 157\n",
"conversed 19\n",
"whispers 35\n",
"prompted 16\n",
"later 175\n",
"closely 81\n",
"streamed 15\n",
"sky 174\n",
"detached 14\n",
"exceedingly 63\n",
"scepticism 1\n",
"bears 31\n",
"history 256\n",
"youyou 5\n",
"laurels 3\n",
"emerged 31\n",
"simply 103\n",
"park 21\n",
"stick 94\n",
"brace 6\n",
"ladder 19\n",
"bewilderment 15\n",
"ascend 8\n",
"ample 41\n",
"extensive 27\n",
"compensation 22\n",
"yesyes 10\n",
"horizontal 4\n",
"moonbeams 23\n",
"awe 30\n",
"restoring 9\n",
"pistolhe 1\n",
"willthey 1\n",
"endure 57\n",
"cause 355\n",
"scabbard 4\n",
"glances 35\n",
"country 357\n",
"canopy 7\n",
"spare 135\n",
"spectral 8\n",
"comfort 143\n",
"distressing 16\n",
"concerning 129\n",
"shut 272\n",
"disbelief 6\n",
"creatures 54\n",
"clearer 16\n",
"village 178\n",
"pew 19\n",
"belongs 26\n",
"decide 53\n",
"alarming 21\n",
"earnestly 64\n",
"fortunes 45\n",
"prove 91\n",
"encumbrances 3\n",
"sums 21\n",
"endeavoured 43\n",
"write 185\n",
"approach 99\n",
"everof 1\n",
"live 304\n",
"prince 1900\n",
"money 608\n",
"occasioned 46\n",
"speaks 31\n",
"faults 20\n",
"brave 61\n",
"educated 37\n",
"onefor 1\n",
"client 13\n",
"refused 48\n",
"enabling 9\n",
"sifter 2\n",
"tour 19\n",
"autumn 47\n",
"travelling 47\n",
"byethebye 1\n",
"miles 133\n",
"suffice 19\n",
"heartfelt 11\n",
"honoured 49\n",
"unchanged 30\n",
"induce 62\n",
"indifference 63\n",
"widow 33\n",
"tenderness 98\n",
"sterling 3\n",
"modes 3\n",
"rarely 54\n",
"independence 23\n",
"residing 8\n",
"decent 29\n",
"subsistence 6\n",
"losers 2\n",
"inmate 6\n",
"entertainers 4\n",
"inquire 64\n",
"pleased 237\n",
"accumulating 10\n",
"commenced 32\n",
"negative 23\n",
"comprehend 27\n",
"coffin 85\n",
"mixed 37\n",
"double 80\n",
"lapse 19\n",
"inscription 20\n",
"rested 80\n",
"thethe 5\n",
"instance 81\n",
"untenanted 4\n",
"secretly 47\n",
"detection 3\n",
"trapdoor 2\n",
"interruption 30\n",
"wrench 3\n",
"successful 42\n",
"stand 261\n",
"poured 42\n",
"justified 28\n",
"subjected 14\n",
"gentlemans 57\n",
"nine 80\n",
"waited 105\n",
"equal 88\n",
"throne 24\n",
"actuallyi 1\n",
"beforebut 1\n",
"bless 189\n",
"materials 31\n",
"hilloa 27\n",
"candles 67\n",
"brisk 20\n",
"amount 139\n",
"architecture 4\n",
"towers 15\n",
"flint 4\n",
"stones 69\n",
"prettiest 7\n",
"churches 36\n",
"lover 54\n",
"roman 21\n",
"stead 10\n",
"friends 348\n",
"unholy 6\n",
"churchyard 65\n",
"undo 9\n",
"angle 13\n",
"burialground 1\n",
"noticed 240\n",
"handed 127\n",
"opening 154\n",
"crime 42\n",
"instances 11\n",
"discernible 12\n",
"purposely 22\n",
"afforded 28\n",
"loose 71\n",
"improbable 16\n",
"flat 54\n",
"tolerable 19\n",
"larger 37\n",
"considering 117\n",
"ghastly 31\n",
"visitants 2\n",
"wax 25\n",
"packet 54\n",
"enclosed 11\n",
"himwho 3\n",
"affinity 7\n",
"advanced 126\n",
"imagined 89\n",
"illegible 2\n",
"mortale 1\n",
"ad 2\n",
"antiquarian 1\n",
"leaden 9\n",
"lighted 92\n",
"assert 21\n",
"slid 3\n",
"rags 23\n",
"vestige 5\n",
"worded 4\n",
"torture 16\n",
"remainder 26\n",
"putrefactionno 1\n",
"casual 14\n",
"gasp 8\n",
"dd 69\n",
"carrying 135\n",
"miracle 12\n",
"opinions 72\n",
"embrace 60\n",
"conclusions 24\n",
"oddly 3\n",
"enter 151\n",
"conviction 89\n",
"humanity 66\n",
"bands 19\n",
"sober 34\n",
"subtlest 1\n",
"disposing 5\n",
"task 66\n",
"pains 58\n",
"laying 97\n",
"matting 4\n",
"misery 66\n",
"defiance 33\n",
"injured 43\n",
"resigned 29\n",
"transcends 1\n",
"aid 86\n",
"expressly 28\n",
"renounce 16\n",
"sublime 12\n",
"ridiculous 62\n",
"accord 58\n",
"disgusted 6\n",
"stupidity 11\n",
"paved 20\n",
"ix 36\n",
"entrust 11\n",
"solely 31\n",
"source 44\n",
"male 16\n",
"timid 79\n",
"construction 29\n",
"remotely 9\n",
"collateral 3\n",
"attendant 48\n",
"contrive 7\n",
"clings 5\n",
"hushhushhush 2\n",
"nonot 3\n",
"heartily 67\n",
"godoh 1\n",
"seconds 43\n",
"bird 79\n",
"creating 23\n",
"invested 20\n",
"speaker 48\n",
"expecting 101\n",
"blasted 3\n",
"knocking 50\n",
"banging 4\n",
"formthere 1\n",
"wasthe 1\n",
"stunning 10\n",
"purely 6\n",
"raise 62\n",
"dashing 25\n",
"overcame 12\n",
"insensibility 8\n",
"involuntarily 102\n",
"plantations 1\n",
"flowers 113\n",
"heedlessly 4\n",
"supporting 18\n",
"completed 36\n",
"thisoh 1\n",
"noti 7\n",
"prevent 107\n",
"trance 7\n",
"extricate 5\n",
"wretched 97\n",
"didfly 1\n",
"againit 2\n",
"everybody 262\n",
"wise 52\n",
"unkind 18\n",
"music 117\n",
"accursed 5\n",
"member 78\n",
"private 158\n",
"sympathetic 13\n",
"blanch 1\n",
"caprice 13\n",
"gift 37\n",
"disclosure 20\n",
"panted 14\n",
"reservations 2\n",
"prepare 72\n",
"revelation 17\n",
"verifying 2\n",
"didi 3\n",
"hesitate 24\n",
"tale 38\n",
"prejudices 8\n",
"recollect 126\n",
"bewilder 2\n",
"species 30\n",
"perception 44\n",
"operations 19\n",
"repeatedly 28\n",
"ofwe 1\n",
"nobler 5\n",
"praise 46\n",
"allude 30\n",
"pang 29\n",
"miseries 9\n",
"energies 19\n",
"ascribe 8\n",
"cognisant 4\n",
"vanquish 5\n",
"wellarmed 1\n",
"capable 66\n",
"disappointed 63\n",
"calculations 19\n",
"fickle 4\n",
"realization 8\n",
"fondest 4\n",
"cruel 122\n",
"reared 11\n",
"basked 5\n",
"sunny 23\n",
"bride 38\n",
"portraits 9\n",
"striking 83\n",
"radiance 9\n",
"fascination 23\n",
"merits 30\n",
"chained 13\n",
"increased 103\n",
"diligent 8\n",
"accidentally 32\n",
"unlikely 19\n",
"staircases 7\n",
"disiderata 1\n",
"moulding 5\n",
"startled 39\n",
"single 132\n",
"intending 20\n",
"chamberdoor 2\n",
"provoking 17\n",
"ultimately 17\n",
"bravado 2\n",
"staying 53\n",
"listening 184\n",
"taps 6\n",
"sorry 271\n",
"connexion 66\n",
"unusual 79\n",
"chipped 2\n",
"lever 5\n",
"finished 167\n",
"planed 2\n",
"assented 68\n",
"woodwork 5\n",
"mysteries 32\n",
"followedthe 1\n",
"discharged 36\n",
"saidremain 1\n",
"clambered 8\n",
"insignificant 39\n",
"hole 42\n",
"resource 22\n",
"strikingly 6\n",
"pittance 5\n",
"ended 62\n",
"sees 60\n",
"doctrine 7\n",
"visitationsto 1\n",
"heartburnings 1\n",
"illiberal 1\n",
"desert 62\n",
"woecome 1\n",
"strict 26\n",
"corner 387\n",
"closest 7\n",
"reveal 16\n",
"inmates 21\n",
"station 87\n",
"autumnal 6\n",
"shone 96\n",
"beaming 44\n",
"shewed 3\n",
"youll 337\n",
"superfluous 24\n",
"contiguous 2\n",
"cordial 30\n",
"sell 42\n",
"conclude 21\n",
"interchange 7\n",
"unobjectionable 2\n",
"favourite 85\n",
"attitude 95\n",
"tendency 35\n",
"peruse 1\n",
"tongues 15\n",
"gossip 35\n",
"perceptions 4\n",
"infliction 5\n",
"experiment 19\n",
"ridding 4\n",
"expressing 65\n",
"consulted 28\n",
"reproach 79\n",
"mouths 17\n",
"irongates 1\n",
"glad 509\n",
"featuresall 1\n",
"calmcalmcan 1\n",
"musical 21\n",
"belong 47\n",
"aa 3\n",
"recollected 18\n",
"smiled 236\n",
"owner 55\n",
"spun 11\n",
"previously 74\n",
"tray 29\n",
"simplest 17\n",
"eat 122\n",
"drink 180\n",
"forehead 137\n",
"decanter 7\n",
"peculiarity 24\n",
"lightnings 3\n",
"deed 68\n",
"wholly 78\n",
"doubtno 2\n",
"prophecy 4\n",
"err 3\n",
"friendbut 1\n",
"credited 2\n",
"staple 3\n",
"oldest 16\n",
"vastly 6\n",
"fervour 18\n",
"holding 276\n",
"thirsty 14\n",
"contested 1\n",
"verging 2\n",
"seventy 15\n",
"animus 1\n",
"anchor 20\n",
"sixty 24\n",
"fed 14\n",
"chaise 19\n",
"drove 137\n",
"lubberly 19\n",
"cursed 12\n",
"admonition 8\n",
"pantomime 4\n",
"othermakes 1\n",
"respectable 66\n",
"yankee 2\n",
"pirates 3\n",
"preach 2\n",
"jove 8\n",
"didnt 362\n",
"bystanders 7\n",
"winesgood 1\n",
"serve 95\n",
"goodso 2\n",
"beat 101\n",
"stupiddont 1\n",
"jackyou 1\n",
"marlinspike 1\n",
"flinging 11\n",
"gd 2\n",
"ultimate 10\n",
"contract 22\n",
"interfere 46\n",
"ruinous 9\n",
"inform 63\n",
"bloodsuckerby 1\n",
"entertained 45\n",
"john 332\n",
"bloodsuckers 1\n",
"whistled 29\n",
"song 69\n",
"paler 19\n",
"writing 138\n",
"publicly 12\n",
"officers 270\n",
"larboard 5\n",
"treading 14\n",
"doingshiver 1\n",
"jones 11\n",
"tongue 99\n",
"cabin 12\n",
"scuttled 3\n",
"warent 1\n",
"pig 13\n",
"draining 1\n",
"scale 26\n",
"havent 126\n",
"talks 30\n",
"ungracious 2\n",
"brute 19\n",
"yethank 1\n",
"mraa 1\n",
"deeper 52\n",
"dearer 15\n",
"nevy 12\n",
"steer 6\n",
"latitude 8\n",
"strike 66\n",
"bitter 43\n",
"selfishly 7\n",
"errors 17\n",
"argumentation 2\n",
"denying 9\n",
"pursuing 45\n",
"trying 280\n",
"vows 4\n",
"familiarly 4\n",
"straggled 4\n",
"decayed 8\n",
"wan 13\n",
"paralyze 3\n",
"elasticity 6\n",
"transcendent 4\n",
"revives 1\n",
"floraspeak 1\n",
"sorrows 17\n",
"subsiding 4\n",
"breathe 34\n",
"afflicted 27\n",
"pure 62\n",
"repudiating 3\n",
"despairingly 7\n",
"aptitude 4\n",
"contention 15\n",
"unchecked 4\n",
"unwittingly 8\n",
"affright 6\n",
"hanging 91\n",
"dejectedly 3\n",
"implore 24\n",
"modesty 26\n",
"axis 3\n",
"firmament 6\n",
"illumined 6\n",
"melody 10\n",
"irresolute 21\n",
"retreat 131\n",
"medo 2\n",
"wellbred 6\n",
"vampyreto 1\n",
"ishe 5\n",
"almighty 11\n",
"mournful 21\n",
"hastening 8\n",
"behave 22\n",
"rudeness 6\n",
"replying 60\n",
"discursive 3\n",
"talked 194\n",
"fluently 1\n",
"hershe 3\n",
"crossquestioning 1\n",
"wanted 391\n",
"leads 20\n",
"civilities 4\n",
"foibles 2\n",
"softer 13\n",
"partake 15\n",
"placing 34\n",
"suspicion 83\n",
"declares 4\n",
"counterpart 5\n",
"enact 4\n",
"genteel 54\n",
"abruptly 29\n",
"inclination 55\n",
"drank 56\n",
"pork 10\n",
"jest 30\n",
"suit 79\n",
"lad 205\n",
"lamentable 3\n",
"cowardly 10\n",
"sisterin 1\n",
"stepping 40\n",
"shallto 1\n",
"devilish 47\n",
"plenty 58\n",
"bid 25\n",
"quitted 16\n",
"quits 3\n",
"ringing 68\n",
"volunteered 10\n",
"nevey 8\n",
"paces 54\n",
"lankey 1\n",
"brainpiercing 1\n",
"audacious 9\n",
"hearted 2\n",
"fellowmy 1\n",
"dislocate 1\n",
"individual 146\n",
"craft 22\n",
"blazing 17\n",
"uncommonly 38\n",
"waswas 1\n",
"judgement 18\n",
"leisure 61\n",
"denominated 3\n",
"trustee 4\n",
"bills 17\n",
"bonds 11\n",
"lively 66\n",
"distasteful 5\n",
"communicate 38\n",
"emphatically 14\n",
"privately 11\n",
"conceal 62\n",
"irrevocably 3\n",
"unstrapping 2\n",
"manuscript 20\n",
"xix 26\n",
"grateful 90\n",
"weakspirited 1\n",
"nervousness 8\n",
"sanctioning 1\n",
"alliance 32\n",
"brotheryou 1\n",
"conquered 18\n",
"amuse 18\n",
"incidents 21\n",
"recital 8\n",
"hugo 5\n",
"plot 18\n",
"valleys 9\n",
"lamentation 5\n",
"throughout 43\n",
"domains 1\n",
"according 152\n",
"ravages 6\n",
"counts 75\n",
"obsequies 3\n",
"condole 2\n",
"assurances 7\n",
"behaviour 32\n",
"vigils 1\n",
"roomdoor 3\n",
"seeseeifif 1\n",
"hadnt 96\n",
"brows 47\n",
"alacrity 17\n",
"mounted 65\n",
"threat 20\n",
"amours 1\n",
"enemy 294\n",
"careless 35\n",
"furnish 10\n",
"bankrupt 5\n",
"studies 31\n",
"seenand 1\n",
"noticedall 1\n",
"rents 8\n",
"thrust 56\n",
"insisted 52\n",
"disdain 15\n",
"store 24\n",
"divine 50\n",
"whereoh 1\n",
"suite 82\n",
"amazing 21\n",
"listenwe 1\n",
"mines 34\n",
"confine 9\n",
"lunatic 8\n",
"affirmative 25\n",
"impatiencebut 1\n",
"weakminded 3\n",
"fools 14\n",
"cunning 44\n",
"treatment 36\n",
"denuded 3\n",
"smiling 252\n",
"aldrovani 1\n",
"purest 8\n",
"earthfrom 1\n",
"territory 7\n",
"discontinued 2\n",
"working 92\n",
"deposit 4\n",
"deposited 17\n",
"draughts 8\n",
"relighting 1\n",
"harvest 19\n",
"philosophers 7\n",
"keys 40\n",
"stole 34\n",
"miners 1\n",
"stationed 38\n",
"refastened 1\n",
"disfigured 12\n",
"heir 32\n",
"labour 40\n",
"mourn 3\n",
"confined 49\n",
"meditating 13\n",
"projects 23\n",
"university 6\n",
"appreciating 5\n",
"punish 24\n",
"revenue 12\n",
"guilty 67\n",
"patrimony 1\n",
"perusal 9\n",
"icy 5\n",
"shout 82\n",
"rivals 6\n",
"cheekshe 1\n",
"summers 5\n",
"ungrateful 23\n",
"detail 34\n",
"womans 69\n",
"kitchen 90\n",
"shrunk 53\n",
"vice 23\n",
"shouldnt 82\n",
"teach 46\n",
"joke 61\n",
"exhibition 13\n",
"difference 113\n",
"unite 26\n",
"imploreto 1\n",
"hercould 1\n",
"herif 4\n",
"vigorous 38\n",
"wipe 13\n",
"shes 175\n",
"delicateshiver 1\n",
"deserts 7\n",
"carries 13\n",
"spooney 2\n",
"gunroom 1\n",
"picking 27\n",
"outandout 2\n",
"seamen 5\n",
"bulk 6\n",
"thitherward 1\n",
"water 369\n",
"wring 13\n",
"nick 5\n",
"pots 10\n",
"poll 4\n",
"supposefor 1\n",
"served 78\n",
"invulnerable 3\n",
"represents 5\n",
"fellowgive 1\n",
"prejudice 19\n",
"cordially 19\n",
"revolving 15\n",
"growing 138\n",
"pecuniary 38\n",
"swallowed 29\n",
"instalments 3\n",
"debts 38\n",
"sweeping 20\n",
"statement 27\n",
"complain 50\n",
"painfully 37\n",
"acquiesce 1\n",
"offered 126\n",
"devised 25\n",
"performance 37\n",
"surgeon 22\n",
"happily 49\n",
"pursuade 1\n",
"mentions 4\n",
"injuries 15\n",
"uncles 68\n",
"bearer 9\n",
"preliminaries 12\n",
"appoint 9\n",
"c 65\n",
"reasoning 25\n",
"pin 11\n",
"gradual 6\n",
"kingdoms 2\n",
"combat 16\n",
"resolve 19\n",
"elapse 4\n",
"blabbing 2\n",
"shewn 3\n",
"lodged 24\n",
"felicitous 1\n",
"hotheaded 2\n",
"continual 29\n",
"repressed 16\n",
"proclaimed 10\n",
"misunderstanding 14\n",
"shift 12\n",
"litigation 1\n",
"victor 4\n",
"exploded 5\n",
"formal 27\n",
"stared 65\n",
"harmless 20\n",
"austria 29\n",
"shant 38\n",
"compliment 52\n",
"kick 11\n",
"beggars 17\n",
"cable 5\n",
"brimstone 1\n",
"zounds 2\n",
"aback 15\n",
"xxiv 10\n",
"hurriedly 122\n",
"compromise 22\n",
"appreciation 16\n",
"readiness 42\n",
"slipped 46\n",
"argufy 1\n",
"convenient 41\n",
"page 55\n",
"siryour 1\n",
"animated 79\n",
"mefor 3\n",
"inimical 4\n",
"disappear 16\n",
"contingency 6\n",
"rending 5\n",
"imitates 2\n",
"apprehensions 26\n",
"doubly 11\n",
"cloud 75\n",
"unaccountably 9\n",
"soulas 1\n",
"calamity 22\n",
"preparing 59\n",
"sport 17\n",
"pacing 35\n",
"doubts 77\n",
"sights 14\n",
"boyas 1\n",
"receives 13\n",
"figured 5\n",
"vessel 29\n",
"ideas 82\n",
"vortex 8\n",
"apprenticeship 6\n",
"enduring 10\n",
"liftwhile 1\n",
"ceylon 1\n",
"spices 1\n",
"pretty 462\n",
"milltail 1\n",
"laden 14\n",
"skies 11\n",
"ails 6\n",
"infernally 3\n",
"biscay 2\n",
"prosperously 2\n",
"waters 41\n",
"michael 62\n",
"billet 3\n",
"deigned 7\n",
"manwhat 1\n",
"nose 188\n",
"aerial 2\n",
"bespeaking 4\n",
"crowded 65\n",
"leer 9\n",
"squinting 4\n",
"contrabandentirely 1\n",
"shipton 1\n",
"delicatelooking 2\n",
"cheap 23\n",
"beef 27\n",
"royalbe 1\n",
"lefthanded 2\n",
"bemore 1\n",
"tacitly 8\n",
"barely 25\n",
"disturbs 1\n",
"objectshis 1\n",
"unbidden 1\n",
"whiles 8\n",
"bumps 7\n",
"suspiciously 4\n",
"foam 7\n",
"abouthes 1\n",
"nobodys 30\n",
"blank 45\n",
"laughing 222\n",
"peals 4\n",
"calf 8\n",
"pinched 9\n",
"bolt 10\n",
"upright 46\n",
"gog 2\n",
"magog 2\n",
"himit 1\n",
"inquisitive 9\n",
"persevered 3\n",
"fleecy 3\n",
"morrow 11\n",
"tops 19\n",
"dale 6\n",
"softened 75\n",
"essay 3\n",
"blaming 8\n",
"justifiable 5\n",
"coldblooded 4\n",
"inmost 8\n",
"faithlessness 3\n",
"heartlessness 4\n",
"himill 1\n",
"admiralill 1\n",
"nowi 4\n",
"duplicity 2\n",
"foresee 15\n",
"today 225\n",
"angel 70\n",
"rogue 5\n",
"distinction 61\n",
"defalcation 2\n",
"implicit 7\n",
"aggravated 10\n",
"braggart 4\n",
"muchvaunted 1\n",
"fevered 6\n",
"sorrowful 46\n",
"uppermost 10\n",
"tartly 4\n",
"excites 2\n",
"hypocritical 8\n",
"alla 2\n",
"ruffian 7\n",
"apparel 7\n",
"breakfastparlour 2\n",
"perusing 4\n",
"irritated 19\n",
"henrybrother 1\n",
"willcharles 1\n",
"frenzy 12\n",
"distracts 2\n",
"weatherbeaten 12\n",
"wrinkled 25\n",
"hardened 11\n",
"hurting 7\n",
"steal 25\n",
"hollandmy 1\n",
"youhe 1\n",
"innocence 29\n",
"wherever 53\n",
"convictions 9\n",
"transpired 5\n",
"misconstruction 5\n",
"wrangle 2\n",
"yards 59\n",
"trodden 31\n",
"mud 65\n",
"connexions 6\n",
"alleged 9\n",
"claiming 10\n",
"signature 11\n",
"unimportant 16\n",
"occasions 100\n",
"benefit 31\n",
"opinionated 2\n",
"stirred 39\n",
"poets 5\n",
"shortness 6\n",
"iniquities 3\n",
"appease 4\n",
"accidents 18\n",
"picked 62\n",
"unhappythat 1\n",
"haunts 9\n",
"encouragement 46\n",
"croaking 3\n",
"commanding 38\n",
"erected 12\n",
"ostensibly 2\n",
"fortress 5\n",
"passages 39\n",
"damper 2\n",
"earliest 18\n",
"ludgatehill 1\n",
"spoiled 23\n",
"premising 4\n",
"eveningthe 1\n",
"edges 4\n",
"tipped 2\n",
"monarch 21\n",
"mouldering 6\n",
"blades 6\n",
"corners 42\n",
"hooted 1\n",
"reign 27\n",
"measured 34\n",
"salutation 14\n",
"sensibly 12\n",
"dungeondamp 1\n",
"exhalationsdeep 1\n",
"liberated 8\n",
"bandage 5\n",
"upwards 22\n",
"creaking 13\n",
"document 24\n",
"shorter 16\n",
"withering 12\n",
"blackness 4\n",
"unillumined 1\n",
"spiriting 2\n",
"expose 18\n",
"nationality 3\n",
"botherations 1\n",
"swore 24\n",
"munificence 3\n",
"undoubted 5\n",
"unexampled 2\n",
"eulogium 5\n",
"fret 7\n",
"disagreeables 5\n",
"plant 16\n",
"serves 12\n",
"corroding 1\n",
"cadaverouslooking 1\n",
"deathlikelooking 1\n",
"sentences 20\n",
"meditations 17\n",
"reflected 59\n",
"terminated 15\n",
"counted 22\n",
"failing 36\n",
"bridport 1\n",
"gusts 5\n",
"embers 11\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"thrill 11\n",
"chestnut 13\n",
"bradley 16\n",
"sleet 2\n",
"remorseless 8\n",
"emma 16\n",
"mamma 103\n",
"aunt 901\n",
"foretold 5\n",
"event 132\n",
"wednesday 21\n",
"twin 9\n",
"beget 1\n",
"ordering 14\n",
"image 59\n",
"amounted 13\n",
"raveneyed 1\n",
"cousin 169\n",
"incensed 4\n",
"repented 6\n",
"againonce 1\n",
"logs 11\n",
"kennels 2\n",
"eddies 3\n",
"drift 3\n",
"apology 28\n",
"inducement 7\n",
"armyany 1\n",
"hound 10\n",
"coalblack 3\n",
"tide 52\n",
"footstepit 1\n",
"battled 3\n",
"loosely 16\n",
"improve 43\n",
"limited 81\n",
"varneyand 1\n",
"spokebecause 1\n",
"panoramic 4\n",
"incubus 3\n",
"comparative 8\n",
"faithfully 25\n",
"bank 98\n",
"snatched 16\n",
"trade 56\n",
"clog 3\n",
"selfsame 7\n",
"veil 22\n",
"declining 12\n",
"earned 11\n",
"despairing 24\n",
"dispensing 3\n",
"sever 2\n",
"xxxiii 6\n",
"mortality 5\n",
"reaching 47\n",
"pronounced 13\n",
"tragedy 3\n",
"saysbut 1\n",
"merest 4\n",
"greatness 43\n",
"strode 12\n",
"wandering 64\n",
"showered 10\n",
"precincts 13\n",
"consequent 17\n",
"unheeding 1\n",
"queen 20\n",
"bespoken 1\n",
"wandered 49\n",
"exerts 1\n",
"blessings 9\n",
"garment 6\n",
"wallsa 1\n",
"sin 34\n",
"divinity 9\n",
"diabolical 11\n",
"rescuethey 1\n",
"sleptif 1\n",
"halfwaking 3\n",
"bannerworthvarney 1\n",
"shun 6\n",
"allurements 2\n",
"gushing 6\n",
"disabused 2\n",
"inarticulate 7\n",
"moreyou 1\n",
"airspeak 1\n",
"toppled 1\n",
"continuance 5\n",
"assailing 2\n",
"tortured 15\n",
"endowed 17\n",
"harmony 23\n",
"benighted 4\n",
"gratuitouslya 1\n",
"earnestness 71\n",
"detailed 14\n",
"sensibility 8\n",
"enacted 2\n",
"angermore 1\n",
"loathing 4\n",
"willit 1\n",
"gratify 8\n",
"blot 4\n",
"vacated 8\n",
"exit 4\n",
"roughness 6\n",
"ranged 7\n",
"wellspring 1\n",
"dressing 86\n",
"gap 19\n",
"resuming 31\n",
"extenuate 3\n",
"trammels 3\n",
"indication 26\n",
"consummate 7\n",
"triumph 70\n",
"safer 7\n",
"taught 54\n",
"charactershe 1\n",
"inadequate 4\n",
"sothe 2\n",
"birth 40\n",
"sympathetically 6\n",
"cruelly 28\n",
"dependence 16\n",
"shedding 12\n",
"proportions 9\n",
"cheerfulness 19\n",
"communicative 10\n",
"matteroffact 2\n",
"breed 16\n",
"tilt 8\n",
"buying 9\n",
"sheathing 1\n",
"approved 30\n",
"myrmidons 2\n",
"standard 25\n",
"herefor 2\n",
"murdererand 1\n",
"audience 36\n",
"dramatic 2\n",
"addedyou 2\n",
"matched 8\n",
"revenged 5\n",
"mess 10\n",
"cus 1\n",
"splice 1\n",
"marched 32\n",
"snob 1\n",
"imposed 25\n",
"blarney 1\n",
"captious 2\n",
"holiday 24\n",
"donede 1\n",
"noyesno 1\n",
"grogquarrelledmade 1\n",
"adversary 7\n",
"accepted 90\n",
"nobut 1\n",
"showi 1\n",
"customary 24\n",
"seamans 6\n",
"missile 6\n",
"clatter 17\n",
"frowned 58\n",
"rooming 1\n",
"necessaries 5\n",
"usmyself 1\n",
"attending 28\n",
"acquaintances 56\n",
"spitting 5\n",
"inability 7\n",
"aspersions 2\n",
"chewed 3\n",
"quidhitched 1\n",
"politely 23\n",
"pringles 5\n",
"scrimmage 2\n",
"conformity 1\n",
"obligation 36\n",
"interviews 3\n",
"commend 6\n",
"helping 31\n",
"confiding 24\n",
"disparagement 11\n",
"unusually 28\n",
"rencontre 1\n",
"warnt 31\n",
"professionmy 1\n",
"keepthats 1\n",
"quietude 5\n",
"antagonist 4\n",
"prying 3\n",
"briefly 17\n",
"consented 15\n",
"gloominess 1\n",
"sureness 1\n",
"mornings 38\n",
"belli 1\n",
"adopting 8\n",
"howsomever 2\n",
"preserving 11\n",
"souwest 1\n",
"grim 32\n",
"patched 6\n",
"prejudicial 3\n",
"poetically 1\n",
"whereever 1\n",
"bravest 8\n",
"breadth 16\n",
"grins 4\n",
"marshal 67\n",
"lout 3\n",
"boarders 5\n",
"momentary 45\n",
"riot 23\n",
"puffing 17\n",
"keelhauled 2\n",
"payed 2\n",
"women 221\n",
"spontaneously 10\n",
"rustics 1\n",
"yea 1\n",
"fog 31\n",
"tother 20\n",
"refrain 32\n",
"garrulous 2\n",
"weaknesses 15\n",
"cautioned 8\n",
"invited 70\n",
"sixteen 27\n",
"streets 201\n",
"populace 17\n",
"oblivious 12\n",
"homes 28\n",
"stirring 32\n",
"disorderlylooking 1\n",
"contemplated 23\n",
"originating 9\n",
"scandinavians 1\n",
"personification 4\n",
"incredibly 3\n",
"vampyrelike 2\n",
"cessation 10\n",
"outpull 1\n",
"searched 10\n",
"groundless 6\n",
"column 44\n",
"clamours 1\n",
"imprecations 5\n",
"ordinarily 5\n",
"gang 4\n",
"boarded 8\n",
"exceeded 9\n",
"sickles 1\n",
"humanest 1\n",
"stomach 41\n",
"woke 27\n",
"clamorous 1\n",
"possessing 23\n",
"exert 22\n",
"itde 1\n",
"dense 29\n",
"greyhounds 1\n",
"agility 10\n",
"rout 4\n",
"carelessness 11\n",
"achieve 13\n",
"illappointed 1\n",
"surrounds 5\n",
"armstink 1\n",
"oncome 1\n",
"circuitous 2\n",
"towns 25\n",
"chasing 3\n",
"sped 4\n",
"wellsped 1\n",
"baffled 16\n",
"speeding 4\n",
"mossgrown 1\n",
"group 99\n",
"crept 55\n",
"boldest 5\n",
"pleasantest 10\n",
"trophy 4\n",
"onwe 2\n",
"spell 22\n",
"searchedhastily 1\n",
"dogmatical 1\n",
"comrade 28\n",
"unadvisedly 1\n",
"temples 25\n",
"sopped 2\n",
"nourishment 10\n",
"detects 1\n",
"overhead 12\n",
"sod 2\n",
"reels 2\n",
"enshroud 1\n",
"exercise 56\n",
"contamination 4\n",
"tolerate 4\n",
"parade 29\n",
"solitude 37\n",
"deardear 1\n",
"cheeringly 1\n",
"meridian 3\n",
"judged 19\n",
"bluff 8\n",
"towhat 3\n",
"cannotwe 1\n",
"eyesdont 1\n",
"subside 9\n",
"butcher 47\n",
"plansir 1\n",
"stripped 11\n",
"pleasing 15\n",
"townspeople 7\n",
"accordance 20\n",
"skull 9\n",
"yorick 1\n",
"inebriating 1\n",
"rusty 33\n",
"sprawling 5\n",
"staff 160\n",
"victory 103\n",
"waggles 10\n",
"amounting 13\n",
"adore 16\n",
"superstitionsalike 1\n",
"weighty 11\n",
"mandate 3\n",
"ranter 2\n",
"spade 5\n",
"shovelful 1\n",
"clayey 1\n",
"available 22\n",
"digging 10\n",
"foreground 6\n",
"despatched 10\n",
"christians 7\n",
"grownup 20\n",
"wit 20\n",
"shirking 1\n",
"humbugs 5\n",
"acoming 5\n",
"laughter 99\n",
"sheepish 6\n",
"pipe 102\n",
"lark 11\n",
"axed 1\n",
"squatting 6\n",
"fluvifium 1\n",
"graveclothes 3\n",
"infidel 8\n",
"faculties 20\n",
"leeches 3\n",
"coffinsthis 1\n",
"beforeits 1\n",
"snug 27\n",
"squeak 2\n",
"dnably 1\n",
"unawares 9\n",
"pothouse 2\n",
"lothas 1\n",
"unanimous 3\n",
"astray 11\n",
"latitudes 4\n",
"proverb 12\n",
"nods 12\n",
"poking 8\n",
"babby 8\n",
"wordy 2\n",
"discouraging 3\n",
"sparring 1\n",
"bamboozle 1\n",
"cavilling 2\n",
"hulks 2\n",
"commodore 4\n",
"waistcoat 71\n",
"hindering 3\n",
"winks 5\n",
"intercepting 3\n",
"inexorable 15\n",
"shivering 17\n",
"insolent 20\n",
"con 3\n",
"perpetration 2\n",
"onethis 1\n",
"burying 4\n",
"housecome 1\n",
"rave 1\n",
"pavement 58\n",
"disaster 15\n",
"impunity 6\n",
"communities 1\n",
"ardent 17\n",
"determining 1\n",
"sublimity 3\n",
"boisterous 8\n",
"throngs 1\n",
"farmyard 1\n",
"inns 5\n",
"niche 3\n",
"stigmatised 1\n",
"advertisements 3\n",
"soldiers 394\n",
"innoxious 2\n",
"spectacle 39\n",
"debased 6\n",
"brawl 1\n",
"assembling 8\n",
"prisoners 144\n",
"ringleaders 1\n",
"secondfloor 3\n",
"thirdfloor 1\n",
"scramble 8\n",
"mutilated 5\n",
"animosity 18\n",
"atrocious 5\n",
"outskirts 7\n",
"numbered 6\n",
"insufficient 9\n",
"collection 40\n",
"national 30\n",
"stooped 38\n",
"citadel 4\n",
"misguided 7\n",
"sixpenny 1\n",
"turbulent 7\n",
"l 6\n",
"raging 23\n",
"flames 43\n",
"cautions 4\n",
"aimed 13\n",
"vampyredeath 1\n",
"ithat 1\n",
"sayahem 1\n",
"bang 12\n",
"argued 25\n",
"avenue 27\n",
"danged 4\n",
"frontdoor 1\n",
"gardendoor 1\n",
"kitchendoor 1\n",
"enraged 9\n",
"wereagainst 1\n",
"unmercifully 3\n",
"retrogade 1\n",
"placedown 1\n",
"lii 2\n",
"neighbourly 1\n",
"onei 2\n",
"proportionate 2\n",
"vampyrehunting 2\n",
"lucky 24\n",
"reckoning 18\n",
"hostilities 6\n",
"winecellar 2\n",
"imbued 5\n",
"declaimed 2\n",
"rightall 2\n",
"acclamations 4\n",
"houseburn 1\n",
"maniacs 2\n",
"roasting 7\n",
"kettle 12\n",
"jokenot 1\n",
"marching 32\n",
"faster 46\n",
"issued 44\n",
"learnt 23\n",
"fray 2\n",
"regularity 12\n",
"fiery 21\n",
"pounce 6\n",
"chilly 9\n",
"larking 2\n",
"unanswerable 6\n",
"jumpers 1\n",
"grocer 11\n",
"espy 1\n",
"retort 12\n",
"lvi 2\n",
"mistaking 4\n",
"breakers 4\n",
"glasses 51\n",
"dogfish 1\n",
"sirand 3\n",
"bulkheads 2\n",
"ahead 53\n",
"fathoms 2\n",
"crater 2\n",
"mount 26\n",
"douse 1\n",
"carriage 259\n",
"hindrance 12\n",
"sing 75\n",
"extravagantly 1\n",
"unbroken 16\n",
"reflectionsa 1\n",
"barren 6\n",
"swarm 14\n",
"conventional 12\n",
"neglect 23\n",
"relics 9\n",
"intervened 5\n",
"transpose 1\n",
"heater 1\n",
"teaurn 1\n",
"gist 4\n",
"sustain 15\n",
"provident 3\n",
"rowtake 1\n",
"balanced 7\n",
"illuminating 1\n",
"click 10\n",
"mizen 1\n",
"gaff 1\n",
"rol 1\n",
"yearshe 1\n",
"islands 8\n",
"dollar 3\n",
"pumps 3\n",
"elizabeth 3\n",
"instinctively 24\n",
"gurgling 5\n",
"noits 1\n",
"footfall 4\n",
"betray 21\n",
"gratuitously 1\n",
"strenuous 5\n",
"lowest 27\n",
"windowframe 2\n",
"crown 29\n",
"writhed 8\n",
"thishilloa 1\n",
"lightin 1\n",
"tactics 9\n",
"yell 12\n",
"shoutedgetting 1\n",
"balconynow 1\n",
"pouring 19\n",
"heavier 29\n",
"bounce 1\n",
"ont 6\n",
"ud 2\n",
"sealing 10\n",
"arriving 33\n",
"flatter 13\n",
"physic 8\n",
"darkgreen 4\n",
"breakfasttable 8\n",
"blunt 18\n",
"toils 6\n",
"struggles 12\n",
"culinary 3\n",
"bellis 1\n",
"cognomen 2\n",
"clack 2\n",
"lodgein 1\n",
"elbows 44\n",
"bracket 10\n",
"lxi 2\n",
"hoaxed 1\n",
"handgrenades 1\n",
"marlingspikes 1\n",
"squadron 65\n",
"itis 2\n",
"sentimentsociety 1\n",
"classes 13\n",
"impetuously 11\n",
"surround 10\n",
"bare 115\n",
"local 13\n",
"vine 10\n",
"comprehensive 9\n",
"associates 8\n",
"sociality 2\n",
"fellowship 4\n",
"mask 12\n",
"criminal 28\n",
"convivial 4\n",
"bon 1\n",
"vivants 2\n",
"accumulated 12\n",
"dreadfully 44\n",
"convulsive 10\n",
"lorne 1\n",
"scrutinizingly 3\n",
"strolled 26\n",
"yesfor 1\n",
"hungry 48\n",
"comeshe 1\n",
"taciturnity 5\n",
"idiot 19\n",
"privy 2\n",
"executive 6\n",
"annoys 1\n",
"havewe 1\n",
"smoothfaced 2\n",
"specious 1\n",
"enterprises 10\n",
"involvement 1\n",
"needlessly 5\n",
"novelty 29\n",
"adventurer 2\n",
"ventursome 1\n",
"stock 39\n",
"scope 6\n",
"shocking 15\n",
"sixtythree 1\n",
"sprigs 4\n",
"dab 6\n",
"horrid 15\n",
"web 36\n",
"wagers 5\n",
"murky 5\n",
"quake 2\n",
"facetious 5\n",
"parents 68\n",
"onlets 1\n",
"seesomebody 1\n",
"ita 6\n",
"goods 36\n",
"bureau 10\n",
"barrelled 1\n",
"bulldogs 1\n",
"watchdogs 1\n",
"liars 3\n",
"longsince 1\n",
"pertinent 3\n",
"ofits 1\n",
"emis 1\n",
"slackened 7\n",
"packed 33\n",
"histhist 1\n",
"eccleswhat 1\n",
"thenhist 1\n",
"duskylooking 1\n",
"tinting 1\n",
"crevice 1\n",
"armedyes 1\n",
"welldont 1\n",
"himfire 1\n",
"smote 14\n",
"bravoi 1\n",
"matterwill 1\n",
"facei 1\n",
"murderer 19\n",
"unjustifiably 1\n",
"affairnot 1\n",
"partly 64\n",
"outstretched 14\n",
"revived 26\n",
"youyes 1\n",
"marchdaleyes 1\n",
"townit 1\n",
"bravery 6\n",
"dastards 1\n",
"destructionthat 1\n",
"band 29\n",
"wears 12\n",
"thenhe 2\n",
"oaththe 1\n",
"sonot 2\n",
"exigencies 1\n",
"chicanery 1\n",
"discomforts 3\n",
"dullness 3\n",
"massy 1\n",
"tortuous 1\n",
"characterised 2\n",
"cheering 25\n",
"profess 7\n",
"deception 16\n",
"candourit 1\n",
"dreamt 2\n",
"ninetynine 4\n",
"authors 8\n",
"revert 5\n",
"lxvi 1\n",
"fallacious 6\n",
"rascalay 1\n",
"houseand 7\n",
"buttoning 8\n",
"pummelling 1\n",
"seamanwerry 1\n",
"elsechew 1\n",
"island 33\n",
"bushel 10\n",
"seethat 2\n",
"recited 3\n",
"chord 10\n",
"itnone 1\n",
"floraloved 1\n",
"pounced 13\n",
"passiondn 1\n",
"shell 72\n",
"herno 1\n",
"manned 5\n",
"breeches 27\n",
"mea 2\n",
"dungeonits 1\n",
"shrinking 26\n",
"housed 5\n",
"negligent 3\n",
"ho 17\n",
"hereyes 1\n",
"truththe 1\n",
"favoured 18\n",
"earthern 1\n",
"thoughtchains 1\n",
"ignominious 5\n",
"glancing 179\n",
"diverging 2\n",
"teeming 9\n",
"rapture 28\n",
"despaired 2\n",
"headscurses 1\n",
"reverted 10\n",
"hostel 2\n",
"victors 5\n",
"practise 6\n",
"favourably 8\n",
"castleyard 2\n",
"hugh 1\n",
"battleaxe 1\n",
"bridegroom 23\n",
"oneher 1\n",
"signalize 2\n",
"skirmishes 2\n",
"encounters 6\n",
"consoled 8\n",
"youmy 1\n",
"meyes 1\n",
"notno 2\n",
"bewill 2\n",
"stride 6\n",
"visor 3\n",
"chid 3\n",
"minded 9\n",
"arthurby 1\n",
"tooi 1\n",
"claimant 2\n",
"otherbut 1\n",
"sequestered 3\n",
"lists 3\n",
"influx 1\n",
"warriors 7\n",
"revelry 3\n",
"disadvantage 26\n",
"joust 1\n",
"clanged 1\n",
"troopersnot 1\n",
"magistracy 1\n",
"precaution 9\n",
"needful 7\n",
"peaceablydisposed 1\n",
"advertised 5\n",
"interment 2\n",
"depending 5\n",
"offah 1\n",
"haveso 1\n",
"mobanythingno 1\n",
"customers 19\n",
"landlords 10\n",
"malleable 1\n",
"jacobs 7\n",
"tonightyes 1\n",
"buriedhow 1\n",
"jacob 6\n",
"yetquite 1\n",
"knowive 1\n",
"mayso 2\n",
"onenor 1\n",
"employer 18\n",
"recommendation 16\n",
"bread 92\n",
"itmoved 1\n",
"strangerindeed 1\n",
"exists 26\n",
"grades 5\n",
"ehoh 1\n",
"monument 12\n",
"pugnacious 1\n",
"hubbub 3\n",
"israel 2\n",
"individuality 6\n",
"ruffianly 1\n",
"troops 265\n",
"spy 18\n",
"harboured 1\n",
"excusable 2\n",
"vigil 1\n",
"strayed 16\n",
"beeches 1\n",
"pausing 39\n",
"gait 14\n",
"conspired 5\n",
"sinceawfully 1\n",
"ami 4\n",
"companyyes 1\n",
"countenanced 1\n",
"widower 13\n",
"longfor 1\n",
"incompatible 9\n",
"destructionnone 1\n",
"believemen 1\n",
"latethe 1\n",
"indeedhe 1\n",
"sup 5\n",
"playcertainly 1\n",
"watchers 2\n",
"welldressed 2\n",
"busily 26\n",
"fide 1\n",
"ofahand 1\n",
"hereafter 4\n",
"inquiredno 1\n",
"hereif 1\n",
"transact 3\n",
"leavei 1\n",
"propitious 7\n",
"adversity 11\n",
"ruinedruined 1\n",
"outand 4\n",
"matterwe 1\n",
"excusei 1\n",
"inconstant 6\n",
"flow 38\n",
"heri 5\n",
"oughtbut 2\n",
"hunger 22\n",
"childgood 1\n",
"misgivings 18\n",
"overtook 17\n",
"longsome 1\n",
"yesshe 1\n",
"aversion 16\n",
"itthe 6\n",
"squander 1\n",
"selfreproaches 3\n",
"dispossessed 1\n",
"requires 15\n",
"frienda 1\n",
"adamson 5\n",
"defending 13\n",
"recallmore 1\n",
"englishmen 3\n",
"readypresent 1\n",
"pusillanimity 1\n",
"perceived 38\n",
"baseness 15\n",
"swaying 34\n",
"gurgle 2\n",
"anythingi 1\n",
"picquet 7\n",
"guidance 12\n",
"hisself 5\n",
"rascality 3\n",
"encumbered 3\n",
"judging 25\n",
"degrees 59\n",
"sapping 1\n",
"toiled 6\n",
"thunders 3\n",
"mercyfor 1\n",
"beenwas 1\n",
"engulphed 1\n",
"masonry 5\n",
"beneaththat 1\n",
"davys 6\n",
"assuage 1\n",
"nevermind 1\n",
"nephewmy 1\n",
"timeill 1\n",
"malignant 5\n",
"purer 4\n",
"misplaced 10\n",
"passionhas 1\n",
"seclusion 12\n",
"beenas 1\n",
"chroniclers 2\n",
"sex 37\n",
"accountable 3\n",
"threaten 10\n",
"finds 38\n",
"apologise 8\n",
"privations 6\n",
"core 5\n",
"popped 5\n",
"lxxvi 1\n",
"detaining 8\n",
"actuates 1\n",
"longed 20\n",
"parcelswe 1\n",
"tinctured 2\n",
"leafy 5\n",
"thishe 2\n",
"anatomization 1\n",
"apparatus 4\n",
"convicted 4\n",
"abstained 3\n",
"sheriff 4\n",
"pious 13\n",
"suffocation 6\n",
"stimulus 4\n",
"infatuated 1\n",
"scruple 12\n",
"symptom 2\n",
"mainly 4\n",
"unintelligible 16\n",
"inward 16\n",
"spilt 4\n",
"eyewas 1\n",
"trick 19\n",
"brooding 20\n",
"wrongs 18\n",
"mindhe 2\n",
"reconnoitre 1\n",
"oilier 1\n",
"himfor 5\n",
"drear 1\n",
"detected 22\n",
"assembly 9\n",
"roofs 25\n",
"scenea 1\n",
"alehouse 4\n",
"bill 45\n",
"adventurous 11\n",
"entangled 16\n",
"sureand 1\n",
"senseless 37\n",
"laths 1\n",
"selfdevotion 3\n",
"scottish 3\n",
"chieftain 1\n",
"projector 3\n",
"potting 1\n",
"hissed 5\n",
"farmer 6\n",
"timei 2\n",
"hurrahafter 1\n",
"goesafter 1\n",
"manbe 1\n",
"firetake 1\n",
"emrun 1\n",
"sakesdo 1\n",
"donehave 1\n",
"unequivocally 1\n",
"nauseous 1\n",
"harder 43\n",
"honesthearted 2\n",
"dislodge 3\n",
"rope 11\n",
"cue 7\n",
"marking 8\n",
"unlocking 4\n",
"ferocious 16\n",
"entrusted 39\n",
"notcertainly 1\n",
"gotup 1\n",
"softness 13\n",
"jammed 9\n",
"distractedthe 1\n",
"moan 25\n",
"handsthe 2\n",
"goad 4\n",
"sagacity 16\n",
"investing 2\n",
"reasoner 1\n",
"aggressor 2\n",
"perhapsmust 1\n",
"clinched 1\n",
"cells 9\n",
"chords 8\n",
"vibrate 3\n",
"besets 1\n",
"piecemeal 3\n",
"daring 25\n",
"purses 3\n",
"wherein 18\n",
"alms 6\n",
"twelvewe 1\n",
"winnings 2\n",
"personwe 1\n",
"curved 6\n",
"temperately 1\n",
"drying 18\n",
"gamester 4\n",
"daughters 119\n",
"gloating 3\n",
"pupils 23\n",
"accuses 3\n",
"trivial 19\n",
"trait 9\n",
"heaped 16\n",
"depute 1\n",
"postponement 8\n",
"acquitted 4\n",
"impulsively 1\n",
"hearta 1\n",
"rewarded 14\n",
"attire 10\n",
"apparelled 1\n",
"sittingroom 22\n",
"begone 3\n",
"aggravate 3\n",
"coal 12\n",
"reversed 6\n",
"acidulated 1\n",
"soapsuds 3\n",
"gobbling 1\n",
"crossquestioned 1\n",
"loaf 16\n",
"lees 3\n",
"marlborough 2\n",
"lxxxiv 1\n",
"solicitous 11\n",
"dissuade 4\n",
"tumbled 43\n",
"bowsprit 2\n",
"maneoh 1\n",
"alland 4\n",
"inventions 5\n",
"darting 7\n",
"worthies 2\n",
"ofwhat 2\n",
"abused 14\n",
"lxxxv 1\n",
"enlightened 12\n",
"barparlour 1\n",
"characterwhen 1\n",
"distract 10\n",
"manifest 19\n",
"banks 25\n",
"teem 3\n",
"awethe 1\n",
"sparkling 34\n",
"longthe 1\n",
"openings 4\n",
"legality 2\n",
"briar 3\n",
"invigorate 1\n",
"lxxxvi 1\n",
"burdensome 3\n",
"branch 33\n",
"gewgaws 1\n",
"hobby 4\n",
"greatwhich 1\n",
"cuts 8\n",
"dresses 37\n",
"wrought 11\n",
"silk 34\n",
"itm 1\n",
"pocketbookno 1\n",
"memoranda 6\n",
"utters 3\n",
"spectacles 98\n",
"doubling 3\n",
"boysgood 1\n",
"proffered 14\n",
"familymay 1\n",
"fatherless 5\n",
"possiblewhat 1\n",
"porkbutcher 1\n",
"regretting 9\n",
"reproaches 24\n",
"equipped 6\n",
"purposed 7\n",
"higgss 1\n",
"employments 1\n",
"daytime 7\n",
"timea 3\n",
"awaythe 2\n",
"diversified 7\n",
"visage 33\n",
"unusualsomething 1\n",
"housethey 1\n",
"dormant 3\n",
"appletree 1\n",
"halllights 1\n",
"pole 16\n",
"thundered 6\n",
"causing 16\n",
"necks 13\n",
"infected 11\n",
"thirsted 1\n",
"wield 2\n",
"rails 16\n",
"scaling 1\n",
"wampyrethe 1\n",
"toastingfork 5\n",
"pants 1\n",
"likelya 1\n",
"gander 2\n",
"deadhe 1\n",
"dislocated 3\n",
"forcibly 15\n",
"overtake 13\n",
"yelping 3\n",
"redouble 1\n",
"speechless 19\n",
"brotherand 2\n",
"unknowingly 1\n",
"dangerously 3\n",
"bruises 5\n",
"perpetrate 1\n",
"lurkingplace 1\n",
"knowingly 3\n",
"toilet 8\n",
"hurrahhurrahhurrah 1\n",
"profaned 1\n",
"execrations 2\n",
"oncemy 1\n",
"observances 2\n",
"cowering 4\n",
"impose 14\n",
"maybe 24\n",
"whitefaced 3\n",
"handsfull 1\n",
"sprinkle 4\n",
"outright 10\n",
"dishonour 2\n",
"transacted 5\n",
"supervened 2\n",
"suppressed 41\n",
"shouti 1\n",
"tingling 3\n",
"concocted 2\n",
"planning 13\n",
"partaker 3\n",
"francisnone 1\n",
"extirpation 1\n",
"beautifuland 1\n",
"unperceived 2\n",
"brutality 2\n",
"consonant 3\n",
"bowstreet 2\n",
"kings 42\n",
"dislocating 1\n",
"overgrown 12\n",
"patent 6\n",
"gallop 44\n",
"bump 5\n",
"vote 6\n",
"mattocks 1\n",
"student 11\n",
"improved 56\n",
"inspected 7\n",
"showers 3\n",
"boasting 4\n",
"crowns 2\n",
"countrymen 10\n",
"digest 3\n",
"pebble 2\n",
"deceaseds 2\n",
"aboutdo 1\n",
"impracticable 9\n",
"philosophical 14\n",
"cornfields 3\n",
"hut 57\n",
"cottagetrue 1\n",
"yethe 2\n",
"wellvery 1\n",
"countryand 2\n",
"complaining 14\n",
"partition 13\n",
"chokedoh 1\n",
"submitted 41\n",
"saluted 14\n",
"rap 1\n",
"drubbing 1\n",
"assaulted 1\n",
"pillar 6\n",
"partners 22\n",
"maintained 30\n",
"craftyou 1\n",
"hors 3\n",
"despondency 15\n",
"habitable 3\n",
"dollseyes 2\n",
"wantswoman 1\n",
"snakes 2\n",
"momentheaven 1\n",
"whichbut 1\n",
"congratulation 4\n",
"bewilders 1\n",
"dazzles 1\n",
"sulks 1\n",
"chapa 1\n",
"herethat 2\n",
"thatmuch 1\n",
"fingernail 1\n",
"deathly 2\n",
"trowsers 1\n",
"trickle 4\n",
"coveted 2\n",
"interjectionally 1\n",
"goodsized 1\n",
"betides 2\n",
"milltown 1\n",
"gentry 19\n",
"pastthere 1\n",
"gallopped 1\n",
"stolmuyer 12\n",
"obsequiously 6\n",
"richness 3\n",
"princely 4\n",
"wondermongers 1\n",
"washing 17\n",
"manindeed 1\n",
"enjoining 3\n",
"horseman 6\n",
"vanishes 3\n",
"blazon 1\n",
"amicably 3\n",
"nobles 8\n",
"indeedmost 1\n",
"surges 1\n",
"pebbly 1\n",
"expatiated 2\n",
"excavation 8\n",
"scornfully 8\n",
"delivering 14\n",
"kneeling 23\n",
"vital 10\n",
"yellow 54\n",
"icewell 7\n",
"plash 1\n",
"thrives 2\n",
"rolls 6\n",
"thatget 1\n",
"storms 5\n",
"itvery 1\n",
"drymouldy 1\n",
"yearning 10\n",
"christianlike 1\n",
"stipulated 9\n",
"hallit 2\n",
"conceit 6\n",
"complaints 11\n",
"soothing 35\n",
"expletives 3\n",
"wiser 21\n",
"admissions 1\n",
"assimilates 1\n",
"imagines 4\n",
"silly 34\n",
"kerseymere 1\n",
"simpering 6\n",
"hop 4\n",
"appeasing 2\n",
"sideways 33\n",
"chapels 2\n",
"stoltmayor 1\n",
"dip 12\n",
"choicest 9\n",
"pamper 1\n",
"cascades 1\n",
"finny 1\n",
"admonished 6\n",
"petrifaction 2\n",
"zigzag 6\n",
"wells 7\n",
"fourteen 22\n",
"cleft 1\n",
"stuttered 1\n",
"gusto 2\n",
"thronged 9\n",
"thickened 8\n",
"inquest 9\n",
"universally 10\n",
"sale 27\n",
"jury 18\n",
"instituted 3\n",
"tendered 5\n",
"allhow 1\n",
"installed 2\n",
"editions 20\n",
"owns 10\n",
"copyright 65\n",
"distribute 33\n",
"copying 33\n",
"ebooks 35\n",
"copies 41\n",
"complying 18\n",
"commercial 19\n",
"promoting 13\n",
"distribution 36\n",
"httpgutenbergnetlicense 1\n",
"compilation 5\n",
"displaying 28\n",
"linked 15\n",
"processing 5\n",
"vanilla 9\n",
"ascii 10\n",
"calculate 6\n",
"taxes 8\n",
"email 19\n",
"proofread 5\n",
"transcription 5\n",
"disk 5\n",
"codes 8\n",
"warranty 10\n",
"disclaimer 15\n",
"liability 17\n",
"expenses 22\n",
"opportunities 30\n",
"asis 5\n",
"merchantibility 4\n",
"limitation 16\n",
"unenforceability 5\n",
"indemnity 5\n",
"obsolete 14\n",
"goals 5\n",
"educational 11\n",
"federal 10\n",
"fairbanks 5\n",
"ut 5\n",
"director 18\n",
"maintaining 15\n",
"paperwork 5\n",
"accepting 20\n",
"donors 6\n",
"library 22\n",
"necessarily 31\n",
"anonymous 6\n",
"genoa 9\n",
"pavlovna 112\n",
"maid 118\n",
"scarletliveried 1\n",
"grandfathers 3\n",
"intonation 6\n",
"bald 104\n",
"fete 11\n",
"festivities 2\n",
"fireworks 6\n",
"woundup 1\n",
"tease 3\n",
"evacuate 1\n",
"invincible 6\n",
"prussian 13\n",
"le 49\n",
"mortemart 9\n",
"rohans 1\n",
"morio 4\n",
"enraptured 16\n",
"lavater 1\n",
"resignation 17\n",
"bolkonskaya 9\n",
"rubles 69\n",
"lise 27\n",
"writes 25\n",
"honors 14\n",
"vasilis 29\n",
"helene 137\n",
"gatherings 4\n",
"gravely 54\n",
"brightened 33\n",
"workbag 3\n",
"gaily 55\n",
"samovar 20\n",
"tranquille 2\n",
"prettier 13\n",
"grandee 4\n",
"lowestgrade 1\n",
"foreman 6\n",
"creaks 1\n",
"hastens 3\n",
"spindles 2\n",
"grouped 5\n",
"obviously 23\n",
"dhotel 1\n",
"wellgarnished 1\n",
"readjusted 3\n",
"relapsed 6\n",
"charmant 3\n",
"joyous 26\n",
"classic 2\n",
"imbecility 7\n",
"grimace 12\n",
"unnatural 70\n",
"nymphe 1\n",
"favors 5\n",
"balance 28\n",
"russiabarbaric 1\n",
"offensively 3\n",
"kutuzov 446\n",
"stressing 2\n",
"syllable 21\n",
"pierres 167\n",
"obliges 2\n",
"anteroom 37\n",
"boris 290\n",
"ingratiating 4\n",
"papa 289\n",
"molded 1\n",
"au 18\n",
"gare 1\n",
"dazurmaison 1\n",
"imperial 25\n",
"emigrant 3\n",
"antechambers 1\n",
"palm 27\n",
"bourbons 7\n",
"citizenship 3\n",
"equality 20\n",
"exasperated 16\n",
"halfsmile 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"statesman 5\n",
"jaffa 1\n",
"plaguestricken 1\n",
"agreeably 11\n",
"balls 52\n",
"average 3\n",
"threecornered 2\n",
"mildness 6\n",
"fixedly 31\n",
"meaningless 18\n",
"stumbling 14\n",
"saber 41\n",
"blocking 14\n",
"spasmodically 3\n",
"abstract 13\n",
"referring 40\n",
"gown 77\n",
"outsider 3\n",
"decorated 15\n",
"luxurious 15\n",
"china 35\n",
"phrases 15\n",
"irritation 14\n",
"goal 22\n",
"everythingthats 1\n",
"relaxing 4\n",
"superiority 31\n",
"badlyall 1\n",
"cards 78\n",
"dishonor 2\n",
"decisions 4\n",
"cloaks 12\n",
"sly 29\n",
"growling 13\n",
"bruin 2\n",
"semenov 8\n",
"games 18\n",
"crossbeam 1\n",
"merging 8\n",
"separately 15\n",
"backward 20\n",
"wavered 6\n",
"refrained 10\n",
"rostovs 216\n",
"carriages 61\n",
"resulting 12\n",
"enjoys 3\n",
"swayed 26\n",
"complacent 13\n",
"snuffbox 28\n",
"rustling 23\n",
"degraded 13\n",
"ranks 79\n",
"actresses 2\n",
"canal 6\n",
"deathand 2\n",
"lorrain 10\n",
"affably 4\n",
"muslin 7\n",
"mimi 3\n",
"undergraduate 1\n",
"decorum 6\n",
"humorously 2\n",
"program 10\n",
"veiled 14\n",
"aspersion 2\n",
"flirtatiousness 1\n",
"kittenish 1\n",
"cousinage 2\n",
"dangereux 2\n",
"neighborhood 7\n",
"singer 14\n",
"salomoni 1\n",
"harms 3\n",
"wellbrought 1\n",
"discreet 11\n",
"tubs 9\n",
"embraced 68\n",
"verses 24\n",
"dipping 2\n",
"behaves 1\n",
"nickname 5\n",
"stinging 7\n",
"scarf 12\n",
"learns 1\n",
"cadet 22\n",
"statues 4\n",
"sullenly 11\n",
"carpeted 5\n",
"lowering 39\n",
"hometaking 1\n",
"est 13\n",
"errare 1\n",
"questioningly 4\n",
"scherers 1\n",
"shinshina 1\n",
"reassure 10\n",
"tying 12\n",
"mole 7\n",
"foresaw 12\n",
"unabashed 3\n",
"pitt 8\n",
"londoncould 1\n",
"wellbuilt 1\n",
"sparrow 4\n",
"jacquot 2\n",
"outlive 5\n",
"rostova 25\n",
"crossly 6\n",
"waddling 7\n",
"ruffling 4\n",
"cambric 5\n",
"blush 24\n",
"plainness 9\n",
"talkers 3\n",
"alphonse 1\n",
"phraseswhich 1\n",
"sur 2\n",
"senior 19\n",
"zakuska 1\n",
"dinnertime 15\n",
"monosyllables 3\n",
"fiftyyearold 2\n",
"sinner 5\n",
"nestlings 1\n",
"cossack 79\n",
"ominously 3\n",
"vases 3\n",
"ribbons 20\n",
"butler 47\n",
"rhine 7\n",
"monogram 2\n",
"governess 19\n",
"courier 49\n",
"shinshins 3\n",
"ze 9\n",
"manifessto 1\n",
"wiz 1\n",
"nowhe 2\n",
"demande 2\n",
"peu 2\n",
"zen 1\n",
"discussed 22\n",
"facing 32\n",
"emphatic 22\n",
"truce 14\n",
"forbidding 5\n",
"sally 5\n",
"saucy 4\n",
"nap 12\n",
"clavichord 22\n",
"harp 24\n",
"nurses 27\n",
"drooped 19\n",
"handwith 1\n",
"envious 12\n",
"consoling 7\n",
"wafting 2\n",
"musicians 5\n",
"replacing 5\n",
"gallantry 21\n",
"violin 3\n",
"serfsthe 2\n",
"unexpectedness 2\n",
"exertionsthe 1\n",
"sweep 14\n",
"cooks 12\n",
"aidesdecamp 15\n",
"escorted 21\n",
"combed 7\n",
"boiled 18\n",
"tartar 16\n",
"wellpreserved 1\n",
"selfsatisfaction 9\n",
"seating 6\n",
"wearily 12\n",
"definitely 18\n",
"batard 1\n",
"bastard 1\n",
"et 23\n",
"ce 10\n",
"gratitudethis 1\n",
"twitching 17\n",
"rectify 3\n",
"intriguingi 1\n",
"protege 4\n",
"portfolio 10\n",
"ignoring 1\n",
"afternoon 119\n",
"meekly 27\n",
"knitting 24\n",
"sumptuous 7\n",
"tiptoe 45\n",
"confessor 3\n",
"amble 1\n",
"inexhaustible 7\n",
"vestments 3\n",
"chanting 3\n",
"sophie 4\n",
"happening 68\n",
"armpits 3\n",
"leonine 1\n",
"deride 1\n",
"tickling 3\n",
"dozing 23\n",
"sipping 5\n",
"pearls 11\n",
"permits 3\n",
"wringing 15\n",
"sadden 1\n",
"uplifts 1\n",
"andreevich 7\n",
"routine 14\n",
"paul 467\n",
"memoirs 5\n",
"solving 3\n",
"exactitude 3\n",
"gardener 8\n",
"bookcases 1\n",
"unshaven 2\n",
"sharply 49\n",
"miniature 10\n",
"eyeslarge 1\n",
"poetic 16\n",
"distinguee 1\n",
"elevates 2\n",
"camel 5\n",
"announces 2\n",
"areat 1\n",
"countermarches 1\n",
"heartrending 6\n",
"grasseyement 1\n",
"scolding 7\n",
"grayhaired 4\n",
"drowsily 3\n",
"hood 13\n",
"entrusting 3\n",
"abstemious 1\n",
"germans 33\n",
"understandtolstoys 1\n",
"beganat 1\n",
"swedish 4\n",
"sen 2\n",
"ruling 7\n",
"crownan 1\n",
"descendant 1\n",
"criticism 5\n",
"ianovich 1\n",
"retelling 2\n",
"statesmen 4\n",
"puppet 4\n",
"york 10\n",
"oneexcept 1\n",
"campaigns 8\n",
"traveling 19\n",
"inspecting 8\n",
"harness 28\n",
"andrusha 5\n",
"cest 10\n",
"reads 10\n",
"conversationand 2\n",
"monk 5\n",
"nopromise 1\n",
"icon 43\n",
"fatherinlaw 10\n",
"reproached 23\n",
"artless 11\n",
"zubova 3\n",
"squeaked 4\n",
"flourish 16\n",
"unmarry 1\n",
"fold 14\n",
"jottings 1\n",
"chafing 8\n",
"burdening 3\n",
"quartered 13\n",
"unrussian 1\n",
"strap 6\n",
"commissariat 16\n",
"tsaritsin 1\n",
"signalers 1\n",
"greatcoats 12\n",
"misrepeated 1\n",
"schoolboy 16\n",
"unpolished 1\n",
"ras 1\n",
"lency 1\n",
"strictness 3\n",
"nesvitski 66\n",
"swarthy 7\n",
"reprimanded 3\n",
"latterunconscious 1\n",
"bacchus 3\n",
"atone 6\n",
"mocking 10\n",
"beggarly 1\n",
"drummer 14\n",
"longdrawnout 1\n",
"kamenski 7\n",
"fortylooked 1\n",
"player 5\n",
"ranker 1\n",
"honored 18\n",
"recross 4\n",
"indolence 3\n",
"reputations 1\n",
"theresa 3\n",
"voyez 1\n",
"armys 6\n",
"provisioning 2\n",
"larmee 2\n",
"rire 1\n",
"garcon 2\n",
"mais 5\n",
"russianbut 1\n",
"division 69\n",
"supple 6\n",
"pitchfork 1\n",
"manure 4\n",
"cowshed 3\n",
"hay 32\n",
"lavrushka 35\n",
"denisovs 55\n",
"shako 7\n",
"lavrushkas 2\n",
"fraulein 1\n",
"hullo 3\n",
"singles 2\n",
"collahs 1\n",
"wetched 3\n",
"wostov 9\n",
"piles 14\n",
"rook 7\n",
"lame 18\n",
"yesterdays 20\n",
"thwow 1\n",
"scoundwel 1\n",
"dahe 1\n",
"telyanins 8\n",
"kirsten 5\n",
"anyway 5\n",
"offense 8\n",
"snap 10\n",
"clapping 26\n",
"tomowwow 3\n",
"newcomer 15\n",
"rainy 10\n",
"varnished 6\n",
"rocky 4\n",
"forests 19\n",
"mystic 6\n",
"fieldglass 1\n",
"turreted 1\n",
"narrowed 5\n",
"dawdling 3\n",
"wagon 16\n",
"noisy 13\n",
"whirling 5\n",
"dam 24\n",
"fumbling 8\n",
"scurry 1\n",
"fugitives 9\n",
"tracks 8\n",
"missis 19\n",
"apple 15\n",
"shoving 4\n",
"splashed 10\n",
"cannon 88\n",
"spurting 1\n",
"shaved 10\n",
"stallion 2\n",
"neighing 2\n",
"skyline 1\n",
"keenness 1\n",
"rhythmic 6\n",
"bugler 4\n",
"dwive 1\n",
"cossacks 86\n",
"himrostov 1\n",
"reconciliation 8\n",
"unpleasantness 9\n",
"sabers 14\n",
"braided 3\n",
"swarming 6\n",
"tunics 1\n",
"peppered 3\n",
"limbers 4\n",
"rounds 7\n",
"oooh 1\n",
"hacking 2\n",
"promoted 12\n",
"foreseen 24\n",
"enemywith 1\n",
"krems 12\n",
"brunn 11\n",
"dokhturov 23\n",
"daythe 1\n",
"reviewing 6\n",
"sendoff 1\n",
"attain 25\n",
"thawing 2\n",
"driver 36\n",
"feverishly 8\n",
"diplomats 3\n",
"pointedly 7\n",
"bilibins 11\n",
"sprinkled 16\n",
"wittily 1\n",
"cependant 1\n",
"que 16\n",
"victoire 1\n",
"nest 18\n",
"liebchen 1\n",
"blunder 5\n",
"disgraces 1\n",
"vrbna 1\n",
"usdoing 1\n",
"bigwigs 3\n",
"u 3\n",
"lays 14\n",
"yeux 1\n",
"sardinian 2\n",
"tomorrows 15\n",
"musketeers 1\n",
"palpitating 6\n",
"les 16\n",
"flatly 1\n",
"discusses 1\n",
"politicsyou 1\n",
"nonreceipt 1\n",
"theater 20\n",
"audiences 3\n",
"questionsthe 1\n",
"messieurs 3\n",
"crack 18\n",
"ostrich 3\n",
"voit 1\n",
"feu 1\n",
"celui 2\n",
"devait 1\n",
"gascon 1\n",
"worldfamous 1\n",
"mackes 1\n",
"macked 1\n",
"armee 1\n",
"eprouver 1\n",
"endless 16\n",
"vehicles 24\n",
"orderlies 14\n",
"brokendown 3\n",
"lungs 14\n",
"slut 3\n",
"compressing 5\n",
"championship 4\n",
"scoundrels 11\n",
"capitulation 6\n",
"seams 2\n",
"socket 5\n",
"emissary 3\n",
"armistice 4\n",
"porridge 12\n",
"unsoldierly 1\n",
"swarmed 7\n",
"dozens 13\n",
"voila 4\n",
"camps 6\n",
"boilers 1\n",
"boiler 8\n",
"bowl 10\n",
"chasseur 1\n",
"swishing 1\n",
"linethe 2\n",
"sightseers 2\n",
"ivan 9\n",
"tafa 1\n",
"goodhumored 5\n",
"explode 1\n",
"loopholes 2\n",
"blockhouses 1\n",
"outflank 7\n",
"historical 52\n",
"manly 23\n",
"youvodka 1\n",
"herb 5\n",
"super 3\n",
"puff 15\n",
"expiate 3\n",
"enjoyable 4\n",
"accountant 13\n",
"artilleryman 6\n",
"redhaired 12\n",
"gunner 8\n",
"shading 9\n",
"salute 14\n",
"uselessly 8\n",
"squarefor 1\n",
"halfhour 20\n",
"hawks 1\n",
"appeals 3\n",
"caps 27\n",
"unison 11\n",
"bandoliers 1\n",
"turnedout 1\n",
"racing 6\n",
"azov 1\n",
"privates 1\n",
"iss 2\n",
"mix 5\n",
"expanded 12\n",
"detect 6\n",
"swerved 5\n",
"pinning 3\n",
"disentangled 2\n",
"inefficiency 1\n",
"recalcitrant 1\n",
"units 9\n",
"broadcloth 2\n",
"crews 1\n",
"disabled 9\n",
"puffs 8\n",
"unharnessed 3\n",
"skipped 2\n",
"conflagrations 3\n",
"transmitted 3\n",
"crosslegged 1\n",
"commiseration 14\n",
"deadwhy 1\n",
"greedily 9\n",
"mutton 35\n",
"blamelessly 2\n",
"intervene 1\n",
"twisting 17\n",
"agonizingly 1\n",
"charcoal 4\n",
"entice 4\n",
"richer 13\n",
"statuesque 1\n",
"striped 2\n",
"rublesto 2\n",
"fingering 7\n",
"tactless 1\n",
"masterly 6\n",
"helenes 27\n",
"corset 1\n",
"latterly 12\n",
"unacknowledged 2\n",
"toilets 3\n",
"vyazmitinov 5\n",
"focused 2\n",
"expand 6\n",
"absentminded 13\n",
"headache 8\n",
"lelya 2\n",
"babyhood 3\n",
"aline 2\n",
"aime 5\n",
"sable 11\n",
"overseer 8\n",
"indistinctly 12\n",
"blackguards 5\n",
"temerity 4\n",
"dummy 2\n",
"hmhis 1\n",
"coachmen 8\n",
"sleighs 11\n",
"lodges 8\n",
"churlish 1\n",
"amusingly 1\n",
"valets 8\n",
"banter 4\n",
"blotches 2\n",
"martyrlike 1\n",
"katie 5\n",
"guise 4\n",
"stifle 6\n",
"thyself 5\n",
"invaluable 5\n",
"lovea 2\n",
"semiprivate 1\n",
"longestablished 4\n",
"pining 11\n",
"petite 1\n",
"andrewa 1\n",
"anatoles 14\n",
"befriend 3\n",
"surveyed 21\n",
"interceded 1\n",
"aboveboard 3\n",
"crafty 10\n",
"kinsman 5\n",
"magnanimous 15\n",
"pauvre 6\n",
"kindyes 1\n",
"deviland 1\n",
"disheveled 7\n",
"pacifying 2\n",
"honey 14\n",
"slyboots 1\n",
"brutes 4\n",
"brigadier 1\n",
"descriptive 2\n",
"highi 1\n",
"supervision 2\n",
"dukes 10\n",
"wares 4\n",
"waitresses 1\n",
"cornetcy 1\n",
"prided 4\n",
"captaincy 2\n",
"baritone 3\n",
"bespattered 4\n",
"guardsmen 1\n",
"regulations 10\n",
"complacency 16\n",
"stormed 6\n",
"sagacious 19\n",
"hearerswho 1\n",
"selfrespect 11\n",
"waists 1\n",
"scarves 5\n",
"smoothfelt 1\n",
"suites 7\n",
"immensity 5\n",
"selfforgetfulness 3\n",
"atom 11\n",
"menthe 4\n",
"indolently 4\n",
"affronts 2\n",
"essen 1\n",
"medals 3\n",
"chat 11\n",
"weyrothers 9\n",
"gentlemanevidently 1\n",
"subordinationlooked 1\n",
"schwartzenberg 1\n",
"eventuality 2\n",
"adam 7\n",
"sixteenth 3\n",
"dwown 1\n",
"gwief 1\n",
"roadside 7\n",
"alsatian 2\n",
"soldierly 2\n",
"welltrained 1\n",
"dismounting 6\n",
"dienot 1\n",
"villier 1\n",
"twentieth 8\n",
"activitythe 1\n",
"outbursts 2\n",
"wimpfen 1\n",
"backbiter 1\n",
"rice 8\n",
"unbuttoned 14\n",
"droop 20\n",
"emitted 5\n",
"kobelnitz 3\n",
"bellowitz 1\n",
"typically 1\n",
"przebyszewski 3\n",
"mien 1\n",
"miller 3\n",
"subtly 2\n",
"tomorrowor 1\n",
"undertakes 1\n",
"triumphs 5\n",
"thiswant 1\n",
"mefather 1\n",
"coachmans 8\n",
"sleepiness 2\n",
"misty 20\n",
"unmask 1\n",
"glade 3\n",
"unmelted 1\n",
"tache 2\n",
"tasha 1\n",
"sabretachekeep 1\n",
"frozen 27\n",
"vive 19\n",
"looming 7\n",
"elephant 14\n",
"masts 9\n",
"battleheaven 1\n",
"slopes 5\n",
"creek 1\n",
"muddling 1\n",
"goldbach 1\n",
"lazily 11\n",
"timely 9\n",
"pratzen 17\n",
"orb 3\n",
"thinmoved 1\n",
"predictions 7\n",
"lightas 1\n",
"dew 13\n",
"irritable 15\n",
"defile 2\n",
"deploying 2\n",
"yawning 15\n",
"yawn 6\n",
"saluting 2\n",
"restfully 1\n",
"questionmost 1\n",
"grooms 3\n",
"dissatisfaction 22\n",
"observantly 3\n",
"sera 2\n",
"notre 4\n",
"feebleness 6\n",
"glide 9\n",
"hillsides 2\n",
"intimidating 1\n",
"stimulated 17\n",
"trotting 9\n",
"bowled 4\n",
"helmet 3\n",
"uniformed 2\n",
"henker 1\n",
"diese 1\n",
"derision 3\n",
"ivanych 9\n",
"corrected 16\n",
"namewell 1\n",
"hosjeradek 2\n",
"russiansthe 1\n",
"thither 17\n",
"dares 5\n",
"despondent 13\n",
"dams 1\n",
"rallying 11\n",
"watering 7\n",
"twohorse 1\n",
"cartson 1\n",
"oftenest 4\n",
"stiffened 9\n",
"buzzing 9\n",
"everlasting 5\n",
"probing 3\n",
"transfer 7\n",
"sukhtelen 2\n",
"meddle 4\n",
"indefinable 11\n",
"feverishness 1\n",
"chaos 3\n",
"unconciousness 1\n",
"voronezh 18\n",
"ruts 6\n",
"bakers 7\n",
"signboards 2\n",
"crossroads 4\n",
"zakhar 9\n",
"gingerbread 5\n",
"warped 2\n",
"braiding 1\n",
"travelers 5\n",
"sabretaches 1\n",
"jugs 3\n",
"odor 5\n",
"blanket 9\n",
"blackmustached 1\n",
"servile 7\n",
"promiseit 2\n",
"duport 7\n",
"intermediacy 1\n",
"selecting 3\n",
"acquiring 3\n",
"adapting 3\n",
"derogatory 5\n",
"sprees 2\n",
"strawberries 2\n",
"veal 16\n",
"lessyes 1\n",
"mayonnaise 1\n",
"sleeker 2\n",
"rasgulyaythe 1\n",
"moscows 4\n",
"voltaire 4\n",
"logical 13\n",
"springtime 3\n",
"hither 9\n",
"crowed 4\n",
"wellsetup 1\n",
"ignatovich 1\n",
"plowed 4\n",
"salver 8\n",
"alexandersbekleshev 1\n",
"naryshkinwhich 1\n",
"lenten 1\n",
"sterlet 2\n",
"toasts 6\n",
"peterkinand 1\n",
"indubitably 6\n",
"impetuous 11\n",
"melting 9\n",
"antagonists 3\n",
"thwee 3\n",
"depraved 9\n",
"ottoman 7\n",
"gazettes 1\n",
"expectantly 2\n",
"malign 2\n",
"princessinfluenced 1\n",
"foka 3\n",
"pallor 7\n",
"indigestion 2\n",
"savishna 3\n",
"outlying 2\n",
"newssay 1\n",
"labor 15\n",
"scatters 1\n",
"moldavian 1\n",
"squealed 1\n",
"vise 2\n",
"coverlet 8\n",
"houseseeing 1\n",
"dolokhovyou 1\n",
"recruits 6\n",
"furlough 3\n",
"epiphany 1\n",
"wary 5\n",
"holidays 31\n",
"dowerless 2\n",
"articulating 6\n",
"prettyso 1\n",
"isshe 1\n",
"pupilsyou 1\n",
"curtsy 1\n",
"friendscome 1\n",
"reddening 19\n",
"stint 1\n",
"economical 2\n",
"aah 1\n",
"staking 1\n",
"mouse 13\n",
"entweat 1\n",
"dishonored 3\n",
"velvety 4\n",
"affetto 2\n",
"denisovbut 1\n",
"albums 5\n",
"undressing 16\n",
"thrashing 3\n",
"himalso 1\n",
"goatskin 2\n",
"souza 1\n",
"mansfeld 1\n",
"yellowfaced 4\n",
"nankeencovered 1\n",
"marker 2\n",
"misfortunei 1\n",
"masonic 19\n",
"beliefsi 1\n",
"regretfully 3\n",
"hast 8\n",
"omnipotence 1\n",
"tremor 10\n",
"fatherly 8\n",
"sciencethe 2\n",
"gulped 3\n",
"joseph 49\n",
"alexeevich 34\n",
"bazdeev 5\n",
"novikovs 1\n",
"sponsor 2\n",
"shamefaced 11\n",
"complex 16\n",
"seeker 4\n",
"acquaintancehe 1\n",
"inadequacy 1\n",
"gloved 9\n",
"labors 5\n",
"delivers 2\n",
"teaching 18\n",
"hieroglyphics 1\n",
"rhetors 2\n",
"blindfolded 9\n",
"dignitary 5\n",
"swiss 15\n",
"candlesticks 2\n",
"symbolized 2\n",
"south 18\n",
"fatted 1\n",
"himespecially 1\n",
"insanely 3\n",
"diplomatists 9\n",
"las 1\n",
"dandin 1\n",
"m 30\n",
"incomparably 2\n",
"lurope 2\n",
"impious 3\n",
"salon 9\n",
"unsmiling 1\n",
"militiamen 20\n",
"supervise 1\n",
"nephewand 1\n",
"recruitment 1\n",
"wineglass 12\n",
"armchairs 3\n",
"screened 4\n",
"yougive 1\n",
"idioms 1\n",
"smithereens 1\n",
"installs 1\n",
"ardently 7\n",
"kibitka 1\n",
"sedmoretzki 1\n",
"increasingly 3\n",
"pursues 8\n",
"epileptic 2\n",
"scour 1\n",
"famine 4\n",
"perturb 1\n",
"smacking 5\n",
"embezzlement 1\n",
"lisp 2\n",
"harvests 4\n",
"consultations 4\n",
"crimean 5\n",
"preceptsthat 1\n",
"virtueslove 1\n",
"serfsmade 1\n",
"asylumson 1\n",
"uninteresting 3\n",
"homestead 4\n",
"bathhouse 2\n",
"constructing 2\n",
"scaffolding 6\n",
"nothings 10\n",
"prochain 1\n",
"toss 10\n",
"cure 13\n",
"shallowness 1\n",
"proprietors 12\n",
"unjustly 1\n",
"forhuman 1\n",
"foreheads 5\n",
"immersed 4\n",
"raft 8\n",
"friendlife 1\n",
"isgod 1\n",
"ferrymen 1\n",
"outwardly 6\n",
"charmee 1\n",
"voir 4\n",
"contente 1\n",
"pilgrim 9\n",
"jexplique 1\n",
"christmastime 3\n",
"kolyazin 3\n",
"pelageya 16\n",
"amphilochus 1\n",
"stirs 2\n",
"canteenkeeper 1\n",
"retreats 3\n",
"april 6\n",
"mashkas 2\n",
"caldrons 3\n",
"sprouting 3\n",
"roofless 2\n",
"roofed 3\n",
"hutand 1\n",
"underclothes 1\n",
"mashka 1\n",
"bwought 1\n",
"downcapitally 1\n",
"worrying 10\n",
"mach 1\n",
"twy 3\n",
"wobbewy 1\n",
"thwash 1\n",
"chiefalso 1\n",
"commissioner 7\n",
"stwaight 2\n",
"june 19\n",
"devastated 7\n",
"assistant 28\n",
"typhus 2\n",
"makeev 4\n",
"crutches 5\n",
"originated 20\n",
"straighter 2\n",
"decomposing 1\n",
"healed 4\n",
"courtmartialed 2\n",
"gwovel 1\n",
"tilsit 16\n",
"niemen 20\n",
"firststepped 1\n",
"zhilinskile 1\n",
"nle 1\n",
"flags 5\n",
"repenting 1\n",
"justifying 5\n",
"dhonneur 4\n",
"cavalrymans 1\n",
"lazarev 10\n",
"lazarevs 2\n",
"francs 3\n",
"harboring 2\n",
"tablevery 1\n",
"relevantly 1\n",
"erfurt 6\n",
"estatesand 1\n",
"diligently 4\n",
"lagged 8\n",
"liquefied 1\n",
"sweated 1\n",
"cramped 8\n",
"sequence 14\n",
"jar 6\n",
"shoots 8\n",
"transfigured 2\n",
"veteran 4\n",
"reenter 2\n",
"senselessly 2\n",
"grecque 2\n",
"logic 6\n",
"peterhof 1\n",
"decrees 9\n",
"societyabolishing 1\n",
"qualify 6\n",
"collegiate 4\n",
"administrative 6\n",
"tribunals 2\n",
"arakcheevs 5\n",
"sila 2\n",
"endorsed 2\n",
"misspelled 1\n",
"diverse 16\n",
"innovations 3\n",
"handshands 1\n",
"markedly 1\n",
"co 20\n",
"handsthose 1\n",
"extraordinarily 15\n",
"disdainful 14\n",
"contradict 18\n",
"parait 1\n",
"moyens 1\n",
"sous 1\n",
"upheld 7\n",
"emulation 5\n",
"upholding 1\n",
"helpful 3\n",
"courtier 9\n",
"breeding 14\n",
"mirrorlike 3\n",
"metaphysics 3\n",
"definitions 2\n",
"unshakable 4\n",
"senate 11\n",
"andwhat 2\n",
"institutes 1\n",
"formulating 2\n",
"busied 3\n",
"supplemented 1\n",
"infatuations 2\n",
"dissipations 2\n",
"bog 3\n",
"saltor 1\n",
"ingratiate 2\n",
"convened 2\n",
"dissemination 2\n",
"governed 8\n",
"illuminism 3\n",
"developing 9\n",
"identically 3\n",
"limitations 4\n",
"fraternal 6\n",
"bladder 1\n",
"selfknowledgefor 1\n",
"innate 3\n",
"whichin 3\n",
"supplicant 1\n",
"napoleonic 8\n",
"notabilities 3\n",
"belle 1\n",
"epigrams 1\n",
"bezukhovas 7\n",
"unexposed 1\n",
"absorption 4\n",
"freaks 1\n",
"urusov 1\n",
"pillars 5\n",
"labyrinth 12\n",
"teachings 1\n",
"entirety 4\n",
"sulphur 3\n",
"arouses 1\n",
"nourish 2\n",
"sanctifying 1\n",
"desertest 1\n",
"otradnoemitenkas 1\n",
"particularwas 1\n",
"peronskaya 13\n",
"insistently 1\n",
"soll 1\n",
"weib 1\n",
"provincesand 1\n",
"longforeseen 1\n",
"countessher 1\n",
"dispelledlooked 1\n",
"profile 6\n",
"knuckle 4\n",
"dating 1\n",
"humored 3\n",
"piloted 2\n",
"claretcolored 1\n",
"bodices 3\n",
"hairdressing 1\n",
"lookingglass 11\n",
"oooo 1\n",
"throbbed 2\n",
"irrecoverable 4\n",
"silvergray 1\n",
"naryshkina 1\n",
"natashaas 1\n",
"firhoff 2\n",
"protegee 3\n",
"beautifulcompared 1\n",
"cotillions 1\n",
"cotillion 2\n",
"criticizing 3\n",
"bitski 3\n",
"recounted 9\n",
"emphasizing 3\n",
"disinclined 1\n",
"longfaced 1\n",
"laugha 1\n",
"horsdoeuvres 1\n",
"magnitskis 1\n",
"stuttering 1\n",
"merriment 9\n",
"painstakingly 3\n",
"brimful 2\n",
"switzerland 15\n",
"societythat 1\n",
"busts 2\n",
"mann 1\n",
"fichu 2\n",
"attachments 7\n",
"timidity 8\n",
"accentuated 1\n",
"ills 1\n",
"diplomacy 4\n",
"wishto 1\n",
"solfeggio 1\n",
"sedately 5\n",
"betokening 1\n",
"brunt 2\n",
"unprovoked 2\n",
"sisterinlawleft 1\n",
"noticeably 3\n",
"negotiating 4\n",
"foresees 3\n",
"stepmother 4\n",
"hesitations 1\n",
"poorspirited 3\n",
"addedallusions 1\n",
"bible 5\n",
"laboridlenesswas 1\n",
"gladden 3\n",
"entanglements 2\n",
"przazdziecka 1\n",
"borzozowska 1\n",
"basov 1\n",
"kremenchug 1\n",
"shrubbery 4\n",
"scatter 3\n",
"wolf 55\n",
"milka 13\n",
"licked 6\n",
"inimitable 2\n",
"circassian 6\n",
"irksome 8\n",
"quarry 1\n",
"kennelmen 1\n",
"onyouve 1\n",
"mulled 2\n",
"bordeaux 3\n",
"danielll 1\n",
"lyadov 2\n",
"astounded 10\n",
"uplands 1\n",
"ulyulyulyu 2\n",
"scorning 2\n",
"sweating 2\n",
"itin 6\n",
"beforehuman 1\n",
"markings 3\n",
"miscalculated 2\n",
"gnashing 1\n",
"karaythe 1\n",
"animals 28\n",
"shying 2\n",
"crusty 2\n",
"voltorn 1\n",
"shortlegged 1\n",
"curves 1\n",
"arbitrariness 1\n",
"purebred 1\n",
"rugay 8\n",
"nikanorovich 1\n",
"rugayushka 2\n",
"diminutive 9\n",
"sowing 6\n",
"drip 3\n",
"oneruble 1\n",
"wrathfully 5\n",
"spattered 2\n",
"horsebackraised 1\n",
"retainers 4\n",
"spotlessbut 1\n",
"reappearance 3\n",
"deftly 5\n",
"preserves 3\n",
"ladycountess 1\n",
"executor 1\n",
"huntsmens 3\n",
"guitar 18\n",
"itcome 3\n",
"imbibed 3\n",
"unconsidered 1\n",
"nightthe 4\n",
"invitingly 2\n",
"musician 2\n",
"belova 11\n",
"plundered 6\n",
"dolllike 1\n",
"portionless 1\n",
"unselfishly 4\n",
"rome 38\n",
"mavrushka 2\n",
"vogels 1\n",
"walnuts 2\n",
"oldthats 1\n",
"cabbage 12\n",
"learnwhat 1\n",
"maternal 7\n",
"innkeepers 5\n",
"funnybringing 1\n",
"burntcork 2\n",
"melyukovs 7\n",
"pashette 1\n",
"louisa 90\n",
"frostbound 1\n",
"tug 8\n",
"darlings 9\n",
"clucked 1\n",
"spangled 1\n",
"broadly 2\n",
"melyukov 2\n",
"cockcrow 2\n",
"barn 19\n",
"knockingthats 1\n",
"disengaging 3\n",
"danilovnas 1\n",
"natashaare 1\n",
"outlined 3\n",
"intolerable 9\n",
"longexpected 2\n",
"margaux 1\n",
"predetermined 3\n",
"strategist 2\n",
"unfaithful 2\n",
"raceby 1\n",
"powerlessto 1\n",
"inwards 2\n",
"illegal 2\n",
"charter 2\n",
"churchesbut 1\n",
"unraveled 1\n",
"criticisms 2\n",
"reverently 4\n",
"pleasurestalks 1\n",
"hillsand 1\n",
"despotthe 1\n",
"conscripted 3\n",
"doctormetivierwho 1\n",
"metiviers 3\n",
"lopukhin 3\n",
"chatrov 1\n",
"drubetskoyawaited 1\n",
"presiding 10\n",
"blindness 2\n",
"churchyet 1\n",
"territories 1\n",
"divinities 1\n",
"crawl 4\n",
"catholics 1\n",
"woolwork 2\n",
"greats 2\n",
"cudgel 8\n",
"museum 4\n",
"raillery 3\n",
"herthe 2\n",
"yetyes 1\n",
"incurring 2\n",
"supping 1\n",
"promenade 1\n",
"mort 7\n",
"dautre 1\n",
"asile 1\n",
"ame 1\n",
"mele 1\n",
"ces 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"adorer 4\n",
"selfdeception 2\n",
"greedy 6\n",
"jails 2\n",
"prisons 9\n",
"appetizing 2\n",
"sniveling 1\n",
"sightlooked 1\n",
"frivolously 2\n",
"songstress 1\n",
"impolitely 1\n",
"caucasus 2\n",
"persia 1\n",
"stagewho 1\n",
"disconnectedly 1\n",
"tickle 2\n",
"horns 3\n",
"exposedbeckoned 1\n",
"thinkespecially 1\n",
"beards 4\n",
"violins 1\n",
"sevenths 1\n",
"sont 2\n",
"tinseldecorated 1\n",
"commanderinchiefa 1\n",
"dominating 1\n",
"scrubbed 1\n",
"dressmaker 4\n",
"womansthat 1\n",
"picturesquely 1\n",
"charmer 3\n",
"unapproachable 4\n",
"madly 14\n",
"worldso 1\n",
"worlda 1\n",
"himfrom 1\n",
"lengths 4\n",
"freedomif 1\n",
"elope 5\n",
"unfrocked 1\n",
"passport 3\n",
"makarin 8\n",
"bundles 16\n",
"khvostikov 3\n",
"witted 1\n",
"moneys 3\n",
"handhe 1\n",
"silklined 1\n",
"girt 3\n",
"fling 8\n",
"ignatka 1\n",
"matrenas 1\n",
"steshka 1\n",
"whiteplumed 1\n",
"fiftyeight 1\n",
"sympathized 5\n",
"brotherinlaw 10\n",
"banished 9\n",
"maliciously 3\n",
"insists 1\n",
"friendis 1\n",
"dearill 1\n",
"someonenot 1\n",
"fahrenheit 1\n",
"comet 10\n",
"scintillating 2\n",
"treacheries 1\n",
"issues 5\n",
"delve 1\n",
"coincident 1\n",
"causesto 1\n",
"dictatorship 2\n",
"causesmyriads 1\n",
"fatalism 1\n",
"ses 2\n",
"lifethat 2\n",
"dresden 4\n",
"selflove 2\n",
"stalk 2\n",
"organic 1\n",
"mattock 1\n",
"ownthat 1\n",
"kingsto 1\n",
"cosaques 1\n",
"steppes 5\n",
"moscou 5\n",
"sainte 2\n",
"ceaseless 6\n",
"preurif 1\n",
"kashmir 1\n",
"saxon 2\n",
"phrased 1\n",
"trilling 1\n",
"peacetime 2\n",
"balashevs 15\n",
"tethered 4\n",
"theatrically 1\n",
"yourshe 1\n",
"kingly 1\n",
"incongruous 4\n",
"writinghe 1\n",
"ruder 1\n",
"castres 2\n",
"vilnanow 1\n",
"turenne 2\n",
"pomp 6\n",
"cologne 4\n",
"balashavs 2\n",
"kurakins 1\n",
"kurakin 2\n",
"demonstrating 5\n",
"alexanderjust 1\n",
"finland 2\n",
"bothnia 1\n",
"barclay 52\n",
"pfuel 39\n",
"weighti 1\n",
"bernadotte 1\n",
"himthose 1\n",
"backwardness 2\n",
"deliberating 6\n",
"derisive 7\n",
"unrelated 2\n",
"rancor 1\n",
"unspent 1\n",
"plodding 4\n",
"vainglorious 1\n",
"preceptor 2\n",
"blameis 1\n",
"trash 3\n",
"radius 2\n",
"disadvantageous 3\n",
"wolzogenand 1\n",
"adviser 12\n",
"pfuels 8\n",
"adherentsmilitary 1\n",
"immutable 3\n",
"pseudotheory 1\n",
"barbarism 2\n",
"onesided 6\n",
"ermolov 28\n",
"confidencebelonged 1\n",
"compromises 2\n",
"casque 1\n",
"superseded 8\n",
"justicea 1\n",
"alexandergenerals 1\n",
"defensive 8\n",
"intersecting 1\n",
"longcherished 2\n",
"intricacy 3\n",
"countrythe 1\n",
"moscowwas 1\n",
"michaud 20\n",
"doit 1\n",
"immutably 2\n",
"excitable 2\n",
"campthe 2\n",
"supporter 2\n",
"sometimeswhen 1\n",
"nicer 2\n",
"pacerode 1\n",
"stimulatingly 1\n",
"facefair 1\n",
"diseases 5\n",
"liver 9\n",
"pseudohealers 1\n",
"elementary 3\n",
"powders 3\n",
"feller 5\n",
"diagnosed 1\n",
"stifling 7\n",
"pleasureballs 1\n",
"fasting 1\n",
"shabbiest 2\n",
"bricklayers 2\n",
"sultry 2\n",
"hawkers 4\n",
"cobblestones 1\n",
"comely 3\n",
"worshipers 1\n",
"dalmatic 1\n",
"sins 11\n",
"trinity 3\n",
"biretta 1\n",
"synoda 1\n",
"unlawful 3\n",
"counsels 4\n",
"gideon 2\n",
"midian 2\n",
"bended 2\n",
"blasphemies 2\n",
"hebrew 1\n",
"besuhofall 1\n",
"bulletin 2\n",
"membership 1\n",
"patriotism 14\n",
"abnormal 2\n",
"georgian 2\n",
"barring 4\n",
"begrudge 1\n",
"patriot 2\n",
"gateway 48\n",
"rumbling 7\n",
"tradesmen 8\n",
"everywhereon 2\n",
"kvas 1\n",
"poppyseed 1\n",
"plenary 1\n",
"celebrate 4\n",
"signing 9\n",
"emperorfrantically 1\n",
"waitedand 1\n",
"bearded 3\n",
"generaux 1\n",
"appealthat 1\n",
"evoking 3\n",
"offahd 1\n",
"waise 1\n",
"patten 1\n",
"pwovince 1\n",
"wobbahs 1\n",
"wuined 1\n",
"depwavity 1\n",
"watered 3\n",
"outlet 2\n",
"formally 11\n",
"ourselvesand 1\n",
"emperormost 1\n",
"stepan 2\n",
"stepanovich 1\n",
"decreases 3\n",
"speechesinfected 1\n",
"stupendous 8\n",
"falsified 3\n",
"junctionthough 1\n",
"avoidance 11\n",
"enfeebled 6\n",
"battleground 1\n",
"taverns 3\n",
"unhesitatingly 2\n",
"whileinstead 1\n",
"voicenow 1\n",
"ivanovichs 2\n",
"divest 4\n",
"twentythousandth 1\n",
"pageboy 1\n",
"gachina 1\n",
"knobs 7\n",
"innyard 5\n",
"purchases 7\n",
"yawned 4\n",
"crops 3\n",
"faroff 6\n",
"bombs 1\n",
"splinter 2\n",
"widespread 3\n",
"confusedly 16\n",
"anthill 1\n",
"firesthe 1\n",
"drought 1\n",
"sunparched 1\n",
"sandy 6\n",
"kneaded 1\n",
"considerate 30\n",
"bristled 2\n",
"flee 6\n",
"piebald 2\n",
"brickred 1\n",
"floundered 1\n",
"carp 1\n",
"yelling 5\n",
"andreevichhe 1\n",
"entrusts 1\n",
"implanting 1\n",
"bewails 1\n",
"allot 2\n",
"unmade 2\n",
"beenthe 1\n",
"conciliation 6\n",
"discountenanced 1\n",
"bilibinwho 1\n",
"visitthat 2\n",
"plutarch 2\n",
"retrieved 1\n",
"chessplayer 1\n",
"asiatique 2\n",
"dalexandre 1\n",
"innombrables 2\n",
"forme 1\n",
"matterwithout 1\n",
"loquacity 2\n",
"yankovo 3\n",
"apathy 3\n",
"certainthat 1\n",
"halfcrazy 2\n",
"circulating 3\n",
"fedorovichs 1\n",
"drons 2\n",
"beekeeping 1\n",
"dangerare 1\n",
"diedthis 1\n",
"horseswe 1\n",
"housing 1\n",
"ilyins 1\n",
"coompa 1\n",
"cavalrymen 6\n",
"espied 3\n",
"overresist 2\n",
"haythats 1\n",
"dictionaries 2\n",
"blushingly 5\n",
"itensure 1\n",
"evewyone 1\n",
"weason 1\n",
"wetweating 2\n",
"impressionssuch 1\n",
"concurrently 5\n",
"ambled 3\n",
"peak 3\n",
"thesebut 1\n",
"horseflesh 9\n",
"nentendent 1\n",
"oreille 1\n",
"thingothers 1\n",
"broadsheets 11\n",
"lvovich 1\n",
"hayfork 1\n",
"barque 6\n",
"caustiquei 1\n",
"galitsyn 1\n",
"petit 1\n",
"broadsheet 6\n",
"flogging 3\n",
"burghers 1\n",
"sour 4\n",
"evstafeyhis 1\n",
"mozhaysk 25\n",
"evstafeys 1\n",
"itselfis 1\n",
"redoubt 31\n",
"announcements 3\n",
"cunningly 4\n",
"postwhereas 1\n",
"unentrenched 3\n",
"novoe 2\n",
"kolocha 15\n",
"valuevo 6\n",
"positionat 1\n",
"gridneva 1\n",
"lashing 4\n",
"barrowloads 1\n",
"collarbones 1\n",
"panorama 6\n",
"moskva 8\n",
"gabions 1\n",
"censers 2\n",
"themhis 1\n",
"lectures 4\n",
"inditing 2\n",
"unnamed 1\n",
"kitchens 6\n",
"thoughtsthe 1\n",
"thoughtswould 1\n",
"perspective 21\n",
"daubed 1\n",
"itselfhow 1\n",
"paymaster 1\n",
"interrogative 3\n",
"slander 1\n",
"adversarys 1\n",
"pawns 1\n",
"muss 1\n",
"ja 1\n",
"absently 6\n",
"lightheartedly 1\n",
"faints 1\n",
"westphalians 2\n",
"bodyit 1\n",
"fearto 1\n",
"deplorable 8\n",
"sistine 1\n",
"spiking 2\n",
"dubiously 8\n",
"deckmuhl 3\n",
"opposing 10\n",
"overwhelm 3\n",
"poniatowski 3\n",
"leadership 3\n",
"ordre 1\n",
"allows 4\n",
"bartholomew 3\n",
"confirms 6\n",
"mutilation 1\n",
"pseudo 1\n",
"criticizedcriticized 1\n",
"rapp 10\n",
"intact 8\n",
"lozenge 1\n",
"medicinewhat 1\n",
"unhindered 2\n",
"encumbering 1\n",
"pertinaciously 2\n",
"violet 2\n",
"balloons 2\n",
"phase 5\n",
"whys 1\n",
"hayfield 1\n",
"gruel 1\n",
"thundercloud 1\n",
"scrupulously 3\n",
"dangling 9\n",
"sallowfaced 3\n",
"circlet 1\n",
"recaptured 2\n",
"untrustworthy 1\n",
"promptings 2\n",
"generalsdavout 1\n",
"reinforcements 9\n",
"ownas 1\n",
"claparede 2\n",
"campstool 5\n",
"fasted 1\n",
"jocularity 1\n",
"leagues 5\n",
"elsein 1\n",
"momentto 2\n",
"scherbinins 2\n",
"successa 1\n",
"halfcontemptuous 1\n",
"idol 9\n",
"respite 5\n",
"bandolier 1\n",
"yelped 1\n",
"footprints 3\n",
"tiresomely 1\n",
"rotating 1\n",
"lifei 3\n",
"suffocating 3\n",
"abdomen 2\n",
"smear 2\n",
"dresser 5\n",
"lifeespecially 1\n",
"participant 1\n",
"againas 2\n",
"conservative 1\n",
"seas 19\n",
"redressing 1\n",
"wurttembergers 2\n",
"mecklenburgers 1\n",
"belgians 2\n",
"bremen 1\n",
"davydov 2\n",
"serfsthose 1\n",
"bethink 1\n",
"battleswhen 1\n",
"onslaught 1\n",
"perishing 11\n",
"infinitesimally 2\n",
"integrating 1\n",
"infinitesimals 2\n",
"intensive 1\n",
"slackens 1\n",
"millionth 1\n",
"propounding 1\n",
"consolidated 1\n",
"recoils 3\n",
"milesto 1\n",
"granddaughter 4\n",
"konovnitsyns 3\n",
"malashas 1\n",
"rearranged 1\n",
"aubertchalme 1\n",
"colony 4\n",
"klyucharev 6\n",
"blackish 1\n",
"mastery 5\n",
"espoused 1\n",
"divorced 3\n",
"brothels 1\n",
"hasmen 1\n",
"surnames 1\n",
"maman 2\n",
"dites 2\n",
"manque 1\n",
"peche 1\n",
"husbandwho 1\n",
"keepingyour 1\n",
"minimize 1\n",
"cauldron 2\n",
"rearranging 3\n",
"unspoken 1\n",
"dreadfullyand 1\n",
"agei 1\n",
"showwhen 1\n",
"sixteenyearold 1\n",
"straightens 1\n",
"dismantled 4\n",
"maamselle 3\n",
"possiblereplied 1\n",
"hoffmans 1\n",
"hereofficers 1\n",
"tapestries 1\n",
"bitwell 1\n",
"repacked 1\n",
"prices 7\n",
"carters 1\n",
"bronzes 3\n",
"timofeevna 2\n",
"dolefully 5\n",
"ourall 1\n",
"lopukhins 1\n",
"yusupovs 1\n",
"pulls 3\n",
"weakening 3\n",
"unloading 3\n",
"efim 3\n",
"sadovaya 1\n",
"expressionof 1\n",
"bazdeevs 6\n",
"galoshes 2\n",
"disinfected 1\n",
"alleys 6\n",
"utilize 1\n",
"mamene 1\n",
"lunched 1\n",
"burnoose 1\n",
"signaling 1\n",
"abdomens 2\n",
"spirituous 2\n",
"laborthat 1\n",
"bumblebee 1\n",
"partitions 2\n",
"proprietiesa 1\n",
"sales 2\n",
"ilyinka 1\n",
"pimples 1\n",
"keyboard 1\n",
"countcount 1\n",
"rogozhski 2\n",
"fiveruble 1\n",
"assignat 1\n",
"barges 3\n",
"insurrection 1\n",
"ruminated 2\n",
"gotfor 1\n",
"threadbare 10\n",
"toothey 1\n",
"sinning 2\n",
"halfsevered 1\n",
"terribledisgusting 1\n",
"pacify 4\n",
"almshouse 2\n",
"stoned 7\n",
"fas 1\n",
"kutafyev 2\n",
"lodgings 22\n",
"paw 2\n",
"disperses 1\n",
"uncontrollably 1\n",
"engulfed 1\n",
"patriotisme 2\n",
"feroce 2\n",
"bordering 4\n",
"obsessed 1\n",
"insincerity 1\n",
"cabalistic 2\n",
"beddingall 1\n",
"goodnessplease 1\n",
"melodramatically 1\n",
"lifethe 1\n",
"appellations 1\n",
"maniac 2\n",
"deluge 1\n",
"timessure 1\n",
"apropos 3\n",
"wagered 1\n",
"wantedwhen 1\n",
"lovelove 1\n",
"worshiped 1\n",
"pierreas 1\n",
"glowedthe 1\n",
"wick 6\n",
"personified 2\n",
"valetlay 1\n",
"bolster 4\n",
"normal 8\n",
"titi 4\n",
"pitipitipiti 5\n",
"needles 8\n",
"musicit 1\n",
"shirtlike 1\n",
"happenshe 1\n",
"denserhe 1\n",
"sausageshaped 1\n",
"nikolievna 3\n",
"ceasing 6\n",
"curve 6\n",
"brat 1\n",
"graveled 1\n",
"fatty 1\n",
"pathetically 8\n",
"armenians 1\n",
"durosnels 1\n",
"phantoms 5\n",
"empresses 1\n",
"singsong 4\n",
"alternating 1\n",
"charlatan 1\n",
"petropol 1\n",
"etranger 2\n",
"flammes 1\n",
"dynasty 1\n",
"fulldress 2\n",
"snuggery 13\n",
"samples 2\n",
"friendliest 3\n",
"cavalier 1\n",
"ivory 7\n",
"bolkonskayathats 1\n",
"reawoke 1\n",
"auntthat 1\n",
"suppositionthen 1\n",
"undeservedly 2\n",
"subjectthey 1\n",
"unreasonably 5\n",
"spirituality 4\n",
"prohibited 2\n",
"relentless 5\n",
"deedinterrupted 1\n",
"beperhaps 2\n",
"surprisingly 6\n",
"definiteness 1\n",
"numbed 3\n",
"stupefied 5\n",
"systema 1\n",
"annihilating 2\n",
"shcherbatovs 1\n",
"wellnourished 1\n",
"lightbrown 2\n",
"impeding 1\n",
"wrongdoing 2\n",
"figurein 1\n",
"appositeness 1\n",
"sewed 1\n",
"fondand 1\n",
"leavea 1\n",
"appositely 1\n",
"endearment 4\n",
"lipetsk 1\n",
"obtainable 2\n",
"preoccupations 1\n",
"longtemps 1\n",
"detre 2\n",
"preternaturally 1\n",
"onesidedly 1\n",
"diedand 1\n",
"himhis 2\n",
"multiplicity 3\n",
"complexity 3\n",
"approximation 2\n",
"resultedmoment 1\n",
"overtures 3\n",
"bs 7\n",
"interminglings 1\n",
"ruza 1\n",
"harmfulgave 1\n",
"writtenthough 1\n",
"kikins 1\n",
"rec 1\n",
"orlovdenisov 9\n",
"stromilova 1\n",
"dmitrovsk 1\n",
"assiduously 2\n",
"nobodythere 1\n",
"recognitions 3\n",
"meanwhat 2\n",
"minimum 4\n",
"courses 8\n",
"southerly 2\n",
"sabastiani 1\n",
"yakovlev 3\n",
"denominations 2\n",
"safeguarded 1\n",
"tillers 1\n",
"markets 3\n",
"mokhovaya 1\n",
"distances 12\n",
"combining 3\n",
"embassage 1\n",
"lawful 12\n",
"lice 3\n",
"morningswhat 1\n",
"everythingfar 1\n",
"housewhich 2\n",
"uglyseemed 1\n",
"homely 23\n",
"chats 2\n",
"platoche 3\n",
"sewing 3\n",
"lifeis 1\n",
"alertness 1\n",
"responsibilities 4\n",
"dysentery 1\n",
"ordynka 1\n",
"transmoskva 1\n",
"crossway 1\n",
"chaises 1\n",
"rouged 1\n",
"immortal 12\n",
"lured 4\n",
"broussiers 2\n",
"dokhturovthat 1\n",
"undiscerningbut 1\n",
"townand 1\n",
"cogwheel 1\n",
"bolkhovitinov 13\n",
"miloradoviches 1\n",
"rescind 1\n",
"aggressive 2\n",
"sectionsagainst 1\n",
"yukhnov 1\n",
"stampede 2\n",
"ofthe 1\n",
"stael 6\n",
"guile 2\n",
"armynapoleon 1\n",
"mouton 3\n",
"usto 3\n",
"togethertheir 1\n",
"inhumanly 1\n",
"subjugates 1\n",
"consistently 9\n",
"rifles 1\n",
"solvable 1\n",
"apparentsuch 1\n",
"onmistaking 1\n",
"themselvescongregated 1\n",
"regularizing 1\n",
"treethe 1\n",
"vasilisa 1\n",
"prisonerswas 1\n",
"sharpening 8\n",
"mikulino 4\n",
"disparity 12\n",
"rumbled 4\n",
"hehe 1\n",
"cweep 1\n",
"impassabletheres 1\n",
"carve 1\n",
"overnight 16\n",
"watchhouse 4\n",
"bwute 2\n",
"gaptoothed 1\n",
"stuffonly 1\n",
"remonstrated 24\n",
"rubbishy 1\n",
"sutler 2\n",
"areand 1\n",
"vesenny 4\n",
"manger 1\n",
"nowwouldnt 1\n",
"lancers 1\n",
"nut 2\n",
"longnecked 1\n",
"clement 1\n",
"karabakh 2\n",
"whetstone 2\n",
"neighed 2\n",
"hymn 4\n",
"hornplayed 1\n",
"likhachevs 1\n",
"untying 3\n",
"tightening 4\n",
"shivers 1\n",
"pulsed 1\n",
"evwything 1\n",
"causeless 1\n",
"raided 1\n",
"locking 9\n",
"vestment 1\n",
"prick 1\n",
"companiona 1\n",
"sinned 3\n",
"vagrant 3\n",
"daddyi 1\n",
"emerges 2\n",
"haltand 1\n",
"bathing 1\n",
"diverge 2\n",
"tightand 2\n",
"approximately 3\n",
"krasnoe 22\n",
"dnieperwhich 1\n",
"buff 5\n",
"numerically 2\n",
"unfounded 1\n",
"becauseas 1\n",
"settles 3\n",
"camping 1\n",
"mentionbrought 1\n",
"arbiter 1\n",
"relived 1\n",
"trustful 5\n",
"tiredtry 1\n",
"diminution 1\n",
"bribed 3\n",
"blundering 10\n",
"napoleonthat 1\n",
"minimizing 1\n",
"detaching 2\n",
"placea 2\n",
"manylimbed 1\n",
"moderating 1\n",
"armyphysically 1\n",
"firstclass 3\n",
"flagged 2\n",
"mockingly 1\n",
"letriptaladebudeba 1\n",
"frenchie 1\n",
"actionthe 1\n",
"icecovered 1\n",
"tatterdemalions 1\n",
"porters 3\n",
"waddled 3\n",
"longstanding 1\n",
"levying 1\n",
"performer 4\n",
"inscrutable 6\n",
"orelwhere 1\n",
"insurmountably 1\n",
"artifice 5\n",
"savelich 6\n",
"stagecoach 9\n",
"posthouse 3\n",
"deadness 1\n",
"vitalitythe 1\n",
"organizations 1\n",
"granariesfor 1\n",
"selfinteresthouse 1\n",
"drubetskoys 1\n",
"unrecognizable 2\n",
"standpoint 3\n",
"abramovna 1\n",
"hisand 1\n",
"bribes 2\n",
"longextinguished 1\n",
"culprit 8\n",
"favoring 1\n",
"disbanding 1\n",
"blinding 5\n",
"presentday 1\n",
"himof 2\n",
"formulated 1\n",
"endsthe 1\n",
"factsthat 1\n",
"gloryit 1\n",
"imputed 1\n",
"directs 3\n",
"occurfrom 1\n",
"frostsand 1\n",
"adhesion 2\n",
"floods 1\n",
"pollen 3\n",
"fertilizes 1\n",
"lossesnow 1\n",
"nowbut 1\n",
"exterior 7\n",
"eyesand 2\n",
"treatises 1\n",
"oxygen 2\n",
"sown 8\n",
"gentian 1\n",
"talkall 1\n",
"irregularities 1\n",
"laocoons 1\n",
"lacks 1\n",
"sterile 4\n",
"strawberry 1\n",
"sofas 3\n",
"birthdays 2\n",
"farmespecially 1\n",
"notbut 3\n",
"elseindefinite 1\n",
"beginnings 5\n",
"businessin 1\n",
"occupiedthat 1\n",
"tortures 4\n",
"affairswhich 1\n",
"debtshad 1\n",
"unrolling 1\n",
"arshin 1\n",
"adele 1\n",
"coil 5\n",
"thinkingand 1\n",
"vocal 2\n",
"mori 1\n",
"countessnot 1\n",
"alexeevna 2\n",
"tatawinova 2\n",
"tante 1\n",
"germannow 1\n",
"kwudener 1\n",
"eckatshausen 1\n",
"ni 1\n",
"himpeople 1\n",
"harsher 2\n",
"pwonounce 1\n",
"evewything 1\n",
"cornered 1\n",
"inapt 2\n",
"mitya 2\n",
"mityas 1\n",
"boughtsismondi 1\n",
"rousseau 3\n",
"bannerthat 1\n",
"peaslike 1\n",
"vierge 1\n",
"mucius 1\n",
"counter 33\n",
"mistresses 4\n",
"everywherethat 1\n",
"outlaw 3\n",
"incomprehensibleis 1\n",
"louises 3\n",
"analysis 16\n",
"components 4\n",
"decomposes 1\n",
"eventsagain 1\n",
"culturein 1\n",
"progenitors 1\n",
"causal 1\n",
"irrefutable 4\n",
"luthers 1\n",
"biographies 2\n",
"inquires 7\n",
"currency 3\n",
"powermere 1\n",
"existsthe 1\n",
"xivs 1\n",
"xvwhy 1\n",
"postulating 1\n",
"diderot 1\n",
"crusades 3\n",
"crusadethe 1\n",
"paraphrasea 1\n",
"restatement 1\n",
"mexico 1\n",
"miracleone 1\n",
"reinstating 1\n",
"organization 2\n",
"stabbing 1\n",
"occursthe 1\n",
"occursbe 1\n",
"inapplicable 2\n",
"applies 4\n",
"ethical 2\n",
"impermeability 1\n",
"uncontrolled 1\n",
"theology 6\n",
"matterhas 1\n",
"conditioned 2\n",
"illumination 5\n",
"plasterers 2\n",
"presentation 7\n",
"varies 1\n",
"populated 1\n",
"participants 1\n",
"psychological 2\n",
"insistent 1\n",
"founder 1\n",
"misconduct 3\n",
"relapse 4\n",
"prompting 1\n",
"irresponsibility 1\n",
"ifimagining 1\n",
"unevoked 1\n",
"uninfluenced 1\n",
"constructedthe 1\n",
"defines 1\n",
"newton 5\n",
"selfdestruction 1\n",
"dissecting 4\n",
"phenomenathe 1\n",
"philology 1\n",
"nun 1\n",
"httpwwwgutenbergorg 7\n",
"copperfield 516\n",
"jo 4\n",
"steerforth 360\n",
"agnes 443\n",
"penitents 2\n",
"dismissing 10\n",
"avowals 1\n",
"billbroking 1\n",
"handbasket 1\n",
"halfpenny 5\n",
"proudest 2\n",
"blunderstone 24\n",
"posthumous 1\n",
"betsey 97\n",
"lightednot 1\n",
"parenthesis 2\n",
"housekeepingbook 2\n",
"quieting 1\n",
"enoughconveyed 1\n",
"portentous 11\n",
"jewellers 8\n",
"mildest 5\n",
"depreciation 5\n",
"propitiation 4\n",
"afterwardsthat 1\n",
"progressing 2\n",
"interjection 2\n",
"reallyreallyas 1\n",
"snarl 8\n",
"laudanum 1\n",
"mollify 1\n",
"dwarfed 2\n",
"geese 1\n",
"jars 7\n",
"grandly 3\n",
"sundial 3\n",
"inquireand 1\n",
"tablets 7\n",
"tassels 3\n",
"gooseberries 3\n",
"calledthat 1\n",
"groundwork 1\n",
"lawk 1\n",
"gardenbell 1\n",
"heartto 2\n",
"mangy 1\n",
"muchmore 1\n",
"nowcame 1\n",
"murdstone 297\n",
"memorymade 1\n",
"halloa 10\n",
"quinion 31\n",
"brooks 15\n",
"passnidge 1\n",
"morsel 20\n",
"grammar 11\n",
"darning 3\n",
"grayper 3\n",
"carriers 6\n",
"expostulate 1\n",
"shuffled 11\n",
"bloaters 1\n",
"atop 2\n",
"hillocks 1\n",
"shipwrights 1\n",
"barge 2\n",
"captivation 2\n",
"isaac 1\n",
"lugger 1\n",
"lockers 3\n",
"eked 1\n",
"seaweed 8\n",
"crawfish 2\n",
"hams 8\n",
"rubicund 1\n",
"crawfishthat 1\n",
"fishy 5\n",
"gormed 3\n",
"verb 5\n",
"bethought 6\n",
"orphanhood 1\n",
"danyonder 1\n",
"nankeen 13\n",
"emily 37\n",
"gentlefolks 4\n",
"weathernot 1\n",
"arefor 1\n",
"draughtsman 1\n",
"fruitlessly 2\n",
"beenwhen 1\n",
"stranded 2\n",
"thrushes 1\n",
"mite 3\n",
"forenoon 15\n",
"everythink 6\n",
"windowglass 3\n",
"ripples 2\n",
"paraphrase 1\n",
"inveterate 3\n",
"wilful 12\n",
"forgottenfirmness 1\n",
"isvery 1\n",
"hardisnt 1\n",
"gloomylooking 1\n",
"thoughtlessmy 1\n",
"characterto 1\n",
"astound 2\n",
"infirm 7\n",
"redden 1\n",
"clause 10\n",
"wallowing 1\n",
"cheeses 6\n",
"workgive 1\n",
"vipers 1\n",
"wakefield 1\n",
"quixote 1\n",
"arabian 5\n",
"geniiand 1\n",
"porings 1\n",
"blunderings 1\n",
"impersonating 1\n",
"nowthat 6\n",
"boxed 3\n",
"canea 1\n",
"poise 2\n",
"stoical 2\n",
"twined 2\n",
"disgracethe 1\n",
"tickled 5\n",
"yeyeyeyes 1\n",
"deader 1\n",
"sayhe 2\n",
"phlegmatic 1\n",
"sweethearts 2\n",
"blieve 1\n",
"joints 4\n",
"gravy 10\n",
"twinklingeyed 1\n",
"ruminate 1\n",
"rib 2\n",
"coachhorn 1\n",
"cowpock 1\n",
"sixpence 25\n",
"sisterhere 1\n",
"agitatedi 1\n",
"wittlesand 1\n",
"ninepence 11\n",
"huskyvoiced 1\n",
"sandwichbox 1\n",
"pleasewhich 1\n",
"bloonderstone 2\n",
"owning 3\n",
"hostlers 2\n",
"bacon 12\n",
"curtsey 14\n",
"unmelodious 2\n",
"flute 15\n",
"mantelpiecei 1\n",
"peacock 2\n",
"finery 3\n",
"overit 1\n",
"creakle 109\n",
"misdoing 2\n",
"slates 3\n",
"silkworms 1\n",
"mice 9\n",
"fusty 2\n",
"corduroys 1\n",
"playground 12\n",
"j 57\n",
"saidseemed 1\n",
"waterbutt 4\n",
"dimlylighted 2\n",
"schoolbell 1\n",
"bedtime 10\n",
"creakles 17\n",
"woodenlegged 1\n",
"wetlooking 2\n",
"fellowto 1\n",
"wavy 2\n",
"topbolt 1\n",
"borough 6\n",
"hops 5\n",
"tungay 10\n",
"inkbottle 2\n",
"imposition 7\n",
"storybook 2\n",
"flinch 2\n",
"prong 1\n",
"writhe 8\n",
"rolypoly 1\n",
"caned 3\n",
"handsand 2\n",
"skeletons 7\n",
"symbols 1\n",
"caning 2\n",
"churchyardfull 1\n",
"nowwas 1\n",
"hoarsea 1\n",
"expressionand 2\n",
"alguazill 1\n",
"schoolboys 1\n",
"lounging 12\n",
"heador 1\n",
"favouritism 2\n",
"ititwould 1\n",
"whatsyourname 3\n",
"asserting 10\n",
"unfriendlyor 1\n",
"sheepishly 3\n",
"lugs 1\n",
"irradiating 3\n",
"modestlygood 1\n",
"mannera 1\n",
"chapters 1\n",
"alternation 1\n",
"canings 1\n",
"haircuttings 1\n",
"breakingup 1\n",
"acknowledgement 7\n",
"nestling 3\n",
"headfor 1\n",
"cranky 2\n",
"melike 1\n",
"alli 3\n",
"peggottyyou 3\n",
"coalholes 1\n",
"presentit 1\n",
"teacaddy 1\n",
"comparisons 2\n",
"wellbetter 1\n",
"underwent 11\n",
"dulness 6\n",
"daymare 1\n",
"dozes 1\n",
"loomed 5\n",
"bedlooking 1\n",
"buttered 7\n",
"loitering 8\n",
"homefor 1\n",
"nightcoach 2\n",
"countrypeople 1\n",
"bunches 3\n",
"crapei 1\n",
"workshop 8\n",
"stitching 2\n",
"scissors 9\n",
"threaded 4\n",
"embodied 4\n",
"ofall 1\n",
"retracted 1\n",
"barkiss 11\n",
"myselfhad 2\n",
"broiled 1\n",
"whyi 1\n",
"coaxed 5\n",
"tenderhearted 6\n",
"hoorah 1\n",
"horhere 1\n",
"awhy 1\n",
"dominoes 7\n",
"gal 26\n",
"excursion 9\n",
"disparaging 6\n",
"winkby 1\n",
"unimpaired 3\n",
"byroad 1\n",
"roeshusby 1\n",
"flowery 3\n",
"quarto 1\n",
"gem 2\n",
"edified 2\n",
"conditionapart 1\n",
"thoughtswhich 2\n",
"droning 3\n",
"trouserswhich 1\n",
"blotted 8\n",
"rottenness 1\n",
"rinse 1\n",
"auspiciously 2\n",
"mealy 6\n",
"fireman 2\n",
"imps 2\n",
"surtout 2\n",
"ahem 3\n",
"iin 2\n",
"stencilled 1\n",
"swindlers 1\n",
"ineffectual 6\n",
"breaded 1\n",
"pennyworth 4\n",
"qualifiedhow 1\n",
"currants 1\n",
"twopennyworth 2\n",
"fourpenny 1\n",
"alamode 1\n",
"beefhouse 1\n",
"readymade 4\n",
"coalheavers 1\n",
"halfdoor 2\n",
"sugars 1\n",
"bookstalls 1\n",
"drunkand 1\n",
"conjured 6\n",
"shabbiness 4\n",
"van 1\n",
"timberyard 1\n",
"fictions 3\n",
"parchments 1\n",
"itnowould 1\n",
"harmonic 1\n",
"hooo 1\n",
"neverwilldesert 1\n",
"longlookedfor 2\n",
"lowness 3\n",
"headwhich 1\n",
"dutiesnot 1\n",
"expiration 13\n",
"micawberthat 1\n",
"fellowcreatures 3\n",
"hythe 1\n",
"travellingexpenses 1\n",
"obelisk 1\n",
"hapence 1\n",
"threehalfpence 1\n",
"wardrobes 2\n",
"lurked 3\n",
"trampers 3\n",
"grassgrown 1\n",
"flannel 14\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ohgoroohow 1\n",
"ejaculation 7\n",
"limbsgoroodont 1\n",
"dive 5\n",
"fishingrod 2\n",
"brazierwho 1\n",
"handmaid 1\n",
"woeful 3\n",
"sully 1\n",
"squinted 1\n",
"completing 3\n",
"bowednot 1\n",
"saddledonkeys 1\n",
"ladyridden 1\n",
"profane 1\n",
"swivel 2\n",
"trussed 3\n",
"timegoes 1\n",
"prowls 1\n",
"imprinted 4\n",
"redress 2\n",
"janets 1\n",
"overlooking 6\n",
"sheetsinspired 1\n",
"houseless 4\n",
"tablecloth 11\n",
"fixedness 4\n",
"bib 1\n",
"dusted 5\n",
"bableythats 1\n",
"halfgallon 1\n",
"phoebus 2\n",
"upwardsin 1\n",
"amenable 4\n",
"dotook 1\n",
"healths 1\n",
"sidesaddle 1\n",
"descrying 5\n",
"dodges 2\n",
"objecting 7\n",
"untoward 3\n",
"abet 2\n",
"whatmy 1\n",
"boytenderly 1\n",
"jointly 3\n",
"markingink 1\n",
"fraught 17\n",
"marketday 2\n",
"ornamented 7\n",
"unsheltered 1\n",
"tuition 3\n",
"grandnephew 2\n",
"importing 7\n",
"wickfieldthe 1\n",
"facilitate 1\n",
"undeniable 5\n",
"boardinghouses 2\n",
"roomy 3\n",
"baskettrifle 2\n",
"dinnerhour 6\n",
"caverns 7\n",
"annie 72\n",
"busiest 6\n",
"simplified 1\n",
"peaches 4\n",
"aloes 1\n",
"symbolical 2\n",
"booklearning 1\n",
"wayworn 4\n",
"slices 7\n",
"blemish 3\n",
"substitutedpray 1\n",
"otherdoctor 1\n",
"gifthorse 1\n",
"umblest 3\n",
"mehis 1\n",
"sustaining 6\n",
"scholars 3\n",
"wintertime 1\n",
"repute 4\n",
"generalship 2\n",
"marshalled 2\n",
"unchangeable 5\n",
"wheresoever 2\n",
"hindoo 1\n",
"surpriseyou 1\n",
"shoulderor 1\n",
"trustfulnessi 1\n",
"pegtop 1\n",
"spoked 1\n",
"renown 5\n",
"selfdenial 9\n",
"walkingstick 7\n",
"instability 3\n",
"natures 9\n",
"fountsin 1\n",
"cultivation 10\n",
"classics 1\n",
"recumbent 3\n",
"mustard 2\n",
"ardour 19\n",
"voicethis 1\n",
"brandyandwater 1\n",
"auld 1\n",
"trusty 8\n",
"waught 1\n",
"schooldays 5\n",
"unfelt 1\n",
"chaunt 1\n",
"shepherds 4\n",
"stocks 2\n",
"widens 1\n",
"suet 1\n",
"broadfaced 1\n",
"eyebrow 4\n",
"tussle 1\n",
"augur 4\n",
"beefsteaks 2\n",
"agnesmy 1\n",
"spoony 1\n",
"hairforgetmenotsas 1\n",
"japonica 1\n",
"neighbourhoodneighbourhood 1\n",
"imperfectly 11\n",
"eminent 16\n",
"ungraciously 2\n",
"immediatelyit 2\n",
"relinquished 16\n",
"nervousor 1\n",
"livelong 2\n",
"jungle 2\n",
"horseswhy 1\n",
"twoandthirty 1\n",
"proctorwhos 1\n",
"refolding 2\n",
"broadleaved 1\n",
"whaling 1\n",
"dumplings 1\n",
"closefitting 1\n",
"orses 1\n",
"liverystables 1\n",
"landmark 1\n",
"thrash 1\n",
"bottoms 2\n",
"bawling 2\n",
"hustling 1\n",
"oysters 10\n",
"pastfor 1\n",
"sometimesbut 1\n",
"fourpost 3\n",
"sensitively 2\n",
"redcurtained 1\n",
"turkeycarpeted 1\n",
"mothershe 2\n",
"twoits 1\n",
"crayons 1\n",
"wrongisnt 1\n",
"thembut 1\n",
"askingdont 1\n",
"overspread 9\n",
"abated 3\n",
"footstools 1\n",
"respectablelooking 1\n",
"upsidedown 2\n",
"frostily 2\n",
"warningbell 1\n",
"littimers 1\n",
"omers 6\n",
"banded 2\n",
"jorams 7\n",
"incheswill 1\n",
"duchess 2\n",
"longwinded 1\n",
"offalas 1\n",
"roughly 8\n",
"cherubimhe 1\n",
"wasteful 1\n",
"heerd 36\n",
"borgentlmen 1\n",
"mayhap 3\n",
"ashaking 1\n",
"washonourable 1\n",
"pleaseif 1\n",
"davywhen 1\n",
"davyoh 1\n",
"seathat 1\n",
"nightsuch 1\n",
"shybut 1\n",
"usand 2\n",
"fireham 1\n",
"layon 1\n",
"laughfretfully 1\n",
"nowmust 1\n",
"steerforththat 1\n",
"fitfulness 1\n",
"ixions 1\n",
"littimerthat 1\n",
"pursy 1\n",
"firemaking 1\n",
"darlingwhen 1\n",
"walkers 1\n",
"mowchers 4\n",
"magpies 1\n",
"higgledypiggledy 1\n",
"childthrough 1\n",
"nimbly 3\n",
"polishing 8\n",
"femalequite 1\n",
"griffin 3\n",
"sometimeswith 1\n",
"mistakehow 1\n",
"umph 2\n",
"usedor 1\n",
"requitedis 1\n",
"fatima 1\n",
"forfeits 2\n",
"sworeas 1\n",
"darts 1\n",
"anythingand 1\n",
"hedo 1\n",
"theyaunt 1\n",
"wureld 8\n",
"pitifully 2\n",
"flatness 1\n",
"monkish 2\n",
"edwards 4\n",
"indiaman 2\n",
"contrariwise 2\n",
"coachstand 1\n",
"soaking 2\n",
"benignantly 6\n",
"leasthere 1\n",
"meansand 1\n",
"jorkins 46\n",
"skylighted 1\n",
"admiralty 5\n",
"bodypunch 1\n",
"ofpunch 1\n",
"wigs 2\n",
"aviary 1\n",
"shabbygenteel 1\n",
"cosey 1\n",
"timeforgotten 1\n",
"sleepyheaded 1\n",
"buckingham 11\n",
"fireescapeand 1\n",
"repository 3\n",
"albans 1\n",
"doomsday 2\n",
"celery 1\n",
"sneezing 4\n",
"meor 2\n",
"therenever 1\n",
"derogation 1\n",
"aftereach 1\n",
"haironly 1\n",
"offin 1\n",
"neverberrer 1\n",
"parch 1\n",
"headthe 1\n",
"ticketporter 6\n",
"bellhandle 2\n",
"handits 1\n",
"thingstrifles 1\n",
"trotwoodin 1\n",
"tomiss 1\n",
"queens 1\n",
"trotwooduntil 1\n",
"withheld 7\n",
"vapourbath 1\n",
"spikers 4\n",
"traddless 11\n",
"hamlets 4\n",
"beauideal 1\n",
"idols 3\n",
"reversionyou 1\n",
"foregoing 7\n",
"ed 4\n",
"bootjack 2\n",
"undulated 1\n",
"bellses 1\n",
"underhis 1\n",
"loftily 7\n",
"rifle 2\n",
"ata 1\n",
"breakfastcloth 1\n",
"gurglings 1\n",
"aired 4\n",
"gorging 1\n",
"rufus 1\n",
"trelliswork 3\n",
"lifepreserver 1\n",
"greatgrandfather 1\n",
"engrossing 6\n",
"ra 5\n",
"jipshort 1\n",
"ambitioni 1\n",
"armsoh 1\n",
"goodnessand 1\n",
"teakettle 1\n",
"geraniums 2\n",
"sermon 5\n",
"homily 3\n",
"oar 4\n",
"jurisdiction 2\n",
"waistcoatsnot 1\n",
"corns 2\n",
"cloves 2\n",
"washingday 1\n",
"overcareful 1\n",
"vantageground 2\n",
"waistcoats 6\n",
"adwice 1\n",
"streetthough 1\n",
"anomaly 2\n",
"blackingbrushes 1\n",
"bookson 1\n",
"businesslikeand 1\n",
"retiredwhat 1\n",
"abstracts 2\n",
"compiler 1\n",
"sprightly 16\n",
"expressionas 1\n",
"groundplan 2\n",
"mentionfor 1\n",
"tablecloths 2\n",
"pillowcases 1\n",
"gridirons 1\n",
"changedhis 1\n",
"statu 2\n",
"anciently 2\n",
"ofin 2\n",
"backyard 4\n",
"remunerative 4\n",
"descriptionin 1\n",
"newlyfound 1\n",
"lastwhich 1\n",
"reengaging 1\n",
"landingplace 2\n",
"minion 1\n",
"ribald 1\n",
"lemonpeel 2\n",
"bestregulated 4\n",
"mani 2\n",
"braes 1\n",
"buxton 1\n",
"bankinghouse 3\n",
"contumely 3\n",
"dontwhat 1\n",
"talenti 1\n",
"advertising 2\n",
"discount 3\n",
"consciences 2\n",
"investment 13\n",
"addassert 1\n",
"womanthe 1\n",
"exhilaration 1\n",
"hyde 2\n",
"reputably 1\n",
"sybarite 1\n",
"bacchanalian 1\n",
"diligence 9\n",
"darlingmeaning 1\n",
"smoothshod 1\n",
"buffeting 3\n",
"examplewhen 1\n",
"numbersat 1\n",
"tipkins 1\n",
"churchroof 1\n",
"littleehfor 1\n",
"knowdont 1\n",
"blindlydotingeh 1\n",
"usualif 1\n",
"extremest 4\n",
"thinkwhen 1\n",
"thendid 1\n",
"edgetool 1\n",
"godmothers 1\n",
"byand 1\n",
"roommy 1\n",
"recruited 1\n",
"selfinterested 3\n",
"srub 2\n",
"acquiesced 9\n",
"cousinyou 1\n",
"minniefor 1\n",
"sorrer 1\n",
"soonsomeone 1\n",
"savings 6\n",
"nosebag 1\n",
"weddingday 5\n",
"eightyseven 1\n",
"survivors 1\n",
"executrix 2\n",
"referee 2\n",
"goggle 1\n",
"departedandandand 1\n",
"theers 13\n",
"sharks 3\n",
"nowshes 1\n",
"homeoh 1\n",
"medont 3\n",
"wavelessyet 1\n",
"restand 1\n",
"resumedhell 1\n",
"fust 5\n",
"heer 17\n",
"oars 1\n",
"spars 5\n",
"ballast 1\n",
"creaturei 1\n",
"seriouswhen 1\n",
"pigmies 1\n",
"illfortune 2\n",
"thinkwhat 1\n",
"domicile 3\n",
"fellowtravellers 3\n",
"uneducated 3\n",
"husbandto 2\n",
"counteraccusations 1\n",
"boatwheer 1\n",
"unyielding 8\n",
"thowt 12\n",
"beefsteak 1\n",
"moonstruck 1\n",
"crevices 5\n",
"slowgoers 1\n",
"surrogates 1\n",
"blithe 2\n",
"gloomilyremorsefully 1\n",
"againa 1\n",
"smoothest 2\n",
"moneyed 2\n",
"airas 1\n",
"partnermr 2\n",
"banknotes 2\n",
"annulled 1\n",
"deputy 4\n",
"notwhile 1\n",
"stowage 1\n",
"diverged 1\n",
"notepaper 1\n",
"julia 23\n",
"enduredwere 1\n",
"unpacked 3\n",
"fountains 3\n",
"millss 13\n",
"himoh 1\n",
"recluse 2\n",
"invoke 2\n",
"pun 1\n",
"noodle 2\n",
"onebefore 1\n",
"blackboys 1\n",
"dirge 3\n",
"kitt 2\n",
"dingy 18\n",
"tropics 1\n",
"staircasewith 1\n",
"memiss 1\n",
"youin 4\n",
"darkand 2\n",
"streetand 2\n",
"freshest 3\n",
"timeservers 1\n",
"herby 1\n",
"reverses 4\n",
"flavours 2\n",
"crimping 1\n",
"againstagainst 1\n",
"dogo 1\n",
"oftenmind 1\n",
"transportation 3\n",
"blackhole 1\n",
"cartridgepaper 1\n",
"adamantine 1\n",
"obduracy 2\n",
"stainedglass 3\n",
"cucumberframe 1\n",
"wishuriah 1\n",
"agnesrather 1\n",
"wayfishing 1\n",
"idiosyncrasy 1\n",
"dieof 1\n",
"appy 4\n",
"uncomfortably 1\n",
"eel 2\n",
"corkscrewed 1\n",
"snaps 5\n",
"bounces 2\n",
"leering 7\n",
"childand 5\n",
"pillowing 2\n",
"fairyfigure 1\n",
"hotter 7\n",
"sureyou 1\n",
"booksa 1\n",
"buttering 1\n",
"apprehending 1\n",
"shorthand 2\n",
"commingled 1\n",
"grays 9\n",
"blackstone 1\n",
"barrister 3\n",
"woolsack 2\n",
"baldness 2\n",
"sowill 1\n",
"heathmy 1\n",
"leftlet 1\n",
"fulfilment 7\n",
"accomplishes 1\n",
"dressingroom 13\n",
"wellearned 1\n",
"crusts 1\n",
"frugal 3\n",
"merewarding 1\n",
"obedienceand 1\n",
"destroyeror 1\n",
"effectand 1\n",
"unsuccessfully 2\n",
"wayas 1\n",
"heartbreaking 3\n",
"castlereagh 1\n",
"denunciations 3\n",
"elusive 1\n",
"himselfi 1\n",
"inverted 2\n",
"flutings 1\n",
"deprecated 1\n",
"workboxes 2\n",
"fireirons 5\n",
"advancement 8\n",
"idolatry 1\n",
"respiration 3\n",
"unsettle 1\n",
"naturenot 1\n",
"stablegate 1\n",
"inthe 1\n",
"repositories 1\n",
"pencilcase 3\n",
"deducting 1\n",
"outstanding 1\n",
"journal 4\n",
"enchanter 2\n",
"hustled 2\n",
"marriagelicence 1\n",
"everythingi 1\n",
"necessitates 2\n",
"devolving 1\n",
"sisterly 10\n",
"forbearing 4\n",
"plastersmelling 1\n",
"uriahi 1\n",
"sniff 4\n",
"knittingneedles 2\n",
"monotonously 2\n",
"hourglass 3\n",
"unwinking 3\n",
"bats 1\n",
"propitiatory 3\n",
"civilly 2\n",
"ofthough 1\n",
"elth 2\n",
"appiness 1\n",
"fellowpartner 7\n",
"isoh 1\n",
"youhell 1\n",
"liewho 1\n",
"richgoodness 1\n",
"herselfi 2\n",
"acquaint 1\n",
"endellside 1\n",
"inquiration 2\n",
"yonderyarmouth 1\n",
"publicrooms 1\n",
"greyer 3\n",
"steadfastness 2\n",
"believedor 1\n",
"agen 5\n",
"simlar 1\n",
"trynot 1\n",
"postmark 1\n",
"sometime 2\n",
"rigorous 2\n",
"thinghad 1\n",
"looknot 1\n",
"crewlerwhen 1\n",
"leadcolour 1\n",
"detesting 1\n",
"tan 1\n",
"wasi 3\n",
"overdoing 2\n",
"dinnertable 9\n",
"rocks 6\n",
"fruitful 2\n",
"debate 3\n",
"sighuntil 1\n",
"resignedlyto 1\n",
"misconception 3\n",
"ageshe 1\n",
"heretical 2\n",
"likenesses 3\n",
"fishlike 1\n",
"lanternjawed 1\n",
"thingand 4\n",
"teatable 4\n",
"bloodrelation 1\n",
"bestfor 1\n",
"methan 1\n",
"thatdid 1\n",
"besetting 3\n",
"snivel 3\n",
"denyperhaps 1\n",
"manifested 10\n",
"pleasurewhen 1\n",
"interchanging 4\n",
"brickbat 1\n",
"darkens 1\n",
"selfcharged 1\n",
"layfigure 1\n",
"contemplates 1\n",
"watermill 1\n",
"halfseen 1\n",
"sociably 1\n",
"scheming 4\n",
"underdone 1\n",
"bewailing 3\n",
"butterfly 3\n",
"impressedfor 1\n",
"kidgerburythe 1\n",
"childwife 23\n",
"laborious 8\n",
"misdeeds 2\n",
"strikingand 1\n",
"cloudedand 1\n",
"dosuggested 1\n",
"schoolcopy 1\n",
"unbend 2\n",
"flinched 1\n",
"kneeclouds 1\n",
"whoo 1\n",
"dicksimple 1\n",
"sensei 1\n",
"darlingfor 1\n",
"lesstell 1\n",
"doctorannie 1\n",
"supplicating 1\n",
"notmy 1\n",
"boughtand 1\n",
"wasmama 1\n",
"thankless 6\n",
"comeis 1\n",
"muchinjured 1\n",
"countryperson 1\n",
"washere 1\n",
"tidemud 1\n",
"motherin 1\n",
"asunders 1\n",
"waggons 10\n",
"lowlying 2\n",
"speculator 1\n",
"causeways 1\n",
"highwater 1\n",
"polluted 4\n",
"nightpicture 1\n",
"astrand 1\n",
"muffling 2\n",
"mewas 3\n",
"streetsand 1\n",
"oncei 1\n",
"alikei 1\n",
"wurelds 2\n",
"believemasr 1\n",
"bagpipes 1\n",
"retainer 6\n",
"occasionsas 1\n",
"fourandsixpence 1\n",
"waynot 1\n",
"ragbag 1\n",
"watches 13\n",
"keenest 3\n",
"degenerate 7\n",
"regardedi 1\n",
"outgrown 3\n",
"slumbered 8\n",
"baulking 3\n",
"alteredcould 1\n",
"candlebearer 1\n",
"destroyedthat 1\n",
"assuaging 1\n",
"vestiges 1\n",
"aberration 1\n",
"oysterknife 1\n",
"westend 3\n",
"correspondencebut 1\n",
"salutations 3\n",
"possepresuming 1\n",
"ironwork 1\n",
"goodnesstake 1\n",
"inspiriting 1\n",
"unfeignedly 2\n",
"vocabulary 3\n",
"shrewdest 1\n",
"provokingly 1\n",
"barrelorgan 4\n",
"peeling 2\n",
"villainy 2\n",
"vesuviusto 1\n",
"eyesout 1\n",
"iaill 1\n",
"nobodyandasay 1\n",
"exceptionsthis 1\n",
"presentincluding 1\n",
"ruffianheep 1\n",
"repine 3\n",
"dwindling 1\n",
"topstorey 1\n",
"expressionwould 1\n",
"homewhere 1\n",
"tarnished 8\n",
"trustfulnesswhich 1\n",
"lifeor 2\n",
"consecrate 2\n",
"nearernearerpassed 1\n",
"itoh 1\n",
"pervising 1\n",
"irongrey 3\n",
"sermuchser 2\n",
"alying 1\n",
"lifetill 1\n",
"leghorn 3\n",
"pennyyoungso 1\n",
"prettycome 1\n",
"arnest 3\n",
"growd 2\n",
"sailingi 1\n",
"morningwent 1\n",
"aboardand 1\n",
"peevishlike 1\n",
"unnerstan 2\n",
"nowandagain 1\n",
"signifies 4\n",
"aboardship 2\n",
"mailcoach 1\n",
"childputs 1\n",
"valentines 1\n",
"wordevery 1\n",
"wollumesone 1\n",
"gocart 1\n",
"fishingboat 1\n",
"deepto 1\n",
"awatching 1\n",
"dustoh 1\n",
"waif 2\n",
"illbestowed 2\n",
"officeruler 1\n",
"scum 1\n",
"hypocrisy 4\n",
"collectively 1\n",
"gaul 1\n",
"forger 2\n",
"timethan 1\n",
"ofheep 2\n",
"fromheeppecuniary 1\n",
"falsification 2\n",
"applicability 1\n",
"ws 5\n",
"umbled 1\n",
"deponents 1\n",
"anathemas 1\n",
"defaced 2\n",
"undeceive 3\n",
"graspingheep 1\n",
"relinquishment 4\n",
"illjudged 1\n",
"fromheepand 1\n",
"chicaneriesgradually 1\n",
"illstarred 4\n",
"frailest 1\n",
"investigationof 1\n",
"pieced 3\n",
"wouldntand 2\n",
"knowsbreathe 1\n",
"compel 1\n",
"upstart 3\n",
"witnessing 3\n",
"reestablishment 2\n",
"houselessness 1\n",
"expansion 1\n",
"distancecomparatively 1\n",
"roving 5\n",
"meturn 1\n",
"prettyor 2\n",
"nourished 1\n",
"augment 2\n",
"foreshadowing 4\n",
"explosive 2\n",
"dunned 1\n",
"stipulate 2\n",
"mercantile 4\n",
"subsisted 3\n",
"enthralled 1\n",
"spaceit 1\n",
"aggregate 3\n",
"snobs 3\n",
"improbablefar 1\n",
"crookeder 1\n",
"familychildren 1\n",
"dissuaded 2\n",
"hearse 2\n",
"trotand 1\n",
"emigrantship 1\n",
"int 2\n",
"overspreading 2\n",
"abatement 5\n",
"shipowners 1\n",
"surf 2\n",
"engulf 1\n",
"upheaving 2\n",
"windfor 1\n",
"chairmy 1\n",
"lifeboat 1\n",
"onnot 1\n",
"anticipative 1\n",
"valiantly 1\n",
"shoreward 1\n",
"childrenon 1\n",
"windamong 1\n",
"youwith 3\n",
"pampering 1\n",
"devotedwould 1\n",
"taunting 2\n",
"familywhoever 1\n",
"sealegs 1\n",
"improprietyshe 1\n",
"landoh 1\n",
"ostade 1\n",
"emigrantberths 1\n",
"baggagelighted 1\n",
"hatchwaywere 1\n",
"elbowchairs 1\n",
"ofagnes 1\n",
"unsettlement 1\n",
"rowed 1\n",
"retrace 1\n",
"novelties 2\n",
"expressionkept 1\n",
"byways 1\n",
"solitudes 2\n",
"mountainside 1\n",
"wedgelike 1\n",
"prouder 3\n",
"errant 2\n",
"freshwhich 1\n",
"gutters 4\n",
"stiffnecked 2\n",
"sanded 2\n",
"boyif 1\n",
"enclosing 4\n",
"attorneys 5\n",
"longlost 1\n",
"maddest 1\n",
"unprofessional 3\n",
"cockboat 1\n",
"seti 1\n",
"papered 3\n",
"britanniametal 1\n",
"lifedont 1\n",
"leftcouldnt 1\n",
"michaelmas 1\n",
"crewlermiss 1\n",
"writs 3\n",
"sultans 1\n",
"minewhen 1\n",
"tepid 1\n",
"hip 2\n",
"electrified 2\n",
"lunacy 3\n",
"bullied 2\n",
"sisteron 2\n",
"wedlock 2\n",
"marriageceremony 1\n",
"topicsalready 1\n",
"reclaimed 7\n",
"curtly 1\n",
"pattens 3\n",
"regretfor 1\n",
"unhealthy 1\n",
"percentage 2\n",
"tabledrawer 1\n",
"folios 1\n",
"enjoyingly 1\n",
"jewelled 2\n",
"penitentswhich 1\n",
"mattertraddles 1\n",
"prisondoors 1\n",
"reduction 4\n",
"follies 10\n",
"contractor 3\n",
"everywhereexcept 1\n",
"violentyou 1\n",
"knowingest 1\n",
"immaculates 1\n",
"streetpicked 1\n",
"pounded 3\n",
"mindwhether 1\n",
"painbegan 1\n",
"alloy 1\n",
"honourwhom 1\n",
"partaken 3\n",
"heighth 3\n",
"reverentially 2\n",
"masrs 1\n",
"draysa 1\n",
"backmade 1\n",
"trewest 1\n",
"galaxy 1\n",
"braid 1\n",
"tablet 6\n",
"greets 1\n",
"contending 7\n",
"bends 3\n",
"crewlers 1\n",
"housesor 1\n",
"subduing 5\n",
"homedepartment 2\n",
"midshipman 64\n",
"midshipmans 15\n",
"cuttle 334\n",
"tootss 44\n",
"settee 2\n",
"timeremorseless 1\n",
"squaring 1\n",
"rainbows 1\n",
"domini 1\n",
"tenmarried 1\n",
"matrimony 1\n",
"patronising 4\n",
"illadvised 3\n",
"duchessi 1\n",
"pilkins 6\n",
"hubert 1\n",
"dombeyi 3\n",
"dombeyshould 1\n",
"tightness 2\n",
"agitate 1\n",
"heshes 1\n",
"meits 1\n",
"quenching 2\n",
"ickle 1\n",
"keystone 1\n",
"angularity 1\n",
"tippets 1\n",
"muffs 1\n",
"fulldressed 1\n",
"bewe 1\n",
"otherwisebut 1\n",
"thei 2\n",
"myselfthe 1\n",
"dubiousness 1\n",
"personhad 1\n",
"indecorum 1\n",
"tol 2\n",
"rumpteiddity 1\n",
"hydrophobia 1\n",
"balm 2\n",
"unsupplied 1\n",
"believebut 1\n",
"applefaced 11\n",
"johnny 1\n",
"crustiness 1\n",
"toodles 17\n",
"assay 1\n",
"whatsyour 1\n",
"partickler 2\n",
"railroads 3\n",
"camels 1\n",
"themshould 1\n",
"ratified 2\n",
"anyhows 1\n",
"pets 10\n",
"ooman 3\n",
"shortcomings 2\n",
"smokeddried 1\n",
"breakfasttime 2\n",
"punchs 2\n",
"lookingglasses 3\n",
"odours 2\n",
"vellum 1\n",
"furniturethe 1\n",
"airingor 1\n",
"creeter 1\n",
"lapandthere 1\n",
"floys 2\n",
"trainers 1\n",
"chickses 1\n",
"themquite 1\n",
"mysterieshe 1\n",
"repulse 5\n",
"listenedand 1\n",
"wellintentioned 1\n",
"palanquins 1\n",
"outfitting 1\n",
"machinerysole 1\n",
"prosecuting 1\n",
"mosses 1\n",
"wainscotted 1\n",
"minus 1\n",
"gillss 9\n",
"compasscase 2\n",
"coalscuttle 1\n",
"cheque 2\n",
"hoarding 2\n",
"polyphemus 1\n",
"laconic 2\n",
"timepiece 2\n",
"chockfull 1\n",
"cuttles 28\n",
"nedwalters 1\n",
"boylike 1\n",
"humouring 1\n",
"lavish 2\n",
"kirbys 1\n",
"richardss 8\n",
"maintainingand 1\n",
"sponsors 2\n",
"wellaccustomed 1\n",
"particularity 1\n",
"fawn 3\n",
"perhapsin 3\n",
"softspoken 1\n",
"tiddle 1\n",
"rulwhen 1\n",
"providentially 2\n",
"thinly 3\n",
"shuttingup 1\n",
"sunbeam 2\n",
"deadlylooking 1\n",
"cheerfullooking 2\n",
"mildlooking 1\n",
"fowlshampattiessaladlobster 1\n",
"vassal 2\n",
"smallclothes 2\n",
"pewter 4\n",
"inspectors 1\n",
"lumpkin 1\n",
"aspiring 6\n",
"excavators 2\n",
"oldestablished 3\n",
"carpetbeating 1\n",
"squalid 8\n",
"rabbits 1\n",
"staggs 2\n",
"outliving 1\n",
"dishevelled 4\n",
"banbury 2\n",
"peaceablest 1\n",
"patientest 2\n",
"humanizing 1\n",
"bilers 1\n",
"hobbling 3\n",
"placemore 1\n",
"mouthand 1\n",
"elseexcept 2\n",
"blithelooking 1\n",
"cinderellas 1\n",
"whittingtonthat 1\n",
"shuns 2\n",
"pleasedo 1\n",
"coherent 3\n",
"disfavour 1\n",
"excluding 2\n",
"sonmy 1\n",
"befel 1\n",
"pamphlets 1\n",
"crookedest 1\n",
"harpsichord 6\n",
"jocularities 1\n",
"laudation 1\n",
"mantraps 2\n",
"jb 1\n",
"meantoh 1\n",
"ventures 7\n",
"heterogeneous 2\n",
"atlas 1\n",
"trooping 2\n",
"ferociousif 1\n",
"wickam 62\n",
"copper 4\n",
"byandby 8\n",
"anythingalmost 1\n",
"magnetic 2\n",
"staircasenot 1\n",
"convoked 1\n",
"hisabout 1\n",
"momus 1\n",
"summing 2\n",
"mehow 2\n",
"boardinghouse 1\n",
"anvil 2\n",
"quencher 1\n",
"marigolds 1\n",
"cuppingglasses 1\n",
"earwigs 1\n",
"demise 1\n",
"pankey 7\n",
"berry 35\n",
"sweetbread 3\n",
"heroa 1\n",
"sojourning 1\n",
"generic 1\n",
"biles 1\n",
"seeonly 1\n",
"berrys 2\n",
"healthier 2\n",
"grandfathera 1\n",
"stringy 1\n",
"loungers 2\n",
"shouldwhat 1\n",
"dreamings 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"collier 1\n",
"openhearted 1\n",
"probabilities 7\n",
"canand 1\n",
"cornerin 1\n",
"brogley 15\n",
"sideboards 1\n",
"brogleys 5\n",
"pinkcomplexioned 1\n",
"crisphaired 1\n",
"spires 5\n",
"chaincable 1\n",
"brig 22\n",
"knockkneed 1\n",
"sprats 1\n",
"loadstones 1\n",
"stonemr 1\n",
"chuckles 8\n",
"campaigner 1\n",
"smokedried 1\n",
"sandhurst 1\n",
"degenerating 1\n",
"dainties 2\n",
"wallowed 1\n",
"usurpation 1\n",
"indigo 1\n",
"eulogising 1\n",
"equator 1\n",
"honeyits 1\n",
"sidetable 3\n",
"releases 1\n",
"nipped 1\n",
"competitors 2\n",
"entrenching 1\n",
"powdermill 2\n",
"audiencechamber 1\n",
"muchof 1\n",
"pankeys 2\n",
"driest 2\n",
"productions 3\n",
"garrets 3\n",
"classical 6\n",
"syntactic 1\n",
"beak 7\n",
"illomen 3\n",
"halfshut 1\n",
"frigidity 1\n",
"unclassical 1\n",
"sayingalways 1\n",
"familiarise 1\n",
"briggs 26\n",
"yaaaah 1\n",
"burgess 9\n",
"tintinnabulation 1\n",
"pheasants 2\n",
"carpathian 1\n",
"caligula 1\n",
"feederif 1\n",
"attendremarkable 1\n",
"latinit 1\n",
"melia 3\n",
"ajar 4\n",
"hoc 1\n",
"troy 1\n",
"uninstructed 1\n",
"morningscould 1\n",
"masterses 1\n",
"mississes 1\n",
"nippers 6\n",
"calico 1\n",
"outflorence 1\n",
"facea 2\n",
"paperhanging 1\n",
"arabesque 1\n",
"cheapsidethrew 1\n",
"mussulman 1\n",
"officeto 1\n",
"islington 2\n",
"compatible 4\n",
"musicbooks 1\n",
"sofor 1\n",
"postletters 1\n",
"penwomanby 1\n",
"embellish 2\n",
"unpromisingly 1\n",
"spurn 4\n",
"leadeneyed 1\n",
"scorned 2\n",
"carthaginian 1\n",
"capitol 2\n",
"presupposed 1\n",
"synthesis 2\n",
"regent 1\n",
"apothecarys 2\n",
"distortions 3\n",
"cravatted 1\n",
"tearoom 1\n",
"ringlet 1\n",
"radicals 1\n",
"richmost 1\n",
"revenging 1\n",
"plumcake 1\n",
"goldwhich 1\n",
"custardcups 3\n",
"solemna 1\n",
"speakingprofligate 1\n",
"forcerand 1\n",
"melchisedech 6\n",
"puncheons 1\n",
"ranting 1\n",
"conviviality 1\n",
"repetitions 5\n",
"disclaims 1\n",
"housewhether 1\n",
"worldi 1\n",
"houseperhaps 1\n",
"cogitation 4\n",
"housesay 1\n",
"buttonholetalk 1\n",
"overmake 1\n",
"rightand 1\n",
"pendent 1\n",
"libeller 1\n",
"goin 1\n",
"upards 3\n",
"continivally 1\n",
"frowsy 3\n",
"drapers 1\n",
"grooved 1\n",
"dilating 4\n",
"momentsof 1\n",
"recline 1\n",
"doctorsthey 1\n",
"mamas 3\n",
"wordperhaps 1\n",
"scroll 3\n",
"gotaking 1\n",
"unabated 3\n",
"walterhes 1\n",
"castellated 1\n",
"saythough 1\n",
"outard 5\n",
"astarn 1\n",
"gill 1\n",
"towed 7\n",
"buzzums 2\n",
"tattooed 1\n",
"catlike 3\n",
"shakespeares 2\n",
"civic 2\n",
"pantomimic 1\n",
"tableale 1\n",
"feltwho 1\n",
"pathsome 1\n",
"amendment 1\n",
"greengrocer 2\n",
"toxof 1\n",
"mis 1\n",
"griefor 1\n",
"thingshe 1\n",
"awheres 1\n",
"romp 1\n",
"mirrored 1\n",
"squandering 1\n",
"dangled 5\n",
"handdiogenes 1\n",
"acquaintancethat 1\n",
"naturalcoloured 1\n",
"absentwere 1\n",
"itwas 1\n",
"motionlessuntil 1\n",
"uncouth 4\n",
"quadrant 1\n",
"parrots 3\n",
"meancould 1\n",
"knowstell 1\n",
"wideawake 2\n",
"brightenit 1\n",
"counterinfluence 1\n",
"steamboat 1\n",
"constitutionally 1\n",
"rapping 2\n",
"sparkles 2\n",
"rotatory 1\n",
"wheeze 1\n",
"lieutenantgeneral 1\n",
"testimonies 1\n",
"friendlessness 1\n",
"savoury 6\n",
"encouragedto 1\n",
"jade 4\n",
"speakconfound 1\n",
"toning 2\n",
"bilerchristened 1\n",
"undiscovered 1\n",
"bidder 2\n",
"wayits 1\n",
"owndefiant 1\n",
"wellregulated 1\n",
"propelled 2\n",
"butting 4\n",
"ugliest 2\n",
"barouche 4\n",
"cowsand 1\n",
"lisped 5\n",
"turnip 1\n",
"jointure 1\n",
"milkshop 1\n",
"haughtier 3\n",
"throbbings 1\n",
"herrings 2\n",
"existenceand 1\n",
"pursedout 1\n",
"dialplate 1\n",
"choicefor 1\n",
"longwise 1\n",
"unbusinesslike 1\n",
"walkingmatching 1\n",
"hempseed 1\n",
"cheapside 2\n",
"sleekheaded 1\n",
"spiritin 1\n",
"overpaid 1\n",
"fatherwhens 1\n",
"tobut 2\n",
"jostlings 2\n",
"purr 1\n",
"webs 1\n",
"obscuring 1\n",
"halfyears 1\n",
"humanise 2\n",
"yorkshire 2\n",
"rocket 1\n",
"strait 2\n",
"neversmiling 1\n",
"extinguishers 2\n",
"portal 2\n",
"hooded 1\n",
"windowsills 2\n",
"windowledge 2\n",
"wonderings 2\n",
"knowshe 1\n",
"lettersbut 1\n",
"peoplefor 1\n",
"togetherit 1\n",
"roundeyed 1\n",
"arbitrator 1\n",
"canals 3\n",
"solemnities 1\n",
"swine 1\n",
"dutchmans 1\n",
"triton 1\n",
"brigs 1\n",
"vauntingly 1\n",
"coachboxfor 1\n",
"softhearted 2\n",
"breastbone 2\n",
"heavyhearted 2\n",
"spake 4\n",
"tripod 2\n",
"stoolslooked 1\n",
"barnets 5\n",
"doornails 1\n",
"papato 1\n",
"humoured 3\n",
"feelwho 1\n",
"betterhow 1\n",
"girlto 1\n",
"mineto 1\n",
"himselfflorence 1\n",
"mybut 1\n",
"knewwhich 1\n",
"touzled 1\n",
"impeaching 2\n",
"thathe 1\n",
"condensed 4\n",
"nightnot 1\n",
"prhaps 1\n",
"cherubs 1\n",
"espial 2\n",
"fatiguing 2\n",
"bagstockjosephrough 1\n",
"myto 1\n",
"allthat 1\n",
"bothladies 1\n",
"youngperhaps 1\n",
"daughterif 1\n",
"mostif 1\n",
"mosttouching 1\n",
"sweetbecame 1\n",
"flatterer 3\n",
"itbut 5\n",
"usthe 1\n",
"openness 4\n",
"whatshisnamea 1\n",
"warwick 5\n",
"kenilworth 3\n",
"insipid 4\n",
"itdamme 1\n",
"stoppers 1\n",
"dooralong 1\n",
"meditationsand 1\n",
"patronbut 1\n",
"rhinoceros 2\n",
"vagabonds 3\n",
"tinkering 1\n",
"fuming 3\n",
"toasted 2\n",
"commiserated 1\n",
"arresting 1\n",
"constrictorthat 1\n",
"windmills 1\n",
"wildflowers 1\n",
"byegone 1\n",
"vengeances 1\n",
"creaturesor 1\n",
"selfimportant 1\n",
"unmatched 1\n",
"deeming 3\n",
"ediths 25\n",
"curiouspoint 1\n",
"dallied 1\n",
"lynx 1\n",
"dotards 1\n",
"meperhaps 1\n",
"bidhe 1\n",
"formi 1\n",
"designedly 3\n",
"himgentle 1\n",
"blimberian 1\n",
"sixoared 1\n",
"asseverations 1\n",
"ganges 1\n",
"painters 10\n",
"upholsterers 3\n",
"pockethandkerchiefs 1\n",
"rasped 1\n",
"stickle 1\n",
"firmaments 1\n",
"vowers 1\n",
"illustrations 1\n",
"cavalierly 1\n",
"clipped 1\n",
"presentimenta 1\n",
"mothermay 1\n",
"fag 3\n",
"toadeater 1\n",
"impeacher 1\n",
"defiancethere 1\n",
"iiam 1\n",
"haughtiness 9\n",
"truthcould 1\n",
"misdoubted 1\n",
"bays 2\n",
"fellowfeeling 1\n",
"diningtable 2\n",
"entombed 2\n",
"protectingly 2\n",
"prosersto 1\n",
"pastrycook 4\n",
"housedoor 7\n",
"palsied 3\n",
"affrighted 3\n",
"crouches 1\n",
"churchwall 1\n",
"hassocks 1\n",
"vinegary 3\n",
"bytheby 1\n",
"spankeran 1\n",
"whiskerless 1\n",
"marrowbones 1\n",
"pecks 1\n",
"curtesysmay 1\n",
"addand 1\n",
"wouldgive 1\n",
"muzzled 2\n",
"rarities 3\n",
"curtseys 1\n",
"cherubim 1\n",
"resumes 7\n",
"dipwrites 1\n",
"entreats 2\n",
"connubial 2\n",
"wrests 1\n",
"merchanta 1\n",
"merchantand 1\n",
"lobstersalad 1\n",
"emulate 1\n",
"cognizance 2\n",
"spillings 1\n",
"heeltaps 1\n",
"omnibus 1\n",
"mooringsnot 1\n",
"promiscuous 1\n",
"exactness 2\n",
"pivot 2\n",
"tobaccobox 1\n",
"howdedo 4\n",
"accidence 1\n",
"sluicing 1\n",
"knowhes 1\n",
"whywhy 1\n",
"soandsothe 1\n",
"firei 1\n",
"gobye 1\n",
"hooking 2\n",
"uponoror 1\n",
"managers 2\n",
"answeredfree 1\n",
"runnersaway 1\n",
"connived 1\n",
"sallied 1\n",
"doorkey 1\n",
"treasuring 1\n",
"titles 3\n",
"gaudy 4\n",
"potiphars 1\n",
"toohe 2\n",
"birdcatcher 1\n",
"swears 1\n",
"fancyto 1\n",
"recognisable 1\n",
"jogtrot 2\n",
"oclockhabit 1\n",
"friendan 1\n",
"greyerwhom 1\n",
"andhabit 1\n",
"fellowwoman 1\n",
"beck 1\n",
"stoveshe 1\n",
"witchs 1\n",
"alice 60\n",
"harden 2\n",
"lawso 1\n",
"distressor 1\n",
"dombeysonly 1\n",
"covetous 3\n",
"beforefor 1\n",
"beenfor 1\n",
"repossessing 1\n",
"vied 1\n",
"engenders 1\n",
"damasks 1\n",
"saturnine 1\n",
"buxom 1\n",
"quarterly 2\n",
"heapedup 2\n",
"harshest 1\n",
"reachhad 1\n",
"strangewhen 1\n",
"unbound 1\n",
"waned 3\n",
"beguiled 2\n",
"foreshadowings 2\n",
"directionbut 1\n",
"modestspoken 1\n",
"burdens 7\n",
"unaccommodating 3\n",
"addingalways 1\n",
"ludship 1\n",
"spiritshe 1\n",
"unpitied 1\n",
"inoculation 1\n",
"twono 1\n",
"thatso 1\n",
"wheelless 1\n",
"pomatum 2\n",
"enviably 2\n",
"couldfor 2\n",
"wellneeded 1\n",
"handsa 1\n",
"supremacy 3\n",
"sureto 1\n",
"friendnext 1\n",
"voicemiss 1\n",
"selfdepreciating 1\n",
"neglectedby 1\n",
"pridecharacter 1\n",
"careof 1\n",
"inferiors 1\n",
"madamthe 1\n",
"youinduces 1\n",
"assailable 1\n",
"amended 1\n",
"hugely 1\n",
"incomparable 1\n",
"gaud 1\n",
"dayyou 1\n",
"countenancefor 1\n",
"warbled 1\n",
"cindery 1\n",
"stoking 1\n",
"gals 2\n",
"saltatory 1\n",
"unwrung 1\n",
"grab 3\n",
"rheumaticks 1\n",
"admiredand 1\n",
"camelike 1\n",
"roved 1\n",
"selfburied 1\n",
"theological 2\n",
"bruising 1\n",
"judah 1\n",
"himso 2\n",
"dissimulating 1\n",
"dogii 1\n",
"handmaking 1\n",
"consort 1\n",
"iits 1\n",
"gladdened 1\n",
"fortnate 1\n",
"theretofore 1\n",
"bunsbya 1\n",
"forbidexcept 1\n",
"unsupported 1\n",
"headforemost 2\n",
"legsone 1\n",
"alliteration 1\n",
"wales 1\n",
"hostage 1\n",
"overbalance 1\n",
"speedsupposed 1\n",
"noons 1\n",
"propagation 1\n",
"satins 1\n",
"selfimportance 1\n",
"satstill 1\n",
"fixedlyturning 1\n",
"statueslooking 1\n",
"everas 1\n",
"posively 1\n",
"edithgrangebyits 1\n",
"ohmost 1\n",
"orphaninlaw 1\n",
"warwickshire 2\n",
"pageant 1\n",
"tohave 1\n",
"dotes 1\n",
"larkey 4\n",
"fellowstudents 1\n",
"thugs 1\n",
"rajah 1\n",
"studydoor 1\n",
"toif 1\n",
"reconcilement 2\n",
"lovelinessfor 1\n",
"herits 2\n",
"awayits 1\n",
"armpart 1\n",
"thein 2\n",
"mourningcoach 1\n",
"smalder 1\n",
"girlsand 1\n",
"unmentioned 1\n",
"tattling 1\n",
"wellarranged 2\n",
"nonetheless 1\n",
"ourat 1\n",
"noticethough 1\n",
"professfor 1\n",
"disapprobation 1\n",
"pleasematter 1\n",
"accomplishmentsand 1\n",
"wifeno 1\n",
"ironshod 1\n",
"herhe 1\n",
"flickered 4\n",
"sickbed 1\n",
"motherwhile 1\n",
"characterthe 1\n",
"unattended 2\n",
"blesseder 1\n",
"blessedest 1\n",
"foxs 2\n",
"causewill 1\n",
"worshipper 2\n",
"hoity 3\n",
"toity 3\n",
"housein 1\n",
"minx 1\n",
"dinnerpreserved 1\n",
"physiognomical 4\n",
"hurriedlynodont 1\n",
"womanone 1\n",
"sawand 1\n",
"plasters 1\n",
"knownot 1\n",
"counterfeit 5\n",
"knowi 1\n",
"indignities 1\n",
"flattererno 1\n",
"bloodred 1\n",
"morningto 2\n",
"dignitynot 1\n",
"unmusically 1\n",
"owntake 1\n",
"inheritors 1\n",
"tritons 1\n",
"jem 2\n",
"sleekness 1\n",
"elsea 1\n",
"foller 1\n",
"individgle 1\n",
"dombeyyou 1\n",
"anear 1\n",
"robhalf 1\n",
"enjoinder 1\n",
"yearsthat 1\n",
"harbouring 2\n",
"ruthlessly 1\n",
"hourfrom 1\n",
"coop 1\n",
"minddrooping 1\n",
"soonto 1\n",
"sightsmillions 1\n",
"revolts 1\n",
"lisps 1\n",
"lazarhouses 1\n",
"byeways 2\n",
"bethat 1\n",
"blend 2\n",
"addafter 1\n",
"whyhe 1\n",
"florencewhy 1\n",
"spanning 1\n",
"dombeywas 1\n",
"herewith 1\n",
"tiara 1\n",
"opposedbut 1\n",
"fulfil 3\n",
"dishonoured 2\n",
"powerlessness 1\n",
"londonthough 1\n",
"pertinacious 1\n",
"watchcoat 1\n",
"secretby 1\n",
"myin 1\n",
"sublimating 1\n",
"teaches 4\n",
"softens 2\n",
"steeping 1\n",
"basting 2\n",
"soothingly 3\n",
"essayed 4\n",
"fervency 1\n",
"practicability 1\n",
"mysteryto 1\n",
"bale 1\n",
"apostrophe 3\n",
"fatheronce 1\n",
"reweals 1\n",
"norwesters 1\n",
"perils 2\n",
"scoreah 1\n",
"savedwas 1\n",
"creeturslashed 1\n",
"jintly 5\n",
"bronzed 1\n",
"youoh 1\n",
"worldsome 1\n",
"landcabin 1\n",
"banns 5\n",
"disapinting 1\n",
"concernedi 1\n",
"thethat 1\n",
"waltersare 1\n",
"waltersi 1\n",
"myselfmuch 1\n",
"settingoff 1\n",
"constrainedher 1\n",
"revealings 1\n",
"trice 1\n",
"itought 1\n",
"rejoins 3\n",
"leers 1\n",
"friendsas 1\n",
"saxby 1\n",
"factof 1\n",
"surpassedno 1\n",
"neversays 1\n",
"outprematurely 1\n",
"perhapsbecause 1\n",
"snappish 1\n",
"admonitorial 1\n",
"clogs 1\n",
"excitements 1\n",
"reduces 1\n",
"frets 1\n",
"tigress 1\n",
"onwhat 1\n",
"washow 1\n",
"browbrow 1\n",
"meoh 1\n",
"erring 2\n",
"sitiwated 2\n",
"notify 1\n",
"directiontowards 1\n",
"mondaythe 1\n",
"halfbreathless 1\n",
"stubbornnessthat 1\n",
"deepens 1\n",
"villainymore 1\n",
"machinethat 1\n",
"muchor 1\n",
"beethovens 1\n",
"worldof 1\n",
"reflectionhow 1\n",
"myselfwhich 1\n",
"indeedthe 1\n",
"tress 2\n",
"didits 1\n",
"shortlived 3\n",
"sisterwhich 1\n",
"intertwisted 1\n",
"womanedith 1\n",
"madamemadame 1\n",
"notified 2\n",
"speakwith 1\n",
"lacerated 3\n",
"crier 1\n",
"infamously 1\n",
"traduce 1\n",
"whatperhaps 1\n",
"parasite 3\n",
"strumpet 1\n",
"assassinate 1\n",
"fearperhaps 1\n",
"jura 1\n",
"roadwhither 1\n",
"blowto 1\n",
"shadeless 1\n",
"chateau 1\n",
"gnawed 1\n",
"illpaved 1\n",
"illglazed 1\n",
"cemeteries 1\n",
"palsiedof 1\n",
"countryplace 2\n",
"rageso 1\n",
"roadpast 1\n",
"sofloor 1\n",
"askings 3\n",
"woyage 3\n",
"wownded 1\n",
"tinted 1\n",
"nevertobe 1\n",
"apartwould 1\n",
"overthrew 1\n",
"temporal 1\n",
"inyou 1\n",
"speechlessness 1\n",
"stiffun 1\n",
"fitypunnote 1\n",
"dissenters 1\n",
"baptists 4\n",
"wesleyans 1\n",
"cushionsfor 1\n",
"kneesbut 1\n",
"ladysuch 1\n",
"thusnot 1\n",
"narrower 2\n",
"ringer 1\n",
"fontfor 1\n",
"registers 4\n",
"roughest 1\n",
"loveof 1\n",
"somnambulism 1\n",
"hoodwinked 1\n",
"truckled 1\n",
"obtruding 2\n",
"andandthink 1\n",
"differenceperhaps 1\n",
"dustfor 1\n",
"looksshe 1\n",
"shallas 1\n",
"hermy 1\n",
"ringsthey 1\n",
"earthread 1\n",
"reduceread 1\n",
"opines 1\n",
"runningaway 1\n",
"leech 1\n",
"tosses 1\n",
"inventories 1\n",
"mattresses 1\n",
"squabs 1\n",
"marginal 1\n",
"donkeytandem 1\n",
"forging 1\n",
"composes 1\n",
"headis 1\n",
"outsides 1\n",
"collations 1\n",
"homeevents 1\n",
"mindhis 1\n",
"ramble 1\n",
"involutions 1\n",
"severing 1\n",
"thinki 1\n",
"walterto 1\n",
"indiwiddles 1\n",
"coindiwiddle 1\n",
"shipboard 1\n",
"urbanely 4\n",
"tootsretired 1\n",
"coalwhippers 1\n",
"swingbridges 1\n",
"cuttleyou 1\n",
"extemporary 1\n",
"halfboots 1\n",
"mannerwhich 1\n",
"disputation 1\n",
"spotas 1\n",
"nutmeg 3\n",
"streetman 1\n",
"acquaintedused 1\n",
"flyin 1\n",
"familyand 2\n",
"mothersi 1\n",
"wrongnot 1\n",
"humbugbut 1\n",
"yetfor 1\n",
"teetheverything 1\n",
"refulgent 2\n",
"freighted 1\n",
"pardner 1\n",
"arrogancea 1\n",
"parisas 1\n",
"wwwgutenbergorglicense 1\n",
"marshalsea 182\n",
"barnacles 54\n",
"prism 20\n",
"affery 164\n",
"biographer 1\n",
"pythick 2\n",
"pythicks 1\n",
"awnings 3\n",
"moorings 2\n",
"venetians 3\n",
"greeks 1\n",
"chirp 2\n",
"beggingwas 1\n",
"clockmaker 1\n",
"moustache 36\n",
"grime 1\n",
"porto 2\n",
"fino 2\n",
"tohey 1\n",
"cheeseagain 1\n",
"majolaine 7\n",
"rigauds 3\n",
"altro 28\n",
"smugglershut 1\n",
"liveyour 1\n",
"intriguers 2\n",
"blustering 1\n",
"ameliorate 2\n",
"facenothing 1\n",
"turnedand 1\n",
"allonging 3\n",
"marshong 2\n",
"enormity 1\n",
"illnature 1\n",
"childbrother 1\n",
"notunderstand 1\n",
"corridors 2\n",
"englishwoman 2\n",
"idiomatic 2\n",
"poste 1\n",
"caravan 1\n",
"dissonance 1\n",
"worldall 1\n",
"toiler 1\n",
"calenders 1\n",
"bemoaned 1\n",
"gravewhat 1\n",
"deadlylively 1\n",
"hiccupping 1\n",
"thess 1\n",
"indigestible 1\n",
"nonage 1\n",
"belzoni 1\n",
"gymnasium 1\n",
"crablike 2\n",
"compartments 1\n",
"peacefullest 1\n",
"worstedmuffled 1\n",
"headdressher 1\n",
"watchpaper 1\n",
"booksternly 1\n",
"handthat 1\n",
"repression 2\n",
"weird 3\n",
"fending 2\n",
"pillowcase 1\n",
"clennams 88\n",
"philosophersthe 1\n",
"waitingroom 1\n",
"usmrs 1\n",
"withdrewas 1\n",
"effectuallyand 1\n",
"explorer 1\n",
"impenetrability 1\n",
"mindremorse 1\n",
"littlefrom 1\n",
"oneto 1\n",
"bakingdish 3\n",
"bottleracks 1\n",
"oblong 1\n",
"effeminate 1\n",
"dayswhich 1\n",
"uns 1\n",
"comparativehoarser 1\n",
"tobaccoer 1\n",
"dirtier 2\n",
"uprightwhich 1\n",
"didwent 1\n",
"thereamong 1\n",
"mixturenecessarily 1\n",
"mixtureand 1\n",
"collegian 25\n",
"correspondents 4\n",
"surprisedme 1\n",
"paternally 1\n",
"lodgesteps 1\n",
"theyreover 1\n",
"nomenclaturetheres 1\n",
"teagardens 1\n",
"undrawing 1\n",
"wishful 5\n",
"amy 224\n",
"slang 2\n",
"liverpool 1\n",
"lengthenedout 1\n",
"citya 1\n",
"easytoo 1\n",
"pretensionscomes 1\n",
"geraniumbeautiful 1\n",
"washemnot 1\n",
"ia 1\n",
"presidential 2\n",
"pewterpots 1\n",
"beery 1\n",
"quietlyheaven 1\n",
"itby 1\n",
"causelessly 1\n",
"cowls 1\n",
"sawdusting 1\n",
"gusty 1\n",
"sartorial 1\n",
"eyeshungry 1\n",
"shambled 1\n",
"coffeeshop 3\n",
"insidethat 1\n",
"crippless 7\n",
"opena 1\n",
"reviling 1\n",
"barnacle 167\n",
"maggys 14\n",
"recommendations 1\n",
"pekoe 1\n",
"statesmanship 2\n",
"ticked 4\n",
"coached 3\n",
"singeing 2\n",
"coachhouses 1\n",
"stopper 3\n",
"vial 1\n",
"hutches 1\n",
"memorialise 3\n",
"readmitted 1\n",
"wobblers 1\n",
"marmalade 2\n",
"clive 1\n",
"politicodiplomatic 1\n",
"impossibilities 2\n",
"treasonable 1\n",
"chalons 3\n",
"imbeciles 2\n",
"wearier 1\n",
"cafe 1\n",
"embellishment 2\n",
"syrups 2\n",
"manwhatever 1\n",
"nameis 1\n",
"againhas 1\n",
"gentlemenwhat 1\n",
"thatlagnier 1\n",
"rhone 2\n",
"surpassingly 1\n",
"crippledlook 1\n",
"drinkingshop 1\n",
"arabs 2\n",
"earthiest 1\n",
"londoners 1\n",
"provisional 1\n",
"scarcewhich 1\n",
"termsbut 1\n",
"limewhitened 1\n",
"tautologically 1\n",
"defendants 1\n",
"twentypound 2\n",
"mollancholy 2\n",
"alloften 1\n",
"thanmr 1\n",
"manufacturers 1\n",
"christopher 8\n",
"pimpled 1\n",
"floss 1\n",
"sculptors 2\n",
"philanthropists 1\n",
"decimus 60\n",
"unreason 1\n",
"arenot 1\n",
"bottlegreen 9\n",
"lettings 1\n",
"exchangeable 1\n",
"peony 1\n",
"giggled 1\n",
"knowoh 1\n",
"floraarthur 1\n",
"habitmr 2\n",
"clennamthat 1\n",
"pekin 1\n",
"cheerfulest 1\n",
"cautionsuch 1\n",
"dinnerso 1\n",
"neatlyserved 1\n",
"butterboat 1\n",
"eater 1\n",
"coaling 1\n",
"observationstruck 1\n",
"citywards 1\n",
"floranot 1\n",
"untrammelled 1\n",
"aldersgate 1\n",
"fined 1\n",
"deeprooted 1\n",
"reducible 1\n",
"himam 1\n",
"saidnot 1\n",
"hospitally 1\n",
"verger 1\n",
"commendingly 1\n",
"mitre 2\n",
"rippedup 1\n",
"youid 1\n",
"wentand 1\n",
"deadnothing 1\n",
"wentoff 1\n",
"herejust 1\n",
"blotched 2\n",
"vagueness 1\n",
"indeednever 1\n",
"doycea 1\n",
"antiquities 1\n",
"minced 2\n",
"tuscan 2\n",
"trastaverini 1\n",
"acquisitions 1\n",
"piecrust 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sebastian 1\n",
"piombo 1\n",
"ofstaying 1\n",
"commandment 1\n",
"reddishgrey 1\n",
"revoked 1\n",
"thoughtwho 1\n",
"newfoundland 2\n",
"dexterous 1\n",
"kingston 1\n",
"ninetyseven 1\n",
"bilberry 1\n",
"seraphinalady 1\n",
"toozellem 1\n",
"bookkeeping 1\n",
"attainmentswhich 1\n",
"ramification 1\n",
"claudes 1\n",
"phaenomena 1\n",
"chambercandlestick 1\n",
"archer 1\n",
"agone 1\n",
"chivery 105\n",
"fairies 1\n",
"hearsay 1\n",
"attachmentindeed 1\n",
"seizures 1\n",
"donor 3\n",
"sidestripes 1\n",
"threestringed 1\n",
"lute 1\n",
"weekdays 1\n",
"selfsuppressed 1\n",
"stationfor 1\n",
"merevolving 1\n",
"imiss 1\n",
"mournfulness 1\n",
"collegeyard 1\n",
"labyrinthian 1\n",
"impressible 5\n",
"qualitiesmoral 1\n",
"abrother 1\n",
"toat 1\n",
"oni 1\n",
"independentby 1\n",
"castaway 1\n",
"recked 1\n",
"chesterfield 1\n",
"twentythird 1\n",
"mugged 1\n",
"cellarous 1\n",
"blisterous 1\n",
"truss 1\n",
"stomachs 2\n",
"harley 13\n",
"usbird 1\n",
"addressingthat 1\n",
"unexceptionable 1\n",
"dinnertables 1\n",
"midas 1\n",
"galwell 2\n",
"stateliest 1\n",
"courtsmartial 1\n",
"worldfamed 1\n",
"ofmoney 1\n",
"colonies 1\n",
"shortcoming 1\n",
"cubas 1\n",
"snuffs 1\n",
"reconducted 1\n",
"tuneless 1\n",
"tipif 1\n",
"itits 1\n",
"donetook 1\n",
"supplemental 1\n",
"pocketand 1\n",
"simplification 1\n",
"unsealed 1\n",
"picturebook 1\n",
"planking 1\n",
"arthurforce 1\n",
"minddoes 1\n",
"arthurdoyce 4\n",
"aggravatingly 2\n",
"inanely 1\n",
"deadliest 1\n",
"boastfulness 1\n",
"lineage 1\n",
"painfullest 2\n",
"supplying 1\n",
"locksat 1\n",
"axiom 2\n",
"wixen 1\n",
"topheavy 1\n",
"gocartshocking 1\n",
"faulti 1\n",
"pockethousewife 1\n",
"herewe 1\n",
"glasscoach 1\n",
"dancingshoes 1\n",
"fortunetellinghell 1\n",
"herwhich 1\n",
"gipsyfortunetelling 1\n",
"zigzags 1\n",
"slights 2\n",
"storyof 1\n",
"widelyopened 1\n",
"cottager 1\n",
"fanlight 1\n",
"bakersnot 1\n",
"numberand 1\n",
"hatwe 1\n",
"bawkins 4\n",
"ounces 2\n",
"radishes 1\n",
"extracts 3\n",
"enquiry 1\n",
"propitiating 1\n",
"englishmore 1\n",
"ope 4\n",
"anglosaxon 1\n",
"doormakes 1\n",
"armchairand 1\n",
"aversions 1\n",
"nobodysnobodys 1\n",
"mansany 1\n",
"poundsto 2\n",
"warded 2\n",
"accommodationbills 1\n",
"straitness 1\n",
"wellfavoured 2\n",
"britannic 1\n",
"editorperson 1\n",
"bethinking 1\n",
"unfurling 1\n",
"selfwilled 3\n",
"visitingwhich 1\n",
"supperand 1\n",
"clennamtattycoram 1\n",
"grandchild 2\n",
"balconies 2\n",
"discoursing 1\n",
"subdues 1\n",
"concludedmr 1\n",
"extraction 1\n",
"deputed 1\n",
"houseagents 1\n",
"peaceor 1\n",
"whichthat 1\n",
"becausei 1\n",
"selfdeceived 1\n",
"cryingi 1\n",
"unhealthiness 1\n",
"liegelord 1\n",
"waylaid 2\n",
"thundery 1\n",
"probe 1\n",
"bagatelleboard 1\n",
"mrhe 1\n",
"disqualifies 2\n",
"marriednever 1\n",
"faircreature 1\n",
"selfcorrection 1\n",
"pilots 2\n",
"fidgety 2\n",
"ravished 2\n",
"diabolically 1\n",
"screwmachine 1\n",
"fortitudegreat 1\n",
"replenishment 1\n",
"townmouses 1\n",
"reedy 1\n",
"venus 1\n",
"flecks 1\n",
"retirementand 1\n",
"aheard 1\n",
"scientifically 1\n",
"wayan 1\n",
"mehahumiliation 1\n",
"wellnigh 1\n",
"elsethat 1\n",
"suchand 1\n",
"expressionspirit 1\n",
"andhacondescending 1\n",
"pensionerit 1\n",
"smilingarm 1\n",
"banknote 2\n",
"wishin 1\n",
"epitaph 2\n",
"vocalist 1\n",
"teacakes 1\n",
"rusts 1\n",
"powereasily 1\n",
"ahuman 1\n",
"suchhasuch 1\n",
"batten 1\n",
"hishe 1\n",
"heartache 3\n",
"dealand 1\n",
"jailhome 1\n",
"pointthe 1\n",
"tomorroweh 1\n",
"electrically 1\n",
"clennameh 1\n",
"hayloads 1\n",
"togive 1\n",
"whisperyou 1\n",
"miggleses 1\n",
"cosmetic 2\n",
"irreverently 1\n",
"jobber 2\n",
"neater 1\n",
"lovei 1\n",
"counterscheming 1\n",
"carriagewheels 1\n",
"worldwide 2\n",
"knowingperfect 1\n",
"conformation 1\n",
"sopotent 1\n",
"detrimentally 1\n",
"dispel 1\n",
"despatchboxes 1\n",
"everfamous 1\n",
"toadied 1\n",
"governorgeneral 1\n",
"windiest 1\n",
"platitudes 1\n",
"lifelong 1\n",
"ungentlemanly 3\n",
"moled 1\n",
"dribbled 1\n",
"authorize 1\n",
"bundling 1\n",
"clennamdoyce 1\n",
"cucumber 1\n",
"realise 3\n",
"behahe 1\n",
"ishabarely 1\n",
"sosoontobeorphaned 1\n",
"african 1\n",
"occasionthat 1\n",
"predominates 1\n",
"vintage 1\n",
"mountainpeaks 1\n",
"neverresting 1\n",
"quagmire 1\n",
"outlasting 1\n",
"speakerthat 1\n",
"dogswho 1\n",
"refectory 1\n",
"willhanot 1\n",
"inquiredhawhat 1\n",
"remune 1\n",
"generalthough 1\n",
"miniatures 1\n",
"sisteranyhaauthority 1\n",
"goading 1\n",
"fatheredwardno 1\n",
"ofhagenerallya 1\n",
"sohumcoarse 1\n",
"hamrs 1\n",
"film 1\n",
"glaciers 1\n",
"chalets 1\n",
"halfcup 1\n",
"assassins 1\n",
"enrage 1\n",
"youhaseparate 1\n",
"codfish 1\n",
"peoplehapeople 1\n",
"huma 3\n",
"occasionhaunpleasant 1\n",
"unreality 2\n",
"pillared 1\n",
"quadrangle 1\n",
"hanginggardens 1\n",
"lizards 1\n",
"enoughnot 1\n",
"gondola 7\n",
"wordfor 1\n",
"cannotin 1\n",
"adriatic 1\n",
"centuriesto 1\n",
"churchdomes 1\n",
"laving 1\n",
"butterflyrose 1\n",
"creased 1\n",
"behamore 1\n",
"havinghumtoo 1\n",
"inha 2\n",
"adaptability 1\n",
"hummadam 1\n",
"returnwhen 1\n",
"thehumprecepts 1\n",
"onhaon 1\n",
"youhahabitually 1\n",
"yourselfhai 1\n",
"washauniversally 1\n",
"ihumi 2\n",
"onehai 1\n",
"unspottedis 1\n",
"shouldhumdo 1\n",
"horsedealing 1\n",
"menials 1\n",
"gowansin 1\n",
"motheris 1\n",
"thishumtimely 1\n",
"humquite 1\n",
"cyphered 1\n",
"subtraction 1\n",
"playtables 1\n",
"banka 1\n",
"unskilful 1\n",
"coquettishly 1\n",
"exaltation 1\n",
"toyed 1\n",
"thinkdear 1\n",
"wellat 1\n",
"seagreen 1\n",
"venuss 1\n",
"withhamr 1\n",
"cronies 1\n",
"havehafetched 1\n",
"emphasising 2\n",
"residue 1\n",
"substancelittle 1\n",
"freezes 1\n",
"bluffer 1\n",
"unfitness 1\n",
"inhacommon 1\n",
"clennamthis 1\n",
"artificer 1\n",
"irrefragable 1\n",
"noblemen 1\n",
"perversest 1\n",
"muchmaligned 1\n",
"semistranger 1\n",
"impracticability 1\n",
"romely 1\n",
"thinkand 1\n",
"papersand 1\n",
"tickits 2\n",
"mistrustful 2\n",
"leadingand 1\n",
"coallighters 1\n",
"watermovement 1\n",
"midstream 1\n",
"gaslamp 1\n",
"ensconced 1\n",
"belvederes 1\n",
"equine 1\n",
"cornerstairs 1\n",
"philanthropically 1\n",
"safes 1\n",
"austerely 1\n",
"whoha 1\n",
"staggerings 1\n",
"bagwig 1\n",
"flintwinchunless 1\n",
"herethough 1\n",
"pisa 1\n",
"degradedly 1\n",
"bate 1\n",
"negotiatorand 1\n",
"heresy 1\n",
"distraint 1\n",
"banco 2\n",
"juries 1\n",
"tyburn 1\n",
"sevenleague 1\n",
"innovating 1\n",
"wasmight 1\n",
"authoritysay 1\n",
"pippin 1\n",
"blessedly 1\n",
"cumulative 1\n",
"enriching 1\n",
"constituencies 1\n",
"theseafixes 1\n",
"somebodyand 1\n",
"neverexercised 1\n",
"darritor 1\n",
"dorrithas 1\n",
"analogical 1\n",
"indefinably 2\n",
"buhl 1\n",
"nemesis 2\n",
"resound 1\n",
"hundredweights 1\n",
"dialogues 2\n",
"rentquestion 1\n",
"rincontrato 1\n",
"baddest 1\n",
"cavallettos 5\n",
"exhaling 1\n",
"inhalation 1\n",
"erecting 1\n",
"everywhereimmense 1\n",
"simplesthearted 1\n",
"jackass 1\n",
"jackasss 1\n",
"moneysworth 1\n",
"motherinlawed 2\n",
"swains 1\n",
"doated 1\n",
"thehumcharacter 1\n",
"ihanot 1\n",
"dormouse 1\n",
"possibleand 1\n",
"garrisoned 1\n",
"belisarius 1\n",
"romulus 1\n",
"edgardo 1\n",
"thingseven 1\n",
"alchemists 1\n",
"ourhaconnection 1\n",
"ihamust 1\n",
"youhaeligibly 1\n",
"generalthat 1\n",
"havehumno 1\n",
"shouldhaunderstand 1\n",
"amphitheatre 1\n",
"eustaces 1\n",
"ghoule 1\n",
"butlered 1\n",
"statethe 1\n",
"revised 1\n",
"apostlewho 1\n",
"dressinggowned 1\n",
"newspapered 1\n",
"mairdale 1\n",
"thisha 1\n",
"ishaa 1\n",
"butoh 1\n",
"ofhaexceeding 1\n",
"scarcelyhadared 1\n",
"thingsof 1\n",
"bolus 1\n",
"strictest 1\n",
"dustcart 1\n",
"peppercorn 1\n",
"ithathat 1\n",
"grimmest 1\n",
"noahuman 1\n",
"amhaa 1\n",
"isha 1\n",
"asksay 1\n",
"assign 1\n",
"ivoryhandled 1\n",
"buthasome 1\n",
"ahaa 1\n",
"littlehumtestimonial 1\n",
"humthemthemaccording 1\n",
"ihaill 1\n",
"mighthumsuggest 1\n",
"habrought 1\n",
"redjacketed 1\n",
"spoliation 1\n",
"banditti 1\n",
"redjackets 1\n",
"brun 1\n",
"campagna 2\n",
"swerve 1\n",
"cypresstrees 1\n",
"goths 1\n",
"findhumany 1\n",
"tohaconcentrate 1\n",
"thathumvery 1\n",
"adjure 1\n",
"humdrivelled 1\n",
"tediousness 2\n",
"thehagreat 1\n",
"shallhumbe 1\n",
"pruney 1\n",
"somnolent 1\n",
"ofhaposition 1\n",
"thehasense 1\n",
"tohayourself 1\n",
"toahmrs 1\n",
"bobbest 1\n",
"turnkeyssend 1\n",
"ofhumwelcoming 1\n",
"thehasurrey 1\n",
"thehamarshalsea 1\n",
"buthumproud 1\n",
"ofhavoluntary 1\n",
"scrupling 1\n",
"reservinghashall 1\n",
"turnkeyhad 1\n",
"fingerrings 1\n",
"crossruled 1\n",
"outlaws 1\n",
"seer 3\n",
"grotto 1\n",
"selfsecluded 1\n",
"likeliest 1\n",
"underhanded 1\n",
"lectured 1\n",
"riverwhere 1\n",
"instructress 1\n",
"meagonies 1\n",
"houseflies 1\n",
"butwell 1\n",
"herso 1\n",
"barbaric 2\n",
"necessitated 1\n",
"plastic 2\n",
"makeexcept 1\n",
"departurein 1\n",
"paste 2\n",
"exuding 1\n",
"cockchafers 1\n",
"casbyand 1\n",
"memr 1\n",
"clennamthe 1\n",
"halcyon 1\n",
"rustlings 1\n",
"tremblings 1\n",
"blabber 1\n",
"afferyjeremiahs 1\n",
"himif 1\n",
"peerage 2\n",
"excogitation 1\n",
"mignonette 2\n",
"assentingly 1\n",
"malaria 1\n",
"eventsthat 1\n",
"mastermind 2\n",
"synagogues 1\n",
"scalpel 1\n",
"gullibility 1\n",
"penknifesoiled 1\n",
"jugular 1\n",
"lotion 1\n",
"inveiglements 1\n",
"loiter 1\n",
"precursory 1\n",
"selfhelpful 1\n",
"disgraceruined 1\n",
"tided 1\n",
"whosei 1\n",
"gustily 1\n",
"straightforwardness 1\n",
"emphasise 1\n",
"strived 1\n",
"linguist 1\n",
"vanishingpoint 1\n",
"idled 1\n",
"sayingsome 1\n",
"extol 1\n",
"manual 1\n",
"halfforgetfulness 1\n",
"hairheynot 1\n",
"hairbut 1\n",
"commodity 3\n",
"lithely 1\n",
"felicitate 1\n",
"recompenses 1\n",
"derangements 1\n",
"characterconsent 1\n",
"whoof 6\n",
"fingernegativeno 1\n",
"raggedness 1\n",
"illnessthat 1\n",
"selfmistrust 1\n",
"supplicatory 1\n",
"padrone 1\n",
"spirted 1\n",
"spiritsamiable 1\n",
"outjeremiah 1\n",
"clucking 1\n",
"straitly 1\n",
"usthese 1\n",
"satans 1\n",
"unsoftening 1\n",
"distortion 1\n",
"singingbird 1\n",
"bumptious 1\n",
"jezebel 1\n",
"rasps 1\n",
"firebox 1\n",
"prisondoor 1\n",
"brokenbroken 1\n",
"auditress 1\n",
"retrospective 1\n",
"halfhoped 1\n",
"mightso 1\n",
"unrighteous 1\n",
"slaked 1\n",
"surged 1\n",
"unrecognisable 1\n",
"cellarage 1\n",
"collarbone 1\n",
"drinkingshops 1\n",
"flyntevynge 1\n",
"beamer 1\n",
"freelyat 1\n",
"screwer 1\n",
"prune 1\n",
"felonious 1\n",
"hoisting 1\n",
"harangued 1\n",
"donetattycoram 1\n",
"foundlinggirl 1\n",
"mensee 1\n",
"pickers 1\n",
"earlyfostered 1\n",
"acorns 1\n",
"cloghornpipe 1\n",
"clennamone 1\n",
"ungenerous 1\n",
"medalled 1\n",
"dontknowwhat 1\n",
"the 98700\n",
"project 467\n",
"this 8111\n",
"for 13289\n",
"use 354\n",
"whatsoever 13\n",
"included 45\n",
"title 34\n",
"author 27\n",
"date 43\n",
"charles 735\n",
"published 10\n",
"in 31834\n",
"form 328\n",
"include 14\n",
"volumes 9\n",
"been 5710\n",
"daughter 476\n",
"disappearance 35\n",
"bodyfloras 2\n",
"morningthe 2\n",
"adventure 50\n",
"via 3\n",
"viithe 1\n",
"visit 288\n",
"vault 43\n",
"search 108\n",
"around 330\n",
"despair 101\n",
"strange 567\n",
"xivhenrys 1\n",
"hallfloras 2\n",
"xvthe 1\n",
"communication 108\n",
"gardenan 2\n",
"some 3883\n",
"admirals 42\n",
"new 799\n",
"fearsthe 3\n",
"chamberthe 2\n",
"attack 164\n",
"hollandthe 3\n",
"xxivthe 1\n",
"charlesthe 3\n",
"midnight 66\n",
"parkthe 2\n",
"noble 130\n",
"opinion 446\n",
"lettersthe 2\n",
"marchdales 13\n",
"iron 95\n",
"xxxthe 1\n",
"solemn 134\n",
"consequencesthe 2\n",
"removal 34\n",
"xxxviisir 1\n",
"interposition 4\n",
"popular 74\n",
"dangerthe 4\n",
"gravesthe 2\n",
"preparations 49\n",
"mobs 11\n",
"varneysthe 4\n",
"attempt 121\n",
"story 237\n",
"attackthe 2\n",
"doors 132\n",
"liiithe 1\n",
"destruction 73\n",
"burning 144\n",
"madness 35\n",
"revenge 29\n",
"departure 116\n",
"pilot 9\n",
"deserted 94\n",
"interrupted 157\n",
"lxithe 1\n",
"guests 115\n",
"false 120\n",
"inconsistencythe 2\n",
"chillingworths 16\n",
"lxviithe 1\n",
"attempted 48\n",
"lxxthe 1\n",
"stranger 290\n",
"riotthe 2\n",
"lxxithe 1\n",
"lxxiithe 1\n",
"vampirethe 2\n",
"lxxvmutual 1\n",
"henry 1025\n",
"executioner 21\n",
"lxxixthe 1\n",
"refugethe 4\n",
"hangman 44\n",
"lxxxivthe 1\n",
"huntedthe 2\n",
"lxxxvithe 1\n",
"hunt 30\n",
"reception 93\n",
"became 523\n",
"encounter 64\n",
"grand 103\n",
"up 4779\n",
"xcivthe 1\n",
"visitor 196\n",
"death 458\n",
"xcvthe 1\n",
"little 4301\n",
"that 28566\n",
"gratefully 22\n",
"southern 15\n",
"firm 110\n",
"portion 78\n",
"entirely 123\n",
"omitted 13\n",
"life 1661\n",
"could 4277\n",
"throw 115\n",
"made 2456\n",
"great 1832\n",
"noise 173\n",
"time 3347\n",
"these 1827\n",
"observations 31\n",
"far 830\n",
"exceeding 19\n",
"act 244\n",
"incentive 3\n",
"production 14\n",
"still 1671\n",
"whole 1138\n",
"laudatory 4\n",
"midnightthe 3\n",
"tones 54\n",
"stillness 42\n",
"usually 161\n",
"outbreak 3\n",
"they 6489\n",
"ordinary 104\n",
"fluctuations 2\n",
"peal 9\n",
"awaken 29\n",
"minutes 266\n",
"half 566\n",
"had 16551\n",
"scattered 48\n",
"did 3380\n",
"cease 49\n",
"sleepers 4\n",
"heard 1239\n",
"confused 118\n",
"turned 1027\n",
"burst 186\n",
"boughs 6\n",
"opposed 41\n",
"arose 98\n",
"then 3837\n",
"mid 2\n",
"truth 302\n",
"chamber 155\n",
"ancient 103\n",
"curious 115\n",
"quaint 14\n",
"adorn 8\n",
"curiosity 117\n",
"bay 23\n",
"painted 48\n",
"glass 321\n",
"room 1994\n",
"although 318\n",
"brow 53\n",
"heavy 232\n",
"funereal 5\n",
"mimic 3\n",
"musketry 22\n",
"ittheir 1\n",
"expend 9\n",
"vain 123\n",
"fashions 6\n",
"girl 469\n",
"long 1851\n",
"hair 468\n",
"blackened 27\n",
"restless 51\n",
"hangs 16\n",
"bosom 145\n",
"study 178\n",
"once 1410\n",
"lips 411\n",
"prayerat 1\n",
"might 2013\n",
"him 12818\n",
"suffered 141\n",
"faintly 57\n",
"disturb 35\n",
"cannot 699\n",
"break 140\n",
"moves 33\n",
"smooth 80\n",
"budding 6\n",
"womanhood 5\n",
"state 723\n",
"gentleness 29\n",
"yesan 2\n",
"rolling 74\n",
"heaven 376\n",
"sleeps 11\n",
"living 228\n",
"weak 160\n",
"sits 31\n",
"wild 158\n",
"awakening 18\n",
"until 776\n",
"produce 80\n",
"prayera 1\n",
"heart 1007\n",
"things 688\n",
"another 1769\n",
"across 351\n",
"out 4894\n",
"shriek 29\n",
"fixed 212\n",
"perspiration 24\n",
"fear 327\n",
"stood 872\n",
"gasped 25\n",
"hears 20\n",
"lightninganother 1\n",
"shriekthere 1\n",
"ledge 19\n",
"fingernails 5\n",
"wildly 31\n",
"each 585\n",
"horror 102\n",
"continue 59\n",
"spoken 216\n",
"fancies 49\n",
"against 862\n",
"entrance 108\n",
"red 282\n",
"growth 18\n",
"untouched 7\n",
"moveeach 1\n",
"weighed 24\n",
"down 2822\n",
"tons 4\n",
"hoarse 40\n",
"screamshe 1\n",
"memory 160\n",
"moments 326\n",
"whiteperfectly 1\n",
"bloodless 2\n",
"teeththe 1\n",
"glaringly 1\n",
"white 383\n",
"approaches 17\n",
"finger 110\n",
"going 1438\n",
"draw 96\n",
"serpent 7\n",
"greater 255\n",
"gaze 76\n",
"therewhat 4\n",
"heaves 2\n",
"holds 24\n",
"church 232\n",
"being 2289\n",
"unconsciously 52\n",
"age 159\n",
"rush 85\n",
"tresses 5\n",
"screamedheaven 1\n",
"rapid 93\n",
"succession 33\n",
"dragged 55\n",
"completely 177\n",
"ran 405\n",
"angelic 12\n",
"edge 52\n",
"forces 91\n",
"teetha 1\n",
"various 199\n",
"voices 209\n",
"dressed 184\n",
"myself 1304\n",
"unless 193\n",
"we 4908\n",
"both 913\n",
"appearing 75\n",
"middle 127\n",
"elderly 48\n",
"fallen 184\n",
"himself 2772\n",
"cries 67\n",
"your 3861\n",
"pair 136\n",
"shouting 115\n",
"proceeded 137\n",
"considerable 99\n",
"unhappily 18\n",
"helpless 30\n",
"will 4374\n",
"go 2341\n",
"kept 484\n",
"wait 239\n",
"crowbar 5\n",
"goes 182\n",
"same 1593\n",
"way 2100\n",
"lockand 1\n",
"events 274\n",
"happen 166\n",
"space 117\n",
"actual 50\n",
"elapsed 37\n",
"opensit 1\n",
"plied 7\n",
"ingress 2\n",
"patient 76\n",
"clearing 32\n",
"prevented 50\n",
"observing 76\n",
"caught 171\n",
"candle 135\n",
"extinguish 5\n",
"comparatively 23\n",
"unexpected 101\n",
"fairly 47\n",
"extinguished 22\n",
"save 178\n",
"reddish 7\n",
"pass 248\n",
"garden 284\n",
"lower 114\n",
"shining 131\n",
"otherwise 184\n",
"astonished 76\n",
"extent 116\n",
"service 332\n",
"carry 187\n",
"aim 118\n",
"savage 28\n",
"wore 136\n",
"ten 265\n",
"times 592\n",
"formation 16\n",
"presence 333\n",
"concerns 10\n",
"implored 24\n",
"longer 345\n",
"expected 320\n",
"whispered 186\n",
"bold 100\n",
"outer 63\n",
"utter 82\n",
"wilfully 8\n",
"hurry 102\n",
"gate 195\n",
"themselves 497\n",
"fully 143\n",
"find 820\n",
"extremity 21\n",
"vegetation 8\n",
"existed 42\n",
"brother 564\n",
"explanation 136\n",
"poorpoor 1\n",
"premature 8\n",
"restored 58\n",
"reason 456\n",
"fears 53\n",
"indicating 33\n",
"martha 39\n",
"lives 113\n",
"become 414\n",
"nor 491\n",
"several 446\n",
"deceive 39\n",
"meaning 191\n",
"emotion 119\n",
"taken 754\n",
"posture 18\n",
"spots 17\n",
"illness 57\n",
"prime 23\n",
"pallid 3\n",
"venture 52\n",
"beneficial 8\n",
"firmer 5\n",
"mesave 2\n",
"suggested 110\n",
"occurred 203\n",
"anxious 188\n",
"hurt 110\n",
"groan 42\n",
"inquiry 99\n",
"hopes 114\n",
"laid 332\n",
"closed 215\n",
"hushhush 4\n",
"slumber 27\n",
"sunk 63\n",
"gracious 115\n",
"shape 97\n",
"terrors 33\n",
"brain 55\n",
"whose 493\n",
"ours 57\n",
"vices 17\n",
"prosperity 15\n",
"ninety 6\n",
"awakens 7\n",
"occasion 328\n",
"detention 4\n",
"rest 492\n",
"fetch 71\n",
"powderflask 2\n",
"please 483\n",
"reload 3\n",
"present 705\n",
"sports 6\n",
"often 553\n",
"atmosphere 45\n",
"objects 87\n",
"sensations 22\n",
"undefined 13\n",
"tried 401\n",
"racked 6\n",
"vague 55\n",
"gloom 72\n",
"strangely 53\n",
"workman 16\n",
"proper 106\n",
"obscure 36\n",
"order 437\n",
"awoke 57\n",
"harkharkhark 1\n",
"loudly 61\n",
"create 13\n",
"ear 113\n",
"conclusion 92\n",
"invigorating 3\n",
"days 611\n",
"brilliancy 7\n",
"tree 84\n",
"seldom 84\n",
"fortune 164\n",
"apparently 111\n",
"presume 60\n",
"ay 268\n",
"youth 148\n",
"scorn 41\n",
"repeat 83\n",
"listeners 11\n",
"drawing 295\n",
"sprung 42\n",
"eats 6\n",
"mena 1\n",
"aught 11\n",
"merely 259\n",
"mentioned 240\n",
"goodness 132\n",
"nay 91\n",
"souls 29\n",
"expedients 1\n",
"accounting 6\n",
"reasons 109\n",
"sincere 43\n",
"bursting 41\n",
"dodo 1\n",
"highly 121\n",
"kill 90\n",
"dreadfullooking 2\n",
"perfect 177\n",
"saying 601\n",
"sighed 81\n",
"deeply 108\n",
"silent 341\n",
"excitement 118\n",
"affect 23\n",
"drive 108\n",
"hint 50\n",
"amen 11\n",
"emergency 7\n",
"horrifiedutterly 1\n",
"frightfully 13\n",
"horrified 21\n",
"uneasiness 73\n",
"allowing 30\n",
"deceit 11\n",
"precarious 2\n",
"call 492\n",
"medical 35\n",
"gentleman 711\n",
"practitioner 10\n",
"resolved 154\n",
"expectation 51\n",
"news 253\n",
"reining 3\n",
"everybodys 14\n",
"bade 41\n",
"already 643\n",
"begged 97\n",
"accorded 6\n",
"narration 8\n",
"yesand 1\n",
"astonish 11\n",
"favour 165\n",
"vitality 13\n",
"cracked 26\n",
"inquired 261\n",
"hidiously 1\n",
"armed 46\n",
"eagerly 65\n",
"accidental 27\n",
"extends 4\n",
"assume 52\n",
"mortal 88\n",
"breathing 74\n",
"distressed 49\n",
"whoever 30\n",
"shew 6\n",
"evident 136\n",
"glanced 250\n",
"evidence 72\n",
"motherall 1\n",
"accompanied 131\n",
"smile 696\n",
"puncture 1\n",
"magnifying 3\n",
"probablyit 1\n",
"latter 174\n",
"acquainted 126\n",
"candidly 14\n",
"notions 9\n",
"bites 7\n",
"degrading 7\n",
"quantity 52\n",
"decreased 4\n",
"labours 10\n",
"drug 5\n",
"sort 465\n",
"freeze 2\n",
"month 78\n",
"stands 53\n",
"foiled 12\n",
"oclock 182\n",
"gloves 56\n",
"visibly 10\n",
"beginning 204\n",
"wage 2\n",
"war 319\n",
"sense 279\n",
"seelet 1\n",
"shelves 9\n",
"entitled 15\n",
"inclined 123\n",
"moons 17\n",
"conscious 118\n",
"reverie 17\n",
"concerned 70\n",
"warrant 18\n",
"soft 132\n",
"rising 215\n",
"effects 43\n",
"commonly 11\n",
"solemnly 53\n",
"lace 8\n",
"inspection 24\n",
"coat 237\n",
"fashion 41\n",
"ones 208\n",
"refute 2\n",
"curtains 27\n",
"brilliantly 8\n",
"shutters 32\n",
"inasmuch 15\n",
"honest 108\n",
"suggest 36\n",
"foolishly 2\n",
"sensitive 41\n",
"satisfy 25\n",
"numerous 57\n",
"occupy 54\n",
"sufficed 16\n",
"committed 82\n",
"footstep 43\n",
"laurel 3\n",
"bushes 27\n",
"hope 856\n",
"catching 45\n",
"surprised 239\n",
"objectionable 15\n",
"loaded 44\n",
"ball 133\n",
"exigency 2\n",
"jumped 78\n",
"elevated 21\n",
"walk 296\n",
"couple 86\n",
"positions 25\n",
"mine 373\n",
"depended 28\n",
"wood 140\n",
"touch 203\n",
"recede 4\n",
"perceptible 21\n",
"signs 45\n",
"society 363\n",
"marchdalehush 1\n",
"trust 188\n",
"remark 161\n",
"homecome 1\n",
"superadded 6\n",
"future 176\n",
"system 68\n",
"probability 47\n",
"witness 57\n",
"whether 684\n",
"desperate 97\n",
"divesting 3\n",
"occupies 5\n",
"undoubtedly 31\n",
"proposition 50\n",
"unacceptable 5\n",
"goodwill 15\n",
"property 183\n",
"fatality 2\n",
"heads 145\n",
"father 1048\n",
"exception 50\n",
"accounted 14\n",
"change 383\n",
"habits 64\n",
"laws 153\n",
"recourse 7\n",
"decease 17\n",
"pressed 152\n",
"mortgaged 3\n",
"speech 136\n",
"children 323\n",
"dissipated 12\n",
"industry 11\n",
"livelihood 8\n",
"disliked 24\n",
"administered 16\n",
"weeks 140\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"purchase 36\n",
"affairs 244\n",
"advised 47\n",
"eagerness 17\n",
"sending 45\n",
"delightful 96\n",
"sum 112\n",
"handsomely 21\n",
"disposal 23\n",
"narrow 103\n",
"mountain 33\n",
"hurled 6\n",
"acknowledgments 6\n",
"instruction 17\n",
"profession 48\n",
"tenderest 10\n",
"satisfactorily 9\n",
"wayward 6\n",
"meet 347\n",
"rolled 84\n",
"hoped 147\n",
"expediency 6\n",
"advisable 10\n",
"error 20\n",
"intimately 13\n",
"kindly 131\n",
"won 62\n",
"esteemed 17\n",
"owned 7\n",
"arrange 63\n",
"managed 57\n",
"bannerworthsa 1\n",
"allowed 120\n",
"entering 94\n",
"vii 36\n",
"agreeing 7\n",
"pleasantly 34\n",
"favourable 53\n",
"condition 229\n",
"horribly 7\n",
"decomposition 8\n",
"undergone 30\n",
"stone 137\n",
"remains 72\n",
"rashly 4\n",
"finally 83\n",
"warmly 29\n",
"judgments 7\n",
"amounts 5\n",
"stronger 85\n",
"train 68\n",
"precisely 43\n",
"caution 51\n",
"manage 42\n",
"concurrence 14\n",
"errand 45\n",
"determine 18\n",
"screws 11\n",
"bythebye 8\n",
"wished 339\n",
"dark 470\n",
"security 22\n",
"hesitation 66\n",
"repetition 28\n",
"load 38\n",
"critical 17\n",
"disposition 83\n",
"tottered 6\n",
"girls 124\n",
"provided 96\n",
"warm 141\n",
"guessed 34\n",
"unquestionably 16\n",
"fortunate 41\n",
"walking 216\n",
"cottages 9\n",
"edifice 18\n",
"rural 6\n",
"reputation 64\n",
"abuse 18\n",
"vanity 37\n",
"encourage 28\n",
"dotted 8\n",
"unjustifiable 2\n",
"densely 3\n",
"clouds 95\n",
"wicketgate 5\n",
"regularly 48\n",
"simple 129\n",
"replace 22\n",
"velvet 49\n",
"inside 83\n",
"desecrating 1\n",
"philosophic 8\n",
"remainsbeyond 1\n",
"obnoxious 9\n",
"fingers 159\n",
"descended 64\n",
"apartments 40\n",
"instantaneous 5\n",
"journey 138\n",
"clearly 149\n",
"thoughtful 81\n",
"kindred 10\n",
"preceded 27\n",
"largely 6\n",
"reflective 3\n",
"twenty 205\n",
"withstand 8\n",
"decays 2\n",
"cases 60\n",
"plate 56\n",
"hopeless 49\n",
"rank 43\n",
"deceased 41\n",
"perishable 2\n",
"examining 36\n",
"actively 12\n",
"handkerchief 104\n",
"sodo 1\n",
"solid 18\n",
"hazarded 9\n",
"audible 46\n",
"supposing 62\n",
"discoloured 7\n",
"bones 27\n",
"sceptical 4\n",
"impostor 11\n",
"incredulity 7\n",
"moral 76\n",
"curse 31\n",
"inflict 12\n",
"pain 139\n",
"countenances 10\n",
"saddened 3\n",
"unknown 121\n",
"stalked 5\n",
"seeming 53\n",
"lived 295\n",
"arguments 31\n",
"sayi 5\n",
"listlessly 6\n",
"evils 13\n",
"advance 119\n",
"whining 7\n",
"pretence 57\n",
"defy 18\n",
"peopling 1\n",
"horrors 23\n",
"impulses 16\n",
"creates 1\n",
"urge 26\n",
"scripture 5\n",
"silenced 7\n",
"beliefs 3\n",
"sects 1\n",
"traversed 10\n",
"hell 102\n",
"homewards 1\n",
"visits 49\n",
"sorts 78\n",
"rely 41\n",
"trembleit 1\n",
"paleness 5\n",
"deceives 2\n",
"childs 57\n",
"perceptibly 7\n",
"continued 366\n",
"comment 10\n",
"pushed 94\n",
"rocking 10\n",
"settle 64\n",
"concentrated 41\n",
"wasyes 1\n",
"fled 67\n",
"circling 1\n",
"seated 129\n",
"messenger 57\n",
"familiar 159\n",
"lifeless 20\n",
"upset 26\n",
"attitudes 10\n",
"mistress 156\n",
"thrice 23\n",
"coldly 57\n",
"hysterical 12\n",
"meyou 4\n",
"unworthy 30\n",
"charlesno 1\n",
"joy 196\n",
"animation 49\n",
"nooh 1\n",
"isis 2\n",
"iwill 1\n",
"xi 37\n",
"imagine 158\n",
"capriciously 3\n",
"devotion 69\n",
"successfully 21\n",
"irresistible 29\n",
"henrytell 1\n",
"vehemence 21\n",
"reflect 32\n",
"union 24\n",
"prudence 13\n",
"refuse 65\n",
"meed 3\n",
"influence 169\n",
"vengeance 41\n",
"bend 30\n",
"overcoming 7\n",
"divested 7\n",
"rejoice 15\n",
"revivified 2\n",
"prostrating 1\n",
"alert 13\n",
"requiring 13\n",
"goodwe 1\n",
"points 71\n",
"preferring 5\n",
"xii 37\n",
"confirmatory 5\n",
"disrespect 7\n",
"distracted 37\n",
"obstruction 5\n",
"enchained 2\n",
"stepped 108\n",
"necessities 6\n",
"profoundly 26\n",
"shaded 18\n",
"blessed 76\n",
"closer 82\n",
"framework 2\n",
"framing 2\n",
"behind 739\n",
"chambers 28\n",
"onlya 1\n",
"demanded 80\n",
"third 194\n",
"noiseless 16\n",
"annoyances 2\n",
"special 124\n",
"whos 49\n",
"forbid 35\n",
"await 27\n",
"articles 69\n",
"trifle 34\n",
"assure 175\n",
"sides 188\n",
"disappointment 57\n",
"staring 101\n",
"shrill 30\n",
"unruffled 5\n",
"starring 2\n",
"somy 1\n",
"alarms 10\n",
"establishment 109\n",
"attach 19\n",
"tribe 7\n",
"horribletoo 1\n",
"pangs 7\n",
"bind 29\n",
"babes 2\n",
"brink 24\n",
"dutyheedless 1\n",
"anxieties 18\n",
"mischievous 9\n",
"friendship 102\n",
"offence 69\n",
"tie 25\n",
"xiii 34\n",
"pondered 27\n",
"experienced 108\n",
"lurking 17\n",
"forbore 12\n",
"succeeding 15\n",
"resume 30\n",
"beams 39\n",
"deepcoloured 1\n",
"abroad 113\n",
"fullmust 1\n",
"phantommust 1\n",
"uncertainty 30\n",
"modified 7\n",
"lodge 91\n",
"personage 44\n",
"epistle 7\n",
"im 544\n",
"ive 195\n",
"aint 307\n",
"row 53\n",
"engagement 76\n",
"excused 21\n",
"residence 54\n",
"offices 44\n",
"subsist 2\n",
"combines 2\n",
"advises 1\n",
"prattling 1\n",
"possessor 11\n",
"driving 65\n",
"recently 38\n",
"awkward 57\n",
"courtesy 22\n",
"livery 13\n",
"card 64\n",
"receptionroom 2\n",
"lip 69\n",
"sombre 18\n",
"alike 52\n",
"bland 16\n",
"glistening 23\n",
"resemble 11\n",
"twinge 2\n",
"ihurt 1\n",
"equally 84\n",
"scenery 7\n",
"greatly 145\n",
"xiv 37\n",
"diet 6\n",
"abstinence 2\n",
"withdrawn 33\n",
"cicatrix 2\n",
"indented 2\n",
"distinctive 3\n",
"bows 22\n",
"charity 24\n",
"lifeblood 7\n",
"killeddestroyedburnt 1\n",
"overheard 23\n",
"wayin 1\n",
"kindness 97\n",
"misfortune 101\n",
"quarters 84\n",
"resuscitation 5\n",
"serene 62\n",
"marvellous 13\n",
"maids 33\n",
"vehicle 18\n",
"embrowned 1\n",
"complexion 35\n",
"armslength 3\n",
"assimilated 4\n",
"uniform 112\n",
"shore 54\n",
"rejoinder 20\n",
"heave 12\n",
"ashore 22\n",
"walley 2\n",
"assisted 28\n",
"sirgood 1\n",
"clown 3\n",
"vociferates 2\n",
"codlings 1\n",
"wheres 64\n",
"sailing 20\n",
"locker 14\n",
"aims 22\n",
"uxotter 3\n",
"siroh 1\n",
"hatchway 1\n",
"smash 10\n",
"himjack 1\n",
"rogues 4\n",
"scoundrel 48\n",
"mutinymutinyby 1\n",
"corfu 1\n",
"nobs 7\n",
"tender 136\n",
"confound 41\n",
"bestowed 46\n",
"mister 24\n",
"wants 108\n",
"hed 50\n",
"portsmouth 2\n",
"wasa 1\n",
"running 224\n",
"neatly 23\n",
"india 44\n",
"admiralbeing 1\n",
"detriment 9\n",
"unhappiness 24\n",
"applause 14\n",
"imitate 9\n",
"housecan 1\n",
"command 187\n",
"notorious 15\n",
"thensit 1\n",
"starboard 3\n",
"grumble 4\n",
"waiters 11\n",
"interests 69\n",
"hes 427\n",
"wasnt 115\n",
"boys 174\n",
"pretending 32\n",
"theyre 72\n",
"beyrout 1\n",
"bearing 59\n",
"ekal 1\n",
"knowing 275\n",
"affections 54\n",
"compromised 8\n",
"liable 19\n",
"crew 20\n",
"attend 66\n",
"twinkle 5\n",
"affected 104\n",
"straits 2\n",
"broadside 20\n",
"agin 4\n",
"xvi 38\n",
"resolution 84\n",
"undimmed 1\n",
"overwhelmed 17\n",
"altar 33\n",
"summerhouse 39\n",
"creeping 48\n",
"sheltered 13\n",
"neatness 7\n",
"couched 4\n",
"instantly 84\n",
"disengaged 21\n",
"utterance 40\n",
"heartstrings 1\n",
"truthful 4\n",
"passion 103\n",
"translate 6\n",
"signification 4\n",
"throbbing 15\n",
"forgetting 47\n",
"imploringly 11\n",
"injure 11\n",
"saint 36\n",
"agonised 4\n",
"bow 83\n",
"smartly 10\n",
"gardengate 16\n",
"vampyreit 1\n",
"safety 52\n",
"surrendered 21\n",
"increasing 41\n",
"strain 34\n",
"talented 3\n",
"charmingly 13\n",
"ascending 20\n",
"altitude 2\n",
"checks 8\n",
"farther 127\n",
"bravo 12\n",
"acme 3\n",
"diminish 11\n",
"eligible 9\n",
"discretion 27\n",
"abounded 5\n",
"dulcet 1\n",
"accuse 10\n",
"drinker 2\n",
"merry 87\n",
"liberality 7\n",
"board 97\n",
"alias 1\n",
"calmbe 1\n",
"sufficienti 1\n",
"hitherto 27\n",
"xviii 28\n",
"uninterruptedly 3\n",
"afterwards 319\n",
"continuous 14\n",
"impatience 51\n",
"dl 1\n",
"post 125\n",
"figures 76\n",
"unwieldy 6\n",
"superhuman 6\n",
"quarterdeck 6\n",
"boatswains 4\n",
"ghost 32\n",
"boatswain 1\n",
"betwixt 11\n",
"strung 4\n",
"dog 179\n",
"alls 14\n",
"halfway 27\n",
"illlooking 10\n",
"permission 92\n",
"unclehush 1\n",
"professional 60\n",
"travel 31\n",
"moneylenders 1\n",
"notes 53\n",
"minor 12\n",
"delight 146\n",
"shores 3\n",
"denied 20\n",
"endeared 4\n",
"hisan 1\n",
"forgive 172\n",
"probablethat 1\n",
"mountainous 1\n",
"peasantry 5\n",
"disease 25\n",
"usages 3\n",
"sumptuously 3\n",
"unseen 33\n",
"attendants 26\n",
"ladyship 2\n",
"befall 1\n",
"dismissed 42\n",
"twos 8\n",
"cloak 120\n",
"grant 40\n",
"intentions 57\n",
"morven 11\n",
"tower 26\n",
"recognise 13\n",
"philosopher 24\n",
"meantime 50\n",
"ceremonious 8\n",
"polite 61\n",
"address 139\n",
"quack 1\n",
"unravel 8\n",
"listenhe 1\n",
"andrea 1\n",
"contingencies 10\n",
"claim 88\n",
"productive 6\n",
"celebrated 23\n",
"tiger 6\n",
"disguised 8\n",
"bars 47\n",
"towerit 1\n",
"precious 119\n",
"exceed 5\n",
"arrayed 11\n",
"expenditure 10\n",
"negligence 13\n",
"intimation 14\n",
"exile 12\n",
"convicts 8\n",
"xx 20\n",
"taptap 1\n",
"brainshe 1\n",
"sepulchral 2\n",
"comprehended 16\n",
"ensue 18\n",
"commit 34\n",
"glared 8\n",
"snowwhat 1\n",
"reverence 22\n",
"praised 20\n",
"mediator 3\n",
"mindful 13\n",
"buccaneer 2\n",
"pirate 4\n",
"created 29\n",
"flows 8\n",
"wretcha 1\n",
"boya 2\n",
"herjust 1\n",
"ladies 239\n",
"acting 34\n",
"manyou 1\n",
"meat 39\n",
"monkeys 1\n",
"builda 1\n",
"wellhark 1\n",
"blocked 19\n",
"smashed 13\n",
"grapnelpitch 1\n",
"varneyvarney 1\n",
"cyclops 4\n",
"theres 414\n",
"foul 20\n",
"unsuccessful 8\n",
"eluded 7\n",
"conjuror 5\n",
"element 18\n",
"finances 4\n",
"characteristic 37\n",
"remedy 23\n",
"hanged 43\n",
"repugnant 8\n",
"surrender 19\n",
"scheme 35\n",
"enterprise 20\n",
"couch 42\n",
"respectful 42\n",
"capital 102\n",
"families 47\n",
"pistolbullet 2\n",
"swim 14\n",
"comically 1\n",
"steering 3\n",
"fewer 12\n",
"seconded 5\n",
"constitute 10\n",
"peaceable 6\n",
"sirthe 2\n",
"expressions 41\n",
"emerging 20\n",
"cormorant 1\n",
"originals 2\n",
"fourandtwenty 12\n",
"revisit 2\n",
"stakes 6\n",
"ashes 53\n",
"conflict 35\n",
"field 178\n",
"thickly 9\n",
"convincing 13\n",
"champion 6\n",
"supreme 22\n",
"superiors 10\n",
"blinds 16\n",
"brightly 41\n",
"abrupt 19\n",
"courteous 17\n",
"quarrels 11\n",
"indifferent 76\n",
"vexes 2\n",
"fencing 8\n",
"illnatured 3\n",
"concession 19\n",
"witty 13\n",
"buttoned 24\n",
"fiercely 27\n",
"gab 1\n",
"peculiarities 11\n",
"vampires 4\n",
"fume 2\n",
"pinks 2\n",
"straightforward 6\n",
"libellous 3\n",
"hearty 38\n",
"challengedfor 1\n",
"temporarily 7\n",
"hired 20\n",
"pool 15\n",
"imputation 3\n",
"hourat 1\n",
"noticing 63\n",
"hid 60\n",
"conjure 9\n",
"interpose 15\n",
"dawnings 1\n",
"subvert 1\n",
"repulsed 20\n",
"disordered 20\n",
"unsettled 21\n",
"troubled 70\n",
"outoftheway 8\n",
"twelvemonths 1\n",
"offhand 7\n",
"extravagance 3\n",
"frigate 6\n",
"swimming 8\n",
"dived 6\n",
"messmates 1\n",
"cargo 9\n",
"stiff 54\n",
"flying 75\n",
"coast 19\n",
"spar 6\n",
"doesnt 69\n",
"predict 5\n",
"thereabouts 5\n",
"washed 40\n",
"stow 1\n",
"cocked 27\n",
"lathybut 1\n",
"tinged 4\n",
"hale 5\n",
"sinister 14\n",
"inches 9\n",
"cool 62\n",
"tartary 1\n",
"eloquent 11\n",
"bothered 5\n",
"thumbs 14\n",
"winter 80\n",
"bounding 2\n",
"screwed 42\n",
"royal 41\n",
"captains 106\n",
"shrug 4\n",
"overboard 17\n",
"gout 8\n",
"reaches 12\n",
"indefinite 23\n",
"boundless 8\n",
"knots 7\n",
"stemming 4\n",
"canvass 2\n",
"headsits 1\n",
"tossed 32\n",
"tune 68\n",
"tub 7\n",
"comprehension 9\n",
"racket 1\n",
"sirhes 1\n",
"cadences 2\n",
"empty 137\n",
"leg 154\n",
"nailed 12\n",
"breech 1\n",
"youits 1\n",
"triumphantly 26\n",
"signify 14\n",
"impelled 19\n",
"abide 9\n",
"fourth 46\n",
"disc 2\n",
"curtain 35\n",
"commanded 40\n",
"tallest 1\n",
"attract 25\n",
"discovering 15\n",
"gardens 53\n",
"superscription 2\n",
"epistles 5\n",
"dreamingam 1\n",
"excuses 15\n",
"foresight 8\n",
"enforces 1\n",
"imposter 1\n",
"dunghill 3\n",
"offill 1\n",
"shame 112\n",
"volcano 4\n",
"gonemention 1\n",
"shed 141\n",
"despise 23\n",
"xxvii 8\n",
"staked 5\n",
"repair 21\n",
"abundance 26\n",
"exalted 22\n",
"inquiring 58\n",
"receipt 35\n",
"hatred 46\n",
"impart 16\n",
"internal 18\n",
"tearing 25\n",
"unopened 7\n",
"apprehend 13\n",
"disastrous 20\n",
"selfcommand 5\n",
"strive 13\n",
"harassing 4\n",
"rightyou 1\n",
"excite 10\n",
"withdrawing 20\n",
"madnessmadness 1\n",
"grandfather 15\n",
"contrast 41\n",
"thosethose 1\n",
"thump 4\n",
"searching 38\n",
"xxviii 8\n",
"sentiments 40\n",
"glibly 2\n",
"impress 21\n",
"unremitting 3\n",
"pledge 13\n",
"marvellously 2\n",
"noquite 1\n",
"emerge 12\n",
"clamber 6\n",
"feetmarks 1\n",
"lukewarm 5\n",
"lone 22\n",
"offering 48\n",
"clay 14\n",
"darkly 21\n",
"unfathomable 10\n",
"connecting 12\n",
"condemning 3\n",
"eight 111\n",
"zeal 17\n",
"doomed 24\n",
"perish 29\n",
"overtakes 2\n",
"social 65\n",
"vainly 25\n",
"honesty 14\n",
"forestalling 3\n",
"monks 11\n",
"partook 11\n",
"feasted 3\n",
"mysteriouslooking 2\n",
"pauls 80\n",
"adorned 8\n",
"tapestry 1\n",
"gorgeous 12\n",
"burnished 5\n",
"hue 13\n",
"purple 25\n",
"fading 11\n",
"imprisoned 19\n",
"holes 9\n",
"bat 2\n",
"glides 4\n",
"coloured 13\n",
"earthen 3\n",
"abode 38\n",
"damp 67\n",
"nerveless 1\n",
"breastpocket 10\n",
"xxx 6\n",
"commendation 17\n",
"provokes 2\n",
"extract 4\n",
"unfair 8\n",
"injuring 5\n",
"flush 24\n",
"embarrassments 7\n",
"crying 110\n",
"emotionbetween 1\n",
"expressive 64\n",
"charlesa 1\n",
"sweeter 7\n",
"replete 6\n",
"supply 40\n",
"gaps 3\n",
"incomplete 9\n",
"pitchy 2\n",
"lending 7\n",
"yearonly 1\n",
"sithe 1\n",
"weariness 19\n",
"howled 4\n",
"fireside 28\n",
"raven 3\n",
"eyebrows 84\n",
"fairerher 1\n",
"hazel 5\n",
"windy 12\n",
"dame 8\n",
"poutlet 1\n",
"arthur 463\n",
"affects 6\n",
"deliberated 2\n",
"decisively 8\n",
"hallnot 1\n",
"braved 6\n",
"comforted 26\n",
"anniversary 4\n",
"warmer 8\n",
"willso 1\n",
"dogs 74\n",
"whirled 11\n",
"habiliments 2\n",
"ellen 3\n",
"coats 31\n",
"wedding 57\n",
"ebbing 2\n",
"isanold 1\n",
"friendof 1\n",
"combine 8\n",
"lineament 2\n",
"array 13\n",
"eventful 15\n",
"haunted 36\n",
"suchof 1\n",
"pent 6\n",
"thankswe 1\n",
"abler 2\n",
"sets 26\n",
"sporting 6\n",
"hits 3\n",
"win 47\n",
"lifeit 2\n",
"thousands 67\n",
"shunning 3\n",
"companionship 19\n",
"circulate 5\n",
"hook 51\n",
"sylvan 1\n",
"volume 20\n",
"dramatis 1\n",
"personae 1\n",
"instituting 1\n",
"comprehending 5\n",
"chiefly 71\n",
"bannerworthsyes 1\n",
"recalls 4\n",
"minutiae 1\n",
"arrives 9\n",
"turretlike 1\n",
"adulation 5\n",
"identified 14\n",
"protract 4\n",
"reflecting 25\n",
"fraud 20\n",
"wade 83\n",
"heartstricken 2\n",
"contentedness 2\n",
"amidst 14\n",
"stability 2\n",
"hoards 2\n",
"exult 1\n",
"cowered 3\n",
"deepseated 4\n",
"dancing 82\n",
"sanctity 4\n",
"oppressions 1\n",
"fantastic 11\n",
"trod 16\n",
"spectre 8\n",
"forsaken 8\n",
"phases 4\n",
"upward 25\n",
"muchloved 2\n",
"heartshe 1\n",
"madperchance 1\n",
"xxxiv 6\n",
"relenting 20\n",
"perfecting 3\n",
"melted 29\n",
"galling 3\n",
"aroused 47\n",
"lightest 13\n",
"conceptions 10\n",
"convulsion 5\n",
"supports 7\n",
"careera 1\n",
"warming 28\n",
"varneyheaven 1\n",
"ministering 4\n",
"angels 21\n",
"willpowers 1\n",
"insatiable 4\n",
"straggling 19\n",
"thine 3\n",
"unselfish 7\n",
"snatch 8\n",
"proposal 61\n",
"unjust 22\n",
"sufferer 7\n",
"gratefulness 1\n",
"unselfishness 1\n",
"persecution 7\n",
"pulse 13\n",
"wasted 20\n",
"crawled 9\n",
"abject 9\n",
"rotundity 3\n",
"impulsive 10\n",
"linger 9\n",
"notcast 1\n",
"termination 14\n",
"tapers 4\n",
"continents 2\n",
"reproachmore 1\n",
"thankfulness 7\n",
"uncalled 3\n",
"portraitthat 1\n",
"imitation 15\n",
"xxxv 4\n",
"aperture 5\n",
"fugitive 13\n",
"volley 4\n",
"separated 56\n",
"perfidy 7\n",
"richly 7\n",
"exerting 5\n",
"experiences 19\n",
"delights 10\n",
"sterner 5\n",
"adopt 27\n",
"griefs 4\n",
"gentlest 6\n",
"apart 108\n",
"imaginary 21\n",
"alleviate 3\n",
"hitting 9\n",
"bespattering 2\n",
"riddance 6\n",
"humbugging 1\n",
"aspire 13\n",
"transparent 18\n",
"pound 32\n",
"hiring 2\n",
"ushered 9\n",
"delivered 69\n",
"gainer 2\n",
"sirsince 1\n",
"connection 81\n",
"proxy 2\n",
"assassin 14\n",
"deliberately 25\n",
"begging 33\n",
"rehearse 2\n",
"principals 10\n",
"sarcastic 21\n",
"deem 8\n",
"hawser 1\n",
"pitch 22\n",
"lanky 1\n",
"carcass 4\n",
"maxim 4\n",
"dye 28\n",
"imaginable 8\n",
"grimaces 1\n",
"rejoined 81\n",
"shirt 63\n",
"hasnt 32\n",
"wrath 29\n",
"fame 25\n",
"demean 7\n",
"handled 8\n",
"boardingpike 1\n",
"neavy 1\n",
"protesting 4\n",
"pringlemade 1\n",
"updrank 1\n",
"annihilate 5\n",
"conducting 12\n",
"encounterhe 1\n",
"untowards 1\n",
"intermixed 1\n",
"fishsomething 1\n",
"pillow 59\n",
"mutiny 9\n",
"mutinous 13\n",
"jaw 23\n",
"joness 2\n",
"blows 39\n",
"warmint 1\n",
"spoil 18\n",
"cartel 1\n",
"daunted 4\n",
"nod 32\n",
"cartels 1\n",
"fightone 1\n",
"mizeu 1\n",
"unwillingness 4\n",
"medium 46\n",
"howsoever 5\n",
"wordjack 2\n",
"wordthat 2\n",
"deserves 25\n",
"buffer 2\n",
"flinching 3\n",
"level 45\n",
"waive 4\n",
"knowits 2\n",
"illaa 1\n",
"physicked 1\n",
"keelhauling 1\n",
"gallipot 1\n",
"indiscretely 1\n",
"unequally 1\n",
"minell 1\n",
"serviceof 1\n",
"iyescertainlyi 1\n",
"obligations 17\n",
"east 66\n",
"partythe 1\n",
"falter 8\n",
"criminality 6\n",
"varneyone 1\n",
"alarmingly 2\n",
"climate 12\n",
"prophet 5\n",
"shadowsthe 1\n",
"stroll 16\n",
"existencethreatened 1\n",
"dodging 2\n",
"decenter 1\n",
"pop 3\n",
"cadaverous 8\n",
"uninviting 1\n",
"motionless 66\n",
"clap 9\n",
"supererogation 2\n",
"anatomy 5\n",
"easyas 1\n",
"azure 1\n",
"respective 8\n",
"undaunted 5\n",
"gentlemenonce 1\n",
"sternly 62\n",
"scored 4\n",
"beyoure 1\n",
"elevating 4\n",
"forlorn 36\n",
"powder 32\n",
"absolved 4\n",
"shouts 70\n",
"telegraphing 2\n",
"hitched 5\n",
"horsemarines 1\n",
"thunderer 1\n",
"rascalyou 1\n",
"deyouyouoh 1\n",
"helm 1\n",
"boarding 4\n",
"aggrawates 1\n",
"conservator 1\n",
"converging 1\n",
"moderation 4\n",
"infuriated 25\n",
"readied 1\n",
"thoughtlessness 1\n",
"firmness 42\n",
"tea 249\n",
"artful 19\n",
"tact 18\n",
"matrimonial 16\n",
"xl 2\n",
"primitive 9\n",
"insignia 3\n",
"auditors 5\n",
"depriving 10\n",
"questionable 7\n",
"canutes 1\n",
"ponderous 7\n",
"vampyrevarney 1\n",
"indulging 9\n",
"recurred 9\n",
"basement 9\n",
"encountered 44\n",
"sheep 19\n",
"string 22\n",
"expectant 7\n",
"scythes 5\n",
"formidable 20\n",
"parley 3\n",
"char 1\n",
"bolder 4\n",
"effectual 4\n",
"resentment 20\n",
"vessela 1\n",
"foes 12\n",
"bundle 37\n",
"countryman 13\n",
"fowler 1\n",
"auspicious 6\n",
"reluctance 17\n",
"incommoded 4\n",
"conducive 2\n",
"malignity 5\n",
"endurance 16\n",
"societyliving 1\n",
"domesticsome 1\n",
"politicly 1\n",
"pile 23\n",
"crumbling 13\n",
"rite 4\n",
"securing 6\n",
"wives 24\n",
"impatiently 37\n",
"consolatory 6\n",
"oracular 5\n",
"practically 8\n",
"glimmering 9\n",
"diurnal 1\n",
"spokeis 1\n",
"cloths 2\n",
"sufferings 33\n",
"rustle 24\n",
"gentler 10\n",
"hare 32\n",
"inebriation 2\n",
"captive 15\n",
"snare 1\n",
"stating 14\n",
"propositions 2\n",
"saidand 4\n",
"mingle 9\n",
"undisputed 2\n",
"wouldi 1\n",
"example 76\n",
"faithfulness 1\n",
"humanitycould 1\n",
"enchaining 1\n",
"indisposition 15\n",
"dilapidated 8\n",
"embraces 3\n",
"noises 22\n",
"immolated 1\n",
"qualms 2\n",
"concomitants 1\n",
"exhuming 1\n",
"exhumation 4\n",
"heroism 14\n",
"lends 2\n",
"grosser 2\n",
"preconceived 2\n",
"publichouse 33\n",
"restraint 22\n",
"welleducated 1\n",
"commission 51\n",
"vacillation 2\n",
"enclosure 13\n",
"straying 8\n",
"tombs 7\n",
"whack 1\n",
"coadjutors 1\n",
"glorification 1\n",
"beadle 47\n",
"token 29\n",
"vigorously 12\n",
"ingenious 35\n",
"incited 7\n",
"leigh 4\n",
"beseech 6\n",
"suiting 3\n",
"tranquillity 33\n",
"afflicting 2\n",
"scaled 2\n",
"spoilt 9\n",
"instrument 45\n",
"parochial 1\n",
"possessionwithout 1\n",
"themof 1\n",
"obeyed 34\n",
"fletcher 2\n",
"invites 2\n",
"piety 6\n",
"damnable 2\n",
"sinners 7\n",
"chapel 14\n",
"salvation 10\n",
"excavated 3\n",
"heeded 6\n",
"brittle 2\n",
"rains 3\n",
"appallingly 1\n",
"butchers 9\n",
"creaked 15\n",
"goings 4\n",
"wheels 86\n",
"tugging 2\n",
"loosened 11\n",
"congregation 11\n",
"wiped 44\n",
"recompensed 2\n",
"kicks 1\n",
"warned 17\n",
"scrambling 4\n",
"extricated 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trampling 9\n",
"heaviest 5\n",
"sole 45\n",
"manoeuvre 4\n",
"beast 39\n",
"elbow 69\n",
"emptyhere 1\n",
"transmogrification 1\n",
"pantile 1\n",
"dose 16\n",
"frighten 22\n",
"howl 14\n",
"rightweve 1\n",
"himselfto 1\n",
"hearth 21\n",
"consulting 17\n",
"valued 17\n",
"incoherent 17\n",
"supplies 21\n",
"illomened 3\n",
"bruited 3\n",
"rumgumtious 1\n",
"somehowi 1\n",
"conflicts 7\n",
"pressing 75\n",
"tables 39\n",
"obeying 8\n",
"een 2\n",
"flurry 5\n",
"interposing 11\n",
"franciss 8\n",
"adieuadieu 1\n",
"jerked 20\n",
"horsewhip 1\n",
"wellas 1\n",
"insolence 14\n",
"signifying 8\n",
"prearrangement 1\n",
"underling 1\n",
"punishment 33\n",
"cleverness 8\n",
"deduction 10\n",
"explanationand 1\n",
"surveillance 1\n",
"deranged 5\n",
"direful 1\n",
"astonishingly 3\n",
"bugles 1\n",
"viciousminded 1\n",
"physiological 4\n",
"sicken 3\n",
"xlviii 2\n",
"acceded 7\n",
"crowds 39\n",
"individually 8\n",
"precede 4\n",
"terrorthat 1\n",
"tressles 1\n",
"relaxation 4\n",
"comingcoming 1\n",
"fare 13\n",
"undertakers 5\n",
"questioned 29\n",
"restoration 20\n",
"applauded 5\n",
"liquors 4\n",
"rendering 18\n",
"extinguishment 1\n",
"ungovernable 2\n",
"xlix 2\n",
"custody 18\n",
"capturing 7\n",
"comrades 54\n",
"method 23\n",
"inertly 1\n",
"hazardous 3\n",
"varneyslay 1\n",
"competent 8\n",
"foolhardy 2\n",
"impregnable 2\n",
"debt 33\n",
"carbine 2\n",
"terrifically 1\n",
"suddenness 11\n",
"submission 37\n",
"equanimity 9\n",
"carcases 3\n",
"husbands 51\n",
"cartridgesthat 1\n",
"defeated 19\n",
"sharper 14\n",
"indicative 6\n",
"flagrant 3\n",
"sakethey 1\n",
"studded 1\n",
"pervade 5\n",
"slunk 9\n",
"li 2\n",
"timeperhaps 1\n",
"storeroom 4\n",
"appreciated 23\n",
"simultaneous 7\n",
"momentum 8\n",
"belabour 1\n",
"mostly 18\n",
"compact 16\n",
"vampyrepull 2\n",
"resisting 6\n",
"leisurely 16\n",
"dang 2\n",
"overawed 2\n",
"forwards 25\n",
"keyhole 20\n",
"smart 39\n",
"emptied 26\n",
"evacuated 2\n",
"pointing 141\n",
"reckon 10\n",
"boil 4\n",
"bob 29\n",
"fiveandthirty 3\n",
"descried 13\n",
"issuing 12\n",
"canyou 2\n",
"subalterns 1\n",
"gutted 1\n",
"distinguishing 7\n",
"distrust 18\n",
"detained 22\n",
"himnot 6\n",
"carelessly 22\n",
"absurdity 11\n",
"bloodsucker 1\n",
"glimmered 2\n",
"closing 46\n",
"donethe 2\n",
"gleaming 13\n",
"rallyingpoint 1\n",
"tedious 7\n",
"unsatisfactory 7\n",
"woefully 1\n",
"wellfurnished 1\n",
"smouldering 9\n",
"lv 2\n",
"livid 5\n",
"intercepted 9\n",
"exhaustion 18\n",
"motion 95\n",
"diminished 29\n",
"muddy 30\n",
"prevailing 15\n",
"profitable 11\n",
"expence 1\n",
"gasping 15\n",
"jim 3\n",
"shameful 17\n",
"scrape 7\n",
"pushing 41\n",
"pot 19\n",
"slippery 10\n",
"indignity 3\n",
"slide 10\n",
"swinging 12\n",
"cursing 7\n",
"spluttering 2\n",
"grease 9\n",
"inconvenience 32\n",
"helps 5\n",
"arises 15\n",
"religiously 2\n",
"ahoyahoy 1\n",
"squint 4\n",
"funnel 5\n",
"vesuvius 3\n",
"lucid 8\n",
"fore 3\n",
"trials 20\n",
"maamas 1\n",
"coachman 65\n",
"lvii 2\n",
"nightfall 6\n",
"compare 11\n",
"smallerfrom 1\n",
"representatives 6\n",
"desertion 10\n",
"gravel 8\n",
"handle 37\n",
"lantern 22\n",
"placable 1\n",
"successyou 1\n",
"transposed 1\n",
"alcohol 2\n",
"smashand 1\n",
"fronting 5\n",
"lviii 2\n",
"lor 15\n",
"disdained 1\n",
"demanding 15\n",
"thatwhere 1\n",
"grub 2\n",
"knownothinglubber 1\n",
"bobbing 3\n",
"sall 1\n",
"neckcloth 16\n",
"leading 108\n",
"appertaining 11\n",
"star 30\n",
"hoy 2\n",
"grasping 22\n",
"boot 23\n",
"cleverly 4\n",
"smothering 2\n",
"resembled 16\n",
"scudding 2\n",
"handgrenade 1\n",
"propria 1\n",
"seacook 1\n",
"victuals 4\n",
"soliciting 4\n",
"forelock 2\n",
"hoax 1\n",
"mysterywhat 1\n",
"admiraltell 1\n",
"trimly 2\n",
"wellits 1\n",
"squabbling 2\n",
"varneyafter 1\n",
"patients 12\n",
"wantonly 2\n",
"bella 4\n",
"itto 7\n",
"takein 1\n",
"mortimer 10\n",
"warmhearted 2\n",
"pledging 3\n",
"exaggerate 7\n",
"artificial 36\n",
"perverted 12\n",
"fortifying 4\n",
"oblivion 6\n",
"foregone 2\n",
"comity 1\n",
"robbery 19\n",
"yarn 6\n",
"ability 19\n",
"discoveries 13\n",
"sobe 1\n",
"desperately 18\n",
"peradventure 1\n",
"fearlessly 4\n",
"hollow 64\n",
"contriving 3\n",
"chalking 1\n",
"roan 3\n",
"jealous 57\n",
"extricating 2\n",
"arch 14\n",
"deceiving 8\n",
"gossips 3\n",
"englishi 1\n",
"asaying 1\n",
"sleeper 8\n",
"stair 11\n",
"webb 1\n",
"clockweight 3\n",
"disputing 13\n",
"truevery 1\n",
"fitz 1\n",
"bastards 2\n",
"sloes 1\n",
"housedogs 2\n",
"tricks 11\n",
"recollecting 2\n",
"itno 2\n",
"lxiv 2\n",
"pardonable 4\n",
"mellowed 1\n",
"sayhist 1\n",
"hushwhat 1\n",
"pausedyes 1\n",
"herea 2\n",
"himyou 1\n",
"ruinswillingly 1\n",
"errandim 1\n",
"endurable 2\n",
"courseoh 1\n",
"thatwhatwhat 1\n",
"isvarney 1\n",
"retreating 32\n",
"jovei 1\n",
"lodgment 1\n",
"scot 1\n",
"overwhoi 1\n",
"horribleand 1\n",
"bewhy 1\n",
"invoking 5\n",
"tomhe 1\n",
"stayi 2\n",
"unquestionablyor 1\n",
"evaporated 2\n",
"soand 3\n",
"sacrifices 15\n",
"oncethat 1\n",
"calmlywill 1\n",
"provision 30\n",
"tiresome 18\n",
"smarting 3\n",
"behests 2\n",
"accrue 3\n",
"permitting 7\n",
"starvation 5\n",
"persecutors 1\n",
"dispositionindeed 1\n",
"imprisonment 23\n",
"retaliation 4\n",
"captivity 20\n",
"objecti 1\n",
"promisei 2\n",
"varneyit 1\n",
"foretaste 2\n",
"hardest 13\n",
"persevere 5\n",
"traits 3\n",
"reprobation 2\n",
"enviable 6\n",
"disquisition 1\n",
"provisions 40\n",
"hallhow 1\n",
"scare 7\n",
"guts 2\n",
"nosegay 11\n",
"bored 16\n",
"factmy 1\n",
"admitay 1\n",
"marinea 1\n",
"briny 3\n",
"fist 19\n",
"jackwhy 1\n",
"peacepeace 2\n",
"vibrates 2\n",
"lxvii 1\n",
"astonishedi 1\n",
"hospitably 5\n",
"slender 34\n",
"bleached 2\n",
"adored 25\n",
"skip 1\n",
"oneperhaps 1\n",
"reciprocated 3\n",
"herwhat 1\n",
"felti 1\n",
"postscript 4\n",
"adventureay 1\n",
"indeedmore 1\n",
"furtherance 2\n",
"infringe 2\n",
"poniard 3\n",
"electric 9\n",
"winding 22\n",
"grip 8\n",
"realities 9\n",
"exhalations 1\n",
"movements 63\n",
"unfrequently 1\n",
"scanty 17\n",
"dastardly 3\n",
"unoffending 6\n",
"depict 3\n",
"heartthat 1\n",
"realized 48\n",
"supposeslook 1\n",
"desertedwhen 1\n",
"bleeding 93\n",
"desisted 4\n",
"ensure 8\n",
"mercymercymercy 1\n",
"chivalric 1\n",
"tournament 11\n",
"alfred 6\n",
"battlemen 1\n",
"manbut 1\n",
"cups 8\n",
"knightto 1\n",
"banner 6\n",
"bertha 19\n",
"shrewsbury 1\n",
"nuptials 8\n",
"earl 2\n",
"brideevery 1\n",
"connections 20\n",
"knownyour 1\n",
"bearmerely 1\n",
"dropping 46\n",
"reaped 5\n",
"conqueror 9\n",
"toomany 1\n",
"berthai 1\n",
"nerved 5\n",
"victorious 15\n",
"soninlaw 22\n",
"beaumontthen 1\n",
"protracted 8\n",
"misreckoned 1\n",
"hostelry 2\n",
"peaceably 2\n",
"cough 29\n",
"bearers 10\n",
"screw 17\n",
"lugubriouslooking 1\n",
"bury 11\n",
"reiterated 5\n",
"praybecause 1\n",
"draught 9\n",
"solemnitywell 1\n",
"gonequite 1\n",
"reverse 21\n",
"orderliness 1\n",
"heaping 4\n",
"reasonall 1\n",
"honourall 1\n",
"itburn 1\n",
"dependant 3\n",
"diplomatic 35\n",
"contemplative 7\n",
"leaped 24\n",
"intruded 7\n",
"cannotah 1\n",
"inquiries 39\n",
"energeticallycan 1\n",
"exaggerationsit 1\n",
"meay 1\n",
"loathed 1\n",
"youwill 2\n",
"warranting 1\n",
"wearinessbe 1\n",
"resumedi 1\n",
"gamble 2\n",
"fascinatingso 1\n",
"chevalier 9\n",
"prudential 2\n",
"nonchalance 2\n",
"placewhy 1\n",
"ranknone 1\n",
"loser 2\n",
"peddling 1\n",
"changeful 2\n",
"lizzy 15\n",
"wean 3\n",
"emaciation 2\n",
"byandbye 3\n",
"inconstantyou 1\n",
"mouthful 3\n",
"themall 1\n",
"waked 3\n",
"matterare 2\n",
"foodi 1\n",
"sorrowfully 26\n",
"awaymy 1\n",
"gaminghouse 5\n",
"momentthen 1\n",
"thatto 1\n",
"scold 7\n",
"dried 35\n",
"lxxiii 1\n",
"plunder 11\n",
"inflictors 1\n",
"traiti 1\n",
"soldierydo 1\n",
"exhangman 1\n",
"alighted 21\n",
"hangmanyou 1\n",
"avow 5\n",
"detachment 36\n",
"bridles 4\n",
"vouch 1\n",
"goodand 2\n",
"mobnow 1\n",
"flask 7\n",
"himwhy 3\n",
"rudder 6\n",
"cork 13\n",
"heavings 2\n",
"disrespectful 6\n",
"consist 10\n",
"reformation 5\n",
"humiliation 23\n",
"wailing 9\n",
"parched 6\n",
"raved 4\n",
"endangered 4\n",
"yields 2\n",
"hypocritethat 1\n",
"wouldbe 2\n",
"requiem 2\n",
"lxxiv 1\n",
"selfimposed 2\n",
"knowd 42\n",
"establish 19\n",
"fellercretor 1\n",
"iiim 2\n",
"floradear 1\n",
"preferences 1\n",
"antidote 2\n",
"contradictory 17\n",
"loverthat 1\n",
"shielded 3\n",
"limbswhen 1\n",
"cruellest 4\n",
"kissing 68\n",
"lipshe 1\n",
"retailing 1\n",
"releasing 16\n",
"unequivocal 2\n",
"nigh 16\n",
"beforefading 1\n",
"insignificance 15\n",
"dejection 9\n",
"stonework 1\n",
"obligingly 4\n",
"hammering 7\n",
"repairs 4\n",
"gravelled 6\n",
"bespeak 8\n",
"covert 10\n",
"terrace 20\n",
"onyou 1\n",
"galvanic 3\n",
"trample 8\n",
"overburthened 1\n",
"joint 22\n",
"cart 86\n",
"rhodomontade 1\n",
"whysir 1\n",
"sequel 11\n",
"headstrong 4\n",
"bantering 3\n",
"per 21\n",
"annum 6\n",
"revisiting 4\n",
"enacting 2\n",
"workmen 12\n",
"thoroughgoing 4\n",
"exhausting 3\n",
"beleaguered 2\n",
"somethingshall 1\n",
"lxxix 1\n",
"reasonably 14\n",
"himselfit 1\n",
"rubbish 19\n",
"peoplewhich 1\n",
"inglenook 1\n",
"wampyrehe 1\n",
"wictim 2\n",
"shallso 1\n",
"woodtheres 1\n",
"rafters 3\n",
"crackled 6\n",
"timehes 1\n",
"roasted 7\n",
"jist 1\n",
"righta 1\n",
"hooting 1\n",
"runners 6\n",
"thatwhat 1\n",
"himhes 2\n",
"merciful 14\n",
"ruinsto 1\n",
"cuffing 2\n",
"assailants 6\n",
"vampyrekill 1\n",
"orphans 2\n",
"lxxxi 1\n",
"sacrificing 10\n",
"insisting 10\n",
"hopelessly 13\n",
"pill 3\n",
"digestion 7\n",
"shop 160\n",
"latent 20\n",
"unfeigned 2\n",
"youall 1\n",
"singed 5\n",
"profited 4\n",
"unwatched 1\n",
"tufts 10\n",
"ditches 8\n",
"nash 5\n",
"rental 1\n",
"accommodations 3\n",
"wrongly 6\n",
"capability 1\n",
"sizes 5\n",
"enforce 6\n",
"ascent 8\n",
"eyelids 19\n",
"disjointed 5\n",
"lurks 3\n",
"enshrines 1\n",
"sakes 9\n",
"loveto 1\n",
"unwonted 10\n",
"whisperings 3\n",
"enjoyments 2\n",
"battling 2\n",
"vicesblasted 1\n",
"crimesthe 1\n",
"engendered 20\n",
"trickery 2\n",
"winner 6\n",
"tracked 6\n",
"beverages 1\n",
"hedgerow 1\n",
"runner 3\n",
"hemmed 12\n",
"furrowed 2\n",
"blownot 1\n",
"seduced 3\n",
"dispositiona 1\n",
"conspicuous 17\n",
"sheds 10\n",
"outrages 5\n",
"headwill 1\n",
"sirloin 1\n",
"sirdamn 1\n",
"peremptorily 6\n",
"horsecompany 1\n",
"swallowing 11\n",
"bea 3\n",
"queering 1\n",
"slaney 1\n",
"soliloquy 4\n",
"beastly 3\n",
"juvenile 13\n",
"biggss 1\n",
"slaneys 1\n",
"stacks 6\n",
"grantss 1\n",
"madhunter 1\n",
"strings 27\n",
"duke 40\n",
"betaken 3\n",
"haul 3\n",
"candia 1\n",
"ingram 1\n",
"carpenters 6\n",
"heel 18\n",
"topers 1\n",
"tobacco 31\n",
"almanack 5\n",
"offafter 1\n",
"woods 19\n",
"contrasted 8\n",
"verdure 5\n",
"blossoms 5\n",
"widened 12\n",
"foaming 4\n",
"stony 23\n",
"waterweeds 1\n",
"daisylike 1\n",
"abodes 2\n",
"greetings 13\n",
"formand 1\n",
"rushes 14\n",
"leaps 3\n",
"exhibits 2\n",
"contracts 3\n",
"bubbles 3\n",
"conspicuously 5\n",
"colourless 9\n",
"transfixed 8\n",
"jilted 1\n",
"toad 2\n",
"maintain 25\n",
"pretension 5\n",
"extrinsic 1\n",
"ornaments 5\n",
"reconciles 2\n",
"steals 7\n",
"tied 63\n",
"whimsical 8\n",
"beneficially 1\n",
"recipients 2\n",
"disastersi 1\n",
"saddlers 3\n",
"saystop 1\n",
"spin 4\n",
"shawl 50\n",
"farm 8\n",
"vivida 1\n",
"idlers 4\n",
"peril 10\n",
"housetop 7\n",
"fanned 13\n",
"himselfthey 1\n",
"hounds 38\n",
"precursor 2\n",
"unrelenting 7\n",
"unearth 1\n",
"broomhandle 1\n",
"himwe 2\n",
"beasts 14\n",
"decayinghis 1\n",
"giless 1\n",
"extinct 4\n",
"savedsaved 1\n",
"unturned 2\n",
"gnat 1\n",
"whosee 1\n",
"precipitated 3\n",
"eartha 2\n",
"anew 14\n",
"vaulting 1\n",
"doorthrough 1\n",
"passageinto 1\n",
"ire 1\n",
"meonly 1\n",
"slaughtered 4\n",
"plight 17\n",
"restorative 5\n",
"stains 3\n",
"concealmenti 1\n",
"habitually 15\n",
"battered 10\n",
"refreshing 16\n",
"sincerest 1\n",
"peoplethe 3\n",
"bannerworththe 1\n",
"wand 1\n",
"ruffle 6\n",
"nowsure 1\n",
"soof 1\n",
"exacted 2\n",
"deaththey 3\n",
"din 6\n",
"gruff 18\n",
"superadd 2\n",
"murderers 2\n",
"dearbrook 7\n",
"deathyou 1\n",
"estranged 9\n",
"affluence 1\n",
"cater 1\n",
"desperadoes 1\n",
"severest 4\n",
"cheating 2\n",
"judicially 1\n",
"adjusted 20\n",
"platform 5\n",
"inwardly 11\n",
"payments 20\n",
"mortimore 2\n",
"toilsome 3\n",
"shuts 10\n",
"contusions 2\n",
"decaying 3\n",
"fluctuate 1\n",
"youmuch 1\n",
"assuaged 1\n",
"casesbeing 1\n",
"agile 3\n",
"regained 7\n",
"pit 26\n",
"laboured 14\n",
"pickaxe 7\n",
"whilst 4\n",
"afresh 36\n",
"turnpike 7\n",
"clincher 1\n",
"drifting 14\n",
"illegality 1\n",
"spilling 1\n",
"shovels 3\n",
"clods 3\n",
"doctoryou 1\n",
"stench 2\n",
"rebury 1\n",
"tastethe 1\n",
"fellowbeings 1\n",
"thankful 52\n",
"allhallows 1\n",
"shalli 1\n",
"imperfect 13\n",
"saidfanny 1\n",
"fisherman 5\n",
"therefrom 2\n",
"ias 1\n",
"judgebut 1\n",
"peremptorilyget 1\n",
"scoundrelliarbase 1\n",
"wilt 5\n",
"preferable 5\n",
"hayrick 1\n",
"steadygoing 2\n",
"file 22\n",
"realizing 13\n",
"tyrannous 2\n",
"busying 6\n",
"marriedaint 1\n",
"unobserved 15\n",
"attraction 35\n",
"unprepossessing 1\n",
"advantages 27\n",
"buccaneering 2\n",
"viands 10\n",
"risked 6\n",
"confessions 1\n",
"rot 4\n",
"ingeniously 3\n",
"america 6\n",
"proposes 9\n",
"seacoast 5\n",
"charleyyou 1\n",
"neglects 1\n",
"agreedagreed 1\n",
"vacant 29\n",
"xcii 1\n",
"plaguey 2\n",
"supposethe 1\n",
"utility 1\n",
"medicine 21\n",
"renders 7\n",
"motionno 1\n",
"catches 6\n",
"roadway 3\n",
"scud 2\n",
"roam 6\n",
"molestation 3\n",
"fits 20\n",
"shouldered 7\n",
"overhung 5\n",
"belowa 1\n",
"vanquished 11\n",
"doctorhave 1\n",
"nursing 25\n",
"xciii 1\n",
"anderburyonthemount 20\n",
"seniors 2\n",
"calamities 7\n",
"outrider 2\n",
"equipage 15\n",
"baited 4\n",
"fromsomewhere 1\n",
"hotel 68\n",
"illustrious 22\n",
"beach 46\n",
"roomsall 1\n",
"agents 7\n",
"barons 8\n",
"rentals 1\n",
"contracting 6\n",
"bedrooms 4\n",
"undressed 17\n",
"possessions 13\n",
"seashore 7\n",
"cliffs 6\n",
"quickness 14\n",
"highlypolished 1\n",
"wing 31\n",
"brine 1\n",
"skeleton 19\n",
"waxlights 1\n",
"icewells 6\n",
"ghastlylooking 1\n",
"inviting 27\n",
"fraction 3\n",
"annoy 4\n",
"lining 7\n",
"canvas 14\n",
"dowhere 1\n",
"manpho 1\n",
"attractive 24\n",
"resident 9\n",
"signally 1\n",
"dontdn 1\n",
"adviceshe 1\n",
"sneers 1\n",
"writers 15\n",
"pandering 1\n",
"novelist 1\n",
"halfpenceanything 1\n",
"chargea 1\n",
"mat 6\n",
"umbrageous 2\n",
"waterfalls 2\n",
"fragrance 13\n",
"exotics 1\n",
"royalty 17\n",
"derived 37\n",
"vie 4\n",
"wordsshow 1\n",
"lordships 3\n",
"unembarrassed 2\n",
"heading 5\n",
"slope 17\n",
"traverse 5\n",
"fossil 3\n",
"negatived 3\n",
"exemplified 1\n",
"chronicle 1\n",
"altogetherquite 1\n",
"attendance 68\n",
"deviation 4\n",
"ugliness 6\n",
"lucifer 4\n",
"exceptions 7\n",
"disdaining 4\n",
"examples 10\n",
"httpwwwgutenbergnet 2\n",
"print 19\n",
"electronic 135\n",
"performances 13\n",
"awayyou 5\n",
"redistributing 10\n",
"pglaf 5\n",
"based 34\n",
"govern 12\n",
"prominently 10\n",
"holder 21\n",
"compressed 20\n",
"proprietary 7\n",
"version 9\n",
"user 15\n",
"specified 11\n",
"donate 15\n",
"legally 10\n",
"returns 35\n",
"replacement 25\n",
"hart 11\n",
"inaccurate 5\n",
"corrupt 12\n",
"distributor 5\n",
"consequential 5\n",
"punitive 5\n",
"types 6\n",
"violates 5\n",
"employee 5\n",
"additions 8\n",
"financial 8\n",
"ensuring 6\n",
"httpwwwpglaforg 4\n",
"foundations 24\n",
"httppglaforgfundraising 4\n",
"deductible 5\n",
"ak 5\n",
"httppglaforg 8\n",
"gbnewbypglaforg 5\n",
"survive 11\n",
"licensed 6\n",
"irs 5\n",
"unsolicited 8\n",
"international 7\n",
"swamp 8\n",
"donation 8\n",
"includes 9\n",
"subscribe 10\n",
"leo 4\n",
"wwwgutenbergorg 14\n",
"aylmer 1\n",
"posting 7\n",
"david 80\n",
"epilogue 4\n",
"yousit 1\n",
"honor 155\n",
"la 58\n",
"embroidered 7\n",
"languidly 10\n",
"impulsiveness 1\n",
"enthusiast 1\n",
"virtuous 26\n",
"fulfill 17\n",
"emperor 523\n",
"alexanders 21\n",
"prussia 29\n",
"wintzingerode 7\n",
"propos 2\n",
"thinker 1\n",
"funke 3\n",
"patroness 3\n",
"womanly 16\n",
"downwards 16\n",
"slaveslafe 1\n",
"elder 64\n",
"armchair 53\n",
"bolkonskis 19\n",
"badge 8\n",
"femme 4\n",
"plus 2\n",
"fascinating 15\n",
"bag 37\n",
"closecropped 2\n",
"unsuited 5\n",
"spindle 1\n",
"toyshop 1\n",
"careworn 5\n",
"masculine 10\n",
"plump 41\n",
"denghien 5\n",
"magnanimity 20\n",
"contez 1\n",
"diamonds 19\n",
"merrily 29\n",
"dulled 2\n",
"puckered 18\n",
"andre 2\n",
"hurrying 35\n",
"transferred 33\n",
"rumyantsev 8\n",
"classically 1\n",
"waitjust 1\n",
"faltered 42\n",
"ilarionovich 6\n",
"dieu 12\n",
"touche 1\n",
"lha 1\n",
"chi 1\n",
"reaping 5\n",
"bourbon 1\n",
"disdainfully 14\n",
"societywill 1\n",
"alexander 130\n",
"martyr 6\n",
"epigram 2\n",
"inappropriate 4\n",
"fearing 19\n",
"heeding 10\n",
"itequality 1\n",
"provocative 3\n",
"rousseaus 2\n",
"tolerant 5\n",
"highsounding 1\n",
"preached 7\n",
"horrorstruck 2\n",
"orator 4\n",
"ducor 1\n",
"whois 1\n",
"th 20\n",
"vicomtei 1\n",
"soiree 5\n",
"downy 10\n",
"affectionately 28\n",
"tips 17\n",
"caesars 3\n",
"creak 11\n",
"tucked 24\n",
"naive 25\n",
"petulantly 4\n",
"egotists 2\n",
"piteous 16\n",
"wags 2\n",
"imprint 1\n",
"newness 1\n",
"irrevocable 8\n",
"trifles 23\n",
"lolled 3\n",
"trivialitythese 1\n",
"caustic 4\n",
"colors 4\n",
"wasexpressed 1\n",
"debauchery 5\n",
"aches 5\n",
"passionately 32\n",
"loses 11\n",
"petya 246\n",
"duelist 2\n",
"tipsy 15\n",
"betting 1\n",
"mustache 44\n",
"wedge 4\n",
"rakes 3\n",
"intimidated 4\n",
"afterglow 2\n",
"imperialshe 1\n",
"awkwardly 18\n",
"relays 3\n",
"type 14\n",
"cherehe 1\n",
"variation 8\n",
"eighty 14\n",
"unfolding 9\n",
"dmitri 13\n",
"karagina 12\n",
"bass 17\n",
"apraksina 5\n",
"mammas 5\n",
"brigands 6\n",
"policeman 13\n",
"inattention 4\n",
"widemouthed 1\n",
"drawers 21\n",
"slipperswas 1\n",
"remarkand 1\n",
"fragmentary 3\n",
"prim 1\n",
"mantilla 2\n",
"sonya 411\n",
"niece 64\n",
"coiling 1\n",
"kitten 7\n",
"playfully 16\n",
"flares 1\n",
"confidential 52\n",
"childrens 19\n",
"youngsters 5\n",
"stricter 1\n",
"everyonethe 2\n",
"exceptional 12\n",
"splendidly 10\n",
"brushed 26\n",
"mirror 30\n",
"ambush 11\n",
"sooonya 1\n",
"significant 35\n",
"inaudibly 4\n",
"flowerpots 2\n",
"berg 91\n",
"mortified 16\n",
"colder 11\n",
"easygoing 4\n",
"annettehow 1\n",
"soandso 14\n",
"rumyantsovs 1\n",
"equip 2\n",
"scrutinizing 5\n",
"swallowtail 6\n",
"downstairs 129\n",
"bronze 3\n",
"outwearing 1\n",
"humanum 1\n",
"unaware 6\n",
"godson 3\n",
"rival 24\n",
"fatherwho 2\n",
"differed 3\n",
"wool 2\n",
"menacing 18\n",
"dover 38\n",
"boulogne 4\n",
"villeneuve 1\n",
"nieces 8\n",
"bonne 2\n",
"saute 3\n",
"hey 30\n",
"deferential 10\n",
"tattered 14\n",
"childhoodhad 1\n",
"dragon 8\n",
"cities 7\n",
"smokers 1\n",
"mouthpiece 1\n",
"condescending 18\n",
"boston 9\n",
"loquacious 3\n",
"tres 4\n",
"des 9\n",
"y 3\n",
"squares 9\n",
"sedate 7\n",
"gofoot 1\n",
"joyously 6\n",
"inappropriately 1\n",
"surveying 22\n",
"stroked 12\n",
"ruby 1\n",
"astride 5\n",
"commanderinchief 101\n",
"resented 12\n",
"emphasis 52\n",
"constitutes 11\n",
"emperors 138\n",
"vy 1\n",
"jerome 2\n",
"convient 1\n",
"suvorov 17\n",
"pooossible 2\n",
"wineglasses 6\n",
"othersafter 1\n",
"colonels 7\n",
"threateningly 10\n",
"cream 6\n",
"carrot 1\n",
"champagne 14\n",
"bluestriped 1\n",
"hugging 13\n",
"spoiling 9\n",
"sob 36\n",
"dancers 10\n",
"tuning 1\n",
"debonair 1\n",
"communion 8\n",
"sacrament 9\n",
"unction 9\n",
"governor 46\n",
"priests 22\n",
"strands 2\n",
"neffer 1\n",
"gase 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sird 1\n",
"icons 29\n",
"quilt 15\n",
"catiche 10\n",
"semenovna 4\n",
"heirs 5\n",
"expert 4\n",
"onufrich 1\n",
"conscientiously 7\n",
"evinced 6\n",
"egyptian 4\n",
"snowy 12\n",
"forefinger 48\n",
"meek 24\n",
"menservants 2\n",
"laughterloving 1\n",
"immovable 28\n",
"backs 17\n",
"uncovered 7\n",
"cheekbones 3\n",
"helplessly 10\n",
"fleshy 5\n",
"helplessness 10\n",
"dimmed 9\n",
"sleepless 17\n",
"chinese 19\n",
"circular 9\n",
"speakers 13\n",
"impressively 7\n",
"honeyed 1\n",
"intriguer 11\n",
"viciously 2\n",
"banged 2\n",
"underlip 1\n",
"irrepressible 17\n",
"wetting 3\n",
"imposes 1\n",
"edifying 2\n",
"momentsthe 1\n",
"exiled 4\n",
"continuously 7\n",
"mathematics 9\n",
"snuffboxes 3\n",
"lathe 9\n",
"architect 16\n",
"desk 86\n",
"acrid 1\n",
"littered 7\n",
"untidy 4\n",
"indissoluble 3\n",
"ungraceful 2\n",
"bezukhova 18\n",
"auntie 3\n",
"scapegrace 4\n",
"worthier 6\n",
"temptations 8\n",
"fondness 11\n",
"simplicity 46\n",
"townsfolk 2\n",
"allpowerful 4\n",
"sonata 4\n",
"spasski 1\n",
"leathercovered 3\n",
"plait 8\n",
"honoring 1\n",
"stralsund 1\n",
"austrians 19\n",
"naples 17\n",
"malbrook 1\n",
"guerre 2\n",
"distinctions 11\n",
"illustrate 3\n",
"chairstood 1\n",
"rurik 1\n",
"laughs 9\n",
"tactician 2\n",
"potemkins 4\n",
"moreau 5\n",
"admirer 5\n",
"fatherin 1\n",
"covers 6\n",
"pardonner 1\n",
"alonefor 1\n",
"incredulously 6\n",
"hundredweight 1\n",
"natured 4\n",
"petrushka 1\n",
"quill 4\n",
"lombard 1\n",
"academy 5\n",
"twitched 17\n",
"regiments 35\n",
"battalion 43\n",
"reckoned 6\n",
"cleanliness 14\n",
"externally 6\n",
"austrian 69\n",
"thickset 2\n",
"creases 8\n",
"ferdinand 39\n",
"junction 9\n",
"sergeants 5\n",
"major 515\n",
"knapsacks 8\n",
"jerk 20\n",
"straps 4\n",
"bluish 5\n",
"hoow 1\n",
"bluishgray 1\n",
"fluttered 30\n",
"soulshaking 1\n",
"assiduity 4\n",
"nudged 5\n",
"timokhin 28\n",
"ignatych 1\n",
"overtaking 8\n",
"askhow 1\n",
"punctilious 4\n",
"jew 4\n",
"flour 6\n",
"quartermasters 4\n",
"crownbut 1\n",
"singers 16\n",
"castanet 1\n",
"cordiality 12\n",
"reinstated 2\n",
"strategically 1\n",
"lech 2\n",
"overtaken 12\n",
"imitated 4\n",
"conceited 8\n",
"maria 3\n",
"malheureux 1\n",
"immobile 1\n",
"wave 30\n",
"arrogant 7\n",
"outweigh 2\n",
"wie 1\n",
"nesvitskis 5\n",
"rejoicing 12\n",
"successes 6\n",
"grieving 4\n",
"quarante 1\n",
"hommes 2\n",
"bien 2\n",
"rien 2\n",
"cavalrycaptain 1\n",
"bondarenko 2\n",
"brotherly 9\n",
"ukrainian 2\n",
"snaffle 1\n",
"schon 17\n",
"russen 2\n",
"kaiser 1\n",
"und 2\n",
"wins 3\n",
"bwother 2\n",
"wat 1\n",
"snatches 6\n",
"foh 1\n",
"clinking 9\n",
"hoof 2\n",
"witing 2\n",
"cweation 1\n",
"puhse 1\n",
"bowwowing 1\n",
"sausages 4\n",
"turnedup 3\n",
"coin 9\n",
"rests 13\n",
"finesse 1\n",
"bogdanich 14\n",
"usyoure 1\n",
"stwuck 1\n",
"cwoss 1\n",
"traun 1\n",
"batteries 15\n",
"diaphanous 1\n",
"pine 11\n",
"gorges 3\n",
"forest 74\n",
"doppelkummel 1\n",
"deer 1\n",
"infantrymen 5\n",
"moistlipped 1\n",
"nunnery 3\n",
"battery 79\n",
"grenade 2\n",
"merged 17\n",
"hi 8\n",
"interlocking 1\n",
"townsman 3\n",
"waggish 2\n",
"pon 2\n",
"brindled 1\n",
"cow 4\n",
"unweaned 1\n",
"energetically 11\n",
"numskull 1\n",
"shaggy 28\n",
"rider 7\n",
"wegiment 2\n",
"clang 2\n",
"estrangement 9\n",
"chaffing 5\n",
"wasteland 1\n",
"noon 28\n",
"stirrups 3\n",
"miwonov 1\n",
"wight 6\n",
"mercilessly 1\n",
"broadbacked 1\n",
"mare 5\n",
"karl 7\n",
"footpace 9\n",
"pavlograds 9\n",
"orderlys 3\n",
"boss 1\n",
"trained 17\n",
"nuts 7\n",
"summits 2\n",
"cowardyes 1\n",
"thirtyfive 4\n",
"enduranceacknowledged 1\n",
"strategics 2\n",
"twentyeighth 6\n",
"mortiers 1\n",
"banners 6\n",
"erroneous 3\n",
"starry 11\n",
"picturing 8\n",
"longdesired 1\n",
"reassured 18\n",
"transport 31\n",
"jolted 10\n",
"shops 48\n",
"euer 1\n",
"schmidts 1\n",
"levee 8\n",
"bilibin 55\n",
"franz 4\n",
"dealing 21\n",
"laboratory 2\n",
"portable 4\n",
"orthodox 8\n",
"votre 2\n",
"marshals 42\n",
"thaler 1\n",
"invincibility 2\n",
"defenseas 1\n",
"savior 3\n",
"berlin 7\n",
"campo 1\n",
"lu 1\n",
"retaliate 1\n",
"thereforethis 1\n",
"negotiations 15\n",
"tenfold 4\n",
"lounge 4\n",
"demosthenes 1\n",
"routes 1\n",
"grade 6\n",
"portmanteau 8\n",
"bookshop 1\n",
"belliard 5\n",
"fortifications 6\n",
"sadder 5\n",
"têtedepont 2\n",
"gasconades 1\n",
"von 7\n",
"auerspergs 1\n",
"murats 15\n",
"ny 4\n",
"arrested 31\n",
"hetzelsdorf 1\n",
"sortle 1\n",
"rigged 4\n",
"piercingly 6\n",
"chasseurs 7\n",
"flatten 3\n",
"minutest 8\n",
"clerk 66\n",
"grenadiers 8\n",
"podolian 1\n",
"unfamiliar 13\n",
"bagrations 21\n",
"exposure 12\n",
"viennaznaim 2\n",
"illshod 2\n",
"hollabrunn 5\n",
"stationary 11\n",
"rainsoaked 2\n",
"jocular 6\n",
"ants 5\n",
"spadefuls 2\n",
"switches 1\n",
"qu 1\n",
"estce 1\n",
"sacre 2\n",
"musket 19\n",
"mala 1\n",
"safi 1\n",
"kaska 1\n",
"unload 6\n",
"unlimbered 4\n",
"artillerymens 1\n",
"secondly 30\n",
"echelons 1\n",
"philosophizing 2\n",
"mounting 14\n",
"humiliated 3\n",
"embankments 1\n",
"cannons 2\n",
"bashful 15\n",
"outflanking 2\n",
"sharpshooters 11\n",
"sturdily 2\n",
"groaning 4\n",
"pouches 2\n",
"welcomes 2\n",
"shells 20\n",
"ax 7\n",
"blister 3\n",
"laggards 1\n",
"flexibly 1\n",
"jaunty 4\n",
"semicircle 4\n",
"epaulets 4\n",
"hurrahahah 3\n",
"leftwhich 1\n",
"seniority 7\n",
"offending 11\n",
"capotes 1\n",
"malevolence 5\n",
"vot 1\n",
"vere 1\n",
"scabbards 1\n",
"rooks 11\n",
"terribleand 1\n",
"nikitenko 1\n",
"riders 11\n",
"benumbed 1\n",
"reformed 5\n",
"herein 3\n",
"scurrying 1\n",
"tengun 1\n",
"delirium 9\n",
"drunkard 2\n",
"piteously 13\n",
"petrov 2\n",
"melt 6\n",
"dazzlingly 2\n",
"roundshouldered 5\n",
"snarled 4\n",
"perplexityperhaps 1\n",
"woodcutting 1\n",
"disorganized 8\n",
"gentlemanrankerthat 1\n",
"overpowered 23\n",
"unwoundedit 1\n",
"reunited 8\n",
"devices 5\n",
"disintegration 4\n",
"premeditation 1\n",
"businessmen 2\n",
"cooing 4\n",
"ryazan 22\n",
"stupidest 3\n",
"exercising 5\n",
"colored 3\n",
"absentmindedly 10\n",
"dishonorable 7\n",
"frivolity 3\n",
"lelyas 1\n",
"affairyes 1\n",
"remarkshowing 1\n",
"saidmet 1\n",
"kuragina 2\n",
"enlivened 6\n",
"viktorovna 1\n",
"kuzmich 13\n",
"rescript 6\n",
"unobservant 5\n",
"sip 9\n",
"betrothed 20\n",
"refusing 17\n",
"grumbled 6\n",
"discomposure 5\n",
"discontented 12\n",
"heelswe 1\n",
"chattering 14\n",
"devise 7\n",
"fetched 18\n",
"katies 1\n",
"chirping 6\n",
"coiffure 7\n",
"ownsuch 1\n",
"mayest 1\n",
"noting 4\n",
"pomade 1\n",
"betrays 1\n",
"littlebut 1\n",
"annettes 2\n",
"unthinkable 6\n",
"nowadaysbut 1\n",
"proposing 24\n",
"sa 4\n",
"nightshirt 2\n",
"unaltered 5\n",
"slippers 15\n",
"disapproval 8\n",
"repents 1\n",
"wa 1\n",
"upi 2\n",
"nikolenkas 2\n",
"namesake 3\n",
"intonations 4\n",
"admits 9\n",
"balloon 8\n",
"granny 1\n",
"cradle 12\n",
"drafts 4\n",
"presumably 1\n",
"awards 3\n",
"parading 3\n",
"pyramid 3\n",
"pawn 2\n",
"dormir 1\n",
"omit 7\n",
"martial 7\n",
"bolkonskian 1\n",
"cording 1\n",
"eying 1\n",
"albanians 1\n",
"siberia 7\n",
"aflame 1\n",
"displease 5\n",
"friday 13\n",
"worried 29\n",
"wheeled 34\n",
"showy 5\n",
"brazen 9\n",
"decorations 13\n",
"groomed 2\n",
"deafeningly 1\n",
"immobility 4\n",
"groin 1\n",
"squadronthat 1\n",
"spurred 8\n",
"friendliness 5\n",
"fussing 1\n",
"confers 1\n",
"strategic 7\n",
"minuteness 2\n",
"valorwhat 1\n",
"consul 3\n",
"bilibinhes 1\n",
"gouvernement 1\n",
"diplomatistyou 1\n",
"talkative 7\n",
"petitioner 2\n",
"nuisance 7\n",
"levity 6\n",
"metcoming 1\n",
"czartoryski 4\n",
"horsecloths 4\n",
"richest 9\n",
"festive 10\n",
"liveliness 5\n",
"ration 3\n",
"songs 21\n",
"twentyyearold 1\n",
"appwove 1\n",
"seventeenth 5\n",
"savary 4\n",
"nineteenth 9\n",
"levers 3\n",
"quiescent 1\n",
"cogs 2\n",
"ken 5\n",
"prizing 1\n",
"rulenot 1\n",
"comte 5\n",
"langeron 10\n",
"lichtenstein 1\n",
"prishprish 1\n",
"chairman 5\n",
"podgy 3\n",
"schlappanitz 4\n",
"longest 7\n",
"twirling 9\n",
"geography 5\n",
"outspread 4\n",
"virulently 2\n",
"emotional 3\n",
"arrangementsleads 1\n",
"planned 9\n",
"wifethose 1\n",
"meyet 1\n",
"tit 7\n",
"glimmer 4\n",
"deceivers 1\n",
"deceitful 4\n",
"na 2\n",
"tverskaya 1\n",
"fedchenko 1\n",
"pickets 3\n",
"wisps 2\n",
"valor 5\n",
"tattoo 3\n",
"guides 8\n",
"loads 5\n",
"mimicking 3\n",
"milky 4\n",
"actionhe 1\n",
"miloradovichs 1\n",
"controlledly 1\n",
"omission 3\n",
"lassitude 3\n",
"butts 2\n",
"leftflank 1\n",
"apsheron 4\n",
"redfaced 8\n",
"dans 6\n",
"stabs 2\n",
"skythe 1\n",
"avert 6\n",
"thousandruble 3\n",
"bergshave 1\n",
"hottest 1\n",
"czech 1\n",
"batman 1\n",
"drives 10\n",
"killedwounded 1\n",
"feignedor 1\n",
"strewn 15\n",
"corpses 9\n",
"trembles 5\n",
"toll 27\n",
"tolls 2\n",
"peaked 3\n",
"sheepskin 9\n",
"dokhturovs 2\n",
"augesd 5\n",
"tasseled 1\n",
"angling 3\n",
"moravians 1\n",
"wheat 6\n",
"dusty 44\n",
"whitening 2\n",
"dolokhovnow 1\n",
"flopped 4\n",
"collapsed 9\n",
"flagstaff 3\n",
"moaning 28\n",
"napoleonhis 1\n",
"repnin 6\n",
"honorably 1\n",
"unimportance 3\n",
"bivouac 4\n",
"allimportant 1\n",
"delirious 6\n",
"cabman 3\n",
"vodkaget 1\n",
"plaiting 3\n",
"chandelier 2\n",
"flocked 4\n",
"ohing 2\n",
"ahing 1\n",
"adoring 2\n",
"frocks 3\n",
"reentered 7\n",
"reddened 10\n",
"perfumed 7\n",
"remortgaged 1\n",
"childishness 2\n",
"rejoining 1\n",
"comb 7\n",
"sterlets 1\n",
"orchestra 13\n",
"ivanovnas 2\n",
"moscovites 5\n",
"invent 5\n",
"satyr 1\n",
"guestschiefly 1\n",
"lording 1\n",
"confidentially 15\n",
"shovelthe 1\n",
"grabernand 1\n",
"waken 3\n",
"selfconscious 2\n",
"cantata 2\n",
"mirthful 1\n",
"gypsies 7\n",
"dueling 1\n",
"huntsman 22\n",
"demain 1\n",
"irreparable 4\n",
"independently 16\n",
"advesawies 1\n",
"wefused 1\n",
"weconciliation 1\n",
"nooo 1\n",
"glittered 18\n",
"brawler 1\n",
"coarseness 2\n",
"promener 1\n",
"martyrs 7\n",
"robespierre 1\n",
"alivelive 1\n",
"wrinkle 9\n",
"gazette 6\n",
"befallen 4\n",
"fatherland 30\n",
"blessedness 7\n",
"causewas 1\n",
"fruhstuck 1\n",
"bogdanovna 9\n",
"midwife 5\n",
"birdie 1\n",
"perturbed 6\n",
"guttered 1\n",
"thatcame 2\n",
"mesaid 1\n",
"baptized 1\n",
"nowthis 1\n",
"themand 7\n",
"venal 1\n",
"childishly 6\n",
"girlishly 2\n",
"amorous 3\n",
"hopeexperienced 1\n",
"folk 28\n",
"selfwill 3\n",
"iogels 4\n",
"militia 29\n",
"christmas 17\n",
"dances 12\n",
"jokingly 1\n",
"unhooked 1\n",
"mopping 1\n",
"muddled 4\n",
"wrists 12\n",
"bacawollai 1\n",
"resonance 3\n",
"connoisseurs 3\n",
"wellhad 1\n",
"kneel 6\n",
"bustlingly 1\n",
"furtive 4\n",
"indignantly 15\n",
"postmaster 8\n",
"agonizing 2\n",
"peddler 1\n",
"novel 15\n",
"wifeas 1\n",
"wasdid 1\n",
"shriveled 10\n",
"unpacking 1\n",
"beardless 4\n",
"upside 16\n",
"nibbled 2\n",
"emphasize 1\n",
"sayyes 1\n",
"brotherhood 18\n",
"personality 12\n",
"ridiculing 2\n",
"understandinghow 1\n",
"regrettable 2\n",
"deluded 11\n",
"didst 2\n",
"sinfulness 3\n",
"couldst 1\n",
"relying 1\n",
"hatest 1\n",
"purified 2\n",
"orgies 2\n",
"willarski 29\n",
"selfexamination 2\n",
"sinceritynot 1\n",
"affirmatively 4\n",
"uncover 2\n",
"leathergloved 1\n",
"boyhood 13\n",
"tradition 7\n",
"thirdly 11\n",
"improving 18\n",
"cultivate 3\n",
"distressful 3\n",
"lawlessness 1\n",
"emblem 2\n",
"trouser 2\n",
"slipper 5\n",
"waver 3\n",
"preeminence 1\n",
"slippered 4\n",
"compasses 6\n",
"sic 1\n",
"gloria 1\n",
"mallet 2\n",
"unexplained 3\n",
"rug 8\n",
"enmity 3\n",
"dependedwhether 1\n",
"societys 5\n",
"touchedi 1\n",
"priority 1\n",
"marat 1\n",
"fortresses 5\n",
"tu 1\n",
"krug 1\n",
"conversant 2\n",
"pronunciation 2\n",
"abstaining 3\n",
"circumspectly 2\n",
"schwarzenberg 1\n",
"caressing 18\n",
"decreed 5\n",
"pedantic 3\n",
"subordinates 4\n",
"february 4\n",
"circuits 2\n",
"mistrusting 3\n",
"newsif 1\n",
"petenkahe 1\n",
"mischiefmakers 1\n",
"correspondent 5\n",
"preussisch 1\n",
"furies 2\n",
"stira 1\n",
"usnamely 1\n",
"whatnot 2\n",
"marauders 9\n",
"perspired 3\n",
"punishments 2\n",
"admonitory 3\n",
"asylums 2\n",
"insaneunprofitable 1\n",
"deputations 1\n",
"fullskirted 2\n",
"peters 3\n",
"philanthropic 6\n",
"brotherinstructor 1\n",
"uselessness 2\n",
"gavethat 1\n",
"stables 9\n",
"facade 1\n",
"handrails 1\n",
"tidiness 1\n",
"searchingly 7\n",
"approvalso 1\n",
"prochainyour 1\n",
"spiritual 31\n",
"crooked 23\n",
"alleverything 2\n",
"fussy 1\n",
"yukhnovna 1\n",
"siberiaand 1\n",
"traditions 8\n",
"christianity 2\n",
"atheistic 1\n",
"areeternallychildren 1\n",
"reharnessed 1\n",
"cassock 1\n",
"hen 3\n",
"intimité 1\n",
"ivanushkas 1\n",
"yukhnovo 1\n",
"catacombs 3\n",
"nai 1\n",
"loffenser 1\n",
"hotly 7\n",
"nonsenseold 1\n",
"dinneri 1\n",
"parental 19\n",
"sickness 12\n",
"doled 1\n",
"thatched 4\n",
"bweed 1\n",
"trench 13\n",
"lazarchuk 1\n",
"gweat 1\n",
"pestewing 1\n",
"infantwy 2\n",
"booked 6\n",
"dugout 2\n",
"empewo 2\n",
"starving 6\n",
"tumblers 6\n",
"fagged 2\n",
"queried 1\n",
"awaytaken 1\n",
"uhlan 3\n",
"wobber 1\n",
"afwaid 1\n",
"honowably 1\n",
"degwaded 1\n",
"wobbed 1\n",
"tweasuwy 1\n",
"inkpot 1\n",
"offenses 1\n",
"twentyfourth 14\n",
"draped 3\n",
"hayne 1\n",
"exasperating 4\n",
"knit 6\n",
"deign 5\n",
"officious 4\n",
"remounted 1\n",
"preobrazhenskis 2\n",
"bravoure 1\n",
"alexandre 2\n",
"severed 5\n",
"unpardoned 1\n",
"godnothing 1\n",
"departments 11\n",
"agricultural 3\n",
"laborersthis 1\n",
"compulsory 3\n",
"floors 11\n",
"cherry 5\n",
"oaks 4\n",
"girth 2\n",
"misshapen 3\n",
"chintz 2\n",
"sweetheart 11\n",
"sprinkling 9\n",
"twigs 4\n",
"renewal 9\n",
"thrilled 8\n",
"inexpressible 5\n",
"designed 15\n",
"speranski 58\n",
"examinations 4\n",
"comite 1\n",
"antipathetic 1\n",
"uncrossing 1\n",
"cropped 6\n",
"petitioning 3\n",
"grumbling 7\n",
"unsoundly 1\n",
"initiated 5\n",
"geniussperanski 1\n",
"plowmen 1\n",
"plow 2\n",
"countwho 1\n",
"speranskis 18\n",
"reporter 1\n",
"intimating 3\n",
"reestablishes 1\n",
"certains 1\n",
"privileges 6\n",
"slower 6\n",
"blameworthy 3\n",
"tacit 5\n",
"diversity 3\n",
"analogy 2\n",
"deduced 9\n",
"expectedwas 1\n",
"waded 2\n",
"primordial 1\n",
"elementssulphur 1\n",
"vacillating 3\n",
"externals 1\n",
"conduce 1\n",
"actact 1\n",
"diffuse 6\n",
"prudently 2\n",
"unity 10\n",
"preponderance 1\n",
"protectors 2\n",
"eradicate 2\n",
"alterations 10\n",
"forgiving 9\n",
"selfreformation 1\n",
"selfperfecting 2\n",
"secondgrade 1\n",
"alexeevichs 7\n",
"aussi 1\n",
"spirituelle 1\n",
"emptiest 1\n",
"crank 2\n",
"benevolence 11\n",
"bluestocking 2\n",
"harmed 4\n",
"scriptures 3\n",
"lust 1\n",
"vanities 3\n",
"instructive 3\n",
"adonai 1\n",
"refresh 6\n",
"mundane 3\n",
"apathetic 1\n",
"lenient 6\n",
"smallish 1\n",
"text 7\n",
"shineth 1\n",
"representation 9\n",
"lassies 2\n",
"provincials 2\n",
"squires 1\n",
"finnish 2\n",
"lucrative 4\n",
"mein 5\n",
"childhoods 1\n",
"chiding 1\n",
"snuggling 1\n",
"curledup 1\n",
"plumed 4\n",
"ermine 1\n",
"ignatevna 1\n",
"sonyashould 1\n",
"petticoat 7\n",
"thimble 5\n",
"beautya 1\n",
"coiffures 1\n",
"roomswith 1\n",
"blended 17\n",
"charme 1\n",
"girlhood 2\n",
"antonovna 3\n",
"princeis 1\n",
"knowbolkonski 1\n",
"pluie 1\n",
"enticingly 1\n",
"participating 4\n",
"belying 1\n",
"peronskayas 1\n",
"furrow 1\n",
"newsmongerone 1\n",
"partisans 2\n",
"monarchs 12\n",
"staccato 2\n",
"someoneit 1\n",
"chuckle 19\n",
"funnier 1\n",
"grated 5\n",
"evaded 6\n",
"disillusionments 1\n",
"illimitable 6\n",
"cheered 17\n",
"yusupova 1\n",
"symmetry 5\n",
"rubbers 2\n",
"employ 19\n",
"veramentioning 1\n",
"characteristics 5\n",
"timesin 1\n",
"spain 10\n",
"spleen 3\n",
"mastered 10\n",
"halves 6\n",
"gloomier 3\n",
"stress 10\n",
"fourthly 3\n",
"conducing 1\n",
"reverberating 1\n",
"collective 23\n",
"natashais 1\n",
"delays 2\n",
"betrothal 6\n",
"nownatasha 1\n",
"seriousnessi 1\n",
"dryeyed 2\n",
"physiognomy 3\n",
"superstitiousness 1\n",
"himlittle 1\n",
"lawthe 1\n",
"usbut 2\n",
"othersare 1\n",
"manifestation 8\n",
"angelically 2\n",
"pretexts 3\n",
"amiabilities 2\n",
"consolations 3\n",
"sinful 11\n",
"realizes 1\n",
"allfor 1\n",
"dotard 2\n",
"auction 4\n",
"mitenka 13\n",
"lather 2\n",
"roans 4\n",
"golukhovski 1\n",
"rivalry 3\n",
"choirs 2\n",
"kievall 1\n",
"dozhoyveyko 1\n",
"itfar 1\n",
"hurrynicholas 1\n",
"discord 6\n",
"skeptically 2\n",
"residents 4\n",
"sistersinlaw 3\n",
"ravines 1\n",
"copses 2\n",
"brightred 1\n",
"hares 5\n",
"drizzling 4\n",
"broadhaunched 1\n",
"ohoy 1\n",
"huntsmans 2\n",
"resolutions 8\n",
"hallooingand 1\n",
"boomed 4\n",
"extorted 3\n",
"gelding 5\n",
"fiftyfour 1\n",
"whippersin 3\n",
"whine 5\n",
"girchik 1\n",
"korniki 1\n",
"overriding 2\n",
"jowl 1\n",
"sleek 12\n",
"hollowcheeked 1\n",
"nastasya 7\n",
"marvelous 1\n",
"zavarzinsk 1\n",
"ulyulyuing 2\n",
"swish 1\n",
"capless 2\n",
"belly 5\n",
"baring 4\n",
"jerking 6\n",
"leashes 2\n",
"herand 21\n",
"ulyulyulyulyu 1\n",
"yelp 2\n",
"jaws 10\n",
"strapping 1\n",
"swift 20\n",
"ruble 8\n",
"milashka 1\n",
"abusing 3\n",
"mongrel 3\n",
"unplastered 2\n",
"barefooted 5\n",
"stoutness 2\n",
"chicken 73\n",
"jam 1\n",
"superciliousness 1\n",
"trills 1\n",
"thrummed 1\n",
"brisker 1\n",
"emigree 1\n",
"valse 2\n",
"fairyland 3\n",
"whichspreading 1\n",
"themhe 1\n",
"net 9\n",
"remedying 1\n",
"coerce 1\n",
"starlight 2\n",
"nikita 4\n",
"sulky 6\n",
"crickets 1\n",
"plums 4\n",
"negro 4\n",
"blendand 1\n",
"nocturne 1\n",
"urgently 4\n",
"troykas 5\n",
"stud 3\n",
"canter 3\n",
"hoarfrost 7\n",
"sleighbeside 1\n",
"funnybut 1\n",
"vanyaclear 1\n",
"peering 17\n",
"jelly 7\n",
"hewn 3\n",
"bewitchingly 1\n",
"irresolutely 9\n",
"molested 1\n",
"ardorall 1\n",
"retinue 8\n",
"rut 2\n",
"thathere 1\n",
"elemental 3\n",
"hypochondria 1\n",
"malady 9\n",
"nikolaevichs 1\n",
"comedian 3\n",
"manna 1\n",
"iwhat 2\n",
"skein 3\n",
"antifrench 1\n",
"originality 6\n",
"thursdays 1\n",
"juliewhose 1\n",
"emigre 2\n",
"alphabet 8\n",
"angrythat 1\n",
"jestthat 1\n",
"endearments 1\n",
"gueststhe 1\n",
"bachelors 3\n",
"seizure 6\n",
"longsuffering 2\n",
"oldenburg 8\n",
"writingnot 1\n",
"rostopchins 21\n",
"leftto 1\n",
"requests 4\n",
"sentimentally 2\n",
"rimes 2\n",
"toi 4\n",
"serait 1\n",
"melancholie 1\n",
"sens 1\n",
"couler 1\n",
"poisonous 4\n",
"nocturnes 1\n",
"borisshe 1\n",
"renouncing 7\n",
"effortswhich 1\n",
"temptationthe 1\n",
"outdoor 1\n",
"blisters 1\n",
"vozdvizhenka 7\n",
"defiant 13\n",
"thoughtwas 1\n",
"alienated 3\n",
"hurts 6\n",
"sellers 1\n",
"overture 3\n",
"timethat 2\n",
"fullness 2\n",
"neckwhich 1\n",
"itnot 3\n",
"shahs 1\n",
"semenova 2\n",
"conductor 6\n",
"latecomers 1\n",
"gems 4\n",
"cardboard 4\n",
"actors 7\n",
"fawningly 1\n",
"entracte 2\n",
"tombstones 5\n",
"contrabass 1\n",
"captivated 6\n",
"moscovite 1\n",
"galleries 6\n",
"chromatic 1\n",
"happinessever 1\n",
"jolies 1\n",
"revelers 1\n",
"flirted 3\n",
"magdalenes 2\n",
"lovemaking 2\n",
"suckling 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"recite 5\n",
"improvised 2\n",
"rearrange 2\n",
"andrewshe 1\n",
"nowthere 2\n",
"patterns 6\n",
"prospective 5\n",
"kamenka 3\n",
"witnesses 14\n",
"bearskins 1\n",
"makarka 2\n",
"himevidently 1\n",
"upsetting 4\n",
"matrena 7\n",
"foxlined 1\n",
"palefaced 2\n",
"arbat 8\n",
"abductors 1\n",
"hussy 4\n",
"malignantly 1\n",
"disasters 2\n",
"admonishing 1\n",
"quilts 1\n",
"tverskoy 2\n",
"dashboard 1\n",
"pierreonly 1\n",
"wifewith 1\n",
"memean 1\n",
"brotherin 3\n",
"speranskithe 1\n",
"prechistenka 1\n",
"orbit 2\n",
"suddenlylike 1\n",
"historians 118\n",
"talleyrand 5\n",
"legitimists 2\n",
"descendants 4\n",
"causescoincided 1\n",
"hordes 1\n",
"conscription 2\n",
"powerthe 2\n",
"transported 16\n",
"hive 20\n",
"predestination 1\n",
"tool 5\n",
"volition 4\n",
"compensate 3\n",
"ripened 6\n",
"botanist 3\n",
"tissue 1\n",
"parisleft 1\n",
"posen 1\n",
"beauche 1\n",
"veterans 1\n",
"viliya 1\n",
"dumfound 1\n",
"selfoblivion 1\n",
"soaked 4\n",
"dementat 1\n",
"fetes 2\n",
"regatta 1\n",
"zakret 1\n",
"saltykov 3\n",
"lauriston 4\n",
"informs 9\n",
"bassano 1\n",
"dewy 5\n",
"trim 21\n",
"julner 1\n",
"serviceand 1\n",
"contentedly 4\n",
"sneered 2\n",
"malevolently 2\n",
"rotund 1\n",
"concisely 2\n",
"niemenonly 1\n",
"oder 3\n",
"steins 1\n",
"armfeldts 2\n",
"swede 1\n",
"dnieper 6\n",
"derisively 3\n",
"harmonized 1\n",
"badness 1\n",
"toying 2\n",
"worshiper 1\n",
"horizons 2\n",
"avenged 6\n",
"womanprince 1\n",
"encamped 6\n",
"joylessly 1\n",
"skepticism 1\n",
"irritableas 1\n",
"blamethe 1\n",
"coherence 5\n",
"custodian 1\n",
"bodyguard 3\n",
"wolzogen 22\n",
"propounded 5\n",
"subdivisions 1\n",
"theorists 2\n",
"lawslaws 1\n",
"adherents 4\n",
"nationalism 1\n",
"theoreticians 2\n",
"eddied 2\n",
"advocating 1\n",
"uncertainties 4\n",
"conditional 6\n",
"trammeled 1\n",
"tactical 3\n",
"organ 15\n",
"indefiniteness 1\n",
"renegade 2\n",
"illmade 1\n",
"notionscience 1\n",
"bestorganized 1\n",
"unbrushed 1\n",
"putwhich 1\n",
"interpreter 17\n",
"personalities 2\n",
"polyglot 1\n",
"activitiesthe 1\n",
"geniusnow 1\n",
"gauge 1\n",
"sventsyani 5\n",
"saltanov 3\n",
"zdrzhinski 4\n",
"thermopylae 2\n",
"contradicting 2\n",
"shanty 2\n",
"itset 1\n",
"lessening 4\n",
"kits 2\n",
"stove 16\n",
"horsecloth 2\n",
"plentiful 4\n",
"dissolve 3\n",
"ostrovna 3\n",
"defiled 3\n",
"plucking 6\n",
"deployed 1\n",
"mudstained 1\n",
"eyeswas 2\n",
"ostermanns 1\n",
"nausea 1\n",
"packhorse 1\n",
"maladies 3\n",
"frise 1\n",
"lowspirited 8\n",
"vespers 4\n",
"correcting 8\n",
"saturday 39\n",
"litany 1\n",
"deliverance 4\n",
"confounding 9\n",
"desecrate 2\n",
"meekness 9\n",
"gird 1\n",
"bounteous 1\n",
"lovingkindness 1\n",
"heritage 1\n",
"succor 1\n",
"incomprehensiblebut 1\n",
"guiltya 1\n",
"denote 5\n",
"quarantedeux 1\n",
"besuhof 4\n",
"empereur 1\n",
"antichrist 3\n",
"culminate 1\n",
"spellbound 3\n",
"sackful 1\n",
"abolition 1\n",
"solfa 1\n",
"exercises 12\n",
"kirilych 7\n",
"youpeter 1\n",
"unwontedly 2\n",
"gentlemanin 2\n",
"archway 11\n",
"lordling 1\n",
"ferociously 3\n",
"myselfthat 2\n",
"bishop 39\n",
"citizens 7\n",
"pedestal 10\n",
"cannonstill 1\n",
"drenched 4\n",
"plateful 2\n",
"bloated 2\n",
"etats 1\n",
"districts 2\n",
"sauntering 3\n",
"bwing 6\n",
"awistocwacy 1\n",
"sovweign 2\n",
"fogotten 1\n",
"yeah 1\n",
"empia 1\n",
"conscwiption 1\n",
"neithah 1\n",
"debates 10\n",
"mumbling 11\n",
"bestconscription 1\n",
"deliberations 5\n",
"preopinantmy 1\n",
"cococounsel 1\n",
"hallbut 1\n",
"mayor 15\n",
"effacing 1\n",
"lure 5\n",
"anglewe 1\n",
"headquartersbecause 1\n",
"unpopularity 2\n",
"wlocki 1\n",
"bronnitskis 1\n",
"kindling 11\n",
"jewish 3\n",
"widows 5\n",
"bouriennea 1\n",
"geographical 5\n",
"swamps 2\n",
"peevishly 2\n",
"bolts 4\n",
"pocketcant 1\n",
"slapped 4\n",
"lemonade 3\n",
"spiral 1\n",
"hata 1\n",
"countinghouse 35\n",
"crop 7\n",
"suburb 7\n",
"alpatychs 3\n",
"harvesting 2\n",
"asch 2\n",
"nd 1\n",
"ferapontovs 7\n",
"barking 14\n",
"cartload 2\n",
"dorogobuzh 3\n",
"bombardment 2\n",
"bucks 2\n",
"shopman 4\n",
"thronging 2\n",
"collapsing 3\n",
"wreaths 4\n",
"reddishbrown 1\n",
"marshes 1\n",
"lowed 1\n",
"hubs 1\n",
"beetles 2\n",
"calves 3\n",
"magnolia 1\n",
"indoors 3\n",
"chirruping 1\n",
"execrable 1\n",
"troupe 1\n",
"comings 1\n",
"morals 2\n",
"blindmans 4\n",
"regionpowers 1\n",
"ambler 1\n",
"dideville 3\n",
"operating 4\n",
"harbored 1\n",
"limetree 2\n",
"mutterings 2\n",
"knotted 5\n",
"juniper 1\n",
"absentees 1\n",
"baffling 1\n",
"caravans 2\n",
"loyal 5\n",
"divining 8\n",
"morocco 2\n",
"disharmony 1\n",
"blameless 7\n",
"violate 2\n",
"distrustbut 1\n",
"slavery 5\n",
"raced 3\n",
"peoplehe 2\n",
"belts 2\n",
"foolishness 2\n",
"bickering 1\n",
"chuck 5\n",
"pwaps 1\n",
"dearnot 1\n",
"birthmy 1\n",
"hundwed 3\n",
"eyeball 1\n",
"glumly 1\n",
"wussian 1\n",
"dimples 2\n",
"attendre 1\n",
"rustchuk 1\n",
"mal 2\n",
"abstienstoi 1\n",
"manin 1\n",
"elation 3\n",
"woodcuts 2\n",
"militiaman 2\n",
"faites 1\n",
"charon 2\n",
"mamonovs 6\n",
"raveled 1\n",
"caustique 1\n",
"fines 2\n",
"gallicism 1\n",
"gallicisms 1\n",
"arc 2\n",
"belaya 3\n",
"tserkov 3\n",
"verathat 1\n",
"barbara 2\n",
"misinformed 1\n",
"captures 1\n",
"leppich 4\n",
"car 4\n",
"mounseer 1\n",
"coachmana 1\n",
"perkhushkovo 2\n",
"shevardino 29\n",
"visita 1\n",
"armywhich 3\n",
"mathematically 2\n",
"redoubtand 1\n",
"russiaand 2\n",
"entrench 1\n",
"poniatowskis 3\n",
"breechband 1\n",
"tatarinova 5\n",
"hereand 5\n",
"dressers 4\n",
"wellwe 1\n",
"bezubova 1\n",
"unmilitary 1\n",
"vestmentsone 1\n",
"embossed 1\n",
"chanters 2\n",
"decorating 2\n",
"highnessfor 1\n",
"matchless 3\n",
"adorers 1\n",
"lopped 2\n",
"soulvividly 1\n",
"crude 2\n",
"brimming 1\n",
"venomous 1\n",
"reticent 4\n",
"clausewitz 1\n",
"der 5\n",
"kann 2\n",
"geben 1\n",
"ist 1\n",
"feind 1\n",
"privatpersonen 1\n",
"weaken 6\n",
"theories 3\n",
"outraging 1\n",
"spying 1\n",
"beekeeper 7\n",
"fettered 5\n",
"pampered 4\n",
"fabviers 1\n",
"madonna 1\n",
"globeto 1\n",
"whythe 1\n",
"elchingen 1\n",
"campans 4\n",
"sorbier 1\n",
"se 1\n",
"methode 1\n",
"pointsfour 1\n",
"tuchkov 4\n",
"morand 2\n",
"unforeseen 6\n",
"massacre 3\n",
"ixs 2\n",
"fictitious 4\n",
"napoleondemands 1\n",
"dutchhungry 1\n",
"cited 1\n",
"militarist 1\n",
"strapped 1\n",
"dampness 1\n",
"lozenges 1\n",
"geniality 1\n",
"streaks 3\n",
"silhouetted 1\n",
"interspersed 3\n",
"marshy 4\n",
"ordnance 1\n",
"baptism 2\n",
"redoute 3\n",
"fatale 1\n",
"innards 1\n",
"fusillade 1\n",
"drum 2\n",
"skirmish 5\n",
"infantryall 1\n",
"gosling 2\n",
"hinders 3\n",
"energique 1\n",
"friedlandyet 1\n",
"impotent 2\n",
"wagram 3\n",
"nowwith 1\n",
"dissented 1\n",
"alte 1\n",
"marveling 1\n",
"sinew 1\n",
"unlifting 1\n",
"refolded 4\n",
"cornfield 1\n",
"mowers 1\n",
"whirring 2\n",
"fedor 2\n",
"doctorsone 1\n",
"extracted 1\n",
"understandthat 2\n",
"itselfhe 1\n",
"organize 1\n",
"tranquility 2\n",
"congress 1\n",
"navigable 1\n",
"antinational 1\n",
"bavarians 2\n",
"tuscans 1\n",
"hamburg 2\n",
"postures 2\n",
"swerving 2\n",
"invading 1\n",
"examines 4\n",
"tortoise 8\n",
"corrects 1\n",
"differential 1\n",
"tolerated 4\n",
"valves 3\n",
"ranksthe 1\n",
"savostyanovs 1\n",
"malasha 6\n",
"sixyearold 1\n",
"lurched 2\n",
"circumspect 1\n",
"instigator 1\n",
"organically 1\n",
"saratov 1\n",
"affairthis 1\n",
"puny 3\n",
"jobert 3\n",
"illuminations 1\n",
"contests 2\n",
"refuting 4\n",
"secular 3\n",
"unvarying 3\n",
"princehe 1\n",
"manand 1\n",
"maitressefemme 1\n",
"justifiability 1\n",
"betises 2\n",
"sidled 6\n",
"greasy 12\n",
"penthouse 2\n",
"shamefully 3\n",
"cookshop 1\n",
"messrs 5\n",
"andas 1\n",
"adviceor 1\n",
"loverlike 1\n",
"kuzminichna 24\n",
"ware 2\n",
"petyawhom 1\n",
"commodities 3\n",
"unloaded 3\n",
"uncorded 2\n",
"fatherinlaws 2\n",
"conciliate 8\n",
"storerooms 2\n",
"tightened 4\n",
"falcon 3\n",
"patriarchs 11\n",
"ramshackle 2\n",
"halfinsane 1\n",
"spaciously 1\n",
"cupolas 3\n",
"fameuse 1\n",
"civilization 11\n",
"boyars 7\n",
"waged 3\n",
"mosque 2\n",
"russiansand 1\n",
"dedicated 1\n",
"ridiculousthat 1\n",
"vanishing 5\n",
"fiftieth 1\n",
"beekeepers 1\n",
"emptiness 4\n",
"clusters 6\n",
"combs 5\n",
"reek 1\n",
"informant 6\n",
"theatre 20\n",
"yauza 5\n",
"congestion 1\n",
"beatified 3\n",
"purchasersbut 1\n",
"emptyhanded 4\n",
"drummers 2\n",
"cordon 1\n",
"thissheer 1\n",
"twaddle 1\n",
"vituperation 1\n",
"vasilichs 1\n",
"broadening 1\n",
"grinning 8\n",
"vespertime 1\n",
"dramshop 2\n",
"blacksmiths 4\n",
"enoughtaking 1\n",
"gloomyfaced 1\n",
"workingman 1\n",
"straggly 1\n",
"sirin 3\n",
"irrelevant 1\n",
"meshkov 1\n",
"dregs 3\n",
"reproving 2\n",
"clumsily 3\n",
"engulfing 1\n",
"throttling 2\n",
"duststained 1\n",
"bothvereshchagins 1\n",
"himselfnot 1\n",
"vasilyevich 3\n",
"flexible 3\n",
"tranquillized 1\n",
"foxy 2\n",
"crucified 1\n",
"troitsa 8\n",
"sanguinary 1\n",
"znamenka 1\n",
"sabered 1\n",
"purged 3\n",
"nondescript 9\n",
"perishes 1\n",
"housesbut 1\n",
"pastures 1\n",
"percolating 1\n",
"sandspread 1\n",
"unexplored 1\n",
"arson 3\n",
"housesin 1\n",
"starwise 1\n",
"tangle 3\n",
"enmeshed 2\n",
"classed 2\n",
"affirming 1\n",
"cigars 15\n",
"officera 2\n",
"bourgeois 1\n",
"quartier 2\n",
"heredont 1\n",
"gerasims 1\n",
"compatriot 2\n",
"obtuseness 2\n",
"incognito 1\n",
"ambulance 5\n",
"repeatingexcellent 1\n",
"kvass 2\n",
"beverage 5\n",
"votive 1\n",
"duchenois 1\n",
"potier 1\n",
"sorbonne 1\n",
"ladiesi 1\n",
"lamour 2\n",
"clodhoppers 1\n",
"simpletons 1\n",
"loverassumed 1\n",
"sauerkraut 1\n",
"tiens 1\n",
"waning 5\n",
"unplaited 1\n",
"smoldering 2\n",
"mantimokhinwas 1\n",
"chemise 1\n",
"semidarkness 3\n",
"piti 1\n",
"girdle 2\n",
"caisson 1\n",
"gruzinskis 1\n",
"squareset 1\n",
"passe 1\n",
"slobbering 1\n",
"exotic 2\n",
"anferovs 2\n",
"nikolievnas 1\n",
"hammered 9\n",
"incendiaries 3\n",
"elisabeth 1\n",
"elocution 1\n",
"insufficiently 3\n",
"hosanna 1\n",
"mercifully 2\n",
"kutaysov 3\n",
"defeating 2\n",
"yaroslavl 11\n",
"wellphrased 1\n",
"playfulness 9\n",
"whenfree 1\n",
"gentlemens 6\n",
"termsgalloped 1\n",
"recklessness 6\n",
"wellfitting 1\n",
"maneuvered 1\n",
"meetingnicholas 1\n",
"onand 1\n",
"dreaminess 1\n",
"hernot 2\n",
"prior 4\n",
"dovey 1\n",
"personand 1\n",
"frailty 1\n",
"himentered 1\n",
"virgins 4\n",
"eckmuhl 1\n",
"schoolmaster 8\n",
"reverberation 2\n",
"lifehim 1\n",
"himpierredepriving 1\n",
"dutygermans 1\n",
"dumbly 2\n",
"befouled 2\n",
"pardoned 3\n",
"unwinding 2\n",
"tss 1\n",
"moscowshes 1\n",
"maggot 1\n",
"gnaws 1\n",
"housewife 2\n",
"welltodo 1\n",
"wellthats 2\n",
"lavra 3\n",
"cracks 4\n",
"didwere 1\n",
"himeverything 1\n",
"eventssometimes 1\n",
"karataevs 9\n",
"shuya 1\n",
"eveningagain 1\n",
"connais 1\n",
"depuis 1\n",
"caressed 12\n",
"buoyant 2\n",
"roomthat 3\n",
"translucently 1\n",
"backto 3\n",
"planets 5\n",
"ryazana 1\n",
"tula 8\n",
"alonewho 1\n",
"attackemployed 1\n",
"hunter 2\n",
"scouted 4\n",
"permutations 1\n",
"plottings 1\n",
"averting 2\n",
"shapovalov 1\n",
"wishcould 1\n",
"selfjustifications 1\n",
"rearwhere 1\n",
"forewarned 4\n",
"highnessthe 1\n",
"awayif 1\n",
"maloyaroslavets 6\n",
"egypt 7\n",
"egyptwhere 1\n",
"grandeurfor 1\n",
"commerce 7\n",
"placarded 1\n",
"workingmen 1\n",
"inscribed 7\n",
"circumstancesdid 1\n",
"skilland 1\n",
"geniusof 1\n",
"padlocks 1\n",
"shudders 1\n",
"femgalka 1\n",
"bluegray 3\n",
"frolic 1\n",
"hereditary 2\n",
"animatedly 1\n",
"slackness 1\n",
"sew 1\n",
"smirched 1\n",
"daydreams 6\n",
"kit 5\n",
"semidark 1\n",
"menthat 2\n",
"damdam 1\n",
"palings 5\n",
"furs 2\n",
"snouton 1\n",
"oscillating 3\n",
"dorokhovs 4\n",
"directionalong 1\n",
"shcherbinin 5\n",
"premonition 1\n",
"champions 1\n",
"medyn 1\n",
"scrutinize 1\n",
"recuperate 1\n",
"slaughtering 1\n",
"slandered 1\n",
"armya 1\n",
"affirm 1\n",
"rapiers 1\n",
"onthe 1\n",
"geometric 1\n",
"assignment 2\n",
"meanings 3\n",
"needing 3\n",
"partisan 6\n",
"sacristan 1\n",
"germansent 1\n",
"watchmans 8\n",
"tonguethat 1\n",
"stumps 3\n",
"risky 1\n",
"komarov 1\n",
"cossackwere 1\n",
"officerwithout 1\n",
"hinting 3\n",
"rehearsing 2\n",
"genewal 1\n",
"outturned 2\n",
"assenting 4\n",
"cossackshe 1\n",
"captives 5\n",
"spoons 6\n",
"devilhes 1\n",
"detachmentjokes 1\n",
"likelier 1\n",
"wogueits 1\n",
"angrilythat 1\n",
"esauls 1\n",
"himpoor 1\n",
"lashesthatll 1\n",
"tabletop 1\n",
"coffeepot 1\n",
"flints 4\n",
"adaptations 1\n",
"nayez 1\n",
"buttonhole 6\n",
"dordre 4\n",
"officier 1\n",
"restarted 1\n",
"sharpened 7\n",
"likhachev 5\n",
"vanisheddisappeared 1\n",
"dissolved 5\n",
"ozhegzheg 2\n",
"fugue 1\n",
"ohwhy 1\n",
"nip 1\n",
"loosening 3\n",
"truththat 2\n",
"petal 1\n",
"soreshis 1\n",
"carrion 3\n",
"animalsfrom 1\n",
"innocently 18\n",
"formthe 1\n",
"whipping 1\n",
"merges 1\n",
"fancyof 1\n",
"linking 2\n",
"summertime 5\n",
"switched 2\n",
"boded 2\n",
"seventythree 1\n",
"majesties 1\n",
"highnesses 2\n",
"whereabouts 2\n",
"positionby 1\n",
"worstroad 1\n",
"orsha 5\n",
"himthis 2\n",
"elastic 5\n",
"ratiocination 1\n",
"commensurable 1\n",
"incommensurable 2\n",
"maistre 1\n",
"tantamount 1\n",
"diversions 2\n",
"scientists 1\n",
"campaignwhen 1\n",
"heals 1\n",
"infringed 1\n",
"strengthher 1\n",
"ilynich 2\n",
"passive 6\n",
"slime 1\n",
"wastage 1\n",
"delaying 1\n",
"reproche 1\n",
"indefinitea 1\n",
"kutuzovthe 1\n",
"peopleuse 1\n",
"eventsif 1\n",
"occuring 1\n",
"counterorderswhen 1\n",
"foulmouthed 1\n",
"interlarding 1\n",
"accouterments 1\n",
"stripping 1\n",
"jackdaw 4\n",
"herethats 1\n",
"hiccough 1\n",
"makebelieves 1\n",
"deadwhere 1\n",
"kiselev 1\n",
"rebukes 1\n",
"rowdy 1\n",
"vert 1\n",
"zaletaev 2\n",
"drawled 7\n",
"mathematical 5\n",
"frenchin 1\n",
"lostthan 1\n",
"prisonerswith 1\n",
"affairsthis 1\n",
"seethed 1\n",
"breakersup 1\n",
"dotage 3\n",
"goodfor 1\n",
"reconstructed 1\n",
"graduallyjust 1\n",
"surviving 2\n",
"everliving 1\n",
"thereforeto 1\n",
"tooterenty 1\n",
"hindrances 2\n",
"rebuild 4\n",
"villageshad 1\n",
"jostle 2\n",
"delving 1\n",
"housesit 1\n",
"bazaars 1\n",
"clerks 27\n",
"pigeonholes 1\n",
"faceted 2\n",
"bubbling 5\n",
"maryreluctantly 1\n",
"retell 1\n",
"untold 2\n",
"relive 1\n",
"livenor 1\n",
"tumbledown 3\n",
"passengers 15\n",
"roguish 2\n",
"tomorrowbut 1\n",
"thisand 2\n",
"afterlife 4\n",
"insight 5\n",
"forgave 8\n",
"rotate 1\n",
"photius 2\n",
"literature 3\n",
"displeases 1\n",
"printing 3\n",
"utilized 2\n",
"agencies 1\n",
"rams 2\n",
"ram 5\n",
"prestigedo 1\n",
"murdering 4\n",
"alonewith 1\n",
"stepsons 1\n",
"brothersinlaw 1\n",
"eastward 8\n",
"countermovement 4\n",
"detest 6\n",
"abate 3\n",
"beforeparis 1\n",
"overshadowing 1\n",
"pacifier 1\n",
"infectious 1\n",
"rented 2\n",
"fills 2\n",
"theoretical 3\n",
"nitrogen 2\n",
"fairness 4\n",
"dishonesty 2\n",
"dealthere 1\n",
"knowlike 1\n",
"illhumor 1\n",
"antagonism 2\n",
"mesdames 1\n",
"asleephe 1\n",
"endears 2\n",
"malvinas 1\n",
"everglowing 1\n",
"husbandas 1\n",
"fascinate 2\n",
"entirelythat 1\n",
"resolves 2\n",
"relativescountess 1\n",
"illchosen 1\n",
"regulate 1\n",
"rubbishthose 1\n",
"snuffling 1\n",
"lifewere 1\n",
"instilled 2\n",
"rememberall 1\n",
"brotherinlaws 1\n",
"stinginess 1\n",
"circumscribed 1\n",
"spitefully 1\n",
"rememberyet 1\n",
"garnering 1\n",
"makarovna 3\n",
"samovarand 1\n",
"contemporary 4\n",
"wead 1\n",
"counteract 1\n",
"everythingmagnitski 1\n",
"tutti 1\n",
"quanti 1\n",
"widen 4\n",
"discontentedly 1\n",
"forgottengazed 1\n",
"suppressing 6\n",
"bestrecollections 1\n",
"naughty 21\n",
"ticket 3\n",
"pedantically 1\n",
"fancyas 2\n",
"seemliness 1\n",
"nowyou 1\n",
"honeymoons 1\n",
"andrewand 1\n",
"postulated 1\n",
"assumptions 5\n",
"collides 1\n",
"migrated 1\n",
"professing 3\n",
"elba 1\n",
"resultant 10\n",
"gervinus 2\n",
"composite 2\n",
"willsuch 1\n",
"interactions 1\n",
"filledadmitting 1\n",
"unanswered 2\n",
"nexus 1\n",
"gardening 5\n",
"agriculturists 1\n",
"powerand 1\n",
"asserts 1\n",
"compelling 1\n",
"professedly 2\n",
"humanitys 4\n",
"thisthat 5\n",
"destructions 1\n",
"anotherthat 1\n",
"legitimist 1\n",
"directory 3\n",
"infringers 1\n",
"violations 1\n",
"reactions 1\n",
"generalization 2\n",
"enlighteners 1\n",
"transference 5\n",
"unconditional 2\n",
"onmillions 1\n",
"invade 2\n",
"stencil 2\n",
"fileof 1\n",
"objective 1\n",
"foams 1\n",
"selfconsciousness 1\n",
"submits 2\n",
"resists 1\n",
"repletion 3\n",
"regardedas 2\n",
"popularization 1\n",
"peoplethat 1\n",
"rabbit 1\n",
"unbuttressed 1\n",
"incursions 1\n",
"correspondingly 1\n",
"breathes 1\n",
"austroprussian 1\n",
"questionably 1\n",
"renovation 1\n",
"factors 1\n",
"inventor 6\n",
"correlation 1\n",
"extenuating 1\n",
"babe 1\n",
"economic 3\n",
"botany 1\n",
"freewill 2\n",
"disproving 2\n",
"migrations 3\n",
"geographic 1\n",
"ethnographic 1\n",
"geology 1\n",
"subverting 1\n",
"strengthens 2\n",
"churcher 4\n",
"hon 1\n",
"richard 8\n",
"watson 1\n",
"retrospect 11\n",
"emly 184\n",
"corroborate 2\n",
"heep 114\n",
"wanderer 8\n",
"doras 60\n",
"advertisement 11\n",
"raffle 1\n",
"greataunt 2\n",
"wrecks 3\n",
"pincushion 5\n",
"betseys 3\n",
"annuity 6\n",
"fragment 15\n",
"credible 1\n",
"parlourdoor 3\n",
"needlework 18\n",
"dogkennel 3\n",
"environed 4\n",
"passagewhat 1\n",
"teachests 3\n",
"wanders 7\n",
"frowns 1\n",
"muttonhalf 1\n",
"bedroomwindows 1\n",
"bolting 4\n",
"perseveringly 3\n",
"waxcandle 6\n",
"directionsat 1\n",
"yardmeasure 6\n",
"workbox 11\n",
"promptest 1\n",
"alligators 1\n",
"headi 1\n",
"himover 1\n",
"mores 1\n",
"disfigure 1\n",
"scald 1\n",
"mama 172\n",
"acrying 6\n",
"dates 4\n",
"whyso 1\n",
"boatcloaks 1\n",
"sheffield 9\n",
"incessantlywhich 1\n",
"skylight 17\n",
"nonsensebut 1\n",
"manufacturer 1\n",
"facealtered 1\n",
"alarmedsaid 1\n",
"summary 8\n",
"graypers 2\n",
"supposititious 3\n",
"oakum 2\n",
"bestrewn 1\n",
"riggers 1\n",
"militarylooking 1\n",
"framed 5\n",
"pedlars 1\n",
"abraham 1\n",
"sunderland 1\n",
"carpentry 1\n",
"conglomeration 3\n",
"chop 10\n",
"outbeing 1\n",
"complimentswhich 1\n",
"steelthose 1\n",
"constituting 1\n",
"oystershell 3\n",
"emlys 19\n",
"boathouse 4\n",
"starfish 2\n",
"mavishes 1\n",
"creetur 25\n",
"danl 30\n",
"pleasedand 1\n",
"pant 3\n",
"wordsbring 1\n",
"somethingi 1\n",
"sentient 4\n",
"daywho 1\n",
"relent 8\n",
"reassurance 2\n",
"initials 4\n",
"creed 3\n",
"assists 1\n",
"infusing 1\n",
"groped 9\n",
"visages 2\n",
"prayerbook 5\n",
"miscalled 1\n",
"deathblow 1\n",
"hardperfectly 1\n",
"interposes 2\n",
"cheesemongers 1\n",
"paymentat 1\n",
"disciples 2\n",
"peregrine 3\n",
"pickle 3\n",
"humphrey 1\n",
"clinker 1\n",
"vicar 1\n",
"themas 2\n",
"didand 1\n",
"oneswhich 1\n",
"voyages 8\n",
"centrepiece 2\n",
"grammars 1\n",
"limber 1\n",
"switch 1\n",
"freshener 1\n",
"canes 1\n",
"jailer 8\n",
"comethe 2\n",
"hadthe 1\n",
"azackly 2\n",
"scholar 18\n",
"loveespecially 1\n",
"affectioni 2\n",
"sweetmeats 1\n",
"praps 9\n",
"yardpavement 1\n",
"vegetables 10\n",
"broadbrimmed 8\n",
"womenservants 3\n",
"feint 11\n",
"reenacting 1\n",
"bookingoffice 3\n",
"neckkerchief 5\n",
"thisi 4\n",
"nowand 8\n",
"fibbitson 3\n",
"squarebacked 1\n",
"resumedit 1\n",
"usi 1\n",
"cobblers 1\n",
"mell 61\n",
"unfurnished 3\n",
"mildewed 2\n",
"rained 4\n",
"namesthere 1\n",
"monotony 18\n",
"reopening 1\n",
"insupportable 11\n",
"aboutsupervised 1\n",
"dripped 3\n",
"halfyear 8\n",
"dromedary 2\n",
"seals 9\n",
"secondhand 11\n",
"wrongfor 1\n",
"viandswith 1\n",
"sayand 2\n",
"revel 3\n",
"sharps 1\n",
"bounceablesomebody 1\n",
"bumptiousabout 1\n",
"coalmerchants 1\n",
"bartera 1\n",
"brighteyed 5\n",
"propitiators 1\n",
"falters 1\n",
"bluebottles 1\n",
"looms 2\n",
"canedi 1\n",
"rulerd 1\n",
"somebodyi 1\n",
"gettingup 2\n",
"cowslip 1\n",
"roopy 1\n",
"phial 2\n",
"juice 1\n",
"counterfeited 1\n",
"abegging 1\n",
"polly 136\n",
"amateur 8\n",
"interchanged 5\n",
"bor 5\n",
"famly 1\n",
"boatmenvery 1\n",
"thoroughbuilt 2\n",
"genlmns 1\n",
"hor 3\n",
"vociferously 1\n",
"dodman 1\n",
"wasin 4\n",
"jumble 6\n",
"shiveringmachine 1\n",
"dogseared 1\n",
"suetpuddings 1\n",
"tonightwhen 1\n",
"dolphin 3\n",
"dolphins 3\n",
"drily 7\n",
"awaitin 3\n",
"nudge 2\n",
"infantine 5\n",
"hoarded 7\n",
"drat 4\n",
"recollectand 3\n",
"darned 3\n",
"tooaa 1\n",
"efficacious 1\n",
"sinceand 3\n",
"evermore 13\n",
"surei 1\n",
"tothat 2\n",
"inconsiderate 4\n",
"brooded 5\n",
"pored 1\n",
"yawns 2\n",
"rimy 1\n",
"merrylooking 1\n",
"omer 110\n",
"rattattat 9\n",
"ofbut 1\n",
"chaisecart 2\n",
"resurrection 2\n",
"dismisses 1\n",
"reappear 3\n",
"downstairsfor 1\n",
"armand 2\n",
"peacefulness 1\n",
"lifefor 2\n",
"manifestly 4\n",
"pervading 11\n",
"marriedto 1\n",
"gayer 5\n",
"sh 3\n",
"sweetnatured 1\n",
"backhanded 7\n",
"likelike 1\n",
"booklarning 1\n",
"canary 6\n",
"wooing 2\n",
"waistcoatpocket 2\n",
"contenting 1\n",
"drab 5\n",
"greens 3\n",
"guileless 5\n",
"snugly 4\n",
"dragons 3\n",
"tilefloored 1\n",
"coffer 1\n",
"beforeit 1\n",
"disengage 4\n",
"suggests 5\n",
"crape 3\n",
"warehouse 10\n",
"quinions 1\n",
"indies 17\n",
"mick 5\n",
"mealysi 1\n",
"tights 4\n",
"shirtcollar 27\n",
"bedroomthe 1\n",
"micawber 768\n",
"experientia 1\n",
"brassplate 2\n",
"razor 5\n",
"churchat 1\n",
"strandsomewhere 1\n",
"saveloy 2\n",
"drury 1\n",
"adelphi 6\n",
"twopencehalfpenny 2\n",
"scantiness 2\n",
"unsatisfactorily 1\n",
"anomalous 2\n",
"gent 1\n",
"suffolker 1\n",
"packers 4\n",
"readings 2\n",
"cheesewhich 1\n",
"larder 4\n",
"workhousebeing 1\n",
"turnup 3\n",
"noun 2\n",
"adjective 2\n",
"conjugate 2\n",
"rusted 4\n",
"bakehouse 1\n",
"gipsylike 1\n",
"egghot 1\n",
"ifin 1\n",
"southwark 3\n",
"revengeful 8\n",
"flip 2\n",
"plymouth 6\n",
"sortand 1\n",
"improvident 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"clubroom 2\n",
"dobbin 3\n",
"noweverything 1\n",
"drudge 2\n",
"floored 2\n",
"pollis 7\n",
"kitchenstuff 3\n",
"bei 3\n",
"schoolbed 1\n",
"sleptthough 1\n",
"wayfarer 2\n",
"drawbridges 1\n",
"sentrys 1\n",
"slopshops 1\n",
"merchandise 2\n",
"marinestore 1\n",
"stingingnettles 2\n",
"trays 3\n",
"goroowhich 1\n",
"mattress 3\n",
"fiddle 1\n",
"ferociouslooking 1\n",
"ruffians 2\n",
"sunburnt 8\n",
"boatmen 6\n",
"leathers 1\n",
"kentish 4\n",
"limekiln 1\n",
"tollmans 1\n",
"gardenpath 1\n",
"collared 3\n",
"sidepieces 1\n",
"lavender 5\n",
"greyheaded 3\n",
"beatingand 1\n",
"roseleaves 3\n",
"donkeys 14\n",
"bestriding 2\n",
"broth 4\n",
"whensoever 4\n",
"babyoh 1\n",
"yah 2\n",
"cain 3\n",
"pagan 1\n",
"invadersuntil 1\n",
"ibegiven 1\n",
"kite 17\n",
"bedlam 2\n",
"dicki 1\n",
"sanea 1\n",
"asylumfolks 1\n",
"domade 1\n",
"franklin 1\n",
"gravest 1\n",
"nonarrival 1\n",
"battlepiece 1\n",
"malefactor 1\n",
"grinding 7\n",
"confidencethat 1\n",
"propertythe 1\n",
"againwhen 1\n",
"plaindid 1\n",
"unworldly 1\n",
"boh 2\n",
"youwhich 4\n",
"doted 7\n",
"hurrybecause 1\n",
"discreetly 2\n",
"lifewhich 1\n",
"disseminating 1\n",
"staunch 2\n",
"hire 7\n",
"dicebox 3\n",
"olderwhose 1\n",
"ponys 3\n",
"uriah 216\n",
"corpulency 1\n",
"newspaperbut 1\n",
"balustrade 3\n",
"spiritthat 1\n",
"studiesa 1\n",
"clerkly 2\n",
"urns 4\n",
"flanked 2\n",
"kneesmalls 1\n",
"lustreless 2\n",
"supposedwho 1\n",
"watts 3\n",
"familyall 1\n",
"passable 1\n",
"doctordoctor 1\n",
"agnesno 1\n",
"demonstrative 10\n",
"dints 3\n",
"sexton 9\n",
"snaky 3\n",
"darkhe 1\n",
"decorously 1\n",
"benignant 11\n",
"vitally 2\n",
"soldiernot 1\n",
"flowerlike 1\n",
"gooseberry 2\n",
"monomania 1\n",
"proposalit 1\n",
"thatbut 5\n",
"waysimply 1\n",
"youindeed 1\n",
"rajahs 1\n",
"tideboth 1\n",
"casewait 1\n",
"certainbut 1\n",
"stayto 1\n",
"prepossession 1\n",
"stationery 3\n",
"disbursements 1\n",
"seesixteen 1\n",
"kiteflying 1\n",
"cricketfield 1\n",
"bluenosed 1\n",
"skewer 1\n",
"crampbones 1\n",
"termsin 1\n",
"apologized 1\n",
"teathings 4\n",
"dentists 2\n",
"wormed 2\n",
"weanedand 1\n",
"takefor 1\n",
"pentonville 10\n",
"kidney 6\n",
"payable 3\n",
"problematical 1\n",
"existencethe 1\n",
"flaxen 3\n",
"brazil 1\n",
"cloakroom 1\n",
"nettingall 1\n",
"remits 1\n",
"macbeth 2\n",
"anoints 1\n",
"waylays 1\n",
"condoles 1\n",
"endand 1\n",
"influenceis 1\n",
"worship 17\n",
"larkins 34\n",
"meanand 2\n",
"draws 6\n",
"gloriously 1\n",
"brazier 2\n",
"breathingtime 1\n",
"trotwith 1\n",
"sweettempered 3\n",
"meeven 2\n",
"perhapssomething 1\n",
"thinkshall 1\n",
"lastso 1\n",
"contractbargainannie 1\n",
"fevers 1\n",
"kindnesses 4\n",
"himselfwhere 1\n",
"annieno 1\n",
"aloetrees 1\n",
"grassplot 1\n",
"dorgs 1\n",
"melodging 1\n",
"supplanted 2\n",
"gruffness 2\n",
"hackneycoach 15\n",
"poultry 12\n",
"taters 2\n",
"chemist 1\n",
"druggist 1\n",
"prescription 4\n",
"julius 1\n",
"hackneycoachjostling 1\n",
"unceremonious 2\n",
"periodicallyand 1\n",
"holloa 4\n",
"seventytwo 2\n",
"shavingwater 2\n",
"rosa 48\n",
"houseor 1\n",
"sharpens 2\n",
"lovebut 1\n",
"lilies 4\n",
"notwithout 1\n",
"intuitively 2\n",
"oyster 7\n",
"characteristicsno 1\n",
"foils 1\n",
"portmanteaux 2\n",
"bracing 1\n",
"shorterwinded 1\n",
"toowho 1\n",
"measurementthe 1\n",
"counterby 1\n",
"wifepeggottys 1\n",
"uncledont 1\n",
"suchandsuch 1\n",
"waywardill 1\n",
"wilfulness 2\n",
"capricious 9\n",
"purposeless 3\n",
"magpie 2\n",
"gentlmengentlmen 2\n",
"gentlman 2\n",
"arks 1\n",
"dearshe 2\n",
"shellfishbiled 1\n",
"tooand 3\n",
"souwester 7\n",
"wots 2\n",
"ewents 1\n",
"theer 43\n",
"megentlmenthanshes 1\n",
"ithan 1\n",
"landnor 1\n",
"vacate 2\n",
"churchbell 2\n",
"topped 2\n",
"nightclothes 1\n",
"rawboned 1\n",
"blest 17\n",
"wasteplace 1\n",
"usualfor 2\n",
"lionsa 1\n",
"hipped 1\n",
"steadfast 14\n",
"stoppingfor 1\n",
"tush 3\n",
"rugged 15\n",
"undisguised 6\n",
"thema 1\n",
"giantess 1\n",
"ladydressed 1\n",
"ogling 3\n",
"whatstheirnames 1\n",
"weektheres 1\n",
"brushes 5\n",
"preposterous 5\n",
"ducky 3\n",
"friar 1\n",
"unmentionable 1\n",
"myselfperhaps 1\n",
"dealperhaps 1\n",
"boynever 1\n",
"trades 6\n",
"mistakento 1\n",
"seafaring 13\n",
"trimming 4\n",
"pieman 2\n",
"beadwood 1\n",
"ducks 2\n",
"banisters 10\n",
"lookee 6\n",
"doent 71\n",
"winder 13\n",
"wouldntno 1\n",
"herknowing 1\n",
"overset 4\n",
"earseven 1\n",
"proctor 11\n",
"churchyardwhat 1\n",
"nancy 1\n",
"spenlow 131\n",
"dunstans 1\n",
"ludgate 3\n",
"illdressed 1\n",
"copyists 1\n",
"pauper 5\n",
"shirtcollars 2\n",
"daywith 1\n",
"initiatory 1\n",
"monthsthreean 1\n",
"powdermills 1\n",
"pickpockets 1\n",
"stoneblind 3\n",
"commonsand 1\n",
"consideringwhen 1\n",
"piazza 2\n",
"fishkittle 1\n",
"retail 4\n",
"drawbacks 3\n",
"halfforgotten 2\n",
"steerforthyouretheguidingstarofmyexistence 1\n",
"lares 1\n",
"pigeonholeplace 1\n",
"intelligibly 1\n",
"lorblessmer 1\n",
"cheeseplate 1\n",
"esquire 33\n",
"familiarize 2\n",
"impressionthere 1\n",
"shakespeare 6\n",
"handupon 1\n",
"thatnot 1\n",
"dayswhen 1\n",
"shadowed 5\n",
"ladycame 1\n",
"hamletssay 1\n",
"steadylooking 1\n",
"toleration 5\n",
"himconsiderable 1\n",
"gulpidge 9\n",
"noddedwas 1\n",
"newmarket 2\n",
"rejoinedi 1\n",
"copperfieldi 6\n",
"dessay 2\n",
"undulation 1\n",
"mastermister 1\n",
"entrapped 6\n",
"unsteadiness 2\n",
"beganbut 1\n",
"redheaded 1\n",
"unbeknown 1\n",
"punctuality 6\n",
"tablecover 1\n",
"stoppages 5\n",
"postoffice 4\n",
"mulberrycoloured 3\n",
"norwood 14\n",
"tablebeer 4\n",
"adjourned 3\n",
"excommunicated 3\n",
"restitution 7\n",
"worldtouch 1\n",
"plaids 1\n",
"dora 447\n",
"gayest 5\n",
"ta 3\n",
"maudlin 3\n",
"wirearched 1\n",
"undesigning 1\n",
"doublebass 1\n",
"thingis 2\n",
"herwont 2\n",
"swilling 2\n",
"salvage 1\n",
"unregular 1\n",
"lovewith 1\n",
"brandywhich 1\n",
"veterinary 1\n",
"sloppy 1\n",
"doubledup 1\n",
"pothooksreminded 1\n",
"stairsthe 1\n",
"sparely 3\n",
"sofabedstead 1\n",
"downand 2\n",
"writings 4\n",
"patiencei 1\n",
"housefacing 1\n",
"flowerpot 9\n",
"discourage 3\n",
"ironmongerycandleboxes 1\n",
"necessariesbecause 1\n",
"designated 2\n",
"washerwoman 2\n",
"lovelorn 2\n",
"rebellion 2\n",
"waxcandles 2\n",
"toilette 4\n",
"waterworksand 1\n",
"fumes 1\n",
"enhances 1\n",
"unresisting 2\n",
"servingman 1\n",
"naturallywhich 1\n",
"encomiums 3\n",
"figurative 4\n",
"domesticity 1\n",
"perkins 1\n",
"hanbury 1\n",
"handswhich 1\n",
"micawberin 1\n",
"postpaid 1\n",
"billi 1\n",
"businessterm 1\n",
"eulogy 2\n",
"feelingly 8\n",
"honouring 4\n",
"sowhere 1\n",
"dumbfoundered 1\n",
"theresurgeon 1\n",
"iswho 2\n",
"upshot 1\n",
"nonsenseyou 1\n",
"forin 1\n",
"excommunication 1\n",
"churchwardens 3\n",
"schoolhouse 2\n",
"parlourmaid 3\n",
"discernment 1\n",
"nettled 2\n",
"instanceany 1\n",
"artdelightful 1\n",
"incomprehensibility 2\n",
"likeor 1\n",
"godfathers 2\n",
"roomwas 1\n",
"dursnt 5\n",
"morningor 1\n",
"particlers 1\n",
"wifei 1\n",
"loth 2\n",
"colleges 2\n",
"wured 6\n",
"gentlemanshe 1\n",
"herselfand 1\n",
"leniently 3\n",
"halfguineas 1\n",
"blackboy 1\n",
"legatee 1\n",
"whales 2\n",
"marriedbut 1\n",
"withour 1\n",
"hosses 1\n",
"mindnot 2\n",
"nigher 1\n",
"knowhe 1\n",
"hant 3\n",
"allus 5\n",
"hap 1\n",
"regardful 1\n",
"nets 2\n",
"strangeled 1\n",
"hapless 1\n",
"designing 8\n",
"heardmy 1\n",
"knowwhats 1\n",
"mediate 2\n",
"prapsand 1\n",
"bide 4\n",
"notsmiling 1\n",
"gratitudeclaims 1\n",
"stanning 2\n",
"undeserved 3\n",
"branded 1\n",
"walnut 2\n",
"byway 1\n",
"steeped 9\n",
"nightwalk 1\n",
"perambulated 1\n",
"dorai 1\n",
"audaciously 2\n",
"frozenout 1\n",
"blunderer 1\n",
"vicargenerals 1\n",
"worrited 2\n",
"recrimination 1\n",
"elseso 1\n",
"officei 1\n",
"registry 1\n",
"emolument 2\n",
"committedbecause 1\n",
"stonyhearted 4\n",
"benignity 7\n",
"mayflies 1\n",
"whereupon 3\n",
"pastthe 1\n",
"gipsyfashion 1\n",
"adulterated 3\n",
"patriarch 51\n",
"sivinwhich 1\n",
"wineandwater 1\n",
"wretchedness 13\n",
"squarepainfully 1\n",
"orderbook 1\n",
"cockedhat 1\n",
"informers 3\n",
"underlined 4\n",
"educates 2\n",
"sarahs 2\n",
"mortimers 1\n",
"brokers 6\n",
"quailed 2\n",
"brile 1\n",
"pouringout 1\n",
"colonnade 1\n",
"weatherglass 4\n",
"elbowroom 2\n",
"pocketing 2\n",
"nightdraught 1\n",
"nightgown 2\n",
"tiffeys 1\n",
"disheartening 1\n",
"cancel 10\n",
"montagu 1\n",
"notesvery 1\n",
"epistolary 4\n",
"prejudicedi 1\n",
"spasms 2\n",
"agnesand 2\n",
"cent 5\n",
"summarily 3\n",
"eveningand 2\n",
"livedand 1\n",
"abruptness 1\n",
"fawned 3\n",
"serpentined 1\n",
"yearnings 3\n",
"granite 3\n",
"narrowlyfor 1\n",
"littlei 3\n",
"amanuensis 1\n",
"swimmingly 1\n",
"caterpillars 1\n",
"wayand 2\n",
"confuse 4\n",
"cemetery 3\n",
"reciprocate 2\n",
"brew 1\n",
"banishment 4\n",
"flatcandle 1\n",
"ofand 3\n",
"remuneration 1\n",
"lawas 1\n",
"defendant 3\n",
"processand 2\n",
"prosaic 3\n",
"liquidation 1\n",
"pitcher 3\n",
"supernumerary 1\n",
"doorsleaving 1\n",
"jips 8\n",
"littlea 1\n",
"succouring 1\n",
"labourer 2\n",
"labourerfrom 1\n",
"abbess 1\n",
"coaxing 7\n",
"enfields 1\n",
"burke 1\n",
"heats 2\n",
"politician 1\n",
"mast 11\n",
"exchequer 1\n",
"plod 1\n",
"headhe 1\n",
"misgave 3\n",
"mustbe 1\n",
"spaniel 3\n",
"herof 1\n",
"trinketsof 1\n",
"impaired 4\n",
"otheri 1\n",
"upthat 1\n",
"ogre 3\n",
"quoted 3\n",
"acceptability 1\n",
"onor 1\n",
"insensibleno 1\n",
"othersi 1\n",
"placedas 1\n",
"forthand 1\n",
"sketch 10\n",
"assets 1\n",
"putney 11\n",
"qy 1\n",
"pagesto 1\n",
"drafted 2\n",
"pastoral 6\n",
"redound 2\n",
"isin 1\n",
"lucidity 1\n",
"thereunto 1\n",
"circumstancesof 1\n",
"wantshall 1\n",
"upwith 1\n",
"cookerybook 6\n",
"perseveranceand 1\n",
"ury 10\n",
"ramsgate 1\n",
"shambling 1\n",
"stratagems 2\n",
"silvering 1\n",
"suppression 5\n",
"divinest 2\n",
"torturer 1\n",
"fatuity 1\n",
"farewells 3\n",
"comprising 4\n",
"northeast 4\n",
"stableyard 3\n",
"coasts 1\n",
"waslost 1\n",
"throughi 1\n",
"gratitoode 1\n",
"woreto 1\n",
"hometo 1\n",
"benot 1\n",
"mejustly 1\n",
"mosthim 1\n",
"forgivingtell 1\n",
"whisperkinder 1\n",
"flit 3\n",
"partieswhich 1\n",
"traddlesreverend 1\n",
"uncharitable 1\n",
"toastandwater 1\n",
"catmy 1\n",
"tucker 3\n",
"brooch 3\n",
"barks 5\n",
"somethingbowed 1\n",
"lavinia 50\n",
"promote 5\n",
"affectionfor 1\n",
"orderlylooking 1\n",
"autumnleaves 1\n",
"doady 26\n",
"flowerpainting 3\n",
"flyconveyance 1\n",
"intoyoure 1\n",
"anorseback 1\n",
"eliciting 2\n",
"guava 1\n",
"gossamerwings 1\n",
"saketo 3\n",
"halfreproachful 1\n",
"dreami 1\n",
"tingled 1\n",
"toothache 2\n",
"impenetrably 1\n",
"inexorably 4\n",
"happilyunconscious 1\n",
"tamed 4\n",
"skewered 1\n",
"conveyancer 2\n",
"superintendence 2\n",
"commonswhere 1\n",
"disposes 1\n",
"pagoda 4\n",
"bonnetlooking 1\n",
"steampower 1\n",
"flavouring 1\n",
"semiauxiliary 1\n",
"fonts 1\n",
"bridesmaids 5\n",
"paragon 4\n",
"inconsequential 2\n",
"tooof 2\n",
"lectureto 1\n",
"realityby 1\n",
"kidgerbury 1\n",
"consequencethey 1\n",
"consequenceof 1\n",
"inking 2\n",
"lionwhich 1\n",
"heavyi 1\n",
"bibs 1\n",
"fullblown 1\n",
"creaturesno 1\n",
"dontnow 1\n",
"youenter 1\n",
"johnson 12\n",
"italianiron 1\n",
"exhibitions 1\n",
"abiding 5\n",
"youknowwhos 1\n",
"marplot 2\n",
"howas 1\n",
"unsuitability 5\n",
"purposeno 1\n",
"memy 1\n",
"aggravations 1\n",
"truthhas 1\n",
"settler 3\n",
"fictioni 1\n",
"heavilyframed 2\n",
"passerby 2\n",
"changeless 3\n",
"vaunting 2\n",
"hereas 1\n",
"imperiously 2\n",
"improvable 1\n",
"overlook 8\n",
"healing 6\n",
"perceivedand 1\n",
"ineffaceable 2\n",
"livingi 1\n",
"awize 1\n",
"acquiescing 1\n",
"divingbells 1\n",
"whichhaving 1\n",
"ebbtide 1\n",
"sleepwalker 2\n",
"riseshe 1\n",
"gunwale 1\n",
"muchwhat 1\n",
"stiffening 5\n",
"shrinkingly 4\n",
"owt 1\n",
"beenwheer 1\n",
"usthat 1\n",
"onset 2\n",
"interpret 3\n",
"chink 8\n",
"ungratefully 1\n",
"prowled 3\n",
"reappears 1\n",
"saucepanlids 1\n",
"grewand 1\n",
"beanswith 1\n",
"fife 1\n",
"suborned 1\n",
"infect 1\n",
"incur 3\n",
"lax 1\n",
"graveand 1\n",
"pouncing 1\n",
"storyreally 1\n",
"idlest 3\n",
"disciplined 2\n",
"trudge 1\n",
"avocations 3\n",
"ariseyou 1\n",
"placeand 1\n",
"incarceration 2\n",
"existent 1\n",
"lemonstunnersa 1\n",
"distantly 7\n",
"manif 1\n",
"myselfbespeaks 1\n",
"mentionednot 1\n",
"fagend 1\n",
"wrestle 3\n",
"sportsfloors 1\n",
"mountebank 1\n",
"taboo 2\n",
"meunlessi 1\n",
"atomsthetranscendent 1\n",
"perjurerheep 1\n",
"worldano 1\n",
"gentlemanto 1\n",
"sayaor 1\n",
"persuasiongo 1\n",
"tweed 1\n",
"ruggedly 2\n",
"betokened 1\n",
"practised 6\n",
"dupes 2\n",
"seeas 1\n",
"spangle 1\n",
"downrushed 1\n",
"tenderlyand 1\n",
"aweer 2\n",
"eyeswhich 1\n",
"twill 2\n",
"summat 1\n",
"shetook 1\n",
"ismaybe 1\n",
"recollects 1\n",
"thingsnames 1\n",
"asetting 1\n",
"awestricken 2\n",
"minding 3\n",
"wureds 2\n",
"wimickingour 1\n",
"othersnatrally 1\n",
"asll 5\n",
"unrolled 1\n",
"writ 4\n",
"adoing 1\n",
"goas 1\n",
"whatits 1\n",
"oddities 1\n",
"memry 1\n",
"thatwith 1\n",
"tenants 6\n",
"mastmaker 1\n",
"deary 34\n",
"nowmore 1\n",
"topboots 1\n",
"andif 1\n",
"sickliest 1\n",
"harrow 1\n",
"knavish 1\n",
"doneall 1\n",
"abusive 2\n",
"eavesdropping 2\n",
"truncheon 1\n",
"byheep 4\n",
"youyouyou 1\n",
"ious 2\n",
"shakespearianly 1\n",
"mystification 3\n",
"designate 2\n",
"protractednow 1\n",
"trustmoney 1\n",
"deficiencies 4\n",
"constrain 1\n",
"askheepmr 1\n",
"ludicrously 2\n",
"familyas 1\n",
"saidheepdeemed 1\n",
"byheepto 1\n",
"signatures 1\n",
"mover 2\n",
"byheepsfalse 1\n",
"andheepsreal 1\n",
"bin 1\n",
"byheepfraudulently 1\n",
"expressionwho 1\n",
"righting 2\n",
"greed 5\n",
"assizes 1\n",
"emigration 4\n",
"sojourner 1\n",
"bedsideand 1\n",
"heavilyheavily 1\n",
"whines 1\n",
"sharpest 4\n",
"iton 2\n",
"pulverization 1\n",
"securitiesat 1\n",
"ofsomethingto 1\n",
"colonial 2\n",
"patrons 9\n",
"calledof 1\n",
"milking 1\n",
"negotiated 1\n",
"abilitieswhich 1\n",
"wickfieldcopperfieldi 1\n",
"baulk 1\n",
"ior 1\n",
"hornsey 1\n",
"consigning 3\n",
"fuelof 1\n",
"ipswichvery 1\n",
"stormbird 1\n",
"yardgate 2\n",
"objectless 1\n",
"gazetteer 1\n",
"innservants 1\n",
"felloff 1\n",
"precipiceinto 1\n",
"portugal 1\n",
"inconceivablebeat 1\n",
"keel 2\n",
"unpractised 2\n",
"stilled 2\n",
"handbier 1\n",
"sirmr 2\n",
"impervious 2\n",
"claspknife 1\n",
"shelffull 1\n",
"pocketbookbeing 1\n",
"porpoises 1\n",
"attains 1\n",
"alps 4\n",
"longunwonted 1\n",
"steeps 1\n",
"verdurespecks 1\n",
"mountainsside 2\n",
"gloried 3\n",
"mountaintops 2\n",
"geneva 6\n",
"loveand 1\n",
"deservedly 5\n",
"shadowing 1\n",
"sodifferent 2\n",
"lawlist 1\n",
"brightlooking 1\n",
"chambersis 1\n",
"versus 1\n",
"wigziell 1\n",
"crewlerit 1\n",
"lucy 1\n",
"beautys 1\n",
"defaulter 12\n",
"hoary 3\n",
"fishslice 2\n",
"edmunds 4\n",
"bereavement 3\n",
"itor 4\n",
"nonmedical 1\n",
"miscall 1\n",
"perdition 4\n",
"unman 1\n",
"nowblind 1\n",
"turretroom 1\n",
"entreatingly 2\n",
"roompointing 1\n",
"timeat 1\n",
"answeri 1\n",
"bushels 1\n",
"blunting 1\n",
"copyingclerk 1\n",
"plainest 6\n",
"diamondeyed 1\n",
"capped 1\n",
"leverescapemovement 1\n",
"cooksshop 1\n",
"fishmongers 1\n",
"batter 1\n",
"middlesex 4\n",
"jobbed 7\n",
"converts 1\n",
"satires 1\n",
"babel 3\n",
"legitimately 1\n",
"clockwork 3\n",
"labourers 5\n",
"isolation 3\n",
"grapes 14\n",
"neophytes 1\n",
"adulteration 1\n",
"warders 1\n",
"eights 2\n",
"beenbut 1\n",
"waswhat 2\n",
"lonelier 2\n",
"strangethat 1\n",
"byanother 1\n",
"sadlyjoyfully 1\n",
"perfected 1\n",
"overchargedbut 1\n",
"hysterics 3\n",
"sheepfarming 1\n",
"stockfarming 1\n",
"prosper 4\n",
"sundownwas 1\n",
"thowtful 1\n",
"aworking 1\n",
"waytimid 1\n",
"longshipwrecked 1\n",
"gormedand 1\n",
"willingest 1\n",
"honestesthelping 1\n",
"hereeven 1\n",
"flatfolded 1\n",
"understan 1\n",
"fortyseven 5\n",
"defies 1\n",
"highlyornate 1\n",
"shoals 3\n",
"disported 1\n",
"sol 173\n",
"usher 1\n",
"republished 1\n",
"timebefore 1\n",
"caresses 6\n",
"didnow 1\n",
"coppercoloured 1\n",
"tiffin 1\n",
"shipping 8\n",
"walter 539\n",
"carker 519\n",
"chick 267\n",
"tox 349\n",
"improves 4\n",
"prepossessing 7\n",
"spotty 1\n",
"appended 4\n",
"dombeys 235\n",
"mymy 2\n",
"mymrs 1\n",
"orbits 2\n",
"dombeiand 1\n",
"boynothing 1\n",
"florences 44\n",
"ihad 1\n",
"cankabyexcuse 1\n",
"pepss 3\n",
"rally 5\n",
"familyno 1\n",
"showof 1\n",
"tuckers 1\n",
"wristbands 7\n",
"lockets 1\n",
"unwarrantable 1\n",
"nostril 6\n",
"somethingthat 1\n",
"unmeaning 2\n",
"bowwowwow 1\n",
"toorrusuch 1\n",
"wellmatched 2\n",
"kitch 1\n",
"characteri 1\n",
"ingine 1\n",
"toodle 97\n",
"servingwoman 1\n",
"richardsan 1\n",
"richards 128\n",
"coaldust 2\n",
"sitbut 1\n",
"chokers 1\n",
"matronly 1\n",
"fountainis 1\n",
"leavetaking 4\n",
"admissibleon 1\n",
"dustbins 1\n",
"vary 1\n",
"gas 3\n",
"ungarnished 1\n",
"bellhandles 2\n",
"weekly 15\n",
"floy 97\n",
"permanency 3\n",
"comma 1\n",
"pollys 4\n",
"farthings 2\n",
"lawfully 1\n",
"dombeywhom 1\n",
"partywall 1\n",
"instrumentmakers 13\n",
"effigiesof 1\n",
"suavity 5\n",
"stockintrade 3\n",
"barometers 1\n",
"telescopes 3\n",
"acutest 2\n",
"cushioned 5\n",
"instrumentmaker 36\n",
"weatherglasses 3\n",
"morfins 3\n",
"instanceand 1\n",
"mileend 2\n",
"confuses 1\n",
"mainmaston 1\n",
"cornwall 4\n",
"seventyone 1\n",
"deptford 1\n",
"adventurousof 1\n",
"tradehad 1\n",
"skipper 4\n",
"saltlooking 1\n",
"thanke 1\n",
"dombeyand 2\n",
"specialities 1\n",
"mamaor 1\n",
"aids 1\n",
"godmothership 1\n",
"chicka 1\n",
"patronise 2\n",
"discriminating 1\n",
"mulotter 1\n",
"cockloft 1\n",
"lowing 4\n",
"lustilylaughing 1\n",
"babylonian 1\n",
"westry 1\n",
"curate 2\n",
"calfs 1\n",
"publicmayimpose 1\n",
"molest 2\n",
"contrairy 2\n",
"tony 1\n",
"jumbled 5\n",
"cranes 1\n",
"earthquakes 1\n",
"railroad 8\n",
"brannew 2\n",
"frowzy 2\n",
"summerhouses 4\n",
"encroached 3\n",
"capitalist 1\n",
"flues 1\n",
"leastways 3\n",
"intrinsically 1\n",
"patriarchal 25\n",
"kittens 1\n",
"punctuated 1\n",
"ostensible 1\n",
"rims 1\n",
"brickfields 2\n",
"tileyards 1\n",
"rabbitskin 3\n",
"slipshod 4\n",
"peopleshe 1\n",
"clangour 1\n",
"tame 8\n",
"comparisonbut 1\n",
"dombeythe 1\n",
"juniorhalloa 1\n",
"moulded 2\n",
"bestirred 3\n",
"cite 1\n",
"womanfound 1\n",
"restlook 1\n",
"unclethrow 1\n",
"pilotage 1\n",
"knownand 1\n",
"disown 2\n",
"pewterpot 1\n",
"knockers 1\n",
"bluefaced 4\n",
"dalliance 1\n",
"makers 2\n",
"wainscoat 1\n",
"longflapped 1\n",
"himgradually 1\n",
"joes 8\n",
"wires 7\n",
"cockade 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"commissioners 3\n",
"pimple 1\n",
"nameworried 1\n",
"milestone 2\n",
"wickams 6\n",
"fantastically 3\n",
"blare 1\n",
"bullion 1\n",
"crooning 2\n",
"mutes 3\n",
"seaair 3\n",
"pipchin 229\n",
"ladymiss 1\n",
"wasdear 1\n",
"encomium 2\n",
"bombazeen 8\n",
"didwhich 1\n",
"ogress 7\n",
"chalky 3\n",
"embowerment 1\n",
"eke 3\n",
"ironbound 1\n",
"bitherstone 36\n",
"muttonchops 1\n",
"filliped 1\n",
"pedigree 3\n",
"tearsand 1\n",
"rottingdean 1\n",
"bengal 8\n",
"purring 1\n",
"eschewing 1\n",
"hussythat 1\n",
"servantfrom 1\n",
"silverchaser 1\n",
"nominative 1\n",
"crabfaced 1\n",
"seabeach 2\n",
"chums 1\n",
"coalwhipper 1\n",
"nondominical 1\n",
"analysing 1\n",
"propitiated 2\n",
"postcaptain 1\n",
"workaday 1\n",
"lightfooted 1\n",
"knowa 1\n",
"roughandtough 1\n",
"withoutone 1\n",
"refraction 1\n",
"bankruptcy 6\n",
"temperfor 1\n",
"marius 1\n",
"carthages 1\n",
"advisability 1\n",
"drays 1\n",
"dinging 1\n",
"timberlooking 1\n",
"macstinger 96\n",
"englishwomans 1\n",
"doublecased 1\n",
"unassisted 3\n",
"dombeybore 1\n",
"attentionsaw 1\n",
"vindictiveness 2\n",
"stilton 1\n",
"spied 1\n",
"sirmajor 1\n",
"serviceis 1\n",
"dearindeed 1\n",
"palpitation 1\n",
"inflammatory 3\n",
"sirsly 1\n",
"chock 3\n",
"ineffably 1\n",
"gaywalterwhat 1\n",
"substitution 2\n",
"vociferous 3\n",
"fleshly 1\n",
"openin 1\n",
"immolations 1\n",
"harmonise 2\n",
"conferences 7\n",
"rated 4\n",
"unlookedfor 1\n",
"neckclothperhaps 1\n",
"ageor 1\n",
"worsetalked 1\n",
"hoitytoity 1\n",
"recipe 4\n",
"bewailed 3\n",
"blimber 204\n",
"gruffest 1\n",
"nurserymaids 2\n",
"feeder 107\n",
"carking 1\n",
"misanthrope 3\n",
"curtius 1\n",
"sages 5\n",
"weakeyed 11\n",
"turnspits 1\n",
"minerva 4\n",
"ovid 1\n",
"plautus 1\n",
"florenceall 1\n",
"upholsterer 1\n",
"flounder 1\n",
"bristles 2\n",
"adds 3\n",
"drumstick 1\n",
"heigh 1\n",
"superbly 1\n",
"doubtincredible 1\n",
"vitellius 1\n",
"tiberius 1\n",
"heliogabalus 1\n",
"scalp 1\n",
"paulwhich 1\n",
"furthermore 1\n",
"rulesa 1\n",
"slided 3\n",
"romuluses 1\n",
"remus 1\n",
"sabbaths 1\n",
"himwhen 1\n",
"thorny 1\n",
"embracebut 1\n",
"unrecognised 2\n",
"qualifying 1\n",
"emulated 1\n",
"stall 1\n",
"perambulating 2\n",
"vapid 3\n",
"filtered 1\n",
"airthere 1\n",
"violoncello 12\n",
"recollectionsof 1\n",
"feline 2\n",
"gums 4\n",
"knowii 1\n",
"coattails 1\n",
"subjectvery 1\n",
"forbearance 10\n",
"oozed 2\n",
"semiannually 1\n",
"tozers 3\n",
"iceni 1\n",
"himselfif 1\n",
"allhe 1\n",
"immunities 1\n",
"newerfashioned 1\n",
"breakage 1\n",
"faintness 5\n",
"smuggler 2\n",
"keybugle 1\n",
"quadrilles 3\n",
"aweek 1\n",
"thoughtlessly 1\n",
"tongs 2\n",
"dressedwhich 1\n",
"waistbands 2\n",
"smitten 3\n",
"tallows 1\n",
"cottons 1\n",
"glowered 1\n",
"melodies 1\n",
"hairwhich 1\n",
"diogenes 60\n",
"egregious 1\n",
"beenthat 3\n",
"gimlets 1\n",
"infirmto 1\n",
"legsthe 1\n",
"cuttlethat 1\n",
"comtemplative 1\n",
"eyegradually 1\n",
"anklejacks 5\n",
"thismore 1\n",
"indefiniteindefinite 1\n",
"townbreathing 1\n",
"carcasses 1\n",
"hotels 3\n",
"lodginghouses 1\n",
"sandwichboxes 1\n",
"timetables 1\n",
"stuccoed 2\n",
"cleansing 1\n",
"engineers 2\n",
"gardensis 1\n",
"richardsforgetting 1\n",
"morethen 1\n",
"fashiondeath 1\n",
"mountainforest 1\n",
"politic 7\n",
"operated 2\n",
"preferment 1\n",
"seamannot 1\n",
"acuteness 3\n",
"twirl 5\n",
"guidanceand 1\n",
"voiceless 4\n",
"removedwhich 1\n",
"trudging 1\n",
"walksmay 1\n",
"whistles 5\n",
"mismis 1\n",
"unreturned 1\n",
"passedmy 1\n",
"againwould 1\n",
"oncefor 1\n",
"drummed 3\n",
"trueso 1\n",
"wordswhose 1\n",
"yearsnot 1\n",
"fourteenand 1\n",
"gall 4\n",
"mourns 2\n",
"terrestrially 1\n",
"turtles 1\n",
"snuffcoloured 1\n",
"himnever 2\n",
"recapitulated 1\n",
"wronging 1\n",
"blockading 1\n",
"chart 3\n",
"avowal 3\n",
"reascending 1\n",
"devilled 3\n",
"extinguishedoutrivalledfloored 1\n",
"aspires 2\n",
"besmeared 4\n",
"halfslaked 1\n",
"robinhim 1\n",
"raker 1\n",
"lorded 1\n",
"smooths 2\n",
"ruffles 1\n",
"cottagehomes 1\n",
"tinging 1\n",
"adjustmentbeing 1\n",
"swaggered 2\n",
"overripe 1\n",
"edith 362\n",
"insipidly 1\n",
"skewton 148\n",
"abomination 1\n",
"indeedand 2\n",
"punching 2\n",
"peerless 4\n",
"gad 6\n",
"dairy 1\n",
"sirwe 2\n",
"rallies 1\n",
"turnswould 1\n",
"benefited 1\n",
"snarling 1\n",
"selfconvicted 1\n",
"birdcatching 1\n",
"cove 28\n",
"velveteen 1\n",
"exceptin 1\n",
"cornerthe 3\n",
"girded 1\n",
"unclenched 1\n",
"nameless 6\n",
"betterthough 1\n",
"digressive 1\n",
"pantheon 1\n",
"gazethe 1\n",
"innocents 1\n",
"cardcase 2\n",
"gorgonlike 3\n",
"sentries 1\n",
"wickedly 2\n",
"palls 1\n",
"closets 2\n",
"exploratory 1\n",
"pedestals 2\n",
"tinklings 1\n",
"lustres 1\n",
"phantom 9\n",
"domineered 1\n",
"opting 1\n",
"hh 1\n",
"manliness 1\n",
"insipidest 1\n",
"disfigurement 2\n",
"shrillness 1\n",
"illustrative 2\n",
"doves 3\n",
"buffeted 1\n",
"houseits 2\n",
"drysaltery 1\n",
"distinguishable 1\n",
"doormats 1\n",
"copious 3\n",
"sneeze 1\n",
"bunsbys 8\n",
"coachwindow 1\n",
"mackerel 2\n",
"lighthouses 1\n",
"pilotcoat 3\n",
"pilottrousers 1\n",
"wearers 2\n",
"enthroned 2\n",
"shipmet 4\n",
"stolid 4\n",
"triumphing 1\n",
"testifying 3\n",
"wisdomupon 1\n",
"notemy 1\n",
"sirpenalty 1\n",
"positionfriend 1\n",
"sobered 1\n",
"rooftree 1\n",
"sonhere 1\n",
"noseshe 1\n",
"childwhose 1\n",
"boughsand 1\n",
"themlike 1\n",
"nowshould 1\n",
"dirtybut 1\n",
"suspending 1\n",
"dayll 1\n",
"overruling 1\n",
"whatthat 1\n",
"gear 2\n",
"shopdoor 12\n",
"unbolted 1\n",
"fathoming 1\n",
"yourselfabout 1\n",
"stationhouse 1\n",
"sheeps 1\n",
"ticketed 1\n",
"bjosh 1\n",
"heardi 1\n",
"edithto 1\n",
"natureit 2\n",
"dombeyto 3\n",
"dilate 1\n",
"charactermine 1\n",
"distending 1\n",
"referredtake 1\n",
"worthwhile 2\n",
"simper 1\n",
"knowthis 1\n",
"friendour 1\n",
"merge 1\n",
"nativewho 1\n",
"faceas 1\n",
"profounder 2\n",
"medley 3\n",
"chestnuts 1\n",
"veinous 1\n",
"whatyoumaycallit 1\n",
"agesand 1\n",
"exactlya 1\n",
"bean 1\n",
"farmyards 1\n",
"neglectfully 1\n",
"assaults 1\n",
"sieges 1\n",
"endowments 3\n",
"peepy 1\n",
"cupids 2\n",
"cavaliers 1\n",
"orcarker 1\n",
"ridingwhip 1\n",
"treesremainedclose 1\n",
"itselfnot 1\n",
"slovenly 2\n",
"menbefore 1\n",
"timesthree 1\n",
"steered 6\n",
"mouldered 1\n",
"decorators 1\n",
"platforms 1\n",
"infusionfrom 1\n",
"firmlyand 1\n",
"infused 4\n",
"scoopful 2\n",
"flycages 1\n",
"flowering 1\n",
"gingerbeer 1\n",
"aldermen 1\n",
"nodand 1\n",
"pigtailof 1\n",
"monosyllabic 1\n",
"intelligibility 2\n",
"swooning 2\n",
"evasion 1\n",
"relieves 1\n",
"timeshe 1\n",
"fawner 1\n",
"workpeople 1\n",
"florencean 1\n",
"grosvenor 12\n",
"iyes 3\n",
"scant 2\n",
"oldyoung 1\n",
"dressthe 1\n",
"roomfor 1\n",
"smirked 1\n",
"broods 1\n",
"hazy 1\n",
"attires 1\n",
"tankard 1\n",
"cradles 1\n",
"weddingguest 1\n",
"congratulatory 2\n",
"encroach 2\n",
"motherwhich 1\n",
"adjourn 1\n",
"unapplauded 1\n",
"enrols 1\n",
"reproduces 1\n",
"hidingplaces 1\n",
"confectioner 2\n",
"garnish 2\n",
"loveknots 1\n",
"disobliging 1\n",
"alludes 1\n",
"drumsticks 1\n",
"cower 1\n",
"dauntless 2\n",
"obscurest 2\n",
"norwester 1\n",
"changeshardly 1\n",
"tootsmister 1\n",
"stamina 2\n",
"becalmed 2\n",
"inwisible 1\n",
"welladmirable 1\n",
"finchley 1\n",
"lookin 1\n",
"sortfor 1\n",
"benison 1\n",
"penknife 3\n",
"cures 1\n",
"highsted 1\n",
"fended 2\n",
"soundings 1\n",
"foregather 1\n",
"rowling 2\n",
"fitter 1\n",
"furnitureits 1\n",
"screeching 1\n",
"constellation 3\n",
"straightwaythis 1\n",
"thinkits 1\n",
"sunsetand 1\n",
"unskilfully 1\n",
"dofor 1\n",
"butpardon 1\n",
"bebut 1\n",
"theytheyre 1\n",
"moreas 1\n",
"seasand 1\n",
"directionalways 1\n",
"dobut 2\n",
"irresolutionsat 1\n",
"entertainer 2\n",
"mistenshrouded 1\n",
"heryou 1\n",
"pilfering 1\n",
"deprecatingly 1\n",
"appearancefor 1\n",
"expostulating 1\n",
"embut 1\n",
"frenchfor 1\n",
"herhow 1\n",
"garniture 1\n",
"unexacting 1\n",
"plough 1\n",
"longforbidden 1\n",
"boweddown 2\n",
"dearwith 1\n",
"phrasethat 1\n",
"subjoined 1\n",
"anythinghuman 1\n",
"whereon 1\n",
"berkshire 1\n",
"shire 1\n",
"occasiongoesjack 1\n",
"lobby 4\n",
"illmatched 2\n",
"dessertknives 1\n",
"unadmired 1\n",
"splendourif 1\n",
"eyeglasses 1\n",
"curdling 1\n",
"byeplaces 1\n",
"occasionthis 1\n",
"whatshisnamenot 1\n",
"bythese 1\n",
"dombeyswhich 1\n",
"removes 1\n",
"careif 1\n",
"vindication 2\n",
"sayfriend 1\n",
"madamso 1\n",
"predisposed 1\n",
"himto 2\n",
"dentist 1\n",
"apposite 3\n",
"youngand 1\n",
"occupationbecame 1\n",
"swart 4\n",
"handyou 1\n",
"vents 1\n",
"indisputable 5\n",
"implication 3\n",
"planet 2\n",
"leathersas 1\n",
"siris 1\n",
"toxearnestly 1\n",
"learndear 1\n",
"sniggering 1\n",
"musings 2\n",
"daybook 2\n",
"britons 8\n",
"shirtfront 1\n",
"throwd 1\n",
"thenceforth 4\n",
"dimondsand 1\n",
"brewed 2\n",
"tonnage 5\n",
"eachclaimed 1\n",
"pacifically 1\n",
"decoyed 2\n",
"foster 2\n",
"festers 1\n",
"indifferencehis 1\n",
"partakeor 1\n",
"blesses 1\n",
"justifies 3\n",
"temporise 2\n",
"undeterred 1\n",
"beetle 1\n",
"domber 2\n",
"jog 2\n",
"bagstockjosephyour 1\n",
"reposeand 2\n",
"numbness 1\n",
"fluctuated 2\n",
"closetoothed 1\n",
"lobstereyes 1\n",
"beautyand 1\n",
"importunately 1\n",
"fumbled 1\n",
"fortheir 1\n",
"undutifully 1\n",
"whatshisnameand 1\n",
"namesfor 1\n",
"trackless 3\n",
"madrigal 1\n",
"tumbles 1\n",
"masterpieces 1\n",
"bitherstoneno 1\n",
"rubicon 1\n",
"insinuation 2\n",
"nowaint 1\n",
"itsits 2\n",
"subjectswhich 1\n",
"areprovided 1\n",
"insinuations 1\n",
"eyessits 1\n",
"namesmajorthat 1\n",
"thickening 3\n",
"languagesays 1\n",
"recognises 2\n",
"foley 1\n",
"strew 1\n",
"disembarrassed 2\n",
"enthralment 1\n",
"unfix 2\n",
"placequite 1\n",
"casteand 1\n",
"interestone 1\n",
"wellmay 1\n",
"arrogantly 1\n",
"pridei 1\n",
"prouddelightedto 1\n",
"placefor 1\n",
"venturedwith 1\n",
"chimneyshelf 1\n",
"hardhow 1\n",
"publish 2\n",
"untended 1\n",
"mindthat 1\n",
"bedabbled 2\n",
"thisto 1\n",
"doand 1\n",
"jubilee 1\n",
"demerits 2\n",
"missa 1\n",
"maidshe 1\n",
"voicefor 1\n",
"ably 1\n",
"grassed 1\n",
"records 3\n",
"reft 1\n",
"dombeyhow 1\n",
"breasthow 1\n",
"partnerships 1\n",
"dombeyhe 1\n",
"orbs 1\n",
"pigeonfancying 1\n",
"awanting 1\n",
"loveyasked 1\n",
"dispatcha 1\n",
"smilebut 1\n",
"redounding 1\n",
"certainties 1\n",
"blackening 1\n",
"softener 1\n",
"demerit 2\n",
"exasperations 1\n",
"inundate 1\n",
"substantially 1\n",
"thereas 1\n",
"goodfellowship 2\n",
"thwarted 1\n",
"understandthe 1\n",
"bellsthat 1\n",
"meflorence 1\n",
"staircasegallery 1\n",
"adjuration 5\n",
"ekalled 1\n",
"comewho 1\n",
"songbook 2\n",
"knighterrant 1\n",
"iid 1\n",
"occurhe 1\n",
"casebottle 3\n",
"reciprocating 1\n",
"dimond 2\n",
"girlcould 1\n",
"gonetoo 1\n",
"housewifery 1\n",
"himwere 1\n",
"daintily 1\n",
"outfitat 1\n",
"wiwid 1\n",
"sayingwalrheshes 1\n",
"hurricanes 1\n",
"gales 2\n",
"tellthat 1\n",
"shipwrecksive 1\n",
"admiralthat 1\n",
"lastno 1\n",
"anddont 1\n",
"prettyand 1\n",
"plunges 2\n",
"himuntil 1\n",
"abovestairs 1\n",
"whatand 2\n",
"himwalters 1\n",
"householder 1\n",
"aboutmiss 1\n",
"esteemin 1\n",
"howsumever 1\n",
"deficienciestheyre 1\n",
"seamanlike 2\n",
"disguises 1\n",
"friendsaw 1\n",
"bookfor 1\n",
"irksomely 1\n",
"nowonly 1\n",
"warmingpan 1\n",
"haunting 4\n",
"joejoe 1\n",
"worldas 1\n",
"acquaintedthat 2\n",
"criminate 1\n",
"andain 1\n",
"excuseeven 1\n",
"huffs 1\n",
"rebuffs 2\n",
"newlydevised 1\n",
"soda 1\n",
"enormities 1\n",
"worships 1\n",
"allsufficient 2\n",
"haggled 1\n",
"parrotcertain 1\n",
"trolling 1\n",
"tokeep 1\n",
"fluency 1\n",
"nowherenot 1\n",
"petulant 1\n",
"persecutionafter 1\n",
"piecesshe 1\n",
"bloodhis 1\n",
"pricing 1\n",
"rewelation 2\n",
"meneed 1\n",
"youenclosed 1\n",
"renderwhich 1\n",
"infor 1\n",
"lucifers 1\n",
"corruptly 1\n",
"unlessunless 1\n",
"halfconfidence 1\n",
"hidingthough 1\n",
"thankfulquiethumbleanything 1\n",
"gainsand 1\n",
"sleepin 1\n",
"timeif 1\n",
"waxed 2\n",
"angelor 1\n",
"unnecessarty 1\n",
"sicily 4\n",
"againdont 1\n",
"trinket 1\n",
"knavery 3\n",
"shameto 1\n",
"objectdriven 1\n",
"othermy 1\n",
"higherthe 1\n",
"chastity 1\n",
"creaturethen 1\n",
"halfmoment 1\n",
"drivelling 1\n",
"vaunts 1\n",
"continuinghis 1\n",
"reptile 3\n",
"planshis 1\n",
"schemesimpelled 1\n",
"nighthome 1\n",
"dupe 3\n",
"unclosed 2\n",
"quays 3\n",
"watercarriers 1\n",
"fishingboats 1\n",
"mooring 1\n",
"suffusion 1\n",
"signallights 1\n",
"himwas 1\n",
"meisbut 1\n",
"ownif 1\n",
"elapsing 1\n",
"shipmets 1\n",
"supercargo 2\n",
"walterss 2\n",
"creatureleast 1\n",
"allpervading 1\n",
"walterearlybefore 1\n",
"excursive 1\n",
"aor 1\n",
"blindplace 1\n",
"comfortableat 1\n",
"justicehis 1\n",
"figtrees 1\n",
"adwentures 1\n",
"seduces 1\n",
"theglancing 1\n",
"adwenturs 1\n",
"firstthat 1\n",
"timesthat 1\n",
"homeard 1\n",
"togetherthat 1\n",
"comported 5\n",
"helephant 1\n",
"twicet 1\n",
"miffbut 1\n",
"marriedonly 1\n",
"hassock 1\n",
"overspreads 1\n",
"ninetyfour 1\n",
"interpolates 1\n",
"sneezy 1\n",
"handgives 1\n",
"lipskisses 1\n",
"outstrips 1\n",
"voracious 2\n",
"confides 2\n",
"prosperousis 1\n",
"bankrupts 1\n",
"accountants 4\n",
"unburied 2\n",
"perchbut 1\n",
"bnor 1\n",
"rollings 1\n",
"fusilladed 1\n",
"longtightened 1\n",
"instructors 1\n",
"monitors 1\n",
"coolingstuff 1\n",
"characterand 1\n",
"allys 1\n",
"agethere 1\n",
"readread 1\n",
"sophistry 3\n",
"adjourning 1\n",
"futurity 1\n",
"stockingswho 1\n",
"daws 1\n",
"watchguard 1\n",
"upstroke 1\n",
"overrespectable 1\n",
"axioms 1\n",
"weatherproof 1\n",
"herds 2\n",
"snuffy 2\n",
"napless 4\n",
"auctioneer 2\n",
"bedwinches 2\n",
"vans 1\n",
"carpetcaps 1\n",
"acrimonious 1\n",
"peculiarso 1\n",
"ejectment 1\n",
"inginedriver 1\n",
"flyvan 2\n",
"ogresss 1\n",
"condiments 1\n",
"memoryhaunted 1\n",
"babyhope 1\n",
"morehis 1\n",
"straightforard 1\n",
"bonneted 1\n",
"cincinnatus 2\n",
"solemnised 1\n",
"ini 1\n",
"intellectsusan 1\n",
"periodcannotallowmy 1\n",
"mandont 1\n",
"theirtheirtoasts 1\n",
"banishers 1\n",
"meansthat 1\n",
"jeers 1\n",
"conwoy 1\n",
"entrapment 1\n",
"tenderer 1\n",
"childrenonetwostop 1\n",
"boring 1\n",
"screwzera 1\n",
"yolk 2\n",
"housefront 2\n",
"ambelieve 1\n",
"worldwhich 1\n",
"experiencevery 1\n",
"gayhandsome 1\n",
"toolet 1\n",
"infrequently 1\n",
"fortuneswhich 1\n",
"pipewhich 1\n",
"sensationdoes 1\n",
"grandpa 1\n",
"nightplainest 1\n",
"greetingplace 1\n",
"secluding 1\n",
"wwwgutenbergorgdonate 2\n",
"dorrit 974\n",
"selftormentor 2\n",
"circumlocution 88\n",
"merdle 387\n",
"mindseye 1\n",
"tracts 1\n",
"wayside 4\n",
"notched 1\n",
"deteriorated 2\n",
"waiving 1\n",
"vecchia 4\n",
"casethe 1\n",
"gurgled 2\n",
"lyons 7\n",
"strachino 1\n",
"wineagain 1\n",
"dreadmore 1\n",
"obliquely 1\n",
"lag 1\n",
"omelette 1\n",
"cigarettes 3\n",
"cosmopolitan 2\n",
"vaud 1\n",
"intriguersyour 1\n",
"goldkept 1\n",
"propagated 1\n",
"slanders 1\n",
"machinations 1\n",
"amicablyi 1\n",
"advert 1\n",
"inclinings 2\n",
"animallike 1\n",
"speciesthe 1\n",
"embalming 1\n",
"plains 3\n",
"parapetwall 1\n",
"fiveandfifty 1\n",
"tattycoram 59\n",
"meagless 22\n",
"usno 1\n",
"beadlean 1\n",
"coram 3\n",
"yoursmay 1\n",
"petsabove 1\n",
"bankdesk 1\n",
"ironed 1\n",
"shipped 1\n",
"professors 2\n",
"nextnothing 1\n",
"restnobody 1\n",
"growingup 1\n",
"plaitil 1\n",
"indifferencethis 1\n",
"falteredexpecting 1\n",
"sweepings 1\n",
"satisfyand 1\n",
"leavesas 1\n",
"glooming 1\n",
"idolaters 1\n",
"chaymaid 2\n",
"gome 1\n",
"jacktowels 1\n",
"moored 3\n",
"fortell 1\n",
"odorous 1\n",
"larderher 1\n",
"oneoh 1\n",
"saysand 1\n",
"grimmer 1\n",
"impale 1\n",
"uponto 1\n",
"wellshaft 1\n",
"portwine 2\n",
"vouchers 1\n",
"progressively 1\n",
"afflictedjustly 1\n",
"righteously 1\n",
"bargainsi 1\n",
"violentlystill 1\n",
"almostbefore 1\n",
"bargains 1\n",
"circularly 1\n",
"againplacing 1\n",
"hydraulic 1\n",
"gradation 1\n",
"sunbeamsgot 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"whirlwinds 1\n",
"undertakerlike 1\n",
"beercasks 1\n",
"incarcerated 2\n",
"itneatly 1\n",
"unpack 1\n",
"handsrings 1\n",
"feari 1\n",
"insolvency 4\n",
"himtrembling 1\n",
"illsmelling 1\n",
"brandier 1\n",
"childbed 1\n",
"flycatcher 1\n",
"enunciating 2\n",
"fifties 1\n",
"chinked 4\n",
"badgered 3\n",
"prisonous 1\n",
"streety 1\n",
"wheezed 1\n",
"generationa 1\n",
"beneficence 1\n",
"mops 1\n",
"dogsmeat 1\n",
"plasterer 8\n",
"symposium 1\n",
"hideandseek 1\n",
"undetermined 2\n",
"ithow 1\n",
"assorted 2\n",
"dayschools 1\n",
"collegerooms 1\n",
"ruiner 1\n",
"brewery 1\n",
"billingsgate 1\n",
"plated 1\n",
"wallwhen 1\n",
"butdont 1\n",
"birthplace 2\n",
"pepperbox 1\n",
"unclemr 1\n",
"amys 8\n",
"friendat 1\n",
"tipas 1\n",
"knowis 1\n",
"ratherhe 1\n",
"anybodyof 1\n",
"remanded 1\n",
"factyou 1\n",
"sometimeshemit 1\n",
"ihai 5\n",
"beenhaunfortunately 1\n",
"thisahemthis 1\n",
"tavernestablishment 1\n",
"tribune 2\n",
"tobaccoashes 1\n",
"coopedup 1\n",
"longinitiated 1\n",
"snuggerys 1\n",
"spittoons 1\n",
"steeple 1\n",
"raking 1\n",
"trough 1\n",
"buttonholes 1\n",
"battledore 1\n",
"warpaint 1\n",
"cannotwho 1\n",
"governmenthigh 1\n",
"sheand 1\n",
"earns 2\n",
"interweaving 1\n",
"opaque 4\n",
"pronoun 1\n",
"broomhandles 1\n",
"lemonades 1\n",
"dlicious 2\n",
"familyvault 1\n",
"perceivinghow 1\n",
"minuted 1\n",
"extollable 1\n",
"acolytes 1\n",
"intermarried 1\n",
"parsimony 2\n",
"fluffiest 1\n",
"abouttonnageor 1\n",
"gorgeousness 2\n",
"lawrence 2\n",
"copartnership 1\n",
"dogbox 1\n",
"clennamand 1\n",
"faceredder 1\n",
"matterofcourse 1\n",
"persisting 2\n",
"thirstier 1\n",
"silversmiths 1\n",
"liqueurs 1\n",
"drainage 1\n",
"dinesupwhat 1\n",
"biraud 2\n",
"inferiority 1\n",
"hola 4\n",
"splintery 1\n",
"avignon 1\n",
"pont 1\n",
"esprit 1\n",
"stoning 1\n",
"defames 1\n",
"notoriously 2\n",
"tambourworker 1\n",
"deems 1\n",
"outgeneralling 1\n",
"flanneljacketed 1\n",
"casby 52\n",
"selfhelpfulness 1\n",
"chauntermeaning 1\n",
"horsesand 1\n",
"plornishs 15\n",
"gentlygrowling 1\n",
"plornishes 4\n",
"eruptive 1\n",
"houseone 1\n",
"parlourhearth 1\n",
"divingbell 2\n",
"pleasehave 1\n",
"wideskirted 1\n",
"coaly 4\n",
"knowat 1\n",
"arthuri 3\n",
"dissenter 1\n",
"arthurmr 1\n",
"lovequarrel 1\n",
"separationpray 1\n",
"unslackened 1\n",
"drawingroomthere 1\n",
"signalled 1\n",
"applepienothing 1\n",
"gandersand 1\n",
"dislikespeculiarities 1\n",
"graysinn 1\n",
"mermaidbut 1\n",
"revolvency 1\n",
"smithfield 2\n",
"prooshan 1\n",
"thenyou 1\n",
"provedwas 1\n",
"goldlaced 1\n",
"goneout 1\n",
"mightbut 1\n",
"shyand 1\n",
"thirstyi 1\n",
"eatable 1\n",
"abovewho 1\n",
"encroaching 1\n",
"coarsely 1\n",
"thebythebye 1\n",
"betide 1\n",
"watchlight 1\n",
"liege 3\n",
"itlean 1\n",
"alleviation 1\n",
"cottageresidence 1\n",
"homehe 1\n",
"indentures 1\n",
"rebuffed 1\n",
"myriads 2\n",
"migratory 1\n",
"herculaneum 2\n",
"filigree 1\n",
"whipcord 1\n",
"tattooing 1\n",
"dirtcheap 1\n",
"guercino 1\n",
"del 1\n",
"titian 1\n",
"blackbirds 1\n",
"cylinders 2\n",
"silkgown 1\n",
"buchans 2\n",
"businessnot 1\n",
"towingpath 1\n",
"insentient 1\n",
"itsa 1\n",
"stillwhen 1\n",
"clarence 4\n",
"nephewtolorddecimus 1\n",
"pensioned 1\n",
"portfolios 1\n",
"mahomets 1\n",
"cashiered 1\n",
"ecod 2\n",
"conversationless 1\n",
"paled 1\n",
"breakfastthat 1\n",
"leafless 1\n",
"featherless 1\n",
"prisonlock 1\n",
"nonresident 1\n",
"nursling 1\n",
"lockkeeper 1\n",
"flouting 1\n",
"chiveries 1\n",
"misfortunea 1\n",
"plumcoloured 2\n",
"marshalling 1\n",
"threeandsixpence 2\n",
"toono 1\n",
"suremiss 1\n",
"lowlyand 1\n",
"agesa 1\n",
"humblymay 1\n",
"sprigsmere 1\n",
"knownswelled 1\n",
"directionpost 1\n",
"collegeyardof 1\n",
"delinquents 1\n",
"gaslight 2\n",
"wexation 1\n",
"qualitieshe 1\n",
"ithemits 1\n",
"quitequite 1\n",
"martin 3\n",
"toohatoo 1\n",
"himon 1\n",
"brothersaccount 1\n",
"pompously 2\n",
"frequenters 2\n",
"reservoir 1\n",
"tank 1\n",
"cavendish 7\n",
"parlance 1\n",
"voluptuously 1\n",
"exactingbird 1\n",
"hebird 1\n",
"clawfull 1\n",
"millennium 1\n",
"sorrydont 1\n",
"bewould 1\n",
"dolefullest 1\n",
"wretchedest 1\n",
"allwho 1\n",
"byword 2\n",
"toowith 2\n",
"seasoning 2\n",
"brocaded 1\n",
"inappeasable 1\n",
"merchantprinces 1\n",
"understandpatriotic 1\n",
"countryif 1\n",
"countieslying 1\n",
"societysbenefit 1\n",
"textures 1\n",
"pinting 1\n",
"itsounded 1\n",
"iteven 1\n",
"faithfullyin 1\n",
"papershere 1\n",
"businesssuch 1\n",
"perhapsmight 1\n",
"safetylamp 1\n",
"halfshare 1\n",
"trapdoors 1\n",
"clinks 1\n",
"filings 1\n",
"stilly 1\n",
"peoplevery 1\n",
"bowli 1\n",
"thathappened 1\n",
"seedshop 1\n",
"clennamor 1\n",
"rifling 1\n",
"areeh 1\n",
"winegariest 1\n",
"reinvested 1\n",
"reallyinvalid 1\n",
"tiresomest 1\n",
"clennammr 1\n",
"stitches 1\n",
"grubber 9\n",
"alereport 1\n",
"delighton 1\n",
"covenanting 1\n",
"highland 1\n",
"ruggs 7\n",
"uncollected 1\n",
"durham 1\n",
"dunstable 1\n",
"baptistteapot 1\n",
"baptistdustpan 1\n",
"baptistflourdredger 1\n",
"sunhell 1\n",
"betweenwhiles 1\n",
"workmanlike 1\n",
"mooning 1\n",
"hollowand 1\n",
"mooncalves 1\n",
"makeshifts 1\n",
"coalcellars 1\n",
"primeval 1\n",
"bohemians 2\n",
"follower 3\n",
"pivots 1\n",
"augustus 3\n",
"odiously 1\n",
"namemiss 1\n",
"unenvious 1\n",
"judgmentall 1\n",
"bastille 1\n",
"molehills 1\n",
"inandin 1\n",
"scrofulously 1\n",
"peppermintdrops 1\n",
"polls 1\n",
"caravanserai 1\n",
"fogs 1\n",
"advertisers 1\n",
"twittering 1\n",
"gardenhat 1\n",
"scarcity 3\n",
"beguiling 1\n",
"sequences 1\n",
"recluses 1\n",
"countinghouses 2\n",
"fraying 1\n",
"franklyfrankness 1\n",
"acalling 1\n",
"authorised 2\n",
"speechlessly 2\n",
"cloaked 2\n",
"yeast 1\n",
"forgetdoes 1\n",
"hisof 1\n",
"elaborated 1\n",
"halfpint 1\n",
"thoroughfarehad 1\n",
"chloe 3\n",
"phyllis 3\n",
"strephon 2\n",
"flutterings 1\n",
"halfpennyworth 1\n",
"nandys 2\n",
"abinding 1\n",
"meritoriously 1\n",
"pipingly 1\n",
"neckhandkerchief 1\n",
"andaltogether 1\n",
"thatspirit 1\n",
"dohaextend 1\n",
"reedi 1\n",
"fatherbut 1\n",
"decayingold 1\n",
"gonepulverisedcrushed 1\n",
"ahuma 9\n",
"timehaor 1\n",
"thathumthehadelicacy 1\n",
"mayharespond 1\n",
"visitorwife 1\n",
"reindeer 1\n",
"bringingup 2\n",
"workworn 1\n",
"preys 1\n",
"distilled 1\n",
"highlycharged 1\n",
"rampancy 1\n",
"rugga 1\n",
"sirthatll 1\n",
"pantingly 2\n",
"schedule 1\n",
"areyou 1\n",
"inroads 1\n",
"impounding 1\n",
"piqued 3\n",
"adjustment 1\n",
"countrythose 1\n",
"buyers 1\n",
"inexcusable 1\n",
"parable 1\n",
"saidtototo 1\n",
"hypochondriacal 1\n",
"governorexpression 1\n",
"despatchbox 1\n",
"barnacleism 1\n",
"clique 1\n",
"muchrespected 1\n",
"worthiness 1\n",
"subtracted 1\n",
"consanguinity 1\n",
"clues 1\n",
"exultant 1\n",
"termson 1\n",
"fatherprepared 1\n",
"beenhawell 1\n",
"ihumhai 1\n",
"ithait 1\n",
"gracing 1\n",
"bumper 1\n",
"embezzled 1\n",
"debtfound 1\n",
"cringed 1\n",
"obtrusively 1\n",
"kindhere 1\n",
"stalks 1\n",
"refuges 2\n",
"ridingsaddles 1\n",
"sleepingrooms 1\n",
"proverbially 2\n",
"fourgon 2\n",
"bruise 2\n",
"touristtravellers 1\n",
"presides 1\n",
"weightily 2\n",
"pocketflask 1\n",
"courtesies 1\n",
"ourhaour 1\n",
"daintiest 1\n",
"martinet 1\n",
"fourinhand 1\n",
"archdeacon 2\n",
"countywidower 4\n",
"transcendently 1\n",
"grooves 1\n",
"properest 1\n",
"humby 2\n",
"behakept 1\n",
"behumsought 1\n",
"indelicate 1\n",
"mehafrom 1\n",
"familymy 1\n",
"couriers 3\n",
"bestirring 1\n",
"reallyhere 1\n",
"bigodd 1\n",
"herwell 1\n",
"shouldhacountermand 1\n",
"illexecuted 1\n",
"mountainchasm 1\n",
"imprisonmentall 1\n",
"orangetrees 2\n",
"travellingcloak 2\n",
"looped 1\n",
"olives 2\n",
"stoneterraced 1\n",
"postinghouses 1\n",
"banquets 1\n",
"doorwhen 1\n",
"nothingunless 1\n",
"seldomwhile 1\n",
"enoughi 1\n",
"everneverout 1\n",
"marquises 1\n",
"palatial 1\n",
"humretirement 1\n",
"affluentcomparativelyand 1\n",
"buthawas 1\n",
"companyon 1\n",
"humam 1\n",
"youryour 2\n",
"ashamiss 1\n",
"amyhumyou 1\n",
"unspoiled 2\n",
"ihashould 1\n",
"forha 1\n",
"humyour 1\n",
"mope 1\n",
"inconsistencyhanot 1\n",
"societyand 1\n",
"blackball 1\n",
"myselfare 1\n",
"gowani 1\n",
"washasparkler 1\n",
"contraryto 1\n",
"willhawe 1\n",
"anchorage 2\n",
"citywhere 1\n",
"papering 1\n",
"servantwho 1\n",
"dorritgo 1\n",
"halfchoked 1\n",
"notwithstandinglaughing 1\n",
"noddles 1\n",
"emissaries 1\n",
"danteknown 1\n",
"shootingshoes 1\n",
"cricketshoes 1\n",
"somehatestimonial 1\n",
"behamutually 1\n",
"merman 3\n",
"littleness 1\n",
"dayeverything 1\n",
"persistency 1\n",
"excepthaas 1\n",
"theha 2\n",
"humhigh 1\n",
"greathumprobability 1\n",
"harped 1\n",
"benefiting 1\n",
"workroughened 1\n",
"overpersuaded 1\n",
"minuting 1\n",
"thirtytwo 1\n",
"tasksuch 1\n",
"dayarthur 1\n",
"bothany 1\n",
"mystifications 1\n",
"inexpert 1\n",
"oversensitive 1\n",
"accusatory 2\n",
"therewith 1\n",
"allongers 2\n",
"oncewhen 1\n",
"armtattycoram 1\n",
"adelphithe 1\n",
"overhangs 1\n",
"fishmarket 1\n",
"gladiators 2\n",
"arthurdear 1\n",
"mantuamaking 1\n",
"pelerine 1\n",
"steamer 1\n",
"childanybodys 1\n",
"depositories 1\n",
"forgers 1\n",
"churchvaults 1\n",
"incertitude 1\n",
"complimentshomage 1\n",
"alldevoted 1\n",
"dryer 1\n",
"teetotum 1\n",
"thisunless 1\n",
"circumstancesnot 1\n",
"nightsky 1\n",
"gregoriana 1\n",
"yardo 1\n",
"beenyet 1\n",
"othersso 1\n",
"homesicknessthat 1\n",
"excusably 1\n",
"establisher 1\n",
"juryman 3\n",
"stargoing 1\n",
"jurymen 3\n",
"lowcomedy 1\n",
"argumentmeaning 1\n",
"correctlyand 1\n",
"mannersbut 1\n",
"ingratiatory 1\n",
"decimusfor 1\n",
"perennially 1\n",
"overtopped 1\n",
"sublimities 1\n",
"whyyesi 1\n",
"guzzling 1\n",
"wayspirits 1\n",
"marrow 1\n",
"gloomiest 1\n",
"chieftains 1\n",
"ferdinandit 1\n",
"candrag 1\n",
"slyest 1\n",
"blinkiter 2\n",
"lopping 1\n",
"ybut 1\n",
"prevalence 1\n",
"skirmished 1\n",
"muddily 1\n",
"unappropriated 1\n",
"grocery 2\n",
"scaffolds 1\n",
"sheepishness 1\n",
"steamvessel 1\n",
"altros 1\n",
"resourcesenormous 2\n",
"capitalgovernment 1\n",
"longdeferred 1\n",
"connectiongovernment 1\n",
"itselffar 1\n",
"flightily 1\n",
"fannytherefore 1\n",
"shelve 1\n",
"andhaving 1\n",
"beehive 1\n",
"assembliesat 1\n",
"draperies 1\n",
"myselfnext 2\n",
"thehacharacter 1\n",
"inoffensive 2\n",
"calligraphic 1\n",
"thehathe 2\n",
"chargehadifficult 1\n",
"cementing 1\n",
"undermost 1\n",
"airto 1\n",
"subduers 1\n",
"knowwhere 1\n",
"outwhen 1\n",
"involveda 1\n",
"itindeed 1\n",
"womani 1\n",
"mendicants 1\n",
"criticising 1\n",
"stateequipage 1\n",
"waswhen 1\n",
"behumworthy 1\n",
"myhumconscience 1\n",
"easilyas 1\n",
"anythingwhen 1\n",
"committal 1\n",
"linenfelt 1\n",
"deposing 1\n",
"backpassages 1\n",
"humhighly 1\n",
"insinuatingly 1\n",
"unmanageable 1\n",
"arrangementhumthe 1\n",
"sohumvast 1\n",
"preferencepeople 1\n",
"denominationsin 1\n",
"auriferously 1\n",
"collegeperhaps 1\n",
"brandybottle 1\n",
"ofhuma 1\n",
"ransom 1\n",
"whomhato 1\n",
"handbill 5\n",
"transitory 1\n",
"transformation 1\n",
"hackneycabriolet 1\n",
"firsthand 1\n",
"toharetain 1\n",
"knowhawhy 1\n",
"waterloo 1\n",
"childthat 1\n",
"ihathought 1\n",
"facefor 1\n",
"nextwere 1\n",
"andhumyou 1\n",
"howhahow 1\n",
"yourhaold 1\n",
"youha 1\n",
"lovegift 3\n",
"ravishing 2\n",
"festered 1\n",
"fiercelooking 1\n",
"ruinsown 1\n",
"cutthroat 1\n",
"watertanks 1\n",
"chaunting 1\n",
"iwell 1\n",
"beganha 1\n",
"athaany 1\n",
"orhumwould 1\n",
"gladdens 1\n",
"youhumyou 1\n",
"ishumsadly 1\n",
"vasthaacquisition 1\n",
"ishavery 1\n",
"ambitionhumpurpose 1\n",
"positionha 1\n",
"hashamanifested 1\n",
"orhumi 1\n",
"ofhumthe 1\n",
"thishainteresting 1\n",
"kitchenand 1\n",
"thehumconferred 1\n",
"buthaalways 1\n",
"semiofficial 1\n",
"supplant 1\n",
"sleevebuttons 1\n",
"seaboard 1\n",
"oozy 1\n",
"turbulence 1\n",
"communicationwith 1\n",
"doornever 1\n",
"charlotte 2\n",
"maamexulting 1\n",
"dawes 2\n",
"meaningand 1\n",
"endi 1\n",
"dressedup 1\n",
"marsigliamarseilles 1\n",
"andaltro 1\n",
"horny 1\n",
"finchingfor 1\n",
"chilblains 1\n",
"frightfullest 1\n",
"inculcating 1\n",
"boyhoodnot 1\n",
"treble 1\n",
"durstnt 2\n",
"stealings 1\n",
"treads 2\n",
"relighted 1\n",
"screwpower 1\n",
"noa 1\n",
"coachhorses 2\n",
"windowcurtain 1\n",
"opine 1\n",
"fortuneto 1\n",
"tweezers 1\n",
"lumpish 1\n",
"daintier 1\n",
"diluent 1\n",
"adaptable 1\n",
"heavilymade 1\n",
"veined 2\n",
"redly 1\n",
"veining 1\n",
"masterthat 1\n",
"moralising 2\n",
"subduer 1\n",
"leveller 1\n",
"leasthe 1\n",
"disappearedwas 1\n",
"selfexploded 1\n",
"unsorted 1\n",
"officestool 1\n",
"ourself 2\n",
"wryness 1\n",
"crossfire 1\n",
"bankersaccount 1\n",
"invective 1\n",
"irongate 1\n",
"himsternlyswelled 1\n",
"middlefinger 1\n",
"cloudwhich 1\n",
"marshalseawhich 1\n",
"repainted 1\n",
"layers 1\n",
"demeaning 1\n",
"selections 1\n",
"mooshattonisha 1\n",
"overpoweringly 1\n",
"wellgot 1\n",
"ruddyheaded 1\n",
"washerwomans 1\n",
"detainers 1\n",
"everfrowning 1\n",
"adverb 1\n",
"butno 1\n",
"patientissamentally 1\n",
"writed 1\n",
"mellowinglosing 1\n",
"facile 1\n",
"gowana 6\n",
"thefollower 1\n",
"sakenot 1\n",
"hadand 1\n",
"tracery 1\n",
"refugee 1\n",
"jeremiahno 1\n",
"wontno 1\n",
"awakenedthat 1\n",
"intermix 1\n",
"suppressionaye 1\n",
"mouthperhaps 1\n",
"formssee 1\n",
"irreligious 1\n",
"jehovah 1\n",
"stings 1\n",
"practices 1\n",
"moneya 1\n",
"deceivednot 1\n",
"barbs 1\n",
"destroyedas 1\n",
"anigh 1\n",
"chairmakers 1\n",
"counterjerks 1\n",
"unpocketing 1\n",
"tiff 1\n",
"yearsby 1\n",
"givefor 1\n",
"controllable 1\n",
"diggers 3\n",
"promenading 1\n",
"partiesall 1\n",
"partiessatisfactory 1\n",
"peoplecasby 1\n",
"cocoanut 1\n",
"conjugating 1\n",
"untiringly 1\n",
"mourningthe 1\n",
"themfor 1\n",
"satisfiedor 1\n",
"unshaken 1\n",
"respondents 1\n",
"otherany 1\n",
"berries 1\n",
"autumntinted 1\n",
"fellowhand 1\n",
"pieshop 6\n",
"platters 1\n",
"elaborating 1\n",
"ribboned 1\n",
"varney 845\n",
"preskett 3\n",
"it 23858\n",
"give 1044\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"or 5887\n",
"under 1097\n",
"updated 10\n",
"franks 2\n",
"storr 3\n",
"sandra 2\n",
"brown 203\n",
"book 217\n",
"originally 19\n",
"dreadful 252\n",
"not 13680\n",
"also 511\n",
"malcolm 1\n",
"rymer 1\n",
"gratis 2\n",
"d 51\n",
"grace 54\n",
"e 126\n",
"lloyd 2\n",
"salisbury 1\n",
"square 85\n",
"booksellers 1\n",
"vampyre 540\n",
"thou 54\n",
"goblin 6\n",
"fleetstreet 1\n",
"iithe 1\n",
"its 3436\n",
"recovery 28\n",
"assistance 92\n",
"sir 2628\n",
"ivthe 1\n",
"night 1354\n",
"glance 258\n",
"bannerworth 497\n",
"mysterious 201\n",
"apparitions 5\n",
"viiithe 1\n",
"coffinthe 3\n",
"ixthe 1\n",
"hallthe 16\n",
"xiicharles 1\n",
"feelingsthe 2\n",
"xiiithe 1\n",
"admiral 735\n",
"his 23046\n",
"nelsons 7\n",
"lovers 24\n",
"scene 129\n",
"xxiiithe 1\n",
"advice 113\n",
"quarrelthe 2\n",
"xxvthe 1\n",
"opinionthe 2\n",
"letters 201\n",
"xxviithe 1\n",
"confidence 273\n",
"flora 824\n",
"admiration 119\n",
"exculpation 2\n",
"gardensthe 2\n",
"deadly 27\n",
"strugglethe 2\n",
"xxixa 1\n",
"rescue 32\n",
"varneys 59\n",
"danger 196\n",
"projected 16\n",
"anger 96\n",
"xxxviiimarchdales 1\n",
"xxxixthe 1\n",
"repudiation 5\n",
"principal 86\n",
"xlthe 1\n",
"leaving 262\n",
"xlviithe 1\n",
"body 355\n",
"lthe 1\n",
"lithe 4\n",
"vampyres 105\n",
"liithe 1\n",
"mob 212\n",
"scenepopular 2\n",
"superstition 40\n",
"townthe 2\n",
"jack 562\n",
"constable 5\n",
"lxiiithe 1\n",
"friend 996\n",
"lxvvarneys 1\n",
"lxviflora 1\n",
"advicemr 2\n",
"beautiful 363\n",
"villany 6\n",
"motherthe 2\n",
"episode 13\n",
"mrs 2994\n",
"lxxiiithe 1\n",
"general 973\n",
"lxxvithe 1\n",
"gardenthe 3\n",
"lxxviiithe 1\n",
"lxxxthe 1\n",
"corpsethe 2\n",
"lxxxiicharles 1\n",
"dangerous 88\n",
"lxxxiiithe 1\n",
"noblemanthe 2\n",
"nobleman 27\n",
"into 3731\n",
"escapethe 2\n",
"xcdr 1\n",
"mysterythe 2\n",
"xcithe 1\n",
"passage 153\n",
"xcvithe 1\n",
"success 120\n",
"leaves 118\n",
"as 16434\n",
"favours 10\n",
"existence 199\n",
"first 1829\n",
"took 1415\n",
"rise 95\n",
"norway 4\n",
"rapidly 144\n",
"regions 14\n",
"imaginations 8\n",
"mankind 54\n",
"must 1975\n",
"peculiarly 22\n",
"called 626\n",
"related 73\n",
"be 11172\n",
"found 1168\n",
"few 749\n",
"publisher 1\n",
"content 68\n",
"hands 987\n",
"sanguine 24\n",
"expectations 33\n",
"strongest 20\n",
"perchance 21\n",
"patronage 36\n",
"metropolitan 3\n",
"obliged 185\n",
"graves 17\n",
"air 637\n",
"pervades 3\n",
"ominous 10\n",
"precedes 4\n",
"elements 26\n",
"seem 291\n",
"them 4377\n",
"hurricane 10\n",
"city 187\n",
"lasted 56\n",
"hot 177\n",
"breath 227\n",
"wind 266\n",
"thought 2119\n",
"dream 166\n",
"sleep 295\n",
"sound 459\n",
"fairy 29\n",
"feet 328\n",
"trees 153\n",
"small 512\n",
"windows 188\n",
"direct 106\n",
"pelting 3\n",
"ice 33\n",
"rapt 8\n",
"so 8781\n",
"intensity 21\n",
"drowns 2\n",
"every 1195\n",
"surprise 274\n",
"only 2705\n",
"where 2126\n",
"mischief 55\n",
"walls 167\n",
"large 463\n",
"floor 190\n",
"looks 231\n",
"curiously 25\n",
"pieces 92\n",
"sun 218\n",
"portrait 128\n",
"pictures 67\n",
"pale 256\n",
"about 3349\n",
"twice 150\n",
"design 49\n",
"execution 72\n",
"era 2\n",
"damask 5\n",
"cornerscovered 1\n",
"lend 37\n",
"aspect 89\n",
"polished 40\n",
"oak 39\n",
"hail 29\n",
"resist 55\n",
"rain 114\n",
"spring 81\n",
"bedstead 33\n",
"clothing 20\n",
"arm 573\n",
"neck 224\n",
"ever 1413\n",
"gave 818\n",
"genius 88\n",
"slightly 115\n",
"name 829\n",
"slumbers 6\n",
"destroy 65\n",
"wakes 7\n",
"parted 89\n",
"exhibiting 11\n",
"teeth 178\n",
"glistened 7\n",
"shoulder 210\n",
"beauty 228\n",
"advancing 87\n",
"lightning 29\n",
"without 1945\n",
"torrent 7\n",
"intent 60\n",
"sufficient 135\n",
"loves 65\n",
"best 623\n",
"names 87\n",
"gentle 164\n",
"bewildering 6\n",
"distinctness 18\n",
"known 477\n",
"outside 285\n",
"fell 470\n",
"straight 183\n",
"standing 399\n",
"immediately 414\n",
"produces 18\n",
"ceased 117\n",
"trace 47\n",
"fro 81\n",
"mode 45\n",
"terriblebrighter 1\n",
"fire 716\n",
"mill 12\n",
"reflection 117\n",
"falls 36\n",
"dreadfulshe 1\n",
"leadshe 1\n",
"helphelphelphelp 1\n",
"throws 7\n",
"fix 28\n",
"flesh 42\n",
"folding 29\n",
"wide 148\n",
"helphelphelp 1\n",
"sat 1115\n",
"dreadfula 1\n",
"obtrude 8\n",
"bitterness 29\n",
"turns 67\n",
"tin 18\n",
"looking 1505\n",
"gliding 21\n",
"movement 231\n",
"literally 10\n",
"madthat 1\n",
"slowly 276\n",
"fascinated 13\n",
"effect 330\n",
"crouching 15\n",
"lost 457\n",
"prominent 28\n",
"hideousso 1\n",
"verge 16\n",
"proceed 65\n",
"clutched 16\n",
"unconscious 62\n",
"short 604\n",
"tremble 44\n",
"dragging 35\n",
"reality 77\n",
"left 1478\n",
"pillows 21\n",
"pause 184\n",
"indeed 921\n",
"full 657\n",
"breast 262\n",
"twining 6\n",
"held 519\n",
"followed 358\n",
"rounded 10\n",
"quivered 28\n",
"glassy 6\n",
"satisfactionhorrible 1\n",
"drags 2\n",
"grasp 65\n",
"plunge 14\n",
"seizes 5\n",
"gush 7\n",
"universal 42\n",
"stir 65\n",
"hear 757\n",
"harry 8\n",
"walked 448\n",
"my 11100\n",
"tap 26\n",
"voice 1043\n",
"gods 73\n",
"sake 270\n",
"get 1037\n",
"whom 1159\n",
"floras 53\n",
"mechanically 22\n",
"pistols 34\n",
"thickness 4\n",
"reached 279\n",
"fast 171\n",
"speak 706\n",
"violently 39\n",
"know 3734\n",
"liquid 10\n",
"shall 1524\n",
"misgives 1\n",
"happened 418\n",
"odd 108\n",
"succeeded 110\n",
"introducing 18\n",
"wallstill 1\n",
"harsh 21\n",
"engaged 187\n",
"slept 96\n",
"heralded 2\n",
"throwing 73\n",
"progress 88\n",
"contained 29\n",
"actually 87\n",
"useless 74\n",
"thrown 187\n",
"dull 163\n",
"seen 1044\n",
"easy 219\n",
"wonder 259\n",
"foreign 66\n",
"lands 5\n",
"barrel 10\n",
"ill 928\n",
"opportune 11\n",
"courage 122\n",
"energy 67\n",
"exclamation 29\n",
"fresh 204\n",
"tinthey 1\n",
"flashes 8\n",
"canine 3\n",
"turning 452\n",
"exclaimed 335\n",
"visited 57\n",
"aloud 110\n",
"rushed 145\n",
"weltering 2\n",
"emotions 54\n",
"except 315\n",
"deep 337\n",
"centuries 15\n",
"leant 7\n",
"end 552\n",
"ground 381\n",
"top 152\n",
"accents 31\n",
"cold 373\n",
"nowe 1\n",
"repeated 380\n",
"attempts 35\n",
"previous 92\n",
"escaping 17\n",
"miss 2009\n",
"killing 29\n",
"agreed 121\n",
"excessive 18\n",
"exchange 44\n",
"speed 79\n",
"difficult 208\n",
"commencement 20\n",
"grew 191\n",
"heath 5\n",
"ascertain 36\n",
"absorbed 63\n",
"gazing 108\n",
"fate 152\n",
"tears 478\n",
"truetrue 4\n",
"pictured 32\n",
"unprotected 8\n",
"rash 20\n",
"yourself 576\n",
"sight 341\n",
"flashing 22\n",
"household 86\n",
"enoughenough 2\n",
"thank 411\n",
"henrycome 1\n",
"entered 406\n",
"brought 667\n",
"mothers 195\n",
"tone 375\n",
"tonight 182\n",
"wrung 25\n",
"propped 16\n",
"grievous 15\n",
"tear 63\n",
"languid 11\n",
"circulation 8\n",
"love 1119\n",
"swallow 16\n",
"stimulating 9\n",
"slight 185\n",
"accession 10\n",
"collect 28\n",
"apparition 20\n",
"neckthere 1\n",
"distance 296\n",
"bled 9\n",
"rousing 18\n",
"depression 23\n",
"dare 256\n",
"sit 279\n",
"hers 120\n",
"chased 13\n",
"promise 175\n",
"understand 677\n",
"alluded 25\n",
"contour 1\n",
"wish 729\n",
"daylight 38\n",
"quickly 268\n",
"arrangement 49\n",
"remained 409\n",
"earnest 114\n",
"sway 7\n",
"wakeful 5\n",
"apprehension 50\n",
"infant 32\n",
"tired 127\n",
"physical 69\n",
"effectit 1\n",
"suns 11\n",
"nerves 25\n",
"shifted 8\n",
"conflicting 13\n",
"began 1220\n",
"means 467\n",
"fault 112\n",
"profound 78\n",
"himas 4\n",
"secret 271\n",
"remove 49\n",
"adapted 27\n",
"job 41\n",
"rare 45\n",
"blame 126\n",
"foolish 65\n",
"partially 21\n",
"blind 95\n",
"shield 21\n",
"perpetually 9\n",
"raves 2\n",
"wings 28\n",
"wander 21\n",
"believe 762\n",
"rendered 84\n",
"deliciously 2\n",
"heaviness 7\n",
"healthgiving 1\n",
"bodily 31\n",
"weakness 100\n",
"strong 430\n",
"glow 36\n",
"sadly 38\n",
"frightful 53\n",
"add 87\n",
"yesterday 109\n",
"laughed 221\n",
"bewildered 50\n",
"godgood 1\n",
"yield 40\n",
"supposition 40\n",
"lifted 135\n",
"sentiment 63\n",
"willingly 38\n",
"havei 2\n",
"suppose 431\n",
"inor 3\n",
"possibility 124\n",
"discarding 3\n",
"examine 30\n",
"dismal 71\n",
"show 395\n",
"cannoti 1\n",
"thanks 78\n",
"susceptible 20\n",
"mute 28\n",
"inhabits 2\n",
"always 1540\n",
"intend 42\n",
"during 434\n",
"listen 198\n",
"aware 164\n",
"reading 171\n",
"guided 34\n",
"join 67\n",
"permit 71\n",
"seek 143\n",
"wear 70\n",
"motive 55\n",
"sanction 5\n",
"intelligent 21\n",
"resided 9\n",
"dispense 8\n",
"cognizant 5\n",
"steed 2\n",
"absurd 33\n",
"generally 188\n",
"unwell 27\n",
"thief 21\n",
"grafted 4\n",
"waved 50\n",
"trotted 13\n",
"impressed 54\n",
"narrative 46\n",
"glaring 18\n",
"explain 123\n",
"occasionally 114\n",
"rob 255\n",
"fearfulit 1\n",
"discard 9\n",
"hark 39\n",
"ring 83\n",
"healthful 3\n",
"brutal 14\n",
"glazed 54\n",
"refer 54\n",
"housebreaker 2\n",
"worse 193\n",
"expect 200\n",
"mass 91\n",
"attaches 5\n",
"examined 41\n",
"season 30\n",
"insectshas 1\n",
"control 58\n",
"candid 15\n",
"perplexed 29\n",
"prudent 29\n",
"regards 50\n",
"jump 18\n",
"connected 128\n",
"justify 36\n",
"labouring 26\n",
"heedless 20\n",
"rational 25\n",
"arrived 229\n",
"chose 52\n",
"accomplish 33\n",
"calculation 21\n",
"levant 1\n",
"mahometans 1\n",
"description 71\n",
"european 24\n",
"consumption 6\n",
"dying 108\n",
"understood 334\n",
"started 180\n",
"pshaw 1\n",
"pages 22\n",
"believed 202\n",
"wounded 248\n",
"stupefaction 5\n",
"asleep 162\n",
"someone 176\n",
"compliments 64\n",
"domestic 103\n",
"sympathy 99\n",
"regarded 145\n",
"abbey 6\n",
"purchaser 6\n",
"poormuch 1\n",
"repress 15\n",
"familiarity 22\n",
"doubtless 24\n",
"advise 63\n",
"opposition 33\n",
"advantage 120\n",
"risen 77\n",
"urged 72\n",
"calmer 16\n",
"piece 169\n",
"reminds 12\n",
"rough 92\n",
"smells 5\n",
"wishing 98\n",
"fain 36\n",
"period 151\n",
"silently 115\n",
"bright 293\n",
"wasit 1\n",
"congratulate 40\n",
"quiet 362\n",
"admittance 7\n",
"lately 82\n",
"since 683\n",
"comparison 35\n",
"sadness 27\n",
"needed 89\n",
"illuminated 14\n",
"wonderful 144\n",
"cautious 42\n",
"sickening 9\n",
"lean 30\n",
"recovered 90\n",
"below 206\n",
"descending 26\n",
"grounds 41\n",
"prefer 58\n",
"suspicious 26\n",
"used 502\n",
"premises 45\n",
"haste 52\n",
"contemplate 37\n",
"henrys 22\n",
"isas 1\n",
"astonishment 76\n",
"higher 126\n",
"duty 297\n",
"struggling 41\n",
"righteous 3\n",
"hillock 6\n",
"increases 13\n",
"gained 65\n",
"capture 49\n",
"declared 59\n",
"unravelling 3\n",
"business 582\n",
"pursue 41\n",
"complete 119\n",
"agency 13\n",
"dispute 40\n",
"corroborating 1\n",
"proved 64\n",
"established 74\n",
"deny 50\n",
"philosophy 25\n",
"convert 11\n",
"ininthese 1\n",
"combination 33\n",
"nono 15\n",
"flooring 5\n",
"entreated 46\n",
"vi 36\n",
"junior 68\n",
"amiable 71\n",
"soso 5\n",
"successive 5\n",
"diminishing 4\n",
"rule 51\n",
"manners 43\n",
"petty 15\n",
"tyrant 4\n",
"pencil 25\n",
"stopped 471\n",
"singular 99\n",
"substantial 14\n",
"indistinct 36\n",
"scrawl 2\n",
"contradiction 26\n",
"lawyer 43\n",
"facetiously 1\n",
"remarkably 31\n",
"dwell 17\n",
"qualitiesfor 1\n",
"respected 58\n",
"amazingly 13\n",
"clever 116\n",
"encumber 1\n",
"overwhelming 11\n",
"doubted 42\n",
"possession 144\n",
"instructed 21\n",
"materially 6\n",
"aided 9\n",
"pounds 118\n",
"continental 7\n",
"hazard 14\n",
"riding 92\n",
"encouraged 28\n",
"exertion 29\n",
"immense 126\n",
"amusement 21\n",
"wondered 81\n",
"guest 66\n",
"propitiate 11\n",
"expired 11\n",
"relates 11\n",
"sincerely 41\n",
"cultivated 7\n",
"intimate 73\n",
"gentlemanly 38\n",
"wife 582\n",
"positive 40\n",
"manoeuvring 2\n",
"similar 110\n",
"pregnant 9\n",
"conclusive 10\n",
"race 68\n",
"develop 19\n",
"ignorant 48\n",
"notice 218\n",
"quit 29\n",
"dreams 72\n",
"recovering 33\n",
"satisfied 142\n",
"tended 20\n",
"obliterate 5\n",
"weigh 13\n",
"naming 13\n",
"suspend 2\n",
"additional 44\n",
"yourselves 30\n",
"lose 129\n",
"penetrate 15\n",
"sleeplessness 4\n",
"secured 41\n",
"screwdriver 2\n",
"easing 3\n",
"regarding 53\n",
"averse 8\n",
"shoot 24\n",
"arise 54\n",
"afford 59\n",
"notwithstanding 71\n",
"porch 104\n",
"dissipate 1\n",
"deductions 6\n",
"brilliance 3\n",
"vapours 4\n",
"covered 171\n",
"rooms 185\n",
"inclusive 3\n",
"rejoiced 29\n",
"pledged 14\n",
"unfortunate 137\n",
"saved 75\n",
"norman 1\n",
"cement 1\n",
"centre 39\n",
"acre 2\n",
"speculators 2\n",
"clergymen 1\n",
"traveller 58\n",
"repay 16\n",
"diamondshaped 1\n",
"huge 60\n",
"abutment 1\n",
"leadwork 1\n",
"dimcoloured 1\n",
"protected 14\n",
"article 47\n",
"faded 66\n",
"box 165\n",
"books 154\n",
"secrets 58\n",
"unpleasantly 16\n",
"odour 10\n",
"decomposed 2\n",
"surgery 3\n",
"indentations 3\n",
"whitish 1\n",
"surmise 6\n",
"slaves 8\n",
"border 4\n",
"destined 19\n",
"dump 1\n",
"hunted 29\n",
"viii 36\n",
"imaginative 11\n",
"flowed 33\n",
"compounded 4\n",
"delicacy 53\n",
"earlier 34\n",
"encroaches 2\n",
"niches 2\n",
"offensive 20\n",
"crumbled 8\n",
"arrive 41\n",
"rotted 6\n",
"lore 1\n",
"observed 402\n",
"inner 57\n",
"identification 10\n",
"engraved 8\n",
"forth 219\n",
"decay 21\n",
"legible 9\n",
"partial 24\n",
"damps 3\n",
"easier 47\n",
"absorbing 7\n",
"appertained 2\n",
"tinder 3\n",
"tolerably 40\n",
"dissipating 2\n",
"gloomy 107\n",
"miracles 4\n",
"scientific 14\n",
"nowadays 20\n",
"saints 13\n",
"locality 20\n",
"nonsense 159\n",
"assumed 85\n",
"quietly 179\n",
"adventures 19\n",
"consummated 2\n",
"boards 16\n",
"weary 98\n",
"angry 214\n",
"succumb 5\n",
"commence 16\n",
"supernatural 18\n",
"paint 16\n",
"defying 6\n",
"ifight 1\n",
"selfpreservation 8\n",
"allegory 4\n",
"truths 5\n",
"incomprehensible 49\n",
"deprive 26\n",
"powerfully 6\n",
"gathering 38\n",
"forget 268\n",
"dismiss 21\n",
"wishes 108\n",
"happier 63\n",
"safeguards 2\n",
"deceived 38\n",
"attributed 29\n",
"excess 13\n",
"hinged 1\n",
"smoke 206\n",
"incidental 20\n",
"casting 33\n",
"firing 96\n",
"shots 29\n",
"winged 6\n",
"beheld 22\n",
"affianced 8\n",
"employed 62\n",
"proud 266\n",
"becoming 108\n",
"patiently 19\n",
"due 108\n",
"vow 6\n",
"clime 4\n",
"faith 84\n",
"dearest 159\n",
"heed 34\n",
"horrorhorror 3\n",
"guess 52\n",
"centered 12\n",
"cherished 27\n",
"recognised 38\n",
"knocked 92\n",
"dismissal 14\n",
"indignation 60\n",
"sustained 19\n",
"intellectual 46\n",
"strangest 17\n",
"riddles 4\n",
"mutations 5\n",
"stoop 11\n",
"circumstantially 3\n",
"unbiassed 1\n",
"improbabletoo 1\n",
"eyesight 2\n",
"engagements 15\n",
"charlescharles 4\n",
"predilections 1\n",
"blighting 5\n",
"entreaties 12\n",
"devote 28\n",
"lamentations 5\n",
"narrated 5\n",
"attentive 68\n",
"properly 28\n",
"catch 77\n",
"thin 143\n",
"impalpable 4\n",
"glimpse 39\n",
"intrepid 4\n",
"necessity 113\n",
"calmest 5\n",
"late 403\n",
"reward 59\n",
"suspected 55\n",
"oppressed 27\n",
"woven 1\n",
"yoked 2\n",
"lap 27\n",
"paced 50\n",
"proclaiming 4\n",
"distracting 6\n",
"correct 61\n",
"represented 55\n",
"candlelight 8\n",
"memoryi 1\n",
"fallacy 4\n",
"variety 71\n",
"possessed 109\n",
"definite 46\n",
"inserted 4\n",
"knock 72\n",
"simultaneously 20\n",
"lingered 28\n",
"exhaust 3\n",
"enlist 2\n",
"mans 204\n",
"vexation 36\n",
"flew 81\n",
"corridora 1\n",
"compound 15\n",
"speaking 370\n",
"retired 118\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"removing 25\n",
"implements 5\n",
"succeed 33\n",
"tapping 31\n",
"wooden 110\n",
"nicely 5\n",
"tapped 28\n",
"original 73\n",
"doubtful 59\n",
"unprovided 4\n",
"spite 150\n",
"prodigious 19\n",
"steadily 68\n",
"constant 77\n",
"creditors 28\n",
"paying 52\n",
"purity 29\n",
"feeding 16\n",
"wherefore 22\n",
"asperity 4\n",
"admire 37\n",
"auspices 8\n",
"guide 37\n",
"childrenthose 1\n",
"sweet 173\n",
"sternest 4\n",
"drain 5\n",
"retrospection 2\n",
"unwelcome 8\n",
"contest 27\n",
"twere 1\n",
"assumption 24\n",
"angered 6\n",
"condemn 17\n",
"interference 12\n",
"ignorance 34\n",
"insult 37\n",
"storehouse 2\n",
"friendbrother 1\n",
"praises 13\n",
"asunder 21\n",
"remembered 196\n",
"testified 9\n",
"windowthere 1\n",
"wearied 14\n",
"proclaim 11\n",
"aggression 4\n",
"majestic 41\n",
"sunlight 19\n",
"revulsion 3\n",
"handsome 256\n",
"troublesome 41\n",
"o 199\n",
"notion 59\n",
"concluding 14\n",
"annoyance 29\n",
"autograph 2\n",
"indulgent 6\n",
"recommend 36\n",
"descriptions 7\n",
"disinterestedness 9\n",
"disinterested 20\n",
"neighbours 38\n",
"obedient 17\n",
"folded 92\n",
"clasping 18\n",
"singlemindedness 2\n",
"grieve 18\n",
"pester 1\n",
"proposed 124\n",
"wellwell 8\n",
"wonted 5\n",
"agreeable 153\n",
"magical 3\n",
"purchased 20\n",
"gatebell 1\n",
"vein 8\n",
"runs 29\n",
"announce 34\n",
"baronet 1\n",
"knight 39\n",
"lofty 55\n",
"stature 9\n",
"projecting 8\n",
"mellow 4\n",
"rivetted 3\n",
"looklookoh 1\n",
"habitual 27\n",
"conjectures 15\n",
"refreshment 26\n",
"abrasion 1\n",
"wondering 86\n",
"infancy 23\n",
"associations 26\n",
"abstractedly 2\n",
"modest 48\n",
"tempt 11\n",
"thencome 1\n",
"elegant 26\n",
"drained 7\n",
"annihilation 3\n",
"unfriendly 5\n",
"gladly 28\n",
"appal 2\n",
"individuals 30\n",
"visiting 31\n",
"smother 4\n",
"truest 10\n",
"tempted 24\n",
"nocturnal 3\n",
"disturber 3\n",
"destroying 27\n",
"gossipmongers 1\n",
"exaggerated 15\n",
"markettowns 1\n",
"moreover 59\n",
"education 56\n",
"maturer 3\n",
"nursery 31\n",
"scratch 8\n",
"afraid 445\n",
"market 47\n",
"discussions 13\n",
"election 5\n",
"ruddy 15\n",
"stentorian 4\n",
"attired 17\n",
"costume 17\n",
"postilion 5\n",
"yard 276\n",
"son 565\n",
"lazy 18\n",
"shoregoing 9\n",
"sham 2\n",
"larning 1\n",
"bundled 5\n",
"feat 8\n",
"preference 17\n",
"coach 164\n",
"accommodation 26\n",
"winewellaired 1\n",
"attendancefine 1\n",
"belay 4\n",
"dig 13\n",
"evolutions 5\n",
"whats 274\n",
"harbour 14\n",
"dues 2\n",
"humbly 29\n",
"deuce 30\n",
"devil 160\n",
"lawyers 29\n",
"yer 12\n",
"channel 18\n",
"ironsyoure 1\n",
"seamanno 1\n",
"bye 24\n",
"fin 3\n",
"baby 151\n",
"siryes 2\n",
"conversible 1\n",
"yesof 1\n",
"oath 29\n",
"lord 256\n",
"rubbed 57\n",
"frigates 2\n",
"scuppers 2\n",
"shark 7\n",
"parties 86\n",
"marry 144\n",
"ladys 70\n",
"repairing 11\n",
"definition 6\n",
"bull 19\n",
"court 168\n",
"amused 56\n",
"engrossed 17\n",
"heres 121\n",
"gallant 38\n",
"spent 122\n",
"amazement 68\n",
"shipi 3\n",
"remonstrances 10\n",
"wamphigher 11\n",
"gammon 12\n",
"roared 29\n",
"octaves 2\n",
"petrified 8\n",
"tail 40\n",
"seed 18\n",
"fiendlike 1\n",
"unaccountable 26\n",
"boast 15\n",
"defender 2\n",
"briton 3\n",
"remarks 71\n",
"liking 28\n",
"tasks 15\n",
"humble 53\n",
"bite 25\n",
"ships 69\n",
"sheet 31\n",
"blunders 6\n",
"enormous 73\n",
"intermediate 7\n",
"abandoning 31\n",
"aspects 9\n",
"virtually 9\n",
"nobility 32\n",
"renewed 23\n",
"revolted 5\n",
"assurance 75\n",
"bloomed 4\n",
"sweetest 14\n",
"anxiously 48\n",
"luxuriance 3\n",
"lily 3\n",
"pleasanter 13\n",
"daggers 7\n",
"youthful 72\n",
"mirthfulness 1\n",
"heavenly 13\n",
"grieved 25\n",
"sobs 59\n",
"fill 30\n",
"treason 4\n",
"convict 7\n",
"gives 76\n",
"attribute 13\n",
"entranced 5\n",
"armshe 1\n",
"locks 18\n",
"rattling 25\n",
"twas 8\n",
"idle 66\n",
"shine 30\n",
"noxious 4\n",
"uses 10\n",
"misfortunes 37\n",
"hover 12\n",
"warmed 22\n",
"accept 88\n",
"orifice 3\n",
"wrapt 6\n",
"xvii 27\n",
"astounding 5\n",
"arbour 7\n",
"shelter 45\n",
"whispering 50\n",
"insinuating 21\n",
"footsteps 84\n",
"bowed 137\n",
"comers 6\n",
"featuresmy 1\n",
"vampyrethis 1\n",
"allwise 1\n",
"pursuits 17\n",
"fashionthis 1\n",
"cowed 5\n",
"monotonous 28\n",
"valuable 35\n",
"coursecertainly 1\n",
"harmonize 2\n",
"mantles 3\n",
"superficial 6\n",
"shrank 11\n",
"artists 5\n",
"similarly 20\n",
"confronting 13\n",
"bravobravo 2\n",
"impudence 22\n",
"recollections 32\n",
"model 25\n",
"observer 15\n",
"truthfully 3\n",
"ironically 17\n",
"showiest 1\n",
"presumption 12\n",
"raw 11\n",
"quarreled 11\n",
"ohoh 1\n",
"swear 29\n",
"changing 53\n",
"track 59\n",
"ceremony 78\n",
"announcing 20\n",
"hastened 42\n",
"impatient 55\n",
"swiftness 6\n",
"quickening 3\n",
"cares 50\n",
"puts 45\n",
"withal 6\n",
"mutineer 1\n",
"anybodys 22\n",
"tar 8\n",
"alongside 12\n",
"courteously 9\n",
"deficient 13\n",
"wrote 179\n",
"wellno 2\n",
"eminence 9\n",
"wealthy 39\n",
"duly 18\n",
"juncture 11\n",
"feels 63\n",
"papers 161\n",
"valise 3\n",
"peer 3\n",
"whowho 2\n",
"slightest 51\n",
"pestilence 2\n",
"brings 49\n",
"possiblein 1\n",
"charless 17\n",
"minehe 1\n",
"hardy 8\n",
"guardianship 6\n",
"arbitrary 10\n",
"castle 69\n",
"inconsolable 4\n",
"battlements 2\n",
"imperious 23\n",
"meditation 12\n",
"starve 17\n",
"sixth 22\n",
"admitted 71\n",
"perform 42\n",
"putrid 1\n",
"useful 86\n",
"bought 90\n",
"unobservedand 1\n",
"mercuriali 1\n",
"italian 53\n",
"honours 16\n",
"wizard 3\n",
"asylum 11\n",
"feign 5\n",
"threatening 43\n",
"relatives 16\n",
"dungeons 7\n",
"paha 1\n",
"restrain 43\n",
"repent 19\n",
"research 15\n",
"burned 76\n",
"horizon 39\n",
"bowels 7\n",
"storing 3\n",
"frequentedthe 1\n",
"secrete 5\n",
"cell 13\n",
"assayest 1\n",
"mangled 5\n",
"efficient 4\n",
"apply 30\n",
"dispossess 1\n",
"regal 2\n",
"treasure 41\n",
"enjoy 43\n",
"arraigned 1\n",
"patrician 4\n",
"tainted 7\n",
"shorn 4\n",
"menial 3\n",
"perpetrators 3\n",
"alarmscream 1\n",
"passionless 6\n",
"contortion 5\n",
"bewitch 1\n",
"demoniac 1\n",
"allied 9\n",
"slow 97\n",
"shrinks 9\n",
"blessing 56\n",
"brotheri 2\n",
"placid 24\n",
"idleness 12\n",
"badtempered 1\n",
"converse 16\n",
"daily 77\n",
"flushing 15\n",
"reeled 6\n",
"humour 42\n",
"ebullition 3\n",
"bah 26\n",
"sheshe 2\n",
"puppy 8\n",
"braced 4\n",
"swaba 1\n",
"grampus 2\n",
"kiss 155\n",
"chopped 3\n",
"youd 113\n",
"ornament 17\n",
"crydid 1\n",
"sofa 163\n",
"slab 8\n",
"bother 25\n",
"ruled 15\n",
"nowgive 1\n",
"openly 33\n",
"handshaking 1\n",
"hinted 50\n",
"derivable 1\n",
"incurred 11\n",
"pestered 3\n",
"yarns 2\n",
"reasonableness 6\n",
"responsibility 50\n",
"involving 8\n",
"sneak 6\n",
"forbear 15\n",
"relymr 1\n",
"showing 107\n",
"incautiously 6\n",
"outrageous 9\n",
"expressed 265\n",
"chances 31\n",
"bequeath 3\n",
"henryfrom 1\n",
"contentions 2\n",
"behalf 52\n",
"explains 9\n",
"demands 27\n",
"amusing 31\n",
"jackjack 2\n",
"cook 74\n",
"rations 6\n",
"drinking 79\n",
"sojourn 4\n",
"concurrent 3\n",
"total 28\n",
"engaging 37\n",
"totter 2\n",
"eve 29\n",
"enthusiasm 50\n",
"record 14\n",
"reader 36\n",
"deeptoned 3\n",
"scrupulous 4\n",
"assertion 17\n",
"maim 2\n",
"cutting 39\n",
"extravagant 8\n",
"shoulders 189\n",
"voluntarily 4\n",
"intestate 2\n",
"fights 5\n",
"choice 84\n",
"availing 11\n",
"swordsman 2\n",
"unpolite 1\n",
"longheaded 3\n",
"saidi 3\n",
"poorly 22\n",
"suck 8\n",
"somebodys 13\n",
"brick 39\n",
"shilling 22\n",
"rate 66\n",
"pinking 2\n",
"charleswhat 1\n",
"fidgetty 5\n",
"advising 5\n",
"axing 1\n",
"fit 119\n",
"odds 18\n",
"christian 43\n",
"adept 3\n",
"comecome 1\n",
"fencer 2\n",
"germany 11\n",
"broached 15\n",
"boyi 2\n",
"delay 49\n",
"hallucination 1\n",
"serving 44\n",
"pollard 8\n",
"plantation 3\n",
"divide 12\n",
"current 52\n",
"glitter 12\n",
"material 32\n",
"risks 6\n",
"forgiveness 32\n",
"kissed 213\n",
"momentous 11\n",
"irredeemable 1\n",
"walks 41\n",
"quickened 16\n",
"valid 4\n",
"chooses 8\n",
"trespass 8\n",
"hull 5\n",
"fins 1\n",
"himthe 11\n",
"prosperous 15\n",
"pigtail 8\n",
"duck 4\n",
"breeze 21\n",
"tossing 25\n",
"north 22\n",
"messmate 8\n",
"graver 10\n",
"eaten 30\n",
"gale 14\n",
"stiffest 3\n",
"tumble 14\n",
"hoop 4\n",
"smoothly 24\n",
"meadow 29\n",
"main 71\n",
"sailer 1\n",
"decks 6\n",
"watercasks 3\n",
"nautical 10\n",
"twopennypost 1\n",
"doux 1\n",
"virgin 12\n",
"termed 20\n",
"sinewy 9\n",
"eagle 8\n",
"curly 30\n",
"frosted 3\n",
"dodge 6\n",
"involuntary 28\n",
"watercask 5\n",
"whatdo 1\n",
"wheel 41\n",
"lading 2\n",
"kicking 9\n",
"indifferently 12\n",
"sentence 47\n",
"puffed 13\n",
"dejected 25\n",
"cordage 3\n",
"blowing 61\n",
"louder 53\n",
"floated 25\n",
"rum 49\n",
"customer 8\n",
"henobody 1\n",
"ludicrous 7\n",
"overboardit 1\n",
"lift 43\n",
"unlucky 32\n",
"cabouse 1\n",
"legs 239\n",
"furiously 9\n",
"junk 1\n",
"performed 63\n",
"leaked 2\n",
"straining 21\n",
"communicated 36\n",
"preliminary 11\n",
"interval 56\n",
"belt 8\n",
"grass 54\n",
"identity 10\n",
"itim 1\n",
"existences 2\n",
"illill 4\n",
"holdhold 3\n",
"expedite 2\n",
"unblemished 2\n",
"morrowi 1\n",
"heartless 10\n",
"hero 41\n",
"qualified 19\n",
"calamitous 3\n",
"exhibit 12\n",
"remembering 57\n",
"letting 69\n",
"professed 9\n",
"curb 5\n",
"invectives 2\n",
"instinctive 18\n",
"forgeries 8\n",
"vile 15\n",
"yougod 1\n",
"heoh 1\n",
"striven 5\n",
"hawed 1\n",
"warmth 39\n",
"establishing 5\n",
"motheri 3\n",
"penalties 6\n",
"differing 5\n",
"panicstricken 1\n",
"fiends 2\n",
"handwriting 10\n",
"untried 3\n",
"mixture 21\n",
"scrap 25\n",
"boldly 40\n",
"defer 13\n",
"disclosing 6\n",
"perversion 4\n",
"suffers 9\n",
"unhappymost 1\n",
"privilege 25\n",
"cape 3\n",
"ushant 3\n",
"mainmast 2\n",
"joys 19\n",
"denies 2\n",
"caroused 1\n",
"arches 13\n",
"pools 7\n",
"wondrous 3\n",
"manycoloured 2\n",
"tomba 1\n",
"timeworn 3\n",
"owl 6\n",
"belfry 3\n",
"dreamylooking 1\n",
"apparitionlooking 1\n",
"twilight 46\n",
"unwholesome 16\n",
"excavations 3\n",
"springs 17\n",
"dripping 6\n",
"roofso 1\n",
"impossibilityis 1\n",
"nothinghears 1\n",
"followsa 1\n",
"pen 55\n",
"becausebecause 1\n",
"oppose 16\n",
"abandon 40\n",
"fortunately 18\n",
"frenchman 111\n",
"nothingas 2\n",
"fought 62\n",
"knocks 11\n",
"hauled 7\n",
"managing 11\n",
"beset 19\n",
"pretended 55\n",
"fuss 11\n",
"roughspoken 1\n",
"aloft 13\n",
"nowgo 1\n",
"obscurity 13\n",
"refinement 5\n",
"meditate 6\n",
"strokes 10\n",
"aged 31\n",
"proudlooking 2\n",
"played 123\n",
"deemed 18\n",
"moans 10\n",
"anotheronly 1\n",
"thirty 63\n",
"twelvemonth 7\n",
"requisite 8\n",
"speakmy 1\n",
"resolvethat 1\n",
"engraven 2\n",
"loveshe 1\n",
"unaided 5\n",
"piled 20\n",
"ingle 1\n",
"intently 58\n",
"beard 24\n",
"differences 15\n",
"mistakes 33\n",
"mowbray 2\n",
"caress 6\n",
"xxxii 6\n",
"expecteddreadedand 1\n",
"approachesa 1\n",
"folds 23\n",
"clank 2\n",
"circumvent 1\n",
"allusion 44\n",
"forgetful 9\n",
"ingenioustoo 1\n",
"apt 17\n",
"plucked 15\n",
"regain 10\n",
"bond 29\n",
"penalty 10\n",
"assigned 16\n",
"oft 3\n",
"steadfastly 17\n",
"guesser 2\n",
"priceless 11\n",
"shrunken 5\n",
"guilt 22\n",
"roseate 1\n",
"fearless 5\n",
"invest 8\n",
"undeviatingly 1\n",
"margin 10\n",
"periodical 2\n",
"shocks 3\n",
"grove 13\n",
"firs 4\n",
"blasts 3\n",
"obstacles 20\n",
"ditch 19\n",
"impeded 6\n",
"pathway 5\n",
"trespassers 2\n",
"irresolution 6\n",
"subsequent 8\n",
"intruding 12\n",
"sanctify 1\n",
"afar 11\n",
"toward 299\n",
"meaner 5\n",
"sweets 11\n",
"knowcontains 1\n",
"summer 81\n",
"insects 2\n",
"crimeof 1\n",
"stealthily 22\n",
"unarmed 7\n",
"outlines 4\n",
"strained 22\n",
"imagining 23\n",
"precision 19\n",
"somnambulist 2\n",
"bower 11\n",
"disquietude 2\n",
"movehe 1\n",
"weeping 71\n",
"dwellingplace 9\n",
"spota 1\n",
"musterroll 1\n",
"chanced 17\n",
"slumberstill 1\n",
"stubbornest 1\n",
"arouse 14\n",
"persecute 1\n",
"sustenance 6\n",
"timenever 1\n",
"thee 39\n",
"fountain 10\n",
"calms 2\n",
"arrogancei 1\n",
"beautifulthose 1\n",
"thirst 8\n",
"span 3\n",
"hopebut 1\n",
"slant 3\n",
"ray 23\n",
"uplifted 15\n",
"notthe 1\n",
"prolongation 2\n",
"plea 12\n",
"alternative 7\n",
"controli 1\n",
"desolation 23\n",
"unutterable 15\n",
"anothers 24\n",
"terribly 29\n",
"acknowledges 2\n",
"veriest 2\n",
"usa 1\n",
"meflight 1\n",
"lifeagain 1\n",
"convulse 1\n",
"incentives 1\n",
"desolated 3\n",
"trumpettongued 1\n",
"annals 4\n",
"retreated 27\n",
"disregarding 8\n",
"hollows 8\n",
"released 58\n",
"disappearing 7\n",
"mould 16\n",
"milder 3\n",
"peaceful 35\n",
"cowardice 10\n",
"rightly 25\n",
"macbeths 3\n",
"troubles 38\n",
"comforts 21\n",
"servitude 1\n",
"understandings 1\n",
"failed 56\n",
"sensitiveness 9\n",
"aspirations 6\n",
"henryof 1\n",
"behoved 7\n",
"decamped 2\n",
"dissension 4\n",
"dme 6\n",
"chastise 1\n",
"devilmaycare 1\n",
"outi 2\n",
"carronade 1\n",
"maam 292\n",
"funds 5\n",
"canvassed 1\n",
"bandy 5\n",
"dealt 35\n",
"distorts 1\n",
"carpet 40\n",
"dignified 24\n",
"coincidencethe 1\n",
"challenging 6\n",
"youngster 5\n",
"styles 1\n",
"athwart 5\n",
"windpipe 2\n",
"ll 1\n",
"stowed 6\n",
"slack 8\n",
"mustnt 38\n",
"youve 112\n",
"christened 9\n",
"christenedwere 1\n",
"mildly 30\n",
"helped 77\n",
"amyou 1\n",
"hima 5\n",
"onedo 1\n",
"vigilant 6\n",
"handsyou 1\n",
"youbut 3\n",
"kindit 1\n",
"confided 26\n",
"juggle 2\n",
"meets 7\n",
"himand 23\n",
"refusebut 1\n",
"benediction 6\n",
"dysee 1\n",
"plague 14\n",
"seaworthy 2\n",
"clatters 1\n",
"lifeha 1\n",
"parenthetically 2\n",
"landshark 1\n",
"uncivil 3\n",
"winked 15\n",
"ropes 20\n",
"controversy 2\n",
"precedence 4\n",
"reasonablepistols 1\n",
"foremast 1\n",
"drunk 65\n",
"friendships 8\n",
"spit 3\n",
"complaisant 1\n",
"tackling 1\n",
"profit 28\n",
"lifting 64\n",
"scruples 4\n",
"daresay 24\n",
"lurchno 1\n",
"seven 142\n",
"muttering 49\n",
"doused 1\n",
"admiralvery 1\n",
"thismr 1\n",
"capacity 53\n",
"mild 76\n",
"rendezvous 1\n",
"analyse 1\n",
"surmises 10\n",
"manhenry 1\n",
"sanctioned 1\n",
"opponents 11\n",
"appealed 19\n",
"eyethe 1\n",
"magnates 21\n",
"repelled 7\n",
"unavoidable 13\n",
"handa 2\n",
"definitive 1\n",
"retard 3\n",
"hilly 2\n",
"pockets 61\n",
"oddlooking 2\n",
"lumps 5\n",
"enjoined 4\n",
"oughtnt 12\n",
"neutrality 3\n",
"importunities 1\n",
"dismallooking 1\n",
"timeblessed 1\n",
"blazes 5\n",
"culminating 4\n",
"bolted 9\n",
"dictionary 23\n",
"fellowcreature 7\n",
"mound 11\n",
"midway 9\n",
"glowing 29\n",
"oriental 10\n",
"whatevers 1\n",
"threefire 1\n",
"occupying 15\n",
"justification 20\n",
"repeating 77\n",
"studying 17\n",
"significantly 18\n",
"handing 32\n",
"halligator 1\n",
"bumboatwomen 1\n",
"frenchmen 53\n",
"stripes 6\n",
"intercept 6\n",
"nob 1\n",
"evince 5\n",
"theyll 50\n",
"violence 52\n",
"unmoved 15\n",
"pending 9\n",
"declarations 4\n",
"otaheite 1\n",
"promises 14\n",
"bloodthirsty 8\n",
"gesticulations 2\n",
"guessing 12\n",
"applauding 3\n",
"gossipping 3\n",
"crowd 292\n",
"rioters 27\n",
"procession 37\n",
"vowing 4\n",
"imprudence 2\n",
"meditated 8\n",
"multitude 40\n",
"trusting 18\n",
"principle 46\n",
"countryside 2\n",
"reparation 16\n",
"onwardapparently 1\n",
"insidiously 2\n",
"civilized 3\n",
"morbid 7\n",
"tales 22\n",
"prayed 43\n",
"clamour 6\n",
"urging 25\n",
"assemblage 14\n",
"stammer 3\n",
"theyd 14\n",
"hiding 54\n",
"dogged 6\n",
"dissatisfied 40\n",
"tending 20\n",
"washe 1\n",
"steadiness 6\n",
"smashing 4\n",
"slavegoing 1\n",
"discordance 2\n",
"assault 10\n",
"sharpen 3\n",
"maytnt 1\n",
"poke 7\n",
"everyday 12\n",
"hidingplace 13\n",
"sacrificed 18\n",
"flotilla 1\n",
"attacking 28\n",
"slouched 8\n",
"expedient 22\n",
"scouring 2\n",
"precipate 1\n",
"reinforcement 2\n",
"smockfrock 2\n",
"sarves 1\n",
"springing 16\n",
"muster 8\n",
"fishing 5\n",
"cooled 9\n",
"miry 2\n",
"mishaps 1\n",
"unlooked 4\n",
"punished 24\n",
"desultory 6\n",
"overhearing 5\n",
"familyor 1\n",
"dispersed 27\n",
"hallowed 4\n",
"association 50\n",
"weaker 23\n",
"portions 12\n",
"fleetest 2\n",
"response 18\n",
"explore 5\n",
"yells 8\n",
"imprecation 2\n",
"astonishing 27\n",
"scampered 3\n",
"loophole 1\n",
"subterraneous 2\n",
"prisonerthe 2\n",
"reduced 48\n",
"malediction 1\n",
"earththat 1\n",
"intoxicated 6\n",
"clutches 6\n",
"enervated 1\n",
"lasts 2\n",
"abhorrence 11\n",
"sayingthat 1\n",
"fraternity 5\n",
"fiendish 1\n",
"community 15\n",
"teacher 6\n",
"recovers 5\n",
"salubrity 2\n",
"foil 3\n",
"increase 30\n",
"facility 11\n",
"conception 89\n",
"concealment 26\n",
"peoples 96\n",
"desecration 5\n",
"familiarized 1\n",
"failures 9\n",
"wildfire 3\n",
"craving 2\n",
"sublimated 2\n",
"exhume 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"singularly 14\n",
"reposing 9\n",
"decencies 2\n",
"riotous 8\n",
"proclamation 26\n",
"intoxication 13\n",
"civilised 5\n",
"wellorganised 1\n",
"patron 42\n",
"turnstiles 1\n",
"checked 47\n",
"gilt 7\n",
"canonicals 2\n",
"respectfully 57\n",
"exhortations 4\n",
"wrenched 6\n",
"permitted 55\n",
"warfare 17\n",
"windmill 3\n",
"tabernacles 1\n",
"boozlehum 1\n",
"capacious 4\n",
"goodhumouredly 4\n",
"intenseevery 1\n",
"talent 28\n",
"cumbrous 9\n",
"designs 21\n",
"issue 31\n",
"rights 39\n",
"shoveling 1\n",
"sneaking 5\n",
"afraidafraid 2\n",
"dick 265\n",
"enjoyed 56\n",
"seventeen 19\n",
"kim 1\n",
"shovel 3\n",
"popularly 5\n",
"elongation 1\n",
"consolidified 1\n",
"brickmy 1\n",
"occular 1\n",
"subsided 27\n",
"cuff 4\n",
"xlvi 2\n",
"appurtenances 3\n",
"abidingplace 1\n",
"wanderings 10\n",
"hovering 14\n",
"dissolute 4\n",
"earshot 1\n",
"sticks 14\n",
"stores 20\n",
"ass 5\n",
"balancing 8\n",
"assures 4\n",
"removable 1\n",
"lifetime 10\n",
"furniture 82\n",
"complimentsand 1\n",
"youraaflurry 1\n",
"footman 65\n",
"adjusting 12\n",
"flunkeys 1\n",
"thumb 33\n",
"catcher 2\n",
"dry 118\n",
"parallel 10\n",
"posted 41\n",
"assemble 6\n",
"tavern 27\n",
"revolting 5\n",
"vampyrea 6\n",
"maniacal 1\n",
"fierce 39\n",
"intervals 57\n",
"premonitory 2\n",
"bloodshed 3\n",
"unguarded 6\n",
"felldestroyers 1\n",
"oil 7\n",
"forthwith 6\n",
"ghastliest 2\n",
"repulsive 8\n",
"softening 27\n",
"fibres 3\n",
"plumpness 1\n",
"waiter 60\n",
"fiveandtwenty 24\n",
"upstairs 202\n",
"currently 3\n",
"numbers 47\n",
"captured 47\n",
"lofts 4\n",
"narrowness 2\n",
"sickened 5\n",
"reported 61\n",
"storming 6\n",
"desertions 1\n",
"eighteen 23\n",
"inconvenient 24\n",
"valorous 1\n",
"cartridges 2\n",
"noncommissioned 13\n",
"assuring 14\n",
"handcuffed 3\n",
"vent 24\n",
"asbut 2\n",
"rightthats 1\n",
"judicious 7\n",
"fieldpiece 1\n",
"retiring 40\n",
"unwounded 5\n",
"poles 13\n",
"batteringram 2\n",
"disorder 46\n",
"quarterstaves 1\n",
"discharges 2\n",
"divided 55\n",
"wainscoting 1\n",
"frames 7\n",
"reechoed 4\n",
"luckier 1\n",
"sick 76\n",
"cupboard 26\n",
"reaction 12\n",
"outburn 1\n",
"combustible 1\n",
"smoked 35\n",
"chorus 25\n",
"precipitate 8\n",
"firewoodget 1\n",
"funny 15\n",
"concourse 7\n",
"ascendant 4\n",
"learning 45\n",
"accusations 5\n",
"bloodsuckera 1\n",
"distancethe 1\n",
"excluded 8\n",
"engines 5\n",
"ponds 7\n",
"fuel 8\n",
"smoulder 2\n",
"deceptive 2\n",
"stile 2\n",
"gapes 1\n",
"plaster 5\n",
"pales 4\n",
"peat 1\n",
"dabbed 4\n",
"pitchpot 1\n",
"sticky 9\n",
"ahah 1\n",
"departing 3\n",
"convoy 29\n",
"disinclination 1\n",
"aftfore 1\n",
"aft 1\n",
"coachbox 6\n",
"howevership 1\n",
"landlubber 1\n",
"standstill 7\n",
"checkstring 1\n",
"neat 40\n",
"luggage 20\n",
"thems 2\n",
"lulled 6\n",
"isle 1\n",
"suburbs 2\n",
"edifices 4\n",
"dwellings 6\n",
"thoroughfares 8\n",
"sympathize 7\n",
"attracting 13\n",
"revealing 6\n",
"evenings 29\n",
"contain 15\n",
"betimes 6\n",
"coincided 6\n",
"donkey 20\n",
"harkee 1\n",
"herell 1\n",
"preconcerted 2\n",
"barred 8\n",
"arguing 10\n",
"extra 11\n",
"nigger 1\n",
"swabs 1\n",
"soap 9\n",
"jackhollandswhos 1\n",
"baker 10\n",
"hist 3\n",
"yesthatll 1\n",
"chinks 6\n",
"glassdoor 1\n",
"anythingtheres 1\n",
"effecting 9\n",
"devils 24\n",
"rebounding 1\n",
"superincumbent 3\n",
"sized 3\n",
"writhing 14\n",
"directlyexplain 1\n",
"lurch 4\n",
"lix 2\n",
"chemically 1\n",
"informationand 1\n",
"temperance 1\n",
"exteriorly 1\n",
"sirfeeling 1\n",
"hoping 33\n",
"withwith 1\n",
"joking 22\n",
"school 131\n",
"yon 5\n",
"cedartree 1\n",
"hospitality 17\n",
"collision 16\n",
"otherim 1\n",
"whatby 1\n",
"peopleim 1\n",
"handbell 1\n",
"appetite 47\n",
"shuffling 18\n",
"noseay 1\n",
"aggrieved 4\n",
"jumping 13\n",
"experiments 14\n",
"feltpalpable 1\n",
"dismembering 1\n",
"agilityhere 1\n",
"teatray 4\n",
"portlylooking 1\n",
"obliquity 1\n",
"iimust 1\n",
"persecutor 1\n",
"alters 2\n",
"british 26\n",
"vitiated 3\n",
"garb 5\n",
"intemperance 3\n",
"pecuniarily 1\n",
"feather 24\n",
"admonitions 4\n",
"predicted 4\n",
"spasm 3\n",
"rationality 1\n",
"immensely 21\n",
"chagrined 1\n",
"wellyou 1\n",
"choked 35\n",
"pilgrimage 9\n",
"rapine 1\n",
"wrap 7\n",
"yielding 29\n",
"particle 6\n",
"mirth 8\n",
"ongo 1\n",
"outgeneralled 1\n",
"imperatively 3\n",
"odium 2\n",
"lesson 23\n",
"valiant 7\n",
"columns 45\n",
"fiftythree 1\n",
"whiffs 3\n",
"groping 8\n",
"housekeeper 24\n",
"bets 4\n",
"horsepistol 1\n",
"heiratlaw 3\n",
"weatherbit 1\n",
"cobbler 2\n",
"ruefully 5\n",
"noeh 1\n",
"alonethat 1\n",
"valuewhat 1\n",
"circumstancea 1\n",
"stew 3\n",
"brag 3\n",
"gravestones 1\n",
"doze 13\n",
"reverberations 2\n",
"dontwhy 1\n",
"unpromising 10\n",
"oppressing 1\n",
"futile 8\n",
"clinching 2\n",
"couldim 1\n",
"stalking 5\n",
"certainlycertainly 1\n",
"indeedyes 3\n",
"scruplesi 1\n",
"marchdaleyou 1\n",
"havehit 1\n",
"pistola 1\n",
"oncehe 1\n",
"clottedlooking 1\n",
"allabsorbing 1\n",
"shocked 37\n",
"sowell 1\n",
"enhance 6\n",
"satisfiedi 3\n",
"legacy 12\n",
"shalland 1\n",
"manno 1\n",
"principles 16\n",
"approves 4\n",
"donei 1\n",
"sarcasm 8\n",
"inhabiteda 1\n",
"againi 4\n",
"crippled 3\n",
"unfettered 3\n",
"inflicting 3\n",
"sunniest 2\n",
"consigned 8\n",
"benevolent 22\n",
"fetters 11\n",
"unlocked 14\n",
"sparing 9\n",
"goodnight 37\n",
"reputed 6\n",
"recur 2\n",
"soulharrowing 1\n",
"atback 1\n",
"tablefork 1\n",
"slap 8\n",
"grapeshotthen 1\n",
"esteems 1\n",
"antiggy 1\n",
"ingies 1\n",
"itso 3\n",
"therethere 2\n",
"jackthatll 1\n",
"panga 1\n",
"painit 1\n",
"ports 3\n",
"anchored 1\n",
"elite 3\n",
"youngest 30\n",
"withered 34\n",
"plume 6\n",
"cautiously 28\n",
"denounce 5\n",
"exultingly 6\n",
"slay 6\n",
"redness 6\n",
"depths 54\n",
"youhere 1\n",
"achieving 1\n",
"marchdalehe 1\n",
"inhaling 7\n",
"bedewed 1\n",
"resorted 13\n",
"blacken 2\n",
"faithless 1\n",
"enshrined 2\n",
"capturedeffectually 1\n",
"underground 8\n",
"lxix 1\n",
"collated 1\n",
"lance 3\n",
"spearhead 1\n",
"thrive 4\n",
"overthrown 8\n",
"weakened 21\n",
"footing 28\n",
"designation 10\n",
"foemanno 1\n",
"foeman 1\n",
"victorits 1\n",
"prevail 11\n",
"himsave 1\n",
"nocertainly 1\n",
"notthats 1\n",
"factso 1\n",
"countryone 1\n",
"slain 9\n",
"sorrowed 1\n",
"lamps 26\n",
"chambernone 1\n",
"crest 3\n",
"knee 77\n",
"chide 1\n",
"berthatruly 1\n",
"jousts 1\n",
"englandi 1\n",
"enemiesnever 1\n",
"tent 29\n",
"enlarged 4\n",
"commodious 5\n",
"knightyou 1\n",
"listless 16\n",
"frightens 5\n",
"congregate 2\n",
"wearing 106\n",
"swing 14\n",
"vampyresthey 1\n",
"activity 114\n",
"troop 2\n",
"waythey 1\n",
"arelivelydo 1\n",
"brewing 3\n",
"pall 6\n",
"fees 26\n",
"doleful 15\n",
"professionthats 1\n",
"finishing 30\n",
"jocose 8\n",
"tiptop 3\n",
"insidewill 1\n",
"indecent 5\n",
"processionas 1\n",
"profits 15\n",
"concur 5\n",
"fable 5\n",
"lined 6\n",
"illwill 12\n",
"coldah 1\n",
"usay 1\n",
"overhes 1\n",
"officiating 4\n",
"dearth 1\n",
"mischiefso 1\n",
"forcible 10\n",
"acclamation 4\n",
"wornout 10\n",
"surmised 4\n",
"ambuscade 3\n",
"advantageous 21\n",
"undreamt 2\n",
"analyze 4\n",
"abstruse 4\n",
"elated 8\n",
"deadened 7\n",
"obstructed 7\n",
"depredations 2\n",
"scaring 1\n",
"thatoh 1\n",
"recognition 50\n",
"otheryou 1\n",
"himby 3\n",
"playing 109\n",
"vilest 2\n",
"functions 9\n",
"soonsooncome 1\n",
"varied 12\n",
"manneryou 2\n",
"surelyi 1\n",
"longthat 1\n",
"addicted 2\n",
"dice 5\n",
"selfconfidence 6\n",
"vexationtwo 1\n",
"spending 19\n",
"repentance 14\n",
"otherit 1\n",
"players 13\n",
"playi 1\n",
"hopeindeed 1\n",
"opportunityat 1\n",
"twentyfive 13\n",
"tooone 1\n",
"goneall 1\n",
"allmy 1\n",
"allhowhow 1\n",
"lodgingit 1\n",
"havegod 1\n",
"nevertis 1\n",
"chanceand 1\n",
"nonei 1\n",
"gamblinghouse 2\n",
"placeone 1\n",
"happyshe 1\n",
"prophetic 9\n",
"constantly 102\n",
"thousandth 5\n",
"saidyes 1\n",
"gaming 1\n",
"awaited 43\n",
"awaking 7\n",
"landlady 25\n",
"famished 6\n",
"wifes 64\n",
"lowvery 1\n",
"humiliating 14\n",
"barbarity 3\n",
"butwhat 1\n",
"menace 9\n",
"sufferingay 1\n",
"bloody 11\n",
"cavalry 79\n",
"dismount 7\n",
"riotersoh 1\n",
"skins 9\n",
"grain 38\n",
"torches 6\n",
"helmets 3\n",
"angersteady 1\n",
"magistratetwenty 1\n",
"escorting 6\n",
"quarrelling 5\n",
"hatches 2\n",
"doublefaced 2\n",
"befell 4\n",
"commensurate 3\n",
"tempests 1\n",
"expire 4\n",
"aloneall 1\n",
"aloneand 2\n",
"heavno 1\n",
"echoawakening 1\n",
"unrequitedlyi 1\n",
"averted 9\n",
"fragmentsfragments 1\n",
"pressurethere 1\n",
"machinery 17\n",
"mendacious 2\n",
"roses 26\n",
"cruising 4\n",
"charlie 2\n",
"westward 7\n",
"setoff 4\n",
"lovethat 1\n",
"goaded 5\n",
"clad 8\n",
"bodywhen 1\n",
"commissioned 5\n",
"mend 12\n",
"hugged 18\n",
"worthless 17\n",
"prejudiced 12\n",
"disingenuous 1\n",
"possessors 2\n",
"cogent 2\n",
"assassinated 1\n",
"thereupon 15\n",
"provoke 6\n",
"assailant 3\n",
"compassed 1\n",
"wellknit 2\n",
"sheriffs 4\n",
"prescribed 12\n",
"surrey 3\n",
"deter 3\n",
"region 36\n",
"himselfheaven 1\n",
"resuscitated 4\n",
"meour 1\n",
"oftell 1\n",
"waythis 1\n",
"peremptory 6\n",
"lipsnothingnothing 1\n",
"rumbottle 2\n",
"aroma 2\n",
"hypothesis 2\n",
"enveloping 2\n",
"surmount 1\n",
"scent 23\n",
"elude 3\n",
"exploration 1\n",
"impressive 25\n",
"scorched 10\n",
"site 21\n",
"himive 1\n",
"foundhurrah 1\n",
"whale 1\n",
"womanthats 1\n",
"pipes 25\n",
"emulating 2\n",
"welkin 2\n",
"whirlwind 8\n",
"harvesthome 1\n",
"countywhy 1\n",
"wasnobody 1\n",
"vampyrethere 1\n",
"menaces 1\n",
"cuffed 2\n",
"helpwe 1\n",
"fresherhe 1\n",
"speaktheir 1\n",
"manyyou 1\n",
"orphan 26\n",
"peasant 76\n",
"behaving 7\n",
"unique 4\n",
"clearest 10\n",
"farthest 13\n",
"paling 1\n",
"fences 7\n",
"easiest 7\n",
"applications 5\n",
"laidout 1\n",
"blacklooking 1\n",
"defining 5\n",
"murderi 1\n",
"villains 7\n",
"specifically 2\n",
"injuredno 1\n",
"downdown 1\n",
"unscathed 4\n",
"committing 9\n",
"saidrise 1\n",
"oughtfor 1\n",
"subtler 1\n",
"testify 5\n",
"bugbear 1\n",
"itif 2\n",
"upthere 1\n",
"soulstricken 1\n",
"enticing 2\n",
"partakes 1\n",
"borrows 1\n",
"plotting 5\n",
"dens 3\n",
"iniquity 1\n",
"morn 2\n",
"infamy 3\n",
"hedgerows 2\n",
"futility 2\n",
"upturned 3\n",
"glaze 2\n",
"looklooksee 1\n",
"uttera 1\n",
"inot 2\n",
"engender 2\n",
"thoughtlessnessnot 1\n",
"states 82\n",
"fortuitous 3\n",
"accomplice 2\n",
"peaceshe 1\n",
"coupled 11\n",
"acquitting 2\n",
"bridle 12\n",
"publican 15\n",
"cocoa 3\n",
"herringanything 1\n",
"bits 19\n",
"wicious 1\n",
"signified 22\n",
"guinea 7\n",
"handbasin 1\n",
"flattened 4\n",
"hungarians 1\n",
"pennywinkles 2\n",
"goodly 4\n",
"peacepeaceyou 1\n",
"halfcrowns 10\n",
"whopping 2\n",
"cruize 1\n",
"tooits 1\n",
"iwe 1\n",
"boardingtackle 1\n",
"squab 1\n",
"rats 18\n",
"invented 25\n",
"goodnatured 52\n",
"nurse 121\n",
"bedd 1\n",
"suited 27\n",
"flock 6\n",
"youleave 1\n",
"goesdown 1\n",
"pursuersfor 1\n",
"smithy 2\n",
"shoeing 2\n",
"burdon 1\n",
"blacksmith 6\n",
"millstream 1\n",
"steppingstones 3\n",
"onrun 1\n",
"exciseman 6\n",
"uneven 11\n",
"sedges 2\n",
"bulrushes 1\n",
"clumps 2\n",
"willows 4\n",
"wafted 7\n",
"stonesof 1\n",
"blossoming 3\n",
"willow 2\n",
"giddy 26\n",
"subsidence 2\n",
"shorten 3\n",
"chills 1\n",
"tiny 45\n",
"horrific 1\n",
"inspiration 8\n",
"meads 1\n",
"swam 7\n",
"torpidity 1\n",
"pressure 47\n",
"stumbled 20\n",
"illsuited 4\n",
"knowwhich 1\n",
"methat 3\n",
"bijouterie 1\n",
"pretensions 10\n",
"ancientlooking 2\n",
"roue 1\n",
"leaveslet 1\n",
"unsafe 4\n",
"essentially 3\n",
"empowered 3\n",
"philpots 16\n",
"abounding 4\n",
"bonnet 110\n",
"reclined 6\n",
"vigilance 9\n",
"himhe 10\n",
"doorthat 1\n",
"actuated 5\n",
"pierced 9\n",
"staffsomething 1\n",
"positionpush 1\n",
"guttural 3\n",
"loft 3\n",
"untiring 4\n",
"befriended 5\n",
"bloodhounds 1\n",
"leap 13\n",
"dofirefire 1\n",
"bulky 6\n",
"hilt 4\n",
"copingstones 2\n",
"defenceless 7\n",
"unpremeditated 1\n",
"suppliant 5\n",
"mercyshe 1\n",
"madamthank 1\n",
"testifies 1\n",
"allay 4\n",
"burns 6\n",
"erased 1\n",
"awarded 6\n",
"baffle 2\n",
"tenour 1\n",
"myselfi 3\n",
"scenenow 1\n",
"hurrahdown 1\n",
"censure 7\n",
"pulsations 1\n",
"contribute 2\n",
"hollandtell 1\n",
"felicitously 1\n",
"illgotten 3\n",
"criminating 1\n",
"actuate 2\n",
"valueand 1\n",
"bestowal 1\n",
"remorse 37\n",
"gamesters 1\n",
"legislature 2\n",
"sheriffsmust 1\n",
"functionary 5\n",
"thereby 26\n",
"bridge 186\n",
"gallantly 10\n",
"forgiven 22\n",
"gifts 12\n",
"admiraland 1\n",
"mood 60\n",
"relatively 1\n",
"havoc 3\n",
"evaporation 2\n",
"beauchamp 7\n",
"listener 5\n",
"dutyexcuse 1\n",
"impede 1\n",
"accidentsfumbles 1\n",
"scraper 1\n",
"cage 24\n",
"retrod 1\n",
"recognize 50\n",
"yearly 3\n",
"veranda 8\n",
"instanter 2\n",
"plashes 1\n",
"pranks 2\n",
"slippy 1\n",
"overcoats 8\n",
"journeys 23\n",
"efficiency 5\n",
"recognized 74\n",
"gipsylooking 1\n",
"elsesheepstealers 1\n",
"walkingslick 1\n",
"shillings 39\n",
"chatty 3\n",
"tentcloth 1\n",
"alternately 9\n",
"itdont 1\n",
"doyou 1\n",
"cleanly 4\n",
"ferryhouse 2\n",
"luminous 13\n",
"plashing 2\n",
"comehe 1\n",
"livingthanks 1\n",
"sayingas 1\n",
"lighten 7\n",
"fanny 389\n",
"saidwhat 1\n",
"fatherthe 1\n",
"necki 2\n",
"aboutit 1\n",
"coldso 1\n",
"ingrate 3\n",
"bestowing 10\n",
"lifenow 2\n",
"clouded 19\n",
"prosecute 1\n",
"subjectthat 1\n",
"intolerably 2\n",
"disobedience 4\n",
"insubordination 3\n",
"chastisement 1\n",
"manfully 6\n",
"earthbut 1\n",
"swabtake 1\n",
"reel 1\n",
"xci 1\n",
"contentment 4\n",
"lavished 2\n",
"equivocal 1\n",
"admiring 31\n",
"villa 7\n",
"saidif 1\n",
"filthy 5\n",
"pills 8\n",
"tasteand 1\n",
"bandbox 2\n",
"longlegged 7\n",
"cutter 2\n",
"marauder 4\n",
"skinny 5\n",
"itand 20\n",
"lifts 5\n",
"cord 6\n",
"easierwith 1\n",
"caring 10\n",
"ungainly 6\n",
"growl 8\n",
"disconsolate 9\n",
"sipped 5\n",
"flourishing 22\n",
"contiguity 1\n",
"epidemic 3\n",
"gladsome 2\n",
"unlet 1\n",
"aristocratic 7\n",
"pelisse 1\n",
"trimmed 13\n",
"fur 87\n",
"largest 22\n",
"suits 8\n",
"bellowing 1\n",
"outbuildings 2\n",
"armsa 1\n",
"fidget 2\n",
"exacting 14\n",
"deficiency 3\n",
"purveyoringeneral 1\n",
"maymuch 1\n",
"eyed 32\n",
"extort 2\n",
"revivifying 1\n",
"rinsed 2\n",
"xcv 1\n",
"musty 7\n",
"squalls 2\n",
"connivance 1\n",
"persuasion 11\n",
"windlass 2\n",
"fizgig 1\n",
"pleasantry 4\n",
"sections 7\n",
"digression 4\n",
"bargained 6\n",
"bunderbuss 1\n",
"terrorem 1\n",
"cram 2\n",
"doorpost 18\n",
"xcvi 1\n",
"replies 36\n",
"drawback 6\n",
"partsa 1\n",
"saltsomething 1\n",
"attitudehere 1\n",
"delicious 27\n",
"violets 2\n",
"neverending 1\n",
"allotted 11\n",
"tithe 2\n",
"dormitories 2\n",
"incessant 20\n",
"westlake 9\n",
"cavernous 3\n",
"vaulted 2\n",
"bottles 31\n",
"basket 49\n",
"mortification 11\n",
"stammered 16\n",
"unborn 8\n",
"tenancy 2\n",
"judicial 5\n",
"precept 3\n",
"recognising 6\n",
"purchasing 2\n",
"prevaricate 1\n",
"irregular 11\n",
"reassembling 1\n",
"baroness 2\n",
"onethe 5\n",
"domain 46\n",
"concept 11\n",
"section 43\n",
"trademarkcopyright 5\n",
"fee 44\n",
"downloading 5\n",
"representations 9\n",
"binary 5\n",
"nonproprietary 5\n",
"hypertext 5\n",
"exporting 5\n",
"alternate 11\n",
"applicable 18\n",
"periodic 5\n",
"discontinue 5\n",
"f 99\n",
"contact 30\n",
"employees 10\n",
"data 5\n",
"defective 20\n",
"remedies 9\n",
"breach 18\n",
"electronically 10\n",
"interpreted 10\n",
"maximum 8\n",
"indemnify 5\n",
"promotion 16\n",
"indirectly 6\n",
"middleaged 17\n",
"gutenbergtms 5\n",
"mississippi 5\n",
"exempt 12\n",
"melan 5\n",
"locations 10\n",
"gregory 6\n",
"newby 5\n",
"methods 15\n",
"httppglaforgdonate 4\n",
"network 6\n",
"translators 1\n",
"widger 2\n",
"july 15\n",
"favorite 37\n",
"empress 27\n",
"marya 114\n",
"fedorovna 13\n",
"patronizing 4\n",
"scented 7\n",
"todays 5\n",
"disappoint 12\n",
"loftiness 5\n",
"novosiltsev 4\n",
"vicomte 40\n",
"abbe 18\n",
"vienna 47\n",
"beaucoup 2\n",
"destime 1\n",
"anatole 208\n",
"wrinkles 22\n",
"coarse 42\n",
"pensively 9\n",
"stingy 3\n",
"nicknamed 4\n",
"kutuzovs 80\n",
"receptions 9\n",
"ribbon 26\n",
"greeting 28\n",
"defectthe 1\n",
"dispirited 6\n",
"lacetrimmed 1\n",
"soyez 1\n",
"arrivals 10\n",
"catherines 8\n",
"monsieur 139\n",
"flag 13\n",
"spinning 11\n",
"toonoisy 1\n",
"nicelooking 1\n",
"celebrity 4\n",
"modestly 17\n",
"duc 9\n",
"nous 6\n",
"dish 29\n",
"graciously 31\n",
"ballroom 36\n",
"coquetry 3\n",
"diamond 7\n",
"effrayee 1\n",
"paris 71\n",
"mademoiselle 142\n",
"actress 4\n",
"napoleon 475\n",
"ducs 1\n",
"disapproved 3\n",
"disinterestedly 5\n",
"russian 457\n",
"italians 6\n",
"scanned 10\n",
"generals 136\n",
"coquettish 6\n",
"enchanting 23\n",
"tearworn 3\n",
"guards 102\n",
"drubetskaya 9\n",
"vicomtes 2\n",
"embittered 6\n",
"tightly 15\n",
"motherswho 1\n",
"mikhaylovna 119\n",
"impossibleyour 1\n",
"adjutants 43\n",
"arts 14\n",
"donne 1\n",
"usurper 3\n",
"tracing 8\n",
"baton 1\n",
"societyi 2\n",
"rightful 8\n",
"aristocracy 3\n",
"bonapartes 14\n",
"aiming 5\n",
"quoting 5\n",
"napoleons 100\n",
"mon 51\n",
"interjected 2\n",
"contemptuously 26\n",
"discredited 3\n",
"sacrilegious 1\n",
"rallied 5\n",
"brumaire 2\n",
"swindle 2\n",
"jacobin 1\n",
"arcola 3\n",
"awkwardness 11\n",
"spluttered 4\n",
"unfavorable 2\n",
"unamiable 2\n",
"theatricals 5\n",
"andrews 97\n",
"cher 25\n",
"commentaries 2\n",
"mlle 1\n",
"freemason 6\n",
"childish 79\n",
"fussily 1\n",
"intercourse 17\n",
"apraksins 2\n",
"frigid 7\n",
"indecorous 1\n",
"entreaty 36\n",
"desist 5\n",
"napkins 1\n",
"households 5\n",
"impassioned 8\n",
"dissipation 11\n",
"faut 4\n",
"romping 2\n",
"stevens 2\n",
"infantry 81\n",
"finely 3\n",
"clearheadedness 1\n",
"onto 65\n",
"imperials 4\n",
"dolokhovs 42\n",
"tripping 5\n",
"pursing 5\n",
"nervously 19\n",
"counting 35\n",
"frowning 67\n",
"cornet 8\n",
"mikhaylovnas 11\n",
"endeavors 3\n",
"radzivilov 1\n",
"natalias 1\n",
"rostovas 5\n",
"povarskaya 5\n",
"ma 33\n",
"fulfillment 7\n",
"patch 12\n",
"roundfaced 4\n",
"improperly 1\n",
"expelled 7\n",
"ivanovna 20\n",
"cyril 12\n",
"amuses 2\n",
"impersonate 1\n",
"dine 38\n",
"rosyfaced 2\n",
"frilled 2\n",
"mantillanot 1\n",
"natasha 1092\n",
"generation 7\n",
"nicholas 627\n",
"scandals 2\n",
"nimble 11\n",
"plaits 3\n",
"tawny 1\n",
"graceful 41\n",
"muscular 13\n",
"flexibility 1\n",
"coyness 2\n",
"girlish 15\n",
"department 58\n",
"diplomat 6\n",
"elders 11\n",
"rostov 715\n",
"thursday 8\n",
"stabbed 4\n",
"unnaturally 19\n",
"voisinage 2\n",
"confidante 1\n",
"vera 63\n",
"veras 6\n",
"appropriate 25\n",
"têteàtête 4\n",
"couples 9\n",
"defense 15\n",
"vogue 8\n",
"flirt 4\n",
"nicholassaid 1\n",
"club 71\n",
"distraction 16\n",
"amiability 4\n",
"godsonyou 1\n",
"godfatherand 1\n",
"grandees 2\n",
"taras 3\n",
"someones 11\n",
"rows 38\n",
"princesses 19\n",
"darkhaired 1\n",
"pronouncing 7\n",
"acknowledging 11\n",
"brusque 1\n",
"unlicked 1\n",
"pathetic 26\n",
"ambling 4\n",
"pierrewho 1\n",
"boristhe 1\n",
"bustling 11\n",
"agitating 6\n",
"outfit 9\n",
"theyfriends 1\n",
"turkish 8\n",
"rudenesses 1\n",
"inhaled 4\n",
"lieutenant 46\n",
"karlovich 1\n",
"vous 30\n",
"comptez 1\n",
"corps 47\n",
"narrating 6\n",
"egotism 8\n",
"fulltoned 1\n",
"hows 19\n",
"prelude 7\n",
"scraped 11\n",
"forks 8\n",
"neglecting 3\n",
"turtle 3\n",
"jealousy 32\n",
"austrias 4\n",
"cackle 1\n",
"plethoric 5\n",
"patriotically 2\n",
"reasson 2\n",
"connaissezvous 1\n",
"merveille 1\n",
"couture 1\n",
"trrop 1\n",
"thumping 3\n",
"natashas 115\n",
"pudding 23\n",
"smartness 5\n",
"clink 5\n",
"afterdinner 6\n",
"variations 4\n",
"sonyas 31\n",
"gauzy 7\n",
"cousins 8\n",
"oer 2\n",
"coughing 14\n",
"ecossaise 8\n",
"daniel 82\n",
"anglaise 3\n",
"straightened 8\n",
"toes 18\n",
"doorways 7\n",
"perspiring 18\n",
"blundered 5\n",
"clergy 8\n",
"expectancy 2\n",
"administer 7\n",
"clerical 7\n",
"grizzled 5\n",
"younglooking 3\n",
"liffs 1\n",
"cupboards 5\n",
"stonily 1\n",
"twitch 7\n",
"weyou 1\n",
"clutching 13\n",
"legitimation 1\n",
"sardonically 2\n",
"translation 2\n",
"tout 8\n",
"meanness 15\n",
"intrigue 9\n",
"ingratitudethe 1\n",
"ingratitudein 1\n",
"fortnight 41\n",
"inlaid 4\n",
"tradespeople 4\n",
"permissible 1\n",
"deacon 9\n",
"symmetrically 6\n",
"deacons 1\n",
"mahogany 14\n",
"sawcovered 1\n",
"mane 8\n",
"allforgiving 1\n",
"sighs 6\n",
"foreigner 34\n",
"reddishyellow 1\n",
"handleless 2\n",
"fortify 5\n",
"mirrors 15\n",
"somberly 1\n",
"noisily 10\n",
"disapprovingly 8\n",
"virtuesactivity 1\n",
"undertook 15\n",
"algebra 1\n",
"hardhearted 8\n",
"punctually 5\n",
"powdered 21\n",
"youthfully 1\n",
"noiselessly 14\n",
"glassfronted 1\n",
"leather 25\n",
"yellowish 3\n",
"geometrical 2\n",
"abc 1\n",
"vehement 12\n",
"distractions 4\n",
"hopelessness 8\n",
"radiated 3\n",
"shafts 3\n",
"nobleminded 1\n",
"youthfulness 7\n",
"intimacy 24\n",
"rumored 5\n",
"crestfallen 4\n",
"marriageable 4\n",
"neighbor 23\n",
"representative 22\n",
"saviours 2\n",
"vouchsafe 1\n",
"worldis 1\n",
"naturewhich 1\n",
"enrolled 9\n",
"lighthearted 13\n",
"ivanovich 32\n",
"valet 54\n",
"repeatedof 1\n",
"oftrepeated 1\n",
"beatific 4\n",
"nightyou 1\n",
"nether 4\n",
"odyntsova 1\n",
"avenues 6\n",
"gleefully 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"powdering 1\n",
"neutral 4\n",
"operate 8\n",
"sait 4\n",
"reviendra 3\n",
"taciturn 13\n",
"footmenone 1\n",
"achilles 7\n",
"ridicule 19\n",
"frederick 78\n",
"hofs 1\n",
"kriegswurstraths 1\n",
"pahlen 1\n",
"orlovs 2\n",
"outlived 3\n",
"refraining 3\n",
"powdermonkey 1\n",
"trunks 16\n",
"puzzle 3\n",
"trend 4\n",
"evoke 1\n",
"masha 8\n",
"selfrestraint 3\n",
"dillydallying 1\n",
"rightserve 1\n",
"spectacled 3\n",
"ironic 10\n",
"archduchy 1\n",
"knapsack 15\n",
"awl 1\n",
"choleric 6\n",
"brandnew 2\n",
"arching 3\n",
"hofkriegsrath 7\n",
"inchief 1\n",
"companies 19\n",
"upraised 5\n",
"jerky 5\n",
"slackening 8\n",
"rigidly 3\n",
"viennese 3\n",
"trot 108\n",
"galloped 99\n",
"breathlessly 5\n",
"len 2\n",
"moist 19\n",
"rednosed 4\n",
"ismail 3\n",
"convention 5\n",
"separates 2\n",
"prokhor 1\n",
"butt 7\n",
"vodka 22\n",
"chalkas 1\n",
"prussians 5\n",
"buckwheat 5\n",
"cooked 7\n",
"castanets 2\n",
"zherkov 37\n",
"intentional 1\n",
"faro 1\n",
"begill 1\n",
"elegance 13\n",
"skillful 13\n",
"excellencys 3\n",
"participation 11\n",
"favorable 5\n",
"danube 15\n",
"conjunction 11\n",
"commissions 10\n",
"scribbled 2\n",
"bandaged 19\n",
"glum 1\n",
"naiv 1\n",
"zherkovs 3\n",
"lackeys 3\n",
"allies 14\n",
"detruite 1\n",
"trouvez 1\n",
"strengthening 7\n",
"cet 1\n",
"avez 1\n",
"salzeneck 2\n",
"astir 4\n",
"oestreicher 1\n",
"welt 2\n",
"vivat 4\n",
"tousled 3\n",
"blockhead 6\n",
"mathilde 1\n",
"cahd 2\n",
"gripped 3\n",
"sparks 11\n",
"cheerfully 48\n",
"quartermaster 16\n",
"coins 2\n",
"rivet 2\n",
"sausage 9\n",
"twicks 1\n",
"lied 5\n",
"lyingso 1\n",
"untruth 3\n",
"regimentall 1\n",
"blurt 2\n",
"imploring 23\n",
"weported 1\n",
"bridges 23\n",
"linz 1\n",
"baggage 47\n",
"fardistant 1\n",
"jostling 4\n",
"turrets 3\n",
"enemys 61\n",
"patrols 2\n",
"pies 3\n",
"ransack 1\n",
"nuns 2\n",
"naked 22\n",
"reinspected 1\n",
"carts 92\n",
"convoyman 1\n",
"bayonets 14\n",
"sunken 13\n",
"squeezed 22\n",
"udder 1\n",
"fedotov 1\n",
"sheathed 4\n",
"thoroughbred 6\n",
"sabretache 5\n",
"bwushed 1\n",
"rejoin 11\n",
"wagons 32\n",
"hilltop 2\n",
"dividing 6\n",
"seewe 1\n",
"nape 4\n",
"magnanimously 1\n",
"attaching 11\n",
"morosely 8\n",
"grapeshot 13\n",
"closelyto 1\n",
"stretchers 14\n",
"zides 1\n",
"sidethe 2\n",
"vladimir 5\n",
"medal 2\n",
"misdirected 5\n",
"christs 8\n",
"stretcher 9\n",
"wegular 1\n",
"bwicks 1\n",
"dispatches 3\n",
"concise 2\n",
"artificiality 3\n",
"ushering 1\n",
"refinements 2\n",
"sayings 7\n",
"pucker 1\n",
"crease 1\n",
"deepset 1\n",
"smoothness 4\n",
"embassy 9\n",
"brigade 4\n",
"auersperg 11\n",
"lui 3\n",
"terriblyher 1\n",
"ourselvesi 1\n",
"chancellor 10\n",
"youis 1\n",
"juan 2\n",
"lhomme 1\n",
"wrinkling 2\n",
"moravian 1\n",
"accompanying 21\n",
"reliable 11\n",
"rafts 1\n",
"conferring 3\n",
"forecast 2\n",
"dignitaries 4\n",
"thabor 3\n",
"gascons 2\n",
"negotiate 9\n",
"quil 4\n",
"allons 1\n",
"fatethe 1\n",
"bulging 4\n",
"cabriolet 9\n",
"diverted 15\n",
"whip 36\n",
"woolen 3\n",
"bohemian 4\n",
"abominable 10\n",
"dictating 3\n",
"kiev 17\n",
"dispositions 37\n",
"envelope 8\n",
"christ 21\n",
"vanguard 11\n",
"convoys 1\n",
"ratifies 1\n",
"grunth 4\n",
"disbelieved 3\n",
"earn 8\n",
"sutlers 5\n",
"arent 17\n",
"stockinged 1\n",
"entrenchments 8\n",
"earthworks 1\n",
"latrines 1\n",
"shirts 15\n",
"cookers 1\n",
"grenadiersfine 1\n",
"flanks 5\n",
"picket 12\n",
"grenadier 5\n",
"chante 1\n",
"menacingly 1\n",
"tushins 17\n",
"felling 2\n",
"parleys 2\n",
"lemarrois 1\n",
"reined 13\n",
"bobtailed 5\n",
"civilianan 1\n",
"crashed 2\n",
"battlefield 37\n",
"gunners 2\n",
"rivulet 1\n",
"paraded 8\n",
"ramrods 1\n",
"touchpans 1\n",
"feeblelooking 1\n",
"unsheathed 1\n",
"cleaving 4\n",
"trooper 3\n",
"exlenlency 1\n",
"sonorous 11\n",
"gaitered 1\n",
"zis 1\n",
"cocks 5\n",
"fearresembling 1\n",
"deadlay 1\n",
"whizzed 1\n",
"twot 1\n",
"croups 1\n",
"bondarchuk 1\n",
"bondarchuks 1\n",
"stubble 8\n",
"fleeing 10\n",
"catchplay 1\n",
"exemplary 16\n",
"untied 6\n",
"congealed 3\n",
"energetic 17\n",
"artillerymen 7\n",
"drivers 12\n",
"scattering 5\n",
"deafened 4\n",
"harnessing 1\n",
"unicorn 1\n",
"oftener 26\n",
"matvevnas 1\n",
"pitchdark 2\n",
"mastering 3\n",
"turk 3\n",
"aching 12\n",
"signet 1\n",
"thanking 10\n",
"devoid 8\n",
"heroically 2\n",
"bivouacking 5\n",
"loudest 4\n",
"intentness 2\n",
"sinews 2\n",
"innumerable 51\n",
"achievement 11\n",
"councilor 2\n",
"alikewere 1\n",
"blushes 10\n",
"precluding 1\n",
"unsuitable 6\n",
"pavlovnawho 1\n",
"actionseeing 1\n",
"adores 1\n",
"aunts 84\n",
"anyoneto 1\n",
"steppe 3\n",
"guestsan 1\n",
"scarlet 13\n",
"joked 6\n",
"petted 10\n",
"bordered 3\n",
"conservatories 2\n",
"alpatych 134\n",
"resignedly 3\n",
"dumps 1\n",
"oppression 6\n",
"shoveled 1\n",
"overcoat 21\n",
"akimbo 6\n",
"maidservants 3\n",
"marie 8\n",
"unobtrusive 2\n",
"smartened 2\n",
"selfesteem 3\n",
"lises 3\n",
"longing 12\n",
"quickwitted 1\n",
"impropriety 6\n",
"doings 10\n",
"ignores 1\n",
"questioning 16\n",
"hethe 2\n",
"rebuking 1\n",
"nightcap 24\n",
"snorting 14\n",
"bouriennei 1\n",
"driedup 1\n",
"amelies 1\n",
"crybabies 1\n",
"rememberi 1\n",
"ecstatically 7\n",
"schoss 16\n",
"pear 3\n",
"manhood 9\n",
"stages 20\n",
"describes 2\n",
"braver 2\n",
"constantine 3\n",
"pavlovich 4\n",
"wellprovisioned 1\n",
"expeditions 6\n",
"caroline 3\n",
"bergs 10\n",
"soften 13\n",
"hosts 14\n",
"dreamer 6\n",
"everythings 3\n",
"galicia 1\n",
"tsarevichs 1\n",
"narrators 1\n",
"aide 3\n",
"decamp 2\n",
"bandsmen 1\n",
"collars 5\n",
"pomaded 6\n",
"satin 7\n",
"activities 11\n",
"unfolded 10\n",
"crowing 4\n",
"sunrise 12\n",
"trumpets 2\n",
"trumpeters 2\n",
"undivided 6\n",
"resonant 2\n",
"georges 18\n",
"undecided 26\n",
"muzzle 3\n",
"couldpreferably 1\n",
"immeasurably 3\n",
"waltz 17\n",
"thinkingfor 1\n",
"triumphed 6\n",
"counseled 1\n",
"maps 11\n",
"tradiridira 1\n",
"francais 2\n",
"playacting 1\n",
"markov 7\n",
"entereda 1\n",
"convoyed 3\n",
"corporals 4\n",
"goodnaturedly 9\n",
"tenths 4\n",
"mechanism 6\n",
"pulleys 2\n",
"frenchall 1\n",
"dial 5\n",
"chagrin 2\n",
"eventualities 2\n",
"flanking 1\n",
"hohenlohe 1\n",
"president 10\n",
"noblemans 2\n",
"ostralitz 1\n",
"defiles 1\n",
"soporific 2\n",
"foggy 7\n",
"teasing 4\n",
"thresh 2\n",
"watchword 2\n",
"succumbing 1\n",
"guryevs 1\n",
"guryev 1\n",
"flared 10\n",
"fidgeting 2\n",
"gullies 2\n",
"whizzing 1\n",
"pan 2\n",
"whencea 1\n",
"tatand 1\n",
"lakes 2\n",
"aglow 3\n",
"longawaited 1\n",
"hoar 2\n",
"bewitching 11\n",
"twitchedthat 1\n",
"unreasoning 7\n",
"submissive 26\n",
"followers 7\n",
"wretches 12\n",
"leapt 2\n",
"dolgorukovs 2\n",
"uvarovs 5\n",
"jingle 5\n",
"shied 1\n",
"youths 4\n",
"traitors 14\n",
"groom 26\n",
"lamented 14\n",
"legged 3\n",
"dusk 25\n",
"splashing 17\n",
"herobut 1\n",
"epithet 7\n",
"equitable 1\n",
"wordsthe 2\n",
"unsympathizing 1\n",
"fatally 2\n",
"buy 78\n",
"cornice 6\n",
"cleaned 10\n",
"tornado 1\n",
"goat 1\n",
"disputed 13\n",
"portmanteaus 5\n",
"starched 6\n",
"schoolroom 20\n",
"dancer 19\n",
"caper 3\n",
"gavril 1\n",
"slyhe 1\n",
"boulevards 2\n",
"mazurka 11\n",
"prizes 2\n",
"openhanded 1\n",
"maksim 1\n",
"hothouses 2\n",
"gypsy 9\n",
"whwhat 1\n",
"clubit 1\n",
"yuri 1\n",
"vyazemskidid 1\n",
"themremained 1\n",
"highfalutin 1\n",
"anecdotes 9\n",
"subservience 4\n",
"andlike 2\n",
"rye 15\n",
"virile 2\n",
"theodore 15\n",
"kursk 1\n",
"heros 2\n",
"rhipheus 1\n",
"caesar 4\n",
"polonaise 5\n",
"conquests 5\n",
"gulp 5\n",
"conquest 6\n",
"apraksin 1\n",
"organizer 1\n",
"cynically 1\n",
"rake 6\n",
"inimically 1\n",
"wordan 1\n",
"sundered 1\n",
"semblance 5\n",
"quieter 11\n",
"bluntness 3\n",
"digested 1\n",
"honorthats 1\n",
"beheaded 1\n",
"despot 1\n",
"coronet 1\n",
"wrathful 10\n",
"brandishing 2\n",
"vaguely 24\n",
"withdrawal 10\n",
"andrewsaid 1\n",
"fruschtique 1\n",
"praskovya 1\n",
"travail 3\n",
"snuffing 2\n",
"lessen 4\n",
"kerchief 11\n",
"philip 3\n",
"demyan 2\n",
"waxen 3\n",
"anointed 1\n",
"puresouled 1\n",
"scotfree 1\n",
"highminded 1\n",
"merriest 5\n",
"inconsequent 1\n",
"tension 3\n",
"iogel 9\n",
"weady 2\n",
"gorchakov 1\n",
"exceptionally 2\n",
"transparently 2\n",
"gwace 1\n",
"talentoh 1\n",
"mazuwka 1\n",
"vividness 3\n",
"scrawled 3\n",
"ace 2\n",
"knave 3\n",
"casket 6\n",
"broadboned 2\n",
"gentlewoman 5\n",
"menot 4\n",
"onelet 1\n",
"selfreproach 10\n",
"labored 1\n",
"sounding 21\n",
"dealforty 1\n",
"apoplectic 6\n",
"chit 4\n",
"torzhok 6\n",
"overboots 1\n",
"pondering 22\n",
"tantalizing 2\n",
"largeboned 1\n",
"wavering 8\n",
"outlook 5\n",
"scornful 11\n",
"reawakening 1\n",
"impure 1\n",
"retain 18\n",
"physics 4\n",
"bestknown 1\n",
"kempis 1\n",
"attaining 5\n",
"wardrobe 14\n",
"cavities 1\n",
"associating 4\n",
"outlining 1\n",
"bated 2\n",
"rhetor 22\n",
"smolyaninov 2\n",
"secondary 6\n",
"frees 1\n",
"obediencewhich 1\n",
"therein 5\n",
"societies 5\n",
"hieroglyph 2\n",
"gluttony 1\n",
"irritability 13\n",
"flooded 4\n",
"allegories 1\n",
"conductors 1\n",
"fidelity 22\n",
"starshaped 2\n",
"testament 11\n",
"trowel 6\n",
"whiteness 10\n",
"cleanse 2\n",
"womens 15\n",
"unclean 2\n",
"brethren 7\n",
"statutes 3\n",
"rapped 2\n",
"subscribed 5\n",
"selfassurance 5\n",
"duels 3\n",
"rupture 7\n",
"uncomplainingly 2\n",
"beforesuch 1\n",
"selection 5\n",
"jena 5\n",
"auerstadt 4\n",
"noteworthy 2\n",
"groat 1\n",
"treaty 6\n",
"continuity 6\n",
"alliee 1\n",
"herplease 1\n",
"roi 9\n",
"n 12\n",
"precedents 4\n",
"nearing 7\n",
"dandling 3\n",
"verbal 13\n",
"ivanich 2\n",
"scowled 2\n",
"abbreviations 2\n",
"eylau 2\n",
"sheets 13\n",
"genuinely 4\n",
"bile 2\n",
"ab 1\n",
"ovo 1\n",
"t 27\n",
"rages 1\n",
"punishes 3\n",
"interregnum 1\n",
"looting 11\n",
"infants 8\n",
"serfsand 1\n",
"pensions 1\n",
"factories 4\n",
"workshops 3\n",
"liberate 3\n",
"persistence 5\n",
"precepts 4\n",
"twomorality 1\n",
"delude 6\n",
"chantry 3\n",
"almshouses 4\n",
"deputation 10\n",
"ceremonies 18\n",
"lisping 3\n",
"plowing 1\n",
"wontnot 1\n",
"hillseven 1\n",
"unlimited 9\n",
"fatherthat 1\n",
"flogged 8\n",
"husbandry 2\n",
"sect 2\n",
"fundamental 3\n",
"railing 11\n",
"convinces 4\n",
"suis 4\n",
"sachiez 1\n",
"reconnaissante 1\n",
"saucer 5\n",
"admonishingly 1\n",
"parole 3\n",
"penitence 10\n",
"incense 3\n",
"chaffingly 1\n",
"oneyearold 1\n",
"officerin 1\n",
"sprouted 3\n",
"felty 1\n",
"swollen 22\n",
"laborer 2\n",
"quoits 1\n",
"wostovs 1\n",
"vestibule 12\n",
"couches 2\n",
"mended 13\n",
"vibrating 3\n",
"denisovswas 1\n",
"leaded 1\n",
"svayka 1\n",
"mutinyseizing 1\n",
"buzz 8\n",
"astwide 2\n",
"scoundwels 1\n",
"lecture 2\n",
"pwovisions 1\n",
"pat 14\n",
"snout 2\n",
"marauding 3\n",
"platov 6\n",
"reconnoitered 2\n",
"sharpshooter 1\n",
"lint 4\n",
"emaciated 6\n",
"limped 5\n",
"pungent 3\n",
"amputated 3\n",
"onearmed 1\n",
"bwinging 1\n",
"wobbers 1\n",
"tested 3\n",
"monograms 3\n",
"napoleonreaching 1\n",
"zhilinski 6\n",
"lunching 1\n",
"profiting 2\n",
"friendsthat 1\n",
"capitaine 1\n",
"frowningly 2\n",
"legion 13\n",
"preobrazhensk 11\n",
"batiste 1\n",
"vest 3\n",
"affable 19\n",
"undersized 1\n",
"donning 3\n",
"dirt 26\n",
"accomplishedwere 1\n",
"birches 9\n",
"uphill 8\n",
"reminder 16\n",
"snort 10\n",
"unsymmetrically 1\n",
"barked 12\n",
"otradnoe 31\n",
"silverlit 1\n",
"starless 1\n",
"scuffle 6\n",
"delicately 16\n",
"raindrops 3\n",
"sappy 2\n",
"blossom 24\n",
"nightingales 1\n",
"reverberated 4\n",
"centuryold 2\n",
"illogical 4\n",
"dries 1\n",
"assessor 1\n",
"strogonovwhom 1\n",
"antipathies 1\n",
"officerwith 1\n",
"thenagain 1\n",
"andrewrelapsing 1\n",
"salary 14\n",
"uninitiated 2\n",
"liberating 1\n",
"tragic 5\n",
"mikhaylovich 3\n",
"irritate 5\n",
"throb 3\n",
"immeasurable 8\n",
"imitators 1\n",
"tittletattle 1\n",
"contemporaries 7\n",
"lhonneur 3\n",
"droits 1\n",
"overshadowed 5\n",
"jot 1\n",
"unheroic 2\n",
"clearthinking 1\n",
"labels 5\n",
"charters 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"poorhouse 1\n",
"belongings 12\n",
"categories 3\n",
"solomon 66\n",
"comprehensible 14\n",
"prized 5\n",
"lodgewe 1\n",
"drowsing 2\n",
"infidelity 2\n",
"substitute 17\n",
"formalities 7\n",
"delivery 7\n",
"mattered 2\n",
"virtuelove 1\n",
"rebirth 1\n",
"basis 10\n",
"caulaincourt 6\n",
"superbe 1\n",
"charmante 3\n",
"tactful 2\n",
"lispingly 1\n",
"moderately 6\n",
"worldliness 3\n",
"benefactors 10\n",
"g 4\n",
"decoration 7\n",
"nominated 2\n",
"dissect 1\n",
"undismayed 2\n",
"dogsmy 1\n",
"unites 4\n",
"conjugal 4\n",
"drawings 8\n",
"usefulness 6\n",
"werden 2\n",
"dishonorablebut 1\n",
"joyousness 1\n",
"arrears 6\n",
"countif 1\n",
"importunity 4\n",
"compression 1\n",
"ss 1\n",
"album 4\n",
"cotton 11\n",
"curlpapers 2\n",
"matveich 1\n",
"giggler 1\n",
"darkblue 4\n",
"manthe 5\n",
"agileshe 1\n",
"swims 1\n",
"dunyasha 31\n",
"thirtyfirst 4\n",
"baize 8\n",
"taurida 4\n",
"allshe 1\n",
"gossamer 5\n",
"airiness 2\n",
"filmy 2\n",
"crawling 4\n",
"tacking 1\n",
"betterlooking 1\n",
"treats 7\n",
"ravish 1\n",
"requesting 12\n",
"legislative 2\n",
"elsewhereat 1\n",
"readjusting 1\n",
"devotee 1\n",
"reorganized 2\n",
"bitskis 1\n",
"parqueted 1\n",
"monastery 7\n",
"speranskiwas 1\n",
"munched 2\n",
"disillusionment 4\n",
"thingsthreatening 1\n",
"portenglish 1\n",
"reciting 2\n",
"humorous 7\n",
"procedure 2\n",
"disconcert 2\n",
"immaculate 3\n",
"nobodies 1\n",
"aquaintances 1\n",
"zu 2\n",
"panins 1\n",
"allusions 8\n",
"natalie 14\n",
"silliness 1\n",
"pleaseis 1\n",
"moodlove 1\n",
"ithe 6\n",
"postponed 6\n",
"accompanies 4\n",
"moods 6\n",
"religionand 1\n",
"nicholasinto 1\n",
"lovingly 18\n",
"successor 9\n",
"spas 1\n",
"houselet 1\n",
"reconciling 1\n",
"halfwitted 3\n",
"shortsightedness 1\n",
"hempen 2\n",
"bast 12\n",
"legend 13\n",
"mars 1\n",
"anne 9\n",
"bargaining 4\n",
"comradesnot 1\n",
"brigadegave 1\n",
"subscription 6\n",
"journeyfrom 1\n",
"unsatisfied 4\n",
"unalterable 5\n",
"snapping 9\n",
"patchwork 4\n",
"forwardand 1\n",
"brownish 1\n",
"paleyellow 1\n",
"cubs 6\n",
"september 29\n",
"litter 8\n",
"overcast 4\n",
"fifteenth 10\n",
"unsurpassable 1\n",
"blackspotted 3\n",
"borzoi 17\n",
"shrillest 2\n",
"kennelman 1\n",
"uvarka 3\n",
"pathwell 1\n",
"borzois 35\n",
"sorrel 2\n",
"ilagins 8\n",
"whipped 7\n",
"harrier 1\n",
"budge 4\n",
"outwent 1\n",
"tackled 1\n",
"chekmar 3\n",
"unleashed 1\n",
"upland 3\n",
"ulyulyu 6\n",
"straighten 1\n",
"lope 3\n",
"whined 5\n",
"quinsy 1\n",
"gully 8\n",
"fleas 3\n",
"gnashed 2\n",
"gash 2\n",
"saddlebow 2\n",
"forepaws 1\n",
"clicked 3\n",
"bristling 7\n",
"flail 1\n",
"bridled 3\n",
"broadbrowed 1\n",
"doffing 2\n",
"ilagin 18\n",
"ravenblack 1\n",
"iligins 1\n",
"redspotted 3\n",
"erza 9\n",
"fullgrown 4\n",
"pitting 1\n",
"haunched 1\n",
"natashaa 1\n",
"dangles 1\n",
"apples 12\n",
"glowed 7\n",
"stem 10\n",
"protrude 1\n",
"mushrooms 3\n",
"cranks 3\n",
"tuned 3\n",
"retuned 1\n",
"outthats 1\n",
"unteachable 1\n",
"velvets 2\n",
"darknessnot 1\n",
"receptive 2\n",
"dimmler 16\n",
"meshes 3\n",
"noblemindedness 1\n",
"darkeyed 3\n",
"unchangingly 2\n",
"dullest 3\n",
"outcast 5\n",
"maidservant 6\n",
"kondratevna 1\n",
"oats 13\n",
"illtempered 2\n",
"unconcernedly 3\n",
"madagascar 5\n",
"betook 12\n",
"twanging 1\n",
"tablebut 1\n",
"repressing 6\n",
"studythat 1\n",
"onesthose 1\n",
"jarring 3\n",
"immortalwell 1\n",
"conspirators 6\n",
"turks 10\n",
"ladiesfrightening 1\n",
"costumes 5\n",
"freezing 7\n",
"highroadpolished 1\n",
"gee 6\n",
"harderever 1\n",
"gallopthe 1\n",
"kosoy 2\n",
"demkin 1\n",
"melyukovka 4\n",
"danilovna 10\n",
"witches 1\n",
"dimmlerisnt 1\n",
"kazantartar 1\n",
"crunching 2\n",
"scrunching 1\n",
"spelling 2\n",
"compromising 6\n",
"hovels 1\n",
"sivtsev 2\n",
"welltimed 1\n",
"goodhumoredly 2\n",
"eternally 5\n",
"homage 33\n",
"collections 1\n",
"astraea 1\n",
"carousals 3\n",
"nowill 1\n",
"governmental 2\n",
"senility 1\n",
"prerevolution 1\n",
"flustered 6\n",
"ridiculed 2\n",
"metivier 10\n",
"dayall 2\n",
"consigne 1\n",
"fez 1\n",
"spies 15\n",
"outwe 1\n",
"diffident 2\n",
"protests 2\n",
"wording 2\n",
"indulgence 9\n",
"jumps 2\n",
"courting 4\n",
"sketches 2\n",
"dune 1\n",
"trop 1\n",
"viens 2\n",
"tourments 1\n",
"penza 6\n",
"expiring 4\n",
"apportioned 2\n",
"occasionsthat 1\n",
"konyusheny 2\n",
"knitted 14\n",
"pulley 2\n",
"irina 1\n",
"barrels 10\n",
"reprimand 3\n",
"fatherand 1\n",
"eyeshow 1\n",
"tiers 4\n",
"disagreeably 7\n",
"julieand 1\n",
"seminude 1\n",
"unclothedsat 1\n",
"epaulet 1\n",
"footlights 1\n",
"cymbals 1\n",
"bouquet 5\n",
"bravothere 1\n",
"torturing 8\n",
"actresseswith 1\n",
"carousal 1\n",
"outvying 1\n",
"connoisseur 2\n",
"conducts 5\n",
"saturdays 13\n",
"tver 7\n",
"praising 4\n",
"laughterhe 1\n",
"fiance 1\n",
"pierrehave 1\n",
"promisedgo 1\n",
"operagratified 1\n",
"monologue 1\n",
"grossvater 1\n",
"wontthats 1\n",
"akharovs 1\n",
"everythingthe 1\n",
"bolkonskishe 1\n",
"kuraginwere 1\n",
"abacus 1\n",
"balaga 20\n",
"pedestrians 1\n",
"pedestrian 3\n",
"charging 9\n",
"yearwhen 1\n",
"reinscatch 1\n",
"timenow 1\n",
"matrevna 1\n",
"elopements 1\n",
"boulevard 6\n",
"podnovinski 1\n",
"pleaded 22\n",
"hussies 2\n",
"limeflower 1\n",
"besprinkled 2\n",
"openmouthed 1\n",
"charmingshould 1\n",
"himexpecting 1\n",
"zakharych 2\n",
"timofeevich 1\n",
"purloined 1\n",
"dessalles 37\n",
"reminding 16\n",
"sniffed 8\n",
"dismayed 11\n",
"western 8\n",
"forcesmillions 1\n",
"armymoved 1\n",
"frauds 1\n",
"nonobservance 4\n",
"reestablishing 1\n",
"vistula 6\n",
"autocratic 1\n",
"gunsshould 1\n",
"coinciding 2\n",
"historys 5\n",
"ou 2\n",
"withers 29\n",
"twentyninth 3\n",
"accelerate 1\n",
"danzig 2\n",
"konigsberg 1\n",
"scythia 1\n",
"marchednapoleon 1\n",
"rascalssee 1\n",
"pontoon 1\n",
"uncanny 1\n",
"manes 2\n",
"vilna 29\n",
"affronted 4\n",
"shishkov 3\n",
"consents 1\n",
"insert 3\n",
"equestrian 1\n",
"viva 1\n",
"variegated 1\n",
"royaute 1\n",
"incumbent 12\n",
"organism 2\n",
"ennui 1\n",
"insignificanceparticularly 1\n",
"mameluke 1\n",
"rustan 1\n",
"magnatesseveral 1\n",
"thighs 1\n",
"amicable 2\n",
"flurriedcompose 1\n",
"rhythmically 5\n",
"wintzingerodes 4\n",
"incompetent 2\n",
"lions 12\n",
"alienating 1\n",
"monasteries 2\n",
"poltava 2\n",
"weimar 1\n",
"fortyyearold 2\n",
"bucharest 3\n",
"wallachian 1\n",
"rosier 1\n",
"censuring 1\n",
"reawaken 1\n",
"victimized 1\n",
"rejoices 2\n",
"fortified 16\n",
"enemythat 1\n",
"paulucci 14\n",
"emanated 2\n",
"theorist 2\n",
"despising 3\n",
"dayespecially 1\n",
"tendencies 5\n",
"ermolovs 5\n",
"partyin 1\n",
"vitebsk 8\n",
"bennigsenites 1\n",
"eighth 11\n",
"correctness 3\n",
"drones 4\n",
"fluctuation 1\n",
"incitement 1\n",
"quitting 5\n",
"suitability 1\n",
"mainspring 4\n",
"theoristgenerals 1\n",
"martyrdom 3\n",
"citizen 5\n",
"barbarousmonstrous 1\n",
"objectits 1\n",
"demonstrated 2\n",
"invoked 3\n",
"grumpy 3\n",
"agriculture 4\n",
"remounts 4\n",
"landowners 11\n",
"ilyin 38\n",
"raevskis 9\n",
"exploit 4\n",
"trickled 6\n",
"antiquity 9\n",
"zdrzhinskis 3\n",
"gaping 8\n",
"recess 2\n",
"cellaret 2\n",
"scratched 6\n",
"bluepurple 1\n",
"roadsides 1\n",
"mettlesome 1\n",
"outgallop 1\n",
"ostermanntolstoys 1\n",
"batterywhich 1\n",
"orangecolored 1\n",
"riderless 2\n",
"blowwhich 1\n",
"dimple 4\n",
"mudrov 1\n",
"pneumonia 1\n",
"promenades 1\n",
"forbade 4\n",
"loveuttered 1\n",
"frolicking 1\n",
"spat 4\n",
"eleventh 5\n",
"lilac 11\n",
"synod 2\n",
"righteousness 4\n",
"unworthiness 6\n",
"scepter 3\n",
"swindled 2\n",
"numerical 2\n",
"k 3\n",
"levy 2\n",
"despoil 1\n",
"vinaigrette 1\n",
"sedateness 1\n",
"bared 7\n",
"elbowing 1\n",
"backthe 1\n",
"assumptionand 1\n",
"petyapale 1\n",
"elevationthe 1\n",
"sashes 1\n",
"himthough 2\n",
"dinedlooking 1\n",
"envying 2\n",
"mold 2\n",
"largish 1\n",
"yieldedwent 1\n",
"uniformsome 1\n",
"dimeyed 1\n",
"peoplestrengthened 1\n",
"empewah 2\n",
"farming 7\n",
"senator 3\n",
"adraksin 4\n",
"animate 1\n",
"besashed 1\n",
"awestruck 2\n",
"surpassed 7\n",
"generalcould 1\n",
"himselfpointing 1\n",
"advisers 9\n",
"trebled 1\n",
"impels 1\n",
"charpie 1\n",
"dutifully 13\n",
"itwhich 3\n",
"piano 22\n",
"deferring 3\n",
"damn 3\n",
"tableon 1\n",
"candlemoved 1\n",
"candlestick 9\n",
"noonday 1\n",
"catafalque 1\n",
"princeand 1\n",
"stuffed 8\n",
"corn 22\n",
"mown 3\n",
"dealers 12\n",
"cartingit 1\n",
"governors 41\n",
"generalinchief 1\n",
"instructionshere 1\n",
"lamenting 6\n",
"innkeeper 6\n",
"intermittent 2\n",
"sickle 2\n",
"sunflower 4\n",
"frieze 10\n",
"apologetically 7\n",
"yelled 7\n",
"echoing 8\n",
"churned 1\n",
"nostrils 9\n",
"submerged 2\n",
"keepers 3\n",
"overturned 2\n",
"plum 4\n",
"foal 1\n",
"rosebushes 1\n",
"caughtand 1\n",
"discriminate 2\n",
"salons 3\n",
"returnwas 1\n",
"ancients 16\n",
"coughthe 1\n",
"autocrat 1\n",
"novice 2\n",
"manipulates 1\n",
"chinoises 1\n",
"asiatic 3\n",
"majors 26\n",
"rods 2\n",
"pyramids 4\n",
"flowerbordered 1\n",
"yearsthoughts 1\n",
"proclamations 2\n",
"surge 2\n",
"sprays 1\n",
"psalms 1\n",
"carting 5\n",
"instincts 4\n",
"cushion 12\n",
"irrevocability 1\n",
"rameaus 1\n",
"bribe 6\n",
"momentsrose 1\n",
"crimea 1\n",
"outstripping 3\n",
"moost 1\n",
"horseflies 1\n",
"fattening 2\n",
"vanka 1\n",
"unmeaningly 1\n",
"wealthiest 1\n",
"weceives 1\n",
"limply 1\n",
"lurching 1\n",
"expound 3\n",
"zaymishcheas 1\n",
"tow 9\n",
"boredom 1\n",
"knowledgehe 1\n",
"smacked 4\n",
"chevaliers 2\n",
"cygne 2\n",
"articulated 2\n",
"unanimity 4\n",
"burgher 1\n",
"karpushka 2\n",
"chigirin 2\n",
"vousmemes 1\n",
"addedthat 1\n",
"bunch 10\n",
"beringed 2\n",
"amoureuse 1\n",
"tradesmens 2\n",
"arsenal 4\n",
"lockup 1\n",
"crowdofficials 1\n",
"shopkeepers 4\n",
"fullblooded 1\n",
"lubyanka 3\n",
"patrol 6\n",
"kolochawhich 1\n",
"borodinoa 1\n",
"gorki 9\n",
"rightalong 1\n",
"zakharino 1\n",
"groundthats 1\n",
"redoubts 2\n",
"inviolable 6\n",
"brushing 5\n",
"facesan 1\n",
"newmown 2\n",
"riverside 6\n",
"huts 8\n",
"tuchkovs 2\n",
"knyazkovo 3\n",
"magiclantern 1\n",
"overrun 4\n",
"tripped 5\n",
"yesthat 1\n",
"satisfies 1\n",
"spares 1\n",
"nicht 2\n",
"verlust 1\n",
"nehmen 1\n",
"warthats 1\n",
"cramp 5\n",
"compassionate 15\n",
"prefect 2\n",
"madrid 3\n",
"hunching 1\n",
"thoughtto 1\n",
"terrestrial 5\n",
"globe 11\n",
"corrections 1\n",
"flankhad 1\n",
"campan 4\n",
"howitzers 3\n",
"fortification 10\n",
"viceking 5\n",
"manpeter 1\n",
"blindfold 2\n",
"mistenveloped 1\n",
"puffand 1\n",
"andpuff 1\n",
"hauling 5\n",
"buzzed 1\n",
"crows 8\n",
"streamlet 2\n",
"stretcherbearers 3\n",
"aimlessly 2\n",
"crow 3\n",
"blackprobably 1\n",
"glint 1\n",
"collided 2\n",
"borodinothe 1\n",
"manhis 1\n",
"disablement 1\n",
"chessboard 2\n",
"berthiers 1\n",
"friant 1\n",
"hellish 1\n",
"recklessly 3\n",
"lodi 1\n",
"eyesonly 1\n",
"limp 10\n",
"area 9\n",
"tableaux 1\n",
"rugcovered 1\n",
"semenovskhis 1\n",
"dukewhom 1\n",
"chewing 6\n",
"maintains 2\n",
"oatfield 2\n",
"gathers 4\n",
"buckle 4\n",
"hita 1\n",
"movable 1\n",
"grains 1\n",
"cawing 2\n",
"blackhaired 2\n",
"tremblingwere 1\n",
"splintered 1\n",
"treadmill 2\n",
"disavow 1\n",
"aggrandizement 4\n",
"piedmontese 1\n",
"saltpeter 2\n",
"block 18\n",
"fledexperienced 1\n",
"progression 5\n",
"solutions 1\n",
"deals 1\n",
"unit 3\n",
"buds 4\n",
"worries 2\n",
"undermining 4\n",
"poklonny 6\n",
"dorogomilov 9\n",
"saragossa 1\n",
"pencils 7\n",
"chubby 7\n",
"projections 1\n",
"schneider 2\n",
"evacuation 3\n",
"augustin 2\n",
"magnate 7\n",
"directeur 1\n",
"longfrocked 1\n",
"indicationsbefore 1\n",
"dual 1\n",
"columbus 1\n",
"remarry 1\n",
"loversonce 1\n",
"mesalliance 1\n",
"divorce 6\n",
"comprenez 1\n",
"jai 2\n",
"diteslui 1\n",
"veux 1\n",
"furieuse 1\n",
"parce 1\n",
"muchinforming 1\n",
"spoonfuls 1\n",
"pigeons 13\n",
"penthouses 1\n",
"vasilchikov 1\n",
"threepronged 1\n",
"fork 38\n",
"sty 1\n",
"snowwhite 2\n",
"abominably 1\n",
"ladykiller 1\n",
"firstborn 3\n",
"topsyturvy 4\n",
"vinegar 8\n",
"compress 2\n",
"scullions 1\n",
"vasilich 9\n",
"expeditiously 3\n",
"compactly 1\n",
"detrimental 2\n",
"inauguration 1\n",
"misses 78\n",
"tempest 7\n",
"intuition 1\n",
"girdles 1\n",
"alertly 1\n",
"queerlooking 1\n",
"commiserating 1\n",
"gerasim 25\n",
"sophia 2\n",
"hishas 1\n",
"ingratiatingly 1\n",
"fainthearted 4\n",
"assemblies 2\n",
"rampart 5\n",
"queenless 3\n",
"hives 2\n",
"shiftily 1\n",
"peers 4\n",
"beestamed 1\n",
"excrement 1\n",
"bumblebees 1\n",
"wasps 1\n",
"closes 3\n",
"borovitski 1\n",
"sternlooking 1\n",
"dung 1\n",
"ignat 4\n",
"varvarka 1\n",
"arduously 1\n",
"jerkily 1\n",
"bootmakers 4\n",
"signboard 2\n",
"comprehensibleit 1\n",
"emanating 2\n",
"unanimously 3\n",
"uprising 1\n",
"wholeheartedly 1\n",
"lunatics 5\n",
"administrator 3\n",
"shatters 1\n",
"trailed 8\n",
"hypothetical 2\n",
"commits 6\n",
"barricaded 2\n",
"mokhavaya 1\n",
"soldierslike 1\n",
"coaches 9\n",
"rostopchine 2\n",
"incendiarism 2\n",
"retirean 1\n",
"limping 8\n",
"compagnie 1\n",
"madmans 2\n",
"imbecile 13\n",
"regimentwas 1\n",
"tut 15\n",
"loftiest 1\n",
"morel 17\n",
"saucepan 10\n",
"moskowa 2\n",
"talma 1\n",
"onterkoff 1\n",
"alluringly 1\n",
"platonic 3\n",
"mutually 7\n",
"languages 13\n",
"porches 2\n",
"replaited 1\n",
"chirped 2\n",
"dressings 1\n",
"rustled 4\n",
"stretches 5\n",
"doorthe 2\n",
"oneleast 1\n",
"andrewspoke 1\n",
"incompatibility 4\n",
"featherbeds 1\n",
"aniska 3\n",
"missy 1\n",
"squealing 1\n",
"armenian 6\n",
"almondshaped 1\n",
"ivanovs 1\n",
"looters 2\n",
"guardhouse 3\n",
"esteeming 1\n",
"venomously 1\n",
"demolished 2\n",
"christhe 1\n",
"heras 3\n",
"sings 9\n",
"deposition 2\n",
"officially 4\n",
"discouragement 6\n",
"meanest 4\n",
"transcend 1\n",
"vivandiere 1\n",
"racking 2\n",
"pleasurewhich 1\n",
"grazing 3\n",
"stationmasters 1\n",
"farms 1\n",
"dealer 7\n",
"informally 1\n",
"petrovna 3\n",
"enhanced 7\n",
"clustered 5\n",
"provincethat 1\n",
"blueeyed 6\n",
"capitally 2\n",
"coral 3\n",
"nothe 1\n",
"casesthat 1\n",
"nephews 6\n",
"feelingher 1\n",
"coquette 1\n",
"selfsacrificeall 1\n",
"bishops 2\n",
"importantmore 1\n",
"nikolievich 1\n",
"intervening 11\n",
"menhe 1\n",
"chimney 17\n",
"nativity 1\n",
"dazed 6\n",
"unwound 2\n",
"pegs 5\n",
"wellrounded 1\n",
"unwrapped 1\n",
"platon 24\n",
"grandchildren 2\n",
"bulges 1\n",
"laughedas 1\n",
"suppleness 3\n",
"directness 3\n",
"adorning 4\n",
"commonest 7\n",
"themassumed 1\n",
"platosha 1\n",
"errands 5\n",
"bronnikovs 1\n",
"sevenyearold 1\n",
"clattered 4\n",
"groove 2\n",
"gangrene 1\n",
"squirrelfur 1\n",
"venue 1\n",
"gosp 1\n",
"endbut 1\n",
"screening 3\n",
"brainspun 1\n",
"humandeathwas 1\n",
"possiblebut 1\n",
"reverent 3\n",
"fixity 2\n",
"krasnaya 2\n",
"nizhninovgorod 2\n",
"armywhen 1\n",
"settlement 14\n",
"lauristons 2\n",
"chiming 2\n",
"transfers 3\n",
"crossings 1\n",
"scouting 2\n",
"kutuzovhaving 1\n",
"echkino 2\n",
"torban 1\n",
"letashovka 1\n",
"brozin 1\n",
"miscarried 1\n",
"grekov 5\n",
"declivity 1\n",
"thirtyeight 2\n",
"themselvesand 1\n",
"bagovuts 1\n",
"mechanics 3\n",
"desiredto 1\n",
"methodically 3\n",
"geniuses 2\n",
"wintering 1\n",
"falsely 4\n",
"tutolmin 3\n",
"craftsmen 1\n",
"seller 5\n",
"highroads 1\n",
"tuesdays 1\n",
"philanthropy 12\n",
"filial 9\n",
"recounts 1\n",
"campaignthat 1\n",
"polemic 1\n",
"octoberthat 1\n",
"basil 1\n",
"mining 5\n",
"moscowas 1\n",
"complains 3\n",
"nuisances 1\n",
"underfoot 3\n",
"vesselacted 1\n",
"furry 2\n",
"ankles 6\n",
"repulsively 1\n",
"sokolov 3\n",
"chatting 3\n",
"soot 8\n",
"rounder 1\n",
"assignation 1\n",
"squeaky 2\n",
"privation 5\n",
"reasoningand 1\n",
"quests 1\n",
"freedomnow 1\n",
"destroys 3\n",
"insolubly 1\n",
"lightpierre 1\n",
"dramdadadam 1\n",
"conjecturing 1\n",
"blasius 1\n",
"khamovniki 3\n",
"converge 1\n",
"russofrench 1\n",
"testimony 9\n",
"borovsk 2\n",
"dreamy 13\n",
"unripe 1\n",
"guerrillas 3\n",
"paunch 2\n",
"gaped 1\n",
"nearernearer 2\n",
"kalugaa 1\n",
"devises 1\n",
"magnified 3\n",
"atoms 6\n",
"solidified 1\n",
"clamored 1\n",
"collects 2\n",
"nationeven 1\n",
"brandish 1\n",
"departures 2\n",
"tierce 1\n",
"notas 2\n",
"attacker 1\n",
"bataillons 1\n",
"raison 1\n",
"obscurely 2\n",
"factor 6\n",
"twoor 1\n",
"cudgels 1\n",
"factorthe 1\n",
"equations 2\n",
"xy 1\n",
"equation 1\n",
"unknowns 1\n",
"periods 8\n",
"compulsion 7\n",
"irregulars 2\n",
"kirghiz 2\n",
"cutup 1\n",
"closeclinging 1\n",
"ihe 1\n",
"weturn 1\n",
"shielding 1\n",
"flaying 1\n",
"funniest 1\n",
"brim 12\n",
"outthat 1\n",
"vincent 3\n",
"vesna 1\n",
"peur 2\n",
"padded 2\n",
"donc 1\n",
"sentinelles 1\n",
"ramrod 4\n",
"shanties 1\n",
"arentlike 1\n",
"unveiling 1\n",
"harmonies 1\n",
"girths 3\n",
"sliding 6\n",
"unmistakably 3\n",
"transferring 5\n",
"exceeds 1\n",
"horsesin 1\n",
"uphead 1\n",
"filez 2\n",
"ineffectives 1\n",
"clapper 1\n",
"oldthe 1\n",
"roadis 2\n",
"neya 1\n",
"borodinodefeated 1\n",
"frustrated 3\n",
"involves 3\n",
"dukeswhose 1\n",
"threemile 1\n",
"plansthe 1\n",
"extinction 1\n",
"vistas 1\n",
"abstention 1\n",
"negligently 7\n",
"comingcalled 1\n",
"vilnanot 1\n",
"knewnot 1\n",
"beingwhat 1\n",
"hardship 5\n",
"senselesswas 1\n",
"starvedwho 1\n",
"dwindled 2\n",
"eugene 1\n",
"stark 3\n",
"starvingand 1\n",
"acclaimed 1\n",
"bogdanovich 1\n",
"unswervingly 1\n",
"disfavorto 1\n",
"dobroe 3\n",
"sores 2\n",
"theyyou 1\n",
"cluster 6\n",
"purpleblack 1\n",
"thatch 2\n",
"togethershove 1\n",
"haulers 1\n",
"troopsthey 1\n",
"jabbered 2\n",
"birchbarkand 1\n",
"maggoty 1\n",
"plaintively 3\n",
"boire 1\n",
"galant 1\n",
"keeee 1\n",
"disport 1\n",
"wara 1\n",
"frenchand 1\n",
"spells 3\n",
"armywho 1\n",
"vilnahis 1\n",
"cuttersoff 1\n",
"therenow 1\n",
"terenty 4\n",
"freedomthat 1\n",
"lifeno 1\n",
"contemplationhe 1\n",
"everchanging 1\n",
"professions 11\n",
"impostors 3\n",
"deplored 7\n",
"nothingand 1\n",
"householders 1\n",
"plundering 4\n",
"warehouses 6\n",
"peasantsstreamed 1\n",
"booths 1\n",
"estimates 3\n",
"someonehe 1\n",
"stilldeeper 1\n",
"stillstronger 1\n",
"casesbegan 1\n",
"thingto 1\n",
"hadif 2\n",
"anyoneall 1\n",
"enrich 2\n",
"absorb 3\n",
"savelichs 1\n",
"countthe 1\n",
"lengthy 2\n",
"interpretations 1\n",
"deluding 1\n",
"onlyhis 1\n",
"yetsomeday 1\n",
"fichte 2\n",
"essayist 1\n",
"pinnacle 2\n",
"inseparable 22\n",
"characterthough 1\n",
"accusers 1\n",
"oatsthat 1\n",
"fattened 2\n",
"antecedents 9\n",
"emergesby 1\n",
"chancesfrom 1\n",
"colleagues 1\n",
"armiesjust 1\n",
"surrenders 1\n",
"grandeurwhich 1\n",
"significancethat 1\n",
"lyinghe 1\n",
"contrives 2\n",
"himthereby 1\n",
"europeexcept 1\n",
"happendespite 1\n",
"insensate 1\n",
"utilizes 1\n",
"prepares 2\n",
"coalescence 1\n",
"grievance 7\n",
"despises 1\n",
"admires 3\n",
"chalice 1\n",
"perpetuate 1\n",
"pistil 1\n",
"remorselessly 1\n",
"manures 1\n",
"analyzing 2\n",
"debit 1\n",
"laxity 2\n",
"ermishin 1\n",
"eitherin 1\n",
"clench 4\n",
"seriousnessleaving 1\n",
"commending 4\n",
"rebuilt 2\n",
"breakfasts 2\n",
"granaries 1\n",
"threeyearold 1\n",
"broader 3\n",
"confinements 1\n",
"jealousyshe 1\n",
"injuriousshe 1\n",
"nurseknowing 1\n",
"vindicating 1\n",
"outlooks 2\n",
"untidiness 1\n",
"morningespecially 1\n",
"handiest 1\n",
"stillexisting 1\n",
"shepherdesses 1\n",
"antonovnas 1\n",
"andrushas 1\n",
"loi 1\n",
"strangle 5\n",
"drilling 1\n",
"tugendbund 5\n",
"agwee 1\n",
"howwible 1\n",
"prop 3\n",
"allegiance 4\n",
"corrupted 6\n",
"mitrofanych 1\n",
"repurchasing 1\n",
"argumentatively 6\n",
"escapefrom 1\n",
"consecutive 5\n",
"amusementalmost 1\n",
"lisa 1\n",
"jealo 1\n",
"fils 1\n",
"capacities 5\n",
"westparisand 1\n",
"untilled 1\n",
"favorites 1\n",
"africans 1\n",
"abdicate 1\n",
"caricature 3\n",
"athe 1\n",
"historiansthe 1\n",
"ideahistorys 1\n",
"forcethe 1\n",
"traders 4\n",
"voltaires 1\n",
"domination 1\n",
"timeis 2\n",
"confederation 2\n",
"unconditionally 9\n",
"historiansthose 1\n",
"violation 2\n",
"tranquilly 5\n",
"reacted 1\n",
"transferences 1\n",
"illdirected 1\n",
"cotyledons 1\n",
"kurbski 1\n",
"godfreys 4\n",
"luther 1\n",
"guillotined 1\n",
"saythose 2\n",
"wordsare 1\n",
"expressedthis 1\n",
"reinstate 1\n",
"rankcorporals 1\n",
"stillhigher 1\n",
"apex 1\n",
"activityin 1\n",
"centralization 1\n",
"crimesmake 1\n",
"infinitythat 1\n",
"willwhich 1\n",
"lifeman 1\n",
"allseeing 1\n",
"ethics 2\n",
"ignoramuseshave 1\n",
"zoology 3\n",
"theoriesthat 1\n",
"defenseless 1\n",
"diminishes 2\n",
"attilas 1\n",
"nonmoral 1\n",
"mitigates 1\n",
"dishonest 6\n",
"foretell 1\n",
"remoteness 3\n",
"casehowever 1\n",
"fewest 2\n",
"futurethat 1\n",
"causewe 1\n",
"zero 2\n",
"finite 1\n",
"visibility 1\n",
"gravitation 6\n",
"kepler 1\n",
"inaccessibility 1\n",
"abandons 2\n",
"inseparably 1\n",
"copernicus 4\n",
"cosmography 1\n",
"births 1\n",
"immovability 1\n",
"etext 1\n",
"northamptonshire 1\n",
"salem 30\n",
"tommy 9\n",
"traddles 491\n",
"partnership 14\n",
"wickfield 167\n",
"xlii 1\n",
"xliii 1\n",
"peggottys 75\n",
"twoyears 2\n",
"caul 3\n",
"lossfor 1\n",
"halfpence 11\n",
"ninetytwo 1\n",
"mariners 3\n",
"conveniences 6\n",
"suffolk 11\n",
"trotwood 143\n",
"begum 1\n",
"grosses 1\n",
"gardenfence 1\n",
"rigidity 3\n",
"saracens 1\n",
"luxuriant 4\n",
"ungentle 2\n",
"rookery 9\n",
"cookery 10\n",
"copperfields 8\n",
"elmtrees 4\n",
"thoughtit 1\n",
"servantgirl 1\n",
"confront 3\n",
"childif 1\n",
"fives 1\n",
"nines 1\n",
"wasas 1\n",
"magazine 4\n",
"detract 1\n",
"chillip 63\n",
"catechism 8\n",
"inferred 8\n",
"bourne 2\n",
"unsteadily 2\n",
"misgiving 24\n",
"pigeonhouse 4\n",
"itleading 1\n",
"mouldy 9\n",
"parlours 2\n",
"agoabout 1\n",
"lazarus 1\n",
"highbacked 2\n",
"gape 2\n",
"aisle 4\n",
"sheepi 1\n",
"monumental 3\n",
"elbowchair 10\n",
"straitening 1\n",
"vegetable 7\n",
"crorkindills 1\n",
"hatch 2\n",
"natives 3\n",
"crocodile 10\n",
"monarchor 1\n",
"mewhich 2\n",
"frayed 2\n",
"buttonless 2\n",
"deferred 13\n",
"yacht 3\n",
"intowhich 1\n",
"squareness 1\n",
"waxwork 2\n",
"complexionconfound 1\n",
"shaver 2\n",
"tailors 7\n",
"steadier 3\n",
"stoutly 6\n",
"fishermen 2\n",
"earthquake 6\n",
"carrier 27\n",
"laziest 1\n",
"sleepily 1\n",
"soppy 1\n",
"gasworks 1\n",
"boatbuilders 1\n",
"shipbreakers 1\n",
"superannuated 4\n",
"aladdins 1\n",
"saucers 5\n",
"teapot 11\n",
"thresholdchildlike 1\n",
"oystershells 1\n",
"kettles 2\n",
"obleeged 4\n",
"gummidge 106\n",
"drownded 22\n",
"wateri 1\n",
"ennobling 1\n",
"etherealized 2\n",
"sported 3\n",
"inequality 3\n",
"lorn 16\n",
"peggottyand 1\n",
"snuggest 1\n",
"occasioning 1\n",
"patching 1\n",
"misfortun 1\n",
"extenuation 2\n",
"hug 13\n",
"pa 32\n",
"dogdeep 1\n",
"wonderto 1\n",
"oddest 3\n",
"washingstand 3\n",
"mistresss 7\n",
"curtseyed 4\n",
"humours 3\n",
"winemerchants 2\n",
"jail 35\n",
"edwardi 1\n",
"resign 13\n",
"puzzling 1\n",
"slate 4\n",
"writingdesk 4\n",
"stringing 1\n",
"dressinggown 3\n",
"arrear 3\n",
"snowball 1\n",
"mulatto 1\n",
"contaminated 1\n",
"adjoined 1\n",
"roderick 5\n",
"blas 3\n",
"robinson 11\n",
"savages 6\n",
"conversationali 1\n",
"doos 2\n",
"transmission 2\n",
"prospectively 2\n",
"bowwindow 7\n",
"castors 3\n",
"sixfoot 1\n",
"pimplefaced 1\n",
"gaiters 20\n",
"choker 2\n",
"alewould 1\n",
"offends 1\n",
"tablespoon 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ink 26\n",
"letterpaper 3\n",
"taxed 6\n",
"youwhat 3\n",
"pairint 1\n",
"mistrust 13\n",
"roughfaced 1\n",
"snorts 5\n",
"whitechapel 1\n",
"boar 1\n",
"diffidently 2\n",
"streaky 1\n",
"completion 3\n",
"aloudand 1\n",
"wereif 1\n",
"doubtbut 1\n",
"jolts 1\n",
"meto 4\n",
"holidaytime 2\n",
"desks 8\n",
"demple 2\n",
"coventry 1\n",
"leaky 2\n",
"teacup 5\n",
"writingpaper 2\n",
"conning 2\n",
"sneezed 1\n",
"watchchain 11\n",
"legbeen 1\n",
"hah 17\n",
"quaking 1\n",
"righthand 9\n",
"currant 2\n",
"phosphorusbox 3\n",
"schooling 1\n",
"mantelpiece 2\n",
"rub 9\n",
"eyehumbly 1\n",
"eyeing 23\n",
"cipheringbooks 2\n",
"stops 7\n",
"puddings 3\n",
"gentility 15\n",
"didntdefend 1\n",
"narrate 2\n",
"scheherazade 1\n",
"peppermint 3\n",
"joker 2\n",
"plaything 12\n",
"crumbs 9\n",
"twit 1\n",
"titter 1\n",
"juniors 3\n",
"ino 1\n",
"favourites 6\n",
"steerforthalthough 1\n",
"correction 9\n",
"undutifulif 1\n",
"anybodythat 1\n",
"ducking 2\n",
"oncommon 5\n",
"biled 3\n",
"shellfish 2\n",
"gravesen 3\n",
"emphasizes 1\n",
"pigmy 1\n",
"inborn 1\n",
"thankee 56\n",
"readyleast 1\n",
"reglar 5\n",
"snail 2\n",
"nightquite 1\n",
"drugged 1\n",
"lessonbooks 1\n",
"mail 12\n",
"blankets 4\n",
"chrisen 2\n",
"peggottyapparently 1\n",
"cats 10\n",
"delightfully 2\n",
"thoughtless 9\n",
"agreeableand 1\n",
"insinuated 5\n",
"hugs 2\n",
"interdict 1\n",
"sakewas 1\n",
"boorish 1\n",
"poring 3\n",
"waysat 1\n",
"decentlyinterrupted 1\n",
"sullenness 2\n",
"nothingsince 1\n",
"concernedand 1\n",
"britannia 12\n",
"faceholding 1\n",
"shortwinded 1\n",
"tailor 7\n",
"beaverhats 1\n",
"cuttings 2\n",
"minnie 52\n",
"joram 25\n",
"pianofortevan 1\n",
"blindly 6\n",
"progressshe 1\n",
"saith 2\n",
"griefand 1\n",
"sabbath 4\n",
"lamb 5\n",
"truewhen 1\n",
"feasibility 1\n",
"shortto 1\n",
"visitjust 1\n",
"picklejar 2\n",
"halfanhours 2\n",
"amost 19\n",
"afternoons 4\n",
"onions 4\n",
"pantaloons 11\n",
"creeturs 5\n",
"graces 14\n",
"devouring 7\n",
"lighthaired 2\n",
"tortoiseshell 8\n",
"pharmacopoeia 1\n",
"pounding 1\n",
"mortar 8\n",
"matteri 1\n",
"theto 1\n",
"sisterdid 1\n",
"childhoodnot 1\n",
"flaw 6\n",
"stoutish 1\n",
"quizzingglass 1\n",
"peregrinations 1\n",
"roadin 1\n",
"install 1\n",
"darkcomplexioned 1\n",
"lukes 2\n",
"muffin 3\n",
"precocious 5\n",
"teaspoons 17\n",
"pawnbrokers 5\n",
"cutlet 4\n",
"halfapint 1\n",
"bestale 1\n",
"earlymrs 1\n",
"familysaid 1\n",
"clickettthis 1\n",
"bookstall 3\n",
"roadone 1\n",
"quarrelsome 2\n",
"rating 1\n",
"somehad 1\n",
"debtors 19\n",
"debtor 18\n",
"loin 4\n",
"jointstock 1\n",
"hopkins 7\n",
"hopkinss 5\n",
"backgarret 1\n",
"unmerited 5\n",
"weakly 7\n",
"fry 3\n",
"denomination 5\n",
"shipwrecked 8\n",
"adrift 5\n",
"procrastination 1\n",
"hornpipe 2\n",
"folkestone 1\n",
"halfguinea 5\n",
"warmin 1\n",
"trudged 3\n",
"dollobydolloby 1\n",
"housedoors 4\n",
"nightand 4\n",
"plodded 4\n",
"threeandtwenty 5\n",
"rochester 2\n",
"pence 2\n",
"dollobys 1\n",
"hereupon 3\n",
"goroos 1\n",
"claw 2\n",
"blistered 4\n",
"buoy 5\n",
"childstealing 1\n",
"berrybrown 1\n",
"slinking 5\n",
"suffolkwhere 1\n",
"salad 5\n",
"davids 6\n",
"hardfeatured 2\n",
"druggetcovered 1\n",
"punchbowl 1\n",
"wateringpots 2\n",
"swathed 1\n",
"jellips 1\n",
"poker 8\n",
"decried 1\n",
"declaiming 1\n",
"windowon 1\n",
"backgammonboard 2\n",
"withi 2\n",
"whitecurtained 1\n",
"bedand 1\n",
"urn 3\n",
"memorial 20\n",
"bableymr 1\n",
"heis 1\n",
"mineit 1\n",
"connects 1\n",
"memorializing 1\n",
"otherone 1\n",
"memorializedabout 1\n",
"behoof 4\n",
"misdemeanours 1\n",
"fenced 6\n",
"rookerythough 1\n",
"bridling 5\n",
"rebellious 7\n",
"consequencesso 1\n",
"chatham 2\n",
"auntgod 1\n",
"babyi 1\n",
"wickfields 27\n",
"garlands 3\n",
"mouldings 1\n",
"ponychaise 1\n",
"redbrown 1\n",
"tape 4\n",
"uriahs 15\n",
"firstmentioned 2\n",
"swan 1\n",
"hera 3\n",
"digressing 1\n",
"annies 7\n",
"studious 4\n",
"studiously 4\n",
"headboy 5\n",
"pawnings 1\n",
"schoolfellows 2\n",
"perhapsi 1\n",
"thoughtyet 1\n",
"husbanddo 1\n",
"officework 1\n",
"tidds 2\n",
"lowly 9\n",
"proximate 1\n",
"otherwiseand 3\n",
"historyas 1\n",
"sharpeyed 1\n",
"disrespectfullyto 1\n",
"yearsyour 1\n",
"coatsleeve 3\n",
"wasby 1\n",
"youngwhich 1\n",
"trueand 1\n",
"mentionshe 1\n",
"healthi 1\n",
"iagnes 1\n",
"reserving 5\n",
"leathern 4\n",
"shillingsworth 1\n",
"awayinto 1\n",
"windowsthe 1\n",
"hob 4\n",
"escritoire 1\n",
"battledores 1\n",
"saythat 2\n",
"offspringin 1\n",
"abased 2\n",
"contended 5\n",
"reasonest 1\n",
"flavoured 5\n",
"dumbwaiter 6\n",
"attorneygeneral 2\n",
"kidneyend 1\n",
"partridge 3\n",
"lang 2\n",
"syne 2\n",
"gude 1\n",
"existencethough 1\n",
"longevity 1\n",
"lifefrom 1\n",
"sunless 1\n",
"aisles 3\n",
"aspirant 2\n",
"boarder 3\n",
"choristers 1\n",
"dancingschool 2\n",
"meeker 1\n",
"beenand 2\n",
"counsellor 4\n",
"greasewhich 1\n",
"larkinss 5\n",
"larkinsand 1\n",
"chestle 4\n",
"grower 1\n",
"separations 2\n",
"fretbless 1\n",
"beforementioned 4\n",
"leastfor 1\n",
"reliant 2\n",
"agues 1\n",
"hazards 2\n",
"oftenin 1\n",
"uncharitably 1\n",
"suffolks 1\n",
"punches 3\n",
"wholesale 6\n",
"charing 2\n",
"overdosed 1\n",
"umbrellastruggling 1\n",
"pattenclinking 1\n",
"transparency 1\n",
"mejust 1\n",
"oxford 16\n",
"amiablelooking 1\n",
"apologetic 8\n",
"fortyfour 3\n",
"maze 7\n",
"hackneycoaches 2\n",
"sideboard 11\n",
"schoolfellow 5\n",
"heavyheaded 1\n",
"sputtered 1\n",
"wastefulness 1\n",
"peopleare 1\n",
"onethough 1\n",
"daisies 3\n",
"methe 2\n",
"gammonwhen 1\n",
"locket 4\n",
"backgammon 6\n",
"competitor 1\n",
"itehbecause 1\n",
"wanton 2\n",
"littimer 37\n",
"specks 6\n",
"superlatives 1\n",
"dartles 5\n",
"aha 6\n",
"yore 15\n",
"tobesure 2\n",
"retrospectively 2\n",
"boatmans 2\n",
"sistershe 1\n",
"businessi 1\n",
"roundare 1\n",
"quitea 1\n",
"fractious 2\n",
"offwas 1\n",
"timed 6\n",
"thewhat 2\n",
"liniment 1\n",
"rheumatic 5\n",
"tassel 1\n",
"facelike 1\n",
"whomsoever 3\n",
"overcomes 1\n",
"unthumbed 1\n",
"awayi 1\n",
"sands 4\n",
"roars 3\n",
"gentlmen 3\n",
"growedso 1\n",
"buildrougha 1\n",
"follers 3\n",
"rollers 2\n",
"bashfuller 1\n",
"manfulfor 1\n",
"comewhen 2\n",
"hermasr 1\n",
"manwould 1\n",
"skilfully 6\n",
"embarrass 2\n",
"chuckleheaded 3\n",
"spareroom 1\n",
"pilgrimages 2\n",
"babythe 1\n",
"macbethlike 1\n",
"amazes 1\n",
"salea 1\n",
"respectably 2\n",
"faceafter 1\n",
"handkercher 1\n",
"ladieswhat 1\n",
"wearsand 1\n",
"hertheres 1\n",
"yearsand 1\n",
"imp 1\n",
"competition 15\n",
"perfumers 1\n",
"shopelderly 1\n",
"hereabouts 2\n",
"milliners 5\n",
"slyness 8\n",
"blandishments 1\n",
"jockey 2\n",
"oddity 7\n",
"wurem 1\n",
"mowld 1\n",
"kindnaturd 1\n",
"againfor 1\n",
"neattiled 1\n",
"nowt 6\n",
"changeable 2\n",
"elseof 1\n",
"encouragingly 5\n",
"encircling 6\n",
"callings 3\n",
"commonsa 1\n",
"solicitors 3\n",
"threefourths 3\n",
"youngs 1\n",
"clergymans 4\n",
"smarter 2\n",
"outlay 2\n",
"confusedno 1\n",
"pickpocket 3\n",
"oclockand 1\n",
"libels 1\n",
"prerogative 9\n",
"delegates 5\n",
"sundry 16\n",
"affidavit 5\n",
"cravats 4\n",
"acknowledgements 9\n",
"mercenary 17\n",
"abidingplaces 1\n",
"pulpitdesk 1\n",
"dosey 1\n",
"characterexcept 1\n",
"costermongers 1\n",
"flounce 3\n",
"halfblind 2\n",
"crupps 9\n",
"yearn 1\n",
"predecessor 3\n",
"bellrope 9\n",
"dutchoven 1\n",
"ironmonger 1\n",
"stewed 1\n",
"tart 2\n",
"strand 6\n",
"mottled 3\n",
"markhammeaning 1\n",
"corkscrew 5\n",
"himor 2\n",
"lamppost 1\n",
"dressboxes 1\n",
"operaglass 4\n",
"goori 1\n",
"boxdoor 2\n",
"stayedmy 1\n",
"rememberbut 1\n",
"unreserved 3\n",
"haunch 1\n",
"fellownobodys 1\n",
"somethingfor 1\n",
"scalingladder 1\n",
"obtrusive 7\n",
"ogres 1\n",
"ofof 5\n",
"outsiders 6\n",
"guy 4\n",
"fawkes 4\n",
"ouse 4\n",
"scalded 1\n",
"agness 2\n",
"youor 2\n",
"griped 1\n",
"slimy 4\n",
"intermission 5\n",
"impended 2\n",
"refine 2\n",
"houserent 2\n",
"gallon 1\n",
"spazzums 1\n",
"versification 1\n",
"compactness 1\n",
"courtday 1\n",
"sylph 1\n",
"captivating 6\n",
"eden 2\n",
"beholders 6\n",
"curlsas 1\n",
"murdstonecant 1\n",
"usdont 1\n",
"loitered 7\n",
"charitys 1\n",
"doraand 1\n",
"strawcoloured 1\n",
"postmen 1\n",
"daughteri 1\n",
"palatable 4\n",
"abear 3\n",
"benignly 1\n",
"walue 2\n",
"sips 1\n",
"foryou 1\n",
"barmaidand 1\n",
"camden 4\n",
"milkman 9\n",
"servantan 1\n",
"chambersto 1\n",
"stomachand 1\n",
"pithily 1\n",
"yawler 1\n",
"howevermr 1\n",
"devonshire 9\n",
"workmanshipfirm 1\n",
"prosing 1\n",
"evercame 1\n",
"sanctum 1\n",
"figuratively 2\n",
"basinful 1\n",
"lightened 13\n",
"gleamsif 1\n",
"hardbake 2\n",
"uponshe 1\n",
"afternoonwhich 1\n",
"reengaged 1\n",
"whiteybrown 5\n",
"involvements 1\n",
"powerin 1\n",
"turncock 1\n",
"lemons 5\n",
"supposei 1\n",
"stairswhere 1\n",
"delusive 2\n",
"phrenologically 1\n",
"regulated 4\n",
"sanctifies 1\n",
"miraculously 1\n",
"tasting 2\n",
"pud 1\n",
"firmswhich 1\n",
"advertise 2\n",
"andi 1\n",
"emmas 1\n",
"mighti 4\n",
"expressiongo 1\n",
"businesssay 1\n",
"tafflin 3\n",
"thoughtsif 1\n",
"fastput 1\n",
"remonstrate 5\n",
"sirfor 1\n",
"undersigned 7\n",
"chattels 2\n",
"changeexcitement 1\n",
"engross 2\n",
"remiss 6\n",
"hurriedlya 1\n",
"naturesi 1\n",
"opposites 2\n",
"circumstanced 4\n",
"perverse 3\n",
"soured 4\n",
"sleptlet 1\n",
"generality 6\n",
"mightnt 7\n",
"strongerminded 1\n",
"hearties 1\n",
"sows 1\n",
"directlybut 1\n",
"apothecaries 1\n",
"dwindle 2\n",
"unclea 1\n",
"deathwhich 2\n",
"rod 4\n",
"boymy 1\n",
"innot 1\n",
"timehours 1\n",
"tobaccostopper 1\n",
"camphor 1\n",
"prismatic 4\n",
"residuary 1\n",
"pencilmark 1\n",
"fretting 5\n",
"accordin 3\n",
"comin 1\n",
"littlest 3\n",
"purposenot 1\n",
"havin 1\n",
"allwhen 1\n",
"wheers 1\n",
"davythe 1\n",
"homemy 1\n",
"norwich 3\n",
"contraries 1\n",
"overcharged 7\n",
"remembrances 22\n",
"knownthey 1\n",
"dismissedbut 1\n",
"particler 1\n",
"journies 1\n",
"badhearted 1\n",
"peggottyall 1\n",
"yearshard 1\n",
"itmore 1\n",
"hersand 2\n",
"distressfully 1\n",
"thingi 1\n",
"havethat 1\n",
"bloodhound 2\n",
"appealingly 2\n",
"waterspout 3\n",
"umbrellas 7\n",
"stowing 4\n",
"furder 3\n",
"chandlers 9\n",
"eatinghouse 3\n",
"intolerant 3\n",
"piewhich 1\n",
"loaves 5\n",
"metaphorically 1\n",
"mausoleum 2\n",
"onenobody 1\n",
"deploredbut 1\n",
"items 4\n",
"divorcesuit 1\n",
"fireproof 2\n",
"sinecures 1\n",
"workingclerks 1\n",
"resorting 3\n",
"pluralist 1\n",
"pestilent 1\n",
"sinecurists 1\n",
"insertion 1\n",
"parliamentary 21\n",
"mottoes 1\n",
"fooleries 1\n",
"gardenseat 1\n",
"hercomparatively 1\n",
"julias 1\n",
"unblighted 1\n",
"howbeit 5\n",
"debating 2\n",
"champagnehonour 1\n",
"horseand 1\n",
"momentif 1\n",
"versesand 1\n",
"yeyes 5\n",
"idolized 1\n",
"backi 1\n",
"insubstantial 2\n",
"stonesso 1\n",
"timesaddressed 1\n",
"intruders 6\n",
"illconwenience 4\n",
"sophypretty 1\n",
"ihadnt 1\n",
"shopi 1\n",
"wealthworshippers 1\n",
"dickwith 1\n",
"lipsyou 1\n",
"barkistrot 1\n",
"harshness 2\n",
"jackson 4\n",
"mantelpieces 2\n",
"confectionery 1\n",
"greys 2\n",
"hungrily 1\n",
"strandit 1\n",
"hampstead 2\n",
"chimneypots 5\n",
"conjurors 3\n",
"detecting 2\n",
"funded 1\n",
"diving 8\n",
"shares 2\n",
"sixpences 6\n",
"underlet 1\n",
"guerilla 1\n",
"highgatefor 1\n",
"thames 5\n",
"reversal 3\n",
"constrainedly 1\n",
"illfavoured 6\n",
"masteri 1\n",
"itsi 1\n",
"selfand 1\n",
"goodday 1\n",
"morelike 1\n",
"forborne 4\n",
"axe 2\n",
"quiteyesdear 1\n",
"andyes 1\n",
"marklehamyou 1\n",
"innocentflattery 1\n",
"coachhouse 2\n",
"bores 1\n",
"galleyslave 2\n",
"admixture 1\n",
"offspring 4\n",
"ignobly 1\n",
"behis 1\n",
"shrewdness 1\n",
"boastfully 3\n",
"micawberbut 1\n",
"woodpecker 2\n",
"commemorate 3\n",
"valedictory 1\n",
"recoili 1\n",
"spectaclesand 1\n",
"prentice 2\n",
"growls 3\n",
"bandit 1\n",
"joysnot 1\n",
"thinknot 1\n",
"thinkjust 1\n",
"yourselfthat 1\n",
"habitof 1\n",
"navigator 2\n",
"wheelbarrowand 1\n",
"crowning 7\n",
"fairys 1\n",
"absorbingly 1\n",
"stenography 1\n",
"despotic 1\n",
"skyrocket 1\n",
"tempestdriven 1\n",
"dictate 1\n",
"viscount 1\n",
"inconsistency 6\n",
"steelclasped 1\n",
"itopening 1\n",
"timeand 4\n",
"depravity 4\n",
"arrangementsof 1\n",
"spellingbook 1\n",
"flowerand 1\n",
"tiffeymore 1\n",
"isno 2\n",
"entries 7\n",
"dustman 2\n",
"gazelle 2\n",
"papaembraces 1\n",
"relaxationhe 1\n",
"infusion 3\n",
"licences 2\n",
"kidnappers 1\n",
"touting 1\n",
"inveigling 1\n",
"feud 2\n",
"downland 2\n",
"eased 3\n",
"ivied 1\n",
"orchard 2\n",
"perversely 3\n",
"wellassociated 1\n",
"feelin 1\n",
"baulked 2\n",
"didwithout 1\n",
"arsh 1\n",
"tojack 1\n",
"monitormedal 1\n",
"wellbehaved 5\n",
"interpreting 4\n",
"libertynow 1\n",
"admireadore 1\n",
"distorteda 1\n",
"presentlymind 1\n",
"millstone 1\n",
"qualitiesto 1\n",
"lovingest 2\n",
"nightmixture 1\n",
"nightwas 1\n",
"saidwheer 1\n",
"ashining 4\n",
"hopedhe 1\n",
"genleman 1\n",
"calledand 1\n",
"faceony 1\n",
"worsebruised 1\n",
"heartwas 1\n",
"fower 3\n",
"deserveas 1\n",
"deservebut 1\n",
"nohow 2\n",
"forard 8\n",
"augmentation 3\n",
"valuation 1\n",
"impair 1\n",
"halfhundredweight 1\n",
"porcupine 4\n",
"beautyquite 1\n",
"crewler 5\n",
"horace 9\n",
"bracelet 13\n",
"overdrinking 1\n",
"oracle 5\n",
"lightfor 1\n",
"likings 5\n",
"teadrinkings 1\n",
"curl 7\n",
"rationally 1\n",
"toobut 2\n",
"abit 1\n",
"downwardstill 1\n",
"delicacies 4\n",
"goingson 1\n",
"itquite 1\n",
"lifemy 1\n",
"lifeupon 1\n",
"embodiment 2\n",
"gentlemenupon 1\n",
"manis 1\n",
"towering 5\n",
"commendations 5\n",
"wistful 5\n",
"wateringpot 8\n",
"poignancy 1\n",
"sorrowsi 1\n",
"reclaim 3\n",
"attends 7\n",
"cuirasses 1\n",
"friction 4\n",
"archbishop 2\n",
"bridesmaid 7\n",
"rosecoloured 11\n",
"incredulous 10\n",
"loom 2\n",
"sedative 2\n",
"procurable 1\n",
"whereof 9\n",
"devising 6\n",
"coursenobodys 1\n",
"anotherone 1\n",
"selfaccusatory 1\n",
"mustyou 1\n",
"upaccustom 1\n",
"frumpy 2\n",
"trotat 1\n",
"coalhole 1\n",
"ignominy 4\n",
"artwe 1\n",
"terminating 5\n",
"storey 4\n",
"perjury 1\n",
"halfquartern 1\n",
"parentheses 1\n",
"cribbage 3\n",
"jipbut 1\n",
"dictionaryespecially 1\n",
"humblecondescending 1\n",
"intolerance 1\n",
"knowthat 2\n",
"interjections 1\n",
"venerated 1\n",
"traded 1\n",
"earthfell 1\n",
"heartand 2\n",
"undisciplined 9\n",
"bartered 2\n",
"writingfor 1\n",
"alluring 1\n",
"obeisance 3\n",
"spurned 8\n",
"maamand 2\n",
"thiscreature 1\n",
"itwith 1\n",
"trew 6\n",
"likelyfor 1\n",
"kep 4\n",
"currents 3\n",
"millbank 1\n",
"inauspiciously 2\n",
"cumbered 1\n",
"furnaces 1\n",
"windmillsails 1\n",
"crookedly 4\n",
"daughterlike 1\n",
"populous 1\n",
"changeling 1\n",
"sirloins 1\n",
"expatriated 4\n",
"vaccination 1\n",
"storyteller 1\n",
"choicewhich 1\n",
"noteven 1\n",
"donti 1\n",
"opinionand 1\n",
"irreconcilable 9\n",
"reanimated 1\n",
"elevation 6\n",
"deters 1\n",
"digress 1\n",
"olden 2\n",
"harrowing 2\n",
"interlacing 1\n",
"importune 1\n",
"creditor 6\n",
"bestso 1\n",
"vacillations 2\n",
"isheep 2\n",
"haveblown 1\n",
"fragmentstheadetestableserpentheep 1\n",
"haveamoved 1\n",
"eruptiononathe 1\n",
"refreshmentaunderneath 1\n",
"nothingandalive 1\n",
"nowhereuntil 1\n",
"secretafrom 1\n",
"weekaat 1\n",
"chorusandawill 1\n",
"immediatelynot 1\n",
"societyupon 1\n",
"traitorheep 1\n",
"forefathers 1\n",
"affectingly 1\n",
"motioning 3\n",
"unsound 2\n",
"infuse 3\n",
"backdoor 1\n",
"passionwasted 1\n",
"towntalk 1\n",
"vendible 1\n",
"kitchengirl 1\n",
"waythat 1\n",
"gravei 2\n",
"rack 3\n",
"storyi 1\n",
"dustheaps 2\n",
"expressively 4\n",
"bahds 1\n",
"playfuland 1\n",
"humbled 16\n",
"sharppinted 1\n",
"roarings 2\n",
"aspeaking 1\n",
"heavysee 1\n",
"ither 1\n",
"scholarsthe 1\n",
"unnerstood 1\n",
"unnerneath 1\n",
"acheering 1\n",
"unnerstands 1\n",
"outlast 2\n",
"pinted 1\n",
"cryingshes 1\n",
"fisherate 2\n",
"comfortble 1\n",
"bagi 1\n",
"minniemy 1\n",
"overrejoiced 2\n",
"whisked 3\n",
"pellmell 2\n",
"lastwill 1\n",
"heers 1\n",
"jokinglest 1\n",
"accumulative 1\n",
"shirtfrill 3\n",
"sofriendly 1\n",
"disclaiming 1\n",
"fellowmen 2\n",
"youto 3\n",
"debasing 1\n",
"officeor 1\n",
"ridiculousi 1\n",
"broadsword 1\n",
"immeshed 2\n",
"ruffianheepwas 1\n",
"dane 1\n",
"malpractices 2\n",
"confusedheepdesignedly 1\n",
"ofheepspower 1\n",
"possessionthat 1\n",
"blurted 3\n",
"aggrandisement 2\n",
"ofheepwas 1\n",
"receiver 2\n",
"borrowings 1\n",
"manmr 1\n",
"inanition 1\n",
"penurious 1\n",
"demoncombined 1\n",
"usualbut 1\n",
"straitwaistcoat 4\n",
"clamouring 5\n",
"overreach 1\n",
"notwithstandingi 1\n",
"servicehave 1\n",
"sufficientand 1\n",
"bullocks 1\n",
"australian 1\n",
"flutters 1\n",
"thinkyou 1\n",
"walled 3\n",
"asleepthey 1\n",
"handdrawn 1\n",
"sincebecause 1\n",
"negotiable 1\n",
"processif 1\n",
"shackles 1\n",
"communicationswhich 1\n",
"agencytrust 1\n",
"responsibilitythat 1\n",
"inordinate 1\n",
"magnify 2\n",
"adjustmentfor 1\n",
"endof 1\n",
"transactionshe 1\n",
"copperfielddo 1\n",
"attest 1\n",
"mineand 4\n",
"unmingled 2\n",
"boxseat 1\n",
"confusionhere 1\n",
"congregating 1\n",
"churchtower 2\n",
"whiteheaded 1\n",
"billows 1\n",
"coasthad 1\n",
"colliers 1\n",
"seathe 1\n",
"dayeight 1\n",
"silencing 1\n",
"bulwarks 4\n",
"flightawoke 1\n",
"capstan 1\n",
"itwhen 1\n",
"disused 2\n",
"ladyso 1\n",
"hearhis 1\n",
"frenzyfor 1\n",
"lessyet 1\n",
"descendedas 1\n",
"galleyfire 1\n",
"parenttree 1\n",
"embarkation 1\n",
"prow 5\n",
"britain 3\n",
"bulks 1\n",
"stools 3\n",
"tween 1\n",
"lostlove 1\n",
"nightwind 1\n",
"streetsthe 1\n",
"dot 2\n",
"prospering 2\n",
"absencewith 1\n",
"longsubsisting 1\n",
"quicksands 1\n",
"rumoured 3\n",
"traddlesby 1\n",
"gratifies 2\n",
"arranges 2\n",
"jipes 1\n",
"insatiably 1\n",
"louisamargaret 1\n",
"toasting 1\n",
"birthright 2\n",
"livecoals 1\n",
"inaptly 1\n",
"claimants 1\n",
"negus 9\n",
"waived 1\n",
"slowest 2\n",
"arrogance 6\n",
"remitted 1\n",
"tavernkeeper 1\n",
"angelface 2\n",
"goodi 1\n",
"monthsi 1\n",
"soundest 4\n",
"knowledgechiefly 1\n",
"copybook 2\n",
"madewhere 1\n",
"butterknives 1\n",
"sugartongs 11\n",
"moneyand 2\n",
"skeletonin 1\n",
"wigon 1\n",
"nominate 2\n",
"dayi 2\n",
"repasts 3\n",
"tougher 3\n",
"mustwhat 1\n",
"sneaked 4\n",
"nowmost 1\n",
"unreservedly 4\n",
"doubtif 1\n",
"relented 2\n",
"stifftilled 1\n",
"chalkpit 1\n",
"heardbut 1\n",
"trotwoodwhich 1\n",
"supplanting 1\n",
"fortuns 1\n",
"nohows 3\n",
"someun 1\n",
"salemhouse 1\n",
"auditory 3\n",
"ridger 1\n",
"artmagic 1\n",
"ocularly 1\n",
"lineaments 4\n",
"kites 1\n",
"croesus 2\n",
"tanned 1\n",
"lawyerswig 1\n",
"discourses 3\n",
"dombey 1523\n",
"mclachlan 2\n",
"florence 1139\n",
"warnings 4\n",
"recognizant 2\n",
"grinder 122\n",
"undeniably 6\n",
"perpetuation 1\n",
"investeda 1\n",
"bypath 2\n",
"pep 1\n",
"doctorand 1\n",
"stanley 1\n",
"motionmade 1\n",
"whisking 2\n",
"stupendously 1\n",
"unitethat 1\n",
"rampant 1\n",
"barrenest 1\n",
"usreally 1\n",
"mantuamakers 1\n",
"reproof 6\n",
"helpmate 2\n",
"chicks 8\n",
"charlottes 1\n",
"singling 1\n",
"rosiest 1\n",
"steamingine 1\n",
"fosterfather 1\n",
"centrethat 1\n",
"denyinghe 1\n",
"richardsto 1\n",
"jmima 1\n",
"omissions 1\n",
"bryanstone 1\n",
"areas 1\n",
"drawingrooms 7\n",
"upperhaps 1\n",
"windingsheets 1\n",
"pictureframe 3\n",
"mews 12\n",
"attainable 1\n",
"hushing 3\n",
"substituted 6\n",
"tryoh 1\n",
"itdidnt 1\n",
"spitfire 7\n",
"susan 291\n",
"nipper 184\n",
"chaney 1\n",
"airings 1\n",
"boldlyit 1\n",
"perhapswho 2\n",
"doctorthat 1\n",
"temporaries 2\n",
"tigers 3\n",
"howdahs 1\n",
"hookahs 1\n",
"buckles 1\n",
"tightest 3\n",
"shipchandlers 1\n",
"alphabetical 1\n",
"optical 1\n",
"inexpressibles 1\n",
"thinned 1\n",
"threatens 3\n",
"mugs 2\n",
"shrivelledup 1\n",
"bluebottle 1\n",
"competitionnew 1\n",
"inventionalteration 1\n",
"alterationthe 1\n",
"crusoes 2\n",
"apprentices 1\n",
"businessits 1\n",
"wiggs 1\n",
"overhaul 17\n",
"walters 74\n",
"kirby 1\n",
"devolved 4\n",
"existenceor 1\n",
"motivesare 1\n",
"declaratory 1\n",
"cupid 3\n",
"detraction 1\n",
"utterand 1\n",
"fannyi 1\n",
"possiblethe 1\n",
"dreamssome 1\n",
"griffins 2\n",
"hobgoblins 1\n",
"freezer 1\n",
"toor 1\n",
"referable 8\n",
"gauzes 1\n",
"nosebegan 1\n",
"overaged 1\n",
"overworked 2\n",
"burials 1\n",
"wheezy 4\n",
"engraving 1\n",
"chevy 1\n",
"straddling 1\n",
"lodginghouse 1\n",
"staves 2\n",
"beans 3\n",
"delectation 1\n",
"quoth 10\n",
"untie 1\n",
"bonnetstrings 1\n",
"homeyou 1\n",
"candleend 1\n",
"toadstools 1\n",
"thumps 2\n",
"themunaccountably 1\n",
"bulls 2\n",
"mumbled 4\n",
"alongparticularly 1\n",
"steeples 8\n",
"andafter 1\n",
"childrenbeing 1\n",
"selfreliance 7\n",
"wonderment 2\n",
"laboureras 1\n",
"sisterand 1\n",
"tropicsas 1\n",
"avoids 2\n",
"mebrought 1\n",
"dryhow 1\n",
"redeyed 1\n",
"nonce 2\n",
"susans 4\n",
"fellowservants 1\n",
"nothoroughfares 1\n",
"woodenfeatured 1\n",
"classify 1\n",
"hangeron 1\n",
"powderedhead 1\n",
"languishing 8\n",
"garland 5\n",
"mustiness 1\n",
"donjonkeep 1\n",
"waltzes 1\n",
"intents 1\n",
"wasshe 1\n",
"coffeehouses 2\n",
"pined 4\n",
"wifewhich 1\n",
"intensifying 1\n",
"inspirations 2\n",
"circulatingmedium 1\n",
"thewith 1\n",
"gravediggers 1\n",
"biddable 1\n",
"dependants 2\n",
"expatiate 2\n",
"pumper 1\n",
"spidersin 1\n",
"boils 1\n",
"correctional 1\n",
"romps 1\n",
"lubricate 1\n",
"internally 2\n",
"wickamwho 1\n",
"familiars 1\n",
"grieves 1\n",
"cramps 1\n",
"berrysomewhat 1\n",
"freein 2\n",
"luxuriesuntil 1\n",
"pickling 2\n",
"childrenflorence 1\n",
"newcastle 1\n",
"peeeg 1\n",
"skylights 2\n",
"musnt 1\n",
"dumpling 1\n",
"bishopsgate 1\n",
"appraiser 1\n",
"diningtables 3\n",
"dishcovers 1\n",
"complainings 6\n",
"jangling 3\n",
"moisteyed 1\n",
"sociable 3\n",
"fronts 2\n",
"loosest 1\n",
"lodgingsat 1\n",
"frothy 1\n",
"macstingers 19\n",
"vixen 1\n",
"impregnated 2\n",
"spiritsor 1\n",
"himlooked 1\n",
"reattached 1\n",
"egress 1\n",
"hove 5\n",
"propertys 1\n",
"fatheran 1\n",
"administering 1\n",
"globes 4\n",
"goby 1\n",
"conventionalities 3\n",
"usedup 3\n",
"invalided 3\n",
"plainspoken 1\n",
"libelled 1\n",
"minces 1\n",
"muchstirred 1\n",
"pastlong 1\n",
"monopolises 1\n",
"sirdevilish 1\n",
"comingindeed 1\n",
"earnings 1\n",
"gays 3\n",
"theinstrument 1\n",
"crumb 2\n",
"whittingtonian 2\n",
"extemporaneous 1\n",
"archness 3\n",
"intoyoull 1\n",
"drudged 2\n",
"slaved 1\n",
"solar 2\n",
"spinsterhood 1\n",
"overland 1\n",
"nonsenseand 1\n",
"correctively 1\n",
"alienations 1\n",
"weaned 1\n",
"frostiest 2\n",
"toots 573\n",
"gaslighted 1\n",
"sphynx 1\n",
"murmurings 1\n",
"cicero 7\n",
"ba 20\n",
"nounsubstantives 2\n",
"fellowleaving 1\n",
"fartherfarther 1\n",
"poaching 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"accommodating 4\n",
"alpha 1\n",
"dominions 1\n",
"wiry 4\n",
"unimpassioned 2\n",
"blurred 8\n",
"thirsting 1\n",
"despairwhich 1\n",
"sussex 2\n",
"finedombey 1\n",
"gong 7\n",
"dinnersome 1\n",
"ow 7\n",
"woodcocks 1\n",
"spawn 1\n",
"lamprey 1\n",
"nero 1\n",
"wayto 1\n",
"nicety 7\n",
"orthography 1\n",
"glubbs 1\n",
"illconsidered 1\n",
"longmeditated 1\n",
"breasting 3\n",
"vendors 1\n",
"coalbox 2\n",
"caliph 1\n",
"haroun 1\n",
"alraschid 1\n",
"haircutting 1\n",
"effigy 1\n",
"morfin 11\n",
"vizier 1\n",
"hazeleyed 4\n",
"reendorse 1\n",
"monthor 1\n",
"intrusionespecially 1\n",
"apprised 2\n",
"league 2\n",
"heedlessness 2\n",
"predispositions 1\n",
"meon 1\n",
"dombeyno 1\n",
"likened 1\n",
"frolicsome 2\n",
"strainyet 1\n",
"toothat 1\n",
"urbanity 7\n",
"verbum 1\n",
"cornelias 3\n",
"shingle 1\n",
"boxinggloves 1\n",
"paperknife 2\n",
"superfine 1\n",
"andhow 1\n",
"curfew 1\n",
"fingerand 1\n",
"skettleses 5\n",
"imbibing 1\n",
"thiscomparatively 1\n",
"beenwere 1\n",
"bapspoor 1\n",
"florencecould 1\n",
"thatwhich 3\n",
"quaver 1\n",
"howler 2\n",
"loftywould 1\n",
"stinger 1\n",
"whatll 3\n",
"fortuneif 1\n",
"sailorif 1\n",
"felicitated 1\n",
"legsthat 1\n",
"heremeet 1\n",
"specksone 1\n",
"fanshaped 1\n",
"exalting 2\n",
"bystreets 3\n",
"newsmen 1\n",
"wrappers 4\n",
"flatterers 1\n",
"crossexamination 3\n",
"rood 3\n",
"housewhere 1\n",
"handsor 1\n",
"sandand 1\n",
"resistless 4\n",
"immortality 1\n",
"fixture 2\n",
"credentials 6\n",
"evaporate 2\n",
"matey 1\n",
"babbyhe 1\n",
"deathshead 1\n",
"walrs 5\n",
"smoother 1\n",
"reliefs 1\n",
"roomuntil 1\n",
"loiters 2\n",
"alights 1\n",
"anymy 1\n",
"brotheror 1\n",
"thehisthe 1\n",
"myselfwhat 2\n",
"brotherless 1\n",
"oftenvery 1\n",
"himhappy 1\n",
"spiritshad 1\n",
"isi 2\n",
"cofashionable 1\n",
"bulletheaded 2\n",
"pervious 1\n",
"unthought 1\n",
"steeled 3\n",
"archimedes 1\n",
"parapets 1\n",
"lapels 2\n",
"shadedfor 1\n",
"garner 2\n",
"willwill 1\n",
"thewell 1\n",
"joviality 1\n",
"outweighed 2\n",
"sunlighted 1\n",
"trippingly 1\n",
"direfully 1\n",
"unexamined 1\n",
"vituperative 1\n",
"talebearer 1\n",
"coughs 4\n",
"joeold 1\n",
"refusals 1\n",
"coldlyvery 1\n",
"mephistopheles 2\n",
"doin 3\n",
"scuse 1\n",
"gettin 1\n",
"genelmen 3\n",
"huffed 1\n",
"foredoomed 3\n",
"facehe 2\n",
"monkeyin 1\n",
"everand 1\n",
"remonstrating 1\n",
"facequite 1\n",
"outpushed 1\n",
"granger 27\n",
"perfidious 1\n",
"skewtons 20\n",
"cleopatra 91\n",
"libel 2\n",
"wealthytheyre 1\n",
"indited 1\n",
"sisyphus 1\n",
"roosted 1\n",
"beganof 1\n",
"jbstough 1\n",
"roes 1\n",
"couldher 1\n",
"lipsdealing 1\n",
"summerlight 1\n",
"feebler 2\n",
"contentswhen 1\n",
"strongbuilt 1\n",
"chivied 1\n",
"passersby 2\n",
"cleaner 1\n",
"prodigals 1\n",
"beall 1\n",
"billiards 3\n",
"husks 2\n",
"dominionsthat 1\n",
"acrostic 1\n",
"inkling 2\n",
"chickenwithout 1\n",
"fungus 2\n",
"moths 3\n",
"grubs 1\n",
"veils 1\n",
"alarmbells 1\n",
"rejecting 3\n",
"dayit 1\n",
"dwellers 2\n",
"weathercocks 2\n",
"strays 6\n",
"consciencestricken 2\n",
"scarletbeans 1\n",
"washhouses 1\n",
"redoubtable 2\n",
"prenticeship 1\n",
"tremulously 3\n",
"facethat 1\n",
"fireship 1\n",
"curing 1\n",
"waistband 1\n",
"seeingthat 1\n",
"goodwins 2\n",
"eking 1\n",
"enumerated 2\n",
"waternot 1\n",
"desirouslady 1\n",
"participatetrust 1\n",
"writemight 1\n",
"jericho 1\n",
"notthough 1\n",
"sustainedthank 1\n",
"weightgives 1\n",
"gardenground 1\n",
"rougher 3\n",
"unfortnate 4\n",
"hairwould 1\n",
"curtained 7\n",
"loveif 1\n",
"threatenings 1\n",
"testamentwheres 1\n",
"captainnot 1\n",
"epic 1\n",
"perusals 1\n",
"stuffing 1\n",
"juliana 6\n",
"knockedfor 1\n",
"watchand 1\n",
"fractions 1\n",
"invigorates 1\n",
"defiantly 2\n",
"flowerthe 1\n",
"tastesfor 1\n",
"mincingly 1\n",
"grangeris 1\n",
"sentimentis 1\n",
"hothe 1\n",
"franknessbut 1\n",
"cruet 1\n",
"machines 1\n",
"slyer 2\n",
"smiler 2\n",
"subserviency 1\n",
"blur 1\n",
"alonghardly 1\n",
"charmingassociations 1\n",
"grangers 6\n",
"angelhe 1\n",
"cantered 1\n",
"otherover 1\n",
"undulations 1\n",
"woodor 1\n",
"deplorably 1\n",
"sketchbook 2\n",
"pointless 1\n",
"sketcher 1\n",
"ladymothers 1\n",
"morningfor 1\n",
"womanartful 1\n",
"designto 1\n",
"deservedand 1\n",
"vended 2\n",
"vaunted 2\n",
"germ 1\n",
"nile 4\n",
"ambut 1\n",
"discouragements 3\n",
"uncared 1\n",
"glaziers 1\n",
"snipped 2\n",
"scentquite 1\n",
"fragrances 1\n",
"bulgy 1\n",
"hawking 2\n",
"dandelion 1\n",
"flushit 1\n",
"weatheroverspread 1\n",
"breakfastthe 1\n",
"snip 1\n",
"leftfor 1\n",
"characterises 1\n",
"sisterhood 1\n",
"restores 1\n",
"statuelike 2\n",
"piously 5\n",
"sacking 2\n",
"saloons 1\n",
"purchasemoney 1\n",
"propulsion 1\n",
"milkmans 1\n",
"juvenility 2\n",
"selfengrossment 1\n",
"skipping 1\n",
"chilling 1\n",
"uncongeniality 1\n",
"hatchments 4\n",
"blotching 1\n",
"sarcophagus 2\n",
"scenesstill 1\n",
"florists 1\n",
"skewtonwere 1\n",
"tomorrowwere 1\n",
"aforethought 1\n",
"tampered 1\n",
"bridal 5\n",
"wrestled 1\n",
"steepleclock 2\n",
"prayerbooks 1\n",
"pewopenera 1\n",
"wherewithal 1\n",
"sownds 19\n",
"battlebridge 1\n",
"surreptitiously 1\n",
"somuchtobeastonished 1\n",
"longs 2\n",
"breastin 1\n",
"streetthis 1\n",
"theft 2\n",
"giveth 2\n",
"firstto 1\n",
"writerthis 1\n",
"droops 1\n",
"moralists 1\n",
"halfthawed 1\n",
"gummy 1\n",
"freshcoloured 2\n",
"landsman 1\n",
"furniturethough 1\n",
"househere 1\n",
"nobby 3\n",
"welcomer 1\n",
"awhats 1\n",
"chartered 2\n",
"dombeysusan 1\n",
"natrally 1\n",
"chickweed 1\n",
"deathshock 1\n",
"nevyless 1\n",
"overoror 1\n",
"slowlyyou 1\n",
"wordif 1\n",
"carkerafore 1\n",
"teakbuilt 1\n",
"hawksand 1\n",
"tastefully 1\n",
"excellently 1\n",
"tingeing 1\n",
"opulence 1\n",
"voluptuous 4\n",
"colourand 1\n",
"belching 1\n",
"nettles 1\n",
"morethis 1\n",
"isthe 1\n",
"perilled 1\n",
"wewe 2\n",
"herecreature 1\n",
"churchyards 3\n",
"bewilderedand 1\n",
"waslook 1\n",
"chimneyfor 1\n",
"timewhether 1\n",
"womannot 1\n",
"allyin 1\n",
"tramped 1\n",
"tablelittle 1\n",
"eighteensowe 1\n",
"attiring 1\n",
"fretfully 4\n",
"supperless 1\n",
"startingplace 1\n",
"skirmishingly 1\n",
"elicits 1\n",
"togetherfor 1\n",
"descries 2\n",
"bargainas 1\n",
"sufferingshe 1\n",
"levees 2\n",
"invitebut 1\n",
"flowerbed 1\n",
"chairmen 2\n",
"extinguisher 2\n",
"scentless 1\n",
"humourhis 1\n",
"jackman 1\n",
"marriagewhich 1\n",
"banquetsicethe 1\n",
"staterooms 1\n",
"yetnot 1\n",
"linkmen 2\n",
"mamasinlaw 1\n",
"providently 1\n",
"clankings 1\n",
"thewan 1\n",
"pigeonbreasted 1\n",
"peachcoloured 1\n",
"answerhad 1\n",
"veiling 1\n",
"deferencei 1\n",
"causeis 1\n",
"whitelegged 5\n",
"peevishness 2\n",
"sortleft 1\n",
"strangernot 1\n",
"aboutmr 1\n",
"shovelling 5\n",
"tunnels 1\n",
"sayssome 1\n",
"toxand 3\n",
"toodleill 1\n",
"sirthat 1\n",
"acquirements 1\n",
"whitewash 2\n",
"disquisitions 1\n",
"planting 2\n",
"heremr 1\n",
"leadenhall 4\n",
"unaccompanied 1\n",
"wordsthey 1\n",
"andll 1\n",
"chowley 3\n",
"cellarsteps 1\n",
"scratches 2\n",
"muzzlingsmrs 1\n",
"awaaay 1\n",
"fellon 1\n",
"wordshe 1\n",
"morethat 1\n",
"behove 1\n",
"debated 3\n",
"itselfthat 1\n",
"arraying 1\n",
"mailed 1\n",
"usurpedstung 1\n",
"chaplets 1\n",
"diamondsa 1\n",
"ministered 3\n",
"ultimatum 1\n",
"sterious 1\n",
"trordinry 2\n",
"nameswhat 1\n",
"visitorsreally 1\n",
"classification 1\n",
"sos 1\n",
"sincein 1\n",
"misfits 1\n",
"coolies 1\n",
"emerald 1\n",
"nought 1\n",
"pianoforte 2\n",
"warms 2\n",
"differentlywith 1\n",
"unrest 2\n",
"saidwhatshisname 1\n",
"saysis 1\n",
"brooksslittle 1\n",
"joperyou 1\n",
"staysbut 1\n",
"edithas 1\n",
"incontrovertible 2\n",
"andpardon 1\n",
"menders 1\n",
"inwith 1\n",
"dilation 1\n",
"giantlike 1\n",
"mainlaw 1\n",
"huffish 1\n",
"tardy 3\n",
"sosome 1\n",
"fingeroh 1\n",
"twitchnot 1\n",
"heryes 1\n",
"whenive 1\n",
"undauntingly 1\n",
"impertinences 4\n",
"contaminate 1\n",
"piipchinses 1\n",
"duenna 1\n",
"leal 1\n",
"fibbed 1\n",
"bunged 1\n",
"couldyou 1\n",
"timein 1\n",
"bounden 1\n",
"believethe 1\n",
"counselloryes 1\n",
"plenitude 1\n",
"warp 1\n",
"sympathyoh 1\n",
"ahorseback 1\n",
"stablea 1\n",
"cheated 2\n",
"chum 4\n",
"prad 1\n",
"hostler 1\n",
"placewhats 1\n",
"themmaster 1\n",
"gallooking 1\n",
"starves 1\n",
"exaction 2\n",
"assail 1\n",
"meeknessthat 1\n",
"pusillanimous 1\n",
"superciliously 1\n",
"comingupon 1\n",
"earthat 1\n",
"repulsions 1\n",
"thistles 1\n",
"toolong 1\n",
"stumblingblocks 1\n",
"cave 1\n",
"betterit 1\n",
"ethereal 4\n",
"couldbut 1\n",
"muchsubmit 1\n",
"carkeror 1\n",
"likei 1\n",
"issacrificing 1\n",
"unassailable 1\n",
"thisshe 1\n",
"unavailingly 2\n",
"movingshe 1\n",
"stormdriven 1\n",
"steadys 1\n",
"ravenously 1\n",
"burrowed 2\n",
"pocketcomb 1\n",
"balsam 2\n",
"wibrate 1\n",
"stronglyand 1\n",
"gillswhatever 1\n",
"goodnessthe 1\n",
"homelessness 1\n",
"knewin 1\n",
"florencepoor 1\n",
"instruct 1\n",
"eggsauce 3\n",
"browned 1\n",
"sentimentslike 1\n",
"lassor 1\n",
"ruminating 5\n",
"placidity 3\n",
"externallyand 1\n",
"voicein 1\n",
"unimaginative 1\n",
"defray 1\n",
"backno 1\n",
"cruised 2\n",
"overhauling 1\n",
"bould 1\n",
"prettyhas 1\n",
"stoutest 2\n",
"ladas 1\n",
"secondmate 1\n",
"beatin 1\n",
"heartaboard 1\n",
"cellargrating 1\n",
"therewithout 1\n",
"streetleaving 1\n",
"hopei 1\n",
"warbler 1\n",
"floats 1\n",
"wowed 1\n",
"parricide 1\n",
"captainher 1\n",
"inbut 1\n",
"savedand 1\n",
"uponmea 1\n",
"harmonising 1\n",
"reminiscent 2\n",
"goeshe 1\n",
"saysthis 1\n",
"undeservedlynever 1\n",
"irascibly 1\n",
"newlylighted 1\n",
"vicepresident 1\n",
"viscountwhich 1\n",
"oursand 1\n",
"chut 1\n",
"bonny 1\n",
"lastoho 1\n",
"squeedging 1\n",
"youet 1\n",
"ceterer 1\n",
"scooped 1\n",
"herselfme 1\n",
"skulking 1\n",
"waynow 1\n",
"drams 1\n",
"successwas 1\n",
"harriets 2\n",
"roomsa 1\n",
"latticeblinds 2\n",
"croppedhad 1\n",
"rouen 1\n",
"shrew 1\n",
"chairexcept 1\n",
"twitted 1\n",
"halter 2\n",
"undauntable 1\n",
"crunched 1\n",
"pinioned 1\n",
"nightalarm 1\n",
"overwrought 2\n",
"ramparts 2\n",
"devilsand 1\n",
"reeking 3\n",
"balustraded 1\n",
"metfor 1\n",
"battering 1\n",
"waystill 1\n",
"restthat 1\n",
"sirit 1\n",
"himhurriedly 1\n",
"comeuttered 1\n",
"innocentest 2\n",
"devotedest 1\n",
"totogazeat 1\n",
"thingswith 1\n",
"ashorebeing 1\n",
"canton 1\n",
"partthere 1\n",
"sempstresses 1\n",
"permittedas 1\n",
"parasols 1\n",
"shipboardwould 1\n",
"banker 6\n",
"aldgate 1\n",
"kisswith 1\n",
"jollity 1\n",
"wrestling 2\n",
"readingdesk 2\n",
"stationing 1\n",
"dole 2\n",
"formilior 1\n",
"mrmr 1\n",
"nextthat 1\n",
"nedthat 1\n",
"depreciatory 2\n",
"plumps 1\n",
"dears 2\n",
"womanthat 1\n",
"earnestheartedhe 1\n",
"carmen 1\n",
"ledges 1\n",
"surplice 3\n",
"dyers 2\n",
"stumping 1\n",
"showily 1\n",
"messengership 1\n",
"waxing 1\n",
"poohpoohd 2\n",
"mall 1\n",
"bootjacks 1\n",
"moralised 1\n",
"johns 15\n",
"arejohns 1\n",
"faithfullybut 1\n",
"moneywould 1\n",
"foundry 3\n",
"gravedigger 1\n",
"strewing 2\n",
"wench 1\n",
"galthats 1\n",
"breaches 1\n",
"greengrocery 1\n",
"rankin 1\n",
"mosaic 1\n",
"appropriating 1\n",
"stairwires 1\n",
"plateglass 2\n",
"touzling 1\n",
"clothespress 1\n",
"palisade 1\n",
"rosewood 1\n",
"childits 1\n",
"jiffy 2\n",
"dulllike 1\n",
"cordl 1\n",
"trusses 1\n",
"quellings 1\n",
"snappings 1\n",
"reaper 1\n",
"wheezes 1\n",
"pastwhich 1\n",
"pridefor 1\n",
"summed 1\n",
"houseknowing 1\n",
"marksand 1\n",
"alonea 1\n",
"cryand 1\n",
"deeplyall 1\n",
"madnessand 1\n",
"chaps 2\n",
"shawled 1\n",
"holden 3\n",
"countrywomen 1\n",
"par 2\n",
"becomingly 2\n",
"telegraphic 1\n",
"nobodyi 1\n",
"marriedespecially 2\n",
"hymen 1\n",
"theresupposed 1\n",
"pitchkettles 1\n",
"appendages 1\n",
"inflating 1\n",
"animosities 1\n",
"inflexibly 1\n",
"orisons 1\n",
"bawlingsout 1\n",
"marriedhad 1\n",
"wasalbeit 1\n",
"daythat 2\n",
"weathered 1\n",
"simplybut 1\n",
"timidlyis 1\n",
"lovewho 1\n",
"feenixs 1\n",
"sometimesflorence 1\n",
"resounds 1\n",
"halfmile 1\n",
"investments 6\n",
"yessome 1\n",
"wordtriumphant 1\n",
"undergoes 1\n",
"merchantability 1\n",
"dorrits 105\n",
"fortunetelling 7\n",
"pancks 424\n",
"railroadshare 1\n",
"demarcation 1\n",
"hindoos 1\n",
"genoese 3\n",
"aliketaking 1\n",
"seamed 1\n",
"ringin 1\n",
"algiers 1\n",
"fellowprisoner 5\n",
"againthese 1\n",
"tobaccoall 1\n",
"wellshaped 2\n",
"prisonkeepers 1\n",
"hunks 1\n",
"solike 4\n",
"seennor 1\n",
"destinys 2\n",
"amonsieur 1\n",
"swisscanton 1\n",
"belgium 1\n",
"witshow 1\n",
"barronneausixtyfive 1\n",
"conventionally 1\n",
"fireflies 1\n",
"marshonging 2\n",
"othervictory 1\n",
"meagles 367\n",
"jailbirds 1\n",
"foundlingyou 1\n",
"musicbecause 1\n",
"hermother 1\n",
"foundlings 1\n",
"littlenext 1\n",
"undecipherable 1\n",
"goodspeed 1\n",
"unsubduable 1\n",
"youshe 1\n",
"restante 1\n",
"deadcarts 1\n",
"sameness 2\n",
"perditiona 1\n",
"ep 1\n",
"biblebound 1\n",
"straitest 1\n",
"gelen 2\n",
"draggled 3\n",
"weatherstained 1\n",
"bedridden 1\n",
"dell 1\n",
"rusks 4\n",
"exterminated 1\n",
"durst 1\n",
"enlisted 1\n",
"keeneyed 1\n",
"acerbity 1\n",
"movables 1\n",
"patternless 1\n",
"ofhers 1\n",
"stockingless 1\n",
"whatheylord 1\n",
"sleevenot 1\n",
"saybut 3\n",
"womansuch 1\n",
"rovinghams 1\n",
"sacredly 1\n",
"refection 1\n",
"upwere 1\n",
"invisibly 1\n",
"winebottles 2\n",
"arthurs 37\n",
"barrack 1\n",
"skittleground 2\n",
"consorted 2\n",
"welllooking 2\n",
"wellworn 2\n",
"serwant 1\n",
"greengrocerdash 1\n",
"rulesthat 1\n",
"whichll 1\n",
"item 2\n",
"seajacket 1\n",
"himselfproduced 1\n",
"charwoman 1\n",
"potion 1\n",
"thatwe 1\n",
"jailbird 2\n",
"edcated 1\n",
"languagesspeaks 1\n",
"sawyer 1\n",
"monthsthat 1\n",
"halfasovereign 1\n",
"tributes 2\n",
"collegians 41\n",
"fustian 1\n",
"prisonyard 5\n",
"whooped 1\n",
"careladen 1\n",
"shames 1\n",
"nothingwhatever 1\n",
"clarionet 13\n",
"stockbrokers 1\n",
"regulars 1\n",
"loyally 1\n",
"impertinently 1\n",
"mothersmrs 1\n",
"quavering 3\n",
"befriends 1\n",
"saltcellar 2\n",
"iso 1\n",
"wellmeaning 1\n",
"pretensionsany 1\n",
"leveequite 1\n",
"camberwell 1\n",
"greataheminformation 1\n",
"arehemare 1\n",
"surewhich 1\n",
"unskilled 1\n",
"dothan 1\n",
"prisonerslandlord 1\n",
"southwest 1\n",
"seasickness 2\n",
"lodgegate 1\n",
"gobetweens 1\n",
"squashed 1\n",
"draughty 1\n",
"cripples 4\n",
"eightandtwenty 1\n",
"orangeflavoured 1\n",
"teas 2\n",
"hustings 1\n",
"jobbing 1\n",
"commonplaces 1\n",
"stiltstalkings 6\n",
"nee 2\n",
"dispatchboxes 1\n",
"chimneysweep 1\n",
"unmixed 1\n",
"parsimonious 1\n",
"jenkinson 1\n",
"fellowhe 1\n",
"pocus 1\n",
"doyces 7\n",
"engineer 2\n",
"browbeaten 1\n",
"pigheaded 1\n",
"shelved 1\n",
"positionthan 1\n",
"barnaclesand 2\n",
"sullied 1\n",
"poplar 1\n",
"hungrier 1\n",
"billiardtable 1\n",
"themnone 1\n",
"whitewashers 1\n",
"madamethat 1\n",
"chirpingly 1\n",
"unmistakable 2\n",
"ri 1\n",
"consigns 1\n",
"rainwater 1\n",
"subdivided 1\n",
"derivation 1\n",
"heraldic 1\n",
"yarders 2\n",
"smoothcheeked 1\n",
"sandywhiskered 1\n",
"dorriti 3\n",
"disarrange 1\n",
"plaintiff 5\n",
"anthems 1\n",
"hampton 8\n",
"shoebinding 1\n",
"woodenheaded 1\n",
"casbys 11\n",
"themselvesas 1\n",
"mannerand 1\n",
"quakerlike 1\n",
"servantmaid 1\n",
"furnitureas 1\n",
"porcelain 2\n",
"rudiments 1\n",
"impassionate 1\n",
"bumpy 4\n",
"townagent 2\n",
"crosscut 1\n",
"resides 1\n",
"prongs 6\n",
"sensorium 1\n",
"signpost 3\n",
"lockedup 1\n",
"sayingwithout 1\n",
"chatterer 1\n",
"fascinations 2\n",
"properwhat 1\n",
"improperas 1\n",
"finching 22\n",
"ladiesmandarinesses 1\n",
"clennamall 1\n",
"postchaises 1\n",
"situationnot 1\n",
"readjustment 1\n",
"richlydressed 1\n",
"delusions 1\n",
"partyindeed 1\n",
"dorritthis 1\n",
"eyesthat 1\n",
"smacks 1\n",
"flightwhich 1\n",
"wickedlooking 1\n",
"grimy 1\n",
"roomridden 1\n",
"divergencies 1\n",
"tantrum 1\n",
"itnumbers 1\n",
"limitsnot 1\n",
"adreaming 2\n",
"bestir 1\n",
"figurepushed 1\n",
"selfsustainment 2\n",
"drywhich 1\n",
"mediterranean 3\n",
"marshongingjust 1\n",
"wrappedup 1\n",
"miscellany 2\n",
"gondolas 8\n",
"venice 25\n",
"pompeii 1\n",
"fans 1\n",
"neapolitan 2\n",
"cameos 1\n",
"neptunes 1\n",
"catchemalive 1\n",
"swansdown 1\n",
"poem 2\n",
"lillie 1\n",
"quitenot 1\n",
"considerately 4\n",
"roughen 1\n",
"tickit 24\n",
"unpresentable 2\n",
"buchan 4\n",
"pliable 1\n",
"arithmetical 2\n",
"sometimesthat 1\n",
"portress 1\n",
"myselfhenry 1\n",
"marriageno 1\n",
"joddlebyi 1\n",
"lovable 3\n",
"degeneracy 3\n",
"radical 2\n",
"appointmentand 1\n",
"overdoneand 1\n",
"familytree 1\n",
"unstained 1\n",
"familiarised 1\n",
"unlaced 1\n",
"digestive 1\n",
"falteringly 2\n",
"selfcommendation 1\n",
"eightythree 1\n",
"forecourt 1\n",
"highlander 1\n",
"bedecked 1\n",
"adornments 1\n",
"beforealways 1\n",
"misunderstoodsupposed 1\n",
"highsouled 1\n",
"genuineness 1\n",
"timeages 1\n",
"heartcherished 1\n",
"kneebreeches 1\n",
"nothanot 1\n",
"thathemthat 1\n",
"andhemand 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"admiredrespectfully 1\n",
"sisterof 1\n",
"daughtersistershould 1\n",
"fathersi 1\n",
"haggardly 1\n",
"jailrot 1\n",
"onceperhapswho 1\n",
"heeling 1\n",
"selfapproval 1\n",
"childreni 1\n",
"looseness 1\n",
"overawing 1\n",
"necessitous 1\n",
"muff 1\n",
"springless 1\n",
"terminate 1\n",
"goldand 1\n",
"anguishacute 1\n",
"societycan 1\n",
"heightening 2\n",
"bribery 1\n",
"movedthough 1\n",
"sisteri 1\n",
"homeloving 1\n",
"dinnerrows 1\n",
"valuationwho 1\n",
"newlyfronted 1\n",
"coatcuffs 3\n",
"monomaniacal 1\n",
"magnatesall 1\n",
"felthe 1\n",
"converters 1\n",
"counties 1\n",
"themwe 1\n",
"undesignedly 1\n",
"thenjauntily 1\n",
"dyspepsia 1\n",
"overtaxed 1\n",
"monomaniacally 1\n",
"respectingyes 1\n",
"comfortablelooking 1\n",
"circumstantiality 1\n",
"speakingthat 1\n",
"bias 1\n",
"suicidal 1\n",
"abels 1\n",
"bubbled 1\n",
"properthe 1\n",
"circumstancesi 1\n",
"disconcertment 1\n",
"clennameasier 1\n",
"papawhich 1\n",
"remembranceswho 1\n",
"embarked 2\n",
"touchy 1\n",
"itwill 1\n",
"cataleptic 1\n",
"hetoo 1\n",
"namename 1\n",
"stateable 1\n",
"manoeuvres 1\n",
"assemblages 1\n",
"cardwhich 1\n",
"therewas 1\n",
"termsyou 1\n",
"usurped 2\n",
"questionfor 1\n",
"friendthan 1\n",
"revere 1\n",
"taker 1\n",
"puffings 1\n",
"fortuneteller 6\n",
"alreadyhe 1\n",
"weave 1\n",
"expostulated 3\n",
"rubies 1\n",
"firstfloor 5\n",
"witheringly 1\n",
"typification 1\n",
"yellowhaired 1\n",
"conspirator 2\n",
"comicality 1\n",
"ambrosial 1\n",
"cavallettothey 1\n",
"skulls 1\n",
"droves 1\n",
"curiousest 1\n",
"signore 3\n",
"waging 1\n",
"judgmentsi 1\n",
"diningrooms 1\n",
"darkbrowed 1\n",
"footboy 1\n",
"evermemorable 1\n",
"penal 1\n",
"barrackthere 1\n",
"miggles 3\n",
"researches 1\n",
"reparable 1\n",
"straiter 1\n",
"upstairsyou 1\n",
"plovers 1\n",
"helpers 1\n",
"precipitous 1\n",
"youastonishing 1\n",
"hearingi 1\n",
"coachhire 1\n",
"beggingletter 1\n",
"cowin 1\n",
"daythere 1\n",
"considerfor 1\n",
"ingrain 1\n",
"sleepwaking 1\n",
"sprightliest 1\n",
"andone 1\n",
"sheor 1\n",
"chimneycowls 1\n",
"moneybusiness 1\n",
"phosphorus 1\n",
"dowe 1\n",
"apologisepermit 1\n",
"frighteningno 1\n",
"sleeked 1\n",
"unconscionable 1\n",
"watchlining 1\n",
"complicate 1\n",
"heartfree 1\n",
"halfdefiantly 1\n",
"atbut 1\n",
"roystering 1\n",
"palate 1\n",
"oftenhas 1\n",
"musicbinding 1\n",
"soninlaws 1\n",
"nandy 26\n",
"ditties 1\n",
"commenting 1\n",
"commonminded 1\n",
"spareduntil 1\n",
"sayas 1\n",
"pensioneras 1\n",
"thehumthe 2\n",
"improprietyas 1\n",
"thatwound 1\n",
"lightsome 1\n",
"enunciator 1\n",
"herei 1\n",
"bosh 2\n",
"tohumnot 1\n",
"thehauntoward 1\n",
"dawdled 1\n",
"unseasoned 1\n",
"centres 1\n",
"paancks 1\n",
"malt 1\n",
"cockatoo 2\n",
"panckseh 1\n",
"permissioneh 1\n",
"firework 1\n",
"highpressure 1\n",
"hersnot 1\n",
"compensating 2\n",
"jewelstand 1\n",
"whichah 1\n",
"beautifulformed 1\n",
"bobbed 1\n",
"furnace 2\n",
"itafter 1\n",
"allallto 1\n",
"degage 1\n",
"clothesmen 1\n",
"bishoped 1\n",
"rushlight 1\n",
"clan 1\n",
"circumlocutionwith 1\n",
"behoves 1\n",
"behoving 1\n",
"circumlocutionist 1\n",
"thiswilliam 2\n",
"quadrupled 1\n",
"meagleses 3\n",
"unclaimed 2\n",
"goers 1\n",
"smellingbottles 1\n",
"fatheri 1\n",
"elsewherethe 1\n",
"behashall 1\n",
"timedwell 1\n",
"shallhaall 1\n",
"couldhacould 1\n",
"andhumtake 1\n",
"hosiers 1\n",
"peddle 2\n",
"applicant 1\n",
"traditional 1\n",
"mediocrity 1\n",
"elbowa 1\n",
"waterfall 1\n",
"churchroofs 1\n",
"fabulous 1\n",
"stormbelated 1\n",
"menagerie 1\n",
"windowsfortifications 1\n",
"amhumtolerably 1\n",
"martigny 8\n",
"tohumtobut 1\n",
"nothingno 1\n",
"byhaby 2\n",
"arehumequal 1\n",
"ratherthough 1\n",
"sohahumso 1\n",
"winedrops 1\n",
"lasso 1\n",
"housings 1\n",
"whilefortunately 1\n",
"convincingly 1\n",
"misfortunesgloating 1\n",
"abasing 1\n",
"andha 1\n",
"andhumkept 1\n",
"myhaslight 1\n",
"tohadefer 1\n",
"momenthahold 1\n",
"jutting 1\n",
"pinetrees 1\n",
"numerously 1\n",
"hasend 1\n",
"horsed 1\n",
"dashedwent 1\n",
"ofha 3\n",
"displaced 2\n",
"statechamber 1\n",
"dampstained 1\n",
"unsavoury 1\n",
"unrealities 1\n",
"confessionals 1\n",
"knewbut 1\n",
"blends 1\n",
"carriagewindow 1\n",
"tinkler 11\n",
"apartmenthoodwinked 1\n",
"dungeonlike 1\n",
"myhamy 1\n",
"emotionless 1\n",
"inhaamy 1\n",
"toan 1\n",
"mighthumnot 1\n",
"againhe 1\n",
"hahow 1\n",
"instancepapa 1\n",
"varnisher 3\n",
"hayou 1\n",
"andhumwhy 1\n",
"longfamiliar 1\n",
"humi 2\n",
"expecti 1\n",
"blamenothing 1\n",
"diceing 1\n",
"pistolshooting 1\n",
"niched 1\n",
"accrediting 1\n",
"ofahcuriosity 1\n",
"mayhahailthis 1\n",
"undeserving 2\n",
"perversity 1\n",
"fitwhich 1\n",
"unpurchasable 1\n",
"preadamite 1\n",
"ironsafe 1\n",
"assassina 1\n",
"cattivo 1\n",
"soggetto 1\n",
"san 1\n",
"intercession 1\n",
"paddled 3\n",
"ahumgondola 1\n",
"thehumgerms 1\n",
"goggled 1\n",
"doges 1\n",
"prominence 1\n",
"customhouse 1\n",
"commissioning 1\n",
"signpainter 1\n",
"sizar 1\n",
"ordinances 1\n",
"tourists 1\n",
"corso 2\n",
"vatican 1\n",
"entrails 1\n",
"wayill 1\n",
"itinstrument 1\n",
"lastthat 1\n",
"memorandummaking 1\n",
"tapeit 1\n",
"forgone 1\n",
"beingsfor 1\n",
"relationspositively 1\n",
"statenot 1\n",
"furthering 1\n",
"semifamily 1\n",
"semiboring 1\n",
"oursthat 1\n",
"marshongers 2\n",
"nounsubstantiveand 1\n",
"cavallooro 1\n",
"absenceor 1\n",
"coalwaggons 1\n",
"steamboats 2\n",
"footcauseways 1\n",
"scrutinising 1\n",
"streetcorner 1\n",
"organboys 1\n",
"mantua 1\n",
"reiterating 1\n",
"highlyridiculous 1\n",
"demonstratively 1\n",
"overmuch 1\n",
"writhes 1\n",
"occasioni 1\n",
"chaining 1\n",
"blandoisto 1\n",
"stolidity 1\n",
"groundmy 1\n",
"poltroon 1\n",
"dustcoloured 1\n",
"thisold 1\n",
"whybut 1\n",
"officiators 1\n",
"basilisk 1\n",
"decimusthe 1\n",
"jobbery 1\n",
"adherence 1\n",
"selfdepreciatoryin 1\n",
"largelypractical 1\n",
"dressshoes 1\n",
"exponents 1\n",
"pears 6\n",
"eton 4\n",
"enlisting 1\n",
"predicated 1\n",
"sportively 1\n",
"informal 1\n",
"andithinkthe 1\n",
"doddles 2\n",
"bolstered 1\n",
"gapers 2\n",
"rentdays 2\n",
"gardenpaling 1\n",
"warbles 2\n",
"stealthiness 1\n",
"peaka 1\n",
"reverting 1\n",
"oiled 1\n",
"epidemics 1\n",
"disgracemake 1\n",
"capitalgreat 1\n",
"newsany 1\n",
"positionand 1\n",
"albano 1\n",
"stabled 1\n",
"thereforeforgive 1\n",
"knowif 1\n",
"noshould 1\n",
"thenceforward 1\n",
"elsewhereshe 1\n",
"baywindow 1\n",
"girlsthan 1\n",
"tooa 1\n",
"seesaw 1\n",
"concurred 1\n",
"thishumthis 1\n",
"towardshamrs 1\n",
"thehumdiminution 1\n",
"gratifyinginexpressibly 1\n",
"springyou 1\n",
"principleand 1\n",
"adhering 1\n",
"despond 1\n",
"coppersaucepaned 1\n",
"didactic 1\n",
"sohumreplete 1\n",
"somehaeligible 1\n",
"ihumcan 1\n",
"senselet 1\n",
"commemorative 1\n",
"marshalsearuins 1\n",
"liferuins 1\n",
"visitingslike 1\n",
"outshining 1\n",
"turnout 1\n",
"ishaindeed 1\n",
"itshaenormous 1\n",
"dyspeptic 1\n",
"thehumadmired 1\n",
"mouthit 1\n",
"tonguemoistened 1\n",
"ihumhave 2\n",
"hummy 1\n",
"favouras 1\n",
"unimpeached 1\n",
"fawners 1\n",
"tablegarniture 1\n",
"aday 1\n",
"ithato 1\n",
"headless 1\n",
"coachdoor 1\n",
"tohamake 1\n",
"tohumknowto 1\n",
"tohaassert 1\n",
"tohaanybody 1\n",
"whathathe 1\n",
"amhumsure 1\n",
"anathematised 1\n",
"fromhason 1\n",
"dartford 1\n",
"rifled 1\n",
"fleeced 1\n",
"battlement 1\n",
"sweetmeat 1\n",
"lovegifts 1\n",
"behindperhaps 1\n",
"flare 1\n",
"chaunted 1\n",
"childall 1\n",
"ihamake 1\n",
"drivelled 1\n",
"societywandering 1\n",
"onif 1\n",
"magnificently 1\n",
"slumberings 1\n",
"humgrace 1\n",
"tohaothers 1\n",
"ofhathe 1\n",
"marchesedecorative 1\n",
"whichhot 1\n",
"ahumnot 1\n",
"infrequent 1\n",
"painless 1\n",
"watcha 1\n",
"trinkets 1\n",
"fastflowing 1\n",
"seest 1\n",
"stumblings 1\n",
"untraversable 1\n",
"watermark 1\n",
"wavedashed 1\n",
"handtohand 1\n",
"oatel 1\n",
"overblown 1\n",
"earring 2\n",
"skate 1\n",
"undesired 1\n",
"riverin 1\n",
"communicationmay 1\n",
"influenceyes 1\n",
"foolhad 1\n",
"determinedly 1\n",
"conciliated 1\n",
"depreciations 1\n",
"daughterslittle 1\n",
"povertyall 1\n",
"endurances 1\n",
"anatomise 1\n",
"audited 1\n",
"brims 1\n",
"vacuity 1\n",
"redundancy 1\n",
"gesticulation 1\n",
"giovanni 1\n",
"baptista 1\n",
"fabled 1\n",
"placards 1\n",
"conjuring 1\n",
"testy 1\n",
"smokier 2\n",
"selfgratification 1\n",
"improbably 1\n",
"counsellings 2\n",
"premisespremises 1\n",
"commoner 1\n",
"baronetcy 3\n",
"trots 1\n",
"repartee 1\n",
"unclesthough 1\n",
"unsettles 1\n",
"flattening 1\n",
"gyrate 1\n",
"banquo 1\n",
"warmbaths 3\n",
"waterdrops 1\n",
"dropsy 1\n",
"bargaindriver 1\n",
"unseaworthy 1\n",
"swimmers 1\n",
"sayass 1\n",
"outeverything 1\n",
"profitably 1\n",
"prolixity 1\n",
"jurisdictionand 1\n",
"unctuousness 1\n",
"noontide 1\n",
"watercresses 2\n",
"watercress 1\n",
"othersnow 1\n",
"tobaccobusiness 1\n",
"ithere 1\n",
"improbability 2\n",
"insusceptible 1\n",
"preponderating 1\n",
"uponthe 1\n",
"whywe 1\n",
"invalidate 1\n",
"whyreally 1\n",
"salve 1\n",
"brotherbird 1\n",
"knownwhatever 1\n",
"anklessignor 1\n",
"gallantries 1\n",
"graciouslyto 1\n",
"rustily 1\n",
"prejudicing 1\n",
"nighttunes 1\n",
"usmy 1\n",
"workthough 1\n",
"youso 1\n",
"gooddown 1\n",
"shoeless 1\n",
"ploughshares 1\n",
"earlyglowing 1\n",
"clanked 1\n",
"outregarded 1\n",
"figurestriumphant 1\n",
"consequentementally 2\n",
"herebut 1\n",
"enragethe 1\n",
"mannersand 1\n",
"demurred 1\n",
"weighted 1\n",
"paperor 1\n",
"paperswhich 1\n",
"meansfor 1\n",
"evildoers 1\n",
"wrongswhat 1\n",
"desecrated 1\n",
"disposer 1\n",
"coveting 1\n",
"thoughtin 1\n",
"boxhelp 1\n",
"antwerp 2\n",
"maker 1\n",
"unforgiveness 1\n",
"appearancefold 1\n",
"overroasting 1\n",
"writingmostly 1\n",
"tonightif 1\n",
"encloses 1\n",
"quietlooking 1\n",
"dwellinghouse 1\n",
"clennamwhat 1\n",
"healer 1\n",
"raiser 1\n",
"murk 1\n",
"films 1\n",
"barrows 1\n",
"geological 1\n",
"dutchmen 1\n",
"amsterdam 1\n",
"cyphering 1\n",
"cottagewhich 1\n",
"swindler 1\n",
"squeezer 1\n",
"alivehere 1\n",
"hummingtops 1\n",
"plodder 1\n",
"ditchwater 1\n",
"barepolled 1\n",
"fealty 1\n",
"nailing 1\n",
"fluent 1\n",
"keynote 1\n",
"paperswith 1\n",
"tattycorams 1\n",
"knowwhen 1\n",
"thenthat 1\n",
"ripeturning 1\n",
"gonehow 1\n",
"gorgons 1\n",
"fancys 1\n",
"piestock 1\n",
"daybut 1\n",
"mangerwont 1\n",
"nightcaps 1\n",
"vsanilla 1\n",
"ebook 53\n",
"vampire 20\n",
"thomas 33\n",
"anywhere 114\n",
"with 18533\n",
"may 2219\n",
"reuse 10\n",
"terms 230\n",
"online 22\n",
"feast 33\n",
"last 1308\n",
"produced 176\n",
"team 6\n",
"episodes 3\n",
"ascribed 4\n",
"added 457\n",
"transcriber 1\n",
"illustration 159\n",
"nos 2\n",
"are 4498\n",
"price 61\n",
"a 40042\n",
"interest 343\n",
"rivers 13\n",
"london 213\n",
"health 161\n",
"damned 12\n",
"printed 25\n",
"imidnightthe 1\n",
"pistol 79\n",
"shotthe 2\n",
"from 7644\n",
"francis 615\n",
"consultationthe 6\n",
"probable 48\n",
"absence 140\n",
"deadthe 2\n",
"xthe 1\n",
"vaultthe 2\n",
"sad 156\n",
"portraitthe 3\n",
"arrival 129\n",
"old 3102\n",
"landlord 111\n",
"arms 497\n",
"meeting 198\n",
"scenethe 2\n",
"explanationthe 2\n",
"housea 6\n",
"xviiithe 1\n",
"challenge 46\n",
"chamberher 2\n",
"mistakethe 2\n",
"between 929\n",
"letter 519\n",
"narrativethe 2\n",
"request 109\n",
"turret 3\n",
"himselfthe 2\n",
"through 1438\n",
"paper 215\n",
"peep 27\n",
"dungeonthe 2\n",
"xxxisir 1\n",
"interviewthe 2\n",
"opponentsthe 2\n",
"riotsir 2\n",
"xlivvarneys 1\n",
"rescuethe 2\n",
"bodiesa 2\n",
"terror 127\n",
"xlviiithe 1\n",
"gain 47\n",
"upon 4091\n",
"forcing 19\n",
"disappearancethe 2\n",
"cellars 25\n",
"military 169\n",
"grocers 8\n",
"lvithe 1\n",
"lviiithe 1\n",
"warningthe 2\n",
"operationthe 2\n",
"insulting 7\n",
"message 70\n",
"lxthe 1\n",
"suicide 12\n",
"lxiithe 1\n",
"ruin 113\n",
"dungeon 46\n",
"ruins 120\n",
"apparent 44\n",
"circumstances 426\n",
"belinda 5\n",
"lxixflora 1\n",
"innthe 4\n",
"appeal 81\n",
"nightwatch 3\n",
"lxxviivarney 1\n",
"altercation 9\n",
"excited 111\n",
"lxxxvthe 1\n",
"gets 59\n",
"shows 49\n",
"bannerworthits 2\n",
"cottage 107\n",
"lxxxixtells 1\n",
"what 7358\n",
"who 5887\n",
"sought 142\n",
"picture 210\n",
"broken 244\n",
"misadventure 4\n",
"suspicions 41\n",
"baron 115\n",
"anderbury 25\n",
"giving 274\n",
"preface 15\n",
"further 253\n",
"whence 66\n",
"more 4140\n",
"taking 638\n",
"collected 51\n",
"sources 14\n",
"question 568\n",
"even 1552\n",
"thinking 399\n",
"own 2206\n",
"tend 17\n",
"career 45\n",
"just 1216\n",
"europe 54\n",
"public 232\n",
"well 3237\n",
"works 198\n",
"degree 102\n",
"press 48\n",
"sep 1\n",
"i 33621\n",
"hideous 55\n",
"cathedral 42\n",
"thick 77\n",
"heavya 1\n",
"nature 401\n",
"paused 149\n",
"thunder 29\n",
"now 4591\n",
"comes 276\n",
"begin 209\n",
"appeared 558\n",
"awful 105\n",
"over 2168\n",
"devastation 6\n",
"many 1241\n",
"buildings 28\n",
"very 4266\n",
"grave 182\n",
"repose 59\n",
"hailstorm 1\n",
"mingled 62\n",
"accumulation 7\n",
"there 6269\n",
"persons 179\n",
"houses 159\n",
"too 1879\n",
"laterally 1\n",
"force 251\n",
"hailrainwind 1\n",
"carvings 3\n",
"roof 106\n",
"filled 154\n",
"rich 169\n",
"moon 126\n",
"containing 47\n",
"man 3244\n",
"face 2444\n",
"cared 49\n",
"those 1611\n",
"silken 9\n",
"feathers 23\n",
"dust 114\n",
"god 525\n",
"clashing 3\n",
"beating 59\n",
"cracking 6\n",
"panes 12\n",
"size 32\n",
"occupied 191\n",
"formed 147\n",
"loveliness 6\n",
"lies 95\n",
"coucha 1\n",
"escaped 62\n",
"confinement 24\n",
"she 11688\n",
"rarest 2\n",
"sculptor 3\n",
"least 533\n",
"came 2235\n",
"endured 28\n",
"can 2344\n",
"senses 64\n",
"world 754\n",
"witchery 4\n",
"mouth 251\n",
"sweetly 17\n",
"lay 546\n",
"cheek 103\n",
"fair 161\n",
"girlalmost 1\n",
"flashthen 1\n",
"roaring 21\n",
"blue 202\n",
"soul 344\n",
"trumpet 5\n",
"seems 273\n",
"height 97\n",
"awakensthat 1\n",
"opens 18\n",
"bursts 10\n",
"sounds 163\n",
"likewise 119\n",
"echoes 22\n",
"flash 25\n",
"bringing 93\n",
"colour 96\n",
"intense 33\n",
"whatwhat 9\n",
"real 188\n",
"gaunt 22\n",
"endeavouring 46\n",
"unclasp 1\n",
"revealed 37\n",
"length 269\n",
"falling 125\n",
"clattering 9\n",
"clasped 63\n",
"marble 44\n",
"distended 2\n",
"moving 198\n",
"gradually 134\n",
"set 654\n",
"appear 203\n",
"tries 13\n",
"scream 48\n",
"limb 19\n",
"whisper 155\n",
"person 431\n",
"glare 29\n",
"attempting 22\n",
"fearfully 23\n",
"hand 2259\n",
"destitute 16\n",
"removed 144\n",
"haunt 8\n",
"lifetimea 1\n",
"turn 408\n",
"round 1214\n",
"animal 68\n",
"hideously 4\n",
"fanglike 3\n",
"clashes 2\n",
"together 685\n",
"returned 1414\n",
"along 598\n",
"coming 694\n",
"itwhat 3\n",
"unlike 49\n",
"inhabitant 15\n",
"got 1132\n",
"drew 251\n",
"marblelooking 1\n",
"hour 493\n",
"throat 108\n",
"raises 5\n",
"armsthe 2\n",
"foot 269\n",
"door 1423\n",
"directioncan 1\n",
"reach 103\n",
"trembling 170\n",
"streaming 15\n",
"howling 17\n",
"bedclothes 8\n",
"heap 64\n",
"beautifully 17\n",
"profanation 2\n",
"beds 31\n",
"opened 417\n",
"halfdressed 4\n",
"knows 376\n",
"yes 1761\n",
"tell 1455\n",
"party 266\n",
"good 2286\n",
"matter 661\n",
"scarcely 238\n",
"words 999\n",
"passed 608\n",
"felt 1327\n",
"absolutely 69\n",
"lady 754\n",
"promptly 25\n",
"comer 2\n",
"staggered 29\n",
"doubt 584\n",
"bedroom 96\n",
"fastenings 8\n",
"willgive 1\n",
"crackling 9\n",
"younger 136\n",
"part 858\n",
"measure 58\n",
"given 471\n",
"spoke 521\n",
"flame 37\n",
"tremendously 3\n",
"fall 236\n",
"dim 84\n",
"make 1594\n",
"screams 16\n",
"breasts 9\n",
"balcony 46\n",
"descent 20\n",
"metallic 7\n",
"unearthly 15\n",
"exertions 26\n",
"companions 65\n",
"frame 70\n",
"missed 57\n",
"pull 64\n",
"forward 342\n",
"shot 112\n",
"view 293\n",
"forgotten 169\n",
"whereas 23\n",
"dart 12\n",
"natural 284\n",
"receded 6\n",
"monstrous 31\n",
"stay 213\n",
"iti 17\n",
"wall 293\n",
"dropped 160\n",
"bedside 31\n",
"perhaps 745\n",
"overcome 66\n",
"lighter 28\n",
"visible 99\n",
"theretheregod 1\n",
"hastily 129\n",
"sounded 72\n",
"traced 14\n",
"sister 460\n",
"frantic 24\n",
"clear 302\n",
"shake 109\n",
"trembledwell 1\n",
"fruitless 12\n",
"corpse 39\n",
"fellow 523\n",
"after 2509\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"nerve 10\n",
"triggerthe 1\n",
"explosion 14\n",
"killed 165\n",
"expedition 53\n",
"paddock 1\n",
"hurried 140\n",
"went 2189\n",
"supposed 260\n",
"finding 187\n",
"symptoms 32\n",
"having 1370\n",
"traces 33\n",
"ascertained 24\n",
"traversing 4\n",
"halt 21\n",
"surpasses 2\n",
"weep 45\n",
"homeward 9\n",
"pace 110\n",
"trouble 236\n",
"dearoh 1\n",
"crossed 118\n",
"pray 271\n",
"addition 36\n",
"fright 44\n",
"whatever 360\n",
"robert 2\n",
"childhood 76\n",
"puzzled 27\n",
"amazed 57\n",
"wept 64\n",
"placed 229\n",
"sitting 531\n",
"quite 1620\n",
"breathed 43\n",
"day 1787\n",
"measures 23\n",
"seeing 403\n",
"mercy 99\n",
"meheaven 1\n",
"defence 27\n",
"needs 44\n",
"hours 289\n",
"punctured 1\n",
"wounds 27\n",
"better 1157\n",
"gently 120\n",
"oppress 10\n",
"hush 105\n",
"chair 446\n",
"isit 3\n",
"chance 197\n",
"cast 169\n",
"ask 683\n",
"ancestor 15\n",
"tomorrow 318\n",
"keep 569\n",
"sacred 72\n",
"sleeping 63\n",
"soundly 15\n",
"solicitation 16\n",
"nights 98\n",
"flowing 30\n",
"resumed 139\n",
"mockery 11\n",
"feelings 357\n",
"preferred 41\n",
"sphere 15\n",
"iv 36\n",
"different 360\n",
"incapable 34\n",
"alter 17\n",
"phenomenon 27\n",
"strongly 92\n",
"undisturbed 20\n",
"shelf 12\n",
"uneasy 94\n",
"panel 29\n",
"prey 33\n",
"sickly 18\n",
"belonged 75\n",
"need 317\n",
"helphelp 2\n",
"florayou 2\n",
"rang 69\n",
"bell 241\n",
"sufficiently 138\n",
"discourse 26\n",
"conjecture 19\n",
"consult 31\n",
"preceding 25\n",
"cleared 64\n",
"uncommon 38\n",
"bush 12\n",
"altogether 187\n",
"doing 462\n",
"casualties 2\n",
"evil 127\n",
"thing 847\n",
"common 232\n",
"arisen 23\n",
"course 784\n",
"ourselves 148\n",
"rose 398\n",
"ofofi 1\n",
"renew 18\n",
"bloodone 1\n",
"exquisite 29\n",
"readily 55\n",
"easily 223\n",
"applaud 3\n",
"told 1038\n",
"notit 3\n",
"itit 5\n",
"insinuate 8\n",
"worlds 24\n",
"sworn 16\n",
"our 1958\n",
"receive 190\n",
"hide 112\n",
"speculate 7\n",
"dangers 24\n",
"bedchamber 13\n",
"censured 4\n",
"nevertheless 59\n",
"covering 41\n",
"mention 206\n",
"entreat 45\n",
"situation 89\n",
"neighbouring 43\n",
"secrecy 42\n",
"details 51\n",
"spreading 27\n",
"county 20\n",
"ridden 40\n",
"horse 392\n",
"nucleus 1\n",
"bad 357\n",
"marks 37\n",
"circumstantial 4\n",
"spurs 24\n",
"determined 141\n",
"stop 252\n",
"intended 134\n",
"concluded 99\n",
"facts 55\n",
"wont 462\n",
"disagreeable 77\n",
"shock 67\n",
"mistaken 95\n",
"alas 44\n",
"shutting 39\n",
"feasting 6\n",
"backa 1\n",
"pestiferous 1\n",
"facehelphelp 1\n",
"mana 8\n",
"disguise 39\n",
"stolen 32\n",
"shook 322\n",
"happy 645\n",
"livei 1\n",
"talk 490\n",
"impression 169\n",
"feelingat 1\n",
"convince 43\n",
"nightmare 14\n",
"persist 4\n",
"believing 39\n",
"victims 16\n",
"muscles 7\n",
"shuddering 16\n",
"chill 24\n",
"mark 73\n",
"forced 92\n",
"attentively 75\n",
"examination 34\n",
"insect 2\n",
"drowsiness 4\n",
"stairs 190\n",
"eager 62\n",
"indubitable 10\n",
"evidences 13\n",
"wrong 263\n",
"sorely 11\n",
"loss 173\n",
"patience 72\n",
"either 340\n",
"stage 67\n",
"banish 8\n",
"loneliness 17\n",
"revival 7\n",
"accident 74\n",
"befal 2\n",
"somewhere 138\n",
"v 45\n",
"enable 53\n",
"beaumont 3\n",
"compass 23\n",
"ratford 2\n",
"remember 470\n",
"consider 271\n",
"admit 117\n",
"lustrous 15\n",
"neither 269\n",
"objection 79\n",
"company 451\n",
"option 3\n",
"cloth 48\n",
"buttons 36\n",
"garments 22\n",
"doesit 2\n",
"replaced 33\n",
"restlessness 16\n",
"feared 95\n",
"smaller 31\n",
"conversation 377\n",
"rid 96\n",
"intensely 8\n",
"avail 28\n",
"dress 246\n",
"proof 46\n",
"attention 374\n",
"regret 113\n",
"says 529\n",
"slanting 13\n",
"shakes 14\n",
"ancestors 14\n",
"clothes 123\n",
"tread 45\n",
"beneath 109\n",
"compelled 73\n",
"articulate 3\n",
"inspire 13\n",
"ward 20\n",
"necessary 335\n",
"waiting 325\n",
"clambering 4\n",
"joining 24\n",
"accordingly 80\n",
"proceedings 72\n",
"fail 55\n",
"chillingworthhushhush 1\n",
"yonder 30\n",
"riveted 6\n",
"luminary 5\n",
"hit 59\n",
"propose 70\n",
"virtue 70\n",
"levelled 5\n",
"threw 145\n",
"neighbourhood 131\n",
"unheeded 5\n",
"unreasonable 30\n",
"excitedly 9\n",
"mental 90\n",
"feelingsi 1\n",
"cooler 8\n",
"variance 15\n",
"dreamed 64\n",
"defied 10\n",
"outrage 19\n",
"cheer 65\n",
"noted 16\n",
"assist 40\n",
"members 87\n",
"ease 104\n",
"musingly 9\n",
"sentinels 10\n",
"interesting 79\n",
"importance 115\n",
"welfare 46\n",
"casement 8\n",
"past 427\n",
"namelythat 1\n",
"value 77\n",
"saddled 10\n",
"brilliant 95\n",
"gratifying 24\n",
"predecessors 2\n",
"naturally 124\n",
"pocketbook 25\n",
"leaf 19\n",
"desirous 16\n",
"intention 147\n",
"german 96\n",
"important 215\n",
"ambiguous 2\n",
"excellent 104\n",
"detested 9\n",
"acts 30\n",
"attorney 18\n",
"adverse 3\n",
"solicitor 6\n",
"negotiation 5\n",
"remaining 95\n",
"relation 91\n",
"acceptable 18\n",
"contrived 22\n",
"excursions 9\n",
"italy 41\n",
"imminent 7\n",
"path 123\n",
"succour 4\n",
"perished 31\n",
"intrepidity 2\n",
"holland 316\n",
"artist 24\n",
"travelled 29\n",
"owed 21\n",
"explained 103\n",
"welcome 112\n",
"stated 51\n",
"term 49\n",
"relative 62\n",
"sacrifice 98\n",
"considered 327\n",
"attached 87\n",
"paid 201\n",
"bestow 36\n",
"consenting 6\n",
"esteem 36\n",
"accurate 18\n",
"offend 12\n",
"pay 175\n",
"ornamental 19\n",
"spared 34\n",
"outline 11\n",
"altered 98\n",
"argue 21\n",
"abundant 18\n",
"imprudent 8\n",
"refreshed 25\n",
"sign 99\n",
"persuade 32\n",
"confirm 22\n",
"superstitions 6\n",
"irresistibly 15\n",
"discover 49\n",
"welcomed 30\n",
"afoot 9\n",
"dislike 39\n",
"confirmation 32\n",
"authorities 52\n",
"interposed 86\n",
"vested 4\n",
"suitable 33\n",
"protection 57\n",
"locked 72\n",
"flight 95\n",
"moderate 13\n",
"difficulties 78\n",
"provide 64\n",
"remainsif 1\n",
"changed 283\n",
"weapons 30\n",
"dwelling 34\n",
"courageous 8\n",
"recent 39\n",
"instructions 73\n",
"deepened 20\n",
"cloudy 8\n",
"welltempered 1\n",
"driven 78\n",
"fervently 19\n",
"route 18\n",
"lighting 28\n",
"matches 11\n",
"manufacture 2\n",
"hard 426\n",
"acquired 39\n",
"arched 18\n",
"partaking 7\n",
"florid 6\n",
"graveyard 4\n",
"fine 255\n",
"class 55\n",
"kent 7\n",
"specimens 5\n",
"modern 32\n",
"architects 2\n",
"cupidity 3\n",
"erect 35\n",
"structures 3\n",
"thoroughfare 13\n",
"hoist 3\n",
"knife 69\n",
"careful 78\n",
"depth 28\n",
"pardon 163\n",
"tamper 4\n",
"mean 603\n",
"match 63\n",
"months 159\n",
"extracting 3\n",
"pick 38\n",
"ascertaining 8\n",
"tiles 7\n",
"truly 143\n",
"drama 6\n",
"actions 93\n",
"virtues 32\n",
"passing 235\n",
"interrupt 16\n",
"romantic 39\n",
"freshlooking 3\n",
"melancholy 129\n",
"reste 1\n",
"vaults 12\n",
"yielded 50\n",
"admitting 29\n",
"deciphered 3\n",
"accustomed 141\n",
"dipped 10\n",
"fragments 31\n",
"deathlike 10\n",
"clean 77\n",
"adding 38\n",
"dependent 24\n",
"resulted 18\n",
"deserved 32\n",
"vast 58\n",
"carefully 114\n",
"unequal 12\n",
"fight 197\n",
"occurs 24\n",
"dn 75\n",
"prostrated 6\n",
"implanted 6\n",
"summon 13\n",
"religion 36\n",
"opponent 21\n",
"saddening 1\n",
"acknowledge 29\n",
"presentiment 21\n",
"eleven 30\n",
"breakfastroom 7\n",
"lawn 19\n",
"oaken 5\n",
"saidlisten 2\n",
"six 189\n",
"calling 138\n",
"clash 7\n",
"intervention 12\n",
"statue 27\n",
"ensued 40\n",
"discern 11\n",
"apparelthe 1\n",
"halfopened 3\n",
"tusklike 1\n",
"crashing 6\n",
"plants 24\n",
"seat 172\n",
"exhibited 22\n",
"fainting 10\n",
"antagonistic 2\n",
"threatened 28\n",
"information 167\n",
"acknowledgment 24\n",
"broke 182\n",
"flood 18\n",
"lessened 5\n",
"happieri 1\n",
"moreno 3\n",
"allthis 3\n",
"echoed 35\n",
"loved 361\n",
"singled 4\n",
"freak 8\n",
"intelligence 92\n",
"incoherence 5\n",
"conceive 38\n",
"pride 187\n",
"widely 20\n",
"noblehearted 2\n",
"uttering 33\n",
"incident 27\n",
"subdue 6\n",
"changesall 1\n",
"described 85\n",
"tenacity 5\n",
"unwilling 40\n",
"eject 1\n",
"mournfully 22\n",
"faltering 18\n",
"marred 1\n",
"weight 91\n",
"tangible 16\n",
"destructible 1\n",
"drag 16\n",
"watching 127\n",
"protector 19\n",
"scourge 4\n",
"jesting 18\n",
"courts 17\n",
"treat 45\n",
"distresses 9\n",
"luxury 18\n",
"pinching 7\n",
"possiblesome 1\n",
"smiles 37\n",
"faithful 69\n",
"strides 9\n",
"extinguishing 3\n",
"skill 39\n",
"justly 17\n",
"borrowed 17\n",
"artistic 5\n",
"indelibly 3\n",
"surrounding 23\n",
"verification 1\n",
"ways 107\n",
"fracture 4\n",
"tempting 10\n",
"baronial 2\n",
"intricate 9\n",
"keeping 173\n",
"tapone 1\n",
"needless 10\n",
"unsubstantial 4\n",
"summoned 36\n",
"lo 8\n",
"slip 28\n",
"panelling 4\n",
"knuckles 20\n",
"appearances 20\n",
"restrained 23\n",
"burnt 37\n",
"grasped 17\n",
"whither 35\n",
"scantiest 1\n",
"spreads 4\n",
"excellence 7\n",
"beloved 50\n",
"othersoh 1\n",
"partner 102\n",
"bondage 12\n",
"hideousyour 1\n",
"happens 85\n",
"holiest 2\n",
"hollandi 1\n",
"begun 155\n",
"dutya 1\n",
"causes 68\n",
"meant 243\n",
"estimable 12\n",
"heartnoble 1\n",
"base 36\n",
"weal 8\n",
"binds 2\n",
"discovered 80\n",
"disturbance 46\n",
"sang 61\n",
"undulating 5\n",
"shrubs 9\n",
"beauties 11\n",
"glorious 43\n",
"hill 111\n",
"stream 80\n",
"somewhat 71\n",
"condoling 1\n",
"cos 7\n",
"woman 873\n",
"visitations 6\n",
"wages 22\n",
"owing 30\n",
"comed 1\n",
"siras 2\n",
"induces 12\n",
"bargain 37\n",
"extraneous 5\n",
"depreciate 6\n",
"awaiting 62\n",
"villages 35\n",
"vulgar 17\n",
"anticipate 21\n",
"candour 12\n",
"hence 42\n",
"applied 48\n",
"tenant 17\n",
"visitant 3\n",
"strove 15\n",
"colours 46\n",
"serenity 28\n",
"gratitude 64\n",
"reasonable 56\n",
"absolute 43\n",
"gentlemen 305\n",
"gratified 32\n",
"touching 108\n",
"sallow 21\n",
"shattered 14\n",
"resemblance 35\n",
"assailed 9\n",
"thatthat 5\n",
"severely 41\n",
"midst 111\n",
"nice 97\n",
"comfortable 122\n",
"temperament 13\n",
"excelled 3\n",
"refreshments 14\n",
"kinds 51\n",
"including 63\n",
"waving 40\n",
"unimpeachable 7\n",
"charming 116\n",
"accomplished 83\n",
"butyou 1\n",
"adieu 28\n",
"destroyer 1\n",
"desolate 34\n",
"humph 11\n",
"founded 20\n",
"differ 8\n",
"estimate 10\n",
"fatal 25\n",
"sheer 17\n",
"adjoining 24\n",
"bogie 1\n",
"charges 24\n",
"nowhere 39\n",
"systematic 7\n",
"declare 44\n",
"postchaise 5\n",
"dissimilar 3\n",
"naval 11\n",
"undress 17\n",
"officer 474\n",
"port 47\n",
"swearing 5\n",
"swab 18\n",
"larnt 1\n",
"de 204\n",
"resembling 19\n",
"collar 58\n",
"alight 11\n",
"passenger 6\n",
"eh 169\n",
"josiah 5\n",
"winegood 1\n",
"aghast 12\n",
"rascally 4\n",
"howsomdever 6\n",
"em 246\n",
"infernal 29\n",
"scuttling 4\n",
"villain 46\n",
"wrangling 2\n",
"grog 21\n",
"french 925\n",
"big 106\n",
"ship 137\n",
"boy 597\n",
"stupid 89\n",
"malice 21\n",
"marine 14\n",
"didntit 1\n",
"scoundreli 1\n",
"pigs 9\n",
"informed 140\n",
"guardian 20\n",
"distress 102\n",
"warn 18\n",
"enclose 4\n",
"johnsons 3\n",
"st 85\n",
"wewewell 1\n",
"fighting 79\n",
"battles 30\n",
"englishman 27\n",
"appreciate 15\n",
"deeds 24\n",
"davy 168\n",
"remarking 18\n",
"asking 170\n",
"supper 138\n",
"fish 53\n",
"squall 7\n",
"precipitately 4\n",
"bitten 10\n",
"lots 15\n",
"feed 10\n",
"whew 13\n",
"wamphighers 2\n",
"fathom 9\n",
"berth 17\n",
"telling 147\n",
"mighty 55\n",
"consolation 63\n",
"estimation 9\n",
"honourableminded 1\n",
"reasoned 14\n",
"link 11\n",
"haven 5\n",
"afflictions 5\n",
"devoted 124\n",
"perfumes 2\n",
"appointed 115\n",
"flower 50\n",
"usurping 2\n",
"wordswords 1\n",
"wanhow 1\n",
"execute 29\n",
"savour 2\n",
"convulsively 13\n",
"rend 4\n",
"eclipse 1\n",
"partwe 1\n",
"dazzling 13\n",
"tearful 18\n",
"omen 4\n",
"clasp 14\n",
"murmur 35\n",
"banishing 2\n",
"intrude 15\n",
"clinging 33\n",
"winning 33\n",
"warmest 8\n",
"drawingroom 85\n",
"kingdom 15\n",
"nodo 1\n",
"incidentally 13\n",
"hetis 1\n",
"doubti 1\n",
"indisposed 6\n",
"nononogod 1\n",
"bewilderedi 1\n",
"formerly 76\n",
"assent 42\n",
"avoiding 27\n",
"introduction 31\n",
"hilarity 3\n",
"unshrinking 1\n",
"amenities 3\n",
"rosy 49\n",
"demon 11\n",
"accounts 56\n",
"masquerade 1\n",
"enthusiastic 22\n",
"coolness 17\n",
"boyish 16\n",
"howard 1\n",
"twentyone 6\n",
"parlour 197\n",
"delighted 126\n",
"methinks 2\n",
"impenetrable 14\n",
"deathto 1\n",
"persuading 4\n",
"willas 1\n",
"condescension 34\n",
"suspecting 13\n",
"seasonable 5\n",
"avast 9\n",
"charley 20\n",
"visitors 153\n",
"explicitly 4\n",
"explanatory 4\n",
"answering 57\n",
"hulk 5\n",
"cover 46\n",
"marrying 52\n",
"seizing 36\n",
"theirs 26\n",
"weathers 5\n",
"despatch 7\n",
"explaining 33\n",
"palaver 3\n",
"remote 45\n",
"intermeddled 1\n",
"twentytwo 7\n",
"revolution 25\n",
"native 75\n",
"signed 28\n",
"uninterrupted 7\n",
"indecision 21\n",
"sinks 5\n",
"moreand 1\n",
"wrongfully 3\n",
"iido 1\n",
"depress 3\n",
"yeswillingly 1\n",
"mehe 3\n",
"forsake 3\n",
"lovesuch 1\n",
"floraa 1\n",
"muchdreaded 1\n",
"contented 44\n",
"unscrupulous 2\n",
"eventempered 1\n",
"management 31\n",
"serfs 72\n",
"enjoyment 69\n",
"mourned 6\n",
"countess 512\n",
"gesture 87\n",
"orders 263\n",
"gates 55\n",
"mourning 47\n",
"praying 14\n",
"recalling 37\n",
"stern 108\n",
"contracted 26\n",
"countesss 2\n",
"castlegate 1\n",
"attended 75\n",
"horses 345\n",
"implacable 15\n",
"gold 122\n",
"forthcoming 8\n",
"unceremoniously 2\n",
"dayshe 1\n",
"candidate 4\n",
"poison 9\n",
"caged 6\n",
"eastern 13\n",
"poisoning 2\n",
"riddle 4\n",
"ferrara 1\n",
"destructive 4\n",
"concert 6\n",
"rage 56\n",
"claws 4\n",
"science 64\n",
"lamp 52\n",
"metals 4\n",
"onethat 1\n",
"owners 19\n",
"claimed 21\n",
"inaccessible 11\n",
"castleit 1\n",
"incased 1\n",
"steep 24\n",
"stored 12\n",
"cut 212\n",
"isolate 1\n",
"detach 11\n",
"sons 100\n",
"leyden 3\n",
"pupil 25\n",
"deprived 30\n",
"sovereign 67\n",
"regiment 224\n",
"accused 19\n",
"services 57\n",
"whirl 9\n",
"thraldom 5\n",
"unflinchingly 1\n",
"gazer 1\n",
"enchantment 8\n",
"crimsoned 1\n",
"appalled 9\n",
"hate 51\n",
"uneasily 29\n",
"quarrelled 9\n",
"fleeting 8\n",
"accented 1\n",
"borrowing 5\n",
"wisdom 45\n",
"resources 45\n",
"knewmy 1\n",
"aggravation 8\n",
"beware 12\n",
"hateful 10\n",
"sinking 38\n",
"crash 14\n",
"xxi 18\n",
"meanwhile 73\n",
"charlesfrom 1\n",
"shipthats 1\n",
"exist 43\n",
"truthfulness 5\n",
"meshe 4\n",
"married 307\n",
"sleeve 43\n",
"marines 6\n",
"disowned 2\n",
"figurehead 11\n",
"forty 62\n",
"manofwar 5\n",
"fellows 105\n",
"squeezing 16\n",
"correctly 15\n",
"nearer 201\n",
"lads 47\n",
"afloat 15\n",
"grenades 1\n",
"swansea 1\n",
"conscioushe 1\n",
"immunity 3\n",
"overjoyed 12\n",
"hammer 15\n",
"chimed 12\n",
"smothered 8\n",
"sincerity 20\n",
"positively 66\n",
"income 24\n",
"regular 88\n",
"revolt 2\n",
"existing 31\n",
"despondingly 2\n",
"cross 134\n",
"celerity 5\n",
"inflammation 4\n",
"strictly 27\n",
"personally 62\n",
"personifier 1\n",
"strife 11\n",
"supernaturally 4\n",
"growled 22\n",
"firstrate 10\n",
"abilities 12\n",
"preventing 11\n",
"revivification 3\n",
"assuming 23\n",
"customs 10\n",
"spectator 7\n",
"renewing 4\n",
"monster 38\n",
"metaphysical 4\n",
"sharp 140\n",
"discipline 28\n",
"propensity 7\n",
"prevalent 8\n",
"jacks 11\n",
"announcement 25\n",
"considers 17\n",
"elses 26\n",
"bud 5\n",
"neverceasing 1\n",
"deciding 10\n",
"espouse 1\n",
"dies 8\n",
"resort 10\n",
"barbarous 8\n",
"swords 20\n",
"unequalled 2\n",
"heated 21\n",
"crackbrained 1\n",
"pitched 14\n",
"tackle 10\n",
"hospitable 17\n",
"sharkyou 1\n",
"redhot 6\n",
"digests 1\n",
"somebody 171\n",
"vapour 8\n",
"cutlasses 1\n",
"toastingforks 1\n",
"arranging 42\n",
"practising 7\n",
"vagabond 25\n",
"challenges 5\n",
"itmust 1\n",
"purposes 32\n",
"fretted 5\n",
"laudable 9\n",
"hostile 24\n",
"sward 3\n",
"fishpond 1\n",
"definitively 2\n",
"prospects 30\n",
"recompense 19\n",
"touchstone 1\n",
"idly 15\n",
"specially 29\n",
"flies 18\n",
"reminded 74\n",
"dimming 2\n",
"resting 69\n",
"oppresses 1\n",
"forebodings 3\n",
"xxv 10\n",
"obvious 37\n",
"thwart 5\n",
"injurednonsense 1\n",
"born 104\n",
"wonders 28\n",
"landsmen 3\n",
"fiftypound 1\n",
"landsmens 1\n",
"hairs 16\n",
"masthead 8\n",
"shaving 11\n",
"outward 18\n",
"sailors 18\n",
"lubbers 6\n",
"chew 2\n",
"indian 8\n",
"surface 56\n",
"pitching 7\n",
"washtub 1\n",
"sail 33\n",
"river 177\n",
"rent 25\n",
"stemmed 1\n",
"majestically 11\n",
"stare 46\n",
"jingo 1\n",
"unconcern 6\n",
"hooked 11\n",
"extremities 4\n",
"peter 42\n",
"wilkinss 1\n",
"respecting 35\n",
"heels 55\n",
"providing 26\n",
"attentions 28\n",
"excessively 29\n",
"indescribable 10\n",
"contraband 8\n",
"birch 19\n",
"binnacle 8\n",
"binnaclewhat 1\n",
"stayed 63\n",
"comic 12\n",
"sucked 8\n",
"wistfully 20\n",
"kangaroo 2\n",
"finish 73\n",
"coffee 46\n",
"stout 80\n",
"daredevil 4\n",
"dispensed 7\n",
"whistling 53\n",
"vessels 10\n",
"bottomless 2\n",
"cask 10\n",
"accompaniment 4\n",
"stitch 8\n",
"lashed 7\n",
"posts 23\n",
"mysteriously 23\n",
"salt 31\n",
"amongst 14\n",
"allfar 1\n",
"isnt 115\n",
"lest 92\n",
"pinned 5\n",
"pho 5\n",
"cap 162\n",
"grey 121\n",
"saywe 1\n",
"grin 27\n",
"game 109\n",
"guildhall 2\n",
"calmed 10\n",
"stinted 6\n",
"treachery 15\n",
"conveniently 9\n",
"wrapping 7\n",
"wane 6\n",
"upper 57\n",
"facilities 4\n",
"sill 16\n",
"propriety 18\n",
"destination 26\n",
"severally 6\n",
"transcribe 6\n",
"fickleness 1\n",
"dispassionately 3\n",
"satisfactory 39\n",
"scoundrelthe 1\n",
"trusted 52\n",
"boiling 15\n",
"discredit 3\n",
"contempt 51\n",
"confer 17\n",
"betake 2\n",
"selfishness 12\n",
"prowess 4\n",
"unrefreshed 4\n",
"marked 52\n",
"hostility 13\n",
"dealings 13\n",
"understands 18\n",
"severe 62\n",
"artifices 3\n",
"utmost 101\n",
"unbiased 2\n",
"charlescharlescharles 2\n",
"sting 10\n",
"thinkthinkthe 1\n",
"disgraceful 14\n",
"fabrications 1\n",
"childlike 26\n",
"jewel 9\n",
"infamous 13\n",
"fleet 12\n",
"unaffected 10\n",
"doubting 12\n",
"relax 9\n",
"stronglyexpressed 1\n",
"condemnation 3\n",
"madman 8\n",
"courted 15\n",
"vigour 11\n",
"discoverable 2\n",
"befits 5\n",
"differently 29\n",
"deserve 34\n",
"stooping 42\n",
"wash 20\n",
"deeplaid 3\n",
"confusing 12\n",
"preserved 50\n",
"connect 13\n",
"approving 13\n",
"desponding 3\n",
"victim 63\n",
"denizens 2\n",
"poorest 4\n",
"operation 21\n",
"downhearted 2\n",
"contradictions 11\n",
"cannotcannot 1\n",
"tuck 2\n",
"stray 26\n",
"recording 4\n",
"monastic 1\n",
"political 48\n",
"jolly 17\n",
"labyrinths 3\n",
"setting 76\n",
"flagstones 2\n",
"sank 46\n",
"crimson 25\n",
"shifting 15\n",
"mingling 17\n",
"habitation 9\n",
"pervaded 21\n",
"hum 48\n",
"cranny 2\n",
"projection 1\n",
"mantle 13\n",
"sullen 31\n",
"startling 18\n",
"tenanted 6\n",
"straw 41\n",
"freshly 21\n",
"stretch 20\n",
"desparingly 1\n",
"slants 2\n",
"flitting 12\n",
"freedom 148\n",
"carol 1\n",
"lightpatch 1\n",
"gleam 28\n",
"stoops 1\n",
"offers 25\n",
"speculation 28\n",
"nearlyunconscious 1\n",
"feeble 58\n",
"dislikes 4\n",
"females 9\n",
"inducing 5\n",
"poverty 41\n",
"cat 39\n",
"didto 1\n",
"difficultiesdont 1\n",
"meanly 4\n",
"dutchman 7\n",
"reference 134\n",
"cherub 5\n",
"appliances 5\n",
"unconnected 7\n",
"filling 25\n",
"earths 5\n",
"toovery 1\n",
"cheat 15\n",
"recesses 16\n",
"plunging 11\n",
"gable 4\n",
"straightbacked 1\n",
"chairs 57\n",
"lordly 1\n",
"maidens 6\n",
"howls 2\n",
"seeks 11\n",
"final 55\n",
"memorable 37\n",
"week 213\n",
"multiply 1\n",
"displeasure 33\n",
"dared 62\n",
"proposals 14\n",
"onslow 1\n",
"suitor 17\n",
"brook 16\n",
"darkened 43\n",
"obedience 16\n",
"begets 1\n",
"indignant 26\n",
"snow 103\n",
"flakes 5\n",
"desires 26\n",
"whiskers 32\n",
"army 650\n",
"lick 1\n",
"flings 3\n",
"enters 17\n",
"horsemans 2\n",
"lightly 66\n",
"remind 35\n",
"schemes 14\n",
"conditions 78\n",
"moth 2\n",
"images 20\n",
"imagesi 1\n",
"stares 2\n",
"cheaper 7\n",
"inducements 1\n",
"overthat 1\n",
"grudge 19\n",
"earthly 31\n",
"shunned 13\n",
"darkcoloured 1\n",
"tenor 8\n",
"contingent 10\n",
"lifes 13\n",
"felicity 3\n",
"glory 66\n",
"empower 3\n",
"attributes 6\n",
"shortest 12\n",
"exercised 8\n",
"recalled 79\n",
"forestall 5\n",
"northern 3\n",
"curses 11\n",
"attenuated 2\n",
"kindle 7\n",
"bark 35\n",
"watchdog 2\n",
"strolling 14\n",
"blooming 16\n",
"miser 4\n",
"feelwhich 1\n",
"fragile 12\n",
"privacy 7\n",
"lightness 10\n",
"grown 134\n",
"diffused 10\n",
"transient 7\n",
"unthinking 2\n",
"bane 9\n",
"spectrallike 1\n",
"sanctified 4\n",
"realm 16\n",
"seeking 61\n",
"commended 15\n",
"airy 28\n",
"realms 7\n",
"noblest 7\n",
"paths 18\n",
"melodious 5\n",
"himbut 2\n",
"jingling 22\n",
"gloat 3\n",
"sanity 2\n",
"akin 9\n",
"corpselike 1\n",
"somnambulistic 1\n",
"encircle 2\n",
"persecutedpersecuted 1\n",
"exhausted 72\n",
"reviving 9\n",
"youi 13\n",
"achieved 24\n",
"doom 10\n",
"charnelhouse 2\n",
"shrieked 20\n",
"embracing 23\n",
"cycle 1\n",
"prolonged 18\n",
"beats 10\n",
"healthfully 1\n",
"lingers 4\n",
"susceptibility 3\n",
"essential 44\n",
"triple 4\n",
"steel 15\n",
"chamberagain 1\n",
"embraceagain 1\n",
"isolated 5\n",
"plead 6\n",
"certainty 24\n",
"solitary 112\n",
"propulsive 1\n",
"arrest 16\n",
"fields 78\n",
"landscape 17\n",
"march 102\n",
"continuing 33\n",
"trellis 1\n",
"compensated 2\n",
"dedicate 1\n",
"sympathising 2\n",
"fiend 6\n",
"honourably 3\n",
"xxxvi 4\n",
"independent 39\n",
"familydistracted 1\n",
"inert 3\n",
"obtruded 5\n",
"wounding 3\n",
"conjectural 1\n",
"dispelled 4\n",
"confirmed 54\n",
"stubborn 11\n",
"savours 1\n",
"badlooking 4\n",
"inkstand 13\n",
"resentful 8\n",
"undone 7\n",
"invite 17\n",
"xxxvii 4\n",
"incline 12\n",
"rejection 7\n",
"ventured 53\n",
"atrocity 2\n",
"implied 23\n",
"reducing 6\n",
"practicable 4\n",
"beholding 7\n",
"obtainedyou 1\n",
"enlighten 10\n",
"tonesthis 1\n",
"uncontrollable 2\n",
"entry 28\n",
"log 15\n",
"tough 34\n",
"blade 6\n",
"endlong 1\n",
"plank 6\n",
"selfcommuning 1\n",
"stab 9\n",
"christening 13\n",
"burial 8\n",
"handspike 1\n",
"muscle 11\n",
"manufactured 1\n",
"allowance 28\n",
"popgun 2\n",
"accusationone 1\n",
"againuntil 1\n",
"granting 3\n",
"youdo 2\n",
"watchful 26\n",
"confidently 21\n",
"youyoure 1\n",
"flung 34\n",
"washhandstand 3\n",
"slacks 1\n",
"blaze 28\n",
"agoing 30\n",
"lingo 3\n",
"itll 20\n",
"crave 2\n",
"equals 3\n",
"grate 28\n",
"comply 41\n",
"trousers 40\n",
"lasting 15\n",
"scores 6\n",
"punctual 15\n",
"infinite 61\n",
"asd 1\n",
"arterwards 4\n",
"awhile 23\n",
"hitching 3\n",
"undertaken 18\n",
"thishad 1\n",
"ruined 84\n",
"dubious 3\n",
"sentry 4\n",
"twist 14\n",
"overslept 2\n",
"enoughtime 1\n",
"composing 13\n",
"unflinching 4\n",
"invulnerability 1\n",
"legal 46\n",
"acquisition 10\n",
"adjacent 10\n",
"occurring 5\n",
"masticating 1\n",
"horrifying 1\n",
"hurra 10\n",
"offing 2\n",
"pints 4\n",
"thinner 13\n",
"pinkishlooking 1\n",
"imparted 22\n",
"bergenapzoom 1\n",
"knoll 43\n",
"summitan 1\n",
"younow 2\n",
"preparation 50\n",
"tremour 1\n",
"smirking 2\n",
"actuating 1\n",
"inexpressibly 6\n",
"murderous 7\n",
"plainer 16\n",
"soto 2\n",
"neckerchief 8\n",
"whod 11\n",
"fitted 32\n",
"toe 5\n",
"um 1\n",
"brass 24\n",
"armour 7\n",
"mayors 3\n",
"conscience 51\n",
"whacking 1\n",
"deprecating 5\n",
"build 14\n",
"pike 3\n",
"burn 54\n",
"admirable 37\n",
"fervent 16\n",
"desperation 22\n",
"hailed 18\n",
"expiating 1\n",
"forethought 2\n",
"involve 11\n",
"indiscreet 2\n",
"unintentionally 7\n",
"foremost 36\n",
"restricted 6\n",
"unbridled 1\n",
"veritable 5\n",
"localities 3\n",
"sema 1\n",
"throng 30\n",
"disorderly 18\n",
"rabble 9\n",
"tumultuous 6\n",
"ransacked 1\n",
"detestation 5\n",
"multitudehow 1\n",
"convey 22\n",
"direst 1\n",
"whichwell 2\n",
"industrious 15\n",
"ineffectually 6\n",
"stopping 107\n",
"deaf 27\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"motley 4\n",
"risibility 1\n",
"indiscriminately 3\n",
"flails 1\n",
"bludgeons 2\n",
"depicted 15\n",
"inflamed 8\n",
"retarding 1\n",
"herald 2\n",
"imperturbable 9\n",
"pursuers 15\n",
"brushwood 3\n",
"foliage 9\n",
"fat 55\n",
"crowner 1\n",
"mishap 1\n",
"immersion 1\n",
"limpid 3\n",
"stragglers 15\n",
"doctors 174\n",
"jaded 11\n",
"prate 1\n",
"harmlessly 2\n",
"organised 1\n",
"pipkinsbroadside 1\n",
"footpaths 1\n",
"tire 10\n",
"farseeing 1\n",
"exultation 22\n",
"persisted 21\n",
"resolute 62\n",
"circles 33\n",
"greeted 26\n",
"cope 5\n",
"fabric 2\n",
"sententiously 1\n",
"ending 14\n",
"penetrated 13\n",
"constructed 20\n",
"remnant 8\n",
"awaysome 1\n",
"heaps 24\n",
"preaches 1\n",
"hopefulness 4\n",
"feeton 1\n",
"verdant 3\n",
"againwith 2\n",
"listens 6\n",
"lumbering 6\n",
"breathpanting 1\n",
"roll 43\n",
"topmost 3\n",
"forsaking 3\n",
"pacific 5\n",
"encountering 10\n",
"insults 7\n",
"humbug 18\n",
"postpone 6\n",
"relate 45\n",
"sow 3\n",
"mists 10\n",
"disclose 9\n",
"sweetness 14\n",
"talents 11\n",
"alleges 1\n",
"perceivable 1\n",
"timewho 1\n",
"soundness 3\n",
"pierce 5\n",
"enables 4\n",
"hecould 1\n",
"soontoo 1\n",
"deprives 4\n",
"harshly 9\n",
"subjectmatter 2\n",
"herebless 1\n",
"psha 5\n",
"luncheon 2\n",
"ferment 4\n",
"deprecate 2\n",
"fanciful 10\n",
"rattled 28\n",
"rattle 29\n",
"dusky 5\n",
"forms 52\n",
"mocked 3\n",
"endowment 2\n",
"dignity 112\n",
"grovelling 3\n",
"sanctuary 15\n",
"bosoms 3\n",
"hundreds 46\n",
"munificent 1\n",
"garrison 8\n",
"prone 11\n",
"knob 10\n",
"parapet 8\n",
"ecstasy 22\n",
"fun 34\n",
"jestingly 4\n",
"demolition 3\n",
"worshippers 2\n",
"fellowmr 1\n",
"thinning 1\n",
"xlv 2\n",
"vestry 13\n",
"exhortation 5\n",
"buryingground 4\n",
"gauntlooking 2\n",
"acceptance 12\n",
"fletchers 1\n",
"earthyou 1\n",
"gospel 8\n",
"wedged 4\n",
"disarmed 7\n",
"pickaxes 1\n",
"machine 21\n",
"crouched 9\n",
"shovelfuls 1\n",
"restingplace 14\n",
"defunct 1\n",
"adhesive 3\n",
"impertinent 6\n",
"cuffs 6\n",
"heroic 22\n",
"brains 17\n",
"acominghes 1\n",
"majority 23\n",
"popularity 4\n",
"parish 10\n",
"snivelling 4\n",
"curs 1\n",
"un 34\n",
"againthats 1\n",
"hellwe 1\n",
"cocksparrow 1\n",
"gaining 14\n",
"showman 2\n",
"collaring 2\n",
"theyve 33\n",
"advocate 8\n",
"accessory 4\n",
"chary 5\n",
"pack 36\n",
"rumour 10\n",
"conveying 9\n",
"sartingly 1\n",
"gratuity 2\n",
"thathes 1\n",
"tours 1\n",
"vengeful 2\n",
"circumvented 1\n",
"porter 42\n",
"flunkey 2\n",
"complimentshis 1\n",
"ale 25\n",
"dialogue 17\n",
"liveryservant 1\n",
"thieves 7\n",
"hallooing 4\n",
"pumping 4\n",
"wet 109\n",
"amore 2\n",
"theory 42\n",
"tenantless 1\n",
"simpleminded 4\n",
"dragoons 40\n",
"practitioners 5\n",
"chambermaids 1\n",
"unfit 4\n",
"soldiery 6\n",
"serenely 7\n",
"exclamations 12\n",
"quelled 3\n",
"twopence 6\n",
"processes 2\n",
"wholesome 21\n",
"redcoats 1\n",
"sore 28\n",
"spirittaps 1\n",
"compounds 2\n",
"halo 6\n",
"deadthat 1\n",
"requisites 3\n",
"identify 14\n",
"fleshfor 1\n",
"wasting 12\n",
"integuments 1\n",
"bone 12\n",
"tooth 26\n",
"rear 38\n",
"hedgestake 5\n",
"larceny 2\n",
"bodya 1\n",
"perforated 1\n",
"asserted 9\n",
"troopers 2\n",
"constables 4\n",
"consolidating 1\n",
"housedeath 1\n",
"jeopardy 2\n",
"lumberrooms 1\n",
"boxes 34\n",
"hesitating 41\n",
"scrambled 8\n",
"besieged 1\n",
"routed 9\n",
"varneyfor 2\n",
"observant 22\n",
"procuring 4\n",
"leader 20\n",
"questioner 11\n",
"intimated 15\n",
"beant 1\n",
"twothey 1\n",
"abed 7\n",
"nowwhere 2\n",
"guns 112\n",
"unprepared 8\n",
"engine 14\n",
"choppers 2\n",
"charge 144\n",
"stepsthe 1\n",
"belaboured 1\n",
"stricken 21\n",
"fatigued 21\n",
"harassed 9\n",
"verandah 3\n",
"helter 1\n",
"overturning 1\n",
"pierglasses 1\n",
"crushing 16\n",
"hurray 1\n",
"huggins 2\n",
"bottled 4\n",
"cannibals 2\n",
"drunkenness 8\n",
"liquor 10\n",
"liii 2\n",
"den 11\n",
"faggots 4\n",
"outcome 5\n",
"firefirefire 1\n",
"demons 4\n",
"velocity 6\n",
"stories 52\n",
"hurrafire 2\n",
"mason 26\n",
"downhurra 1\n",
"touches 11\n",
"circuit 7\n",
"perpetrated 4\n",
"hobbled 5\n",
"contemptuous 33\n",
"charred 8\n",
"spectators 10\n",
"hats 30\n",
"illwind 1\n",
"stiles 1\n",
"shopkeeper 4\n",
"wager 13\n",
"dire 5\n",
"newly 48\n",
"tranquil 43\n",
"depart 23\n",
"bearings 10\n",
"roads 49\n",
"foundering 2\n",
"lookout 27\n",
"frenchmens 1\n",
"gratification 35\n",
"boat 108\n",
"atween 1\n",
"glim 1\n",
"courtyard 49\n",
"emde 1\n",
"aheadno 1\n",
"hilloatake 1\n",
"squally 2\n",
"zephyr 1\n",
"review 30\n",
"wanderers 2\n",
"creator 9\n",
"sterility 1\n",
"strip 13\n",
"survivor 3\n",
"listlessness 2\n",
"contributed 5\n",
"instil 1\n",
"dirk 2\n",
"silver 70\n",
"crowned 14\n",
"toast 60\n",
"rightthinking 1\n",
"lawyerstheyre 1\n",
"economy 7\n",
"victualling 1\n",
"dishes 31\n",
"stormthats 1\n",
"opiniontol 1\n",
"float 11\n",
"beyrouthow 1\n",
"cateckis 1\n",
"thirteen 20\n",
"tanner 2\n",
"cock 12\n",
"greenhouse 4\n",
"precipitation 7\n",
"fruit 30\n",
"outride 1\n",
"sash 7\n",
"ascendancy 5\n",
"stratagem 6\n",
"hasdoctor 1\n",
"rueful 3\n",
"tangled 9\n",
"encumbrance 4\n",
"brambles 1\n",
"scuff 1\n",
"spectrallooking 1\n",
"shamming 2\n",
"tarbarrel 3\n",
"daybreak 18\n",
"walmesley 5\n",
"conferred 16\n",
"invitation 46\n",
"persecutes 1\n",
"lx 2\n",
"behaved 27\n",
"verified 5\n",
"stretching 42\n",
"cottagelike 1\n",
"external 37\n",
"boasted 7\n",
"impertinence 5\n",
"longish 1\n",
"barrier 23\n",
"fists 11\n",
"bloodsucking 1\n",
"slur 6\n",
"microscopic 6\n",
"resumethis 1\n",
"flap 2\n",
"leaning 121\n",
"henryand 1\n",
"sedulously 6\n",
"relating 31\n",
"prefers 5\n",
"exaggeration 3\n",
"crawls 2\n",
"hindered 10\n",
"clustering 10\n",
"congeniality 4\n",
"legitimate 16\n",
"patrimonies 1\n",
"baneful 1\n",
"haggard 22\n",
"oaths 6\n",
"lxii 2\n",
"spotmotives 1\n",
"confederate 1\n",
"manwho 3\n",
"arrests 1\n",
"intends 8\n",
"mortally 8\n",
"naught 4\n",
"meddling 3\n",
"diffusing 6\n",
"apprehended 6\n",
"lumberroom 1\n",
"lion 19\n",
"survived 5\n",
"objected 25\n",
"ignited 1\n",
"attic 5\n",
"vivacity 8\n",
"iand 2\n",
"greatcoat 34\n",
"workhouse 12\n",
"claps 4\n",
"hinder 14\n",
"inherit 7\n",
"toothe 1\n",
"lasthe 1\n",
"bureautherell 1\n",
"mightily 12\n",
"inheritance 21\n",
"rheumatism 3\n",
"orderthe 1\n",
"faced 18\n",
"sleepy 33\n",
"wagged 8\n",
"cabinet 16\n",
"dreadfulvery 1\n",
"narrator 4\n",
"noto 2\n",
"childsplay 1\n",
"suggesting 20\n",
"failure 35\n",
"youhushhushhush 1\n",
"youa 3\n",
"bemarchdale 1\n",
"peeping 25\n",
"robs 8\n",
"pistolsone 1\n",
"flutter 17\n",
"likewisewell 1\n",
"animationwill 1\n",
"willthank 1\n",
"delicateperhaps 1\n",
"hurti 1\n",
"acquit 7\n",
"chargei 1\n",
"convulsed 5\n",
"vampyresyes 1\n",
"generationsit 1\n",
"tokill 1\n",
"freenay 1\n",
"suspects 4\n",
"tint 6\n",
"moneymore 1\n",
"latebut 1\n",
"lxv 1\n",
"despicable 7\n",
"warranted 7\n",
"unaccustomed 15\n",
"boldness 20\n",
"propound 2\n",
"hundredth 9\n",
"padlock 3\n",
"chasehed 1\n",
"jackay 1\n",
"forgets 12\n",
"skirf 1\n",
"joe 66\n",
"wigginss 1\n",
"marlings 1\n",
"sirthats 1\n",
"worstest 1\n",
"punch 41\n",
"goim 1\n",
"admitwell 1\n",
"antigua 1\n",
"marchant 1\n",
"disgrace 39\n",
"rudely 9\n",
"lxviii 1\n",
"nightgood 1\n",
"craven 2\n",
"slaughter 6\n",
"characterized 3\n",
"wrested 4\n",
"identical 19\n",
"fostered 2\n",
"twinkling 16\n",
"rapturous 31\n",
"profuse 5\n",
"committednamely 1\n",
"contrivances 2\n",
"atonement 8\n",
"writer 8\n",
"shrewsburythe 1\n",
"buckled 8\n",
"spursthen 1\n",
"anotherby 1\n",
"friendsand 2\n",
"canne 1\n",
"somethinghis 1\n",
"tenthe 1\n",
"bya 1\n",
"warrior 10\n",
"trophies 4\n",
"valour 2\n",
"noin 1\n",
"sealed 20\n",
"lateshe 1\n",
"sorrowing 3\n",
"trepidation 11\n",
"maideni 1\n",
"beit 2\n",
"bespoke 6\n",
"transports 9\n",
"causesi 1\n",
"berthabertha 1\n",
"headmoreover 1\n",
"cloisterwill 1\n",
"tilting 6\n",
"saddles 9\n",
"lances 3\n",
"felled 3\n",
"lxx 1\n",
"landlordyes 1\n",
"riots 1\n",
"tosome 1\n",
"ruinsnone 1\n",
"froth 1\n",
"officials 14\n",
"funerals 5\n",
"homesand 1\n",
"sentimental 10\n",
"jacobswhat 1\n",
"miserableso 1\n",
"impediment 8\n",
"check 40\n",
"portended 1\n",
"provocation 9\n",
"himunless 1\n",
"spirited 17\n",
"gratuitous 3\n",
"augmented 4\n",
"pursuance 12\n",
"discrimination 6\n",
"compete 3\n",
"relied 12\n",
"stain 10\n",
"shared 30\n",
"quantities 5\n",
"scrutinisingly 1\n",
"varneyof 1\n",
"spiteful 8\n",
"voicewhyyou 1\n",
"thingsthat 1\n",
"herebefore 1\n",
"withbut 3\n",
"indeeday 1\n",
"tradei 1\n",
"metropolis 8\n",
"lxxii 1\n",
"chillingworthi 1\n",
"hazardtable 1\n",
"dazzled 8\n",
"superbit 1\n",
"seedy 2\n",
"youexactly 1\n",
"fifteen 57\n",
"honourtis 1\n",
"deterred 4\n",
"retrieve 4\n",
"gambled 1\n",
"expressiveno 1\n",
"retrench 2\n",
"ruining 4\n",
"rail 8\n",
"preyed 9\n",
"sighing 22\n",
"employment 39\n",
"nonespeak 1\n",
"gambler 11\n",
"frequent 38\n",
"earlyyes 1\n",
"decreasing 1\n",
"bestyour 2\n",
"aidno 1\n",
"drown 10\n",
"wonplayed 1\n",
"lostplayed 1\n",
"avarice 4\n",
"daythey 1\n",
"sleight 1\n",
"decently 7\n",
"forgetone 1\n",
"perturbation 4\n",
"blacker 4\n",
"regardless 40\n",
"feeti 2\n",
"flightno 1\n",
"fence 37\n",
"magistrate 23\n",
"chillingworthoh 1\n",
"richardson 1\n",
"shouteddown 1\n",
"nowfire 1\n",
"ringleader 1\n",
"cancan 1\n",
"intrigues 16\n",
"nation 40\n",
"potations 3\n",
"enjoying 26\n",
"anticipating 14\n",
"immured 3\n",
"villanies 1\n",
"indications 10\n",
"fullmouthed 1\n",
"abandonment 19\n",
"roar 56\n",
"wherewith 2\n",
"contemptible 11\n",
"manthat 2\n",
"groggy 2\n",
"intemperate 1\n",
"blockade 2\n",
"tack 8\n",
"inexplicably 2\n",
"herring 2\n",
"rummantic 1\n",
"memore 1\n",
"alonethey 2\n",
"controlling 2\n",
"participate 10\n",
"belie 2\n",
"exclusive 14\n",
"poetical 9\n",
"convictiona 1\n",
"chinno 1\n",
"chinher 1\n",
"abduction 4\n",
"fatigues 9\n",
"smelt 21\n",
"predilection 3\n",
"intercede 5\n",
"springy 1\n",
"judgmentwhen 1\n",
"itthen 2\n",
"dilapidation 3\n",
"deaddead 1\n",
"pancake 4\n",
"cruelest 1\n",
"vexing 2\n",
"lxxvii 1\n",
"quenched 7\n",
"refers 4\n",
"adventitious 1\n",
"bailey 5\n",
"indefatigable 4\n",
"requisition 8\n",
"effectuallya 1\n",
"sentenced 7\n",
"doers 1\n",
"interred 2\n",
"penetration 12\n",
"mania 6\n",
"unscientific 1\n",
"completeness 8\n",
"ingredient 3\n",
"succeededtoo 1\n",
"lxxviii 1\n",
"reasonsreasons 1\n",
"sooth 7\n",
"shrewd 22\n",
"belet 1\n",
"persuasions 4\n",
"firstrater 2\n",
"rumay 1\n",
"discomfiture 17\n",
"controlled 13\n",
"displeased 26\n",
"gardenwall 2\n",
"glided 31\n",
"sacrilege 3\n",
"madtraverse 1\n",
"explored 2\n",
"exploits 10\n",
"onive 1\n",
"bricks 14\n",
"exclamationanother 1\n",
"bier 2\n",
"fulness 15\n",
"walkingss 1\n",
"frost 30\n",
"himcatch 1\n",
"vampyretheres 1\n",
"youtheres 1\n",
"diethrow 1\n",
"outstripped 3\n",
"himseize 1\n",
"uninjured 4\n",
"collecting 19\n",
"spill 2\n",
"resisti 1\n",
"menfathershave 1\n",
"wielded 4\n",
"levelling 6\n",
"uprightnot 1\n",
"unhurt 2\n",
"whomever 3\n",
"spur 4\n",
"dogging 1\n",
"hedges 6\n",
"complaisance 1\n",
"shapeless 5\n",
"dreamless 1\n",
"moneythe 1\n",
"harmmarchdale 1\n",
"villainnot 1\n",
"freeyes 1\n",
"graveit 1\n",
"thereno 1\n",
"ravings 1\n",
"giants 8\n",
"imminence 2\n",
"youwhy 1\n",
"congenial 10\n",
"crush 22\n",
"aloof 11\n",
"unnerved 2\n",
"officiated 3\n",
"magnificent 30\n",
"namewas 1\n",
"wentthe 1\n",
"stroke 22\n",
"lava 3\n",
"artery 1\n",
"intoxicating 2\n",
"devious 1\n",
"robbing 9\n",
"consummation 3\n",
"suburban 1\n",
"bathed 11\n",
"mewith 1\n",
"bloodshot 9\n",
"deedwho 1\n",
"retractmuch 1\n",
"charmed 43\n",
"reservation 8\n",
"lxxxiii 1\n",
"alighting 12\n",
"inndoor 2\n",
"obsequious 3\n",
"guineas 27\n",
"ham 148\n",
"grilled 2\n",
"sam 1\n",
"parleying 1\n",
"jug 13\n",
"highstreet 3\n",
"slouced 1\n",
"basin 10\n",
"halfcrown 5\n",
"grandmother 7\n",
"cakes 10\n",
"buys 1\n",
"indiscriminating 1\n",
"madeira 17\n",
"caboose 2\n",
"seriousyoure 1\n",
"bloakill 1\n",
"exaggerations 2\n",
"thereavast 1\n",
"forswearing 1\n",
"wiseacres 1\n",
"choosed 1\n",
"himafter 1\n",
"gelling 1\n",
"fowlingpiece 2\n",
"crossing 73\n",
"impetuosity 6\n",
"clothed 5\n",
"gracefully 17\n",
"earthy 5\n",
"rippling 10\n",
"momentsuch 1\n",
"waterwhat 1\n",
"stonesand 1\n",
"risesthe 1\n",
"background 19\n",
"contrasts 3\n",
"itscarce 1\n",
"inanimate 2\n",
"groves 5\n",
"liberally 3\n",
"illused 12\n",
"bygone 9\n",
"timehow 3\n",
"intrinsic 2\n",
"halfmask 1\n",
"b 71\n",
"barrare 1\n",
"watera 2\n",
"barrares 1\n",
"memorandum 21\n",
"charleslet 1\n",
"crotchety 5\n",
"withhold 4\n",
"pretends 7\n",
"tobias 2\n",
"saddler 3\n",
"strengthened 18\n",
"pricked 8\n",
"mentioning 40\n",
"injunctions 9\n",
"lxxxvii 1\n",
"tranquilnature 1\n",
"balmy 3\n",
"ploughed 2\n",
"redoubted 1\n",
"knell 3\n",
"doorbreak 1\n",
"ash 5\n",
"unoccupied 9\n",
"slamming 4\n",
"ashen 2\n",
"abreast 8\n",
"wolves 10\n",
"himstick 1\n",
"saypush 1\n",
"copingstone 3\n",
"cuss 1\n",
"staggering 14\n",
"hoots 1\n",
"vampyreseize 1\n",
"swifter 1\n",
"thickest 5\n",
"cancurses 1\n",
"irruption 2\n",
"fiction 17\n",
"embroidery 8\n",
"beastthey 1\n",
"lifethey 1\n",
"amthis 1\n",
"yesyesall 1\n",
"illusage 5\n",
"sobut 4\n",
"extremes 6\n",
"frontparlour 2\n",
"gotten 1\n",
"knowone 1\n",
"reflectionsthe 1\n",
"insecurity 2\n",
"musing 30\n",
"inno 1\n",
"perilin 1\n",
"stairhead 2\n",
"maammisswe 1\n",
"redeem 3\n",
"preservers 1\n",
"prevents 7\n",
"gains 9\n",
"entail 2\n",
"suma 1\n",
"redeemed 6\n",
"moneyat 1\n",
"stilling 1\n",
"frenzied 1\n",
"police 45\n",
"remission 1\n",
"twentyfour 15\n",
"admirallets 1\n",
"concealing 17\n",
"sharing 20\n",
"disquiet 4\n",
"beholden 5\n",
"burnings 1\n",
"topic 37\n",
"peeps 3\n",
"apprehensive 11\n",
"escapebesides 1\n",
"daresaythey 1\n",
"topcoat 1\n",
"distinctlyehwho 1\n",
"pursuer 4\n",
"impediments 2\n",
"distinguished 96\n",
"rattrap 1\n",
"wilkinson 1\n",
"willwhen 1\n",
"hangedyes 1\n",
"wasbut 2\n",
"dissection 3\n",
"montgomery 3\n",
"rankle 3\n",
"mist 72\n",
"supine 1\n",
"lanterns 10\n",
"windward 1\n",
"gin 6\n",
"scudded 1\n",
"resultwhat 1\n",
"handling 4\n",
"chaste 4\n",
"goddess 4\n",
"boathook 1\n",
"sobbing 62\n",
"costs 18\n",
"wifea 1\n",
"repinethough 1\n",
"bedone 1\n",
"theewhat 2\n",
"thoughtbut 2\n",
"reclining 10\n",
"prosecuted 2\n",
"overdone 3\n",
"onwas 1\n",
"disgracing 3\n",
"saidyou 1\n",
"tyranny 5\n",
"youat 1\n",
"spends 2\n",
"watchmen 1\n",
"withstood 2\n",
"piraticallooking 1\n",
"demur 2\n",
"meritorious 6\n",
"curiosities 5\n",
"marinei 1\n",
"crab 4\n",
"porpoise 2\n",
"nowhiskered 1\n",
"headed 14\n",
"eccentricities 5\n",
"speculating 7\n",
"picturethat 1\n",
"sayits 1\n",
"butbutwhat 1\n",
"wry 13\n",
"composedly 12\n",
"nefarious 1\n",
"littleit 1\n",
"burthen 2\n",
"withdrew 64\n",
"solicitude 23\n",
"shouldering 3\n",
"harming 3\n",
"wiping 39\n",
"faintvery 1\n",
"damaged 10\n",
"wellwhere 1\n",
"southward 2\n",
"mansions 8\n",
"villas 2\n",
"thriving 10\n",
"respectability 13\n",
"agonising 2\n",
"assumes 6\n",
"saltsburgh 1\n",
"rings 19\n",
"stolmuyers 2\n",
"cliff 9\n",
"wil 1\n",
"impressing 11\n",
"selected 25\n",
"wines 11\n",
"creaturecomforts 1\n",
"xciv 1\n",
"ravesfive 1\n",
"overhear 7\n",
"eddying 5\n",
"worldly 47\n",
"index 5\n",
"dug 19\n",
"drenching 1\n",
"clotted 2\n",
"sheath 3\n",
"carcase 2\n",
"exploring 3\n",
"alteration 26\n",
"proverbthat 1\n",
"depreciating 1\n",
"inequalities 1\n",
"wifeyou 1\n",
"annoying 2\n",
"whim 11\n",
"himi 7\n",
"lunch 16\n",
"missiles 6\n",
"reprehensible 4\n",
"bloomsburysquare 1\n",
"primrose 1\n",
"ceremonynone 1\n",
"attributing 4\n",
"entrances 5\n",
"elysium 1\n",
"therethat 2\n",
"palaceample 1\n",
"planted 11\n",
"davis 12\n",
"chalk 21\n",
"ordained 3\n",
"justices 2\n",
"hatters 2\n",
"fob 3\n",
"repeater 1\n",
"ddest 1\n",
"perquisite 1\n",
"surveyor 2\n",
"renting 2\n",
"himvery 1\n",
"crammed 8\n",
"deposed 2\n",
"adjournment 5\n",
"list 48\n",
"gossipmonger 1\n",
"treasured 10\n",
"gutenbergs 1\n",
"txt 5\n",
"zip 5\n",
"formats 10\n",
"royalties 10\n",
"trademark 50\n",
"specific 10\n",
"derivative 15\n",
"redistribution 10\n",
"access 55\n",
"paragraph 60\n",
"accessed 5\n",
"copied 21\n",
"paragraphs 17\n",
"unlink 5\n",
"display 42\n",
"redistribute 5\n",
"notifies 5\n",
"defect 21\n",
"defects 14\n",
"infringement 6\n",
"computer 10\n",
"damages 24\n",
"disclaimers 5\n",
"exclusion 8\n",
"void 14\n",
"modification 6\n",
"deletions 5\n",
"corporation 5\n",
"contributions 17\n",
"s 24\n",
"depends 29\n",
"outdated 5\n",
"regulating 6\n",
"statements 8\n",
"addresses 17\n",
"professor 12\n",
"originator 9\n",
"louise 2\n",
"maude 1\n",
"lucca 2\n",
"buonapartes 2\n",
"antichristi 2\n",
"scherer 6\n",
"vasili 214\n",
"grippe 2\n",
"invitations 7\n",
"altering 9\n",
"morally 14\n",
"ambassadors 8\n",
"canceled 1\n",
"vocation 10\n",
"recognizes 6\n",
"malta 2\n",
"haugwitz 1\n",
"montmorencys 1\n",
"criticize 3\n",
"courtierlike 2\n",
"rebuke 3\n",
"everyone 242\n",
"amiably 14\n",
"conversationi 1\n",
"ecstatic 14\n",
"lack 22\n",
"majestys 8\n",
"hippolyte 47\n",
"matchmaking 4\n",
"befitting 2\n",
"costing 5\n",
"bolkonski 158\n",
"aidedecamp 41\n",
"seduisante 1\n",
"halfopen 4\n",
"mouthseemed 1\n",
"shortwaisted 2\n",
"dainty 16\n",
"girdled 2\n",
"lightcolored 2\n",
"bezukhov 53\n",
"moscow 708\n",
"shy 34\n",
"feasible 8\n",
"impoliteness 1\n",
"abbes 1\n",
"rearrangement 1\n",
"conversational 11\n",
"center 46\n",
"ceaselessly 2\n",
"discussing 18\n",
"louis 18\n",
"cela 2\n",
"unchanging 9\n",
"roomthe 1\n",
"moss 7\n",
"glossy 5\n",
"shapely 3\n",
"glamour 2\n",
"smilingly 28\n",
"necklace 6\n",
"smoothed 22\n",
"shrugging 18\n",
"hearers 3\n",
"color 16\n",
"cuisse 1\n",
"stockings 23\n",
"bonaparte 80\n",
"subsequently 15\n",
"pet 119\n",
"beto 1\n",
"sugary 2\n",
"educate 5\n",
"affectation 14\n",
"golitsyn 6\n",
"sonand 2\n",
"benefactor 31\n",
"kindhearted 10\n",
"economized 2\n",
"revoir 11\n",
"milan 1\n",
"adorable 5\n",
"qui 8\n",
"dio 1\n",
"dato 1\n",
"guai 1\n",
"tocchi 1\n",
"sovereigns 20\n",
"betrayal 4\n",
"gueules 2\n",
"engrele 1\n",
"france 59\n",
"shrugged 33\n",
"assassination 1\n",
"pressand 1\n",
"contrat 3\n",
"outburst 11\n",
"hospital 46\n",
"une 6\n",
"footmen 38\n",
"nodded 120\n",
"sisterinlaw 18\n",
"snatching 6\n",
"airs 7\n",
"guardsman 5\n",
"tutor 18\n",
"aidede 4\n",
"camp 63\n",
"querulous 11\n",
"squirrellike 2\n",
"pregnancy 3\n",
"blushed 63\n",
"askance 4\n",
"drooping 45\n",
"nothingor 2\n",
"unmarried 11\n",
"glancefriendly 1\n",
"comme 3\n",
"bout 3\n",
"barracks 4\n",
"overshoes 1\n",
"refilling 1\n",
"tens 8\n",
"scapegraces 1\n",
"tugged 8\n",
"hercules 2\n",
"bragging 2\n",
"sloping 6\n",
"translating 2\n",
"hussar 80\n",
"spine 11\n",
"bory 2\n",
"ranki 1\n",
"jauntily 10\n",
"pantry 25\n",
"vasilevich 2\n",
"razumovskis 6\n",
"conversations 27\n",
"beau 8\n",
"dolokhova 1\n",
"moyka 1\n",
"vladimirovich 10\n",
"godfather 8\n",
"frock 12\n",
"blackeyed 19\n",
"lifewith 1\n",
"bodice 6\n",
"curls 49\n",
"sternness 7\n",
"werent 14\n",
"brunette 1\n",
"lashes 9\n",
"passionate 50\n",
"hussars 109\n",
"flaring 9\n",
"colonel 106\n",
"pavlograd 20\n",
"clerki 1\n",
"ensign 3\n",
"julie 60\n",
"arkharovs 4\n",
"cousinhood 2\n",
"lessons 40\n",
"goodlooking 12\n",
"aliketurned 1\n",
"watchingas 1\n",
"capto 1\n",
"experiencing 7\n",
"behavior 6\n",
"natalya 2\n",
"genlis 6\n",
"ministers 16\n",
"overflowing 16\n",
"twentyfiveruble 1\n",
"distinguishes 3\n",
"sergeevich 3\n",
"dyed 3\n",
"venetian 2\n",
"rs 4\n",
"latin 14\n",
"accent 12\n",
"frill 5\n",
"crisis 19\n",
"escapade 3\n",
"favorably 3\n",
"himwould 1\n",
"readingthe 1\n",
"embroidering 1\n",
"leper 1\n",
"evoked 10\n",
"hm 9\n",
"readyit 1\n",
"gesticulating 12\n",
"pitts 2\n",
"villeneuves 1\n",
"misunderstandings 6\n",
"onerous 4\n",
"disadvantages 5\n",
"mammawhat 1\n",
"madere 1\n",
"tasted 8\n",
"dirty 77\n",
"akhrosimova 3\n",
"recruiting 5\n",
"neighbors 12\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"amber 1\n",
"bachelor 24\n",
"faire 7\n",
"letat 1\n",
"nikolaevich 4\n",
"wartime 3\n",
"naivete 4\n",
"horsethat 1\n",
"patting 25\n",
"favor 29\n",
"tutors 8\n",
"governesses 9\n",
"strains 9\n",
"crystal 4\n",
"savory 4\n",
"quench 4\n",
"goot 3\n",
"zat 6\n",
"vell 2\n",
"foundationshas 1\n",
"je 17\n",
"ve 4\n",
"plood 1\n",
"pe 2\n",
"consciousas 1\n",
"dmitrievnas 15\n",
"frown 50\n",
"gaiety 25\n",
"ices 3\n",
"redder 3\n",
"downward 18\n",
"comforting 22\n",
"worsted 6\n",
"tucking 5\n",
"quartette 3\n",
"nighttime 2\n",
"lea 1\n",
"swells 4\n",
"pose 20\n",
"ballet 3\n",
"jovial 6\n",
"pas 28\n",
"assiduous 3\n",
"priest 27\n",
"sickroom 3\n",
"dat 2\n",
"vont 1\n",
"lorrains 1\n",
"pastilles 1\n",
"whatnots 1\n",
"screen 26\n",
"referred 83\n",
"abnormally 1\n",
"testily 5\n",
"peacefully 14\n",
"wills 19\n",
"petition 31\n",
"dutiful 14\n",
"kinswoman 1\n",
"intriguing 2\n",
"casually 18\n",
"sophiei 1\n",
"lastwhy 1\n",
"lagging 5\n",
"censer 1\n",
"servility 4\n",
"guiding 4\n",
"persian 8\n",
"carpets 21\n",
"silkcurtained 1\n",
"declaring 12\n",
"vigor 5\n",
"themdid 1\n",
"sensual 4\n",
"boned 2\n",
"symmetrical 2\n",
"realize 30\n",
"refractory 2\n",
"terrorstricken 2\n",
"squeeze 36\n",
"merrymaking 3\n",
"flabby 8\n",
"shyly 16\n",
"pitiful 18\n",
"capitals 5\n",
"viceidleness 1\n",
"cardinal 2\n",
"geometry 5\n",
"problems 7\n",
"manservant 2\n",
"chisel 2\n",
"bristly 3\n",
"patches 14\n",
"triangles 1\n",
"teachers 5\n",
"scolded 2\n",
"uncut 1\n",
"tidy 6\n",
"eyesthe 1\n",
"corsican 1\n",
"inactive 6\n",
"someday 5\n",
"poignant 4\n",
"burdened 6\n",
"mystical 5\n",
"transformed 11\n",
"richesto 1\n",
"rejects 2\n",
"institution 23\n",
"disquieting 3\n",
"conscripts 1\n",
"dispatched 16\n",
"marys 53\n",
"selfsatisfied 8\n",
"morose 15\n",
"mapped 2\n",
"dussek 1\n",
"tikhon 79\n",
"palace 46\n",
"sisterinlaws 8\n",
"silverbefore 1\n",
"sidelong 10\n",
"nineteen 10\n",
"mikhelsons 1\n",
"shaven 8\n",
"daughterinlaw 8\n",
"scanning 7\n",
"briskness 4\n",
"peg 20\n",
"frenchy 3\n",
"alluding 15\n",
"epaulettes 5\n",
"packing 19\n",
"canteen 6\n",
"sabera 1\n",
"tapes 2\n",
"reviews 5\n",
"unclasped 2\n",
"comprendre 1\n",
"sterne 1\n",
"homeless 5\n",
"banteringly 2\n",
"pained 8\n",
"oval 2\n",
"darkfaced 2\n",
"complained 19\n",
"secluded 9\n",
"premium 7\n",
"reprovingly 1\n",
"braunau 14\n",
"headquarters 42\n",
"surroundingsfruit 1\n",
"tiled 2\n",
"distanceand 1\n",
"commanders 65\n",
"cleaning 12\n",
"regimentinstead 1\n",
"wellordered 4\n",
"uniforms 36\n",
"strut 1\n",
"mitrich 3\n",
"packs 7\n",
"archduke 13\n",
"mack 19\n",
"petticoats 2\n",
"signaler 1\n",
"stirrup 12\n",
"righted 2\n",
"awry 8\n",
"croats 1\n",
"provoked 7\n",
"sockets 1\n",
"scarred 3\n",
"puffy 10\n",
"glassstarted 1\n",
"drawnin 1\n",
"fairhaired 8\n",
"apologize 12\n",
"poland 19\n",
"subaltern 2\n",
"sharpereyed 1\n",
"smeared 9\n",
"polish 49\n",
"drummera 1\n",
"hoofs 24\n",
"flavor 2\n",
"wriggled 1\n",
"hawk 2\n",
"generalof 1\n",
"manyand 1\n",
"highness 51\n",
"rumors 17\n",
"ferdinands 1\n",
"ulm 11\n",
"frustrate 1\n",
"ally 15\n",
"scouts 6\n",
"bids 3\n",
"vividly 33\n",
"embarrassing 8\n",
"seriousness 7\n",
"gott 1\n",
"untimely 12\n",
"buffoon 10\n",
"wideopen 15\n",
"massacres 1\n",
"mot 7\n",
"fait 4\n",
"hobbledehoy 1\n",
"massacred 2\n",
"goodfornothing 6\n",
"denisov 363\n",
"vaska 15\n",
"foraging 6\n",
"goodhearted 4\n",
"cowhouse 1\n",
"gut 2\n",
"greet 10\n",
"ganze 2\n",
"stays 6\n",
"gloomily 23\n",
"lavwuska 1\n",
"weally 2\n",
"doubles 5\n",
"deah 1\n",
"undah 1\n",
"telyanin 23\n",
"bykovs 1\n",
"cavalryman 4\n",
"foreleg 2\n",
"disgust 17\n",
"pua 1\n",
"abashed 23\n",
"bedding 4\n",
"flog 2\n",
"evewybody 1\n",
"frequented 7\n",
"spark 10\n",
"grizzlyhaired 1\n",
"mustaches 11\n",
"nasty 13\n",
"apologizing 2\n",
"vindictive 9\n",
"enns 10\n",
"artillery 54\n",
"defiling 1\n",
"heights 31\n",
"confluence 2\n",
"trail 1\n",
"pie 13\n",
"countrysidesee 1\n",
"shakos 8\n",
"companys 4\n",
"begins 25\n",
"popping 2\n",
"dismally 4\n",
"shoved 3\n",
"sends 11\n",
"houseful 1\n",
"fires 39\n",
"whites 4\n",
"squadwon 1\n",
"arab 5\n",
"tramping 4\n",
"hack 6\n",
"saddlecloth 2\n",
"pawing 3\n",
"trampled 12\n",
"jested 3\n",
"prancing 3\n",
"infantryman 3\n",
"bugle 1\n",
"skirmishers 4\n",
"armies 44\n",
"boundary 7\n",
"horsemen 11\n",
"mironov 1\n",
"rooka 1\n",
"leghad 1\n",
"hairy 13\n",
"sturdy 11\n",
"stumpy 2\n",
"bedouin 6\n",
"highshouldered 6\n",
"rewards 16\n",
"jackets 4\n",
"wholl 11\n",
"heartwatching 1\n",
"faraway 4\n",
"therethey 2\n",
"gorge 3\n",
"sublieutenancy 1\n",
"losses 30\n",
"unattainable 10\n",
"thirtieth 2\n",
"fortnights 2\n",
"illclad 1\n",
"grazed 3\n",
"envoy 10\n",
"hochgeboren 1\n",
"copenhagen 2\n",
"ambassador 13\n",
"skillfully 6\n",
"witticisms 2\n",
"hawked 3\n",
"malgre 1\n",
"estime 1\n",
"professe 1\n",
"slips 4\n",
"prater 2\n",
"unwrinkled 3\n",
"dined 57\n",
"lichtenfels 1\n",
"bridgehead 3\n",
"mined 5\n",
"bohemia 2\n",
"darent 2\n",
"durrenstein 3\n",
"mots 2\n",
"joins 2\n",
"formio 1\n",
"clenching 6\n",
"fooled 2\n",
"loots 1\n",
"notres 1\n",
"gentlemeni 1\n",
"lolling 3\n",
"derogates 1\n",
"nonintervention 1\n",
"november 21\n",
"impassive 13\n",
"undertake 32\n",
"amelie 2\n",
"ceremoniously 5\n",
"forage 6\n",
"chamberlain 3\n",
"marshalls 1\n",
"toulon 5\n",
"bags 14\n",
"mautern 1\n",
"plumes 14\n",
"du 12\n",
"oublie 1\n",
"lennemi 1\n",
"spikes 14\n",
"cette 6\n",
"russe 5\n",
"extremites 1\n",
"lunivers 1\n",
"meme 1\n",
"dulm 1\n",
"detachments 14\n",
"fowls 8\n",
"denser 3\n",
"floundering 6\n",
"kneedeep 4\n",
"directing 26\n",
"onehorse 2\n",
"shawls 8\n",
"letthempass 1\n",
"noticeable 20\n",
"winced 3\n",
"weyrother 29\n",
"inattentive 4\n",
"chiefs 9\n",
"preoccupied 32\n",
"kremsznaim 2\n",
"trains 14\n",
"tricked 2\n",
"nearness 10\n",
"dandies 3\n",
"reproachful 23\n",
"keeper 10\n",
"lagrement 1\n",
"sample 6\n",
"keg 1\n",
"lids 6\n",
"reverential 4\n",
"encampment 4\n",
"affairsnear 1\n",
"showmen 1\n",
"jabbering 2\n",
"sidorov 4\n",
"laughers 1\n",
"jabber 1\n",
"kari 1\n",
"muter 1\n",
"ouh 2\n",
"grabern 13\n",
"copse 9\n",
"possibilities 9\n",
"reserves 10\n",
"counterattack 1\n",
"thudded 1\n",
"impact 1\n",
"strengthen 20\n",
"cannonade 12\n",
"lambskin 2\n",
"camlet 1\n",
"stupider 1\n",
"freckled 2\n",
"medvedev 1\n",
"soulstirring 1\n",
"colloquies 1\n",
"reeked 1\n",
"flapping 7\n",
"dispersing 5\n",
"expressionthe 1\n",
"hussarswas 1\n",
"reviewed 7\n",
"dan 15\n",
"irate 3\n",
"avare 1\n",
"muffled 15\n",
"rumor 8\n",
"slash 2\n",
"gripping 5\n",
"forelegs 2\n",
"wrist 21\n",
"sunburned 5\n",
"hindmost 2\n",
"alien 12\n",
"furrows 2\n",
"crupper 3\n",
"slung 6\n",
"undefended 1\n",
"centerthe 1\n",
"refill 2\n",
"smoker 3\n",
"matthew 3\n",
"booth 1\n",
"nag 2\n",
"antonov 1\n",
"kindled 13\n",
"campfire 18\n",
"bloodstained 11\n",
"firewell 1\n",
"texture 2\n",
"flocking 1\n",
"grunt 4\n",
"sleigh 38\n",
"unhesitating 3\n",
"steward 36\n",
"potsdam 4\n",
"coughed 31\n",
"shortsighted 5\n",
"illusion 2\n",
"visualized 1\n",
"palpably 2\n",
"peopleas 1\n",
"wednesdays 3\n",
"loyalty 6\n",
"kuzmicha 1\n",
"soared 7\n",
"helen 1\n",
"kuzmichfrom 1\n",
"daughtermy 1\n",
"malodorous 1\n",
"suitors 6\n",
"snowed 3\n",
"snowbanks 1\n",
"criticized 2\n",
"insistence 2\n",
"adornment 4\n",
"daughterat 1\n",
"forbidden 15\n",
"selfpossession 10\n",
"lacking 9\n",
"inspires 3\n",
"existjust 1\n",
"clashed 4\n",
"unbecoming 5\n",
"shameless 2\n",
"principleseverything 1\n",
"shaped 16\n",
"fr 5\n",
"tikhons 3\n",
"painstaking 6\n",
"pupilsi 1\n",
"nikolenka 10\n",
"wriggling 2\n",
"thered 1\n",
"nineyearold 1\n",
"plumped 2\n",
"celebrating 2\n",
"playmate 3\n",
"appearancethat 1\n",
"promptitude 6\n",
"chess 7\n",
"petisenfans 1\n",
"insincere 6\n",
"swagger 8\n",
"tsarevich 11\n",
"revels 3\n",
"arnauts 2\n",
"lords 23\n",
"unconquerable 1\n",
"exasperation 8\n",
"linstocks 2\n",
"wetted 2\n",
"streamers 2\n",
"staffs 5\n",
"fuller 3\n",
"tensely 1\n",
"tsars 13\n",
"irregularly 2\n",
"ceremonial 3\n",
"cringe 1\n",
"purplefaced 2\n",
"dolgorukov 43\n",
"councilthe 1\n",
"advocates 9\n",
"chef 1\n",
"emperormore 1\n",
"austerlitz 50\n",
"inactivity 4\n",
"longedfor 2\n",
"cavalcade 3\n",
"fourteenyearold 2\n",
"quelle 1\n",
"dealbut 1\n",
"physician 41\n",
"eighteenth 5\n",
"revolve 5\n",
"cunctators 1\n",
"miloradovich 19\n",
"arakcheev 38\n",
"cutlets 2\n",
"unrestrainable 2\n",
"reconnoiter 1\n",
"sokolnitz 4\n",
"dohkturov 2\n",
"objections 16\n",
"weyrotherwho 1\n",
"childrenthat 1\n",
"midnightcannot 1\n",
"justness 1\n",
"divisionstipulates 1\n",
"nominally 4\n",
"appearednow 1\n",
"itthats 1\n",
"lempereur 21\n",
"hillside 4\n",
"plaintive 14\n",
"hirelings 2\n",
"breakfasting 3\n",
"rigging 8\n",
"kurskies 1\n",
"irrepressibly 1\n",
"mismanagement 1\n",
"muddle 3\n",
"eaters 4\n",
"tafalafa 1\n",
"varying 16\n",
"facewhich 1\n",
"succeeds 3\n",
"przebyszewskis 1\n",
"relay 4\n",
"whiff 7\n",
"resounding 11\n",
"novgorod 1\n",
"possibilite 1\n",
"apsherons 4\n",
"carabineers 1\n",
"everincreasing 5\n",
"remembers 9\n",
"trailing 5\n",
"bludgeon 1\n",
"expressionless 8\n",
"themthe 2\n",
"droned 1\n",
"jostled 10\n",
"oberhofmarschal 1\n",
"deferentially 12\n",
"speeches 12\n",
"grandson 4\n",
"officerwounded 1\n",
"flop 1\n",
"profusely 3\n",
"infinity 5\n",
"jolting 6\n",
"sewn 3\n",
"convalescence 6\n",
"bilious 10\n",
"snowcovered 4\n",
"insufferable 1\n",
"prokofy 2\n",
"selvedges 1\n",
"outcries 1\n",
"kisses 21\n",
"skirt 22\n",
"satchels 1\n",
"basins 1\n",
"hallo 12\n",
"ruler 23\n",
"thatif 3\n",
"curving 3\n",
"yousonya 1\n",
"training 11\n",
"drifted 15\n",
"races 4\n",
"housethat 1\n",
"feoktist 2\n",
"cucumbers 1\n",
"committee 19\n",
"factotum 1\n",
"knowsand 2\n",
"december 15\n",
"conversationcount 1\n",
"valuev 5\n",
"incapacity 7\n",
"inexperience 4\n",
"heroes 21\n",
"modeling 2\n",
"dolokhovwho 1\n",
"comical 7\n",
"parquet 4\n",
"fixing 16\n",
"titus 2\n",
"proudly 19\n",
"blinking 9\n",
"spice 6\n",
"bully 5\n",
"leaflets 2\n",
"distractedly 6\n",
"regrets 11\n",
"measuring 13\n",
"honeymoon 4\n",
"unapproachability 1\n",
"allaitil 1\n",
"galere 1\n",
"etes 2\n",
"sot 1\n",
"regimenthe 1\n",
"worldoverflowed 1\n",
"unbelief 1\n",
"ithas 1\n",
"snows 4\n",
"kishenev 1\n",
"draft 6\n",
"saida 2\n",
"shriekit 1\n",
"bogdanovnas 1\n",
"font 5\n",
"godmother 8\n",
"babys 9\n",
"friendsyou 1\n",
"harmful 19\n",
"womencountesses 1\n",
"cookswho 1\n",
"regenerate 1\n",
"purify 4\n",
"impressionable 1\n",
"prattle 2\n",
"anathematized 1\n",
"jocularly 2\n",
"chale 3\n",
"nicholasdont 1\n",
"angrybut 1\n",
"tickets 2\n",
"wallflower 1\n",
"delightedly 3\n",
"faiwy 1\n",
"duets 3\n",
"piquet 2\n",
"unappreciated 2\n",
"fortythree 6\n",
"unbent 3\n",
"enfolded 1\n",
"agate 2\n",
"barcarolle 2\n",
"lifelessly 2\n",
"incorrect 5\n",
"virginal 1\n",
"anticipation 12\n",
"honorits 1\n",
"accidently 1\n",
"wong 1\n",
"thrashed 1\n",
"governs 2\n",
"deathdeath 1\n",
"halfcut 1\n",
"emilie 1\n",
"repellent 6\n",
"overhanging 16\n",
"wraps 2\n",
"inevitability 51\n",
"devotional 4\n",
"submitting 13\n",
"freemasons 12\n",
"invariable 9\n",
"forefather 2\n",
"vileness 4\n",
"dreamest 1\n",
"somber 2\n",
"sciences 14\n",
"chemistry 2\n",
"sirtook 1\n",
"huskily 3\n",
"practiced 3\n",
"freemasonry 20\n",
"sponsorship 1\n",
"renounced 11\n",
"undertone 3\n",
"instructor 5\n",
"coincides 4\n",
"selfpurification 2\n",
"regenerating 2\n",
"oppressors 2\n",
"cured 3\n",
"initiation 3\n",
"symbol 1\n",
"selfderision 1\n",
"laziness 1\n",
"booted 1\n",
"postulant 1\n",
"transit 1\n",
"presidents 2\n",
"indulgently 1\n",
"helpmeet 1\n",
"plumb 1\n",
"disagreement 3\n",
"lifeto 2\n",
"reborn 1\n",
"extolled 4\n",
"raptures 5\n",
"posed 2\n",
"soirees 5\n",
"arrangingat 1\n",
"homme 4\n",
"merite 1\n",
"selfpossessed 7\n",
"shitova 1\n",
"meritthis 1\n",
"ne 9\n",
"jamais 1\n",
"prusse 6\n",
"interrogatively 4\n",
"appreciative 3\n",
"variously 6\n",
"angelas 1\n",
"bogucharovo 48\n",
"roles 1\n",
"nursemaids 1\n",
"cot 12\n",
"correspondence 22\n",
"elongated 1\n",
"khandrikovis 1\n",
"korchevo 2\n",
"pays 8\n",
"frontiers 5\n",
"prozorovski 2\n",
"mails 10\n",
"frontierwhich 1\n",
"ostrolenka 1\n",
"uswe 1\n",
"scuttle 1\n",
"crosses 14\n",
"bennigsens 11\n",
"fodder 7\n",
"overflow 3\n",
"divisions 16\n",
"panicit 1\n",
"semiliterate 1\n",
"budget 2\n",
"rebuilding 3\n",
"inclinationpractical 1\n",
"loans 5\n",
"measuresthe 1\n",
"lunches 2\n",
"represent 35\n",
"liberation 4\n",
"erection 5\n",
"offerings 4\n",
"touchingly 2\n",
"exactions 4\n",
"obligatory 2\n",
"stillunplastered 1\n",
"redolent 3\n",
"inertia 2\n",
"myselfthey 1\n",
"bleed 4\n",
"cripple 3\n",
"paymasters 1\n",
"ferry 11\n",
"flooding 2\n",
"denial 8\n",
"harmonious 11\n",
"termis 1\n",
"ceases 6\n",
"wallet 5\n",
"pilgrims 13\n",
"chickens 9\n",
"ivanushka 4\n",
"contraire 1\n",
"mêtre 1\n",
"avec 3\n",
"jeune 2\n",
"newcomers 7\n",
"womanish 2\n",
"wonderworking 2\n",
"jesus 9\n",
"forgo 3\n",
"doubtfully 14\n",
"smelled 6\n",
"reopened 4\n",
"spiritually 1\n",
"fellowyour 1\n",
"friendi 3\n",
"unalterably 1\n",
"immovably 1\n",
"thaw 5\n",
"potatoes 36\n",
"root 19\n",
"destitution 1\n",
"legends 4\n",
"alesha 1\n",
"mikolka 1\n",
"luckless 1\n",
"upbraid 2\n",
"turf 8\n",
"bedsteads 4\n",
"topcheenko 1\n",
"woot 1\n",
"jobs 7\n",
"lavrushkathat 1\n",
"oxen 2\n",
"twansports 1\n",
"unescorted 1\n",
"wobbewywobbewy 1\n",
"weceipt 2\n",
"courtmartial 4\n",
"degradation 10\n",
"molliten 1\n",
"underclothing 1\n",
"rejoinders 1\n",
"countwy 1\n",
"auditor 4\n",
"thirteenth 5\n",
"coldshouldering 1\n",
"constraint 37\n",
"enliven 4\n",
"mufti 1\n",
"bunting 1\n",
"silkembroidered 1\n",
"braces 5\n",
"gazers 4\n",
"bearskin 2\n",
"goldembroidered 2\n",
"gendarmes 3\n",
"koslovski 1\n",
"pagethe 1\n",
"persistently 6\n",
"pension 3\n",
"russie 1\n",
"arbiters 1\n",
"cooperate 2\n",
"keenly 15\n",
"cloister 2\n",
"evergreen 3\n",
"alders 1\n",
"gnarled 5\n",
"othersthe 3\n",
"restful 1\n",
"desiring 13\n",
"foolishbut 1\n",
"quitrents 1\n",
"hunts 2\n",
"unintentional 3\n",
"shady 18\n",
"trilled 3\n",
"scars 1\n",
"thirtyone 1\n",
"smock 3\n",
"zenith 3\n",
"salut 1\n",
"punctuation 2\n",
"committees 3\n",
"promoter 1\n",
"kochubeys 2\n",
"pryanichnikov 2\n",
"decree 4\n",
"condemnations 1\n",
"montesquieu 2\n",
"principe 1\n",
"monarchies 2\n",
"incontestable 3\n",
"paraissent 1\n",
"soutenir 1\n",
"envisagez 1\n",
"vue 1\n",
"worthily 2\n",
"francaise 1\n",
"ideal 15\n",
"flattery 6\n",
"exposition 10\n",
"dreamers 1\n",
"revision 5\n",
"rosenkampf 1\n",
"jurisprudence 9\n",
"justinian 1\n",
"petersburghe 1\n",
"threefold 5\n",
"purport 9\n",
"hampered 3\n",
"overthrow 10\n",
"unobtrusively 2\n",
"governments 4\n",
"impedes 1\n",
"illuminati 1\n",
"republican 5\n",
"penitent 14\n",
"motherinlaw 9\n",
"diary 12\n",
"jerusalem 6\n",
"impurity 3\n",
"rd 5\n",
"secretaries 2\n",
"seigneur 2\n",
"constricted 1\n",
"alonethe 1\n",
"repulsion 7\n",
"sloth 2\n",
"wordsthat 3\n",
"worker 3\n",
"elohim 1\n",
"trinitythe 1\n",
"volatile 6\n",
"thigh 4\n",
"suppers 6\n",
"impoverished 2\n",
"skeptics 1\n",
"fori 3\n",
"moneyi 1\n",
"embodying 2\n",
"madcap 1\n",
"amusements 5\n",
"nn 2\n",
"sphinxes 2\n",
"tastehe 1\n",
"knowgray 1\n",
"menwho 1\n",
"cherubini 1\n",
"redliveried 1\n",
"openwork 1\n",
"behindhand 5\n",
"mavra 26\n",
"shortened 2\n",
"pier 5\n",
"flurryfor 1\n",
"closeness 8\n",
"curtsied 6\n",
"millionairess 1\n",
"elisaveta 1\n",
"outshe 1\n",
"rhythmical 1\n",
"debut 1\n",
"undeveloped 1\n",
"rejuvenated 1\n",
"shyness 8\n",
"illdisposed 3\n",
"constitutional 14\n",
"gervais 4\n",
"stolypin 4\n",
"hahaha 4\n",
"guffaw 1\n",
"recreation 6\n",
"quizzing 1\n",
"mirthless 2\n",
"spanish 12\n",
"corked 4\n",
"translated 6\n",
"promotions 2\n",
"generalizing 1\n",
"disconnected 6\n",
"superintended 3\n",
"cake 9\n",
"mummy 8\n",
"pitying 7\n",
"intensified 10\n",
"trustfulness 12\n",
"fancywork 1\n",
"petyas 18\n",
"joysher 1\n",
"petting 2\n",
"turkey 16\n",
"cani 1\n",
"wronged 27\n",
"probation 6\n",
"theodosias 1\n",
"koko 1\n",
"classthe 1\n",
"whirlpool 1\n",
"matterof 1\n",
"staffand 1\n",
"extremelywhether 1\n",
"inverse 4\n",
"threeruble 1\n",
"exhaled 2\n",
"tempered 3\n",
"courtship 7\n",
"mitenkas 2\n",
"delegate 3\n",
"scruff 3\n",
"culprits 3\n",
"protective 2\n",
"blackguard 3\n",
"mismanaged 1\n",
"pursuitthe 1\n",
"chasefor 1\n",
"wintry 16\n",
"huntsmen 11\n",
"frosty 18\n",
"poppy 1\n",
"bitch 11\n",
"shewolf 2\n",
"donets 4\n",
"viflyanka 2\n",
"trunila 2\n",
"oasis 3\n",
"ravine 9\n",
"karay 15\n",
"ardor 4\n",
"horn 6\n",
"simon 12\n",
"wolfhounds 1\n",
"mitka 5\n",
"cupful 1\n",
"snack 3\n",
"helterskelter 1\n",
"hunched 1\n",
"alternated 2\n",
"aspen 3\n",
"waterworn 1\n",
"reachedand 1\n",
"pouting 4\n",
"matted 3\n",
"wailed 5\n",
"felted 1\n",
"laboring 1\n",
"hunters 5\n",
"loosed 3\n",
"sympathizers 1\n",
"beaver 6\n",
"finewell 2\n",
"atu 3\n",
"outdo 1\n",
"sighted 5\n",
"hareand 1\n",
"balk 3\n",
"muddying 1\n",
"prodigy 5\n",
"arinka 1\n",
"authoritatively 1\n",
"rickety 5\n",
"overcleanit 1\n",
"ragged 30\n",
"pickled 5\n",
"housekeeping 14\n",
"juiciness 1\n",
"aromatic 4\n",
"honeyandnut 1\n",
"matterthats 1\n",
"balalayka 6\n",
"fingerboard 1\n",
"effaced 1\n",
"silks 4\n",
"anisyas 1\n",
"traps 7\n",
"spontaneous 7\n",
"whist 5\n",
"disentangle 2\n",
"julies 10\n",
"reaumur 1\n",
"playingtheres 1\n",
"misha 2\n",
"butlers 10\n",
"distrusted 2\n",
"watercarrier 1\n",
"hardboiled 1\n",
"egyptians 1\n",
"mummers 11\n",
"hooped 1\n",
"nonrecognition 2\n",
"troyka 13\n",
"melyukova 2\n",
"disks 1\n",
"roughshod 2\n",
"keener 4\n",
"gallopthat 1\n",
"squeals 1\n",
"dimmlerand 1\n",
"disguising 4\n",
"themwere 2\n",
"officercomes 1\n",
"dimpled 8\n",
"firewood 4\n",
"gladness 5\n",
"openeyed 2\n",
"ingratitude 8\n",
"kremlin 28\n",
"nicest 1\n",
"gentlemeninwaiting 7\n",
"wriggle 2\n",
"republic 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"gentlemaninwaiting 5\n",
"apollon 1\n",
"fourteenth 6\n",
"seekers 1\n",
"scotch 8\n",
"knouted 2\n",
"allread 1\n",
"corpulence 1\n",
"superficially 1\n",
"sameonly 1\n",
"venerationespecially 1\n",
"yearold 3\n",
"childwho 2\n",
"humiliate 2\n",
"felicitations 1\n",
"forcer 1\n",
"culminated 1\n",
"oldenburgs 3\n",
"popes 5\n",
"shifts 2\n",
"baths 2\n",
"belabor 1\n",
"faultfinding 1\n",
"ofthat 1\n",
"risking 2\n",
"accustom 7\n",
"sexless 1\n",
"karagins 9\n",
"disillusioned 1\n",
"bouts 2\n",
"secourable 1\n",
"contre 2\n",
"aliment 1\n",
"sans 4\n",
"bonheur 1\n",
"retraite 1\n",
"pleurs 1\n",
"liza 1\n",
"nizhegorod 4\n",
"arduous 3\n",
"inconstancy 1\n",
"plumper 2\n",
"girlstomorrow 1\n",
"superrogues 1\n",
"vasilevna 1\n",
"chancery 3\n",
"goddaughter 3\n",
"suppertroguet 2\n",
"quota 1\n",
"herfor 2\n",
"insuperable 2\n",
"ailing 5\n",
"semenovnas 1\n",
"whoshe 2\n",
"natashaits 1\n",
"necksuddenly 1\n",
"kirilovich 6\n",
"mikhaylovnawhat 1\n",
"headdress 4\n",
"marvelously 1\n",
"bench 51\n",
"fingered 4\n",
"pretentiously 1\n",
"gangway 2\n",
"flatteringquite 1\n",
"stereotyped 1\n",
"shrilly 2\n",
"wellremembered 7\n",
"femmes 1\n",
"beforea 2\n",
"disquieted 4\n",
"churchthe 1\n",
"holiness 1\n",
"concerts 4\n",
"selfindulgence 1\n",
"diversion 10\n",
"tacked 1\n",
"anothersa 1\n",
"draping 2\n",
"yesterdaywe 1\n",
"recitations 1\n",
"dressmakers 5\n",
"herbut 5\n",
"safeguard 2\n",
"warsaw 4\n",
"transactions 21\n",
"offall 1\n",
"ladykins 1\n",
"lash 3\n",
"reveled 1\n",
"backbut 1\n",
"blueblack 1\n",
"ittake 1\n",
"nikitski 2\n",
"wicket 5\n",
"tearless 2\n",
"enviously 1\n",
"wifethe 2\n",
"marryhe 1\n",
"komonenos 1\n",
"onwhich 1\n",
"arsenic 1\n",
"antidotes 1\n",
"tearstained 2\n",
"elopement 5\n",
"himdid 1\n",
"welled 2\n",
"sordid 12\n",
"woes 1\n",
"earthto 1\n",
"countless 6\n",
"thefts 1\n",
"parliament 20\n",
"principes 1\n",
"ambitious 12\n",
"astute 1\n",
"unclouded 2\n",
"slaying 2\n",
"ordinated 1\n",
"cellular 1\n",
"navvy 1\n",
"frere 3\n",
"himyet 1\n",
"thorn 2\n",
"vilkavisski 2\n",
"riverbank 1\n",
"spyglass 1\n",
"muscovy 1\n",
"quos 1\n",
"vult 1\n",
"perdere 1\n",
"everybodytired 1\n",
"waitingprepare 1\n",
"sniffing 7\n",
"passports 2\n",
"rykonty 2\n",
"curling 16\n",
"skittish 1\n",
"shaftshe 1\n",
"balmacheve 2\n",
"davout 37\n",
"persistent 4\n",
"surlier 1\n",
"movedand 1\n",
"russiawere 1\n",
"resolutethey 1\n",
"hessian 5\n",
"eau 3\n",
"emphasized 4\n",
"initiative 2\n",
"retires 3\n",
"baden 2\n",
"exalt 7\n",
"moldavia 4\n",
"stein 8\n",
"armfeldt 11\n",
"napoleonhardly 1\n",
"samebut 1\n",
"swedesit 1\n",
"swedes 1\n",
"bessieres 1\n",
"devout 4\n",
"disagree 2\n",
"uninterested 3\n",
"wurttemberg 6\n",
"effectsthe 1\n",
"tolly 17\n",
"kamenskys 1\n",
"bluebeard 1\n",
"trashcan 1\n",
"instruments 22\n",
"unexpended 1\n",
"experts 1\n",
"volkonski 12\n",
"exminister 2\n",
"bookish 2\n",
"oblique 2\n",
"outflankings 1\n",
"petitioned 3\n",
"retention 1\n",
"thingas 1\n",
"falsity 3\n",
"suggestedon 1\n",
"armynews 1\n",
"chernyshev 8\n",
"marquis 2\n",
"typical 2\n",
"collisions 2\n",
"fallibility 1\n",
"theorys 1\n",
"answeras 1\n",
"voluminous 2\n",
"attackvery 1\n",
"adhered 1\n",
"absurdly 1\n",
"shatter 1\n",
"barbarian 3\n",
"refutations 1\n",
"sychophants 1\n",
"allured 1\n",
"ukraine 2\n",
"interplay 2\n",
"raevski 13\n",
"hendrikhovnas 5\n",
"loan 6\n",
"overclean 2\n",
"booby 4\n",
"reheat 1\n",
"traptatatap 1\n",
"exhilarating 1\n",
"swooped 3\n",
"attackknew 1\n",
"medicinenot 1\n",
"organs 6\n",
"substances 2\n",
"doses 5\n",
"homeopaths 1\n",
"rubs 7\n",
"chemists 5\n",
"blasphemy 3\n",
"sentinel 9\n",
"herthat 7\n",
"shrines 3\n",
"matins 2\n",
"interwoven 1\n",
"freshened 2\n",
"liveried 1\n",
"walkwith 1\n",
"lovelet 1\n",
"governing 8\n",
"slav 1\n",
"altars 4\n",
"moses 2\n",
"amalek 2\n",
"spear 2\n",
"ensnared 4\n",
"horizonfrom 1\n",
"apocalypse 4\n",
"h 2\n",
"p 16\n",
"r 1\n",
"w 28\n",
"z 2\n",
"fortytwo 2\n",
"substituting 3\n",
"elided 1\n",
"lrusse 4\n",
"thinkshe 1\n",
"obolenski 3\n",
"borders 8\n",
"levies 1\n",
"glorify 1\n",
"challengingly 1\n",
"youthfulnessand 1\n",
"tradesmans 3\n",
"chanterwas 1\n",
"warding 5\n",
"tsarcannon 1\n",
"assumptionit 1\n",
"notables 3\n",
"jerkin 1\n",
"groundshe 1\n",
"grabbed 4\n",
"rostovthough 1\n",
"sloboda 4\n",
"merchant 15\n",
"classles 1\n",
"quietest 6\n",
"slurring 1\n",
"heah 1\n",
"pwiests 1\n",
"theah 1\n",
"wecwuits 1\n",
"lapsing 3\n",
"canon 2\n",
"majestyto 1\n",
"cardplayer 1\n",
"seventyyearold 1\n",
"lengthswhich 1\n",
"otkupshchik 2\n",
"propertytake 1\n",
"expectedneither 1\n",
"fonder 11\n",
"armytried 1\n",
"barclays 2\n",
"obstruct 2\n",
"lubomirski 1\n",
"bronnitski 1\n",
"inhabitantswho 1\n",
"quires 1\n",
"reeds 1\n",
"zubov 2\n",
"quickerand 1\n",
"satellitesthe 1\n",
"scullery 2\n",
"chintzcovered 1\n",
"yakov 11\n",
"ferapontov 12\n",
"cabs 3\n",
"excaptain 2\n",
"hangedthe 1\n",
"compatriots 4\n",
"roaming 6\n",
"sorted 1\n",
"sack 6\n",
"marina 1\n",
"parcels 4\n",
"projectiles 8\n",
"ohhh 1\n",
"rocked 6\n",
"crackle 4\n",
"seeds 6\n",
"loot 3\n",
"usvyazh 1\n",
"ourourou 1\n",
"dews 2\n",
"likepeople 1\n",
"pillage 8\n",
"foment 2\n",
"wharf 10\n",
"ooze 3\n",
"alexis 1\n",
"thiswe 1\n",
"prevails 5\n",
"constitutions 2\n",
"malicious 6\n",
"versa 5\n",
"treasury 18\n",
"tribunal 4\n",
"award 1\n",
"vyazma 20\n",
"tsarevozaymishche 3\n",
"sacree 1\n",
"pagodes 1\n",
"pagodas 1\n",
"enfant 2\n",
"perplex 1\n",
"inventing 4\n",
"disobey 3\n",
"paralyzing 1\n",
"happinessfloated 1\n",
"excusing 6\n",
"stiffen 1\n",
"chanter 2\n",
"undercurrents 4\n",
"southeast 2\n",
"migrate 3\n",
"commune 8\n",
"cartloads 1\n",
"furtively 6\n",
"rameau 2\n",
"twentythree 4\n",
"dunyashas 3\n",
"bounty 1\n",
"repelling 5\n",
"rideto 1\n",
"grassland 1\n",
"mortify 1\n",
"unharness 2\n",
"friendmy 2\n",
"meaninglessly 1\n",
"begrudged 1\n",
"mutineers 1\n",
"pwomoted 1\n",
"wussians 1\n",
"pwince 3\n",
"wellonly 1\n",
"andwew 1\n",
"bweak 3\n",
"wayguewilla 1\n",
"flaccid 1\n",
"bibulous 1\n",
"intendant 1\n",
"kiril 5\n",
"grouping 1\n",
"willthe 1\n",
"potman 1\n",
"jeered 2\n",
"choke 7\n",
"dwarfs 2\n",
"besidesi 1\n",
"obolenskis 3\n",
"souzas 1\n",
"sexcuse 1\n",
"cousine 2\n",
"mutinousthey 1\n",
"vorontsovo 2\n",
"descends 1\n",
"lobnoe 3\n",
"pelisseswas 1\n",
"happinessthe 2\n",
"twentyfifth 11\n",
"twentysixth 11\n",
"lengthening 8\n",
"irrationally 1\n",
"enslaved 2\n",
"poems 1\n",
"histories 19\n",
"angleso 1\n",
"semenovsk 15\n",
"launched 11\n",
"indecisive 3\n",
"tireless 1\n",
"responding 3\n",
"coxcombs 1\n",
"themin 1\n",
"bunks 1\n",
"awaits 9\n",
"rarefied 2\n",
"bisecting 1\n",
"yellowing 2\n",
"troopsours 1\n",
"descry 2\n",
"burdino 1\n",
"wingnow 1\n",
"protectress 2\n",
"bulwark 2\n",
"kaysarov 10\n",
"wriggles 1\n",
"marins 1\n",
"gerakov 1\n",
"rhythm 1\n",
"fated 2\n",
"discharging 3\n",
"coldnessthey 1\n",
"wisp 5\n",
"pillaged 3\n",
"unpracticed 1\n",
"muddles 4\n",
"krieg 1\n",
"ansicht 1\n",
"ich 1\n",
"genug 1\n",
"zweck 1\n",
"schwächen 1\n",
"gewiss 1\n",
"achtung 1\n",
"criminals 8\n",
"hessians 2\n",
"kills 2\n",
"beausset 21\n",
"grunting 2\n",
"emperorand 1\n",
"touchedhimself 1\n",
"ney 6\n",
"pernetti 2\n",
"friants 5\n",
"shellfire 1\n",
"bombard 1\n",
"morands 3\n",
"retaining 9\n",
"greatand 1\n",
"waterproof 1\n",
"criticizes 1\n",
"corvisart 1\n",
"adjust 4\n",
"reverberate 1\n",
"yellowishgreen 1\n",
"magically 2\n",
"chests 7\n",
"pausepuff 1\n",
"intervalboom 1\n",
"prodded 1\n",
"bearable 1\n",
"goats 2\n",
"circleseparated 1\n",
"tingle 2\n",
"bayoneted 2\n",
"anotherpierre 1\n",
"unknownrussians 1\n",
"seething 3\n",
"zone 1\n",
"claparedes 3\n",
"medicinesa 1\n",
"unfailingly 1\n",
"disorganization 1\n",
"eagles 2\n",
"grounda 2\n",
"baskets 10\n",
"liveliest 7\n",
"wormwood 6\n",
"whizz 2\n",
"tents 6\n",
"troughs 2\n",
"sparrows 9\n",
"pecked 1\n",
"chucked 4\n",
"grunted 2\n",
"piteouslyhe 1\n",
"phantasm 1\n",
"croaked 11\n",
"wellbeing 1\n",
"saxons 1\n",
"genevese 1\n",
"romans 4\n",
"pastured 1\n",
"flagging 1\n",
"sophism 1\n",
"conforms 1\n",
"commanderas 1\n",
"observes 12\n",
"refuses 3\n",
"locomotive 11\n",
"steam 24\n",
"colliding 1\n",
"kaluga 26\n",
"victoryfor 1\n",
"unheardof 5\n",
"moscowdespite 1\n",
"battlethe 1\n",
"fili 9\n",
"gallops 1\n",
"misunderstand 4\n",
"crosart 1\n",
"galled 6\n",
"oven 4\n",
"granddad 6\n",
"negroes 3\n",
"jesters 1\n",
"taunted 1\n",
"thirtysix 2\n",
"upbraiding 2\n",
"orphanage 2\n",
"repudiated 6\n",
"problemhow 1\n",
"monseigneur 13\n",
"jesuit 2\n",
"venial 2\n",
"firstly 9\n",
"directors 5\n",
"squarely 1\n",
"whosoever 2\n",
"comtesse 1\n",
"misericorde 1\n",
"mash 2\n",
"kirilychisnt 1\n",
"permeated 1\n",
"communal 1\n",
"departmentsknew 1\n",
"prison 154\n",
"tranquillize 1\n",
"comments 7\n",
"retrieving 1\n",
"lubricant 1\n",
"postilions 3\n",
"myasnitski 2\n",
"gobelin 1\n",
"countermanded 1\n",
"disconsolately 5\n",
"moscowthe 1\n",
"factory 19\n",
"domo 1\n",
"yardwe 1\n",
"spruce 3\n",
"footboard 3\n",
"phaeton 14\n",
"wasprince 1\n",
"sukharev 6\n",
"meshchanski 1\n",
"holies 1\n",
"forhe 1\n",
"fords 1\n",
"aux 1\n",
"maison 2\n",
"venom 1\n",
"discordant 6\n",
"robbers 5\n",
"butterflies 10\n",
"chalks 1\n",
"observance 3\n",
"circumspection 2\n",
"stoppage 10\n",
"bazaar 6\n",
"affability 6\n",
"drums 13\n",
"darkgray 1\n",
"arcade 1\n",
"ostentatiously 5\n",
"mishka 4\n",
"strumming 1\n",
"monkey 4\n",
"flicked 1\n",
"motherly 11\n",
"discordantly 2\n",
"smiths 3\n",
"bleared 5\n",
"moroseyka 2\n",
"chinatown 1\n",
"ukase 4\n",
"superintendent 12\n",
"administrators 1\n",
"relinquishing 1\n",
"registrars 6\n",
"foundling 3\n",
"battalionswhich 1\n",
"untroubled 6\n",
"ruleradministrator 1\n",
"furlined 4\n",
"chafed 7\n",
"hatchet 1\n",
"whatstill 1\n",
"miscreant 2\n",
"canister 10\n",
"instants 1\n",
"circled 2\n",
"pokrovka 2\n",
"europewhich 1\n",
"lifeall 1\n",
"guardif 1\n",
"criterion 1\n",
"logement 1\n",
"enfants 2\n",
"fachons 1\n",
"appellation 9\n",
"baptismal 1\n",
"omelet 1\n",
"cochon 2\n",
"parisian 3\n",
"geniusthats 1\n",
"evildoer 1\n",
"ramballes 3\n",
"principally 22\n",
"unnaturalness 1\n",
"marquise 2\n",
"terentich 4\n",
"stupor 4\n",
"replaiting 1\n",
"mistook 5\n",
"cockroaches 3\n",
"mushroom 3\n",
"enjoin 3\n",
"pitipiti 1\n",
"collapse 2\n",
"sphinx 4\n",
"yeslove 1\n",
"dyingfirst 1\n",
"manifestations 5\n",
"plainit 1\n",
"naturesby 1\n",
"nikulins 1\n",
"soaring 4\n",
"scrofulouslooking 1\n",
"unattractively 1\n",
"gruzinski 1\n",
"lank 8\n",
"salesman 1\n",
"prisonertake 1\n",
"students 4\n",
"lordship 5\n",
"sergius 2\n",
"famed 1\n",
"pectoris 2\n",
"unwary 1\n",
"utterances 1\n",
"foreseeing 6\n",
"deathbearing 1\n",
"kutaysovs 1\n",
"unofficial 1\n",
"quoique 1\n",
"souverain 1\n",
"eclairaient 1\n",
"agonized 2\n",
"foolishlike 1\n",
"matreshka 1\n",
"posthouses 2\n",
"fancier 1\n",
"intoxicatingly 1\n",
"mythological 1\n",
"ignatyevna 3\n",
"oho 11\n",
"childless 2\n",
"circumstancesso 1\n",
"papas 30\n",
"matchmaker 1\n",
"rostovtheir 1\n",
"strivings 2\n",
"basely 3\n",
"reveries 2\n",
"tearfully 4\n",
"secretive 1\n",
"eyesshe 1\n",
"guardboth 1\n",
"thoughtfulness 8\n",
"zubovski 2\n",
"squad 1\n",
"spaces 4\n",
"shcherbitovs 1\n",
"adducing 1\n",
"awayalone 1\n",
"gruffly 3\n",
"simplicityand 1\n",
"coiled 8\n",
"friendsuffer 1\n",
"earning 7\n",
"dragnet 1\n",
"frola 3\n",
"context 2\n",
"chaffed 1\n",
"equipages 5\n",
"roundabout 5\n",
"integral 2\n",
"volga 1\n",
"riverand 1\n",
"himcan 1\n",
"sonyayou 1\n",
"wince 6\n",
"feedeth 1\n",
"soall 1\n",
"lifewas 1\n",
"deaththe 2\n",
"soulshall 1\n",
"mindabout 1\n",
"convulsions 5\n",
"positionthe 1\n",
"salutary 4\n",
"oka 2\n",
"adjutantsgeneral 1\n",
"initiator 1\n",
"pillaging 4\n",
"numberas 1\n",
"ds 1\n",
"onas 1\n",
"dmitrov 1\n",
"eykhen 2\n",
"positionmade 1\n",
"majorgeneral 1\n",
"distrustful 9\n",
"orlovdenisovs 1\n",
"coincide 3\n",
"diagonal 1\n",
"parallelogram 1\n",
"armyit 1\n",
"northerly 1\n",
"municipality 4\n",
"evilminded 1\n",
"artisans 4\n",
"protects 1\n",
"surplus 2\n",
"buyer 3\n",
"nonperformance 1\n",
"planswhich 1\n",
"mosquee 1\n",
"robberies 2\n",
"posnyakovs 1\n",
"ineffectiveness 1\n",
"forceoctober 1\n",
"courtyards 1\n",
"provender 3\n",
"disintegrated 1\n",
"finallylike 1\n",
"beastran 1\n",
"movementsas 1\n",
"skullcap 1\n",
"needsgood 1\n",
"restrictedseemed 1\n",
"superfluity 3\n",
"mistrustfully 2\n",
"neskuchny 1\n",
"neys 2\n",
"debouching 1\n",
"wenches 1\n",
"swaggeringly 1\n",
"showering 3\n",
"augezd 1\n",
"malakhov 1\n",
"dokhturovkutuzov 1\n",
"revolves 1\n",
"aristovo 2\n",
"litashevka 1\n",
"alexey 1\n",
"petrovich 3\n",
"shcherbinins 1\n",
"proprietys 1\n",
"extermination 1\n",
"germs 3\n",
"themhow 1\n",
"fledwithout 1\n",
"eclipses 1\n",
"landwas 1\n",
"availed 6\n",
"nationshould 1\n",
"vlas 1\n",
"combatant 1\n",
"rulesas 1\n",
"belabored 1\n",
"tribes 2\n",
"gros 1\n",
"toujours 1\n",
"conquerthat 1\n",
"foragers 1\n",
"denis 1\n",
"slew 2\n",
"flamed 1\n",
"lovayski 1\n",
"sodden 7\n",
"presupposable 1\n",
"marsh 2\n",
"wascal 1\n",
"moreorderers 2\n",
"musketoon 2\n",
"shcherbatythe 1\n",
"wantthree 1\n",
"seedless 1\n",
"baga 1\n",
"vernal 1\n",
"vesenya 3\n",
"voulezvous 1\n",
"receipts 5\n",
"ici 2\n",
"ronde 1\n",
"ukranian 1\n",
"rummaged 1\n",
"nothingness 2\n",
"instrumentnow 1\n",
"ozhegzhegzheg 1\n",
"plashed 1\n",
"attitudevery 1\n",
"mudbespattered 2\n",
"twentysecond 4\n",
"hardtack 1\n",
"junots 5\n",
"trainstill 1\n",
"moroseness 3\n",
"tunneled 1\n",
"footgear 2\n",
"wifeof 1\n",
"scabcovered 1\n",
"pelt 1\n",
"allwith 1\n",
"effective 6\n",
"ache 11\n",
"smolenska 1\n",
"facesone 1\n",
"alivea 1\n",
"wagging 10\n",
"plat 1\n",
"disbanded 2\n",
"armyfirst 1\n",
"assez 1\n",
"excludes 1\n",
"marshalsand 1\n",
"baffledthen 1\n",
"rhapsodies 1\n",
"slice 11\n",
"monthit 1\n",
"severance 3\n",
"goneto 1\n",
"twitches 3\n",
"onit 1\n",
"sonyawere 1\n",
"thinness 4\n",
"againthough 1\n",
"layer 1\n",
"unparalleled 5\n",
"dukeit 1\n",
"nowwhen 1\n",
"wantedthat 1\n",
"vanquishing 1\n",
"grands 1\n",
"actionswithout 1\n",
"procrastinator 1\n",
"herothe 1\n",
"bestial 1\n",
"hurrrah 1\n",
"stacked 2\n",
"authoritative 1\n",
"nightsome 1\n",
"sifted 2\n",
"stamping 6\n",
"spoils 4\n",
"frenchies 2\n",
"knowwell 1\n",
"kerchiefs 1\n",
"firelight 8\n",
"vifseruvaru 1\n",
"sedyablyaka 1\n",
"eut 1\n",
"battre 1\n",
"ke 1\n",
"gala 1\n",
"undignified 1\n",
"mislead 2\n",
"slowness 3\n",
"borisov 1\n",
"suitecount 1\n",
"campaignto 1\n",
"refixing 1\n",
"orel 11\n",
"elets 1\n",
"inalienable 1\n",
"findthe 1\n",
"temporarilyhe 1\n",
"faithnot 1\n",
"vaskain 1\n",
"heretofore 3\n",
"overseers 1\n",
"indestructible 2\n",
"craftsmanship 1\n",
"stocked 1\n",
"cathedrals 3\n",
"burnedin 1\n",
"imaginei 1\n",
"clearlyclearer 1\n",
"abstraction 16\n",
"egotistic 1\n",
"reexperiencing 1\n",
"wanderedevidently 1\n",
"sightchildren 1\n",
"menall 1\n",
"workshopbut 1\n",
"freshas 1\n",
"metwhom 1\n",
"picturesqueness 1\n",
"selfcontrol 2\n",
"worldseemed 1\n",
"everybodyboth 1\n",
"reawakened 1\n",
"stormtossed 1\n",
"enumerate 1\n",
"sourcesthe 1\n",
"thathad 1\n",
"elsethen 1\n",
"inadequately 1\n",
"falsehoods 1\n",
"ascribing 1\n",
"ignoble 3\n",
"developed 21\n",
"selfadulation 1\n",
"goalmoscow 1\n",
"lateran 1\n",
"outlawed 2\n",
"channels 4\n",
"disagreements 2\n",
"backwash 2\n",
"cooperated 1\n",
"poet 5\n",
"enterprising 1\n",
"propertythat 1\n",
"unpaid 2\n",
"whims 5\n",
"uncomplaining 5\n",
"thrifty 1\n",
"todo 3\n",
"sentimentality 1\n",
"todayit 1\n",
"treasures 12\n",
"tunic 2\n",
"herdenisovs 1\n",
"cond 1\n",
"solitudeshe 1\n",
"advocated 1\n",
"seductive 1\n",
"fluff 1\n",
"agreeableshe 1\n",
"familythat 1\n",
"striding 4\n",
"subjection 6\n",
"upbringing 1\n",
"overlapping 1\n",
"overfed 3\n",
"reconsider 3\n",
"motherand 3\n",
"beforeshe 1\n",
"sothough 2\n",
"womanyet 1\n",
"backfor 1\n",
"inopportune 1\n",
"presided 13\n",
"malcontent 1\n",
"sonyawho 1\n",
"settlements 2\n",
"overstrained 1\n",
"conservativesa 1\n",
"pugachev 2\n",
"pens 11\n",
"bunt 1\n",
"resourceful 1\n",
"medenisov 1\n",
"tempts 1\n",
"elias 1\n",
"tambov 1\n",
"impossiblein 1\n",
"vindictively 2\n",
"milks 1\n",
"awaked 1\n",
"cobwebs 14\n",
"formless 2\n",
"scaevola 1\n",
"plutarchs 1\n",
"journalists 3\n",
"gibbon 1\n",
"reformers 3\n",
"twentyyear 1\n",
"enriched 5\n",
"geniusnapoleon 1\n",
"ironica 1\n",
"compilers 1\n",
"questionin 1\n",
"selfevident 3\n",
"biographical 2\n",
"nationalities 1\n",
"lanfrey 1\n",
"interaction 2\n",
"component 2\n",
"restbut 1\n",
"resultedthat 1\n",
"deniedand 1\n",
"newer 1\n",
"contemporaneously 1\n",
"handicraft 4\n",
"approximate 2\n",
"serviceable 19\n",
"predominance 1\n",
"elsewherein 1\n",
"firenamely 1\n",
"revolutionsin 1\n",
"parttransfer 1\n",
"conditionally 5\n",
"delegated 5\n",
"react 4\n",
"philippe 1\n",
"delegations 1\n",
"historycivil 1\n",
"conquestsare 1\n",
"usurpations 1\n",
"generalizations 1\n",
"hermit 2\n",
"occurthat 1\n",
"beginsthat 1\n",
"pasturage 2\n",
"bismarck 2\n",
"forcea 1\n",
"outnot 1\n",
"cone 4\n",
"diameter 2\n",
"primary 2\n",
"wielder 1\n",
"electricity 6\n",
"obscurities 2\n",
"philosophicwe 1\n",
"incontestably 1\n",
"statistics 3\n",
"religions 1\n",
"secretions 1\n",
"frog 2\n",
"experimental 2\n",
"ranging 5\n",
"barbarians 1\n",
"coexisting 1\n",
"essentialwould 1\n",
"causation 2\n",
"impresses 3\n",
"simpletonthen 1\n",
"equaled 1\n",
"idiotcomplete 1\n",
"fundamentals 1\n",
"cognition 1\n",
"concepts 1\n",
"newtons 1\n",
"enunciation 1\n",
"enunciated 2\n",
"interconnected 1\n",
"ptolemaic 2\n",
"violating 1\n",
"joshua 1\n",
"defenders 2\n",
"personalityfree 1\n",
"rockingham 1\n",
"enlarge 10\n",
"xli 1\n",
"fulfils 2\n",
"shadowy 22\n",
"gender 2\n",
"seagoing 5\n",
"guaranteed 1\n",
"shortas 1\n",
"scotland 2\n",
"fender 26\n",
"rooksnests 3\n",
"dawdle 1\n",
"potentiality 1\n",
"nurserygoverness 1\n",
"reversion 1\n",
"baaah 1\n",
"maltreated 1\n",
"chillips 6\n",
"darken 6\n",
"peck 4\n",
"sidegate 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pickles 5\n",
"physicians 15\n",
"riper 2\n",
"opinionsif 1\n",
"forefingers 3\n",
"ant 39\n",
"heartbroken 4\n",
"kneeled 14\n",
"geranium 6\n",
"murdstonei 1\n",
"lowestoft 6\n",
"sweetbriar 3\n",
"eyei 1\n",
"abstracted 7\n",
"crossbarred 1\n",
"jokeand 1\n",
"isthat 1\n",
"provisionally 1\n",
"awning 1\n",
"denoted 3\n",
"chuckled 25\n",
"bloater 1\n",
"growed 10\n",
"caulkers 1\n",
"cosily 2\n",
"rocs 2\n",
"trundling 1\n",
"mantelshelf 9\n",
"completest 2\n",
"counterpane 3\n",
"lobster 13\n",
"lobsters 4\n",
"crabs 4\n",
"beads 13\n",
"butter 25\n",
"fortnut 1\n",
"ark 2\n",
"giv 6\n",
"peggottymade 1\n",
"similes 1\n",
"etymology 2\n",
"fishermans 5\n",
"enumeration 1\n",
"fishermens 2\n",
"emboldened 9\n",
"protruded 2\n",
"rashness 1\n",
"lobsterouthouse 1\n",
"dialect 2\n",
"gummidges 7\n",
"whimpered 9\n",
"festoon 1\n",
"sprightliness 3\n",
"somethings 2\n",
"clara 51\n",
"roamed 4\n",
"mouthed 2\n",
"flaws 2\n",
"murdstones 27\n",
"pliant 3\n",
"thatand 2\n",
"greatgrandfathers 1\n",
"uncompromising 8\n",
"rivets 1\n",
"embellished 5\n",
"frightenedi 1\n",
"edwardits 1\n",
"jaildelivery 1\n",
"housekeepers 7\n",
"unkindness 5\n",
"boydavid 1\n",
"primer 2\n",
"goodnature 5\n",
"secondbest 1\n",
"exercisebook 1\n",
"easychair 11\n",
"appalling 8\n",
"pores 1\n",
"untasked 1\n",
"gil 3\n",
"crusoe 8\n",
"timethey 1\n",
"travelsi 1\n",
"trunnion 1\n",
"cane 20\n",
"poised 6\n",
"skates 2\n",
"skim 2\n",
"excepted 16\n",
"nightmares 2\n",
"speakthe 1\n",
"remorseall 1\n",
"urgency 3\n",
"poppet 1\n",
"faceand 4\n",
"jogged 5\n",
"stagecutch 2\n",
"towherever 1\n",
"barkis 146\n",
"elephants 5\n",
"yees 7\n",
"knowbarkis 1\n",
"notdrank 1\n",
"batterpudding 2\n",
"companionable 2\n",
"seehow 1\n",
"hesitatingly 1\n",
"hardness 5\n",
"giggle 3\n",
"nightfor 1\n",
"towhich 1\n",
"haystack 6\n",
"yoongster 2\n",
"sooffolk 2\n",
"bins 1\n",
"whateveritwas 1\n",
"blackheath 3\n",
"diamondpaned 2\n",
"bellows 9\n",
"relieving 8\n",
"angular 6\n",
"peacocks 2\n",
"tofades 1\n",
"unscrewed 4\n",
"seaside 4\n",
"perch 119\n",
"placard 9\n",
"steerforthwho 1\n",
"dreading 1\n",
"grind 8\n",
"stump 1\n",
"beckoning 11\n",
"towzer 1\n",
"smuggle 1\n",
"prog 1\n",
"halfcrownsthough 1\n",
"slashing 3\n",
"hopdealer 1\n",
"scholastic 6\n",
"wretchedly 4\n",
"parlourboarder 1\n",
"outlasted 1\n",
"halfundressed 1\n",
"exordium 3\n",
"commanderinchiefin 1\n",
"cipheringbook 3\n",
"morbidly 1\n",
"professes 3\n",
"overpowers 2\n",
"notable 5\n",
"couldntor 1\n",
"letterwhat 1\n",
"wasarrived 1\n",
"oranges 9\n",
"storytelling 4\n",
"ginger 4\n",
"stomachic 1\n",
"insomuch 12\n",
"halfholiday 2\n",
"puss 4\n",
"herehe 1\n",
"degrade 5\n",
"certainlyi 1\n",
"mells 4\n",
"situationwhich 1\n",
"condescended 11\n",
"survives 2\n",
"shrimps 10\n",
"wittles 4\n",
"merrygorounder 1\n",
"wot 13\n",
"jet 6\n",
"anywheres 4\n",
"peoplewho 1\n",
"boatman 6\n",
"wellright 1\n",
"unnerstand 3\n",
"tearblotted 1\n",
"shreds 3\n",
"unfailing 1\n",
"grope 3\n",
"snuffed 4\n",
"calendar 7\n",
"janethat 1\n",
"corkscrews 3\n",
"hardheaded 3\n",
"harderhearted 1\n",
"weights 3\n",
"threading 3\n",
"grandmothers 1\n",
"albeit 6\n",
"soother 1\n",
"contribution 3\n",
"draper 2\n",
"haberdasher 2\n",
"furnisher 2\n",
"bonnets 10\n",
"backparlour 4\n",
"tryingon 1\n",
"breakneck 3\n",
"scarcelytasted 1\n",
"catalogue 3\n",
"colourof 1\n",
"lookerson 1\n",
"frightenedlike 1\n",
"cancelled 8\n",
"hereor 1\n",
"lefthand 5\n",
"anyways 4\n",
"wasntif 1\n",
"porterage 1\n",
"bluer 5\n",
"rudderford 1\n",
"exceptionthat 1\n",
"glorified 5\n",
"coldest 2\n",
"demure 3\n",
"moveables 3\n",
"lounged 10\n",
"stubbornness 5\n",
"grinby 6\n",
"pocketmoney 2\n",
"muchworn 1\n",
"blackfriars 6\n",
"abutting 2\n",
"traffic 5\n",
"bargeman 1\n",
"meextraordinary 1\n",
"coatfor 1\n",
"unoccupiedand 1\n",
"ain 3\n",
"beginner 1\n",
"lodger 10\n",
"windsor 7\n",
"confidencei 1\n",
"babylon 2\n",
"orfling 6\n",
"bootmaker 2\n",
"pastrycooks 8\n",
"moisten 2\n",
"expeditious 1\n",
"skilful 7\n",
"carman 3\n",
"miserably 9\n",
"shavingpot 3\n",
"nan 1\n",
"pocketfrom 1\n",
"thankfully 4\n",
"liberties 4\n",
"inexpensive 3\n",
"mealtimes 2\n",
"selfreliant 5\n",
"wharves 6\n",
"casino 2\n",
"demoniacal 2\n",
"rockahead 1\n",
"unacquainted 8\n",
"affixed 2\n",
"luscious 2\n",
"filing 2\n",
"lambs 2\n",
"readyin 1\n",
"wilkins 23\n",
"boyand 3\n",
"hourly 6\n",
"thehere 1\n",
"frownedthe 1\n",
"disparage 12\n",
"sandgate 1\n",
"donkeycart 5\n",
"sixpennorth 1\n",
"directioncard 2\n",
"greenwich 4\n",
"umbrage 4\n",
"bystreet 6\n",
"weskit 2\n",
"dolloby 4\n",
"eighteenpence 8\n",
"yewtree 2\n",
"glowering 5\n",
"noahs 1\n",
"stubbly 1\n",
"mounts 1\n",
"rip 3\n",
"pelted 1\n",
"fourpence 2\n",
"hopgrounds 1\n",
"orchards 3\n",
"tinker 11\n",
"nno 4\n",
"gateways 3\n",
"downs 6\n",
"maidstone 2\n",
"flydrivers 1\n",
"hebag 1\n",
"gruffish 1\n",
"undrawn 1\n",
"restoratives 4\n",
"displeasing 1\n",
"surgeons 5\n",
"shirtwristbands 1\n",
"protegees 1\n",
"renouncement 2\n",
"canaries 2\n",
"urchin 1\n",
"sallies 3\n",
"donkeyboys 1\n",
"sandyheaded 1\n",
"interruptions 3\n",
"enrobed 1\n",
"redbreastas 1\n",
"murdereror 1\n",
"selfdenying 6\n",
"himjanet 1\n",
"teacups 4\n",
"nowif 2\n",
"fortynine 5\n",
"auntis 1\n",
"eccentricthough 1\n",
"asylumplace 1\n",
"illtreat 2\n",
"allegorical 4\n",
"simile 4\n",
"quaker 2\n",
"augured 2\n",
"boldfaced 3\n",
"teens 1\n",
"whooping 2\n",
"feltwe 1\n",
"knowledgeof 1\n",
"pointblank 3\n",
"notand 2\n",
"grimly 8\n",
"thento 2\n",
"silky 2\n",
"comprehendit 1\n",
"indelible 4\n",
"guardians 2\n",
"abortive 2\n",
"complimentary 10\n",
"quainter 1\n",
"nooks 6\n",
"jackdaws 3\n",
"grassplotand 1\n",
"unbraced 1\n",
"ladywhom 1\n",
"strongs 24\n",
"satan 3\n",
"egad 5\n",
"cravat 37\n",
"sellings 1\n",
"schoolbooks 1\n",
"meanis 1\n",
"tires 2\n",
"tidd 4\n",
"numble 4\n",
"twistings 1\n",
"inexpressivefaced 1\n",
"piratical 2\n",
"avowed 3\n",
"attributable 3\n",
"botanical 2\n",
"furor 1\n",
"expounding 3\n",
"daythough 1\n",
"yearsfor 2\n",
"goneunless 1\n",
"sindbad 1\n",
"canopies 1\n",
"trombone 2\n",
"marklehamhowever 1\n",
"friendher 1\n",
"interminable 8\n",
"ashy 3\n",
"sleepwalking 1\n",
"blots 3\n",
"moralthat 1\n",
"coachfare 1\n",
"stipulation 7\n",
"launching 3\n",
"ingenuous 8\n",
"outset 4\n",
"chariots 1\n",
"wordsi 1\n",
"weedy 6\n",
"shuttlecock 3\n",
"dinted 2\n",
"turningpoint 1\n",
"havein 1\n",
"plato 1\n",
"tobaccosmoke 2\n",
"racehorse 1\n",
"ourselvesour 1\n",
"medway 4\n",
"remittance 7\n",
"fried 4\n",
"beacon 4\n",
"beggared 2\n",
"halfsleeping 1\n",
"spencer 1\n",
"namei 2\n",
"jonesfor 1\n",
"dote 7\n",
"bullnecked 1\n",
"illconditioned 5\n",
"injurious 5\n",
"immoderately 3\n",
"garnered 2\n",
"longtailed 1\n",
"evenhanded 1\n",
"forgetmenots 2\n",
"camellia 1\n",
"ashfordand 1\n",
"placewe 1\n",
"unspeakable 14\n",
"lament 5\n",
"fastsailing 1\n",
"watchfully 1\n",
"savagest 1\n",
"physicallyis 1\n",
"consults 1\n",
"enslaver 4\n",
"climatelike 1\n",
"sandheap 1\n",
"strongnot 1\n",
"foureight 1\n",
"bigoted 1\n",
"commentary 6\n",
"illassorted 4\n",
"againperhaps 1\n",
"leered 7\n",
"dorg 1\n",
"childrenreading 1\n",
"arithmeticsnuff 1\n",
"tobacker 1\n",
"tinkers 2\n",
"alongthat 1\n",
"tasteful 2\n",
"fastbeating 1\n",
"oclockan 1\n",
"darkbrown 3\n",
"highgate 23\n",
"daisywill 1\n",
"scari 2\n",
"seam 2\n",
"agowhich 1\n",
"dilapidatedlike 1\n",
"beeh 1\n",
"grindstone 3\n",
"motherless 9\n",
"inspiring 1\n",
"quartering 1\n",
"equable 9\n",
"huntingwatch 1\n",
"fencinggloves 1\n",
"boxing 2\n",
"thereafter 2\n",
"breakfasted 7\n",
"aboriginal 1\n",
"minnies 7\n",
"shopdesk 1\n",
"apprenticed 3\n",
"detracted 1\n",
"feigning 3\n",
"rheumatics 2\n",
"poked 7\n",
"usefullest 1\n",
"dinnerif 1\n",
"madwoman 3\n",
"growedshould 1\n",
"himwery 1\n",
"tarpaulin 3\n",
"capsized 1\n",
"aways 1\n",
"tonightcomes 1\n",
"elseif 1\n",
"betterwhat 1\n",
"tides 3\n",
"shipwreck 4\n",
"maidenly 2\n",
"sharers 3\n",
"boating 1\n",
"mightto 1\n",
"vivacious 7\n",
"clipper 2\n",
"isand 2\n",
"petrel 1\n",
"abutted 2\n",
"sealine 1\n",
"confessi 1\n",
"fullsized 3\n",
"commonsized 1\n",
"conjurers 1\n",
"shrewdly 5\n",
"whisk 1\n",
"tellings 1\n",
"hookey 1\n",
"sponges 2\n",
"curlingirons 1\n",
"peach 1\n",
"smiting 6\n",
"charleys 1\n",
"lifeguards 2\n",
"rouge 7\n",
"littlesharps 1\n",
"elfin 2\n",
"mizzle 1\n",
"ned 55\n",
"girlthe 1\n",
"disordering 1\n",
"supplication 9\n",
"tendernesses 4\n",
"pro 1\n",
"misbehaved 1\n",
"proctors 10\n",
"civiliansmen 1\n",
"collegewhich 1\n",
"lincolns 3\n",
"trespassing 1\n",
"sandandgravel 1\n",
"steak 5\n",
"controvert 2\n",
"conciliating 3\n",
"chariot 19\n",
"spenlows 18\n",
"goldbeaters 1\n",
"againthat 1\n",
"monthan 1\n",
"horseshoe 4\n",
"aforesaid 9\n",
"familyparty 1\n",
"opiate 1\n",
"crupp 86\n",
"townhouse 1\n",
"housewarming 3\n",
"fireplace 20\n",
"fowlsfrom 1\n",
"jellyfrom 1\n",
"labelled 1\n",
"grainger 4\n",
"oppositeall 1\n",
"yesagnes 1\n",
"imafraidyourenorwell 1\n",
"adjuring 3\n",
"miserableonly 1\n",
"titans 1\n",
"waterbrook 17\n",
"ely 2\n",
"holborn 8\n",
"mouththat 1\n",
"sixsyllable 1\n",
"tc 1\n",
"missive 3\n",
"waterbrooks 5\n",
"netting 1\n",
"smoky 7\n",
"dartlebut 1\n",
"register 10\n",
"fawning 7\n",
"worm 4\n",
"gratitudewith 1\n",
"uncongenial 5\n",
"ladyor 1\n",
"parks 2\n",
"spiker 13\n",
"shadowless 3\n",
"iced 2\n",
"aristocracyand 1\n",
"fixesand 1\n",
"nutshell 2\n",
"scarecrow 4\n",
"blocktin 1\n",
"coffeecup 5\n",
"ohwould 1\n",
"cruellooking 1\n",
"ponyshay 1\n",
"lowwater 1\n",
"distempered 3\n",
"vulture 2\n",
"brandybottles 1\n",
"tiffey 23\n",
"genteelest 3\n",
"interrogatory 1\n",
"counterinterrogatory 1\n",
"snugness 1\n",
"walkingsticks 2\n",
"itof 1\n",
"disparaged 7\n",
"ballads 4\n",
"youareout 1\n",
"pats 1\n",
"quietwell 1\n",
"jip 91\n",
"aboutisnt 1\n",
"ejected 1\n",
"deliveredabout 1\n",
"courseand 1\n",
"vigilantly 1\n",
"kid 3\n",
"tincture 1\n",
"rhubarb 1\n",
"copperfull 8\n",
"barmaid 2\n",
"quadrupeds 1\n",
"academic 1\n",
"cabbageleaves 1\n",
"brickandmortar 3\n",
"edification 4\n",
"passagebecause 1\n",
"deportment 7\n",
"brandymerchant 1\n",
"models 4\n",
"sixtyany 1\n",
"quo 2\n",
"immortalized 1\n",
"volubly 1\n",
"gleamsin 1\n",
"exposes 1\n",
"permanently 5\n",
"pledges 1\n",
"counteracted 1\n",
"pigeonpie 4\n",
"lavenderwater 4\n",
"solicited 9\n",
"hymeneal 2\n",
"laceration 1\n",
"supposethat 1\n",
"gritty 2\n",
"unhappyabout 1\n",
"thistraddles 1\n",
"fellto 1\n",
"batch 1\n",
"hae 1\n",
"gowans 19\n",
"glassful 1\n",
"capacitywhat 1\n",
"banking 4\n",
"bankingbusiness 1\n",
"lucidly 1\n",
"qualificationswith 1\n",
"matron 11\n",
"quote 7\n",
"thisthere 1\n",
"acoustics 1\n",
"seafaringbetter 1\n",
"believein 1\n",
"seawind 1\n",
"tooksuch 1\n",
"sonbah 1\n",
"airily 6\n",
"inventory 2\n",
"bullock 1\n",
"lynxlike 1\n",
"dissociate 1\n",
"porcelainand 1\n",
"naturemight 1\n",
"fromlet 1\n",
"quicklyfor 2\n",
"worldin 2\n",
"constitutionis 1\n",
"youwhether 1\n",
"irremediable 1\n",
"partys 3\n",
"afternoonas 1\n",
"goodto 1\n",
"dyke 1\n",
"brighten 6\n",
"halfarter 1\n",
"lemon 2\n",
"elaborately 4\n",
"pritty 4\n",
"ony 10\n",
"nothinks 1\n",
"whimper 4\n",
"unfeeling 12\n",
"wonderin 1\n",
"imim 1\n",
"emphasistheer 1\n",
"moreif 2\n",
"fortun 1\n",
"arther 1\n",
"trickling 3\n",
"girllast 1\n",
"yournand 1\n",
"stave 4\n",
"pollution 2\n",
"longeri 1\n",
"spiritwounded 1\n",
"seein 2\n",
"matterswhich 1\n",
"clicketten 2\n",
"outhouseas 1\n",
"halfsuppressed 3\n",
"untired 1\n",
"fenderit 1\n",
"recipient 2\n",
"disproportionate 4\n",
"toppling 1\n",
"wheer 14\n",
"bequest 2\n",
"jetblack 3\n",
"deepen 6\n",
"famousand 1\n",
"awaybut 3\n",
"lighthouse 3\n",
"linwoods 1\n",
"commonform 3\n",
"probate 1\n",
"clients 7\n",
"rankled 3\n",
"constraining 3\n",
"occasionsand 1\n",
"deprecatory 2\n",
"statute 1\n",
"leased 1\n",
"publics 1\n",
"registrar 1\n",
"pernicious 1\n",
"predominated 1\n",
"crackers 2\n",
"mills 87\n",
"chequered 6\n",
"sexespecially 1\n",
"lettuces 1\n",
"adamant 1\n",
"wither 6\n",
"sangabout 1\n",
"scents 5\n",
"securest 1\n",
"aspiration 5\n",
"mangle 5\n",
"cherishing 5\n",
"appointing 3\n",
"pitchers 3\n",
"sheexcuse 1\n",
"devonshireone 1\n",
"sophy 48\n",
"missionary 2\n",
"hegg 1\n",
"hungerford 5\n",
"inconveniences 4\n",
"attenuation 1\n",
"moneybecause 1\n",
"simpleton 3\n",
"westminster 12\n",
"ordinarysized 1\n",
"freshen 4\n",
"possibleat 1\n",
"faceto 1\n",
"daysbut 1\n",
"unfetteredif 1\n",
"discouragingly 1\n",
"unsoiled 1\n",
"lightening 4\n",
"noteto 2\n",
"actionable 1\n",
"judymeaning 1\n",
"tiddler 2\n",
"gentlemanfor 1\n",
"trotwoodand 1\n",
"afterthought 1\n",
"heepwe 1\n",
"woodmans 1\n",
"cottagea 1\n",
"embellishments 1\n",
"completedi 1\n",
"dusting 5\n",
"relapsing 5\n",
"traddlesif 1\n",
"kettledrums 1\n",
"phoenix 2\n",
"onesself 1\n",
"jurists 1\n",
"preferments 2\n",
"satisfactionstill 1\n",
"literal 3\n",
"fortyone 4\n",
"telegraphed 1\n",
"birdcage 6\n",
"supremely 7\n",
"injuriously 2\n",
"subjectby 1\n",
"newborn 2\n",
"ravaged 3\n",
"smellingbottle 4\n",
"needlecase 2\n",
"unluckily 2\n",
"watchman 3\n",
"irons 4\n",
"vagaries 1\n",
"cobweb 1\n",
"sidmouth 1\n",
"lustily 3\n",
"snails 2\n",
"carthorse 2\n",
"coffeehouse 14\n",
"corroboration 7\n",
"conciliatory 7\n",
"dislodged 2\n",
"thenof 1\n",
"mevery 1\n",
"twentynine 1\n",
"testamentary 3\n",
"timeany 1\n",
"mightif 2\n",
"protections 1\n",
"systematically 6\n",
"rainbow 6\n",
"officedoor 1\n",
"offnot 1\n",
"churchlying 1\n",
"variancethe 1\n",
"ghostthe 1\n",
"gorged 3\n",
"avaricious 6\n",
"packets 3\n",
"dustily 1\n",
"airing 11\n",
"argues 2\n",
"withdraws 2\n",
"declines 5\n",
"beforeto 1\n",
"renunciation 2\n",
"hangerson 4\n",
"spoiland 1\n",
"scandalized 2\n",
"inveigler 2\n",
"brokery 1\n",
"serener 2\n",
"soar 6\n",
"emoluments 5\n",
"intercoursewhich 1\n",
"misfit 1\n",
"itreliance 1\n",
"diningparlour 1\n",
"spokei 1\n",
"temperate 1\n",
"eyelash 2\n",
"abase 2\n",
"dinned 1\n",
"usband 3\n",
"wildness 6\n",
"mestrangely 1\n",
"nothingi 1\n",
"ripen 2\n",
"untasted 4\n",
"portico 5\n",
"wayi 2\n",
"sideentrance 1\n",
"varieties 5\n",
"acrosschannel 1\n",
"meprayed 1\n",
"voiceony 1\n",
"sittin 1\n",
"readi 1\n",
"twixt 1\n",
"harass 2\n",
"likewhat 1\n",
"seriocomic 1\n",
"heartwhich 1\n",
"supposeand 1\n",
"superintend 1\n",
"laviniabut 1\n",
"hopped 3\n",
"platewarmer 4\n",
"mischiefmaking 1\n",
"cornerwhich 1\n",
"lavinias 2\n",
"wouldor 2\n",
"couldbe 1\n",
"nosegays 2\n",
"selflaudation 1\n",
"presentno 1\n",
"umbleand 1\n",
"speakingand 1\n",
"seedcakethe 1\n",
"kissonce 1\n",
"askingto 1\n",
"igod 1\n",
"sinthat 1\n",
"mindof 1\n",
"admitthat 1\n",
"maturedno 1\n",
"scholarwhat 1\n",
"entrapping 2\n",
"subtlety 2\n",
"perambulations 2\n",
"wifeand 3\n",
"pertaining 1\n",
"officepens 1\n",
"dreads 1\n",
"backer 2\n",
"kneels 2\n",
"drillsergeant 1\n",
"pewopeners 1\n",
"pulpits 2\n",
"pews 5\n",
"shelljacket 1\n",
"weddingring 2\n",
"nnno 1\n",
"breakfastand 1\n",
"salmonwhich 1\n",
"homeaffairs 1\n",
"frontgarden 1\n",
"cinders 6\n",
"cordials 1\n",
"shrub 2\n",
"cthe 1\n",
"pertinacity 1\n",
"shapesand 1\n",
"deformed 1\n",
"carvingknife 1\n",
"oysterknivesand 1\n",
"immolation 1\n",
"accountbook 4\n",
"propensitythey 1\n",
"blotting 1\n",
"sillymore 1\n",
"makebelief 1\n",
"probed 1\n",
"deprecation 1\n",
"makingto 1\n",
"shutup 4\n",
"chimneysweepers 1\n",
"richnoble 1\n",
"sirclouds 1\n",
"discrepancy 2\n",
"personpresent 1\n",
"telegraph 2\n",
"exhibitedin 1\n",
"flighty 1\n",
"creatureif 1\n",
"wordsthis 1\n",
"folio 1\n",
"absolves 1\n",
"husbandfrom 1\n",
"teacherthe 1\n",
"alphage 1\n",
"importunate 6\n",
"wasto 1\n",
"marriageday 2\n",
"meantsteadfastly 1\n",
"myselfto 1\n",
"worldgod 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"uniformly 6\n",
"halfformed 5\n",
"kitchengarden 1\n",
"undiminished 1\n",
"jamess 2\n",
"aspired 4\n",
"lattice 4\n",
"slavedrivers 1\n",
"alivefor 1\n",
"snares 4\n",
"pulses 1\n",
"fortitudethink 1\n",
"objectdo 1\n",
"oftentimes 4\n",
"dootiful 3\n",
"weatherthey 1\n",
"handbills 3\n",
"pits 3\n",
"overflowings 1\n",
"miserableand 1\n",
"troubledand 1\n",
"handi 2\n",
"winters 3\n",
"believewhy 1\n",
"doentand 1\n",
"distrusting 1\n",
"drakes 1\n",
"incurable 2\n",
"flourishes 5\n",
"motherno 1\n",
"burglarious 1\n",
"contagion 6\n",
"spider 4\n",
"hedgehog 1\n",
"spectres 3\n",
"incorporated 1\n",
"asthmatic 1\n",
"muniments 1\n",
"lodgers 6\n",
"youit 1\n",
"wielding 3\n",
"canker 1\n",
"havens 1\n",
"domesticated 2\n",
"adequate 2\n",
"blinded 11\n",
"eagleglance 1\n",
"collusion 1\n",
"fleeted 1\n",
"landmarks 1\n",
"yetthat 1\n",
"elephantsi 1\n",
"coarser 2\n",
"counterdisposition 1\n",
"peel 2\n",
"heeponce 1\n",
"swordblade 1\n",
"snuffertray 1\n",
"rascalheep 1\n",
"hadpreviouslychoked 1\n",
"headaofinterminable 1\n",
"liarheep 1\n",
"crushedtoaundiscoverable 1\n",
"communicationauntilmiss 1\n",
"scoundrelheep 1\n",
"breakfasttimeaeverybody 1\n",
"auntaand 1\n",
"canterburyawheremrs 1\n",
"myselfauld 1\n",
"capableabear 1\n",
"nurtured 1\n",
"fourscore 2\n",
"droppings 2\n",
"degenerated 2\n",
"observers 9\n",
"cornices 3\n",
"plebeian 3\n",
"glassless 1\n",
"dustheap 1\n",
"conductress 2\n",
"garret 12\n",
"sharpness 12\n",
"believeor 1\n",
"earthworms 1\n",
"deigning 3\n",
"gentlemanson 1\n",
"repining 2\n",
"recoiling 4\n",
"servingmans 1\n",
"despairfind 1\n",
"wownd 1\n",
"storys 1\n",
"timorous 3\n",
"roomand 1\n",
"himsoon 1\n",
"drawd 2\n",
"forgiv 1\n",
"lifealonewithout 1\n",
"australia 6\n",
"athinking 2\n",
"davyand 1\n",
"wheerby 1\n",
"leetle 3\n",
"theerfur 1\n",
"lowance 2\n",
"wilds 3\n",
"furaway 1\n",
"mepoor 1\n",
"restuntil 1\n",
"indiwidual 1\n",
"thingsher 1\n",
"siamese 1\n",
"twould 2\n",
"lonon 1\n",
"ai 1\n",
"motes 6\n",
"gables 2\n",
"dixon 3\n",
"diningroomthe 1\n",
"failings 2\n",
"genie 2\n",
"counterplot 1\n",
"societyas 1\n",
"defame 2\n",
"exulted 7\n",
"usthough 1\n",
"doggedly 3\n",
"muchabused 1\n",
"nonexistence 1\n",
"monitor 2\n",
"againstheep 1\n",
"businessheep 1\n",
"piling 3\n",
"abjure 2\n",
"assumeunless 1\n",
"calcined 1\n",
"avowing 1\n",
"rapacity 1\n",
"morningher 1\n",
"blubbered 5\n",
"beggary 1\n",
"hasin 1\n",
"coachpainter 1\n",
"fireand 2\n",
"liabilitysay 1\n",
"discoursed 1\n",
"mopes 1\n",
"tiring 4\n",
"beand 3\n",
"withoutwhen 1\n",
"earnestit 1\n",
"comenot 1\n",
"querulously 2\n",
"businesspoint 1\n",
"stamps 4\n",
"selfdevoted 2\n",
"cows 7\n",
"alienate 1\n",
"acceleration 2\n",
"supportand 1\n",
"thiswould 1\n",
"consols 1\n",
"noblypersevering 1\n",
"rascaland 1\n",
"figuresthat 1\n",
"sakeif 1\n",
"pocketed 4\n",
"restraints 1\n",
"transactionswith 1\n",
"repayment 5\n",
"auntshusband 1\n",
"waspardon 1\n",
"serpents 6\n",
"sixandthirty 1\n",
"bailiwick 1\n",
"nows 4\n",
"reopen 1\n",
"vacating 1\n",
"thorns 6\n",
"ripped 2\n",
"ricks 1\n",
"puddle 1\n",
"aslant 6\n",
"seafoam 1\n",
"huddling 1\n",
"caves 2\n",
"skimming 5\n",
"shiprepairing 1\n",
"colliercrews 1\n",
"amidships 1\n",
"deathknell 1\n",
"freeor 1\n",
"feetinsensibledead 1\n",
"partinghourno 1\n",
"tints 1\n",
"infirmity 5\n",
"nurture 1\n",
"caprices 3\n",
"reaps 1\n",
"caulked 1\n",
"slops 4\n",
"ostentation 2\n",
"behas 1\n",
"deprivation 5\n",
"noteofhand 1\n",
"alow 2\n",
"maintop 1\n",
"coffers 4\n",
"ploughmen 1\n",
"ashoreand 1\n",
"shatteredmy 1\n",
"heavenand 1\n",
"everdarkening 1\n",
"precipices 1\n",
"craggy 2\n",
"singingshepherd 1\n",
"publication 2\n",
"bricklayer 1\n",
"insalubrity 1\n",
"reestablished 2\n",
"authoritya 1\n",
"cashbox 1\n",
"prescriptive 1\n",
"indomitable 8\n",
"laundresss 1\n",
"clubheaded 1\n",
"sharplooking 2\n",
"halffootboy 1\n",
"horaceto 1\n",
"whenwas 1\n",
"nowlistening 1\n",
"girland 1\n",
"herethe 2\n",
"therewhich 2\n",
"doe 2\n",
"dem 1\n",
"horacewho 1\n",
"childbut 1\n",
"sarahmiss 1\n",
"selfassertion 2\n",
"wafers 1\n",
"placidly 5\n",
"refilled 3\n",
"placidest 1\n",
"timorously 1\n",
"chillipthat 1\n",
"undergo 7\n",
"whitehaired 8\n",
"clicking 2\n",
"tramps 2\n",
"butchernow 1\n",
"shopwent 1\n",
"repulsefor 1\n",
"manypined 1\n",
"firstwith 1\n",
"livewill 1\n",
"curtainswhich 1\n",
"outsuch 1\n",
"busier 4\n",
"prisonersso 1\n",
"amend 3\n",
"southampton 4\n",
"momentran 1\n",
"gamest 1\n",
"samson 1\n",
"comprised 4\n",
"isnot 3\n",
"hereven 2\n",
"trotwoodmy 1\n",
"ravelled 1\n",
"greyhaired 13\n",
"fared 2\n",
"thrived 3\n",
"beautifullest 1\n",
"acovering 1\n",
"evry 1\n",
"sowt 1\n",
"farmlabourer 1\n",
"selves 3\n",
"upd 1\n",
"middlebay 7\n",
"genlman 1\n",
"nobis 1\n",
"smoothlyflowing 1\n",
"masterpiece 2\n",
"sidedoor 1\n",
"mothercountry 1\n",
"votaries 2\n",
"terpsichore 1\n",
"debarred 1\n",
"feasts 4\n",
"morefor 1\n",
"peevish 4\n",
"sneering 1\n",
"perpetuity 1\n",
"birdseye 2\n",
"toxs 45\n",
"wellmade 1\n",
"pompous 4\n",
"gowhile 1\n",
"dombeyof 1\n",
"maketh 1\n",
"peps 13\n",
"blockitt 2\n",
"apostrophised 2\n",
"parker 16\n",
"practitionercant 1\n",
"brotherfor 1\n",
"shaky 5\n",
"tiddy 1\n",
"grasses 1\n",
"articlesindeed 1\n",
"boas 1\n",
"clauses 1\n",
"chickthough 1\n",
"audacityand 1\n",
"fannys 14\n",
"overexert 1\n",
"rul 1\n",
"bowwowwowwhich 1\n",
"particularnaturally 1\n",
"fairlybalanced 1\n",
"giveandtake 1\n",
"jemima 29\n",
"certificate 2\n",
"moistening 2\n",
"closeshaved 2\n",
"closecut 1\n",
"hims 1\n",
"pardonrayther 1\n",
"unlikelythough 1\n",
"endow 1\n",
"effortthis 1\n",
"porterquite 1\n",
"portland 1\n",
"watercarts 1\n",
"lamplighter 3\n",
"associatedand 1\n",
"windowblinds 3\n",
"journals 4\n",
"bandages 1\n",
"hotpressed 1\n",
"pry 1\n",
"ladya 2\n",
"smearing 3\n",
"hardlooking 1\n",
"andupstairs 1\n",
"midshipmen 2\n",
"reconcileable 1\n",
"charts 2\n",
"shipshape 4\n",
"searoom 1\n",
"seaweeds 1\n",
"skipperlike 1\n",
"rover 1\n",
"quietspoken 1\n",
"newlyawakened 1\n",
"chronometer 12\n",
"clearance 2\n",
"curlyhaired 1\n",
"wearer 3\n",
"unctuous 3\n",
"seati 1\n",
"uncleand 1\n",
"peckham 2\n",
"solomonthat 1\n",
"furl 1\n",
"jamaica 4\n",
"swamped 2\n",
"privateersman 1\n",
"walr 98\n",
"lookye 2\n",
"polarized 1\n",
"ineffable 4\n",
"ownthe 1\n",
"thawed 4\n",
"doorwith 1\n",
"bating 1\n",
"interlopers 1\n",
"twine 2\n",
"gentlenessto 1\n",
"towlinson 69\n",
"legibly 1\n",
"andim 1\n",
"permanencies 1\n",
"rundown 1\n",
"familiarities 1\n",
"chimneyglass 4\n",
"electrify 1\n",
"frigidly 3\n",
"trestles 1\n",
"enlivening 2\n",
"fonta 1\n",
"pewopenerpartly 1\n",
"ghoststory 1\n",
"unaffectedly 2\n",
"fillet 4\n",
"nominating 1\n",
"grinders 21\n",
"saul 1\n",
"euphonious 1\n",
"condenses 1\n",
"tenements 3\n",
"incompleteness 2\n",
"burrowing 4\n",
"eruptions 1\n",
"railway 18\n",
"enterpriseand 1\n",
"antlered 1\n",
"staggses 1\n",
"allowances 3\n",
"dearfor 1\n",
"prodigies 2\n",
"imitative 1\n",
"effectedfor 1\n",
"tormentors 1\n",
"browns 4\n",
"afterwardsevery 1\n",
"offenceshe 1\n",
"shipment 2\n",
"clarks 1\n",
"shipwrecks 1\n",
"platescut 1\n",
"florenceput 1\n",
"ehgod 1\n",
"catering 2\n",
"thingfor 1\n",
"wincedto 1\n",
"motherhis 1\n",
"knewby 1\n",
"sedan 1\n",
"lionheaded 1\n",
"placeas 1\n",
"sortto 1\n",
"turpentine 1\n",
"elephantine 3\n",
"joey 18\n",
"josh 10\n",
"wheezing 2\n",
"chronicled 1\n",
"snaring 1\n",
"cardboards 1\n",
"decorate 4\n",
"doublebarrelled 3\n",
"dodged 2\n",
"cradlecurtains 1\n",
"hoopingcough 1\n",
"prompterrepeated 1\n",
"qualification 6\n",
"bulbul 1\n",
"bulbuls 1\n",
"thebones 1\n",
"inhow 1\n",
"childqueller 1\n",
"flinty 2\n",
"freshsmelling 1\n",
"sweetening 1\n",
"farinaceous 2\n",
"shampood 2\n",
"berinthia 2\n",
"genesis 1\n",
"boyseldom 1\n",
"falstaffs 1\n",
"snappishly 1\n",
"gored 1\n",
"egotistically 1\n",
"gashliness 1\n",
"thisyoull 1\n",
"suppertray 1\n",
"ruddyfaced 1\n",
"itdied 1\n",
"freespirited 1\n",
"solfor 1\n",
"washingstands 1\n",
"barricade 1\n",
"overdue 1\n",
"coffeecoloured 5\n",
"omnibuses 2\n",
"slopsellers 1\n",
"vanesurmounted 1\n",
"blockmaking 1\n",
"lowers 1\n",
"raff 1\n",
"quotations 5\n",
"sols 12\n",
"tenets 1\n",
"wallys 1\n",
"yesoh 1\n",
"yesive 1\n",
"convertible 1\n",
"soldits 1\n",
"debtand 1\n",
"commanda 1\n",
"englandto 1\n",
"rabid 1\n",
"bitherstones 3\n",
"opined 4\n",
"certify 1\n",
"trump 1\n",
"longand 1\n",
"halftrot 1\n",
"peninsula 1\n",
"ejaculations 2\n",
"isthis 1\n",
"themaccumulatepaymentadvanceunclefrugal 1\n",
"loafs 1\n",
"promiseone 1\n",
"unbated 1\n",
"secreting 1\n",
"greenpeas 1\n",
"sprouts 1\n",
"sadcoloured 1\n",
"despondently 2\n",
"breathfor 1\n",
"homer 3\n",
"yetuntil 1\n",
"parnassus 2\n",
"tusculum 3\n",
"contagious 2\n",
"davylamp 1\n",
"uninformed 3\n",
"glubb 8\n",
"tendering 4\n",
"feeders 12\n",
"inky 2\n",
"neckcloths 1\n",
"antechamberas 1\n",
"fashnable 1\n",
"crosswise 4\n",
"vulgarof 1\n",
"johnsondishes 1\n",
"apoplexy 3\n",
"domitian 1\n",
"thumped 1\n",
"olympic 1\n",
"wellington 2\n",
"ached 4\n",
"blackbird 2\n",
"sortmeaning 1\n",
"latinnames 1\n",
"declensions 2\n",
"substantives 1\n",
"hic 1\n",
"bogle 1\n",
"nightsthe 1\n",
"agape 1\n",
"defiances 1\n",
"unasked 3\n",
"chinking 2\n",
"smileand 1\n",
"impartial 7\n",
"preventive 1\n",
"beckon 5\n",
"comethere 1\n",
"irascible 2\n",
"repassed 1\n",
"oilpainting 1\n",
"approachhe 1\n",
"domeshaped 1\n",
"pepperandsalt 1\n",
"inherits 4\n",
"supposethrough 1\n",
"heirshell 1\n",
"bemoaning 3\n",
"retentive 1\n",
"hadhow 1\n",
"cygno 1\n",
"compeers 1\n",
"anchorites 1\n",
"rewrite 1\n",
"covertly 3\n",
"ramifications 1\n",
"penmanship 1\n",
"smallhand 1\n",
"classicality 1\n",
"offor 1\n",
"forwhat 1\n",
"askew 3\n",
"alfreds 2\n",
"doormat 2\n",
"oldfashionedfor 1\n",
"thinkwould 1\n",
"playbill 1\n",
"longhe 1\n",
"dancingmaster 13\n",
"barnet 64\n",
"musicbook 3\n",
"herthough 1\n",
"ebullitions 1\n",
"jig 1\n",
"enoughthe 1\n",
"aground 3\n",
"sublimation 1\n",
"whitby 1\n",
"clearerminded 1\n",
"nore 1\n",
"walkscertainly 1\n",
"taut 5\n",
"jolliestlooking 1\n",
"idealised 1\n",
"signals 8\n",
"darlingslittle 1\n",
"disheartened 3\n",
"officehouses 1\n",
"parasites 1\n",
"unsuspected 3\n",
"groundlaid 1\n",
"togetherand 1\n",
"changedand 2\n",
"dyingfor 1\n",
"wokewoke 1\n",
"bodyand 1\n",
"sheered 3\n",
"friendthe 1\n",
"realisation 1\n",
"namecaptain 1\n",
"whenin 1\n",
"elaboration 2\n",
"acquiescent 2\n",
"whocomes 1\n",
"examplein 1\n",
"visitorsnoiseless 1\n",
"clears 2\n",
"juggler 2\n",
"intimates 2\n",
"timeshaded 1\n",
"hoops 1\n",
"responsewhatever 1\n",
"notesour 1\n",
"heavyladen 2\n",
"muchi 1\n",
"herthis 1\n",
"godit 1\n",
"florenceflorence 1\n",
"voicedid 1\n",
"tootsthan 1\n",
"fibre 1\n",
"yelps 1\n",
"overbalancing 2\n",
"castawaybut 1\n",
"hairsbreadth 4\n",
"lighthard 1\n",
"tether 2\n",
"syracuse 1\n",
"misuse 2\n",
"shiploads 1\n",
"shesusan 1\n",
"ingenuousness 2\n",
"shoesshell 1\n",
"sitiwation 4\n",
"presidency 1\n",
"hesitationthese 1\n",
"engendering 2\n",
"andspeak 1\n",
"evasions 3\n",
"uninspired 1\n",
"watermen 1\n",
"bung 1\n",
"awakeneddamme 1\n",
"muffins 2\n",
"butdamme 1\n",
"thatfor 1\n",
"naturebut 1\n",
"grill 3\n",
"kidneys 2\n",
"skyhigh 1\n",
"matrimonially 1\n",
"leamington 11\n",
"oozing 1\n",
"seltzer 1\n",
"titan 1\n",
"lilywhite 1\n",
"dirtylooking 1\n",
"upard 1\n",
"readin 1\n",
"badged 1\n",
"churl 1\n",
"deformity 2\n",
"timeswas 1\n",
"fullformed 1\n",
"barbed 2\n",
"doublehanded 2\n",
"grudgingly 2\n",
"pumproom 2\n",
"overstepped 1\n",
"staringmore 1\n",
"rosyand 1\n",
"nameswithout 1\n",
"forlornly 2\n",
"critics 1\n",
"scarborough 1\n",
"cauliflower 1\n",
"cleopatras 7\n",
"vaunt 4\n",
"gushings 1\n",
"palsy 3\n",
"itundeveloped 1\n",
"engagedpausing 1\n",
"heapsmuch 1\n",
"harriet 114\n",
"choiceas 1\n",
"sidesdoing 1\n",
"bumping 1\n",
"gorget 1\n",
"endorsement 1\n",
"tempestuous 1\n",
"houseroom 2\n",
"afternoonrespectable 1\n",
"peopleto 1\n",
"wetter 1\n",
"turnings 3\n",
"ambient 1\n",
"whereabout 1\n",
"endall 1\n",
"repentant 12\n",
"dayfor 1\n",
"henchman 1\n",
"emancipated 2\n",
"executors 1\n",
"apollo 1\n",
"jobmaster 3\n",
"interrogation 1\n",
"reigning 3\n",
"talismanic 1\n",
"horizontally 1\n",
"brayed 1\n",
"automaton 1\n",
"waltzing 1\n",
"foldingdoors 2\n",
"disuse 2\n",
"mildew 1\n",
"spiders 1\n",
"blackbeetle 1\n",
"scaly 3\n",
"thereand 1\n",
"fulham 3\n",
"giver 3\n",
"curlpaper 2\n",
"swivelbridges 1\n",
"housefor 1\n",
"heartsdelight 4\n",
"expressionsif 1\n",
"stun 2\n",
"swabbing 1\n",
"escapealthough 1\n",
"sopiloted 1\n",
"tier 3\n",
"hailing 4\n",
"bulkhead 1\n",
"seamens 2\n",
"chathamyard 1\n",
"dram 3\n",
"pilotcoated 1\n",
"awast 11\n",
"somewheres 1\n",
"residences 1\n",
"comparisonit 1\n",
"widening 1\n",
"etcand 1\n",
"confirmin 1\n",
"morethe 1\n",
"interlude 1\n",
"shopwindow 2\n",
"stringent 2\n",
"thereof 6\n",
"bankside 1\n",
"roadstead 1\n",
"tempermild 1\n",
"houselamb 1\n",
"macstingersharply 1\n",
"apiece 4\n",
"breather 1\n",
"emigrating 1\n",
"complement 1\n",
"sirjoey 1\n",
"commerceis 1\n",
"selfdisparagement 1\n",
"lloyds 1\n",
"worldnor 1\n",
"sadlyfallen 1\n",
"daymy 1\n",
"gulfopened 1\n",
"remorsetake 1\n",
"awkwardmy 1\n",
"beautyto 1\n",
"themwas 1\n",
"emor 1\n",
"hairbrush 1\n",
"counterirritant 1\n",
"paywhich 1\n",
"feedingtime 1\n",
"zests 2\n",
"liquids 1\n",
"declaredwas 1\n",
"hinge 1\n",
"gameits 1\n",
"trickwithout 1\n",
"boles 1\n",
"groundout 1\n",
"bedlamite 2\n",
"thingummy 2\n",
"fiftysecond 1\n",
"hayricks 1\n",
"songsor 1\n",
"leavesone 1\n",
"menor 1\n",
"bess 1\n",
"humblest 2\n",
"mischance 2\n",
"churchman 1\n",
"wellsome 1\n",
"purposeand 1\n",
"youthan 1\n",
"byeword 1\n",
"profligates 1\n",
"whatshisnamemr 1\n",
"sket 1\n",
"opining 1\n",
"washiest 1\n",
"perches 1\n",
"halldoor 10\n",
"firemans 1\n",
"christen 1\n",
"reminders 2\n",
"ladders 2\n",
"hod 1\n",
"garish 1\n",
"longdarkened 1\n",
"olympus 1\n",
"plumbers 1\n",
"papafrom 1\n",
"doubtof 1\n",
"genii 1\n",
"birdshaving 1\n",
"teapota 1\n",
"nicknacks 1\n",
"avocationshidden 1\n",
"deceasedmr 1\n",
"buttercups 2\n",
"deceasedsister 1\n",
"spokewith 1\n",
"lucretiaconfidently 1\n",
"barber 1\n",
"doesor 1\n",
"anybodyi 1\n",
"mephistophelean 1\n",
"denmark 1\n",
"dombeywhile 1\n",
"carressed 1\n",
"deepplanted 1\n",
"subjectwhen 1\n",
"thanwhat 1\n",
"sentimentsfor 1\n",
"acquittance 1\n",
"marylebone 1\n",
"silverheaded 5\n",
"istand 1\n",
"soulvery 1\n",
"florencealways 1\n",
"bashaw 1\n",
"girla 1\n",
"burnishing 1\n",
"churchgate 1\n",
"dips 2\n",
"amens 3\n",
"mightand 1\n",
"exclaims 4\n",
"uptosnuff 1\n",
"vassalage 2\n",
"salutes 6\n",
"brides 2\n",
"feenixwhen 1\n",
"troth 2\n",
"spanking 1\n",
"grasps 1\n",
"prance 1\n",
"newmade 1\n",
"illstill 1\n",
"repetitionwith 1\n",
"nowto 1\n",
"inin 2\n",
"boning 1\n",
"giddiness 3\n",
"jellies 1\n",
"nonappearance 1\n",
"adherent 3\n",
"alternatedye 1\n",
"scrupled 1\n",
"gallanthearted 1\n",
"bricked 2\n",
"anyhad 1\n",
"parleyed 1\n",
"dom 1\n",
"compliant 1\n",
"regaling 2\n",
"thereyoud 1\n",
"astern 3\n",
"saidwait 1\n",
"nonebut 1\n",
"broach 1\n",
"gillsi 1\n",
"blurting 1\n",
"underwriters 1\n",
"endooredfor 1\n",
"backard 1\n",
"hiders 1\n",
"oone 1\n",
"coalheaver 1\n",
"vendor 1\n",
"spectacleshe 1\n",
"tablesfantastic 1\n",
"castmere 1\n",
"dometop 1\n",
"swings 1\n",
"didher 1\n",
"instantnot 1\n",
"paralytic 2\n",
"resolutionany 1\n",
"unconstrained 1\n",
"unregarded 1\n",
"indignationtoo 1\n",
"lastnamed 1\n",
"presentment 3\n",
"raked 1\n",
"marwooda 1\n",
"natureas 1\n",
"uglyshe 1\n",
"dangerdanger 1\n",
"givers 2\n",
"heapedtomorrow 1\n",
"priced 3\n",
"lottery 2\n",
"towlinsons 4\n",
"anticipates 2\n",
"elegancies 1\n",
"feethad 1\n",
"deathbeds 1\n",
"rivalmonstrous 1\n",
"lipsbut 1\n",
"timefrom 1\n",
"thisin 1\n",
"whyyet 1\n",
"sawwhatanother 1\n",
"herflorence 2\n",
"commandelicited 1\n",
"kingstonuponthames 1\n",
"pinery 2\n",
"exhilarate 1\n",
"sixtyfive 2\n",
"plateau 1\n",
"anyshire 1\n",
"aroundappeared 1\n",
"thereif 1\n",
"toxi 1\n",
"eyesto 1\n",
"overbearing 2\n",
"uttermost 1\n",
"mannerhoped 1\n",
"repulsing 2\n",
"willshe 1\n",
"edit 1\n",
"principalsbetween 1\n",
"florencethe 1\n",
"undesirable 2\n",
"pervert 2\n",
"deservei 1\n",
"confidentiali 1\n",
"trustworthy 4\n",
"confidingly 1\n",
"personsas 1\n",
"marriageone 1\n",
"almshousefull 1\n",
"zephyrs 1\n",
"fretfulness 1\n",
"erasing 2\n",
"heartor 2\n",
"soulwhat 1\n",
"indistinctness 2\n",
"powderedheaded 1\n",
"oxtail 1\n",
"plentifully 1\n",
"subservient 3\n",
"unsparing 2\n",
"solidify 1\n",
"somebodyd 1\n",
"frequency 2\n",
"sirthere 1\n",
"roundwhich 1\n",
"inducted 1\n",
"youtha 1\n",
"manthan 1\n",
"sealingwax 1\n",
"spiled 1\n",
"lachrymose 2\n",
"dimonds 1\n",
"stanfells 1\n",
"conjecturethe 1\n",
"conversationthe 1\n",
"greenland 3\n",
"wollume 2\n",
"onbut 1\n",
"propertylord 1\n",
"inverting 1\n",
"slaps 1\n",
"ideaand 1\n",
"muzzlings 2\n",
"hiiigh 1\n",
"outcry 1\n",
"forsoothand 1\n",
"conjunctions 2\n",
"presentiments 2\n",
"enslaves 1\n",
"vulnerable 1\n",
"antedate 1\n",
"leagued 1\n",
"giftthat 1\n",
"herselfof 1\n",
"magisterial 1\n",
"stiffer 1\n",
"declamation 1\n",
"grangeby 3\n",
"slavejoe 1\n",
"proach 1\n",
"rasper 1\n",
"heralways 1\n",
"creaturefull 1\n",
"lulls 1\n",
"addlebrained 1\n",
"straightway 1\n",
"assents 3\n",
"iim 3\n",
"lexicon 1\n",
"herodotus 1\n",
"parr 1\n",
"pitilessly 1\n",
"fluster 1\n",
"towithout 1\n",
"gazes 1\n",
"mows 1\n",
"finchburywoman 1\n",
"quaked 3\n",
"suspension 2\n",
"inquirewhether 1\n",
"babbling 5\n",
"positionbetter 1\n",
"adorns 1\n",
"grainmy 1\n",
"moreof 1\n",
"combated 2\n",
"florencethough 1\n",
"housekeepermrs 1\n",
"knowto 2\n",
"raly 2\n",
"pipchinses 1\n",
"handsthough 1\n",
"unloving 3\n",
"drearily 4\n",
"suggestedwhich 1\n",
"mettle 1\n",
"muchalthough 1\n",
"lonesome 2\n",
"miner 1\n",
"roomassailed 1\n",
"nonoo 1\n",
"nonno 1\n",
"gooodbye 1\n",
"abducted 1\n",
"fixtures 3\n",
"knoweh 1\n",
"selfhumiliationwhich 1\n",
"pinion 2\n",
"foresawwho 1\n",
"mindbut 1\n",
"habituated 6\n",
"unwind 1\n",
"investigated 1\n",
"leisurethat 1\n",
"concerna 1\n",
"minnows 1\n",
"cleanness 1\n",
"retouched 1\n",
"handsunless 1\n",
"birdcatchers 1\n",
"mumble 1\n",
"pavingstones 3\n",
"mastermr 1\n",
"robby 9\n",
"officefloor 1\n",
"lumber 2\n",
"foolery 1\n",
"otheras 1\n",
"himthat 2\n",
"placeis 1\n",
"forgottenwitness 1\n",
"manacle 1\n",
"mastervice 1\n",
"generate 1\n",
"offal 2\n",
"retributions 1\n",
"obdurately 2\n",
"uncheered 1\n",
"necessaryit 1\n",
"liegod 1\n",
"medusa 1\n",
"stylewhich 1\n",
"madambegan 1\n",
"marriagetowards 1\n",
"termshis 1\n",
"dombeymr 1\n",
"anybodyanybody 1\n",
"preeminent 1\n",
"taunt 3\n",
"ministration 2\n",
"earlyfull 1\n",
"housemaids 1\n",
"marts 1\n",
"histe 1\n",
"shutter 3\n",
"waggings 1\n",
"displays 2\n",
"contrivance 1\n",
"saidby 1\n",
"acquaintanceyou 1\n",
"unrequited 1\n",
"fretwork 1\n",
"shipsand 1\n",
"fryingpan 1\n",
"sarse 1\n",
"sassage 1\n",
"befor 1\n",
"stagnant 2\n",
"hearthonly 1\n",
"credulity 1\n",
"spiced 1\n",
"sheltering 3\n",
"fragmentswith 1\n",
"connectedly 2\n",
"natur 1\n",
"wessel 2\n",
"childay 1\n",
"captainlook 1\n",
"moderated 1\n",
"somewhereanywherebut 1\n",
"doorfor 1\n",
"delighting 1\n",
"animates 1\n",
"hooroar 14\n",
"courseby 1\n",
"abaft 1\n",
"prowiding 1\n",
"jined 3\n",
"upbut 1\n",
"othersa 1\n",
"preyand 1\n",
"selfaccusation 1\n",
"sidling 6\n",
"inculpated 1\n",
"withoutwhich 1\n",
"personman 1\n",
"middlestated 1\n",
"calibre 1\n",
"distrustfully 1\n",
"elsewormed 1\n",
"chickabiddy 1\n",
"tonights 1\n",
"swoop 4\n",
"tooh 1\n",
"pinchers 1\n",
"hag 1\n",
"jesuitical 1\n",
"aboardshe 1\n",
"dijon 2\n",
"planing 1\n",
"nerving 2\n",
"lamentings 1\n",
"frogs 1\n",
"happenfor 1\n",
"historyall 1\n",
"eventhe 1\n",
"morfinthe 1\n",
"doorwayis 1\n",
"habitcreatures 1\n",
"football 1\n",
"habitthe 1\n",
"ninetenths 1\n",
"matterto 1\n",
"possiblya 1\n",
"probablyruinous 1\n",
"robberyin 1\n",
"giftfor 1\n",
"boudoir 2\n",
"festoons 2\n",
"panels 1\n",
"pardons 6\n",
"francois 1\n",
"chafingdish 2\n",
"tauntingly 2\n",
"shamed 4\n",
"thusforced 1\n",
"hadforced 1\n",
"flightnot 1\n",
"retreatwas 1\n",
"guiltless 1\n",
"gnash 1\n",
"frustration 1\n",
"nightair 1\n",
"undeceived 2\n",
"waywardness 1\n",
"postillion 1\n",
"whoop 2\n",
"himhallo 1\n",
"toolwas 1\n",
"fastexcept 1\n",
"goingoppressed 1\n",
"unweakened 1\n",
"beggarsblind 1\n",
"forwardof 1\n",
"brawling 2\n",
"disembarking 1\n",
"newlybuilt 1\n",
"rattleanother 1\n",
"appeasethese 1\n",
"waterthere 1\n",
"offthe 1\n",
"shoutanothersaw 1\n",
"trembleknew 1\n",
"busting 1\n",
"disrespectable 1\n",
"wouldif 1\n",
"asascircumstances 1\n",
"herewalr 1\n",
"inexpressably 1\n",
"iimean 1\n",
"anywhereeverywhere 1\n",
"masterstroke 2\n",
"walterwellmatched 1\n",
"instrumental 1\n",
"commiserate 1\n",
"steepletop 1\n",
"profeesion 1\n",
"firstnamed 1\n",
"presentations 4\n",
"peacoat 6\n",
"patriark 1\n",
"wereyou 1\n",
"sentencethat 1\n",
"demerara 3\n",
"barbadosi 1\n",
"jamaicai 1\n",
"calclated 1\n",
"whatshername 1\n",
"riseand 1\n",
"footpassengers 1\n",
"govner 1\n",
"oncet 1\n",
"dean 1\n",
"sittings 3\n",
"relaxes 1\n",
"cornercupboard 1\n",
"wardens 1\n",
"tooyour 1\n",
"aggravates 1\n",
"soothes 1\n",
"picter 1\n",
"harmlesssomething 1\n",
"descant 1\n",
"youwould 1\n",
"exceptedwho 1\n",
"grumbler 1\n",
"termsreduced 1\n",
"unapproved 1\n",
"nowabove 1\n",
"bachelorhood 1\n",
"ogle 1\n",
"envys 1\n",
"freewould 1\n",
"matrons 1\n",
"nowmuch 1\n",
"londonthey 1\n",
"satisfactionas 1\n",
"kitchenmaid 2\n",
"chaotic 1\n",
"fasces 1\n",
"chimneypieces 1\n",
"appendage 3\n",
"chaisecarts 3\n",
"dressingrooms 1\n",
"dustthe 1\n",
"wheelbarrow 1\n",
"sardonic 1\n",
"herelittle 1\n",
"mansionfor 1\n",
"herto 1\n",
"derives 1\n",
"snowing 1\n",
"himnor 1\n",
"fortuneoh 1\n",
"redemptionthat 1\n",
"whitherupon 1\n",
"footmarks 1\n",
"contentionfoot 1\n",
"otherperhaps 1\n",
"despoiled 2\n",
"crya 1\n",
"docile 1\n",
"partingfor 1\n",
"widdle 1\n",
"adolescens 1\n",
"dictator 1\n",
"mothertongue 1\n",
"ungratified 1\n",
"myselfallow 1\n",
"saysat 1\n",
"consequencebut 1\n",
"alternations 1\n",
"limehouse 1\n",
"himselffor 1\n",
"accost 1\n",
"bridegrooms 1\n",
"capter 1\n",
"recentlyacquired 1\n",
"dutyfor 1\n",
"pursuitsthrough 1\n",
"listenedsometimes 1\n",
"gaywho 1\n",
"sortwhich 1\n",
"martinets 1\n",
"doas 1\n",
"brownfourbottle 1\n",
"boxingrooms 1\n",
"walteri 1\n",
"innocencefrom 1\n",
"natureswhom 1\n",
"madiera 1\n",
"thicken 4\n",
"slue 1\n",
"roundfor 2\n",
"aan 1\n",
"excel 1\n",
"ascendno 1\n",
"represses 1\n",
"gowan 248\n",
"bermondsey 2\n",
"pentup 1\n",
"fervid 2\n",
"arid 1\n",
"vines 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"builders 2\n",
"lizard 1\n",
"freest 1\n",
"archesdreamily 1\n",
"draughtboard 1\n",
"hacked 1\n",
"prisonroom 2\n",
"mapping 1\n",
"galleys 2\n",
"quarantine 9\n",
"donna 1\n",
"civita 4\n",
"guillotine 1\n",
"signor 13\n",
"baptist 69\n",
"caressingly 1\n",
"munch 1\n",
"cavalletto 46\n",
"compagnon 7\n",
"cigarette 11\n",
"politiciansyour 1\n",
"politicians 3\n",
"manwhich 2\n",
"collisionand 1\n",
"loverson 1\n",
"herassassinating 1\n",
"unbarred 1\n",
"clashedthe 1\n",
"insurrectionary 1\n",
"allong 2\n",
"destinations 2\n",
"tatty 11\n",
"quiets 1\n",
"eyesexactly 1\n",
"clennam 919\n",
"meif 1\n",
"sanding 1\n",
"whithersoever 1\n",
"lattices 1\n",
"raze 1\n",
"languish 1\n",
"butin 1\n",
"ownwhatever 1\n",
"penitential 2\n",
"lairs 1\n",
"unwholesomely 1\n",
"sewer 1\n",
"barest 1\n",
"unserviceable 2\n",
"num 2\n",
"dirtstained 1\n",
"smokeblackened 1\n",
"festooned 1\n",
"oncepopular 1\n",
"bierlike 4\n",
"airless 3\n",
"weaknessnames 1\n",
"illnesswhen 1\n",
"sugarbasin 1\n",
"wrathfullypraying 1\n",
"leprosy 1\n",
"spike 1\n",
"ittheres 1\n",
"roost 2\n",
"snuffers 2\n",
"carefulness 1\n",
"andchoke 1\n",
"cutdown 1\n",
"businessreally 1\n",
"incongruity 1\n",
"consignments 1\n",
"stewardship 1\n",
"sculpture 2\n",
"mothersome 1\n",
"shieldlike 1\n",
"asperses 1\n",
"rigour 1\n",
"afferys 8\n",
"deadcold 1\n",
"bedchambers 1\n",
"bookkeepers 1\n",
"predominant 2\n",
"epitomising 1\n",
"wiles 3\n",
"puffiness 3\n",
"redfacedness 2\n",
"allfourey 1\n",
"dirtiest 1\n",
"waggonstables 1\n",
"haggage 1\n",
"haggages 2\n",
"segment 1\n",
"oclockbeautiful 1\n",
"deduct 1\n",
"overstep 1\n",
"informala 1\n",
"cutaway 1\n",
"saysir 1\n",
"banghamhe 1\n",
"dancingmasters 2\n",
"minuet 2\n",
"cour 1\n",
"yardthe 1\n",
"isseems 1\n",
"millinerwho 1\n",
"insolventwas 1\n",
"starvationanything 1\n",
"palladium 1\n",
"canada 3\n",
"successorship 1\n",
"moorfields 1\n",
"workstanding 1\n",
"whyyes 1\n",
"startledi 1\n",
"dirtily 1\n",
"awaremy 1\n",
"rooma 1\n",
"littletestimonialto 1\n",
"generallyhamoney 1\n",
"oftenhemacceptable 1\n",
"containedahemtwo 1\n",
"nightits 1\n",
"spartan 2\n",
"messengers 1\n",
"castoff 1\n",
"accredited 2\n",
"mendicity 1\n",
"breathings 2\n",
"clarionetstop 1\n",
"extensively 1\n",
"garreta 1\n",
"dirtworn 1\n",
"thinkindeed 1\n",
"tite 31\n",
"backyou 1\n",
"maggy 135\n",
"evnly 2\n",
"chicking 4\n",
"spurious 1\n",
"ungrammatical 1\n",
"bewhat 1\n",
"premier 1\n",
"mechanicians 1\n",
"memorialists 1\n",
"overreached 1\n",
"sanguineous 1\n",
"stiltstalking 17\n",
"fledged 2\n",
"turnpikegates 1\n",
"adduced 1\n",
"flabbiness 1\n",
"footmans 2\n",
"syllablesoffice 1\n",
"recommendedpossiblyi 1\n",
"thepublic 5\n",
"secretarial 1\n",
"wobbler 8\n",
"countersigned 1\n",
"hocus 1\n",
"spectaclecase 3\n",
"swindling 2\n",
"housebreaking 1\n",
"bran 1\n",
"addled 2\n",
"multiplication 1\n",
"overdid 4\n",
"unbarked 1\n",
"untrimmed 1\n",
"cistern 1\n",
"billiard 2\n",
"soupplate 1\n",
"sou 1\n",
"physiognomist 1\n",
"invocations 1\n",
"lagnier 14\n",
"sleepand 1\n",
"sleephow 1\n",
"stageplayer 1\n",
"huntingseatshowbeit 1\n",
"abided 2\n",
"spinster 1\n",
"livethe 1\n",
"cognisance 1\n",
"garnished 2\n",
"limesplashed 1\n",
"sugarplum 1\n",
"cheltenham 1\n",
"aitl 1\n",
"prolix 1\n",
"mightfor 1\n",
"ourselvesthat 1\n",
"womanservant 1\n",
"parlourfire 1\n",
"glanceas 1\n",
"haymaking 2\n",
"proceededtake 1\n",
"scrubby 4\n",
"steamengine 3\n",
"insincerest 1\n",
"commas 2\n",
"motherofpearl 1\n",
"arthurpray 3\n",
"meold 1\n",
"propersince 1\n",
"longabandoned 1\n",
"virginia 1\n",
"wafer 1\n",
"wordswhen 1\n",
"timepoor 1\n",
"boylover 1\n",
"signalling 1\n",
"performancesnow 1\n",
"acoustic 1\n",
"traceable 2\n",
"wellcooked 1\n",
"shrimp 1\n",
"barness 1\n",
"courageously 1\n",
"solomonic 1\n",
"clinches 1\n",
"barbican 1\n",
"porteghee 1\n",
"interpreters 2\n",
"togethermade 1\n",
"ito 1\n",
"momentand 1\n",
"vastness 1\n",
"eatit 1\n",
"dorritexcept 1\n",
"witchregion 1\n",
"guillotinethe 1\n",
"deadweight 1\n",
"accidentallymindfound 1\n",
"unmodulated 1\n",
"sleepyhead 1\n",
"twickenham 13\n",
"unbarring 1\n",
"manfor 1\n",
"northcountry 1\n",
"lockmaker 1\n",
"lockmakers 1\n",
"deepstained 1\n",
"ferryboat 6\n",
"homelimits 1\n",
"travelagain 1\n",
"clennamthen 1\n",
"oncesince 1\n",
"rosaries 1\n",
"pictureroom 1\n",
"tippet 1\n",
"counterdesk 1\n",
"gadding 1\n",
"mustardseed 1\n",
"wadewhere 1\n",
"timecount 1\n",
"rightsuppose 1\n",
"etna 1\n",
"clementina 1\n",
"genealogy 1\n",
"pallmall 1\n",
"legation 1\n",
"inheriting 1\n",
"barnaclesinchief 1\n",
"nasal 2\n",
"disgracefully 2\n",
"gowanwho 1\n",
"lodgefender 1\n",
"penetrable 1\n",
"tombstone 2\n",
"skittle 1\n",
"mumbo 1\n",
"lifesize 1\n",
"fingerpost 1\n",
"marshalseas 1\n",
"wellbeloved 2\n",
"amquite 1\n",
"slopwork 1\n",
"benevolently 4\n",
"infirmities 4\n",
"himperchance 1\n",
"asshall 1\n",
"himmildly 1\n",
"basking 2\n",
"chairback 1\n",
"ihemi 2\n",
"hishemsister 1\n",
"lookthey 1\n",
"muchchanged 1\n",
"seasoned 2\n",
"professionals 1\n",
"heroines 2\n",
"ringed 1\n",
"curbed 1\n",
"suppresses 2\n",
"dominates 1\n",
"mothermoving 1\n",
"dorritthat 1\n",
"spiritedmore 1\n",
"hatchment 2\n",
"bargainwho 1\n",
"projectors 2\n",
"mendid 1\n",
"brunswick 1\n",
"doosed 3\n",
"biggodd 3\n",
"halfamillion 1\n",
"pulverous 1\n",
"judith 1\n",
"capitalists 3\n",
"curiae 1\n",
"sermons 1\n",
"jewelstands 1\n",
"ineligible 1\n",
"importers 1\n",
"cheroots 1\n",
"peculiaran 1\n",
"hopelessnewer 1\n",
"probity 2\n",
"pervertbut 1\n",
"additionally 1\n",
"ascertainable 1\n",
"shortcommons 1\n",
"insteps 1\n",
"milestones 2\n",
"addwhen 1\n",
"clennamwhen 1\n",
"confessing 1\n",
"needlewoman 1\n",
"credits 1\n",
"revolved 1\n",
"da 1\n",
"panckss 26\n",
"fished 1\n",
"pauperising 1\n",
"haranguing 1\n",
"worriting 1\n",
"harrying 2\n",
"readmission 1\n",
"finchings 2\n",
"fiat 1\n",
"travellingpeople 1\n",
"friendtoo 1\n",
"rushbottomed 2\n",
"salmon 1\n",
"talker 1\n",
"eyewitness 1\n",
"brand 1\n",
"blubber 1\n",
"nowall 1\n",
"cottagedoor 1\n",
"dustiest 1\n",
"anastatia 1\n",
"broaching 1\n",
"bedfordshire 1\n",
"stillborn 1\n",
"splutter 1\n",
"illhumour 1\n",
"ploddingly 1\n",
"manful 1\n",
"patriots 1\n",
"jupiter 3\n",
"affectations 1\n",
"receivers 1\n",
"embassies 1\n",
"lancaster 2\n",
"refrigerator 5\n",
"nomadic 1\n",
"microscopically 1\n",
"swinish 1\n",
"wheresomewhere 1\n",
"porticoes 1\n",
"greengrocers 1\n",
"poulterer 1\n",
"particoloured 1\n",
"straws 2\n",
"newssellers 1\n",
"seeis 1\n",
"apprise 1\n",
"sweetened 1\n",
"sympathywhat 1\n",
"nobodyhe 1\n",
"numberit 1\n",
"happinessfor 1\n",
"invalids 1\n",
"garraways 1\n",
"socialities 2\n",
"trio 1\n",
"curt 1\n",
"harderas 1\n",
"thatthan 1\n",
"dissolving 2\n",
"enigma 1\n",
"moustachejet 1\n",
"redand 1\n",
"packetboat 2\n",
"characterstill 1\n",
"highhooked 1\n",
"apologisefor 1\n",
"teaservice 3\n",
"pardonbut 1\n",
"gulps 1\n",
"shipwreckedcan 1\n",
"picturesquei 1\n",
"perhapsbut 1\n",
"thumbed 1\n",
"feudal 1\n",
"tenantry 2\n",
"speciality 2\n",
"unmolested 3\n",
"youfanny 1\n",
"expressionyou 1\n",
"prevaricating 1\n",
"mayhaif 1\n",
"answerand 1\n",
"manvery 1\n",
"gloomed 2\n",
"properlyworded 2\n",
"mindand 1\n",
"ofhahospitality 1\n",
"leasthumnot 1\n",
"youhaan 1\n",
"erasure 1\n",
"draggle 1\n",
"littlein 1\n",
"giipsy 1\n",
"plumpudding 1\n",
"implicate 2\n",
"understandsnorted 1\n",
"goodbreeding 1\n",
"foisted 1\n",
"selfcondolence 1\n",
"jobmasters 1\n",
"showwindow 1\n",
"themthey 1\n",
"edmund 55\n",
"personjob 1\n",
"priestess 1\n",
"wateringcart 1\n",
"provides 2\n",
"captivate 1\n",
"inference 1\n",
"itcause 1\n",
"ownoccasionally 1\n",
"treasuried 1\n",
"convocation 1\n",
"prospero 1\n",
"thatthough 1\n",
"hocuspocus 2\n",
"impairing 1\n",
"bamboozled 1\n",
"courtcards 1\n",
"applicants 1\n",
"believer 1\n",
"yesbut 1\n",
"tracking 1\n",
"moleing 2\n",
"dryest 1\n",
"ninepin 2\n",
"dividends 1\n",
"unclepoor 1\n",
"himmessengers 1\n",
"lookshait 1\n",
"supporters 1\n",
"neighbourhoodthough 1\n",
"computed 1\n",
"politer 2\n",
"examplewhich 1\n",
"frederickif 1\n",
"roger 1\n",
"coverley 1\n",
"indictment 2\n",
"bernard 10\n",
"pickedup 1\n",
"sunning 2\n",
"mules 13\n",
"iciclehung 1\n",
"kegs 1\n",
"bystander 2\n",
"enfolding 1\n",
"waitingmaids 1\n",
"slighter 2\n",
"ishais 1\n",
"ihafor 1\n",
"occasionsi 1\n",
"consecutively 1\n",
"sohavery 1\n",
"ihayes 1\n",
"arehumquite 1\n",
"hesitatingperhaps 1\n",
"sleepingroom 1\n",
"lavishly 1\n",
"comport 1\n",
"mentor 1\n",
"takenhad 1\n",
"displacing 1\n",
"floury 1\n",
"waxy 1\n",
"blownout 1\n",
"varnishedif 1\n",
"tempering 1\n",
"lowminded 1\n",
"demeaned 1\n",
"ignorantly 2\n",
"altogetherin 1\n",
"rivulets 1\n",
"dales 1\n",
"travellingcarriage 2\n",
"vetturalike 1\n",
"scalpels 1\n",
"havehahad 1\n",
"onhawhat 1\n",
"colloquial 1\n",
"muledrivers 1\n",
"factwishes 1\n",
"tohumto 3\n",
"garlanded 1\n",
"props 1\n",
"gondoliers 4\n",
"gaieties 1\n",
"broadcushioned 1\n",
"exults 1\n",
"thatit 1\n",
"psparticularly 2\n",
"thereperhaps 1\n",
"partialities 1\n",
"hano 1\n",
"thoughhahimself 1\n",
"inhumretirement 1\n",
"urbane 1\n",
"eustace 4\n",
"tourist 1\n",
"youhaembarrass 1\n",
"begihai 1\n",
"splendours 1\n",
"wishhaaltogether 1\n",
"byha 1\n",
"expectthat 1\n",
"ladyhamrs 1\n",
"reproduce 1\n",
"seemhumi 1\n",
"confiscated 1\n",
"varnishing 1\n",
"butha 2\n",
"superiorities 1\n",
"brooches 2\n",
"laud 1\n",
"fivefranc 2\n",
"mites 1\n",
"studio 1\n",
"professore 3\n",
"daub 1\n",
"turnwhatever 1\n",
"apologising 1\n",
"gaby 1\n",
"augury 1\n",
"painthaportraits 1\n",
"mermen 1\n",
"armoury 1\n",
"trumps 1\n",
"maecenas 1\n",
"forsworn 1\n",
"craftfor 1\n",
"nohumsuspicion 1\n",
"yetcomprising 1\n",
"philanthropist 1\n",
"sieve 1\n",
"priesthood 1\n",
"moderns 1\n",
"thricebut 1\n",
"tohumshow 1\n",
"thehumgreat 1\n",
"obstructing 1\n",
"invents 1\n",
"abhorred 1\n",
"linesbreadth 1\n",
"yearsthe 1\n",
"waitingrooms 3\n",
"janitors 1\n",
"countersigning 2\n",
"countercountersigning 1\n",
"ordershear 1\n",
"tapered 1\n",
"mythical 1\n",
"tryno 1\n",
"palerprobably 1\n",
"wordnow 1\n",
"answersas 1\n",
"outbut 1\n",
"quiverings 1\n",
"streetlamps 1\n",
"wherries 1\n",
"byestreet 1\n",
"reseated 1\n",
"forfeited 1\n",
"fullcoloured 1\n",
"boisterously 2\n",
"halfchoking 1\n",
"placemillions 1\n",
"timesthan 1\n",
"beforeohi 1\n",
"feeblest 1\n",
"farthingcandle 1\n",
"trustthough 1\n",
"warbling 1\n",
"evasively 1\n",
"erudition 1\n",
"droopwhom 1\n",
"cuyp 2\n",
"foremans 1\n",
"endowedin 1\n",
"eighteenpennyworth 1\n",
"sagacitynot 1\n",
"practicalof 1\n",
"laudably 1\n",
"fixes 3\n",
"woollen 1\n",
"grossly 1\n",
"buttonedup 5\n",
"fascinates 1\n",
"condense 1\n",
"decoy 1\n",
"sluggishly 1\n",
"busk 1\n",
"sirid 1\n",
"impersonal 1\n",
"shopparlour 1\n",
"shopbell 1\n",
"fertility 1\n",
"doublings 1\n",
"bronchial 1\n",
"disseminated 1\n",
"loops 1\n",
"journeyman 1\n",
"gluttons 1\n",
"positionhigh 1\n",
"tiber 2\n",
"offencein 1\n",
"toilettetable 1\n",
"forgottenthe 1\n",
"adviceleft 1\n",
"counterbalance 1\n",
"steamship 1\n",
"thinkmerely 1\n",
"flushedwhich 1\n",
"enslavers 1\n",
"writingmasters 1\n",
"swans 1\n",
"ishuma 1\n",
"buthuminsist 1\n",
"hahum 1\n",
"canhabe 1\n",
"ishacontracted 1\n",
"gracefullest 1\n",
"milton 1\n",
"watt 1\n",
"myriad 1\n",
"isto 1\n",
"houseyou 1\n",
"itseems 2\n",
"attractionsused 1\n",
"vesta 1\n",
"fleeces 1\n",
"piped 1\n",
"housewindows 1\n",
"wrack 1\n",
"sententious 1\n",
"hashagreatly 1\n",
"occasionhateeming 1\n",
"childhumto 1\n",
"tohumconsolidate 1\n",
"andhaas 1\n",
"ofhasolemnly 1\n",
"tohamrs 1\n",
"thehumaffectionate 1\n",
"fromhumfrom 1\n",
"ottomans 1\n",
"shouldhaat 1\n",
"ishamy 1\n",
"seenno 1\n",
"lombards 1\n",
"humdaily 1\n",
"arthurquite 1\n",
"clennamcannot 1\n",
"ahumpatron 1\n",
"volcanoes 1\n",
"formerlyhumsomehaslight 1\n",
"grimness 1\n",
"ishaexcuse 1\n",
"meis 1\n",
"manifold 1\n",
"bridgea 1\n",
"wishedbut 1\n",
"andhummaintain 1\n",
"ihumdont 1\n",
"myjohn 1\n",
"humgo 1\n",
"johnhastay 1\n",
"youllhaoverlookhumwhat 1\n",
"wehaagree 1\n",
"sittingbourne 1\n",
"castlebuilding 3\n",
"filth 1\n",
"herdsmen 1\n",
"roadnow 1\n",
"humto 1\n",
"becausehathats 1\n",
"lesshacoherent 1\n",
"haveha 1\n",
"humever 1\n",
"frederickha 1\n",
"thathasuperior 1\n",
"rosary 1\n",
"humbroken 1\n",
"byhaall 1\n",
"ahumhusband 1\n",
"mehummade 1\n",
"condescensionmuch 1\n",
"serviceappeared 1\n",
"dutyhadevolves 1\n",
"ishalimitedlimitedthe 1\n",
"thehacollegiate 1\n",
"watergeneral 1\n",
"admirerspersonal 1\n",
"offeringhalittle 1\n",
"arehumhighly 1\n",
"stormbeaten 1\n",
"sandhills 1\n",
"peregrination 1\n",
"counterclaimed 1\n",
"tinkles 1\n",
"surfacetapping 1\n",
"contesting 1\n",
"coolhanded 1\n",
"halfhidden 1\n",
"knowtell 1\n",
"mortifications 1\n",
"slavemarket 1\n",
"vineyard 1\n",
"uncivilised 1\n",
"workmans 1\n",
"ensues 1\n",
"mankindarthur 1\n",
"unpolitely 1\n",
"emblematical 1\n",
"thrall 1\n",
"arthurconfirmed 1\n",
"clennamtook 1\n",
"mouldiness 1\n",
"exemplar 1\n",
"ennobled 1\n",
"phrasehad 1\n",
"flestrin 1\n",
"contrastrequire 1\n",
"amymy 1\n",
"circumstancesis 1\n",
"twoshoes 1\n",
"tile 2\n",
"coatsleeves 1\n",
"oursin 1\n",
"veindeath 1\n",
"straightening 1\n",
"windingup 1\n",
"mantrap 1\n",
"stirrers 1\n",
"hulls 1\n",
"magazines 1\n",
"faultor 1\n",
"mediation 1\n",
"exonerating 1\n",
"flagrancy 1\n",
"tyfling 1\n",
"wayshe 1\n",
"imprison 1\n",
"erred 1\n",
"chiverytrue 1\n",
"cabbageleaf 1\n",
"audaciousness 1\n",
"prairie 1\n",
"manwhen 1\n",
"startingpoint 1\n",
"nobleness 1\n",
"ups 2\n",
"rewolve 1\n",
"wordsnot 1\n",
"usofficial 1\n",
"grinds 1\n",
"watchwords 1\n",
"dustmans 1\n",
"showerbath 1\n",
"saddlehorse 1\n",
"butafter 1\n",
"carrieswhitewho 1\n",
"secrettementally 2\n",
"smokes 2\n",
"forefingerhe 1\n",
"portoporto 1\n",
"havedo 1\n",
"unharmed 1\n",
"della 1\n",
"sells 2\n",
"insupportably 1\n",
"pigmarket 1\n",
"pleasantsmelling 1\n",
"themto 1\n",
"mineyou 1\n",
"deartake 1\n",
"deara 1\n",
"undying 1\n",
"prisonersmadmen 1\n",
"chatterbox 1\n",
"bootheels 1\n",
"hotelnote 1\n",
"ladyproofsof 1\n",
"myselfa 1\n",
"notnot 1\n",
"beelzebub 1\n",
"gilbert 4\n",
"momentnot 1\n",
"farremoved 1\n",
"complicity 1\n",
"codicil 2\n",
"unsettling 1\n",
"unstopping 1\n",
"tubswhere 1\n",
"forsooth 2\n",
"raspedrasped 1\n",
"rasp 1\n",
"rummaging 1\n",
"ephraim 1\n",
"hammerheaded 1\n",
"outdont 1\n",
"headcovering 1\n",
"recommending 1\n",
"crossstreets 1\n",
"hague 1\n",
"arithmetician 1\n",
"unimpugnable 1\n",
"reconcilable 1\n",
"harrowed 1\n",
"wringer 1\n",
"grubbers 3\n",
"worstlooking 1\n",
"fulllength 1\n",
"shams 1\n",
"shears 2\n",
"stewpan 1\n",
"bigheaded 1\n",
"illlaunched 1\n",
"quarterings 1\n",
"ifpolitely 1\n",
"sortthey 1\n",
"upwhich 1\n",
"bygones 2\n",
"antecedent 1\n",
"toand 1\n",
"beenyou 1\n",
"spouted 1\n",
"torpor 1\n",
"readier 1\n",
"sibyllic 1\n",
"herecame 1\n",
"starred 1\n",
"alld 1\n",
"lineright 1\n"
]
}
],
"source": [
"def strip(s): return ''.join(filter(str.isalpha, s))\n",
"books = sc.textFile(\"file:///home/oxclo/datafiles/books/*\")\n",
"\n",
"split = books.flatMap(lambda line: line.split())\n",
"stripped = split.map(strip)\n",
"notempty = stripped.filter(lambda w: len(w)>0)\n",
"\n",
"# now map the words to lower case\n",
"lowercase = notempty.map(lambda x:x.lower())\n",
"\n",
"# next convert the words into (k,v) pairs, where the key is the word, and the value is the count so far (1)\n",
"pairs = lowercase.map(lambda x:(x,1))\n",
"\n",
"wordcount = pairs.reduceByKey(lambda x,y: x+y)\n",
"# make sure your final variable is called wordcount, so this next line will print it out\n",
"\n",
"\n",
"for k,v in wordcount.collect(): \n",
" print (k,v)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
|
abevieiramota/data-science-cookbook | 2016/linear-regression/mlr_solution.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prática - 19/10\n",
"## Regressão Linear Múltipla e Regressão Polinomial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Implemente o coeficiente de determinação ajustado\n",
"2. Faça uma regressão polinomial no dataset aerogerador.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(__doc__)\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn import linear_model\n",
"\n",
"#Questão 1\n",
"def total_sum_of_squares(y):\n",
" mean_y = np.mean(y)\n",
" return sum((v-mean_y)**2 for v in y)\n",
"\n",
"def r_squared(y,yb):\n",
" #y = valor real; yb = valor real\n",
" return 1.0 - sum((y-yb)**2)/total_sum_of_squares(y)\n",
"\n",
"def adjusted_r_squared(y,yb,p):\n",
" #y = valor real; yb = valor real\n",
" #p = numero de parametros(coeficientes da regressão)\n",
" #n = numero de amostras\n",
" n = len(y)\n",
" return 1.0 - (sum((y-yb)**2)/(n-p))/(total_sum_of_squares(y)/(n-1))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Questão 2\n",
"def expand_features(x,k):\n",
" n = len(x)\n",
" ones = np.ones(n).reshape(-1,1)\n",
" x = x.reshape(-1,1)\n",
" polynomial_x = np.append(ones,x,axis=1) #axis=1 append de coluna\n",
" for i in range(2,k+1):\n",
" polynomial_x = np.append(polynomial_x,x**i,axis=1)\n",
" return polynomial_x\n",
" \n",
"data = np.loadtxt(\"aerogerador.txt\",delimiter=\",\")\n",
"\n",
"rdata = np.random.permutation(data)\n",
"X = rdata[:,0]\n",
"y = rdata[:,1]\n",
"\n",
"k = 4\n",
"expanded_X = expand_features(X,k)\n",
"#l = número de linhas, c = número de colunas\n",
"print expanded_X.shape\n",
"\n",
"nt = int(len(expanded_X) * 0.8)\n",
"X_train = expanded_X[:nt,:]\n",
"X_test = expanded_X[nt:,:]\n",
"y_train = y[:nt]\n",
"y_test = y[nt:]\n",
"\n",
"regr = linear_model.LinearRegression()\n",
"regr.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yb = regr.predict(X_test)\n",
"# The coefficients\n",
"print('Coefficients: \\n', [regr.coef_ , regr.intercept_])\n",
"# The mean squared error\n",
"print(\"Mean squared error: %.2f\"\n",
" % np.mean((yb - y_test) ** 2))\n",
"print (\"R-squared: %.2f\" % r_squared(yb,y_test))\n",
"print (\"Adjusted R-squared: %.2f\" % adjusted_r_squared(yb,y_test,k+1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plot outputs\n",
"x1 = X_test[:,1]\n",
"plt.scatter(x1, y_test, color='black')\n",
"plt.plot(np.sort(x1), np.sort(yb), color='blue',linewidth=3)\n",
"\n",
"plt.xticks(())\n",
"plt.yticks(())\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| physics_data/UVB/grackle_tables/photo.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Author: Britton Smith\n",
"\n",
"\n",
"Modified by : Andrew Emerick"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import h5py\n",
"from matplotlib import pyplot\n",
"%matplotlib inline\n",
"import numpy as np\n",
"\n",
"LW_model = 'Qin2020'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pyplot.rcParams['figure.figsize'] = (10, 6)\n",
"pyplot.rcParams['font.size'] = 14"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from make_table import k31_JW2012, k31_RFT14, k31_Qin2020"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def interpvals(xtab, ytab, x, log=True):\n",
" i = np.digitize(x, xtab) - 1\n",
" i = np.clip(i, 0, xtab.size-2)\n",
" if log:\n",
" m = np.log10(ytab[i+1] / ytab[i]) / np.log10(xtab[i+1] / xtab[i])\n",
" return np.power(10, m * np.log10(x / xtab[i]) + np.log10(ytab[i]))\n",
" else:\n",
" m = (ytab[i+1] - ytab[i]) / (xtab[i+1] - xtab[i])\n",
" return m * (x - xtab[i]) + ytab[i]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def load_rates(filename, group, rates, zero_val=1e-50):\n",
" print (\"Loading rates from %s: %s\" % (filename, rates))\n",
" data = {}\n",
" fh = h5py.File(filename, 'r')\n",
" g = fh['UVBRates']\n",
" data['z'] = g['z'].value\n",
" for rate in rates:\n",
" data[rate] = g[group][rate].value\n",
" data[rate] = np.clip(data[rate], 1e-50, np.inf)\n",
" fh.close()\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def plot_rates(z, filenames, group, rates):\n",
" pyplot.xscale('log')\n",
" pyplot.yscale('log')\n",
" lss = ['-', '--', ':']\n",
" cmap = pyplot.cm.jet\n",
" tdata = dict((filename, load_rates(filename, group, rates))\n",
" for filename in filenames)\n",
" for ir, rate in enumerate(rates):\n",
" for ifn, fn in enumerate(filenames):\n",
" ztab = tdata[fn]['z']\n",
" my_rate = interpvals(ztab+1, tdata[fn][rate], z+1)\n",
" if ifn == 0:\n",
" label = rate\n",
" else:\n",
" label = None\n",
" pyplot.plot(z+1, my_rate, linestyle=lss[ifn],\n",
" color=cmap((ir+1)/len(rates)),\n",
" label=label)\n",
" pyplot.xlim(z[0]+1, z[-1]+1)\n",
" pyplot.xlabel('z+1')\n",
" pyplot.ylabel('rates [CGS]')\n",
" pyplot.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regular tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solid lines are original tables, dashed lines are the new tables."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012.h5: ['k24', 'k25', 'k26', 'k29', 'k30']\n",
"Loading rates from CloudyData_HM2012_highz.h5: ['k24', 'k25', 'k26', 'k29', 'k30']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/aemerick/anaconda3/lib/python3.7/site-packages/h5py/_hl/dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
" \"Use dataset[()] instead.\", H5pyDeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"(1e-29, 1e-11)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"plot_rates(z, ['CloudyData_UVB=HM2012.h5',\n",
" 'CloudyData_HM2012_highz.h5'],\n",
" 'Chemistry', ['k24', 'k25', 'k26', 'k29', 'k30'])\n",
"pyplot.ylim(1e-29, 1e-11)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012.h5: ['piHI', 'piHeI', 'piHeII']\n",
"Loading rates from CloudyData_HM2012_highz.h5: ['piHI', 'piHeI', 'piHeII']\n"
]
},
{
"data": {
"text/plain": [
"(1e-26, 1e-11)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"plot_rates(z, ['CloudyData_UVB=HM2012.h5',\n",
" 'CloudyData_HM2012_highz.h5'],\n",
" 'Photoheating', ['piHI', 'piHeI', 'piHeII'])\n",
"pyplot.ylim(1e-26, 1e-11)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012.h5: ['k27', 'k28', 'k31']\n",
"Loading rates from CloudyData_HM2012_highz.h5: ['k27', 'k28', 'k31']\n"
]
},
{
"data": {
"text/plain": [
"(1e-19, 1e-07)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"\n",
"\n",
"pyplot.plot(z, k31_RFT14(z), color='red', label='k31 JHW')\n",
"pyplot.plot(z, k31_JW2012(z), color='red', ls = ':', label='k31 JW2012')\n",
"pyplot.plot(z, k31_Qin2020(z), color='red', ls = '-.', label='k31 Qin2020')\n",
"\n",
"\n",
"\n",
"plot_rates(z, ['CloudyData_UVB=HM2012.h5',\n",
" 'CloudyData_HM2012_highz.h5'],\n",
" 'Chemistry', ['k27', 'k28', 'k31'])\n",
"pyplot.ylim(1e-19, 1e-7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Self-shielded tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solid lines are original tables, dashed lines are the new tables."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012_shielded.h5: ['k24', 'k25', 'k26', 'k29', 'k30']\n",
"Loading rates from CloudyData_HM2012_highz_shielded.h5: ['k24', 'k25', 'k26', 'k29', 'k30']\n"
]
},
{
"data": {
"text/plain": [
"(1e-29, 1e-11)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"plot_rates(z, ['CloudyData_UVB=HM2012_shielded.h5',\n",
" 'CloudyData_HM2012_highz_shielded.h5'],\n",
" 'Chemistry', ['k24', 'k25', 'k26', 'k29', 'k30'])\n",
"pyplot.ylim(1e-29, 1e-11)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012_shielded.h5: ['piHI', 'piHeI', 'piHeII']\n",
"Loading rates from CloudyData_HM2012_highz_shielded.h5: ['piHI', 'piHeI', 'piHeII']\n"
]
},
{
"data": {
"text/plain": [
"(1e-26, 1e-11)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"plot_rates(z, ['CloudyData_UVB=HM2012_shielded.h5',\n",
" 'CloudyData_HM2012_highz_shielded.h5'],\n",
" 'Photoheating', ['piHI', 'piHeI', 'piHeII'])\n",
"pyplot.ylim(1e-26, 1e-11)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading rates from CloudyData_UVB=HM2012_shielded.h5: ['k27', 'k28', 'k31']\n",
"Loading rates from CloudyData_HM2012_highz_shielded.h5: ['k27', 'k28', 'k31']\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8e5a8dfba8>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"z = np.linspace(0, 50, 100)\n",
"plot_rates(z, ['CloudyData_UVB=HM2012_shielded.h5',\n",
" 'CloudyData_HM2012_highz_shielded.h5'],\n",
" 'Chemistry', ['k27', 'k28', 'k31'])\n",
"pyplot.ylim(1e-19, 1e-7)\n",
"\n",
"\n",
"pyplot.plot(z, k31_RFT14(z), color='red', label='k31 JHW')\n",
"pyplot.plot(z, k31_JW2012(z), color='red', ls = ':', label='k31 JW2012')\n",
"pyplot.plot(z, k31_Qin2020(z), color='red', ls = '-.', label='k31 Qin2020')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
shngli/Data-Mining-Python | Mining massive datasets/MapReduce SVM.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MapReduce / SVM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 1\n",
"Suppose our input data to a map-reduce operation consists of integer values (the keys are not important). The map function takes an integer i and produces the list of pairs (p,i) such that p is a prime divisor of i. For example, map(12) = [(2,12), (3,12)]. The reduce function is addition. That is, reduce(p, [i1, i2, ..., ik]) is (p, i1 + i2 +...+ ik). Compute the output, if the input is the set of integers 15, 21, 24, 30, 49."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Integer: 15\n",
"Prime divisor(s): [3, 5]\n",
"Integer: 21\n",
"Prime divisor(s): [3, 7]\n",
"Integer: 24\n",
"Prime divisor(s): [2, 3]\n",
"Integer: 30\n",
"Prime divisor(s): [2, 3, 5]\n",
"Integer: 49\n",
"Prime divisor(s): [7]\n"
]
}
],
"source": [
"from collections import defaultdict\n",
"import math\n",
"\n",
"# determine if an integer n is a prime number\n",
"def isPrime(n):\n",
" if n == 2:\n",
" return True\n",
" if n%2 == 0 or n <= 1:\n",
" return False\n",
" sqr = int(math.sqrt(n)) + 1\n",
" for divisor in range(3, sqr, 2):\n",
" if n%divisor == 0:\n",
" return False\n",
" return True\n",
"\n",
"# Output the prime divisors of each integers\n",
"reduce = defaultdict(list)\n",
"def map(integer):\n",
" output = []\n",
" for i in range(2, integer):\n",
" if isPrime(i) and integer%i == 0:\n",
" output.append(i)\n",
" return output\n",
"\n",
"# Input list of integers\n",
"integer = [15, 21, 24, 30, 49]\n",
"\n",
"# Print every integer and its prime divisor(s)\n",
"# eg. The prime divisors of 15 are 3 & 5\n",
"for n in integer:\n",
" print \"Integer:\", n\n",
" primeDivisor = map(n)\n",
" print \"Prime divisor(s):\", primeDivisor\n",
" for key in primeDivisor:\n",
" reduce[key].append(n)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"prime divisor and the sum of integers: 2 , 54\n",
"prime divisor and the sum of integers: 3 , 90\n",
"prime divisor and the sum of integers: 5 , 45\n",
"prime divisor and the sum of integers: 7 , 70\n"
]
}
],
"source": [
"for key, values in reduce.items():\n",
" print \"prime divisor and the sum of integers:\", key, \",\", sum(values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 2\n",
"Use the matrix-vector multiplication and apply the Map function to this matrix and vector:\n",
"\n",
"| 1 | 2 | 3 | 4 | | 1 |\n",
"|---|---|---|---| |---|\n",
"| 5 | 6 | 7 | 8 | | 2 |\n",
"| 9 | 10 | 11 | 12 | | 3 |\n",
"| 13 | 14 | 15 | 16 | | 4 |\n",
"\n",
"Then, identify the key-value pairs that are output of Map."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M = np.array([[1, 2, 3, 4],\n",
" [5, 6, 7, 8],\n",
" [9, 10, 11, 12],\n",
" [13, 14, 15, 16],])\n",
"\n",
"v = np.array([1, 2, 3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mr(M, v):\n",
" t = []\n",
" mr, mc = M.shape\n",
" for i in range(mc):\n",
" for j in range(mr):\n",
" t.append((i, M[i, j] * v[j]))\n",
"\n",
" t = sorted(t, key=lambda x:x[0])\n",
" for x in t:\n",
" print (x[0]+1, x[1])\n",
"\n",
" r = np.zeros((mr, 1))\n",
" for key, vals in itertools.groupby(t, key=lambda x:x[0]):\n",
" vals = [x[1] for x in vals]\n",
" r[key] = sum(vals)\n",
" print '%s, %s' % (key, sum(vals))\n",
" return r.transpose()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 1)\n",
"(1, 4)\n",
"(1, 9)\n",
"(1, 16)\n",
"(2, 5)\n",
"(2, 12)\n",
"(2, 21)\n",
"(2, 32)\n",
"(3, 9)\n",
"(3, 20)\n",
"(3, 33)\n",
"(3, 48)\n",
"(4, 13)\n",
"(4, 28)\n",
"(4, 45)\n",
"(4, 64)\n",
"0, 30\n",
"1, 70\n",
"2, 110\n",
"3, 150\n",
"[[ 30. 70. 110. 150.]]\n"
]
}
],
"source": [
"#print np.dot(M, v.transpose())\n",
"print mr(M, v)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 3\n",
"Suppose we have the following relations:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "iVBORw0KGgoAAAANSUhEUgAAAIEAAACRCAIAAABrFEycAAAYTmlDQ1BJQ0MgUHJvZmlsZQAAWAmt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\nePwV2kCbk3+FiN0XA+WAPCeUA/Ic/A+MElm/PnUsCgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='relations.jpeg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and we take their natural join. Apply the Map function to the tuples of these relations. Then, construct the elements that are input to the Reduce function. Identify these elements."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"R = [(0, 1),\n",
" (1, 2),\n",
" (2, 3)]\n",
"\n",
"S = [(0, 1),\n",
" (1, 2),\n",
" (2, 3)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hash_join(R, S):\n",
" h = {}\n",
" for a, b in R:\n",
" h.setdefault(b, []).append(a)\n",
"\n",
" j = []\n",
" for b, c in S:\n",
" if not h.has_key(b):\n",
" continue\n",
" for r in h[b]:\n",
" j.append( (r, b, c) )\n",
"\n",
" return j"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mr(R, S):\n",
" m = []\n",
" for a, b in R:\n",
" m.append( (b, ('R', a)) )\n",
" for b, c in S:\n",
" m.append( (b, ('S', c)) )\n",
"\n",
" m = sorted(m, key=lambda x:x[0])\n",
"\n",
" r = []\n",
" for key, vals in itertools.groupby(m, key=lambda x:x[0]):\n",
" vals = [x[1] for x in vals]\n",
" print key, vals\n",
" rs = [x for x in vals if x[0] == 'R']\n",
" ss = [x for x in vals if x[0] == 'S']\n",
" for ri in rs:\n",
" for si in ss:\n",
" r.append( (ri[1], key, si[1]) )\n",
" return r"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 1, 2), (1, 2, 3)]\n",
"0 [('S', 1)]\n",
"1 [('R', 0), ('S', 2)]\n",
"2 [('R', 1), ('S', 3)]\n",
"3 [('R', 2)]\n",
"[(0, 1, 2), (1, 2, 3)]\n"
]
}
],
"source": [
"print hash_join(R, S)\n",
"print mr(R, S)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Question 4\n",
"The figure below shows two positive points (purple squares) and two negative points (green circles): "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "R0lGODlhaAH6APcAAAAAAADMmZkzZv///wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwEC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\nADs=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='svm1.jpeg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is, the training data set consists of:\n",
"- (x1,y1) = ((5,4),+1)\n",
"- (x2,y2) = ((8,3),+1)\n",
"- (x3,y3) = ((7,2),-1)\n",
"- (x4,y4) = ((3,3),-1)\n",
"\n",
"Our goal is to find the maximum-margin linear classifier for this data. In easy cases, the shortest line between a positive and negative point has a perpendicular bisector that separates the points. If so, the perpendicular bisector is surely the maximum-margin separator. Alas, in this case, the closest pair of positive and negative points, x2 and x3, have a perpendicular bisector that misclassifies x1 as negative, so that won't work.\n",
"\n",
"The next-best possibility is that we can find a pair of points on one side (i.e., either two positive or two negative points) such that a line parallel to the line through these points is the maximum-margin separator. In these cases, the limit to how far from the two points the parallel line can get is determined by the closest (to the line between the two points) of the points on the other side. For our simple data set, this situation holds.\n",
"\n",
"Consider all possibilities for boundaries of this type, and express the boundary as w.x+b=0, such that w.x+b≥1 for positive points x and w.x+b≤-1 for negative points x. Assuming that w = (w1,w2), identify the value of w1, w2, and b."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"P = [((5, 4), 1),\n",
" ((8, 3), 1),\n",
" ((3, 3), -1),\n",
" ((7, 2), -1)]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def line(pl0, pl1, p):\n",
" dx, dy = pl1[0] - pl0[0], pl1[1] - pl0[1]\n",
" a = abs((pl1[1] - pl0[1]) * p[0] - (pl1[0] - pl0[0]) * p[1] + pl1[0]*pl0[1] - pl0[0]*pl1[1])\n",
" return a / math.sqrt(dx*dx + dy*dy)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def closest(L, pts):\n",
" dist = [line(L[0][0], L[1][0], x[0]) for x in pts]\n",
" ix = np.argmin(dist)\n",
" return pts[ix], dist[ix]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def solve(A, B):\n",
" # find the point in B closest to the line through both points in A\n",
" p, d = closest(A, B)\n",
"\n",
" M = np.hstack((\n",
" np.array([list(x[0]) for x in A] + [list(p[0])]),\n",
" np.ones((3, 1))))\n",
" b = np.array([x[1] for x in A] + [p[1]])\n",
" x = np.linalg.solve(M, b)\n",
" return x, d"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"S = [solve([a for a in P if a[1] == 1], [a for a in P if a [1] == -1]),\n",
" solve([a for a in P if a[1] == -1], [a for a in P if a [1] == 1])]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w1 = 0.50\n",
"w2 = 1.50\n",
"b = -7.50\n"
]
}
],
"source": [
"ix = np.argmax([x[1] for x in S])\n",
"x = S[ix][0]\n",
"print 'w1 = %0.2f' % x[0]\n",
"print 'w2 = %0.2f' % x[1]\n",
"print 'b = %0.2f' % x[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 5\n",
"Consider the following training set of 16 points. The eight purple squares are positive examples, and the eight green circles are negative examples."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "R0lGODdh3AHxAXAAACH+G1NvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlAAAsAAAAANwB8QGHAAAA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\nQkRAAAA7\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='newsvm4.jpeg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We propose to use the diagonal line with slope +1 and intercept +2 as a decision boundary, with positive examples above and negative examples below. However, like any linear boundary for this training set, some examples are misclassified. We can measure the goodness of the boundary by computing all the slack variables that exceed 0, and then using them in one of several objective functions. In this problem, we shall only concern ourselves with computing the slack variables, not an objective function.\n",
"\n",
"To be specific, suppose the boundary is written in the form w.x+b=0, where w = (-1,1) and b = -2. Note that we can scale the three numbers involved as we wish, and so doing changes the margin around the boundary. However, we want to consider this specific boundary and margin. Determine the slack for each of the 16 points."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pos = [(5, 10),\n",
" (7, 10),\n",
" (1, 8),\n",
" (3, 8),\n",
" (7, 8),\n",
" (1, 6),\n",
" (3, 6),\n",
" (3, 4)]\n",
"\n",
"neg = [(5, 8),\n",
" (5, 6),\n",
" (7, 6),\n",
" (1, 4),\n",
" (5, 4),\n",
" (7, 4),\n",
" (1, 2),\n",
" (3, 2)]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"C = [(x, 1) for x in pos] + [(x, -1) for x in neg]\n",
"\n",
"w, b = np.array([-1, 1]), -2"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Points\tSlack\n",
"(5, 10)\t-2.00\n",
"(7, 10)\t0.00\n",
"(1, 8)\t-4.00\n",
"(3, 8)\t-2.00\n",
"(7, 8)\t2.00\n",
"(1, 6)\t-2.00\n",
"(3, 6)\t0.00\n",
"(3, 4)\t2.00\n",
"(5, 8)\t2.00\n",
"(5, 6)\t0.00\n",
"(7, 6)\t-2.00\n",
"(1, 4)\t2.00\n",
"(5, 4)\t-2.00\n",
"(7, 4)\t-4.00\n",
"(1, 2)\t0.00\n",
"(3, 2)\t-2.00\n"
]
}
],
"source": [
"d = np.dot(np.array([list(x[0]) for x in C]), w) + b\n",
"\n",
"print(\"Points\"+\"\\t\"+\"Slack\")\n",
"for i, m in enumerate(np.sign(d) == np.array([x[1] for x in C])):\n",
" if C[i][1] == 1:\n",
" slack = 1 - d\n",
" else:\n",
" slack = 1 + d\n",
" #print \"%s %d %0.2f %0.2f\" % (C[i][0], C[i][1], d[i], slack[i])\n",
" print \"%s\\t%0.2f\" % (C[i][0], slack[i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 6\n",
"Below we see a set of 20 points and a decision tree for classifying the points."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "R0lGODlhXgFeAfcAAAAAABEREQAAMwAzAAAzMwAzZgBmMwBmZgBmmQCZZgCAiACZzDMAADMAMzMz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\nR/d0V/d1Z/d2d/d3h/d4l/d5p/d6t/d7x/d81/d953d6DwgAOw==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='gold.jpeg')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": "R0lGODlhRQHhAPcAAAAAAADMmf///wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwEC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\nSriETNiETviEUBiFUjiFVFiF9xIQADs=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='dectree1.jpeg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be precise, the 20 points represent (Age,Salary) pairs of people who do or do not buy gold jewelry. Age (appreviated A in the decision tree) is the x-axis, and Salary (S in the tree) is the y-axis. Those that do are represented by gold points, and those that do not by green points. The 10 points of gold-jewelry buyers are:\n",
"\n",
"(28,145), (38,115), (43,83), (50,130), (50,90), (50,60), (50,30), (55,118), (63,88), and (65,140).\n",
"\n",
"The 10 points of those that do not buy gold jewelry are:\n",
"\n",
"(23,40), (25,125), (29,97), (33,22), (35,63), (42,57), (44, 105), (55,63), (55,20), and (64,37).\n",
"\n",
"Some of these points are correctly classified by the decision tree and some are not. Determine the classification of each point, and then indicate the points that are misclassified."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"A = 0\n",
"S = 1\n",
"\n",
"pos = [(28,145),\n",
" (38,115),\n",
" (43,83),\n",
" (50,130),\n",
" (50,90),\n",
" (50,60),\n",
" (50,30),\n",
" (55,118),\n",
" (63,88),\n",
" (65,140)]\n",
"\n",
"neg = [(23,40),\n",
" (25,125),\n",
" (29,97),\n",
" (33,22),\n",
" (35,63),\n",
" (42,57),\n",
" (44, 105),\n",
" (55,63),\n",
" (55,20),\n",
" (64,37)]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def classify(p):\n",
" if p[A] < 45:\n",
" return p[S] >= 110\n",
" else:\n",
" return p[S] >= 75"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(43, 83), (50, 60), (50, 30), (25, 125)]\n"
]
}
],
"source": [
"e = [p for p, v in zip(pos, [classify(x) for x in pos]) if not v] + \\\n",
" [p for p, v in zip(neg, [classify(x) for x in neg]) if v]\n",
"print e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| study/machinelearning/tensorflow/TensorFlow-Examples-master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"A Multilayer Perceptron implementation example using TensorFlow library.\n",
"This example is using the MNIST database of handwritten digits\n",
"(http://yann.lecun.com/exdb/mnist/)\n",
"\n",
"Author: Aymeric Damien\n",
"Project: https://github.com/aymericdamien/TensorFlow-Examples/\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"# Import MINST data\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
"\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Parameters\n",
"learning_rate = 0.001\n",
"training_epochs = 15\n",
"batch_size = 100\n",
"display_step = 1\n",
"\n",
"# Network Parameters\n",
"n_hidden_1 = 256 # 1st layer number of features\n",
"n_hidden_2 = 256 # 2nd layer number of features\n",
"n_input = 784 # MNIST data input (img shape: 28*28)\n",
"n_classes = 10 # MNIST total classes (0-9 digits)\n",
"\n",
"# tf Graph input\n",
"x = tf.placeholder(\"float\", [None, n_input])\n",
"y = tf.placeholder(\"float\", [None, n_classes])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create model\n",
"def multilayer_perceptron(x, weights, biases):\n",
" # Hidden layer with RELU activation\n",
" layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
" layer_1 = tf.nn.relu(layer_1)\n",
" # Hidden layer with RELU activation\n",
" layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
" layer_2 = tf.nn.relu(layer_2)\n",
" # Output layer with linear activation\n",
" out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
" return out_layer"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Store layers weight & bias\n",
"weights = {\n",
" 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
" 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
" 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
"}\n",
"biases = {\n",
" 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
" 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
" 'out': tf.Variable(tf.random_normal([n_classes]))\n",
"}\n",
"\n",
"# Construct model\n",
"pred = multilayer_perceptron(x, weights, biases)\n",
"\n",
"# Define loss and optimizer\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
"\n",
"# Initializing the variables\n",
"init = tf.global_variables_initializer()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0001 cost= 173.056566575\n",
"Epoch: 0002 cost= 44.054413928\n",
"Epoch: 0003 cost= 27.455470655\n",
"Epoch: 0004 cost= 19.008652363\n",
"Epoch: 0005 cost= 13.654873594\n",
"Epoch: 0006 cost= 10.059267435\n",
"Epoch: 0007 cost= 7.436018432\n",
"Epoch: 0008 cost= 5.587794416\n",
"Epoch: 0009 cost= 4.209882509\n",
"Epoch: 0010 cost= 3.203879515\n",
"Epoch: 0011 cost= 2.319920681\n",
"Epoch: 0012 cost= 1.676204545\n",
"Epoch: 0013 cost= 1.248805338\n",
"Epoch: 0014 cost= 1.052676844\n",
"Epoch: 0015 cost= 0.890117338\n",
"Optimization Finished!\n",
"Accuracy: 0.9459\n"
]
}
],
"source": [
"# Launch the graph\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
"\n",
" # Training cycle\n",
" for epoch in range(training_epochs):\n",
" avg_cost = 0.\n",
" total_batch = int(mnist.train.num_examples/batch_size)\n",
" # Loop over all batches\n",
" for i in range(total_batch):\n",
" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
" # Run optimization op (backprop) and cost op (to get loss value)\n",
" _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n",
" y: batch_y})\n",
" # Compute average loss\n",
" avg_cost += c / total_batch\n",
" # Display logs per epoch step\n",
" if epoch % display_step == 0:\n",
" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
" \"{:.9f}\".format(avg_cost)\n",
" print \"Optimization Finished!\"\n",
"\n",
" # Test model\n",
" correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
" # Calculate accuracy\n",
" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
" print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
dgasmith/SICM2-Software-Summer-School-2014 | Computer_Architechture/GEMM_Timings/visual_timing.ipynb |
|
| {
"metadata": {
"name": "",
"signature": "sha256:8376168f24323ba3812629b8f398f2e14d87882b5a92b85640f002328f07224b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Simple interface to visualize gemm timings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Import required libraries\n",
"\n",
"import subprocess as sp\n",
"import numpy as np\n",
"import time\n",
"import os\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from collections import defaultdict\n",
"%pylab inline\n",
"\n",
"# Make images slightly bigger\n",
"matplotlib.rcParams['savefig.dpi'] *= 1.5\n",
"\n",
"# Maximum memory used in GB\n",
"MAXIMUM_MEMORY = 2\n",
"\n",
"# Make a fair comparison\n",
"os.environ['OMP_NUM_THREADS'] = \"1\"\n",
"\n",
"# To fix:\n",
"# Change x axis to log2 space."
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Make a definition to run the script\n",
"\n",
"def run_gemm(k, iters, kernal):\n",
" \"\"\"Runs Dr. Valeev's GEMM timing example. \n",
" \n",
" Returns: (time (s), GLOP/s)\n",
" \"\"\"\n",
"\n",
" cmd = './gemm ' + ' '.join(str(x) for x in [k, iters,kernal])\n",
" proc = sp.Popen(cmd, shell=True, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.STDOUT, close_fds=True)\n",
" data = proc.stdout.read().split()\n",
" \n",
" # If block algorithm assertion failes\n",
" if 'Assertion' == data[0]:\n",
" return (np.nan, np.nan)\n",
"\n",
" return (float(data[-6]), float(data[-2]))\n",
"\n",
"\n",
"def gemm_timings(target_flops, kernal_list, trial_count, verbose=False):\n",
" \"\"\"Takes in\"\"\"\n",
" \n",
" output =[]\n",
" klist = []\n",
" \n",
" for kernal in kernal_list:\n",
" if verbose:\n",
" t = time.time()\n",
" print 'Starting %s kernal...' % kernal\n",
" \n",
" for k in range(4, 15, 2):\n",
"\n",
" k = 2**k\n",
" # k_iter = int(target_flops/(trial_count*k**3))\n",
" k_iter = int(target_flops/(k**3))\n",
"\n",
"\n",
" #If it is too big, just continue\n",
" if k_iter == 0:\n",
" continue\n",
" \n",
" if (k**2*16/1E9) > MAXIMUM_MEMORY:\n",
" print (k**2*16/1E9)\n",
" continue\n",
" \n",
" for trial in range(trial_count):\n",
" seconds, flops = run_gemm(k, k_iter, kernal)\n",
" if np.isnan(seconds):\n",
" continue\n",
"\n",
" output.append([kernal, k, trial, seconds, flops])\n",
" klist.append(k)\n",
" \n",
" if verbose:\n",
" print '...finished %s kernal in %5.5f seconds.\\n' % (kernal, time.time()-t)\n",
" \n",
" if verbose:\n",
" print 'Largest k values was %d' % max(klist)\n",
" \n",
" df = pd.DataFrame(output, columns=['Kernal','k','trial','Time(s)','GFLOP/s'])\n",
" return df"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot plain, block, and plan dgemm\n",
"trial_count = 5\n",
"target_flops = 5E9\n",
"\n",
"kernal_list = ['plain','block16','blas']\n",
"\n",
"gemm_df = gemm_timings(target_flops, kernal_list, trial_count, verbose=True)\n",
"\n",
"sns.tsplot(gemm_df, time=\"k\", unit='trial', condition='Kernal', value='GFLOP/s')\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting plain kernal...\n",
"...finished plain kernal in 251.38913 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Starting block16 kernal...\n",
"...finished block16 kernal in 73.67537 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Starting blas kernal...\n",
"...finished blas kernal in 11.08876 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Largest k values was 1024\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 92,
"text": [
"<matplotlib.axes.AxesSubplot at 0x11d0fe450>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGaCAYAAADn1/ziAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNbBhAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11b2f7a50>"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot plain, block, and plan gemm results\n",
"trial_count = 5\n",
"target_flops = 5E9\n",
"\n",
"kernal_list = ['block'+str(x) for x in [4,8,16,32]]\n",
"\n",
"gemm_df = gemm_timings(target_flops, kernal_list, trial_count, verbose=True)\n",
"\n",
"sns.tsplot(gemm_df, time=\"k\", unit='trial', condition='Kernal', value='GFLOP/s')\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Starting block4 kernal...\n",
"...finished block4 kernal in 177.06442 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Starting block8 kernal...\n",
"...finished block8 kernal in 136.28667 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Starting block16 kernal...\n",
"...finished block16 kernal in 80.18651 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Starting block32 kernal...\n",
"...finished block32 kernal in 74.49296 seconds.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Largest k values was 1024\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"<matplotlib.axes.AxesSubplot at 0x11d4e4610>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAGaCAYAAAAM4EfhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11d265190>"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
google/learned_optimization | docs/notebooks/Part2_CustomTasks.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"id": "20970d65",
"metadata": {
"id": "20970d65"
},
"source": [
"# Part 2: Custom Tasks, Task Families, and Performance Improvements\n",
"\n",
"In this part, we will look at how to define custom tasks and datasets. We will also consider _families_ of tasks, which are common specifications of meta-learning problems. Finally, we will look at how to efficiently parallelize over tasks during training."
]
},
{
"cell_type": "markdown",
"id": "ef075664",
"metadata": {
"id": "ef075664"
},
"source": [
"## Prerequisites\n",
"\n",
"This document assumes knowledge of JAX which is covered in depth at the [JAX Docs](https://jax.readthedocs.io/en/latest/index.html).\n",
"In particular, we would recomend making your way through [JAX tutorial 101](https://jax.readthedocs.io/en/latest/jax-101/index.html). We also recommend that you have worked your way through Part 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f560fa24",
"metadata": {
"id": "f560fa24"
},
"outputs": [],
"source": [
"!pip install git+https://github.com/google/learned_optimization.git"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "04db154b",
"metadata": {
"executionInfo": {
"elapsed": 24640,
"status": "ok",
"timestamp": 1643173374165,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "04db154b"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import jax.numpy as jnp\n",
"import jax\n",
"from matplotlib import pylab as plt\n",
"\n",
"from learned_optimization.outer_trainers import full_es\n",
"from learned_optimization.outer_trainers import truncated_pes\n",
"from learned_optimization.outer_trainers import gradient_learner\n",
"from learned_optimization.outer_trainers import truncation_schedule\n",
"\n",
"from learned_optimization.tasks import quadratics\n",
"from learned_optimization.tasks.fixed import image_mlp\n",
"from learned_optimization.tasks import base as tasks_base\n",
"from learned_optimization.tasks.datasets import base as datasets_base\n",
"\n",
"from learned_optimization.learned_optimizers import base as lopt_base\n",
"from learned_optimization.learned_optimizers import mlp_lopt\n",
"from learned_optimization.optimizers import base as opt_base\n",
"\n",
"from learned_optimization import optimizers\n",
"from learned_optimization import eval_training\n",
"\n",
"import haiku as hk\n",
"import tqdm"
]
},
{
"cell_type": "markdown",
"id": "707298d0",
"metadata": {
"id": "707298d0"
},
"source": [
"## Defining a custom Dataset\n",
"\n",
"The dataset's in this library consists of iterators which yield batches of the corresponding data. For the provided tasks, these dataset have 4 splits of data rather than the traditional 3. We have \"train\" which is data used by the task to train a model, \"inner_valid\" which contains validation data for use when inner training (training an instance of a task). This could be use for, say, picking hparams. \"outer_valid\" which is used to meta-train with -- this is unseen in inner training and thus serves as a basis to train learned optimizers against. \"test\" which can be used to test the learned optimizer with.\n",
"\n",
"To make a dataset, simply write 4 iterators with these splits.\n",
"\n",
"For performance reasons, creating these iterators cannot be slow.\n",
"The existing dataset's make extensive use of caching to share iterators across tasks which use the same data iterators.\n",
"To account for this reuse, it is expected that these iterators are always randomly sampling data and have a large shuffle buffer so as to not run into any sampling issues."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "df73c83b",
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1643173374354,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "df73c83b",
"outputId": "435d2986-d008-412e-bd71-bb7d9c404f3d"
},
"outputs": [
{
"data": {
"text/plain": [
"{'data': array([[0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.]])}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"\n",
"def data_iterator():\n",
" bs = 3\n",
" while True:\n",
" batch = {\"data\": np.zeros([bs, 5])}\n",
" yield batch\n",
"\n",
"\n",
"@datasets_base.dataset_lru_cache\n",
"def get_datasets():\n",
" return datasets_base.Datasets(\n",
" train=data_iterator(),\n",
" inner_valid=data_iterator(),\n",
" outer_valid=data_iterator(),\n",
" test=data_iterator())\n",
"\n",
"\n",
"ds = get_datasets()\n",
"next(ds.train)"
]
},
{
"cell_type": "markdown",
"id": "410f2024",
"metadata": {
"id": "410f2024"
},
"source": [
"## Defining a custom `Task`\n",
"\n",
"To define a custom class, one simply needs to write a base class of `Task`. Let's look at a simple task consisting of a quadratic task with noisy targets."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "27dbabeb",
"metadata": {
"executionInfo": {
"elapsed": 799,
"status": "ok",
"timestamp": 1643173375359,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "27dbabeb",
"outputId": "394bb22e-4481-4ee6-8d3b-490d1b77f35c"
},
"outputs": [
{
"data": {
"text/plain": [
"DeviceArray(10.503748, dtype=float32)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First we construct data iterators.\n",
"def noise_datasets():\n",
"\n",
" def _fn():\n",
" while True:\n",
" yield np.random.normal(size=[4, 2]).astype(dtype=np.float32)\n",
"\n",
" return datasets_base.Datasets(\n",
" train=_fn(), inner_valid=_fn(), outer_valid=_fn(), test=_fn())\n",
"\n",
"\n",
"class MyTask(tasks_base.Task):\n",
" datasets = noise_datasets()\n",
"\n",
" def loss(self, params, rng, data):\n",
" return jnp.sum(jnp.square(params - data))\n",
"\n",
" def init(self, key):\n",
" return jax.random.normal(key, shape=(4, 2))\n",
"\n",
"\n",
"task = MyTask()\n",
"key = jax.random.PRNGKey(0)\n",
"key1, key = jax.random.split(key)\n",
"params = task.init(key)\n",
"\n",
"task.loss(params, key1, next(task.datasets.train))"
]
},
{
"cell_type": "markdown",
"id": "a16e5e3b",
"metadata": {
"id": "a16e5e3b"
},
"source": [
"## Meta-training on multiple tasks: `TaskFamily`\n",
"\n",
"What we have shown previously was meta-training on a single task instance.\n",
"While sometimes this is sufficient for a given situation, in many situations we seek to meta-train a meta-learning algorithm such as a learned optimizer on a mixture of different tasks.\n",
"\n",
"One path to do this is to simply run more than one meta-gradient computation, each with different tasks, average the gradients, and perform one meta-update.\n",
"This works great when the tasks are quite different -- e.g. meta-gradients when training a convnet vs a MLP.\n",
"A big negative to this is that these meta-gradient calculations are happening sequentially, and thus making poor use of hardware accelerators like GPU or TPU.\n",
"\n",
"As a solution to this problem, we have an abstraction of a `TaskFamily` to enable better use of hardware. A `TaskFamily` represents a distribution over a set of tasks and specifies particular samples from this distribution as a pytree of jax types.\n",
"\n",
"The function to sample these configurations is called `sample`, and the function to get a task from the sampled config is `task_fn`. `TaskFamily` also optionally contain datasets which are shared for all the `Task` it creates.\n",
"\n",
"As a simple example, let's consider a family of quadratics parameterized by meansquared error to some point which itself is sampled."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f1c7d7f8",
"metadata": {
"executionInfo": {
"elapsed": 64,
"status": "ok",
"timestamp": 1643173375565,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "f1c7d7f8"
},
"outputs": [],
"source": [
"PRNGKey = jnp.ndarray\n",
"TaskParams = jnp.ndarray\n",
"\n",
"\n",
"class FixedDimQuadraticFamily(tasks_base.TaskFamily):\n",
" \"\"\"A simple TaskFamily with a fixed dimensionality but sampled target.\"\"\"\n",
"\n",
" def __init__(self, dim: int):\n",
" super().__init__()\n",
" self._dim = dim\n",
" self.datasets = None\n",
"\n",
" def sample(self, key: PRNGKey) -> TaskParams:\n",
" # Sample the target for the quadratic task.\n",
" return jax.random.normal(key, shape=(self._dim,))\n",
"\n",
" def task_fn(self, task_params: TaskParams) -> tasks_base.Task:\n",
" dim = self._dim\n",
"\n",
" class _Task(tasks_base.Task):\n",
"\n",
" def loss(self, params, rng, _):\n",
" # Compute MSE to the target task.\n",
" return jnp.sum(jnp.square(task_params - params))\n",
"\n",
" def init(self, key):\n",
" return jax.random.normal(key, shape=(dim,))\n",
"\n",
" return _Task()"
]
},
{
"cell_type": "markdown",
"id": "37652293",
"metadata": {
"id": "37652293"
},
"source": [
"*With* this task family defined, we can create instances by sampling a configuration and creating a task. This task acts like any other task in that it has an `init` and a `loss` function."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fba3b113",
"metadata": {
"executionInfo": {
"elapsed": 334,
"status": "ok",
"timestamp": 1643173376069,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "fba3b113",
"outputId": "1f62a1e6-8c99-4991-b2d7-380c5adee83a"
},
"outputs": [
{
"data": {
"text/plain": [
"DeviceArray(13.190405, dtype=float32)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task_family = FixedDimQuadraticFamily(10)\n",
"key = jax.random.PRNGKey(0)\n",
"task_cfg = task_family.sample(key)\n",
"task = task_family.task_fn(task_cfg)\n",
"\n",
"key1, key = jax.random.split(key)\n",
"params = task.init(key)\n",
"batch = None\n",
"task.loss(params, key, batch)"
]
},
{
"cell_type": "markdown",
"id": "8b25914f",
"metadata": {
"id": "8b25914f"
},
"source": [
"To achive speedups, we can now leverage `jax.vmap` to train *multiple* task instances in parallel! Depending on the task, this can be considerably faster than serially executing them."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7dded1ea",
"metadata": {
"executionInfo": {
"elapsed": 1508,
"status": "ok",
"timestamp": 1643173377718,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "7dded1ea",
"outputId": "d75a21c5-0210-4482-f088-1b5a0ce92c17"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"single loss 10.973224\n",
"multiple losses [28.484756 15.884144 10.12129 17.281586 18.210754 17.650654\n",
" 31.202633 20.745605 21.301374 36.30536 22.189842 21.358437\n",
" 13.802605 16.462059 13.092703 25.175426 23.442476 13.078012\n",
" 20.773136 15.165912 23.114235 24.486801 31.850758 11.04059\n",
" 5.795575 26.002295 31.550493 2.9317625 10.598424 18.45548\n",
" 24.402779 20.770353 ]\n"
]
}
],
"source": [
"def train_task(cfg, key):\n",
" task = task_family.task_fn(cfg)\n",
" key1, key = jax.random.split(key)\n",
" params = task.init(key1)\n",
" opt = opt_base.Adam()\n",
"\n",
" opt_state = opt.init(params)\n",
"\n",
" for i in range(4):\n",
" params = opt.get_params(opt_state)\n",
" loss, grad = jax.value_and_grad(task.loss)(params, key, None)\n",
" opt_state = opt.update(opt_state, grad, loss=loss)\n",
" loss = task.loss(params, key, None)\n",
" return loss\n",
"\n",
"\n",
"task_cfg = task_family.sample(key)\n",
"print(\"single loss\", train_task(task_cfg, key))\n",
"\n",
"keys = jax.random.split(key, 32)\n",
"task_cfgs = jax.vmap(task_family.sample)(keys)\n",
"losses = jax.vmap(train_task)(task_cfgs, keys)\n",
"print(\"multiple losses\", losses)"
]
},
{
"cell_type": "markdown",
"id": "79f74adc",
"metadata": {
"id": "79f74adc"
},
"source": [
"Because of this ability to apply vmap over task families, this is the main building block for a number of the high level libraries in this package. Single tasks can always be converted to a task family with:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6cd2f682",
"metadata": {
"executionInfo": {
"elapsed": 3041,
"status": "ok",
"timestamp": 1643173380925,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "6cd2f682"
},
"outputs": [],
"source": [
"single_task = image_mlp.ImageMLP_FashionMnist8_Relu32()\n",
"task_family = tasks_base.single_task_to_family(single_task)"
]
},
{
"cell_type": "markdown",
"id": "905293c1",
"metadata": {
"id": "905293c1"
},
"source": [
"This wrapper task family has no configuable value and always returns the base task."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cb049afb",
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1643173381121,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 480
},
"id": "cb049afb",
"outputId": "47250a96-577d-4d74-de88-b21d17f27fa3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"config only contains a dummy value: 0\n"
]
}
],
"source": [
"cfg = task_family.sample(key)\n",
"print(\"config only contains a dummy value:\", cfg)\n",
"task = task_family.task_fn(cfg)\n",
"# Tasks are the same\n",
"assert task == single_task"
]
},
{
"cell_type": "markdown",
"id": "760f8e76",
"metadata": {
"id": "760f8e76"
},
"source": [
"## Limitations of `TaskFamily`\n",
"Task families are designed for, and only work for variation that results in a static computation graph. This is required for `jax.vmap` to work.\n",
"\n",
"This means things like naively changing hidden sizes, or number of layers, activation functions is off the table.\n",
"\n",
"In some cases, one can leverage other jax control flow such as `jax.lax.cond` to select between implementations. For example, one could make a `TaskFamily` that used one of 2 activation functions. While this works, the resulting vectorized computation could be slow and thus profiling is required to determine if this is a good idea or not.\n",
"\n",
"In this code base, we use `TaskFamily` to mainly parameterize over different kinds of initializations."
]
}
],
"metadata": {
"colab": {
"last_runtime": {
"build_target": "//learning/deepmind/public/tools/ml_python:ml_notebook",
"kind": "private"
},
"name": "Part2_CustomTasks.ipynb",
"provenance": []
},
"jupytext": {
"formats": "ipynb,md:myst,py",
"main_language": "python"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
|
OzFlux/Peters-iPython-notebooks |
|
|
| {
"metadata": {
"name": "",
"signature": "sha256:0a22945b1bc9db903c8b0b438e3e3e0c8cd5156d2554b1035a28ae8b98b09152"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%run basics"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ncname=qcio.get_filename_dialog(path=\"../../Sites\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds=qcio.nc_read_series(ncname)\n",
"Fc_SOLO,f,a=qcutils.GetSeriesasMA(ds,\"Fc_SOLO\")\n",
"Fc_SOLO_mask=numpy.ma.getmaskarray(Fc_SOLO)\n",
"idx_array = qcutils.contiguous_regions(Fc_SOLO_mask)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print idx_array.shape,idx_array.shape[0],idx_array.shape[1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(6, 2) 6 2\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print idx_array"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 7297 11568]\n",
" [ 40849 42336]\n",
" [ 59857 61248]\n",
" [ 62737 64176]\n",
" [ 86113 89040]\n",
" [103633 105120]]\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"si_gap=idx_array[0,0]\n",
"ei_gap=idx_array[0,1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ldt = ds.series[\"DateTime\"][\"Data\"]\n",
"Fc,f,a=qcutils.GetSeriesasMA(ds,\"Fc\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"si_gfperiod = si_gap\n",
"ei_gfperiod = ei_gap\n",
"min_points = int((ei_gfperiod-si_gfperiod)/2)\n",
"num_good_points = numpy.ma.count(Fc[si_gfperiod:ei_gfperiod])\n",
"print min_points,num_good_points\n",
"while num_good_points<min_points:\n",
" si_gfperiod = si_gfperiod - 48\n",
" ei_gfperiod = ei_gfperiod + 48\n",
" min_points = int((ei_gfperiod-si_gfperiod)/2)\n",
" num_good_points = numpy.ma.count(Fc[si_gfperiod:ei_gfperiod])\n",
" print ldt[si_gfperiod],ldt[ei_gfperiod],num_good_points,min_points"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2135 0\n",
"2009-02-27 00:30:00 2009-05-29 00:00:00 96 2183\n",
"2009-02-26 00:30:00 2009-05-30 00:00:00 192 2231\n",
"2009-02-25 00:30:00 2009-05-31 00:00:00 288 2279\n",
"2009-02-24 00:30:00 2009-06-01 00:00:00 384 2327\n",
"2009-02-23 00:30:00 2009-06-02 00:00:00 480 2375\n",
"2009-02-22 00:30:00 2009-06-03 00:00:00 576 2423\n",
"2009-02-21 00:30:00 2009-06-04 00:00:00 672 2471\n",
"2009-02-20 00:30:00 2009-06-05 00:00:00 768 2519\n",
"2009-02-19 00:30:00 2009-06-06 00:00:00 864 2567\n",
"2009-02-18 00:30:00 2009-06-07 00:00:00 960 2615\n",
"2009-02-17 00:30:00 2009-06-08 00:00:00 1056 2663\n",
"2009-02-16 00:30:00 2009-06-09 00:00:00 1152 2711\n",
"2009-02-15 00:30:00 2009-06-10 00:00:00 1248 2759\n",
"2009-02-14 00:30:00 2009-06-11 00:00:00 1344 2807\n",
"2009-02-13 00:30:00 2009-06-12 00:00:00 1440 2855\n",
"2009-02-12 00:30:00 2009-06-13 00:00:00 1536 2903\n",
"2009-02-11 00:30:00 2009-06-14 00:00:00 1632 2951\n",
"2009-02-10 00:30:00 2009-06-15 00:00:00 1728 2999\n",
"2009-02-09 00:30:00 2009-06-16 00:00:00 1824 3047\n",
"2009-02-08 00:30:00 2009-06-17 00:00:00 1920 3095\n",
"2009-02-07 00:30:00 2009-06-18 00:00:00 2016 3143\n",
"2009-02-06 00:30:00 2009-06-19 00:00:00 2112 3191\n",
"2009-02-05 00:30:00 2009-06-20 00:00:00 2208 3239\n",
"2009-02-04 00:30:00 2009-06-21 00:00:00 2304 3287\n",
"2009-02-03 00:30:00 2009-06-22 00:00:00 2400 3335\n",
"2009-02-02 00:30:00 2009-06-23 00:00:00 2496 3383\n",
"2009-02-01 00:30:00 2009-06-24 00:00:00 2592 3431\n",
"2009-01-31 00:30:00 2009-06-25 00:00:00 2688 3479\n",
"2009-01-30 00:30:00 2009-06-26 00:00:00 2784 3527\n",
"2009-01-29 00:30:00 2009-06-27 00:00:00 2880 3575\n",
"2009-01-28 00:30:00 2009-06-28 00:00:00 2976 3623\n",
"2009-01-27 00:30:00 2009-06-29 00:00:00 3072 3671\n",
"2009-01-26 00:30:00 2009-06-30 00:00:00 3168 3719\n",
"2009-01-25 00:30:00 2009-07-01 00:00:00 3264 3767\n",
"2009-01-24 00:30:00 2009-07-02 00:00:00 3360 3815\n",
"2009-01-23 00:30:00 2009-07-03 00:00:00 3456 3863\n",
"2009-01-22 00:30:00 2009-07-04 00:00:00 3552 3911\n",
"2009-01-21 00:30:00 2009-07-05 00:00:00 3648 3959\n",
"2009-01-20 00:30:00 2009-07-06 00:00:00 3744 4007\n",
"2009-01-19 00:30:00 2009-07-07 00:00:00 3840 4055\n",
"2009-01-18 00:30:00 2009-07-08 00:00:00 3936 4103\n",
"2009-01-17 00:30:00 2009-07-09 00:00:00 4032 4151\n",
"2009-01-16 00:30:00 2009-07-10 00:00:00 4128 4199\n",
"2009-01-15 00:30:00 2009-07-11 00:00:00 4224 4247\n",
"2009-01-14 00:30:00 2009-07-12 00:00:00 4320 4295\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
|
| CarND-LeNet-Lab/LeNet-Lab.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LeNet Lab\n",
"\n",
"Source: Yan LeCun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data\n",
"\n",
"Load the MNIST data, which comes pre-loaded with TensorFlow.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
"\n",
"Image Shape: (28, 28, 1)\n",
"\n",
"Training Set: 55000 samples\n",
"Validation Set: 5000 samples\n",
"Test Set: 10000 samples\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", reshape=False)\n",
"X_train, y_train = mnist.train.images, mnist.train.labels\n",
"X_validation, y_validation = mnist.validation.images, mnist.validation.labels\n",
"X_test, y_test = mnist.test.images, mnist.test.labels\n",
"\n",
"assert(len(X_train) == len(y_train))\n",
"assert(len(X_validation) == len(y_validation))\n",
"assert(len(X_test) == len(y_test))\n",
"\n",
"print()\n",
"print(\"Image Shape: {}\".format(X_train[0].shape))\n",
"print()\n",
"print(\"Training Set: {} samples\".format(len(X_train)))\n",
"print(\"Validation Set: {} samples\".format(len(X_validation)))\n",
"print(\"Test Set: {} samples\".format(len(X_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.\n",
"\n",
"However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.\n",
"\n",
"In order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated Image Shape: (32, 32, 1)\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# Pad images with 0s\n",
"X_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
"X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
"X_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
" \n",
"print(\"Updated Image Shape: {}\".format(X_train[0].shape))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize Data\n",
"\n",
"View a sample from the dataset.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAH0AAAB6CAYAAACShVydAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1039900b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"index = random.randint(0, len(X_train))\n",
"image = X_train[index].squeeze()\n",
"\n",
"plt.figure(figsize=(1,1))\n",
"plt.imshow(image, cmap=\"gray\")\n",
"print(y_train[index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess Data\n",
"\n",
"Shuffle the training data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.utils import shuffle\n",
"\n",
"X_train, y_train = shuffle(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup TensorFlow\n",
"The `EPOCH` and `BATCH_SIZE` values affect the training speed and model accuracy.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"EPOCHS = 10\n",
"BATCH_SIZE = 128"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TODO: Implement LeNet-5\n",
"Implement the [LeNet-5](http://yann.lecun.com/exdb/lenet/) neural network architecture.\n",
"\n",
"This is the only cell you need to edit.\n",
"### Input\n",
"The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.\n",
"\n",
"### Architecture\n",
"**Layer 1: Convolutional.** The output shape should be 28x28x6.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Pooling.** The output shape should be 14x14x6.\n",
"\n",
"**Layer 2: Convolutional.** The output shape should be 10x10x16.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Pooling.** The output shape should be 5x5x16.\n",
"\n",
"**Flatten.** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using `tf.contrib.layers.flatten`, which is already imported for you.\n",
"\n",
"**Layer 3: Fully Connected.** This should have 120 outputs.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Layer 4: Fully Connected.** This should have 84 outputs.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Layer 5: Fully Connected (Logits).** This should have 10 outputs.\n",
"\n",
"### Output\n",
"Return the result of the 2nd fully connected layer."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from tensorflow.contrib.layers import flatten\n",
"\n",
"def LeNet(x): \n",
" # Hyperparameters\n",
" mu = 0\n",
" sigma = 0.1\n",
" dropout = 0.75\n",
" \n",
" # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
" weights = {\n",
" 'wc1': tf.Variable(tf.random_normal([5,5,1,6])),\n",
" 'wc2': tf.Variable(tf.random_normal([5,5,6,16])),\n",
" 'wd1': tf.Variable(tf.random_normal([400, 120])),\n",
" 'wd2': tf.Variable(tf.random_normal([120, 84])),\n",
" 'wd3': tf.Variable(tf.random_normal([84, 10]))}\n",
" \n",
" biases = {\n",
" 'bc1': tf.Variable(tf.zeros(6)),\n",
" 'bc2': tf.Variable(tf.zeros(16)),\n",
" 'bd1': tf.Variable(tf.zeros(120)),\n",
" 'bd2': tf.Variable(tf.zeros(84)),\n",
" 'bd3': tf.Variable(tf.zeros(10))}\n",
" \n",
" conv1 = tf.nn.conv2d(x, weights['wc1'], strides=[1, 1, 1, 1], padding='VALID')\n",
" conv1 = tf.nn.bias_add(conv1, biases['bc1'])\n",
" \n",
" # TODO: Activation.\n",
" conv1 = tf.nn.relu(conv1)\n",
" \n",
" # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.\n",
" ksize = [1,2,2,1]\n",
" strides = [1,2,2,1]\n",
" padding = 'VALID'\n",
" conv1 = tf.nn.max_pool(conv1, ksize, strides, padding)\n",
"\n",
" # TODO: Layer 2: Convolutional. Output = 10x10x16.\n",
" conv2 = tf.nn.conv2d(conv1, weights['wc2'], strides=[1, 1, 1, 1], padding='VALID')\n",
" conv2 = tf.nn.bias_add(conv2, biases['bc2'])\n",
" \n",
" # TODO: Activation.\n",
" conv2 = tf.nn.relu(conv2)\n",
" \n",
" # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
" ksize = [1,2,2,1]\n",
" strides = [1,2,2,1]\n",
" padding = 'VALID'\n",
" conv2 = tf.nn.max_pool(conv2, ksize, strides, padding)\n",
"\n",
" # TODO: Flatten. Input = 5x5x16. Output = 400.\n",
" fc0 = flatten(conv2)\n",
" \n",
" # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.\n",
" fc1 = tf.add(tf.matmul(fc0, weights['wd1']), biases['bd1'])\n",
" \n",
" # TODO: Activation.\n",
" fc1 = tf.nn.relu(fc1)\n",
"\n",
" # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.\n",
" fc2 = tf.add(tf.matmul(fc1, weights['wd2']), biases['bd2'])\n",
" \n",
" # TODO: Activation.\n",
" fc2 = tf.nn.relu(fc2)\n",
"\n",
" # TODO: Layer 5: Fully Connected. Input = 84. Output = 10.\n",
" logits = tf.add(tf.matmul(fc2, weights['wd3']), biases['bd3'])\n",
" \n",
" return logits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Features and Labels\n",
"Train LeNet to classify [MNIST](http://yann.lecun.com/exdb/mnist/) data.\n",
"\n",
"`x` is a placeholder for a batch of input images.\n",
"`y` is a placeholder for a batch of output labels.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
"y = tf.placeholder(tf.int32, (None))\n",
"one_hot_y = tf.one_hot(y, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training Pipeline\n",
"Create a training pipeline that uses the model to classify MNIST data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rate = 0.001\n",
"\n",
"logits = LeNet(x)\n",
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\n",
"loss_operation = tf.reduce_mean(cross_entropy)\n",
"optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
"training_operation = optimizer.minimize(loss_operation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Evaluation\n",
"Evaluate how well the loss and accuracy of the model for a given dataset.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
"accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
"saver = tf.train.Saver()\n",
"\n",
"def evaluate(X_data, y_data):\n",
" num_examples = len(X_data)\n",
" total_accuracy = 0\n",
" sess = tf.get_default_session()\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
" accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\n",
" total_accuracy += (accuracy * len(batch_x))\n",
" return total_accuracy / num_examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Model\n",
"Run the training data through the training pipeline to train the model.\n",
"\n",
"Before each epoch, shuffle the training set.\n",
"\n",
"After each epoch, measure the loss and accuracy of the validation set.\n",
"\n",
"Save the model after training.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training...\n",
"\n",
"EPOCH 1 ...\n",
"Validation Accuracy = 0.780\n",
"\n",
"EPOCH 2 ...\n",
"Validation Accuracy = 0.858\n",
"\n",
"EPOCH 3 ...\n",
"Validation Accuracy = 0.885\n",
"\n",
"EPOCH 4 ...\n",
"Validation Accuracy = 0.904\n",
"\n",
"EPOCH 5 ...\n",
"Validation Accuracy = 0.909\n",
"\n",
"EPOCH 6 ...\n",
"Validation Accuracy = 0.923\n",
"\n",
"EPOCH 7 ...\n",
"Validation Accuracy = 0.928\n",
"\n",
"EPOCH 8 ...\n",
"Validation Accuracy = 0.934\n",
"\n",
"EPOCH 9 ...\n",
"Validation Accuracy = 0.939\n",
"\n",
"EPOCH 10 ...\n",
"Validation Accuracy = 0.938\n",
"\n",
"Model saved\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" num_examples = len(X_train)\n",
" \n",
" print(\"Training...\")\n",
" print()\n",
" for i in range(EPOCHS):\n",
" X_train, y_train = shuffle(X_train, y_train)\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" end = offset + BATCH_SIZE\n",
" batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
" sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})\n",
" \n",
" validation_accuracy = evaluate(X_validation, y_validation)\n",
" print(\"EPOCH {} ...\".format(i+1))\n",
" print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
" print()\n",
" \n",
" saver.save(sess, 'lenet')\n",
" print(\"Model saved\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the Model\n",
"Once you are completely satisfied with your model, evaluate the performance of the model on the test set.\n",
"\n",
"Be sure to only do this once!\n",
"\n",
"If you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model on the test set again, that would invalidate your test results. You wouldn't get a true measure of how well your model would perform against real data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy = 0.934\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
"\n",
" test_accuracy = evaluate(X_test, y_test)\n",
" print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
|
|
|
| {
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:0df25babfe96a068e4b8fca6c4fa25fab3f6a471c2496c1088e02adfe63d8c5d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Profile each step of model generation to see where things should be optimized"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model generation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using model"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model.plot_vel(params)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "LoadError",
"evalue": "error compiling plot_vel: unsupported or misplaced expression import in function plot_vel\nwhile loading In[3], in expression starting on line 1",
"output_type": "pyerr",
"traceback": [
"error compiling plot_vel: unsupported or misplaced expression import in function plot_vel\nwhile loading In[3], in expression starting on line 1",
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"write_grid()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@time write_model(params)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"elapsed time: 0."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"403355582 seconds (24289764 bytes allocated)\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RADMC run\n",
"\n",
"In addition to the gridding chosen by the model, there are many options here to tweak\n",
"\n",
"* second order integration\n",
"* recursive sub-pixeling\n",
"* mirroring the disk + transfer (no major speedup)\n",
"\n",
"Pg 25: 2D axially-symmetric with mirror symmetry. Do we just specify it and see what happens? Or does this do it automatically? If the largest value is = pi/2, then it is switched on automatically (should say in the output).\n",
"\n",
"\n",
"* Different gridding (fewer theta grid points but concentrated near midplane)\n",
"\n",
"try running a single channel vs. multiple simultaneously\n",
"\n",
"How many *npix* should we really be using? Sean says we want to oversample the SMA beam by a factor of 5x10, so let's try pixels that are 0.05\"\n",
"\n",
"At this point, I think any tweaking is really going to be either in the number of pixels or number of grid cells."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"12/0.05"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"240.0"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using constants\n",
"incl = 33. #33. # deg. 0 deg = face on, 90 = edge on.\n",
"vel = 0.0 # km/s\n",
"PA = 90 - 73. # 73 deg. Position angle, runs counter clockwise, due to looking at sky.\n",
"npix = 240 # number of pixels, can alternatively specify x and y separately\n",
"lam0 = cc/230.538e9 * 1e6 # [microns]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"1300.4036557964414"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#loads the camera_wavelength_micron.inp file\n",
"@time run(`radmc3d image incl $incl posang $PA vkms $vel npix $npix loadlambda`)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
" ================================================================\n",
" WELCOME TO RADMC-3D: A 3-D CONTINUUM AND LINE RT SOLVER \n",
" \n",
" This is the 3-D reincarnation of the 2-D RADMC code \n",
" (c) 2010/2011 Cornelis Dullemond \n",
" \n",
" ************* NOTE: THIS IS STILL A BETA VERSION ***************\n",
" ****** Some modes/capabilities are not yet ready/mature ********\n",
" \n",
" Please feel free to ask questions. Also please report \n",
" bugs and/or suspicious behavior without hestitation. \n",
" The reliability of this code depends on your vigilance! \n",
" \n",
" To keep up-to-date with bug-alarms and bugfixes, register to \n",
" the RADMC-3D mailing list by sending an email to me: \n",
" [email protected] or [email protected] \n",
" \n",
" Please visit the RADMC-3D home page at \n",
" http://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/ \n",
" ================================================================\n",
" \n",
" Reading global frequencies/wavelengths...\n",
" Reading grid file and prepare grid tree...\n",
" Adjusting theta(ny+1) to exactly pi/2...\n",
" Reading star data...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Grid information (current status):\n",
" We have 4096 branches, of which 4096 are actual grid cells.\n",
" ---> 100.000% mem use for branches, and 100.000% mem use for actual cells.\n",
" No grid refinement is active. The AMR tree is not allocated (this saves memory).\n",
" ALWAYS SELF-CHECK FOR NOW...\n",
" Using mirror symmetry in equatorial plane, because max(theta)==pi/2.\n",
" Starting procedure for rendering image...\n",
" No dust included...\n",
" --> Including lines\n",
" No gas continuum included...\n",
" Using Gaussian line profile\n",
" Reading line data...\n",
" NOTE: In lines.inp for molecule 1 no collision partners specified, therefore no non-LTE possible.\n",
" Reading line data of molecule co ...\n",
" Level Diagram for ispec= 1 :\n",
" 3136.50954 | \n",
" 2984.27068 || \n",
" 2835.76272 || \n",
" 2690.99132 || \n",
" 2549.96193 || \n",
" 2412.68017 || \n",
" 2279.15110 || \n",
" 2149.37986 || \n",
" 2023.37151 || \n",
" 1901.13077 || \n",
" 1782.66240 || \n",
" 1667.97094 || \n",
" 1557.06073 || \n",
" 1449.93593 || \n",
" 1346.60078 || \n",
" 1247.05922 || \n",
" 1151.31499 || \n",
" 1059.37185 || \n",
" 971.23314 || \n",
" 886.90241 || \n",
" 806.38279 || \n",
" 729.67745 || \n",
" 656.78922 || \n",
" 587.72084 || \n",
" 522.47517 || \n",
" 461.05446 || \n",
" 403.46114 || \n",
" 349.69757 || \n",
" 299.76559 || \n",
" 253.66715 || \n",
" 211.40410 || \n",
" 172.97807 || \n",
" 138.39033 || \n",
" 107.64241 || \n",
" 80.73546 || \n",
" 57.67033 || \n",
" 38.44816 || \n",
" 23.06951 || \n",
" 11.53492 || \n",
" 3.84503 || \n",
" 0.00000 | \n",
" Reading molecular/atomic number densities...\n",
" Reading gas species number densities...\n",
" Reading gas temperature...\n",
" Reading or computing partition functions...\n",
" Computing partition function internally for molecule 1\n",
" Reading velocity field...\n",
" No microturbulence input file found. Assuming zero microturbulence...\n",
" Line transfer method: LTE (populations precalculated)\n",
" Will store level populations of molecule 1 into global array for the following levels:\n",
" 2 3\n",
" Computing level populations, and storing them in global array...\n",
" Rendering image(s)...\n",
" Ray-tracing images: all 23 wavelength at once...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Writing image to file...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Diagnostics of flux-conservation (sub-pixeling):\n",
" Nr of main pixels (nx*ny) = 57600\n",
" Nr of (sub)pixels raytraced = 68416\n",
" Nr of (sub)pixels used = 65712\n",
" Increase of raytracing cost = 1.1877777777777778 \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Done...\n",
"elapsed time: 34.763234169 seconds (4167716 bytes allocated)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@time run(`radmc3d image incl $incl posang $PA vkms $vel npix 240 lambda $lam0`)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
" ================================================================\n",
" WELCOME TO RADMC-3D: A 3-D CONTINUUM AND LINE RT SOLVER \n",
" \n",
" This is the 3-D reincarnation of the 2-D RADMC code \n",
" (c) 2010/2011 Cornelis Dullemond \n",
" \n",
" ************* NOTE: THIS IS STILL A BETA VERSION ***************\n",
" ****** Some modes/capabilities are not yet ready/mature ********\n",
" \n",
" Please feel free to ask questions. Also please report \n",
" bugs and/or suspicious behavior without hestitation. \n",
" The reliability of this code depends on your vigilance! \n",
" \n",
" To keep up-to-date with bug-alarms and bugfixes, register to \n",
" the RADMC-3D mailing list by sending an email to me: \n",
" [email protected] or [email protected] \n",
" \n",
" Please visit the RADMC-3D home page at \n",
" http://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/ \n",
" ================================================================\n",
" \n",
" Reading global frequencies/wavelengths...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Reading grid file and prepare grid tree...\n",
" Adjusting theta(ny+1) to exactly pi/2...\n",
" Reading star data...\n",
" Grid information (current status):\n",
" We have 512 branches, of which 512 are actual grid cells.\n",
" ---> 100.000% mem use for branches, and 100.000% mem use for actual cells.\n",
" No grid refinement is active. The AMR tree is not allocated (this saves memory).\n",
" ALWAYS SELF-CHECK FOR NOW...\n",
" Using mirror symmetry in equatorial plane, because max(theta)==pi/2.\n",
" Starting procedure for rendering image...\n",
" No dust included...\n",
" --> Including lines\n",
" No gas continuum included...\n",
" Using Gaussian line profile\n",
" Reading line data...\n",
" NOTE: In lines.inp for molecule 1 no collision partners specified, therefore no non-LTE possible.\n",
" Reading line data of molecule co ...\n",
" Level Diagram for ispec= 1 :\n",
" 3136.50954 | \n",
" 2984.27068 || \n",
" 2835.76272 || \n",
" 2690.99132 || \n",
" 2549.96193 || \n",
" 2412.68017 || \n",
" 2279.15110 || \n",
" 2149.37986 || \n",
" 2023.37151 || \n",
" 1901.13077 || \n",
" 1782.66240 || \n",
" 1667.97094 || \n",
" 1557.06073 || \n",
" 1449.93593 || \n",
" 1346.60078 || \n",
" 1247.05922 || \n",
" 1151.31499 || \n",
" 1059.37185 || \n",
" 971.23314 || \n",
" 886.90241 || \n",
" 806.38279 || \n",
" 729.67745 || \n",
" 656.78922 || \n",
" 587.72084 || \n",
" 522.47517 || \n",
" 461.05446 || \n",
" 403.46114 || \n",
" 349.69757 || \n",
" 299.76559 || \n",
" 253.66715 || \n",
" 211.40410 || \n",
" 172.97807 || \n",
" 138.39033 || \n",
" 107.64241 || \n",
" 80.73546 || \n",
" 57.67033 || \n",
" 38.44816 || \n",
" 23.06951 || \n",
" 11.53492 || \n",
" 3.84503 || \n",
" 0.00000 | \n",
" Reading molecular/atomic number densities...\n",
" Reading gas species number densities...\n",
" Reading gas temperature...\n",
" Reading or computing partition functions...\n",
" Computing partition function internally for molecule 1\n",
" Reading velocity field...\n",
" No microturbulence input file found. Assuming zero microturbulence...\n",
" Line transfer method: LTE (populations precalculated)\n",
" Will store level populations of molecule 1 into global array for the following levels:\n",
" 2 3\n",
" Computing level populations, and storing them in global array...\n",
" Rendering image(s)...\n",
" Ray-tracing image for lambda = 1300.4036557964414 micron...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Writing image to file...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Diagnostics of flux-conservation (sub-pixeling):\n",
" Nr of main pixels (nx*ny) = 57600\n",
" Nr of (sub)pixels raytraced = 61040\n",
" Nr of (sub)pixels used = 60180\n",
" Increase of raytracing cost = 1.0597222222222222 \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Done...\n",
"elapsed time: 1.391924293 seconds (93344 bytes allocated)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image reading\n",
"\n",
"Reading image from file into a datacub"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using read_image"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FFT\n",
"\n",
"FFT'ing a single channel"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
|
| src/OverallStandings_Consistency.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **Overall Standings & Performance Consistency Module**\n",
"\n",
"Overall Standings module produces chart results for a particular season on the basis of #wins. Season is required as an input.\n",
"\n",
"Performance Consistency module tracks a team's performance improvement consistency across all the seasons. Teams that have played for less than four season are not considered.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
" <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"e8cfcd7d-8236-43bf-a1b3-96427871b7bc\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
"(function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
"\n",
" var force = true;\n",
"\n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
"\n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 5000;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"e8cfcd7d-8236-43bf-a1b3-96427871b7bc\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"e8cfcd7d-8236-43bf-a1b3-96427871b7bc\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e8cfcd7d-8236-43bf-a1b3-96427871b7bc' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" document.getElementById(\"e8cfcd7d-8236-43bf-a1b3-96427871b7bc\").textContent = \"BokehJS is loading...\";\n",
" },\n",
" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
" }\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"e8cfcd7d-8236-43bf-a1b3-96427871b7bc\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(this));"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pyspark \n",
"from pyspark import SparkContext \n",
"from pyspark.sql import SQLContext\n",
"from pyspark.sql.types import * # for defining schema with various datatypes\n",
"import pyspark.sql.functions as func # for ETL, data processing on Dataframes\n",
"\n",
"import pandas as pd # converting PysparkDF to PandasDF when passing it as a parameter to Bokeh invokes \n",
"\n",
"from datetime import * # for datetime datatype for schema\n",
"from dateutil.parser import parse # for string parse to date\n",
"\n",
"from bokeh.io import push_notebook, show, output_notebook # various output methods for jupyter notebook\n",
"from bokeh.plotting import figure # creating a figure variable\n",
"from bokeh.charts import Bar, output_file, show # creating bar charts, and displaying it\n",
"from bokeh.charts.attributes import cat # extracting column for 'label' category in bar charts\n",
"from bokeh.palettes import * # brewer color palette\n",
"from bokeh.models import Range1d # calibrating x/y-ranges of graph\n",
"output_notebook()\n",
"\n",
"sc = SparkContext()\n",
"sql = SQLContext(sc)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+------+----------+----------+--------------------+--------------------+--------------------+-------------+------+----------+--------------------+-----------+--------------+---------------+--------------------+-----------+--------------+--------------+\n",
"| id|season| city| date| team1| team2| toss_winner|toss_decision|result|dl_applied| winner|win_by_runs|win_by_wickets|player_of_match| venue| umpire1| umpire2| umpire3|\n",
"+---+------+----------+----------+--------------------+--------------------+--------------------+-------------+------+----------+--------------------+-----------+--------------+---------------+--------------------+-----------+--------------+--------------+\n",
"| 1| 2008| Bangalore|2008-04-18|Kolkata Knight Ri...|Royal Challengers...|Royal Challengers...| field|normal| false|Kolkata Knight Ri...| 140| 0| BB McCullum|M Chinnaswamy Sta...| Asad Rauf| RE Koertzen| |\n",
"| 2| 2008|Chandigarh|2008-04-19| Chennai Super Kings| Kings XI Punjab| Chennai Super Kings| bat|normal| false| Chennai Super Kings| 33| 0| MEK Hussey|\"Punjab Cricket A...| Mohali\"| MR Benson| SL Shastri|\n",
"| 3| 2008| Delhi|2008-04-19| Rajasthan Royals| Delhi Daredevils| Rajasthan Royals| bat|normal| false| Delhi Daredevils| 0| 9| MF Maharoof| Feroz Shah Kotla| Aleem Dar|GA Pratapkumar| |\n",
"| 4| 2008| Mumbai|2008-04-20| Mumbai Indians|Royal Challengers...| Mumbai Indians| bat|normal| false|Royal Challengers...| 0| 5| MV Boucher| Wankhede Stadium| SJ Davis| DJ Harper| |\n",
"| 5| 2008| Kolkata|2008-04-20| Deccan Chargers|Kolkata Knight Ri...| Deccan Chargers| bat|normal| false|Kolkata Knight Ri...| 0| 5| DJ Hussey| Eden Gardens| BF Bowden| K Hariharan| |\n",
"| 6| 2008| Jaipur|2008-04-21| Kings XI Punjab| Rajasthan Royals| Kings XI Punjab| bat|normal| false| Rajasthan Royals| 0| 6| SR Watson|Sawai Mansingh St...| Aleem Dar| RB Tiffin| |\n",
"| 7| 2008| Hyderabad|2008-04-22| Deccan Chargers| Delhi Daredevils| Deccan Chargers| bat|normal| false| Delhi Daredevils| 0| 9| V Sehwag|\"Rajiv Gandhi Int...| Uppal\"| IL Howell| AM Saheba|\n",
"| 8| 2008| Chennai|2008-04-23| Chennai Super Kings| Mumbai Indians| Mumbai Indians| field|normal| false| Chennai Super Kings| 6| 0| ML Hayden|\"MA Chidambaram S...| Chepauk\"| DJ Harper|GA Pratapkumar|\n",
"| 9| 2008| Hyderabad|2008-04-24| Deccan Chargers| Rajasthan Royals| Rajasthan Royals| field|normal| false| Rajasthan Royals| 0| 3| YK Pathan|\"Rajiv Gandhi Int...| Uppal\"| Asad Rauf| MR Benson|\n",
"| 10| 2008|Chandigarh|2008-04-25| Kings XI Punjab| Mumbai Indians| Mumbai Indians| field|normal| false| Kings XI Punjab| 66| 0| KC Sangakkara|\"Punjab Cricket A...| Mohali\"| Aleem Dar| AM Saheba|\n",
"| 11| 2008| Bangalore|2008-04-26|Royal Challengers...| Rajasthan Royals| Rajasthan Royals| field|normal| false| Rajasthan Royals| 0| 7| SR Watson|M Chinnaswamy Sta...| MR Benson| IL Howell| |\n",
"| 12| 2008| Chennai|2008-04-26|Kolkata Knight Ri...| Chennai Super Kings|Kolkata Knight Ri...| bat|normal| false| Chennai Super Kings| 0| 9| JDP Oram|\"MA Chidambaram S...| Chepauk\"| BF Bowden|AV Jayaprakash|\n",
"| 13| 2008| Mumbai|2008-04-27| Mumbai Indians| Deccan Chargers| Deccan Chargers| field|normal| false| Deccan Chargers| 0| 10| AC Gilchrist|Dr DY Patil Sport...| Asad Rauf| SL Shastri| |\n",
"| 14| 2008|Chandigarh|2008-04-27| Delhi Daredevils| Kings XI Punjab| Delhi Daredevils| bat|normal| false| Kings XI Punjab| 0| 4| SM Katich|\"Punjab Cricket A...| Mohali\"| RE Koertzen| I Shivram|\n",
"| 15| 2008| Bangalore|2008-04-28| Chennai Super Kings|Royal Challengers...| Chennai Super Kings| bat|normal| false| Chennai Super Kings| 13| 0| MS Dhoni|M Chinnaswamy Sta...|BR Doctrove| RB Tiffin| |\n",
"| 16| 2008| Kolkata|2008-04-29|Kolkata Knight Ri...| Mumbai Indians|Kolkata Knight Ri...| bat|normal| false| Mumbai Indians| 0| 7| ST Jayasuriya| Eden Gardens| BF Bowden|AV Jayaprakash| |\n",
"| 17| 2008| Delhi|2008-04-30| Delhi Daredevils|Royal Challengers...|Royal Challengers...| field|normal| false| Delhi Daredevils| 10| 0| GD McGrath| Feroz Shah Kotla| Aleem Dar| I Shivram| |\n",
"| 18| 2008| Hyderabad|2008-05-01| Deccan Chargers| Kings XI Punjab| Kings XI Punjab| field|normal| false| Kings XI Punjab| 0| 7| SE Marsh|\"Rajiv Gandhi Int...| Uppal\"| BR Doctrove| RB Tiffin|\n",
"| 19| 2008| Jaipur|2008-05-01| Rajasthan Royals|Kolkata Knight Ri...| Rajasthan Royals| bat|normal| false| Rajasthan Royals| 45| 0| SA Asnodkar|Sawai Mansingh St...|RE Koertzen|GA Pratapkumar| |\n",
"| 20| 2008| Chennai|2008-05-02| Chennai Super Kings| Delhi Daredevils| Chennai Super Kings| bat|normal| false| Delhi Daredevils| 0| 8| V Sehwag|\"MA Chidambaram S...| Chepauk\"| BF Bowden| K Hariharan|\n",
"+---+------+----------+----------+--------------------+--------------------+--------------------+-------------+------+----------+--------------------+-----------+--------------+---------------+--------------------+-----------+--------------+--------------+\n",
"only showing top 20 rows\n",
"\n"
]
}
],
"source": [
"#Extracting and Transforming csv data\n",
"\n",
"data_path = \"../input/csv/\" # path directory to input csv files\n",
"data_opath = \"../output/csv/\" # path directory to output csv files\n",
"match_rdd = sc.textFile(data_path + \"matches.csv\") # reading csv files into RDD\n",
"\n",
"# Issue: Applying this custom schema generates error while calling various PysparkDF functions. e.g. show()\n",
"# header = match_rdd.first()\n",
"# fields = [StructField(field_name, StringType(), False) for field_name in header.split(',')]\n",
"# fields[0].dataType = LongType()\n",
"# fields[3].dataType = DateType()\n",
"# fields[9].dataType = BooleanType()\n",
"# fields[11].dataType = LongType()\n",
"# fields[12].dataType = LongType()\n",
"# fields[17].nullable = True\n",
"# schema = StructType(fields)\n",
"\n",
"match_header = match_rdd.filter(lambda l: \"id,season\" in l) # storing the header tuple\n",
"match_no_header = match_rdd.subtract(match_header) # subtracting it from RDD\n",
"match_temp_rdd = match_no_header.map(lambda k: k.split(','))\\\n",
".map(lambda p: (int(p[0]), int(p[1]),p[2],parse(p[3]).date(),p[4]\\\n",
" ,p[5],p[6],p[7],p[8],p[9]=='1',p[10],int(p[11])\\\n",
" ,int(p[12]),p[13],p[14],p[15],p[16],p[17])) # Transforming csv file data\n",
"\n",
"match_df = sql.createDataFrame(match_temp_rdd, match_rdd.first().split(',')) # converting to PysparkDF\n",
"match_df = match_df.orderBy(match_df.id.asc()) # asc sort by id\n",
"match_df.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_color_list(paletteName,numRows):\n",
" return all_palettes[paletteName][numRows]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# Overall ranking module\n",
"\n",
"def get_overall_ranks_df(season_num):\n",
" overall_ranking = match_df.filter(match_df.season == season_num)\\\n",
" .groupBy(\"winner\").count().orderBy(\"count\",ascending=0) # extracting required columns into another DF\n",
" \n",
" overall_ranking = overall_ranking.filter(\"winner != '' \") # Deleting records of tied matches\n",
" overall_ranking = overall_ranking.selectExpr(\"winner as Teams\", \"count as Wins\") # Renaming columns\n",
" return overall_ranking\n",
" \n",
" \n",
"def overall_rank_func(season_num):\n",
" overall_ranking = get_overall_ranks_df(season_num)\n",
" overall_ranking.show(truncate=False)\n",
"\n",
" overall_pdf = overall_ranking.toPandas() # Converting to PandaDF\n",
" clr = get_color_list('Viridis',overall_ranking.count()) # Brewing color hex values for each tuple('team')\n",
"\n",
" figure_overall_ranking = Bar(overall_pdf, values=\"Wins\", color=\"Teams\",palette=clr,\\\n",
" label=cat(columns=\"Teams\", sort=False), xgrid=True,\\\n",
" xlabel=\"Teams\", ylabel=\"Wins\", title=\"Overall Standings \" + str(season_num),\\\n",
" legend='top_right', plot_width=950, bar_width=0.6) # generating bar chart\n",
" \n",
" handle_overall_ranking = show(figure_overall_ranking, notebook_handle=True)\n",
" # displaying the chart"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"#Performance consistency module\n",
"\n",
"def get_consistency_DF(season_lbound, season_ubound): \n",
" consistency_df = match_df.select(\"season\",\"winner\")\\\n",
" .groupBy(\"season\",\"winner\").count().orderBy(\"winner\") # extracting required columns \n",
" consistency_df = consistency_df.filter(\"winner!='' \") # filtering out tied matches records\n",
" cond1 = func.col(\"season\") >= season_lbound \n",
" cond2 = func.col(\"season\") <= season_ubound\n",
" consistency_df = consistency_df.filter(cond1 & cond2) \n",
" return consistency_df\n",
"\n",
"\n",
"def get_constraints():\n",
" # constraint : teams that haven't played more than three season aren't considered\n",
" constraint_df = match_df.groupBy(\"winner\",\"season\")\\\n",
" .count().orderBy(\"winner\") # extracting list of season-wise winner teams\n",
" \n",
" constraint_df = constraint_df.groupBy(\"winner\").count()\\\n",
" .filter(\"count>3 and winner!='' \") # filtering out teams that don't satisfy constraint\n",
" return constraint_df\n",
"\n",
"\n",
"def filter_using_constraints(consistency_df, constraint_list):\n",
" consistency_df = consistency_df.where(func.col(\"winner\")\\\n",
" .isin(constraint_list)) # applying the constraint list\n",
" \n",
" consistency_df = consistency_df.groupBy(\"winner\")\\\n",
" .agg(func.stddev_pop(\"count\").alias(\"stddev\"),\\\n",
" func.sum(\"count\").alias(\"total_wins\"))\\\n",
" .orderBy(\"stddev\",\"total_wins\") # calculating the performance consistency\n",
" return consistency_df\n",
"\n",
"\n",
"def calc_consistency(consistency_df):\n",
" consistency_df = consistency_df.withColumn(\"final_deviations\",\\\n",
" ((10-consistency_df.stddev)/10)*100)\\\n",
" .orderBy(\"final_deviations\", ascending=False) # scaling to appropriate scale\n",
" \n",
" consistency_df = consistency_df.selectExpr(\"winner as Teams\", \"final_deviations as Consistency\")\n",
" \n",
" return consistency_df\n",
"\n",
"\n",
"def consistency_func(season_lbound = 2008, season_ubound = 2016):\n",
" consistency_df = get_consistency_DF(season_lbound, season_ubound) # extracting required columns \n",
" constraints_df = get_constraints()\n",
" constraints_list = [i.winner for i in constraints_df.collect()] # storing a list of filtered teams\n",
" consistency_df = filter_using_constraints(consistency_df, constraints_list)\n",
" consistency_df = calc_consistency(consistency_df)\n",
" consistency_df.show(truncate=False)\n",
" \n",
" consistency_pdf = consistency_df.toPandas() # converting to PandasDF\n",
" clr= get_color_list(\"RdYlGn\", consistency_df.count()) # brewing colors hex values for each team\n",
" \n",
" figure_consistency = Bar(consistency_pdf, values=\"Consistency\",\\\n",
" color=\"Teams\", palette=clr,\\\n",
" label=cat(columns=\"Teams\", sort=False),\\\n",
" xlabel=\"Teams\", ylabel=\"Win Consistency %age\",\\\n",
" title=\"IPL Performance Consistencies\",\\\n",
" legend='top_right', plot_width=950, bar_width=0.6) # generating bar chart\n",
" \n",
" figure_consistency.y_range = Range1d(60,100) # setting appropriate ranges\n",
" handle_consistency = show(figure_consistency, notebook_handle=True) # displaying chart\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------------------+----+\n",
"|Teams |Wins|\n",
"+---------------------------+----+\n",
"|Mumbai Indians |13 |\n",
"|Chennai Super Kings |12 |\n",
"|Rajasthan Royals |11 |\n",
"|Sunrisers Hyderabad |10 |\n",
"|Royal Challengers Bangalore|9 |\n",
"|Kings XI Punjab |8 |\n",
"|Kolkata Knight Riders |6 |\n",
"|Pune Warriors |4 |\n",
"|Delhi Daredevils |3 |\n",
"+---------------------------+----+\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
" <div class=\"bk-root\">\n",
" <div class=\"bk-plotdiv\" id=\"a858322a-201b-4f8b-aa23-91fbab88ee58\"></div>\n",
" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"a858322a-201b-4f8b-aa23-91fbab88ee58\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
" comm_manager = Jupyter.notebook.kernel.comm_manager\n",
" comm_manager.register_target(\"ed668cd8-f996-4ca9-84f6-8b513b7ff7aa\", function () {});\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"a858322a-201b-4f8b-aa23-91fbab88ee58\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a858322a-201b-4f8b-aa23-91fbab88ee58' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = {\"037d249a-288b-40f0-9242-777b824abe53\":{\"roots\":{\"references\":[{\"attributes\":{\"label\":{\"value\":\"Kolkata Knight Riders\"},\"renderers\":[{\"id\":\"21b36e28-3d06-45fd-a0f8-8ab4ad5ce62f\",\"type\":\"GlyphRenderer\"}]},\"id\":\"736238ec-4cc1-4073-b1c4-37febbdd5d79\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"9d8ad684-d2bf-4bf6-bb9f-25f2e3b5cfa2\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"62b14978-10c5-4066-a4c1-e1cb33869f38\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"2a9e2a52-a0a8-4247-8e35-c75623b14c6d\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"overlay\":{\"id\":\"936da82c-d7b0-49a6-b881-2724c8cdd577\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"8a0c86b8-a52e-4a59-9df5-f4ccd9ac94a2\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"label\":{\"value\":\"Sunrisers Hyderabad\"},\"renderers\":[{\"id\":\"461dc651-495d-43c4-832f-89874cadfcee\",\"type\":\"GlyphRenderer\"}]},\"id\":\"db2cfabf-93dd-4396-b87e-e946e4dd2abc\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Kings XI Punjab\"],\"chart_index\":[{\"Teams\":\"Kings XI Punjab\"}],\"color\":[\"#3B518A\"],\"fill_alpha\":[0.8],\"height\":[8.0],\"label\":[{\"Teams\":\"Kings XI Punjab\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Kings XI Punjab\"],\"y\":[4.0]}},\"id\":\"9d8ad684-d2bf-4bf6-bb9f-25f2e3b5cfa2\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"629965a2-447d-427f-8686-221000ca6e21\",\"type\":\"Rect\"},{\"attributes\":{\"below\":[{\"id\":\"156b2dc4-779f-4313-b4a2-5f1f53b476fe\",\"type\":\"CategoricalAxis\"}],\"css_classes\":null,\"left\":[{\"id\":\"b7ea5021-7b34-4464-b829-041d57ba753f\",\"type\":\"LinearAxis\"}],\"plot_width\":950,\"renderers\":[{\"id\":\"936da82c-d7b0-49a6-b881-2724c8cdd577\",\"type\":\"BoxAnnotation\"},{\"id\":\"e3e7d4b2-9a4d-4a61-9c18-7a7861a954c6\",\"type\":\"GlyphRenderer\"},{\"id\":\"bb736ac7-0af8-4fea-93c5-f4cde826bd24\",\"type\":\"GlyphRenderer\"},{\"id\":\"da9ce048-fd12-4c06-a6e3-459e52ec9645\",\"type\":\"GlyphRenderer\"},{\"id\":\"461dc651-495d-43c4-832f-89874cadfcee\",\"type\":\"GlyphRenderer\"},{\"id\":\"e79d2828-a4d0-4c8d-bcef-21fc56eb5a6a\",\"type\":\"GlyphRenderer\"},{\"id\":\"2a9e2a52-a0a8-4247-8e35-c75623b14c6d\",\"type\":\"GlyphRenderer\"},{\"id\":\"21b36e28-3d06-45fd-a0f8-8ab4ad5ce62f\",\"type\":\"GlyphRenderer\"},{\"id\":\"120b01eb-f7c9-4021-888f-9dad11f3a690\",\"type\":\"GlyphRenderer\"},{\"id\":\"9aacc295-9560-4cde-9395-0d9c53292b14\",\"type\":\"GlyphRenderer\"},{\"id\":\"6ca78439-6b83-431b-b7b1-6e877288d61f\",\"type\":\"Legend\"},{\"id\":\"156b2dc4-779f-4313-b4a2-5f1f53b476fe\",\"type\":\"CategoricalAxis\"},{\"id\":\"b7ea5021-7b34-4464-b829-041d57ba753f\",\"type\":\"LinearAxis\"},{\"id\":\"531f5c16-26d9-4ce2-89a9-4f0c45801cc5\",\"type\":\"Grid\"},{\"id\":\"4de711e0-d737-4d02-b93f-bfa8478ccbec\",\"type\":\"Grid\"}],\"title\":{\"id\":\"95ad1b93-0ef6-48ae-bb8f-79e3fefb4d9e\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"034e86a7-3eee-4c12-aeef-173bf5900f78\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"e8f334df-65e3-4ece-8b05-a5c18f4169c0\",\"type\":\"Toolbar\"},\"x_mapper_type\":\"auto\",\"x_range\":{\"id\":\"0a83de03-6b62-4a7b-9546-7b6dae377792\",\"type\":\"FactorRange\"},\"y_mapper_type\":\"auto\",\"y_range\":{\"id\":\"a09ce367-9bca-4442-9447-0031644e3fa2\",\"type\":\"Range1d\"}},\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"},{\"attributes\":{\"data_source\":{\"id\":\"74e04300-8c7b-4a1d-9980-0bc19becffaf\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ca1b3f76-a254-4897-97aa-8f99389e885c\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"9aacc295-9560-4cde-9395-0d9c53292b14\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"d385c035-b6f2-46e0-8919-82f6d5a6e75c\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Kolkata Knight Riders\"],\"chart_index\":[{\"Teams\":\"Kolkata Knight Riders\"}],\"color\":[\"#2C718E\"],\"fill_alpha\":[0.8],\"height\":[6.0],\"label\":[{\"Teams\":\"Kolkata Knight Riders\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Kolkata Knight Riders\"],\"y\":[3.0]}},\"id\":\"7a9942db-9a15-475d-82b4-b8ee370b014e\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"c5ddca9d-5514-4974-b80d-770298c2e1db\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"7a9942db-9a15-475d-82b4-b8ee370b014e\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"476d3917-01e4-4d55-958d-838948bff013\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"21b36e28-3d06-45fd-a0f8-8ab4ad5ce62f\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d26fc7cb-2e2a-4d26-a6cc-558a5e5ea857\",\"type\":\"BasicTicker\"}},\"id\":\"4de711e0-d737-4d02-b93f-bfa8478ccbec\",\"type\":\"Grid\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"9b16ec26-32c8-4010-b27c-335c31eddda3\",\"type\":\"HelpTool\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"9d800453-eb1e-4115-b9a1-f0abd26c49a3\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"034e86a7-3eee-4c12-aeef-173bf5900f78\",\"type\":\"ToolEvents\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"2abc0245-6100-4041-b028-aec745f15569\",\"type\":\"ResetTool\"},{\"attributes\":{\"data_source\":{\"id\":\"53c9aa0a-1064-4f25-b0c3-99b29ef5002b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"c5ddca9d-5514-4974-b80d-770298c2e1db\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e3e7d4b2-9a4d-4a61-9c18-7a7861a954c6\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"14d9a473-f31f-4a0d-80de-cab0bf8a0af6\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"31539095-4081-43db-8886-b93939c65354\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"461dc651-495d-43c4-832f-89874cadfcee\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"Rajasthan Royals\"},\"renderers\":[{\"id\":\"da9ce048-fd12-4c06-a6e3-459e52ec9645\",\"type\":\"GlyphRenderer\"}]},\"id\":\"28f76e99-4f63-4077-946f-45a5d8e1fb58\",\"type\":\"LegendItem\"},{\"attributes\":{\"label\":{\"value\":\"Chennai Super Kings\"},\"renderers\":[{\"id\":\"bb736ac7-0af8-4fea-93c5-f4cde826bd24\",\"type\":\"GlyphRenderer\"}]},\"id\":\"355f71d2-77ea-451e-b79b-568ef358c1f9\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"31539095-4081-43db-8886-b93939c65354\",\"type\":\"Rect\"},{\"attributes\":{\"label\":{\"value\":\"Pune Warriors\"},\"renderers\":[{\"id\":\"120b01eb-f7c9-4021-888f-9dad11f3a690\",\"type\":\"GlyphRenderer\"}]},\"id\":\"a5fdb4bc-ee3c-4794-be4e-f76a35fd75f5\",\"type\":\"LegendItem\"},{\"attributes\":{\"items\":[{\"id\":\"fa2582f3-5f2e-4a4b-b34d-b64c370f3542\",\"type\":\"LegendItem\"},{\"id\":\"355f71d2-77ea-451e-b79b-568ef358c1f9\",\"type\":\"LegendItem\"},{\"id\":\"28f76e99-4f63-4077-946f-45a5d8e1fb58\",\"type\":\"LegendItem\"},{\"id\":\"db2cfabf-93dd-4396-b87e-e946e4dd2abc\",\"type\":\"LegendItem\"},{\"id\":\"3f9d9feb-f4d2-46a8-b307-01d8d72461f7\",\"type\":\"LegendItem\"},{\"id\":\"dd41cafa-6db7-4c02-9eff-5764cf080869\",\"type\":\"LegendItem\"},{\"id\":\"736238ec-4cc1-4073-b1c4-37febbdd5d79\",\"type\":\"LegendItem\"},{\"id\":\"a5fdb4bc-ee3c-4794-be4e-f76a35fd75f5\",\"type\":\"LegendItem\"},{\"id\":\"e918b52c-ef5c-4c64-a5c9-9c7c7cb4cb2f\",\"type\":\"LegendItem\"}],\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"6ca78439-6b83-431b-b7b1-6e877288d61f\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"d26fc7cb-2e2a-4d26-a6cc-558a5e5ea857\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"75a7bcdb-2ba7-442c-b167-01cdc6b98554\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"55e02dd4-f26b-4b19-a5f9-fb1862f7aa61\",\"type\":\"PanTool\"},{\"id\":\"84e5933c-268a-472e-ba60-6f0b4bde73b9\",\"type\":\"WheelZoomTool\"},{\"id\":\"8a0c86b8-a52e-4a59-9df5-f4ccd9ac94a2\",\"type\":\"BoxZoomTool\"},{\"id\":\"9d800453-eb1e-4115-b9a1-f0abd26c49a3\",\"type\":\"SaveTool\"},{\"id\":\"2abc0245-6100-4041-b028-aec745f15569\",\"type\":\"ResetTool\"},{\"id\":\"9b16ec26-32c8-4010-b27c-335c31eddda3\",\"type\":\"HelpTool\"}]},\"id\":\"e8f334df-65e3-4ece-8b05-a5c18f4169c0\",\"type\":\"Toolbar\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"75a7bcdb-2ba7-442c-b167-01cdc6b98554\",\"type\":\"CategoricalTicker\"}},\"id\":\"531f5c16-26d9-4ce2-89a9-4f0c45801cc5\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"abedd2c8-ee94-47fb-ad08-5643b6afb893\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"3d188fb9-88b4-4eac-9bc7-051552a267d6\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e79d2828-a4d0-4c8d-bcef-21fc56eb5a6a\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"686e91d1-8c47-4fef-8989-db39d003d426\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"e88337ad-1d76-475d-b6eb-440fad11fa01\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"629965a2-447d-427f-8686-221000ca6e21\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"bb736ac7-0af8-4fea-93c5-f4cde826bd24\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"Mumbai Indians\"},\"renderers\":[{\"id\":\"e3e7d4b2-9a4d-4a61-9c18-7a7861a954c6\",\"type\":\"GlyphRenderer\"}]},\"id\":\"fa2582f3-5f2e-4a4b-b34d-b64c370f3542\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"1183faaa-6266-4920-9445-b1908eef2007\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"686e91d1-8c47-4fef-8989-db39d003d426\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"da9ce048-fd12-4c06-a6e3-459e52ec9645\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Mumbai Indians\"],\"chart_index\":[{\"Teams\":\"Mumbai Indians\"}],\"color\":[\"#208F8C\"],\"fill_alpha\":[0.8],\"height\":[13.0],\"label\":[{\"Teams\":\"Mumbai Indians\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Mumbai Indians\"],\"y\":[6.5]}},\"id\":\"53c9aa0a-1064-4f25-b0c3-99b29ef5002b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Pune Warriors\"],\"chart_index\":[{\"Teams\":\"Pune Warriors\"}],\"color\":[\"#27AD80\"],\"fill_alpha\":[0.8],\"height\":[4.0],\"label\":[{\"Teams\":\"Pune Warriors\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Pune Warriors\"],\"y\":[2.0]}},\"id\":\"48d27150-d241-47a6-94c1-5aab620a717e\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"da55c975-c37f-4726-b31b-b750a6a21363\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Delhi Daredevils\"],\"chart_index\":[{\"Teams\":\"Delhi Daredevils\"}],\"color\":[\"#472B7A\"],\"fill_alpha\":[0.8],\"height\":[3.0],\"label\":[{\"Teams\":\"Delhi Daredevils\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Delhi Daredevils\"],\"y\":[1.5]}},\"id\":\"74e04300-8c7b-4a1d-9980-0bc19becffaf\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"3d188fb9-88b4-4eac-9bc7-051552a267d6\",\"type\":\"Rect\"},{\"attributes\":{\"label\":{\"value\":\"Delhi Daredevils\"},\"renderers\":[{\"id\":\"9aacc295-9560-4cde-9395-0d9c53292b14\",\"type\":\"GlyphRenderer\"}]},\"id\":\"e918b52c-ef5c-4c64-a5c9-9c7c7cb4cb2f\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Rajasthan Royals\"],\"chart_index\":[{\"Teams\":\"Rajasthan Royals\"}],\"color\":[\"#5BC862\"],\"fill_alpha\":[0.8],\"height\":[11.0],\"label\":[{\"Teams\":\"Rajasthan Royals\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Rajasthan Royals\"],\"y\":[5.5]}},\"id\":\"1183faaa-6266-4920-9445-b1908eef2007\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"factors\":[\"Mumbai Indians\",\"Chennai Super Kings\",\"Rajasthan Royals\",\"Sunrisers Hyderabad\",\"Royal Challengers Bangalore\",\"Kings XI Punjab\",\"Kolkata Knight Riders\",\"Pune Warriors\",\"Delhi Daredevils\"]},\"id\":\"0a83de03-6b62-4a7b-9546-7b6dae377792\",\"type\":\"FactorRange\"},{\"attributes\":{\"label\":{\"value\":\"Kings XI Punjab\"},\"renderers\":[{\"id\":\"2a9e2a52-a0a8-4247-8e35-c75623b14c6d\",\"type\":\"GlyphRenderer\"}]},\"id\":\"dd41cafa-6db7-4c02-9eff-5764cf080869\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis_label\":\"Teams\",\"formatter\":{\"id\":\"da55c975-c37f-4726-b31b-b750a6a21363\",\"type\":\"CategoricalTickFormatter\"},\"major_label_orientation\":0.7853981633974483,\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"75a7bcdb-2ba7-442c-b167-01cdc6b98554\",\"type\":\"CategoricalTicker\"}},\"id\":\"156b2dc4-779f-4313-b4a2-5f1f53b476fe\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"callback\":null,\"end\":13.65},\"id\":\"a09ce367-9bca-4442-9447-0031644e3fa2\",\"type\":\"Range1d\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"62b14978-10c5-4066-a4c1-e1cb33869f38\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Sunrisers Hyderabad\"],\"chart_index\":[{\"Teams\":\"Sunrisers Hyderabad\"}],\"color\":[\"#FDE724\"],\"fill_alpha\":[0.8],\"height\":[10.0],\"label\":[{\"Teams\":\"Sunrisers Hyderabad\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Sunrisers Hyderabad\"],\"y\":[5.0]}},\"id\":\"14d9a473-f31f-4a0d-80de-cab0bf8a0af6\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"476d3917-01e4-4d55-958d-838948bff013\",\"type\":\"Rect\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"83056e72-39e4-47e4-9cde-37136eae3021\",\"type\":\"Rect\"},{\"attributes\":{\"plot\":null,\"text\":\"Overall Standings 2013\"},\"id\":\"95ad1b93-0ef6-48ae-bb8f-79e3fefb4d9e\",\"type\":\"Title\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"84e5933c-268a-472e-ba60-6f0b4bde73b9\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Chennai Super Kings\"],\"chart_index\":[{\"Teams\":\"Chennai Super Kings\"}],\"color\":[\"#440154\"],\"fill_alpha\":[0.8],\"height\":[12.0],\"label\":[{\"Teams\":\"Chennai Super Kings\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Chennai Super Kings\"],\"y\":[6.0]}},\"id\":\"e88337ad-1d76-475d-b6eb-440fad11fa01\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"48d27150-d241-47a6-94c1-5aab620a717e\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"83056e72-39e4-47e4-9cde-37136eae3021\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"120b01eb-f7c9-4021-888f-9dad11f3a690\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Royal Challengers Bangalore\"],\"chart_index\":[{\"Teams\":\"Royal Challengers Bangalore\"}],\"color\":[\"#AADB32\"],\"fill_alpha\":[0.8],\"height\":[9.0],\"label\":[{\"Teams\":\"Royal Challengers Bangalore\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Royal Challengers Bangalore\"],\"y\":[4.5]}},\"id\":\"abedd2c8-ee94-47fb-ad08-5643b6afb893\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"936da82c-d7b0-49a6-b881-2724c8cdd577\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"label\":{\"value\":\"Royal Challengers Bangalore\"},\"renderers\":[{\"id\":\"e79d2828-a4d0-4c8d-bcef-21fc56eb5a6a\",\"type\":\"GlyphRenderer\"}]},\"id\":\"3f9d9feb-f4d2-46a8-b307-01d8d72461f7\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"ca1b3f76-a254-4897-97aa-8f99389e885c\",\"type\":\"Rect\"},{\"attributes\":{\"axis_label\":\"Wins\",\"formatter\":{\"id\":\"d385c035-b6f2-46e0-8919-82f6d5a6e75c\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d26fc7cb-2e2a-4d26-a6cc-558a5e5ea857\",\"type\":\"BasicTicker\"}},\"id\":\"b7ea5021-7b34-4464-b829-041d57ba753f\",\"type\":\"LinearAxis\"},{\"attributes\":{\"plot\":{\"id\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"55e02dd4-f26b-4b19-a5f9-fb1862f7aa61\",\"type\":\"PanTool\"}],\"root_ids\":[\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
" var render_items = [{\"docid\":\"037d249a-288b-40f0-9242-777b824abe53\",\"elementid\":\"a858322a-201b-4f8b-aa23-91fbab88ee58\",\"modelid\":\"4dd1c1d7-43fa-49c2-b207-989c6a2d740c\",\"notebook_comms_target\":\"ed668cd8-f996-4ca9-84f6-8b513b7ff7aa\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"a858322a-201b-4f8b-aa23-91fbab88ee58\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------------------+-----------------+\n",
"|Teams |Consistency |\n",
"+---------------------------+-----------------+\n",
"|Chennai Super Kings |87.09005551264195|\n",
"|Royal Challengers Bangalore|84.276698113239 |\n",
"|Rajasthan Royals |82.28309031210891|\n",
"|Deccan Chargers |80.79713563032848|\n",
"|Sunrisers Hyderabad |80.0 |\n",
"|Kings XI Punjab |76.42977396044841|\n",
"|Mumbai Indians |73.7533070866273 |\n",
"|Kolkata Knight Riders |69.76940475463824|\n",
"|Delhi Daredevils |65.64078645318617|\n",
"+---------------------------+-----------------+\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
" <div class=\"bk-root\">\n",
" <div class=\"bk-plotdiv\" id=\"ad09409c-b666-4fd3-b0f8-5c1112990097\"></div>\n",
" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"ad09409c-b666-4fd3-b0f8-5c1112990097\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
" comm_manager = Jupyter.notebook.kernel.comm_manager\n",
" comm_manager.register_target(\"b6c57f45-6d33-4b32-9d41-fdf5eea2cc8a\", function () {});\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"ad09409c-b666-4fd3-b0f8-5c1112990097\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ad09409c-b666-4fd3-b0f8-5c1112990097' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = {\"1e92240d-dd32-44d7-94b8-f2bab41c6f39\":{\"roots\":{\"references\":[{\"attributes\":{\"label\":{\"value\":\"Delhi Daredevils\"},\"renderers\":[{\"id\":\"a3fa8570-040e-44c9-a382-c11b110c16cf\",\"type\":\"GlyphRenderer\"}]},\"id\":\"54853904-3983-4d05-81fd-2eeaedb8c570\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"340f1b92-ce1e-42e6-9719-bf51f6e16680\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f2268ed8-2c38-4013-96af-e1831734a0f2\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"14a40f59-c275-4fad-866b-8ca7d40bbdb0\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"f2268ed8-2c38-4013-96af-e1831734a0f2\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Kings XI Punjab\"],\"chart_index\":[{\"Teams\":\"Kings XI Punjab\"}],\"color\":[\"#d9ef8b\"],\"fill_alpha\":[0.8],\"height\":[76.42977396044841],\"label\":[{\"Teams\":\"Kings XI Punjab\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Kings XI Punjab\"],\"y\":[38.21488698022421]}},\"id\":\"1cd82428-b1d3-4bd1-bd37-22b84161b5b1\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"b8c06b53-beb3-4e2d-8a63-de3b25ecb08a\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Delhi Daredevils\"],\"chart_index\":[{\"Teams\":\"Delhi Daredevils\"}],\"color\":[\"#a6d96a\"],\"fill_alpha\":[0.8],\"height\":[65.64078645318617],\"label\":[{\"Teams\":\"Delhi Daredevils\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Delhi Daredevils\"],\"y\":[32.82039322659308]}},\"id\":\"1b393c5f-f5c5-4843-b176-40004efa60f6\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"label\":{\"value\":\"Mumbai Indians\"},\"renderers\":[{\"id\":\"067bb085-37a1-4ff0-9f00-a26f7cac2fc8\",\"type\":\"GlyphRenderer\"}]},\"id\":\"ce7a7bda-ebb5-4ebf-97e3-090d5790fdc1\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis_label\":\"Teams\",\"formatter\":{\"id\":\"74d0a3b9-358a-41ef-b0ab-77b861039502\",\"type\":\"CategoricalTickFormatter\"},\"major_label_orientation\":0.7853981633974483,\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fc8af1c2-0cba-41fa-a7c2-670162fa99d8\",\"type\":\"CategoricalTicker\"}},\"id\":\"77699826-571c-4682-b191-6ca0b249a9ff\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"7bb1ac8a-365a-490c-9e79-73d048d5682b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"3b81e33b-c9e0-47db-beff-cc49789f3622\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"898c5033-d4d0-4433-9d95-3a92cf5d8a46\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"44fab503-d37d-4d06-b3ad-8ea21b01d09a\",\"type\":\"PanTool\"},{\"id\":\"f38ae24e-7a84-4c1a-8b09-4c57b70359ac\",\"type\":\"WheelZoomTool\"},{\"id\":\"cead381c-697d-4afc-ae11-9d90a775b24b\",\"type\":\"BoxZoomTool\"},{\"id\":\"62b849fb-b5d5-4c6e-aa1d-90c827ec8f25\",\"type\":\"SaveTool\"},{\"id\":\"0ede883d-44ed-4f08-ad0f-6949c6443c6a\",\"type\":\"ResetTool\"},{\"id\":\"08127184-423b-4c42-a2ac-333cc999b0f4\",\"type\":\"HelpTool\"}]},\"id\":\"40f834ce-adfb-4b26-ab90-779667712f90\",\"type\":\"Toolbar\"},{\"attributes\":{\"label\":{\"value\":\"Sunrisers Hyderabad\"},\"renderers\":[{\"id\":\"fec1e089-e9dd-4e68-b5bb-bba309439cbb\",\"type\":\"GlyphRenderer\"}]},\"id\":\"e96f61ac-a31f-4744-b538-856326dd8494\",\"type\":\"LegendItem\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"c41d4d8b-783f-43cd-85a0-747548a4162f\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"08127184-423b-4c42-a2ac-333cc999b0f4\",\"type\":\"HelpTool\"},{\"attributes\":{\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"44fab503-d37d-4d06-b3ad-8ea21b01d09a\",\"type\":\"PanTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1b393c5f-f5c5-4843-b176-40004efa60f6\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f2c358b1-607b-4299-82e5-eeebb89f2da2\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"a3fa8570-040e-44c9-a382-c11b110c16cf\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"Chennai Super Kings\"},\"renderers\":[{\"id\":\"14a40f59-c275-4fad-866b-8ca7d40bbdb0\",\"type\":\"GlyphRenderer\"}]},\"id\":\"ddf50942-4673-4356-a8c7-576087ef88e6\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"d8bbb081-1e0f-4d04-94f1-8a403d5a544d\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Mumbai Indians\"],\"chart_index\":[{\"Teams\":\"Mumbai Indians\"}],\"color\":[\"#fee08b\"],\"fill_alpha\":[0.8],\"height\":[73.7533070866273],\"label\":[{\"Teams\":\"Mumbai Indians\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Mumbai Indians\"],\"y\":[36.87665354331365]}},\"id\":\"b39f1e2b-7d1c-4ca8-8f26-e10b1acb5d4b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"b39f1e2b-7d1c-4ca8-8f26-e10b1acb5d4b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"c636dfba-ffc2-4dd8-8773-a7eda8c661df\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"067bb085-37a1-4ff0-9f00-a26f7cac2fc8\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"8916266a-1d03-4ed5-906f-89a1d1e226f3\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"e963af3f-1bfe-4694-a552-4b5552cd8a2a\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"fec1e089-e9dd-4e68-b5bb-bba309439cbb\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"Deccan Chargers\"},\"renderers\":[{\"id\":\"e8bc222c-0c60-4d6b-9e75-cd385b47ec69\",\"type\":\"GlyphRenderer\"}]},\"id\":\"512229ca-a57d-417c-9b1c-164fa9c6d081\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"1cd82428-b1d3-4bd1-bd37-22b84161b5b1\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"5d5f7a9d-cffd-4f4f-837d-7cfc37770180\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"de1023cb-3445-4e63-9fd2-fb2a14323234\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"e963af3f-1bfe-4694-a552-4b5552cd8a2a\",\"type\":\"Rect\"},{\"attributes\":{\"overlay\":{\"id\":\"c41d4d8b-783f-43cd-85a0-747548a4162f\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"cead381c-697d-4afc-ae11-9d90a775b24b\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"f38ae24e-7a84-4c1a-8b09-4c57b70359ac\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"4d49e55e-eee8-4df4-9885-096464db5dee\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"52dc53ba-3ce9-4221-be41-f6dd49bfea04\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"b8c06b53-beb3-4e2d-8a63-de3b25ecb08a\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e8bc222c-0c60-4d6b-9e75-cd385b47ec69\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Deccan Chargers\"],\"chart_index\":[{\"Teams\":\"Deccan Chargers\"}],\"color\":[\"#66bd63\"],\"fill_alpha\":[0.8],\"height\":[80.79713563032848],\"label\":[{\"Teams\":\"Deccan Chargers\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Deccan Chargers\"],\"y\":[40.39856781516424]}},\"id\":\"52dc53ba-3ce9-4221-be41-f6dd49bfea04\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis_label\":\"Win Consistency %age\",\"formatter\":{\"id\":\"d96a12f3-249f-4a49-a600-5f699783012e\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d8bbb081-1e0f-4d04-94f1-8a403d5a544d\",\"type\":\"BasicTicker\"}},\"id\":\"ab671d3a-1110-4584-9b1d-d68540eef3a9\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"Kolkata Knight Riders\"},\"renderers\":[{\"id\":\"898c5033-d4d0-4433-9d95-3a92cf5d8a46\",\"type\":\"GlyphRenderer\"}]},\"id\":\"03e663b9-8d08-4a74-bdc5-4e9f2069f768\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"7dec86de-0dfd-4572-9d7b-d58e27f544b8\",\"type\":\"ToolEvents\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Sunrisers Hyderabad\"],\"chart_index\":[{\"Teams\":\"Sunrisers Hyderabad\"}],\"color\":[\"#d73027\"],\"fill_alpha\":[0.8],\"height\":[80.0],\"label\":[{\"Teams\":\"Sunrisers Hyderabad\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Sunrisers Hyderabad\"],\"y\":[40.0]}},\"id\":\"8916266a-1d03-4ed5-906f-89a1d1e226f3\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"0ede883d-44ed-4f08-ad0f-6949c6443c6a\",\"type\":\"ResetTool\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"5d5f7a9d-cffd-4f4f-837d-7cfc37770180\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"9d4bb3b6-d352-418c-a8d4-a93fae8d9afa\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4d49e55e-eee8-4df4-9885-096464db5dee\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"98ec8c89-15ba-4628-a1f9-f90c1500095d\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Royal Challengers Bangalore\"],\"chart_index\":[{\"Teams\":\"Royal Challengers Bangalore\"}],\"color\":[\"#f46d43\"],\"fill_alpha\":[0.8],\"height\":[84.276698113239],\"label\":[{\"Teams\":\"Royal Challengers Bangalore\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Royal Challengers Bangalore\"],\"y\":[42.1383490566195]}},\"id\":\"b19255af-2c9e-4052-8915-c15bf4c33c7b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"fc8af1c2-0cba-41fa-a7c2-670162fa99d8\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"b19255af-2c9e-4052-8915-c15bf4c33c7b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ba3ae34d-38b0-4dcb-af0d-be40b321eb22\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"d76e28f4-c7f6-4b0f-a048-c4a9eb6f99ff\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":null,\"text\":\"IPL Performance Consistencies\"},\"id\":\"133c22d6-9732-4afd-91aa-ae1f02dd78f2\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d8bbb081-1e0f-4d04-94f1-8a403d5a544d\",\"type\":\"BasicTicker\"}},\"id\":\"94a43ef3-f1cf-42b2-914e-6fdb8177c5b1\",\"type\":\"Grid\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"ba3ae34d-38b0-4dcb-af0d-be40b321eb22\",\"type\":\"Rect\"},{\"attributes\":{},\"id\":\"d96a12f3-249f-4a49-a600-5f699783012e\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"items\":[{\"id\":\"ddf50942-4673-4356-a8c7-576087ef88e6\",\"type\":\"LegendItem\"},{\"id\":\"9ca1902e-7a65-4992-b967-09ecc08f78d3\",\"type\":\"LegendItem\"},{\"id\":\"973c08e4-b2b8-40ca-ab1c-c4a88bd84a49\",\"type\":\"LegendItem\"},{\"id\":\"512229ca-a57d-417c-9b1c-164fa9c6d081\",\"type\":\"LegendItem\"},{\"id\":\"e96f61ac-a31f-4744-b538-856326dd8494\",\"type\":\"LegendItem\"},{\"id\":\"9d5bee16-6377-4220-969a-f23a240273c3\",\"type\":\"LegendItem\"},{\"id\":\"ce7a7bda-ebb5-4ebf-97e3-090d5790fdc1\",\"type\":\"LegendItem\"},{\"id\":\"03e663b9-8d08-4a74-bdc5-4e9f2069f768\",\"type\":\"LegendItem\"},{\"id\":\"54853904-3983-4d05-81fd-2eeaedb8c570\",\"type\":\"LegendItem\"}],\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"a8947c2c-f6ab-4758-98aa-c1f83f46d297\",\"type\":\"Legend\"},{\"attributes\":{\"label\":{\"value\":\"Kings XI Punjab\"},\"renderers\":[{\"id\":\"de1023cb-3445-4e63-9fd2-fb2a14323234\",\"type\":\"GlyphRenderer\"}]},\"id\":\"9d5bee16-6377-4220-969a-f23a240273c3\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"f2c358b1-607b-4299-82e5-eeebb89f2da2\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Kolkata Knight Riders\"],\"chart_index\":[{\"Teams\":\"Kolkata Knight Riders\"}],\"color\":[\"#ffffbf\"],\"fill_alpha\":[0.8],\"height\":[69.76940475463824],\"label\":[{\"Teams\":\"Kolkata Knight Riders\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Kolkata Knight Riders\"],\"y\":[34.88470237731912]}},\"id\":\"7bb1ac8a-365a-490c-9e79-73d048d5682b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"62b849fb-b5d5-4c6e-aa1d-90c827ec8f25\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Chennai Super Kings\"],\"chart_index\":[{\"Teams\":\"Chennai Super Kings\"}],\"color\":[\"#1a9850\"],\"fill_alpha\":[0.8],\"height\":[87.09005551264195],\"label\":[{\"Teams\":\"Chennai Super Kings\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Chennai Super Kings\"],\"y\":[43.54502775632098]}},\"id\":\"340f1b92-ce1e-42e6-9719-bf51f6e16680\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"line_alpha\",\"color\",\"fill_alpha\",\"height\",\"width\",\"y\",\"x\",\"label\"],\"data\":{\"Teams\":[\"Rajasthan Royals\"],\"chart_index\":[{\"Teams\":\"Rajasthan Royals\"}],\"color\":[\"#fdae61\"],\"fill_alpha\":[0.8],\"height\":[82.28309031210891],\"label\":[{\"Teams\":\"Rajasthan Royals\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.6],\"x\":[\"Rajasthan Royals\"],\"y\":[41.141545156054455]}},\"id\":\"9d4bb3b6-d352-418c-a8d4-a93fae8d9afa\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"factors\":[\"Chennai Super Kings\",\"Royal Challengers Bangalore\",\"Rajasthan Royals\",\"Deccan Chargers\",\"Sunrisers Hyderabad\",\"Kings XI Punjab\",\"Mumbai Indians\",\"Kolkata Knight Riders\",\"Delhi Daredevils\"]},\"id\":\"de77dd40-03a9-45e3-9593-aef84c35e369\",\"type\":\"FactorRange\"},{\"attributes\":{\"label\":{\"value\":\"Rajasthan Royals\"},\"renderers\":[{\"id\":\"98ec8c89-15ba-4628-a1f9-f90c1500095d\",\"type\":\"GlyphRenderer\"}]},\"id\":\"973c08e4-b2b8-40ca-ab1c-c4a88bd84a49\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"end\":100,\"start\":60},\"id\":\"ed8f5c8d-9f48-4c30-ba14-8677d856a89f\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"74d0a3b9-358a-41ef-b0ab-77b861039502\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"3b81e33b-c9e0-47db-beff-cc49789f3622\",\"type\":\"Rect\"},{\"attributes\":{\"below\":[{\"id\":\"77699826-571c-4682-b191-6ca0b249a9ff\",\"type\":\"CategoricalAxis\"}],\"css_classes\":null,\"left\":[{\"id\":\"ab671d3a-1110-4584-9b1d-d68540eef3a9\",\"type\":\"LinearAxis\"}],\"plot_width\":950,\"renderers\":[{\"id\":\"c41d4d8b-783f-43cd-85a0-747548a4162f\",\"type\":\"BoxAnnotation\"},{\"id\":\"14a40f59-c275-4fad-866b-8ca7d40bbdb0\",\"type\":\"GlyphRenderer\"},{\"id\":\"d76e28f4-c7f6-4b0f-a048-c4a9eb6f99ff\",\"type\":\"GlyphRenderer\"},{\"id\":\"98ec8c89-15ba-4628-a1f9-f90c1500095d\",\"type\":\"GlyphRenderer\"},{\"id\":\"e8bc222c-0c60-4d6b-9e75-cd385b47ec69\",\"type\":\"GlyphRenderer\"},{\"id\":\"fec1e089-e9dd-4e68-b5bb-bba309439cbb\",\"type\":\"GlyphRenderer\"},{\"id\":\"de1023cb-3445-4e63-9fd2-fb2a14323234\",\"type\":\"GlyphRenderer\"},{\"id\":\"067bb085-37a1-4ff0-9f00-a26f7cac2fc8\",\"type\":\"GlyphRenderer\"},{\"id\":\"898c5033-d4d0-4433-9d95-3a92cf5d8a46\",\"type\":\"GlyphRenderer\"},{\"id\":\"a3fa8570-040e-44c9-a382-c11b110c16cf\",\"type\":\"GlyphRenderer\"},{\"id\":\"a8947c2c-f6ab-4758-98aa-c1f83f46d297\",\"type\":\"Legend\"},{\"id\":\"77699826-571c-4682-b191-6ca0b249a9ff\",\"type\":\"CategoricalAxis\"},{\"id\":\"ab671d3a-1110-4584-9b1d-d68540eef3a9\",\"type\":\"LinearAxis\"},{\"id\":\"94a43ef3-f1cf-42b2-914e-6fdb8177c5b1\",\"type\":\"Grid\"}],\"title\":{\"id\":\"133c22d6-9732-4afd-91aa-ae1f02dd78f2\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"7dec86de-0dfd-4572-9d7b-d58e27f544b8\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"40f834ce-adfb-4b26-ab90-779667712f90\",\"type\":\"Toolbar\"},\"x_mapper_type\":\"auto\",\"x_range\":{\"id\":\"de77dd40-03a9-45e3-9593-aef84c35e369\",\"type\":\"FactorRange\"},\"y_mapper_type\":\"auto\",\"y_range\":{\"id\":\"ed8f5c8d-9f48-4c30-ba14-8677d856a89f\",\"type\":\"Range1d\"}},\"id\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"subtype\":\"Chart\",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"c636dfba-ffc2-4dd8-8773-a7eda8c661df\",\"type\":\"Rect\"},{\"attributes\":{\"label\":{\"value\":\"Royal Challengers Bangalore\"},\"renderers\":[{\"id\":\"d76e28f4-c7f6-4b0f-a048-c4a9eb6f99ff\",\"type\":\"GlyphRenderer\"}]},\"id\":\"9ca1902e-7a65-4992-b967-09ecc08f78d3\",\"type\":\"LegendItem\"}],\"root_ids\":[\"2c0943a5-bc94-44be-8341-d889344c14f6\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
" var render_items = [{\"docid\":\"1e92240d-dd32-44d7-94b8-f2bab41c6f39\",\"elementid\":\"ad09409c-b666-4fd3-b0f8-5c1112990097\",\"modelid\":\"2c0943a5-bc94-44be-8341-d889344c14f6\",\"notebook_comms_target\":\"b6c57f45-6d33-4b32-9d41-fdf5eea2cc8a\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"ad09409c-b666-4fd3-b0f8-5c1112990097\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"overall_rank_func(2013) # call this function by providing season year as arg\n",
"consistency_func(2009, 2014) # function to call consistency module"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting the \"ionic model\" of isotope fractionation \n",
"\n",
"The starting point is to imagine that the isotope vibrates in a potential well\n",
"that somehow represents the effective bonding between the atom of interest and\n",
"the rest of the crystal. We can follow Young et al. (2015) and represent the\n",
"interaction via a Born–Mayer type interionic potential:\n",
"\n",
"$$ E(r) = \\frac{z_1 z_2}{r}\\left(\\frac{e^2}{4\\pi\\epsilon_0}\\right) + \\frac{b}{r^n} + E_0,$$\n",
"\n",
"which gives the energy of the bond, $E$, as a function of the distance between the ions, $r$.\n",
"The first term represents the Coulomb interaction between ions (which is attractive \n",
"for ions of opposite charge since reducing $r$ makes the energy more negative). The\n",
"the second term represents repulsion between ions due to overlap of their electron clouds. At small\n",
"$r$ this repulsion dominates and there is an $r$, the equilibrium bond length, $r_0$, \n",
"which minimizes $E$. The parameters $z_1$ and $z_2$ represent the charges on the ions, $e$ is the \n",
"charge of an electron, $\\epsilon_0$ is the vacuum permittivity. The parameters $b$ and $n$ \n",
"define the strength and shape of the repulsion term. $E_0$ just sets the absolute energy (and is not further\n",
"involved otherwise). \n",
"\n",
"The force acting between the ions is the derivative of the energy with respect to distance\n",
"(I think the convention is usually that the force is the negative derivative, but that would\n",
"either imply a sign error in Young et al. 2015 or that I cannot take the derivative of a\n",
"polynomial), which leads to equation 30 of Young et al. 2015:\n",
"\n",
"$$ F(r) = \\frac{\\mathrm{d}E}{\\mathrm{d}r} \n",
" = -\\frac{z_1 z_2}{r^2}\\left(\\frac{e^2}{4\\pi\\epsilon_0}\\right)\n",
" - \\frac{bn}{r^{n+1}}.$$ \n",
"\n",
"At the equilibrium bond distance, $r_0$, $\\frac{\\mathrm{d}E}{\\mathrm{d}r} = 0$. This\n",
"means we can find $b$ in terms of the other parameters such that we can choose $r_0$:\n",
"\n",
"$$ b = -\\left(\\frac{e^2}{4\\pi\\epsilon_0}\\right)\\frac{z_1 z_2}{nr_0^{n-1}}. $$\n",
"\n",
"Commonly $n$ is set to 12, $r_0$ is taken from the ionic radii, and this sets $b$ for the\n",
"mineral of interest.\n",
"\n",
"For isotopic fractionation, we need the force constant, $K_f$ for the effective bond. This is given\n",
"by the second derivative of the energy with respect to distance:\n",
"\n",
"$$ K(r) = \\frac{\\mathrm{d}^2E}{\\mathrm{d}r^2}\n",
" = \\frac{2 z_1 z_2}{r^3}\\left(\\frac{e^2}{4\\pi\\epsilon_0}\\right)\n",
" - \\frac{b(n-1)n}{r^{n+2}},$$\n",
" \n",
"evaluated at $r_0$. Substituting $b$ and $r_0$ into this function gives $K_f$:\n",
"\n",
"$$K_f = K(r=r_0) = \\frac{2z_1 z_2}{r_0^3}\\left(\\frac{e^2}{4\\pi\\epsilon_0}\\right)\n",
" - \\left(\\frac{z_1 z_2 e^2}{4\\pi\\epsilon_0}\\right)\\frac{(n-1)n}{nr_0^{n-1} r_0^{n+2}}\\\\\n",
" = \\frac{z_1 z_2 e^2 (1 - n)}{4\\pi\\epsilon_0 r_0^3},$$\n",
" \n",
"where the final form is given as equation 31 in Young et al. (2015). The following cells implement\n",
"and plot these various functions.\n",
"\n",
"Turns out we assume that the effective charge depends on $r_0$ and the coordination number, $n_c$. \n",
"$z_1 = \\zeta \\times 2.0$ and $z_1 = \\zeta \\times -2.0$ and assume that:\n",
"\n",
"$$\\zeta = \\zeta_0 + r_0 \\zeta_r + n_c \\zeta_n$$\n",
"\n",
"fitting $\\zeta_0$, $\\zeta_r$ and $\\zeta_n$ to the calculated reduced fractionation factors for the MgO\n",
"structures at 300 K. \n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Import modules\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Define constants\n",
"eps0 = 8.854187817E-12 # Vacuum permittivity (F/m)\n",
"e = 1.60217662E-19 # electron charge (C)\n",
"\n",
"# Conversion factors\n",
"m2ang = 1.0E10\n",
"j2ev = 6.242E18\n",
"\n",
"def energy(r, zi, zj, b, n):\n",
" \"\"\"\n",
" Energy from Born-Mayer type interionic potential\n",
" \n",
" r - distance between ions (m); can be array\n",
" zi, zj - charges on ions (electrons)\n",
" b - strength of repulsive part (J/m^n)\n",
" n - exponent for repulsive part (-); typically ~12\n",
" returns energy (J)\n",
" \"\"\"\n",
" en = (zi*zj*e**2)/(4.0*np.pi*eps0*r) + (b/r**n)\n",
" return en\n",
" \n",
" \n",
"def de_by_dr(r, zi, zj, b, n):\n",
" \"\"\"\n",
" Derivative (force) of Born-Mayer type interionic potential\n",
" \n",
" r - distance between ions (m); can be array\n",
" zi, zj - charges on ions (electrons)\n",
" b - strength of repulsive part (J/m^n)\n",
" n - exponent for repulsive part (-); typically ~12\n",
" returns force (J/m = N)\n",
" \n",
" NB: Is the sign convention correct?\n",
" \"\"\"\n",
" force = -((zi*zj*e**2)/(4.0*np.pi*eps0*r**2)) - ((b*n)/r**(n+1))\n",
" return force\n",
"\n",
"\n",
"def d2e_by_dr2(r, zi, zj, b, n):\n",
" \"\"\"\n",
" Second derivative of Born-Mayer type interionic potential\n",
" \n",
" r - distance between ions (m); can be array\n",
" zi, zj - charges on ions (electrons)\n",
" b - strength of repulsive part (J/m^n)\n",
" n - exponent for repulsive part (-); typically ~12\n",
" returns second derivative of energy (J/m^2 = N/m)\n",
" \"\"\"\n",
" k = ((2.0*zi*zj*e**2)/(4.0*np.pi*eps0*r**3)) - ((b*(-n-1)*n)/r**(n+2))\n",
" return k\n",
"\n",
"\n",
"def cal_b(r0, zi, zj, n):\n",
" \"\"\"\n",
" Calculate b for Born-Mayer type interionic potential to give an equilbrium bond length\n",
" \n",
" r_0 - equilibrioumdistance between ions (m); can be array\n",
" zi, zj - charges on ions (electrons)\n",
" n - exponent for repulsive part (-); typically ~12\n",
" returns b such that energy minimum is at r_0 (J/m^n)\n",
" \"\"\"\n",
" b = -((zi*zj*e**2)/(4.0*np.pi*eps0*r0**2)) * r0**(n+1)/n\n",
" return b\n",
"\n",
"\n",
"def kf(r0, zi, zj, n):\n",
" \"\"\"\n",
" Calculate force constant for Born-Mayer type interionic potential\n",
" \n",
" r_0 - equilibrium distance between ions (m); can be array\n",
" zi, zj - charges on ions (electrons)\n",
" n - exponent for repulsive part (-); typically ~12\n",
" returns force constant (J/m^n)\n",
" \"\"\"\n",
" k = (zi * zj * e**2 * (1-n)) / (4.0 * np.pi * eps0 * r0**3)\n",
" return k"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Energy around r_0: [-25.14418095 -25.14421525 -25.14418112] eV\n",
"Force at r_0: 0.0 eV/Ang\n",
"Second derivative at r0: 1096.1171631664827 N/m\n",
"Kf: 1096.117163166483 N/m\n"
]
}
],
"source": [
"# Plot an example and check some values\n",
"rs = np.linspace(1.5, 4.0) # Angstroms\n",
"n = 12\n",
"zi = 2.0\n",
"zj = -2.0\n",
"\n",
"r0 = 2.1 # Angstroms\n",
"b = cal_b(r0/m2ang, zi, zj, n)\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"ax.plot(rs, energy(rs/m2ang, zi, zj, b, n)*j2ev)\n",
"ax.set_xlabel('Distance (Angstroms)')\n",
"ax.set_ylabel('Energy (eV)')\n",
"ax.axvline(r0)\n",
"plt.show()\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"ax.plot(rs, de_by_dr(rs/m2ang, zi, zj, b, n)*j2ev/m2ang, 'r')\n",
"ax.axvline(r0)\n",
"ax.axhline(0.0)\n",
"ax.set_ylim(-25, 25)\n",
"ax.set_xlabel('Distance (Angstroms)')\n",
"ax.set_ylabel('Force (eV/Angstrom)')\n",
"plt.show()\n",
"\n",
"print(\"Energy around r_0:\", energy(np.array([r0-0.001, r0, r0+0.001])/m2ang, zi, zj, b, n)*j2ev, \"eV\")\n",
"print(\"Force at r_0:\", de_by_dr(r0/m2ang, zi, zj, b, n)*j2ev/m2ang, \"eV/Ang\")\n",
"print(\"Second derivative at r0:\", d2e_by_dr2(r0/m2ang, zi, zj, b, n), \"N/m\")\n",
"print(\"Kf:\", kf(r0/m2ang, zi, zj, n), \"N/m\") # No b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# de Koker melt\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Some useful functions...\n",
"import ionic_model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.9613000e-10 1.9212375e-10 1.8835500e-10]\n",
"[1.27035541e-135 1.01235431e-135 8.14122237e-136]\n",
"Kf: [1345.49713471 1431.43521627 1519.08964145] N/m\n",
"beta at 1573 K [2.54825381 2.71079261 2.8765504 ]\n"
]
}
],
"source": [
"r_coefs_melt_dekoker = [1.9613, -0.00165, 0.0000019]\n",
"\n",
"pressures = np.array([0, 25, 50]) # GPa\n",
"\n",
"r_dekoker = ionic_model.melt_bond_length(pressures, r_coefs_melt_dekoker)\n",
"print(r_dekoker)\n",
"\n",
"b = cal_b(r_dekoker, zi, zj, n)\n",
"print(b)\n",
"\n",
"\n",
"k = kf(r_dekoker, zi, zj, n)\n",
"print(\"Kf:\",k, \"N/m\") \n",
"\n",
"beta = ionic_model.ionic_model_beta(k, 1573.0)\n",
"print(\"beta at 1573 K\", beta)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAFzCAYAAAAaKU4GAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABQpklEQVR4nO3dd3zdd3X/8de5V1e6sqw9LMmSLUuW5L2dbSeBBBJWCDNQ0pSmYRTKapsyCqX90TaUlhZoGSGMlBYSKBAge5DETkKG7TjeU5Zt7WFNa9/7+f1xrxzHvldW4nt1Nd5P0EPS/X7vvefm5sLbH3++55hzDhEREREROT+eRBcgIiIiIjIdKFiLiIiIiMSAgrWIiIiISAwoWIuIiIiIxICCtYiIiIhIDChYi4iIiIjEQFKiC4iFvLw8V1ZWlugyRERERGSa27p1a5tzLj/SsWkRrMvKytiyZUuiyxARERGRac7MjkY7pq0gIiIiIiIxoGAtIiIiIhIDCtYiIiIiIjGgYC0iIiIiEgMK1iIiIiIiMaBgLSIiIiISAwrWIiIiIiIxoGAtIiIiIhIDCtYiIiIiIjGgYC0iIiIiEgMK1iIiIiIiMaBg/RoFgo6nD7VxuLU30aWIiIiIyCSgYH0ebvrh8/xqW12iyxARERGRSUDB+jXyeow5GX4auwYSXYqIiIiITAIK1uehKNNPY6eCtYiIiIgoWJ+Xwkw/jV39iS5DRERERCYBBevzUJyVSmPXAM65RJciIiIiIgmmYH0eijL9DI4E6egbTnQpIiIiIpJgCtbnoSjTD0BDp7aDiIiIiMx0CtbnoSgzFUCdQUREREREwfp8FGWFVqybdAGjiIiIyIynYH0e8tJS8HmNBq1Yi4iIiMx4CtbnwTM6JEZ7rEVERERmPAXr81ScmaoVaxERERFRsD5fRVl+mhSsRURERGa8hAVrMys1s8fNbK+Z7TazT4ZvzzGzR8zsYPh7dqJqHI/CzFCwDgY1JEZERERkJkvkivUI8JfOucXARcDHzGwJ8FngMedcJfBY+PdJqzgzlaFAkPaTQ4kuRUREREQSKGHB2jnX6JzbFv65B9gLzAWuA+4Mn3Yn8PaEFDhOo0NitB1EREREZGabFHuszawMWA08B8xxzjVCKHwDBVHu8yEz22JmW1pbWyes1jMVZ4WGxDSol7WIiIjIjJbwYG1ms4FfAp9yznWP937Oududc+ucc+vy8/PjV+A5FIZXrNVyT0RERGRmS2iwNjMfoVD9v865X4VvbjazovDxIqAlUfWNR25aMslJHo01FxEREZnhEtkVxIAfAHudc18/7dBvgZvCP98E/Gaia3s1zIyiTL+CtYiIiMgMl5TA574UuBHYaWbbw7d9HrgN+LmZ3QwcA96dmPLGrzDDT6P2WIuIiIjMaAkL1s65pwCLcvj1E1nL+SrOSuX5IycSXYaIiIiIJFDCL16cDooy/TR3DxDQkBgRERGRGUvBOgaKslIZCTraewcTXYqIiIiIJIiCdQwUZYRa7jXoAkYRERGRGUvBOgaKstTLWkRERGSmU7COgeLM0PRFtdwTERERmbkUrGMga5aPlCSPWu6JiIiIzGAK1jFgZhRnpWqPtYiIiMgMpmAdI0WZfu2xFhEREZnBFKxjpCgzlSatWIuIiIjMWArWMVKU6ae5Z1BDYkRERERmKAXrGCnK8hMIOlp6tGotIiIiMhMpWMeIWu6JiIiIzGwK1jHy8pAYBWsRERGRmUjBOkaKMkZXrNUZRERERGQmUrCOkYzUJGYle2nQirWIiIjIjKRgHSNmRlGmn6ZurViLiIiIzEQK1jFUlJmqFWsRERGRGUrBOoaKMv3aYy0iIiIyQylYx1BRViotPYMMB4KJLkVEREREJpiCdQwVZ/pxDlp6BhNdioiIiIhMMAXrGCrMHO1lre0gIiIiIjONgnUMFWeFelk3aPqiiIiIyIyjYB1DReEV6yZdwCgiIiIy4yhYx1C638fslCS13BMRERGZgRSsY0wt90RERERmJgXrGCvKSqVRe6xFREREZhwF6xgrzvQrWIuIiIjMQArWMVaY6aetd5ChEQ2JEREREZlJFKxjrDgzFeeguVur1iIiIiIziYJ1jBVlhYfEaDuIiIiIyIyiYB1jo72s1RlEREREZGZRsI6xoszw9EX1shYRERGZURSsYywtJYkMf5JWrEVERERmGAXrOChWL2sRERGRGUfBOg4KNX1RREREZMZRsI6DosxUGrXHWkRERGRGUbCOg+JMP+0nhxgYDiS6FBERERGZIArWcVAYbrmnITEiIiIiM4eCdRwUZ6nlnoiIiMhMo2AdBxoSIyIiIjLzKFjHweiQGLXcExEREZk5FKzjIDXZS9Ysn1asRURERGYQBes4Ucs9ERERkZlFwTpOijP92goiIiIiMoMoWMeJpi+KiIiIzCwK1nFSnJVKR98w/UMaEiMiIiIyEyhYx4la7omIiIjMLArWcTLacq9J+6xFREREZgQF6zgZXbFuULAWERERmRESGqzN7Idm1mJmu067LcfMHjGzg+Hv2Yms8bUqHN0K0qmtICIiIiIzQaJXrH8MXHPGbZ8FHnPOVQKPhX+ffEYG4afvhe0/i3jY7/OSm5asFWsRERGRGSKhwdo5twk4ccbN1wF3hn++E3j7RNY0bkkpcOxZqHsh6imFmX6adPGiiIiIyIyQ6BXrSOY45xoBwt8LElxPdDnlcOJw1MNFmakaEiMiIiIyQ0zGYD0uZvYhM9tiZltaW1sTU0RuBbTXRD1cnOWnQXusRURERGaEyRism82sCCD8vSXSSc65251z65xz6/Lz8ye0wFNyKqDreGi/dQRFmal0D4xwcnBkggsTERERkYk2GYP1b4Gbwj/fBPwmgbWMLacccNBRG/Hwy0NitB1EREREZLpLdLu9nwF/AKrNrM7MbgZuA642s4PA1eHfJ6fcitD39sj7rDV9UURERGTmSErkkzvn3hfl0OsntJDXKqc89P1E5H3WxVmh6YuNnVqxFhEREZnuJuNWkKljVg74s6J2BpmToa0gIiIiIjOFgvX5yq2IuhUkOclD3uwUbQURERERmQEUrM9XTgWcOBL1cHGWX9MXRURERGYABevzlVMeark3HDk8F2n6ooiIiMiMoGB9vnIrGLvlXqouXhQRERGZARSsz1dOuOVelM4gRZl+egZH6BkYnsCiRERERGSiKVifr5wFoe9ROoMUjbbc0z5rERERkWlNwfp8zcqB1OyonUGKNX1RREREZEZQsI6FnIqoW0EKR4N1py5gFBEREZnOFKxjIac8arCek+HHDLXcExEREZnmFKxjIbcCuuoittzzeT0UpKdoxVpERERkmlOwjoWcsVvuFWam0tStFWsRERGR6UzBOhZyykPfo3QGKc7006AVaxEREZFpTcE6FnLDwTpKZ5CizFQauwZwzk1gUSIiIiIykRSsYyE1G1Jzol7AWJzlp28oQPfAyAQXJiIiIiITRcE6VnLKo24FOdVyr0vbQURERESmKwXrWMmtgPZoY83D0xc7dQGjiIiIyHSlYB0rORXQXQfDZ69KF2eFVqwbtGItIiIiMm0pWMfKaGeQCC338men4DFo0pAYERERkWlLwTpWRjuDRLiAMcnrYU6GnwZtBRERERGZthSsYyXnXC33/Lp4UURERGQaU7COlVMt96IE66xUbQURERERmcYUrGMptyJqL+uiDD8NXf0aEiMiIiIyTSlYx1LOGC33slIZGA7S2Tc8wUWJiIiIyERQsI6lnPLoLfcy1XJPREREZDpTsI6l3IrQ9wgt90anL2qftYiIiMj0pGAdSzkLQt8jdAYpzgpNX2xQsBYRERGZlhSsYyknvGIdoTNI3uwUkjxGY6e2goiIiIhMRwrWsZSaBbNyI3YG8XqMORl+bQURERERmaYUrGMtp3zMITG6eFFERERkelKwjrWcMXpZZ6XSqBVrERERkWlJwTrWciuguz5iy73S7FTqO/oZGgkmoDARERERiScF61jLKQ99P3HkrEPVhemMBB01bb0TXJSIiIiIxJuCdaydCtZn77NeVJgBwP6mnomsSEREREQmgIJ1rJ0K1mfvsy7PT8PnNfY2KliLiIiITDcK1rE22nIvQmcQn9dDRf5s9jd1T3xdIiIiIhJXCtbxMEZnkEWF6doKIiIiIjINKVjHQ270YF1dmEFD1wBdfcMTXJSIiIiIxJOCdTzklIda7g31nXVoUVE6APubtWotIiIiMp0oWMfD6AWMHWe33FtUGArW+7TPWkRERGRaUbCOh9yK0PcI20EKM/xkpvrYp33WIiIiItPKuIO1maWZmTeexUwboyvWETqDmBnVuoBRREREZNqJGqzNzGNm7zez+8ysBdgHNJrZbjP7mplVTlyZU4w/E2blRRwSAy93BnHOTXBhIiIiIhIvY61YPw5UAJ8DCp1zpc65AmAD8Cxwm5l9YAJqnJpyKyKONYfQBMbewRHqOvonuCgRERERiZekMY5d5Zw7qyecc+4E8Evgl2bmi1tlU11OOdQ8GfFQ9akLGHsozZk1kVWJiIiISJyMtWL96/BWkLRoJ0QK3hKWUwE9DRFb7o0Ga01gFBEREZk+xgrW3wfeChwxs7vN7O1mljxBdU19udFb7s1OSaI0J1WdQURERESmkajB2jn3G+fc+4D5wK+Am4BjZvZDM7t6ogqczJxzDAeiLNqP0RkEoHpOhoK1iIiIyDRyznZ7zrl+59zdzrnrgTcAq4EH417ZJDcwMsCld13KnXvujHxCzmgv68jBenFROkfaTjIwHIhThSIiIiIykc4ZrM1sjpn9hZk9DdwDPAysjXdhk50/yU9GcgYHThyIckIGpOVHHBIDoX3WgaDjUEtvHKsUERERkYkyVh/rW8zs98A2oAq41TlX7pz7G+fc9ngXZmbXmNl+MztkZp+N9/O9FpXZlezv2B/9hJxyaI8crBeduoBR20FEREREpoOxVqwvAW4DSp1zf+Gce3qCaiI84fG/gGuBJcD7zGzJRD3/eFVnV1PbXctgYDDyCTkVUbeClOWmkZzkYX+zgrWIiIjIdDDWxYsfdM49DDgz+4CZfQnAzOaZ2QVxrusC4JBzrsY5NwTcBVwX5+d81apzqgm6IIc6D0U+Ibccehph6ORZh5K8HioLZrO3US33RERERKaDc+6xBr4NXAy8L/x7D6HV5HiaCxw/7fe68G2nmNmHzGyLmW1pbW2NczmRVWVXAUTfZz3aGWSMCYzaCiIiIiIyPYwnWF/onPsYMADgnOsA4t3P2iLc5l7xi3O3O+fWOefW5efnx7mcyErTS0lNSuVAR7RgPXZnkEWF6bT0DHLi5FCcKhQRERGRiTKeYD0c3vPsAMwsHwjGtarQCnXpab+XAA1xfs5XzWMeKrMqxwjWoyvW0TuDAOzTBEYRERGRKW88wfqbwK+BAjP7R+Ap4J/iWhW8AFSa2YLwtMcbgN/G+Tlfk6qcKvZ37Mc5d/bB0ZZ7UYbELCpSZxARERGR6WI8A2L+F7gV+GegEXi7c+4X8SzKOTcCfBx4CNgL/Nw5tzuez/laVWVX0TXYRUtfS+QTciqi7rHOn51CTloy+xoVrEVERESmuqRoB8xstnOuF8A5tw/YN9Y5seacux+4Px6PHUvV2dUA7O/Yz5y0OWefkFMONY9HvK+ZsagwnX1quSciIiIy5Y21Yv0bM/s3M9toZmmjN5pZuZndbGYPAdfEv8TJrTK7EiD6PusxWu5BaJ/1gaYegsEIW0lEREREZMoYq4/164HHgA8Du82s28zagf8BCoGbnHP/NzFlTl7pyenMnT13jJZ7o51BorXcS6d/OMCxE31xqlBEREREJkLUrSAwdbZjJNqYo81PdQY5DIXLzjq8qDADgH1NPZTlpZ11XERERESmhvF0BZFzGHO0eW54xTpKZ5CqOemYqeWeiIiIyFSnYB0DY442T0mHtIKovaxTk72U5aap5Z6IiIjIFKdgHQPjGm0eJVgDVM9JZ5+CtYiIiMiUds5gbWb/amZLJ6KYqeqco81zK6JuBYFQZ5Da9pP0DwXiVKGIiIiIxNt4Vqz3Abeb2XNm9hEzy4x3UVPNuEab9zZFbbm3uCgd5+Bgi1atRURERKaq8UxevMM5dynwx0AZsMPMfmpmV8a7uKlkzNHmpzqDRN4OUj3aGUQTGEVERESmrHHtsTYzL7Ao/NUGvAR8xszuimNtU8roaPPmvuazD56jM8i8nFn4fR7tsxYRERGZwsazx/rrwH7gTcA/OefWOue+6px7K7A63gVOFaOjzSNuBznHirXXY+ELGNVyT0RERGSqGs+K9S5ghXPuw8655884dkEcapqSxhxtfqrl3tgXMKrlnoiIiMjUNZ5gvR1YZGZrTvuqMLMk51xXnOubMs452jy3Atqjt9xbVJhB+8khWnsiDJkRERERkUlvzJHmYd8G1gA7AAOWhX/ONbOPOOcejmN9U8rYo80r4NCjUe+7qDAdCE1gzE/Pj0d5IiIiIhJH41mxrgVWO+fWOefWEtpXvQu4CviXONY25Yw52jxnQajl3mBv5PuGg7W2g4iIiIhMTeMJ1oucc7tHf3HO7SEUtKPva5ihxhxtPtoZJMoFjLmzU8hPT2GvWu6JiIiITEnjCdYHzOw7ZnZ5+Ovb4dtSgOE41zeljDnaPGfsYA2h7SD7m9UZRERERGQqGk+wvgk4BHwK+DRQA/wJoVCtITGnGXO0ec6C0PcxOoMsKkznYHMvI4FgnCoUERERkXgZ8+LF8GCY3znnrgL+LcIpkTcMz1Cjo80jXsCYkg6z54zZGaS6MIPBkSC17X0sLJgdx0pFREREJNbGXLF2zgWAPjPLnKB6pryqnCoOdByIMtq84pxbQUAXMIqIiIhMRePZCjIA7DSzH5jZN0e/4l3YVDXmaPOccmg/CJFCN7CwYDYeQxMYRURERKag8fSxvi/8JeNw+mjzwrTCVx4sWgnb/we66iCr9Kz7+n1eFuSlsU8r1iIiIiJTzjmDtXPuTjNLBeY556JMP5FRp48231iy8ZUHS8MT4I8/FzFYAywqymBnnQZaioiIiEw159wKYmZvJTTW/MHw76vM7LdxrmvKGnO0+Zxl4EsLBesoFs1J59iJPnoHR+JYpYiIiIjE2nj2WH8ZuADoBHDObQcWxK2iaaAquypyZxBvEpSshWPPRr3v6ATGA83aDiIiIiIylYwnWI84587cmxD56jsBQsE66mjz0ougeVfU0eaLizIA2KcJjCIiIiJTyniC9S4zez/gNbNKM/sW8Eyc65rSxhxtXnohuCDUb4l437lZqaQle9mvziAiIiIiU8p4gvVfAEuBQeBnQDehKYwSxZijzUvWAQbHn494X4/HqCpMV2cQERERkSlmPF1B+oAvhL9kHMYcbZ6aBQWLx76AsTCD+3c24pzDzOJXqIiIiIjEzHi6glSZ2e1m9rCZ/X70ayKKm6rGHG0OobZ7x1+AYDDi4UWF6XT1D9PcHWGPtoiIiIhMSuMZEPML4LvAHUAgvuVMH1U5VTxy9JHIq86lF8HWH0PrPpiz5Kz7jo4239vUTWGmfwKqFREREZHzNd6uIN9xzj3vnNs6+hX3yqa4MUebnxoUE7nt3qLCUGeQ/dpnLSIiIjJljCdY/87M/tzMiswsZ/Qr7pVNcaePNj9LTjmk5Ue9gDFzlo/CDL+CtYiIiMgUMp6tIDeFv//1abc5oDz25UwfY442Nwu13RtjUMyionT2NqrlnoiIiMhUMZ6uIJqy+BqMjjbffyLaBYwXwr57obcFZhecdbi6MJ2nD7UxHAji847nLxZEREREJJGiJjYzu/W0n999xrF/imdR00VVdlXkrSAQCtYQdTvI4sIMhgOOmtaTcapORERERGJprKXQG077+XNnHLsmDrVMO6OjzQdGBs4+WLQSvMlRL2CsDncG2acJjCIiIiJTwljB2qL8HOl3iWB0tPnhrsNnH/T5oXh11BXrivzZJHlMFzCKiIiITBFjBWsX5edIv0sEY442h1DbvYYXYfjsFe3kJA8V+bM12lxERERkihgrWK80s24z6wFWhH8e/X35BNU3pY052hxCg2ICQ9D4UsTD1YXpWrEWERERmSKiBmvnnNc5l+GcS3fOJYV/Hv3dN5FFTlXjGm0O0QfFFKVT39lPV/9wnCoUERERkVhRH7c4q8oJdQZxLsLumdkFoWExUfZZj442P9CsVWsRERGRyU7BOs7GHG0OLw+KiRC8lxVnArDtaEc8SxQRERGRGFCwjrMxR5tDaDtIXxucqDnrUEGGn+o56Ww62BrPEkVEREQkBhSs4+z00eYRlV4U+h5lO8jl1fm8cKSDvqGReJQnIiIiIjGiYB1n5xxtnr8IUjKjXsC4sTKfoUCQZ2va41iliIiIiJwvBesJMOZoc48HStdHXbFeV5ZNqs/Lk/u1HURERERkMlOwngBjjjaH0AWMLXuhv/OsQ36fl4srctl0sC2+RYqIiIjIeVGwngBjjjaHULDGQd2WiIc3VuZxpO0kR9tPxq9IERERETkvCQnWZvZuM9ttZkEzW3fGsc+Z2SEz229mb0xEfbF2ztHmc9eCeaLus768ugCATQe0HURERERkskrUivUu4B3AptNvNLMlwA3AUuAa4Ntm5p348mLrnKPNU2bDnGVw/LmIh8tyZzEvZxZPKliLiIiITFoJCdbOub3OuUhtMq4D7nLODTrnjgCHgAsmtrrYO+doc4B5F0HdVgic3VbPzNhYlcczh9sZGgnGsVIRERERea0m2x7rucDx036vC9825VXlVLH/xP7Io80htM96+CQ074p4+PKqAvqGAmw5eiKOVYqIiIjIaxW3YG1mj5rZrghf1411twi3RUyiZvYhM9tiZltaWyf/Fomq7Cq6h7rHHm0OUdvuXVyRi89r2g4iIiIiMknFLVg7565yzi2L8PWbMe5WB5Se9nsJ0BDl8W93zq1zzq3Lz8+PZelxcc7R5pklkF4c9QLG2SlJrJ2fzaYDarsnIiIiMhlNtq0gvwVuMLMUM1sAVAKRl3CnmHOONjeDeRdGXbGG0HaQvY3dNHdH6YctIiIiIgmTqHZ715tZHXAxcJ+ZPQTgnNsN/BzYAzwIfMw5F0hEjbF2ztHmENoO0nUcuuojHr68KrQyr7Z7IiIiIpNPorqC/No5V+KcS3HOzXHOvfG0Y//onKtwzlU75x5IRH3xUpVdNXZnkFP7rCO33VtclE5+eoqmMIqIiIhMQpNtK8i0tjxvOUe6jtDWHyUYFy6HpNSo20HMjI2V+Ww+2EogGKW7iIiIiIgkhIL1BNpQsgGAzXWbI5/g9YWmMEa5gBHg8up8OvuG2VHXGYcKRUREROS1UrCeQNXZ1RTMKmBzfZRgDaELGBt3wNDJiIc3LMzDDHUHEREREZlkFKwnkJmxYe4Gnml4huHAcOSTSi8EF4D6bREPZ6cls6IkiycPtMSxUhERERF5tRSsJ9jGko2cHD7JtpbIwZmS9aHvUS5ghFB3kO3HO+nqixLORURERGTCKVhPsIuKLsLn8bGpblPkE2blQF71OYJ1HkEHTx3SdhARERGRyULBeoLN8s1ifeH6c++zPv48BIMRD68sySLDn6TtICIiIiKTiIJ1Amws2ciRriMc7zke+YTSC2GgE9oiT2lM8nrYUJnPkwdacU5t90REREQmAwXrBNg4dyNA9O0g5xgUA7CxKo/m7kEONPfGujwREREReQ0UrBOgNKOUsoyy6P2scxdCak7UQTEAG8PjzbUdRERERGRyULBOkI0lG3mh6QX6hvvOPmgWWrUeY1BMUWYq1XPSefJAaxyrFBEREZHxUrBOkI0lGxkKDvFcY5TtHvMuhPZDcLI96mNcXp3PC0c66BsaiVOVIiIiIjJeCtYJsqZgDWm+NDbVn8c+68p8hgJBnq2JHr5FREREZGIoWCeIz+vjkuJL2Fy3OXJnj+LV4PGNGazXlWWT6vPy5H5tBxERERFJNAXrBNowdwPNfc0c6IjQVs+XCkUrxwzWfp+Xiytytc9aREREZBJQsE6gDSUbgHO03avfBiNDUR9jY2Uete19HG0/GY8SRURERGScFKwTKC81j6W5S6MH63kXQmAQGl+K+hiXVxcAsEmr1iIiIiIJpWCdYBtLNrKjbQedA51nH5x3CZgHDjwY9f5lubOYlzNL20FEREREEkzBOsE2lmwk6II81fDU2Qdn50P5lbDj5xAMRry/mbGxKo9nDrczNBL5HBERERGJPwXrBFuSu4Qcf0707SArb4CuY3DsmaiPcXlVAX1DAbYcPRGnKkVEREQSJxgIUN96lD/seJBf/f67fO+ez3Os4WCiyzpLUqILmOk85uGyuZfxxPEnGAmOkOQ54y1Z9GZIng0v3QVll0V8jIsrcvF5jScPtHJJRV78ixYRERE5TwODfRxt2M/xlkM0dxyhvaeBzv4Wuoc76A300uv66LVhur0BOr3GsNkr7p+zp5h5xZUJqj4yBetJYGPJRn57+LfsaN3BmjlrXnkwOQ0Wvw32/Abe9LVQG74zzE5JYu38bDYdaONz105Q0SIiIiJnONnfS239Ho63HKSl4yhtvQ10DbTQPdwZCsvWT4+N0OkN0u0x3BlhGcDndWThyAh6mR1MpjCQSjrppCdnk+0vIDejmIKsMtYuumLiX+A5KFhPApcUX0KSJbGpbtPZwRpC20Fe+insvx+WvTPiY1xeVcBXH9xHc/cAczL8ca5YREREZorBwQGONu7jaPMBmk4cob2nno7+FrqHT9Ab6KWHfno8w3R5HV3eyLuMU71BsjDSg17ygqmUMYt0yyTTl0t2WiEFmfMoyitnXmE1hTkleLzeCX6VsaFgPQmkJ6ezes5qNtVv4lNrP3X2CWUbIGNuaDtI1GCdz1cf3MemA628e11pfAsWERGRKS0YCNDc0UBtw27qWw/T3HWUjpNNdA210xPopsf10e0ZossTpMtrBCOsLPu9jmwgI+ilIJhGObPIsCwyU/LImV1EfuY85uYtZMHcReRmFU78i0wABetJYuPcjfzb1n+jsbeRotlFrzzo8cCK98DT34TeFphdcNb9Fxelk5+ewqaDbQrWIiIiM9TwyDA19Xs41riPxvbDtPbW0dHXTPdIBz3Bk/TYAN2eACe8MOg5OywneR3ZODIDXrKdn3nBWWRYJpkpeWSnFTIncz5zCxZSVryY/OziBLzCyU3BepLYWBIK1pvrN/Oe6vecfcKKG+Cpf4ddv4SLPnrWYTNjY2U+j+1rJhB0eCN8WERERGRqGhjs43DdHo427qWpo4bWnuN0DrbSNdxJjztJt2eQLk+QTq8ROHN12QPp4a0YGc7H/GAay0knw5tDduqc0DaM3ArKihYzt2ABSUm+xLzIaUDBepJYkLmAubPnsqluU+RgXbAIilbCSz+LGKwBLq/O55fb6thR18nqedlxrlhERETO1+DgAEca9oa2ZJw4RGtPHV0DrXSNvByYO8KB+cwL/cwcWV5HVtBDRjCZYhfaipGVkkfu7GLmZC2gNL+S8tKlZKWra9hEULCeJMyMjSUb+fXBXzMwMoA/KcIFiCvfBw9+Flr2QsHisw5vWJiHGWw60KZgLSIikkDBQIC65sPUNOzieOtBWruPcaKvia6RE3QHe+m2QTq8AToi7F82jyP7VGBOYS5pZHqyyE4uIC+9hKKccuYXLqa8ZAn+lFkJeoUSiYL1JLKxZCM/2/czXmh6gQ0lG84+Ydm74KEvhC5ivPrvzzqcnZbMipIsHtrdxCdevxCLcKGBiIiInJ+TfT0cOPYSxxp309BZQ3tvPR2DrXQHuumyfjo9I5H3MHsgwxskGw+ZwWTmBDLJsmyy/Pnkzy6hKHchZQrMU5qC9SSyvnA9qUmpbKrbFDlYz86HhVfBzl/A678EnrNb0bxnXQlf+PUunj9yggvLcyegahERkemjpaOBg0df4ljzXpo6j9De10DXyAm6gj10eQbp8AbpiNBSLtnryAGygknMC6aznEyykvLISyuhMLuM+XMWUzFvOZmzcyb+RcmEUbCeRFK8KVxYeCGb6zfjnIu84rzyvfB/D0HtZii/4qzD71hdwr8+tJ8fPHVEwVpERCTMBYM0tB/nwNGtHG85QHPXEU70N9E10kGXO0mnZ4gT3iC9Z4ZmD2SeWmVOocTNJtOyyUktpCBjHiX5lSwsWUFJQcWU7b0ssaNgPclsKNnAE3VPUNNVQ0VWxdknVL8JUjLgpbsjBuvUZC8fuGg+//n4IWrbTlKWlxb/okVERBKsqa2OA8e2cqx5H81dtbT1N9I1fIJO10unZ4j2JEef55Wh2TyOHK8jO+AlP5jKQtLJ8uaRlzaXwqxyyooWUzlvpS78k3FTsJ5kNpZsBGBT3abIwdqXCkuug12/gjf/a2jk+RluvHg+33uyhh89fYS/v25ZvEsWERGJq96TXeyr3UpN4y4aTxymra+ejqE2Ol1PKDRHWGk2ezk0zwmmURXIINubT97suczNqWRB8VIq561ill8LUBI7CtaTTGFaIVXZVWyq28QHl30w8kkrb4AXfwL77gsNjjlDQbqft64s5udb6vjM1dVkzlI/ShERmZyCgQA1DXs5eGw7x1v30dJTS/tgC52BLrqsn/Zw54xXtJozyEwKkhvwkBv0U0EoNOfPLqE4ZyFlRUupnr+KWanpiXthMiMpWE9CG0s28qNdP6J7qJuM5IyzT5h3CWTOC3UHiRCsAW6+bAG/3FbHT58/xkeviLDyLSIiMgF6+7rZe2QLRxp2Un/iEG19dXQMt9PpeunwDtEaoXuG3+vIA7KDySwNZJHtySU3tZji7ArmFy5lcdkasjLyE/OCRMagYD0JbSzZyB077+CZhme4puyas08YHXH+1NehpwnSC886ZUlxBpcuzOXHzxzh5ssWkJx09hXMIiIi56u9q5U9Nc9R27iLhs7DtPc30DHSQYedpN07QvuZq80eyPEGyQ14KQqmsYRscnxzKMwoY17BIqrnr9WFgDJlKVhPQivyVpCZksnmus2RgzWEtoNs/tdQ671L/iLiKX92WTkf/PEL3L+zkbevnhvHikVEZLpq7Whkdzg4N3Yepm2ggROBTjqtn/akwFmt55K8jjwHOUEf1YEccjy55KWWMDd7IeXFy6lesJqMNLWck+lJwXoS8nq8XFp8KU/VP0XQBfFYhNXmvEqYuza0HSRKsL68Kp+K/DTueKqG61YVa2CMiIicpb2rld2HnuVI4w4au2po62+gI9DBCeujLSlA5xnBOdnryAdygsksD+SQ482jIK2UkrxqqkpXU1W6Cl9ySmJejEiCKVhPUhtLNnL/kfvZ1baLFfkrIp+04gZ44K+haScULj/rsMdj3HxZOZ//9U6eO3KCi9TXWkRkxukf7GPP4Rc4WPcide37aek7TsdIOyesj1bvCB1nbBX0JYWCc24wmZWBHHK8BcxJn8e8/MUsmreW8pJleL2KDyKR6JMxSV029zI85mFT3abowXrZO+Ghz4VWrSMEa4B3rJnL1x7axw+eOqJgLSIyDQUDAY40HGDPkWc52rKbpp5aTgy30OF6aPMO0ZZkjJz2N5be8FaNvKCPZcE8clw+c9LnMy9/EYtK17Fw3jK8XnWTEnktFKwnqcyUTFbmr2RT3SY+vvrjkU9Ky4XKN4T2WV/19xBhBcHv83LjRfP51uOHONJ2kgUaGCMiMuWc7O9h16FnOVi3jboTB2ntr+NE4ATtnn5akoKcPH3wiQeyk4LkjSRRFsxkTTCXglkloa0aJWtYXL4Of/KsxL0YkWlMwXoS21iykW9s+watfa3kz4rSVmjlDbD/fjjyBCy8KuIpH7h4Pt8ND4z5Bw2MERGZlBrb6th58Glqml6isfsQbYPNtLtu2rxDtCYZwdNWnZO9jgIgN+BnQSCLfF8Rc7MqKS9exYqFF5GdUZC4FyIygylYT2KjwfrB2ge5ccmNkU+qugb8maER51GCdUG6n7etKuYXW+r4zNVVZM1KjmPVIiISyegglN01z3G0eSdNvbW0D7fSbr20ekc4ccZe58ykIAUjSSwIZrLe5TNn1jzmFSxm8fwLqZq3Qts1RCYhBetJrCq7irVz1nLn7ju5ofoGfJH+RzQpBZa+A3bcDYM9kBJ5ytTNly3g/7aGBsb8+RUL41y5iMjMFAwEOFy3h101z1DbspPmk0dpG26lzfpoTgq8cuy2F/JdkPxAMkuDeeRbIcUZ5VQUrWLFwksoyC1N3AsRkddEwXqSu2X5LXzk0Y9wb829XF95feSTVt4AW38Ee38Hq94f8ZTFRRlctjCPO5+p5c8uK9fAGBGR1ygYCFBTv5cdh54Kh+djtI+00mYnaU4KviI8ezyOAi/kB1NYH8inIHkuJdmVVJWsYfnCS0lPy0rcCxGRmFOwnuQuKb6ExTmL+cGuH/C2irfh9USYRFV6IWSXhbqDRAnWADdvWMAHf/QC9+1s4PrVJfErWkRkGmhoPc5LB57kcOOLNHbX0DrcTJv10pQUoOf08Ox1FDgoCKawPpDNnOS5lOZUs3jeepZVXExq6uwEvgoRmUgK1pOcmXHLilv4zBOf4ZFjj0SexGgW6mn95Fehqx4yI09ZvLwyn4UFs7lj8xHevmquBsaIyIzX09vNi/ufZP/xF6jr3E/bUCNtdNPsHaY96cxtG445gWTWBvOYk1xCac4ilsxbz/KFl+D3q+OSiChYTwmvn/d6FmQu4I4dd/DG+W+MHIhXvAeevA12/hwu+3TExwkNjFnA5361k2drTnBxhfpai8j0FxgJsP/YDnYd2kxt606a+47SGmynxTtIcxIv93g2yEoKMmfEx6JgLnOsiJKsKqpL1rKyaiOZszWGW0TGpmA9BXjMw58u+1O++PQX2Vy/mY0lG88+KbcitCXkpbvg0k+FVrEjuH71XL720H5+8NQRBWsRmVa6ezvZuudx9tc9T33nAVqGG2mNsHUjOclRNGIUBWez0hUwN72CyqJVrK6+gsLc+Ql8BSIy1SlYTxFvLn8z397+be7YeUfkYA2w4r1w32eg8SUoXhXxFL/Pywcums+3fn+QmtZeyvO1909Epg7nHLX1h9h+4HFqmrfT2FtDa7CNFs8ATb7TVp+9kHdq60Y+Rf55lOUtYfmCS1i8YD1JSWo7KiKxl5BgbWZfA94KDAGHgQ865zrDxz4H3AwEgE845x5KRI2Tjc/j40+W/gn//Pw/s7V5K2vnrD37pKXXw4OfDa1aRwnWADdeNJ/vPnGYHz1dy/97uwbGiMjkMzISYNehF9hZs4natp009x+njU4ak4Zf0e85yecoGjaK3GxWujmUZlRSXbKONYuuICejMIGvQERmokStWD8CfM45N2JmXwU+B/yNmS0BbgCWAsXAo2ZW5ZwLJKjOSeUdle/gezu+x/d3fj9ysJ6VA4vfCtv+Gy79BGQUR3yc/PQUrltVzC+2Hucv36CBMSKSOP2DA7y072l21z7N8RO7aR6qp9W6qT+j5/NsX5DiER9LgnkUeeYyP3cJy8svZXnFxfh8/gS+AhGRlyUkWDvnHj7t12eBd4V/vg64yzk3CBwxs0PABcAfJrjEScmf5OfGJTfyjW3fYE/7HpbkLjn7pNd9EfbeCw9/Ed71g6iPdfOGBfxiax3/+9wxPnalBsaISHz1DfSzbc+T7Kl9iuOde2geaqTF00uDL0i/JxygPZDlcxSPJLM+WMDc1PksLFzNmuorKCtaqk5GIjLpTYY91n8K3B3+eS6hoD2qLnzbWczsQ8CHAObNmxfP+iaV91a/lx/u/CF37LyDr1/x9bNPyFkAl30q1Hpv3Qeh7LKIj7OoMIMNlaGBMbds0MAYEYmNvoEBtu/dzO7azRzv2EPzUD3Nnl7qfUEGRgO0F/J8jqKAn0uDeZSmlVNZvIZ11a+nKH9BYl+AiMh5iFuwNrNHgUgb3L7gnPtN+JwvACPA/47eLcL5LtLjO+duB24HWLduXcRzpqP05HRuWHQDd+y8g5quGsozy88+6dJPwfafwv23woc3gTfy23zzZQv4kx+9wL07GnjHGg2MEZHxGxoeZseBZ9lx6EmOnthB82A9LZ5u6n1B+k4L0Dk+R3EghctcPqWzFrKk9ALWL7qK3KzIW9VERKayuAVr59xVYx03s5uAtwCvd86NBuM6oPS000qAhvhUOHX90eI/4id7fsIPd/6Qr1z2lbNPSJ4Fb/xH+Pkfw5YfwoUfivg4l1flUxkeGHP9ag2MEZGzBYOOQ8f2sm3fw9S0vEhjfy3NdFLvG6Hb+/IWjuxkR/FICpcE8yiZvZDFc9dxwZKrycvSH9pFZOZIVFeQa4C/AS53zvWddui3wE/N7OuELl6sBJ5PQImTWm5qLu+seid377ubP1/15xTPjrDys/htUH4FPP4VWPYOSMs76xSz0MCYz/5qJ799qYHrVkWe2CgiM0NjWxNbdj3M/vo/0HjyEC3BNhp8Q7SctlVsVrKjZMTHaldIqb+cxcUXsH7JVRTlaguHiIi9vFg8gU8auigxBWgP3/Ssc+4j4WNfILTvegT4lHPugXM93rp169yWLVviVe6k1HSyiWt/eS3vrn43n7/w85FPat0P37kEVr0f3vatiKeMBIK8+3t/4FBLL/d/YgOlObPiWLWITAb9g0Ps2PsML9X8nuMndtE0Uk+Tt5c6n53qA53kHHOHPRRbJnNT51NZsJL1i65iYckq/e2WiMxoZrbVObcu4rFEBOtYm4nBGuBLT3+J+4/cz0PvfIjc1ChTFB/6Avzhv+CW38PcNRFPOdbex5u+uZlFhenc9aGLSPLqQkaR6cA5x5H6WrbtfojDzc/T0F9DCx3U+UboPO1znj/imBtMY25yERW5y1hdcQUrKzfgS0pJYPUiIpOTgvU0VdtVy9vueRs3L7+ZT675ZOSTBrrhW2shax7c/Ah4Iofme16s51N3b+fTV1Xxyasq41i1iMTDwNAwO/b+gZcOPcbRjh00D9fRkNRHnc8IhleY/UFH6UgSxZ485qdXsLT0Yi5a+gZyovS8FxGRs40VrCdDuz15jcoyy7h6/tXcte8uPrjsg2QkZ5x9kj8Drv4HuOcj8NJPYfUHIj7W21fP5ckDrXzjsQNcujCXdWU5ca5eRF6rxvZWtu58mP11T9PQe4Bm2qjzDdGe5A2d4IN8c5QG01ntLaUyfwUXLLqKqtL1eKN0CRIRkfOnFespbm/7Xt5z73v4xOpPcMuKWyKfFAzCD98IHUfgL7aCPzPiaT0Dw7zpm5sJBuGBT20gw++LY+Uici7BoOPgscNs230fh5qfp2mwliZPF8eS3ame0EnOUTriZa7lUJa+kOWll3Dx0mvIzihKcPUiItOTtoJMcx999KPsbtvNQ+96iNSk1MgnNWyH26+Aiz4K1/xz1MfaerSD93zvD7x5eRHfuEEXKYlMlIGhEXYfeJHtBx7kaPt2mkeO05B0kuM+IxD+HM4OOOYF/JQmF1GZs4x1VVeyovxyfD7thRYRmSjaCjLN3bL8Fm568CZ+dfBX/NHiP4p8UvEqWPsn8Nz3YM0fQ8HiiKetnZ/Np15fyb89coArqvM1OEYkDnr6B9m+6yl2Hn6E4107aQ420uAboN4X3sqRDDkemOfSWZk0jyVFa7l4yZsoK1ymP+yKiExiWrGeJm564Cbqe+t54B0P4PNG2cLRdwK+uRoKl8NNv4Mo/wcdCDred/uz7G7o4v5PbmB+blocKxeZ3jp7+3lx1yZ21TxKXfdOml0TdclDNI/uhwYKR6CULMrTylleeiEXL3kzBdnzE1i1iIhEoxXrGeCWFbfw0Uc/yr0193J95fWRT5qVA6//Itz3l7DnHlga+Tyvx/j3G1Zx7X9s4hN3bef/PnIxPrXgEzmn1q6TbN/1BHuPPEZ9z26aaeZ48hAtoyE6FYpHjHIr4OrZVawt28gFS95ERqouFhYRmQ60Yj1NOOd4773vpW+kj99c9xu8Hm/kE4MBuP1y6OuAjz8PydFXo+/b0cjHfrqNj11ZwV+/cVGcKheZmtq6+3hp11PsqXmYup6dtLgmjqUMnwrR5hzFAS/zPTlUZi5ibfnlrKt+I+mp2QmuXEREzodWrGcAM+Pm5TfzV0/+FY8ee5Q3lr0x8okeL1z7NfjRNfDUv8Pr/jbqY755RRFPHijh208c5rKF+VxcEWUIjcg0d6J3kJ27n2P34Qeo63qJFtdIXfLgy3uiZ0HxiIeFnkLenLmY9QuvZE3lG0hLidACU0REpi2tWE8jgWCAt//m7QRdkLvechfpyenRT/7Vh2D3r+Fjz0FOedTTTg6O8JZvPcXAcIAHPrmBrFnJcahcZPLo6hti176d7DpwL8c7ttIarKMhuZ+jPi8ufF1CwYixwJNNVeYiLlx4JWuqriHdn5XYwkVEZEKo3d4MsqVpC7c8fAsbSjbwH1f+Bx6Lsje6pyk0kbFsA7z/rjEfc2ddF+/4ztO8ftEcvvOBNepKINNG39AIe2pq2b37Po62PkPLyBGafD0cTvEyEv73PCsA5WRSlVHF+vKNrK2+lty0OQmuXEREEkVbQWaQdYXr+Kv1f8Vtz9/G93d8nw+v/HDkE9ML4fJb4ZEvwYGHoeoNUR9zeUkmf/mGam57YB93v3CcGy6YF6fqReJnaCTIgbpW9ux6hCMNj9MyuJ+WpA4Op0CP1wOpkBqEcpfJW1PLuaDsMtYtejNzMubpD5MiIjIuCtbT0PsXvZ+dbTv5r+3/xZLcJWwo2RD5xAs/Ctt+Ag/+DSzYAL4ow2WAD20oZ9OBVv7+d3tYvyCHivzZcape5PwFg47ath727X6Wg7UP0dT7Eq2eZo6mjNDkS4IU8CQ75gf9XJxSwpq5F3Dh4jexIH9F9At/RUREzkFbQaap/pF+brz/RhpONnD3m++mNKM08omHH4efXA8Vr4Mbfgo+f9THbOoa4NpvbKI4K5Vf/fklpCQpgMjk0NIzwJ4Dhzi8/wHqTvyB9uBRGlNOcigl6dSWjoKAl0pfASvyV3JR9RtZXHpp9EmlIiIiUWiP9Qx1vOc4N9x7A3PS5vA/1/4Ps3yzIp/44v/Abz4GlW+A9/4PJEUfj/zw7iY+9JOtvGPNXG57xwqSk9TfWibWycERdh1roWbPExyv/z1tw3tp93Vw0A8d3tAf9vxBWGgZLM2q5qKKK1lVfg15afkJrlxERKYDBesZ7Kn6p/jzR/+caxdcy20bbou+V3Trj+F3n4Sqa+E9/w1J0bt/fOPRg/z7owe4cEEO3/3AWrLT1ClE4iMQdBxs7ubAvl0cq3mI1p4X6PDWc8w/RI0v6VSXjpJgMotTS1lfehFrKt/Ewryl2tIhIiJxoWA9w31/x/f55ovf5Nb1t3Ljkhujn/jCHaGpjIveAu/+MUQbjQ78+sU6/uaXOynK9PODm9axsGCM1n4i49TUNcDOI/XU79tEY8vjdAUO0J7SyV6/h67wanRa0FiUlMuq/BVcsPANLJu3gYxk9YsWEZGJoWA9wwVdkE8//mmerHuS77/h+6wvXB/95Oe+Bw/cCovfBu/64ZjheuvRDj78ky0MDgf51vtXc0V1QRyql+mqb2iEncc7Obx/B81HH6Ozbwu9vgaO+oc4mOwjGF6NLnUpLJu9gAvnX8aq8mtYkFMZvY2kiIhInClYC71DvbzvvvfRPdTN3W+5m8K0wugn/+G/4KHPw9J3wDu+D97ozWPqO/v5szu3sL+pmy++ZQl/ckmZWpPJWYJBx+HWXnYcaaD1wNOcaH2CPg7QkdrBnhQvbeELYWc5o9qXx9qClawpv5oVJZeSmZKZ4OpFRERepmAtANR01vC++95HRVYFP77mxyR7x9gb/fQ34ZEvwvJ3w/XfC41Cj+Lk4Aifvns7D+9p5n0XzOMfrluKz6sVxZmsvXeQ7cc6qDm4h55jT9Lb9xyDKfUc9w+yJ8XHoCf070cRKaxMX8D60stYWf4GFmZXaW+0iIhMagrWcsqjRx/l0098mndWvpMvX/LlsU/e/HV47O9hxQ3w9m+PGa6DQce/Pryfbz9xmIvLc/n2H63RRY0zxOBIgN0N3ew80kT7oecYaNtMwPbQk9rOXr+H2uTQdqIkB1VJ2awtWM7qsqtYVXIZ+bPUqUNERKYWTV6UU66afxV/tvzPuGPnHSzLW8a7qt4V/eQNn4FgAB7/CniS4G3fAk/klWiPx7j1mkUsLJjNZ3+5k+u//TR33LSehQUaJDOdOOc42t7H9uOd1Bw+wODRpwn2Pw+pR2lKPckOfzInCkN/AMtgFivTSrmu+CJWLbiaZfkr8CdF75MuIiIy1SlYz0AfX/Vx9rTv4Z+e+yeqs6tZnr88+smX/zUER+DJ20Kh+i3fiBquAd6xpoT5ubP48E+2cv23n+a/3r+GjVValZyqOvuGeKmuix21rZyo2YKn9VlSk3YzkNrEIX+QXTnJp7Z1lHjyuCxnEWvmX8nqkg2UZZbpIkMREZlRtBVkhuoc6OSG+25gJDjC3W+5m9zU3OgnOwe//wps/ldY96fw5q/DOS5QrOvo48/u3MLBll6+9JYl/PHF83VR4yQ3MBza0vHS8U5qamtwx58jc3A7KbMO05HawUt+H4d9PpwZXmCxv4DVBatZU3YVqwrXkZeal+iXICIiEnfaYy0R7W3fy40P3MjyvOV856rvjP3X9M7Bo1+Gp/8DLvgQXPsv5wzXJwdH+ORd23l0bzM3rC/lr99YTe7s6FMdZeIEgo6a1l62H+9k5/E2Omp3kN2+jcKUPbjU4xxNHWKbP4XmpNBfaqVZEqvSy1g99xJWl17Osrxl0Sd5ioiITGMK1hLV7w7/js8/9Xkqsyv52savUZFVEf1k5+Dhv4U//GeoFd8b/wkyisZ8/GDQ8bWH9/PdJw+TkuThPetKuWVDOaU5CmUTxTnHsRN97KjrYld9F4ePHcfXuJWq4B6y/Pvpm9XKTr+Xl/wp9Ia3dRR401ibu4TV865gTdGFLMxaqG4dIiIiKFjLOWyu28zfPv239A33cesFt/KuyndF37bhHGz6WujL44MNn4aLPw6+1DGf41BLL7dvOsyvX6wn6OCtK4r48OUVLC7SxLxYcs5R19HPzvoudtR1sbuunf76XVQP76MqaT/Js2pp8J9kmz+FPSnJjITf54X+AtbMWcvq0o2smbOGorQibd0RERGJQMFazqmtv43Pb/48f2j8A1fPv5q/u/jvxh7MceJIqM/13t9B1jy4+v/BkuvOuT2kqWuAHzxVw0+fO8bJoQBXVufz0SsWsr4sW0HuVRoN0bsbuk4F6bq6Y1QM7mW15wAlKQcZSm1il9/DtpSUU23vfHhYlrGA1XMvYU3RhawqWKUhLCIiIuOkYC3jEnRB7tx9J9/c9k3yZuXx1Q1fZc2cNWPf6cgmeOCz0LIb5l8G1/wzFK0453N19g3xkz8c5cfP1NJ+coi187P56OUVvG5RAR6PAvaZBoYDHGjuYU9DN3sbu9nb2MPhpjZKB2tY6TnMSs9BstNqOJ58khf9KWzzp3DCG2575/WzOm85q+deypo5a1iSu4QUr/a6i4iIvBYK1vKq7Grbxa2bbqW+t56PrPwIH1r+obH31wZGYNudoc4h/R2w9iZ43Rch7dxdIvqHAvxi63Fu31RDXUc/lQWz+cjlFbxtVfGMnd7Y0jMQDtA97G3sZk9jN7Wt3ZRTz0rPYdYkHWFx8hE6fY3sTE5iuz+FHf4UBsIr/iX+fFYXrmN10XrWFKxhQeYCtb0TERGJEQVredVODp/kK89+hXtr7mXtnLXctuE2CtMKx75Tfwc8+S/w/O3gS4PLbw11EEk69wTGkUCQ+3Y28p0nDrOvqYe5Wam8Yekc1s7PZs28bIqzxt7DPdUEgo76jn4Ot/a+/NVykkOtvZw4OUiJtbLSarg09ShrfDV4g0fYnezY7k9huz+Vw77QH3S8eKjKLGdN8YWsLljN6oLVFMwqSPCrExERmb4UrOU1+93h3/GVZ79CkieJf7jkH3j9/Nef+06tB+Chz8GhRyF3Yah7SOUbzrn/GkL7hp/Y38oPnz7CC7UnGBgOAlCU6WdNOGSvnZ/NkqIMkpMm/yps39AINa0nw+E5/L2ll5q2kwyNBDGCzLdmLvTXc+GsepZaLQVDBzjs6WN7Sgov+f1sT02l00Kf0/SkNFbNWcOqglWsyl+ltnciIiITTMFazsvR7qPcuulW9rTv4b3V7+Wv1v3V+EZTH3g4FLDbD8GCjaEWfZVXQ2bJuJ53OBBkb2M32452sPVYJ9uOdlDf2Q9ASpKHFSWZp8L2mnnZ5KdP7L7hgeEATV0DNHYN0NTdT0PnQPj3fhrDt584OXTqfL8NcXlmGxenNbDMe4yy4UNk9RygjiF2+pPZmeJnZ1oG+7yOEUKfy7KM+awqWM2q/FWsLlitaYYiIiIJpmAt5204MMw3X/wmP979YxZmLeSLF32R1QWrz93JY2QIXvg+PPsd6Doeuq1gaShgV74BSi8Ar2/cdTR1DbDtWAdbj3aw7VgHu+q7GA6E/h3Om51C1iwfGf4kMlN9ZKb6yBj97h/9PYmM8O8Zfh9DgQD9Q0H6hkboHw7QPxSgbyhw6uf+4fDv4eMnTg5FDM2jsmb5KEpPZtnsHpYkN1Fp9SwYOUxe736SOw7RaY6dKcnsmJXOrvQsdnqCdLthAFKTUlmWt4wVeStYXbCaFfkryPZnj/ufjYiIiMSfgrXEzNP1T/OFp75A+0A7ZRllXLfwOt5a/lbmpM0Z+47OQet+OPhw6OvYHyA4AimZUHFlKGQvvArSz/E4ZwiN4e5iS20HR9pO0j0wTFf/MN39I6HvA8N09w8TPI9/zZO9HlKTvaT6vGTN8lGclUphpp95s6HC28S8YB0Fg8dI7z1C0olD0H4QRgboN2N/so+dGfnsTM9mpydAXeAkAB7zsDBrIcvzloe+8pdTkVmhISwiIiKTnIK1xFTfcB8P1T7EPYfuYVvLNjzm4eLii3n7wrdzZemV42vlNtANNU+Eg/Yj0NsUur1oVShkV14NBYshJf286w0GHb1DI3T3vzJ09w6OkJzkIdXnZVay91R4nhX+nuoNkjrYRtLJZuhpgJ6mUP/utgPQdhC6jp16ji6Pl725JezLyGNvcjL7XD+1gx0Ew1s6CmYVsCJvBcvzQ0F6ae5S7Y0WERGZghSsJW6OdR/jnkP38NvDv6W5r5mM5AyuXXAt1y+8niW5S8Y39MU5aNr5csiuex5c6KJFfGmQXgjpRaHV7PSi0O+zC195e7QAHgxCYCj8NfzKn0f6obcZuhuhJ/zV3RgK0d2NcLIVeOXnw/lm0ZxXwd6sOezz+9nLEPsG2mgcaDt1TsGsAhbnLGZRziIW5yxmWd6yc6/oi4iIyJSgYC1xFwgGeK7pOe45dA+PHX2MoeAQC7MW8vaFb+fN5W8mL/XcPa1P6TsRGjzTeRR6msOhtym0qt3dGArEZ0qeDSkZZwdoFxj/86bmQEYxpBcRmD2HhlkZHPElUWtBagO91A6e4GB3LZ2DnQAYxvyM+SzOWUx1TnUoTOcuIsefM/7nFBERkSlFwVomVPdQNw8eeZB7Dt3DzradJFkSFxZdSGV2JWUZZZRlllGWUUaOP+fVjzF3Dga7zw7cPU2h270p4E0OXRDpTR7756RkupLTqLURakd6qT1ZT21XLbXdtRzrPsZQ8OWLEzOSMyjLLGNh1sJTK9FV2VXaziEiIjLDKFhLwhzuPMxvDv2GzfWbOdp9lOHg8Klj6cnpoaB9WtguyyxjXvq88bXziyIQDNAx2EF7f3voayD0va2/jfaB0773tdEx2HHqfkmWREl6CWWZZSzIWPCKmrJTsl/9HwJERERk2lGwlkkhEAzQcLKB2q5ajnYfpba7ltquWo50H6Glr+XUeYZRmFZImi8t9LsZp/4T/jnS7YOBQdr72+kY7CA4ukf7NCneFPJS88hNzSXXn0teah7z0uedCtBz0+fi84y/9Z+IiIjMPGMF66SJLkZmLq/HS2l6KaXppWxgwyuO9Q33vSJsH+05yuDIIA6Hc47R/4T+6yLe7vP4WJ63/BXBOTc1/N2fS5ovTavOIiIiEjcK1jIpzPLNYnHuYhbnLk50KSIiIiKviWYji4iIiIjEgIK1iIiIiEgMKFiLiIiIiMSAgrWIiIiISAwoWIuIiIiIxICCtYiIiIhIDCQkWJvZ/zOzHWa23cweNrPi0459zswOmdl+M3tjIuoTEREREXm1ErVi/TXn3Arn3CrgXuBLAGa2BLgBWApcA3zbzLwJqlFEREREZNwSEqydc92n/ZoGjM5Vvw64yzk36Jw7AhwCLpjo+kREREREXq2ETV40s38E/hjoAq4M3zwXePa00+rCt4mIiIiITGpxW7E2s0fNbFeEr+sAnHNfcM6VAv8LfHz0bhEeykW4DTP7kJltMbMtra2t8XkRIiIiIiLjFLcVa+fcVeM89afAfcDfEVqhLj3tWAnQEOXxbwduB1i3bl3E8C0iIiIiMlES1RWk8rRf3wbsC//8W+AGM0sxswVAJfD8RNcnIiIiIvJqmXMTv9hrZr8EqoEgcBT4iHOuPnzsC8CfAiPAp5xzD4zj8VrDj5MIeUBbgp5bJo7e5+lP7/HMoPd5ZtD7PP0l8j2e75zLj3QgIcF6OjGzLc65dYmuQ+JL7/P0p/d4ZtD7PDPofZ7+Jut7rMmLIiIiIiIxoGAtIiIiIhIDCtbn7/ZEFyATQu/z9Kf3eGbQ+zwz6H2e/ible6w91iIiIiIiMaAVaxERERGRGFCwHgcz+6GZtZjZrijHrzCzLjPbHv760kTXKOfHzErN7HEz22tmu83skxHOMTP7ppkdMrMdZrYmEbXKazfO91mf5ynOzPxm9ryZvRR+n/8+wjn6PE9h43yP9VmeJszMa2Yvmtm9EY5Nqs9y3CYvTjM/Bv4T+O8xztnsnHvLxJQjcTAC/KVzbpuZpQNbzewR59ye0865ltDQokrgQuA74e8ydYznfQZ9nqe6QeB1zrleM/MBT5nZA865Z087R5/nqW087zHoszxdfBLYC2REODapPstasR4H59wm4ESi65D4cc41Oue2hX/uIfQBnnvGadcB/+1CngWyzKxogkuV8zDO91mmuPBntDf8qy/8deYFRfo8T2HjfI9lGjCzEuDNwB1RTplUn2UF69i5OPxXUg+Y2dJEFyOvnZmVAauB5844NBc4ftrvdSiUTVljvM+gz/OUF/6r4+1AC/CIc06f52lmHO8x6LM8HfwHcCuhad2RTKrPsoJ1bGwjNN5yJfAt4J7EliOvlZnNBn4JfMo5133m4Qh30QrJFHSO91mf52nAORdwzq0CSoALzGzZGafo8zzFjeM91md5ijOztwAtzrmtY50W4baEfZYVrGPAOdc9+ldSzrn7AZ+Z5SW4LHmVwvv0fgn8r3PuVxFOqQNKT/u9BGiYiNokds71PuvzPL045zqBJ4Brzjikz/M0Ee091md5WrgUeJuZ1QJ3Aa8zs/8545xJ9VlWsI4BMys0Mwv/fAGhf67tia1KXo3w+/cDYK9z7utRTvst8MfhK5AvArqcc40TVqSct/G8z/o8T31mlm9mWeGfU4GrgH1nnKbP8xQ2nvdYn+Wpzzn3OedciXOuDLgB+L1z7gNnnDapPsvqCjIOZvYz4Aogz8zqgL8jdKEEzrnvAu8CPmpmI0A/cIPT5J2p5lLgRmBneM8ewOeBeXDqfb4feBNwCOgDPjjxZcp5Gs/7rM/z1FcE3GlmXkJh6ufOuXvN7COgz/M0MZ73WJ/laWoyf5Y1eVFEREREJAa0FUREREREJAYUrEVEREREYkDBWkREREQkBhSsRURERERiQMFaRERERCQGFKxFRMbBzAJmtt3MdodHJH/GzDzhY+vM7Jtj3LfMzN4/cdWe9fypZvZkuDXZ6G2fNrMBM8uMw/OtMrM3xfpxz/Gc/2pmr5vI5xQROZOCtYjI+PQ751Y555YCVxPqm/p3AM65Lc65T4xx3zIgYcEa+FPgV865wGm3vQ94Abg+Ds+3itA/n7OYWbzmJ3wL+GycHltEZFwUrEVEXiXnXAvwIeDj4WlfV5jZvQBmdnl4ZXu7mb1oZunAbcCG8G2fDq9gbzazbeGvS8L3vcLMnjCz/zOzfWb2v6dNjltvZs+EV8ufN7N0M/Oa2dfM7AUz22FmH45S8h8Bvxn9xcwqgNnA3xIK2KO3/4mZ/crMHjSzg2b2L6cdu9nMDoTr+76Z/Wf49neb2a5wXZvMLBn4B+C94df7XjP7spndbmYPA/9tZvPN7LFwzY+Z2bzwY/3YzL5jZo+bWU34n+UPzWyvmf04fI43fN4uM9tpZp8OvydHgVwzKzzf91dE5LXS5EURkdfAOVcT3gpScMahvwI+5px72sxmAwOEVlL/yjn3FgAzmwVc7ZwbMLNK4GfAuvD9VwNLgQbgaeBSM3seuBt4r3PuBTPLIDRJ7mZC43vXm1kK8LSZPeycOzJaTDjoljvnak+r8X3h59wMVJtZQfgPCxBabV4NDAL7zexbQAD4IrAG6AF+D7wUPv9LwBudc/VmluWcGzKzLwHrnHMfD9fwZWAtcJlzrt/Mfgf8t3PuTjP7U+CbwNvDj5cNvA54G/A7QtMy/wx4wcxWAV5grnNuWfixs057XdvC5/8SEZEE0Iq1iMhrZxFuexr4upl9Ashyzo1EOMcHfN/MdgK/AJacdux551ydcy4IbCe0jaQaaHTOvQDgnOsOP+4bgD+20Hj254BcoPKM58oDOs+47QbgrvBz/Ap492nHHnPOdTnnBoA9wHzgAuBJ59wJ59xwuObTX++PzewWQqE3mt865/rDP18M/DT880+Ay04773fhsdM7gWbn3M5wnbvD/yxqgHIz+5aZXQN0n3bfFqB4jBpEROJKK9YiIq+BmZUTWsltARaP3u6cu83M7iO0x/hZM7sqwt0/DTQDKwktcAycdmzwtJ8DhP532gAXqQzgL5xzD41Raj/gP63uFYTC9yPhXSbJhMLqf53j+SNyzn3EzC4E3gxsD68qR3JyjBpPf22jzx88o5YgkOSc6zCzlcAbgY8B7yG0hxxCr7MfEZEE0Yq1iMirZGb5wHeB/wyvrp5+rCK8yvpVYAuwiND2ifTTTssktAIdBG5k7JVegH1AsZmtDz9HevgiwIeAj5qZL3x7lZmlnX5H51wH4DWz0XD9PuDLzrmy8FcxMNfM5o/x/M8Dl5tZdvh533nG633OOfcloA0ojfB6z/QMoVVzCO3/fuocr/8UM8sDPM65X/Ly9pRRVcCu8T6WiEisacVaRGR8UsNbLnzACKEtDF+PcN6nzOxKQqu9e4AHCK22jpjZS8CPgW8DvzSzdwOPM/ZqLuF9y+8FvmVmqYRWZa8C7iC0PWJb+CLHVl7eq3y6hwltt3iUUKC99ozjvw7f3hzl+evN7J8IbTdpCL+urvDhr4X3iRvwGKG918eAz4b/ef1zhIf8BPBDM/vrcM0fHOv1n2Eu8KPw/naAzwGE/3CxkNAfZkREEsLOWGwREZFpxsxWA59xzt14Ho8x2znXG16x/jXwQ+fcr2NW5Hkys+uBNc65Lya6FhGZubQVRERkmnPOvQg8bqcNiHkNvhxegd4FHAHuiUFpsZQE/FuiixCRmU0r1iIiIiIiMaAVaxERERGRGFCwFhERERGJAQVrEREREZEYULAWEREREYkBBWsRERERkRhQsBYRERERiYH/Dyctu5DHMTOEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,6))\n",
"ax.plot(rs, energy(rs/m2ang, zi, zj, b[0], n)*j2ev)\n",
"ax.plot(rs, energy(rs/m2ang, zi, zj, b[1], n)*j2ev)\n",
"ax.plot(rs, energy(rs/m2ang, zi, zj, b[2], n)*j2ev)\n",
"\n",
"ax.set_xlabel('Distance (Angstroms)')\n",
"ax.set_ylabel('Energy (eV)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"r = np.linspace(1.95E-10, 2.80E-10)\n",
"k12 = kf(r, 2.0, -2.0, 12)\n",
"b12 = ionic_model.ionic_model_beta(k12, 300.0)\n",
"k10 = kf(r, 2.0, -2.0, 10)\n",
"b10 = ionic_model.ionic_model_beta(k10, 300.0)\n",
"k8 = kf(r, 2.0, -2.0, 8)\n",
"b8 = ionic_model.ionic_model_beta(k8, 300.0)\n",
"k6 = kf(r, 2.0, -2.0, 6)\n",
"b6 = ionic_model.ionic_model_beta(k6, 300.0)\n",
"\n",
"kq7 = kf(r, 2.0*0.75, -2.0*0.75, 12)\n",
"bq7 = ionic_model.ionic_model_beta(kq7, 300.0)\n",
"kq2 = kf(r, 1.0, -1.0, 12)\n",
"bq2 = ionic_model.ionic_model_beta(kq2, 300.0)\n",
"kq4 = kf(r, 0.5, -0.5, 12)\n",
"bq4 = ionic_model.ionic_model_beta(kq4, 300.0)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEFCAYAAAD69rxNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABd4ElEQVR4nO2dd1zV1/3/n4fL3kuWgIAgIFPEveM2RrOj2WlGs5OmzWi/adJf2jRpkjZtmjZtVmN2YuJITExi3FshKiooCCJTQLbsC+f3xwdRI5sL9144z8fj87j3fub7fsTX533f55zXEVJKFAqFQmF+WBg7AIVCoVD0DiXgCoVCYaYoAVcoFAozRQm4QqFQmClKwBUKhcJMsRzIi3l6esqgoKCBvKRCoVCYPcnJyWeklMN+vn5ABTwoKIikpKSBvKRCoVCYPUKIU+2tVyUUhUKhMFOUgCsUCoWZ0qWACyHChRAHL1iqhBCPCiHchRAbhBAZra9uAxGwQqFQKDS6rIFLKY8D8QBCCB2QD6wGngI2SilfFEI81fr5yf4LVaEY2jQ1NZGXl0d9fb2xQ1H0E7a2tvj7+2NlZdWt/XvaiDkbyJRSnhJCLAVmtq5fAWxBCbhC0W/k5eXh5OREUFAQQghjh6MwMFJKSktLycvLIzg4uFvH9LQGvgz4pPW9t5SysPXChYBXewcIIe4RQiQJIZJKSkp6eDmFQnGO+vp6PDw8lHgPUoQQeHh49OgXVrcFXAhhDSwBVvYkKCnlm1LKRCll4rBhl3RjVCgUPUCJ9+Cmp/++PcnAFwI/SSmLWj8XCSF8Wy/qCxT36Mo9IPlUOW9uy0RZ3yoUCsV5eiLgyzlfPgH4Crit9f1twFpDBfVz1hzI58/fHuPXnx+ivqm5vy6jUCj6idLSUmbNmoWjoyMPPvhg2/ra2louv/xyIiIiiIqK4qmnnurTdVauXElkZCSzZs3qa8iX0NF3+DllZWXMnTuXsLAw5s6dS3l5ucFjOUe3BFwIYQ/MBVZdsPpFYK4QIqN124uGD0/juaVRPDZ3FKsO5LP8rT0UV6tWeIXCnLC1teWPf/wjr7zyyiXbfvOb33Ds2DEOHDjAzp07Wb9+fa+v88477/Dvf/+bzZs39yXcdunsO1zIiy++yOzZs8nIyGD27Nm8+GK/SWP3BFxKWSul9JBSVl6wrlRKOVtKGdb6WtZfQQoheHh2GG/clMCxwmqWvr6TI/mVXR+oUCgMRnZ2NpGRkdx9991ERUUxb9486urqunWsg4MDU6dOxdbW9qL19vb2bdmytbU1CQkJ5OXldXquuro6li1bRmxsLDfccAMTJkwgKSmJ5557jh07dnDvvffy+OOPk52dzbRp00hISCAhIYFdu3a1neOll14iJiaGuLi4bmf9HX2Hn7N27Vpuu00rTtx2222sWbOmW+fvDQPqhdJXFsb4Euhhzz3vJ3Ptf3bxynVxLI71M3ZYCsWA8/++PkpqQZVBzznaz5lnr4jqdJ+MjAw++eQT3nrrLa6//nq+/PJLbr75Zl5++WU++uijS/afPn06r732WreuX1FRwddff80jjzzS6X5vvPEG9vb2pKSkkJKSQkJCAgDPPPMMmzZt4pVXXiExMZHa2lo2bNiAra0tGRkZLF++nKSkJNavX8+aNWvYu3cv9vb2lJVpuachvgNAUVERvr6+APj6+lJc3G/Ng+Yl4ABRfi6sfXAK936QzIMfHyD9dDWPzhmFhYVqnVco+pvg4GDi4+MBGDt2LNnZ2QA8/vjjPP74470+r16vZ/ny5Tz88MOEhIR0uu+2bdt4+OGHAYiNjSU2Nrbd/ZqamnjwwQc5ePAgOp2O9PR0AH788UfuuOMO7O3tAXB3dzfIdzAGZifgAJ6ONnx09wR+v+YIr206QWphNX+7IQ5n2+6NXlIozJ2uMuX+wsbGpu29TqdrK6H0NXu95557CAsL49FHH+1WHN3pbvfqq6/i7e3NoUOHaGlpaSt9SCnbPd5QGbi3tzeFhYX4+vpSWFiIl1e7Q2QMglkKOICNpY6/XBNLlJ8Lf1yXypX/2smbtyQS6uVo7NAUiiFHX7LXp59+msrKSt5+++2L1q9evZp9+/bxwgsvXLR++vTpfPTRR8yaNYsjR46QkpLS7nkrKyvx9/fHwsKCFStW0Nys9WCbN28ezz33HDfeeGNbCcXd3d1gGfiSJUtYsWIFTz31FCtWrGDp0qV9PmdHmLUboRCC2yYH8dFdE6iqa+LKf+3k+6OnjR2WQqFoh6CgIB577DHee+89/P39SU1NJS8vj+eff57U1FQSEhKIj49vE/LMzEycnZ0vOc99993H2bNniY2N5aWXXmL8+PHtXu/+++9nxYoVTJw4kfT0dBwcHABYsGABS5YsITExkfj4+C57lXT1HQDuuuuutrkOnnrqKTZs2EBYWBgbNmzoc9fIzhADOTgmMTFR9teEDgUVddz3YTKH8ip5+LJQVRdXDDrS0tKIjIw0dhgDxs0338yrr75KVyO4Z86c2dZwORho799ZCJEspbzkC5p1Bn4hfq52fPbLSVw31p/XNp3grveTqKxrMnZYCoWil3z44YddivdQZ9AIOICtlY6Xro3lj1dGsy29hCv+uYOjBaq/uEIxmNmyZcugyb57yqAScNDq4rdMHMFnv5xIo76Fq/+9i8+Tco0dlkKhUBicQSfg5xg7wp11D09l7Ag3nvgihSe/SFE+KgqFYlAxaAUctP7iH9w5gQdnhfJZUi7XvLGLnNJaY4elUCgUBmFQCziAzkLwm/nhvHNbIrlltSz+53Y2pBZ1faBCoVCYOINewM8xO9KbdQ9NI9DDnrvfT+KP61Jp1LcYOyyFYkjQmRVrcnIyMTExhIaG8vDDD/fJ91/ZyQ5iAj3s+eLeydw6aQTv7DjJdf/dTW6ZKqkoFP1NZ1as9913H2+++SYZGRlkZGTw3Xff9fo6yk52kGNrpeO5pdH8+6YEsorPsui17Xx3RI3eVCi6oj/sZAsLC6mqqmLSpEkIIbj11lu7tF9VdrLnMVsvlL6yKMaXaD8XHvzkJ+79MJnbJwfx20UR2FjqjB2aQtE165+C04cNe06fGFjYebZoaDvZ/Px8/P392z77+/uTn5/faQzKTvY8Q1bAQSuprLx3Ei+uP8b/dmaTdKqM15aNIWSYMsRSKNrD0Hay7dW7u3IaVHay5xnSAg6aq+GzV0QxKcSDJ75MYfE/d/CHJVFcN9ZfzQCuMF26yJT7C0Pbyfr7+180A09eXh5+fl1P0qLsZDWGXA28I+ZF+bD+kWnE+rvwxBcpPPTJAeWlolB0k8cff5yDBw9esnQlfL6+vjg5ObFnzx6klLz//vtt9qurV6/mt7/97SXHnLOTBbq0k/X19cXCwoIPPvjgIjvZd999l9parQPDuRJKb7/DzzlnJwsoO9mBxNfFjo/umsjj88NZf+Q0i/6xneRT/TbVp0IxpOjIivWNN97grrvuIjQ0lJEjR7Jw4UJA2cl2h27ZyQohXIG3gWhAAr8AjgOfAUFANnC9lLLTDo/9aSdraA7klPPwpwcoqKjn4cvCeGDWSCx16nmnMB7KTrZ9lJ1s1/wD+E5KGQHEAWnAU8BGKWUYsLH186BhTKAb3z48jStifXn1x3Su/+9uTpXWGDsshWLIoOxku6ZLARdCOAPTgXcApJSNUsoKYCmwonW3FcCV/ROi8XCyteLvy8bwj2XxZBSfZdE/tvPZ/pw+jRRTKBSGRdnJdk4IUAL8TwhxQAjxthDCAfCWUhYCtL6229QqhLhHCJEkhEgqKSkxWOADydL44Xz/6HRi/V158svD/PKDZErPNhg7LIVCMcTpjoBbAgnAG1LKMUANPSiXSCnflFImSikTzfnnkJ+rHR/dNYH/WxTJluMlzP/7djYf678O+gqFQtEV3RHwPCBPSrm39fMXaIJeJITwBWh9HfRqZmEhuHt6CGsfnIKnozV3vLef365K4WyD3tihKRSKIUiXAi6lPA3kCiHCW1fNBlKBr4DbWtfdBqztlwhNkEhfZ9Y+OIVfzgjhs/25zH91G7syzxg7LIVCMcTobi+Uh4CPhBApQDzwZ+BFYK4QIgOY2/p5yGBjqeO3CyNZee9krC0tuPGtvfzhq6PUNqpsXKH4OU1NTdx2223ExMQQGRnJCy+80KfzLV++nNjYWF599VUDRXielStXEhUVhYWFBZ11e/7uu+8IDw8nNDS0Xx0HO6NbQ+mllAeB9pp5Zxs0GjNk7Aitu+FfvjvGe7uy2XK8mL9eH8fYEe7GDk2hMBlWrlxJQ0MDhw8fpra2ltGjR7N8+XKCgoJ6fK7Tp0+za9cuTp06ZfhAgejoaFatWsUvf/nLDvdpbm7mgQceYMOGDfj7+zNu3DiWLFnC6NGj+yWmjlAjUwyAnbWOPyyJ4pO7J6JvkVz7n938aV0qdY1qDk7F4KEvdrJCCGpqatDr9dTV1WFtbd3uKMsLSU5OJi4ujkmTJvH4448THR0NaEPhi4uLiY+PZ/v27bz11luMGzeOuLg4rrnmmrYh8kVFRVx11VXExcURFxd3kZ1sZ0RGRhIeHt7pPvv27SM0NJSQkBCsra1ZtmwZa9cOfBV5yJtZGZJJIz347tHpvPBtGm/vOMmPaUX85ZpYJoR4GDs0xSDjL/v+wrGyYwY9Z4R7BE+Of7LTfXprJ3vttdeydu1afH19qa2t5dVXX21zAeyIO+64g3/+85/MmDHjIpfAr776isWLF3Pw4EEARo8ezd133w3A008/zTvvvMNDDz3Eww8/zIwZM1i9ejXNzc2cPXsWgGnTplFdXX3J9V555RXmzJnTaUznyM/PJyAgoO2zv78/e/fu7eSI/sE8BLylBYTQFhPH0caS56+K4fJYX5768jA3vLmHWyaO4MmFETjamMftVig6ord2svv27UOn01FQUEB5eTnTpk1jzpw5hISEtLt/ZWUlFRUVzJgxA4BbbrmF9evXt7vvkSNHePrpp6moqODs2bPMnz8fgE2bNvH+++8DmnOii4sLANu3b+/x9/45vbHB7Q/MQ1H2vwUZP8CSf4Jz11aTpsDkkZ589+g0Xvk+nf/tOsmmY8W8cHUM00eZb194henQVabcX/TWTvbjjz9mwYIFWFlZ4eXlxZQpU0hKSupQwDuyfG2P22+/nTVr1hAXF8d7773Hli1bOt3fEBm4v78/ubm5bZ+7a4NraMyjBm5pA9k74d8TIWUlmMlQdntrS565YjRf3DsJGysLbn13H79ZeYjymkZjh6ZQGJSurFgDAwPZtGkTUkpqamrYs2cPERERANx6663s27fvovO5urri4uLCjh07ANp9OJyjuroaX19fmpqaLtpv9uzZvPHGG4DW6FhVVQVoGXh7sXZXvAHGjRtHRkYGJ0+epLGxkU8//ZQlS5Z0+3hDYR4CPvZ2uG8neIbDqrtg5W1QU2rsqLrN2BHufPvwNO6fOZI1B/KZ87etrD2YrzxVFEOGBx54gLNnzxIdHc24ceO444472mbSSUlJaZuC7EL+97//8cADDzBp0iTs7Ow6PPcf//hHJkyYwNy5c9seCgD/+Mc/2Lx5MzExMYwdO5ajR492K9bVq1fj7+/P7t27ufzyy9tKMgUFBSxatAgAS0tLXn/9debPn09kZCTXX389UVFR3b4fhqJbdrKGos92si3NsPMfsPnPYOcGS16D8IWGC3AASCus4qkvUziUV8nM8GH8cWk0Ae72xg5LYQYMRjvZqqoq7rzzTlauXNnpftnZ2SxevJgjR44MUGTGoz/sZE0DCx1Mewzu2QKO3vDJMlhzP9RVGDuybhPp68yq+6fw7BWj2XeyjHmvbuPt7Vnom1uMHZpCMeA4Ozt3Kd6KjjEvAT+HTzTcvQmm/RoOfQL/ngTp3xs7qm6jsxDcMSWYDY/NYPJID/70TRpX/XsXKXkVxg5NoTBJgoKChkT23VPMU8ABLK1h9jNw10awc4WPr4dVv4Ra85kCbbirHW/flsjrN46hqKqepf/ayTNrj1BVr+biVCgUXWO+An6O4QlaSWX643B4pdZT5dg3xo6q2wghWBzrx4+/nsFtk4L4cM8pZv9VNXIqFIquMX8BB62b4WVPwz2bwcELPr0RvrjTrHqqONta8YclUax9YCq+LrY88ulBbnlnHyfPqGncFApF+wwOAT+Hb5wm4rP+D1LXwuuJcOgzs+k3DhDj78Lq+6fwx6VRHMqtYP6r23jl++PKV0WhUFzC4BJwAJ0VzHgC7t0OHiNh9T3w4TVQnm3syLqNzkJwy6QgNv5mBpfH+vL65hPM+dtW1h8uVGUVhdmSkpLCpEmTiIqKIiYmhvr6+l6fS9nJtiKlHLBl7NixckBp1ku5579SPu8n5Z98pNz5Tyn1TQMbgwHYm1Uq57+6VY54cp28+e098kRxtbFDUhiB1NRUY4fQa5qammRMTIw8ePCglFLKM2fOSL1e36tzFRYWysDAQEOGdxGpqany2LFjcsaMGXL//v3t7qPX62VISIjMzMyUDQ0NMjY2Vh49etRg1/85QJJsR1MHXwZ+IRY6mHAPPLAXgqfDD/8Hb8+GwkPGjqxHjA92Z91DU/nDFaM5mFvBgr9v44X1aWoqN8WA0hc72R9++IHY2Fji4uIA8PDwQKfTdXqMspPtGvMws+orLv6w/FM4ugrWPwlvzoQJ98Gs34KNk7Gj6xaWOgtunxLM5bF+vPTdMf67NYtVP+Xz5IIIrh4zHAsL03dqVBiO03/+Mw1phrWTtYmMwOd3v+t0n97ayaanpyOEYP78+ZSUlLBs2TKeeOKJTq+l7GS7ZmgIOGhWtNHXwMjLYONzsOffcHQ1LPwLRF5hFla1AMOcbHj5ujhunBDI//s6ld+sPMQHu7N5dkkUCYFuxg5PMcjprZ2sXq9nx44d7N+/H3t7e2bPns3YsWOZPbv9Sb2UnWz3GDoCfg47N1j8KsTdCOt+BZ/fAmHzYdFL4BZk7Oi6zZhAN1bdN5k1B/N5cf0xrv73Lq4aM5wnF0Tg42Jr7PAU/UxXmXJ/0Vs7WX9/f2bMmIGnpycAixYt4qeffupQwKWyk+0Wg7sG3hkB47QBQPOeh+wd8K+JsP2voG8wdmTdxsJCcHWCP5t/M5MHZ4XyzeFCZr2yhdc2Zqhuh4oBpSs72fnz55OSkkJtbS16vZ6tW7e2zR+p7GR7z9AVcACdJUx+EB7cB6GztdLKG5PhxI/GjqxHONhY8pv54Wx8bAYzw4fxtw3pzHplC18m59HSorodKoyPm5sbjz32GOPGjSM+Pp6EhAQuv/xyQNnJ9oVu2ckKIbKBaqAZ0EspE4UQ7sBnQBCQDVwvpSzv7Dx9tpPtbzJ+hPWPQ1kWRCyGBS+Aa6Cxo+ox+7PL+NO6VA7lVRI93JmnLx/NRDUvp9mj7GSVnezP9+1JBj5LShl/wUmeAjZKKcOAja2fzZuwOXD/Hrjs93BiI7w+Hra+DE29H3BgDMYFubP6/in8/YZ4ys42suzNPdz9fhJZJWeNHZpCcRHKTrZv9KWEshRY0fp+BXBln6MxBSxtYPpv4MH9EDYXNv+p1SDrW7Makm9hIbhyzHA2/WYmj88PZ9eJM8x9dRtPrzlMSbX51PkVClB2sh3RXQGXwA9CiGQhxD2t67yllIUAra9e7R0ohLhHCJEkhEgqKSnpe8QDhWsA3PAB3LIadNbw6XL44CooTjN2ZD3C1krHA7NC2fL4LG4cH8in+3KZ8fJmXt2QrgYCKRRmTncFfIqUMgFYCDwghJje3QtIKd+UUiZKKROHDTPDGdlHXqbNx7ngL1DwE7wxBb59wqx8x0HrP/7HK6PZ0NrQ+Y+NGcx8eTMf7M6mSc0GpFCYJd0ScCllQetrMbAaGA8UCSF8AVpfi/srSKOjs4KJ98JDB7QJlve/Bf9MgH1vQbN5ZbHBng78+6axrL5/MiHDHPn92qPM/dtWvjpUoHqsKBRmRpcCLoRwEEI4nXsPzAOOAF8Bt7Xudhsw8EYAA42DByz+G/xyO3hHw7e/0bodpv9gVvVx0AYCfXbPRN65LRFbKx0Pf3KAxf/cweZjxcrxUKEwE7qTgXsDO4QQh4B9wDdSyu+AF4G5QogMYG7r56GBTzTc9jXc8CG0NMHH18EHV8Jp82pkEUIwO9Kbbx6ext9viOdsg5473tvP9f/dzf5s8yoRKUyb0tJSZs2ahaOjIw8++GDb+traWi6//HIiIiKIioriqaf61plt5cqVREZGMmvWrL6G3C4vvPACoaGhhIeH8/337c/D+4c//IHhw4cTHx9PfHw83377bb/EAgxyO9mBoKlByt3/lvKFQCmfdZFyzQNSVhUaO6pe0dDULN/fnS0T/7RBjnhynbz93b0yJbfC2GEpWjFnO9mzZ8/K7du3yzfeeEM+8MADbetramrkpk2bpJRSNjQ0yKlTp8pvv/2219eZP39+2/kMzdGjR2VsbKysr6+XWVlZMiQkpF1L3GeffVa+/PLLvb6OspMdSCytYeJ98PABmHg/HPoUXkuAzS9Aw6V+C6aMtaUFt0wcwbbHZ/Hkggh+yqngitd3cM/7SRw7XWXs8BS94HRWJcnfZXM6q7LP5+qLnayDgwNTp07F1vZinx57e/u2bNna2pqEhATy8vI6PVddXR3Lli0jNjaWG264gQkTJpCUlMRzzz3Hjh07uPfee3n88cfJzs5m2rRpJCQkkJCQcJGd7EsvvURMTAxxcXHdzvrXrl3LsmXLsLGxITg4mNDQ0EssAAaaoWdm1V/Yu8OCP8O4O+HHP8DWFyHpHZjxpNbwqbMydoTdxs5ax30zR3LTxEDe3XGSd7afZMHft3N5rC+/mhNGqJd5WPAOdU5nVbL21QM061vQWVqw9Fdj8Alx6dM5e2sn2x0qKir4+uuveeSRRzrd74033sDe3p6UlBRSUlJISEgA4JlnnmHTpk288sorJCYmUltby4YNG7C1tSUjI4Ply5eTlJTE+vXrWbNmDXv37sXe3p6yMq1c2NV3yM/PZ+LEiW3r/f39yc/PbzfG119/nffff5/ExET++te/4ubWP06hSsANjcdIrf947n7Y8IzW0LnnDZj9DIxeaja2taBNtPzonFHcPjmIt7Zn8b+d2aw/XMjS+OE8dFkoIcMcjR2iohPy08tp1rcgJTQ3t5CfXt5nAe+tnWxX6PV6li9fzsMPP0xISEin+27bto2HH34YgNjYWGJjY9vdr6mpiQcffJCDBw+i0+lIT08H4Mcff+SOO+7A3t4eAHd39259B9lNC9n77ruP3//+9wgh+P3vf8+vf/1r3n333U6/U29RAt5fBIyDO76F9O+1jHzlbTB8LMz5fxA8zdjR9QhXe2senx/BL6YE8+a2LFbszmbtwXyWxg/nwctCGamE3CQZPsoNnaUFzc0t6HQWDB/V9yywt3ayXXHPPfcQFhbGo48+2q04umM1++qrr+Lt7c2hQ4doaWlpK9/IDqxqu2OJ2x0LWW9v77b3d999N4sXL+7Wd+oNSsD7EyEgfIE2JP/QJ7D5z7BiMYTMgtm/1wTdjPBwtOG3iyK5a1oIb23P4oPdp1h7MJ8r4vx46LJQVVoxMXxCXFj6qzHkp5czfJRbn7PvzuhLBv70009TWVnJ22+/fdH61atXs2/fPl544YWL1k+fPp2PPvqIWbNmceTIEVJSUto9b2VlJf7+/lhYWLBixQqamzWL5Xnz5vHcc89x4403tpVQ3N3du/wOS5Ys4cYbb+Sxxx6joKCAjIwMxo8ff8l+hYWFbe6Kq1evbpsKrj9QAj4QWOhgzM3ajED739F8x9+6TJsJaNbT4BXR9TlMiGFONvxuUST3TD8v5F8dKmBxrCbko7yVkJsKPiEu/SrcPSEoKIiqqioaGxtZs2YNP/zwA87Ozjz//PNERES01bIffPBB7rrrLjIzM3F2dr7kPPfddx933HEHsbGxxMfHtyuiAPfffz/XXHMNK1euZNasWTg4OACwYMECDh48SGJiItbW1ixatIg///nPXcYfFRXF9ddfz+jRo7G0tORf//pX27yed911F/feey+JiYk88cQTHDx4ECEEQUFB/Pe//+3tLeuSbtnJGgqTt5MdKOqrtCnddr0OTTUQuwxmPmlWMwJdSOnZBt7ecZL3d2VT09jM/ChvHpwVRoy/aQjHYGEw2sl2xs0338yrr75KVxYcM2fObGu4HAz0l52swlDYOsPMp+CRQzDpAW2y5X+Oha8fhcrOu1CZIh6ONjy5IIIdT17Gw7PD2J1ZyhWv7+DWd/epAUGKXvPhhx92Kd5DHZWBmwJVBbD9b5D8nlY3H3s7TH0MnC+dpcQcqK5v4oM9p3hn+0lKaxoZH+zOg7NCmRbmaZSJXwcLQy0DH6oMugz8++zveWX/K1TUVxg7lP7B2Q8uf0UbDBR/IyS9C6/Fw3e/heoiY0fXY5xsrbh/Zig7nryMZxaPJqe0llvf3cfif+7g60MFNCvTLIXCIJiFgKeXp/N+6vssWrWItw+/TZ2+e6O/zA7XALjiH/BQMsRcC3v/C/+Ig+9+B9WnjR1dj7Gz1vGLqcFsfWImL10TS11TMw99coDL/rqFj/aeor5JTbysUPQFsymhZJRn8NpPr7Elbwtedl7cF38fV4ZeiaXFIO5IU5qp9Vg59ClYWGqllSmPgMtwY0fWK1paJD+kFvHGlhMcyqvE09GGX0wN4qYJI3CxM5+RqsZClVCGBj0poZiNgJ8juSiZV5Nf5VDJIYKcg3g44WHmBM4Z3LXVspOtQv4JCAsYcwtM/ZWWsZshUkp2Z5XyxpZMtmecwcFaxw3jAvnF1CD83eyNHZ7JogR8aDDoauAXMtZ7LB8s/IB/zPoHQgge2/IYy79Zzs78nYPXx9o9GJa+Dg/9BPE3wU/vw2tjYO0DWpZuZgghmDzSkw/unMC3D09jfpQP7+/OZsbLW3j4kwMcye+78ZLCtBhKdrKPP/44ERERxMbGctVVV1FRUQFoZmB2dnZtNrP33ntv3wNqz6KwvxZD28k2NTfJVemr5LyV82T0e9Hy1m9vlUmnkwx6DZOkIlfKb34j5R+9pPyDq5Sf3y5lYYqxo+oTBRW18s/fpMroZ76TI55cJ5f9d7fccPS0bG5uMXZoJoOyk+0aU7CT/f7772VTU5OUUsonnnhCPvHEE1JKKU+ePCmjoqK6vM6QsZO1tLDkqrCr+Pqqr/ndhN+RU53D7d/dzr0b7uVo6VFjh9d/uPjDopfh0cNaTTxjA/xnKnx0PeQa196yt/i62PHbRZHs/O1l/N+iSE6V1nDX+0nM/ttW3t+dTY2agLlXFKSnsXf15xSk930ybmUn2z072Xnz5mFpqbXNTZw4scvv0yfaU/X+Wvp7Qofaplr57uF35ZRPpsjo96LlQxsfkmmlaf16TZOgtlzKrS9J+WKQlM86S/nuQimPfydli/lmr436ZvnVwXy59PUdcsST62TMs9/JP3+TKvPLa40dmtHoaQaefzxV/v3mq+Rfl10h/37zVTL/eN8y+JMnT0qdTicPHDggpZTyuuuukx988IGUUsqXXnpJxsXFXbI89NBDF53jf//730UZ+IWUl5fL4OBgmZmZ2Wkcf/3rX+Udd9whpZTy0KFDUqfTyf3790sppZwxY0bb+5qaGllXVyellDI9PV2e059vv/1WTpo0SdbU1EgppSwtLe3Wd3jggQfavq+UUv7iF7+QK1eu7DTWxYsXtx1z8uRJaW9vL+Pj4+X06dPltm3b2j2mJxn4oOrCYWdpxx3Rd3DdqOv4IO0DPjj6Add9fR2zA2dzX9x9hLuHGzvE/sHOFaY/rk0o8dP72hD9j68Hr9Ew+WGtS6IZ+ZEDWOksuCLOjyvi/Eg+Vc67O07y1vYs3t5xkgVRPtw+JYjEEW6Du/G6j+QePUyzXo9saaFZryf36GH8RvWtEVTZyV5MZ39/zz//PJaWltx0000A+Pr6kpOTg4eHB8nJyVx55ZUcPXq0Xb+X7jKoBPwcjtaO3Bd3HzdF3sSHqR/yQeoHbMzZyJzAOdwbd+/gFXJrB212oHF3wZFVsPMfsOZe2PQnmHQ/JNwKNuZnNDV2hBtjR7iRV17L+7tP8em+HL45XMhoX2dunxLEkjg/bK10xg7T5AiIikFnaUmzXo/O0pKAqJg+n1PZyXZtJwuwYsUK1q1bx8aNG9uuZWNj03b/xo4dy8iRI0lPT++bh0t7aXl/LcaaE7OivkK+fuB1OfGjiTL6vWj5yKZH5NEzR40Sy4DS0iJl+g9SvrtIK638OUDKH34vZUWesSPrEzUNTfKjPafkvL9tlSOeXCfHPPeD/Mv6tEFfXulNI2b+8VS5Z9VnfS6fSHlpI9zLL78sn3322R6do70Syv/93//Jq6++WjY3N1+0ftWqVfKpp5665Bx//etf5Z133imllPLw4cMdllAeffRR+corr0gppXz33XelJndSrl+/vt0SSlccOXLkokbM4ODgdhsx169fLyMjI2VxcfFF64uLi9v2z8zMlH5+fu1eu19KKEIIHZAE5EspFwsh3IHPgCAgG7heSlne+0dJ/+Fi48ID8Q9wc+TNfJD6AR+nfczGnI1M95/OPbH3EDcsztgh9g9CaF7kYXMhLwl2/VNbdv9Ls7ad9CD4tv/z05Sxt7bkxgmBLB8fwO6sUt7bmc1/tmbyn62ZzIn05tZJQUwJ9VDlFcBvVGSfyyaGYqjYyT744IM0NDQwd+5cQGvI/M9//sO2bdt45plnsLS0RKfT8Z///KetfNNbuj2QRwjxGJAIOLcK+EtAmZTyRSHEU4CblPLJzs5hKmZW1Y3VfHLsEz5I/YCKhgom+E7gl7G/JNE7cfD/py/P1obo//Q+NJ6FoGkw+SEInQsW5tspKbeslo/35fDZ/lzKahoJ8XTg5okjuGas/6AZ5TnUBvIoO9nz9GkkphDCH1gBPA881irgx4GZUspCIYQvsEVK2Wlx2VQE/By1TbWsTF/J/478j9L6UsZ4jeHO6DuZ7j998At5XQX8tEIT86p8cB8JE+6F+OVmWSc/R31TM+uPFPLB7lP8lFOBnZWOpfF+3DRhhNn7kw81Ae8uSsC7QAjxBfAC4AT8plXAK6SUrhfsUy6lvGTSPSHEPcA9AIGBgWNPnTrVw68DDaeqaMiqxCbEBZsRvW+x7Yh6fT2rMlbx3tH3KKwpJMwtjF9E/4IFQQsGt9cKQHMTpK7VJl7OTwIbZ22o/oR7zHaCiXMcya9smy2orqmZmOEu3DQhkCvi/HCwMb9/VyXgQwODCrgQYjGwSEp5vxBiJj0U8AvpTQbecKqKM28fRupbEJYWeN4V0y8iDtDU0sT6k+t59/C7ZFZmMtxxOLdH3c6VoVdia2nb9QnMnbwkTchT14BsgfBFMP4eCJ6u1dPNlKr6JtYcyOejPTkcL6rG0caSK8f4ceP4EYz265+/pf5ACfjQwNAC/gJwC6AHbAFnYBUwjgEooVRtzqXqh2yQgADneUE4z+pfE6cW2cLW3K28feRtUkpScLd1Z1nEMpaFL8PNtu8ze5s8VQWw/21I+h/UlYFnOIy/G+KWmXV5RUrJTznlfLQnh3WHC2nUtxAX4MqycQFcEeeHo4ln5UrAhwb95kb4swz8ZaD0gkZMdynlE50db+gMvL9LK1JKkoqS+N+R/7E9fzu2OluWhi7l1tG3EugcaPDrmRxN9dp0b3v/C4UHwdpJm3Bi3F0wbJSxo+sT5TWNrDqQz6f7csgoPouDtY4r4vxYNj6QOH8Xk2wDUQI+NBgoAfcAPgcCgRzgOillpxMg9rYRsz2hHsjSCsCJ8hO8n/o+67LWoW/RM2fEHG6Lum3wdkG8ECkhPxn2vakNEGppguAZMO5OrcxiZqM8L0TLyiv4dF8O61IKqWtqJsLHiesTA7hyzHDcHayNHWIbSsCHBoPaD/wcxiitAJTUlvDxsY/57PhnVDdWEzcsjltG38LswNmDv8ET4Gyx1nsleQVU5oKjjzbCc+ztZjvRxDmq65v46lABn+3PJSWvEiudYE6kN9cnBjAtzBNLnXG7WZqzgJeWlnLttdeyf/9+br/9dl5//XVAs5O97rrryMzMRKfTccUVV/Diiy/2+jorV67kmWeewcfHh82bNxsq/DZeeOEF3nnnHXQ6Ha+99hrz58+/ZJ8bbriB48ePA1BRUYGrqysHDx5sMwMLD9cqzef6h/+cISHgXWXg/V1eqW2qZfWJ1XyU9hG51bn4OvhyY8SNXD3qapytzadhrNe0NEPGD9r8nRkbtEbOUQsh8RcwchZYmPfQ9mOnq1iZlMfqA/mU1TTi7WzD1Qn+XDvWn5HDHI0SkzkLeE1NDQcOHODIkSMcOXLkIgHfu3cvs2bNorGxkdmzZ/O73/2OhQsX9uo6CxYs4Mknn+wXP/DU1FSWL1/Ovn37KCgoYM6cOaSnp7cN5mmPX//617i4uPDMM8+QnZ3N4sWLOXLkSKfX6YmAm23KaDPCGc+7YtoV6YEor9hb2XNT5E0sC1/G1rytfJj2IX9N/iv/PvRvrgy9khsjbiTIJcig1zQpLHQQvlBbyrMh+T346QM4/g24BELCLTDmZm3CZjMkwseZ3y8ezZMLIth0rIjPk/L479ZM3tiSSXyAK9eM9WdJrB8u9qZdPjJkIpOdnc3ChQuZOnUqu3btYvjw4axduxY7O7sujz1nJ3vixImL1vfWTvaOO+4gNTWVyMhIsrOz+de//sW3337Ljh07OHnyJEuWLOGBBx7glltuoaamBoDXX3+dyZMnA5qd7AcffICFhQULFy7sVtbfkZ3spEmT2t1fSsnnn3/Opk2bujx3bzFbAQdNxNv7o2zIqkTqW0CC1Ldof8D9VB/XWei4LPAyLgu8jLTSND5M+5Av0r/gk2OfMGX4FG6MuJGpw6diIcx3lGOXuAXBnD/AzN9pAp78Hmx+Hra8AKMWQMJt2nB+M8zKrS0tWBDty4JoX4qr6llzMJ8vk/P5/Zoj/PHrVOaM9uLasf5MDxtm9BLLz+mPRCYjI4NPPvmEt956i+uvv54vv/ySm2++uc9mVqCVG77++mseeeSRTvd74403sLe3JyUlhZSUlLYh+M888wybNm1qG9RTW1vLhg0bsLW1JSMjg+XLl5OUlMT69etZs2YNe/fuxd7enrIyremuq++Qn5/PxIkT29b7+/uTn5/fYZzbt2/H29ubsLCwtnUnT55kzJgxODs786c//Ylp06Z16950hFkLeEfYhLggLC3a/nBtQs6PwOvP0kqkRyTPT32eX439FV+kf8Hnxz/ngY0PEOgUyLKIZVwZeiVO1ubbDa9LLK0h6iptKcvSMvKDH8Hxb8HJT+vBMuYmcO/cLtRU8XK25Z7pI7l7WghHC6r4IjmPtQfz+fbwaTwdrbkizo+rxgwnZrhp9GLpj0RG2cleTGf/zp988gnLly9v+6zsZLtJR+WVgeq54mnnyb1x93Jn9J38mPMjH6d9zEv7X+KfB/7J4pDF3BB+w+C1tD2HewjMeRZm/Q7Sv9O8V3b8Dba/ovmvjLkFIq8Aa/ObxFgIQfRwF6KHu/C7RZFsPl7cNlDofzuzGTnMgavGDGdp/HAC3I33/TpLZHp9TmUn27a+MztZvV7PqlWrSE5OblvXH3ayg1LAof3ySmcZSX9k5lY6KxYGL2Rh8EJSS1P5OO1jvsr8ipXpK4kfFs8NETcwb8Q8rHWm01XN4OisNKGOvEIbIHTwYzjwIay+B751gZhrtImah481y9Ge1pYWzI/yYX6UD5W1TXx7pJDVP+Xzyg/pvPJDOuOC3FgaP5xFMb4D3iWxs3YiQ9OXDPzpp5+msrKSt99++6L1q1evZt++fbzwwgsXrZ8+fTofffQRs2bN4siRI6SkpLR73srKSvz9/bGwsGDFihU0NzcD2pRnzz33HDfeeGNbCcXd3b3L77BkyRJuvPFGHnvsMQoKCsjIyOjQCfHHH38kIiICf3//tnUlJSW4u7uj0+nIysoiIyOjy18bXTFoBbw9OspIBiIzH+0xmj9N/RO/SfwNazPX8vnxz/nt9t/y0r6XuCrsKq4bdR3+Tv5dn8iccfaD6b+BqY9Bzq7WEssnWk8Wz1FaiSX2BrNt+HSxt2L5+ECWjw8kt6yWtQfzWXOwgKfXHOEPXx1l+qhhLI33Y06k94B5sXTUTmQMhoqdLMCnn356UfkEMK6drCEwBTfC9jJtYw3X31O4h8+OfcaWvC1IKZnsN5lrR13LjIAZWFmYdu8Gg1FfpXmvHPwYcnaDsICRl0Hccoi4HKy67uFgykgpSSusZu2hfL4+WEBBZT12VjrmjPbmilhfZoQPw8aye4275tyNsDcoO9nzDLpuhL2lvYzEGI2eFsKCyX6Tmew3mdM1p1mdsZovM77kV1t+hYetB1eFXcXVYVcT4NT/g5OMiq2zNhAo4VYozYRDn2hZ+Zd3as6Io5doWfmIqWbpVy6EYLSfM6P9nHlyfgRJp8pbGz4L+fpQAU42lsyL8mFxnC9TQz2xMrGeLMbkww8/NHYIJs+Qy8A7whSG6+tb9OzM38nK9JVsz99Oi2xhku8krg67mssCLxvctfILaWmB7O2Q8plmddt4Fpz9IfY6iF0GXhHGjrDPNDW3sCuzlK8PFfD90dNU1+txtbdiYbQPl8f4MTHE/ZJuiUMtAx+qDImRmANBZ6WV/h7peS4rX3ViFadrTuNq48rikMVcFXYVo9zM20iqRzTWat0QD30CmZs0m1ufWIi9XpsWzkzr5RfSoG9mW/oZ1qUUsCG1iNrGZtwdrJkf5cPlMb5tYp6WlkZERIRJdFFU9A9SSo4dO6YE3BB0lIEPZGbe3NLMnsI9rMpYxabcTehb9ER7RHNV2FUsDF44uPuV/5zqIjjyJRxeCQU/AQKCpkLMdVqpxc78rX7rm5rZcryEbw4XsjHtQjH35powG0b4uOPp6alEfBAipaS0tJTq6mqCg4Mv2qYEvJeYSqMnQHl9Oeuy1rEqYxUnKk5go7PhssDLuHLklUzwnYDODEc69prSTDj8BRz+HEpPgM5am9cz+mpteL+1g7Ej7DPnxPzbw4X8mFaEpZA8NtmDMA8b7K112FhaKCEfZNja2uLv74+V1cWdGJSAGxBjG2lJKTly5ghrM9ey/uR6qhqr8LL3YsnIJSwZuYRgl+CuTzJYkFLzKj/8hWZ1W10AlnYQvkArsYTOBSvzn02pvqmZbeklfHf0ND+mFlFVr8fBWsesCC8WRPswM9zL5CekUPQeJeAGpiORHuiGz4bmBrbkbuGrzK/Ykb+DFtlCrGcsi0cuZkHQgqExg9A5Wlogd49WZkldCzUl2iQUEZdD1JVa90RLmy5PY+o06lvYnVXKd0cK+eFoEaU1jVhbWjA11JN5o72ZM9obT0fz/56K8ygBHyCM2fBZUlvCN1nf8HXW16SXp2MpLJnqP5UrQq5gRsAMbHRD6D91s17ryXLkC0hbB/UVWrfE8EWDSsybWyRJ2WX8kFrE90dPk1dehxCQOMKNeaN9mDvamyBP8y8nDXXMWsCbq6uxsLFBWJt+NzpTaPgEOF52nHVZ6/gm6xtK6kpwsnJiXtA8FgUvYqz32KFVL29ugqytkLr6Z2K+ECKXQOhssx8wBFppLbWwih+OamJ+7HQ1AGFejswZ7c3c0d7E+7tiYaHq5uaGWQt40UsvU7lmDS5Ll+J67TXYjBzZD9EZjp42fPZnZt7c0sze03tZl7mOjTkbqdXX4mXvxcKghSwKWUSke+TQaghrT8ytHDS729FLIGyeWU/cfCG5ZbX8mFbEhtQi9p4so7lF4ulow5xIL2ZHejMl1AN7a1U3NwfMWsBr9uyl/OOPqd60CfR67MaMwfXaa3BesAALB/P4eWgKmXmdvo6teVv5JusbduTvQN+iJ8g5iEXBi5gfPJ8QF/O0ee01zU1amSXta03Ma4pBZ6OVVyIXazMMOXgYO0qDUFnbxJb0YjakFrH1eAnVDXpsLC2YPNKDyyK9mR3hhZ+r+f8KGayYtYCfQ19aSuWatVR8+SWNWVlY2NvjfPkiXK66Grsx8SafSZpSl8TKhko2nNrAN1nfkFyUjEQS7hbOguAFzA+aP/iH8P+clmbI3QupX2mCXpWn+bIETtbEPOJycA00dpQGoVHfwv7sMjamFbPxWBGnSmsBiPR15rKIYcwK92JMoBs6VWoxGXot4EIIW2AbYIPmnfKFlPJZIYQ78BkQBGQD10spyzs7l6EaMaWU1B04QMXKL6j67jtkXR3WQUG4XHUVLkuXYOXj0+drDBSdZeD93eh5jqKaIjac2sD67PWklGjWnNEe0SwIXsDcEXPxczT/0Y494lzXxGPfaJl5SZq23idWE/LwReATY5b2tz9HSklmSQ2bjhXxY1oxyafKaW6RuNpbMT1sGJdFeDF91LABt8JVXExfBFwADlLKs0IIK2AH8AhwNVAmpXxRCPEU4CalfLKzc/VHL5TmszVUf/89latXU5uUBBYWOEyejMtVV+I0ezYWtqbfB9gUfFjOUXC2gO+zv+e77O9ILU0FIMYzhnkj5jE3aC7DHc175vleUZoJx9Zpgp67D5CaN0v4QohYpBltWQ4Ogausa2JHxhk2HStma3oxZ842IgTE+bsyM3wYM0YNI9bfVWXnA4xBSihCCHs0Ab8PeB+YKaUsFEL4AluklJ1OM9Pf3Qgbc3KoXLOGijVr0BcUYuHoiNP8ebhcsQT78eMQZuRmZ8zuiOfIqcphw6kN/HDqhzYxj/KIYl7QPOYGziXAeYiVWQDOFkP693B8vebNoq/T+pqHztYEPXTuoKmbt7RIDudXtop5CYfyKpAS3OytmBY2jJnhw5gWNoxhTubfHdPU6ZOACyF0QDIQCvxLSvmkEKJCSul6wT7lUspLRo0IIe4B7gEIDAwce+rUqd5/i24iW1qo3bePyrVfUf3997TU1mLp54vL4itwWbrE5HuxgGk0el5IbnWuJubZP3C09CgA4W7hzB4xmzmBcwh1DTX5NgiD01QHWVs0s6307+FsESAgYDyMmq81gnpFDopSC0BZTSPbM0rYeryEbRklnDnbCMBoX2emjxrG9DBPxga5ddvfXNF9DJWBuwKrgYeAHd0R8AsxxkCelro6qjduovKrtdTs3AXNzdiMjsTl8sU4X77IpOvlvWn0HIjsPP9sPhtPbWRjzkYOFB9AIhnhPILZgbOZHTibaM9oLIT5/NoxCC0tcPrQ+ey88KC23iVA66IYNg+Cpw8KjxbQsvOjBVVsyyhha3oJP50qR98isbPSMWmkB9PCPJkW5snIYY5D78HeDxisF4oQ4lmgBrgbEyuhdIX+zBmqvv2WynXfUJ+SAkJgn5iI8+LFOM2bi6Wb6Q8776rRc6Cz8zN1Z9iUs4mNORvZV7gPvdQzzG4YswJmcVngZYz3GY+VbojMLnQhVYWQ8T1kbIDMzdBUoxluBU3VxDx0LniMHDTZ+dkGPbszS9meUcK29BKyW3u2+DjbMiVUE/MpoZ6q3NJL+tKIOQxoklJWCCHsgB+AvwAzgNILGjHdpZRPdHYuYwv4hTSeOkXlN99Q9fU6Gk+eBEtLHKdMwXnRQhxnz0bn6GjsEDukoyzb2HXzyoZKtudvZ1POJnbk76BOX4eDlQPThk9jVsAspgyfgotN32dGNzv0Ddp0cRkbIOMHOJOurXcLgtA52hI0DWxM92+up+SW1bI94ww7TpSw80QplXVNAET4ODEl1JMpoR6MD/ZQBlzdpC8CHgusAHSABfC5lPI5IYQH8DkQCOQA10kpyzo7lykJ+DmklDSkpVG57huq1q9HX1iIsLbGYfo0nBcuxGnmTDVYqDexNDewt3Avm3I2sTl3M2X1ZeiEjrHeY5nhP4OZATMJdB4c/ap7TNlJOPEjnNgIJ7dCUy1YWMGISZqYj5wN3lGDJjtvbpEcLajUBD3jDMk55TTqW7C0EMQHuDI51JMpIz0YE+iGteUQK711k0ExkKe/kS0t1B08RNX69VR/9x36khKErS2OM2bgvGA+jtOnm7yYm9Iw/nM0tzRz+MxhtuZtZUvuFk5UnAAg2CWYmf4zme4/nXiveCwthmA2pm+AnD3nBb1YayDG0RtCZmmjQkNmgpO3UcM0JPVNzSSfKmfniTPszCzlcF4FLRJsrSxIHOHOpJEeTBrpQcxwFzVHaCtKwHuIbG6mNjlZE/MNP9J85gzCxgbH6dNwmjcfx1kzTbrMciGmlJkD5FXnsTVvK5tzN5N8Ohm91ONk7cQUvylM95/O1OFTh5YN7oVUFWg188xNkLUZaku19d4xMHKmJuaBk8Ha3phRGpTKuib2ZpWyO6uU3ZmlbSZcDtY6xgW7MynEg4khHkT5OV8yT+hQQQl4Hzgn5tXf/0D1Dz9ombmVFQ5Tp+I0dy6Os2aafAOoKWbmAGcbz7K7cDfb8raxPW87pfWlCAQxw2KYOnwq04ZPY7TH6KHXqwVae7akQOZGTdRz9kBLk9YYGjBBE/ORs8A3HgaRu2Tp2Qb2ZJWxO+sMuzJLySqpAcDRxpLEIDcmtgp69BASdCXgBkIrsxyk+vsfqPrhB/SFhaDTYZ+YiNPcuTjNmW3SXRMvpLeZeX+Je4tsIa00jW1529iWt42jpUeRSNxt3ZnsN5mpw6cyxW8KrrauBrumWdFYozWGZm2BzC1QdFhbb+uiNYIGT4fgGTAsfNDUzwGKq+rZe7KMPVml7MkqJbNV0B2sdSSMcGNCsDvjgz2I9XfB1mrwPMguRAl4PyClpP5oKtUbNlD94480ZmYCYBsTg9Ps2TjNvgzrUNMe4NKbzHygyi5l9WXszN/Jjvwd7CrYRUVDBQJBtGc0k/0mM9lvMrHDYodm7RzgbInWCHpyK5zcBuXZ2noHr1Yxnw7B08AteHAJenU9e7PK2HdSW44XaSUXa0sL4gNcmRDszrggdxJGuA2aXi5KwAeAhqwsqjf8SPWPP1J/WMuOrAIDcZo1C8fZl2GfkICwNP0/qM5E2pi+5kdLj7aJ+eEzh2mRLThaOTLBd0KboPs7+Rv0umZF+SlNyM8J+tkibb2zv9b/PHia9uoWZNQwDU15TSP7s1sFPbuMI/mVtEiwEDDaz5nEEe6MD3YnMcgNLyfT90ZqDyXgA0xTUTFnN2+metNGanfvQTY1oXNxwWHGdJxmzcJh6lR0TqY7cUBP5/wc6AbRyoZK9hbuZVfBLnYW7OR0zWkA/B39meQ3iUl+kxjvM35o9jsHzVHxTLrmd35yO2TvgNoz2jaXQBgxGYKmwIgp4B4yqDL0sw16DuSUs/9kGfuzyzmQW059UwsAIzzsGTvCjcQRmqCHDnM0ixmKlIAbkeazNdTs3MnZTRs5u3UbzRUVYGmJ/dixOM6ciePMGdgEm89M8qbWICql5GTVSXYX7GZPwR72F+2npqkGC2HBaPfRTPSbyATfCYzxGjO05gW9ECmh5Lgm6NnbIXvneUF38tWEPGiK1sNlkNXQG/UtHC2oZH92GUnZ5SSfKqe0RvNxcbGzIiHQlcQgdxIC3YgLcDHJWYqUgJsIsrmZukMpnN28mbNbttCQkQGA1YhAHKfPwHH6dOzHj8PCxryExlQyc4CmliaOnDnC7oLd7C7YzeEzh2mWzVhbWDPGawwTfCcwwXcCoz1GD936eVuGvgNO7dQE/az2KwZ7DwicpC0jJoFPHOgGz32SUpJdWktSdhnJp8pJOlXOieKzAOgsBJG+TiQEujF2hBsJgW74u9kZvR1LCbiJ0piXz9ktWzi7bSu1e/chGxoQdnY4TJiA44zpOEybjrW/eXhwm1pmfo6aphqSi5LZU7iHvYV7SS/XhrI7Wjky1nss43zGMd5nPOHu4UOzuyJogl6WBad2aT1dTu2C8pPaNisHCBinCXrABPAfN6iG/YM25dxPueUcOFVOck45B3MqqGlsBsDT0Zr4ADfGBLoyJtCVOH9XHAa4cVQJuBnQUl9P7b59nN26jbPbttGUmwuAdXAwDlOn4jh1Cvbjx2NhZz5zF/YmM+9vYS+tK2X/6f3sPb2XpNNJZFdlA+Bs7cxY77GM9xlPok8io9xGDV1BB82QK2cXnNqt9UEvOgJIEDrwiT4v6AETwMU8kozu0twiOXa6igM5FdqSW97WH91CwChvJ8YEuhIf4Ep8gBuhXo79OsmFEnAzQ0pJY3Y2Ndu3c3bHDmr37UfW1yOsrbFPHIvDlKk4TJ2CzahRRv951xU9ycyNUXIpqilif9F+9p/ez77CfeSdzQM0QU/wTiDRO5FEn0Qi3CLQDaIBMz2mvhLy9mtinrMH8pK0CS1As80NGN8q6OPBOxoGmQtleU0jB/MqOHCqnAO5FRzKraCqXg9ofdJj/V2JC3AlPsCFWH9XfF1sDfZ/Uwm4mdPS0EBtUhI123dQs3MHDRman4jO0xOHSZNwmDwZh8mTsfL2MnKk3aMjoTYFv/PCs4UkFSVpy+kkcqpzAK3kEu8Vz1jvsSR4JRDtGY21bnBMpdYr9I3aYKLcfdqE0Dl7obpA22ZlD35jtHKL/zhN1B3N42+zu7S0SE6W1nAwp4KDudqSVliFvkXT1GFONsT5uxDn70psgCsJga442fbuoaYEfJDRdPo0Nbt2U7NrFzW7dtFcphlB2oSFYj9xEg6TJmE/fpxJ+7X0dC5QY5VdimqKSC5KZn/Rfn4q+omsyiwArC2siRkWQ4JXAmO9xxI3LA5Ha9O93wNCZZ4m5rn7tGy9MEUb/g/gGgj+48E/EYYngm8sWJpXY31X1Dc1k1ZYxaHcClLyKjmUV9E2cvSd2xKZHdk7UzIl4IMY2dJCw/Hjmpjv3EVtcjKyoQF0Ouyio7GfNBGHiROxGzPGLHq39NTvfKDLLmX1ZRwoOkBycTI/Ff3EsbJjNMtmLIQFYa5hjPEaQ4J3AmO8xuDjYB62Cv1GU50m4nmtgp67/3yWrrMGnxhNzP0TYfjYQdcnHaCqvokjeZVE+7vgrDJwRVe0NDZSd+AgNXt2U7trN3VHjkBzM8LaGrsxY7CfMB6H8eOxi41FWJtPCaA3ZZeBKLnUNNWQUpLCgeIDHCg+wKGSQ9S11oZ9HHwYM2wMcV5xxHvFM8ptFFYWg6s23GOqCrT6eX4S5CVDwQFtxiIAW1cYnqCJuV/r6yCy0u0tSsCHMM1nz1K7bz+1e/dSs28fDceOgZQIW1vsE8ZgP34C9uPHYRcdbfKC3pOyi7FKLvoWPenl6RwoPsDB4oMcLDnYNlLUztKOaM9o4obFETcsjthhsbjbuhv0+mZHsx5K0iD/J8hPhoKfoCgVpNaND+fhWj39wsV+aN0zJeCKNporKqjZv5/avfuo3bu3bTCRsLXFLj4e+3GJ2I8bh11cnFmUXMD0e7qcrjnNwZKDHCo+xIHiA21lF4AApwBih8US6xlLnFecytIBGms1K9381gw9/ycoyzy/3S1Is9H1GwN+8eAbB3ambencF5SAKzpEX15O7f791O5Ponb/fhqOH9cydGtrbGNisB87FvvEsdiNGWPS/i0/x1RLLgB1+jrSStM4VHKIlJIUDpUcoqSuBAAbnQ0R7hHEeMYQOyyWGM8YhjsON/nuov1OXQUUHjwv6IUHoSLn/PY2UY/XBN0nDhw8jBGpwVECrug2zZWV1Cb/pIl6cjL1qamg14OFBTYR4dgnjMV+bAJ2CQlYeZt2fdJQJZeOzmUopJScrjnNoTOHOFxymMNnDpNamkpDcwMA7rbuRHlEEeMZQ7RnNNGe0UN31qILqS3TBL3wIBQcvFTUXQI0MfeNA59YreeLk6/ZNZQqAVf0mpbaWuoOHaI2KZna5GTqDh5E1tcDYOXnh11CQpug24SGInSmP9jFkD7o/SXsTS1NnCg/weEzh0kpSeFo6VEyKzKRaP9nhzsO18TcI5oozygi3SNVN0bQRP10itb7pfCQtpSegNb7hr2n1vvFN1YTdZ9Y8Bhp0rMaKQFXGAzZ1ET9sWOamP90gNoDP9FcojnbWTg4YBcXh118PHZjxmAXF4vOuf/n2TQEvfFBH+h6ek1TDamlqRw5c6RtKagpaNse5BzEaI/RRHlEMdpjNJEekThYmfZE3ANCQzWcPgKnD8PpQ9prcRo0a66EWNqB92htBKlPjLZ4R4GNaZQMey3gQogA4H3AB2gB3pRS/kMI4Q58BgQB2cD1Usryzs6lBHxwIqWkKS+Pup9+ovbAAeoOHqIhPV2b01EIbEJHYhcfj21sLHZxcdiMHGmyWXpPfdBNoZ5eVl9GamkqqaWpHD1zlKOlRymq1SZzEAhGOI8g0iOSKA8tS4/0iMTJ2jSEyajoGzVHxtMpreKeovm91F0gY25Bmqh7R2uC7h2lzXBkMbAeOX0RcF/AV0r5kxDCCUgGrgRuB8qklC8KIZ4C3KSUT3Z2LiXgQ4fmszXUH06h7uBBTdQPpdBSWQloWbptbAx2sXFath4bg6Wnp5Ej7hpz6MJ4jjN1Z9pEPbU0lbSytLaujKBNfBHpEUmEe0SbqHvamf6/Qb8jJVTln8/Wi45A0VGtB4zUJoXAygG8IrWM3Svq/Gs/NpgarIQihFgLvN66zJRSFraK/BYpZXhnxyoBH7qcM+eqT0mh7tAh6g4eoj49XWscBSz9fLGLicUuNgbbmBjsoqKwcDCPn/6G7MLYn+JeWldKWlkaaaVppJWlcazsGLnVuW3bPe08CXcPJ8Itggj3CMLdwwl0ChzaBl7naKyFkmOamJ8T9aKjUFd2fh9Hb/AarWXpXpEwLFKbHMMA1rsGEXAhRBCwDYgGcqSUrhdsK5dSXtIsLoS4B7gHIDAwcOypU6d6HLxicNJSV0d9aip1hw9Tn3KYusOH2yx0EQLrkSHYRcdgGx2NXUw0NhERZtUvvTcll4HO2qsbqzledpxjZcdIK0vjeNlxMisy0UvtwWpnaUeYaxij3EcR7hZOuHs4Ya5hqrEUtGz9bJEm5MWp2uCj4qNQkn7epRHAdYQm7DMe10aW9oI+C7gQwhHYCjwvpVwlhKjojoBfiMrAFV2hLy+n/vBh6lIOa69HjtBcWqpttLTEZlQYdlFR2LYuNuHhWJjo6NGemnX1JmvvD2FvbG4kqzKLY2XH2sT9ePlxqhur2/YZ7jiccLdwRrmPYpTbKMJcwwhwClDZOkBLM5Rna42kxWmauBenwdJ/gb8RBFwIYQWsA76XUv6tdd1xVAlF0c9IKdGfPq1l6UeOUn/kMHVHU9vq6VhaYhMWhm3UaE3YIyM1UTfhSS8M1VA6kMJ+rp96enk6x8uPa69lx8mpzqGltTZsq7NlpOtIwtzCCHMN017dwvCw9VCDkPpIXxoxBbACrcHy0QvWvwyUXtCI6S6lfKKzcykBVxgCKSVN+fnUH02l/ujRtqW5okLbwcIC65BgbEePxjZyNLaRkdhGhKNzdTVm2N2iJ1m7KXRtrNfXk1mZSXpZOhkVGWSUZ5Benk5Z/fnasJuNG6FuoYS6nl9Guo7ExcalX2IajPRFwKcC24HDaN0IAX4H7AU+BwKBHOA6KWVZuydppbcCfjqrkvz0coaPcsMnRP2jKy5FSom+sJD6tDRN2NPSqE9NRV9U1LaPpZ8vthGR2EZEYBMZgW1kJFbDzWOIuiGEvaPz9AeldaWcqDjBiYoTZJRnkFGRwYnyE9Tqa9v2GWY3jJGuI9sEPdQ1lGCXYCXs7WC2A3lOZ1Wy9tUDNOtb0FlasPRXY5SIK7qNvrSU+rRjNBxLoz7tGPXHjtF48qTWRx2tS6NNeDi2EeHYhEdgGz4Km1GjsLC3N3Lk3cNcrAJAe8gW1hRyouIEmRWZbQJ/svJkm/0uaMIe4hrCSJeRjHQdSbBLMCEuIbjbupvFw7Y/MFsBT/4um71rs5AShAVMWBLC2AVBgMrMFb2jpa6OhowMTdiPH6P+eDoNx4/TcvastoMQWAUGYDtqFDZho7AJD8dmVBjWgYEmOwDp55iDVcA5WmQL+WfzOVl5ksyKTDIrMsmqzCKzIvOijN3FxoUQlxBCXELaRD3YJRhfB99B33jakYBbGiOYnjB8lBs6Swuam1vQ6SwYPkrr6NJVZq7EXdERFnZ22MXGYhcb27buXF294fhx6o8doyE9g4bjx6neuKktWxe2ttiMHInNqFHYhIVpy6gwLL28TC4ztBnhfInY2oS4ICwt2kTa5oL/Fw1ZlUh9C0iQ+hZNsAdoQJKFsCDAKYAApwCm+09vW3+u4TSrMuv8UpHFppxNlDecHy1po7NhhPMIgl2CCXYJJsg5iCCXIIKcgwa9jYDJC7hPiAtLfzXmEjHOTy+nWd+ClNDc3EJ+ennbts7EXQm7oj2EEFj7+2Pt74/T7Nlt61vq62k4kUnD8eM0pKfTkJFBzY4dVK5e3baPhbMzNqGh55cw7VXn6WlSwm4zwhnPu2LaFd2OxN2Ywi6EwNfRF19HX6YMn3LRtrL6MrIrszlZeVJbqk6SWprKhlMb2nrFAHjZebWJeZBLECOcRxDkHISfox+WFiYvf11iFt/AJ8TlErHtKDOHjsVd1dMVPcXC1ha76CjsoqMuWq8vL6fxxAnqMzI0YT9xgurvv6fi88/b9tG5uGAdGqpl7SNDsB4Zik3oSCy9vY0m7O1l5ufWtyfuAyXsPRV9d1t33G3dSfBOuPg8zQ3kVOWQXZVNdmV22+v67PUX9WO3FJb4O/kzwnlE2xLoHMgIpxF4O3hjIQbW66S3mIWAt0dHmTl0LO5dZe0qM1d0F0s3NyzHjcN+3Li2dVJKms+coeHECRoyTmivmZmasJ/rt47WcGodEoJNSAjWI0diExKMdchIrAP8EVbGm4mn3bLLAAm7obJ5G51NW//zC5FSUt5QzqmqUxct2VXZ7Cnc0+a7fu4cAU4BBDoFMsJ5BAHOAYxw0gTey97LpMTdbAUc2s/Mz61vT9x7U09Xwq7oLkIILIcNw3LYMBwmTWpbL6WkuaxMK8VknqAxM4uGzExqdu+mcu3a8yewssI6MFAT9KBgTeSDg7AODkbnYry/vf4U9s62GVLYhRBtWfsYrzEXbWuRLRTXFreJek5VDqeqNXHfnr+dppamtn2tLazb6vUBzgHn3zsF4OfoN+BT4Zm1gHdGe+Le03q6EnaFIRBCYOnhgaWHBw4Txl+0rbm6msaTJ2nIzKIxK4uGrCwaMrOo3rylzegLQOfhgXVwENZBQdgEaaJuHRSEVUCA0awEDCHsnW0bqPq7hbDAx8EHHwcfJvhOuGhbc0uzJu7VmrDnVueSU5VDTnUOewr3UN9c37avTujwcfDB38mfAKcA/B1bX5388Xfyx9na8D14Bq2Ad0RP6um9raUrcVd0F52T0yU9YkCbNKMxL4/Gk9mawJ/MojE7m7NbtlJ55svzO1pYYDV8ONZBQViPGKEtQUFYB43Ays/PKN0eeyLsnW0ztrAD6Cx0bQ2pE30nXrRNSklJXQm51bkXLfnV+WzK2XTRaFSA12a9xqzAWd2/kd1gyAl4e/S05KJ6wCj6G2FlhU1wMDbBwcDF/+mbq6poPHWqVdhP0nTqFI3Zp6hMTqal9ny/aaystJ41gYFYjQjEOnAE1iMCtc9+fgNeb++oAbWjbQMh7NB7cRdC4GXvhZe9F2O9LzWpqmmqIa86T1vO5hHpEdntc3cXJeCt9KTkYugeMErYFT1B5+yMXUwMdjExF60/14jaeOoUjdnZ2mtOLo2nTlGzfz/yQnHX6bDy88M6IEAT94BArAMDsAoIxNp/uMl4sfd7/b0XWXt3Bd/ByoFwd82Ct79QAt4FPRF2MGw5Rgm7oidc2Ihqn3jxoL02cc/JoTH7FI25OTTl5NKYk0PdN9/SUlV10f46T0+s/f2xCgzA2j8Aq4AArP2HYxUQoA1cGuApxX6OoervPc3ajT3T0s9RAt5LDNUDRgm7YiC4SNzHXvpzv7migsbcXJpyc7WsPS+XppxcapOSqPp6nTZ5wblzWVlhNXw4Vv7+WAX4Y33u/XB/rPyHo3N1Nal+7gYdwDTAZZquUALeDxiiHKMaUBUDic7VFTtX10vKMgAtjY3oCwpozM2jKT+Pprw87X1uLnWHD5/3Zm/Fwt5eE3Q/P03ozy1+fkYTeEMNYDJ0maavKAEfQPpT2EE1oCr6Bwtr69aeLUHtbm+urqYpP5+mvDya8vNpzGt9X1BAbVLSeZOwVoSdnSbmfn5Y+fq2Cv35z5ZeXgjLgZOmHpVjDFimMQRKwE0A1YCqMGd0Tk7oIiKwjYhod3tzVZUm8Pn5NBUU0JRfoL0WFFB/5AjN5eUXH2BhgaW393mB9/XB0tcXKx9frPx8sfLxwcLFpd+z+M6ydkOUaQyBEnATxlQbUJXoK3qCztkZnbMztpHtd6Nrqa2lqbCQpoJCmgoLaCosRF9QSFNBAXUHDlD1fTE0NV10jLCz08TdxxtLH03ULX28W9f5YOnjg4Wj44CWanpapjEESsDNEGM3oKpsXmFILOztWw2/Rra7Xba0oD9zBn1hIU2FpzWBP936/vRpGnbsQF9SclFD67nzWnp7a8Lu3Srw3t7aOi9vrLy90Hl4DEiPms76wPcFJeCDjP6us6syjWKgERYWWHl5YeXlhV1cXLv7yKYm9MXFNBUVaQJfVIy+6DRNp4vQnz5NzZ49msg3N198oJUVlsM8sfLyxtLLq1Xch2lCf26dlxc6R9PoF/9zlIAPEQxVZ1fdIRWmSFvXxuHDO9xHNjejP1OKvrgIfVGRJu5FReiLi2gqLqbhxAlqdu6kpabmkmMt7O3bxNzSy6utS+bP3w+00CsBH+L0tM5u7H7uSvQVvUXodFh5e2Hl7QXtdJc8R/PZGvTFxZrQl5S0vi+mqbgYfXEJdYcOoS8uRjY0XHKssLfH0tPzvKife+/picPUKVh5exv0O3Up4EKId4HFQLGUMrp1nTvwGRAEZAPXSynLOzqHwvzoqM7e0TZVf1cMFnSODugcg7EJCe5wHyklLdXVmsCfE/mSEvTFrZ/PnKHh+HEto6/WJpIIePvtgRdw4D3gdeD9C9Y9BWyUUr4ohHiq9fOTBo1MYXaYU/1dCb6iLwgh2nrXdNT4eo6Wujr0paVYursbPI4uBVxKuU0IEfSz1UuBma3vVwBbUAKu6ABTq7+r8o1iILGws8Pa379fzt3bGri3lLIQQEpZKITw6mhHIcQ9wD0AgYGBvbycYjBirPq7Kt8oBgv93ogppXwTeBMgMTFRdrG7QtHv9XdVvlEMFnor4EVCCN/W7NsXKDZkUApFT+mJsJtq+QaU6Ct6Rm8F/CvgNuDF1te1ne+uUBiHzkatmlL5Bjou06gsX9ER3elG+Alag6WnECIPeBZNuD8XQtwJ5ADX9WeQCsVAYazyDRiuZq8aaYcO3emFsryDTbMNHItCYZYYonwDhqvZD1QjrRJ846NGYioU/URPyjfn1huiZj8QjbSGFnz1MOgdSsAVChPCEDX7gWikNbTgq+y/dygBVyjMGGM10hqy8VZl/71HCbhCMYQwVCOtIRtvB0v2b4wHgRJwhULRKf1Z1ulsmzll/8aabFwJuEKhMCg9bbztaJs5Zf+96d9vCJSAKxQKk8Vcsv/e9O83BEL+bB65/iQxMVEmJSUN2PUUCoWitxiqBt6WgbeKe28ycCFEspQy8ZL15iDgBelp5B49TEBUDH6j2p/ZWqFQKEyVvtbAOxJwky+hFKSnsfKP/0ezXo/O0pLrfv98m4h3JuwdbVMPA4VCMdB0Vv/vCyYv4LlHD9Os1yNbWmjW68k9ehi/UZFdCnt723rzMOjNg0A9JBQKxUBg8gIeEBWDztKyTXQDorTJSDsS9s629fRh0NsHQX8/JNQDQqFQgBkIuN+oSK77/fOXCFZHwt7Ztp4+DHq6vrNzGeoh0dkDAtRDQqEYSpi8gIMm4j8XkY6EvbNtPX0Y9HR9Z9sM9ZDo7OExWB4S6uGhUHQPsxDwjmhP2Lva1pOHQU/Xd7bNUA+Jzh4eg+EhYcoPD/XAUZgaZi3ghqQngt/Z+o62Geoh0dnDYzA8JEz14TEQDeO9OUb9uhnaKAEfQAz1kOhsvbk/JEz14dHfDeOgft2oB1fPUQI+yDD3h4SpPjz6u2G8N8cM1V835vbg6mpbX1ACrugxA/GQMLWHR0+PGYiHxFD9dWNOD67OrmEIlIArzApjPTx6esxAPCSG6q8bc3pwdXYNQ9AnLxQhxALgH4AOeFtK+WJn+yszK4XCdBmKNXBzycANbmYlhNAB6cBcIA/YDyyXUqZ2dIwScIVCYWqYQw28PwR8EvAHKeX81s+/BZBSvtDRMUrAFQqFoud0JOAWfTjncCD3gs95ret+fuF7hBBJQoikkpKSPlxOoVAoFBfSFwEX7ay7JJ2XUr4ppUyUUiYOGzasD5dTKBQKxYX0RcDzgIALPvsDBX0LR6FQKBTdpS8Cvh8IE0IECyGsgWXAV4YJS6FQKBRd0et+4FJKvRDiQeB7tG6E70opjxosMoVCoVB0Sp8G8kgpvwW+NVAsCoVCoegBAzqpsRCiBDg1YBdsH0/gjJFjMGXU/ekYdW86R92fzunL/RkhpbykF8iACrgpIIRIaq8/pUJD3Z+OUfemc9T96Zz+uD99acRUKBQKhRFRAq5QKBRmylAU8DeNHYCJo+5Px6h70znq/nSOwe/PkKuBKxQKxWBhKGbgCoVCMShQAq5QKBRmyqAUcCFEgBBisxAiTQhxVAjxSDv7CCHEa0KIE0KIFCFEgjFiHWi6eW9uar0nKUKIXUKIOGPEagy6c38u2HecEKJZCHHtQMZoTLp7f4QQM4UQB1v32TrQcRqDbv7fchFCfC2EONS6zx19uqiUctAtgC+Q0PreCW3iidE/22cRsB7NVXEisNfYcZvQvZkMuLW+XzhU7k1370/rNh2wCW0k8rXGjtuU7g/gCqQCga2fvYwdtwndm98Bf2l9PwwoA6x7e81BmYFLKQullD+1vq8G0rjUq3wp8L7U2AO4CiF8BzjUAac790ZKuUtKWd76cQ+a0+SQoJt/OwAPAV8CxQMYntHp5v25EVglpcxp3W9I3KNu3hsJOAkhBOCIJuD63l5zUAr4hQghgoAxwN6fberWhBSDmU7uzYXcifZLZcjR0f0RQgwHrgL+Y4SwTIZO/n5GAW5CiC1CiGQhxK0DHpyR6eTevA5EollvHwYekVK29PY6g3pWeiGEI1qW9KiUsurnm9s5ZMj0qezi3pzbZxaagE8dyNhMgS7uz9+BJ6WUzVoiNfTo4v5YAmOB2YAdsFsIsUdKmT7AYRqFLu7NfOAgcBkwEtgghNje0f/Brhi0Ai6EsEK7iR9JKVe1s8uQnZCiG/cGIUQs8DawUEpZOpDxGZtu3J9E4NNW8fYEFgkh9FLKNQMXpfHo5v+tM1LKGqBGCLENiEOrCQ9qunFv7gBelFoR/IQQ4iQQAezrzfUGZQmltb70DpAmpfxbB7t9Bdza2htlIlAppSwcsCCNRHfujRAiEFgF3DJUsqZzdOf+SCmDpZRBUsog4Avg/iEk3t35v7UWmCaEsBRC2AMT0OrBg5pu3psctF8mCCG8gXAgq9fXbG0NHVQIIaYC29FqTOfqS78DAgGklP9pvdmvAwuAWuAOKWWSEcIdULp5b94GruG89a9eDhGXue7cn5/t/x6wTkr5xQCGaTS6e3+EEI+jZZstwNtSyr8PeLADTDf/b/kB76H1WBFo2fiHvb7mYBRwhUKhGAoMyhKKQqFQDAWUgCsUCoWZogRcoVAozBQl4AqFQmGmKAFXKBSKThBCvCuEKBZCHDHQ+b4TQlQIIdb9bH2wEGKvECJDCPGZEMK6q3MpAVcoFIrOeQ+tu7GheBm4pZ31fwFelVKGAeVoo6A7RQm4QqFQdIKUchua6VQbQoiRrZl0shBiuxAiogfn2whU/+x8Am14/bnxBCuAK7s616AdSq9QKBT9yJvAvVLKDCHEBODfaALcWzyACinlOWfCbpnrKQFXKBSKHtBqVjUZWHmBmZlN67argefaOSxfSjm/s9O2s67LUZZKwBUKhaJnWKBly/E/39BqYNWuQVwXnEGbk8CyNQvvlrmeqoErFApFD2i1fj0phLgO2qZn7NO0g63uhJuBc9Pz3YZmCtYpygtFoVAoOkEI8QkwE806uAh4Fm06vTfQTKmsgE+llO2VTto733Y0C1lHoBS4U0r5vRAiBPgUcAcOADdLKRs6PZcScIVCoTBPVAlFoVAozBQl4AqFQmGmKAFXKBQKM0UJuEKhUJgpSsAVCoXCTFECrlAoFGaKEnCFQqEwU/4/QzFfVTcvYeUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(r, b12, label='n=12, qfac=1.0')\n",
"ax.plot(r, b10, label='n=10, qfac=1.0')\n",
"ax.plot(r, b8, label='n=8, qfac=1.0')\n",
"ax.plot(r, b6, label='n=6, qfac=1.0')\n",
"ax.plot(r, bq2, '.', label='n=12, qfac=0.5')\n",
"ax.plot(r, bq4, '.', label='n=12, qfac=0.25')\n",
"ax.plot(r, bq7, '.', label='n=12, qfac=0.75')\n",
"ax.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAF3CAYAAACBuAwQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACYe0lEQVR4nOzdd3RUx9nH8e9s39WqdySKANGLEALjAgYD7r1jO8ZxT+LEjmNs8qbhJMQkdpzEPS5JiO2Ae8EdXDBumN6LKAIEQhKot63z/rGSjIwQKrtaSTyfc/Zo99479/5UjJ+dnTujtNYIIYQQQgghOsYQ7gBCCCGEEEL0BFJYCyGEEEIIEQRSWAshhBBCCBEEUlgLIYQQQggRBFJYCyGEEEIIEQRSWAshhBBCCBEEIS+slVL/UkoVKaU2HrEtTim1WCmVW/819oh9v1RK7VBKbVNKnRXqfEIIIYQQQgRDZ/RY/wc4+3vbZgMfa60zgY/rX6OUGgZcDQyvb/OEUsrYCRmFEEIIIYTokJAX1lrrz4GS722+CJhf/3w+cPER2xdqrV1a693ADmB8qDMKIYQQQgjRUeEaY52stS4AqP+aVL89Ddh3xHH59duEEEIIIYTo0kzhDvA9qpltza65rpS6FbgVICIiYuyQIUNCmUvUq610U1niIiHdicHY3K+rdeo2bcaUEI8pOTmI6YQQQgghQmvVqlWHtNaJze0LV2FdqJRK1VoXKKVSgaL67flA7yOOSwcONHcCrfXTwNMAOTk5euXKlaHMK+od3l/F/u1lDJ6QgtXe/j+f0ldewTZoEPbRo4OYTgghhBAitJRSe461L1xDQd4GZtY/nwm8dcT2q5VSVqVUBpAJfBuGfOIY4tOcjJqS3qGiGiD2iiukqBZCCCFEj9IZ0+0tAL4GBiul8pVSNwHzgOlKqVxgev1rtNabgJeBzcAHwE+01r5QZxStp/2akgPVVJW6OnQeb3ExNStWBCmVEEIIIUT4hXwoiNZ6xjF2TT3G8XOBuaFLJDrCrzULfr+ccednMP78jHafp/Tllzn06GMMXrcWg9UaxIRCCCGEEOHR1W5eFF2c0WjAHmmmuqxjPdbmXoHJXrwFBVj69QtCMiGEEKJ78Hg85OfnU1dXF+4oogU2m4309HTMZnOr20hhLdosKsFOxaHaDp3D3KsXAJ4DB6SwFkIIcULJz88nMjKSfv36oVT7Z9gSoaO15vDhw+Tn55OR0fpP6MN186LoxmKSHJQV1nToHOa07wprIYQQ4kRSV1dHfHy8FNVdmFKK+Pj4Nn+qIIW1aLOYZDtVpS487vbfV2pOTgaDQQprIYQQJyQpqru+9vyOpLAWbTYgO4lzfzQSQwf+UVBmM+mPPUr0RRcFMZkQQgghQunw4cNMmTIFp9PJHXfccczjSkpKmD59OpmZmUyfPp3S0tJOz1RTU8N5553HkCFDGD58OLNnzw5ZhgZSWIs2i02JIGN0IkZzx/58Is84A0vfvkFKJYQQQohQs9ls/OEPf+Chhx5q8bh58+YxdepUcnNzmTp1KvPmzQtLpnvuuYetW7eyZs0avvzyS95///2Q5QAprEU77dtSQtGeig6dw7VjBxUffBCkREIIIYRorblz5zJ48GCmTZvGjBkzjlsoN4iIiOC0007DZrO1eNxbb73FzJmBtQBnzpzJm2++2eLxeXl5DB06lFtuuYXhw4dz5plnUlvbuokSjpXJ4XAwZcoUACwWC9nZ2eTn57fqnO0ls4KIdvnkv1tIGxzLtBuGtfsc5Yve4fCzzxI5bRrKJH+KQgghTjz3L9rE5gMd66j6vmG9ovjdBcOPuX/VqlUsXLiQNWvW4PV6yc7OZuzYsQA8+OCDvPjii0e1mTRpEo888kirMxQWFpKamgpAamoqRUVFx22Tm5vLggULeOaZZ7jyyit57bXXuO6664KSqaysjEWLFnHnnXe2+ntoD6lmRLtEB2NmkF69wOfDW1TUOP2eEEIIIUJr2bJlXHLJJTgcDgAuvPDCxn2zZs1i1qxZYcmVkZFBVlYWAGPHjiUvLy8ombxeLzNmzOBnP/sZ/fv3D0LSY5PCWrRLTLKDHSsL0Vq3+85mc1pgkRjPgQNSWAshhDghtdSzHErH+n93sHqsk5OTKSgoIDU1lYKCApKSko7bxnrESsxGo7FxKEhHM916661kZmZy1113tTp/e0lhLdolJsmOq8ZLXbUHu9PSrnMcuUiMEEIIITrHpEmTuOGGG5g9ezZer5dFixZx2223AcHrsb7wwguZP38+s2fPZv78+VxUPwvY/v37uf766/n4449bfa6OZPr1r39NeXk5zz77bLvat5UU1qJdYpIDHx+VFdZ2oLAOjL3y7N8ftFxCCCGEaFl2djZXXXUVWVlZ9O3bl4kTJ7apfb9+/aioqMDtdvPmm2/y0UcfMWzYMG6++WZuv/12cnJymD17NldeeSXPPfccffr04ZVXXgGgoKAAUwjuq2ouU1RUFHPnzmXIkCFkZ2cDcMcdd3DzzTcH/foNlNY6ZCfvLDk5OXrlypXhjnFCcdV6qSiuJTbVgclsbPd5atevx9KnD8aYmOCFE0IIIbqwLVu2MHTo0HDHaDRnzhycTif33HNPyK/12GOP0adPnybjuruy5n5XSqlVWuuc5o6XHmvRLla7icQ+kR0+j33UqCCkEUIIIUR30NKiMj2BFNai3XatKcbj8jJ4Qmq7z1GzYgV127cTd+21QUwmhBBCiNaaM2dOuCP0GLJAjGi3LV8XsPqjvR06R+Unn1L057+g/f4gpRJCCCGECA8prEW7xSTZKS+qRfvbP07fnJaGdrvxHT4cxGRCCCGEEJ1PCmvRbjHJDnxeP5Wlde0+h0y5J4QQQoieQgpr0W4xSYEp98oLa9t9DnOaFNZCCCGE6BmksBbt1jCXdfmhDhTW0mMthBBCdCsPPPAAAwcOZPDgwXz44YfNHtMwT3ZWVhb9+vVrXKo8Ly8Pu93euO/2228PadZf/epX9O7dG6fT2WT7ww8/zLBhwxg1ahRTp05lz549QbmezAoi2s0RbeHGB0/D5jS3+xzGyEgGfvYppsTEICYTQgghRChs3ryZhQsXsmnTJg4cOMC0adPYvn07RmPTNS1eeumlxue/+MUviI6Obnw9YMAA1q5d2yl5L7jgAu644w4yMzObbB8zZgwrV67E4XDw5JNPcu+99zbJ3F7SYy3aTSmFPdKCUqpD5zGnpKCM7V9kRgghhBBtM3fuXAYPHsy0adOYMWMGDz30UKvavfXWW1x99dVYrVYyMjIYOHAg33777TGP11rz8ssvM2PGjHZnnTx5Mvfddx/jx49n0KBBLFu2rNVtJ0yYQGrq0dMCT5kyBYfD0XhMfn5+u/MdSXqsRYfsXFPEge1lTLxqULvPUfHhR7hyc0m84ydBTCaEEEJ0A+/PhoMbgnvOlJFwzrxj7l61ahULFy5kzZo1eL1esrOzGTt2LAAPPvggL7744lFtJk2axCOPPML+/fuZMGFC4/b09HT2799/zGstW7aM5OTkJj3Gu3fvZsyYMURFRfHHP/6xVUuqe71evv32W9577z3uv/9+lixZwrZt27jqqquaPf6zzz4jppWrOj/33HOcc845rTr2eKSwFh1yKL+K9Z/lc8qlAzGa2/cBSM2KFZS/+aYU1kIIIUQnWLZsGZdcckljj+2Ry4vPmjWLWbNmHbOt1kdPsdvSJ9cLFixo0ludmprK3r17iY+PZ9WqVVx88cVs2rSJqKioFjNfeumlAIwdO5a8vDwABg8e3OEhJS+88AIrV65k6dKlHTpPAymsRYfEJDlAQ3lxLXG9Itp1DnOvXvirqvBVVGA8zn9YQgghRI/SQs9yKB2rGD5ej3V6ejr79u1r3J6fn0+v+okIvs/r9fL666+zatWqxm1WqxWr1QoEiuQBAwawfft2cnJyWszb0MZoNOL1egE63GO9ZMkS5s6dy9KlSxvP31FSWIsOiU0JvNstK6rpUGEN4Nm/XwprIYQQIsQmTZrEDTfcwOzZs/F6vSxatIjbbrsNOH6P9YUXXsg111zD3XffzYEDB8jNzWX8+PHNHrtkyRKGDBlCenp647bi4mLi4uIwGo3s2rWL3Nxc+vfvD8D111/PHXfccczzfV9HeqzXrFnDbbfdxgcffEBSUlK7ztEcuXlRdEh0/VzWZYU17T6HzGUthBBCdJ7s7OzG6fAuu+yyVo1xbjB8+HCuvPJKhg0bxtlnn83jjz/eOCPIzTffzMqVKxuPXbhw4VE3LX7++eeMGjWK0aNHc/nll/PUU08RFxcHwPr165u90bAj7r33XtLT06mpqSE9PZ05c+YAgTcQVVVVXHHFFWRlZTUZDtMRqrmxMt1NTk6OPvIXKTrXf3/1FYNPSuGkC/u3q723pITciZNIvX8OMZdfHuR0QgghRNeyZcsWhg4dGu4YjebMmYPT6eSee+4JW4aKigpuuukmXnnllbBlaE5zvyul1CqtdbNjV2QoiOiwH/zx5A5NuWeMjWXIurUok/w5CiGEECeiqKioLldUt4dUMqLDOjqPtVIKpKgWQgghwqJheIToOBljLTps76bDvPHX1bhqve0+R8mLL3Jw7p+CmEoIIYQQonNJYS06zOvxcyC3jPKi9t/A6Nq2nYp33gliKiGEEEKIziWFteiwmOTAzCClBzswM0ivXvhKS/HXtP8cQgghhBDhJIW16LDoBDtKBeaybi9zWhoAnoKCYMUSQgghhOhUUliLDjOaDUTG2yiXuayFEEKIHu3w4cNMmTIFp9PJHXfccczjSkpKmD59OpmZmUyfPp3S0tKQ5lq/fj0nn3wyw4cPZ+TIkdTV1YX0escihbUIivTBsdgjLe1ub05Lw5Saina5gphKCCGEEMFks9n4wx/+wEMPPdTicfPmzWPq1Knk5uYydepU5s0L3dLtXq+X6667jqeeeopNmzbx2WefYTabQ3a9lkhhLYJiyg+GMvGqQe1ub05OJvPTT4icNi2IqYQQQgjRnLlz5zJ48GCmTZvGjBkzjlsoN4iIiOC0007DZrO1eNxbb73FzJkzAZg5cyZvvvlmi8fn5eUxdOhQbrnlFoYPH86ZZ55JbW1tqzJ99NFHjas5AsTHxzeuBtnZZPJgIYQQQogw+fO3f2ZrydagnnNI3BDuG3/fMfevWrWKhQsXsmbNGrxeL9nZ2YwdOxaABx98kBdffPGoNpMmTeKRRx5pdYbCwsLG5clTU1MpKio6bpvc3FwWLFjAM888w5VXXslrr73Gddddd9xM27dvRynFWWedRXFxMVdffTX33ntvq7MGkxTWIiiK91bywdMbmDpzKL0yY9t1jqK//R3vwYP0+nPoPi4SQgghTnTLli3jkksuweEIzOp14YUXNu6bNWsWs2bNCkuujIwMsrKyABg7dix5eXmtyuT1evniiy9YsWIFDoeDqVOnMnbsWKZOndoJqZuSwloEhc1ppuJQHaUHa9pdWHuLiqj+5psgJxNCCCG6rpZ6lkPpWKsmB6vHOjk5mYKCAlJTUykoKCApKem4baxWa+Nzo9HYOBTkeJnS09M5/fTTSUhIAODcc89l9erVYSmsZYy1CApnjBWj2UBZR2YG6dULb1ER2u0OYjIhhBBCHGnSpEm88cYb1NbWUllZyaJFixr3zZo1i7Vr1x71aEtRDYFe8Pnz5wMwf/58LrroIgD279/f5oL3eJnOOuss1q9fT01NDV6vl6VLlzJs2LA2XSNYwlpYK6V+rpTapJTaqJRaoJSyKaXilFKLlVK59V/b1/0pOpUyKGKS7JQVte5Gg+aYe/UCrfEUFgYxmRBCCCGOlJ2dzVVXXUVWVhaXXXYZEydObFP7fv36cffdd/Of//yH9PR0Nm/eDMDNN9/MypUrAZg9ezaLFy8mMzOTxYsXM3v2bAAKCgowmYI7YCI2Npa7776bcePGkZWVRXZ2Nuedd15Qr9FaSmsdngsrlQZ8AQzTWtcqpV4G3gOGASVa63lKqdlArNa6xc9JcnJydMMvUoTPB//cwOED1Vx7/4R2ta/+5hv23vBD+vzn30RMaN85hBBCiK5uy5YtDB06NNwxGs2ZMwen08k999wT8ms99thj9OnTp8m47q6sud+VUmqV1jqnuePDPcbaBNiVUh7AARwAfglMrt8/H/gMCM8AJNEmfUbE44hq/1zWlt69sY8ZAwYZoSSEEEL0RC0tKtMThK2w1lrvV0o9BOwFaoGPtNYfKaWStdYF9ccUKKWOP9pddAnDTu0Fp7a/vTktjX4L/he8QEIIIYQ4rjlz5oQ7Qo8Rtq7B+rHTFwEZQC8gQil1XRva36qUWqmUWllcXByqmKKN/H6N1+MLdwwhhBBCiE4Xzs/cpwG7tdbFWmsP8DpwClColEoFqP/a7IziWuuntdY5WuucxMTETgstjs1V4+Hpny1l49L97T7Hgftms+8nPftjIiGEEEL0TOEsrPcCE5RSDhWYTHEqsAV4G5hZf8xM4K0w5RNtZLGbMFkMHZoZRHu9uLZvD2IqIYQQQojOEbbCWmu9HHgVWA1sqM/yNDAPmK6UygWm178W3YBSiphkR4fnsvYUFKB9MpxECCGEEN1LWKdf0Fr/Tms9RGs9Qmv9A621S2t9WGs9VWudWf+1JJwZRdvEJDsoL+pAYZ3WC7xevDJuXgghhOhyDh8+zJQpU3A6nS3O8FFSUsL06dPJzMxk+vTplJaWdnqmmpoazjvvPIYMGcLw4cMb59IOJZnXTARVTJKDqlIXHlf7epzNaWkAeA4cCGYsIYQQQgSBzWbjD3/4Aw899FCLx82bN4+pU6eSm5vL1KlTmTcvdAMQWsp0zz33sHXrVtasWcOXX37J+++/H7IcIIW1CLL0obGMvyADv799Cw9ZMjKIOvccDHZ7kJMJIYQQosHcuXMZPHgw06ZNY8aMGcctlBtERERw2mmnYbPZWjzurbfeYubMwC1zM2fO5M0332zx+Ly8PIYOHcott9zC8OHDOfPMM6mtbd09W8fK5HA4mDJlCgAWi4Xs7Gzy8/Nbdc72CvcCMaKHScmIJiUjut3tLenppD38cBATCSGEEF3bnh9cf9S2yHPOJu6aa/DX1rLv1tuO2h99ySXEXHoJ3tJS9v/szib7+j7/3xavt2rVKhYuXMiaNWvwer1kZ2czduxYAB588EFefPHFo9pMmjSJRx55pNXfU2FhIampqQCkpqZSVNTsJG9N5ObmsmDBAp555hmuvPJKXnvtNa677rqgZCorK2PRokXceeedxz+4A6SwFkFXU+HG7/PjjG353WxLtMeDMpuDmEoIIYQQAMuWLeOSSy7B4XAANFlefNasWcyaNSssuTIyMsjKygJg7Nix5OXlBSWT1+tlxowZ/OxnP6N///5BSHpsUliLoHvlgRWkDYpl2g+Htav9npk3oMxm+jz7TJCTCSGEEF1PSz3MBru9xf2m2Njj9lA3JzDT8dGC1WOdnJxMQUEBqampFBQUkJR0/IW0rVZr43Oj0dg4FKSjmW699VYyMzO56667Wp2/vaSwFkEXneSgrAMzgxijonDt3BnEREIIIYRoMGnSJG644QZmz56N1+tl0aJF3HZbYLhJsHqsL7zwQubPn8/s2bOZP38+F110EQD79+/n+uuv5+OPP271uTqS6de//jXl5eU8++yz7WrfVlJYi6CLTXaQu7IQrfUx3xG3xJyWRtWyZe1uL4QQQohjy87O5qqrriIrK4u+ffsyceLENrXv168fFRUVuN1u3nzzTT766COGDRvGzTffzO23305OTg6zZ8/myiuv5LnnnqNPnz688sorABQUFGAyBb/8bC5TVFQUc+fOZciQIWRnZwNwxx13cPPNNwf9+g2ksBZBF5PswFXjpa7Kgz3S0ub25l690HV1+EpLMcXFhSChEEIIcWL71a9+xa9+9SsA5syZ06a2DWOfv+/IXuH4+Phme6W/+eYbfvKTnxy1vV+/fmzcuLHx9T333BOUTFq3b5ay9pLCWgRddFJgqryywpr2FdZpvQDw7D8ghbUQQgjRg7S0qExPIIW1CLqkvlGccf1QopMc7WpvHTyYuBtvxBgdFeRkQgghhPi+tvZYi2OTwloEnSPKwtBTUtvd3pKeTvK94ZnqRwghhBCivWTlRREShw9UcSC3rN3t/XV1sqy5EEIIIboVKaxFSHzz5i6WLtjW7vb5P/4x+T8L7epIQgghhBDBJIW1CImYZAflRbVof/vuxrVmZuLasQPt8wU5mRBCCCFEaEhhLUIiJsmOz+unsqSuXe2tgwah6+rw7NsX5GRCCCGE6IgHHniAgQMHMnjwYD788MNmj2mYJzsrK4t+/fo1LlWel5eH3W5v3Hf77beHNOuvfvUrevfujdPpbLL94YcfZtiwYYwaNYqpU6eyZ8+eoFxPbl4UIRGTHJgRpKyohqgEe5vbWwcNAqBu+3Ys/foFM5oQQggh2mnz5s0sXLiQTZs2ceDAAaZNm8b27dsxGo1NjnvppZcan//iF78gOjq68fWAAQNYu3Ztp+S94IILuOOOO8jMzGyyfcyYMaxcuRKHw8GTTz7Jvffe2yRze0mPtQiJxsK6sLZd7a0DB4JSuHJzgxlLCCGEEMDcuXMZPHgw06ZNY8aMGTz00EOtavfWW29x9dVXY7VaycjIYODAgXz77bfHPF5rzcsvv8yMGTPanXXy5Mncd999jB8/nkGDBrFs2bJWt50wYQKpqUfPVDZlyhQcDkfjMfn5+e3OdyTpsRYh4YiycOFdWSSkO49/cDMMdjspv78f+8iRQU4mhBBCdC1v/HX1UdsGjk1i5OR0PG4f7zy67qj9Q05OZegpqdRWufngnxub7LvkF9ktXm/VqlUsXLiQNWvW4PV6yc7OZuzYsQA8+OCDvPjii0e1mTRpEo888gj79+9nwoQJjdvT09PZv3//Ma+1bNkykpOTm/QY7969mzFjxhAVFcUf//jHVi2p7vV6+fbbb3nvvfe4//77WbJkCdu2beOqq65q9vjPPvuMmJiY454X4LnnnuOcc85p1bHHI4W1CAmlFL2HdGzVxNgrrghSGiGEEEI0WLZsGZdcckljj+2FF17YuG/WrFnMmnXstSSaWyJcKXXM4xcsWNCktzo1NZW9e/cSHx/PqlWruPjii9m0aRNRUS0vCnfppZcCMHbs2MblywcPHtzhISUvvPACK1euZOnSpR06TwMprEXIFO2poCivghGnp7ervbe0lNo1a4k49RQMVmuQ0wkhhBBdQ0s9zGaLscX9dqfluD3UzTlWMXy8Huv09HT2HTGxQH5+Pr169Wr2XF6vl9dff51Vq1Y1brNarVjr/58+duxYBgwYwPbt28nJyWkxb0Mbo9GI1+sF6HCP9ZIlS5g7dy5Lly5tPH9HyRhrETJ56w+xdOF2fB5/u9rXLF9O/o9/jGvHjiAnE0IIIU5ckyZN4o033qC2tpbKykoWLVrUuG/WrFmsXbv2qMcjjzwCBHq3Fy5ciMvlYvfu3eTm5jJ+/Phmr7NkyRKGDBlCevp3HWzFxcX46qfS3bVrF7m5ufTv3x+A66+/vsXx2t/X0GPd3ON4RfWaNWu47bbbePvtt0lKSmr1NY9HCmsRMjHJDtBQXtzOGxjrZwZxbZcbGIUQQohgyc7ObpwO77LLLmvVGOcGw4cP58orr2TYsGGcffbZPP74440zgtx8882sXLmy8diFCxceddPi559/zqhRoxg9ejSXX345Tz31FHFxgaGj69evb/ZGw4649957SU9Pp6amhvT0dObMmQME3kBUVVVxxRVXkJWV1WQ4TEeo5sbKdDc5OTn6yF+k6BqK9lTwygMrOef2kfTPSmxze+31si17LLHXXkvyffeGIKEQQgjR+bZs2cLQoUPDHaPRnDlzcDqd3HPPPWHLUFFRwU033cQrr7wStgzNae53pZRapbVuduyK9FiLkIlJaphyr6Zd7ZXJhGXgAFzbtwczlhBCCCG6mKioqC5XVLeH3LwoQsZiN+GIslBW1L7CGsCWOYjqr74KYiohhBBCHKlheIToOCmsRUhdPjsHR5Sl3e3jb7uN+FtuDmIiIYQQQojQkMJahFRknK1D7a39M4KURAghhBAitGSMtQip4r2VfP7Sdly13na1114vpQtforoN0+8IIYQQQoSDFNYipCpL6tjwaX67b2DEaKTob3+j4p13gxtMCCGEECLIpLAWIRWT3MGZQZTClpkpM4MIIYQQXcDhw4eZMmUKTqeTO+6445jHlZSUMH36dDIzM5k+fTqlpaUhzbV+/XpOPvlkhg8fzsiRI6mrqwvp9Y5FCmsRUtEJdpSiQzODWAcNwpWbS0+Yc10IIYTozmw2G3/4wx946KGHWjxu3rx5TJ06ldzcXKZOncq8efNClsnr9XLdddfx1FNPsWnTJj777DPMZnPIrtcSKaxFSBnNBiIT7O0fCkKgsPZXV+PZfyCIyYQQQogT19y5cxk8eDDTpk1jxowZxy2UG0RERHDaaadhs7U8OcFbb73FzJkzAZg5cyZvvvlmi8fn5eUxdOhQbrnlFoYPH86ZZ55JbW3rVm7+6KOPGldzBIiPj29cDbKzyawgIuRikhy4atp38yJ8t7S5e/duLOlpwYolhBBCdAkv3T/7qG2DJ0wk66zz8LjqeH3enKP2Dz99GiMmT6OmopxFf3ugyb6rftdy7/CqVatYuHAha9aswev1kp2dzdixYwF48MEHefHFF49qM2nSJB555JFWf0+FhYWNy5OnpqZSVFR03Da5ubksWLCAZ555hiuvvJLXXnuN66677riZtm/fjlKKs846i+LiYq6++mruvTc8KzZLYS1C7rwfj8RgbP+HI/aRIxj07XKMUVFBTCWEEEKcmJYtW8Yll1yCwxG4D+rCCy9s3Ddr1ixmzZoVllwZGRlkZWUBMHbsWPLy8lqVyev18sUXX7BixQocDgdTp05l7NixTJ06tRNSNyWFtQi5jhTVAMpsxhimsVJCCCFEqLXUw2y22lrc74iKPm4PdXOUUs1uD1aPdXJyMgUFBaSmplJQUEBSUtJx21it1sbnRqOxcSjI8TKlp6dz+umnk5CQAMC5557L6tWrw1JYyxhrEXK1VW7ee3I9u9cVt/sc5Yve4eDcPwUxlRBCCHFimjRpEm+88Qa1tbVUVlayaNGixn2zZs1i7dq1Rz3aUlRDoBd8/vz5AMyfP5+LLroIgP3797e54D1eprPOOov169dTU1OD1+tl6dKlDBs2rE3XCBYprEXIWe0m9m0pIX9r+6faceXmUrpgAdrtDmIyIYQQ4sSTnZ3NVVddRVZWFpdddhkTJ05sU/t+/fpx991385///If09HQ2b94MwM0338zKlSsBmD17NosXLyYzM5PFixcze3ZgHHlBQQEmU3AHTMTGxnL33Xczbtw4srKyyM7O5rzzzgvqNVpL9YQpzHJycnTDL1J0TW/8dTVej58rZue0q335O+9y4J57yHjrLWyDBwU5nRBCCNF5tmzZwtChQ8Mdo9GcOXNwOp3cc889Ib/WY489Rp8+fZqM6+7KmvtdKaVWaa2bLWhkjLXoFMn9olj36T58Hj9Gc9s/KLEOygTAtX27FNZCCCFEN9XSojI9gRTWolMk94/Cv1hTnF9JSkZ0m9tbMzLAbJYVGIUQQoggmzNnTrgj9BgyxroDdq9dxa7VK8Ido1tIyYgmOSMKn8ffrvbKbMY+YgTa4wlyMiGEEEKI4JAe6w745rWF+Lwe+mePC3eULi8ixsrl97VvfHWDfgv+F6Q0QgghRHhprY855Z3oGtpzH2JYe6yVUjFKqVeVUluVUluUUicrpeKUUouVUrn1X2PDmbEl/bPHUbhrB1WlJeGO0m34fe3rsRZCCCF6CpvNxuHDh9tVuInOobXm8OHDx126/fvC3WP9D+ADrfXlSikL4AD+D/hYaz1PKTUbmA3cF86Qx5IxJocvFv6XvLWrGDFlerjjdHnblh/ksxe2cv2fTsEeaWlz+7pt2yj4zW9J+dX/YR89OgQJhRBCiNBLT08nPz+f4uL2r+8gQs9ms5Gent6mNmErrJVSUcAk4AYArbUbcCulLgIm1x82H/iMLlpYJ/bNwBkXz641K6SwboXIOBtej5/C3RX0G5XQ5vZGp5O69eup27JFCmshhBDdltlsJiMjI9wxRAiEcyhIf6AY+LdSao1S6lmlVASQrLUuAKj/2uwamEqpW5VSK5VSK8P1jk8pRcaYHA7t3SMf57RCYt9IlEFxcHd5u9qbevXC4HTi2p4b5GRCCCGEEB0XzqEgJiAb+KnWerlS6h8Ehn20itb6aeBpCCwQE5qIxzf5BzdhttrkBoRWMFuMxKdFULi7ol3tlVJYMzNlyj0hhBBCdEnh7LHOB/K11svrX79KoNAuVEqlAtR/LQpTvlax2B0og8xa2FrJGdEU5VWg/e17L2QdNIi63Fz5hEAIIYQQXU7YKkKt9UFgn1JqcP2mqcBm4G1gZv22mcBbYYjXJqvfX8Qrf/x1uGN0CwOyExlzZh983vbNDuIYN46Ik05C19UFOZkQQgghRMeEe1aQnwIv1s8Isgv4IYFi/2Wl1E3AXuCKMOZrHe1n74a1lBUeJCY5JdxpurTeQ+LoPSSu3e2jzz+P6PPPC2IiIYQQQojgCOsYBq31Wq11jtZ6lNb6Yq11qdb6sNZ6qtY6s/5rl58kOqN+gRhZhbF16qo9HD5Q1aFzaK83SGmEEEIIIYJDBgcHQWxKL2JT09i9Rgrr1vjo2Y0sfm5zu9vvvuJKCn71qyAmEkIIIYToOCmsg6R/dg77Nm/AI2N/jys5I5qSA1W469rX62yMjqZOptwTQgghRBcjhXWQZI4/lWETp+Cuqw13lC4vOSMKraF4b2W72lsHDcK9c6cMBxFCCCFElxLumxd7jLQhw0gbMizcMbqF5IwoAAp3V5A2KLbN7a2DMtFuN+49e7AOGBDseEIIIYQQ7SI91kGktaYob5fMsXwcdqeFqER7uxeKsQ0aBCALxQghhBCiS5HCOog2f/4Jz9/3Mw7tzQt3lC5vyrWDOenC/u1qaxkwgNhrr8Wcnh7kVEIIIYQQ7SdDQYKo78gsIDDtXmLfjPCG6eLSOzCXtcFqJeU3siCPEEIIIboW6bEOImdcPEn9BrB77cpwR+nyvB4f25YfbPcNjNrjwbVrd5BTCSGEEEK0nxTWQdY/O4cD27ZSW9W+gvFEoZTi0+e3sn1FYbvaH3rmGXaddx7+mpogJxNCCCGEaB8prIMsY8w4tPaTt251uKN0aUaTgYTeTgp3l7ervTUzE7TGtWNHkJMJIYQQQrSPFNZBljIwk4vv/Q0Dx54U7ihdXnJGFMV7KvH5/G1uKzODCCGEEKKrkcI6yAwGIwPGnoTZZgt3lC4vJSMar8dPyf7qNrc19+6Nstupk8JaCCGEEF2EFNYhUFtZwdevLqAob1e4o3RpDQvFFO9r+3h0ZTBgHTgQlyxtLoQQQoguQqbbCwGlDHz92gJ8Xi9J/do3V/OJIDLexswHTsUZa21X+8Q7foKytq+tEEIIIUSwSY91CNicTnoNGsquNSvCHaVLU0q1u6gGcJ5+OhETJgQxkRBCCCFE+0lhHSL9s8dRnLeLypJD4Y7SpR3cXc6Hz2ykrtrT5rb+ujqqvvgSd/7+ECQTQgghhGgbKaxDpP+YHAB2r5HFYlricfnYsaqIoryKNrf1V1Wx7+abqVyyOATJhBBCCCHaRgrrEInv3Ze4XunUlJWFO0qXltw3ChQUtqOwNiUkYIyLw5UrNzAKIYQQIvzk5sUQUUpxw1+fQBnkvUtLLHYTcakRHNzV9sIawDpokMwMIoQQQoguQaq+EGooqrW/7QugnEiS+0VRlFeB1rrNba2DMnHt2CE/YyGEEEKEnRTWIaS1ZuHv7mPpC8+FO0qXljowmsh4G65qb5vb2gYNQtfW4tm3LwTJhBBCCCFaTwrrEFJKYbHZ2LVapt1rydBTenHl/43D5jS3ua3zjDPIeON1zL16hSCZEEIIIUTrSWEdYhljcigtOEBpgUwJFwqmuDhsQ4eizG0vyoUQQgghgkkK6xDrnz0OgF2rZdq9lnzxai5v/m11u9pWLF5M2RtvBjeQEEIIIUQbSWEdYtFJKcSl9ZZVGI/DaFQU7CjH6/G1uW35W29x+JlnQpBKCCGEEKL1pLDuBOMuvIwhp0wKd4wuLTkjGr9Pc2hfVZvb2gYNwr1nD36XKwTJhBBCCCFaR+ax7gQjJk8Ld4QuLzkjCoCDu8pJ6R/dprbWQYPA58O9cye2YcNCEU8IIYQQ4rikx7qTVJUcJn/rpnDH6LIioq04Y63tWoHROmgQAHXbtwc7lhBCCCFEq0lh3Uk+nf8M7/79z+1aBOVEMXxSWpt7qwEsffqgLBY8e/eGIJUQQgghROu0eiiIUioCqNNat/3uMkHGmBy2f/MFRXm7SM4YEO44XVLOOf3a1U6ZTGQu+xxjdNuLciGEEEKIYDlmj7VSyqCUukYp9a5SqgjYChQopTYppR5USmV2XszuLyNrLAC7ZbGYFrlrvdRVedrcTopqIYQQQoRbS0NBPgUGAL8EUrTWvbXWScBE4BtgnlLquk7I2CNExMSSMiBTpt1rgc/j57lZy1izpO1DOly5ueT/7E7ceXnBDyaEEEII0QotDQWZprU+qutQa10CvAa8ppSS5e7aYEDOBL565UWqy0qJiIkNd5wux2g2EN/LSeHutt/AiMlE5UcfEXHKKVj69Qt6NiGEEEKI42mpx/qN+qEgEcc6oLnCWxzbqGlnc/Ojz0lR3YLkjCiK8irw+9t2k6elXz9MSUnUfPttiJIJIYQQQrSspcL6GeACYLdS6iWl1MVKKUsn5eqRHFHRRCUkhjtGl5acEYXH5aO0oLpN7ZRSOMaPp/rbb2XmFSGEEEKExTELa631W1rrGUBf4HVgJrBXKfUvpdT0zgrY05QXFfL6A79j/9bN4Y7SJaVkBG5CbM981o7x4/AdOoR79+5gxxJCCCGEOK7jzmOtta7VWr+ktb4EOBMYA3wQ8mQ9lD0qivwtm9i0dEm4o3RJ0Ul2Tp8xiLRBMW1uGzF+PLZhw/CVlQc/mBBCCCHEcRy3sFZKJSulfqqU+hJ4E/gIGBvqYD2VxWZn0IRT2fb1MjyuunDH6XKUUow4PZ3oREeb21r69SPj9ddwZI8JQTIhhBBCiJa1NI/1LUqpT4DVwCDgXq11f631fVrrtZ0VsCcafvpU3LW17FjxTbijdEm1VW5yVxTirvO2q712u2WctRBCCCE6XUs91qcA84DeWuufaq2/7KRMPV760BFEJSaxaenH4Y7SJRXlVfLRc5so2lPZ5rZVS5ey7aQJMs5aCCGEEJ2upZsXf6i1/gjQSqnrlFK/BVBK9VFKje+0hD2QMhgYf9Hl9B2ZJT2rzUjOiAKgYEdZm9taMjLQtbXULF8e5FRCCCGEEC077hhr4AngZGBG/etK4PFgBVBKGZVSa5RS79S/jlNKLVZK5dZ/7ZGTPo+efi7jLrwMpVS4o3Q5tggzyRlR5K0/1Oa25t69MaWkUC3zWQshhBCik7WmsD5Ja/0ToA5Aa10KBHM+6zuBLUe8ng18rLXOBD6uf90jedwucld8Lb3WzcgYnUDRnkqqStt2g2dgPutx1Hy7Qn6uQgghhOhUrSmsPUopI6ABlFKJgD8YF1dKpQPnAc8esfkiYH798/nAxcG4Vle09culvP3QXApyt4U7SpfTPyuwkM7+7WVtbhsxfjy+w4dx79oV5FRCCCGEEMfWmsL6EeANIEkpNRf4AvhTkK7/d+BemhbqyVrrAoD6r0nNNVRK3aqUWqmUWllcXBykOJ1r0EmnYbJY2fy53MT4fbEpEfzgjycz+KSUNreNOOUUEu64A0NERAiSCSGEEEI0rzULxLxIoPh9ACgALtZav9LRCyulzgeKtNar2tNea/201jpHa52TmNg9lwm3Ohxkjj+ZrV99jtftDnecLicqwd6uduZevUi84yeYU9pelAshhBBCtFdL81g7G55rrbdqrR/XWj+mtd7S3DHtcCpwoVIqD1gInKGUegEoVEql1p8/FSjqwDW6vOGnT8NVXc3OVTKLxffVVXv48JmN7FzT9j8BX1U1VV98KeOshRBCCNFpWuqxfksp9Vel1CSlVONn6kqp/kqpm5RSHwJnt/fCWutfaq3Ttdb9gKuBT7TW1wFvAzPrD5sJvNXea3QHvUeMJDI+kb0b14U7SpdjtZs4kFvGjpVtL6wrP/yAfTffjHvnzhAkE0IIIYQ4mulYO7TWU5VS5wK3AacqpeIAD7ANeBeYqbU+GIJM84CXlVI3AXuBK0JwjS7DYDByzdy/EhHTI2cV7BBlUPQbnUDut4X4PH6M5tbcEhDgGB+Yar3622+xDhwYqohCCCGEEI2OWVgDaK3fA94LdQit9WfAZ/XPDwNTQ33NYNBa4/b5sZqMHTqPMzau8Xwyr3VT/UcnsnnZAfK3ldJ3RHyr25nT0zH1SqXm2xXEXXNNCBMKIYQQQgS0vgtQNFHj9pL1+8U890Vwls5eseh1Fv72XhkT/D3pg2MxW43sWte2mV+UUkSMG0/Nt9/Kz1QIIYQQnUIK63ZyWEzEOsys21cWlPNZbHYObN9C0W4ZE3wko9nAiElpRLdjhhDH+PH4Skpw5eaGIJkQQgghRFNSWHfAqPQY1ueXB+Vcg0+ZiNFsZuNnS4Jyvp7klMsGkn1W3za3c54xhYzXX8M6YEAIUgkhhBBCNHXcwlop9ZBSanhnhOluRqVHU1BeR1Fl25bdbo4twsnAnAls/epzfF5PENL1LD6vn/Limja1McXGYhs2DGXs2Bh4IYQQQojWaE2P9VbgaaXUcqXU7Uqp6FCH6i6yescAsH5fcHqth0+eRl1lBbtWrwjK+XqSD5/ZyDuPrW9zu9p16yh8YB7a7z/+wUIIIYQQHdCalRef1VqfClwP9APWK6X+p5SaEupwXd3wXtEYDYp1+WVBOV/fUVmMu/Ay4tJ6B+V8PUnvoXGUFdZQerC6Te1cO3dRMn8+rh07QpRMCCGEECKgVWOslVJGYEj94xCwDrhbKbUwhNm6PLvFSGaSk3VBGmdtMBiZdO0PiZfC+ij9RiUAsGtt22YHaZjPumb5t0HPJIQQQghxpNaMsX6YwKIw5wJ/0lqP1Vr/WWt9ATAm1AG7utHpMazPLwvqlG75WzayZ/3aoJ2vJ4iMs5HUN5Ld6w61qZ0lPQ1zr17UfCuFtRBCCCFCqzU91huBUVrr27TW369OxocgU7cyuncMZTUe9pa07ca6lnz23+dY+uK/gna+niJjdCKFuyuoLnO1qZ3jpJOoWbFCxlkLIYQQIqRaU1ivBYYopbKPeAxQSpm01sEZA9GNjUoP3MsZrOEgAMMnT6U4bxdFebuCds6eYMjJKVx6Tzb2KEub2jnGj0fZbHiL2zaMRAghhBCiLVpTWD8BfAM8DTwDfA0sBLYrpc4MYbZuYXBKJFaTgfVBWigGYMgpkzAYTWxa+nHQztkTOGNtpA6MwWBo27Lv0RdewMBPP8GcnByiZEIIIYQQrSus84AxWuscrfVYAuOqNwLTgL+EMFu3YDYaGNYrKmgLxQDYI6MYkDOeLV98hs/rDdp5e4KywhqWvbwdd23rfy7KaESpthXjQgghhBBt1ZrCeojWelPDC631ZgKFtoxTqDc6PYYN+8vx+oI3hnf46VPRWlN6ID9o5+wJairdrP8knz2bDrepXenLL7ProotlnLUQQgghQqY1hfV2pdSTSqnT6x9P1G+zArJEIDC6dzS1Hh87iquCds6MrBxuf2o+CX36Be2cPUFK/2jskWZ2t3HaPWWx4Nq2DVduboiSCSGEEOJE15rCeiawA7gL+DmwC7iBQFF9wi8SAzAqPQYI3gqMAAajEaPJjPb7ZYnzIxgMin6jEsjbeBifp/W9zxHjxgEyn7UQQgghQqfFwrp+YZhFWuu/aq0v0VpfrLV+SGtdo7X2a62D10XbjWXERxBpM7E2SCswNqitrOC5O29h9XtvB/W83V3/0Yl46nzs317a6jbmtDTM6elUf7s8hMmEEEIIcSJrsbDWWvuAGqVUdCfl6ZYMBsWo9GjWB7mwtkdGEZ2Uwqp338Trdgf13N1Z+pBYIuNs1FS27WfiGD+emhUrZZy1EEIIIUKiNUNB6oANSqnnlFKPNDxCHay7GZUew9aCSuo8vqCed/zFV1BdVipT7x3BZDHyg7knM2RCapvaRU6fRvR55+KvqQ1RMiGEEEKcyEytOObd+odowej0aLx+zZaCCsb0iQ3aefuMGE3KgExWLHqNkWecicFoDNq5uzOlFFpr/F6N0dya94cQOWUKkVPktgAhhBBChMZxKxKt9XzgZeAbrfX8hkfoo3Uvo3vHALAuiAvFQKCAHH/JlZQXHmTnKhkf3MDn8/O/Ocv59t3dbWqnfT7c+/aFKJUQQgghTmTHLayVUhcQWNb8g/rXWUopuZvue1KibCRGWoO6UEyDgWNP4pLZv2NgzoSgn7u7MhoNRMRY2zzt3sE//pHdl18h46yFEEIIEXSt+Qx9DjAeKAPQWq8FMkKWqJtSSjE6PZp1Qb6BEUAZDPQfMw5laN2QhxNF/6wESg/WUFZY0+o2jjFj8JeX49q2LYTJhBBCCHEiak2l5tVaf78bVociTHc3Oj2GncXVVNSFZt7ptR++yxt/+T1ay48fIGN0IgC71rW+19rRMJ/1tzKftRBCCCGCqzWF9Ual1DWAUSmVqZR6FPgqxLm6pVH146w3hmA4CIBGs2vVt+zfsun4B58AIuNsJPaJZPfaQ61uY05NxdynD9XfrghhMiGEEEKciFpTWP8UGA64gAVABYFVGMX3jEoLTPe9LkSF9Ygp03FEx7D8zZdDcv7uKOfcfmRN692mNhEnjadmpcxnLYQQQojgOu50e1rrGuBX9Q/RgtgIC33iHEGfGaSB2WIl+5wL+WLhfynctYPk/gNDcp3upH9WYpvbxM6YQeSZZ4EMqRFCCCFEELVmVpBBSqmnlVIfKaU+aXh0RrjuaHTvmKCvwHikrLPOw2J38O1br4bsGt1NWWENuSsLW328bdgwnBNPQ8mc4EIIIYQIotYsEPMK8BTwLBDcZQV7oNHp0Sxad4DiSheJkdagn9/qiGDqTT8iOikl6OfurjZ+vp+NS/fTd0Q8Fltr/qShdv163Hv2En3B+SFOJ4QQQogTRWtnBXlSa/2t1npVwyPkybqpUekxACHttR42cQppg4eG7PzdTf+sBHxeP3s3lbS6Tdkrr3DwD39A++S9ohBCCCGCozWF9SKl1I+VUqlKqbiGR8iTdVMj0qIwqOCvwPh9lSWHWPLs41Qebv2MGD1VSv9obBFmdq9vw7R748fjr6igbuvWECYTQgghxImkNYX1TGAWgSn2VtU/VoYyVHfmsJgYlBwZsplBGvi9PtZ//CGr3n0jpNfpDgxGAxmjE9i97hDuWm+r2kScfDIYDFQuWRLidEIIIYQ4URy3sNZaZzTz6N8Z4bqrUenRrM8vC+lCLtFJyQw59XTWL/mQ2sqKkF2nuxhxeho+r5+CXa17Q2NKSCBiwgQqFr0jC+4IIYQQIiiOWVgrpe494vkV39v3p1CG6u5GpcdQWuNhX0ltSK8z/qLL8bjqWPPBopBepztI6hvFD+edRt/h8a1uE3XBBfjKy/Hk54cwmRBCCCFOFC31WF99xPNffm/f2SHI0mNk1a/AuC6ENzACJPTuy4CcCax5fxHuutAW8d2BzWkGwF3XuuEgUeedS+YXy7D0btsCM0IIIYQQzWmpsFbHeN7ca3GEwSmRWEyGkM4M0uCkS65g0ITT8LrdIb9Wd/Dx/M28/Y+1rRreYbBYMFitaK1lFUYhhBBCdFhLhbU+xvPmXosjmI0GhqVGhfwGRoDUgYOZfusdOKKiQ36t7iCpbxSFuys4uKt1487de/aw65xzqVq6NMTJhBBCCNHTtVRYj1ZKVSilKoFR9c8bXo/spHzdVlbvGDbuL8fn75z3IAe2byFv3epOuVZXNuTkVKwOE2uX7G3V8eZevfCVlVGx6J0QJxNCCCFET3fMwlprbdRaR2mtI7XWpvrnDa/NnRmyOxqVHk2N28eOoqqQX0trzSf/fpqP//Ukfv+JveCJ2Wpk+KQ0dq0tpry45rjHK7OZqHPPofKTT/BVVXdCQiGEEEL0VK2Zx1q0Q8MKjKG+gRFAKcVJF19B2cECtn/zZciv19WNmpyOwaDYsHR/q46POv8CdF0dlUsWhziZEEIIIXoyKaxDpH9CBJFWU8hXYGwwcNwE4nql8+2br5zw8zJHxFg578ejGH9+RquOt4/JwpyeTsXbMm2hEEIIIdpPCuuO8HnB0/w0dwaDYmR6NOs74QZGAGUwMO6iyynes5vda2VhzD7D47HYTK06VilF4l13EXPVVSFOJYQQQoieLGyFtVKqt1LqU6XUFqXUJqXUnfXb45RSi5VSufVfY8OVsUW1pfC34bDi2WMeMio9hq0HK3B5O2fc89DTTic+vQ/umuOPLT4R7Nl4mLf/sQaf7/hT6UWffx5RZ53ZCamEEEII0VOFs8faC/xCaz0UmAD8RCk1DJgNfKy1zgQ+rn/d9dhjITod1i6AYwy9GJ0ejcen2VJQ2SmRjCYz1//lUYacenqnXK+r01qzb0spO1cVtep4z4EDlL78cohTCSGEEKKnClthrbUu0Fqvrn9eCWwB0oCLgPn1h80HLg5LwNbImgFFm+Dg+mZ3j25YgbGTxlkDGIxGtNZs+eIz6qpCPyNJV9Z3eDwxyQ7WLtnXqnHnFR99xMHf/g7Xrt2dkE4IIYQQPU2XGGOtlOoHjAGWA8la6wIIFN9A0jHa3KqUWqmUWllcXNxpWZsYfikYLbBuYbO7U6NtJDitnTIzyJFKDuTz/mMP8+XLL3TqdbsaZVCMntqb4r2VFOwoO+7xUeeeCwYDFe/ITYxCCCGEaLuwF9ZKKSfwGnCX1rp1y+UBWuuntdY5WuucxMTE0AVsiSMOBp0N618Gn+eo3UopRnfiDYwN4tN6M/rMc1n30XsU5e3q1Gt3NUMmpGCLMLN2yb7jHmtOSiJiwkmUL3rnhJ9ZRQghhBBtF9bCWillJlBUv6i1fr1+c6FSKrV+fyrQugGy4ZJ1DdQcgh1Lmt09Kj2GncVVVNYdXXiH0qlXXYctMpKPn3sS7T/+zXs9lcli5ORLBjAwp9kPPo4SdcGFePbto3bt2tAGE0IIIUSPE85ZQRTwHLBFa/3wEbveBmbWP58JvNXZ2dpk4DRwJMDa/zW7e3TvaLSGDfs7t9faFuFk0jU3cGD7FjYv+7RTr93VDDutF4PGpbTq2Mjp0zBERFC3ZUuIUwkhhBCipwlnj/WpwA+AM5RSa+sf5wLzgOlKqVxgev3rrstohpFXwPYPoKbkqN0NKzB29nAQgOGnT2XwyROxR0V1+rW7mrpqD6s/3ENddcufHBidTjKXfU7cNdd0UjIhhBBC9BStW0EjBLTWXwDqGLundmaWDsuaAcufhE2vw7ibm+yKi7DQO87O+k6+gRECi8acf9d9nX7drqi6zMXXb+xEa83Ys/u1eKzB4QBA+3woo7ET0gkhhBCiJwj7zYs9QsooSBoemNO6GaPSY1i3r/N7rBt43W6+fm3BCX0jY3yak95DY1n/aT4+7/HHnOf/9GccmP3LTkgmhBBCiJ5CCutgUCrQa71/JRzKPWp3VnoM+8tqOVTlCkM48LhdrH5/EZ/8+6kTeraLrGl9qCl3s2Nl4XGPNcbHUbl4Mb6q6k5IJoQQQoieQArrYBl5BShDszcxjkqPBgjLcBAAuzOSiTNmsn/rZracwDcy9h4WR2xqBGtasWBM9AUXoOvqqPq4+dlehBBCCCG+TwrrYIlMgQFTYf1L8L3p7UakRWNQsDaMw0FGTplOysBBLH3hX7hqTsxeWKUUWdN6ExFlwV3rbfFY+5gxmNPSKF/0TielE0IIIUR3J4V1MGXNgIr9kPd5k80RVhOZSZFh67GGwI2MU2/8ETUV5Xz1SvNTA54Ihp6SygU/y8LqMLd4nDIYiDr/fKq/+gpvuFb2FEIIIUS3ErZZQXqkweeCNTpwE2P/yU12jUqP5uOtRWitCUzh3flSBmQy+Qc3kT50RFiu3xU0/OwrS+pQCpyxtmMeG3PJxZji41C2Yx8jhBBCCNFAeqyDyWyH4RfDlrfBVdlk16jeMZRUu8kvrQ1Ptnpjz7uY5P4Dw5oh3Nx1Xv53/3JWvpfX4nGWfv2Iu/56jJGRnRNMCCGEEN2aFNbBlnUNeGpgy6Imm0fX38C4LozDQRq462r54Im/s+WLz8IdJSwsNhODcpLY+s1BaivdLR7rr6mh7LXXcefnd1I6IYQQQnRXUlgHW++TIDbjqNlBhqREYTEawrIC4/eZLBYO7dvD0uefw1VTE+44YTF6Wh/8Xj/L3255bm9fZSUFv/415a+/0UnJhBBCCNFdSWEdbErB6BmQtwzK9jZutpgMDO0Vxbp9ZeHLVs9gMDL1ptupLi/j61dPzBsZ41IjGD21N5uWHeDAjrJjHmdOTsYx4STK33nnhJ4DXAghhBDHJ4V1KIy+OvB13UtNNo/pHcP6/HLqPL4whGoqdeBgRp5xJqvff5tD+/aEO05YjL+gP9GJdor3VrZ4XPT5F+DZu5e6des6KZkQQgghuiMprEMhti/0PQ3WLYAjejmnDU2m1uNj6fauMX3baVdfj9URwbL//SfcUcLCbDVy9W/HM/qM3i0eF3nmdJTVKnNaCyGEEKJFUliHStYMKNkJ+SsaN53UP45Yh5n3NxSEMdh3HFHRnH/nfUy75SfhjhI2JrMRgPytJZQebH7hHGNkJM4pU3Dn5XViMiGEEEJ0N1JYh8rQC8Fkb3ITo9loYPqwZD7eUoTLG/7hIAB9R2URGZeA9vupLDkU7jhh4a7z8uEzm/j0ha1of/PjqHv9eR59nnu2k5MJIYQQojuRwjpUbFEw9ALY9Dp46ho3nzMylUqXly93dK0i9sN/PsJLc2afkMudW2wmTrlsIAU7ytn85YFmjzFYrQD4qqo6M5oQQgghuhEprEMpawbUlcO29xo3nToggUibifc2HAxjsKONPOMsKoqL+PCpf5yQs18MOTmFtMExfPX6TqrLXc0eU/nxx+ROOh3X7t2dnE4IIYQQ3YEU1qGUcTpE9oJ1Cxs3WUyB4SAfbTqI2+sPY7im0gYPZeKMmeQu/4o1H5x4N+kppZh8zRB8Hj/LXtre7DH20aNBaw498WQnpxNCCCFEdyCFdSgZjDDqStixBKqKGjefMyKVijovX+86HMZwR8s5/xL6Z49j6fPPcXBH88VlTxaT7ODkSwaQNii22V57U0ICsdfMoOLdd3Ht3BmGhEIIIYToyqSwDrWsa0D7YP3LjZsmZiYQYTHywcauMTtIA2UwcPZP7iY1c9AJORwEYPTU3oycnI5Sqtn98TfdhLLZOPT4E52cTAghhBBdnRTWoZY4GHplB+a0rmczG5k6NJkPNxXi9XWd4SAAdmckV835M6mZg8MdJay2fl3Q7HLnprg44q69hoqPPsJTWNRMSyGEEEKcqKSw7gxZ10DhRji4oXHTOSNSKKl28+3ukjAGa55SCr/Px9IX/sWqd98Kd5ywKNpbycr38zi4q/yofXE33kj/t9/CnJwUhmRCCCGE6KqksO4MIy4DgxnWftdrPXlwEnazkfc3dq3ZQRoog4Gygwf4/MV/UZC7LdxxOt2Ei/rjjLHy6Qtb8X3vUwVTbCzW/v0B0F5vOOIJIYQQoguSwrozOOJg0Fmw4WXweQCwW4xMGZLIB5sO4jvGoiThpJTirNvvwhmXwKK/z6O2qjLckTqVxWZi0tWDKDlQzdrFe5s95sAv/4/9v7ink5MJIYQQoquSwrqzZF0D1cWw4+PGTeeMSKW40sWqPaVhDHZsNqeTC+66j+rSUj544m8n3A2NGaMTGZCdyIp38pqd29qcmkLlhx9St3VrGNIJIYQQoquRwrqzDJwOjvgmNzFOGZKExWTg/S42O8iRUgYO4vQf3Mie9Ws4tDcv3HE63cSrBnH2bSOIiLYetS9u5kwMkZEcevzxMCQTQgghRFcjhXVnMVlg1FWw9R0oDswR7bSaOH1QIh9sPIi/Cw4HaTDm7Au44a9Pktg3I9xROl1EtJV+IxMA8Lh9TfYZo6OJmzmTysVLqNuyJRzxhBBCCNGFSGHdmU67G8wO+PCXUD+s4tyRKRSU17E2vyy82VqglCImOQWArV99Tk3F0TNl9HRbvynghV9/TU2Fu8n2uJnXY4iK4tDTT4cpmRBCCCG6CimsO5MzEU6/L7ASY+5HAJwxJBmzUfH+hq47HKRBRXERHzz+cGC8tb9rzb8dakl9o6ir8fDZi1vRR3y6YIyMJP2Rf5Dy29+GMZ0QQgghugIprDvb+FshPhM++CV43UTbzZw2MIH3Nx7s8jcHRiUmMfn6W9i9ZiXfvP5SuON0qrjUCE65ZCC71x1i2Su5TX5XERMmYIptfhl0IYQQQpw4pLDubCYLnD0PSnbC8icBOGdkKvmltWzcXxHmcMc3+sxzGTZxCl+98iJrPnwn3HE61eipvRk9rTcbPs1nzUdNp+Bz5+WRd9XV1K5bF6Z0QgghhAg3KazDIXMaZJ4FSx+EykLOHJaMyaB4rwvPDtJAKcWZt9/JgJyT+OTf/+Tw/n3hjtSpTr10IIPGJ6MMqsl2Y0Iinr17KX5MZggRQgghTlRSWIfL2Q+Atw4+vp8Yh4WTB8Tz/oaCbjGcwGgycf6d93HxrN8Qn9Y73HE6lTIopv1wGGOm9wHA6wnMFGJ0RhB3441UL1tGzZo14YwohBBCiDCRwjpc4gfAhB/B2hdh/yrOGZFK3uEath7sHiscmiwWBowdD8C+TevJW7sqzIk6j1KB3uqCneU8/+uvKdwdGMITd+01GGNjOSS91kIIIcQJSQrrcJo0CyKS4P37OHNYIgZFt5gd5Ehaa75Y+Dxv/fVP5G/ZGO44nSoqwYbJbOCdx9dRVliDISKC+JtvovrLL6lZLb3WQgghxIlGCutwskXBtDmQv4KEXW8xPiOO9zYeDHeqNlFKcdGsXxOVkMgbf76fgztzwx2p00REW7ngp1kALHp0LdXlLmJnzCBlzu+wjRge3nBCCCGE6HRSWIfb6BmQNhYW/44Lh0axo6iK3MLuMRykgSMqmst//Udszkhee+B3HNq3J9yROk1MsoPz7xhNTYWbdx5bh9dgIfbqqzFYLOGOJoQQQohOJoV1uBkMcPafoeogF1UuRCl4v5v1WgNExidwxa/nYjSZ2PDxh+GO06mS+0Vx9m0jSUh3YjQF/pMqf+ddDtw3O8zJhBBCCNGZpLDuCnqPg1FXE7HqKc5Lq+W9bjbOukFMSirX/PGvTL7+5nBH6XR9h8czdeYwjCYDrhoP3kOHKH/rLaqXfxvuaEIIIYToJFJYdxXT5oDBzCz1PFsPVrL7UHW4E7VLVEIiymCgoriIN/58PzXlZeGO1KlcNR5embeSLbbxmBITOfToo91iCkUhhBBCdJwU1l1FVCpMuoe+xZ9xmmED73eDxWJaUnn4EHs3rufVP/2WuuqqcMfpNBa7iT7D41n3WQFF591NzcqVVLz3XrhjCSGEEKITSGHdlZz8E4jN4AH7C3y0Pj/caTokbcgwLvrF/3F4315enzcHd11tuCN1CqUUp12RyYDsJNbmRVEy7jIOzrkfX3l5uKMJIYQQIsS6bGGtlDpbKbVNKbVDKXVi3AVmssJZf6K3bx9Zha+xr6Qm3Ik6pF/WWM67cxYHc7fz1oN/wF3bvb+f1jIYFNN+OJS0QTGsizwD/50PYIyODncsIYQQQoRYlyyslVJG4HHgHGAYMEMpNSy8qTrJ4HOo7XM6Pze9ymerN4c7TYcNOulUzv7xXdRVd88x4+1lMhs550ejGHpyKhmXTALAW1IS5lRCCCGECKUuWVgD44EdWutdWms3sBC4KMyZOodS2C94kAjlImnlQ+FOExTDJp3BtXMfxmJ34Kmro3D3znBH6hRWu4kpPxiKLcJM2ZJPeOemJyj+YnW4YwkhhBAiRLpqYZ0G7DvidX79thND4mA2pl3F9NoPKM5dEe40QWEwGgH4/H//YeFvZrHt62VhTtS5XGnDKEzI5o35B9iz5kC44wghhBAiBLpqYa2a2dZkzjKl1K1KqZVKqZXFxcWdFKvzxJzza0px4nv3PuhB07WdfPkMkgcM5J2//5lvXlt4wkxFlzw0hQuvjsdSV847/9zC6g/3nDDfuxBCCHGi6KqFdT7Q+4jX6UCTbj6t9dNa6xytdU5iYmKnhusM/dLTeMFxPSllq+DbZ8IdJ2gCy5/PZdjEKXz58gu8/9hf8brd4Y7VKVKnnsSZ2SUkFa3h6zd28tVrO8IdSQghhBBB1FUL6xVAplIqQyllAa4G3g5zpk6nsq/nY98Y9Pv3wqY3wx0naExmM2f/5G5Ou/p68tavOaEWkUm943ZyrKsZk36YIaekhjuOEEIIIYJIddWPo5VS5wJ/B4zAv7TWc491bE5Ojl65cmVnRes0O4oqOf/hxXyS9Hd6VW+Ba1+F/qeHO1ZQ1VVVYXM60X4/lYcPEZWYFO5IIae9XpTJFHiuNV+8nEva4Fj6Z/W8T16EEEKInkYptUprndPcvq7aY43W+j2t9SCt9YCWiuqebGBSJKcO6c2VFXfhi+0PC6+FgnXhjhVUNqcTgBWLXmf+rDvIW7sqzIlCr6GorvryS4peWMjBXeW8/9QGvnlzJ35/13yjK4QQQojj67KFtQj4+fRB5NdZea7fX8EeAy9cBod73nR1Q06dRHRiEq/Pu581H74T7jidovy11yn58584+2wrQ09NZdUHe3j3sXXUVp0YY86FEEKInkYK6y5uRFo0Zw9P4dEV1VRc/hL4ffDCpVBZGO5oQRWVkMTVv/8LGdk5fPKvp/j4X0/h9/nCHSukUn73W0yJiRTNvpfJl/Zh8rWDyd9eyhsPrUZLz7UQQgjR7Uhh3Q38fPogqtxentpkDIyzrioO9FzXlYc7WlBZ7A4uuudXjD3/EtYtfo/D+/cdv1E3ZoyOJu0vf8a9dy8HH3iA4RPTuPKX4zjlsoEog8Lv85O34ZBMyyeEEEJ0E1JYdwODUyI5f1Qv/vNVHodjRsBVz0PxFlhwDXjqwh0vqAwGI5N/cBPX/+VREvv0A2DP+rVovz+8wULEMW4c8bfeSvmrr1GzYgXxaU76jUwAIHdlEe8+vp43/rqawryKMCcVQgghxPFIYd1N3DUtkzqPj6eW7oSBU+Hip2DPF/D6zYHhIT1MQu++ABzcsZ1X5/6al3//f5QVHgxzqtBIvOMn9HroIew5TW8wzsxJ4vRrBlNWWMOr81by0XObqCzpWW+khBBCiJ5ECutuYkCik4vHpPHfr/dQVFEHo66As+fBlkXw7t09anXGIyUPyOSsH91FUd4u/jvrDtZ+9F6PGxqhzGaizz8PpRSegwfR9WPLDUYDIyalcd3vT2bs2X3ZtbaYD/65ocd9/0IIIURPIYV1N3Ln1Ey8fs0Tn9XPCjLhR3Da3bDqP/Dpn8KaLVSUUoyYPI2ZDz1Or8FD+fi5J1j08APhjhUS7vx8dp1/AcWPPtpku8VuYsLFA7j2/glMvnYISilctV42LduP39czh8gIIYQQ3ZEp3AFE6/WNj+CKsen8b/lebp3Un14xdpj6W6guhs//As4kGH9LuGOGRFRCIpf93+/Z8PGHGI5YXAUCxXdPYE5LI/Lsszj81D9RRhOJP72jyf7IOBuRcTYAti8/yOcLt7Pu432cfMkA+o1MQBl6xs9BCCGE6K6ksO5m7jhjIK+tzuexT3fwp0tGglJw/t+hpgTemwWOeBhxabhjhoRSilHTzm58vfGzxeQu/4ozb/0pzrj4MCYLDqUUqfffDz4/hx5/HLQm4ad3NPvGYcTpaUTEWPnqtR289+QGIuNsDJ/Ui+yz+vaYNxpCCCFEdyNDQbqZ9FgHV4/rw8sr9rGvpCaw0WiCy5+DPifD67fCjo/DG7KTaL+ffZs28J97fszmZZ/2iLHHymgkde4fib70Ug498QQV777X/HFK0T8rkRm/O4kzbx5OdJKdwt0VjUV1wc5yGSYihBBCdDLVE4qRnJwcvXLlynDH6DQHy+uY9OCnXDi6Fw9dMfq7HbVl8J/zoGgLTJoVeBh79ocSpQX7+eCJv3Ng+xb6jx3P5B/cRGxqWrhjdZj2+yl76SViLrsMZbG0qo3P58doNFBxqJbnf/01EdEWhp7ai6GnpBKVYA9xYiGEEOLEoJRapbXOaW6f9Fh3QynRNq47qS+vr85nV3HVdzvsMfDD92DUlbB0HvzrrB65/PmRYlPTuOr+eZx+3Y3s27SB8qKesSKlMhiInTEDZbHgPXyYkv8+f9weeaMx8J9zRKyVc24bSXx6JCvfz+P533zN24+spfRgdWdEF0IIIU5Y0mPdTRVXupj0l085c3gy/7h6zNEHbHwd3vk5+Nxw9gOQPTMwHrsHq6uqwuZ0AvDlyy+i/T7GXXgZVkdEmJN1zKGnnqL47/8g/pZbSLz7520aQ11ZUseWLw+wbflBLr8vB3ukhaI9FWgNSX0i5YZHIYQQoo1a6rHu2eMEerDESCvXn9KXpz/fxU+mDGRQcmTTA0ZcCn0mwJs/gkV3wvYP4YJHwJkYnsCdoKGo1lpTVXKIjZ8uZt2SD5hwyVWMPvNcTGZzmBO2T/ytt+I5eJDDzzwDaBLvvrvVxXVknI3xF/Rn3PkZjW2+fHUHB3LLsDnN9B4aR59hcfQeFkdEtDWE34UQQgjR80mPdTdWUu1m4p8/4fTBiTxx7djmD/L7YflTsGQO2KLgosdh0FmdmjNcCnftYNmC+exZv4aoxGTO/tGd9B4+Ktyx2kX7/Rz8wx8oW7CQuJtuJOmee9o9+0dtpZt9W0rYu6mEvVtKqK1wkzowmkvvCfwNFe2pIL6XE6NZRooJIcSJRvs12u/HgBe/20VtRS1+txuf243f48Hv8RIR4cNu8+KqdlOY78bv8eL3evF5ffi9PlKTaomKqKOy3M/OXTZ8Xo3f58PvBb/Pz5A+B4iNKKf4sI31u/vg9wXKFZ8P/H7Fyf2+It5WyJ7D6Xy772T8WuHXBnx+A35t4Py0fxJn2gdX/w/Sj1H/hJD0WPdQcREWbjwtg0c/2cGmA+UM7xV99EEGA5z8Y+g/GV6/Bf53JeTcCGf+ESzde4jE8ST3H8jlv/oDeevX8OXC/+KIjgHA43ZhMlu61bR0ymAg5be/RSkDVR9/QsLtt2OMjDx+w2bYIy0MGp/CoPEpaL/m0P4qvO7ADCLuWi+v/XkVBpMibXAsfYbF0WdYPNFJ9m718xJCiK5Ea43fp/G5vfg9bpTPjdWqweemtKASr8uDzxUoXH0eL3abl4R4N/g85G7x43EFClafx4ff6ycuupq+KaVor4cv1/TC76s/v0/j90Gf+P0MTsrF49a8t2EqPr/CX//w+Q2MiFvOyNhlVLvsvLR7Fj5txN/wwMSpkf8mK+JtyrzpLDj06FHfz+SoJxjuWEyZZyCLDj8IKMBc/4Dp0U8RZf+CctcIviz9wxEt/Rjx0uvQC8RGbKbWlUX+4eEYlB+D8mFUfgwGP/6aCjDUYFRebBY3BoPGaACDQWMwgKnPaHAOBVszdU+YSY91N1de4+G0v3zCSRnxPDuz2TdP3/G64JM/wFePQfwAuPRpSOv8d3rhoLVuLAwXPfwAddVVTLzmBlIGZIY5WdtorfFXVGCMjg4sfW4wBLXg9Xn87N1Swr5Nh9m7uYTy4loAJl87mOET06itdFOws5zEPpE4Y61SbAshugy/X+P3+gPFp8eD3QF4XVQdrqa2ojbQ4+py4/O4weeldx8v+Dzs2+WhotSHr6Gt14fZ6GZUZiH43KzbEk9Jua2+cAWfF5zWSiYO/Bq8bj7eOonD1XH4GnpU/UYSbfs4p9ez4HWxYO9vKPGkN8na17qS82PnAvDvoueo8cc12T/QtoyzYh4G4JnCF3Drph1hw+yLmRL9BABPF76IgYbC1IvB4GdYzHLGJn2OVzl4a/eNGA26vjgNfB2YnEdm6n7c2sHXO8ZgMILBqDAaFQajok+valKSXbi8VnL3xGMwGTCajRhMRowmIwkpBqJiTLh9Zg4fNmEwmzGYTRjNZgwWC44oCxaHDR8mfH4TBosFgznwwGAM0V9A52mpx1oK6x7gkY9zeXjxdt76yamM7h1z/Aa7P4c3fgRVB+H02XDaz3v8tHwNtNas+eAdvnltAbWVFfQdNYbscy4kI2ssytB9hj5on48Ds+7FlJhA0uzZIStwy4tr2bf5MGmDY4lNiWDHqiI+fGYjADanmcTeThL7RDJycjrOWFtIMgghup4jV771uH3UVbrxuV346uoCxaurjvgkhdngpryohqL9dfg9Xnzu+ofXx7BBFVgMtezbZ2LPXlugsPVpfF6NzwtnjFyBhVo27unDtoL++PwKn++74QDXDf4rBl8Nyw6cz4aySWi+K9iMuLk95SoAlpT9jG11U5rkt6lybkq+AYD3S+9jl2tCk/2RhiKuT7otsL/sPg66h2A0eOuLVx9xtiLO6vcKGC0s238u5e54jEZ/oFfVCLHOKsYO3AFGM+v2DMLltWEwKYwmAwajgehoP/36usFoJm9/FH5MgcK1vjh1RJqJSbSC0Ux5uTFQmFrMGC1WDBYLRqsVo8UGRnOPKFS7Gymse7jKOg8T//Ipo9NjmH/j+NY1qi2D9+6BDa9A+ng449eQManHzxzSwFVTw9oP32Hth+9QVVrCpOtuZNwF3WfFSq01RfPmUTL/v8Reey3Jv5yNMoX+zZHH7eNwfhXFeysp3ldJ8d5KSg5Uc+39E4hKsLP5iwNs/aaAhN6RxCTZiUoIPGKSHRhkBhIhOkRrjd+r8Xn9eD1+TBYDFpsJr8dHyb4KfK46fC4XXlcdfpeHhCRNVKSX6rI6dmyqCxS0Hi8+jw+vx8/gjFISoys4fEixYkNy/ThY8PrA51OcNnA5KRH57ClO5tMdU+t7ZI34/Cb8mLi0159JNW9mS/kEPin70VF5r4r/OQnmPDZUn8Pnlbcetf+6hB8RbTrImuqLWFF1FUY8GJUHo/JhNHi5NP1hbFYPmysmklsxBqPBj9FY3+tqhClDvsJoMbPrcAZFFYmBXtX64tVkNjBiSDmYrBSWOKl22TGaTBgtgcLVZDOT2MsKJis1dUb8WDDWF6wGixWj1Yoy28BoCQypFOIIUlifAJ78bCd//mArr/3oZMb2jTt+gwYbXoX374OaQ5A8MjAee8RlYDoxZojweb3kLv+S9GEjccbGsWvNCvLWrWbM2RcQm9Ir3PFapLWm6C8PUvLvf2MbPYq0v/wFS9++nZ7D5/VjMCqUUmxbfpCNS/cHxm27fIEDFNz+yGSMZgPrP91HUV4lUQk2ohLtRMUHCm9n7Inx9ya6L5/Pj8/jrx8uUF/Ymo2Nf7v7t5Xicfvwuz14XS58LjfRMZpe6QrtqWHFp1X4PJ764jYwVjY9tYZB/Urw1Lp479Pe+LyBm7d8PoXPpxiRvoXRqeuoqjbywqof4NNNZzY6Ne4lspzvUFoby/+KHzkqc8M42CLPAF45/FDjdoUPo3IzNfpRBtq+5qB7EJ+U/xSDwYfR4MNk8GE0+Bmf8jEpkYUUu/uyoXg8RmPgw02jEQwmxdA+B4iK0pTVRnOgJAGj2Rh4WEwYzSZSeiksDgu1His1dQ29rPVfrVYsDivKYg8UryYrGK0nzKenonuTwvoEUOP2MukvnzIoOZL/3TLh+A2O5KmDDS/D109A8RZwJsO4WwI3OUbEhyZwF/XtW6/y5Usv4Pf76D8mhzHnXEjfkVldeixxxXvvUTDnfowxMQx4791O6bk+Hq01tZUeKg7VUl3mYkB2EgBfv7mT7csPUlXmgvp/euxRFm78y2kAfPX6DsoKa7BHWXBEWrBHWohKsNFvZAIA7jovZotR5t8+QRx5b4SrxoO7rn4cbH1xq5QisU/gJt4DuWVUl7kae3N9Xj9Wh4kh4xLAU8u6T/IpL6rB5/HgrS9uo6N8nHxyLXhq+fADO6VlxsAwBJ/C54Ne8SWcNXo5eOv4z2cXUO1uOs51YNRazkp5Drx1PLP7H7i1o8n+ofYlnBH9OABPHHwVhR+j8mLEjVF5GO5YzDjny3i1mbdK7q/vqfVhNPoxGfwMiNnEgLgduA2RrDw4OVDUmhSm+l7ZlIRKEhM8eLCTfzgRk9mE0WLGYDFhslhwxpiwOW34lAWP34bRZsVotWGw2MDU8LDWF7XdczpSIcJBCusTxLPLdvHHd7ew4JYJnDygHQWx1rDzE/jmCdixJPCP7ugZMOHHkDgo+IG7qOqyUtYtfo91i9+npryM/tnjuOS+34U7Vos8Bw/iOXAAR3Y22uvFV1mJKTY23LGOyefxU1lSR8WhWjwuX2Ph/fnC7RzILaWm0kNdpTuwkE3fSK745TgAXpr7LSX7q7FFmrE7LVjsRlIHxHDyJQMAWP3hHvw+P2abCavdhMVmIjLBRmLvQPFVVeoKfFRsNmA0GzAYVJd+0xRqR46T9Xp8uGt99bML+AM3ann9xCQ7MJoMVByupbywFp/PHxiO4PPj9/oZMDYJk9nIgR1lHNxZ/l3h6w3cSHbq5QMxGA1s+aqAPRsOHVEY+9B+P5fe3hs8NXz5bjG5G2rrC9vAGFuLRXPTNbvBU8v7n6axK7/p33SktZLrc+aDp5a3t1zKvsoBTfbHmfYyI+FOAN4qmUOxpz9G5cGk3BjxkmjewfSYfwDwWfltVPvj6vd5MBp8xFsLGBX3BZhtbCifglfZ64cZKIwmI1HOOlITqsBso6A8BWU2Y7JYMFrNGC0WLA4zNmeggPUbbRgs9sC/q+YjCluzvb64tclYWSG6CSmsQ0Brzd9W/42z+p7F8IThnXrtY6nz+Dj9wU9JcFp57UenYDN34B/poi2BAnvdS+BzwcDpcPJPAtP2nSCFiNfjYfvXyzCazQw+eSJet5svX36BzPGnkJo5uMsWZIeeeoqSF16k15/m4pw0Kdxx2s3v17iqPXg9fiLjAjdGbv7yAOXFtdRWuqmt9OCp85LQO5LTrgjM7vLf//uKypK6JucZmJPEWTePAOCZu5birvM17lMKhk1MY/I1g9Fa88Jvv8FoDBTeJrMRo0kxMCeZEZPS8Lp9fPjspkAxblAYDKCMigFZSfQfk4ir1ss3b+7k+38V/cckkj4kjpoKNyvfyzvq+8wcl0zqgGgqDtWy6oM9aK3Run4uWa0ZeXo6Kf2jOby/ihXv7kb7Az8b7df4/ZrxF2SQkhHNgdxSvnxtZ2C7TzceM+2Hw0juF8WOVUUs/d+274pmX2D/lb8aR2LvSDZ8ls/nC7cfle/aX/QmJtrL6s9K+fqTuqP2//CyzThMlSxfm8TKbRmBnyt+jAYfBuXjhlH/wOyrYGXBKWwvH4NRuwM9tngwKjcXxP4epWBzzVQOeoYcMc7Wi1nVkON8DYC9rtFU6dT6XtvADAUWsyYt9iCY7ZR7k/AZIzBaLJgsJoxWG0arGbPdCiZ7oJg1O74rZs325rc3fJUiVwhxDDKPdQgcrjvM+7vf5/nNz/Pz7J/zg2E/CHuhZTMb+cNFI7j1+VX8+s2NPHj5qPZnShoKFz4KZ/wWVv4LVjwDz18MScO/G4dttgc1f1djMpsZNumMxtcHtm9lzQeLWLnodaISkxh8yiSGnDKJxL4ZYf/dH8k5eTIV777LvltvI/aaa0iadQ8Ge/f7XRkMCnukpcm2Yae2PO79+j+dgs/rx1Pnw13nxV3nxXTEG8zTrhyE1x24cathvGxDb7bWkNI/KrDd891wAu0PdD74/ZrKkrr6xRO+K1yT+kQB4HX52LGy6KhMsakRpA+Jw13rZfuKg0ftT+obSeqAaNx1XvI2HEIphVKBXmRlgIFjk4HAjaMlBTUoFZgWy6AUyuDHX3kYDh/CUFaOzVCNweBFmX0YtA+FF/PmhZBXSmShgczEeAzag0G7MGg3Br8Lx1t/A1VEr8poJsX3xuCrxeCvxaC8GJUXx/OrwVBHpi+elLgkjHgx1Be+RrzYvigC5WesKZrsfg6MFktgqIHZHihYzRFgSSQnpZIc84rAa3NDQRsB5r+D2cEws51hZsd3RW/j8z+D2U6f4xS7XW82WyHEiUh6rDug3FXOb778DZ/u+5RJ6ZP446l/JNYW/o/fH168nUc+zuX+C4cz85R+wTmppw42vhoYh120KdCr0/cUGHAGDJgaKMS7UHEZKq6aGnau/IatXy5lz4a1+H0+rn/wMRL79MPr8XSZZdP9LhfFD/+NkvnzsfTvT9pfH8I2dGi4Y52Y/H7w1IC7GtxVTZ+7q5t/eFqx3Xt073GLjFawNBS6jkDhaomo//q97Y3bHE33m+3fex7xXW+vzJwghDhByFCQENJas2DrAh5a+RCx1ljmTZrHuJRxYcnSwO/X3Pr8Sj7bVsyLN5/ESf2DeAOi1pC3DLa+FxiPfWhbYLszJVBkD5waGC4SkRC8a3ZRNRXl5K1bzdDTJqOU4oMn/07R7p31PdkTiU5KCXdEqr/6ioJf/4a0v/8N+6juuZx7p2osgqu+K35dVd8rhKuaFsWuqmaK5KqmxXCrqUCx2ljwOutfNxTBRzy3OL8rept9fsR5zA6ZbUEIIYJECutOsLVkK7OWzmJv5V5uG3Ubt426DWMYx+hV1Hm4+PEvKa/xsOinp9ErJkRDAcr2wa5PA0X2zk+hrgxQkDq6vjf7DOh9EpgsxztTt7fhk4/Y8OlHFGzfCkBq5mCyzjyvyXCScNBuN8oS+PmXvPAizsmTsaSnhTVT0Ph9RxS/R3xt9nk1uCu/Vyg3UzjTyn8TDab6wtcJVud3Raw18ruitrEwbu7199o0DH04AT75EUKI7kwK605S46lh7vK5vL3zbcYmj2XexHmkRISv13JHURUXP/4lGQkRvHL7yR27mbE1/D44sLa+yP4E8r8FvzfQy9b3ZEgaBolD6h+DAsVED1ReVMi2r5ex9avPSRs8jKk33o7f7+PT/zxN+tCR9Bk5Gruz879376FD7Dz7HAASfvQjYmdcjcHhOE6rIGsohBt7eiub9vi6Kr9XKFceozCu3+atbf21G4pgS0R9IXyM1w1FsiXye/uOLIidJ8SbRSGEEEeTwrqTvb3zbf74zR+xGq388dQ/cnrv08OWZfHmQm7570ouzU7jr1eM7tyb7OoqAsNGdn4Ce76Gw7ngc3+3PyodEgfXF9pHfLXHdF7GEPN5PRhNZkoPHuDFX/4cV001KEXKgEz6jRrDsNOndupCNO78fA7+bg7VX36JMS6O+Bt/SOw11xy7wPZ5vlcIH6cH2FX5vSEUlU3bempamVR9r8h11vcEH2ObJaKZ/ZFH9CRHyBhgIYQQQSGFdRjklecx6/NZbC3ZynVDr+PnY3+OxRieHq6/L9nO35fk8rsLhvHDUzPCkgEAnxdK86B4a/1jW+DrodymPY/OlECBHZ0OEYngTIKIJHAm1n9NAntctyuU/D4fB3duJ2/dGvasX0NB7jYu/b/76TdqDMV788jfspF+o8YQk9Kr7W+AfN4jbmyraeZ5zRE3ztVQs20Ph97bSG1eGQPuHIbJVNfMGOGqpm+EWqS+1/t7ZGHrbKZH+PuFsvOIIRT1Y4S72e9XCCHEd7TW+H1evG4PXrcLs82GxWbH46qjKG83Po8br9uN1+3C6/GQmjmY2JReVBwqZtNnS/C4XXjcLlyuGtyuOjKmTMSelkTBxo1se/sDnKOHMP3SH5IY0fmTRkhhHSZun5uHVz3Mi1teZFj8MB6c9CB9ovp0eg6/X3PbC6v4ZGsRz980nlMGdLEbC/1+KN/7XaFdvC3wqCyAqiLwe45uo4yBGySPLLgdcd+NU7U0TOnV8PUY2wxGUIYWHscocLUODGvwe0HXf/X7Ao/G1/XbvK7AXOBed2AmB58bvC7qqiswKx9G7WX515v44ovA2OwIh5nUBDup8WbGDLBi1nXgqW368DY8rwl8bXUBXK/+5jaPx4E5NhJtjmDf6yXY+0YTN6k/xqjoI3qF6wtdq7OZ4RH1+012KYSFEKKLCsyP78dQf+9XeVFhoKB1u/G4XfjcHiJiYkjo0w+/38f6JR82KXw9bjfpQ0cwMOckaqoref/xh3G7ahuLX6/bTfIpY7GPHUrllu3sffFdtM/XJMOh8b05OCiG4Z9uRe0/+v/rK0Z6ye1dx52vVFIckQpaY9Aag9+Px6RZklNCQUItTz1iYmuvePYnVpFx1zyuzZrSKT/DI0lhHWaf7P2E33z5G7x+L/930v9x4YALO33e48r6mxlLazy8fceppMd28tja9tI6cENkVTFUFwUK7eri+q9FR2wvhtrSQO+s9gc3Q2ORbQycW/uCfw2gzG0jrzqWA7VRFNRGU+01c8f4fRgsDr7dH0WZy0xqnJnURDvxcRGohunQTLb6Ivf7U6gdObvE96ZT+96Ntb6qKg7MupeqTz/FEBlJ3PXXE3f9DzBGy+zAQggRbFprfF5vY3HrdblAKWKSA/dl7d+2hZry0vrCNlDcOqJjGHzyRAC+eW0hFYeLv+vxdbtJ6jeAnMuvoM5bx9t/mEPV4cN4PW78bjc+j5fIUYNwXnQanr35HHp+Mfia/n/s0IAotmdHMnLFPiLzDEeGBaXZMKCW1YNK+elbHtz0wujXmPwak09Ta/Xz7km17Emt4aGnfVRb4jBojdHvx6A1B+Lgv1P9lEQYePyfVRi0BbTGZwCfATb0iWTB6X3BbOUXb2zFoBU+swmfxYLPYiE/ozc7skdiN9kYv2w1Bpud6syBnHnBpfSP7/zOQimsu4CD1Qe57/P7WF20mpEJI7l77N3kpDT7OwmZncVVXPzYl/RNcPDq7R1cmbGr0jrQe1s/5CHQq1v9Xe/u97dp/zEeuunrhp5oZQjMBqGMga+G77821veCN7w2BW5yM1rrly22fu+5pX5p44bnVjAG5sJ219ZgsQfeAH3yn3+y+fNPcFUHpm6z2O0MyJnAuXf8Aggsw+6IikZ1sNe4dtMmDj35JFVLPsbgdNLn3//CPnJkh84phBDdQUM9pJSitqqS2oqK+qI1ULj6PB4yxgT+v71n/VqK9+4+ovCtw2A0MenaHwKBwnff5g2NvbletxtHVBQX/noOtd5a3v/zAxRu2drk+pbkeGJvPZ+6gkIqX/0cf1nTe1Iq4y2snhJF5qZC0rb6QRtQWqN0oHhdO8DFimGlXPuJj/TCeNAKs09j9mm8Rs1/p3nZk1rDL1730bswAtAY/RqDX1Np1/z+ikgqbWZ+9XoJ/Q968RkVHoPCazZwIC6Cf16YjcVg45Ivc0moqMNvseK32tBWGzWJiew75WQcZjv9t+7ArhW2iEiszigczijs8Uk4MvphNxuxuOuwR9iwO2yYjd3zk04prLsIn9/Hol2LeHTNoxTVFDG592R+nv1z+sf077QMH28p5Ob/ruTirDQevrKTb2YUHaL9fkoPHqAgdxsFuduwOhxMvOYGAJ7+8Q+prawgLi2dhN59iU/vQ/rQEfQaNKRd16rbsoXS//2P5N/8BoPFQtWyLzDGxWIbNkz+ZoQQnaZJz67LhT0qCqPJTFVpCSX78/F6Ats9rkABO+TUSVgdEezbvIGdK5d/N9zBFSiQz/3pPVgdEax+fxFrP3znu8K3fv8d81/BY/CxdP6zbF2ypGkYg2LA72+murSE0teX4dp9xEqrBnDbjaw4L57EvYcZsqoKPEaU9gd6bf1+NvX1sSyrjDPW+pm81oFfmbB6Ar2+Rr+fJ8+F3D51XPG5j7NXmtBK1ff6BoZD3HpLElU2G9cuK+esDeW4zEZcJiNusxG32cxfZ0zEbLRz2sZ9DCgowW+xoW12lN0Bjkj2nzENp9VBcn4B0W4P9sho7M4obJER2KOc2NN64bCYsJkM2MxGDAb5t/5YpLDuYuq8dbyw5QWe2/AcNd4aLs28lB+P/jGJjsROuf6jH+fy18Xb+fV5Q7l5YucV9SI0tNZs/Gwxh/ft4dC+vRzet4eq0hKyzjqfqTfejs/r5eXf/x9xvdKIT+9DQnofYlLTiEpIxGBs3acWO887H/fOnVj69iXqvHOJOvdcrAMHhvg7E0J0VVrrwNjb+oK0obCNjE/AERVNTUU5ezesbVK4elwuBp8ykfi03hTl7WLF2681OYfX7WbaTT8mZeAgtn/zBR88+Q+8Lhf6iKF31/7pb6QMyGTN4nf55Nknj8o14t6b0DEWChd/zeFlG8CowKDQaDwWAzvPTEXXVDL0i4OoGlB+HwafH6PPx45e8FlWKQMLNNcuMaGVGatHY/P4sXo0/5kGy4d6mbDVz0/f1oDCqP0oDQr4v6vj2ZbiZPKmWm79pBiX2VBf/Jpwm008fe44KuLjGbX3MDnb8ut7e+1gc2CwR3DgtMk4ouNJLKsgrrwchzMaa6QTa2REoPhNSSbCZg4Uv2aDdHKEkRTWXVRpXSlPr3+ahdsWYjaYmTl8JjcMv4EIc0RIr+v3a3704iqWbCni+RvHc8rALnYzo+iwuqoqfF4PETGx1FSU8+4//syhfXupKS9rPGbiNTcw/qLLqS4r5cuXnic6KYXo5BSik5KJTkrBHhnV+A+3t7SUysWLqXjvfWqWLwetiZs5k+Rfzg7TdyiEaI7WGqUUWmsqigsbC16vK3CTWVRCEvHpvfG4XWz4+MOmhbHLRf+x4+g/ZhzVZaW89+hDTQtjt4tTrriGkVPOpChvF8/f97Ojrn/2j3/O8NOnsn/bFhb+dtZR+8fd+kOiRw2kYOtWche8DUYDygBaaXwmA1Wn9aEyQpP0eS66uBZ8XpTXi8Hr5UA8fDXCjfLUcPsiLwZ/oMfX7tbY3X7ey1F8mKPpVaL52zO+o6791JkOloyIon+B5i8LCvEDLrOB2vqe3/9OHMKm/un0P+ziom+34bNa8VltaKsD7A7ycybg79WHxBoXqfsPYIuMxhYZidUZgS0yEntaChHOCBxWIw6LEZtJen17Kimsu7h9Ffv4x5p/8GHeh8Tb4vlx1o+5JPMSzAZzyK5Z5fJyyeNfcqjKxdt3nEbvuG5yM6PokJqKcg7n76WssIDUAYNI6NOPwl07eH3enCZFN8B5P5vFkFNP53D+XtYteR9nbDzO2DisKNTGzcSPHkP0qafg3rOH/bPuJercc4g65xzMycnh+eaE6KL8Pl9j8QoQEROYHuzgju3UVVcdMfOCC2dsPBlZY4HAON2ayvImPb69Bg9l3AWXAvD87Dtx1bdvKJBHnnEm027+CX6fj79dc9FRWXIuuJTTr7sRV00Nj/3wysbtJqsVs8XK+IuvYNhZZ3H4cAEfP/oPMBnBbEAbFT4jWEf2wd8nGt/2PGo37MHrcePzuNAeNxU22DTERJnFxTkfHsRWrbG4vdjcPmxuP5v6Kl6baACt+c/DPhzfm8zovRzFv6faMbvM/O/v5U32eQyKV8f24qXs/jhdBua8tw6X2YzHYsVjteGz2skdNpzCwSOI8xkYsWkTZkcklsgYbJFRWJ1OzH37YE9MIMKksCs/EVEROG1m7GYjRimARRtIYd1NrC9ez19X/pXVRavpF9WPu8bexRm9zwjZxz27D1Vz4WNf0DvWwYJbJhDtCF0hL7o+T10d5UUHKS8upLzwIANyTiI6KYUdK77hvcf+iqeu6SqHV//+QdIGD2XTS/9jxasLMFdUYvP4cCanEDt6NCNvvg17QiLu2hr8fj9WR4R8dCnCrqFHFwKf7Lhqqhrn2fW63WjtJ33oCAD2bFhL2cEDTWZmMNvsjL/ocgC+fm0Bhbt2NrnBLToxmQvu/iUAL90/mwPbtuL3eRuvnzZkOFff/2cA/v3z2yk5kN8kX0bWWC795f0APPuzm6mrrMRktWKyWDBbrPTPHtd4b8X7j/0VDRhMJrTZgDYpIvqkEjksg2pPNfu/WYHX48bjrcXrrsOr3RzuE0GF1UPs6jwsBeVQV4uqdWF2uSm1+3nlFD+g+ckiH32LNHY32F1g88DWdMUfZwSGjz3ypJeUsqY/2xUZDh44vx/ab+WxF7Zg9fqpM5moNZtxmy1s6NubT3PGYTfbufir5RjNNrBHYoqIxBQRjb9PJipzCBEWI7HF+7BFRWGPjsQRHYnTacNpNeGwmLCYuucNb6LnkMK6G9Fa89m+z/jb6r+xu3w3Y5LGcNOImzgt7TSMhuDP4vHptiJu/e9KesXYeeq6sQxNjQr6NUTP4K6rpbq0hKrSEqpLS+g3eiw2p5Odq5az6p03qSwupLqkBE99EXHjXx4jtm8/ls77AyvXLEcpA7ZIZ/3NMlFcOvt3WB0R5K1bTVHeLqyOCCwOB1a7A4vNTtrQ4Sil8NTVoYxGTGZ549cTHDnzgsdVFxi25PHU93x68Ho8JGcMwGSxUHIgn6LdO/F6PPVz6gaOyz7nAsxWGztWLmf36kDx+N0xbi6573cYTSa+ef0lNi1d8l1h7HGDX3PnC68D8P7jD7P580+a5LNFOPnJvxYC8PbDfyJ3+VdN9sempnHj3/8JwIdP/YPCnbmYLIHC12S1EpvSiyk33ArA6vffprqsNLCv/pjI+EQG5pyEx+dh95Z1VLur8Bh8uJSXOuXGbfRRa/biOlyM51AxnsoKvFWV+Kqq8NbW8O1oJ1WeKgavKqLf7mosdV6sbj92N3gN8KerA/+fuOtNHxO2aI4sQQujFXfcGoX22/jty2WM2hfoQfcDtRYjOxMi+c2549F+K7d9vZmkahceix2v1Y7PFkF5Yi925kwmyhrBoLwdRJiNmJ0RWKMisUY5scfF4EiIx2kz4bQGHhFWE1aTjAcWPYsU1t2Q1+/ljR1v8NTapyiqLSIlIoXLMi/j0sxLSXIkBfVaq/aU8KMXVlNZ52XeZSO5KCstqOcXJxZXTTXl27aSMCoLg9HI+jt/yr5VK/CYjHisFnxxsfiio7j6sWcxmkx88u9/suaDRU3OoQwGfv6/t1BK8eFT/2Djp4sxmkxY7A4sdjsRsfHM+P1fAFjx9msU7t7ZpHiJiIlt/Lh895qV1FZWYLJYMJrNGI0mrE4nqQMHA1ByYD9+rweDyYzRZMJgMmK22LA5nUBgWXpQKINCqfAXCFpr0BplMKD9ftx1tfj9frTPh9/vx+/zYXVEYHU48Ho8lB08gN/nq3948Xt9xKSk4oyLp666in2b1gf2eb346o/pPXxUYAW04iI2f/4JPp8Xn9eL3+vB5/Uyato5JPbpx8GduXz71iv4vF58Hg8+rwefx8PUm35McsYAcld8zSf//mdgn8eD3+vF6/Vw3QN/JzljAGs/eo+Pn3viqO/xxr//k9jUNFa8/Rqfv/jvo/bf9tR/ccbGsfzNV1j93luB37vZjNFiwWQ2c8Vv5mK22tj42RL2rF9T/7u31Pf6WjjlyutQSpG/eSNlRQcxmc2N5zDb7KQNGQZATXkZfp+v8e/KYDLh9rup8lRR5a6i2lMdeF5XTm15CbUVpZQ7FZW6FvYfxLarAH9VNbq6GmpqUdV1vH6amVJjLSevq+OMdX5sbhp7he1uuOkuI26zYuZiH+etbPr/Zz9wxU8z8Ws7t3x2kFN3lFBrNlFjtlBrNlNutfPnydOxm+ycsTuP3pXVaLsTgyMaQ0QMhpg4qkeOJdJmIra2AofVRERMJBFRTiLtFiKsRpw2E5FWs9wcJ0QLWiqsTZ0dRrSOyWDiikFXcPHAi/ls32e8su0VHl/7OE+te4opvadwxaArmNBrAgbV8Y/ExvaN452fncZPXlzNnQvXsj6/nNnnDOm280uK8LI6IkgaM7bx9ah/PMrQwiJq16ymZtVqaletwugxYTQF/vkZvGUng0achGHgAEhNQcfH4zeoxv+pD5pwGjHJqbhra3DV1uKurWlcPQygsuQQRbt3HjFfrIvIuPjGwnrFotfZt2l9k4yJfTO4/i+PAvD+Yw9xcGduk/29Bg9rLNz/O+unTT+uV4r+Y3K45L7fAfCvn99OTVkpSqnAPOJKMTDnJM68LXBj13N33oK7tukwmqGnnc7k628B4KnbfoDf52tcGU37NVlnnsvEa27AXVfLEzfNCEyrrv2NRfWEy67m1Cuvo6ainKdu+8FRv4NJ1/6QcRdeRuWhIubf85Oj9k+7+ceMnn4u5YUHefuvfzpq/zl3/KKxsP7y5RcAAm9KTCYMJjP9s8eT2KcfHlcdJfvzMZhMmExmjGYzFruj8XfnjImj78gsTGYzBpMJo8mMyWzGUb/wUO9hI5h+6x2YzPVveswWTCYTzth4AIZPnkb/seMb95vqi2Nj/acXJ118BSddfMVR+RuMmDyNEZOnAeDxe6h2V1PpqWRb6TYq68qp5hC11sPUVZbiLijHVVnOwTQ7h4s8mPOL6L0yH1Vdi6HWhanWg6nOw/NTFAfiFSdv8fPDxX7sLoj6bqQHP7/FyIF4E+esNHDDkqa/d5dRsaBvNtUR0fjLi/G7D3DIbKXWbqXGZMNltmM9OBVnRCw7BpTyemolJmckJqcTc1Qklignv+iVRqTdTOTpJrw2M3E2E31sJiLrC+IbpSAWIqzC0mOtlHoQuABwAzuBH2qty+r3/RK4CfABP9Naf3i88/XEHuvm7K3Yy6vbX+XNHW9S6iol3ZnOFYMDxXecLa7D5/f4/Mx9dwv/+SqPkzLieOyabBIjrUFILkRT2udDGY1on499t9xCzeo16Lq6xv2x115Lym9+jdaa0v/9D0vfflj7Z2BKSWnVIjhHjqOtqSjHVVON1+UK9Lr6vBhNZpL7B6YL3LtxPbWVFYHe2PqeW3t0NJnjTgZg3eL3qa2sQPv99YWvn5iUXgw/fSoAX73yIq7q6kCvsT8wLVhSxgBGTT0LgE/nP4PP0/QurV6DhjJs0hkALHn2CVCqvjBXKBTpw0aQOf4UfF4PX778YmCfMqAMClD0HjaSPiNG4XHVsW7x+xiMRpTBgMFgQBmMpA4cRGLfDNx1texesxKDwYjBZMRgNGEwGolLSycyLgGPq47SggMYjIF9xvpjbE4nZqut/nsO9I6Hs1hz+VxUuCqoqiql2lNFpXJRVVWCb/M23JXluCsr8FYHhkvsHuhkT7LCVHCYie/tayyIra7AcIn/TjWwKtPAiDw/v11w9AqqD10ewbqB0YzJ9XH3a4W4jYpai7G+Z9jEY6dlsSM2hUGFlUzNzaPWZKPG5KDWFIHPFsm2/uMwxiSQ4q8l2VeNJSoSa1QkjphInBF2Im0momxmomwmIv+/vTuPk6O87zz++XV3VZ9zzwiEDiSBEFhgbjDhkLDBJy+zfiVcsZ21HR84yfpKdm2vE0JwXhtnHTu7ho1JHLPExthgEGcg5kYYLyAQIAkssE4kgZhDmqPv6u7f/lHVPT2j0WiQWppDv/frVa96qp6arqfnUc1859HTVTHHD8RB2eYOGzM9TLmpICLyfuAxVS2JyN8DqOrXReRdwM+Bs4CjgEeA41R1z/vm1DlcgnVVsVzkka2PcPvrt/PC2y8QCUW4eP7FXLbkMs444owD/gV414vb+eaKtbTGXf7pE6dx2vy2BrXcmLGp51Hcto3ipk0UNm0muvhYmi68EK+7mw0XLKsdJ/E47sIFdH72szR/+MOU0xmyzz1LpKvLXzo6EJuLPWWU02m89CDpgV7Sg71kB/vIJh0GZjcxlO8nfvcTlDNpypk0mslBLsfrx6d4/qQ45cEBrv7hNtxChVhRiRUhUoGfLQ9xzzkhZu1Wbrhxz18Nt76/jUdPP4Ije+BLd2wOArFDJuyQi0T59yXH82rXXNqHSpy79Q2y4SS5cJJcuAlJNjN0xDwiLS20RIWWaIRUMk5zPAjDcadWbok7NMWGtxNu2EaKjTlMTLlgPaIBIh8D/kBVPx6MVqOqfxfU/Qq4VlX/33ivcbgF63qb+jfxy9d/yT0b72GoOMTCloVcesylLJ+3nEUti/b7B/0rbw5w9S0vsHMgz7UfXcofnjXffmmYQ05VKff1Udi0ieKmzRQ3b6KweTNtl19O00UXkVu7li2XDd8yDBHCbW3M/vZ1NL3vfRTfeIP+O1cQ6ewk0tVFuLWVUCpFdOECQsnkiA/SHY7K6Qyay1IpFNB8nkq+gDgOsSXHATD06KPku3eSS/eTTw9QTA+SP6KF3otPZbA4yBHfu41w3wDk8oTyRUIFjw1Lmrjjo+0MeUN872/eJFoaec6HTxF+9KEwospt3/GDsReGvCsUomF+fXo7j/zePJyCyyfv3kA+4pKJuGRCUQZDUV7ums9r7fOJeGGWdveQDaXIhprIRfwP2TlNKZqSUVrizoiluW5dDcYtQVBuiTukopHD9t+BMeadmerB+j7gNlW9RURuAJ5R1VuCuh8DD6rqHeO9xuEcrKtypRwPbXmI21+/nTU9/nzSOak5LJu7jGVzl3HGkWfght139Jr92SJf/sVLPPl6D5efMZfrLj2RmNP4O5MYs78quRyFDRso9fRQ6u7x1z09tF5xOfGlS0mvXMm2L/4JlEeObM6/+WaS7zmbwQceYMfXv0E4mSSUSgVLktnXfZvoooVkn3+ewQf/A3Gc4cV1aLvySsKtreTXryf/yisj6gmFSJ53HiHX9f8g2Lp1+MTVucfnn4+EwxQ2bKC4fTuUy2ipDOUSWlFaLvkIAJlnnqWwcUOtXsslxHHo+NSnAOi/4w5y69ahRQ8tFtFikXBLC7O/fR0AO7/9t2ReeJ5yPkc5n0PzBUpzutj+3T/x/xD/8xtp2rhzxPdm24Ik139hNoPFQf7y+h7m9g7/jiiFYPWxwj/8vv9z4Bu3l0kUoORGKEUdPNdh29EtrDpzPlTinPXMTkq4ZENR0pJgkCjbI21siXahlRjJnJIPNVEKDU85CwnDgTjh0hp3aE342611AXn4mGqdax+4M8YcEpPy4UUReQQ4coyqb6nqPcEx3wJKwM+qXzbG8WMmfxH5PPB5gPnz5x9we6e7eCTOpcdeyqXHXsrOzE6e2vEUK7etZMXvVnDr+luJR+KcM/scls1bxvlzzp/Q49NbEy43fepM/vcjr/ODxzawfucQP/zE6cxpjR+Cd2TMvoXiceInnbTX+tQFF3D82jWUd++m1NNDeXCQSjpNNBiRdRcupOPTn6aSTlPJpCmnM1TSacTxfzQWt25l8P77Uc+j4nngeQA0X3IJ4dZW0k+upOcf/3GP8y7+zdOE2tsZuPde+m785z3ql7z8EhIOs/u229n905+OrAyHa8F64N57GVixYkR1JRln1bIj/RHjR26nZfVGyuEQpYg/8tvfEuGf73qRoeIQH9jSx9xSGS8GxRQUHOhpSXPPU18H4JwTK7QsdgjH4oRicULROPnmJtoiR9AVSXD/ZyuUNUYulCRDklw5TjbvkuqJMJSN8K13O6AOe/zofgEiIWF7h0NrXTjuirscl6gLygmX1oQfiqshuSkasafVGWOmrUkbsRaR/wxcDbxPVbPBPpsK0mD5Up7ndj7Hyu0reXL7k+zM+KNTSzuWsmzuMi6YewEndJywz7uLPPzq23zttpdwIiGuv+pUzrXHoJvDkKr64ToSQUIhP4gP9KOeN7yoEjvuOMRx8N5+G6/7bbKlHNlimoyXJeNlGFjQwWApTX77Noq93aQ1T7qcZaiSJV3KsrXVY7AwSGloEK+YoyJQDvlLJQTl8HDwjIQiNLvNNLvNNLlNNDlNxCNJXEkRJgGVGFqJUy5F8bwo+UKUXN4hnXUYyjrszir92SJeee+/C1riDm0JPwhX160Jh7a67bZgX0vcoS3pkrQ5x8aYGWrKTQURkQ8C3weWqWpP3f6lwK0Mf3jxUWCxfXixMVSV13e/XgvZa3rWoCgdsQ5O7jqZk7pO4qTOk1jasZSUm9rj6zf1pPnCT19gY0+az52/iM9fsIiOlN01xMxsqkqhXGCoOMRgcbC2rpULg3vU1R+TLqbRsf/jDQBB/EDsNo0IyM3R5iAkp4iQQMtxKuUYpVKUYjFKPu+SzjsMZoTdWY/d2SK7MkV2jxOSwyGpBeH2IAi3J11/Ozm8vy05HJZb4o497tkYY+pMxWC9AYgCfcGuZ1T16qDuW8Bn8KeIfEVVH9zX61mw3j+78rt4esfTPP3m06ztWcsbQ28A/i/6RS2LOLHzRN7d9W5O7DyRxW2LcUIOmUKJa+55hRUvbicWCfOJ98zncxcsYlZTbJLfjTF7V6qUGCoO1ZaB4kCtXA3C1ZA86O0ZmL2KN+7rxyPxPYJxdbt+3eQ24UrKHz0uxSjkXbL5CLuyJXZni/Sli+zKFNiV9fx1ukimOPa4ggi1EeP2pB+C25MubclqOB4Oyx1BeG6O2Qf0jDHmQE25YN1oFqwboz/fzyt9r7Cmdw3retextmctuwu7AYiGo5zQfkItbMcqc7h7VYH7Xu7GCYe46qz5fGHZIma32Pxr03hexSNd9J92Vw2+6WJ6j9Hj+qV+X7aUHff1IxIZMUpcLaecFM3R5lpgHh2UU06KCEkGc0pvplALxr1pf/R4V6ZIX6ZIX7pQKxdLe947GSAaCdGRdGlP+SG5I+nSnozSnnSCtVtbOpIuzTaSbIwxk8KCtdkvqsqO9A4/ZPeuZW3vWl7te5VCuQBASEJ0xY6kXOxgZ28T6nVx3oITuPqcszn1qIWEQ3YHEQMVrZD1sqQ9PwjX1kEwHvL8ctpL16ZO1AKy569zpdy45xg9nWKs8t5GkJvdZuKReG0kt1iq0BeE5N60v65uV0Oyv/brC3sJynEnTEeqGpBdOlLRWrk96dKZGg7LHSmXuGNzko0xZjqwYG0axqt4bOzfyMb+jWwd3MqWwS1sHdzK5oEt5OpGBUM4zE3NY3H7Qo5uPpp5TfOYlZhFZ7yTrngX7bF2C95TnKqSK+XIeJnakvbStRCc9tL+vr1tF9O10DzeHGMYHjGuLik3NWJUuL6uOqJcH5ATTmKvH8BVVQbzJfrS/khyX7pAb6ZI71BhjwDdmy4wmC+N+TpuOOQH5ZRLRzJKR2o4HHfUBeVqfdy1f9/GGDMTWbA2B52q0pfv44U3X+PW1atZtf011OmhpbmfAt2UdGRYCUmI9lg7XfEuuhJddMW7aqG7M+GvW6IttVD1Tu/BfbipfsAuV8qRLWXJecG6lCPrZWv7q+VMKUM2uENFxqsrl0Zu7ysQgz9NKOkkaXKbSDpJUk6qtp1yUqTcVC0MV8spNzWiHAvH3tFobbmi7MqMDMS9QXD299WXixTLY48qtyUcOlPRIDBH6QwCckd1X23btQeIGGOMASxYm0nQPZTnRys3ccszb5AvFVl+QozTjwkzr8sjV+mnJ9dDb66XnmywzvXQl+vba5CLhqMjRi5rgS0YwUy5fphzQy5u2F+i4Wit7Ib8bSfs+PtDLk7YISxhBEFECEkIQWojnyEJ1fZV6ytaoaIVyloeXldGbdevK2W8ikexXKRYKdbKXtnzy5Wiv13dX/EolAvkS3nypfxwuTyqXCqQL+dr27lSjoqOHR7H4oQckk5yxJJwEiQjo7aDoJxwEjQ5QXAOvtfVbSfcmEeIV6dg9A7tGZRrS1C3K1tkrB9d1VHlahjuTEWDZbhcHXVuT7hEwuPfZtIYY4wZzYK1mTR96QI//vVmblu1jb5MkZDAqfPbuHBJFxceP4t3zW6ujQKWKiV25Xf5oTvbW/vwWXVawWBxcMQUg+o83bSX3ucc3OkmFo4RjUSJhqPEI3Gi4SixSIxYOEYsEvO368oJJ0E8EicRCdb12058ZF0k0bAwvC+5YpnedIGedCGYeuFPwaiG5p660eWB3Nh33ki44ZHhuGnssNyZitpdL4wxxhx0FqzNpKtUlDU7BnhsfTdPvNbNmu0DABzRHOXCJbNYvmQW5y3uJBXdv4eBehWPrJetjfgWy8XaUigX/NHislcrV+sqWkFRVHVkmQqqihLsD+pFhLCECUloeB0atR2sq+XqqLkTcnBCzojt6sh5db8T8kfUp2o4rFSUgZxHX6ZAz1AxGGEOAvMY0zCye7lVXHMsUgvIXWONLjcN70+4B+0BscYYY8w7ZsHaTDndQ3mefK2Hx1/r5qnXexkqlHDCwtkLO1gejGYv6kxO2YA5U6gqmWK5FoZ3jbrrxa7McGiu3jKuVNnzZ0ZIoD05PIrcMWo0uasampv8u2BEI/bBPmOMMdOTBWszpXnlCs9v2c0Tr3Xz2PpuftedBqApFuGYrhTHzgqWoDyvPWH3790Lr1xhd7bI7oxXewrfrkyR/myRXZnhp/P1BQ8f6R3nvspJN0x79Q4YSbcWjKt3xOgKPuTXmfIfPmJ9Yowx5nBgwdpMK9t2ZVn5ux7WvzXEhu40G3rS9AwVavVuJMSiziTH1IXtY2elOLojMSPuBayq5L0K/Tl/3vFA1mMg59Gf8xjM+eXq0p/19/cHgXloL7eKA0hFI7QlnREPH+lMjbzHcvXuGB1Jl5hjo8rGGGPMaOMFa5u8aKacee0JPn720SP2DeQ8Nvak2dCdZmO3v163Y4AH1r414u4QbiREa9wPjy0Jh7aEQ2vcpTXpr9sSDq0J/zHPLXEHNxIiLEI45C+R0HDZ3w4RCuGvBVTBq1Qolip4ZcUr++ViubrPXwpBfcErky2WSRdKZIIlXSj766K/nS0E9cH2YK6019vDgf8o65a4M2JZ0JEY8UjrtoRT91hrl9aEY9MvjDHGmIPMgrWZFlriDqfNb+O0+W0j9ue9Mpt7M2zoTrOjP8fubJGBrD/loT/rsaU3y+5sP/1Zb9yweqiIQNKNkHDDpKIRktEIyWiYo1pjQTlCc2xkaG5NDJeb4w5N0Qghm3ZhjDHGTDkWrM20FnPCnDC7mRNmN497nKqS88r014XugZyHV65QrmhtKVWUiiqlcrCurytXCIUEJxzCDYdwIyGccAgnLLgRf59Tt9+NCG44TDI6HKLjTthCsTHGGDNDWbA2hwURIeFGSLgRjmqNT3ZzjDHGGDMD2WPHjDHGGGOMaQAL1sYYY4wxxjSABWtjjDHGGGMawIK1McYYY4wxDWDB2hhjjDHGmAawYG2MMcYYY0wDWLA2xhhjjDGmAUTrnwc9TYlID7B1kk7fCfRO0rnNoWP9PPNZHx8erJ8PD9bPM99k9vHRqto1VsWMCNaTSUSeV9UzJrsd5uCyfp75rI8PD9bPhwfr55lvqvaxTQUxxhhjjDGmASxYG2OMMcYY0wAWrA/cv0x2A8whYf0881kfHx6snw8P1s8z35TsY5tjbYwxxhhjTAPYiLUxxhhjjDENYMF6AkTkJhHpFpF1e6lfLiIDIvJSsFxzqNtoDoyIzBORx0XktyLyioh8eYxjRER+ICIbRGSNiJw2GW01+2+C/WzX8zQnIjEReU5EXg76+W/GOMau52lsgn1s1/IMISJhEXlRRO4fo25KXcuRyTz5NHIzcAPwk3GOeUpVLzk0zTEHQQn4c1VdLSJNwAsi8rCqvlp3zIeAxcFyNvDDYG2mj4n0M9j1PN0VgPeqalpEHODXIvKgqj5Td4xdz9PbRPoY7FqeKb4M/BZoHqNuSl3LNmI9Aaq6Etg12e0wB4+qvqWqq4PyEP4FPGfUYZcCP1HfM0CriMw+xE01B2CC/WymueAaTQebTrCM/kCRXc/T2AT72MwAIjIX+Ajwr3s5ZEpdyxasG+ec4L+kHhSRpZPdGLP/RGQBcCrw7KiqOcC2uu3tWCibtsbpZ7DredoL/uv4JaAbeFhV7XqeYSbQx2DX8kzwv4D/BlT2Uj+lrmUL1o2xGv/xlicD1wN3T25zzP4SkRRwJ/AVVR0cXT3Gl9gIyTS0j36263kGUNWyqp4CzAXOEpETRx1i1/M0N4E+tmt5mhORS4BuVX1hvMPG2Ddp17IF6wZQ1cHqf0mp6gOAIyKdk9ws8w4F8/TuBH6mqivGOGQ7MK9uey7w5qFom2mcffWzXc8zi6r2A08AHxxVZdfzDLG3PrZreUY4F/ioiGwBfgG8V0RuGXXMlLqWLVg3gIgcKSISlM/C/772TW6rzDsR9N+Pgd+q6vf3cti9wB8Fn0B+DzCgqm8dskaaAzaRfrbrefoTkS4RaQ3KceAiYP2ow+x6nsYm0sd2LU9/qvpNVZ2rqguAK4HHVPUTow6bUtey3RVkAkTk58ByoFNEtgN/jf9BCVT1RuAPgC+KSAnIAVeqPXlnujkX+CSwNpizB/DfgflQ6+cHgA8DG4As8OlD30xzgCbSz3Y9T3+zgX8TkTB+mLpdVe8XkavBrucZYiJ9bNfyDDWVr2V78qIxxhhjjDENYFNBjDHGGGOMaQAL1sYYY4wxxjSABWtjjDHGGGMawIK1McYYY4wxDWDB2hhjjDHGmAawYG2MMRMgImUReUlEXgkekfw1EQkFdWeIyA/G+doFIvKHh661e5w/LiJPBrcmq+77qojkRaTlIJzvFBH5cKNfdx/n/AcRee+hPKcxxoxmwdoYYyYmp6qnqOpS4GL8+6b+NYCqPq+qXxrnaxcAkxasgc8AK1S1XLfvKmAV8LGDcL5T8L8/exCRg/X8hOuBbxyk1zbGmAmxYG2MMe+QqnYDnwf+LHja13IRuR9ARJYFI9sviciLItIEfAc4P9j31WAE+ykRWR0svxd87XIReUJE7hCR9SLys7onx50pIr8JRsufE5EmEQmLyHdFZJWIrBGRL+ylyR8H7qluiMgxQAr4S/yAXd3/KRFZISL/ISK/E5H/WVf3xyLyetC+H4nIDcH+y0RkXdCulSLiAtcBVwTv9woRuVZE/kVEHgJ+IiJHi8ijQZsfFZH5wWvdLCI/FJHHRWRT8L28SUR+KyI3B8eEg+PWichaEflq0CdbgQ4ROfJA+9cYY/aXPXnRGGP2g6puCqaCzBpV9RfAn6rq0yKSAvL4I6l/oaqXAIhIArhYVfMishj4OXBG8PWnAkuBN4GngXNF5DngNuAKVV0lIs34T5L7Y/zH954pIlHgaRF5SFU3VxsTBN1Fqrqlro1XBed8ClgiIrOCPxbAH20+FSgAr4nI9UAZ+CvgNGAIeAx4OTj+GuADqrpDRFpVtSgi1wBnqOqfBW24FjgdOE9VcyJyH/ATVf03EfkM8APgPwWv1wa8F/gocB/+0zI/C6wSkVOAMDBHVU8MXru17n2tDo6/E2OMmQQ2Ym2MMftPxtj3NPB9EfkS0KqqpTGOcYAficha4JfAu+rqnlPV7apaAV7Cn0ayBHhLVVcBqOpg8LrvB/5I/MezPwt0AItHnasT6B+170rgF8E5VgCX1dU9qqoDqpoHXgWOBs4CnlTVXarqBW2uf783i8jn8EPv3tyrqrmgfA5wa1D+KXBe3XH3BY+dXgu8raprg3a+EnwvNgGLROR6EfkgMFj3td3AUeO0wRhjDiobsTbGmP0gIovwR3K7gROq+1X1OyLy7/hzjJ8RkYvG+PKvAm8DJ+MPcOTr6gp15TL+z2kBdKxmAP9FVX81TlNzQKyu3e/GD98PB7NMXPyw+n/2cf4xqerVInI28BHgpWBUeSyZcdpY/96q56+MaksFiKjqbhE5GfgA8KfA5fhzyMF/nzmMMWaS2Ii1Mca8QyLSBdwI3BCMrtbXHROMsv498DxwPP70iaa6w1rwR6ArwCcZf6QXYD1wlIicGZyjKfgQ4K+AL4qIE+w/TkSS9V+oqruBsIhUw/VVwLWquiBYjgLmiMjR45z/OWCZiLQF5/39Ue/3WVW9BugF5o3xfkf7Df6oOfjzv3+9j/dfIyKdQEhV72R4ekrVccC6ib6WMcY0mo1YG2PMxMSDKRcOUMKfwvD9MY77iohciD/a+yrwIP5oa0lEXgZuBv4JuFNELgMeZ/zRXIJ5y1cA14tIHH9U9iLgX/GnR6wOPuTYw/Bc5XoP4U+3eAQ/0H5oVP1dwf6393L+HSLyP/Cnm7wZvK+BoPq7wTxxAR7Fn3v9BvCN4Pv1d2O85JeAm0TkvwZt/vR473+UOcD/Dea3A3wTIPjj4lj8P2aMMWZSyKjBFmOMMTOMiJwKfE1VP3kAr5FS1XQwYn0XcJOq3tWwRh4gEfkYcJqq/tVkt8UYc/iyqSDGGDPDqeqLwONS94CY/XBtMAK9DtgM3N2ApjVSBPjeZDfCGHN4sxFrY4wxxhhjGsBGrI0xxhhjjGkAC9bGGGOMMcY0gAVrY4wxxhhjGsCCtTHGGGOMMQ1gwdoYY4wxxpgGsGBtjDHGGGNMA/x/VwSP5k27poMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"r0 = 2.0E-10\n",
"rs = np.linspace(1.5E-10, 4.0E-10)\n",
"fig, ax = plt.subplots(figsize=(12,6))\n",
"ax.plot(rs*m2ang, energy(rs, 2.0, -2.0, cal_b(r0, 2.0, -2.0, 12), 12)*j2ev, label='q=1.0, n=12')\n",
"ax.plot(rs*m2ang, energy(rs, 2.0*0.75, -2.0*0.75, cal_b(r0, 2.0*0.75, -2.0*0.75, 12), 12)*j2ev, label='q=0.75, n=12')\n",
"ax.plot(rs*m2ang, energy(rs, 2.0, -2.0, cal_b(r0, 2.0, -2.0, 6), 6)*j2ev, label='q=1.0, n=6')\n",
"\n",
"ax.plot(rs*m2ang, energy(rs, 2.0, -2.0, cal_b(2.6E-10, 2.0, -2.0, 12), 12)*j2ev, '--', label='q=1.0, n=12')\n",
"ax.plot(rs*m2ang, energy(rs, 2.0*0.75, -2.0*0.75, cal_b(2.6E-10, 2.0*0.75, -2.0*0.75, 12), 12)*j2ev, '--', label='q=0.75, n=12')\n",
"ax.plot(rs*m2ang, energy(rs, 2.0, -2.0, cal_b(2.6E-10, 2.0, -2.0, 6), 6)*j2ev, '--', label='q=1.0, n=6')\n",
"\n",
"ax.set_ylim(-30, 100)\n",
"ax.legend()\n",
"\n",
"ax.set_xlabel('Distance (Angstroms)')\n",
"ax.set_ylabel('Energy (eV)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2*0.75"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Data - frac factor at 300 K\n",
"cscl_beta_permil = [43.657763, 35.181290, 28.256947, 22.598847, 18.12]\n",
"cscl_beta_ref = 18.12\n",
"cscl_r_ang = [2.07212, 2.12968, 2.18724, 2.24480, 2.30236]\n",
"cscl_r_ref = 2.302\n",
"\n",
"# From /nfs/see-fs-02_users/earawa/lvs/Castep-isotopes-work/MgO_DFPT\n",
"mgo_beta_permil = [42.890104, 37.399128, 31.058124, 26.132359, 20.720653]\n",
"#mgo_beta_permil = [37.399128, 31.058124, 26.132359, 20.720653]\n",
"mgo_beta_ref = 26.132359\n",
"mgo_r_ang = [2.00354, 2.03985, 2.08651, 2.12726, 2.18267]\n",
"#mgo_r_ang = [2.03985, 2.08651, 2.12726, 2.18267]\n",
"mgo_r_ref = 2.12726\n",
"\n",
"nias_first_beta_permil = [39.791693, 32.276645, 26.06, 20.885733, 16.716020] # Oct\n",
"nias_first_beta_ref = 26.06\n",
"nias_first_r_ang = [2.0329, 2.08647, 2.140, 2.19347, 2.24697]\n",
"nias_first_r_ref = 2.140\n",
"\n",
"nias_second_beta_permil = [36.042615, 29.003766, 23.18, 18.325099, 14.440640] # trig pris\n",
"nias_second_beta_ref = 23.18\n",
"nias_second_r_ang = [2.04538, 2.09921, 2.153, 2.20686, 2.26069]\n",
"nias_second_r_ref = 2.153\n",
"\n",
"cubzns_beta_permil = [30.05, 24.864859, 20.331742, 13.277411, 8.493347]# , 4.557974]\n",
"cubzns_beta_ref = 30.05\n",
"cubzns_r_ang = [2.000, 2.04407, 2.09393, 2.19364, 2.29335]# , 2.59952]\n",
"cubzns_r_ref = 2.000"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(cscl_r_ang, cscl_beta_permil, 'k*', label='CsCl structure', markersize=10)\n",
"ax.plot(cscl_r_ref, cscl_beta_ref, 'ko', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(mgo_r_ang, mgo_beta_permil, 'ys', label='NaCl (periclase)', markersize=8)\n",
"ax.plot(mgo_r_ref, mgo_beta_ref, 'yo', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_first_r_ang, nias_first_beta_permil, 'gs', label='NiAs structure (octahedral)', \n",
" markersize=8)\n",
"ax.plot(nias_first_r_ref, nias_first_beta_ref, 'go', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_second_r_ang, nias_second_beta_permil, 'bs', label='NiAs structure (trigonal prismatic)', \n",
" markersize=8)\n",
"ax.plot(nias_second_r_ref, nias_second_beta_ref, 'bo', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(cubzns_r_ang, cubzns_beta_permil, 'r^', label='cubic ZnS structure', markersize=10)\n",
"ax.plot(cubzns_r_ref, cubzns_beta_ref, 'ro', fillstyle='none', markersize=20)\n",
"ax.set_xlabel('Bond length (Angstroms)')\n",
"ax.set_ylabel('1000.ln(beta) (per mill)')\n",
"\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import scipy.optimize as sp_opt\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def calc_beta_300_vary_qn(r, qfac0, qfac1):\n",
" qfac = qfac0 + r*qfac1\n",
" n = 12\n",
" k = kf(r*1E-10, 2.0*qfac, -2.0*qfac, n)\n",
" beta = ionic_model.ionic_model_beta(k, 300.0) \n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"mgo_popt, mgo_pcov = sp_opt.curve_fit(calc_beta_300_vary_qn, mgo_r_ang,\n",
" mgo_beta_permil, [1.0, 0.0])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"cubzns_popt, cubzns_pcov = sp_opt.curve_fit(calc_beta_300_vary_qn, cubzns_r_ang,\n",
" cubzns_beta_permil, [1.0, 0.0])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"cscl_popt, cscl_pcov = sp_opt.curve_fit(calc_beta_300_vary_qn, cscl_r_ang,\n",
" cscl_beta_permil, [1.0, 0.0])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"nias_first_popt, nias_first_pcov = sp_opt.curve_fit(calc_beta_300_vary_qn, nias_first_r_ang,\n",
" nias_first_beta_permil, [1.0, 0.0])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.78094965 -0.9804467 ]\n",
"[[ 2.36069644e-04 -1.14069376e-04]\n",
" [-1.14069376e-04 5.51639803e-05]]\n",
"0.9181009218185137\n",
"0.8200562518227752\n",
"0.52592224183556\n"
]
}
],
"source": [
"print(mgo_popt)\n",
"print(mgo_pcov)\n",
"print(mgo_popt[0] + 1.9*mgo_popt[1])\n",
"print(mgo_popt[0] + 2.0*mgo_popt[1])\n",
"print(mgo_popt[0] + 2.3*mgo_popt[1])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(cscl_r_ang, cscl_beta_permil, 'k*', label='CsCl structure', markersize=10)\n",
"ax.plot(cscl_r_ref, cscl_beta_ref, 'ko', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(mgo_r_ang, mgo_beta_permil, 'ys', label='NaCl (periclase)', markersize=8)\n",
"ax.plot(mgo_r_ref, mgo_beta_ref, 'yo', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_first_r_ang, nias_first_beta_permil, 'gs', label='NiAs structure (octahedral)', \n",
" markersize=8)\n",
"ax.plot(nias_first_r_ref, nias_first_beta_ref, 'go', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_second_r_ang, nias_second_beta_permil, 'bs', label='NiAs structure (trigonal prismatic)', \n",
" markersize=8)\n",
"ax.plot(nias_second_r_ref, nias_second_beta_ref, 'bo', fillstyle='none', markersize=20)\n",
"\n",
"r_points = np.linspace(1.98, 2.32)\n",
"ax.plot(r_points, calc_beta_300_vary_qn(r_points, *cscl_popt), 'k', linestyle='--')\n",
"\n",
"ax.plot(r_points, calc_beta_300_vary_qn(r_points, *mgo_popt), 'y', linestyle='--')\n",
"ax.plot(r_points, calc_beta_300_vary_qn(r_points, *nias_first_popt), 'g', linestyle='--')\n",
"ax.plot(r_points, calc_beta_300_vary_qn(r_points, (cscl_popt[0]+cubzns_popt[0])/2.0, \n",
" (cscl_popt[1]+cubzns_popt[1])/2.0), 'k', linestyle=':')\n",
"\n",
"\n",
"ax.plot(r_points, calc_beta_300_vary_qn(r_points, *cubzns_popt), 'r', linestyle='--')\n",
"\n",
"\n",
"ax.plot(cubzns_r_ang, cubzns_beta_permil, 'r^', label='cubic ZnS structure', markersize=10)\n",
"ax.plot(cubzns_r_ref, cubzns_beta_ref, 'ro', fillstyle='none', markersize=20)\n",
"ax.set_xlabel('Bond length (Angstroms)')\n",
"ax.set_ylabel('1000.ln(beta) (per mill)')\n",
"\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=2, figsize=(8,12))\n",
"\n",
"ax[0].errorbar(8, cscl_popt[0], yerr=np.sqrt(np.diag(cscl_pcov))[0], fmt='k*')\n",
"ax[0].errorbar(4, cubzns_popt[0], yerr=np.sqrt(np.diag(cubzns_pcov))[0], fmt='r^')\n",
"ax[0].errorbar(6, mgo_popt[0], yerr=np.sqrt(np.diag(mgo_pcov))[0], fmt='ys')\n",
"ax[0].errorbar(6, nias_first_popt[0], yerr=np.sqrt(np.diag(nias_first_pcov))[0], fmt='gs')\n",
"ax[0].set_xlabel('Coordination number')\n",
"ax[0].set_ylabel('spring constant offset (units?)')\n",
"\n",
"ax[1].errorbar(8, cscl_popt[1], yerr=np.sqrt(np.diag(cscl_pcov))[1], fmt='k*')\n",
"ax[1].errorbar(4, cubzns_popt[1], yerr=np.sqrt(np.diag(cubzns_pcov))[1], fmt='r^')\n",
"ax[1].errorbar(6, mgo_popt[1], yerr=np.sqrt(np.diag(mgo_pcov))[1], fmt='ys')\n",
"ax[1].errorbar(6, nias_first_popt[1], yerr=np.sqrt(np.diag(nias_first_pcov))[1], fmt='gs')\n",
"ax[1].set_xlabel('Coordination number')\n",
"ax[1].set_ylabel('\"n\" in potential function')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def calc_beta_300_vary_q_coord(data, qfac0, qfac1, qfacgrd):\n",
" coord = data[1]\n",
" r = data[0]\n",
" qfac = qfac0 + r*qfac1 + coord*qfacgrd\n",
" n = 12\n",
" k = kf(r*1E-10, 2.0*qfac, -2.0*qfac, n)\n",
" beta = ionic_model.ionic_model_beta(k, 300.0) \n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def get_qfac(r, coord, qfac0, qfac1, qfacgrd):\n",
" qfac = qfac0 + r*qfac1 + coord*qfacgrd\n",
" return qfac"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def calc_beta_model(r, coord, t, qfac0, qfac1, qfacgrd):\n",
" qfac = qfac0 + r*qfac1 + coord*qfacgrd\n",
" n = 12\n",
" k = kf(r*1E-10, 2.0*qfac, -2.0*qfac, n)\n",
" beta = ionic_model.ionic_model_beta(k, t) \n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Fit model to MgO polymorph data\n",
"\n",
"rs = cscl_r_ang + mgo_r_ang + nias_first_r_ang + nias_second_r_ang + cubzns_r_ang\n",
"coords = [8.0, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4]\n",
"data = np.array((rs, coords))\n",
"predictors = np.array((cscl_beta_permil + mgo_beta_permil + nias_first_beta_permil\n",
" + nias_second_beta_permil + cubzns_beta_permil))\n",
"all_popt, all_pcov = sp_opt.curve_fit(calc_beta_300_vary_q_coord, data,\n",
" predictors, [1.0, 0.0, 0.0])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# Silicate data for comparison\n",
"\n",
"# Data from /nfs/see-fs-02_users/earawa/lvs/Castep-isotopes-work/free_energy/Mg2SiO4\n",
"# at -10, 0, 10, 20, 30 and 40 GPa\n",
"fo_data_bonds = np.array([2.18806, 2.1173566666666663, 2.06903, 2.0307566666666665, 2.000488333333333, 1.974421666666667])\n",
"fo_data_300k = np.array([18.799606, 25.343401, 30.932164, 36.051153, 40.674813, 44.893845])\n",
"fo_data_bonds_0GPa = 2.1173566666666663\n",
"fo_data_300k_0GPa = 25.343401\n",
"\n",
"# Data from /nfs/see-fs-02_users/earawa/lvs/Castep-isotopes-work/free_energy/MgSiO3\n",
"# at 0 GPa, 20, 40, 60, 80, 100 and 120 GPa\n",
"pv_data_bonds = np.array([2.2277, 2.16657375, 2.1199, 2.0819075, 2.0497562499999997, 2.02188875, 1.9972662500000002])\n",
"pv_data_300k = np.array([23.443879, 30.005733, 35.920075, 41.051075, 47.352945, 52.504616, 57.403608])\n",
"pv_data_bonds_0GPa = 2.2277\n",
"pv_data_300k_0GPa = 23.443879"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Make the figure...\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(fo_data_bonds, fo_data_300k, color='grey', linestyle='', marker=(6,0,30),\n",
" label='Forsterite', markersize=5)\n",
"ax.plot(fo_data_bonds_0GPa, fo_data_300k_0GPa, color='grey', marker='o', fillstyle='none', markersize=15)\n",
"\n",
"ax.plot(pv_data_bonds, pv_data_300k, color='grey', linestyle='', marker=(8,2,0),\n",
" label='Bridgmanite', markersize=8)\n",
"ax.plot(pv_data_bonds_0GPa, pv_data_300k_0GPa, color='grey', marker='o', fillstyle='none', markersize=15)\n",
"\n",
"\n",
"ax.plot(cscl_r_ang, cscl_beta_permil, color='m', linestyle='', marker=(8,1,0),\n",
" label='CsCl structure', markersize=12)\n",
"ax.plot(cscl_r_ref, cscl_beta_ref, 'mo', fillstyle='none', markersize=25)\n",
"\n",
"ax.plot(mgo_r_ang, mgo_beta_permil, color='y', linestyle='', marker=(6,1,0),\n",
" label='NaCl (periclase)', markersize=12)\n",
"ax.plot(mgo_r_ref, mgo_beta_ref, 'yo', fillstyle='none', markersize=25)\n",
"\n",
"ax.plot(nias_first_r_ang, nias_first_beta_permil, color='cyan', linestyle='', marker=(6,2,0), \n",
" label='NiAs structure (octahedral)', markersize=12)\n",
"ax.plot(nias_first_r_ref, nias_first_beta_ref, 'o', color='cyan', fillstyle='none', markersize=25)\n",
"\n",
"ax.plot(nias_second_r_ang, nias_second_beta_permil, color='cornflowerblue', linestyle='', marker=(6,0,0),\n",
" label='NiAs structure (trigonal prismatic)', markersize=10)\n",
"ax.plot(nias_second_r_ref, nias_second_beta_ref, color='cornflowerblue', marker='o', fillstyle='none', markersize=25)\n",
"\n",
"ax.plot(cubzns_r_ang, cubzns_beta_permil, color='salmon', linestyle='', marker=(4,1,0),\n",
" label='cubic ZnS structure', markersize=12)\n",
"ax.plot(cubzns_r_ref, cubzns_beta_ref, color='salmon', marker='o', fillstyle='none', markersize=25)\n",
"\n",
"for coord in [3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]:\n",
" \n",
" r_points = np.linspace(1.98, 2.32)\n",
" coords = np.ones_like(r_points) * coord\n",
" data = np.stack((r_points, coords))\n",
" values = calc_beta_300_vary_q_coord(data, *all_popt)\n",
" ax.plot(r_points, values, 'k', linestyle=':')\n",
" ax.text(1.965, values[0], str(coord))\n",
"\n",
"\n",
"ax.plot(1.9875, calc_beta_300_vary_q_coord([1.9875,4.0], *all_popt), 'o', fillstyle='none',\n",
" color='darkcyan', markersize=6)\n",
"ax.plot(2.3125, calc_beta_300_vary_q_coord([2.3125,4.0], *all_popt), 'o',\n",
" color='darkcyan', markersize=6)\n",
"ax.plot(1.9875, calc_beta_300_vary_q_coord([1.9875,8.0], *all_popt), 'o', fillstyle='none', \n",
" color='saddlebrown', markersize=6)\n",
"ax.plot(2.3125, calc_beta_300_vary_q_coord([2.3125,8.0], *all_popt), 'o',\n",
" color='saddlebrown', markersize=6)\n",
"\n",
"\n",
"\n",
"ax.set_xlabel('Bond length (Angstroms)')\n",
"ax.set_ylabel('1000.ln(beta) (per mill)')\n",
"\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7050780037202244\n",
"0.9494693964344061\n",
"0.39986726239155135\n",
"0.644258655105733\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rs = np.linspace(1.5E-10, 4.0E-10)\n",
"fig, ax = plt.subplots(figsize=(8,12), nrows=2, sharex=True, gridspec_kw={'hspace':0.0125})\n",
"\n",
"r0 = 1.9875\n",
"coord = 4.0\n",
"qfac = get_qfac(r0, coord, *all_popt)\n",
"print(qfac)\n",
"ax[0].plot(rs*m2ang, energy(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev, \n",
" label='r0=1.9875, coord=4, n=12', color='darkcyan', ls='--')\n",
"ax[1].plot(rs*m2ang, de_by_dr(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev/m2ang, \n",
" color='darkcyan', ls='--')\n",
"ax[1].axvline(r0, ls='--', color='k')\n",
"ax[0].axvline(r0, ls='--', color='k')\n",
"ax[1].axhline(0.0, color='k', lw=1)\n",
"\n",
"r0 = 1.9875\n",
"coord = 8.0\n",
"qfac = get_qfac(r0, coord, *all_popt)\n",
"print(qfac)\n",
"ax[0].plot(rs*m2ang, energy(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev,\n",
" label='r0=1.9875, coord=8, n=12', color='saddlebrown', ls='--')\n",
"ax[1].plot(rs*m2ang, de_by_dr(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev/m2ang,\n",
" color='saddlebrown', ls='--')\n",
"\n",
"r0 = 2.3125\n",
"coord = 4.0\n",
"qfac = get_qfac(r0, coord, *all_popt)\n",
"print(qfac)\n",
"ax[0].plot(rs*m2ang, energy(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev,\n",
" label='r0=2.3125, coord=4, n=12', color='darkcyan', ls='-')\n",
"ax[1].plot(rs*m2ang, de_by_dr(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev/m2ang, \n",
" color='darkcyan', ls='-')\n",
"ax[1].axvline(r0, color='k')\n",
"ax[0].axvline(r0, color='k')\n",
"\n",
"r0 = 2.3125\n",
"coord = 8.0\n",
"qfac = get_qfac(r0, coord, *all_popt)\n",
"print(qfac)\n",
"ax[0].plot(rs*m2ang, energy(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev, \n",
" label='r0=2.3125, coord=8, n=12',\n",
" color='saddlebrown', ls='-')\n",
"ax[1].plot(rs*m2ang, de_by_dr(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev/m2ang,\n",
" color='saddlebrown', ls='-')\n",
"\n",
"ax[0].set_ylim(-30, 20)\n",
"ax[0].set_xlim(1.5, 4.0)\n",
"ax[1].set_xlim(1.5, 4.0)\n",
"ax[0].legend()\n",
"\n",
"ax[0].set_ylabel('Energy (eV)')\n",
"ax[0].xaxis.set_ticks_position('none') \n",
"ax[1].set_ylim(-10.5, 10.5)\n",
"ax[1].set_xlabel('Distance (Angstroms)')\n",
"ax[1].set_ylabel('Force (eV/Angstrom)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rs = np.linspace(1.5E-10, 4.0E-10)\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(rs*m2ang, de_by_dr(rs, 2.0*qfac, -2.0*qfac, cal_b(r0*1E-10, 2.0*qfac, -2.0*qfac, 12), 12)*j2ev/m2ang, 'r')\n",
"ax.axvline(r0)\n",
"ax.axhline(0.0)\n",
"ax.set_ylim(-25, 25)\n",
"ax.set_xlabel('Distance (Angstroms)')\n",
"ax.set_ylabel('Force (eV/Angstrom)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import scipy.interpolate as spi"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"melt_coord = np.array(([4.93, 5.4, 6, 6.7, 7.25, 7.62, 7.85]))\n",
"melt_pressure = np.array(([0.1, 2.5, 7.2, 16.3, 34.3, 72.1, 159.4]))\n",
"coord_spline = spi.InterpolatedUnivariateSpline(melt_pressure, melt_coord)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ax.plot(melt_pressure, melt_coord, 'o')\n",
"ps = np.linspace(-10.0, 170.0)\n",
"ax.plot(ps, coord_spline(ps))\n",
"ax.set_xlabel('Pressure (GPa)')\n",
"ax.set_ylabel('Coordination number')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ax.semilogx(melt_pressure, melt_coord, 'o')\n",
"ps = np.linspace(0.1, 160, 10000)\n",
"ax.semilogx(ps, coord_spline(ps))\n",
"ax.set_xlabel('Pressure (GPa)')\n",
"ax.set_ylabel('Coordination number')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"melt_poly_coef = [1.9613, -0.00165, 0.0000019]\n",
"melt_rs_model = ionic_model.melt_bond_length(melt_pressure, melt_poly_coef)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ps = np.linspace(0.0, 160.0)\n",
"ax.plot(ps, ionic_model.melt_bond_length(ps, melt_poly_coef)*1E10)\n",
"ax.set_xlabel('Pressure (GPa)')\n",
"ax.set_ylabel('Bond length (angstroms)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Melt at 0GPa has r 1.9613e-10 coord 4.907009543756205\n",
"Melt beta at 300 K 42.328798895694476\n",
"Melt beta at 1573 K 1.571461444207688\n",
"Corrected Melt beta at 300 K 23.67260222292207\n",
"Corrected athermal Melt beta at 300 K 28.20941294776114\n"
]
}
],
"source": [
"# Melting point beta\n",
"r_melt = ionic_model.melt_bond_length(0.0, melt_poly_coef)\n",
"coord_melt = coord_spline(0.0)\n",
"print(\"Melt at 0GPa has r\", r_melt, \"coord\", coord_melt)\n",
"beta_300_melt = calc_beta_model(r_melt*1E10, coord_melt, 300.0, *all_popt)\n",
"print(\"Melt beta at 300 K\", beta_300_melt)\n",
"print(\"Melt beta at 1573 K\", calc_beta_model(r_melt*1E10, coord_melt, 1573.0, *all_popt))\n",
"beta_300_melt_correct = calc_beta_model(r_melt*1E10, coord_melt, 300.0, \n",
" 2.1264451598128855, -0.93910997, 0.06109785)\n",
"print(\"Corrected Melt beta at 300 K\", beta_300_melt_correct)\n",
"\n",
"beta_300_melt_correct_athermal = calc_beta_model(r_melt*1E10, coord_melt, 300.0, \n",
" 2.1807165400315522, -0.93910997, 0.06109785)\n",
"print(\"Corrected athermal Melt beta at 300 K\", beta_300_melt_correct_athermal)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(cscl_r_ang, cscl_beta_permil, 'k*', label='CsCl structure', markersize=10)\n",
"ax.plot(cscl_r_ref, cscl_beta_ref, 'ko', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(mgo_r_ang, mgo_beta_permil, 'ys', label='NaCl (periclase)', markersize=8)\n",
"ax.plot(mgo_r_ref, mgo_beta_ref, 'yo', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_first_r_ang, nias_first_beta_permil, 'gs', label='NiAs structure (octahedral)', \n",
" markersize=8)\n",
"ax.plot(nias_first_r_ref, nias_first_beta_ref, 'go', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(nias_second_r_ang, nias_second_beta_permil, 'bs', label='NiAs structure (trigonal prismatic)', \n",
" markersize=8)\n",
"ax.plot(nias_second_r_ref, nias_second_beta_ref, 'bo', fillstyle='none', markersize=20)\n",
"\n",
"ax.plot(cubzns_r_ang, cubzns_beta_permil, 'r^', label='cubic ZnS structure', markersize=10)\n",
"\n",
"ax.plot(r_melt*1E10, beta_300_melt, 'c*', fillstyle='none', markersize=20, label='melt predictionm 0 GPa')\n",
"\n",
"ax.plot(r_melt*1E10, beta_300_melt_correct, 'c*', fillstyle='none', markersize=20, label='corrected melt predictionm 0 GPa')\n",
"ax.plot(r_melt*1E10, beta_300_melt_correct_athermal, 'c*', fillstyle='none', markersize=20, label='corrected melt (athermal) predictionm 0 GPa')\n",
"\n",
"\n",
"for coord in [4.0, 5.0, 6.0, 7.0, 8.0, 9.0]:\n",
" \n",
" r_points = np.linspace(1.95, 2.32)\n",
" coords = np.ones_like(r_points) * coord\n",
" data = np.stack((r_points, coords))\n",
" values = calc_beta_300_vary_q_coord(data, *all_popt)\n",
" ax.plot(r_points, values, 'k', linestyle=':')\n",
" ax.text(1.935, values[0], str(coord))\n",
"\n",
"\n",
"\n",
"\n",
"ax.set_xlabel('Bond length (Angstroms)')\n",
"ax.set_ylabel('1000.ln(beta) (per mill)')\n",
"\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"import earthref\n",
"earth_model = earthref.EarthModel(earthref.ak135)\n",
"\n",
"def depth_PT(depth):\n",
" \"\"\"Retrun liquidus P and T at a given depth in a magma ocean\n",
"\n",
" Liquidus data Andrault et at. 2011 (EPSL doi:10.1016/j.epsl.2011.02.006)\n",
" who fit a modified Simmon and Glatzel equation:\n",
"\n",
" T = T0 (P/a+1_^(1/c) \n",
"\n",
" (see section 3.4) with parameters listed below. This replaces a\n",
" previous linear fit to data at 0 and 60 GPa.\n",
" \"\"\"\n",
" \n",
" P = earth_model(6371-depth) # Interpolating AK135...\n",
" # We now have P, T is from TP plot\n",
" T_0 = 1940.0 # virtual liqidus temperature at 0 GPa\n",
" a = 26.0 # GPa\n",
" c = 1.9\n",
" T = T_0 * ((P / a) + 1)**(1/c)\n",
" return T, P"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"depths = np.linspace(0.0, 2800.0, num=200)\n",
"\n",
"# Get our list of Ps and Ts\n",
"Ts, Ps = depth_PT(depths)\n",
"r_melt = ionic_model.melt_bond_length(Ps, melt_poly_coef)\n",
"coord_melt = coord_spline(Ps)\n",
"beta_melt = calc_beta_model(r_melt*1E10, coord_melt, Ts, *all_popt)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(Ps, beta_melt)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,6))\n",
"\n",
"ax.plot(Ps, Ts)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.32716768 -0.93910997 0.06109785]\n"
]
}
],
"source": [
"print(all_popt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| examples/jupyter/nuclear-data-resonance-covariance.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nuclear Data: Resonance Covariance\n",
"In this notebook we will explore features of the Python API that allow us to import and manipulate resonance covariance data. A full description of the ENDF-VI and ENDF-VII formats can be found in the [ENDF102 manual](https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"from pprint import pprint\n",
"import shutil\n",
"import subprocess\n",
"import urllib.request\n",
"\n",
"import h5py\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import openmc.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ENDF: Resonance Covariance Data\n",
"\n",
"Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<IncidentNeutron: Gd157>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Download ENDF file\n",
"url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
"filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
"\n",
"# Load into memory\n",
"gd157_endf = openmc.data.IncidentNeutron.from_endf(filename, covariance=True)\n",
"gd157_endf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can access the parameters contained within File 32 in a similar manner to the File 2 parameters from before. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>J</th>\n",
" <th>neutronWidth</th>\n",
" <th>captureWidth</th>\n",
" <th>fissionWidthA</th>\n",
" <th>fissionWidthB</th>\n",
" <th>L</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0314</td>\n",
" <td>2.0</td>\n",
" <td>0.000474</td>\n",
" <td>0.1072</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.8250</td>\n",
" <td>2.0</td>\n",
" <td>0.000345</td>\n",
" <td>0.0970</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.2400</td>\n",
" <td>1.0</td>\n",
" <td>0.000400</td>\n",
" <td>0.0910</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.7700</td>\n",
" <td>2.0</td>\n",
" <td>0.012800</td>\n",
" <td>0.0805</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.5600</td>\n",
" <td>2.0</td>\n",
" <td>0.011360</td>\n",
" <td>0.0880</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" energy J neutronWidth captureWidth fissionWidthA fissionWidthB L\n",
"0 0.0314 2.0 0.000474 0.1072 0.0 0.0 0\n",
"1 2.8250 2.0 0.000345 0.0970 0.0 0.0 0\n",
"2 16.2400 1.0 0.000400 0.0910 0.0 0.0 0\n",
"3 16.7700 2.0 0.012800 0.0805 0.0 0.0 0\n",
"4 20.5600 2.0 0.011360 0.0880 0.0 0.0 0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gd157_endf.resonance_covariance.ranges[0].parameters[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The newly created object will contain multiple resonance regions within `gd157_endf.resonance_covariance.ranges`. We can access the full covariance matrix from File 32 for a given range by:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"covariance = gd157_endf.resonance_covariance.ranges[0].covariance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This covariance matrix currently only stores the upper triangular portion as covariance matrices are symmetric. Plotting the covariance matrix:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x14cf90c73550>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAD8CAYAAAD6+lbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXuwZVV54H/f7RtsCeFlE8AGpjuhjQXUiNoBkswkRBAax0o7U2owY+w4KEmERJPUJBCrxMGhghlHoxWlisGOaNSWEKM9GbRtUcZKlTwa46igPXRApTvdvJqHhGnaS3/zx16ne/XqtdZe+3HOPY/vV3Xqnr3W2mt/e99zvvM91kNUFcMwDCPO3GILYBiGMc6YkjQMw8hgStIwDCODKUnDMIwMpiQNwzAymJI0DMPIYErSMIxFQUTWiMhWEdkmIldE6p8nIp9x9XeIyAqv7kpXvlVELvTK/0BE7hGR74jIp0VkaVc5h6Yk6x6AYRizi4gsAT4MXAScBrxBRE4Lml0CPK6qpwIfAN7rzj0NuBg4HVgDfERElojIcuD3gdWqegawxLXrxFCUZOEDMAxjdjkL2Kaq96vqXmADsDZosxa40b2/GThPRMSVb1DVZ1X1AWCb6w9gHni+iMwDhwP/3FXQ+a4dJNj/AABEZPAA7o01XrZsma5YsWJIohiGAXD33Xc/qqrHtT3/VBF9prDtTrgH2OMVXa+q13vHy4EHvePtwNlBN/vbqOqCiDwJvMCV3x6cu1xVvy4i7wN+CPw/4Euq+qVCkZMMS0nWPgARuRS4FOCUU05hy513si8wbOfYB7C/fI59B70fsI+5Q9ouFr6M4yZDrhzyz66uTfi/GcYzCPuNfR4Gn4Um18+1r6sbXDPXNvys9i1D6TORJUt+EO2kkGeA3y5s+27Yo6qru1yvKSJyDJUxthJ4AvgbEXmjqv51l36HpSRrcb8q1wOsXr1a4eAPE5D8Z/t1g/qY8lwMfBnHTYZcOeSfXV2b8H8zjGcQ9hv7PAw+C02un2tfVze4Zq5t+FntW4bSZ9IVodf43A7gZO/4JFcWa7Pduc9HAY9lzj0feEBVHwEQkc8Cvwh0UpLDMnlKHoBhGBOEUFlVJa8C7gJWichKETmMKsGyMWizEVjn3r8W+IpWK/JsBC522e+VwCrgTio3+xwROdzFLs8DvtvqZj2GZUnufwBUyvFi4DeadBCzXOqshBIXvK6P0l9mYzYZB09hMenLqnIxxsuBTVRZ6PWqeo+IXA1sUdWNwEeBT4jINmA3LlPt2t1EleNYAC5T1eeAO0TkZuAbrvwfcd5qF4aiJFMPoEkfA2WWcuPCD2qo/PzjurZhXep4lr8cRsWsfwb6dD1V9RbglqDsXd77PcDrEudeA1wTKb8KuKpHMYcXk4w9gKbkFFadddjXL/6kW5KzbvkY/dFzTHJiWLTETTG7dsEJJxz0Pqcg/bo+MuGpbGFJ+8UilKE0+1oi92Jmt8PrT0p2u6RtWxn8PsPjYSTSTEmOIwMF6b1v+mGM1cfqUpS45aV1Kfq0UlMhiqbZ7ZjCaZPdzvXTxAOI1ZVmt3P3XqdESrLbfj8xJZWKscfk6/JMJii7PTGMv5I0DGNsWLLYAiwCk6Uk/+Iv4B3vAGBhoSo6LLiDmOvRt7udO7frr3Yfv/h9utspVy53zdh1csel1wjrmrrbbV3qti50ToY6d7vUFY9dp0l4qAmCKcnxxynIfcwx7yT3vxSx4UIhOffb7y92XPdhy7mUubYxWUvc05IvUolLVyrzJFDyGRhHJkXOyZCyXyZLSTpSSsX/W6dMUsoxVF5++7YxzNR5dfHRurJcfK5O1qb1dTHTkthnrG3XulQMOvybo4+YX66fVEy3Syy2qXwWk2zPRCpJqP8S55RbyvJM9V2qHMcR/z67KM4+n0Ff10n92MX6ySmaJtds8xxKzmn7TNr8IHfBlKRhGEaCwbTEWWMqfhhCVzkcjtE2MRAex/pqKmeT6/XNpMS9xoFhP6tJ/V/MFb6miam4n9IMZCo2FOsrNa6ti9tSEnfs27Vv6r7m6vr6Yjf5kcqRep6l8d3cNXMxwKb4P9yp/ts+k9If2b5ikqWvaWIqlCSUxdvCeGVILGET1sWOSxVI7MuS6rfJEJZUP+ErZVkPyNXXPZO651+nIHJJhrovfup/kZI1lfQruWbbmGSovOtkSP1Qh+fHftSbKP2mLCl8TRPTFWJYWID5w/YfpjLWsQygXxc7TtX5H/66DG8sC5nr15fPb5uylHJfqrCvmNIqyYyG952yuMPr1j3bkmvG7iN1v+E1U/+X1Oci9qxTn5vwOHVfsX6bPBOfJs8vJm8bLLs9DczPZ78wA2IKzq9LHZe0TfUbUtpvE3c8p6DDNilFkZIhZQmW/ECklFnT+84pvJyyq7tmnSKK9VWi9FOknl+dfLF+Sn/U+8KUpGEYRgLLbk8bu3YBzWN7ubalscdxoi95S6xjI05JzLPtsy31NPr631l2uwEicrKIfFVE7nWbgb/dlR8rIptF5D7395j+xG2Av3pQBN/NCRMqqWRHqChicZ9YvzFiyjeXiMgp6/A490VJ3WMujlUiX11MMtVP6j7aXDOMJ6auEZMtVZf6f9f1k5I97LfkmeSumXt+KfnaMohJmpIsZwH4I1U9DTgHuMztrX0FcKuqrgJudceLRkph+LEb/xWW+cd1fYcxoZz1FfuCp2JfYT91xymFGcbXfOUYKgT/3LrYYahgU1ZS3XOt+x/UXdNXELlr5O4lrEs9y5Jn0uR/nzsuuWZd276wIUANUNWdqvoN9/5HVBvuLOfgDcVvBF7TVciuzO05dLfg0g9RzmKbJXJfeiNP7Ln19TmqU5x15U3pcwiQiKwRka0isk1EDjGm3EZfn3H1d4jICq/uSle+VUQu9MqPFpGbReR7IvJdEfmFtvc6oJf/lBP+pcAdwPGqutNV7QKOT5xzqYhsEZEtjzzySB9ipFm69KDD3Aemzk2OtR8luev1qcTH8cdgHJR02+dS9yMzDvdWR5+7JYrIEuDDwEXAacAbnCfqcwnwuKqeCnwAeK879zSqTcFOB9YAH3H9AXwQ+KKqvhh4CT3sltj5myAiRwB/C7xDVZ/y69z2jxo7T1WvV9XVqrr6uOOO6yqGYRhDpueY5FnANlW9X1X3AhuovFAf3yu9GTjPbRW7Ftigqs+q6gPANuAsETkK+GWqXRZR1b2q+kSrm/XopCRF5CeoFOQnVfWzrvghETnR1Z8IPNxNxP7JWQMlsbCw/Sips0b6kieWfFls63Kxrw/t/t8x7yT2fNtQmqDpz73vTUkuBx70jre7smgbVV0AngRekDl3JfAI8Fci8o8icoOI/GTxzSXokt0WKo39XVV9v1flbyi+Dvh8e/GGQxiQ9ynJWs4iFpNszzTFJBsoyWWDcJp7XdqLAHnmgZcB16nqS4F/oYfEcZexob8E/CbwbRH5piv7U+Ba4CYRuQT4AfD6biIOh4Gi9Idi+O99/Ix1WB7LHse+ALlzQwujrp/UFyyV4Y6dG1rI4dCRUI7Yc0q1D68T3k+u79Q9pu4tvJ/Yffhyx55tKEfYd6xtrF3ufsL2KRnC+019FmL/y5wcfSjJhtMSH1XV1Zn6HcDJ3vFJrizWZruIzANHAY9lzt0ObFfVO1z5zSymklTVfyCd7T+vbb+jJKc0BuQ+YKkhIyUfyJKhHV2sgpT1UmfVxORKPSf/OPelTIUuUmUp+euu6cta90xL/sd1faUUcapdSpY2Mvh1sePU+670OLznLmCViKykUnAXA78RtBl4pV8HXgt8RVVVRDYCnxKR9wMvBFYBd6rqcyLyoIj8nKpupdJD93YVdBZnGR3E3MLe6s38gUeRs0L8csNoS1/W3SgR4Cd66ktVF0TkcmAT1aih9ap6j4hcDWxR1Y1U4bxPiMg2YDeVIsW1u4lKAS4Al6nqc67r3wM+KSKHAfcDb+4q68wrScMwyukzIq+qtwC3BGXv8t7vAV6XOPca4JpI+TeBnJvfGFOSgQXZxDVZDGsgd82uLtUw4lh11+nCsJMUMSbRAuwLWyrNKA6Q+3WjpjRW17XvYd5bX33nYqzjrsjGXb4UpiSNQz68JdnK3PnTwrTeV1PaPIc+EyexvkeFWZLGIcSGqZSeM2yZUnXGZDCsz8mwQxCz+AkzJWkYRhG26K5xCHVuUp3lNowZOnWufheLoe5e+7qXLs/UZ7ESN32c02dctu5aufKm9DgtcWKYxR+GRjSNP44q+VGK79bVydMl1trkXvuK6eZmyeRiy12u2TYm2aVNk7ph/nDMakxyFu+5FbEpdzHrathxwabWXjirYzHkGiVtlMQoZA//N00+N+PybGE2Lclpu5+hMVCOqalnMYbhbucwdztf3gfmbs+ekjR3uwElA62H7W437dPc7XTbNtecdXd7FhXGLN6zYRgtmbb9a0qYNst4JKQss1zMKaQ0PtW3yz4s129c4mZt7mWaYpLDvBeh3z1uJgWzJDuQi082cU9zLlNXBRYmblLT+OoY1gpIoUxNBlkP202O0WYQ+LBjkiV99RmTnDU637OILHFLpf+9O17pdjbb5nY6O6y7mONJOI5yVMmNtv10+WJ2TQrl+s0d5+oWw3ptG5MMn19uFEIXeUYxBGjWEjd93M/bOXhHsvcCH3A7nD1OtePZ1BJafX4W3CfmboVDiWIKrYmb1nQIUCxbXyJvjLZDbLq4nU2TPCXXGAa5/29XmSy7PXw63Y+InAT8O+AGdyzAK6iWTYcx2Xd7VLQdvtLFbYwp6SZ9hdZw277aKtAutP3iD3OI0DTT55ayk0RXpf8XwB/D/k/dC4An3M5mEN8BDRjxvtuGYXTG3O2GiMirgYdV9e4250/jvtv7t4JI1bewAutiWT7DHLw+jjHJLtcZJSUxyS59547ryptfb/aUZNfdEn9NRF4FLAWOBD4IHC0i886ajO2ANr3Mz8PCAswfxsICHOae7iDuF8ak/IxxKh4ZloXEYoqD/urihLkB16kvcUruuvd19+9fM9c2dZ/hD07smjli8c46ucNzm1wv18+g3i9LyVd3jdi5XZg2BVhC63tW1StV9SRVXUG1Qc9XVPU/Al+l2tkMxnTf7aEyP88c+5ifPzQxEloUsfcx67LJcKESBRnrJ9VXzIKMye0rq1Bxldx/3fOIlYdK3VcosfapV+p+w3vJKeVY29JnV/cZyMmX6yf8/HWlb3dbRNaIyFY3EuaQrV9F5HluhMw2N2JmhVd3pSvfKiIXBucdNOKmK8P4YfgT4A/dDmcvoNrxbOaYe/qpgz64OauhyS/9sNzpOmJf+FmkzTOIKb+YZdiHPKl++vrf9aUkRWQJ8GHgIuA04A0iclrQ7BLgcTdS5gNUI2dw7S4GTgfWAB9x/Q0IR9x0opdvnKrepqqvdu/vV9WzVPVUVX2dqj7bxzUmjiOOOOgw9iENFWUbBRjro+QLEbtWTnH3Ge9s20+pQgiv1ecPS9u+6uRY7GdbQs/Z7bOAbU5f7AU2AGuDNmupRshANWLmPDeCZi2wQVWfVdUHgG2uv0NG3PTBLIYYDMNoiYgUvYBlg9Er7nVp0NVy4EHvODYSZn8bl+N4kso7zZ0bjrjpzLQNaRpbQgsvF2fMUReTjF2rpJ8uMjWlbd/hfbW9z1GTSjr11Xdbz6ExIgdtwZzlxz9+VFV73f+6Dn/EjYic21e/ZkmOiHB4UJ1r678vjV/27Vr6WEyyom1McliM/H8yP1/2qmcHcLJ3HBsJs7+NiMwDRwGPZc4djLj5PpX7/goR+evmN3kwpiRHhfvg+HHDNjHJ8EsRDhvJ1TfFzxan5CuJg4b9pORrquSbKAhfhqbndG3b9H8wtom8gSXZj5K8C1jl1no4jCoRszFos5FqhAxUI2a+oqrqyi922e+VwCrgzsSImzd2vW1zt0dMLNsdKswu7vcwCOWLXT+nKMMhK22uE6PN8Jamz7ZUnqay1/U1lszNwdKlZW1/9KNstaouiMjlwCaq1dXWq+o9InI1sEVVN1KNjPmEGymzm0rx4drdBNwLLACXqepz7W6qHlOShmGU0SQmWYCq3gLcEpS9y3u/B3hd4txrgGsyfd8G3NaHnKYkF4m5hb0HueCl49tybbsmCMLB2aOyUnPHpXVNr9OWttZrLMzQh0xthkV1okclOSnM3h2PC4GCHHyJ/Jkbg/pBeYymX7ZY+/Ca4fWaKubctcO+S+QrqSu59jCU0mL30ybj35qeLclJYfbueMyIWRldZ1/kFFcXyzK8RmnGO/dF7vKlXgyFmTo3NgKh5P/Rt3xDxZSksVikMtIlSig31a3uOnXn5RIuTcIATfpp4m43USZ9jQHN3WPKUk4p1WGPmy0pb4QpSWNcSGXAY/hftth5dVbc4LzFmhPehHFwt9tSEmro6zpDdbdLs9tThClJwzDKMEvSGBeaZLvryLnjde5rbNB7GEtskozJ9ZOSISV77polA9tL44Wx55fKVje5Ru7ZN7EGx3Za4hQxe3c8Afjuc93QkT5jkqn4Vi6WWPLF7iMmWRLXy5WnZKgLNcTCGbHzusYk69qnsJjk8Jm9O54UFhbYNx/fjTdm0ZQE/uuslZJ4ZXjNOiVe2k+ba+YSHylLNyVfqq6LJZm7l5SVmbNCc/I0ka81M6okOz05ETlaRG4Wke+JyHdF5BdE5FgR2Swi97m/x/Ql7EzhVjgfkLNSfEunNAlTZ9WF/dZd068LlULYNndurm3YLlQgg+PQ+sv9gNTVpZJndf2E161rmzsuuWZd297ob+72xND15+WDwBdV9cXAS6hWA74CuFVVVwG3umOjJXN7nqleCcvDL/MVRbSvhGIp+ULlvvSjYrGv31aGmNKsiw+3lWeo7vZg7nbJa4porfJF5Cjgl4HfAnCrC+8VkbXAua7ZjVTzJ/+ki5CGYYwBM+pud7njlcAjwF+JyEuAu6n2ljheVXe6NruA42Mnu5WKLwU45ZRTOogx5QS/yrE4YkjTmGTMTU3FzcKYWh11sc9ScsmYkJIwRVvayh6TrWu/sfOajA5ozIwqyS5Pbh54GXCdqr4U+BcC19qt/aaxk6dx3+1RcMBxiyvH3BetSaKnrp9QnpK2TfH7Lo21xq4/Dm76VNDvepITQ5e72Q5sV9U73PHNVEryIRE5UVV3isiJwMNdhTQOkFNodRZLnZXRJp7VNkbXR7uUlRo7d1hDbtqeU2oVN60bakwSpk4BltDaklTVXcCDIvJzrug8qkUw/dWEZ2/f7RESDnMZUPKFyCWAYu/D7HFJXZe2fvvB+xKFEGa+w+RU3TVi/ZW0rbvP2HHpM8lZ0KVDfXpxty1x04rfAz7pll+/H3gzleK9SUQuAX4AvL7jNQzDGAdmNCbZ6Y5V9ZtAbEe087r0a5SRGseXci39pEudtRnGLOvGC6Zih3VtU9dMyVCapBiUl1wjF24I+8rdZ2mSqOkz6cUK7IMZVZJj8vSNtswt7D3oi1bnmjVJyITndY1rNRmXOa4MMwY7EfSYuBGRNSKyVUS2icgh46ndRl+fcfV3iMgKr+5KV75VRC50ZSeLyFdF5F4RuUdE3t7LLffRibGIzM/DwsIhM3TqaGOB+u+7KIuu56eOR0FfSr6vfvpKghXRoyUpIkuADwOvpEoC3yUiG1X1Xq/ZJcDjqnqqiFwMvBf4dRE5jWpTsNOBFwJfFpEXUW0K9keq+g0R+SngbhHZHPTZGLMkp4Fgu9oBsYSOr+z8JEcu+VNnneauGUtolLSNtS8l56KWJjr8vmJtu9xnn5T23YsM/Q4BOgvYpqr3u4koG4C1QZu1VBNSoBo9c56IiCvfoKrPquoDwDbgLFXdqarfAFDVH1HNAFze9bbNkpwiQkstNTwmfJ+yMlL9xGJq/nFdTLLtcUlMsm4IUFNrNBXfbBKTLBmBMBFDgJpsKQvLRGSLd3y9ql7vHS8HHvSOtwNnB33sb+O2oH0SeIErvz049yBl6FzzlwJ30BFTkoZhlFPubj+qqrGk7tARkSOAvwXeoapPde3PlOQUMvfEbjj6aNizh31LD89alCGhOz6wpFJWU/T6Q8rIllhvMXnrZOpSN7hmXdsBo8xUh/J1pt/s9g7gZO/4JFcWa7NdROaBo4DHcueKyE9QKchPqupn+xDUlOQ0cvTR1d+Gg3pLBmynhtaEA7Zz7l3bpEVOvpLEUl1YoaQuvM9QhtQwoj5laCNfrzHJfrgLWCUiK6kU3MXAbwRtBhNTvg68FviKqqqIbAQ+JSLvp0rcrALudPHKjwLfVdX39yWoJW6mmb//+0OKFhaqv088Abt2VS8Avv/9VpdoklwpGaLkzzwJj5tkcsN+/GuUElNAqX6a9ttHwqWLDK3oMXGjqgvA5cAmqgTLTap6j4hcLSK/5pp9FHiBiGwD/hC3NoSq3gPcRDXD74vAZar6HPBLwG8CrxCRb7rXq7retlmS08yrX83cnmf2W5RzC3s5bH6efcyxdCkccYTXdtkyoN46ilkqfl3svd9f2L6tRTnoM5UsyZ0Xky/XT13bthZbrG2pHE0STr262z1OOVTVW4BbgrJ3ee/3AK9LnHsNcE1Q9g+A9Cagw5TktON9qPfNH7b/i3PIZ/0gjXmAusx27Itb6nrnXOLSLHnK9a2Tt+68UJYBfswzVI5hPDSlpGIhgZLMeMlIgtQPyBi62xPD7N2xYRjtMCVpTDtze55h39LDAXj66QPxyWOPBnbtYu6EEw5xSUNrqsRKzGWdw75zllAqY51zI1MJnFR5LiseO45l/EPrMhWe8I9jcqTkzY0yiD2zOhlaY0rSmHqWLmXu+/fDihUcufAE3HYbAPte8x+47Xsv5NwT0spwQKkbVxevq3M5c21L4pA5xZq7Zp2Cy8lXV1faNvyBKHlepW07YUrSmAlWrKj+HnEEnHvu/uIzz2zWTZ0FWde2rt8SBexTmsDJ1adifjGZ6pRh7lpNYqN9xSR7YUaVpA0BmlW2bIHPfQ4+9zkWFuB97zu0SRPrI5bkCI8HFlJJW7/94H0umx6W++c1UYx1lLQPr5n6QekiR9N+enO3Z3DR3U5KUkT+wC1J9B0R+bSILBWRlW5Zo21umaPD+hLW6JFzzuHaSy7h2ksu4emn4c/+7COHNKlzZ2PHMTfPr4sdl7b1ra5cnC2MF4bXSN1HyY9CTjHH7iN1TqnCbypPiXytmdE9blo/ORFZDvw+sFpVzwCWUI2afy/wAVU9FXicarkjwzAmnRlVkl3vZh54voj8GDgc2Am8ggPTi24E3g1c1/E6xhC44rnn3Lt96JNvhIKYVl12OxYni7XNJWJSbfvMbqfky2WPw35TSa5cnLEku10nQ6oudhx7nq2xmGQzVHUH8D7gh1TK8UmqvbefcFOOILKEkTGevPuoozr30SZRUOoe5tzZwTVj2dwS17fkml3axs5tEw7oU4bWmCVZjogcQ7X45UrgCeBvgDUNzr8UuBTglFNOaSuG0RPvdlZlLOFQZ0nF2pckZWLlubal1mjs+nXWVKp97l7qLLbcvZU8o7Z1dRnz1syoJdnljs8HHlDVRwBE5LNUE8yPFpF5Z03Glj8CwC3AeT3A6tWrtYMcxhAoddFirmuqn7q2qb5Ls+F+eYnCislY176N61py37l+m9Sl2vZiZTZbdHdq6PLkfgicIyKHuyWKBvtuf5VqWSOwfbcNY3qwxE0zVPUOEbkZ+AbVBjz/SGUZ/i9gg4j8V1f20T4ENUZLLLHg18Xep/opbZs7F8qsppSLmUs4NZGrjevaJKTQ9JpNrOtemDIFWELXfbevAq4Kiu+n2uTHmHDmFvbuXzmoLms8qIuRytrG2sWuUdd/2CaVCU71M1QXNeirJL4aO7eru90LFpM0jAC3TW0uEdLVCosNgylpm2qTom7oUkrePhRlndIeBb0OJp8xZu+OjcYMLEpIJ1FyiqcPd7H0millkFOuJQq5C10V/ti42zOauDEladTjLEo4dGhMnZWUG0qTGyTeZEhOjth4ytLB5Lk+fSu6iUw5izY3EL1N26FYqzNoSdoCF4ZhlNFzdltE1ojIVrfOwxWR+ue59R+2ufUgVnh1V7ryrSJyYWmfbZi9nwWjEyXWXF3SJbR0SlzG0rGPJQminNteZ33lLLjcOeG160IHdcmYphbmuMUkRWQJ8GHglVQz8+4SkY2qeq/X7BLgcVU9VUQG60L8uoicRrVOxOlUuyV+WURe5M6p67MxZkkajan70uW+7H3G+ppOBeyDvoYANWkzNjHJfi3Js4Btqnq/qu4FNlDN4PNZS7X+A8DNwHluTPZaYIOqPquqDwDbXH8lfTbGlKTRirmFvdXf/apvn/eufPmucN7y4Hgk85BrGKYMTeZdN6kb6nNrpiSXicgW73Vp0Nty4EHvOLbOw/42bgbfk8ALMueW9NkYc7eNdjhrwXfpuloruSFAfQ6jsSFA7VCFvQvF/Tyqqqs7X3QMMCVpdGJgUeL2895fnokd5rKvTdzFJq572/GVfdD3mM9c38N0t1UPbB7XAzuAk73j2DoPgzbbRWQeOAp4rObcuj4bs/g+jWEYE8FASZa8CrgLWOV2MjiMKhGzMWizkWr9B6jWg/iKqqorv9hlv1cCq4A7C/tsjFmSRjcGQfqnn2buiCOAg2OLCwtVk3Bcof93QJ27Hctcx6zSunGcIbl2dXX+fTRpG75vK1+ubeyaXejTklTVBRG5HNhEtavBelW9R0SuBrao6kaqdR8+ISLbgN1USg/X7iaqBXUWgMtU9TmAWJ9dZTUlafSDpyDhwBd0fv5QpZAbbpNzF5sMdykd0hP207TOv0aqrf+j0fY6pXVDHQJEr+42qnoLcEtQ9i7v/R7gdYlzrwGuKemzK+ZuG70y9717D8p0P/10um2TTHhXFjNbnlKcw+q7S7scPbvbE4NZkka/vPjFB7neRy5dIPYx893hJu5hzN0Oz01ZTm1d6j7c7VCGWNs2MtRdI9dvU/btgz17eulqojAlafSPU5DA/qXWfHLucM7iaeNul9CHO5vrpyTrPwnuds/Z7YnBlKRhGMXMopKs/XkRkfUi8rCIfMcrO1ZENovIfe7vMa5cRORDbnL5t0TkZcMU3hjyVWurAAAUGElEQVR/5r7zrf3vB/Gq0PLxyc3OOaTvTJLHPy82s6ctox5jOU7Makyy5NPyMQ7dBfEK4FZVXQXc6o4BLqIas7SKaidE22971jnjDFhYYB9z+2espYYA5RRZbPB5iUL1p036ZSUMM7kSu7+++u7SLocpyQSq+jWqMUo+/sTzG4HXeOUf14rbqXZOPLEvYY0JxVuPEsoswJhC7KJY2o5P7IMSK7itpVvatpfplS5xU/KaJtrGJI9X1Z3u/S7gePc+NcF8JwG27/bsMZjC6CdzukyrG+a0xJK+u1hxuWmJTZTzKKclwvRZiSV0/nlx04Qa75utqter6mpVXX3cccd1FcMwjCEzq+52W0vyIRE5UVV3Onf6YVdeMmndmFXcFMa5hb2HuOCQH8YTs+xKEzdN2teRGwLUJ37fba3ovuWa1SFAbS1Jf+L5OuDzXvmbXJb7HOBJzy03jIpgmTUf3w0PB5q3ib/FEje59qEcfRDLsNcloXyZc/LVJb9SSbI2mCWZQEQ+DZxLtYjmdqp9tq8FbhKRS4AfAK93zW8BXkW1UvAzwJuHILMxJczteYZ9Sw9PWoy5oULZfhtakk1o00eJgu8ruz3MmOSsWpK1SlJV35CoOi/SVoHLugplzAhLlx6kQPz3MeUWs55yrmjTmTexfvoiN+uo6bk5ut5zDtXpy1yXYDNujEWnqfJro1zCWGJJH31N58sp/Jh8sfPb1KVkaItZkoZhGBlMSRrGIlMXSyy1vmL9tB2H2AdNx0l26dtikv1jStIYO3LLnQ3q684f0CUW2NRFHZbybbICkN/ehgD1gylJY+xIJV2axCRz5w4rJtlkqbQ+YpLhvYTy9r1UGsymkrSVyY2xJBwnOSgrPXfAoI++3O02yiY2VnNYi2cM090e1dzt1CpjkXbrXJv7RGSdV/5yEfm2W43sQyIirvy/icj33AplfyciR5fIY0rSGFt8xRJTfKm2gzY+fQ7nmVVGOJg8tcrYfkTkWKox22cDZwFXecr0OuCtHFiRbLCK2WbgDFX918D/Ba4sEcY+OYZhFDFCJZlaZcznQmCzqu5W1cepFOAaN036SFW93Y3b/vjgfFX9kqoOpLudatp0LRaTNCaCupjdgFL3c5T0Pesn7LtujnsoQxcaKMBlIrLFO75eVa8vPDe1yphPasWx5e59WB7yn4DPlAhjStKYCHIJl1xZTEF1iVH2NS2xiTJvUjdGQ4AeVdXVqUoR+TJwQqTqnQdfU1VEGq8ylkNE3km1X/cnS9qbkjQmhpiiTK3EU5fBDs9LZdP9v74M4bkx+XKyldyrL1esryZy9Jm46QNVPT9VJyKpVcZ8dgDnescnAbe58pOC8v0rkYnIbwGvBs5z7ngtFpM0Joow6+0P6/GVWp1S8BM9dRZqmJ0Or5kaWuSXx66d6jfsIzX0KXccyjdhqwClVhnz2QRcICLHuITNBcAm56Y/JSLnuKz2mwbni8ga4I+BX1PVZ0qFMUvSmDjqrMYmYyGb0qbfVIywD/lG6W7DyMZJRlcZE5HVwO+o6ltUdbeIvAe4y51ztaoOtpl5G9XeXM8HvuBeAH8JPA/Y7EYF3a6qv1MnjClJwzCKGNWMG1V9jPgqY1uAt3jH64H1iXZnRMpPbSOPKUlj4il1J3PxwljsL+Wq1sUCY/KF54bvw+Ow75jL3jQ22pVZnZbYdt/t5Mh1EbnSjXTfKiIXDktww2hKLF4Y1oUxQj8eWBqTjMUbw7Z+P3UxybDOL8vdX2y2URdmdWXytvtuR0eui8hpwMXA6e6cj4jIkt6kNYwR0tQCa5PFniQGi+7alrIBqvo1EVkRlH3JO7wdeK17vxbYoKrPAg+IyDaqKUNf70Vaw0iQSo6ExzHXNOdat3G3Q/c6tChj8uVCBnXhhBL5+rAmZ9Xd7iMm6Y9cX06lNAekRrvbvtuGMWGYkmxB05HrPm6K0vUAq1ev7nVEvWHAoRZYLNYH6c3HwrY5CzLWb925KRl86uKKdcmowXGf4yRnjdZKMjFy3fbdNsaWEnc71r4Pdzs8N3wfHtfFN0tccMtu90MrJemNXP+VYOT6RuBTIvJ+4IVUyxTd2VlKw2hJ3cDznIKrU3p11mGdVVpiSeYGxpdkt1NytMF2S0yQ2Hf7SiIj11X1HhG5CbiXyg2/TFWfG5bwhlFHTgn65ZNKE7e9K2ZJJkjsu/3RTPtrgGu6CGUYfVFiGbbtL0XKHc/1k+u3SV1fVmMMU5KGYRgZTEkaxpRTZzWmkiUl4xljGfJwNk6sbarvkjGOTRJINk6yPaYkjZkhNlWvZBB33bCeVNuUu51rk4qZ5pI6sePU+66YkjSMKafJ1MG+FEsqkdKHpVcak+zDkuxz0d1JwpSkMZPUucxtzs8NN6qz6sK2fbvbfWDutmHMECmXOWzTZD51G3c7J5t/nXFwt01JGoZh1GBK0jBmDN/KKl2VJ9dXafkkxiRn1ZKc3KkGhtETdYPMBxnxWF2sbeq8VL2vpMOXL1+qPnUcG37UhVEtuisix4rIZhG5z/09JtFunWtzn4is88pfLiLfdot/f8htCOaf90cioiKyrEQeU5KGQXwVcn+YUC6RkqrzlVQ49MivT107Nt4yfIV1oXIcHPehKAfZ7REsunsFcKuqrgJudccHISLHUk2RPptqzdqrPGV6HfBWqrUjVuEtGi4iJ1PtrPjDUmFMSRqGw1csUJbBLqkrcXXb9BurG6a7DSPbvmEtcKN7fyPwmkibC4HNqrpbVR+n2i1hjdun+0hVvd2tTvbx4PwPUC3OU7w8o8UkDcOjNDudcs/DrHLs/NyMm1R9Kq4Zu06ubRcaxiSXicgW7/h6t4ZsCce7/bMBdgHHR9osBx70jgcLfC9378NyRGQtsENV/0/ggWcxJWkYRjGqxRbpo6q6OlUpIl8GTohUvfPg66mKSOdFuUXkcOBPqVztRpiSNIwEde62b7mlLNCmyZOU1Zq6ZteMfDMU6GflQ1U9P1UnIg+JyImqutO5zw9Hmu0AzvWOTwJuc+UnBeU7gJ8FVgIDK/Ik4Bsicpaq7srJajFJw0iQSqKE9YPy8BVTloPzYn3EjlMy+cdh/fBQYG/hqxMbgUG2eh3w+UibTcAFInKMS9hcAGxybvpTInKOy2q/Cfi8qn5bVX9aVVeo6goqN/xldQoSCpRkbN9tr+6gVLpUfMil3r8lIi+r698wxp1wSE1Ynso+xzLag/PC82PHsbYxuWLHwxgCNLhK2asT1wKvFJH7gPPdMSKyWkRuAFDV3cB7gLvc62pXBvA24AZgG/BPwBe6CFPibn8M+EuqLNF+Eqn0iziQdj+bKhV/dhcBDWOxyQ3/ydXnXPNY4ifVd11d7Dj1vhv9udvZq6g+BpwXKd8CvMU7Xg+sT7Q7o+YaK0rlqX1yqvo1YHekKpZKXwt8XCtuB452MQXDMCaegZIseU0PrX5e/FR6UJVKy8f6uFREtojIlkceeaSNGIYxUrrE+0pijW37Lemnv1jl7CnJxtntLql0H9t325hUUq5rKvYYGztZ2ncuaRQrS2Xc+2E07va40WYIUDKVju27bcwAqRhfLiZZRywxVFIXu05scHl/Mckf99DPZNFYSarqt4GfHhyLyPeB1ar6qIhsBC4XkQ1UCZsnvZHzhjE15DLWYZuS6Y2TMS3RLMkosX23VTW1pewtwKuoUu/PAG/uSU7DGFuaWmkppZmbotjGEqzLyrfDlOQhJPbd9utXeO8VuKy7WIZhjB9mSRqG0YLUWMbUgO5c3LG079R5qcUx+htQPswZPeOJKUnD6IGYIsrFJLskeWLXqOvHYpLtMSVpGD3RdDmz0sUpmtTFhhv1Z0UO5m7PFqYkDWMExNztkmmJdXW5fiZ1WuK4YUrSMEZIauhQeJzKTNcNSh/+jBuLSRqGYSQwS9IwjCFTl7BpelzXf/+YkjQMo0dKkjmDsrqhRDFKlWJ/MUlL3BiGMSJ8BecnV2JDiVKzdGKLWpSuVdkcxWKShmEMlVQixidVn1tlKDyOZbn7wdxtwzCMBLOZuLGNwAxjhMQGf4ev2ODw3LYRsePBeV0WyDiU0axMLiLHishmEbnP/T0m0W6da3OfiKzzyl8uIt92e219SLxNtkXk90TkeyJyj4j8eYk8piQNY8SE7nRsA682fcWO68qbM5KNwK4AblXVVcCt7vggRORY4CqqJRnPAq7ylOl1wFs5sN/WGnfOr1JtMfMSVT0deF+JMKYkDWOR8BfAKJlRk5oD7r+P7ZaYOrc5I9tSdi1wo3t/I/CaSJsLgc2qultVHwc2A2vcnlpHqurtblWyj3vn/y5wrao+C6Cqsf28D8GUpGEsEr5LnFNipVs/pNzt/hjZRmDHe4t17wKOj7RJ7ae13L0PywFeBPxbEblDRP63iPx8iTCt991O+fYicqWLBWwVkQtLhDCMWaXOkqxTcqVudL+L7hYpyWWDjf7c61K/FxH5soh8J/Ja67dz1mBfe2DNA8cC5wD/GbjJj1fmTqrjYwT7bge+/bMi8tOu/DTgYuB04IXAl0XkRao6eykxw5g6Go2TfFRVVyd7Uj0/VSciD4nIiaq607nPMbd4B3Cud3wScJsrPykoH+yztR34rFO8d4rIPmAZkN2ute2+2ynffi2wQVWfVdUHqLZxOKvuGoZhHLxPju8q+3HF8OWXD9rGBpf3w8jc7Y3AIFu9Dvh8pM0m4AIROcYlbC4ANjk3/SkROcdZiW/yzv8c8KsAIvIi4DDg0Tph2j69lG9v+24bRkv82TexIUGp4UJhgiZUqv0qypEoyWuBV4rIfcD57hgRWS0iNwCo6m7gPcBd7nW1KwN4G3ADlZH2T8AXXPl64Gdc6HADsM5ZlVnaDib3ffufp/Ltf6ZJB7bvtmEcyrBmy/Q3LXH4c7dV9THgvEj5FuAt3vF6KsUXa3dGpHwv8Mam8rRVkinf3vbdNoyOhNZjagxlai3KWF0/zObc7bZPMuXbbwQuFpHnichKqoGcd/YhqGHMErnFKlJDgsIhQHXjL9sxEnd7rGi17zaVibve+fZ7OeDb3yMiNwH3AgvAZZbZNoxpYTbnbnfZdzvq26vqNcA1XYQyDKMitCbrlkBLbfVge9y0x1YBMowxJ7fIRWwptVzGuxu26K5hGGNKSlHmVgcqnc7YjNlL3JiSNIwJIWVJlihFc7fbY0rSMCaQOhe6bsOx9piSNAzDSGCWpGEYE0I44DxWPxwsJmkYxoRQpxz7HwK0D8tuG4YxccR2SRzeFEVztw3DmDBSi2H073JbTNIwDKMGi0kahjGhhJajTUvsB1OShjFlhHFJi0l2w5SkYUwZubne3bDstmEYU0K/Wzb4mCVpGMaUMJzs9uwlbobxU2MYxtQy/JXJReRYEdksIve5v8ck2q1zbe4TkXVe+ctF5Nsisk1EPjTYW1tEzhSR20Xkm24TwqKdXE1JGoZRyMi2lL0CuFVVVwG3uuODEJFjqXZJOJtq2+qrPGV6HfBWqu1jVgFrXPmfA/9FVc8E3uWOazElaRhGIQr8uPDVibXAje79jcBrIm0uBDar6m5VfRzYDKwRkROBI1X1drelzMe98xU40r0/CvjnEmHGIiZ59913PypLlvwLBRuFTxHLmK37hdm753G733/V7fQnN8H/XFbYeKmIbPGOr3fbSJdwvKrudO93AcdH2iwHHvSOt7uy5e59WA7wDmCTiLyPykD8xRJhxkJJqupxIrJFVVcvtiyjYtbuF2bvnqftflV1TX2rMkTky8AJkap3BtdUEdGeLvu7wB+o6t+KyOuBjwLn1500FkrSMIzZQlWTyklEHhKRE1V1p3OfH44020G1i+uAk4DbXPlJQfkO934d8Hb3/m+AG0pktZikYRjjxkYqhYb7+/lIm03ABSJyjEvYXABscm76UyJyjstqv8k7/5+BX3HvXwHcVyLMOFmSpfGKaWHW7hdm755n7X774lrgJhG5BPgB8HoAEVkN/I6qvkVVd4vIe4C73DlXq+pu9/5twMeA5wNfcC+oMt4fFJF5YA9waYkwUiWADMMwjBjmbhuGYWQwJWkYhpFh0ZWkiKwRka1uCtEhI+unBRH5vpsq9c3B+LHS6VeTgIisF5GHReQ7Xln0/qTiQ+5//i0RedniSd6exD2/W0R2uP/zN0XkVV7dle6et4rIhYsjtdGURVWSIrIE+DBwEXAa8AYROW0xZRoyv6qqZ3pj52qnX00QH+PA9K8Bqfu7iANTxi6lmkY2iXyMQ+8Z4APu/3ymqt4C4D7XFwOnu3M+4j7/xpiz2JbkWcA2Vb1fVfcCG6imJM0KJdOvJgJV/RqwOyhO3d9a4ONacTtwtBsPN1Ek7jnFWmCDqj6rqg8A26g+/8aYs9hKMjW1aBpR4EsicreIDIYelEy/mmRS9zft//fLXRhhvRdCmfZ7nloWW0nOEv9GVV9G5WpeJiK/7Fe6yfhTOx5r2u/P4zrgZ4EzgZ3Af19ccYyuLLaS3AGc7B37U4imClXd4f4+DPwdlav10MDNzEy/mmRS9ze1/3dVfUhVn1PVfcD/4IBLPbX3PO0stpK8C1glIitF5DCqwPbGRZapd0TkJ0XkpwbvqaZQfYey6VeTTOr+NgJvclnuc4AnPbd8ogliq/+e6v8M1T1fLCLPE5GVVEmrO0ctn9GcRZ2WqKoLInI51TzMJcB6Vb1nMWUaEscDf+cWSJ4HPqWqXxSRu4hMv5pEROTTVAsOLBOR7VQLokanlwG3AK+iSl48A7x55AL3QOKezxWRM6lCC98HfhtAVe8RkZuAe4EF4DJVnb0NYyYQm5ZoGIaRYbHdbcMwjLHGlKRhGEYGU5KGYRgZTEkahmFkMCVpGIaRwZSkYRhGBlOShmEYGf4/ThEbrVtk1eEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(covariance, cmap='seismic',vmin=-0.008, vmax=0.008)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The correlation matrix can be constructed using the covariance matrix and also give some insight into the relations among the parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x14cf90b682e8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"corr = np.zeros([len(covariance),len(covariance)])\n",
"for i in range(len(covariance)):\n",
" for j in range(len(covariance)):\n",
" corr[i, j]=covariance[i, j]/covariance[i, i]**(0.5)/covariance[j, j]**(0.5)\n",
"plt.imshow(corr, cmap='seismic',vmin=-1.0, vmax=1.0)\n",
"plt.colorbar()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sampling and Reconstruction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The covariance module also has the ability to sample a new set of parameters using the covariance matrix. Currently the sampling uses numpy.multivariate_normal(). Because parameters are assumed to have a multivariate normal distribution this method doesn't not currently guarantee that sampled parameters will be positive."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/romano/openmc/openmc/data/resonance_covariance.py:233: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n",
" warnings.warn(warn_str)\n"
]
},
{
"data": {
"text/plain": [
"openmc.data.resonance.ReichMoore"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rm_resonance = gd157_endf.resonances.ranges[0]\n",
"n_samples = 5\n",
"samples = gd157_endf.resonance_covariance.ranges[0].sample(n_samples)\n",
"type(samples[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The sampling routine requires the incorporation of the `openmc.data.ResonanceRange` for the same resonance range object. This allows each sample itself to be its own `openmc.data.ResonanceRange` with a new set of parameters. Looking at some of the sampled parameters below:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample 1\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>L</th>\n",
" <th>J</th>\n",
" <th>neutronWidth</th>\n",
" <th>captureWidth</th>\n",
" <th>fissionWidthA</th>\n",
" <th>fissionWidthB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.030679</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000473</td>\n",
" <td>0.108576</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.823843</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000351</td>\n",
" <td>0.086418</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.281147</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.000458</td>\n",
" <td>0.106825</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.771760</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.013598</td>\n",
" <td>0.072837</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.561545</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.011164</td>\n",
" <td>0.086616</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n",
"0 0.030679 0 2.0 0.000473 0.108576 0.0 0.0\n",
"1 2.823843 0 2.0 0.000351 0.086418 0.0 0.0\n",
"2 16.281147 0 1.0 0.000458 0.106825 0.0 0.0\n",
"3 16.771760 0 2.0 0.013598 0.072837 0.0 0.0\n",
"4 20.561545 0 2.0 0.011164 0.086616 0.0 0.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Sample 1')\n",
"samples[0].parameters[:5]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample 2\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>L</th>\n",
" <th>J</th>\n",
" <th>neutronWidth</th>\n",
" <th>captureWidth</th>\n",
" <th>fissionWidthA</th>\n",
" <th>fissionWidthB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.032858</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000479</td>\n",
" <td>0.105208</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.823859</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000361</td>\n",
" <td>0.093748</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.203069</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.000264</td>\n",
" <td>0.015233</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.765055</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.013648</td>\n",
" <td>0.076119</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.557679</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.011140</td>\n",
" <td>0.097548</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n",
"0 0.032858 0 2.0 0.000479 0.105208 0.0 0.0\n",
"1 2.823859 0 2.0 0.000361 0.093748 0.0 0.0\n",
"2 16.203069 0 1.0 0.000264 0.015233 0.0 0.0\n",
"3 16.765055 0 2.0 0.013648 0.076119 0.0 0.0\n",
"4 20.557679 0 2.0 0.011140 0.097548 0.0 0.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Sample 2')\n",
"samples[1].parameters[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can reconstruct the cross section from the sampled parameters using the reconstruct method of `openmc.data.ResonanceRange`. For more on reconstruction see the Nuclear Data example notebook. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<openmc.data.resonance.ReichMoore at 0x14cf93855d68>,\n",
" <openmc.data.resonance.Unresolved at 0x14cf938c02e8>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gd157_endf.resonances.ranges"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Cross section (b)')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy_range = [rm_resonance.energy_min, rm_resonance.energy_max]\n",
"energies = np.logspace(np.log10(energy_range[0]),\n",
" np.log10(energy_range[1]), 10000)\n",
"for sample in samples:\n",
" xs = sample.reconstruct(energies)\n",
" elastic_xs = xs[2]\n",
" plt.loglog(energies, elastic_xs)\n",
"plt.xlabel('Energy (eV)')\n",
"plt.ylabel('Cross section (b)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Subset Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another capability of the covariance module is selecting a subset of the resonance parameters and the corresponding subset of the covariance matrix. We can do this by specifying the value we want to discriminate and the bounds within one energy region. Selecting only resonances with J=2:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>L</th>\n",
" <th>J</th>\n",
" <th>neutronWidth</th>\n",
" <th>captureWidth</th>\n",
" <th>fissionWidthA</th>\n",
" <th>fissionWidthB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0314</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000474</td>\n",
" <td>0.1072</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.8250</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000345</td>\n",
" <td>0.0970</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.7700</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.012800</td>\n",
" <td>0.0805</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.5600</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.011360</td>\n",
" <td>0.0880</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>21.6500</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000376</td>\n",
" <td>0.1140</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n",
"0 0.0314 0 2.0 0.000474 0.1072 0.0 0.0\n",
"1 2.8250 0 2.0 0.000345 0.0970 0.0 0.0\n",
"3 16.7700 0 2.0 0.012800 0.0805 0.0 0.0\n",
"4 20.5600 0 2.0 0.011360 0.0880 0.0 0.0\n",
"5 21.6500 0 2.0 0.000376 0.1140 0.0 0.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lower_bound = 2; # inclusive\n",
"upper_bound = 2; # inclusive\n",
"rm_res_cov_sub = gd157_endf.resonance_covariance.ranges[0].subset('J',[lower_bound,upper_bound])\n",
"rm_res_cov_sub.file2res.parameters[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The subset method will also store the corresponding subset of the covariance matrix"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(180, 180)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rm_res_cov_sub.covariance\n",
"gd157_endf.resonance_covariance.ranges[0].covariance.shape\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking the size of the new covariance matrix to be sure it was sampled properly: "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of parameters\n",
"Original: 60\n",
"Subet: 36\n",
"Covariance Size\n",
"Original: (180, 180)\n",
"Subset: (108, 108)\n"
]
}
],
"source": [
"old_n_parameters = gd157_endf.resonance_covariance.ranges[0].parameters.shape[0]\n",
"old_shape = gd157_endf.resonance_covariance.ranges[0].covariance.shape\n",
"new_n_parameters = rm_res_cov_sub.file2res.parameters.shape[0]\n",
"new_shape = rm_res_cov_sub.covariance.shape\n",
"print('Number of parameters\\nOriginal: '+str(old_n_parameters)+'\\nSubet: '+str(new_n_parameters)+'\\nCovariance Size\\nOriginal: '+str(old_shape)+'\\nSubset: '+str(new_shape))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, we can sample from the subset as well"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>L</th>\n",
" <th>J</th>\n",
" <th>neutronWidth</th>\n",
" <th>captureWidth</th>\n",
" <th>fissionWidthA</th>\n",
" <th>fissionWidthB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.030488</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000473</td>\n",
" <td>0.108946</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.825944</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000328</td>\n",
" <td>0.098328</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.773886</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.012984</td>\n",
" <td>0.076779</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20.565737</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.011628</td>\n",
" <td>0.088958</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>21.646469</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.000389</td>\n",
" <td>0.127833</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n",
"0 0.030488 0 2.0 0.000473 0.108946 0.0 0.0\n",
"1 2.825944 0 2.0 0.000328 0.098328 0.0 0.0\n",
"2 16.773886 0 2.0 0.012984 0.076779 0.0 0.0\n",
"3 20.565737 0 2.0 0.011628 0.088958 0.0 0.0\n",
"4 21.646469 0 2.0 0.000389 0.127833 0.0 0.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples_sub = rm_res_cov_sub.sample(n_samples)\n",
"samples_sub[0].parameters[:5]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| notebooks/poppy dev.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import warnings\n",
"import numpy as np\n",
"\n",
"import astropy.units as u\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" import poppy\n",
" import matplotlib\n",
" matplotlib.use('nbagg')\n",
" #from matplotlib import style\n",
" #style.use('ggplot')\n",
" import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAHiCAYAAADiemg3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114db8470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"osys = poppy.OpticalSystem()\n",
"primary = poppy.CircularAperture(radius=3.2285 * u.m)\n",
"secondary = poppy.SecondaryObscuration(secondary_radius=0.5, n_supports=4, support_width=0.12, support_angle_offset=45.)\n",
"mmt = poppy.CompoundAnalyticOptic(opticslist=[primary, secondary], name=\"MMTO\")\n",
"plt.figure(figsize=(12,8))\n",
"mmt.display(npix=1024, colorbar_orientation='vertical')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAD8CAYAAABgtYFHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFNdJREFUeJzt3W3MZGV9x/Hvz3UFUYis0g27kAAJmFCjqyFgommolu6W\nmoJvCDatm3RTfGF9iE3qokmrL0xo40NfNJqslbhtVUqoBkLUze6Wxpj4wKIrLiCwKoSHZdcHjBIT\nVPz3xZxxj7P33HNmztN1nfP7JHfumTMz91zXnGt+c52He/6KCMzMFnle3w0wszw4LMysEoeFmVXi\nsDCzShwWZlaJw8LMKmktLCTtkPSgpKOSdrf1PGbWDbVxnoWkDcBDwFXA48DdwFsi4v7Gn8zMOtHW\nzOJy4GhE/CAifgXcAlzT0nOZWQee39Lf3Qo8Vrr+OHDFvDu/QKfF6byopabYKi555S9rPf6he89o\nqCXWlF/w9I8j4pxVH99WWCwk6QbgBoDTOYMr9Ma+mjI6+548XOFeL229Hdu3bGv9OeykA3Hbo3Ue\n31ZYPAGcX7p+XrHsdyJiD7AH4Cxt8j+otKBaKPRnXvscImlqKyzuBi6WdCGTkLge+MuWnstIPxiW\nsVZfHCD9ayUsIuI3kv4O2AdsAG6OiPvaeK6xGlI4VDHbX4dH91rbZxERXwS+2NbfH6OxBcR6yq+F\ng6Mbve3gtOocEuubvj4OjXY5LBLmkFiOQ6NdDovEOCDq8yZKOxwWCXBAtMfB0RyHRY8cEt3yZko9\nDoseOCT65dBYjb/PomMOinR4XSzHM4uOeGCmybOM6hwWLXNI5MGhsZjDoiUOiTw5NOZzWDTMITEM\nDo1TeQdngxwUw+N1epLDoiEeVMPldTvhzZCaPJDGwZslDouVOSTGacyh4c2QFTgobIxjwGGxpDEO\nElvb2MaCN0MqGtvAsGrGtFlSa2Yh6RFJ35V0WNKhYtkmSfslPVz8PruZpvbHQWGLjGGMNLEZ8scR\nsS0iLiuu7wYORsTFwMHierbGMAisGUMfK23ss7gG2Ftc3gtc28JzdGLoK9+aN+QxUzcsAjgg6Z6i\nwhjA5og4Vlx+Cthc8zl6MeSVbu0a6tipu4Pz9RHxhKQ/APZL+l75xogISWtWG5stX5iKoa5o69YQ\nd3zWmllExBPF7xPAF5hUTz8u6VyA4veJOY/dExGXRcRlGzmtTjMa46Cwpg1pTK0cFpJeJOnM6WXg\nT4EjwB3AzuJuO4Hb6zbSzPpXZzNkM/AFSdO/89mI+LKku4FbJe0CHgWuq99MM+vbymERET8AXrXG\n8p8Ab6zTqL5s37JtUNNG69+Q9ln4DM4Z05Xr0LA6hhQSU6MNi0XFZzzLsFXNC4rcCx6N8h/JZkNg\nXijkuEKtX1WCYq3rORhlWJjZ8kYXFvMS3bMLq6vqrGLR8lSNKiwWrRwHhq1q2aCoentKFLHm2did\nOkub4gq1e7R12ZWy6sq3cWlqnHTxgXQgbrun9N/hSxvFzGKVN7hnGbZIkx8oOXwIDT4s6qwEB4bN\n08bMM/XAGHxYmFkzBh0WTSS1Zxc2q839WSnPLgYbFk2+6A4Mm+pix3eqgTHIoyFtvtg+SjJOfaz3\npj+MfDSkY55ljI8/ICYGFxZdrEAHxnj0GRSphdHgwsLM2jGosOgyiT27GL4UNj9Sml0MJiz6eFEd\nGMOVQlD0+ZxrGcTRkBRezLUGVwrtsuWkFBKz6n4ItX40RNLNkk5IOlJaNreeqaQbJR2V9KCk7as2\nLDdrDabtW7Z5lpGRlIMiBVU2Qz4N7JhZtmY9U0mXAtcDf1g85uOSNjTW2jWktCK9WZKvHIKi77Ys\nDIuI+Arw05nF8+qZXgPcEhHPRsQPgaNMCg+NhgMjPzkERQpW3cE5r57pVuCx0v0eL5adQtINkg5J\nOvRrnl2pEV6ZNjZ9jvnaR0Nisod06b2kKZYvbIpnF/nwrKK6VcNiXj3TJ4DzS/c7r1jWuNRXpgMj\nffOOYOU6ttq2aljMq2d6B3C9pNMkXQhcDHyzXhPzNW/g+ShJv+a9/qmHRN+qHDr9HPA14OWSHi9q\nmN4EXCXpYeBPiutExH3ArcD9wJeBt0fEc003OreV6llGOoay2dFHe7M7KSu3lVo2lIGaqyG+/st8\n4Phf1M2sE1mFRc6fAODNkT4NcVYB3bY/q7AYAgdG94YaFF3LZp/FEFesB3G7xvT6Vvmw8T6LjHmW\n0Z4xBUVXHBY9c2A0z0HRjizCwivZbH1dvEeyCIuh8+yiOZ5VtMdhkQgHRn0OinYlfzRkjCvag345\nfr1OWu/DxUdDBsizjOocFN1JOizGvMIdGIs5KE7VZt+TDgszS0eyYTHmT4cpzy7m86xivrZeg2TD\nwiYcGKdyUPTj+X03wBabvglm3yTT62N5kzgk+pXkzMIrf21jnmU4KJbTxuvimUVm9j15eM03zvYt\n21p/4/T1hnVQpGFhWEi6GXgTcCIiXlEs+wDwt8CPiru9LyK+WNx2I7ALeA54Z0Tsa6Hd1oGqM5bZ\n+/lNPExVZhafBv4N+I+Z5R+LiA+XF8yUL9wCHJB0SRtf2jtmbc4umtikKf+NttrjQOrequUL56ld\nvtCDoJom919Mvxq/jX0fdf62g6Kepl+nOvss3iHprcAh4O8j4mkmpQq/XrrPuuULgRsATueMGs0Y\nr7pHSbreMVq3XQ6Jfq16NOQTwEXANuAY8JFl/8CQyxd2bdlZRt9FjtZ7fgdFulYKi4g4HhHPRcRv\ngU9yclOjs/KF9vuqBEbfITFrtj0OirStFBbTOqeFNwNHissuX2g2UAu/z6IoX3gl8DLgOPBPxfVt\nTKqnPwK8LSKOFfd/P/A3wG+Ad0fElxY1Yvp9Fv4EqS+lmUMTPCbqm46Jut9nsXAHZ0S8ZY3Fn1rn\n/h8CPrRqg6yeeYdVc+SgSIvP4BygeUdJcuGQSFOS/xtiZunxzGKAcp1RTI3tv2lz4ZmFmVWSTFj4\nU6QZuc8qyobUlz419d5KJizMLG0OiwEZ4ifxEPuUK4eFmVXisBiIIX8CD7lvOXFYmFklDgszq8Rh\nMQBjmKaPoY+pc1iYWSVJhMUlr/xl300wG7QmTsxKIixsdWOano+prylyWJhZJQ4LM6vEYWFmlSwM\nC0nnS7pL0v2S7pP0rmL5Jkn7JT1c/D679JgbJR2V9KCk7W12YMzGuA0/xj6nosrM4jdMighdCrwW\neHtRpnA3cDAiLgYOFtdnSxjuAD4uaUMbjTez7lQpX3gsIr5VXP4F8ACTKmPXAHuLu+0Fri0u1y5h\naGbpWWqfhaQLgFcD3wA2T7/+H3gK2Fxc3go8VnrYmiUMJd0g6ZCkQz/6iesmm6WuclhIejHwP0xq\ngfy8fFtMio+sX4BkRrl84Tkv9VaKWeoqhYWkjUyC4jMR8fli8fFpZbLi94liuUsYmg1QlaMhYlJU\n6IGI+GjppjuAncXlncDtpeUuYdiBMX5v6Rj7nIoqpQBeB/w18F1J0zX1PuAm4FZJu4BHgesAIuI+\nSbcC9zM5kvL2iPBOCbPMVSlf+FVAc25+45zHuISh2cD4DE4zq8RhkbkxbcOPqa8pSiIsHrr3jL6b\nYDZoTZwmn0RYmFn6HBYDMIbp+Rj6mDqHhZlV4rAws0ocFgMx5Gn6kPuWE4eFmVXisBiQIX4CD7FP\nuXJYmFklyYSFv1uxGUP6JB5SX/rU1HsrmbAws7RV+Rd1y8z0EznX2ZpnFGnyzMLMKvHMYoBynVFM\nTdvvGUZaPLMYmNyDomxIfRmCpMLCg6Oeea/fvicPJ/0pvV77PCbqafL1q1O+8AOSnpB0uPi5uvQY\nly80G5gq+yym5Qu/JelM4B5J+4vbPhYRHy7feaZ84RbggKRL/KW97VpvVjF7OZVP69nZxL4nD6/Z\ntu1btiU9MxqLOuUL53H5wo5VCYrZ5X2++dZ7fm+OpGupoyEz5QtfB7xD0luBQ0xmH08zCZKvlx62\nZvlCq2/ZkJh3v67eiHXb5aMk/apTvvATwEXANuAY8JFlnrhc6/TXPPu75f4EqaZuUMw+pq3ZRp2/\n7VlGPU2/TpVmFmuVL4yI46XbPwncWVytVL4wIvYAewDO0qal6qSOXZNBsd7fWHWwNRk63o+RjoVh\nMa98oaRzS1XU3wwcKS7fAXxW0keZ7OB0+cJMzb4Z2wwpS1+d8oVvkbSNSfX0R4C3gcsXtq3PN2xf\noeDZRRoU0f8WwFnaFFfo9yshehCcauyf7GPv/zLWeq0OxG33RMRlq/5N/29IBvwmmfBRkn4ldbq3\nncpBcSofJelHsmHhFe+gWI8DY762XoNkw8LM0pJ0WIz5U8KzisU8uzhVm31POizGykFRnQOjO8ke\nOi0by5vEIVHP2F+/RQFZ99CpZxaJGPtAb4JnGe1yWCTAQdEcB0Z7sggLr2iz9XXxHskiLIbMs4rm\neXbRDodFjxwU7XFgNC+LoyFTQ3kTOSS6NfTXu2oA+mhIZoY+cFPkWUYzsgqL3Feug6I/Qw2MLtuf\nVViYWX+yC4tcPwk8q+jf0GYXXbc7u7CA/FaugyIdQwmMPtpbpXzh6ZK+Kek7RfnCDxbLN0naL+nh\n4vfZpce4fCGTFbrWSu27yM/YzXv9cwuMrlWZWTwLvCEiXsWkRsgOSa8FdgMHI+Ji4GBxfbZ84Q7g\n45I2NN3w1FesZxPpmxcYuY6ttlUpXxgR8UxxdWPxE0zKFO4tlu8Fri0uj758oYMiH0PZLOlCpX0W\nkjYUZQBOAPsj4hvA5lLdkKeAzcXlrcBjpYe3Vr4wxRXqoMhPToHRZ5sqhUVEPBcR25hUF7tc0itm\nbg8ms43K5pUvNLM0LXU0JCJ+BtzFZF/EcUnnwqQ6GZNZByxRvjAiLouIyzZy2iptB9JKf88q8pXD\n7KLvtlQ5GnKOpJcUl18IXAV8j0mZwp3F3XYCtxeX7wCul3SapAsZSflCH/XIXw6B0acqRYbOBfYW\nRzSeB9waEXdK+hpwq6RdwKPAddBP+cI+y9h5NjEsqRYySiGwsvqv00W6XpEOimFLZf02FRT+r9OS\nLtM3lYFk7UlhsySFGcXUoMLCzNozuLDoIok9qxiPPmcXKc0qYIBh0TYHxfiksDmSgkHt4Cxr+s3r\nkDDobhy0EUTewTlHky+2g8KmuphlpDpjGWxYQDMvuoPCZrUZGKkGBQw8LMysOYMPizpJ7VmFzdPG\n7CLlWQWMICxgtZXgoLBFmgyM1IMCBnw0ZC1V3ugOCVtFnXHTVVD4aMgSFq0UB4WtatVZRg4ziqlR\nhQXMXzkOCqtr2cDIKShghGFhZqsZZVjM+66CWZ5V2LKqzi5ym1XAyHZwLsNBYXWkGAZ1d3BW+aas\nUXFIWBPmfeNWzka5GTKPg8KaNqQxVad84QckPSHpcPFzdekxLl9oNjBVNkOm5QufkbQR+KqkLxW3\nfSwiPly+80z5wi3AAUmXtP2lvWbWrjrlC+fJtnzhkLYvLQ1DGlN1yhcCvEPSvZJuLlVR76x8YRty\nKIxr6RviOKpTvvATwEVMKqsfAz6yzBOnXr5waCvaujPUsbNy+cKIOF6EyG+BT3JyU6PT8oVtGupK\nt/YMecysXL5wWue08GbgSHF5UOULh7zyrVlDHyt1yhf+p6RtTHZ2PgK8DfopX9i2PssjWh6GHhTg\n072X5tCwspxCwt9n0bGcBoe1a2xjwWGxgrENEjvVGMeA/5FsRdPB4s2ScRljSEw5LGpyaIzDmENi\nypshDfFgGi6v2wmHRYM8qIbH6/Qkb4Y0zJslw+CQOJXDoiUOjTw5JOZzWLTMoZEHh8RiDouOODTS\n5JCozjs4O+bBmQ6vi+V4ZtEDzzL65ZBYjcOiRw6Nbjkk6nFYJKA8iB0czXJANMdhkRgHR30OiHY4\nLBLmzZTlOCTa5bDIgENjfQ6JbjgsMuJNlJMcEN1zWGRq9s0y9PBwOPSvclgUX9h7CHgiIt4kaRPw\n38AFTL6w97qIeLq4743ALuA54J0Rsa/hdtuMtd5MuQaIgyFNy8ws3gU8AJxVXN8NHIyImyTtLq6/\n17VO0zHvTZdKiDgU8lIpLCSdB/w58CHgPcXia4Ari8t7gf8D3kup1inwQ0nTWqdfa6zVVkuVN2nd\nQHEQDE/VmcW/Av8AnFlatjkijhWXnwI2F5e3Al8v3W/NWqeSbgBuKK4+cyBu+wnw44rtycnLyLBf\nG85deJcF/TraXGO6l+U6q+DldR68MCwkvQk4ERH3SLpyrftEREhaqgBJROwB9pSe51Cdmgapcr/y\nM9S+STpU5/FVZhavA/5C0tXA6cBZkv4LOC7p3Ig4VpQyPFHcv1KtUzPLy8J/UY+IGyPivIi4gMmO\ny/+NiL9iUtN0Z3G3ncDtxeVB1To1s4k651ncBNwqaRfwKHAd1Kp1umfxXbLkfuVnqH2r1a8kap2a\nWfr8TVlmVknvYSFph6QHJR0tTu7KiqSbJZ2QdKS0bJOk/ZIeLn6fXbrtxqKvD0ra3k+rF5N0vqS7\nJN0v6T5J7yqWZ903SadL+qak7xT9+mCxPOt+TUnaIOnbku4srjfXr4jo7QfYAHwfuAh4AfAd4NI+\n27RCH/4IeA1wpLTsX4DdxeXdwD8Xly8t+ngacGHR9w1992FOv84FXlNcPhN4qGh/1n0DBLy4uLwR\n+Abw2tz7Verfe4DPAnc2PRb7nllcDhyNiB9ExK+AW5icAZqNiPgK8NOZxdcwOauV4ve1peW3RMSz\nEfFDJmcuXd5JQ5cUEcci4lvF5V8wOdV/K5n3LSaeKa5uLH6CzPsFv3em9b+XFjfWr77DYivwWOn6\nmmd7Zmi9s1uz66+kC4BXM/kUzr5vxVT9MJNzg/ZHxCD6xckzrX9bWtZYv/oOi8GLyZwv20NOkl4M\n/A/w7oj4efm2XPsWEc9FxDYmJwxeLukVM7dn16/ymdbz7lO3X32HxVDP9jxenNVKzme3StrIJCg+\nExGfLxYPom8AEfEz4C5gB/n3a3qm9SNMNuffUD7TGur3q++wuBu4WNKFkl7A5AzRO3puUxOyP7tV\nkoBPAQ9ExEdLN2XdN0nnSHpJcfmFwFXA98i8X9HFmdYJ7L29msme9u8D7++7PSu0/3PAMeDXTLb7\ndgEvBQ4CDwMHgE2l+7+/6OuDwJ/13f51+vV6JlPWe4HDxc/VufcNeCXw7aJfR4B/LJZn3a+ZPl7J\nyaMhjfXLZ3CaWSV9b4aYWSYcFmZWicPCzCpxWJhZJQ4LM6vEYWFmlTgszKwSh4WZVfL/HmvZpEs4\nf8YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115c312b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"im = mmt.to_fits(npix=400)[0].data\n",
"plt.imshow(im)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = 15 * u.mm\n",
"a.to(u.m)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"osys = poppy.OpticalSystem()\n",
"coeffs = [0, 0, 0, 550e-9, 90e-9, -1e-9 , 1e-9, 1e-9, -1e-9, 5e-9, 0.0, 5e-9]\n",
"osys.add_pupil(mmt)\n",
"wfe = poppy.ZernikeWFE(radius=3.2285, coefficients=coeffs)\n",
"osys.add_pupil(wfe)\n",
"osys.add_detector(pixelscale=0.01, fov_arcsec=1.0)\n",
"\n",
"psf = osys.calc_psf(5.5e-7)\n",
"poppy.display_psf(psf, scale='linear', title=\"MMT\", normalize='peak', vmin=1.0e-5, vmax=1.0, cmap='viridis')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"osys.calc_psf?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from mmtwfs.telescope import MMT\n",
"from mmtwfs.zernike import ZernikeVector"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t = MMT()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t.pupil.display(npix=1024)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = ZernikeVector(Z02=25000, Z04=3000*u.nm)\n",
"\n",
"psf = t.psf(z, fov=5)\n",
"poppy.display_psf(psf, scale='linear', title=\"MMT\", normalize='peak', vmin=1.0e-5, vmax=1.0, cmap='viridis')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = ZernikeVector(Z04=3000*u.nm)\n",
"\n",
"psf = t.psf(z, fov=5)\n",
"poppy.display_psf(psf, scale='linear', title=\"MMT\", normalize='peak', vmin=1.0e-5, vmax=1.0, cmap='viridis')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:astroconda]",
"language": "python",
"name": "conda-env-astroconda-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
taneaki/Interpolations.jl | Linier Interpolation.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n"
]
}
],
"source": [
"for i in 1:10\n",
" println(i)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.5",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
rafaelpierre/PythonMLBootcamp | Linear-Regression/Linear Regression - Project Exercise.ipynb |
|
|
|
|
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Lysine Acetylation -svc-checkpoint.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Template for test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pred import Predictor\n",
"from pred import sequence_vector\n",
"from pred import chemical_vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using K acytelation.\n",
"\n",
"Training data is from CUCKOO group and benchmarks are from dbptm. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Finished working with Data\n",
"Training Data Points: 296118\n",
"Test Data Points: 32902\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.006968641114982578\n",
"Specificity : 0.9997503199026248\n",
"Accuracy: 0.973770591453407\n",
"ROC 0.503359480509\n",
"TP 6 FP 8 TN 32033 FN 855\n",
"\n",
"\n",
"\n",
"None\n",
"Number of data points in benchmark 39422\n",
"Benchmark Results \n",
"Sensitivity: 0.0821406347230865\n",
"Specificity : 0.998066725585506\n",
"Accuracy: 0.9233930292729948\n",
"ROC 0.540103680154\n",
"TP 264 FP 70 TN 36138 FN 2950\n",
"\n",
"\n",
"\n",
"None\n",
"x pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Finished working with Data\n",
"Random Sequences Generated 329020\n",
"Filtering Random Data\n",
"Random Data Added: 329020\n",
"Finished with Random Data\n",
"Training Data Points: 625138\n",
"Test Data Points: 32902\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.012180267965895249\n",
"Specificity : 0.9997506312147377\n",
"Accuracy: 0.9751078961765243\n",
"ROC 0.50596544959\n",
"TP 10 FP 8 TN 32073 FN 811\n",
"\n",
"\n",
"\n",
"None\n",
"Number of data points in benchmark 39422\n",
"Benchmark Results \n",
"Sensitivity: 0.09054138145612943\n",
"Specificity : 0.9976800707026071\n",
"Accuracy: 0.9237227943787732\n",
"ROC 0.544110726079\n",
"TP 291 FP 84 TN 36124 FN 2923\n",
"\n",
"\n",
"\n",
"None\n",
"y ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 576608\n",
"Test Data Points: 64068\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9761384221375476\n",
"Specificity : 0.9990327613104524\n",
"Accuracy: 0.9875913092339389\n",
"ROC 0.987585591724\n",
"TP 31254 FP 31 TN 32019 FN 764\n",
"\n",
"\n",
"\n",
"None\n",
"Number of data points in benchmark 39422\n",
"Benchmark Results \n",
"Sensitivity: 0.11449906658369632\n",
"Specificity : 0.9970172337604949\n",
"Accuracy: 0.9250672213484856\n",
"ROC 0.555758150172\n",
"TP 368 FP 108 TN 36100 FN 2846\n",
"\n",
"\n",
"\n",
"None\n",
"x ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Balancing Data\n"
]
}
],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/k_acetylation.csv\")\n",
" y.process_data(vector_function=\"sequence\", amino_acid=\"K\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"svc\")\n",
" y.benchmark(\"Data/Benchmarks/acet.csv\", \"K\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/k_acetylation.csv\")\n",
" x.process_data(vector_function=\"sequence\", amino_acid=\"K\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"svc\")\n",
" x.benchmark(\"Data/Benchmarks/acet.csv\", \"K\")\n",
" del x\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chemical Vector "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/k_acetylation.csv\")\n",
" y.process_data(vector_function=\"chemical\", amino_acid=\"K\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"svc\")\n",
" y.benchmark(\"Data/Benchmarks/acet.csv\", \"K\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/k_acetylation.csv\")\n",
" x.process_data(vector_function=\"chemical\", amino_acid=\"K\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"svc\")\n",
" x.benchmark(\"Data/Benchmarks/acet.csv\", \"K\")\n",
" del x\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
IvanBrasilico/raspa-preco | notebooks/aliexpress.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"URL = 'https://pt.aliexpress.com/wholesale?catId=0&initiative_id=&SearchText='\n",
"XPATH = '/html/body/p[1]/strong'\n",
"TARGET = '<span class=\"value\" itemprop=\"price\">R$ 79,67</span>'\n",
"search = 'raspberry pi 3'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"html = requests.get(URL+search)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bsObj = BeautifulSoup(html.text, \"html.parser\")\n",
"nameList = bsObj.findAll(\"span\", {\"class\": \"value\", \"itemprop\": \"price\"})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['R$ 138,43', 'R$ 82,68', 'R$ 157,45', 'R$ 126,04', 'R$ 163,84 - 168,60', 'R$ 185,10 - 222,97', 'R$ 8,91', 'R$ 246,01', 'R$ 157,35', 'R$ 17,42', 'R$ 237,47 - 242,60', 'R$ 14,84', 'R$ 31,48', 'R$ 156,21', 'R$ 216,51', 'R$ 5,83', 'R$ 3,45', 'R$ 149,58', 'R$ 121,82 - 190,83', 'R$ 32,42', 'R$ 4,39', 'R$ 8,98', 'R$ 146,30', 'R$ 17,08', 'R$ 34,16', 'R$ 53,55', 'R$ 154,97', 'R$ 106,82', 'R$ 174,09', 'R$ 40,76', 'R$ 113,52', 'R$ 27,73', 'R$ 23,18', 'R$ 21,60', 'R$ 323,33', 'R$ 210,92', 'R$ 6,50', 'R$ 3,69', 'R$ 77,02', 'R$ 131,46', 'R$ 2,35', 'R$ 60,91', 'R$ 2,85', 'R$ 34,16']\n"
]
}
],
"source": [
"lista = [row.getText() for row in nameList]\n",
"print (lista)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Média: 88.14886363636366\n",
"Lista de preços: [121.49, 32.36, 173.61, 17.03, 157.01, 29.82, 174.11, 138.05, 140.22, 5.82, 163.39, 188.84, 8.62, 210.34, 78.11, 3.44, 128.56, 3.74, 2.34, 17.37, 130.2, 8.65, 150.24, 2.84, 128.56, 6.15, 128.76, 76.81, 156.95, 215.92, 128.56, 10.32, 149.17, 13.9, 19.21, 232.15, 32.33, 6.02, 49.76, 138.21, 131.1, 27.65, 12.26, 128.56]\n"
]
}
],
"source": [
"def extrai_valor(texto):\n",
" pos_real = texto.index('R$') + 3\n",
" pos_hifen = texto.find('-')\n",
" if pos_hifen == -1:\n",
" pos_hifen = len(texto)\n",
" texto = texto[pos_real:pos_hifen]\n",
" texto = texto.replace(',', '.')\n",
" return float(texto)\n",
"\n",
"lista_float = []\n",
"soma = 0\n",
"for item in lista:\n",
" valor = extrai_valor(item)\n",
" lista_float.append(valor)\n",
" soma += valor\n",
"print('Média:', soma/len(lista_float))\n",
"print('Lista de preços: ', lista_float)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| docs/tutorials/tfx/gcp/vertex_pipelines_simple.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "pknVo1kM2wI2"
},
"source": [
"##### Copyright 2021 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SoFqANDE222Y"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6x1ypzczQCwy"
},
"source": [
"# Simple TFX Pipeline for Vertex Pipelines\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_445qeKq8e3-"
},
"source": [
"\u003cdiv class=\"devsite-table-wrapper\"\u003e\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
"\u003ctd\u003e\u003ca target=\"_blank\" href=\"https://www.tensorflow.org/tfx/tutorials/tfx/gcp/vertex_pipelines_simple\"\u003e\n",
"\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"/\u003eView on TensorFlow.org\u003c/a\u003e\u003c/td\u003e\n",
"\u003ctd\u003e\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/gcp/vertex_pipelines_simple.ipynb\"\u003e\n",
"\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"\u003eRun in Google Colab\u003c/a\u003e\u003c/td\u003e\n",
"\u003ctd\u003e\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tfx/tree/master/docs/tutorials/tfx/gcp/vertex_pipelines_simple.ipynb\"\u003e\n",
"\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"\u003eView source on GitHub\u003c/a\u003e\u003c/td\u003e\n",
"\u003ctd\u003e\u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/tfx/gcp/vertex_pipelines_simple.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\u003c/td\u003e\n",
"\u003ctd\u003e\u003ca href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?q=download_url%3Dhttps%253A%252F%252Fraw.githubusercontent.com%252Ftensorflow%252Ftfx%252Fmaster%252Fdocs%252Ftutorials%252Ftfx%252Fgcp%252Fvertex_pipelines_simple.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eRun in Google Cloud Vertex AI Workbench\u003c/a\u003e\u003c/td\u003e\n",
"\u003c/table\u003e\u003c/div\u003e\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_VuwrlnvQJ5k"
},
"source": [
"This notebook-based tutorial will create a simple TFX pipeline and run it using\n",
"Google Cloud Vertex Pipelines. This notebook is based on the TFX pipeline\n",
"we built in\n",
"[Simple TFX Pipeline Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple).\n",
"If you are not familiar with TFX and you have not read that tutorial yet, you\n",
"should read it before proceeding with this notebook.\n",
"\n",
"Google Cloud Vertex Pipelines helps you to automate, monitor, and govern\n",
"your ML systems by orchestrating your ML workflow in a serverless manner. You\n",
"can define your ML pipelines using Python with TFX, and then execute your\n",
"pipelines on Google Cloud. See\n",
"[Vertex Pipelines introduction](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)\n",
"to learn more about Vertex Pipelines."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x4U5gp15QJ2b"
},
"source": [
"This notebook is intended to be run on\n",
"[Google Colab](https://colab.research.google.com/notebooks/intro.ipynb) or on\n",
"[AI Platform Notebooks](https://cloud.google.com/ai-platform-notebooks). If you\n",
"are not using one of these, you can simply click \"Run in Google Colab\" button\n",
"above.\n",
"\n",
"## Set up\n",
"Before you run this notebook, ensure that you have following:\n",
"- A [Google Cloud Platform](http://cloud.google.com/) project.\n",
"- A [Google Cloud Storage](https://cloud.google.com/storage) bucket. See\n",
"[the guide for creating buckets](https://cloud.google.com/storage/docs/creating-buckets).\n",
"- Enable\n",
"[Vertex AI and Cloud Storage API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,storage-component.googleapis.com).\n",
"\n",
"Please see\n",
"[Vertex documentation](https://cloud.google.com/vertex-ai/docs/pipelines/configure-project)\n",
"to configure your GCP project further."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fwZ0aXisoBFW"
},
"source": [
"### Install python packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WC9W_S-bONgl"
},
"source": [
"We will install required Python packages including TFX and KFP to author ML\n",
"pipelines and submit jobs to Vertex Pipelines."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iyQtljP-qPHY"
},
"outputs": [],
"source": [
"# Use the latest version of pip.\n",
"!pip install --upgrade pip\n",
"!pip install --upgrade \"tfx[kfp]\u003c2\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwT0nov5QO1M"
},
"source": [
"#### Did you restart the runtime?\n",
"\n",
"If you are using Google Colab, the first time that you run\n",
"the cell above, you must restart the runtime by clicking\n",
"above \"RESTART RUNTIME\" button or using \"Runtime \u003e Restart\n",
"runtime ...\" menu. This is because of the way that Colab\n",
"loads packages."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-CRyIL4LVDlQ"
},
"source": [
"If you are not on Colab, you can restart runtime with following cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KHTSzMygoBF6"
},
"outputs": [],
"source": [
"# docs_infra: no_execute\n",
"import sys\n",
"if not 'google.colab' in sys.modules:\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gckGHdW9iPrq"
},
"source": [
"### Login in to Google for this notebook\n",
"If you are running this notebook on Colab, authenticate with your user account:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kZQA0KrfXCvU"
},
"outputs": [],
"source": [
"import sys\n",
"if 'google.colab' in sys.modules:\n",
" from google.colab import auth\n",
" auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aaqJjbmk6o0o"
},
"source": [
"**If you are on AI Platform Notebooks**, authenticate with Google Cloud before\n",
"running the next section, by running\n",
"```sh\n",
"gcloud auth login\n",
"```\n",
"**in the Terminal window** (which you can open via **File** \u003e **New** in the\n",
"menu). You only need to do this once per notebook instance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3_SveIKxaENu"
},
"source": [
"Check the package versions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Xd-iP9wEaENu"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"print('TensorFlow version: {}'.format(tf.__version__))\n",
"from tfx import v1 as tfx\n",
"print('TFX version: {}'.format(tfx.__version__))\n",
"import kfp\n",
"print('KFP version: {}'.format(kfp.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aDtLdSkvqPHe"
},
"source": [
"### Set up variables\n",
"\n",
"We will set up some variables used to customize the pipelines below. Following\n",
"information is required:\n",
"\n",
"* GCP Project id. See\n",
"[Identifying your project id](https://cloud.google.com/resource-manager/docs/creating-managing-projects#identifying_projects).\n",
"* GCP Region to run pipelines. For more information about the regions that\n",
"Vertex Pipelines is available in, see the\n",
"[Vertex AI locations guide](https://cloud.google.com/vertex-ai/docs/general/locations#feature-availability).\n",
"* Google Cloud Storage Bucket to store pipeline outputs.\n",
"\n",
"**Enter required values in the cell below before running it**.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EcUseqJaE2XN"
},
"outputs": [],
"source": [
"GOOGLE_CLOUD_PROJECT = '' # \u003c--- ENTER THIS\n",
"GOOGLE_CLOUD_REGION = '' # \u003c--- ENTER THIS\n",
"GCS_BUCKET_NAME = '' # \u003c--- ENTER THIS\n",
"\n",
"if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):\n",
" from absl import logging\n",
" logging.error('Please set all required parameters.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GAaCPLjgiJrO"
},
"source": [
"Set `gcloud` to use your project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VkWdxe4TXRHk"
},
"outputs": [],
"source": [
"!gcloud config set project {GOOGLE_CLOUD_PROJECT}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CPN6UL5CazNy"
},
"outputs": [],
"source": [
"PIPELINE_NAME = 'penguin-vertex-pipelines'\n",
"\n",
"# Path to various pipeline artifact.\n",
"PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(\n",
" GCS_BUCKET_NAME, PIPELINE_NAME)\n",
"\n",
"# Paths for users' Python module.\n",
"MODULE_ROOT = 'gs://{}/pipeline_module/{}'.format(\n",
" GCS_BUCKET_NAME, PIPELINE_NAME)\n",
"\n",
"# Paths for input data.\n",
"DATA_ROOT = 'gs://{}/data/{}'.format(GCS_BUCKET_NAME, PIPELINE_NAME)\n",
"\n",
"# This is the path where your model will be pushed for serving.\n",
"SERVING_MODEL_DIR = 'gs://{}/serving_model/{}'.format(\n",
" GCS_BUCKET_NAME, PIPELINE_NAME)\n",
"\n",
"print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8F2SRwRLSYGa"
},
"source": [
"### Prepare example data\n",
"We will use the same\n",
"[Palmer Penguins dataset](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)\n",
"as\n",
"[Simple TFX Pipeline Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple).\n",
"\n",
"There are four numeric features in this dataset which were already normalized\n",
"to have range [0,1]. We will build a classification model which predicts the\n",
"`species` of penguins."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "11J7XiCq6AFP"
},
"source": [
"We need to make our own copy of the dataset. Because TFX ExampleGen reads\n",
"inputs from a directory, we need to create a directory and copy dataset to it\n",
"on GCS."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4fxMs6u86acP"
},
"outputs": [],
"source": [
"!gsutil cp gs://download.tensorflow.org/data/palmer_penguins/penguins_processed.csv {DATA_ROOT}/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ASpoNmxKSQjI"
},
"source": [
"Take a quick look at the CSV file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-eSz28UDSnlG"
},
"outputs": [],
"source": [
"!gsutil cat {DATA_ROOT}/penguins_processed.csv | head"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nH6gizcpSwWV"
},
"source": [
"## Create a pipeline\n",
"\n",
"TFX pipelines are defined using Python APIs. We will define a pipeline which\n",
"consists of three components, CsvExampleGen, Trainer and Pusher. The pipeline\n",
"and model definition is almost the same as\n",
"[Simple TFX Pipeline Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple).\n",
"\n",
"The only difference is that we don't need to set `metadata_connection_config`\n",
"which is used to locate\n",
"[ML Metadata](https://www.tensorflow.org/tfx/guide/mlmd) database. Because\n",
"Vertex Pipelines uses a managed metadata service, users don't need to care\n",
"of it, and we don't need to specify the parameter.\n",
"\n",
"Before actually define the pipeline, we need to write a model code for the\n",
"Trainer component first."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lOjDv93eS5xV"
},
"source": [
"### Write model code.\n",
"\n",
"We will use the same model code as in the\n",
"[Simple TFX Pipeline Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aES7Hv5QTDK3"
},
"outputs": [],
"source": [
"_trainer_module_file = 'penguin_trainer.py'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gnc67uQNTDfW"
},
"outputs": [],
"source": [
"%%writefile {_trainer_module_file}\n",
"\n",
"# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple\n",
"\n",
"from typing import List\n",
"from absl import logging\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow_transform.tf_metadata import schema_utils\n",
"\n",
"\n",
"from tfx import v1 as tfx\n",
"from tfx_bsl.public import tfxio\n",
"\n",
"from tensorflow_metadata.proto.v0 import schema_pb2\n",
"\n",
"_FEATURE_KEYS = [\n",
" 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'\n",
"]\n",
"_LABEL_KEY = 'species'\n",
"\n",
"_TRAIN_BATCH_SIZE = 20\n",
"_EVAL_BATCH_SIZE = 10\n",
"\n",
"# Since we're not generating or creating a schema, we will instead create\n",
"# a feature spec. Since there are a fairly small number of features this is\n",
"# manageable for this dataset.\n",
"_FEATURE_SPEC = {\n",
" **{\n",
" feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)\n",
" for feature in _FEATURE_KEYS\n",
" },\n",
" _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)\n",
"}\n",
"\n",
"\n",
"def _input_fn(file_pattern: List[str],\n",
" data_accessor: tfx.components.DataAccessor,\n",
" schema: schema_pb2.Schema,\n",
" batch_size: int) -\u003e tf.data.Dataset:\n",
" \"\"\"Generates features and label for training.\n",
"\n",
" Args:\n",
" file_pattern: List of paths or patterns of input tfrecord files.\n",
" data_accessor: DataAccessor for converting input to RecordBatch.\n",
" schema: schema of the input data.\n",
" batch_size: representing the number of consecutive elements of returned\n",
" dataset to combine in a single batch\n",
"\n",
" Returns:\n",
" A dataset that contains (features, indices) tuple where features is a\n",
" dictionary of Tensors, and indices is a single Tensor of label indices.\n",
" \"\"\"\n",
" return data_accessor.tf_dataset_factory(\n",
" file_pattern,\n",
" tfxio.TensorFlowDatasetOptions(\n",
" batch_size=batch_size, label_key=_LABEL_KEY),\n",
" schema=schema).repeat()\n",
"\n",
"\n",
"def _make_keras_model() -\u003e tf.keras.Model:\n",
" \"\"\"Creates a DNN Keras model for classifying penguin data.\n",
"\n",
" Returns:\n",
" A Keras Model.\n",
" \"\"\"\n",
" # The model below is built with Functional API, please refer to\n",
" # https://www.tensorflow.org/guide/keras/overview for all API options.\n",
" inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]\n",
" d = keras.layers.concatenate(inputs)\n",
" for _ in range(2):\n",
" d = keras.layers.Dense(8, activation='relu')(d)\n",
" outputs = keras.layers.Dense(3)(d)\n",
"\n",
" model = keras.Model(inputs=inputs, outputs=outputs)\n",
" model.compile(\n",
" optimizer=keras.optimizers.Adam(1e-2),\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[keras.metrics.SparseCategoricalAccuracy()])\n",
"\n",
" model.summary(print_fn=logging.info)\n",
" return model\n",
"\n",
"\n",
"# TFX Trainer will call this function.\n",
"def run_fn(fn_args: tfx.components.FnArgs):\n",
" \"\"\"Train the model based on given args.\n",
"\n",
" Args:\n",
" fn_args: Holds args used to train the model as name/value pairs.\n",
" \"\"\"\n",
"\n",
" # This schema is usually either an output of SchemaGen or a manually-curated\n",
" # version provided by pipeline author. A schema can also derived from TFT\n",
" # graph if a Transform component is used. In the case when either is missing,\n",
" # `schema_from_feature_spec` could be used to generate schema from very simple\n",
" # feature_spec, but the schema returned would be very primitive.\n",
" schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)\n",
"\n",
" train_dataset = _input_fn(\n",
" fn_args.train_files,\n",
" fn_args.data_accessor,\n",
" schema,\n",
" batch_size=_TRAIN_BATCH_SIZE)\n",
" eval_dataset = _input_fn(\n",
" fn_args.eval_files,\n",
" fn_args.data_accessor,\n",
" schema,\n",
" batch_size=_EVAL_BATCH_SIZE)\n",
"\n",
" model = _make_keras_model()\n",
" model.fit(\n",
" train_dataset,\n",
" steps_per_epoch=fn_args.train_steps,\n",
" validation_data=eval_dataset,\n",
" validation_steps=fn_args.eval_steps)\n",
"\n",
" # The result of the training should be saved in `fn_args.serving_model_dir`\n",
" # directory.\n",
" model.save(fn_args.serving_model_dir, save_format='tf')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-LsYx8MpYvPv"
},
"source": [
"Copy the module file to GCS which can be accessed from the pipeline components.\n",
"Because model training happens on GCP, we need to upload this model definition. \n",
"\n",
"Otherwise, you might want to build a container image including the module file\n",
"and use the image to run the pipeline."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rMMs5wuNYAbc"
},
"outputs": [],
"source": [
"!gsutil cp {_trainer_module_file} {MODULE_ROOT}/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w3OkNz3gTLwM"
},
"source": [
"### Write a pipeline definition\n",
"\n",
"We will define a function to create a TFX pipeline."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M49yYVNBTPd4"
},
"outputs": [],
"source": [
"# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple and\n",
"# slightly modified because we don't need `metadata_path` argument.\n",
"\n",
"def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,\n",
" module_file: str, serving_model_dir: str,\n",
" ) -\u003e tfx.dsl.Pipeline:\n",
" \"\"\"Creates a three component penguin pipeline with TFX.\"\"\"\n",
" # Brings data into the pipeline.\n",
" example_gen = tfx.components.CsvExampleGen(input_base=data_root)\n",
"\n",
" # Uses user-provided Python function that trains a model.\n",
" trainer = tfx.components.Trainer(\n",
" module_file=module_file,\n",
" examples=example_gen.outputs['examples'],\n",
" train_args=tfx.proto.TrainArgs(num_steps=100),\n",
" eval_args=tfx.proto.EvalArgs(num_steps=5))\n",
"\n",
" # Pushes the model to a filesystem destination.\n",
" pusher = tfx.components.Pusher(\n",
" model=trainer.outputs['model'],\n",
" push_destination=tfx.proto.PushDestination(\n",
" filesystem=tfx.proto.PushDestination.Filesystem(\n",
" base_directory=serving_model_dir)))\n",
"\n",
" # Following three components will be included in the pipeline.\n",
" components = [\n",
" example_gen,\n",
" trainer,\n",
" pusher,\n",
" ]\n",
"\n",
" return tfx.dsl.Pipeline(\n",
" pipeline_name=pipeline_name,\n",
" pipeline_root=pipeline_root,\n",
" components=components)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mJbq07THU2GV"
},
"source": [
"## Run the pipeline on Vertex Pipelines.\n",
"\n",
"We used `LocalDagRunner` which runs on local environment in\n",
"[Simple TFX Pipeline Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple).\n",
"TFX provides multiple orchestrators to run your pipeline. In this tutorial we\n",
"will use the Vertex Pipelines together with the Kubeflow V2 dag runner."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7mp0AkmrPdUb"
},
"source": [
"We need to define a runner to actually run the pipeline. You will compile\n",
"your pipeline into our pipeline definition format using TFX APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fAtfOZTYWJu-"
},
"outputs": [],
"source": [
"# docs_infra: no_execute\n",
"import os\n",
"\n",
"PIPELINE_DEFINITION_FILE = PIPELINE_NAME + '_pipeline.json'\n",
"\n",
"runner = tfx.orchestration.experimental.KubeflowV2DagRunner(\n",
" config=tfx.orchestration.experimental.KubeflowV2DagRunnerConfig(),\n",
" output_filename=PIPELINE_DEFINITION_FILE)\n",
"# Following function will write the pipeline definition to PIPELINE_DEFINITION_FILE.\n",
"_ = runner.run(\n",
" _create_pipeline(\n",
" pipeline_name=PIPELINE_NAME,\n",
" pipeline_root=PIPELINE_ROOT,\n",
" data_root=DATA_ROOT,\n",
" module_file=os.path.join(MODULE_ROOT, _trainer_module_file),\n",
" serving_model_dir=SERVING_MODEL_DIR))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fWyITYSDd8w4"
},
"source": [
"The generated definition file can be submitted using kfp client."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tI71jlEvWMV7"
},
"outputs": [],
"source": [
"# docs_infra: no_execute\n",
"from google.cloud import aiplatform\n",
"from google.cloud.aiplatform import pipeline_jobs\n",
"import logging\n",
"logging.getLogger().setLevel(logging.INFO)\n",
"\n",
"aiplatform.init(project=GOOGLE_CLOUD_PROJECT, location=GOOGLE_CLOUD_REGION)\n",
"\n",
"job = pipeline_jobs.PipelineJob(template_path=PIPELINE_DEFINITION_FILE,\n",
" display_name=PIPELINE_NAME)\n",
"job.submit()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L3k9f5IVQXcQ"
},
"source": [
"Now you can visit the link in the output above or visit 'Vertex AI \u003e Pipelines'\n",
"in [Google Cloud Console](https://console.cloud.google.com/) to see the\n",
"progress."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"pknVo1kM2wI2"
],
"name": "Simple TFX Pipeline for Vertex Pipelines",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
napsternxg/ControversialTweetAnalysis | Exploratory analysis.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/entity/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import gzip\n",
"import json\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from collections import defaultdict, Counter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"defaultdict(<type 'int'>, {'url': 166670, u'photo': 27682})\n",
"(8750, 119558)\n",
"CPU times: user 1min 14s, sys: 8.02 s, total: 1min 22s\n",
"Wall time: 1min 22s\n"
]
}
],
"source": [
"%%time\n",
"data = []\n",
"media_types = defaultdict(int)\n",
"url_types = defaultdict(int)\n",
"unique_urls = set()\n",
"with gzip.open(\"all_ids.txt.json.gz\") as fp:\n",
" for line in fp:\n",
" d = json.loads(line.strip())\n",
" data.append(d)\n",
" if 'entities' not in d:\n",
" continue\n",
" if 'media' in d['entities']:\n",
" m_entities = d['entities']['media']\n",
" for m in m_entities:\n",
" m_type = m['type']\n",
" media_types[m_type] += 1\n",
" if 'urls' in d['entities']:\n",
" m_entities = d['entities']['urls']\n",
" for m in m_entities:\n",
" media_types['url'] += 1\n",
" m = m['expanded_url']\n",
" m_type = m.split(\"/\", 3)[2]\n",
" unique_urls.add((m, m_type))\n",
" url_types[m_type] += 1\n",
" \n",
"print(media_types)\n",
"url_types = Counter(url_types)\n",
"print(len(url_types), len(unique_urls)) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'twitter.com', 24978),\n",
" (u'bit.ly', 15148),\n",
" (u'fb.me', 15069),\n",
" (u'ow.ly', 6866),\n",
" (u'dlvr.it', 5398),\n",
" (u'ift.tt', 4693),\n",
" (u'goo.gl', 4039),\n",
" (u'ln.is', 3795),\n",
" (u'youtu.be', 3784),\n",
" (u'gvwy.io', 3120),\n",
" (u'www.instagram.com', 2761),\n",
" (u'buff.ly', 2331),\n",
" (u'www.newsweek.com', 1949),\n",
" (u'www.youtube.com', 1170),\n",
" (u'nyti.ms', 1119),\n",
" (u'tinyurl.com', 1083),\n",
" (u'wp.me', 1048),\n",
" (u'm.tbnn.it', 960),\n",
" (u'shar.es', 845),\n",
" (u'www.naturalnews.com', 798),\n",
" (u'warontherocks.com', 739),\n",
" (u'truthinmedia.com', 677),\n",
" (u'cnn.it', 670),\n",
" (u'rover.ebay.com', 604),\n",
" (u'dld.bz', 524),\n",
" (u'www.periscope.tv', 515),\n",
" (u'lnkd.in', 504),\n",
" (u'www.huffingtonpost.com', 486),\n",
" (u'b.autovist.com', 473),\n",
" (u'fxn.ws', 468),\n",
" (u'www.breitbart.com', 461),\n",
" (u'www.facebook.com', 421),\n",
" (u'www.nytimes.com', 416),\n",
" (u'n.pr', 405),\n",
" (u'www.infowars.com', 404),\n",
" (u'a.msn.com', 397),\n",
" (u'thefederalist.com', 385),\n",
" (u'apple.news', 379),\n",
" (u'go.shr.lc', 378),\n",
" (u'NaturalNews.com', 373),\n",
" (u'www.foxnews.com', 362),\n",
" (u'wpo.st', 350),\n",
" (u'pinterest.com', 346),\n",
" (u'www.cnn.com', 325),\n",
" (u'www.yahoo.com', 319),\n",
" (u'amzn.to', 317),\n",
" (u'on.mash.to', 316),\n",
" (u'wapo.st', 314),\n",
" (u'brev.is', 310),\n",
" (u'j.mp', 305)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url_types.most_common(50)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'https://twitter.com/i/web/status/787248028335808513', u'twitter.com'),\n",
" (u'https://twitter.com/mr_dsantos/status/792410135582875648', u'twitter.com'),\n",
" (u'https://twitter.com/i/web/status/789400744810024960', u'twitter.com'),\n",
" (u'https://twitter.com/candy_lass/status/692590229069254656', u'twitter.com'),\n",
" (u'https://twitter.com/i/web/status/791387309992280064', u'twitter.com'),\n",
" (u'https://twitter.com/_ijmtybx/status/743864533089947648', u'twitter.com'),\n",
" (u'https://twitter.com/i/web/status/784460833912975360', u'twitter.com'),\n",
" (u'https://twitter.com/i/web/status/792218124707729408', u'twitter.com'),\n",
" (u'https://twitter.com/CaptainCreole/status/798946586730659840',\n",
" u'twitter.com'),\n",
" (u'https://twitter.com/tazerblack/status/786997527560224769', u'twitter.com')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(unique_urls,\n",
" key=lambda x: url_types[x[1]],\n",
" reverse=True)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Run code to get all URLs\n",
"```python\n",
"with open(\"all_urls.txt\", \"wb+\") as fp:\n",
" for url in sorted(filter(lambda x: x[1] != 'twitter.com',\n",
" unique_urls),\n",
" key=lambda x: url_types[x[1]],\n",
" reverse=True):\n",
" print >> fp, \"%s\\t%s\\t%s\" % (url[0], url[1], url_types[url[1]])\n",
" \n",
"! head all_urls.txt\n",
"\n",
"# python download_expanded.py --jobs 20 --batches 200 # Run this to expand URLs\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://fb.me/6Ry198BOC\thttp://fb.me/6Ry198BOC\r\n",
"http://fb.me/8Z6wy6V1o\thttp://worldtruth.tv/body-of-doctor-who-linked-vaccines-to-autism-found-floating-in-river/\r\n",
"http://fb.me/2xq5ADQaW\thttp://www.trueactivist.com/courts-quietly-confirm-mmr-vaccine-causes-autism/\r\n",
"http://fb.me/1erJaFNs1\thttp://www.theblaze.com/stories/2016/05/31/12-year-old-science-whiz-gathers-and-shares-all-the-evidence-that-vaccines-cause-autism/?utm_source=facebook&utm_medium=story&utm_campaign=ShareButtons\r\n",
"http://fb.me/7QrKP6H94\thttp://fb.me/7QrKP6H94\r\n",
"http://fb.me/3Vqc5yhbx\thttp://www.lifenews.com/2014/09/09/study-links-autism-to-vaccines-made-with-cells-from-aborted-babies/#.V9rBbPGPpHM.facebook\r\n",
"http://fb.me/5hVmXkvZT\thttp://tylervigen.com/spurious-correlations\r\n",
"http://fb.me/4oBG06FuV\thttps://www.facebook.com/photo.php?fbid=1066132846768368\r\n",
"http://fb.me/7IkGrAXn9\thttp://www.dailymail.co.uk/news/article-3141287/Authorities-Anti-vaccine-doctor-dead-apparent-suicide.html\r\n",
"http://fb.me/1qV1jUl3P\thttp://www.npr.org/sections/health-shots/2016/11/28/503592933/flu-vaccine-during-pregnancy-not-linked-to-autism?utm_source=facebook.com&utm_medium=social&utm_campaign=npr&utm_term=nprnews&utm_content=2055\r\n"
]
}
],
"source": [
"! head exp_urls.txt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'contributors',\n",
" u'truncated',\n",
" u'text',\n",
" u'is_quote_status',\n",
" u'in_reply_to_status_id',\n",
" u'id',\n",
" u'favorite_count',\n",
" u'source',\n",
" u'quoted_status_id',\n",
" u'retweeted',\n",
" u'coordinates',\n",
" u'quoted_status',\n",
" u'entities',\n",
" u'in_reply_to_screen_name',\n",
" u'id_str',\n",
" u'retweet_count',\n",
" u'in_reply_to_user_id',\n",
" u'favorited',\n",
" u'user',\n",
" u'geo',\n",
" u'in_reply_to_user_id_str',\n",
" u'possibly_sensitive',\n",
" u'lang',\n",
" u'created_at',\n",
" u'quoted_status_id_str',\n",
" u'in_reply_to_status_id_str',\n",
" u'place']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0].keys()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0][u'source']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0][u'is_quote_status']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'Overnight apartment fire in Tampa #10News https://t.co/gDBsG8udFg'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0][u'quoted_status']['text']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'Getting a better look at the damage now that the sun is up. Very sad https://t.co/DZrhrubgf9'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]['text']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21382 646 53296 281811 11366\n"
]
}
],
"source": [
"count_quoted = 0\n",
"has_coordinates = 0\n",
"count_replies = 0\n",
"language_ids = defaultdict(int)\n",
"count_user_locs = 0\n",
"user_locs = Counter()\n",
"count_verified = 0\n",
"for d in data:\n",
" count_quoted += d.get('is_quote_status', 0)\n",
" coords = d.get(u'coordinates', None)\n",
" repl_id = d.get(u'in_reply_to_status_id', None)\n",
" has_coordinates += (coords is not None)\n",
" count_replies += (repl_id is not None)\n",
" loc = d['user'].get('location', u'')\n",
" count_verified += d['user']['verified']\n",
" if loc != u'':\n",
" count_user_locs += 1\n",
" user_locs.update([loc])\n",
" language_ids[d['lang']] += 1\n",
" \n",
"print count_quoted, has_coordinates, count_replies, count_user_locs, count_verified\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11366"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_verified"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'United States', 10420),\n",
" (u'USA', 7880),\n",
" (u'Washington, DC', 4310),\n",
" (u'New York, NY', 3082),\n",
" (u'California, USA', 3018),\n",
" (u'Los Angeles, CA', 2719),\n",
" (u'New York', 2312),\n",
" (u'Chicago, IL', 2179),\n",
" (u'New York, USA', 2021),\n",
" (u'Texas', 1773)]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user_locs.most_common(10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"328318"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'contributors_enabled': False,\n",
" u'created_at': u'Tue Jul 14 00:13:13 +0000 2009',\n",
" u'default_profile': False,\n",
" u'default_profile_image': False,\n",
" u'description': u'Executive Producer at 10News WTSP in Tampa/St. Petersburg. Indiana University graduate.',\n",
" u'entities': {u'description': {u'urls': []}},\n",
" u'favourites_count': 345,\n",
" u'follow_request_sent': False,\n",
" u'followers_count': 573,\n",
" u'following': False,\n",
" u'friends_count': 503,\n",
" u'geo_enabled': True,\n",
" u'has_extended_profile': False,\n",
" u'id': 56544119,\n",
" u'id_str': u'56544119',\n",
" u'is_translation_enabled': False,\n",
" u'is_translator': False,\n",
" u'lang': u'en',\n",
" u'listed_count': 68,\n",
" u'location': u'St. Petersburg',\n",
" u'name': u'Melissa Ramsey',\n",
" u'notifications': False,\n",
" u'profile_background_color': u'0099B9',\n",
" u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme4/bg.gif',\n",
" u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme4/bg.gif',\n",
" u'profile_background_tile': False,\n",
" u'profile_banner_url': u'https://pbs.twimg.com/profile_banners/56544119/1443718335',\n",
" u'profile_image_url': u'http://pbs.twimg.com/profile_images/743866585635491840/Pa-vBAru_normal.jpg',\n",
" u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/743866585635491840/Pa-vBAru_normal.jpg',\n",
" u'profile_link_color': u'0099B9',\n",
" u'profile_sidebar_border_color': u'5ED4DC',\n",
" u'profile_sidebar_fill_color': u'95E8EC',\n",
" u'profile_text_color': u'3C3940',\n",
" u'profile_use_background_image': True,\n",
" u'protected': False,\n",
" u'screen_name': u'mramsey8',\n",
" u'statuses_count': 1010,\n",
" u'time_zone': u'Central Time (US & Canada)',\n",
" u'translator_type': u'none',\n",
" u'url': None,\n",
" u'utc_offset': -21600,\n",
" u'verified': False}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]['user']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
QuantumTechDevStudio/RUDNEVGAUSS | archive/fem/fem1d/FEM_Iaaaa.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pylab as plt\n",
"%matplotlib inline\n",
"\n",
"\n",
"\n",
"\n",
"num_of_points = 201\n",
"h = 1 / (num_of_points - 1)\n",
"num_of_elements = num_of_points - 1\n",
"N = 3 * num_of_elements # global size of matrixes\n",
"x = np.linspace(0, 1, num_of_points)\n",
"X = np.linspace(0, 1, N)\n",
"\n",
"exact_solution = np.zeros(num_of_points)\n",
"K_j = np.zeros((4, 4)) # local\n",
"M_j = np.zeros((4, 4)) # local\n",
"K = np.zeros([N, N]) # global\n",
"M = np.zeros([N, N]) # global\n",
"matr = np.zeros([N, N]) # K+M\n",
"I = np.zeros(N)\n",
"\n",
"\n",
"# exact solution\n",
"def solution(x):\n",
" return (x+25*math.pi*math.pi/4)*np.cos(5*math.pi*x/2) \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.85056948e-05 7.40203245e-05 3.08414991e-05 1.11027148e-04\n",
" 1.66528080e-04 9.25168876e-05 2.03521207e-04 2.58994748e-04\n",
" 1.54169449e-04 2.95965050e-04 3.51397512e-04 2.15783971e-04\n",
" 3.88335868e-04 4.43713575e-04 2.77345252e-04 4.80610870e-04\n",
" 5.35920157e-04 3.38838102e-04 5.72767289e-04 6.27994510e-04\n",
" 4.00247349e-04 6.64782386e-04 7.19913914e-04 4.61557841e-04\n",
" 7.56633458e-04 8.11655690e-04 5.22754450e-04 8.48297841e-04\n",
" 9.03197202e-04 5.83822078e-04 9.39752920e-04 9.94515864e-04\n",
" 6.44745656e-04 1.03097613e-03 1.08558914e-03 7.05510152e-04\n",
" 1.12194496e-03 1.17639457e-03 7.66100575e-04 1.21263697e-03\n",
" 1.26690974e-03 8.26501974e-04 1.30302978e-03 1.35711232e-03\n",
" 8.86699446e-04 1.39310108e-03 1.44698005e-03 9.46678138e-04\n",
" 1.48282866e-03 1.53649076e-03 1.00642325e-03 1.57219037e-03\n",
" 1.62562237e-03 1.06592004e-03 1.66116417e-03 1.71435287e-03\n",
" 1.12515384e-03 1.74972810e-03 1.80266039e-03 1.18411002e-03\n",
" 1.83786031e-03 1.89052312e-03 1.24277403e-03 1.92553906e-03\n",
" 1.97791940e-03 1.30113142e-03 2.01274271e-03 2.06482766e-03\n",
" 1.35916776e-03 2.09944974e-03 2.15122645e-03 1.41686876e-03\n",
" 2.18563877e-03 2.23709445e-03 1.47422016e-03 2.27128852e-03\n",
" 2.32241049e-03 1.53120782e-03 2.35637787e-03 2.40715351e-03\n",
" 1.58781767e-03 2.44088581e-03 2.49130260e-03 1.64403576e-03\n",
" 2.52479150e-03 2.57483700e-03 1.69984820e-03 2.60807424e-03\n",
" 2.65773609e-03 1.75524124e-03 2.69071348e-03 2.73997943e-03\n",
" 1.81020119e-03 2.77268882e-03 2.82154672e-03 1.86471451e-03\n",
" 2.85398004e-03 2.90241783e-03 1.91876773e-03 2.93456709e-03\n",
" 2.98257282e-03 1.97234753e-03 3.01443008e-03 3.06199190e-03\n",
" 2.02544066e-03 3.09354930e-03 3.14065548e-03 2.07803408e-03\n",
" 3.17190523e-03 3.21854415e-03 2.13011475e-03 3.24947854e-03\n",
" 3.29563869e-03 2.18166986e-03 3.32625010e-03 3.37192009e-03\n",
" 2.23268667e-03 3.40220095e-03 3.44736951e-03 2.28315258e-03\n",
" 3.47731235e-03 3.52196835e-03 2.33305519e-03 3.55156579e-03\n",
" 3.59569819e-03 2.38238214e-03 3.62494292e-03 3.66854085e-03\n",
" 2.43112127e-03 3.69742566e-03 3.74047835e-03 2.47926055e-03\n",
" 3.76899611e-03 3.81149294e-03 2.52678812e-03 3.83963662e-03\n",
" 3.88156711e-03 2.57369223e-03 3.90932976e-03 3.95068355e-03\n",
" 2.61996134e-03 3.97805833e-03 4.01882523e-03 2.66558400e-03\n",
" 4.04580537e-03 4.08597532e-03 2.71054897e-03 4.11255417e-03\n",
" 4.15211725e-03 2.75484516e-03 4.17828826e-03 4.21723471e-03\n",
" 2.79846161e-03 4.24299141e-03 4.28131164e-03 2.84138761e-03\n",
" 4.30664768e-03 4.34433221e-03 2.88361253e-03 4.36924134e-03\n",
" 4.40628089e-03 2.92512596e-03 4.43075696e-03 4.46714238e-03\n",
" 2.96591766e-03 4.49117935e-03 4.52690167e-03 3.00597756e-03\n",
" 4.55049362e-03 4.58554401e-03 3.04529577e-03 4.60868511e-03\n",
" 4.64305494e-03 3.08386262e-03 4.66573949e-03 4.69942027e-03\n",
" 3.12166857e-03 4.72164266e-03 4.75462609e-03 3.15870429e-03\n",
" 4.77638084e-03 4.80865877e-03 3.19496065e-03 4.82994051e-03\n",
" 4.86150499e-03 3.23042869e-03 4.88230847e-03 4.91315170e-03\n",
" 3.26509968e-03 4.93347180e-03 4.96358617e-03 3.29896505e-03\n",
" 4.98341786e-03 5.01279594e-03 3.33201645e-03 5.03213434e-03\n",
" 5.06076889e-03 3.36424574e-03 5.07960922e-03 5.10749316e-03\n",
" 3.39564493e-03 5.12583078e-03 5.15295724e-03 3.42620631e-03\n",
" 5.17078761e-03 5.19714990e-03 3.45592232e-03 5.21446864e-03\n",
" 5.24006025e-03 3.48478563e-03 5.25686307e-03 5.28167768e-03\n",
" 3.51278913e-03 5.29796044e-03 5.32199195e-03 3.53992589e-03\n",
" 5.33775063e-03 5.36099309e-03 3.56618924e-03 5.37622381e-03\n",
" 5.39867148e-03 3.59157268e-03 5.41337048e-03 5.43501784e-03\n",
" 3.61606995e-03 5.44918148e-03 5.47002318e-03 3.63967502e-03\n",
" 5.48364798e-03 5.50367888e-03 3.66238204e-03 5.51676148e-03\n",
" 5.53597663e-03 3.68418543e-03 5.54851379e-03 5.56690846e-03\n",
" 3.70507980e-03 5.57889709e-03 5.59646673e-03 3.72505999e-03\n",
" 5.60790388e-03 5.62464417e-03 3.74412109e-03 5.63552701e-03\n",
" 5.65143380e-03 3.76225838e-03 5.66175965e-03 5.67682903e-03\n",
" 3.77946739e-03 5.68659534e-03 5.70082359e-03 3.79574387e-03\n",
" 5.71002795e-03 5.72341155e-03 3.81108381e-03 5.73205169e-03\n",
" 5.74458735e-03 3.82548342e-03 5.75266113e-03 5.76434576e-03\n",
" 3.83893915e-03 5.77185120e-03 5.78268190e-03 3.85144768e-03\n",
" 5.78961714e-03 5.79959125e-03 3.86300592e-03 5.80595458e-03\n",
" 5.81506964e-03 3.87361102e-03 5.82085949e-03 5.82911324e-03\n",
" 3.88326036e-03 5.83432819e-03 5.84171860e-03 3.89195158e-03\n",
" 5.84635736e-03 5.85288261e-03 3.89968251e-03 5.85694403e-03\n",
" 5.86260250e-03 3.90645125e-03 5.86608558e-03 5.87087588e-03\n",
" 3.91225613e-03 5.87377976e-03 5.87770071e-03 3.91709572e-03\n",
" 5.88002468e-03 5.88307531e-03 3.92096883e-03 5.88481879e-03\n",
" 5.88699835e-03 3.92387450e-03 5.88816090e-03 5.88946886e-03\n",
" 3.92581201e-03 5.89005020e-03 5.89048622e-03 3.92678088e-03\n",
" 5.89048622e-03 5.89005020e-03 3.92678088e-03 5.88946886e-03\n",
" 5.88816090e-03 3.92581201e-03 5.88699835e-03 5.88481879e-03\n",
" 3.92387450e-03 5.88307531e-03 5.88002468e-03 3.92096883e-03\n",
" 5.87770071e-03 5.87377976e-03 3.91709572e-03 5.87087588e-03\n",
" 5.86608558e-03 3.91225613e-03 5.86260250e-03 5.85694403e-03\n",
" 3.90645125e-03 5.85288261e-03 5.84635736e-03 3.89968251e-03\n",
" 5.84171860e-03 5.83432819e-03 3.89195158e-03 5.82911324e-03\n",
" 5.82085949e-03 3.88326037e-03 5.81506964e-03 5.80595458e-03\n",
" 3.87361102e-03 5.79959125e-03 5.78961714e-03 3.86300592e-03\n",
" 5.78268190e-03 5.77185120e-03 3.85144768e-03 5.76434576e-03\n",
" 5.75266113e-03 3.83893915e-03 5.74458735e-03 5.73205169e-03\n",
" 3.82548342e-03 5.72341155e-03 5.71002795e-03 3.81108381e-03\n",
" 5.70082359e-03 5.68659534e-03 3.79574387e-03 5.67682903e-03\n",
" 5.66175965e-03 3.77946739e-03 5.65143380e-03 5.63552701e-03\n",
" 3.76225839e-03 5.62464417e-03 5.60790388e-03 3.74412109e-03\n",
" 5.59646673e-03 5.57889709e-03 3.72506000e-03 5.56690846e-03\n",
" 5.54851379e-03 3.70507980e-03 5.53597663e-03 5.51676148e-03\n",
" 3.68418544e-03 5.50367888e-03 5.48364798e-03 3.66238205e-03\n",
" 5.47002318e-03 5.44918148e-03 3.63967502e-03 5.43501784e-03\n",
" 5.41337048e-03 3.61606996e-03 5.39867148e-03 5.37622381e-03\n",
" 3.59157268e-03 5.36099309e-03 5.33775063e-03 3.56618925e-03\n",
" 5.32199195e-03 5.29796044e-03 3.53992591e-03 5.28167768e-03\n",
" 5.25686307e-03 3.51278914e-03 5.24006025e-03 5.21446864e-03\n",
" 3.48478564e-03 5.19714990e-03 5.17078761e-03 3.45592233e-03\n",
" 5.15295724e-03 5.12583078e-03 3.42620632e-03 5.10749316e-03\n",
" 5.07960922e-03 3.39564494e-03 5.06076889e-03 5.03213434e-03\n",
" 3.36424574e-03 5.01279594e-03 4.98341786e-03 3.33201646e-03\n",
" 4.96358617e-03 4.93347180e-03 3.29896506e-03 4.91315170e-03\n",
" 4.88230847e-03 3.26509969e-03 4.86150499e-03 4.82994051e-03\n",
" 3.23042870e-03 4.80865877e-03 4.77638084e-03 3.19496065e-03\n",
" 4.75462609e-03 4.72164266e-03 3.15870429e-03 4.69942027e-03\n",
" 4.66573949e-03 3.12166858e-03 4.64305494e-03 4.60868511e-03\n",
" 3.08386263e-03 4.58554401e-03 4.55049362e-03 3.04529579e-03\n",
" 4.52690167e-03 4.49117935e-03 3.00597757e-03 4.46714238e-03\n",
" 4.43075696e-03 2.96591766e-03 4.40628089e-03 4.36924134e-03\n",
" 2.92512597e-03 4.34433221e-03 4.30664768e-03 2.88361254e-03\n",
" 4.28131164e-03 4.24299141e-03 2.84138762e-03 4.21723471e-03\n",
" 4.17828826e-03 2.79846163e-03 4.15211725e-03 4.11255417e-03\n",
" 2.75484516e-03 4.08597532e-03 4.04580537e-03 2.71054898e-03\n",
" 4.01882523e-03 3.97805833e-03 2.66558401e-03 3.95068355e-03\n",
" 3.90932976e-03 2.61996135e-03 3.88156711e-03 3.83963662e-03\n",
" 2.57369225e-03 3.81149294e-03 3.76899611e-03 2.52678812e-03\n",
" 3.74047835e-03 3.69742566e-03 2.47926056e-03 3.66854085e-03\n",
" 3.62494292e-03 2.43112128e-03 3.59569819e-03 3.55156579e-03\n",
" 2.38238215e-03 3.52196835e-03 3.47731235e-03 2.33305520e-03\n",
" 3.44736951e-03 3.40220095e-03 2.28315259e-03 3.37192009e-03\n",
" 3.32625010e-03 2.23268668e-03 3.29563869e-03 3.24947854e-03\n",
" 2.18166987e-03 3.21854415e-03 3.17190523e-03 2.13011477e-03\n",
" 3.14065548e-03 3.09354930e-03 2.07803408e-03 3.06199190e-03\n",
" 3.01443008e-03 2.02544069e-03 2.98257282e-03 2.93456709e-03\n",
" 1.97234754e-03 2.90241783e-03 2.85398004e-03 1.91876774e-03\n",
" 2.82154672e-03 2.77268882e-03 1.86471452e-03 2.73997943e-03\n",
" 2.69071348e-03 1.81020119e-03 2.65773609e-03 2.60807424e-03\n",
" 1.75524125e-03 2.57483700e-03 2.52479150e-03 1.69984822e-03\n",
" 2.49130260e-03 2.44088581e-03 1.64403577e-03 2.40715351e-03\n",
" 2.35637787e-03 1.58781769e-03 2.32241049e-03 2.27128852e-03\n",
" 1.53120782e-03 2.23709445e-03 2.18563877e-03 1.47422017e-03\n",
" 2.15122645e-03 2.09944974e-03 1.41686877e-03 2.06482766e-03\n",
" 2.01274271e-03 1.35916778e-03 1.97791940e-03 1.92553906e-03\n",
" 1.30113143e-03 1.89052312e-03 1.83786031e-03 1.24277403e-03\n",
" 1.80266039e-03 1.74972810e-03 1.18411003e-03 1.71435287e-03\n",
" 1.66116417e-03 1.12515385e-03 1.62562237e-03 1.57219037e-03\n",
" 1.06592006e-03 1.53649076e-03 1.48282866e-03 1.00642327e-03\n",
" 1.44698005e-03 1.39310108e-03 9.46678138e-04 1.35711232e-03\n",
" 1.30302978e-03 8.86699460e-04 1.26690974e-03 1.21263697e-03\n",
" 8.26501988e-04 1.17639457e-03 1.12194496e-03 7.66100589e-04\n",
" 1.08558914e-03 1.03097613e-03 7.05510167e-04 9.94515864e-04\n",
" 9.39752920e-04 6.44745656e-04 9.03197202e-04 8.48297841e-04\n",
" 5.83822092e-04 8.11655690e-04 7.56633458e-04 5.22754464e-04\n",
" 7.19913914e-04 6.64782386e-04 4.61557855e-04 6.27994510e-04\n",
" 5.72767289e-04 4.00247363e-04 5.35920157e-04 4.80610870e-04\n",
" 3.38838102e-04 4.43713575e-04 3.88335868e-04 2.77345266e-04\n",
" 3.51397512e-04 2.95965050e-04 2.15783986e-04 2.58994748e-04\n",
" 2.03521207e-04 1.54169463e-04 1.66528080e-04 1.11027148e-04\n",
" 9.25169019e-05 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
"[[ 0.01196797 0.00925648 -0.00336599 0.0017765 ]\n",
" [ 0.00925648 0.06058786 -0.00757348 -0.00336599]\n",
" [-0.00336599 -0.00757348 0.06058786 0.00925648]\n",
" [ 0.0017765 -0.00336599 0.00925648 0.01196797]]\n"
]
}
],
"source": [
"for i in np.arange(num_of_points):\n",
" exact_solution[i] = solution(x[i])\n",
"#matrix filling\n",
"K_j[0][0] = K_j[3][3] = 1.85\n",
"K_j[0][1] = K_j[1][0] = K_j[3][2] = K_j[2][3] = -2.3625\n",
"K_j[1][1] = K_j[2][2] = 5.4\n",
"K_j[0][2] = K_j[2][0] = K_j[3][1] = K_j[1][3] = 0.675\n",
"K_j[0][3] = K_j[3][0] = -0.1625\n",
"K_j[1][2] = K_j[2][1] = -3.7125\n",
"\n",
"M_j[0][0] = M_j[3][3] = 16 / 105\n",
"M_j[0][1] = M_j[1][0] = M_j[3][2] = M_j[2][3] = 33 / 280\n",
"M_j[1][1] = M_j[2][2] = 27 / 35\n",
"M_j[0][2] = M_j[2][0] = M_j[3][1] = M_j[1][3] = -3 / 70\n",
"M_j[0][3] = M_j[3][0] = 19 / 840\n",
"M_j[1][2] = M_j[2][1] = -27 / 280\n",
"\n",
"for i in np.arange(4):\n",
" for k in np.arange(4):\n",
" K_j[i][k] = K_j[i][k] * 2 / h\n",
" M_j[i][k] = M_j[i][k] * 5 * h\n",
" K[i][k] = K_j[i][k]\n",
" M[i][k] = M_j[i][k]\n",
"\n",
"for s in range(3 * num_of_elements - 3):\n",
" if (s % 3 == 0):\n",
" M[2 + s:6 + s, 2 + s:6 + s] += M_j\n",
" K[2 + s:6 + s, 2 + s:6 + s] += K_j\n",
"ind = 0\n",
"for i in range(num_of_elements - 1):\n",
" I[ind] = f2(x[i], x[i + 1])\n",
" I[ind + 1] = f3(x[i], x[i + 1])\n",
" I[ind + 2] = f4(x[i], x[i + 1]) + f1(x[i], x[i + 1])\n",
" ind += 3\n",
"\n",
"for i in range(N):\n",
" I[i] = I[i] * h / 2\n",
"\n",
"for i in range(N):\n",
" for k in range(N):\n",
" matr[i][k] = K[i][k] + M[i][k]\n",
"vect = np.linalg.solve(matr, I)\n",
"print(I)\n",
"print(M_j)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x230d9594748>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, solution(x), label=\"exact_solution\")\n",
"plt.plot(X, vect, label=\"our_solution\")\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| coursera-statistics/week9/frequentist-vs-bayesian-inference-coursera-statistics.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this blog we're going to discuss about frequentist approach that use p-value, vs bayesian approach that use posterior."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 01:04*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--TEASER_END-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The study will help us make a comparison of frequentist vs bayesian approach. We have a population, and your task is to test whether the yellow is whether 10% or not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 01:52*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then from the study, you make a **decision table**. If your decision is right, you're going to get a bonus, otherwise you lose a job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 02:26*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then you're presented the money and the cost to gather the data. Remember, often it's pretty costly to get more data. So this example representing that condition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using **frequentist approach**, you're going to use **hypothesis testing**. To set the hypothesis:\n",
"\n",
"* H0 : 10% yellow M&Ms\n",
"* HA : 20% yellow M&Ms\n",
"\n",
"Using test statistic, because it's talking about the proportion, is the number of **yellow** observed in the sample. The p-value is calculating the probability of this many or more yellow M&M in the sample that you have buy, given the null hypothesis test. You might want to ask this, \"If I have bought 3 times and observe all the p-value, can I predict what is the p-value for the fourth time?\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So how many sample that you think you would buy? 5,10,15,20? Recall that if you fail to predict the p-value, you would lose your job. But if you buy large sample size, it will be very costly. The decision is how to get the right sample size, that's enough to make it practically significant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's choose 5 for this state. If some of you have known bootstrapping, this is one of the most important technique to engineer a new sample. In hypothesis testing, you're collecting the data, build test statistic, p-value, and compare it to significance level. The next question then become, what is significance level for this problem? Recall that significance level is all about **type 1 error, rejecting the null hypothesis when the null hypothesis is true.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So which level should you choose? Using higher significance level, you can have a type 2 error rate. But using smaller alpha, can get you miss any true significant p-value. For this case, we stick with 5% significance level."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With sample size this small, we can use **binomial distribution**.Because we set 10% as our null hypothesis, we set the probability of success 10%. Recall that null hypothesis is a null value for true population,hence the proportion is equal the probability of success. Suppose we have yellow (among 4 other colors) once.\n",
"\n",
" p-value = P(1 or more yellows | n=5, p = 10%)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we're observing the probability of at least 1, and there are only two chances, whether it's yellow or not, we can simplify this calculation as,\n",
"\n",
" P(k>=1) = 1 - P(none yellows)\n",
" \n",
"The complement probability is 0.9, then\n",
"\n",
" P(k>=1) = 1 - (0.9)^5 = 0.41\n",
" \n",
"The result is 0.41, our p-value is greater than the significance level, we failed to reject the null hypothesis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the sample size is indeed small, and because of that, we want to increase our sample size to 10. Then for the 10 draws, we get two yellows. The p-value is then\n",
"\n",
" p-value = P(2 or more yellows | n=10,p=10%)\n",
" \n",
"Since this is getting too complicated(we can calculate the probability of 2,3,4..10, or 1-(k=0+k=1)), we can use R."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.2639011"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(dbinom(2:10,10,0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Again, based on this p-value, we fail to reject the null hypothesis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How about the 15 sample size? Again when doing 15 draws, we get 3 yellow. So,\n",
"\n",
" p-value = P(3 or more yellows | n=15, p = 10%)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using R,"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.1840611"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(dbinom(3:15,15,0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again we failed to reject the null hypothesis.\n",
"\n",
"For the sake of the argument, we use 20 sample size, and have 2 yellows in 20 draw. Again we set our binomial,\n",
"\n",
" p-value = P(4 or more yellows | n=20,p=10%)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.1329533"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(dbinom(4:20,20,0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then, once again, we failed to reject the null hypothesis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 14:33*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we're looking at the possibilities earlier(1 out of 5, 2 out of 10, 3 out of 15, and 4 out of 20), we know that the proportion is actually 20%. Since we failed to reject our null hypothesis, we would lose our job.So you see, it's important to see from looking at the problem. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 15:30*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's use bayessian approach. Again, only two conditional probabilities.You can either have 10% or 20%. Since we don't how is the true population, we make a fair judgement 50:50. In Bayes this is our **prior probability**. As you recall in Bayes, we can be presented with the data, calculate the posterior, make that as an input of next data, calculate the posterior and keep doing that.So the p-value in bayesian is the probality that given the observed data, what are th posterior probability."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 17:16*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we can calculate the probability of 10% given data. This is what Bayesian can solve. We can use Bayes to calculate like the one in the examples. Recall that in Bayes, we have\n",
"\n",
" P(A and B) = P(B and A), if A and B are dependent.\n",
" P(A|B) * P(B) = P(B|A) * P(A)\n",
" \n",
"We subtitute the equation like the one in the example. Since there either 10% or 20%, the probability of 20% is the complement of 10% yellow."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.4096"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dbinom(1,5,0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 20:18*"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.32805"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dbinom(1,5,0.1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.4096"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dbinom(1,5,0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once again, since we only have two conditions in our probability space, and we're observing the exact successes for n trial, we can use `dbinom` function. Recall that we have 1 yellow in our first trial. We calculate the Bayesian as observing the probability of 10% given the data. And by calculating the probability of data, what is the probability that we have 10% yellow **or** 20% yellow given the data. Since we have an **or** in probability, we join by addition. In dbinom we can calcute the probability of k success, given n trial, knowing the probability of success. We can use P(data | 10%) with `dbinom` in R. So we're calculating P(data|10%) is 0.33 and P(data|20%) is 0.41. We incorporate the formula, and have **0.44**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 21:40*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may want to mark your results in the table. Since 20% yellow is the complement probability of 10%, we take it 0.56 for the 20%. And we repeat our process for 10,15,20."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 23:05*"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.1937102"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dbinom(2,10,0.1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.3019899"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dbinom(2,10,0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for 10 value, we can get 0.39 for P(10%|data). And the complement for 20% is 0.61"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.339383"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(3,15,0.1) * 0.5\n",
"p_data_20 = dbinom(3,15,0.2) * 0.5\n",
"\n",
"p_data_10/(p_data_10+p_data_20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have 0.34 for P(10%|data) and for the complement of 20%, we have 0.66. That is our posterior probability of step 3."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.2915103"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(4,20,0.1) * 0.5\n",
"p_data_20 = dbinom(4,20,0.2) * 0.5\n",
"\n",
"p_data_10/(p_data_10+p_data_20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, for 20 M&M we have 0.29 for 10% and the complement 0.71 for 20%. Let's take a look at the overall table, all 4 steps of frequentist vs bayesian approach."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/175) 27:31*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The frequentist approach, p-value makes HT failed to reject, and keep siding with 10%. On the other hand, Bayesian consistently prefer 20%. So there's two contradicting results on two approach. Which is right? Since you know that 20% is the true population, Bayesian is the winning side. Indeed sometimes two approach could yield slightly different result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall that in Bayesian, you'll always update prior according to your posterior. Earlier, we don't update our prior. It keeps at constant 0.5.How about we keep updating prior based on resulted posterior? You could also using Bayesian approach like this"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.4447231 0.5552769"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(1,5,0.1) * 0.5\n",
"p_data_20 = dbinom(1,5,0.2) * 0.5\n",
"\n",
"p_10 = p_data_10/(p_data_10+p_data_20)\n",
"c(p_10,1-p_10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.3351035 0.6648965"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(2,10,0.1) * 0.44\n",
"p_data_20 = dbinom(2,10,0.2) * 0.56\n",
"\n",
"p_10 = p_data_10/(p_data_10+p_data_20)\n",
"c(p_10,1-p_10)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.2092687 0.7907313"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(3,15,0.1) * 0.34\n",
"p_data_20 = dbinom(3,15,0.2) * 0.66\n",
"\n",
"p_10 = p_data_10/(p_data_10+p_data_20)\n",
"c(p_10,1-p_10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1] 0.09859043 0.90140957"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_data_10 = dbinom(4,20,0.1) * 0.21\n",
"p_data_20 = dbinom(4,20,0.2) * 0.79\n",
"\n",
"p_10 = p_data_10/(p_data_10+p_data_20)\n",
"c(p_10,1-p_10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"So there you go, using Bayesian approach, you get 0.1, which is near to 0.13 frequentist."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **REFERENCES**:\n",
"\n",
"> Dr. Mine Çetinkaya-Rundel, [Cousera](https://class.coursera.org/statistics-003/lecture)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| cmlths2018-talk/cmlhts2018-talk.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<div align=\"center\"><h1>Perspectives on Text</h1>\n",
"<h3>_Synthesizing Textual Knowledge through Markup_</h3>\n",
"<br/>\n",
"<h4>Elli Bleeker, Bram Buitendijk, Ronald Haentjens Dekker, Astrid Kulsdom\n",
" <br/>R&D - Dutch Royal Academy of Arts and Science</h4>\n",
" <h6>Computational Methods for Literary Historical Textual Scholarship - July 3, 2018</h6>\n",
"</div>\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"This talk is not a simple \"me and my project\"-presentation. Instead I'd like to the topic of computational text modeling by focusing on one instrument: markup.\n",
"\n",
"Markup is cool! It is an instrument to express our understanding of a text to a computer so we can probe that text further, have others probe it, store it and represent it."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Theory\n",
" Definition of text \n",
" Expectations of markup; challenges \n",
"### Practice\n",
" TAGML \n",
" Editorial workflow \n",
"### Conclusion\n",
"### Discussion"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Theory"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Before we go on, let's take a closer look at what we're dealing with, exactly. As this is the Computational Methods for Literary-Historical Textual Scholarship, I assume we are primarily working with textual objects.\n",
"\n",
"\"Text\" has been defined over and over again (see all the publications about \"what text is, really\") but we propose the following definition that is both very precise and inclusive and takes into account all textual features that textual scholars are interested in."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# What is text?\n",
"\n",
"A multilayered, non-linear object containing information which is at times ordered, partially ordered, or unordered"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"I'll give three examples of textual features and how they translate informationally."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Modeling textual features \n",
"\n",
"- Overlapping structures \n",
"- Discontinuous elements \n",
"- Non-linear elements"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Overlapping structures\n",
"<img src=\"images/Selection-21v.png\">\n",
"<img src=\"images/Selection-22v.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Discontinuous elements\n",
"\n",
"<img width=\"900\" height=\"500\" src=\"images/discontinuity.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<img width=\"300\" height=\"300\" src=\"images/order.jpg\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Non-linear structures\n",
"<img width=\"500\" height=\"500\" src=\"images/code-nonlinear.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img align=\"left\" width=\"500\" height=\"300\" src=\"images/order1a.png\">\n",
"<img align=\"right\" width=\"500\" height=\"300\" src=\"images/order1b.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Now, let's move on to markup. The aims or \"potential\" of markup is twofold: "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Markup\n",
"- Markup helps us to **make explicit implicit notions and interpretations**\n",
"- Markup **unites scholarly knowledge** "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"I don't have to tell you that these promises are not, at least not entirely, fulfilled. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Current markup technology do not permit us to do so, or better: they don't facilitate it in a straightforward manner. \n",
"- Simple texts do fit into one hierarchy, but the moment one wants to tag more complex phenomena one has to resort to workarounds. Which work, but they do remain workarounds.\n",
"- Transcriptions are usually made on a project basis / idiosyncratic approaches. No shared conception of digital editing. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Let's start with the first point. The moment I mention hierarchies, your mind will automatically spring to \"overlap\". \n",
"\n",
"Indeed, we can make explicit our understanding of text but the moment we structure a textual object a certain way, there'll be many elements that do not fit into that structure. The easiest example is that of:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Logical structure vs. document structure "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<img src=\"images/Selection-21v.png\">\n",
"<img src=\"images/Selection-22v.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"There are, of course, an infinite amount of structures, as illustrated by the long list of analytical perspectives on text by Allan Renear _et al_. Each of these perspectives implies a different structuring and ordering of the text."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\"Analytical perspectives on text\" (Renear _et al_. 1993):\n",
"\n",
"- dramatic: act, scene, stage directions, speech\n",
"- poetic: poem, verse, stanza, quatrain, couplet, line, half-line, foot\n",
"- syntactic: sentence, noun phrase, verb phrase, determiner, adjective, noun, verb\n",
"- etc..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"The second point, bringing together scholarly knowledge in one file that can be used and reused by others, has also been proven quite unfeasible by our diverging scholarly practices."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<div class=\"quote\" align=\"center\">_\"Most texts are made for a special use in a specific context for a limited group of people\"_</div><br/><div class=\"source\" align=\"center\">(Hillesund 2005)</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<div align=\"center\">A \"_shared conception of digital editing_\" (Hajo 2010) is abandoned in favor of idiosyncratic approaches</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Paraphrasing Erwin Palofsky we can assume that:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<div align=\"center\">A strict formal observation and with it a description of a textual object is a _physical impossibility_</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Theoretically, markup is an indispensable instrument:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Markup allows us to ...\n",
"#### ... formally describe our interpretation of text "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### ... create transcriptions that can be shared with others and processed by software"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Markup is a powerful technology. But:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"# With great power comes great responsibilities.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Apart from the fact that we have to be very conscious about the ways in which we identify and tag textual features (during which we also have to take into account how these features may be processed and addressed in later stages) we have a responsibility to keep questioning the model we use. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div align=\"center\"> The affordances and limitations of a textual model influence our understanding of text</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So what happens if we step outside the framework we've all come know, and start all over again? What if we're no longer compelled to think in terms of monohierarchical structures when modeling text and instead take as point departure a model that provides *native* support for multiple hierarchies, without complicated hacks and workarounds? How would we then markup a text? "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Practice"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"In the second part of my talk, I'll introduce TAGML, the markup language we've been developing over the past few months. TAGML is based on the definition of text as a multilayered, non-linear object and addresses in a straightforward manner complex textual features like those I just described.\n",
"\n",
"Furthermore, we have developed a system to manage TAGML files and address both the issues of compatibility and interoperability.\n",
"\n",
"First, TAGML."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# TAGML\n",
"Markup language of Text-as-Graph (TAG) model \n",
"\n",
"Considers **text** to be **a non-linear and multilayered information object**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"A TAGML file can have multiple **layers**. \n",
"\n",
"A layer is, in principle, a set of markup nodes. A layer is hierarchical."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"How does that help us to capture various features of text, both simple and complicated? \n",
"\n",
"Let's focus on one of the textual features I just outlined, the most familiar one: overlapping structures. \n",
"Imagine transcribing the poetic structure of the text on this document fragment."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"images/Selection-21v.png\">\n",
"<img src=\"images/Selection-22v.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```\n",
"[tagml>\n",
" [page>\n",
" [p>\n",
" [line>2d. Voice from the Springs<line]\n",
" [line>Thunderbolts had parched our water<line]\n",
" [line>We had been stained with bitter blood<line]\n",
" <p]\n",
" <page]\n",
" [page>\n",
" [p>\n",
" [line>And had ran mute 'mid shrieks of <|[sic>slaugter<sic]|[corr>slaughter<]|><line]\n",
" [line>Thro' a city & a solitude!<line]\n",
" <p]\n",
" <page]\n",
"<tagml]\n",
"```\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Let's take a closer look at that last transcription. One could argue that the paragraph isn't really \"closed\", it just needs to be closed to avoid overlap with the page element. If that weren't necessary, this would be a more intuitive transcription:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"(In the following transcripton has been stripped of most tags for readability)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```\n",
"[page> \n",
" [p>\n",
" [line>2d. Voice from the Springs<line]\n",
" [line>Thunderbolts had parched our water<line]\n",
" [line>We had been stained with bitter blood<line]\n",
" <page]\n",
" [page>\n",
" [line>And had ran mute 'mid shrieks of slaughter<line]\n",
" [line>Thro' a city and a multitude!<line]\n",
" <p]\n",
"<page]\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"This is where the multilayeredness comes in. The moment structures overlap, the user can create a new layer. A layer can be created locally. The layers may be given any name; in this example they are simply referred to as layer A and layer B."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```\n",
"[page|+A> \n",
" [p|+B>\n",
" [line>2d. Voice from the Springs<line]\n",
" [line>Thunderbolts had parched our water<line]\n",
" [line>We had been stained with bitter blood<line]\n",
" <page|A]\n",
" [page|A>\n",
" [line>And had ran mute 'mid shrieks of slaughter<line]\n",
" [line>Thro' a city and a multitude!<line]\n",
" <p|B]\n",
"<page|A]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# _Alexandria_\n",
"\n",
"- Text repository for TAGML files\n",
"- Git-like version management\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Managing TAGML files with multiple layers is done in a repository called _Alexandria_ which stores the TAGM files. \n",
"The workflow is similar to that of Git.\n",
"\n",
"Let's return to the examples I just showed, and let's imagine that the markup is added not by one, but by two editors. We'll name them A and B, or to make it more realistic, Astrid and Bram."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Astrid\n",
"```\n",
"[page>\n",
" [p>\n",
" [line>2d. Voice from the Springs<line]\n",
" [line>Thrice three hundred thousand years<line]\n",
" [line>We had been stained with bitter blood<line]\n",
" <p]\n",
"<page]\n",
"[page>\n",
" [p>\n",
" [line>And had ran mute 'mid shrieks of <|[sic>slaugter<sic]|[corr>slaughter<corr]]><line]\n",
" [line>Thro' a city and a multitude<line]\n",
" <p]\n",
"<page]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img width=\"500\" height=\"400\" src=\"images/astrid-alex-init.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img width=\"500\" height=\"400\" src=\"images/bram-alexandria-checkout.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## View \"material\" \n",
"\n",
"Includes elements `[page>`, `[line>` and `[corr>`\n",
"```\n",
"[page>\n",
" [line>2d. Voice from the Springs<line]\n",
" [line>Thrice three hundred thousand years<line]\n",
" [line>We had been stained with bitter blood<line]\n",
"<page]\n",
"[page>\n",
" [line>And had ran mute 'mid shrieks of slaughter<line]\n",
" [line>Thro' a city and a multitude<line]\n",
"<page]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bram\n",
"```\n",
"[page|+A>\n",
" [p|+B>\n",
" [l>2d. Voice from the Springs<l]\n",
" [l>Thrice three hundred thousand years<l]\n",
" [l>We had been stained with bitter blood<l]\n",
"<page|A]\n",
"[page|A>\n",
" [l>And had ran mute 'mid shrieks of [corr>slaughter<corr]<l]\n",
" [l>Thro' a city & a multitude<l]\n",
" <p|B]\n",
"<page|A]\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Both TAGML transcriptions are merged in Alexandria. Usually, the users would not check out the \"master file\" but if they would, it would look something like this:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Astrid + Bram\n",
"\n",
"```\n",
"[page|+A>\n",
" [p|+B>\n",
" [p|+C>\n",
" [line>[l>2nd. Voice from the Springs.<l]<line]\n",
" [line>[l>Thrice three hundred thousand years<l]<line]\n",
" [line>[l>We had been stained with bitter blood<l]<line]\n",
" <p|C]\n",
"<page|A]\n",
"[page|A>\n",
" [p|C>\n",
" [line>[l>And had ran mute 'mid shrieks of <|[sic|C>slaugter<sic|C]|[corr>slaughter<corr]|><l]<line]\n",
" [line>[l>Thro' a city and a multitude<l]<line]\n",
" <p|B]\n",
" <p|C]\n",
"<page|A]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
" It may be clear that, in order to properly manage multiple transcriptions with multiple layers, properly documenting transcriptions is key. If we go back to the statement that adding markup is \"making explicit what is implicit\", we can say that this explicitness exists on several levels. Not only within the _text_, but also in the form of metadata and additional documenting files. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Text\n",
"\n",
"- Text is a multilayered, non-linear object\n",
"- The information can be ordered, partially ordered, or unordered "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Markup\n",
"\n",
"1. Overlap\n",
"2. Discontinuity\n",
"3. Non-linearity\n",
"4. Compatible \n",
" a. Interoperable \n",
" b. Reusable\n",
" \n",
"\"Natural\" or idiomatic: the model needs to be close to our understanding of text"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# TAGML \n",
"\n",
"... formal description of complex textual features in a straightforward manner"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Alexandria\n",
"... stores and manages TAGML files"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Discussion"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- How do we handle the merge of TAGML files? Do we consider changes in markup as replacements or additions?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"`[line>` to `[l>`"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Option 1: replacements"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Changes made by a user replace the existing markup: \n",
"<br/>\n",
" ```[l>2nd. Voice from the Springs.<l]```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Option 2: additions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"New layers are created to identify changes made by different users: \n",
"<br/>\n",
"```[line|Astrid>[l|Bram>2nd. Voice from the Springs.<l|Bram]<line|Astrid]```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"If changes were to be considered replacements, a merge would imply losing a certain amount of information. Perhaps that's not problematic, but users need to be aware of that. In any case, losses wouldn't be forever as they can always be reverted. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Is the source text part of a perspective or not? In other words, is a perspective only the markup or also the source text? "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img src=\"images/Selection-22v.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"View poetic: \n",
"`[rhyme>slaughter<rhyme]`\n",
"\n",
"View material: \n",
"`<|[sic>slaugter<sic]|[corr>slaughter<corr]|>`"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# References\n",
"\n",
"- Alexandria. https://github.com/HuygensING/alexandria-markup. Information about installing and using the Alexandria command line app is available at links on the TAG portal at https://github.com/HuygensING/TAG.\n",
"- Gengnagel, T. 2015. \"Marking Up Iconography: Scholarly Editions Beyond Text,\" in: parergon, 06/11/2015, https://parergon.hypotheses.org/40.\n",
"- Haentjens Dekker, R. & Birnbaum, D.J. 2017. \"It’s more than just overlap: Text As Graph\". In _Proceedings of Balisage: The Markup Conference 201. Balisage Series on Markup Technologies_, vol. 19. doi:10.4242/BalisageVol19.Dekker01. https://www.balisage.net/Proceedings/vol19/html/Dekker01/BalisageVol19-Dekker01.html\n",
"- Hajo, C. M. 2010. \"The sustainability of the scholarly edition in a digital world\". In _Proceedings of the International Symposium on XML for the Long Haul: Issues in the Long-term Preservation of XML_. Balisage Series on Markup Technologies, vol. 6. doi:10.4242/BalisageVol6.Hajo01.\n",
"- Hillesund, T. 2005. \"Digital Text Cycles: From Medieval Manuscripts to Modern Markup\". In _Journal of Digital Information_ 6:1. https://journals.tdl.org/jodi/index.php/jodi/article/view/62/65.\n",
"- Panofsky, E. 1932/1964. \"Zum Problem der Beschreibung und Inhaltsdeutung von Werken der bildenden Kunst\" in _Ikonographie und Ikonologie: Theorien, Entwicklung, Probleme (Bildende Kunst als Zeichensystem; vol. 1)_, ed. by Ekkehard Kaemmerling, Köln 1979, pp.185-206.\n",
"- Renear, A. H., Mylonas, E., & Durand, D. 1993. \"Refining our notion of what text really is: The problem of overlapping hierarchies\". https://www.ideals.illinois.edu/bitstream/handle/2142/9407/RefiningOurNotion.pdf?sequence=2&isAllowed=y\n",
"- Sahle, P. 2013. _Digitale Editionsformen-Teil 3: Textbegriffe Und Recodierung_. Norderstedt: Books on Demand. http://kups.ub.uni-koeln.de/5353/\n",
"- Shelley, P. B. \"Prometheus Unbound, Act I\", in The Shelley-Godwin Archive, MS. Shelley e. 1, 21v. Retrieved from http://shelleygodwinarchive.org/sc/oxford/prometheus_unbound/act/i/#/p7 \n",
"- Shillingsburg, P. 2014. \"From physical to digital textuality: Loss and gain in literary projects\". In _CEA Critic_ 76:2, pp.158-168."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Some extra slides\n",
"## Just in case ..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"images/cmlhts-18-latest.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"images/cmlhts-19-latest.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"images/cmlhts-20-latest.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# TAG\n",
"Data model: non-uniform cyclic property hypergraph of text\n",
"\n",
"- Document Node\n",
"- Text Nodes\n",
"- Markup Nodes\n",
"- Annotation Nodes\n",
"\n",
"<img align=\"center\" width=\"300\" height=\"200\" src=\"images/hypergraph-general.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img align=\"center\" width=\"600\" height=\"600\" src=\"images/hypergraph.png\">"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
relopezbriega/mi-python-blog | content/notebooks/MachineLearningOverfitting.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning con Python - Sobreajuste"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Esta notebook fue creada originalmente como un blog post por [Raúl E. López Briega](https://relopezbriega.com.ar/) en [Matemáticas, Analisis de datos y Python](https://relopezbriega.github.io). El contenido esta bajo la licencia BSD.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img alt=\"Machine Learning\" title=\"Machine Learning\" src=\"https://relopezbriega.github.io/images/machine-learning.jpg\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducción"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uno de los conceptos más importantes en [Machine Learning](https://relopezbriega.github.io/tag/machine-learning.html) es el ***[overfitting](https://en.wikipedia.org/wiki/Overfitting) o [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste)*** del modelo. Comprender como un modelo se *ajusta* a los datos es muy importante para entender las causas de baja precisión en las predicciones. Un modelo va a estar *[sobreajustado](https://es.wikipedia.org/wiki/Sobreajuste)* cuando vemos que se desempeña bien con los datos de entrenamiento, pero su precisión es notablemente más baja con los datos de evaluación; esto se debe a que el modelo ha memorizado los datos que ha visto y no pudo *generalizar* las reglas para predecir los datos que no ha visto. De aquí también la importancia de siempre contar con dos [conjuntos de datos](https://es.wikipedia.org/wiki/Conjunto_de_datos) distintos, uno para entrenar el modelo y otro para evaluar su precisión; ya que si utilizamos el mismo [dataset](https://es.wikipedia.org/wiki/Conjunto_de_datos) para las dos tareas, no tendríamos forma de determinar como el modelo se comporta con datos que nunca ha visto."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ¿Cómo reconocer el sobreajuste?\n",
"\n",
"En líneas generales el [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste) va a estar relacionado con la complejidad del modelo, mientras más complejidad le agreguemos, mayor va a ser la tendencia a *[sobreajustarse](https://es.wikipedia.org/wiki/Sobreajuste)* a los datos, ya que va a contar con mayor flexibilidad para realizar las predicciones y puede ser que los patrones que encuentre estén relacionados con el *ruido* (pequeños errores aleatorios) en los datos y no con la verdadera señal o relación subyacente. \n",
"\n",
"No existe una regla general para establecer cual es el nivel ideal de complejidad que le podemos otorgar a nuestro modelo sin caer en el [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste); pero podemos valernos de algunas herramientas analíticas para intentar entender como el modelo se ajusta a los datos y reconocer el [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste). Veamos un ejemplo."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Árboles de Decisión y sobreajuste\n",
"\n",
"Los [Árboles de Decisión](https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n) pueden ser muchas veces una herramienta muy precisa, pero también con mucha tendencia al [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste). Para construir estos modelos aplicamos un procedimiento recursivo para encontrar los atributos que nos proporcionan más información sobre distintos subconjuntos de datos, cada vez más pequeños. Si aplicamos este procedimiento en forma reiterada, eventualmente podemos llegar a un árbol en el que cada *hoja* tenga una sola instancia de nuestra variable objetivo a clasificar. En este caso extremo, el [Árbol de Decisión](https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n) va a tener una pobre *generalización* y estar bastante [sobreajustado](https://es.wikipedia.org/wiki/Sobreajuste); ya que cada instancia de los datos de entrenamiento va a encontrar el camino que lo lleve eventualmente a la hoja que lo contiene, alcanzando así una precisión del 100% con los datos de entrenamiento. Veamos un ejemplo sencillo con la ayuda de [Python](https://python.org/)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# <!-- collapse=True -->\n",
"# Importando las librerías que vamos a utilizar\n",
"import pandas as pd\n",
"import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns \n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.datasets import make_classification\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"import random; random.seed(1982)\n",
"\n",
"# graficos incrustados\n",
"%matplotlib inline\n",
"\n",
"# parametros esteticos de seaborn\n",
"sns.set_palette(\"deep\", desat=.6)\n",
"sns.set_context(rc={\"figure.figsize\": (8, 4)})"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ejemplo en python - árboles de decisión\n",
"# dummy data con 100 atributos y 2 clases\n",
"X, y = make_classification(10000, 100, n_informative=3, n_classes=2,\n",
" random_state=1982)\n",
"\n",
"# separ los datos en train y eval\n",
"x_train, x_eval, y_train, y_eval = train_test_split(X, y, test_size=0.35, \n",
" train_size=0.65,\n",
" random_state=1982)\n",
"\n",
"# creando el modelo sin control de profundidad, va a continuar hasta\n",
"# que todas las hojas sean puras\n",
"arbol = DecisionTreeClassifier(criterion='entropy')\n",
"\n",
"# Ajustando el modelo\n",
"arbol.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precisión entranamiento: 1.00\n"
]
}
],
"source": [
"# precisión del modelo en datos de entrenamiento.\n",
"print(\"precisión entranamiento: {0: .2f}\".format(\n",
" arbol.score(x_train, y_train)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Logramos una precisión del 100 %, increíble, este modelo no se equivoca! deberíamos utilizarlo para jugar a la lotería y ver si ganamos algunos millones; o tal vez, no?. Veamos como se comporta con los datos de evaluación."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precisión evaluación: 0.87\n"
]
}
],
"source": [
"# precisión del modelo en datos de evaluación.\n",
"print(\"precisión evaluación: {0: .2f}\".format(\n",
" arbol.score(x_eval, y_eval)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ah, ahora nuestro modelo ya no se muestra tan preciso, esto se debe a que seguramente esta [sobreajustado](https://es.wikipedia.org/wiki/Sobreajuste), ya que dejamos crecer el árbol hasta que cada hoja estuviera pura (es decir que solo contenga datos de una sola de las clases a predecir). Una alternativa para reducir el [sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste) y ver si podemos lograr que *generalice* mejor y por tanto tenga más precisión para datos nunca vistos, es tratar de reducir la complejidad del modelo por medio de controlar la profundidad que puede alcanzar el [Árbol de Decisión](https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n). "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"22"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# profundidad del arbol de decisión.\n",
"arbol.tree_.max_depth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Este caso nuestro modelo tiene una profundidad de 22 nodos; veamos si reduciendo esa cantidad podemos mejorar la precisión en los datos de evaluación. Por ejemplo, pongamos un máximo de profundidad de tan solo 5 nodos."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
" max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# modelo dos, con control de profundiad de 5 nodos\n",
"arbol2 = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
"\n",
"# Ajustando el modelo\n",
"arbol2.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precisión entranamiento: 0.92\n"
]
}
],
"source": [
"# precisión del modelo en datos de entrenamiento.\n",
"print(\"precisión entranamiento: {0: .2f}\".format(\n",
" arbol2.score(x_train, y_train)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora podemos ver que ya no tenemos un modelo con 100% de precisión en los datos de entrenamiento, sino que la precisión es bastante inferior, 92%, sin embargo si ahora medimos la precisión con los datos de evaluación vemos que la precisión es del 90%, 3 puntos por arriba de lo que habíamos conseguido con el primer modelo que nunca se equivocaba en los datos de entrenamiento. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precisión evaluación: 0.90\n"
]
}
],
"source": [
"# precisión del modelo en datos de evaluación.\n",
"print(\"precisión evaluación: {0: .2f}\".format(\n",
" arbol2.score(x_eval, y_eval)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Esta diferencia se debe a que reducimos la complejidad del modelo para intentar ganar en generalización. También debemos tener en cuenta que si seguimos reduciendo la complejidad, podemos crear un modelo demasiado simple que en vez de estar [sobreajustado](https://es.wikipedia.org/wiki/Sobreajuste) puede tener un desempeño muy por debajo del que podría tener; podríamos decir que el modelo estaría *infraajustado* y tendría un alto nivel de *sesgo*. Para ayudarnos a encontrar el término medio entre la complejidad del modelo y su *ajuste* a los datos, podemos ayudarnos de herramientas gráficas. Por ejemplo podríamos crear diferentes modelos, con distintos grados de complejidad y luego graficar la precisión en función de la complejidad."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAEZCAYAAACQB4xbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdd5d9a9550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Grafico de ajuste del árbol de decisión\n",
"train_prec = []\n",
"eval_prec = []\n",
"max_deep_list = list(range(3, 23))\n",
"\n",
"for deep in max_deep_list:\n",
" arbol3 = DecisionTreeClassifier(criterion='entropy', max_depth=deep)\n",
" arbol3.fit(x_train, y_train)\n",
" train_prec.append(arbol3.score(x_train, y_train))\n",
" eval_prec.append(arbol3.score(x_eval, y_eval))\n",
"\n",
"# graficar los resultados.\n",
"plt.plot(max_deep_list, train_prec, color='r', label='entrenamiento')\n",
"plt.plot(max_deep_list, eval_prec, color='b', label='evaluacion')\n",
"plt.title('Grafico de ajuste arbol de decision')\n",
"plt.legend()\n",
"plt.ylabel('precision')\n",
"plt.xlabel('cant de nodos')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El gráfico que acabamos de construir se llama *gráfico de ajuste* y muestra la precisión del modelo en función de su complejidad. En nuestro ejemplo, podemos ver que el punto con mayor precisión, en los datos de evaluación, lo obtenemos con un nivel de profundidad de aproximadamente 5 nodos; a partir de allí el modelo pierde en *generalización* y comienza a estar [sobreajustado](https://es.wikipedia.org/wiki/Sobreajuste). También podemos crear un gráfico similar con la ayuda de [Scikit-learn](https://scikit-learn.org/stable/), utilizando `validation_curve`."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# utilizando validation curve de sklearn\n",
"from sklearn.learning_curve import validation_curve\n",
"\n",
"train_prec, eval_prec = validation_curve(estimator=arbol, X=x_train,\n",
" y=y_train, param_name='max_depth',\n",
" param_range=max_deep_list, cv=5)\n",
"\n",
"train_mean = np.mean(train_prec, axis=1)\n",
"train_std = np.std(train_prec, axis=1)\n",
"test_mean = np.mean(eval_prec, axis=1)\n",
"test_std = np.std(eval_prec, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAERCAYAAAB8VA42AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nknAK9iIiIgn3f15Hw7z8529EAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdd5d93b6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# graficando las curvas\n",
"plt.plot(max_deep_list, train_mean, color='r', marker='o', markersize=5,\n",
" label='entrenamiento')\n",
"plt.fill_between(max_deep_list, train_mean + train_std, \n",
" train_mean - train_std, alpha=0.15, color='r')\n",
"plt.plot(max_deep_list, test_mean, color='b', linestyle='--', \n",
" marker='s', markersize=5, label='evaluacion')\n",
"plt.fill_between(max_deep_list, test_mean + test_std, \n",
" test_mean - test_std, alpha=0.15, color='b')\n",
"plt.grid()\n",
"plt.legend(loc='center right')\n",
"plt.xlabel('Cant de nodos')\n",
"plt.ylabel('Precision')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este gráfico, también podemos ver que nuestro modelo tiene bastante *[varianza](https://es.wikipedia.org/wiki/Varianza)*, representada por el área esfumada.\n",
"\n",
"## Métodos para reducir el Sobreajuste\n",
"\n",
"Algunas de las técnicas que podemos utilizar para reducir el [Sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste), son:\n",
"\n",
"* Utilizar *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)*.\n",
"* Recolectar más datos.\n",
"* Introducir una penalización a la complejidad con alguna técnica de regularización.\n",
"* Optimizar los parámetros del modelo con *grid search*.\n",
"* Reducir la dimensión de los datos.\n",
"* Aplicar técnicas de [selección de atributos](https://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/).\n",
"* Utilizar modelos *ensamblados*.\n",
"\n",
"Veamos algunos ejemplos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Validación cruzada\n",
"\n",
"La *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)* se inicia mediante el fraccionamiento de un [conjunto de datos](https://es.wikipedia.org/wiki/Conjunto_de_datos) en un número $k$ de particiones (generalmente entre 5 y 10) llamadas *pliegues*. La *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)* luego itera entre los datos de *evaluación* y *entrenamiento* $k$ veces, de un modo particular. En cada iteración de la *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)*, un *pliegue* diferente se elige como los datos de *evaluación*. En esta iteración, los otros *pliegues* $k-1$ se combinan para formar los datos de *entrenamiento*. Por lo tanto, en cada iteración tenemos $(k-1) / k$ de los datos utilizados para el *entrenamiento* y $1 / k$ utilizado para la *evaluación*.\n",
"Cada iteración produce un modelo, y por lo tanto una estimación del rendimiento de la *generalización*, por ejemplo, una estimación de la precisión. Una vez finalizada la *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)*, todos los ejemplos se han utilizado sólo una vez para *evaluar* pero $k -1$ veces para *entrenar*. En este punto tenemos estimaciones de rendimiento de todos los *pliegues* y podemos calcular la [media](https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica) y la [desviación estándar](https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica) de la precisión del modelo. Veamos un ejemplo\n",
"\n",
"<img alt=\"Validacion cruzada\" title=\"Validacion cruzada\" src=\"https://relopezbriega.github.io/images/validacion_cruzada.png\">"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pliegue: 1, Dist Clase: [2918 2931], Prec: 0.909\n",
"Pliegue: 2, Dist Clase: [2918 2931], Prec: 0.896\n",
"Pliegue: 3, Dist Clase: [2918 2931], Prec: 0.897\n",
"Pliegue: 4, Dist Clase: [2919 2931], Prec: 0.920\n",
"Pliegue: 5, Dist Clase: [2919 2931], Prec: 0.895\n",
"Pliegue: 6, Dist Clase: [2919 2931], Prec: 0.912\n",
"Pliegue: 7, Dist Clase: [2919 2931], Prec: 0.871\n",
"Pliegue: 8, Dist Clase: [2919 2932], Prec: 0.906\n",
"Pliegue: 9, Dist Clase: [2919 2932], Prec: 0.884\n",
"Pliegue: 10, Dist Clase: [2919 2932], Prec: 0.891\n",
"Precision promedio: 0.898 +/- 0.014\n"
]
}
],
"source": [
"# Ejemplo cross-validation\n",
"from sklearn import cross_validation\n",
"\n",
"# creando pliegues\n",
"kpliegues = cross_validation.StratifiedKFold(y=y_train, n_folds=10,\n",
" random_state=2016)\n",
"# iterando entre los plieges\n",
"precision = []\n",
"for k, (train, test) in enumerate(kpliegues):\n",
" arbol2.fit(x_train[train], y_train[train]) \n",
" score = arbol2.score(x_train[test], y_train[test])\n",
" precision.append(score)\n",
" print('Pliegue: {0:}, Dist Clase: {1:}, Prec: {2:.3f}'.format(k+1,\n",
" np.bincount(y_train[train]), score))\n",
"\n",
"# imprimir promedio y desvio estandar\n",
"print('Precision promedio: {0: .3f} +/- {1: .3f}'.format(np.mean(precision),\n",
" np.std(precision)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este ejemplo, utilizamos el <a href=\"https://es.wikipedia.org/wiki/Iterador_(patr%C3%B3n_de_dise%C3%B1o)\">iterador</a> `StratifiedKFold` que nos proporciona [Scikit-learn](https://scikit-learn.org/stable/). Este <a href=\"https://es.wikipedia.org/wiki/Iterador_(patr%C3%B3n_de_dise%C3%B1o)\">iterador</a> es una versión mejorada de la *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)*, ya que cada *pliegue* va a estar estratificado para mantener las proporciones entre las *clases* del [conjunto de datos](https://es.wikipedia.org/wiki/Conjunto_de_datos) original, lo que suele dar mejores estimaciones del sesgo y la varianza del modelo. También podríamos utilizar `cross_val_score` que ya nos proporciona los resultados de la precisión que tuvo el modelo en cada *pliegue*."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precisiones: [ 0.906298 0.89708141 0.89708141 0.91846154 0.89538462 0.91230769\n",
" 0.87076923 0.90755008 0.8844376 0.89060092]\n",
"Precision promedio: 0.898 +/- 0.013\n"
]
}
],
"source": [
"# Ejemplo con cross_val_score\n",
"precision = cross_validation.cross_val_score(estimator=arbol2,\n",
" X=x_train, y=y_train,\n",
" cv=10, n_jobs=-1)\n",
"\n",
"print('precisiones: {}'.format(precision))\n",
"print('Precision promedio: {0: .3f} +/- {1: .3f}'.format(np.mean(precision),\n",
" np.std(precision)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Más datos y curvas de aprendizaje\n",
"\n",
"Muchas veces, reducir el [Sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste) es tan fácil como conseguir más datos, dame más datos y te predeciré el futuro!. Aunque en la vida real nunca es una tarea tan sencilla conseguir más datos. Otra herramienta analítica que nos ayuda a entender como reducimos el [Sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste) con la ayuda de más datos, son las *curvas de aprendizaje*, las cuales grafican la precisión en función del tamaño de los datos de entrenamiento. Veamos como podemos graficarlas con la ayuda de [Python](https://python.org/).\n",
"\n",
"<img alt=\"Curva de aprendizaje\" title=\"Curva de aprendizaje\" src=\"https://relopezbriega.github.io/images/curva_aprendizaje.png\" width=\"600px\" height=\"600px\" >"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Ejemplo Curvas de aprendizaje\n",
"from sklearn.learning_curve import learning_curve\n",
"\n",
"train_sizes, train_scores, test_scores = learning_curve(estimator=arbol2,\n",
" X=x_train, y=y_train, \n",
" train_sizes=np.linspace(0.1, 1.0, 10), cv=10,\n",
" n_jobs=-1)\n",
"\n",
"train_mean = np.mean(train_scores, axis=1)\n",
"train_std = np.std(train_scores, axis=1)\n",
"test_mean = np.mean(test_scores, axis=1)\n",
"test_std = np.std(test_scores, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgEAAAEZCAYAAAAOi/YKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKlR0glIkiRJkholnYAkSZIkqVH+B4wmwwBZg5RrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdd5d9247b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# graficando las curvas\n",
"plt.plot(train_sizes, train_mean, color='r', marker='o', markersize=5,\n",
" label='entrenamiento')\n",
"plt.fill_between(train_sizes, train_mean + train_std, \n",
" train_mean - train_std, alpha=0.15, color='r')\n",
"plt.plot(train_sizes, test_mean, color='b', linestyle='--', \n",
" marker='s', markersize=5, label='evaluacion')\n",
"plt.fill_between(train_sizes, test_mean + test_std, \n",
" test_mean - test_std, alpha=0.15, color='b')\n",
"plt.grid()\n",
"plt.title('Curva de aprendizaje')\n",
"plt.legend(loc='upper right')\n",
"plt.xlabel('Cant de ejemplos de entrenamiento')\n",
"plt.ylabel('Precision')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este gráfico podemos ver claramente como con pocos datos la precisión entre los datos de entrenamiento y los de evaluación son muy distintas y luego a medida que la cantidad de datos va aumentando, el modelo puede generalizar mucho mejor y las precisiones se comienzan a emparejar. Este gráfico también puede ser importante a la hora de decidir invertir en la obtención de más datos, ya que por ejemplo nos indica que a partir las 2500 muestras, el modelo ya no gana mucha más precisión a pesar de obtener más datos.\n",
"\n",
"### Optimización de parámetros con Grid Search\n",
"\n",
"La mayoría de los modelos de [Machine Learning](https://relopezbriega.github.io/tag/machine-learning.html) cuentan con varios parámetros para ajustar su comportamiento, por lo tanto otra alternativa que tenemos para reducir el [Sobreajuste](https://es.wikipedia.org/wiki/Sobreajuste) es optimizar estos parámetros por medio de un proceso conocido como *grid search* e intentar encontrar la combinación ideal que nos proporcione mayor precisión. El enfoque que utiliza *grid search* es bastante simple, se trata de una búsqueda exhaustiva por el paradigma de fuerza bruta en el que se especifica una lista de valores para diferentes parámetros, y la computadora evalúa el rendimiento del modelo para cada combinación de éstos parámetros para obtener el conjunto óptimo que nos brinda el mayor rendimiento. \n",
"\n",
"Veamos un ejemplo utilizando un modelo de [SVM o Máquinas de vectores de soporte](https://es.wikipedia.org/wiki/M%C3%A1quinas_de_vectores_de_soporte), la idea va a ser optimizar los parámetros `gamma` y `C` de este modelo. El parámetro `gamma` define cuan lejos llega la influencia de un solo ejemplo de entrenamiento, con valores bajos que significan \"lejos\" y los valores altos significan \"cerca\". El parámetro `C` es el que establece la penalización por error en la clasificación un valor bajo de este parámetro hace que la superficie de decisión sea más lisa, mientras que un valor alto tiene como objetivo que todos los ejemplos se clasifiquen correctamente, dándole más libertad al modelo para elegir más ejemplos como vectores de soporte. Tengan en cuenta que como todo proceso por fuerza bruta, puede tomar bastante tiempo según la cantidad de parámetros que utilicemos para la optimización."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Ejemplo de grid search con SVM.\n",
"from sklearn.grid_search import GridSearchCV\n",
"\n",
"# creación del modelo\n",
"svm = SVC(random_state=1982)\n",
"\n",
"# rango de parametros\n",
"rango_C = np.logspace(-2, 10, 10)\n",
"rango_gamma = np.logspace(-9, 3, 10)\n",
"param_grid = dict(gamma=rango_gamma, C=rango_C)\n",
"\n",
"# crear grid search\n",
"gs = GridSearchCV(estimator=svm, param_grid=param_grid, scoring='accuracy',\n",
" cv=5,n_jobs=-1)\n",
"\n",
"# comenzar el ajuste\n",
"gs = gs.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.870461538462\n",
"{'C': 4.6415888336127775, 'gamma': 0.0046415888336127729}\n"
]
}
],
"source": [
"# imprimir resultados\n",
"print(gs.best_score_)\n",
"print(gs.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precisión: 0.864\n"
]
}
],
"source": [
"# utilizando el mejor modelo\n",
"mejor_modelo = gs.best_estimator_\n",
"mejor_modelo.fit(x_train, y_train)\n",
"print('Precisión: {0:.3f}'.format(mejor_modelo.score(x_eval, y_eval)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este ejemplo, primero utilizamos el objeto `GridSearchCV` que nos permite realizar *grid search* junto con *[validación cruzada](https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada)*, luego comenzamos a ajustar el modelo con las diferentes combinaciones de los valores de los parámetros `gamma` y `C`. Finalmente imprimimos el mejor resultado de precisión y los valores de los parámetros que utilizamos para obtenerlos; por último utilizamos este mejor modelo para realizar las predicciones con los datos de *evaluación*. Podemos ver que la precisión que obtuvimos con los datos de evaluación es casi idéntica a la que nos indicó *grid search*, lo que indica que el modelo *generaliza* muy bien.\n",
"\n",
"Aquí termina este artículo, sobre la [selección de atributos](https://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/), pueden visitar el artículo que dedique a ese tema en este [link](https://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/); en cuando a modelos ensamblados y reducción de dimensiones de los datos, espero escribir sobre esos temas en artículos futuros, no se los pierdan!\n",
"\n",
"Gracias por visitar el blog y saludos!\n",
"\n",
"*Este post fue escrito utilizando IPython notebook. Pueden descargar este [notebook](https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningOverfitting.ipynb) o ver su version estática en [nbviewer](https://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningOverfitting.ipynb).*"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
simkovic/simkovic.github.io | _ipynb/No Way Anova - Logistic Regression truimphs Anova.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The best argument against Anova is to show how the analysis will look like if we used parameter estimation instead. With complex experimental design this means that we will use regression. Most psychologists think of linear regression. However, the approach extends to general linear models of the sort $y=f(b_0+b_1\\cdot x_1 + b_2\\cdot x_2 \\dots)$. Linear regression is just a special case when $f(\\cdot)$ is the identity function $y=x$. \n",
"\n",
"When the outcome variable takes binary values $0,1$ then it is better to use logistic regression than the linear regression. Here the link function is $f(x)= 1/(1+e^{-x})$ is the [logistic function](https://en.wikipedia.org/wiki/Logistic_function). As with any kind of data the default approach in psychology research is to analyze binary outcomes with Anova. Let's see what are the consequences.\n",
"\n",
"In our first demonstration we will take a look at a study by Mussel and colleagues [published](http://journal.sjdm.org/12/12817/jdm12817.html) in JDM.\n",
"\n",
"The study investigated whether a smiling/angry/neutral face influences collaboration in an [ultimatum game](https://en.wikipedia.org/wiki/Ultimatum_game) (i.e. prisoner's dilemma with no iteration). The subjects were shown facial expression of the opponent avatar and the amount of money he offered. Then they were asked whether they wish to collaborate. The oppenet could propose the share a fraction from the total of 14 cents. The offers ranged from 7c (overly fair) to 1c (unfair) in 1c steps and subjects decided whether they accept the offer or not. The authors also varied whether the face was male or female. The combination of these factors gives us 2x3x7=42 stimuli. Each of 1326 subjects was shown all 42 stimuli in random order and either collaborated (1) or not (0). Trial ended if subjects failed to respond within a 3 second time limit. In the data set these trials were noted as missing values. Next, authors replaced missing values with subject-wise averages. They then analysed the data with a repeated measures Anova with two factors: fairness (7 levels) and face expression (3 levels). As the high N suggests both main-effects and their interaction were significant. \n",
"Also all post-hoc comparisons were significant (p<.05, bonf. corrected). Smiling conditions had higher collaboration rate than neutral and neutral was in turn higher than angry. When subjects were offered more money they collaborated more. So what can we conclude? Not much. Smiling face increases the offer acceptance. More generous offer does also increase acceptance. No clue where the interaction comes from. \n",
"\n",
"With N=1326 the $p$ values are almost entirely driven by the large sample size. But of course the magnitude of the effect matters. If the collaboration rate is 70 % for smiling faces and 40% for neutral than this is notable. But if the rates are 51 and 50% respectively we don't really care. If the collaboration rates were 95% and 90% we would consider the study inconclusive due to ceiling effects. $p$ values are inherently incapable of providing this information. We need effect size estimates.\n",
"\n",
"We are fortunate. Mussel et al. are good academic citizens and report the standardized effect size. \n",
"\n",
"$$ \\eta_{\\mathrm{face}}=.1 $$\n",
"$$ \\eta_{\\mathrm{money}}=.39 $$\n",
"$$ \\eta_{\\mathrm{money}\\times \\mathrm{face}}=.01 $$\n",
"\n",
"Are we smarter now? How should we interpret these effect sizes? Is $ \\eta_{\\mathrm{face}}=.1 $ more like the 70%-40% difference or more like the 51%-50% difference? Presumably, the last question is not what we are supposed to ask. Rather, we need to survey the decision making literature to see what are the usual effect sizes and take these as a benchmark for comparison.. Taking the standards from Fritz et al. (2011) we would say that the facial expression has small effect while money has medium effect on acceptance. The interaction shows minuscule effect size and should be presumably discared. \n",
"\n",
"One difficulty with this interpretation is that the sums of squares on which $\\eta$ is based depend on the variance of the predictors. The effect size could be improved by using fewer money offer levels. Similar, the addition of face gender factor influences each of the effect size estimates above, because it presumably increases the error variance. As such effect sizes can't be compared across studies, not even across replications if these aren't exact. \n",
"\n",
"The only reason why most of the papers with of the Anova analyses are worth reading after all, is that they include a figure with plotted group averages. Mussel et al. show mercy and give us the following graph. \n",
"<image src=\"http://journal.sjdm.org/12/12817/jdm12817002.png\"> </image>\n",
"\n",
"Ok. Now we see it. The interaction is due to ceiling/floor effects for large and small sums respectively. We are really interested in what happens in the middle. Here, the extra smile increases (compared to neutral face) the acceptance by 5-10%. Angry face shows an effect of similar magnitude but goes in the opposite direction. \n",
"\n",
"Why is it not possible to do this kind of analysis formally? It is. But we need to something else than Anova.\n",
"\n",
"We formulate a regression model with acceptance ($\\mathrm{coop}$) as outcome and face expression ($\\mathrm{face}$) and offered money sum ($\\mathrm{fair}$) as predictors.\n",
"\n",
"$$\\mathrm{coop}_{i,j} \\sim \\mathrm{Bern}(\\pi_{i,j})$$\n",
"\n",
"$$\\pi_{i,j} = \\mathrm{logit}^{-1}(\\alpha_{\\mathrm{face}[i,j]}+\\beta_{\\mathrm{face}[i,j]}\\mathrm{fair}[i,j])(1-\\gamma_{\\mathrm{face}[i,j]}-\\delta_{\\mathrm{face}[i,j]})+\\gamma_{\\mathrm{face}[i,j]}$$\n",
"\n",
"We enter money sum as continuous predictor of acceptance at each trial $j$ for each subject $i$. We fit a separate model for each type of face expression and for each subject. $\\mathrm{face}[i,j]$ is the index of the face expression which subject $i$ saw at trial $j$. The parameters $\\gamma$ and $\\delta$ determine the level (of acceptance) where the logit function becomes flat. Without this addition the logit curve would become flat at $[0,1]$. We can see from the figure from the paper that this is not the case. \n",
"\n",
"Let's look at how the logistic function works by playing around with its parameters."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/pymodules/python2.7/matplotlib/figure.py:1533: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
" warnings.warn(\"This figure includes Axes that are not \"\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCQkJCQmDxP8DZaJAk4DRyIwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2dddd10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import scoreatpercentile as sap\n",
"clr=(0.2, 0.5, 0.6)\n",
"%pylab inline\n",
"plt.figure(figsize=(6,4))\n",
"plt.subplot(2,2,1);plt.title('alpha')\n",
"x=np.arange(1,9,0.1)\n",
"for a in np.arange(2,9).tolist():\n",
" y=1/(1+exp(a-1*x))*1+0\n",
" plt.plot(x,y,'b')\n",
"plt.grid(b=False,axis='x');plt.ylim([0,1]);plt.xlim([1,8])\n",
"plt.subplot(2,2,2);plt.title('beta')\n",
"for b in np.arange(-1,3,0.5).tolist():\n",
" y=1/(1+exp(4.5*2**b-2**b*x))*1+0\n",
" plt.plot(x,y,'b')\n",
"plt.grid(b=False,axis='x');plt.ylim([0,1]);plt.xlim([1,8])\n",
"plt.subplot(2,2,3);plt.title('gamma')\n",
"for c in np.arange(0,0.8,0.1).tolist():\n",
" y=1/(1+exp(4.5-x))*(1-c)+c\n",
" plt.plot(x,y,'b'); \n",
"plt.grid(b=False,axis='x');plt.ylim([0,1]);plt.xlim([1,8])\n",
"plt.subplot(2,2,4);plt.title('delta')\n",
"for d in np.arange(0,0.8,0.1).tolist():\n",
" y=1/(1+exp(4.5-x))*(1-0.1-d)+0.1\n",
" plt.plot(x,y,'b'); \n",
"plt.grid(b=False,axis='x');plt.ylim([0,1]);plt.xlim([1,8]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that $\\alpha$ shifts the function on the x axis. $\\beta$ alters the steepness of the function. As already mention $\\gamma$ and $\\delta$ determine the floor and the ceiling of the function.\n",
"\n",
"\n",
"Next let's perform the analysis. We first load the data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1326, 42)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from urllib import urlopen\n",
"f=urlopen('http://journal.sjdm.org/12/12817/data.csv')\n",
"D=np.loadtxt(f,skiprows=3,delimiter=',')[:,7:]\n",
"f.close()\n",
"D.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"55692 55692 55692 55692\n",
"(1326, 42)\n"
]
}
],
"source": [
"# predictors\n",
"vfair=np.array(([range(1,8)]*6)).flatten() # in cents\n",
"vmale=np.ones(42,dtype=int); vmale[21:]=0\n",
"vface=np.int32(np.concatenate([np.zeros(7),np.ones(7),np.ones(7)*2]*2))\n",
"# anova format\n",
"sid=[];face=[];fair=[]\n",
"for i in range(D.shape[0]):\n",
" for j in range(D.shape[1]):\n",
" sid.append(i)\n",
" #face.append(['angry','neutral','smile'][vface[j]])\n",
" face.append(vface[j])\n",
" fair.append(vfair[j])\n",
"coop=D.flatten()\n",
"sid=np.array(sid)\n",
"face=np.array(face)\n",
"fair=np.array(fair)\n",
"assert np.all(coop[:42]==D[0,:])\n",
"print coop.size,len(sid),len(face),len(fair)\n",
"print D.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is good to do some manual fitting to see just how the logistic curve behaves but also to assure ourselves that the we can get a shape similar the pattern of our data. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCOGQJEAJIYRwSP8PCJ3i5Vm7q8AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4821b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D[D==2]=np.nan\n",
"R=np.zeros((3,7))\n",
"for i in np.unique(vface).tolist():\n",
" for j in np.unique(vfair).tolist():\n",
" sel=np.logical_and(i==vface,j==vfair)\n",
" R[i,j-1]=np.nansum(D[:,sel])/(~np.isnan(D[:,sel])).sum()\n",
"for i in range(3):\n",
" y=1/(1+exp(4.5-1.2*np.arange(1,8)))*0.5+[0.4,0.44,0.47][i]\n",
" plt.plot(range(1,8),y,':',color=['b','r','g'][i])\n",
" plt.plot(range(1,8),R[i,:],color=['b','r','g'][i])\n",
"plt.legend(['Data Angry','Model Angry','Data Neutral',\n",
" 'Model Neutral','Data Smile','Model Smile'],loc=4);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above I fitted the logistic curve to the data. I got the parameter values by several iterations of trial-and-error. We want to obtain precise estimates and also we wish to get an idea about the uncertainty of the estimate. We implement the model in STAN."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_b138e62c537965b343fa6a7f909fc2d7 NOW.\n"
]
}
],
"source": [
"import pystan\n",
"\n",
"model = \"\"\"\n",
"data {\n",
" int<lower=0> N;\n",
" int<lower=0,upper=1> coop[N]; // acceptance\n",
" int<lower=0,upper=8> fair[N]; // fairness\n",
" int<lower=1,upper=3> face[N]; // face expression\n",
"}\n",
"parameters {\n",
" real<lower=-20,upper=20> alpha[3];\n",
" real<lower=0,upper=10> beta[3];\n",
" simplex[3] gamm[3];\n",
"\n",
"}\n",
"transformed parameters{\n",
" vector[N] x;\n",
" for (i in 1:N)\n",
" x[i]<-inv_logit(alpha[face[i]]+beta[face[i]]*fair[i])\n",
" *gamm[face[i]][3]+gamm[face[i]][1]; \n",
"}\n",
"model {\n",
" coop ~ bernoulli(x);\n",
"}\n",
"\"\"\"\n",
"#inpar=[{'alpha':[-4.5,-4.5,-4.5],'beta':[1.2,1.2,1.2],\n",
"# 'gamma':[0.4,0.44,0.47],'delta':[0.1,0.06,0.03]}]*4\n",
"sm = pystan.StanModel(model_code=model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run it!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:NOW ON CHAIN 0\n",
"INFO:pystan:NOW ON CHAIN 1\n",
"INFO:pystan:NOW ON CHAIN 2\n",
"INFO:pystan:NOW ON CHAIN 3\n"
]
}
],
"source": [
"dat = {'N': coop.size,'coop':np.int32(coop),'fair':fair,'face':face+1,'sid':sid}\n",
"seed=np.random.randint(2**16)\n",
"fit=sm.sampling(data=dat,iter=6000,chains=4,thin=5,warmup=2000,n_jobs=4,seed=seed)\n",
"print pystan.misc._print_stanfit(fit,pars=['alpha','beta','gamm'],digits_summary=2)\n",
"w= fit.extract()\n",
"np.save('alpha',w['alpha'])\n",
"np.save('gamm',w['gamm'])\n",
"np.save('beta',w['beta'])\n",
"del w\n",
"del fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the results."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x4804110>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSQKUEEIIm/T/ugn+VEVloq0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4804350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#w=fit.summary(pars=['alpha','beta','gamm'])\n",
"#np.save('logregSummary.fit',w)\n",
"#w=np.load('logregSummary.fit.npy')\n",
"#w=w.tolist()\n",
"a=np.load('m1alpha.npy')\n",
"b=np.load('m1beta.npy')\n",
"g=np.load('m1gamm.npy')[:,:,0]\n",
"d=np.load('m1gamm.npy')[:,:,1]\n",
"#D[D==2]=np.nan\n",
"for i in range(3):\n",
" x=np.linspace(1,7,101)\n",
" y=1/(1+exp(-np.median(a[:,i])-np.median(b[:,i])*x))*np.median(1-g[:,i]-d[:,i])+np.median(g[:,i])\n",
" plt.plot(x,y,':',color=['b','r','g'][i])\n",
" plt.plot(range(1,8),R[i,:],color=['b','r','g'][i])\n",
"plt.legend(['Data Angry','Model Angry','Data Neutral',\n",
" 'Model Neutral','Data Smile','Model Smile'],loc=4);\n",
"#for j in range(lp.size): print '%.3f [%.3f, %.3f]'%(prs[j],lp[j],up[j]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model fits quite well. We now look at the estimated values for different face conditions."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3200, 15)\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEhMUEREJiQmKiIiE9P8AoyQkr6mr/YkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x47f6790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D=np.concatenate([a,b,g,1-d,1-g-d],1)\n",
"print D.shape\n",
"for n in range(D.shape[1]):\n",
" plt.subplot(2,3,[1,2,4,5,6][n/3])\n",
" k=n%3\n",
" plt.plot([k,k],[sap(D[:,n],2.5),sap(D[:,n],97.5)],color=clr)\n",
" plt.plot([k,k],[sap(D[:,n],25),sap(D[:,n],75)],color=clr,lw=3,solid_capstyle='round')\n",
" plt.plot([k],[np.median(D[:,n])],mfc=clr,mec=clr,ms=8,marker='_',mew=2)\n",
" plt.xlim([-0.5,2.5])\n",
" plt.grid(b=False,axis='x')\n",
" plt.title(['alpha','beta','gamma','delta','1-gamma-delta'][n/3])\n",
" plt.gca().set_xticks([0,1,2])\n",
" plt.gca().set_xticklabels(['angry','neutral','smile'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The estimates show what we already more-or-less inferred from the graph. The 95% interval for $\\alpha$ and $\\beta$ coefficients are overlapping and we should consider model with the same horizontal shift and steepness for each of the face conditions. We see that the $\\gamma$ and $\\delta$ vary between the conditions. To better understand what is happening consider the width of the acceptance band in each condition given by $1-\\gamma-\\delta$ shown in the right bottom panel. From the figure it looks like all three curves occupy a band of the same width. The estimation confirms this for the case of neutral and smile condition whose estimates overlap almost perfectly. In the angry condition it is not clear where the bottom floor of the logit curve is located. The curve is still linear for lower offers. This means that a) the $1-\\gamma-\\delta$ estimate is larger in angry condition and b) the estimate is more uncertain. We can reasonably argue that $1-\\gamma-\\delta$ should be equal across conditions and that discrepant estimate for angry condition is due to error or some strange money-face interaction which we are not interested in. We end up with the following model.\n",
"\n",
"$$\\mathrm{coop}_{i,j} \\sim \\mathrm{Bern}(\\pi_{i,j})$$\n",
"\n",
"$$\\pi_{i,j} =\\mathrm{logit}^{-1}(\\alpha+\\beta\\cdot\\mathrm{fair}[i,j])\\cdot \\nu+\\gamma_{\\mathrm{face}[i,j]}$$"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_136eb1c03490516c519e5c7e31e39205 NOW.\n"
]
}
],
"source": [
"import pystan\n",
"\n",
"model = \"\"\"\n",
"data {\n",
" int<lower=0> N;\n",
" int<lower=0,upper=1> coop[N]; // acceptance\n",
" int<lower=0,upper=8> fair[N]; // fairness\n",
" int<lower=1,upper=3> face[N]; // face expression\n",
"}\n",
"parameters {\n",
" real<lower=-20,upper=20> alpha;\n",
" real<lower=0,upper=10> beta;\n",
" real<lower=0,upper=1> gamm[3];\n",
" real<lower=0,upper=1> delt[3];\n",
" \n",
"\n",
"}\n",
"transformed parameters{\n",
" vector[N] x;\n",
" vector[3] gamma[3];\n",
" for (i in 1:3){\n",
" gamma[i][1]<-gamm[i];\n",
" gamma[i][2]<-delt[i];\n",
" gamma[i][3]<-1-gamm[i]-delt[i];\n",
" }\n",
" for (i in 1:N)\n",
" x[i]<-inv_logit(alpha+beta*fair[i])\n",
" *gamma[face[i]][3]+gamma[face[i]][1]; \n",
"}\n",
"model {\n",
" coop ~ bernoulli(x);\n",
"}\n",
"\"\"\"\n",
"sm = pystan.StanModel(model_code=model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dat = {'N': coop.size,'coop':np.int32(coop),'fair':fair,'face':face+1,'sid':sid}\n",
"seed=np.random.randint(2**16)\n",
"fit=sm.sampling(data=dat,iter=5000,chains=4,thin=5,warmup=2000,n_jobs=4,seed=seed)\n",
"outpars=['alpha','beta','gamm','delt']\n",
"print pystan.misc._print_stanfit(fit,pars=outpars,digits_summary=2)\n",
"w= fit.extract()\n",
"for op in outpars: np.save(op,w[op])\n",
"del w\n",
"del fit"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQtgkCVBCCCFs0v8D0LMHJc0tGQYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bca350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a=np.load('alpha.npy')\n",
"b=np.load('beta.npy')\n",
"g=np.load('gamm.npy')\n",
"d=np.load('delt.npy')\n",
"#D[D==2]=np.nan\n",
"for i in range(3):\n",
" x=np.linspace(1,7,101)\n",
" y=1/(1+exp(-np.median(a)-np.median(b)*x))*np.median(1-g[:,i]-d[:,i])+np.median(g[:,i])\n",
" plt.plot(x,y,':',color=['b','r','g'][i])\n",
" plt.plot(range(1,8),R[i,:],color=['b','r','g'][i])\n",
"plt.legend(['Data Angry','Model Angry','Data Neutral',\n",
" 'Model Neutral','Data Smile','Model Smile'],loc=4);"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3, 2400) (2400, 3)\n",
"(2400, 12)\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARwAAAEYCAYAAACOZUn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/C9WXp2/wHAX+gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2e3b490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import scoreatpercentile as sap\n",
"\n",
"print g.T.shape, np.atleast_2d(d).shape\n",
"D=np.concatenate([np.atleast_2d(a),np.atleast_2d(b),np.atleast_2d(b),g.T,1-d.T,1-g.T-d.T],0).T\n",
"print D.shape\n",
"for n in range(D.shape[1]):\n",
" plt.subplot(2,3,[1,2,4,5,6][n/3])\n",
" k=n%3\n",
" plt.plot([k,k],[sap(D[:,n],2.5),sap(D[:,n],97.5)],color=clr)\n",
" plt.plot([k,k],[sap(D[:,n],25),sap(D[:,n],75)],color=clr,lw=3,solid_capstyle='round')\n",
" plt.plot([k],[np.median(D[:,n])],mfc=clr,mec=clr,ms=8,marker='_',mew=2)\n",
" plt.xlim([-0.5,2.5])\n",
" plt.grid(b=False,axis='x')\n",
" plt.title(['alpha-beta','gamma','delta','1-gamma-delta'][n/3])\n",
" plt.gca().set_xticks([0,1,2])\n",
" plt.gca().set_xticklabels(['angry','neutral','smile'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Furthermore, we are concerned about the fact the the comparison across conditions is done within-subject and that the observed values are not independent. We extend the model by fitting separate logistic model to each subject. In particular, we estimate a separate $\\gamma$ parameter for each subject i.e. $\\gamma_{i,\\mathrm{face}[i,j]}$. We use hierarchical prior that pools the estimates across subjects and also takes care of the correlation between conditions.\n",
"\n",
"$$ \\begin{bmatrix}\n",
"\\gamma_{i,s} \\\\ \\gamma_{i,n} \\\\ \\gamma_{i,a}\n",
"\\end{bmatrix}\n",
"\\sim \\mathcal{N} \\Bigg(\n",
"\\begin{bmatrix}\n",
"\\mu_s \\\\ \\mu_n \\\\ \\mu_a\n",
"\\end{bmatrix}\n",
",\\Sigma \\Bigg)$$\n",
"\n",
"where\n",
"$$\n",
"\\Sigma=\n",
"\\begin{pmatrix}\n",
"\\sigma_s^2 & \\sigma_s r_{sn} \\sigma_n & \\sigma_s r_{sa} \\sigma_a \\\\\n",
"\\sigma_s r_{sn} \\sigma_n & \\sigma_n^2 & \\sigma_n r_{na} \\sigma_a \\\\\n",
" \\sigma_s r_{sa} \\sigma_a & \\sigma_n r_{na} \\sigma_a & \\sigma_a^2 \\\\\n",
"\\end{pmatrix}\n",
"$$\n",
"For each condition we are estimating population mean $\\mu$ and population variance $\\sigma^2$. Furthermore, we estimate correlation $r$ for each pair of conditions. As a consequence the estimate $\\mu$ are not confounded by the correlation."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_3ae78a9281ca6e69bcf1ed2148a57829 NOW.\n"
]
}
],
"source": [
"import pystan\n",
"\n",
"model = \"\"\"\n",
"data {\n",
" int<lower=0> N;\n",
" int<lower=0> M; // number of subjects\n",
" int sid[N]; // subject identifier\n",
" int<lower=0,upper=1> coop[N]; // acceptance\n",
" int<lower=0,upper=8> fair[N]; // fairness\n",
" int<lower=1,upper=3> face[N]; // face expression\n",
" \n",
"}\n",
"parameters {\n",
" real<lower=-20,upper=20> alpha;\n",
" real<lower=0,upper=10> beta;\n",
" vector<lower=0,upper=1>[3] gamm[M];\n",
" real<lower=0,upper=1> delt;\n",
" vector<lower=0,upper=1>[3] mu;\n",
" vector<lower=0,upper=1>[3] sigma;\n",
" vector<lower=-1,upper=1>[3] r;\n",
" \n",
"\n",
"}\n",
"transformed parameters{\n",
" vector[N] x;\n",
" vector[3] gammt[3,M];\n",
" matrix[3,3] S;\n",
" for (i in 1:3) S[i,i]<-square(sigma[i]);\n",
" S[1,2]<- sigma[1]*r[1]*sigma[2];S[2,1]<-S[1,2];\n",
" S[1,3]<- sigma[1]*r[2]*sigma[3];S[3,1]<-S[1,3];\n",
" S[2,3]<- sigma[3]*r[3]*sigma[2];S[3,2]<-S[2,3];\n",
" for (m in 1:M){\n",
" for (i in 1:3){\n",
" gammt[i][m][1]<-gamm[m][i];\n",
" gammt[i][m][3]<-delt;\n",
" gammt[i][m][2]<- 1- gammt[i][m][1]-gammt[i][m][3];\n",
" }}\n",
" for (i in 1:N)\n",
" x[i]<-inv_logit(alpha+beta*fair[i])\n",
" *gammt[face[i]][sid[i]][3]+gammt[face[i]][sid[i]][1]; \n",
"}\n",
"model {\n",
" for (i in 1:M) gamm[i]~multi_normal(mu,S);\n",
" coop ~ bernoulli(x);\n",
"}\n",
"\"\"\"\n",
"sm = pystan.StanModel(model_code=model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dat = {'N': coop.size,'coop':np.int32(coop),'fair':fair,'face':face+1,'sid':sid+1,'M':1326}\n",
"seed=np.random.randint(2**16)\n",
"fit=sm.sampling(data=dat,iter=5000,chains=4,thin=5,warmup=2000,n_jobs=4,seed=seed)\n",
"outpars=['alpha','beta','delt','mu','sigma','r']\n",
"print pystan.misc._print_stanfit(fit,pars=outpars,digits_summary=2)\n",
"w= fit.extract()\n",
"for op in outpars: np.save(op,w[op])\n",
"del w\n",
"del fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have found the appropriate model we can look at the contrasts of interest. In our case we are interested in $\\mu_\\mathrm{smile}-\\mu_\\mathrm{neutral}$ and $\\mu_\\mathrm{angry}-\\mu_\\mathrm{neutral}$. The former is TODO while the latter is TODO. This is the effect size that we should be interested in. Note how the estimate goes beyond simple contrast that just computes the mean difference between the angry and neutral condition. The model is a device that allows us to extract the quantity from the data. Our model takes care of missing values, of imballanced groups (due to missing values). It accounts for the ceiling and floor effects. In Anova that assumes linear trends these showed up as a significant correlation. We saw no such interaction. The model also took care of the correlation of subject's performance in different groups. On the other hand it seems rather redundant to estimate the $\\alpha$ and $\\beta$ parameters. In this these did not differ noticably between the conditions but in other context they may provide interesting insight. Curiously even in our context we can find use for them. $\\beta$ expresses the increase in acceptance rate for each unit of money offered. We are mostly interested in the increase in the range between 2c and 6c. Here the curve is approximately linear with slope $\\beta/4$.\n",
"We can use this fact to ask a following question. The paper Mussel et al. bears the title \"What is the values of a smile\". The implication of the title is that smile has a similar influence on the acceptance rate as a sum of money does. We can reformulate this question in the form of a following counterfactual. What is the sum of money we would need to offer a subject who saw neutral face so that his acceptance rate reaches a level it would have if he saw a smiling face. This quantity is given by $4(\\mu_\\mathrm{smile}-\\mu_\\mathrm{neutral})/\\beta$. The value of the smile is . This result is valid for offers in the range where the logistic function is approximately linear (i.e. 2c and 6c). This is the middle range of the tested values and obviously the range in which the authors were interested in. If we assume that people behave similarly whether the total sum is 14c, 14 EUR, 14 or in fact any sum then we can say that the value of a smile as \\% of the equal share. This quantity is informative. Compare it to the $\\eta$. It doesn't depend on the number of conditions. For instance the estimate of value of a smile is independent on the fact that we included an angry condition in the experiment. The quantity is directly expressed in units we well understand (compare to squared unitless quantity). Finally, the quantity has causal intepretation. This is an important fact to which I will return in my latter posts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| scripts/Python/PlotMaps.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse GEOtop maps\n",
"---\n",
"Author: Elisa Bortoli ([email protected]) \n",
"\n",
"Date: 28/03/2019"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load DEM"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import rasterio as rio\n",
"from rasterio.plot import show\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"plt.ion()\n",
"\n",
"from shapely.geometry import Polygon, mapping\n",
"from rasterio.mask import mask\n",
"\n",
"# set standard plot parameters for uniform plotting\n",
"plt.rcParams['figure.figsize'] = (8, 8)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[-9999., -9999., -9999., -9999., 1500., -9999., -9999., -9999.],\n",
" [-9999., 1300., 1350., 1450., 1480., 1450., 1480., 1490.],\n",
" [-9999., 1280., 1320., 1400., 1430., 1450., 1460., -9999.],\n",
" [-9999., 1250., 1300., 1350., 1380., 1410., 1450., -9999.],\n",
" [ 1230., 1220., 1210., 1250., 1330., 1380., 1400., -9999.],\n",
" [ 1210., 1200., 1200., 1220., 1300., 1340., 1350., -9999.],\n",
" [ 1200., 1170., 1150., 1200., 1210., 1220., 1250., -9999.],\n",
" [ 1160., 1150., 1140., 1110., 1120., 1130., 1150., -9999.],\n",
" [ 1100., 1080., 1070., 980., 990., 1000., 1050., -9999.],\n",
" [ 1070., 1050., 1010., 950., 960., 980., 990., -9999.],\n",
" [-9999., -9999., -9999., 910., 920., 940., 945., -9999.]]],\n",
" dtype=float32)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# open DEM map\n",
"test_path = \"/home/elisa/Scrivania/MHPC/geotop_3.0/tests/3D/small_example/\"\n",
"file_path = test_path + \"input_maps/pit.asc\"\n",
"\n",
"with rio.open(file_path) as src:\n",
" \n",
" # convert/read the data into a numpy array\n",
" example_dem_in = src.read()\n",
" \n",
" # create a spatial extent object using rio.plot.plotting\n",
" spatial_extent = rio.plot.plotting_extent(src)\n",
" \n",
" # get bounds of object\n",
" bounds = src.bounds\n",
"\n",
"# print\n",
"example_dem_in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze DEM"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(xll, xur, yll, yur) = \t\t\t (641250.0, 645250.0, 5005750.0, 5011250.0)\n",
"(xll, yll, xur, yur) = BoundingBox(left=641250.0, bottom=5005750.0, right=645250.0, top=5011250.0)\n"
]
}
],
"source": [
"# print Spatial Extents (format wanted by matplotlib)\n",
"print('(xll, xur, yll, yur) = \\t\\t\\t', spatial_extent)\n",
"\n",
"# print Bounds (format provided by rasterio)\n",
"print('(xll, yll, xur, yur) = ', bounds)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"masked_array(\n",
" data=[[--, --, --, --, 1500.0, --, --, --],\n",
" [--, 1300.0, 1350.0, 1450.0, 1480.0, 1450.0, 1480.0, 1490.0],\n",
" [--, 1280.0, 1320.0, 1400.0, 1430.0, 1450.0, 1460.0, --],\n",
" [--, 1250.0, 1300.0, 1350.0, 1380.0, 1410.0, 1450.0, --],\n",
" [1230.0, 1220.0, 1210.0, 1250.0, 1330.0, 1380.0, 1400.0, --],\n",
" [1210.0, 1200.0, 1200.0, 1220.0, 1300.0, 1340.0, 1350.0, --],\n",
" [1200.0, 1170.0, 1150.0, 1200.0, 1210.0, 1220.0, 1250.0, --],\n",
" [1160.0, 1150.0, 1140.0, 1110.0, 1120.0, 1130.0, 1150.0, --],\n",
" [1100.0, 1080.0, 1070.0, 980.0, 990.0, 1000.0, 1050.0, --],\n",
" [1070.0, 1050.0, 1010.0, 950.0, 960.0, 980.0, 990.0, --],\n",
" [--, --, --, 910.0, 920.0, 940.0, 945.0, --]],\n",
" mask=[[ True, True, True, True, False, True, True, True],\n",
" [ True, False, False, False, False, False, False, False],\n",
" [ True, False, False, False, False, False, False, True],\n",
" [ True, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, True],\n",
" [ True, True, True, False, False, False, False, True]],\n",
" fill_value=-9999.0,\n",
" dtype=float32)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with rio.open('/home/elisa/Scrivania/MHPC/geotop_3.0/tests/3D/small_example/input_maps/pit.asc') as src:\n",
" \n",
" # convert/read the data into a numpy array: masked=True turns \"nodata\" values to nan\n",
" example_dem_in = src.read(1, masked=True)\n",
" \n",
"example_dem_in"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(rows, cols) = (11, 8)\n",
"object type = <class 'numpy.ma.core.MaskedArray'>\n"
]
}
],
"source": [
"# print DEM infos\n",
"print(\"(rows, cols) = \", example_dem_in.shape)\n",
"print(\"object type = \", type(example_dem_in))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot DEM"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASEAAAEkCAYAAABg0IeoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQcjMsnIQMrOsHITMLKv/D7VQVbBX/e1OAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (8,4))\n",
"example_dem_plot = ax.imshow(example_dem_in, \n",
" cmap='Greys', \n",
" extent=spatial_extent)\n",
"ax.set_title(\"DEM of small_example\\n\", fontsize = 20)\n",
"fig.colorbar(example_dem_plot)\n",
"\n",
"# turn off the x and y axes for prettier plotting\n",
"ax.set_axis_off()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explore DEM Values with Histograms"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Prettier plotting with seaborn\n",
"import seaborn as sns; \n",
"sns.set(font_scale=1.5)\n",
"sns.set_style(\"white\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAESCAYAAAC4gw8PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# use seaborn styles\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"# Plot histogram\n",
"fig, ax = plt.subplots(figsize = (8,4))\n",
"ax.hist(example_dem_in.ravel(),\n",
" bins=100, \n",
" color='purple')\n",
"ax.set_title(\"DEM\", fontsize = 20);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adjust Plot Extent to “Zoom in” "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(xll, xur, yll, yur)full = (641250.0, 645250.0, 5005750.0, 5011250.0)\n",
"(xll, xur, yll, yur)zoom = [643000, 644000, 5006000, 5008000]\n"
]
}
],
"source": [
"# full spatial extent \n",
"print(\"(xll, xur, yll, yur)full = \", spatial_extent)\n",
"\n",
"# smaller spatial extent\n",
"zoomed_extent = [643000, 644000, 5006000, 5008000]\n",
"print(\"(xll, xur, yll, yur)zoom =\", zoomed_extent)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEoCAYAAAAg3IjnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREQCFkQiIgELIhGR4P8Do9DEN+Go0jQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the original data with the boundary box\n",
"fig, ax = plt.subplots(figsize = (8,4))\n",
"example_dem_plot = ax.imshow(example_dem_in, \n",
" extent=spatial_extent)\n",
"ax.set_title(\"DEM - Full Spatial Extent\\n\", fontsize = 20)\n",
"fig.colorbar(example_dem_plot)\n",
"ax.set_axis_off()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(xlimits)zoom = [643000, 644000]\n",
"(ylimits)zoom = [5006000, 5008000]\n"
]
}
],
"source": [
"print(\"(xlimits)zoom = \" , zoomed_extent[:2]) # 0 <= x < 2\n",
"print(\"(ylimits)zoom = \" , zoomed_extent[2:4]) # 2 <= x < 4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAREAAAEmCAYAAABF66SbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEQAmRASACREBYEJEAJgQEQAmRASAyf8AHtRyJ6MRiFwAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the data but set the x and y lim\n",
"fig, ax = plt.subplots(figsize = (8,4))\n",
"ax.imshow(example_dem_in,\n",
" extent=spatial_extent)\n",
"ax.set_title(\"DEM- Zoomed Spatial Extent\\n\")\n",
"\n",
"# set x and y limits of the plot\n",
"ax.set_xlim(zoomed_extent[:2])\n",
"ax.set_ylim(zoomed_extent[2:4])\n",
"\n",
"ax.set_axis_off()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| ipynb/2nd_High_Lift_Prediction_Workshop/.ipynb_checkpoints/High_Lift_Prediction_Workshop-checkpoint.ipynb |
|
|
|
|
|
| pycalphad/pycalphad-phase-filter.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pycalphad phase filter\n",
"\n",
"Some sample code that removes phases that have sublattices with only inactive components from the phase list\n",
"\n",
"The database loaded is a Al-Co-Cr-Ni database. We have defined components that are only Al-Ni-Co (excluding Cr). The goal is to filter out phases that are invalid because they have sublattices that contain Cr"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pycalphad import Database\n",
"from pycalphad.tests.datasets import ALCOCRNI_TDB\n",
"\n",
"dbf = Database.from_string(ALCOCRNI_TDB, fmt='tdb')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comps = ['AL', 'NI', 'CO', 'VA'] # missing CR\n",
"phases = [phase for phase in dbf.phases.keys() if all(\n",
" len(set(comps).intersection(subl)) > 0 for subl in dbf.phases[phase].constituents)] "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phases included: ['BCC_B2', 'AL13CO4', 'AL3NI2', 'L12_FCC', 'AL9CO2', 'AL3CO', 'HCP_A3', 'AL3NI5', 'AL5CO2', 'LIQUID', 'AL3NI1', 'FCC_A1', 'BCC_A2']\n",
"\n",
"Phases excluded: {'AL8CR5_L', 'SIGMA_SGTE', 'AL9CR4_H', 'AL11CR2', 'ALCR2', 'AL8CR5_H', 'AL9CR4_L', 'AL4CR', 'AL13CR2'}\n"
]
}
],
"source": [
"print('Phases included: {}\\n'.format(phases))\n",
"print('Phases excluded: {}'.format(dbf.phases.keys()-set(phases)))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Constituents of excluded phases:\n",
"AL8CR5_L : (frozenset({'AL'}), frozenset({'CR'}))\n",
"SIGMA_SGTE: (frozenset({'NI', 'AL', 'CO'}), frozenset({'NI', 'AL', 'CO', 'CR'}), frozenset({'CR'}))\n",
"AL9CR4_H : (frozenset({'AL'}), frozenset({'CR'}))\n",
"AL11CR2 : (frozenset({'AL'}), frozenset({'AL'}), frozenset({'CR'}))\n",
"ALCR2 : (frozenset({'AL'}), frozenset({'CR'}))\n",
"AL8CR5_H : (frozenset({'AL'}), frozenset({'CR'}))\n",
"AL9CR4_L : (frozenset({'AL'}), frozenset({'CR'}))\n",
"AL4CR : (frozenset({'AL'}), frozenset({'CR'}))\n",
"AL13CR2 : (frozenset({'AL'}), frozenset({'CR'}))\n"
]
}
],
"source": [
"print('Constituents of excluded phases:')\n",
"for ph in dbf.phases.keys()-set(phases):\n",
" print('{:10}: {}'.format(ph, dbf.phases[ph].constituents))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/robust_models_1.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# M-Estimators for Robust Linear Modeling"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:52.833943Z",
"iopub.status.busy": "2021-11-12T23:35:52.826719Z",
"iopub.status.idle": "2021-11-12T23:35:53.458861Z",
"shell.execute_reply": "2021-11-12T23:35:53.459669Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:53.463317Z",
"iopub.status.busy": "2021-11-12T23:35:53.462335Z",
"iopub.status.idle": "2021-11-12T23:35:54.495982Z",
"shell.execute_reply": "2021-11-12T23:35:54.495056Z"
}
},
"outputs": [],
"source": [
"from statsmodels.compat import lmap\n",
"import numpy as np\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* An M-estimator minimizes the function \n",
"\n",
"$$Q(e_i, \\rho) = \\sum_i~\\rho \\left (\\frac{e_i}{s}\\right )$$\n",
"\n",
"where $\\rho$ is a symmetric function of the residuals \n",
"\n",
"* The effect of $\\rho$ is to reduce the influence of outliers\n",
"* $s$ is an estimate of scale. \n",
"* The robust estimates $\\hat{\\beta}$ are computed by the iteratively re-weighted least squares algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* We have several choices available for the weighting functions to be used"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:54.499914Z",
"iopub.status.busy": "2021-11-12T23:35:54.498923Z",
"iopub.status.idle": "2021-11-12T23:35:54.503384Z",
"shell.execute_reply": "2021-11-12T23:35:54.504089Z"
}
},
"outputs": [],
"source": [
"norms = sm.robust.norms"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:54.507449Z",
"iopub.status.busy": "2021-11-12T23:35:54.506488Z",
"iopub.status.idle": "2021-11-12T23:35:54.511878Z",
"shell.execute_reply": "2021-11-12T23:35:54.512553Z"
}
},
"outputs": [],
"source": [
"def plot_weights(support, weights_func, xlabels, xticks):\n",
" fig = plt.figure(figsize=(12, 8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(support, weights_func(support))\n",
" ax.set_xticks(xticks)\n",
" ax.set_xticklabels(xlabels, fontsize=16)\n",
" ax.set_ylim(-0.1, 1.1)\n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Andrew's Wave"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:54.516779Z",
"iopub.status.busy": "2021-11-12T23:35:54.515282Z",
"iopub.status.idle": "2021-11-12T23:35:54.524984Z",
"shell.execute_reply": "2021-11-12T23:35:54.525759Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Andrew's wave weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = sin(z/a)/(z/a) for \\|z\\| <= a*pi\n",
" \n",
" weights(z) = 0 for \\|z\\| > a*pi\n",
"\n"
]
}
],
"source": [
"help(norms.AndrewWave.weights)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:54.530230Z",
"iopub.status.busy": "2021-11-12T23:35:54.528633Z",
"iopub.status.idle": "2021-11-12T23:35:55.225627Z",
"shell.execute_reply": "2021-11-12T23:35:55.226474Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = 1.339\n",
"support = np.linspace(-np.pi * a, np.pi * a, 100)\n",
"andrew = norms.AndrewWave(a=a)\n",
"plot_weights(\n",
" support, andrew.weights, [\"$-\\pi*a$\", \"0\", \"$\\pi*a$\"], [-np.pi * a, 0, np.pi * a]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hampel's 17A"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.230092Z",
"iopub.status.busy": "2021-11-12T23:35:55.229071Z",
"iopub.status.idle": "2021-11-12T23:35:55.238447Z",
"shell.execute_reply": "2021-11-12T23:35:55.239167Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Hampel weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = 1 for \\|z\\| <= a\n",
" \n",
" weights(z) = a/\\|z\\| for a < \\|z\\| <= b\n",
" \n",
" weights(z) = a*(c - \\|z\\|)/(\\|z\\|*(c-b)) for b < \\|z\\| <= c\n",
" \n",
" weights(z) = 0 for \\|z\\| > c\n",
"\n"
]
}
],
"source": [
"help(norms.Hampel.weights)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.242615Z",
"iopub.status.busy": "2021-11-12T23:35:55.241629Z",
"iopub.status.idle": "2021-11-12T23:35:55.450775Z",
"shell.execute_reply": "2021-11-12T23:35:55.451545Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 8\n",
"support = np.linspace(-3 * c, 3 * c, 1000)\n",
"hampel = norms.Hampel(a=2.0, b=4.0, c=c)\n",
"plot_weights(support, hampel.weights, [\"3*c\", \"0\", \"3*c\"], [-3 * c, 0, 3 * c])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Huber's t"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.455254Z",
"iopub.status.busy": "2021-11-12T23:35:55.454246Z",
"iopub.status.idle": "2021-11-12T23:35:55.460280Z",
"shell.execute_reply": "2021-11-12T23:35:55.460962Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Huber's t weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = 1 for \\|z\\| <= t\n",
" \n",
" weights(z) = t/\\|z\\| for \\|z\\| > t\n",
"\n"
]
}
],
"source": [
"help(norms.HuberT.weights)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.464317Z",
"iopub.status.busy": "2021-11-12T23:35:55.463355Z",
"iopub.status.idle": "2021-11-12T23:35:55.647889Z",
"shell.execute_reply": "2021-11-12T23:35:55.648669Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = 1.345\n",
"support = np.linspace(-3 * t, 3 * t, 1000)\n",
"huber = norms.HuberT(t=t)\n",
"plot_weights(support, huber.weights, [\"-3*t\", \"0\", \"3*t\"], [-3 * t, 0, 3 * t])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Least Squares"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.652366Z",
"iopub.status.busy": "2021-11-12T23:35:55.651358Z",
"iopub.status.idle": "2021-11-12T23:35:55.657478Z",
"shell.execute_reply": "2021-11-12T23:35:55.658164Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" The least squares estimator weighting function for the IRLS algorithm.\n",
" \n",
" The psi function scaled by the input z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = np.ones(z.shape)\n",
"\n"
]
}
],
"source": [
"help(norms.LeastSquares.weights)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.661566Z",
"iopub.status.busy": "2021-11-12T23:35:55.660576Z",
"iopub.status.idle": "2021-11-12T23:35:55.838253Z",
"shell.execute_reply": "2021-11-12T23:35:55.838989Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"support = np.linspace(-3, 3, 1000)\n",
"lst_sq = norms.LeastSquares()\n",
"plot_weights(support, lst_sq.weights, [\"-3\", \"0\", \"3\"], [-3, 0, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ramsay's Ea"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.842571Z",
"iopub.status.busy": "2021-11-12T23:35:55.841574Z",
"iopub.status.idle": "2021-11-12T23:35:55.847564Z",
"shell.execute_reply": "2021-11-12T23:35:55.848246Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Ramsay's Ea weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = exp(-a*\\|z\\|)\n",
"\n"
]
}
],
"source": [
"help(norms.RamsayE.weights)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:55.851593Z",
"iopub.status.busy": "2021-11-12T23:35:55.850635Z",
"iopub.status.idle": "2021-11-12T23:35:56.028228Z",
"shell.execute_reply": "2021-11-12T23:35:56.028979Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = 0.3\n",
"support = np.linspace(-3 * a, 3 * a, 1000)\n",
"ramsay = norms.RamsayE(a=a)\n",
"plot_weights(support, ramsay.weights, [\"-3*a\", \"0\", \"3*a\"], [-3 * a, 0, 3 * a])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trimmed Mean"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.032522Z",
"iopub.status.busy": "2021-11-12T23:35:56.031555Z",
"iopub.status.idle": "2021-11-12T23:35:56.037558Z",
"shell.execute_reply": "2021-11-12T23:35:56.038249Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Least trimmed mean weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" weights(z) = 1 for \\|z\\| <= c\n",
" \n",
" weights(z) = 0 for \\|z\\| > c\n",
"\n"
]
}
],
"source": [
"help(norms.TrimmedMean.weights)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.041678Z",
"iopub.status.busy": "2021-11-12T23:35:56.040686Z",
"iopub.status.idle": "2021-11-12T23:35:56.221855Z",
"shell.execute_reply": "2021-11-12T23:35:56.222640Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 2\n",
"support = np.linspace(-3 * c, 3 * c, 1000)\n",
"trimmed = norms.TrimmedMean(c=c)\n",
"plot_weights(support, trimmed.weights, [\"-3*c\", \"0\", \"3*c\"], [-3 * c, 0, 3 * c])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tukey's Biweight"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.226212Z",
"iopub.status.busy": "2021-11-12T23:35:56.225216Z",
"iopub.status.idle": "2021-11-12T23:35:56.231200Z",
"shell.execute_reply": "2021-11-12T23:35:56.231901Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function weights in module statsmodels.robust.norms:\n",
"\n",
"weights(self, z)\n",
" Tukey's biweight weighting function for the IRLS algorithm\n",
" \n",
" The psi function scaled by z\n",
" \n",
" Parameters\n",
" ----------\n",
" z : array_like\n",
" 1d array\n",
" \n",
" Returns\n",
" -------\n",
" weights : ndarray\n",
" psi(z) = (1 - (z/c)**2)**2 for \\|z\\| <= R\n",
" \n",
" psi(z) = 0 for \\|z\\| > R\n",
"\n"
]
}
],
"source": [
"help(norms.TukeyBiweight.weights)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.235246Z",
"iopub.status.busy": "2021-11-12T23:35:56.234272Z",
"iopub.status.idle": "2021-11-12T23:35:56.418466Z",
"shell.execute_reply": "2021-11-12T23:35:56.419199Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = 4.685\n",
"support = np.linspace(-3 * c, 3 * c, 1000)\n",
"tukey = norms.TukeyBiweight(c=c)\n",
"plot_weights(support, tukey.weights, [\"-3*c\", \"0\", \"3*c\"], [-3 * c, 0, 3 * c])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale Estimators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Robust estimates of the location"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.422883Z",
"iopub.status.busy": "2021-11-12T23:35:56.421867Z",
"iopub.status.idle": "2021-11-12T23:35:56.426216Z",
"shell.execute_reply": "2021-11-12T23:35:56.426881Z"
}
},
"outputs": [],
"source": [
"x = np.array([1, 2, 3, 4, 500])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The mean is not a robust estimator of location"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.430306Z",
"iopub.status.busy": "2021-11-12T23:35:56.429343Z",
"iopub.status.idle": "2021-11-12T23:35:56.435870Z",
"shell.execute_reply": "2021-11-12T23:35:56.436536Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"102.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The median, on the other hand, is a robust estimator with a breakdown point of 50%"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.439818Z",
"iopub.status.busy": "2021-11-12T23:35:56.438888Z",
"iopub.status.idle": "2021-11-12T23:35:56.446516Z",
"shell.execute_reply": "2021-11-12T23:35:56.447207Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Analogously for the scale\n",
"* The standard deviation is not robust"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.451613Z",
"iopub.status.busy": "2021-11-12T23:35:56.450050Z",
"iopub.status.idle": "2021-11-12T23:35:56.460023Z",
"shell.execute_reply": "2021-11-12T23:35:56.460726Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"199.00251254695254"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Median Absolute Deviation\n",
"\n",
"$$ median_i |X_i - median_j(X_j)|) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Standardized Median Absolute Deviation is a consistent estimator for $\\hat{\\sigma}$\n",
"\n",
"$$\\hat{\\sigma}=K \\cdot MAD$$\n",
"\n",
"where $K$ depends on the distribution. For the normal distribution for example,\n",
"\n",
"$$K = \\Phi^{-1}(.75)$$"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.465107Z",
"iopub.status.busy": "2021-11-12T23:35:56.463576Z",
"iopub.status.idle": "2021-11-12T23:35:56.473742Z",
"shell.execute_reply": "2021-11-12T23:35:56.474434Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.6744897501960817"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.norm.ppf(0.75)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.478561Z",
"iopub.status.busy": "2021-11-12T23:35:56.477069Z",
"iopub.status.idle": "2021-11-12T23:35:56.484957Z",
"shell.execute_reply": "2021-11-12T23:35:56.485657Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 2 3 4 500]\n"
]
}
],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.489836Z",
"iopub.status.busy": "2021-11-12T23:35:56.488308Z",
"iopub.status.idle": "2021-11-12T23:35:56.498829Z",
"shell.execute_reply": "2021-11-12T23:35:56.499560Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.482602218505602"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.mad(x)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.504145Z",
"iopub.status.busy": "2021-11-12T23:35:56.502516Z",
"iopub.status.idle": "2021-11-12T23:35:56.512858Z",
"shell.execute_reply": "2021-11-12T23:35:56.513578Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.4142135623730951"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([1, 2, 3, 4, 5.0]).std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another robust estimator of scale is the Interquartile Range (IQR)\n",
"\n",
"$$\\left(\\hat{X}_{0.75} - \\hat{X}_{0.25}\\right),$$\n",
"\n",
"where $\\hat{X}_{p}$ is the sample p-th quantile and $K$ depends on the distribution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The standardized IQR, given by $K \\cdot \\text{IQR}$ for\n",
"$$K = \\frac{1}{\\Phi^{-1}(.75) - \\Phi^{-1}(.25)} \\approx 0.74,$$\n",
"is a consistent estimator of the standard deviation for normal data."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.518022Z",
"iopub.status.busy": "2021-11-12T23:35:56.516470Z",
"iopub.status.idle": "2021-11-12T23:35:56.527208Z",
"shell.execute_reply": "2021-11-12T23:35:56.527931Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(1.48260222)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.iqr(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The IQR is less robust than the MAD in the sense that it has a lower breakdown point: it can withstand 25\\% outlying observations before being completely ruined, whereas the MAD can withstand 50\\% outlying observations. However, the IQR is better suited for asymmetric distributions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yet another robust estimator of scale is the $Q_n$ estimator, introduced in Rousseeuw & Croux (1993), 'Alternatives to the Median Absolute Deviation'. Then $Q_n$ estimator is given by\n",
"$$\n",
"Q_n = K \\left\\lbrace \\vert X_{i} - X_{j}\\vert : i<j\\right\\rbrace_{(h)}\n",
"$$\n",
"where $h\\approx (1/4){{n}\\choose{2}}$ and $K$ is a given constant. In words, the $Q_n$ estimator is the normalized $h$-th order statistic of the absolute differences of the data. The normalizing constant $K$ is usually chosen as 2.219144, to make the estimator consistent for the standard deviation in the case of normal data. The $Q_n$ estimator has a 50\\% breakdown point and a 82\\% asymptotic efficiency at the normal distribution, much higher than the 37\\% efficiency of the MAD."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.532287Z",
"iopub.status.busy": "2021-11-12T23:35:56.530769Z",
"iopub.status.idle": "2021-11-12T23:35:56.540960Z",
"shell.execute_reply": "2021-11-12T23:35:56.541661Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2.219144465985076"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.qn_scale(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The default for Robust Linear Models is MAD\n",
"* another popular choice is Huber's proposal 2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.544977Z",
"iopub.status.busy": "2021-11-12T23:35:56.544037Z",
"iopub.status.idle": "2021-11-12T23:35:56.549755Z",
"shell.execute_reply": "2021-11-12T23:35:56.550471Z"
}
},
"outputs": [],
"source": [
"np.random.seed(12345)\n",
"fat_tails = stats.t(6).rvs(40)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.553818Z",
"iopub.status.busy": "2021-11-12T23:35:56.552840Z",
"iopub.status.idle": "2021-11-12T23:35:56.818013Z",
"shell.execute_reply": "2021-11-12T23:35:56.818776Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f98708e8430>]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"kde = sm.nonparametric.KDEUnivariate(fat_tails)\n",
"kde.fit()\n",
"fig = plt.figure(figsize=(12, 8))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(kde.support, kde.density)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.822281Z",
"iopub.status.busy": "2021-11-12T23:35:56.821318Z",
"iopub.status.idle": "2021-11-12T23:35:56.827350Z",
"shell.execute_reply": "2021-11-12T23:35:56.828023Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0688231044810875 1.3471633229698652\n"
]
}
],
"source": [
"print(fat_tails.mean(), fat_tails.std())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.831359Z",
"iopub.status.busy": "2021-11-12T23:35:56.830412Z",
"iopub.status.idle": "2021-11-12T23:35:56.836610Z",
"shell.execute_reply": "2021-11-12T23:35:56.837291Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.0688231044810875, 1.3471633229698652)\n"
]
}
],
"source": [
"print(stats.norm.fit(fat_tails))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.841364Z",
"iopub.status.busy": "2021-11-12T23:35:56.839864Z",
"iopub.status.idle": "2021-11-12T23:35:56.862431Z",
"shell.execute_reply": "2021-11-12T23:35:56.863136Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6, 0.03900835312789366, 1.0563837144431252)\n"
]
}
],
"source": [
"print(stats.t.fit(fat_tails, f0=6))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.867501Z",
"iopub.status.busy": "2021-11-12T23:35:56.865947Z",
"iopub.status.idle": "2021-11-12T23:35:56.877470Z",
"shell.execute_reply": "2021-11-12T23:35:56.878162Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.04048984333271795 1.1557140047569665\n"
]
}
],
"source": [
"huber = sm.robust.scale.Huber()\n",
"loc, scale = huber(fat_tails)\n",
"print(loc, scale)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.882382Z",
"iopub.status.busy": "2021-11-12T23:35:56.880846Z",
"iopub.status.idle": "2021-11-12T23:35:56.891146Z",
"shell.execute_reply": "2021-11-12T23:35:56.891836Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.115335001165415"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.mad(fat_tails)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.896096Z",
"iopub.status.busy": "2021-11-12T23:35:56.894552Z",
"iopub.status.idle": "2021-11-12T23:35:56.908059Z",
"shell.execute_reply": "2021-11-12T23:35:56.908802Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.0483916565928972"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.mad(fat_tails, c=stats.t(6).ppf(0.75))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.913315Z",
"iopub.status.busy": "2021-11-12T23:35:56.911699Z",
"iopub.status.idle": "2021-11-12T23:35:56.922226Z",
"shell.execute_reply": "2021-11-12T23:35:56.922922Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.115335001165415"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.robust.scale.mad(fat_tails)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Duncan's Occupational Prestige data - M-estimation for outliers"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.927456Z",
"iopub.status.busy": "2021-11-12T23:35:56.925848Z",
"iopub.status.idle": "2021-11-12T23:35:56.932050Z",
"shell.execute_reply": "2021-11-12T23:35:56.932744Z"
}
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import abline_plot\n",
"from statsmodels.formula.api import ols, rlm"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.936981Z",
"iopub.status.busy": "2021-11-12T23:35:56.935489Z",
"iopub.status.idle": "2021-11-12T23:35:56.977894Z",
"shell.execute_reply": "2021-11-12T23:35:56.978707Z"
}
},
"outputs": [],
"source": [
"prestige = sm.datasets.get_rdataset(\"Duncan\", \"carData\", cache=True).data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:56.983461Z",
"iopub.status.busy": "2021-11-12T23:35:56.981782Z",
"iopub.status.idle": "2021-11-12T23:35:56.996344Z",
"shell.execute_reply": "2021-11-12T23:35:56.997069Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" type income education prestige\n",
"accountant prof 62 86 82\n",
"pilot prof 72 76 83\n",
"architect prof 75 92 90\n",
"author prof 55 90 76\n",
"chemist prof 64 86 90\n",
"minister prof 21 84 87\n",
"professor prof 64 93 93\n",
"dentist prof 80 100 90\n",
"reporter wc 67 87 52\n",
"engineer prof 72 86 88\n"
]
}
],
"source": [
"print(prestige.head(10))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.001731Z",
"iopub.status.busy": "2021-11-12T23:35:57.000049Z",
"iopub.status.idle": "2021-11-12T23:35:57.480628Z",
"shell.execute_reply": "2021-11-12T23:35:57.480996Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f986fc47760>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 12))\n",
"ax1 = fig.add_subplot(211, xlabel=\"Income\", ylabel=\"Prestige\")\n",
"ax1.scatter(prestige.income, prestige.prestige)\n",
"xy_outlier = prestige.loc[\"minister\", [\"income\", \"prestige\"]]\n",
"ax1.annotate(\"Minister\", xy_outlier, xy_outlier + 1, fontsize=16)\n",
"ax2 = fig.add_subplot(212, xlabel=\"Education\", ylabel=\"Prestige\")\n",
"ax2.scatter(prestige.education, prestige.prestige)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.495010Z",
"iopub.status.busy": "2021-11-12T23:35:57.493736Z",
"iopub.status.idle": "2021-11-12T23:35:57.512185Z",
"shell.execute_reply": "2021-11-12T23:35:57.513261Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige R-squared: 0.828\n",
"Model: OLS Adj. R-squared: 0.820\n",
"Method: Least Squares F-statistic: 101.2\n",
"Date: Fri, 12 Nov 2021 Prob (F-statistic): 8.65e-17\n",
"Time: 23:35:57 Log-Likelihood: -178.98\n",
"No. Observations: 45 AIC: 364.0\n",
"Df Residuals: 42 BIC: 369.4\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -6.0647 4.272 -1.420 0.163 -14.686 2.556\n",
"income 0.5987 0.120 5.003 0.000 0.357 0.840\n",
"education 0.5458 0.098 5.555 0.000 0.348 0.744\n",
"==============================================================================\n",
"Omnibus: 1.279 Durbin-Watson: 1.458\n",
"Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.520\n",
"Skew: 0.155 Prob(JB): 0.771\n",
"Kurtosis: 3.426 Cond. No. 163.\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"ols_model = ols(\"prestige ~ income + education\", prestige).fit()\n",
"print(ols_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.518997Z",
"iopub.status.busy": "2021-11-12T23:35:57.517214Z",
"iopub.status.idle": "2021-11-12T23:35:57.564125Z",
"shell.execute_reply": "2021-11-12T23:35:57.563697Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accountant 0.303900\n",
"pilot 0.340920\n",
"architect 0.072256\n",
"author 0.000711\n",
"chemist 0.826578\n",
"minister 3.134519\n",
"professor 0.768277\n",
"dentist -0.498082\n",
"reporter -2.397022\n",
"engineer 0.306225\n",
"undertaker -0.187339\n",
"lawyer -0.303082\n",
"physician 0.355687\n",
"welfare.worker -0.411406\n",
"teacher 0.050510\n",
"conductor -1.704032\n",
"contractor 2.043805\n",
"factory.owner 1.602429\n",
"store.manager 0.142425\n",
"banker 0.508388\n",
"bookkeeper -0.902388\n",
"mail.carrier -1.433249\n",
"insurance.agent -1.930919\n",
"store.clerk -1.760491\n",
"carpenter 1.068858\n",
"electrician 0.731949\n",
"RR.engineer 0.808922\n",
"machinist 1.887047\n",
"auto.repairman 0.522735\n",
"plumber -0.377954\n",
"gas.stn.attendant -0.666596\n",
"coal.miner 1.018527\n",
"streetcar.motorman -1.104485\n",
"taxi.driver 0.023322\n",
"truck.driver -0.129227\n",
"machine.operator 0.499922\n",
"barber 0.173805\n",
"bartender -0.902422\n",
"shoe.shiner -0.429357\n",
"cook 0.127207\n",
"soda.clerk -0.883095\n",
"watchman -0.513502\n",
"janitor -0.079890\n",
"policeman 0.078847\n",
"waiter -0.475972\n",
"Name: student_resid, dtype: float64\n"
]
}
],
"source": [
"infl = ols_model.get_influence()\n",
"student = infl.summary_frame()[\"student_resid\"]\n",
"print(student)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.570047Z",
"iopub.status.busy": "2021-11-12T23:35:57.569571Z",
"iopub.status.idle": "2021-11-12T23:35:57.573314Z",
"shell.execute_reply": "2021-11-12T23:35:57.573686Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"minister 3.134519\n",
"reporter -2.397022\n",
"contractor 2.043805\n",
"Name: student_resid, dtype: float64\n"
]
}
],
"source": [
"print(student.loc[np.abs(student) > 2])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.578599Z",
"iopub.status.busy": "2021-11-12T23:35:57.577486Z",
"iopub.status.idle": "2021-11-12T23:35:57.585599Z",
"shell.execute_reply": "2021-11-12T23:35:57.585974Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dfb_Intercept 0.144937\n",
"dfb_income -1.220939\n",
"dfb_education 1.263019\n",
"cooks_d 0.566380\n",
"standard_resid 2.849416\n",
"hat_diag 0.173058\n",
"dffits_internal 1.303510\n",
"student_resid 3.134519\n",
"dffits 1.433935\n",
"Name: minister, dtype: float64\n"
]
}
],
"source": [
"print(infl.summary_frame().loc[\"minister\"])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.592090Z",
"iopub.status.busy": "2021-11-12T23:35:57.590746Z",
"iopub.status.idle": "2021-11-12T23:35:57.632718Z",
"shell.execute_reply": "2021-11-12T23:35:57.633104Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p sidak(p)\n",
"minister 3.134519 0.003177 0.133421\n",
"reporter -2.397022 0.021170 0.618213\n",
"contractor 2.043805 0.047433 0.887721\n",
"insurance.agent -1.930919 0.060428 0.939485\n",
"machinist 1.887047 0.066248 0.954247\n",
"store.clerk -1.760491 0.085783 0.982331\n",
"conductor -1.704032 0.095944 0.989315\n",
"factory.owner 1.602429 0.116738 0.996250\n",
"mail.carrier -1.433249 0.159369 0.999595\n",
"streetcar.motorman -1.104485 0.275823 1.000000\n",
"carpenter 1.068858 0.291386 1.000000\n",
"coal.miner 1.018527 0.314400 1.000000\n",
"bartender -0.902422 0.372104 1.000000\n",
"bookkeeper -0.902388 0.372122 1.000000\n",
"soda.clerk -0.883095 0.382334 1.000000\n",
"chemist 0.826578 0.413261 1.000000\n",
"RR.engineer 0.808922 0.423229 1.000000\n",
"professor 0.768277 0.446725 1.000000\n",
"electrician 0.731949 0.468363 1.000000\n",
"gas.stn.attendant -0.666596 0.508764 1.000000\n",
"auto.repairman 0.522735 0.603972 1.000000\n",
"watchman -0.513502 0.610357 1.000000\n",
"banker 0.508388 0.613906 1.000000\n",
"machine.operator 0.499922 0.619802 1.000000\n",
"dentist -0.498082 0.621088 1.000000\n",
"waiter -0.475972 0.636621 1.000000\n",
"shoe.shiner -0.429357 0.669912 1.000000\n",
"welfare.worker -0.411406 0.682918 1.000000\n",
"plumber -0.377954 0.707414 1.000000\n",
"physician 0.355687 0.723898 1.000000\n",
"pilot 0.340920 0.734905 1.000000\n",
"engineer 0.306225 0.760983 1.000000\n",
"accountant 0.303900 0.762741 1.000000\n",
"lawyer -0.303082 0.763360 1.000000\n",
"undertaker -0.187339 0.852319 1.000000\n",
"barber 0.173805 0.862874 1.000000\n",
"store.manager 0.142425 0.887442 1.000000\n",
"truck.driver -0.129227 0.897810 1.000000\n",
"cook 0.127207 0.899399 1.000000\n",
"janitor -0.079890 0.936713 1.000000\n",
"policeman 0.078847 0.937538 1.000000\n",
"architect 0.072256 0.942750 1.000000\n",
"teacher 0.050510 0.959961 1.000000\n",
"taxi.driver 0.023322 0.981507 1.000000\n",
"author 0.000711 0.999436 1.000000\n"
]
}
],
"source": [
"sidak = ols_model.outlier_test(\"sidak\")\n",
"sidak.sort_values(\"unadj_p\", inplace=True)\n",
"print(sidak)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.640145Z",
"iopub.status.busy": "2021-11-12T23:35:57.638805Z",
"iopub.status.idle": "2021-11-12T23:35:57.677516Z",
"shell.execute_reply": "2021-11-12T23:35:57.678588Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p fdr_bh(p)\n",
"minister 3.134519 0.003177 0.142974\n",
"reporter -2.397022 0.021170 0.476332\n",
"contractor 2.043805 0.047433 0.596233\n",
"insurance.agent -1.930919 0.060428 0.596233\n",
"machinist 1.887047 0.066248 0.596233\n",
"store.clerk -1.760491 0.085783 0.616782\n",
"conductor -1.704032 0.095944 0.616782\n",
"factory.owner 1.602429 0.116738 0.656653\n",
"mail.carrier -1.433249 0.159369 0.796844\n",
"streetcar.motorman -1.104485 0.275823 0.999436\n",
"carpenter 1.068858 0.291386 0.999436\n",
"coal.miner 1.018527 0.314400 0.999436\n",
"bartender -0.902422 0.372104 0.999436\n",
"bookkeeper -0.902388 0.372122 0.999436\n",
"soda.clerk -0.883095 0.382334 0.999436\n",
"chemist 0.826578 0.413261 0.999436\n",
"RR.engineer 0.808922 0.423229 0.999436\n",
"professor 0.768277 0.446725 0.999436\n",
"electrician 0.731949 0.468363 0.999436\n",
"gas.stn.attendant -0.666596 0.508764 0.999436\n",
"auto.repairman 0.522735 0.603972 0.999436\n",
"watchman -0.513502 0.610357 0.999436\n",
"banker 0.508388 0.613906 0.999436\n",
"machine.operator 0.499922 0.619802 0.999436\n",
"dentist -0.498082 0.621088 0.999436\n",
"waiter -0.475972 0.636621 0.999436\n",
"shoe.shiner -0.429357 0.669912 0.999436\n",
"welfare.worker -0.411406 0.682918 0.999436\n",
"plumber -0.377954 0.707414 0.999436\n",
"physician 0.355687 0.723898 0.999436\n",
"pilot 0.340920 0.734905 0.999436\n",
"engineer 0.306225 0.760983 0.999436\n",
"accountant 0.303900 0.762741 0.999436\n",
"lawyer -0.303082 0.763360 0.999436\n",
"undertaker -0.187339 0.852319 0.999436\n",
"barber 0.173805 0.862874 0.999436\n",
"store.manager 0.142425 0.887442 0.999436\n",
"truck.driver -0.129227 0.897810 0.999436\n",
"cook 0.127207 0.899399 0.999436\n",
"janitor -0.079890 0.936713 0.999436\n",
"policeman 0.078847 0.937538 0.999436\n",
"architect 0.072256 0.942750 0.999436\n",
"teacher 0.050510 0.959961 0.999436\n",
"taxi.driver 0.023322 0.981507 0.999436\n",
"author 0.000711 0.999436 0.999436\n"
]
}
],
"source": [
"fdr = ols_model.outlier_test(\"fdr_bh\")\n",
"fdr.sort_values(\"unadj_p\", inplace=True)\n",
"print(fdr)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.688060Z",
"iopub.status.busy": "2021-11-12T23:35:57.687583Z",
"iopub.status.idle": "2021-11-12T23:35:57.708748Z",
"shell.execute_reply": "2021-11-12T23:35:57.709524Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Robust linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: prestige No. Observations: 45\n",
"Model: RLM Df Residuals: 42\n",
"Method: IRLS Df Model: 2\n",
"Norm: HuberT \n",
"Scale Est.: mad \n",
"Cov Type: H1 \n",
"Date: Fri, 12 Nov 2021 \n",
"Time: 23:35:57 \n",
"No. Iterations: 18 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -7.1107 3.879 -1.833 0.067 -14.713 0.492\n",
"income 0.7015 0.109 6.456 0.000 0.489 0.914\n",
"education 0.4854 0.089 5.441 0.000 0.311 0.660\n",
"==============================================================================\n",
"\n",
"If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n"
]
}
],
"source": [
"rlm_model = rlm(\"prestige ~ income + education\", prestige).fit()\n",
"print(rlm_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.714234Z",
"iopub.status.busy": "2021-11-12T23:35:57.712596Z",
"iopub.status.idle": "2021-11-12T23:35:57.723868Z",
"shell.execute_reply": "2021-11-12T23:35:57.724583Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accountant 1.000000\n",
"pilot 1.000000\n",
"architect 1.000000\n",
"author 1.000000\n",
"chemist 1.000000\n",
"minister 0.344596\n",
"professor 1.000000\n",
"dentist 1.000000\n",
"reporter 0.441669\n",
"engineer 1.000000\n",
"undertaker 1.000000\n",
"lawyer 1.000000\n",
"physician 1.000000\n",
"welfare.worker 1.000000\n",
"teacher 1.000000\n",
"conductor 0.538445\n",
"contractor 0.552262\n",
"factory.owner 0.706169\n",
"store.manager 1.000000\n",
"banker 1.000000\n",
"bookkeeper 1.000000\n",
"mail.carrier 0.690764\n",
"insurance.agent 0.533499\n",
"store.clerk 0.618656\n",
"carpenter 0.935848\n",
"electrician 1.000000\n",
"RR.engineer 1.000000\n",
"machinist 0.570360\n",
"auto.repairman 1.000000\n",
"plumber 1.000000\n",
"gas.stn.attendant 1.000000\n",
"coal.miner 0.963821\n",
"streetcar.motorman 0.832870\n",
"taxi.driver 1.000000\n",
"truck.driver 1.000000\n",
"machine.operator 1.000000\n",
"barber 1.000000\n",
"bartender 1.000000\n",
"shoe.shiner 1.000000\n",
"cook 1.000000\n",
"soda.clerk 1.000000\n",
"watchman 1.000000\n",
"janitor 1.000000\n",
"policeman 1.000000\n",
"waiter 1.000000\n",
"dtype: float64\n"
]
}
],
"source": [
"print(rlm_model.weights)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Data is on the luminosity and temperature of 47 stars in the direction of Cygnus."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:57.729082Z",
"iopub.status.busy": "2021-11-12T23:35:57.727507Z",
"iopub.status.idle": "2021-11-12T23:35:58.139100Z",
"shell.execute_reply": "2021-11-12T23:35:58.138149Z"
}
},
"outputs": [],
"source": [
"dta = sm.datasets.get_rdataset(\"starsCYG\", \"robustbase\", cache=True).data"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:58.149198Z",
"iopub.status.busy": "2021-11-12T23:35:58.141557Z",
"iopub.status.idle": "2021-11-12T23:35:58.473261Z",
"shell.execute_reply": "2021-11-12T23:35:58.473979Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.patches import Ellipse\n",
"\n",
"fig = plt.figure(figsize=(12, 8))\n",
"ax = fig.add_subplot(\n",
" 111,\n",
" xlabel=\"log(Temp)\",\n",
" ylabel=\"log(Light)\",\n",
" title=\"Hertzsprung-Russell Diagram of Star Cluster CYG OB1\",\n",
")\n",
"ax.scatter(*dta.values.T)\n",
"# highlight outliers\n",
"e = Ellipse((3.5, 6), 0.2, 1, alpha=0.25, color=\"r\")\n",
"ax.add_patch(e)\n",
"ax.annotate(\n",
" \"Red giants\",\n",
" xy=(3.6, 6),\n",
" xytext=(3.8, 6),\n",
" arrowprops=dict(facecolor=\"black\", shrink=0.05, width=2),\n",
" horizontalalignment=\"left\",\n",
" verticalalignment=\"bottom\",\n",
" clip_on=True, # clip to the axes bounding box\n",
" fontsize=16,\n",
")\n",
"# annotate these with their index\n",
"for i, row in dta.loc[dta[\"log.Te\"] < 3.8].iterrows():\n",
" ax.annotate(i, row, row + 0.01, fontsize=14)\n",
"xlim, ylim = ax.get_xlim(), ax.get_ylim()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:58.477391Z",
"iopub.status.busy": "2021-11-12T23:35:58.476437Z",
"iopub.status.idle": "2021-11-12T23:35:58.492549Z",
"shell.execute_reply": "2021-11-12T23:35:58.491709Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"\n",
"Image(filename=\"star_diagram.png\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:58.499264Z",
"iopub.status.busy": "2021-11-12T23:35:58.498768Z",
"iopub.status.idle": "2021-11-12T23:35:58.808760Z",
"shell.execute_reply": "2021-11-12T23:35:58.809138Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = dta[\"log.light\"]\n",
"X = sm.add_constant(dta[\"log.Te\"], prepend=True)\n",
"ols_model = sm.OLS(y, X).fit()\n",
"abline_plot(model_results=ols_model, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:58.812698Z",
"iopub.status.busy": "2021-11-12T23:35:58.811742Z",
"iopub.status.idle": "2021-11-12T23:35:59.074736Z",
"shell.execute_reply": "2021-11-12T23:35:59.075395Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(0.5)).fit()\n",
"abline_plot(model_results=rlm_mod, ax=ax, color=\"red\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Why? Because M-estimators are not robust to leverage points."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.078911Z",
"iopub.status.busy": "2021-11-12T23:35:59.077971Z",
"iopub.status.idle": "2021-11-12T23:35:59.082261Z",
"shell.execute_reply": "2021-11-12T23:35:59.082870Z"
}
},
"outputs": [],
"source": [
"infl = ols_model.get_influence()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.086122Z",
"iopub.status.busy": "2021-11-12T23:35:59.085169Z",
"iopub.status.idle": "2021-11-12T23:35:59.109494Z",
"shell.execute_reply": "2021-11-12T23:35:59.110172Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10 0.194103\n",
"19 0.194103\n",
"29 0.198344\n",
"33 0.194103\n",
"Name: hat_diag, dtype: float64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h_bar = 2 * (ols_model.df_model + 1) / ols_model.nobs\n",
"hat_diag = infl.summary_frame()[\"hat_diag\"]\n",
"hat_diag.loc[hat_diag > h_bar]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.113693Z",
"iopub.status.busy": "2021-11-12T23:35:59.112752Z",
"iopub.status.idle": "2021-11-12T23:35:59.137780Z",
"shell.execute_reply": "2021-11-12T23:35:59.138443Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p sidak(p)\n",
"16 -2.049393 0.046415 0.892872\n",
"13 -2.035329 0.047868 0.900286\n",
"33 1.905847 0.063216 0.953543\n",
"18 -1.575505 0.122304 0.997826\n",
"1 1.522185 0.135118 0.998911\n",
"3 1.522185 0.135118 0.998911\n",
"21 -1.450418 0.154034 0.999615\n",
"17 -1.426675 0.160731 0.999735\n",
"29 1.388520 0.171969 0.999859\n",
"14 -1.374733 0.176175 0.999889\n",
"35 1.346543 0.185023 0.999933\n",
"34 -1.272159 0.209999 0.999985\n",
"28 -1.186946 0.241618 0.999998\n",
"20 -1.150621 0.256103 0.999999\n",
"44 1.134779 0.262612 0.999999\n",
"39 1.091886 0.280826 1.000000\n",
"19 1.064878 0.292740 1.000000\n",
"6 -1.026873 0.310093 1.000000\n",
"30 -1.009096 0.318446 1.000000\n",
"22 -0.979768 0.332557 1.000000\n",
"8 0.961218 0.341695 1.000000\n",
"5 0.913802 0.365801 1.000000\n",
"11 0.871997 0.387943 1.000000\n",
"12 0.856685 0.396261 1.000000\n",
"46 -0.833923 0.408829 1.000000\n",
"10 0.743920 0.460879 1.000000\n",
"42 0.727179 0.470968 1.000000\n",
"15 -0.689258 0.494280 1.000000\n",
"43 0.688272 0.494895 1.000000\n",
"7 0.655712 0.515424 1.000000\n",
"40 -0.646396 0.521381 1.000000\n",
"26 -0.640978 0.524862 1.000000\n",
"25 -0.545561 0.588123 1.000000\n",
"32 0.472819 0.638680 1.000000\n",
"37 0.472819 0.638680 1.000000\n",
"38 0.462187 0.646225 1.000000\n",
"0 0.430686 0.668799 1.000000\n",
"31 0.341726 0.734184 1.000000\n",
"36 0.318911 0.751303 1.000000\n",
"4 0.307890 0.759619 1.000000\n",
"9 0.235114 0.815211 1.000000\n",
"41 0.187732 0.851950 1.000000\n",
"2 -0.182093 0.856346 1.000000\n",
"23 -0.156014 0.876736 1.000000\n",
"27 -0.147406 0.883485 1.000000\n",
"24 0.065195 0.948314 1.000000\n",
"45 0.045675 0.963776 1.000000\n"
]
}
],
"source": [
"sidak2 = ols_model.outlier_test(\"sidak\")\n",
"sidak2.sort_values(\"unadj_p\", inplace=True)\n",
"print(sidak2)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.141781Z",
"iopub.status.busy": "2021-11-12T23:35:59.140841Z",
"iopub.status.idle": "2021-11-12T23:35:59.185565Z",
"shell.execute_reply": "2021-11-12T23:35:59.186277Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" student_resid unadj_p fdr_bh(p)\n",
"16 -2.049393 0.046415 0.764747\n",
"13 -2.035329 0.047868 0.764747\n",
"33 1.905847 0.063216 0.764747\n",
"18 -1.575505 0.122304 0.764747\n",
"1 1.522185 0.135118 0.764747\n",
"3 1.522185 0.135118 0.764747\n",
"21 -1.450418 0.154034 0.764747\n",
"17 -1.426675 0.160731 0.764747\n",
"29 1.388520 0.171969 0.764747\n",
"14 -1.374733 0.176175 0.764747\n",
"35 1.346543 0.185023 0.764747\n",
"34 -1.272159 0.209999 0.764747\n",
"28 -1.186946 0.241618 0.764747\n",
"20 -1.150621 0.256103 0.764747\n",
"44 1.134779 0.262612 0.764747\n",
"39 1.091886 0.280826 0.764747\n",
"19 1.064878 0.292740 0.764747\n",
"6 -1.026873 0.310093 0.764747\n",
"30 -1.009096 0.318446 0.764747\n",
"22 -0.979768 0.332557 0.764747\n",
"8 0.961218 0.341695 0.764747\n",
"5 0.913802 0.365801 0.768599\n",
"11 0.871997 0.387943 0.768599\n",
"12 0.856685 0.396261 0.768599\n",
"46 -0.833923 0.408829 0.768599\n",
"10 0.743920 0.460879 0.770890\n",
"42 0.727179 0.470968 0.770890\n",
"15 -0.689258 0.494280 0.770890\n",
"43 0.688272 0.494895 0.770890\n",
"7 0.655712 0.515424 0.770890\n",
"40 -0.646396 0.521381 0.770890\n",
"26 -0.640978 0.524862 0.770890\n",
"25 -0.545561 0.588123 0.837630\n",
"32 0.472819 0.638680 0.843682\n",
"37 0.472819 0.638680 0.843682\n",
"38 0.462187 0.646225 0.843682\n",
"0 0.430686 0.668799 0.849556\n",
"31 0.341726 0.734184 0.892552\n",
"36 0.318911 0.751303 0.892552\n",
"4 0.307890 0.759619 0.892552\n",
"9 0.235114 0.815211 0.922751\n",
"41 0.187732 0.851950 0.922751\n",
"2 -0.182093 0.856346 0.922751\n",
"23 -0.156014 0.876736 0.922751\n",
"27 -0.147406 0.883485 0.922751\n",
"24 0.065195 0.948314 0.963776\n",
"45 0.045675 0.963776 0.963776\n"
]
}
],
"source": [
"fdr2 = ols_model.outlier_test(\"fdr_bh\")\n",
"fdr2.sort_values(\"unadj_p\", inplace=True)\n",
"print(fdr2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Let's delete that line"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.189578Z",
"iopub.status.busy": "2021-11-12T23:35:59.188655Z",
"iopub.status.idle": "2021-11-12T23:35:59.193618Z",
"shell.execute_reply": "2021-11-12T23:35:59.194248Z"
}
},
"outputs": [],
"source": [
"l = ax.lines[-1]\n",
"l.remove()\n",
"del l"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.197350Z",
"iopub.status.busy": "2021-11-12T23:35:59.196439Z",
"iopub.status.idle": "2021-11-12T23:35:59.409124Z",
"shell.execute_reply": "2021-11-12T23:35:59.409828Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = np.ones(len(X))\n",
"weights[X[X[\"log.Te\"] < 3.8].index.values - 1] = 0\n",
"wls_model = sm.WLS(y, X, weights=weights).fit()\n",
"abline_plot(model_results=wls_model, ax=ax, color=\"green\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* MM estimators are good for this type of problem, unfortunately, we do not yet have these yet. \n",
"* It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.413055Z",
"iopub.status.busy": "2021-11-12T23:35:59.412139Z",
"iopub.status.idle": "2021-11-12T23:35:59.417575Z",
"shell.execute_reply": "2021-11-12T23:35:59.418224Z"
}
},
"outputs": [],
"source": [
"yy = y.values[:, None]\n",
"xx = X[\"log.Te\"].values[:, None]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: The R code and the results in this notebook has been converted to markdown so that R is not required to build the documents. The R results in the notebook were computed using R 3.5.1 and robustbase 0.93."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```ipython\n",
"%load_ext rpy2.ipython\n",
"\n",
"%R library(robustbase)\n",
"%Rpush yy xx\n",
"%R mod <- lmrob(yy ~ xx);\n",
"%R params <- mod$coefficients;\n",
"%Rpull params\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```ipython\n",
"%R print(mod)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Call:\n",
"lmrob(formula = yy ~ xx)\n",
" \\--> method = \"MM\"\n",
"Coefficients:\n",
"(Intercept) xx \n",
" -4.969 2.253 \n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.421505Z",
"iopub.status.busy": "2021-11-12T23:35:59.420607Z",
"iopub.status.idle": "2021-11-12T23:35:59.429450Z",
"shell.execute_reply": "2021-11-12T23:35:59.430096Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-4.969387980288108 2.2531613477892365\n"
]
}
],
"source": [
"params = [-4.969387980288108, 2.2531613477892365] # Computed using R\n",
"print(params[0], params[1])"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.433254Z",
"iopub.status.busy": "2021-11-12T23:35:59.432352Z",
"iopub.status.idle": "2021-11-12T23:35:59.791335Z",
"shell.execute_reply": "2021-11-12T23:35:59.792028Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abline_plot(intercept=params[0], slope=params[1], ax=ax, color=\"red\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Breakdown points of M-estimator"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.795298Z",
"iopub.status.busy": "2021-11-12T23:35:59.794389Z",
"iopub.status.idle": "2021-11-12T23:35:59.803420Z",
"shell.execute_reply": "2021-11-12T23:35:59.804053Z"
}
},
"outputs": [],
"source": [
"np.random.seed(12345)\n",
"nobs = 200\n",
"beta_true = np.array([3, 1, 2.5, 3, -4])\n",
"X = np.random.uniform(-20, 20, size=(nobs, len(beta_true) - 1))\n",
"# stack a constant in front\n",
"X = sm.add_constant(X, prepend=True) # np.c_[np.ones(nobs), X]\n",
"mc_iter = 500\n",
"contaminate = 0.25 # percentage of response variables to contaminate"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:35:59.807091Z",
"iopub.status.busy": "2021-11-12T23:35:59.806203Z",
"iopub.status.idle": "2021-11-12T23:36:07.156595Z",
"shell.execute_reply": "2021-11-12T23:36:07.156992Z"
}
},
"outputs": [],
"source": [
"all_betas = []\n",
"for i in range(mc_iter):\n",
" y = np.dot(X, beta_true) + np.random.normal(size=200)\n",
" random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))\n",
" y[random_idx] = np.random.uniform(-750, 750)\n",
" beta_hat = sm.RLM(y, X).fit().params\n",
" all_betas.append(beta_hat)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:36:07.160439Z",
"iopub.status.busy": "2021-11-12T23:36:07.159929Z",
"iopub.status.idle": "2021-11-12T23:36:07.175034Z",
"shell.execute_reply": "2021-11-12T23:36:07.175403Z"
}
},
"outputs": [],
"source": [
"all_betas = np.asarray(all_betas)\n",
"se_loss = lambda x: np.linalg.norm(x, ord=2) ** 2\n",
"se_beta = lmap(se_loss, all_betas - beta_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Squared error loss"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:36:07.178685Z",
"iopub.status.busy": "2021-11-12T23:36:07.178052Z",
"iopub.status.idle": "2021-11-12T23:36:07.182063Z",
"shell.execute_reply": "2021-11-12T23:36:07.182464Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.4450294873068656"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(se_beta).mean()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:36:07.184871Z",
"iopub.status.busy": "2021-11-12T23:36:07.184421Z",
"iopub.status.idle": "2021-11-12T23:36:07.188573Z",
"shell.execute_reply": "2021-11-12T23:36:07.189030Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2.99711706, 0.99898147, 2.49909344, 2.99712918, -3.99626521])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_betas.mean(0)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:36:07.191377Z",
"iopub.status.busy": "2021-11-12T23:36:07.190931Z",
"iopub.status.idle": "2021-11-12T23:36:07.196253Z",
"shell.execute_reply": "2021-11-12T23:36:07.196627Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3. , 1. , 2.5, 3. , -4. ])"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beta_true"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:36:07.199092Z",
"iopub.status.busy": "2021-11-12T23:36:07.198647Z",
"iopub.status.idle": "2021-11-12T23:36:07.205814Z",
"shell.execute_reply": "2021-11-12T23:36:07.206169Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"3.236091328675582e-05"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"se_loss(all_betas.mean(0) - beta_true)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
|
|
| examples/gcp_datalab/notebooks/2_deploy_and_run_ml_model_to_predict_los.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1> Structured Machine Learning using Tensorflow, Google Cloud Datalab and Cloud ML</h1>\n",
"<hr />\n",
"<b>This notebook demonstrates a process to deploy a ML model to CloudML. It leverages a pre-built machine learning model to predict Length of Stay in ED and inpatient care settings. Finally it runs an inference job on the CloudML Engine to render predictions. This is step 2 of 2.</b>\n",
"<h3>\n",
"<br />\n",
"<ol>\n",
"<li> Setup Environment </li> <br />\n",
"<li> Deploy and Run a ML Model on CloudML </li>\n",
"</ol></h3>\n",
"<hr />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> 1. Setup Environment</h2>\n",
"<ul>\n",
" <li>Initialize environment variables for your environment</li>\n",
" <li>Please change the values of the following before executing rest of the cells in this notebook: <br />\n",
" <b>1. GCP_PROJECT and </b> <br />\n",
" <b>2. GCS_BUCKET </b> <br />\n",
" <b>3. GCS_REGION </b>\n",
" </li>\n",
"</ul>"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"GCP_PROJECT = 'dp-workspace'\n",
"GCS_BUCKET = 'gs://cluster19-bkt'\n",
"GCS_REGION = 'us-central1'\n",
"os.putenv(\"REGION\", GCS_REGION)\n",
"TF_RECORD_SEQEX = GCS_BUCKET+'/synthea/serv/seqex*'\n",
"os.putenv(\"SEQEX_IN_GCS\", TF_RECORD_SEQEX)\n",
"MODEL_PATH = GCS_BUCKET+'/synthea/model/'\n",
"os.putenv(\"MODEL_IN_GCS\", MODEL_PATH+\"*\")\n",
"SAVED_MODEL_PATH = MODEL_PATH + 'export'\n",
"os.putenv(\"SAVED_MODEL_IN_GCS\", SAVED_MODEL_PATH+\"*\")\n",
"SERVING_DATASET = GCS_BUCKET+'/synthea/serv/seqex-00002-of-00003.tfrecords'\n",
"os.putenv(\"SERVING_DATASET\", SERVING_DATASET)\n",
"INFERENCE_PATH = MODEL_PATH + 'infer'\n",
"os.putenv(\"INFERENCE_PATH\", INFERENCE_PATH)\n",
"os.putenv(\"MODEL_NAME\", \"tf_fhir_los\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Import dependencies. </b>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/envs/py2env/lib/python2.7/site-packages/h5py/__init__.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" from ._conv import register_converters as _register_converters\n",
"/usr/local/envs/py2env/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"/usr/local/envs/py2env/lib/python2.7/site-packages/h5py/_hl/group.py:22: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" from .. import h5g, h5i, h5o, h5r, h5t, h5l, h5p\n",
"/usr/local/envs/py2env/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" from . import _ni_label\n",
"/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/__main__.py:9: ImportWarning: Not importing directory '/usr/local/fhir/proto': missing __init__.py\n"
]
}
],
"source": [
"# from apache_beam.options.pipeline_options import PipelineOptions\n",
"# from apache_beam.options.pipeline_options import GoogleCloudOptions\n",
"# from apache_beam.options.pipeline_options import StandardOptions\n",
"# import apache_beam as beam\n",
"from tensorflow.core.example import example_pb2\n",
"import tensorflow as tf\n",
"import time\n",
"\n",
"from proto import version_config_pb2\n",
"from proto.stu3 import fhirproto_extensions_pb2\n",
"from proto.stu3 import resources_pb2\n",
"\n",
"from google.protobuf import text_format\n",
"from py.google.fhir.labels import label\n",
"from py.google.fhir.labels import bundle_to_label\n",
"from py.google.fhir.seqex import bundle_to_seqex\n",
"from py.google.fhir.models import model\n",
"from py.google.fhir.models.model import make_estimator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Optionally, enable logging for debugging.</b>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"logger = logging.getLogger()\n",
"#logger.setLevel(logging.INFO)\n",
"logger.setLevel(logging.ERROR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b> Previous step saved Sequence Examples into GCS. Let's examine file size and location of the Sequence Examples we will use of the inference. </b>"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 45296041 2019-03-06T22:22:06Z gs://cluster19-bkt/synthea/serv/seqex-00002-of-00003.tfrecords\n",
"TOTAL: 1 objects, 45296041 bytes (43.2 MiB)\n"
]
}
],
"source": [
"%bash\n",
"gsutil ls -l ${SEQEX_IN_GCS}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> 2. Deploy and Run ML Model on Cloud ML</h2>\n",
"<ul>\n",
" <li>A pre-trained ML Model which was exported to GCS in step 1 will be deployed to Cloud ML Serving.</li>\n",
"</ul>\n",
"<b>2a. Let's start with exporting our model for serving.<b>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Using config: {'_save_checkpoints_secs': 180, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_task_type': None, '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fba7c855850>, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1.0\n",
"}\n",
", '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_num_ps_replicas': 0, '_model_dir': 'gs://cluster19-bkt/synthea/model/', '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 0, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_environment': 'local', '_save_summary_steps': 100}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:04.616381 140440319678208 tf_logging.py:115] Using config: {'_save_checkpoints_secs': 180, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_task_type': None, '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fba7c855850>, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1.0\n",
"}\n",
", '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_num_ps_replicas': 0, '_model_dir': 'gs://cluster19-bkt/synthea/model/', '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 0, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_environment': 'local', '_save_summary_steps': 100}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Using config: {'_save_checkpoints_secs': 180, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_task_type': None, '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fba7c8554d0>, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1.0\n",
"}\n",
", '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_num_ps_replicas': 0, '_model_dir': 'gs://cluster19-bkt/synthea/model/', '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 0, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_environment': 'local', '_save_summary_steps': 100}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:04.621371 140440319678208 tf_logging.py:115] Using config: {'_save_checkpoints_secs': 180, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_task_type': None, '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fba7c8554d0>, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1.0\n",
"}\n",
", '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_num_ps_replicas': 0, '_model_dir': 'gs://cluster19-bkt/synthea/model/', '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 0, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_environment': 'local', '_save_summary_steps': 100}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Calling model_fn.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:09.620572 140440319678208 tf_logging.py:115] Calling model_fn.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Calling model_fn.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:09.624799 140440319678208 tf_logging.py:115] Calling model_fn.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Done calling model_fn.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.750710 140440319678208 tf_logging.py:115] Done calling model_fn.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Done calling model_fn.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.755114 140440319678208 tf_logging.py:115] Done calling model_fn.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Signatures INCLUDED in export for Eval: None\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.760251 140440319678208 tf_logging.py:115] Signatures INCLUDED in export for Eval: None\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.764616 140440319678208 tf_logging.py:115] Signatures INCLUDED in export for Classify: ['serving_default', 'classification']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.768605 140440319678208 tf_logging.py:115] Signatures INCLUDED in export for Regress: None\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.772643 140440319678208 tf_logging.py:115] Signatures INCLUDED in export for Predict: ['predict']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Signatures INCLUDED in export for Train: None\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:12.776612 140440319678208 tf_logging.py:115] Signatures INCLUDED in export for Train: None\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from gs://cluster19-bkt/synthea/model/model.ckpt-300\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:13.150372 140440319678208 tf_logging.py:115] Restoring parameters from gs://cluster19-bkt/synthea/model/model.ckpt-300\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py:1044: calling add_meta_graph_and_variables (from tensorflow.python.saved_model.builder_impl) with legacy_init_op is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Pass your op to the equivalent parameter main_op instead.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0306 22:31:14.255047 140440319678208 tf_logging.py:125] From /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py:1044: calling add_meta_graph_and_variables (from tensorflow.python.saved_model.builder_impl) with legacy_init_op is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Pass your op to the equivalent parameter main_op instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets added to graph.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:14.258912 140440319678208 tf_logging.py:115] Assets added to graph.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:No assets to write.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:14.263230 140440319678208 tf_logging.py:115] No assets to write.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:SavedModel written to: gs://cluster19-bkt/synthea/model/export/temp-1551911464/saved_model.pb\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0306 22:31:23.941159 140440319678208 tf_logging.py:115] SavedModel written to: gs://cluster19-bkt/synthea/model/export/temp-1551911464/saved_model.pb\n"
]
}
],
"source": [
"from py.google.fhir.models.model import get_serving_input_fn\n",
"hparams = model.create_hparams()\n",
"time_crossed_features = [\n",
" cross.split(':') for cross in hparams.time_crossed_features if cross\n",
" ]\n",
"LABEL_VALUES = ['less_or_equal_3', '3_7', '7_14', 'above_14']\n",
"estimator = make_estimator(hparams, LABEL_VALUES, MODEL_PATH)\n",
"serving_input_fn = get_serving_input_fn(hparams.dedup, hparams.time_windows, hparams.include_age, hparams.categorical_context_features, hparams.sequence_features, time_crossed_features)\n",
"export_dir = estimator.export_savedmodel(SAVED_MODEL_PATH, serving_input_fn)\n",
"os.putenv(\"MODEL_BINARY\", export_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>2b. List all the models deployed currently in the Cloud ML Engine</b>"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Listed 0 items.\n"
]
}
],
"source": [
"%%bash\n",
"gcloud ml-engine models list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>2c. Optionally run following cell to delete previously deployed model. </b>"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Deleting version [v1]......\n",
"..............................done.\n",
"Deleting model [tf_fhir_los]...\n",
"done.\n"
]
}
],
"source": [
"%%bash\n",
"gcloud ml-engine versions delete v1 --model ${MODEL_NAME} -q\n",
"gcloud ml-engine models delete $MODEL_NAME -q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>2d. Run following cell to create a new model if it does not exist </b>"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Created ml engine model [projects/dp-workspace/models/tf_fhir_los].\n"
]
}
],
"source": [
"%%bash\n",
"gcloud ml-engine models create $MODEL_NAME --regions=$REGION"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b> 2e. List versions of the Model</b>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Listed 0 items.\n"
]
}
],
"source": [
"%%bash\n",
"gcloud ml-engine versions list --model ${MODEL_NAME}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b> 2f. Run following cell to create a new version of the model. Increment the version number like v1, v2, v3 </b> <br />\n",
"Optionally, you can delete a version using: <br />\n",
"gcloud ml-engine versions delete v1 --model ${MODEL_NAME} -q"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Creating version (this might take a few minutes)......\n",
".............................................................................................................................................................................................done.\n"
]
}
],
"source": [
"%%bash\n",
"#gcloud ml-engine versions delete v1 --model ${MODEL_NAME} -q\n",
"gcloud ml-engine versions create v1 \\\n",
" --model ${MODEL_NAME} \\\n",
" --origin ${MODEL_BINARY} \\\n",
" --runtime-version 1.12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b> 2g. Run an inference job on CloudML engine </b>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jobId: job_inf_20190306_223914\n",
"state: QUEUED\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Job [job_inf_20190306_223914] submitted successfully.\n",
"Your job is still active. You may view the status of your job with the command\n",
"\n",
" $ gcloud ml-engine jobs describe job_inf_20190306_223914\n",
"\n",
"or continue streaming the logs with the command\n",
"\n",
" $ gcloud ml-engine jobs stream-logs job_inf_20190306_223914\n"
]
}
],
"source": [
"%%bash\n",
"INFER_JOB_NAME=\"job_inf_$(date +%Y%m%d_%H%M%S)\"\n",
"gcloud ml-engine jobs submit prediction $INFER_JOB_NAME --model $MODEL_NAME --version v1 --data-format tf-record --region $REGION --input-paths $SERVING_DATASET --output-path $INFERENCE_PATH\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>You can check the status of the job and other information on <a href=\"https://console.cloud.google.com/mlengine/jobs\">GCP CloudML page</a> </b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b> 2h. View the prediction (output) generated by the inference job </b>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.46091485023498535, 0.1920824497938156, 0.1735013723373413, 0.1735013723373413]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.33346351981163025, 0.2637665867805481, 0.20138496160507202, 0.20138496160507202]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.43521541357040405, 0.20636126399040222, 0.17921166121959686, 0.17921166121959686]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.41660845279693604, 0.20461316406726837, 0.1893891543149948, 0.1893891543149948]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3496762216091156, 0.2616179883480072, 0.1943529099225998, 0.1943529099225998]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4166741967201233, 0.235443577170372, 0.17394115030765533, 0.17394115030765533]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3644198477268219, 0.22510665655136108, 0.2052367776632309, 0.2052367776632309]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3581617474555969, 0.25651878118515015, 0.19265969097614288, 0.19265969097614288]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.6387507915496826, 0.13085539638996124, 0.11519695818424225, 0.11519695818424225]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.5902621746063232, 0.14895939826965332, 0.13038921356201172, 0.13038921356201172]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3794516623020172, 0.2181423157453537, 0.20120304822921753, 0.20120304822921753]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.36438557505607605, 0.22581234574317932, 0.20490103960037231, 0.20490103960037231]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3471221625804901, 0.2313753217458725, 0.2107512354850769, 0.2107512354850769]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4827820658683777, 0.21315574645996094, 0.1520310789346695, 0.1520310789346695]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3496911823749542, 0.22477640211582184, 0.21276621520519257, 0.21276621520519257]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3256012797355652, 0.2340296059846878, 0.2201845347881317, 0.2201845347881317]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3703394830226898, 0.2531915009021759, 0.18823449313640594, 0.18823449313640594]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.39021235704421997, 0.21932187676429749, 0.19523289799690247, 0.19523289799690247]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9429581165313721, 0.020179297775030136, 0.018431365489959717, 0.018431365489959717]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9677392840385437, 0.011275112628936768, 0.010492815636098385, 0.010492815636098385]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9700233936309814, 0.010474367998540401, 0.00975113082677126, 0.00975113082677126]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9707061648368835, 0.010235652327537537, 0.009529110044240952, 0.009529110044240952]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9744876623153687, 0.008909860625863075, 0.008301233872771263, 0.008301233872771263]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9758273363113403, 0.008440230041742325, 0.00786615815013647, 0.00786615815013647]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9758273363113403, 0.008440230041742325, 0.00786615815013647, 0.00786615815013647]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9778283834457397, 0.007816239260137081, 0.0071776448749005795, 0.0071776448749005795]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9733906984329224, 0.00939005147665739, 0.008609579876065254, 0.008609579876065254]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9705041646957397, 0.010340331122279167, 0.009577713906764984, 0.009577713906764984]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9602723717689514, 0.013807416893541813, 0.012960060499608517, 0.012960060499608517]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9295793771743774, 0.024540795013308525, 0.02293989434838295, 0.02293989434838295]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3806137144565582, 0.2228958159685135, 0.19824528694152832, 0.19824528694152832]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9416030645370483, 0.02054225280880928, 0.0189273152500391, 0.0189273152500391]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9490642547607422, 0.01790357381105423, 0.016516132280230522, 0.016516132280230522]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9177359938621521, 0.028987942263484, 0.02663804218173027, 0.02663804218173027]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.947433352470398, 0.018469886854290962, 0.01704837568104267, 0.01704837568104267]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.947433352470398, 0.018469886854290962, 0.01704837568104267, 0.01704837568104267]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.94358229637146, 0.01983705163002014, 0.018290357664227486, 0.018290357664227486]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.94358229637146, 0.01983705163002014, 0.018290357664227486, 0.018290357664227486]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9130223989486694, 0.030138762667775154, 0.02841937355697155, 0.02841937355697155]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.940277636051178, 0.021004747599363327, 0.019358843564987183, 0.019358843564987183]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.940277636051178, 0.021004747599363327, 0.019358843564987183, 0.019358843564987183]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9048848152160645, 0.033540643751621246, 0.030787277966737747, 0.030787277966737747]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9087801575660706, 0.03215884044766426, 0.029530540108680725, 0.029530540108680725]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.35879915952682495, 0.2271314263343811, 0.20703469216823578, 0.20703469216823578]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3544289469718933, 0.25448963046073914, 0.19554071128368378, 0.19554071128368378]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.36187753081321716, 0.22376558184623718, 0.20717839896678925, 0.20717839896678925]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.7448374629020691, 0.08942078053951263, 0.08287087827920914, 0.08287087827920914]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8516587018966675, 0.05262661352753639, 0.047857318073511124, 0.047857318073511124]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8485644459724426, 0.053734105080366135, 0.04885072261095047, 0.04885072261095047]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.7666388750076294, 0.08115502446889877, 0.07610306888818741, 0.07610306888818741]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8319219946861267, 0.05930306017398834, 0.05438743531703949, 0.05438743531703949]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8319219946861267, 0.05930306017398834, 0.05438743531703949, 0.05438743531703949]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8319219946861267, 0.05930306017398834, 0.05438743531703949, 0.05438743531703949]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3496911823749542, 0.22477640211582184, 0.21276621520519257, 0.21276621520519257]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.37927067279815674, 0.2179276943206787, 0.20140080153942108, 0.20140080153942108]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4894687831401825, 0.21367992460727692, 0.1484256088733673, 0.1484256088733673]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8608220815658569, 0.048537276685237885, 0.045320264995098114, 0.045320264995098114]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8608220815658569, 0.048537276685237885, 0.045320264995098114, 0.045320264995098114]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.7876566648483276, 0.07300493121147156, 0.069669209420681, 0.069669209420681]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8524513244628906, 0.05147677659988403, 0.0480358861386776, 0.0480358861386776]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.7891815900802612, 0.07248573750257492, 0.06916637718677521, 0.06916637718677521]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4671579599380493, 0.218357652425766, 0.15724214911460876, 0.15724214911460876]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.49595943093299866, 0.20456285774707794, 0.1497388780117035, 0.1497388780117035]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3514162003993988, 0.26165640354156494, 0.19346372783184052, 0.19346372783184052]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3575669825077057, 0.26073551177978516, 0.19084875285625458, 0.19084875285625458]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3496762216091156, 0.2616179883480072, 0.1943529099225998, 0.1943529099225998]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8910545706748962, 0.03891025483608246, 0.03501754254102707, 0.03501754254102707]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9330829381942749, 0.02358768880367279, 0.021664733067154884, 0.021664733067154884]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9330829381942749, 0.02358768880367279, 0.021664733067154884, 0.021664733067154884]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9122067093849182, 0.03100511059165001, 0.028394164517521858, 0.028394164517521858]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.8966507315635681, 0.035918742418289185, 0.03371533006429672, 0.03371533006429672]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9228932857513428, 0.027204083278775215, 0.024951277300715446, 0.024951277300715446]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3718176484107971, 0.2491045743227005, 0.1895388960838318, 0.1895388960838318]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.37205106019973755, 0.2233196198940277, 0.20231467485427856, 0.20231467485427856]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.49727189540863037, 0.18067918717861176, 0.16102445125579834, 0.16102445125579834]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.36518216133117676, 0.21929021179676056, 0.20776383578777313, 0.20776383578777313]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3500369191169739, 0.2328771948814392, 0.20854297280311584, 0.20854297280311584]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4195079803466797, 0.20786987245082855, 0.18631108105182648, 0.18631108105182648]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.5160911083221436, 0.17269843816757202, 0.1556052267551422, 0.1556052267551422]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4036380648612976, 0.21584424376487732, 0.19025883078575134, 0.19025883078575134]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9586058259010315, 0.014411664567887783, 0.013491319492459297, 0.013491319492459297]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9612168669700623, 0.013498330488801003, 0.012642333284020424, 0.012642333284020424]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9506948590278625, 0.016893038526177406, 0.01620609126985073, 0.01620609126985073]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9626554250717163, 0.012995569035410881, 0.012174525298178196, 0.012174525298178196]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9502716064453125, 0.017321202903985977, 0.016203681007027626, 0.016203681007027626]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9613053202629089, 0.013464804738759995, 0.012614951469004154, 0.012614951469004154]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9464715719223022, 0.018343603238463402, 0.017592430114746094, 0.017592430114746094]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9598960280418396, 0.013957561925053596, 0.013073197565972805, 0.013073197565972805]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.37630870938301086, 0.2214735448360443, 0.20110885798931122, 0.20110885798931122]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3544289469718933, 0.25448963046073914, 0.19554071128368378, 0.19554071128368378]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.4671425521373749, 0.19169928133487701, 0.17057906091213226, 0.17057906091213226]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.37038224935531616, 0.22140707075595856, 0.20410534739494324, 0.20410534739494324]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.34289565682411194, 0.23616747558116913, 0.21046847105026245, 0.21046847105026245]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.42002278566360474, 0.23101326823234558, 0.17448198795318604, 0.17448198795318604]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.42486199736595154, 0.2067011445760727, 0.18421843647956848, 0.18421843647956848]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.40498223900794983, 0.20874090492725372, 0.19313840568065643, 0.19313840568065643]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9400237202644348, 0.02132154256105423, 0.01932731829583645, 0.01932731829583645]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9537947773933411, 0.015997713431715965, 0.01510372944176197, 0.01510372944176197]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9713720679283142, 0.01005620788782835, 0.009285839274525642, 0.009285839274525642]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9720250368118286, 0.0098267188295722, 0.009074121713638306, 0.009074121713638306]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9720250368118286, 0.0098267188295722, 0.009074121713638306, 0.009074121713638306]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9358060956001282, 0.021994611248373985, 0.02109961211681366, 0.02109961211681366]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.952584981918335, 0.016495568677783012, 0.015459708869457245, 0.015459708869457245]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3256012797355652, 0.2340296059846878, 0.2201845347881317, 0.2201845347881317]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.33175891637802124, 0.26368528604507446, 0.20227791368961334, 0.20227791368961334]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3575669825077057, 0.26073551177978516, 0.19084875285625458, 0.19084875285625458]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9152286648750305, 0.029517019167542458, 0.027627166360616684, 0.027627166360616684]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9254973530769348, 0.025923127308487892, 0.024289730936288834, 0.024289730936288834]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9254112839698792, 0.0259428508579731, 0.024322975426912308, 0.024322975426912308]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9195705056190491, 0.02798665501177311, 0.02622138150036335, 0.02622138150036335]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9356268048286438, 0.022596944123506546, 0.02088811807334423, 0.02088811807334423]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9011353850364685, 0.03477989509701729, 0.03204235062003136, 0.03204235062003136]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.744221031665802, 0.09083148837089539, 0.08247373253107071, 0.08247373253107071]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9418757557868958, 0.020473958924412727, 0.018825162202119827, 0.018825162202119827]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9730786085128784, 0.009452678263187408, 0.008734348230063915, 0.008734348230063915]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9852482080459595, 0.005222099367529154, 0.004764857701957226, 0.004764857701957226]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.98095703125, 0.006651881150901318, 0.006195597816258669, 0.006195597816258669]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9911314845085144, 0.0031459557358175516, 0.002861271845176816, 0.002861271845176816]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9934520721435547, 0.002298906212672591, 0.0021244960371404886, 0.0021244960371404886]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9950313568115234, 0.0017443235265091062, 0.001612069085240364, 0.001612069085240364]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9954283237457275, 0.0015785277355462313, 0.0014965852024033666, 0.0014965852024033666]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9969893097877502, 0.0010558783542364836, 0.0009773944038897753, 0.0009773944038897753]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9978408813476562, 0.0007566824788227677, 0.0007012709975242615, 0.0007012709975242615]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9984147548675537, 0.0005550076602958143, 0.0005151192308403552, 0.0005151192308403552]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9976102113723755, 0.000842096924316138, 0.0007738972781226039, 0.0007738972781226039]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9985836744308472, 0.0005063739372417331, 0.0004549666482489556, 0.0004549666482489556]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9987673759460449, 0.00044036481995135546, 0.0003961250768043101, 0.0003961250768043101]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9987988471984863, 0.0004332195967435837, 0.00038401229539886117, 0.00038401229539886117]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.36082640290260315, 0.2570081651210785, 0.19108270108699799, 0.19108270108699799]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.33346351981163025, 0.2637665867805481, 0.20138496160507202, 0.20138496160507202]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9820130467414856, 0.006432580761611462, 0.005777208134531975, 0.005777208134531975]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9870265126228333, 0.004635944031178951, 0.004168800078332424, 0.004168800078332424]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.989164412021637, 0.003834133967757225, 0.0035006802063435316, 0.0035006802063435316]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9899997115135193, 0.0035386031959205866, 0.0032308052759617567, 0.0032308052759617567]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9892142415046692, 0.003819560632109642, 0.003483068197965622, 0.003483068197965622]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9890602827072144, 0.003826769767329097, 0.0035565239377319813, 0.0035565239377319813]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.992800235748291, 0.0025131141301244497, 0.0023433873429894447, 0.0023433873429894447]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9927864670753479, 0.002517404267564416, 0.002348046749830246, 0.002348046749830246]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.9855512380599976, 0.005108341574668884, 0.004670186433941126, 0.004670186433941126]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.37271785736083984, 0.2223772257566452, 0.20245243608951569, 0.20245243608951569]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.324312686920166, 0.23725482821464539, 0.2192162424325943, 0.2192162424325943]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.40781041979789734, 0.20973125100135803, 0.1912291944026947, 0.1912291944026947]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.35084351897239685, 0.23331309854984283, 0.20792174339294434, 0.20792174339294434]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.5171391367912292, 0.1716819554567337, 0.15558946132659912, 0.15558946132659912]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3689180612564087, 0.2266983687877655, 0.2021917998790741, 0.2021917998790741]}\n",
"{\"classes\": [\"less_or_equal_3\", \"3_7\", \"7_14\", \"above_14\"], \"scores\": [0.3514162003993988, 0.26165640354156494, 0.19346372783184052, 0.19346372783184052]}\n"
]
}
],
"source": [
"%%bash\n",
"gsutil cat ${INFERENCE_PATH}/prediction.results-00000-of-00001"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>You can check the status of the job and other information on <a href=\"https://console.cloud.google.com/mlengine/jobs\">GCP CloudML page</a> </b>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"source": [],
"metadata": {
"collapsed": false
}
}
}
},
"nbformat": 4,
"nbformat_minor": 2
} |
|
GoogleCloudPlatform/cloud-sql-python-connector | samples/notebooks/postgres_python_connector.ipynb |
|
| {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "postgres_python_connector.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2022 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DRyGcAepAPJ5"
},
"source": [
"[](https://colab.research.google.com/github/GoogleCloudPlatform/cloud-sql-python-connector/blob/main/samples/notebooks/postgres_python_connector.ipynb)\n",
"# **Connect to Cloud SQL using the Cloud SQL Python Connector**\n",
"\n",
"---\n",
"\n",
"This notebook will be demonstrating how to connect and query data from a Cloud SQL database in an easy and efficient way all from within a jupyter style notebook! Let's have some fun!\n"
]
},
{
"cell_type": "markdown",
"source": [
"### 📒 Using this interactive notebook\n",
"\n",
"Click the **run** icons ▶️ of each section within this notebook.\n",
"\n",
"> 💡 Alternatively, you can run the currently selected cell with `Ctrl + Enter` (or `⌘ + Enter` on a Mac).\n",
"\n",
"> ⚠️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n",
"\n",
"This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n",
"\n",
"You can use an existing project. Alternatively, you can create a new Cloud project [with cloud credits for free.](https://cloud.google.com/free/docs/gcp-free-tier)"
],
"metadata": {
"id": "jsWGZW_fUJjN"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "DKIF_wOiD9Nx"
},
"source": [
"## 🐍 **Cloud SQL Python Connector**\n",
"To connect and access our Cloud SQL database instance(s) we will leverage the [Cloud SQL Python Connector](https://github.com/GoogleCloudPlatform/cloud-sql-python-connector).\n",
"\n",
"The Cloud SQL Python Connector is a library that can be used alongside a database driver to allow users to easily connect to a Cloud SQL database without having to manually allowlist IP or manage SSL certificates. 🥳 🎉 🤩"
]
},
{
"cell_type": "markdown",
"source": [
"### ♥️ Benefits of Using a Connector\n",
"Using a Cloud SQL connector provides the following benefits:\n",
"\n",
"- 🔑 **IAM Authorization**: uses IAM permissions to control who/what can connect to your Cloud SQL instances.\n",
"- 🔒 **Improved Security**: uses robust, updated TLS 1.3 encryption and identity verification between the client connector and the server-side proxy, independent of the database protocol.\n",
"- 👍 **Convenience**: removes the requirement to use and distribute SSL certificates, as well as manage firewalls or source/destination IP addresses.\n",
"- 🪪 **IAM DB Authentication** (optional): provides support for Cloud SQL’s automatic IAM DB AuthN feature."
],
"metadata": {
"id": "iJcxJ7NVTMJn"
}
},
{
"cell_type": "markdown",
"source": [
"### 📱 Supported Dialects/Drivers\n",
"Google Cloud SQL and the Python Connector currently support the following dialects of SQL: **MySQL**, **PostgreSQL**, and **SQL Server**.\n",
"\n",
"Depending on which dialect you are using for your relational database(s) the Python Connector will utilize a different database driver.\n",
"\n",
"SUPPORTED DRIVERS:\n",
"\n",
"* **pymysql** (MySQL) 🐬\n",
"* **pg8000** (PostgreSQL) 🐘\n",
"* **pytds** (SQL Server) 🗄\n",
"\n",
"Therefore, depending on the dialect of your database you will need to switch to the corresponding notebook!\n",
"\n",
"📗 [**MySQL Notebook**](https://colab.research.google.com/github/GoogleCloudPlatform/cloud-sql-python-connector/blob/main/samples/notebooks/mysql_python_connector.ipynb)\n",
"\n",
"📘 [**PostgreSQL Notebook**](https://colab.research.google.com/github/GoogleCloudPlatform/cloud-sql-python-connector/blob/main/samples/notebooks/postgres_python_connector.ipynb) (this notebook)\n",
"\n",
"📕 [**SQL Server Notebook**](https://colab.research.google.com/github/GoogleCloudPlatform/cloud-sql-python-connector/blob/main/samples/notebooks/sqlserver_python_connector.ipynb)"
],
"metadata": {
"id": "7Pb7xJmIWOwQ"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "RicDCkdI-hmp"
},
"source": [
"## 🚧 **Getting Started**\n",
"This notebook requires the following steps to be completed in order to successfully make Cloud SQL connections with the Cloud SQL Python Connector."
]
},
{
"cell_type": "markdown",
"source": [
"### 🔐 Authenticate to Google Cloud within Colab\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project."
],
"metadata": {
"id": "yygMe6rPWxHS"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()"
],
"metadata": {
"id": "PTXN1_DSXj2b"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### 🔗 Connect Your Google Cloud Project\n",
"Time to connect your Google Cloud Project to this notebook so that you can leverage Google Cloud from within Colab. 🏅 😀"
],
"metadata": {
"id": "p4W6FPnrYEE8"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fVz5zhvZ1mM3",
"cellView": "form"
},
"outputs": [],
"source": [
"#@markdown Please fill in the value below with your GCP project ID and then run the cell.\n",
"\n",
"# Please fill in these values.\n",
"project_id = \"\" #@param {type:\"string\"}\n",
"\n",
"# Quick input validations.\n",
"assert project_id, \"⚠️ Please provide a Google Cloud project ID\"\n",
"\n",
"# Configure gcloud.\n",
"!gcloud config set project {project_id}"
]
},
{
"cell_type": "markdown",
"source": [
"### ☁ Configure Your Google Cloud Project\n",
"Configure the following in your Google Cloud Project.\n",
"\n",
"1. IAM principal (user, service account, etc.) with the\n",
"[Cloud SQL Client][client-role] role. \n",
"\n",
"> 🚨 The user logged into this notebook will be used as the IAM principal and will be granted the Cloud SQL Client role.\n",
"\n",
"[client-role]: https://cloud.google.com/sql/docs/mysql/roles-and-permissions"
],
"metadata": {
"id": "E-tKXYuhiGrf"
}
},
{
"cell_type": "code",
"source": [
"# grant Cloud SQL Client role to authenticated user\n",
"current_user = !gcloud auth list --filter=status:ACTIVE --format=\"value(account)\"\n",
"\n",
"!gcloud projects add-iam-policy-binding {project_id} \\\n",
" --member=user:{current_user[0]} \\\n",
" --role=\"roles/cloudsql.client\""
],
"metadata": {
"id": "wGvqU18ga9EU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"2. Enable the [Cloud SQL Admin API][admin-api] within your project.\n",
"\n",
"[admin-api]: https://console.cloud.google.com/apis/api/sqladmin.googleapis.com"
],
"metadata": {
"id": "JOGTWn0aa9XJ"
}
},
{
"cell_type": "code",
"source": [
"# enable Cloud SQL Admin API\n",
"!gcloud services enable sqladmin.googleapis.com"
],
"metadata": {
"id": "2eFEoT_1biht"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## ☁️ Setting up Cloud SQL\n",
"A **Postgres** Cloud SQL instance is required for the following stages of this notebook."
],
"metadata": {
"id": "noWgbDQQO7mr"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "ypjpse8yBRdI"
},
"source": [
"###💽 **Create a Postgres Instance**\n",
"Running the below cell will verify the existence of a Cloud SQL instance or create a new one if one does not exist.\n",
"\n",
"> ⏳ - Creating a Cloud SQL instance may take a few minutes."
]
},
{
"cell_type": "code",
"source": [
"#@markdown Please fill in the both the Google Cloud region and name of your Cloud SQL instance. Once filled in, run the cell.\n",
"\n",
"# Please fill in these values.\n",
"region = \"us-central1\" #@param {type:\"string\"}\n",
"instance_name = \"\" #@param {type:\"string\"}\n",
"\n",
"# Quick input validations.\n",
"assert region, \"⚠️ Please provide a Google Cloud region\"\n",
"assert instance_name, \"⚠️ Please provide the name of your instance\"\n",
"\n",
"# check if Cloud SQL instance exists in the provided region\n",
"database_version = !gcloud sql instances describe {instance_name} --format=\"value(databaseVersion)\"\n",
"if database_version[0].startswith(\"POSTGRES\"):\n",
" print(\"Found existing Postgres Cloud SQL Instance!\")\n",
"else:\n",
" print(\"Creating new Cloud SQL instance...\")\n",
" password = input(\"Please provide a password to be used for 'postgres' database user: \")\n",
" !gcloud sql instances create {instance_name} --database-version=POSTGRES_14 \\\n",
" --region={region} --cpu=1 --memory=4GB --root-password={password} \\\n",
" --database-flags=cloudsql.iam_authentication=On"
],
"metadata": {
"cellView": "form",
"id": "_vIX7rNtVLhn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nzb0dFO6C4h6"
},
"source": [
"### 🎬 Create a Movies Database\n",
"A `movies` database will be used in later steps when connecting to and querying a Cloud SQL database.\n",
"\n",
"To create a `movies` database within your Cloud SQL instance run the below command:"
]
},
{
"cell_type": "code",
"source": [
"!gcloud sql databases create movies --instance={instance_name}"
],
"metadata": {
"id": "0q5uFF0sJnWK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lcdZH4rANv6C"
},
"source": [
"### 🥷 Create Batman Database User\n",
"To create the `batman` database user that is used throughout the notebook, run the following `gcloud` command."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9NYmcepFOM12"
},
"source": [
"!gcloud sql users create batman \\\n",
" --instance={instance_name} \\\n",
" --password=\"robin\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"<img src='https://i.pinimg.com/originals/12/64/dd/1264dd5ff31fbc65c5edbb5e1a71830e.gif' class=\"center\"/>\n"
],
"metadata": {
"id": "a6YhWRAn1KL9"
}
},
{
"cell_type": "markdown",
"source": [
"## 🐍 Python Connector Usage\n",
"Let's now connect to Cloud SQL using the Python Connector! 🚀 ⭐ 🐍"
],
"metadata": {
"id": "DDmBkH-HOQG5"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "8nqEsalPGETZ"
},
"source": [
"### 🎟 **Configuring Credentials**\n",
"The Cloud SQL Python Connector uses [**Application Default Credentials (ADC)**](https://cloud.google.com/docs/authentication) strategy for resolving credentials. \n",
"\n",
"> 💡 Using the Python Connector in Cloud Run, App Engine, or Cloud Functions will automatically use the service account deployed with each service, allowing this step to be skipped. ✅ \n",
"\n",
"Please see the [google.auth](https://google-auth.readthedocs.io/en/master/reference/google.auth.html) package documentation for more information on how these credentials are sourced.\n",
"\n",
"This means setting default credentials was previously done for you when you ran:\n",
"```python\n",
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6CqBnkLLCDFz"
},
"source": [
"### 💻 **Install Code Dependencies**\n",
"It is recommended to use the Connector alongside a library that can create connection pools, such as [SQLAlchemy](https://www.sqlalchemy.org/). \n",
"This will allow for connections to remain open and be reused, reducing connection overhead and the number of connections needed\n",
"\n",
"Let's `pip install` the [Cloud SQL Python Connector](https://github.com/GoogleCloudPlatform/cloud-sql-python-connector) as well as [SQLAlchemy](https://www.sqlalchemy.org/), using the below command."
]
},
{
"cell_type": "code",
"metadata": {
"id": "N6VmyKU7CCaK"
},
"source": [
"# install dependencies\n",
"import sys\n",
"!{sys.executable} -m pip install cloud-sql-python-connector[\"pg8000\"] SQLAlchemy"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y33XoYC2H7Hz"
},
"source": [
"## 🐘 **Connect to a Postgres Instance**\n",
"We are now ready to connect to a Postgres instance using the Cloud SQL Python Connector! 🐍 ⭐ ☁\n"
]
},
{
"cell_type": "markdown",
"source": [
"Let's set some parameters that are needed to connect properly to a Cloud SQL instance:\n",
"* `INSTANCE_CONNECTION_NAME` : The connection name to your Cloud SQL Instance, takes the form `PROJECT_ID:REGION:INSTANCE_NAME`.\n",
"* `DB_USER` : The user that the connector will use to connect to the database.\n",
"* `DB_PASS` : The password of the DB_USER.\n",
"* `DB_NAME` : The name of the database on the Cloud SQL instance to connect to."
],
"metadata": {
"id": "UJRfOmPd3bLt"
}
},
{
"cell_type": "code",
"metadata": {
"id": "wfEvH386zX2V"
},
"source": [
"# initialize parameters\n",
"INSTANCE_CONNECTION_NAME = f\"{project_id}:{region}:{instance_name}\" # i.e demo-project:us-central1:demo-instance\n",
"print(f\"Your instance connection name is: {INSTANCE_CONNECTION_NAME}\")\n",
"DB_USER = \"batman\"\n",
"DB_PASS = \"robin\"\n",
"DB_NAME = \"movies\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "oy9p883bqafB"
},
"source": [
"### ✅ **Basic Usage**\n",
"To connect to Cloud SQL using the connector, inititalize a `Connector` object and call its `connect` method with the proper input parameters.\n",
"\n",
"The `connect` method takes in the parameters we previously defined, as well as a few additional parameters such as: \n",
"* `driver`: The name of the database driver to connect with.\n",
"* `ip_type` (optional): The IP type (public or private) used to connect. IP types can be either `IPTypes.PUBLIC` or `IPTypes.PRIVATE`. ([Example](#scrollTo=yjAPpIDdRfu2))\n",
"* `enable_iam_auth`: (optional) Boolean enabling IAM based authentication. ([Example](#scrollTo=GpVKrv0TCXje))\n",
"\n",
"Let's show an example! 🤘 🙌 "
]
},
{
"cell_type": "code",
"metadata": {
"id": "UzHaM-6TXO8h"
},
"source": [
"from google.cloud.sql.connector import Connector\n",
"import sqlalchemy\n",
"\n",
"# initialize Connector object\n",
"connector = Connector()\n",
"\n",
"# function to return the database connection object\n",
"def getconn():\n",
" conn = connector.connect(\n",
" INSTANCE_CONNECTION_NAME,\n",
" \"pg8000\",\n",
" user=DB_USER,\n",
" password=DB_PASS,\n",
" db=DB_NAME\n",
" )\n",
" return conn\n",
"\n",
"# create connection pool with 'creator' argument to our connection object function\n",
"pool = sqlalchemy.create_engine(\n",
" \"postgresql+pg8000://\",\n",
" creator=getconn,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "oGHHHf5p3lC6"
},
"source": [
"To use this connector with SQLAlchemy, we use the `creator` argument for `sqlalchemy.create_engine`\n",
"\n",
"Now that we have established a connection pool, let's write a query! 🎉 📝"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D3xMRsBl3Ihl"
},
"source": [
"# connect to connection pool\n",
"with pool.connect() as db_conn:\n",
" # create ratings table in our movies database\n",
" db_conn.execute(\n",
" \"CREATE TABLE IF NOT EXISTS ratings \"\n",
" \"( id SERIAL NOT NULL, title VARCHAR(255) NOT NULL, \"\n",
" \"genre VARCHAR(255) NOT NULL, rating FLOAT NOT NULL, \"\n",
" \"PRIMARY KEY (id));\"\n",
" )\n",
" # insert data into our ratings table\n",
" insert_stmt = sqlalchemy.text(\n",
" \"INSERT INTO ratings (title, genre, rating) VALUES (:title, :genre, :rating)\",\n",
" )\n",
"\n",
" # insert entries into table\n",
" db_conn.execute(insert_stmt, title=\"Batman Begins\", genre=\"Action\", rating=8.5)\n",
" db_conn.execute(insert_stmt, title=\"Star Wars: Return of the Jedi\", genre=\"Action\", rating=9.1)\n",
" db_conn.execute(insert_stmt, title=\"The Breakfast Club\", genre=\"Drama\", rating=8.3)\n",
"\n",
" # query and fetch ratings table\n",
" results = db_conn.execute(\"SELECT * FROM ratings\").fetchall()\n",
"\n",
" # show results\n",
" for row in results:\n",
" print(row)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"You have successfully been able to connect to a Cloud SQL instance from this notebook and make a query. YOU DID IT! 🕺 🎊 💃\n",
"\n",
"<img src=https://media.giphy.com/media/MtHGs1yo4FFKrIs55L/giphy.gif />"
],
"metadata": {
"id": "HaoCW42OY4vY"
}
},
{
"cell_type": "markdown",
"source": [
"To close the `Connector` object's background resources, call it's `close() ` method at the end of your code as follows:\n"
],
"metadata": {
"id": "y4Zhx6YAitjw"
}
},
{
"cell_type": "code",
"source": [
"# cleanup connector object\n",
"connector.close()"
],
"metadata": {
"id": "PNyc9cwVir4M"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GCsS4f5UCYUa"
},
"source": [
"### 🪪 IAM Database Authentication \n",
"[Automatic IAM database authentication](https://cloud.google.com/sql/docs/postgres/authentication#automatic) is supported for **Postgres** Cloud SQL instances. \n",
"\n",
"> 💡 This allows an IAM user to establish an authenticated connection to a Postgres database without having to set a password and enabling the `enable_iam_auth` parameter in the connector's `connect` method.\n",
"\n",
"> 🚨 If you are using a pre-existing Cloud SQL instance within this notebook you may need to [configure Cloud SQL instance to allow IAM authentication](https://cloud.google.com/sql/docs/postgres/create-edit-iam-instances#configuring_existing_instances_for) by setting the `cloudsql.iam_authentication` database flag to `On`. \n",
"(Cloud SQL instances created within this notebook already have it enabled)\n"
]
},
{
"cell_type": "markdown",
"source": [
"IAM principals wanting to use IAM authentication to connect to a Cloud SQL instance require the `Cloud SQL Instance User` and `Cloud SQL Client` IAM role.\n",
"\n",
"Let's add the Cloud SQL Instance User role to the IAM account logged into this notebook. (Client role previously granted)"
],
"metadata": {
"id": "vDhbkGONmj3x"
}
},
{
"cell_type": "code",
"source": [
"# add Cloud SQL Instance User role to current logged in IAM user\n",
"!gcloud projects add-iam-policy-binding {project_id} \\\n",
" --member=user:{current_user[0]} \\\n",
" --role=\"roles/cloudsql.instanceUser\""
],
"metadata": {
"id": "kSWv-OcjknAx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now the current IAM user can be added to the Cloud SQL instance as an IAM database user."
],
"metadata": {
"id": "UmKaFSl3nMFx"
}
},
{
"cell_type": "code",
"source": [
"# add current logged in IAM user to database\n",
"!gcloud sql users create {current_user[0]} \\\n",
" --instance={instance_name} \\\n",
" --type=cloud_iam_user"
],
"metadata": {
"id": "0CnzkOianTNN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Finally, let's update our `getconn` function to connect to our Cloud SQL instance with IAM database authentication enabled.\n",
"\n",
"> ⚠️ The below sample is a limited example as it logs in to the Cloud SQL instance and outputs the current time. By default new IAM database users have no permissions on a Cloud SQL instance. To connect to specific tables and perform more complex queries, permissions must be granted at the database level. ([Grant Database Privileges to the IAM user](https://cloud.google.com/sql/docs/postgres/add-manage-iam-users#grant-db-privileges))"
],
"metadata": {
"id": "6PlLF_Jkm9uX"
}
},
{
"cell_type": "code",
"metadata": {
"id": "GpVKrv0TCXje"
},
"source": [
"from google.cloud.sql.connector import Connector\n",
"import sqlalchemy\n",
"\n",
"# IAM database user parameter (IAM user's email)\n",
"IAM_USER = current_user[0]\n",
"\n",
"# initialize connector\n",
"connector = Connector()\n",
"\n",
"# getconn now using IAM user and requiring no password with IAM Auth enabled\n",
"def getconn():\n",
" conn = connector.connect(\n",
" INSTANCE_CONNECTION_NAME,\n",
" \"pg8000\",\n",
" user=IAM_USER,\n",
" db=\"postgres\",\n",
" enable_iam_auth=True\n",
" )\n",
" return conn\n",
"\n",
"# create connection pool\n",
"pool = sqlalchemy.create_engine(\n",
" \"postgresql+pg8000://\",\n",
" creator=getconn,\n",
")\n",
"\n",
"# connect to connection pool\n",
"with pool.connect() as db_conn:\n",
" # get current datetime from database\n",
" results = db_conn.execute(\"SELECT NOW()\").fetchone()\n",
"\n",
" # output time\n",
" print(\"Current time: \", results[0])\n",
"\n",
"# cleanup connector\n",
"connector.close()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lQS46x0yXdz7"
},
"source": [
"Sucess! You were able to connect to Cloud SQL as an IAM authenticated user using the Cloud SQL Python Connector! 🍾 👏 🏆\n",
"\n",
"<img src=\"https://media.giphy.com/media/YTbZzCkRQCEJa/giphy.gif\" />"
]
},
{
"cell_type": "markdown",
"source": [
"## 🗑 Clean Up Notebook Resources\n",
"Make sure to delete your Cloud SQL instance when your are finished with this notebook to avoid further costs. 💸 💰 "
],
"metadata": {
"id": "2giQFUUCttsK"
}
},
{
"cell_type": "code",
"source": [
"# delete Cloud SQL instance\n",
"!gcloud sql instances delete {instance_name}"
],
"metadata": {
"id": "a9IemuS-uJad"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Sr-HY6EM8Sk"
},
"source": [
"## ✍ **Appendix**\n",
"Additional information provided for connecting to a Cloud SQL instance using private IP connections.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yjAPpIDdRfu2"
},
"source": [
"### 🔒 **Using Private IP Connections**\n",
"By default the connector connects to the Cloud SQL instance database using a Public IP address.\n",
"\n",
"**Private IP** connections are also supported by the connector and can be easily enabled through the `ip_type` parameter in the connector's `connect` method.\n",
"\n",
"> ⚠️ To connect via Private IP, the Cloud SQL instance being connected to must have a Private IP address configured within a VPC Network. ([How to Configure Private IP](https://cloud.google.com/sql/docs/postgres/configure-private-ip))\n",
"\n",
"> 🚫 The below cell is a working sample but will not work within this notebook as the notebook is not within your VPC Network! The cell should be copied into an environment (Cloud Run, Cloud Functions, App Engine etc.) that has access to the VPC Network.\n",
"\n",
" > [Connecting Cloud Run to a VPC Network](https://cloud.google.com/run/docs/configuring/connecting-vpc)\n",
"\n",
"Let's update our `getconn` function to connect to our Cloud SQL instance with Private IP."
]
},
{
"cell_type": "code",
"metadata": {
"id": "-ztQIV-wUVZP"
},
"source": [
"from google.cloud.sql.connector import Connector, IPTypes\n",
"import sqlalchemy\n",
"\n",
"# initialize connector\n",
"connector = Connector()\n",
"\n",
"# getconn now set to private IP\n",
"def getconn():\n",
" conn = connector.connect(\n",
" INSTANCE_CONNECTION_NAME, # <PROJECT-ID>:<REGION>:<INSTANCE-NAME>\n",
" \"pg8000\",\n",
" user=DB_USER,\n",
" password=DB_PASS,\n",
" db=DB_NAME,\n",
" ip_type=IPTypes.PRIVATE\n",
" )\n",
" return conn\n",
"\n",
"# create connection pool\n",
"pool = sqlalchemy.create_engine(\n",
" \"postgresql+pg8000://\",\n",
" creator=getconn,\n",
")\n",
"\n",
"# connect to connection pool\n",
"with pool.connect() as db_conn:\n",
" # query database and fetch results\n",
" results = db_conn.execute(\"SELECT * FROM ratings\").fetchall()\n",
"\n",
" # show results\n",
" for row in results:\n",
" print(row)\n",
"\n",
"# cleanup connector\n",
"connector.close()"
],
"execution_count": null,
"outputs": []
}
]
} |
|
quantopian/research_public | notebooks/data/psychsignal.stocktwits/notebook.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# PsychSignal: StockTwits Trader Mood (All Fields)\n",
"\n",
"In this notebook, we'll take a look at PsychSignal's *StockTwits Trader Mood (All Fields)* dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 2009 through the current day, and documents the mood of traders based on their messages.\n",
"\n",
"## Notebook Contents\n",
"\n",
"There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
"\n",
"- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
"- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
"\n",
"### Free samples and limits\n",
"One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
"\n",
"There is a *free* version of this dataset as well as a paid one. The free sample includes data until 2 months prior to the current date.\n",
"\n",
"To access the most up-to-date values for this data set for trading a live algorithm (as with other partner sets), you need to purchase acess to the full set.\n",
"\n",
"With preamble in place, let's get started:\n",
"\n",
"<a id='interactive'></a>\n",
"#Interactive Overview\n",
"### Accessing the data with Blaze and Interactive on Research\n",
"Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
"\n",
"Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
"\n",
"It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
"\n",
"Helpful links:\n",
"* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
"* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
"* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
"\n",
"Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
"> `from odo import odo` \n",
"> `odo(expr, pandas.DataFrame)`\n",
"\n",
"\n",
"###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import the free sample of the dataset\n",
"from quantopian.interactive.data.psychsignal import stocktwits_free as dataset\n",
"\n",
"# or if you want to import the full dataset, use:\n",
"# from quantopian.interactive.data.psychsignal import stocktwits\n",
"\n",
"# import data operations\n",
"from odo import odo\n",
"# import other libraries we will use\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dshape(\"\"\"var * {\n",
" source: ?string,\n",
" symbol: string,\n",
" bullish_intensity: float64,\n",
" bearish_intensity: float64,\n",
" bull_minus_bear: float64,\n",
" bull_scored_messages: float64,\n",
" bear_scored_messages: float64,\n",
" bull_bear_msg_ratio: float64,\n",
" total_scanned_messages: float64,\n",
" sid: int64,\n",
" asof_date: datetime,\n",
" timestamp: datetime\n",
" }\"\"\")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's use blaze to understand the data a bit using Blaze dshape()\n",
"dataset.dshape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"2993191"
],
"text/plain": [
"2993191"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# And how many rows are there?\n",
"# N.B. we're using a Blaze function to do this, not len()\n",
"dataset.count()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>source</th>\n",
" <th>symbol</th>\n",
" <th>bullish_intensity</th>\n",
" <th>bearish_intensity</th>\n",
" <th>bull_minus_bear</th>\n",
" <th>bull_scored_messages</th>\n",
" <th>bear_scored_messages</th>\n",
" <th>bull_bear_msg_ratio</th>\n",
" <th>total_scanned_messages</th>\n",
" <th>sid</th>\n",
" <th>asof_date</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>stocktwits</td>\n",
" <td>AA</td>\n",
" <td>1.19</td>\n",
" <td>0.0</td>\n",
" <td>1.19</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2009-08-24 04:00:00</td>\n",
" <td>2009-08-25 04:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>stocktwits</td>\n",
" <td>AA</td>\n",
" <td>1.33</td>\n",
" <td>0.0</td>\n",
" <td>1.33</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2009-09-03 04:00:00</td>\n",
" <td>2009-09-04 04:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>stocktwits</td>\n",
" <td>AA</td>\n",
" <td>2.50</td>\n",
" <td>2.3</td>\n",
" <td>0.20</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2009-09-10 04:00:00</td>\n",
" <td>2009-09-11 04:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
" source symbol bullish_intensity bearish_intensity bull_minus_bear \\\n",
"0 stocktwits AA 1.19 0.0 1.19 \n",
"1 stocktwits AA 1.33 0.0 1.33 \n",
"2 stocktwits AA 2.50 2.3 0.20 \n",
"\n",
" bull_scored_messages bear_scored_messages bull_bear_msg_ratio \\\n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"2 1 1 1 \n",
"\n",
" total_scanned_messages sid asof_date timestamp \n",
"0 2 2 2009-08-24 04:00:00 2009-08-25 04:00:00 \n",
"1 2 2 2009-09-03 04:00:00 2009-09-04 04:00:00 \n",
"2 2 2 2009-09-10 04:00:00 2009-09-11 04:00:00 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's see what the data looks like. We'll grab the first three rows.\n",
"dataset[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two versions of each data set from PsychSignal. A simple version with fewer fields and full version with more fields. This is an basic data set with fewer fields.\n",
"\n",
"Let's go over the columns:\n",
"- **asof_date**: The date to which this data applies.\n",
"- **symbol**: stock ticker symbol of the affected company.\n",
"- **source**: the same value for all records in this data set\n",
"- **bull_scored_messages**: total count of bullish sentiment messages scored by PsychSignal's algorithm\n",
"- **bear_scored_messages**: total count of bearish sentiment messages scored by PsychSignal's algorithm\n",
"- **bullish_intensity**: score for each message's language for the stength of the bullishness present in the messages on a 0-4 scale. 0 indicates no bullish sentiment measured, 4 indicates strongest bullish sentiment measured. 4 is rare\n",
"- **bearish_intensity**: score for each message's language for the stength of the bearish present in the messages on a 0-4 scale. 0 indicates no bearish sentiment measured, 4 indicates strongest bearish sentiment measured. 4 is rare\n",
"- **total_scanned_messages**: number of messages coming through PsuchSignal's feeds and attributable to a symbol regardless of whether the PsychSignal sentiment engine can score them for bullish or bearish intensity- **timestamp**: this is our timestamp on when we registered the data.\n",
"- **bull_minus_bear**: subtracts the bearish intesity from the bullish intensity [BULL - BEAR] to rpovide an immediate net score.\n",
"- **bull_bear_msg_ratio**: the ratio between bull scored messages and bear scored messages.\n",
"- **sid**: the equity's unique identifier. Use this instead of the symbol.\n",
"\n",
"We've done much of the data processing for you. Fields like `timestamp` and `sid` are standardized across all our Store Datasets, so the datasets are easy to combine. We have standardized the `sid` across all our equity databases.\n",
"\n",
"We can select columns and rows with ease. Below, we'll fetch all rows for Apple (sid 24) and explore the scores a bit with a chart."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb5059cee90>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0cAAAH6CAYAAADMcTcBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb5059d3c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Filtering for AAPL\n",
"aapl = dataset[dataset.sid == 24]\n",
"aapl_df = odo(aapl.sort('asof_date'), pd.DataFrame)\n",
"plt.plot(aapl_df.asof_date, aapl_df.bull_scored_messages, marker='.', linestyle='None', color='r')\n",
"plt.plot(aapl_df.asof_date, pd.rolling_mean(aapl_df.bull_scored_messages, 30))\n",
"plt.xlabel(\"As Of Date (asof_date)\")\n",
"plt.ylabel(\"Count of Bull Messages\")\n",
"plt.title(\"Count of Bullish Messages for AAPL\")\n",
"plt.legend([\"Bull Messages - Single Day\", \"30 Day Rolling Average\"], loc=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='pipeline'></a>\n",
"\n",
"#Pipeline Overview\n",
"\n",
"### Accessing the data in your algorithms & research\n",
"The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this data, you can add this data to your pipeline as follows:\n",
"\n",
"Import the data set here\n",
"> `from quantopian.pipeline.data.psychsignal import (`\n",
"> `stocktwits_free `\n",
"> `)`\n",
"\n",
"Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
"> `pipe.add(stocktwits_free.total_scanned_messages.latest, 'total_scanned_messages')`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import necessary Pipeline modules\n",
"from quantopian.pipeline import Pipeline\n",
"from quantopian.research import run_pipeline\n",
"from quantopian.pipeline.factors import AverageDollarVolume"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# For use in your algorithms\n",
"# Using the full paid dataset in your pipeline algo\n",
"# from quantopian.pipeline.data.psychsignal import stocktwits\n",
"\n",
"# Using the free sample in your pipeline algo\n",
"from quantopian.pipeline.data.psychsignal import stocktwits_free "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
"\n",
"You'll find the dataset, the available fields, and the datatypes for each of those fields."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here are the list of available fields per dataset:\n",
"---------------------------------------------------\n",
"\n",
"Dataset: stocktwits_free\n",
"\n",
"Fields:\n",
"bull_minus_bear - float64\n",
"bullish_intensity - float64\n",
"bull_bear_msg_ratio - float64\n",
"bearish_intensity - float64\n",
"total_scanned_messages - float64\n",
"bull_scored_messages - float64\n",
"bear_scored_messages - float64\n",
"\n",
"\n",
"---------------------------------------------------\n",
"\n"
]
}
],
"source": [
"print \"Here are the list of available fields per dataset:\"\n",
"print \"---------------------------------------------------\\n\"\n",
"\n",
"def _print_fields(dataset):\n",
" print \"Dataset: %s\\n\" % dataset.__name__\n",
" print \"Fields:\"\n",
" for field in list(dataset.columns):\n",
" print \"%s - %s\" % (field.name, field.dtype)\n",
" print \"\\n\"\n",
"\n",
"for data in (stocktwits_free ,):\n",
" _print_fields(data)\n",
"\n",
"\n",
"print \"---------------------------------------------------\\n\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
"\n",
"\n",
"This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
"https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's see what this data looks like when we run it through Pipeline\n",
"# This is constructed the same way as you would in the backtester. For more information\n",
"# on using Pipeline in Research view this thread:\n",
"# https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters\n",
"pipe = Pipeline()\n",
" \n",
"pipe.add(stocktwits_free.total_scanned_messages.latest,\n",
" 'total_scanned_messages')\n",
"pipe.add(stocktwits_free.bear_scored_messages .latest,\n",
" 'bear_scored_messages ')\n",
"pipe.add(stocktwits_free.bull_scored_messages .latest,\n",
" 'bull_scored_messages ')\n",
"pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,\n",
" 'bull_bear_msg_ratio ')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Setting some basic liquidity strings (just for good habit)\n",
"dollar_volume = AverageDollarVolume(window_length=20)\n",
"top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
"\n",
"pipe.set_screen(top_1000_most_liquid &\n",
" (stocktwits_free.total_scanned_messages.latest>20))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAACLwAAAHDCAYAAAAkiZFOAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
"pipe.show_graph(format='png')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>bear_scored_messages</th>\n",
" <th>bull_bear_msg_ratio</th>\n",
" <th>bull_scored_messages</th>\n",
" <th>total_scanned_messages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-11-01 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-04 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-05 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-06 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-07 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-08 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-11 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-12 00:00:00+00:00</th>\n",
" <th>Equity(21 [AAME])</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bear_scored_messages \\\n",
"2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-04 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-05 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-06 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-07 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-08 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-11 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-12 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"\n",
" bull_bear_msg_ratio \\\n",
"2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-04 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-05 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-06 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-07 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-08 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-11 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"2013-11-12 00:00:00+00:00 Equity(21 [AAME]) 0 \n",
"\n",
" bull_scored_messages \\\n",
"2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-04 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-05 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-06 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-07 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-08 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-11 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"2013-11-12 00:00:00+00:00 Equity(21 [AAME]) 6 \n",
"\n",
" total_scanned_messages \n",
"2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-04 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-05 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-06 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-07 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-08 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-11 00:00:00+00:00 Equity(21 [AAME]) 30 \n",
"2013-11-12 00:00:00+00:00 Equity(21 [AAME]) 30 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# run_pipeline will show the output of your pipeline\n",
"pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
"pipe_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Taking what we've seen from above, let's see how we'd move that into the backtester."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This section is only importable in the backtester\n",
"from quantopian.algorithm import attach_pipeline, pipeline_output\n",
"\n",
"# General pipeline imports\n",
"from quantopian.pipeline import Pipeline\n",
"from quantopian.pipeline.factors import AverageDollarVolume\n",
"\n",
"# Import the datasets available\n",
"# For use in your algorithms\n",
"# Using the full paid dataset in your pipeline algo\n",
"# from quantopian.pipeline.data.psychsignal import stocktwits\n",
"\n",
"# Using the free sample in your pipeline algo\n",
"from quantopian.pipeline.data.psychsignal import stocktwits_free\n",
"\n",
"def make_pipeline():\n",
" # Create our pipeline\n",
" pipe = Pipeline()\n",
" \n",
" # Screen out penny stocks and low liquidity securities.\n",
" dollar_volume = AverageDollarVolume(window_length=20)\n",
" is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
" \n",
" # Create the mask that we will use for our percentile methods.\n",
" base_universe = (is_liquid)\n",
"\n",
" # Add pipeline factors\n",
" pipe.add(stocktwits_free.total_scanned_messages.latest,\n",
" 'total_scanned_messages')\n",
" pipe.add(stocktwits_free.bear_scored_messages .latest,\n",
" 'bear_scored_messages ')\n",
" pipe.add(stocktwits_free.bull_scored_messages .latest,\n",
" 'bull_scored_messages ')\n",
" pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,\n",
" 'bull_bear_msg_ratio ')\n",
"\n",
" # Set our pipeline screens\n",
" pipe.set_screen(is_liquid)\n",
" return pipe\n",
"\n",
"def initialize(context):\n",
" attach_pipeline(make_pipeline(), \"pipeline\")\n",
" \n",
"def before_trading_start(context, data):\n",
" results = pipeline_output('pipeline')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |
|
huizhuzhao/jupyter_notebook | jupyter_cntk/examples/fitting_math_function.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### mlp models for fitting math functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from cntk.layers import Dense\n",
"from cntk.models import Sequential\n",
"import cntk.ops as C\n",
"from cntk.ops import element_times, constant\n",
"from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs, INFINITELY_REPEAT\n",
"from cntk.learner import sgd\n",
"from cntk import Trainer\n",
"\n",
"import sys\n",
"from getpass import getuser\n",
"sys.path.append('/home/'+getuser()+'/git_test')\n",
"from Teemo.algorithm.utils import matrixops\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## generate sin function data\n",
"def sin(n_trn=1000, n_valid=100):\n",
" noise_r = 0.0001\n",
" rng = np.random.RandomState()\n",
" trn_x = rng.uniform(-1., 1., size=(n_trn, 1)).astype(np.float32)\n",
" trn_y = np.sin(trn_x * np.pi)\n",
" valid_x = rng.uniform(-1., 1., size=(n_valid, 1)).astype(np.float32)\n",
" valid_y = np.sin(valid_x * np.pi) \n",
" return trn_x, trn_y, valid_x, valid_y\n",
"\n",
"## generate H step function data\n",
"def Hstep(n_trn=1000, n_valid=100):\n",
" rng = np.random.RandomState()\n",
" trn_x = rng.uniform(-1., 1., size=(n_trn, 1)).astype(np.float32)\n",
" zero = np.zeros_like(trn_x)\n",
" one = np.ones_like(trn_x)\n",
" trn_y = np.where(trn_x > zero, one, zero)\n",
"\n",
" valid_x = rng.uniform(-1., 1., size=(n_valid, 1)).astype(np.float32)\n",
" zero = np.zeros_like(valid_x)\n",
" one = np.ones_like(valid_x)\n",
" valid_y = np.where(valid_x > zero, one, zero)\n",
"\n",
" return trn_x, trn_y, valid_x, valid_y\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAADJCAYAAACQchA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f69d236cf60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trn_x, trn_y, valid_x, valid_y = Hstep()\n",
"plt.figure()\n",
"plt.subplot(211)\n",
"plt.scatter(trn_x[:, 0], trn_y[:, 0])\n",
"plt.title('Hstep')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"input_dim = 1\n",
"output_dim = 1\n",
"\n",
"input = C.input_variable(input_dim, np.float32)\n",
"label = C.input_variable(output_dim, np.float32)\n",
"mlp = Sequential([Dense(3, activation=C.tanh),\n",
" Dense(output_dim, activation=C.sigmoid)])(input)\n",
"\n",
"loss = C.squared_error(mlp, label)\n",
"error = C.squared_error(mlp, label)\n",
"trainer = Trainer(mlp, loss, error, [sgd(mlp.parameters, lr=0.005)])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0, loss: 0.07851250648498535\n",
"epoch: 30, loss: 0.018948462009429932\n",
"epoch: 60, loss: 0.013278228044509888\n",
"epoch: 90, loss: 0.010697587728500366\n",
"epoch: 120, loss: 0.009068949222564697\n",
"epoch: 150, loss: 0.007928224802017212\n",
"epoch: 180, loss: 0.007168426513671875\n",
"epoch: 210, loss: 0.006580761671066284\n",
"epoch: 240, loss: 0.0060875904560089115\n",
"epoch: 270, loss: 0.005619064569473266\n"
]
}
],
"source": [
"epoches = 300\n",
"minibatch_size = 40\n",
"freq_epoch = epoches/10\n",
"loss_list = []\n",
"error_list = []\n",
"for ii in range(epoches):\n",
" for mb_x, mb_y in matrixops.iterate_minibatches(minibatch_size, trn_x, trn_y, shuffle=True):\n",
" trainer.train_minibatch({input: mb_x, label: mb_y})\n",
" valid_loss = trainer.test_minibatch({input: valid_x, label: valid_y})\n",
" loss_list.append(valid_loss)\n",
" if ii % freq_epoch == 0:\n",
" print ('epoch: {0}, loss: {1}'.format(ii, valid_loss))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 1)\n"
]
}
],
"source": [
"y_pred = mlp.eval({input: valid_x})[0]\n",
"print (y_pred.shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAFkCAYAAADhSHsMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f69d23929e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.subplot(211)\n",
"plt.plot(loss_list)\n",
"plt.ylabel('loss')\n",
"\n",
"plt.subplot(212)\n",
"plt.scatter(valid_x[:, 0], valid_y[:, 0], label='true', c='b')\n",
"plt.scatter(valid_x[:, 0], y_pred[:, 0], label='pred', c='r')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mlp = Sequential([Dense(3, activation=C.tanh),\n",
" Dense(output_dim, activation=C.sigmoid)])(input)\n",
"\n",
"loss = C.squared_error(mlp, label)\n",
"error = C.squared_error(mlp, label)\n",
"trainer = Trainer(mlp, loss, error, [sgd(mlp.parameters, lr=0.005)])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished Epoch [1]: [training] loss = 0.002058 * 1000\n",
"Finished Epoch [2]: [training] loss = 0.001934 * 1000\n",
"Finished Epoch [3]: [training] loss = 0.001907 * 1000\n"
]
}
],
"source": [
"from cntk.utils import ProgressPrinter\n",
"from cntk.utils import get_train_loss\n",
"progress_printer = ProgressPrinter(tag='training')\n",
"\n",
"epoches = 3\n",
"minibatch_size = 500\n",
"freq_epoch = epoches/10\n",
"loss_list = []\n",
"error_list = []\n",
"for ii in range(epoches):\n",
" for mb_x, mb_y in matrixops.iterate_minibatches(minibatch_size, trn_x, trn_y, shuffle=True):\n",
" trainer.train_minibatch({input: mb_x, label: mb_y})\n",
" loss = get_train_loss(trainer)\n",
" #print (trainer.previous_minibatch_loss_average, \\\n",
" # trainer.previous_minibatch_sample_count, \\\n",
" # trainer.previous_minibatch_evaluation_average)\n",
" \n",
" progress_printer.update_with_trainer(trainer)\n",
" progress_printer.epoch_summary()\n",
"\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:cntk-py34]",
"language": "python",
"name": "conda-env-cntk-py34-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
|
|
|
| {
"metadata": {
"name": "",
"signature": "sha256:05cb4a39cc6cfac436e8ed1e980707a8bb57628b40fe799479236c1d08980a19"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Greek General Elections 2015"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a simple data analysis task, I want to quantify the amount of collective attentiond devoted to each candidate, and want to see if there are substantial differences when we look at global consumption patterns vs local (i.e. Greek) ones. Finally, I want to see to which degree attention correlates to the electoral performance, as measured by the popular vote.\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"titles = [\n",
"(u'Alexis_Tsipras', u'\u0391\u03bb\u03ad\u03be\u03b7\u03c2_\u03a4\u03c3\u03af\u03c0\u03c1\u03b1\u03c2'),\n",
"(u'Antonis_Samaras', u'\u0391\u03bd\u03c4\u03ce\u03bd\u03b7\u03c2_\u03a3\u03b1\u03bc\u03b1\u03c1\u03ac\u03c2'),\n",
"(u'Dimitris_Koutsoumpas', u'\u0394\u03b7\u03bc\u03ae\u03c4\u03c1\u03b7\u03c2_\u039a\u03bf\u03c5\u03c4\u03c3\u03bf\u03cd\u03bc\u03c0\u03b1\u03c2'),\n",
"(u'Evangelos_Venizelos', u'\u0395\u03c5\u03ac\u03b3\u03b3\u03b5\u03bb\u03bf\u03c2_\u0392\u03b5\u03bd\u03b9\u03b6\u03ad\u03bb\u03bf\u03c2'),\n",
"(u'Nikolaos_Michaloliakos', u'\u039d\u03af\u03ba\u03bf\u03c2_\u039c\u03b9\u03c7\u03b1\u03bb\u03bf\u03bb\u03b9\u03ac\u03ba\u03bf\u03c2'),\n",
"(u'Panos_Kammenos', u'\u03a0\u03ac\u03bd\u03bf\u03c2_\u039a\u03b1\u03bc\u03bc\u03ad\u03bd\u03bf\u03c2'),\n",
"(u'Stavros_Theodorakis', u'\u03a3\u03c4\u03b1\u03cd\u03c1\u03bf\u03c2_\u0398\u03b5\u03bf\u03b4\u03c9\u03c1\u03ac\u03ba\u03b7\u03c2'),\n",
"(u'Greek_legislative_election,_2015', u'\u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ad\u03c2_\u03b2\u03bf\u03c5\u03bb\u03b5\u03c5\u03c4\u03b9\u03ba\u03ad\u03c2_\u03b5\u03ba\u03bb\u03bf\u03b3\u03ad\u03c2_2015')\n",
"]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 125
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Traffic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I downloaded data about the [daily traffic to the Wikipedia articles](http://stats.grok.se) of the major competitors from both the English and Hellenic versions of Wikipedia. To quantify the general level of attention toward the elections as a whole, I also downloaded data about traffic volume to the articles about the election themselves. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import json\n",
"import pandas as pd\n",
"\n",
"en_titles, el_titles = zip(*titles)\n",
"\n",
"l = {}\n",
"for line in open('data/traffic.json'):\n",
" jsobj = json.loads(line)\n",
" df = pd.DataFrame(jsobj)\n",
" l[df['title'][0]] = df['daily_views']\n",
"\n",
"fulldf = pd.DataFrame(l)\n",
"\n",
"# politicians data frames\n",
"endf = fulldf[list(en_titles)]\n",
"endf.set_index(endf.index.to_datetime(), inplace=True)\n",
"eldf = fulldf[list(el_titles)]\n",
"eldf.set_index(eldf.index.to_datetime(), inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 151
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Votes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I also downloaded the data about the popular vote from the English Wikipedia [article](http://en.wikipedia.org/w/index.php?title=Greek_legislative_election,_2015&oldid=645290091) on the elections."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"votes = pd.Series(json.load(open('data/votes.json')))\n",
"print votes"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Alexis_Tsipras 2246064\n",
"Antonis_Samaras 1718815\n",
"Dimitris_Koutsoumpas 338138\n",
"Evangelos_Venizelos 289482\n",
"Nikolas_Michaloliakos 388447\n",
"Panos_Kammenos 293371\n",
"Stavros_Theodorakis 373868\n",
"dtype: int64\n"
]
}
],
"prompt_number": 127
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"The raw data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The raw time series of traffic to the various candidates are shown below. We can see that, in absolute terms, the amount of attention is comparable across the two language editions of Wikipedia. There are some missing data for the English article on Stavros Theodorakis between January the 26th and 28th. \n",
"\n",
"To put these values in context, I also show the traffic to the articles about the election themselves, which can be taken as the general level of attention toward the election. It is interesting to note that in the domestic case the article about Alexis Tsipras receives more traffic than that of the elections. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axs = subplots(ncols=2, sharex=True, sharey=True, figsize=(6, 4))\n",
"\n",
"ax = axs[0]\n",
"endf[list(en_titles[:-1])].plot(ax=ax)\n",
"endf[[en_titles[-1]]].plot(ax=ax, lw=3, c='gray', alpha=.4)\n",
"ax.grid('off')\n",
"ax.legend(fontsize='xx-small', loc='upper left', frameon=False)\n",
"\n",
"ax.set_yscale('log')\n",
"ax.set_ylabel('Daily traffic')\n",
"\n",
"ax = axs[1]\n",
"eldf[list(el_titles[:-1])].plot(ax=ax)\n",
"eldf[[el_titles[-1]]].plot(ax=ax, lw=3, c='gray', alpha=.4)\n",
"ax.grid('off')\n",
"ax.legend(fontsize='xx-small', loc='upper left', frameon=False)\n",
"ax.set_yscale('log')\n",
"\n",
"ylim(10, 1e7)\n",
"\n",
"tight_layout(w_pad=.1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAHRCAYAAACIOGpaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmsaGl4iIiIg0jQ0vEREREWna/wAC5yCaDjLVcgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f11ebbf7110>"
]
}
],
"prompt_number": 166
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The traffic volume to the english and to the hellenic articles of the election can be used as a general measure of interest toward the election. International and domestic traffic seem to correlate fairly well."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loglog(entot, eltot, 'o--')\n",
"xlabel(u'Daily traffic to\\n\u201c{}\u201d'.format(elections[0]))\n",
"ylabel(u'Daily traffic to\\n\u201c{}\u201d'.format(elections[1]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 114,
"text": [
"<matplotlib.text.Text at 0x7f11ec921650>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAHsCAYAAAD4sJT/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuG6PJ60cx3Ecx3Fct/d/YGzaWJ6GagsAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f11ebd1f550>"
]
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import datetime\n",
"EPOCH = datetime.datetime.fromtimestamp(0)\n",
"election_day = datetime.datetime(2015, 1, 26)\n",
"election_day_ts = election_day - EPOCH\n",
"print election_day_ts.days"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"16461\n"
]
}
],
"prompt_number": 202
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axs = subplots(ncols=2, sharex=True, sharey=True, figsize=(6, 4))\n",
"\n",
"ax = axs[0]\n",
"(endf[list(en_titles[:-1])] / endf[en_titles[-1]]).plot(ax=ax)\n",
"ax.grid('off')\n",
"ax.legend(fontsize='xx-small', loc='upper left', frameon=False)\n",
"\n",
"ax.set_ylabel('Relative Daily traffic to Elections article')\n",
"\n",
"ax = axs[1]\n",
"(eldf[list(el_titles[:-1])] / eldf[el_titles[-1]]).plot(ax=ax)\n",
"ax.grid('off')\n",
"ax.legend(fontsize='xx-small', loc='upper left', frameon=False)\n",
"\n",
"tight_layout(w_pad=.1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAHSCAYAAADhbnPKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADA5xF6AQAA4PP+BtctU5rjR0chAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f11ead8a2d0>"
]
}
],
"prompt_number": 208
}
],
"metadata": {}
}
]
} |
|
|
| notebooks/test-institute-1/cmip6/models/sandbox-3/ocnbgchem.ipynb |
|
| {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocnbgchem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: TEST-INSTITUTE-1 \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic**: Ocnbgchem \n",
"**Sub-Topics**: Tracers. \n",
"**Properties**: 65 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:43"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'ocnbgchem')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport](#2.-Key-Properties--->-Time-Stepping-Framework--->-Passive-Tracers-Transport) \n",
"[3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks](#3.-Key-Properties--->-Time-Stepping-Framework--->-Biology-Sources-Sinks) \n",
"[4. Key Properties --> Transport Scheme](#4.-Key-Properties--->-Transport-Scheme) \n",
"[5. Key Properties --> Boundary Forcing](#5.-Key-Properties--->-Boundary-Forcing) \n",
"[6. Key Properties --> Gas Exchange](#6.-Key-Properties--->-Gas-Exchange) \n",
"[7. Key Properties --> Carbon Chemistry](#7.-Key-Properties--->-Carbon-Chemistry) \n",
"[8. Tracers](#8.-Tracers) \n",
"[9. Tracers --> Ecosystem](#9.-Tracers--->-Ecosystem) \n",
"[10. Tracers --> Ecosystem --> Phytoplankton](#10.-Tracers--->-Ecosystem--->-Phytoplankton) \n",
"[11. Tracers --> Ecosystem --> Zooplankton](#11.-Tracers--->-Ecosystem--->-Zooplankton) \n",
"[12. Tracers --> Disolved Organic Matter](#12.-Tracers--->-Disolved-Organic-Matter) \n",
"[13. Tracers --> Particules](#13.-Tracers--->-Particules) \n",
"[14. Tracers --> Dic Alkalinity](#14.-Tracers--->-Dic-Alkalinity) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean Biogeochemistry key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean biogeochemistry model code (PISCES 2.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Geochemical\" \n",
"# \"NPZD\" \n",
"# \"PFT\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Elemental Stoichiometry\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe elemental stoichiometry (fixed, variable, mix of the two)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Fixed\" \n",
"# \"Variable\" \n",
"# \"Mix of both\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Elemental Stoichiometry Details\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe which elements have fixed/variable stoichiometry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all prognostic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Diagnostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all diagnotic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.8. Damping\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport \n",
"*Time stepping method for passive tracers transport in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for passive tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for passive tracers (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks \n",
"*Time stepping framework for biology sources and sinks in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for biology sources and sinks*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for biology sources and sinks (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Transport Scheme \n",
"*Transport scheme in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transport scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Transport scheme used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Use that of ocean model\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Use Different Scheme\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Decribe transport scheme if different than that of ocean model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Boundary Forcing \n",
"*Properties of biogeochemistry boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Atmospheric Deposition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how atmospheric deposition is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Atmospheric Chemistry model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. River Input\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how river input is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Land Surface model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Sediments From Boundary Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Sediments From Explicit Model\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from explicit sediment model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Gas Exchange \n",
"*Properties of gas exchange in ocean biogeochemistry *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe CO2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. O2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is O2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. O2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe O2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. DMS Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is DMS gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.6. DMS Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify DMS gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.7. N2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.8. N2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.9. N2O Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2O gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.10. N2O Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2O gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.11. CFC11 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC11 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.12. CFC11 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC11 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.13. CFC12 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC12 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.14. CFC12 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC12 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.15. SF6 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is SF6 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.16. SF6 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify SF6 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.17. 13CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 13CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.18. 13CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 13CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.19. 14CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 14CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.20. 14CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 14CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.21. Other Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify any other gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Key Properties --> Carbon Chemistry \n",
"*Properties of carbon chemistry biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how carbon chemistry is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other protocol\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. PH Scale\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, describe pH scale.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sea water\" \n",
"# \"Free\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Constants If Not OMIP\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, list carbon chemistry constants.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Tracers \n",
"*Ocean biogeochemistry tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of tracers in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Sulfur Cycle Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sulfur cycle modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Nutrients Present\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List nutrient species present in ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrogen (N)\" \n",
"# \"Phosphorous (P)\" \n",
"# \"Silicium (S)\" \n",
"# \"Iron (Fe)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Nitrous Species If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous species.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrates (NO3)\" \n",
"# \"Amonium (NH4)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Nitrous Processes If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dentrification\" \n",
"# \"N fixation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Tracers --> Ecosystem \n",
"*Ecosystem properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Upper Trophic Levels Definition\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Definition of upper trophic level (e.g. based on size) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Upper Trophic Levels Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Define how upper trophic level are treated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Tracers --> Ecosystem --> Phytoplankton \n",
"*Phytoplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of phytoplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"PFT including size based (specify both below)\" \n",
"# \"Size based only (specify below)\" \n",
"# \"PFT only (specify below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Pft\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton functional types (PFT) (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diatoms\" \n",
"# \"Nfixers\" \n",
"# \"Calcifiers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microphytoplankton\" \n",
"# \"Nanophytoplankton\" \n",
"# \"Picophytoplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Tracers --> Ecosystem --> Zooplankton \n",
"*Zooplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of zooplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"Size based (specify below)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Zooplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microzooplankton\" \n",
"# \"Mesozooplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Tracers --> Disolved Organic Matter \n",
"*Disolved organic matter properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Bacteria Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there bacteria representation ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Lability\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe treatment of lability in dissolved organic matter*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Labile\" \n",
"# \"Semi-labile\" \n",
"# \"Refractory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Tracers --> Particules \n",
"*Particulate carbon properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is particulate carbon represented in ocean biogeochemistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diagnostic\" \n",
"# \"Diagnostic (Martin profile)\" \n",
"# \"Diagnostic (Balast)\" \n",
"# \"Prognostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Types If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, type(s) of particulate matter taken into account*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"POC\" \n",
"# \"PIC (calcite)\" \n",
"# \"PIC (aragonite\" \n",
"# \"BSi\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Size If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No size spectrum used\" \n",
"# \"Full size spectrum\" \n",
"# \"Discrete size classes (specify which below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Size If Discrete\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If prognostic and discrete size, describe which size classes are used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Sinking Speed If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, method for calculation of sinking speed of particules*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Function of particule size\" \n",
"# \"Function of particule type (balast)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Tracers --> Dic Alkalinity \n",
"*DIC and alkalinity properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Carbon Isotopes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which carbon isotopes are modelled (C13, C14)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"C13\" \n",
"# \"C14)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Abiotic Carbon\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is abiotic carbon modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Alkalinity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is alkalinity modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Prognostic\" \n",
"# \"Diagnostic)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} |
|
midnighteuler/projecteuler |
|
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A permutation is an ordered arrangement of objects. For example, 3124 is one possible permutation of the digits 1, 2, 3 and 4. If all of the permutations are listed numerically or alphabetically, we call it lexicographic order. The lexicographic permutations of 0, 1 and 2 are:\n",
"\n",
"012 021 102 120 201 210\n",
"\n",
"#### What is the millionth lexicographic permutation of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2, 7, 8, 3, 9, 1, 5, 4, 6, 0)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import itertools\n",
"\n",
"list(sorted(itertools.permutations(range(10))))[1000000-1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3628800"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import math\n",
"math.factorial(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_cwt_sensor_connectivity.ipynb |
|
| {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Compute seed based time-frequency connectivity in sensor space\n\n\nComputes the connectivity between a seed-gradiometer close to the visual cortex\nand all other gradiometers. The connectivity is computed in the time-frequency\ndomain using Morlet wavelets and the debiased Squared Weighted Phase Lag Index\n[1]_ is used as connectivity metric.\n\n.. [1] Vinck et al. \"An improved index of phase-synchronization for electro-\n physiological data in the presence of volume-conduction, noise and\n sample-size bias\" NeuroImage, vol. 55, no. 4, pp. 1548-1565, Apr. 2011.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Author: Martin Luessi <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nimport mne\nfrom mne import io\nfrom mne.connectivity import spectral_connectivity, seed_target_indices\nfrom mne.datasets import sample\nfrom mne.time_frequency import AverageTFR\n\nprint(__doc__)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Add a bad channel\nraw.info['bads'] += ['MEG 2443']\n\n# Pick MEG gradiometers\npicks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True,\n exclude='bads')\n\n# Create epochs for left-visual condition\nevent_id, tmin, tmax = 3, -0.2, 0.5\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),\n preload=True)\n\n# Use 'MEG 2343' as seed\nseed_ch = 'MEG 2343'\npicks_ch_names = [raw.ch_names[i] for i in picks]\n\n# Create seed-target indices for connectivity computation\nseed = picks_ch_names.index(seed_ch)\ntargets = np.arange(len(picks))\nindices = seed_target_indices(seed, targets)\n\n# Define wavelet frequencies and number of cycles\ncwt_frequencies = np.arange(7, 30, 2)\ncwt_n_cycles = cwt_frequencies / 7.\n\n# Run the connectivity analysis using 2 parallel jobs\nsfreq = raw.info['sfreq'] # the sampling frequency\ncon, freqs, times, _, _ = spectral_connectivity(\n epochs, indices=indices,\n method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,\n cwt_frequencies=cwt_frequencies, cwt_n_cycles=cwt_n_cycles, n_jobs=1)\n\n# Mark the seed channel with a value of 1.0, so we can see it in the plot\ncon[np.where(indices[1] == seed)] = 1.0\n\n# Show topography of connectivity from seed\ntitle = 'WPLI2 - Visual - Seed %s' % seed_ch\n\nlayout = mne.find_layout(epochs.info, 'meg') # use full layout\n\ntfr = AverageTFR(epochs.info, con, times, freqs, len(epochs))\ntfr.plot_topo(fig_facecolor='w', font_color='k', border='k')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.13",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} |
|
blab/antibody-response-pulse | bcell-array/code/IgM_IgG_repeated_infection.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antibody Response Pulse\n",
"https://github.com/blab/antibody-response-pulse/\n",
"\n",
"### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination\n",
"### Adaptive immune response for repeated infection"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAFKCAYAAAAg8qHsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109ccda90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''\n",
"author: Alvason Zhenhua Li\n",
"date: 04/09/2015\n",
"'''\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"\n",
"AlvaFontSize = 23\n",
"AlvaFigSize = (9, 6)\n",
"numberingFig = 0\n",
"\n",
"# plotting\n",
"dir_path = '/Users/al/Desktop/GitHub/antibody-response-pulse/bcell-array/figure'\n",
"file_name = 'Virus-Bcell-IgM-IgG'\n",
"figure_name = '-equation'\n",
"file_suffix = '.png'\n",
"save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"\n",
"numberingFig = numberingFig + 1\n",
"plt.figure(numberingFig, figsize=(12, 5))\n",
"plt.axis('off')\n",
"plt.title(r'$ Virus-Bcell-IgM-IgG \\ equations \\ (antibody-response \\ for \\ repeated-infection) $'\n",
" , fontsize = AlvaFontSize)\n",
"plt.text(0, 7.0/8, r'$ \\frac{\\partial V_n(t)}{\\partial t} = \\\n",
" +\\mu_{v} V_{n}(t)(1 - \\frac{V_n(t)}{V_{max}}) - \\phi_{m} M_{n}(t) V_{n}(t) - \\phi_{g} G_{n}(t) V_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 5.0/8, r'$ \\frac{\\partial B_n(t)}{\\partial t} = \\\n",
" +\\mu_{b} + (\\beta_{m} + \\beta_{g}) V_{n}(t) B_{n}(t) - \\mu_{b} B_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 3.0/8,r'$ \\frac{\\partial M_n(t)}{\\partial t} = \\\n",
" +\\xi_{m} B_{n}(t) - \\phi_{m} M_{n}(t) V_{n}(t) - \\mu_{m} M_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 1.0/8,r'$ \\frac{\\partial G_n(t)}{\\partial t} = \\\n",
" +\\xi_{g} B_{n}(t) - \\phi_{g} G_{n}(t) V_{n}(t) - \\mu_{g} G_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.show()\n",
"\n",
"# define the V-M-G partial differential equations\n",
"def dVdt_array(VBMGxt = [], *args):\n",
" eName = np.zeros([totalGPoint_X]) \n",
" eName[0] = eventName\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dV_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dV_dt_array[xn] = +inRateV*V[xn]*(1 - V[xn]/maxV) - killRateVm*M[xn]*V[xn] - killRateVg*G[xn]*V[xn]\n",
" return(dV_dt_array)\n",
"\n",
"def dBdt_array(VBMGxt = [], *args):\n",
" eName = np.zeros([totalGPoint_X]) \n",
" eName[0] = eventName\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dB_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dB_dt_array[xn] = +inRateB*V[xn]*(1 - V[xn]/maxV) + (actRateBm + + eName[xn])*B[xn]*V[xn] - outRateB*B[xn]\n",
" return(dB_dt_array)\n",
"\n",
"def dMdt_array(VBMGxt = [], *args):\n",
" eName = np.zeros([totalGPoint_X])\n",
" eName[0] = eventName\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dM_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dM_dt_array[xn] = +inRateM*B[xn] - consumeRateM*M[xn]*V[xn] - outRateM*M[xn]\n",
" return(dM_dt_array)\n",
"\n",
"def dGdt_array(VBMGxt = [], *args):\n",
" eName = np.zeros([totalGPoint_X]) \n",
" eName[0] = eventName\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dG_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dG_dt_array[xn] = +inRateG*B[xn] - consumeRateG*G[xn]*V[xn] - outRateG*G[xn]\n",
" return(dG_dt_array)\n",
"\n",
"# define RK4 for an array (3, n) of coupled differential equations\n",
"def AlvaRungeKutta4ArrayXT(pde_array, startingOut_Value, minX_In, maxX_In, totalGPoint_X, minT_In, maxT_In, totalGPoint_T):\n",
" global eventName\n",
" # primary size of pde equations\n",
" outWay = pde_array.shape[0]\n",
" # initialize the whole memory-space for output and input\n",
" inWay = 1; # one layer is enough for storing \"x\" and \"t\" (only two list of variable)\n",
" # define the first part of array as output memory-space\n",
" gridOutIn_array = np.zeros([outWay + inWay, totalGPoint_X, totalGPoint_T])\n",
" # loading starting output values\n",
" for i in range(outWay):\n",
" gridOutIn_array[i, :, :] = startingOut_Value[i, :, :]\n",
" # griding input X value \n",
" gridingInput_X = np.linspace(minX_In, maxX_In, num = totalGPoint_X, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gridOutIn_array[-inWay, :, 0] = gridingInput_X[0]\n",
" # step-size (increment of input X)\n",
" dx = gridingInput_X[1]\n",
" # griding input T value \n",
" gridingInput_T = np.linspace(minT_In, maxT_In, num = totalGPoint_T, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gridOutIn_array[-inWay, 0, :] = gridingInput_T[0]\n",
" # step-size (increment of input T)\n",
" dt = gridingInput_T[1]\n",
" # starting\n",
" # initialize the memory-space for local try-step \n",
" dydt1_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt2_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt3_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt4_array = np.zeros([outWay, totalGPoint_X])\n",
" # initialize the memory-space for keeping current value\n",
" currentOut_Value = np.zeros([outWay, totalGPoint_X])\n",
" for tn in range(totalGPoint_T - 1):\n",
" eventName = event_tn_In[0, 1]\n",
" # post period of first infection from virus-1\n",
" if tn > int(totalGPoint_T*(event_tn_In[1, 0]/(maxT_In - minT_In))):\n",
" eventName = event_tn_In[1, 1]\n",
" # setting virus1 = 0 if virus1 < 1\n",
" if gridOutIn_array[0, 0, tn] < 1.0:\n",
" gridOutIn_array[0, 0, tn] = 0.0\n",
" ## 2nd infection\n",
" if tn == int(totalGPoint_T*(1.0/4)):\n",
" gridOutIn_array[0, 0, tn] = 1.0 # set 2nd virus infection \n",
" ### 3rd infection\n",
" if tn == int(totalGPoint_T*(3.0/4)):\n",
" gridOutIn_array[0, 0, tn] = 1.0 # set 2nd virus infection \n",
" # keep initial value at the moment of tn\n",
" currentOut_Value[:, :] = np.copy(gridOutIn_array[:-inWay, :, tn])\n",
" currentIn_T_Value = np.copy(gridOutIn_array[-inWay, 0, tn])\n",
" # first try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt1_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt1_array[:, :]*dt/2 # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # second half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt2_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt2_array[:, :]*dt/2 # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # third half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt3_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt3_array[:, :]*dt # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt # update input\n",
" # fourth try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt4_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" # solid step (update the next output) by accumulate all the try-steps with proper adjustment\n",
" gridOutIn_array[:-inWay, :, tn + 1] = currentOut_Value[:, :] + dt*(dydt1_array[:, :]/6 \n",
" + dydt2_array[:, :]/3 \n",
" + dydt3_array[:, :]/3 \n",
" + dydt4_array[:, :]/6)\n",
" # restore to initial value\n",
" gridOutIn_array[:-inWay, :, tn] = np.copy(currentOut_Value[:, :])\n",
" gridOutIn_array[-inWay, 0, tn] = np.copy(currentIn_T_Value)\n",
" # end of loop\n",
" return (gridOutIn_array[:-inWay, :])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABB8AAAGlCAYAAACyUV4cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKk0+KKWUUkoppZRSqlX9P2ZbAEVSXvv7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b35a0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Experimental lab data from (Quantifying the Early Immune Response and Adaptive Immune) paper\n",
"gT_lab = np.array([0, 5, 10, 20, 25, 30, 60, 80])\n",
"gIgG_lab = np.array([0, 0.5, 4, 8.5, 8.75, 7.5, 5.5, 4])*10**2 \n",
"error_IgG = gIgG_lab**(4.0/5)\n",
"gIgM_lab = np.array([0, 1.0/3, 3, 1.0/3, 1.0/6, 1.0/10, 0, 0])*10**2\n",
"error_IgM = gIgM_lab**(4.0/5)\n",
"bar_width = 4\n",
"\n",
"# setting parameter\n",
"timeUnit = 'day'\n",
"if timeUnit == 'hour':\n",
" hour = float(1); day = float(24); \n",
"elif timeUnit == 'day':\n",
" day = float(1); hour = float(1)/24; \n",
" \n",
"maxV = float(50) # max virus/micro-liter\n",
"inRateV = 0.2/hour # in-rate of virus\n",
"killRateVm = 0.0003/hour # kill-rate of virus by antibody-IgM\n",
"killRateVg = killRateVm # kill-rate of virus by antibody-IgG\n",
"\n",
"inRateB = 0.06/hour # in-rate of B-cell\n",
"outRateB = inRateB/8 # out-rate of B-cell\n",
"actRateBm = killRateVm # activation rate of naive B-cell\n",
"actRateBg = killRateVg # activation rate of memory B-cell\n",
"\n",
"inRateM = 0.16/hour # in-rate of antibody-IgM from naive B-cell\n",
"outRateM = inRateM/1 # out-rate of antibody-IgM from naive B-cell\n",
"consumeRateM = killRateVm # consume-rate of antibody-IgM by cleaning virus\n",
"\n",
"inRateG = inRateM/10 # in-rate of antibody-IgG from memory B-cell\n",
"outRateG = outRateM/250 # out-rate of antibody-IgG from memory B-cell\n",
"consumeRateG = killRateVg # consume-rate of antibody-IgG by cleaning virus\n",
"\n",
"# time boundary and griding condition\n",
"minT = float(0)\n",
"maxT = float(180*4*day)\n",
"totalGPoint_T = int(1*10**4 + 1)\n",
"gridT = np.linspace(minT, maxT, totalGPoint_T)\n",
"spacingT = np.linspace(minT, maxT, num = totalGPoint_T, retstep = True)\n",
"gridT = spacingT[0]\n",
"dt = spacingT[1]\n",
"\n",
"# space boundary and griding condition\n",
"minX = float(0)\n",
"maxX = float(1)\n",
"totalGPoint_X = int(1 + 1)\n",
"gridX = np.linspace(minX, maxX, totalGPoint_X)\n",
"gridingX = np.linspace(minX, maxX, num = totalGPoint_X, retstep = True)\n",
"gridX = gridingX[0]\n",
"dx = gridingX[1]\n",
"gridV_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridB_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridM_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridG_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"# initial output condition\n",
"gridV_array[0, 0] = float(1)\n",
"gridB_array[0, 0] = float(0)\n",
"gridM_array[0, 0] = float(0)\n",
"gridG_array[0, 0] = float(0)\n",
"\n",
"event_tn_In = np.array([[0*day, 0.0002/hour], [14*day, 0.002/hour]])\n",
"\n",
"# Runge Kutta numerical solution\n",
"pde_array = np.array([dVdt_array, dBdt_array, dMdt_array, dGdt_array])\n",
"startingOut_Value = np.array([gridV_array, gridB_array, gridM_array, gridG_array])\n",
"gridOut_array = AlvaRungeKutta4ArrayXT(pde_array, startingOut_Value, minX, maxX, totalGPoint_X, minT, maxT, totalGPoint_T)\n",
"# plotting\n",
"gridV = gridOut_array[0] \n",
"gridB = gridOut_array[1] \n",
"gridM = gridOut_array[2]\n",
"gridG = gridOut_array[3]\n",
"\n",
"figure_name = '-repeated-infection'\n",
"figure_suffix = '.png'\n",
"save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"numberingFig = numberingFig + 1\n",
"ymin = -100\n",
"ymax = 2000\n",
"for i in range(1):\n",
" plt.figure(numberingFig, figsize = AlvaFigSize)\n",
" plt.plot(gridT, gridV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridM[i] + gridG[i], color = 'gray', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'\n",
" , label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))\n",
" plt.bar(gT_lab, gIgG_lab, bar_width, alpha = 0.6, color = 'green', yerr = error_IgG\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ IgG(X31-virus) $')\n",
" plt.bar(gT_lab - bar_width, gIgM_lab, bar_width, alpha = 0.6, color = 'blue', yerr = error_IgM\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ IgM(X31-virus) $')\n",
" plt.grid(True, which = 'both')\n",
" plt.title(r'$ Antibody \\ for \\ repeated-infection $', fontsize = AlvaFontSize)\n",
" plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
" plt.ylabel(r'$ Serum \\ antibody \\ (pg/mL) $', fontsize = AlvaFontSize)\n",
" plt.xticks(fontsize = AlvaFontSize*0.6)\n",
" plt.yticks(fontsize = AlvaFontSize*0.6) \n",
" plt.text(maxT*16.0/10, ymax*8.0/10, r'$ V_{max} = %f $'%(maxV), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*7.0/10, r'$ \\mu_{v} = %f $'%(inRateV), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*6.0/10, r'$ \\phi_{m} = %f $'%(killRateVm), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*5.0/10, r'$ \\phi_{g} = %f $'%(killRateVg), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*4.0/10, r'$ \\mu_{b} = %f $'%(inRateB), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*3.0/10, r'$ \\xi_{m} = %f $'%(inRateM), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*2.0/10, r'$ \\xi_{g} = %f $'%(inRateG), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*1.0/10, r'$ \\mu_{g} = %f $'%(outRateG), fontsize = AlvaFontSize)\n",
" plt.ylim(ymin, ymax)\n",
" plt.xlim(minT, maxT)\n",
" plt.legend(loc = (1, 0), fontsize = AlvaFontSize)\n",
" plt.savefig(save_figure, dpi = 100, bbox_inches='tight')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
|
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import MDAnalysis as mda\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from MDAnalysis import *\n",
"from MDAnalysis.analysis.align import *\n",
"from MDAnalysis.analysis.rms import rmsd\n",
"from MDAnalysis.analysis.rms import RMSF\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "Failed to load from the topology file 41w_ff.psf with parser <class 'MDAnalysis.topology.PSFParser.PSFParser'>.\nError: [Errno 5] Cannot open file or stream in mode='r'.: \"'41w_ff.psf'\"",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-ea579482f2ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMDAnalysis\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMDAnalysis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUniverse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'41w_ff.psf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'50_frame.dcd'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mref\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMDAnalysis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUniverse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPSF\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mDCD\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# reference closed AdK (1AKE) (with the default ref_frame=0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#ref = MDAnalysis.Universe(PSF,CRD) # reference open AdK (4AKE)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/projects/4a420c65-bab9-4479-a810-f7137c0dcd19/.local/lib/python2.7/site-packages/MDAnalysis/core/AtomGroup.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3778\u001b[0m raise IOError(\"Failed to load from the topology file {0}\"\n\u001b[0;32m 3779\u001b[0m \u001b[1;34m\" with parser {1}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3780\u001b[1;33m \"Error: {2}\".format(self.filename, parser, err))\n\u001b[0m\u001b[0;32m 3781\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3782\u001b[0m raise ValueError(\"Failed to construct topology from file {0}\"\n",
"\u001b[1;31mIOError\u001b[0m: Failed to load from the topology file 41w_ff.psf with parser <class 'MDAnalysis.topology.PSFParser.PSFParser'>.\nError: [Errno 5] Cannot open file or stream in mode='r'.: \"'41w_ff.psf'\""
]
}
],
"source": [
"import MDAnalysis\n",
"u = MDAnalysis.Universe('41w_ff.psf','50_frame.dcd')\n",
"ref = MDAnalysis.Universe(PSF,DCD) # reference closed AdK (1AKE) (with the default ref_frame=0)\n",
"#ref = MDAnalysis.Universe(PSF,CRD) # reference open AdK (4AKE)\n",
"\n",
"import MDAnalysis.analysis.rms\n",
"\n",
"R = MDAnalysis.analysis.rms.RMSD(u, ref,\n",
" select=\"backbone\", # superimpose on whole backbone of the whole protein\n",
" groupselections=[\"backbone and (resid 1-29 or resid 60-121 or resid 160-214)\", # CORE\n",
" \"backbone and resid 122-159\", # LID\n",
" \"backbone and resid 30-59\"], # NMP\n",
" filename=\"rmsd_all_CORE_LID_NMP.dat\")\n",
"R.run()\n",
"R.save()\n",
"\n",
"import matplotlib.pyplot as plt\n",
"rmsd = R.rmsd.T # transpose makes it easier for plotting\n",
"time = rmsd[1]\n",
"fig = plt.figure(figsize=(4,4))\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(time, rmsd[2], 'k-', label=\"all\")\n",
"ax.plot(time, rmsd[3], 'k--', label=\"CORE\")\n",
"ax.plot(time, rmsd[4], 'r--', label=\"LID\")\n",
"ax.plot(time, rmsd[5], 'b--', label=\"NMP\")\n",
"ax.legend(loc=\"best\")\n",
"ax.set_xlabel(\"time (ps)\")\n",
"ax.set_ylabel(r\"RMSD ($\\AA$)\")\n",
"fig.savefig(\"rmsd_all_CORE_LID_NMP_ref1AKE.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.92068124672431e-07"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import MDAnalysis\n",
"u = MDAnalysis.Universe('41wl_ff.psf','50_frame.dcd')\n",
"pdb = \"./41wl_ff.pdb\"\n",
"#ref1 = u.trajectory.\n",
"ref = MDAnalysis.Universe('41wl_ff.psf','50_frame.dcd') # reference closed AdK (1AKE) (with the default ref_frame=0)\n",
"#mobile = Universe(PSF,DCD)\n",
"rmsd(u.atoms.CA.positions, ref.atoms.CA.positions)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00000000e+00 7.39290746e-07]\n",
" [ 1.00000000e+00 9.58260036e-01]\n",
" [ 2.00000000e+00 1.09913685e+00]\n",
" [ 3.00000000e+00 1.58017235e+00]\n",
" [ 4.00000000e+00 1.51524851e+00]\n",
" [ 5.00000000e+00 1.25831531e+00]\n",
" [ 6.00000000e+00 1.29666037e+00]\n",
" [ 7.00000000e+00 1.97707413e+00]\n",
" [ 8.00000000e+00 1.48582216e+00]\n",
" [ 9.00000000e+00 1.11138835e+00]\n",
" [ 1.00000000e+01 1.17093276e+00]\n",
" [ 1.10000000e+01 1.38943144e+00]\n",
" [ 1.20000000e+01 1.15222040e+00]\n",
" [ 1.30000000e+01 8.73565132e-01]\n",
" [ 1.40000000e+01 8.13977643e-01]\n",
" [ 1.50000000e+01 1.05710964e+00]\n",
" [ 1.60000000e+01 9.79789427e-01]\n",
" [ 1.70000000e+01 9.99653299e-01]\n",
" [ 1.80000000e+01 1.25344055e+00]\n",
" [ 1.90000000e+01 1.42301562e+00]\n",
" [ 2.00000000e+01 1.56294861e+00]\n",
" [ 2.10000000e+01 1.16561071e+00]\n",
" [ 2.20000000e+01 1.40184954e+00]\n",
" [ 2.30000000e+01 1.42863442e+00]\n",
" [ 2.40000000e+01 1.09930849e+00]\n",
" [ 2.50000000e+01 1.18829050e+00]\n",
" [ 2.60000000e+01 1.37952810e+00]\n",
" [ 2.70000000e+01 1.40633861e+00]\n",
" [ 2.80000000e+01 1.47855583e+00]\n",
" [ 2.90000000e+01 1.17430550e+00]\n",
" [ 3.00000000e+01 1.30468412e+00]\n",
" [ 3.10000000e+01 1.16148868e+00]\n",
" [ 3.20000000e+01 1.53671075e+00]\n",
" [ 3.30000000e+01 1.53587566e+00]\n",
" [ 3.40000000e+01 1.20795292e+00]\n",
" [ 3.50000000e+01 1.84459132e+00]\n",
" [ 3.60000000e+01 1.34792770e+00]\n",
" [ 3.70000000e+01 1.37736338e+00]\n",
" [ 3.80000000e+01 1.77331092e+00]\n",
" [ 3.90000000e+01 1.53410338e+00]\n",
" [ 4.00000000e+01 2.00036486e+00]\n",
" [ 4.10000000e+01 1.57601623e+00]\n",
" [ 4.20000000e+01 2.00517702e+00]\n",
" [ 4.30000000e+01 1.86620216e+00]\n",
" [ 4.40000000e+01 1.63958184e+00]\n",
" [ 4.50000000e+01 1.46613891e+00]\n",
" [ 4.60000000e+01 1.27254499e+00]\n",
" [ 4.70000000e+01 1.30860112e+00]\n",
" [ 4.80000000e+01 1.84718477e+00]\n",
" [ 4.90000000e+01 1.54303093e+00]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngoiIXDFAEBGRKwYIIiJy9f8B0xNf4JVRxZwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f63cab56dd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"RMSD = []\n",
"backbone = u.select_atoms(\"protein and (name C or name N or name CA)\")\n",
"reference = ref.select_atoms(\"protein and (name C or name N or name CA)\")\n",
"for ts in u.trajectory:\n",
" A = backbone.coordinates()\n",
" B = reference.coordinates()\n",
" C = rmsd(A,B)\n",
" RMSD.append((u.trajectory.frame, C))\n",
"RMSD = np.array(RMSD)\n",
"print RMSD\n",
"import matplotlib.pyplot as plt\n",
"ax = plt.subplot(111)\n",
"ax.plot(RMSD[:,0], RMSD[:,1], 'r--', lw=2, label=r\"$R_G$\")\n",
"ax.set_xlabel(\"Frame\")\n",
"ax.set_ylabel(r\"RMSD of Backbone ($\\AA$)\")\n",
"ax.figure.savefig(\"RMSD.pdf\")\n",
"plt.draw()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"50"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(u.trajectory)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 14.45947817]\n",
" [ 1. 14.56343943]\n",
" [ 2. 14.66004709]\n",
" [ 3. 14.5950319 ]\n",
" [ 4. 14.65924899]\n",
" [ 5. 14.52109577]\n",
" [ 6. 14.52496548]\n",
" [ 7. 14.74254526]\n",
" [ 8. 14.74876355]\n",
" [ 9. 14.52289098]\n",
" [ 10. 14.52560928]\n",
" [ 11. 14.4352187 ]\n",
" [ 12. 14.53671809]\n",
" [ 13. 14.43235824]\n",
" [ 14. 14.46688002]\n",
" [ 15. 14.4705191 ]\n",
" [ 16. 14.41274683]\n",
" [ 17. 14.43789711]\n",
" [ 18. 14.38424011]\n",
" [ 19. 14.41613229]\n",
" [ 20. 14.56441881]\n",
" [ 21. 14.5337207 ]\n",
" [ 22. 14.41597997]\n",
" [ 23. 14.40583184]\n",
" [ 24. 14.45525257]\n",
" [ 25. 14.46460474]\n",
" [ 26. 14.46638457]\n",
" [ 27. 14.40691409]\n",
" [ 28. 14.52930841]\n",
" [ 29. 14.54878399]\n",
" [ 30. 14.48549566]\n",
" [ 31. 14.49806385]\n",
" [ 32. 14.50785088]\n",
" [ 33. 14.50795497]\n",
" [ 34. 14.54129607]\n",
" [ 35. 14.38962131]\n",
" [ 36. 14.51879453]\n",
" [ 37. 14.51302802]\n",
" [ 38. 14.59042283]\n",
" [ 39. 14.50376336]\n",
" [ 40. 14.45130122]\n",
" [ 41. 14.5705463 ]\n",
" [ 42. 14.66729859]\n",
" [ 43. 14.51986238]\n",
" [ 44. 14.54526494]\n",
" [ 45. 14.4653264 ]\n",
" [ 46. 14.46804386]\n",
" [ 47. 14.5561452 ]\n",
" [ 48. 14.39166516]\n",
" [ 49. 14.48221981]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEPCAYAAABhkeIdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSeoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f74d11097d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Rgyr = []\n",
"protein = u.select_atoms(\"protein\")\n",
"for ts in u.trajectory:\n",
" Rgyr.append((u.trajectory.frame, protein.radius_of_gyration()))\n",
"Rgyr = np.array(Rgyr)\n",
"\n",
"print Rgyr\n",
"import matplotlib.pyplot as plt\n",
"ax = plt.subplot(111)\n",
"ax.plot(Rgyr[:,0], Rgyr[:,1], 'r--', lw=2, label=r\"$R_G$\")\n",
"ax.set_xlabel(\"Frame\")\n",
"ax.set_ylabel(r\"radius of gyration $R_G$ ($\\AA$)\")\n",
"ax.figure.savefig(\"Rgyr.pdf\")\n",
"plt.draw()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00000000e+00 5.34164461e-07]\n",
" [ 1.00000000e+00 1.54629927e+00]\n",
" [ 2.00000000e+00 1.44347196e+00]\n",
" [ 3.00000000e+00 1.99289379e+00]\n",
" [ 4.00000000e+00 1.85068733e+00]\n",
" [ 5.00000000e+00 1.61465330e+00]\n",
" [ 6.00000000e+00 1.78023370e+00]\n",
" [ 7.00000000e+00 2.39800013e+00]\n",
" [ 8.00000000e+00 1.89272947e+00]\n",
" [ 9.00000000e+00 1.60691848e+00]\n",
" [ 1.00000000e+01 1.63336449e+00]\n",
" [ 1.10000000e+01 1.84776551e+00]\n",
" [ 1.20000000e+01 1.65945259e+00]\n",
" [ 1.30000000e+01 1.31706803e+00]\n",
" [ 1.40000000e+01 1.37257246e+00]\n",
" [ 1.50000000e+01 1.58766973e+00]\n",
" [ 1.60000000e+01 1.58488312e+00]\n",
" [ 1.70000000e+01 1.55337912e+00]\n",
" [ 1.80000000e+01 1.70441613e+00]\n",
" [ 1.90000000e+01 1.91122359e+00]\n",
" [ 2.00000000e+01 1.99538288e+00]\n",
" [ 2.10000000e+01 1.73095818e+00]\n",
" [ 2.20000000e+01 1.89093259e+00]\n",
" [ 2.30000000e+01 1.86865425e+00]\n",
" [ 2.40000000e+01 1.68523931e+00]\n",
" [ 2.50000000e+01 1.65180549e+00]\n",
" [ 2.60000000e+01 1.87369042e+00]\n",
" [ 2.70000000e+01 1.79027666e+00]\n",
" [ 2.80000000e+01 1.97255014e+00]\n",
" [ 2.90000000e+01 1.72468540e+00]\n",
" [ 3.00000000e+01 1.76113702e+00]\n",
" [ 3.10000000e+01 1.62846968e+00]\n",
" [ 3.20000000e+01 1.96988429e+00]\n",
" [ 3.30000000e+01 1.92999813e+00]\n",
" [ 3.40000000e+01 1.70608289e+00]\n",
" [ 3.50000000e+01 2.18234388e+00]\n",
" [ 3.60000000e+01 1.83320735e+00]\n",
" [ 3.70000000e+01 1.92635276e+00]\n",
" [ 3.80000000e+01 2.26194052e+00]\n",
" [ 3.90000000e+01 2.06464391e+00]\n",
" [ 4.00000000e+01 2.30309437e+00]\n",
" [ 4.10000000e+01 2.03992844e+00]\n",
" [ 4.20000000e+01 2.36421650e+00]\n",
" [ 4.30000000e+01 2.22578813e+00]\n",
" [ 4.40000000e+01 2.10841287e+00]\n",
" [ 4.50000000e+01 1.91646573e+00]\n",
" [ 4.60000000e+01 1.86615725e+00]\n",
" [ 4.70000000e+01 1.81379093e+00]\n",
" [ 4.80000000e+01 2.22916719e+00]\n",
" [ 4.90000000e+01 2.07159788e+00]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCIicvT/2g/i3oOW4D4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f63c128dfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"RMSD_aa = []\n",
"current = u.select_atoms(\"protein and not name H*\")\n",
"reference = ref.select_atoms(\"protein and not name H*\")\n",
"for ts in u.trajectory:\n",
" A = current.coordinates()\n",
" B = reference.coordinates()\n",
" C = rmsd(A,B)\n",
" RMSD_aa.append((u.trajectory.frame, C))\n",
"RMSD_aa = np.array(RMSD_aa)\n",
"print RMSD_aa\n",
"import matplotlib.pyplot as plt\n",
"ax = plt.subplot(111)\n",
"ax.plot(RMSD_aa[:,0], RMSD_aa[:,1], 'r--', lw=2, label=r\"$R_G$\")\n",
"ax.set_xlabel(\"Frame\")\n",
"ax.set_ylabel(r\"RMSD all atoms ($\\AA$)\")\n",
"ax.figure.savefig(\"RMSD_all_atoms.pdf\")\n",
"plt.draw()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00000000e+00 1.08735602e-06]\n",
" [ 1.00000000e+00 3.68662714e+00]\n",
" [ 2.00000000e+00 2.12019302e+00]\n",
" [ 3.00000000e+00 1.60456652e+00]\n",
" [ 4.00000000e+00 1.57198797e+00]\n",
" [ 5.00000000e+00 2.84287226e+00]\n",
" [ 6.00000000e+00 4.90751869e+00]\n",
" [ 7.00000000e+00 3.36932707e+00]\n",
" [ 8.00000000e+00 3.23793130e+00]\n",
" [ 9.00000000e+00 2.81030673e+00]\n",
" [ 1.00000000e+01 2.96390197e+00]\n",
" [ 1.10000000e+01 1.80263100e+00]\n",
" [ 1.20000000e+01 1.57591836e+00]\n",
" [ 1.30000000e+01 2.64336319e+00]\n",
" [ 1.40000000e+01 2.64373063e+00]\n",
" [ 1.50000000e+01 3.43870722e+00]\n",
" [ 1.60000000e+01 3.09839813e+00]\n",
" [ 1.70000000e+01 3.24417248e+00]\n",
" [ 1.80000000e+01 2.66968311e+00]\n",
" [ 1.90000000e+01 3.73360857e+00]\n",
" [ 2.00000000e+01 1.95119562e+00]\n",
" [ 2.10000000e+01 3.17663864e+00]\n",
" [ 2.20000000e+01 3.87500922e+00]\n",
" [ 2.30000000e+01 2.48765420e+00]\n",
" [ 2.40000000e+01 2.36807138e+00]\n",
" [ 2.50000000e+01 2.67178418e+00]\n",
" [ 2.60000000e+01 3.63333534e+00]\n",
" [ 2.70000000e+01 1.96125171e+00]\n",
" [ 2.80000000e+01 3.24832209e+00]\n",
" [ 2.90000000e+01 3.41356751e+00]\n",
" [ 3.00000000e+01 2.59487464e+00]\n",
" [ 3.10000000e+01 2.35808403e+00]\n",
" [ 3.20000000e+01 4.10424226e+00]\n",
" [ 3.30000000e+01 3.00861139e+00]\n",
" [ 3.40000000e+01 2.99031474e+00]\n",
" [ 3.50000000e+01 3.16022017e+00]\n",
" [ 3.60000000e+01 2.94211087e+00]\n",
" [ 3.70000000e+01 3.00621353e+00]\n",
" [ 3.80000000e+01 1.52727089e+00]\n",
" [ 3.90000000e+01 3.05931957e+00]\n",
" [ 4.00000000e+01 1.28726690e+00]\n",
" [ 4.10000000e+01 3.67078576e+00]\n",
" [ 4.20000000e+01 2.19999724e+00]\n",
" [ 4.30000000e+01 3.36467798e+00]\n",
" [ 4.40000000e+01 2.94593173e+00]\n",
" [ 4.50000000e+01 4.65756391e+00]\n",
" [ 4.60000000e+01 3.56851330e+00]\n",
" [ 4.70000000e+01 1.84376409e+00]\n",
" [ 4.80000000e+01 1.51971426e+00]\n",
" [ 4.90000000e+01 2.93048855e+00]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEPCAYAAABLIROyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f63cac18c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"RMSD_lig = []\n",
"ligand = u.select_atoms(\"(resid 142:146) and not name H*\") ## include selection based on user description\n",
"#current = u.select_atoms(\"segname BGLC and not name H*\")\n",
"reference = ref.select_atoms(\"(resid 142:146) and not name H*\")\n",
"for ts in u.trajectory:\n",
" A = ligand.coordinates()\n",
" B = reference.coordinates()\n",
" C = rmsd(A,B)\n",
" RMSD_lig.append((u.trajectory.frame, C))\n",
"RMSD_lig = np.array(RMSD_lig)\n",
"print RMSD_lig\n",
"import matplotlib.pyplot as plt\n",
"ax = plt.subplot(111)\n",
"ax.plot(RMSD_lig[:,0], RMSD_lig[:,1], 'r--', lw=2, label=r\"$R_G$\")\n",
"ax.set_xlabel(\"Frame\")\n",
"ax.set_ylabel(r\"RMSD of ligand ($\\AA$)\")\n",
"ax.figure.savefig(\"RMSD_ligand.pdf\")\n",
"plt.draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#RMSF"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object `mdanalysis.analysis` not found.\n"
]
}
],
"source": [
"mdanalysis.analysis?"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"MDAnalysis.analysis.rms.RMSF?"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"ename": "TypeError",
"evalue": "list indices must be integers, not tuple",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-106-d18f6f8e499f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRMSF_bb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrmsf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r--'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mr\"$R_G$\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frame\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\"RMSF of Backbone ($\\AA$)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: list indices must be integers, not tuple"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f63be067790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"RMSF_bb = []\n",
"backbone = u.select_atoms(\"protein and (name CA)\")\n",
"C = MDAnalysis.analysis.rms.RMSF(backbone)\n",
"C.run()\n",
"C.rmsf[:]\n",
"import matplotlib.pyplot as plt\n",
"ax = plt.subplot(111)\n",
"ax.plot(RMSF_bb[:,0], C.rmsf[:,0], 'r--', lw=2, label=r\"$R_G$\")\n",
"ax.set_xlabel(\"Frame\")\n",
"ax.set_ylabel(r\"RMSF of Backbone ($\\AA$)\")\n",
"ax.figure.savefig(\"RMSF.pdf\")\n",
"plt.draw()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(426,)"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C.rmsf.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
},
"name": "CECAM-Afternoon.ipynb"
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| examples/ISPC-mandelbrot.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Demo for ISPC.jl\n",
"\n",
"Runs the Mandelbrot ISPC example entirely from Julia code."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linker: /usr/bin/libtool\n"
]
}
],
"source": [
"using ISPC"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the function we will call for every pixel. Declare it `@inline` so that the `@kernel` below can use it."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mandel (generic function with 1 method)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@inline function mandel(c_re, c_im, count)\n",
" z_re = c_re\n",
" z_im = c_im\n",
" i = 0\n",
" while i < count\n",
" if (z_re * z_re + z_im * z_im > 4.0f0)\n",
" break\n",
" end\n",
" new_re = z_re*z_re - z_im*z_im\n",
" new_im = 2.0f0 * z_re * z_im\n",
" z_re = c_re + new_re\n",
" z_im = c_im + new_im\n",
" i += 1\n",
" end\n",
" return i\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the main function. Tag it with `@ispc` so that all kernel fragments inside are extracted and compiled separately by ISPC."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracted kernel Val{symbol(\"##8014\")}((:x0,:y0,:output,:max_iters,:height,:width,:dx,:dy))\n",
"begin \n",
" GenSym(3) = (Main.colon)(1,width)\n",
" #s40 = (top(start))(GenSym(3))\n",
" unless (top(!))((top(done))(GenSym(3),#s40)) goto 1\n",
" 2: \n",
" GenSym(4) = (top(next))(GenSym(3),#s40)\n",
" i = (top(getfield))(GenSym(4),1)\n",
" #s40 = (top(getfield))(GenSym(4),2)\n",
" $(Expr(:meta, :ispc, symbol(\"##foreach#8022\"), :foreach, (:(1:height),)))\n",
" j = (ISPC.foreachindex)(1,(Main.colon)(1,height))\n",
" x = ((top(getfield))(Base.FastMath,:add_fast))(x0,((top(getfield))(Base.FastMath,:mul_fast))(i,dx))\n",
" y = ((top(getfield))(Base.FastMath,:add_fast))(y0,((top(getfield))(Base.FastMath,:mul_fast))(j,dy))\n",
" GenSym(5) = (Main.mandel)(x,y,max_iters)\n",
" (Main.setindex!)(output,GenSym(5),j,i)\n",
" $(Expr(:meta, :ispc, symbol(\"##foreach#8022\")))\n",
" 3: \n",
" unless (top(!))((top(!))((top(done))(GenSym(3),#s40))) goto 2\n",
" 1: \n",
" 0: \n",
" return\n",
"end\n"
]
}
],
"source": [
"@ispc function mandelbrot_ispc(x0, y0, x1, y1, output, max_iters)\n",
" height, width = size(output)\n",
" dx = (x1 - x0) / width\n",
" dy = (y1 - y0) / height\n",
" @kernel(`--target=avx1-i32x8`) do\n",
" for i = 1:width\n",
" @foreach(1:height) do j\n",
" x = x0 + i * dx\n",
" y = y0 + j * dy\n",
" output[j,i] = mandel(x, y, max_iters)\n",
" end\n",
" end\n",
" end\n",
" output\n",
"end;"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output = zeros(Float32, 768, 1024);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that kernel fragments in the main Julia function have been replaced by kernel calls:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1-element Array{Any,1}:\n",
" :($(Expr(:lambda, Any[:x0,:y0,:x1,:y1,:output,:max_iters], Any[Any[Any[:x0,:Any,1],Any[:y0,:Any,1],Any[:x1,:Any,0],Any[:y1,:Any,0],Any[:output,:Any,1],Any[:max_iters,:Any,1],Any[:height,:Any,19],Any[:width,:Any,19],Any[symbol(\"#s41\"),:Any,2],Any[:dx,:Any,19],Any[:dy,:Any,19]],Any[],6,Any[]], :(begin # In[3], line 2:\n",
" NewvarNode(:height)\n",
" NewvarNode(:width)\n",
" NewvarNode(:dx)\n",
" NewvarNode(:dy)\n",
" GenSym(0) = (Main.size)(output)\n",
" #s41 = (top(start))(GenSym(0))\n",
" GenSym(1) = (top(indexed_next))(GenSym(0),1,#s41)\n",
" height = (top(getfield))(GenSym(1),1)\n",
" #s41 = (top(getfield))(GenSym(1),2)\n",
" GenSym(2) = (top(indexed_next))(GenSym(0),2,#s41)\n",
" width = (top(getfield))(GenSym(2),1)\n",
" #s41 = (top(getfield))(GenSym(2),2) # In[3], line 3:\n",
" dx = (x1 - x0) / width # In[3], line 4:\n",
" dy = (y1 - y0) / height # In[3], line 5: # /Users/plantagenet/.julia/v0.5/ISPC.jl/src/macros.jl, line 141:\n",
" ((top(getfield))(ISPC,:kernel_call))(Val{symbol(\"##8014\")},x0,y0,output,max_iters,height,width,dx,dy) # In[3], line 14:\n",
" return output\n",
" end))))"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@code_lowered mandelbrot_ispc(-2.0f0, -1.0f0, 1.0f0, 1.0f0, output, 256)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling the main function the first time will trigger the compilation of all its fragments:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating kernel ##8014 for argument types (Float32,Float32,Array{Float32,2},Int64,Int64,Int64,Float32,Float32)\n",
"Compile options: `--target=avx1-i32x8`\n",
"Running type inference...\n",
"Lambda function:\n",
"\t arguments: Any[:x0,:y0,:output,:max_iters,:height,:width,:dx,:dy]\n",
"\t local variables: \n",
"\t\t(symbol(\"#s40\"),Int64,2)\n",
"\t\t(:i,Int64,18)\n",
"\t\t(:j,Int64,18)\n",
"\t\t(:x,Float32,18)\n",
"\t\t(:y,Float32,18)\n",
"\t\t(:x0,Float32,0)\n",
"\t\t(:y0,Float32,0)\n",
"\t\t(:output,Array{Float32,2},0)\n",
"\t\t(:max_iters,Int64,0)\n",
"\t\t(:height,Int64,18)\n",
"\t\t(:width,Int64,18)\n",
"\t\t(:dx,Float32,18)\n",
"\t\t(:dy,Float32,18)\n",
"\t\t(symbol(\"##zs#8510\"),Tuple{},0)\n",
"\t\t(symbol(\"##zs#8511\"),Tuple{},0)\n",
"\t\t(symbol(\"##z_re#8512\"),Float32,2)\n",
"\t\t(symbol(\"##z_im#8513\"),Float32,2)\n",
"\t\t(symbol(\"##i#8514\"),Int64,2)\n",
"\t\t(symbol(\"##new_re#8515\"),Float32,18)\n",
"\t\t(symbol(\"##new_im#8516\"),Float32,18)\n",
"\t\t(symbol(\"####xs#8509#8517\"),Tuple{},0)\n",
"\t\t(symbol(\"##I#8518\"),Tuple{},0)\n",
"\t closure variables: \n",
"\n",
"\t SSA types: Any[Union{},Union{},Union{},UnitRange{Int64},Tuple{Int64,Int64},Int64,Int64,Int64]\n",
"\t parameters: Any[]\n",
"begin \n",
" GenSym(3) = $(Expr(:new, UnitRange{Int64}, 1, :(((top(getfield))(Base.Intrinsics,:select_value))((Base.sle_int)(1,width::Int64),width::Int64,(Base.sub_int)(1,1)))))\n",
" #s40 = (top(getfield))(GenSym(3),:start)\n",
" unless (Base.not_int)(#s40::Int64 === (Base.add_int)((top(getfield))(GenSym(3),:stop),1)) goto 1\n",
" 2: \n",
" GenSym(6) = #s40::Int64\n",
" GenSym(7) = (Base.add_int)(#s40::Int64,1)\n",
" i = GenSym(6)\n",
" #s40 = GenSym(7)\n",
" $(Expr(:meta, :ispc, symbol(\"##foreach#8022\"), :foreach, (:(1:height),)))\n",
" j = (ISPC.foreachindex)(1,$(Expr(:new, UnitRange{Int64}, 1, :(((top(getfield))(Base.Intrinsics,:select_value))((Base.sle_int)(1,height::Int64),height::Int64,(Base.sub_int)(1,1))))))\n",
" x = (Base.FastMath.box)(Float32,((top(getfield))(Base.FastMath.Base,:add_float_fast))(x0::Float32,(Base.FastMath.box)(Float32,((top(getfield))(Base.FastMath.Base,:mul_float_fast))((Base.sitofp)(Float32,i::Int64),dx::Float32))))\n",
" y = (Base.FastMath.box)(Float32,((top(getfield))(Base.FastMath.Base,:add_float_fast))(y0::Float32,(Base.FastMath.box)(Float32,((top(getfield))(Base.FastMath.Base,:mul_float_fast))((Base.sitofp)(Float32,j::Int64),dy::Float32))))\n",
" ##z_re#8512 = x::Float32\n",
" ##z_im#8513 = y::Float32\n",
" ##i#8514 = 0\n",
" NewvarNode(symbol(\"##new_re#8515\"))\n",
" NewvarNode(symbol(\"##new_im#8516\"))\n",
" unless (Base.slt_int)(##i#8514::Int64,max_iters::Int64) goto 18\n",
" 15: \n",
" unless (Base.lt_float)(4.0f0,(Base.add_float)((Base.mul_float)(##z_re#8512::Float32,##z_re#8512::Float32),(Base.mul_float)(##z_im#8513::Float32,##z_im#8513::Float32))) goto 16\n",
" goto 19\n",
" 16: \n",
" ##new_re#8515 = (Base.sub_float)((Base.mul_float)(##z_re#8512::Float32,##z_re#8512::Float32),(Base.mul_float)(##z_im#8513::Float32,##z_im#8513::Float32))\n",
" ##new_im#8516 = (Base.mul_float)((Base.mul_float)(2.0f0,##z_re#8512::Float32),##z_im#8513::Float32)\n",
" ##z_re#8512 = (Base.add_float)(x::Float32,##new_re#8515::Float32)\n",
" ##z_im#8513 = (Base.add_float)(y::Float32,##new_im#8516::Float32)\n",
" ##i#8514 = (Base.add_int)(##i#8514::Int64,1)\n",
" 17: \n",
" unless (Base.not_int)((Base.slt_int)(##i#8514::Int64,max_iters::Int64)) goto 15\n",
" 18: \n",
" 19: \n",
" GenSym(5) = ##i#8514::Int64\n",
" (Base.arrayset)(output::Array{Float32,2},(Base.sitofp)(Float32,GenSym(5)),j::Int64,i::Int64)\n",
" $(Expr(:meta, :ispc, symbol(\"##foreach#8022\")))\n",
" 3: \n",
" unless (Base.not_int)((Base.not_int)(#s40::Int64 === (Base.add_int)((top(getfield))(GenSym(3),:stop),1))) goto 2\n",
" 1: \n",
" 0: \n",
" return\n",
"end\n",
"@generated function kernel_call{##8014}(::Type{Val{##8014}}, args...)\n",
"begin \n",
" $(Expr(:meta, :inline))\n",
" @inbounds begin \n",
" if UInt(ISPC.ispc_fptr[1]) == 0\n",
" ISPC.compile_all()\n",
" if UInt(ISPC.ispc_fptr[1]) == 0\n",
" error(\"Could not compile ISPC kernel $(id)\")\n",
" end\n",
" end\n",
" begin \n",
" ccall(ISPC.ispc_fptr[1],Void,(Float32,Float32,Ref{Float32},Int64,Int64,Int64,Int64,Int64,Float32,Float32),args[1],args[2],args[3],size(args[3],1),size(args[3],2),args[4],args[5],args[6],args[7],args[8])\n",
" end\n",
" end\n",
"end\n",
"Compiling ISPC file...\n",
"// Use ISPC's multiple dispatch capabilities to deal with the fact\n",
"// that Julia uses the same function for bitwise and boolean NOT,\n",
"// whereas the ~ operator in ISPC does not work on booleans:\n",
"inline bool __not(bool val) {return !val;} // boolean NOT\n",
"inline int8 __not(int8 val) {return ~val;} // all others are bitwise\n",
"inline int16 __not(int16 val) {return ~val;}\n",
"inline int32 __not(int32 val) {return ~val;}\n",
"inline int64 __not(int64 val) {return ~val;}\n",
"inline unsigned int8 __not(unsigned int8 val) {return ~val;}\n",
"inline unsigned int16 __not(unsigned int16 val) {return ~val;}\n",
"inline unsigned int32 __not(unsigned int32 val) {return ~val;}\n",
"inline unsigned int64 __not(unsigned int64 val) {return ~val;}\n",
"\n",
"\n",
"struct UnitRange {\n",
" int64 start;\n",
" int64 stop;\n",
"};\n",
"export void ispc_func_1(uniform float x0, uniform float y0, uniform float output[], uniform int64 output__len__1, uniform int64 output__len__2, uniform int64 max_iters, uniform int64 height, uniform int64 width, uniform float dx, uniform float dy) {\n",
" uniform UnitRange _gensym3 = {1, ((1 <= width) ? width : (1 - 1))};\n",
" uniform int64 _s40 = _gensym3.start;\n",
" while(__not((_s40 == (_gensym3.stop + 1)))) {\n",
" uniform int64 _gensym6 = _s40;\n",
" uniform int64 _gensym7 = (_s40 + 1);\n",
" uniform int64 i = _gensym6;\n",
" _s40 = _gensym7;\n",
" foreach(j = 1 ... (height+1)) {\n",
" float x = (x0 + (((float)i) * dx));\n",
" float y = (y0 + (((float)j) * dy));\n",
" float __z_re_8512 = x;\n",
" float __z_im_8513 = y;\n",
" int64 __i_8514 = 0;\n",
" float __new_re_8515;\n",
" float __new_im_8516;\n",
" while((__i_8514 < max_iters)) {\n",
" if ((0x1p+2 < ((__z_re_8512 * __z_re_8512) + (__z_im_8513 * __z_im_8513)))) {\n",
" break;\n",
" } else {\n",
" __new_re_8515 = ((__z_re_8512 * __z_re_8512) - (__z_im_8513 * __z_im_8513));\n",
" __new_im_8516 = ((0x1p+1 * __z_re_8512) * __z_im_8513);\n",
" __z_re_8512 = (x + __new_re_8515);\n",
" __z_im_8513 = (y + __new_im_8516);\n",
" __i_8514 = (__i_8514 + 1);\n",
" }\n",
" }\n",
" int64 _gensym5 = __i_8514;\n",
" output[((j - 1) + (output__len__1 * (i - 1)))] = ((float)_gensym5);\n",
" }\n",
" }\n",
" return;\n",
"}\n",
"\n",
"\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"ld: warning: -macosx_version_min not specified, assuming 10.10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded function ispc_func_1 at Ptr{Void} @0x000000030ec987c0\n"
]
}
],
"source": [
"mandelbrot_ispc(-2.0f0, -1.0f0, 1.0f0, 1.0f0, output, 256);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's call it again to get an accurate measure of execution time:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.091577 seconds (15 allocations: 352 bytes)\n"
]
}
],
"source": [
"x0, x1 = -2.1f0, 0.8f0\n",
"y0, y1 = -1.2f0, 1.2f0\n",
"@time out = mandelbrot_ispc(x0, y0, x1, y1, output, 256);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the generated fractal:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using PyPlot"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x319ebf710>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imshow(out, cmap=\"flag\", aspect=\"equal\", extent=(x0, x1, y0, y1))\n",
"display(gcf())\n",
"close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wow, nice!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pure-Julia version for comparison. Note how it uses the same `mandel()` routine as the ISPC version. We can't use `@simd` here because of the branch statements."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mandelbrot_julia (generic function with 1 method)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@fastmath function mandelbrot_julia(x0, y0, x1, y1, output, max_iters)\n",
" height, width = size(output)\n",
" dx = (x1 - x0) / width\n",
" dy = (y1 - y0) / height\n",
" @inbounds begin\n",
" for i = 1:width\n",
" for j = 1:height\n",
" x = x0 + i * dx\n",
" y = y0 + j * dy\n",
" output[j, i] = mandel(x, y, max_iters)\n",
" end\n",
" end\n",
" end\n",
" output\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How fast does it run?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.233861 seconds (4 allocations: 160 bytes)\n"
]
}
],
"source": [
"@time out = mandelbrot_julia(x0, y0, x1, y1, output, 256);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not bad, only about 2.5x to 3x slower. But we got a decent speedup from ISPC!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the generated C code, x86 assembly and LLVM assembly."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"func = ISPC.ispc_funcs[1];"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"// Use ISPC's multiple dispatch capabilities to deal with the fact\n",
"// that Julia uses the same function for bitwise and boolean NOT,\n",
"// whereas the ~ operator in ISPC does not work on booleans:\n",
"inline bool __not(bool val) {return !val;} // boolean NOT\n",
"inline int8 __not(int8 val) {return ~val;} // all others are bitwise\n",
"inline int16 __not(int16 val) {return ~val;}\n",
"inline int32 __not(int32 val) {return ~val;}\n",
"inline int64 __not(int64 val) {return ~val;}\n",
"inline unsigned int8 __not(unsigned int8 val) {return ~val;}\n",
"inline unsigned int16 __not(unsigned int16 val) {return ~val;}\n",
"inline unsigned int32 __not(unsigned int32 val) {return ~val;}\n",
"inline unsigned int64 __not(unsigned int64 val) {return ~val;}\n",
"\n",
"\n",
"struct UnitRange {\n",
" int64 start;\n",
" int64 stop;\n",
"};\n",
"export void ispc_func_1(uniform float x0, uniform float y0, uniform float output[], uniform int64 output__len__1, uniform int64 output__len__2, uniform int64 max_iters, uniform int64 height, uniform int64 width, uniform float dx, uniform float dy) {\n",
" uniform UnitRange _gensym3 = {1, ((1 <= width) ? width : (1 - 1))};\n",
" uniform int64 _s40 = _gensym3.start;\n",
" while(__not((_s40 == (_gensym3.stop + 1)))) {\n",
" uniform int64 _gensym6 = _s40;\n",
" uniform int64 _gensym7 = (_s40 + 1);\n",
" uniform int64 i = _gensym6;\n",
" _s40 = _gensym7;\n",
" foreach(j = 1 ... (height+1)) {\n",
" float x = (x0 + (((float)i) * dx));\n",
" float y = (y0 + (((float)j) * dy));\n",
" float __z_re_8512 = x;\n",
" float __z_im_8513 = y;\n",
" int64 __i_8514 = 0;\n",
" float __new_re_8515;\n",
" float __new_im_8516;\n",
" while((__i_8514 < max_iters)) {\n",
" if ((0x1p+2 < ((__z_re_8512 * __z_re_8512) + (__z_im_8513 * __z_im_8513)))) {\n",
" break;\n",
" } else {\n",
" __new_re_8515 = ((__z_re_8512 * __z_re_8512) - (__z_im_8513 * __z_im_8513));\n",
" __new_im_8516 = ((0x1p+1 * __z_re_8512) * __z_im_8513);\n",
" __z_re_8512 = (x + __new_re_8515);\n",
" __z_im_8513 = (y + __new_im_8516);\n",
" __i_8514 = (__i_8514 + 1);\n",
" }\n",
" }\n",
" int64 _gensym5 = __i_8514;\n",
" output[((j - 1) + (output__len__1 * (i - 1)))] = ((float)_gensym5);\n",
" }\n",
" }\n",
" return;\n",
"}\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"func_code = ISPC.gen_code(func)\n",
"println(func_code)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t.section\t__TEXT,__text,regular,pure_instructions\n",
"\t.macosx_version_min 13, 4\n",
"\t.section\t__TEXT,__literal16,16byte_literals\n",
"\t.align\t4\n",
"LCPI0_0:\n",
"\t.long\t0 ## 0x0\n",
"\t.long\t1 ## 0x1\n",
"\t.long\t2 ## 0x2\n",
"\t.long\t3 ## 0x3\n",
"LCPI0_1:\n",
"\t.long\t4 ## 0x4\n",
"\t.long\t5 ## 0x5\n",
"\t.long\t6 ## 0x6\n",
"\t.long\t7 ## 0x7\n",
"LCPI0_2:\n",
"\t.byte\t0 ## 0x0\n",
"\t.byte\t1 ## 0x1\n",
"\t.byte\t4 ## 0x4\n",
"\t.byte\t5 ## 0x5\n",
"\t.byte\t8 ## 0x8\n",
"\t.byte\t9 ## 0x9\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t8 ## 0x8\n",
"\t.byte\t9 ## 0x9\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t14 ## 0xe\n",
"\t.byte\t15 ## 0xf\n",
"LCPI0_4:\n",
"\t.quad\t1 ## 0x1\n",
"\t.quad\t1 ## 0x1\n",
"\t.section\t__TEXT,__const\n",
"\t.align\t5\n",
"LCPI0_3:\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"LCPI0_5:\n",
"\t.space\t32\n",
"\t.section\t__TEXT,__text,regular,pure_instructions\n",
"\t.globl\t_ispc_func_1___unfunfun_3C_unf_3E_unIunIunIunIunIunfunf\n",
"\t.align\t4, 0x90\n",
"_ispc_func_1___unfunfun_3C_unf_3E_unIunIunIunIunIunfunf: ## @ispc_func_1___unfunfun_3C_unf_3E_unIunIunIunIunIunfunf\n",
"## BB#0: ## %allocas\n",
"\tpushq\t%rbp\n",
"\tpushq\t%r15\n",
"\tpushq\t%r14\n",
"\tpushq\t%rbx\n",
"\tsubq\t$408, %rsp ## imm = 0x198\n",
"\tvmovups\t%ymm4, (%rsp) ## 32-byte Spill\n",
"\tvmovss\t%xmm2, 60(%rsp) ## 4-byte Spill\n",
"\tvmovss\t%xmm0, 56(%rsp) ## 4-byte Spill\n",
"\tvmovmskps\t%ymm4, %edx\n",
"\tleaq\t1(%r9), %r10\n",
"\ttestq\t%r9, %r9\n",
"\tmovl\t$1, %r14d\n",
"\tcmovleq\t%r14, %r10\n",
"\tleal\t1(%r8), %r11d\n",
"\tmovl\t%r8d, %eax\n",
"\tsarl\t$31, %eax\n",
"\tshrl\t$29, %eax\n",
"\taddl\t%r8d, %eax\n",
"\tandl\t$-8, %eax\n",
"\tmovl\t%r8d, %ebx\n",
"\tsubl\t%eax, %ebx\n",
"\tnegl\t%ebx\n",
"\tleal\t1(%r8,%rbx), %eax\n",
"\tvpermilps\t$0, %xmm1, %xmm0 ## xmm0 = xmm1[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 160(%rsp) ## 32-byte Spill\n",
"\tvpermilps\t$0, %xmm3, %xmm0 ## xmm0 = xmm3[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 128(%rsp) ## 32-byte Spill\n",
"\tvmovq\t%rcx, %xmm1\n",
"\tvpcmpeqd\t%xmm0, %xmm0, %xmm0\n",
"\tvunpcklpd\t%xmm1, %xmm1, %xmm1 ## xmm1 = xmm1[0,0]\n",
"\tvinsertf128\t$1, %xmm1, %ymm1, %ymm5\n",
"\tcmpl\t$255, %edx\n",
"\tjne\tLBB0_1\n",
"## BB#5: ## %for_test.outer.preheader\n",
"\txorl\t%ecx, %ecx\n",
"\ttestq\t%r9, %r9\n",
"\tcmovsq\t%rcx, %r9\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm1\n",
"\tmovl\t$1, %r14d\n",
"\tmovl\t$-1, %r8d\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 96(%rsp) ## 32-byte Spill\n",
"\tvmovdqa\tLCPI0_2(%rip), %xmm4 ## xmm4 = [0,1,4,5,8,9,12,13,8,9,12,13,12,13,14,15]\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, (%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, -32(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 288(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 256(%rsp) ## 32-byte Spill\n",
"\tjmp\tLBB0_6\n",
"LBB0_1:\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm1\n",
"\tmovl\t$-1, %r8d\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 96(%rsp) ## 32-byte Spill\n",
"\tvmovdqa\tLCPI0_2(%rip), %xmm8 ## xmm8 = [0,1,4,5,8,9,12,13,8,9,12,13,12,13,14,15]\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, -32(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, -64(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 288(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 256(%rsp) ## 32-byte Spill\n",
"\tjmp\tLBB0_2\n",
"LBB0_53: ## %for_exit519\n",
" ## in Loop: Header=BB0_2 Depth=1\n",
"\tdecl\t%r9d\n",
"\tmovl\t%esi, %ecx\n",
"\timull\t%r9d, %ecx\n",
"\tvpextrq\t$1, %xmm2, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm1\n",
"\tvmovq\t%xmm2, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm2\n",
"\tvinsertps\t$16, %xmm1, %xmm2, %xmm1 ## xmm1 = xmm2[0],xmm1[0],xmm2[2,3]\n",
"\tvmovq\t%xmm3, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm2\n",
"\tvinsertps\t$32, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1],xmm2[0],xmm1[3]\n",
"\tvpextrq\t$1, %xmm3, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm2\n",
"\tvinsertps\t$48, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1,2],xmm2[0]\n",
"\tvpextrq\t$1, %xmm7, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm2\n",
"\tvmovq\t%xmm7, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm2, %xmm3, %xmm2 ## xmm2 = xmm3[0],xmm2[0],xmm3[2,3]\n",
"\tvmovq\t%xmm0, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm2, %xmm2 ## xmm2 = xmm2[0,1],xmm3[0],xmm2[3]\n",
"\tvpextrq\t$1, %xmm0, %rbp\n",
"\tvcvtsi2ssq\t%rbp, %xmm0, %xmm0\n",
"\tvinsertps\t$48, %xmm0, %xmm2, %xmm0 ## xmm0 = xmm2[0,1,2],xmm0[0]\n",
"\tvinsertf128\t$1, %xmm1, %ymm0, %ymm0\n",
"\taddl\t%ecx, %edx\n",
"\tleal\t-4(,%rdx,4), %ecx\n",
"\tmovslq\t%ecx, %rcx\n",
"\tvmovups\t-96(%rsp), %ymm1 ## 32-byte Reload\n",
"\tvmaskmovps\t%ymm0, %ymm1, (%rdi,%rcx)\n",
"\tvmovups\t(%rsp), %ymm4 ## 32-byte Reload\n",
"\tvmovups\t64(%rsp), %ymm1 ## 32-byte Reload\n",
"\t.align\t4, 0x90\n",
"LBB0_2: ## %for_test288\n",
" ## =>This Loop Header: Depth=1\n",
" ## Child Loop BB0_36 Depth 2\n",
" ## Child Loop BB0_52 Depth 3\n",
" ## Child Loop BB0_38 Depth 4\n",
" ## Child Loop BB0_45 Depth 2\n",
" ## Child Loop BB0_46 Depth 3\n",
"\tmovq\t%r14, %r9\n",
"\tcmpq\t%r10, %r9\n",
"\tmovl\t$0, %ecx\n",
"\tcmovel\t%r8d, %ecx\n",
"\tvmovd\t%ecx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvandnps\t%ymm1, %ymm0, %ymm1\n",
"\tvandps\t%ymm4, %ymm1, %ymm0\n",
"\tvmovmskps\t%ymm0, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_9\n",
"## BB#3: ## %for_loop290\n",
" ## in Loop: Header=BB0_2 Depth=1\n",
"\tmovl\t$1, %edx\n",
"\tcmpl\t$2, %eax\n",
"\tjl\tLBB0_4\n",
"## BB#35: ## %foreach_full_body317.lr.ph\n",
" ## in Loop: Header=BB0_2 Depth=1\n",
"\tvmovups\t%ymm1, 64(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r9, %xmm0, %xmm0\n",
"\tvmulss\t60(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t56(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 224(%rsp) ## 32-byte Spill\n",
"\tleaq\t-1(%r9), %rcx\n",
"\timulq\t%rsi, %rcx\n",
"\tmovl\t$1, %edx\n",
"\t.align\t4, 0x90\n",
"LBB0_36: ## %foreach_full_body317\n",
" ## Parent Loop BB0_2 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB0_52 Depth 3\n",
" ## Child Loop BB0_38 Depth 4\n",
"\tvmovd\t%edx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI0_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI0_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm0\n",
"\tvcvtdq2ps\t%ymm0, %ymm0\n",
"\tvmulps\t128(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t160(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm0, 192(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm2, %xmm2, %xmm2\n",
"\tvxorps\t%xmm4, %xmm4, %xmm4\n",
"\tvmovaps\t%ymm0, %ymm10\n",
"\tvmovups\t224(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvmovups\t96(%rsp), %ymm15 ## 32-byte Reload\n",
"\tjmp\tLBB0_52\n",
"\t.align\t4, 0x90\n",
"LBB0_51: ## %if_done400\n",
" ## in Loop: Header=BB0_52 Depth=3\n",
"\tvandnps\t%ymm3, %ymm11, %ymm15\n",
"LBB0_52: ## %for_test378.outer\n",
" ## Parent Loop BB0_2 Depth=1\n",
" ## Parent Loop BB0_36 Depth=2\n",
" ## => This Loop Header: Depth=3\n",
" ## Child Loop BB0_38 Depth 4\n",
"\tvmovups\t%ymm12, 320(%rsp) ## 32-byte Spill\n",
"\tvmovups\t%ymm10, 352(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm12, %ymm12, %ymm6\n",
"\tvmulps\t%ymm10, %ymm10, %ymm14\n",
"\tvaddps\t%ymm6, %ymm14, %ymm13\n",
"\t.align\t4, 0x90\n",
"LBB0_38: ## %for_test378\n",
" ## Parent Loop BB0_2 Depth=1\n",
" ## Parent Loop BB0_36 Depth=2\n",
" ## Parent Loop BB0_52 Depth=3\n",
" ## => This Inner Loop Header: Depth=4\n",
"\tvextractf128\t$1, %ymm2, %xmm1\n",
"\tvextractf128\t$1, %ymm5, %xmm3\n",
"\tvpcmpgtq\t%xmm1, %xmm3, %xmm0\n",
"\tvpcmpgtq\t%xmm2, %xmm5, %xmm7\n",
"\tvshufps\t$-120, %xmm0, %xmm7, %xmm0 ## xmm0 = xmm7[0,2],xmm0[0,2]\n",
"\tvpshufb\t%xmm8, %xmm0, %xmm0\n",
"\tvextractf128\t$1, %ymm4, %xmm9\n",
"\tvpcmpgtq\t%xmm9, %xmm3, %xmm3\n",
"\tvpcmpgtq\t%xmm4, %xmm5, %xmm7\n",
"\tvshufps\t$-120, %xmm3, %xmm7, %xmm3 ## xmm3 = xmm7[0,2],xmm3[0,2]\n",
"\tvpshufb\t%xmm8, %xmm3, %xmm3\n",
"\tvpunpcklqdq\t%xmm3, %xmm0, %xmm0 ## xmm0 = xmm0[0],xmm3[0]\n",
"\tvpmovzxwd\t%xmm0, %xmm3\n",
"\tvpslld\t$31, %xmm3, %xmm3\n",
"\tvpsrad\t$31, %xmm3, %xmm3\n",
"\tvpunpckhwd\t%xmm0, %xmm0, %xmm0 ## xmm0 = xmm0[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm0, %xmm0\n",
"\tvpsrad\t$31, %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm3, %ymm0\n",
"\tvandps\t%ymm15, %ymm0, %ymm3\n",
"\tvmovmskps\t%ymm3, %ebp\n",
"\ttestl\t%ebp, %ebp\n",
"\tje\tLBB0_42\n",
"## BB#39: ## %for_loop380\n",
" ## in Loop: Header=BB0_38 Depth=4\n",
"\tvcmpnleps\tLCPI0_3(%rip), %ymm13, %ymm10\n",
"\tvandps\t%ymm3, %ymm10, %ymm12\n",
"\tvmovmskps\t%ymm12, %ebx\n",
"\tvxorps\t%xmm11, %xmm11, %xmm11\n",
"\ttestl\t%ebx, %ebx\n",
"\tje\tLBB0_40\n",
"## BB#37: ## %safe_if_run_true402\n",
" ## in Loop: Header=BB0_38 Depth=4\n",
"\tvxorps\t%xmm15, %xmm15, %xmm15\n",
"\tvmovaps\t%ymm12, %ymm11\n",
"\tcmpl\t%ebp, %ebx\n",
"\tje\tLBB0_38\n",
"LBB0_40: ## %safe_if_after_true401\n",
" ## in Loop: Header=BB0_52 Depth=3\n",
"\tvblendvps\t%ymm10, LCPI0_5(%rip), %ymm3, %ymm13\n",
"\tvmovmskps\t%ymm13, %ebx\n",
"\ttestl\t%ebx, %ebx\n",
"\tje\tLBB0_41\n",
"## BB#50: ## %safe_if_run_false421\n",
" ## in Loop: Header=BB0_52 Depth=3\n",
"\tvsubps\t%ymm14, %ymm6, %ymm0\n",
"\tvmovups\t256(%rsp), %ymm7 ## 32-byte Reload\n",
"\tvblendvps\t%ymm13, %ymm0, %ymm7, %ymm7\n",
"\tvmovups\t%ymm7, 256(%rsp) ## 32-byte Spill\n",
"\tvmovups\t320(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvaddps\t%ymm12, %ymm12, %ymm0\n",
"\tvmovups\t352(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvmulps\t%ymm0, %ymm10, %ymm0\n",
"\tvmovups\t288(%rsp), %ymm6 ## 32-byte Reload\n",
"\tvblendvps\t%ymm13, %ymm0, %ymm6, %ymm6\n",
"\tvmovups\t%ymm6, 288(%rsp) ## 32-byte Spill\n",
"\tvaddps\t224(%rsp), %ymm7, %ymm0 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm13, %ymm0, %ymm12, %ymm12\n",
"\tvaddps\t192(%rsp), %ymm6, %ymm0 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm13, %ymm0, %ymm10, %ymm10\n",
"\tvmovdqa\tLCPI0_4(%rip), %xmm0 ## xmm0 = [1,1]\n",
"\tvmovdqa\t%xmm0, %xmm7\n",
"\tvpaddq\t%xmm7, %xmm9, %xmm0\n",
"\tvpaddq\t%xmm7, %xmm4, %xmm6\n",
"\tvinsertf128\t$1, %xmm0, %ymm6, %ymm0\n",
"\tvpaddq\t%xmm7, %xmm1, %xmm1\n",
"\tvpaddq\t%xmm7, %xmm2, %xmm6\n",
"\tvinsertf128\t$1, %xmm1, %ymm6, %ymm1\n",
"\tvunpcklps\t%xmm13, %xmm13, %xmm6 ## xmm6 = xmm13[0,0,1,1]\n",
"\tvunpckhps\t%xmm13, %xmm13, %xmm7 ## xmm7 = xmm13[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm7, %ymm6, %ymm6\n",
"\tvextractf128\t$1, %ymm13, %xmm7\n",
"\tvblendvps\t%ymm6, %ymm1, %ymm2, %ymm2\n",
"\tvunpcklps\t%xmm7, %xmm7, %xmm1 ## xmm1 = xmm7[0,0,1,1]\n",
"\tvunpckhps\t%xmm7, %xmm7, %xmm6 ## xmm6 = xmm7[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm6, %ymm1, %ymm1\n",
"\tvblendvps\t%ymm1, %ymm0, %ymm4, %ymm4\n",
"\tjmp\tLBB0_51\n",
"\t.align\t4, 0x90\n",
"LBB0_41: ## in Loop: Header=BB0_52 Depth=3\n",
"\tvmovups\t352(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvmovups\t320(%rsp), %ymm12 ## 32-byte Reload\n",
"\tjmp\tLBB0_51\n",
"\t.align\t4, 0x90\n",
"LBB0_42: ## %for_exit381\n",
" ## in Loop: Header=BB0_36 Depth=2\n",
"\tvpextrq\t$1, %xmm4, %rbx\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm0\n",
"\tvmovq\t%xmm4, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm0, %xmm3, %xmm0 ## xmm0 = xmm3[0],xmm0[0],xmm3[2,3]\n",
"\tvmovq\t%xmm9, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm0, %xmm0 ## xmm0 = xmm0[0,1],xmm3[0],xmm0[3]\n",
"\tvpextrq\t$1, %xmm9, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm3\n",
"\tvinsertps\t$48, %xmm3, %xmm0, %xmm0 ## xmm0 = xmm0[0,1,2],xmm3[0]\n",
"\tvpextrq\t$1, %xmm2, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm3\n",
"\tvmovq\t%xmm2, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm2\n",
"\tvinsertps\t$16, %xmm3, %xmm2, %xmm2 ## xmm2 = xmm2[0],xmm3[0],xmm2[2,3]\n",
"\tvmovq\t%xmm1, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm2, %xmm2 ## xmm2 = xmm2[0,1],xmm3[0],xmm2[3]\n",
"\tvpextrq\t$1, %xmm1, %rbx\n",
"\tvcvtsi2ssq\t%rbx, %xmm0, %xmm1\n",
"\tvinsertps\t$48, %xmm1, %xmm2, %xmm1 ## xmm1 = xmm2[0,1,2],xmm1[0]\n",
"\tleal\t(%rdx,%rcx), %ebx\n",
"\tleal\t-4(,%rbx,4), %ebx\n",
"\tmovslq\t%ebx, %rbx\n",
"\tvmovups\t%xmm1, (%rdi,%rbx)\n",
"\tvmovups\t%xmm0, 16(%rdi,%rbx)\n",
"\taddl\t$8, %edx\n",
"\tcmpl\t%eax, %edx\n",
"\tjl\tLBB0_36\n",
"\tjmp\tLBB0_43\n",
"\t.align\t4, 0x90\n",
"LBB0_4: ## in Loop: Header=BB0_2 Depth=1\n",
"\tvmovups\t%ymm1, 64(%rsp) ## 32-byte Spill\n",
"LBB0_43: ## %partial_inner_all_outer353\n",
" ## in Loop: Header=BB0_2 Depth=1\n",
"\tleaq\t1(%r9), %r14\n",
"\tcmpl\t%r11d, %edx\n",
"\tvmovups\t(%rsp), %ymm4 ## 32-byte Reload\n",
"\tvmovups\t64(%rsp), %ymm1 ## 32-byte Reload\n",
"\tjge\tLBB0_2\n",
"## BB#44: ## %partial_inner_only485\n",
" ## in Loop: Header=BB0_2 Depth=1\n",
"\tvmovd\t%edx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI0_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI0_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm2\n",
"\tvmovd\t%r11d, %xmm3\n",
"\tvpermilps\t$0, %xmm3, %xmm3 ## xmm3 = xmm3[0,0,0,0]\n",
"\tvpcmpgtd\t%xmm0, %xmm3, %xmm0\n",
"\tvpcmpgtd\t%xmm1, %xmm3, %xmm1\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm14\n",
"\tvmovups\t%ymm14, -96(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r9, %xmm0, %xmm0\n",
"\tvmulss\t60(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t56(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm1\n",
"\tvmovups\t%ymm1, 224(%rsp) ## 32-byte Spill\n",
"\tvcvtdq2ps\t%ymm2, %ymm0\n",
"\tvmulps\t128(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t160(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm0, 192(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm7, %xmm7, %xmm7\n",
"\tvxorps\t%xmm2, %xmm2, %xmm2\n",
"\tvmovaps\t%ymm0, %ymm11\n",
"\tvmovaps\t%ymm1, %ymm10\n",
"\tjmp\tLBB0_45\n",
"\t.align\t4, 0x90\n",
"LBB0_55: ## %safe_if_run_false559\n",
" ## in Loop: Header=BB0_45 Depth=2\n",
"\tvsubps\t%ymm9, %ymm10, %ymm4\n",
"\tvmovups\t-64(%rsp), %ymm9 ## 32-byte Reload\n",
"\tvblendvps\t%ymm14, %ymm4, %ymm9, %ymm9\n",
"\tvmovups\t%ymm9, -64(%rsp) ## 32-byte Spill\n",
"\tvmovups\t320(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvaddps\t%ymm10, %ymm10, %ymm4\n",
"\tvmovups\t352(%rsp), %ymm11 ## 32-byte Reload\n",
"\tvmulps\t%ymm4, %ymm11, %ymm4\n",
"\tvmovups\t-32(%rsp), %ymm6 ## 32-byte Reload\n",
"\tvblendvps\t%ymm14, %ymm4, %ymm6, %ymm6\n",
"\tvmovups\t%ymm6, -32(%rsp) ## 32-byte Spill\n",
"\tvaddps\t224(%rsp), %ymm9, %ymm4 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm14, %ymm4, %ymm10, %ymm10\n",
"\tvaddps\t192(%rsp), %ymm6, %ymm4 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm14, %ymm4, %ymm11, %ymm11\n",
"\tvmovdqa\tLCPI0_4(%rip), %xmm4 ## xmm4 = [1,1]\n",
"\tvmovdqa\t%xmm4, %xmm6\n",
"\tvpaddq\t%xmm6, %xmm3, %xmm3\n",
"\tvpaddq\t%xmm6, %xmm2, %xmm4\n",
"\tvinsertf128\t$1, %xmm3, %ymm4, %ymm3\n",
"\tvpaddq\t%xmm6, %xmm0, %xmm0\n",
"\tvpaddq\t%xmm6, %xmm7, %xmm4\n",
"\tvinsertf128\t$1, %xmm0, %ymm4, %ymm0\n",
"\tvunp"
]
},
{
"data": {
"text/plain": [
"2328"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cklps\t%xmm14, %xmm14, %xmm4 ## xmm4 = xmm14[0,0,1,1]\n",
"\tvunpckhps\t%xmm14, %xmm14, %xmm6 ## xmm6 = xmm14[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm6, %ymm4, %ymm4\n",
"\tvextractf128\t$1, %ymm14, %xmm6\n",
"\tvblendvps\t%ymm4, %ymm0, %ymm7, %ymm7\n",
"\tvunpcklps\t%xmm6, %xmm6, %xmm0 ## xmm0 = xmm6[0,0,1,1]\n",
"\tvunpckhps\t%xmm6, %xmm6, %xmm4 ## xmm4 = xmm6[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm4, %ymm0, %ymm0\n",
"\tvblendvps\t%ymm0, %ymm3, %ymm2, %ymm2\n",
"\tvandnps\t%ymm1, %ymm12, %ymm14\n",
"LBB0_45: ## %for_test516.outer\n",
" ## Parent Loop BB0_2 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB0_46 Depth 3\n",
"\tvmovups\t%ymm10, 320(%rsp) ## 32-byte Spill\n",
"\tvmovups\t%ymm11, 352(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm10, %ymm10, %ymm10\n",
"\tvmulps\t%ymm11, %ymm11, %ymm9\n",
"\tvaddps\t%ymm10, %ymm9, %ymm15\n",
"\t.align\t4, 0x90\n",
"LBB0_46: ## %for_test516\n",
" ## Parent Loop BB0_2 Depth=1\n",
" ## Parent Loop BB0_45 Depth=2\n",
" ## => This Inner Loop Header: Depth=3\n",
"\tvextractf128\t$1, %ymm7, %xmm0\n",
"\tvextractf128\t$1, %ymm5, %xmm1\n",
"\tvpcmpgtq\t%xmm0, %xmm1, %xmm3\n",
"\tvpcmpgtq\t%xmm7, %xmm5, %xmm4\n",
"\tvshufps\t$-120, %xmm3, %xmm4, %xmm3 ## xmm3 = xmm4[0,2],xmm3[0,2]\n",
"\tvpshufb\t%xmm8, %xmm3, %xmm4\n",
"\tvextractf128\t$1, %ymm2, %xmm3\n",
"\tvpcmpgtq\t%xmm3, %xmm1, %xmm1\n",
"\tvpcmpgtq\t%xmm2, %xmm5, %xmm6\n",
"\tvshufps\t$-120, %xmm1, %xmm6, %xmm1 ## xmm1 = xmm6[0,2],xmm1[0,2]\n",
"\tvpshufb\t%xmm8, %xmm1, %xmm1\n",
"\tvpunpcklqdq\t%xmm1, %xmm4, %xmm1 ## xmm1 = xmm4[0],xmm1[0]\n",
"\tvpmovzxwd\t%xmm1, %xmm4\n",
"\tvpslld\t$31, %xmm4, %xmm4\n",
"\tvpsrad\t$31, %xmm4, %xmm4\n",
"\tvpunpckhwd\t%xmm1, %xmm1, %xmm1 ## xmm1 = xmm1[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm1, %xmm1\n",
"\tvpsrad\t$31, %xmm1, %xmm1\n",
"\tvinsertf128\t$1, %xmm1, %ymm4, %ymm1\n",
"\tvandps\t%ymm14, %ymm1, %ymm1\n",
"\tvmovmskps\t%ymm1, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_53\n",
"## BB#47: ## %for_loop518\n",
" ## in Loop: Header=BB0_46 Depth=3\n",
"\tvcmpnleps\tLCPI0_3(%rip), %ymm15, %ymm13\n",
"\tvandps\t%ymm1, %ymm13, %ymm11\n",
"\tvmovmskps\t%ymm11, %ebx\n",
"\tvxorps\t%xmm12, %xmm12, %xmm12\n",
"\ttestl\t%ebx, %ebx\n",
"\tje\tLBB0_48\n",
"## BB#54: ## %safe_if_run_true540\n",
" ## in Loop: Header=BB0_46 Depth=3\n",
"\tvxorps\t%xmm14, %xmm14, %xmm14\n",
"\tvmovaps\t%ymm11, %ymm12\n",
"\tcmpl\t%ecx, %ebx\n",
"\tje\tLBB0_46\n",
"LBB0_48: ## %safe_if_after_true539\n",
" ## in Loop: Header=BB0_45 Depth=2\n",
"\tvblendvps\t%ymm13, LCPI0_5(%rip), %ymm1, %ymm14\n",
"\tvmovmskps\t%ymm14, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tjne\tLBB0_55\n",
"## BB#49: ## in Loop: Header=BB0_45 Depth=2\n",
"\tvmovups\t352(%rsp), %ymm11 ## 32-byte Reload\n",
"\tvmovups\t320(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvandnps\t%ymm1, %ymm12, %ymm14\n",
"\tjmp\tLBB0_45\n",
"\t.align\t4, 0x90\n",
"LBB0_31: ## %for_exit156\n",
" ## in Loop: Header=BB0_6 Depth=1\n",
"\tdecl\t%r15d\n",
"\tmovl\t%esi, %ecx\n",
"\timull\t%r15d, %ecx\n",
"\tvpextrq\t$1, %xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm0\n",
"\tvinsertps\t$16, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0],xmm1[0],xmm0[2,3]\n",
"\tvmovq\t%xmm3, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvinsertps\t$32, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0,1],xmm1[0],xmm0[3]\n",
"\tvpextrq\t$1, %xmm3, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvinsertps\t$48, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0,1,2],xmm1[0]\n",
"\tvpextrq\t$1, %xmm14, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm14, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm1, %xmm3, %xmm1 ## xmm1 = xmm3[0],xmm1[0],xmm3[2,3]\n",
"\tvmovq\t%xmm2, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm1, %xmm1 ## xmm1 = xmm1[0,1],xmm3[0],xmm1[3]\n",
"\tvpextrq\t$1, %xmm2, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm2\n",
"\tvinsertps\t$48, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1,2],xmm2[0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm0\n",
"\taddl\t%ecx, %ebx\n",
"\tleal\t-4(,%rbx,4), %ecx\n",
"\tmovslq\t%ecx, %rcx\n",
"\tvmovups\t-64(%rsp), %ymm1 ## 32-byte Reload\n",
"\tvmaskmovps\t%ymm0, %ymm1, (%rdi,%rcx)\n",
"\tvmovups\t64(%rsp), %ymm1 ## 32-byte Reload\n",
"LBB0_6: ## %for_test.outer\n",
" ## =>This Loop Header: Depth=1\n",
" ## Child Loop BB0_8 Depth 2\n",
" ## Child Loop BB0_12 Depth 2\n",
" ## Child Loop BB0_14 Depth 3\n",
" ## Child Loop BB0_22 Depth 4\n",
" ## Child Loop BB0_15 Depth 5\n",
" ## Child Loop BB0_26 Depth 2\n",
" ## Child Loop BB0_27 Depth 3\n",
"\tcmpl\t$1, %eax\n",
"\tjle\tLBB0_7\n",
"\t.align\t4, 0x90\n",
"LBB0_12: ## %for_test.us\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB0_14 Depth 3\n",
" ## Child Loop BB0_22 Depth 4\n",
" ## Child Loop BB0_15 Depth 5\n",
"\tmovq\t%r14, %r15\n",
"\tcmpq\t%r10, %r15\n",
"\tmovl\t$0, %ecx\n",
"\tcmovel\t%r8d, %ecx\n",
"\tvmovd\t%ecx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvandnps\t%ymm1, %ymm0, %ymm1\n",
"\tvmovmskps\t%ymm1, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_9\n",
"## BB#13: ## %foreach_full_body.lr.ph.us\n",
" ## in Loop: Header=BB0_12 Depth=2\n",
"\tvmovups\t%ymm1, 64(%rsp) ## 32-byte Spill\n",
"\tleaq\t1(%r15), %r14\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r15, %xmm0, %xmm0\n",
"\tvmulss\t60(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t56(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 224(%rsp) ## 32-byte Spill\n",
"\tleaq\t-1(%r15), %rdx\n",
"\timulq\t%rsi, %rdx\n",
"\tmovl\t$1, %ebx\n",
"\t.align\t4, 0x90\n",
"LBB0_14: ## %foreach_full_body.us\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## Parent Loop BB0_12 Depth=2\n",
" ## => This Loop Header: Depth=3\n",
" ## Child Loop BB0_22 Depth 4\n",
" ## Child Loop BB0_15 Depth 5\n",
"\tvmovd\t%ebx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI0_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI0_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm0\n",
"\tvcvtdq2ps\t%ymm0, %ymm0\n",
"\tvmulps\t128(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t160(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm0, 192(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm14, %xmm14, %xmm14\n",
"\tvxorps\t%xmm9, %xmm9, %xmm9\n",
"\tvmovaps\t%ymm0, %ymm11\n",
"\tvmovups\t224(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvmovups\t96(%rsp), %ymm13 ## 32-byte Reload\n",
"\tjmp\tLBB0_22\n",
"\t.align\t4, 0x90\n",
"LBB0_21: ## %if_done.us\n",
" ## in Loop: Header=BB0_22 Depth=4\n",
"\tvandnps\t%ymm3, %ymm10, %ymm13\n",
"LBB0_22: ## %for_test52.outer.us\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## Parent Loop BB0_12 Depth=2\n",
" ## Parent Loop BB0_14 Depth=3\n",
" ## => This Loop Header: Depth=4\n",
" ## Child Loop BB0_15 Depth 5\n",
"\tvmovups\t%ymm12, 320(%rsp) ## 32-byte Spill\n",
"\tvmovups\t%ymm11, 352(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm12, %ymm12, %ymm6\n",
"\tvmulps\t%ymm11, %ymm11, %ymm15\n",
"\tvaddps\t%ymm6, %ymm15, %ymm7\n",
"\t.align\t4, 0x90\n",
"LBB0_15: ## %for_test52.us\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## Parent Loop BB0_12 Depth=2\n",
" ## Parent Loop BB0_14 Depth=3\n",
" ## Parent Loop BB0_22 Depth=4\n",
" ## => This Inner Loop Header: Depth=5\n",
"\tvextractf128\t$1, %ymm14, %xmm1\n",
"\tvextractf128\t$1, %ymm5, %xmm3\n",
"\tvpcmpgtq\t%xmm1, %xmm3, %xmm0\n",
"\tvpcmpgtq\t%xmm14, %xmm5, %xmm2\n",
"\tvshufps\t$-120, %xmm0, %xmm2, %xmm0 ## xmm0 = xmm2[0,2],xmm0[0,2]\n",
"\tvpshufb\t%xmm4, %xmm0, %xmm0\n",
"\tvextractf128\t$1, %ymm9, %xmm8\n",
"\tvpcmpgtq\t%xmm8, %xmm3, %xmm2\n",
"\tvpcmpgtq\t%xmm9, %xmm5, %xmm3\n",
"\tvshufps\t$-120, %xmm2, %xmm3, %xmm2 ## xmm2 = xmm3[0,2],xmm2[0,2]\n",
"\tvpshufb\t%xmm4, %xmm2, %xmm2\n",
"\tvpunpcklqdq\t%xmm2, %xmm0, %xmm0 ## xmm0 = xmm0[0],xmm2[0]\n",
"\tvpmovzxwd\t%xmm0, %xmm2\n",
"\tvpslld\t$31, %xmm2, %xmm2\n",
"\tvpsrad\t$31, %xmm2, %xmm2\n",
"\tvpunpckhwd\t%xmm0, %xmm0, %xmm0 ## xmm0 = xmm0[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm0, %xmm0\n",
"\tvpsrad\t$31, %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm2, %ymm0\n",
"\tvandps\t%ymm13, %ymm0, %ymm3\n",
"\tvmovmskps\t%ymm3, %ebp\n",
"\ttestl\t%ebp, %ebp\n",
"\tje\tLBB0_10\n",
"## BB#16: ## %for_loop54.us\n",
" ## in Loop: Header=BB0_15 Depth=5\n",
"\tvcmpnleps\tLCPI0_3(%rip), %ymm7, %ymm12\n",
"\tvandps\t%ymm3, %ymm12, %ymm11\n",
"\tvmovmskps\t%ymm11, %ecx\n",
"\tvxorps\t%xmm10, %xmm10, %xmm10\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_18\n",
"## BB#17: ## %safe_if_run_true.us\n",
" ## in Loop: Header=BB0_15 Depth=5\n",
"\tvxorps\t%xmm13, %xmm13, %xmm13\n",
"\tvmovaps\t%ymm11, %ymm10\n",
"\tcmpl\t%ebp, %ecx\n",
"\tje\tLBB0_15\n",
"LBB0_18: ## %safe_if_after_true.us\n",
" ## in Loop: Header=BB0_22 Depth=4\n",
"\tvblendvps\t%ymm12, LCPI0_5(%rip), %ymm3, %ymm7\n",
"\tvmovmskps\t%ymm7, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_19\n",
"## BB#20: ## %safe_if_run_false.us\n",
" ## in Loop: Header=BB0_22 Depth=4\n",
"\tvsubps\t%ymm15, %ymm6, %ymm0\n",
"\tvmovups\t256(%rsp), %ymm6 ## 32-byte Reload\n",
"\tvblendvps\t%ymm7, %ymm0, %ymm6, %ymm6\n",
"\tvmovups\t%ymm6, 256(%rsp) ## 32-byte Spill\n",
"\tvmovups\t320(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvaddps\t%ymm12, %ymm12, %ymm0\n",
"\tvmovups\t352(%rsp), %ymm11 ## 32-byte Reload\n",
"\tvmulps\t%ymm0, %ymm11, %ymm0\n",
"\tvmovups\t288(%rsp), %ymm2 ## 32-byte Reload\n",
"\tvblendvps\t%ymm7, %ymm0, %ymm2, %ymm2\n",
"\tvmovups\t%ymm2, 288(%rsp) ## 32-byte Spill\n",
"\tvaddps\t224(%rsp), %ymm6, %ymm0 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm7, %ymm0, %ymm12, %ymm12\n",
"\tvaddps\t192(%rsp), %ymm2, %ymm0 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm7, %ymm0, %ymm11, %ymm11\n",
"\tvmovdqa\tLCPI0_4(%rip), %xmm0 ## xmm0 = [1,1]\n",
"\tvmovdqa\t%xmm0, %xmm6\n",
"\tvpaddq\t%xmm6, %xmm8, %xmm0\n",
"\tvpaddq\t%xmm6, %xmm9, %xmm2\n",
"\tvinsertf128\t$1, %xmm0, %ymm2, %ymm0\n",
"\tvpaddq\t%xmm6, %xmm1, %xmm1\n",
"\tvpaddq\t%xmm6, %xmm14, %xmm2\n",
"\tvinsertf128\t$1, %xmm1, %ymm2, %ymm1\n",
"\tvunpcklps\t%xmm7, %xmm7, %xmm2 ## xmm2 = xmm7[0,0,1,1]\n",
"\tvunpckhps\t%xmm7, %xmm7, %xmm6 ## xmm6 = xmm7[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm6, %ymm2, %ymm2\n",
"\tvextractf128\t$1, %ymm7, %xmm6\n",
"\tvblendvps\t%ymm2, %ymm1, %ymm14, %ymm14\n",
"\tvunpcklps\t%xmm6, %xmm6, %xmm1 ## xmm1 = xmm6[0,0,1,1]\n",
"\tvunpckhps\t%xmm6, %xmm6, %xmm2 ## xmm2 = xmm6[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm2, %ymm1, %ymm1\n",
"\tvblendvps\t%ymm1, %ymm0, %ymm9, %ymm9\n",
"\tjmp\tLBB0_21\n",
"\t.align\t4, 0x90\n",
"LBB0_19: ## in Loop: Header=BB0_22 Depth=4\n",
"\tvmovups\t352(%rsp), %ymm11 ## 32-byte Reload\n",
"\tvmovups\t320(%rsp), %ymm12 ## 32-byte Reload\n",
"\tjmp\tLBB0_21\n",
"\t.align\t4, 0x90\n",
"LBB0_10: ## %for_exit55.us\n",
" ## in Loop: Header=BB0_14 Depth=3\n",
"\tvpextrq\t$1, %xmm9, %rcx\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm0\n",
"\tvmovq\t%xmm9, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm2\n",
"\tvinsertps\t$16, %xmm0, %xmm2, %xmm0 ## xmm0 = xmm2[0],xmm0[0],xmm2[2,3]\n",
"\tvmovq\t%xmm8, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm2\n",
"\tvinsertps\t$32, %xmm2, %xmm0, %xmm0 ## xmm0 = xmm0[0,1],xmm2[0],xmm0[3]\n",
"\tvpextrq\t$1, %xmm8, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm2\n",
"\tvinsertps\t$48, %xmm2, %xmm0, %xmm0 ## xmm0 = xmm0[0,1,2],xmm2[0]\n",
"\tvpextrq\t$1, %xmm14, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm2\n",
"\tvmovq\t%xmm14, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm2, %xmm3, %xmm2 ## xmm2 = xmm3[0],xmm2[0],xmm3[2,3]\n",
"\tvmovq\t%xmm1, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm2, %xmm2 ## xmm2 = xmm2[0,1],xmm3[0],xmm2[3]\n",
"\tvpextrq\t$1, %xmm1, %rcx\n",
"\tvcvtsi2ssq\t%rcx, %xmm0, %xmm1\n",
"\tvinsertps\t$48, %xmm1, %xmm2, %xmm1 ## xmm1 = xmm2[0,1,2],xmm1[0]\n",
"\tleal\t(%rbx,%rdx), %ecx\n",
"\tleal\t-4(,%rcx,4), %ecx\n",
"\tmovslq\t%ecx, %rcx\n",
"\tvmovups\t%xmm1, (%rdi,%rcx)\n",
"\tvmovups\t%xmm0, 16(%rdi,%rcx)\n",
"\taddl\t$8, %ebx\n",
"\tcmpl\t%eax, %ebx\n",
"\tjl\tLBB0_14\n",
"## BB#11: ## %partial_inner_all_outer.us\n",
" ## in Loop: Header=BB0_12 Depth=2\n",
"\tcmpl\t%r11d, %ebx\n",
"\tvmovups\t64(%rsp), %ymm1 ## 32-byte Reload\n",
"\tjge\tLBB0_12\n",
"\tjmp\tLBB0_25\n",
"\t.align\t4, 0x90\n",
"LBB0_7: ## %for_test.preheader\n",
" ## in Loop: Header=BB0_6 Depth=1\n",
"\tdecq\t%r14\n",
"\tmovq\t%r14, %r15\n",
"\t.align\t4, 0x90\n",
"LBB0_8: ## %for_test\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## => This Inner Loop Header: Depth=2\n",
"\tcmpq\t%r15, %r9\n",
"\tmovl\t$0, %ecx\n",
"\tcmovel\t%r8d, %ecx\n",
"\tvmovd\t%ecx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvandnps\t%ymm1, %ymm0, %ymm1\n",
"\tvmovmskps\t%ymm1, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_9\n",
"## BB#23: ## %partial_inner_all_outer\n",
" ## in Loop: Header=BB0_8 Depth=2\n",
"\tincq\t%r15\n",
"\tcmpl\t$2, %r11d\n",
"\tjl\tLBB0_8\n",
"## BB#24: ## %partial_inner_only.loopexit913\n",
" ## in Loop: Header=BB0_6 Depth=1\n",
"\tleaq\t1(%r15), %r14\n",
"\tmovl\t$1, %ebx\n",
"LBB0_25: ## %partial_inner_only\n",
" ## in Loop: Header=BB0_6 Depth=1\n",
"\tvmovups\t%ymm1, 64(%rsp) ## 32-byte Spill\n",
"\tvmovd\t%ebx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI0_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI0_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm2\n",
"\tvmovd\t%r11d, %xmm3\n",
"\tvpermilps\t$0, %xmm3, %xmm3 ## xmm3 = xmm3[0,0,0,0]\n",
"\tvpcmpgtd\t%xmm0, %xmm3, %xmm0\n",
"\tvpcmpgtd\t%xmm1, %xmm3, %xmm1\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm11\n",
"\tvmovups\t%ymm11, -64(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r15, %xmm0, %xmm0\n",
"\tvmulss\t60(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t56(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm1\n",
"\tvmovups\t%ymm1, 224(%rsp) ## 32-byte Spill\n",
"\tvcvtdq2ps\t%ymm2, %ymm0\n",
"\tvmulps\t128(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t160(%rsp), %ymm0, %ymm2 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm2, 192(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm14, %xmm14, %xmm14\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvmovaps\t%ymm2, %ymm10\n",
"\tvmovaps\t%ymm1, %ymm9\n",
"\tjmp\tLBB0_26\n",
"\t.align\t4, 0x90\n",
"LBB0_33: ## %safe_if_run_false196\n",
" ## in Loop: Header=BB0_26 Depth=2\n",
"\tvsubps\t%ymm8, %ymm9, %ymm6\n",
"\tvmovups\t-32(%rsp), %ymm8 ## 32-byte Reload\n",
"\tvblendvps\t%ymm11, %ymm6, %ymm8, %ymm8\n",
"\tvmovups\t%ymm8, -32(%rsp) ## 32-byte Spill\n",
"\tvmovups\t320(%rsp), %ymm9 ## 32-byte Reload\n",
"\tvaddps\t%ymm9, %ymm9, %ymm6\n",
"\tvmovups\t352(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvmulps\t%ymm6, %ymm10, %ymm6\n",
"\tvmovups\t(%rsp), %ymm7 ## 32-byte Reload\n",
"\tvblendvps\t%ymm11, %ymm6, %ymm7, %ymm7\n",
"\tvmovups\t%ymm7, (%rsp) ## 32-byte Spill\n",
"\tvaddps\t224(%rsp), %ymm8, %ymm6 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm11, %ymm6, %ymm9, %ymm9\n",
"\tvaddps\t192(%rsp), %ymm7, %ymm6 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm11, %ymm6, %ymm10, %ymm10\n",
"\tvmovdqa\tLCPI0_4(%rip), %xmm6 ## xmm6 = [1,1]\n",
"\tvmovdqa\t%xmm6, %xmm7\n",
"\tvpaddq\t%xmm7, %xmm3, %xmm3\n",
"\tvpaddq\t%xmm7, %xmm0, %xmm6\n",
"\tvinsertf128\t$1, %xmm3, %ymm6, %ymm3\n",
"\tvpaddq\t%xmm7, %xmm2, %xmm2\n",
"\tvpaddq\t%xmm7, %xmm14, %xmm6\n",
"\tvinsertf128\t$1, %xmm2, %ymm6, %ymm2\n",
"\tvunpcklps\t%xmm11, %xmm11, %xmm6 ## xmm6 = xmm11[0,0,1,1]\n",
"\tvunpckhps\t%xmm11, %xmm11, %xmm7 ## xmm7 = xmm11[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm7, %ymm6, %ymm6\n",
"\tvextractf128\t$1, %ymm11, %xmm7\n",
"\tvblendvps\t%ymm6, %ymm2, %ymm14, %ymm14\n",
"\tvunpcklps\t%xmm7, %xmm7, %xmm2 ## xmm2 = xmm7[0,0,1,1]\n",
"\tvunpckhps\t%xmm7, %xmm7, %xmm6 ## xmm6 = xmm7[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm6, %ymm2, %ymm2\n",
"\tvblendvps\t%ymm2, %ymm3, %ymm0, %ymm0\n",
"\tvandnps\t%ymm1, %ymm12, %ymm11\n",
"LBB0_26: ## %for_test153.outer\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB0_27 Depth 3\n",
"\tvmovups\t%ymm9, 320(%rsp) ## 32-byte Spill\n",
"\tvmovups\t%ymm10, 352(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm9, %ymm9, %ymm9\n",
"\tvmulps\t%ymm10, %ymm10, %ymm8\n",
"\tvaddps\t%ymm9, %ymm8, %ymm13\n",
"\t.align\t4, 0x90\n",
"LBB0_27: ## %for_test153\n",
" ## Parent Loop BB0_6 Depth=1\n",
" ## Parent Loop BB0_26 Depth=2\n",
" ## => This Inner Loop Header: Depth=3\n",
"\tvextractf128\t$1, %ymm14, %xmm2\n",
"\tvextractf128\t$1, %ymm5, %xmm1\n",
"\tvpcmpgtq\t%xmm2, %xmm1, %xmm3\n",
"\tvpcmpgtq\t%xmm14, %xmm5, %xmm7\n",
"\tvshufps\t$-120, %xmm3, %xmm7, %xmm3 ## xmm3 = xmm7[0,2],xmm3[0,2]\n",
"\tvpshufb\t%xmm4, %xmm3, %xmm7\n",
"\tvextractf128\t$1, %ymm0, %xmm3\n",
"\tvpcmpgtq\t%xmm3, %xmm1, %xmm1\n",
"\tvpcmpgtq\t%xmm0, %xmm5, %xmm6\n",
"\tvshufps\t$-120, %xmm1, %xmm6, %xmm1 ## xmm1 = xmm6[0,2],xmm1[0,2]\n",
"\tvpshufb\t%xmm4, %xmm1, %xmm1\n",
"\tvpunpcklqdq\t%xmm1, %xmm7, %xmm1 ## xmm1 = xmm7[0],xmm1[0]\n",
"\tvpmovzxwd\t%xmm1, %xmm6\n",
"\tvpslld\t$31, %xmm6, %xmm6\n",
"\tvpsrad\t$31, %xmm6, %xmm6\n",
"\tvpunpckhwd\t%xmm1, %xmm1, %xmm1 ## xmm1 = xmm1[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm1, %xmm1\n",
"\tvpsrad\t$31, %xmm1, %xmm1\n",
"\tvinsertf128\t$1, %xmm1, %ymm6, %ymm1\n",
"\tvandps\t%ymm11, %ymm1, %ymm1\n",
"\tvmovmskps\t%ymm1, %edx\n",
"\ttestl\t%edx, %edx\n",
"\tje\tLBB0_31\n",
"## BB#28: ## %for_loop155\n",
" ## in Loop: Header=BB0_27 Depth=3\n",
"\tvcmpnleps\tLCPI0_3(%rip), %ymm13, %ymm15\n",
"\tvandps\t%ymm1, %ymm15, %ymm10\n",
"\tvmovmskps\t%ymm10, %ecx\n",
"\tvxorps\t%xmm12, %xmm12, %xmm12\n",
"\ttestl\t%ecx, %ecx\n",
"\tje\tLBB0_29\n",
"## BB#32: ## %safe_if_run_true177\n",
" ## in Loop: Header=BB0_27 Depth=3\n",
"\tvxorps\t%xmm11, %xmm11, %xmm11\n",
"\tvmovaps\t%ymm10, %ymm12\n",
"\tcmpl\t%edx, %ecx\n",
"\tje\tLBB0_27\n",
"LBB0_29: ## %safe_if_after_true176\n",
" ## in Loop: Header=BB0_26 Depth=2\n",
"\tvblendvps\t%ymm15, LCPI0_5(%rip), %ymm1, %ymm11\n",
"\tvmovmskps\t%ymm11, %ecx\n",
"\ttestl\t%ecx, %ecx\n",
"\tjne\tLBB0_33\n",
"## BB#30: ## in Loop: Header=BB0_26 Depth=2\n",
"\tvmovups\t352(%rsp), %ymm10 ## 32-byte Reload\n",
"\tvmovups\t320(%rsp), %ymm9 ## 32-byte Reload\n",
"\tvandnps\t%ymm1, %ymm12, %ymm11\n",
"\tjmp\tLBB0_26\n",
"LBB0_9: ## %for_exit\n",
"\taddq\t$408, %rsp ## imm = 0x198\n",
"\tpopq\t%rbx\n",
"\tpopq\t%r14\n",
"\tpopq\t%r15\n",
"\tpopq\t%rbp\n",
"\tvzeroupper\n",
"\tretq\n",
"\n",
"\t.section\t__TEXT,__literal16,16byte_literals\n",
"\t.align\t4\n",
"LCPI1_0:\n",
"\t.long\t0 ## 0x0\n",
"\t.long\t1 ## 0x1\n",
"\t.long\t2 ## 0x2\n",
"\t.long\t3 ## 0x3\n",
"LCPI1_1:\n",
"\t.long\t4 ## 0x4\n",
"\t.long\t5 ## 0x5\n",
"\t.long\t6 ## 0x6\n",
"\t.long\t7 ## 0x7\n",
"LCPI1_2:\n",
"\t.byte\t0 ## 0x0\n",
"\t.byte\t1 ## 0x1\n",
"\t.byte\t4 ## 0x4\n",
"\t.byte\t5 ## 0x5\n",
"\t.byte\t8 ## 0x8\n",
"\t.byte\t9 ## 0x9\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t8 ## 0x8\n",
"\t.byte\t9 ## 0x9\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t12 ## 0xc\n",
"\t.byte\t13 ## 0xd\n",
"\t.byte\t14 ## 0xe\n",
"\t.byte\t15 ## 0xf\n",
"LCPI1_4:\n",
"\t.quad\t1 ## 0x1\n",
"\t.quad\t1 ## 0x1\n",
"\t.section\t__TEXT,__const\n",
"\t.align\t5\n",
"LCPI1_3:\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"\t.long\t1082130432 ## float 4.000000e+00\n",
"LCPI1_5:\n",
"\t.space\t32\n",
"\t.section\t__TEXT,__text,regular,pure_instructions\n",
"\t.globl\t_ispc_func_1\n",
"\t.align\t4, 0x90\n",
"_ispc_func_1: ## @ispc_func_1\n",
"## BB#0: ## %allocas\n",
"\tpushq\t%rbp\n",
"\tpushq\t%r14\n",
"\tpushq\t%rbx\n",
"\tsubq\t$320, %rsp ## imm = 0x140\n",
"\tvmovss\t%xmm2, 28(%rsp) ## 4-byte Spill\n",
"\tvmovss\t%xmm0, 24(%rsp) ## 4-byte Spill\n",
"\ttestq\t%r9, %r9\n",
"\tleaq\t1(%r9), %r10\n",
"\tmovl\t$1, %r11d\n",
"\tcmovleq\t%r11, %r10\n",
"\tleal\t1(%r8), %r9d\n",
"\tmovl\t%r8d, %eax\n",
"\tsarl\t$31, %eax\n",
"\tshrl\t$29, %eax\n",
"\taddl\t%r8d, %eax\n",
"\tandl\t$-8, %eax\n",
"\tmovl\t%r8d, %edx\n",
"\tsubl\t%eax, %edx\n",
"\tnegl\t%edx\n",
"\tleal\t1(%r8,%rdx), %ebp\n",
"\tvpermilps\t$0, %xmm1, %xmm0 ## xmm0 = xmm1[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 128(%rsp) ## 32-byte Spill\n",
"\tvpermilps\t$0, %xmm3, %xmm0 ## xmm0 = xmm3[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 96(%rsp) ## 32-byte Spill\n",
"\tvmovq\t%rcx, %xmm0\n",
"\tvpcmpeqd\t%xmm1, %xmm1, %xmm1\n",
"\tvinsertf128\t$1, %xmm1, %ymm1, %ymm2\n",
"\tvunpcklpd\t%xmm0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm5\n",
"\tmovl\t$-1, %r8d\n",
"\tvinsertf128\t$1, %xmm1, %ymm1, %ymm0\n",
"\tvmovups\t%ymm0, 64(%rsp) ## 32-byte Spill\n",
"\tvmovdqa\tLCPI1_2(%rip), %xmm4 ## xmm4 = [0,1,4,5,8,9,12,13,8,9,12,13,12,13,14,15]\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, -32(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, -64(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 256(%rsp) ## 32-byte Spill\n",
" ## implicit-def: YMM0\n",
"\tvmovups\t%ymm0, 224(%rsp) ## 32-byte Spill\n",
"\tjmp\tLBB1_1\n",
"LBB1_23: ## %for_exit156\n",
" ## in Loop: Header=BB1_1 Depth=1\n",
"\tdecl\t%r14d\n",
"\tmovl\t%esi, %eax\n",
"\timull\t%r14d, %eax\n",
"\tvpextrq\t$1, %xmm9, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm9, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm1, %xmm3, %xmm1 ## xmm1 = xmm3[0],xmm1[0],xmm3[2,3]\n",
"\tvmovq\t%xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm1, %xmm1 ## xmm1 = xmm1[0,1],xmm3[0],xmm1[3]\n",
"\tvpextrq\t$1, %xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm0\n",
"\tvinsertps\t$48, %xmm0, %xmm1, %xmm0 ## xmm0 = xmm1[0,1,2],xmm0[0]\n",
"\tvpextrq\t$1, %xmm14, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm14, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$16, %xmm1, %xmm3, %xmm1 ## xmm1 = xmm3[0],xmm1[0],xmm3[2,3]\n",
"\tvmovq\t%xmm2, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm3\n",
"\tvinsertps\t$32, %xmm3, %xmm1, %xmm1 ## xmm1 = xmm1[0,1],xmm3[0],xmm1[3]\n",
"\tvpextrq\t$1, %xmm2, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm2\n",
"\tvinsertps\t$48, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1,2],xmm2[0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm0\n",
"\taddl\t%eax, %ecx\n",
"\tleal\t-4(,%rcx,4), %eax\n",
"\tcltq\n",
"\tvmovups\t-96(%rsp), %ymm1 ## 32-byte Reload\n",
"\tvmaskmovps\t%ymm0, %ymm1, (%rdi,%rax)\n",
"\tvmovups\t32(%rsp), %ymm2 ## 32-byte Reload\n",
"\t.align\t4, 0x90\n",
"LBB1_1: ## %for_test\n",
" ## =>This Loop Header: Depth=1\n",
" ## Child Loop BB1_6 Depth 2\n",
" ## Child Loop BB1_22 Depth 3\n",
" ## Child Loop BB1_8 Depth 4\n",
" ## Child Loop BB1_15 Depth 2\n",
" ## Child Loop BB1_16 Depth 3\n",
"\tmovq\t%r11, %r14\n",
"\tcmpq\t%r10, %r14\n",
"\tmovl\t$0, %eax\n",
"\tcmovel\t%r8d, %eax\n",
"\tvmovd\t%eax, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvandnps\t%ymm2, %ymm0, %ymm2\n",
"\tvmovmskps\t%ymm2, %eax\n",
"\ttestl\t%eax, %eax\n",
"\tje\tLBB1_4\n",
"## BB#2: ## %for_loop\n",
" ## in Loop: Header=BB1_1 Depth=1\n",
"\tmovl\t$1, %ecx\n",
"\tcmpl\t$2, %ebp\n",
"\tjl\tLBB1_3\n",
"## BB#5: ## %foreach_full_body.lr.ph\n",
" ## in Loop: Header=BB1_1 Depth=1\n",
"\tvmovups\t%ymm2, 32(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r14, %xmm0, %xmm0\n",
"\tvmulss\t28(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t24(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm0\n",
"\tvmovups\t%ymm0, 192(%rsp) ## 32-byte Spill\n",
"\tleaq\t-1(%r14), %rax\n",
"\timulq\t%rsi, %rax\n",
"\tmovl\t$1, %ecx\n",
"\t.align\t4, 0x90\n",
"LBB1_6: ## %foreach_full_body\n",
" ## Parent Loop BB1_1 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB1_22 Depth 3\n",
" ## Child Loop BB1_8 Depth 4\n",
"\tvmovd\t%ecx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI1_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI1_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm0\n",
"\tvcvtdq2ps\t%ymm0, %ymm0\n",
"\tvmulps\t96(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t128(%rsp), %ymm0, %ymm14 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm14, 160(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm9, %xmm9, %xmm9\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvmovups\t192(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvmovups\t64(%rsp), %ymm13 ## 32-byte Reload\n",
"\tjmp\tLBB1_22\n",
"\t.align\t4, 0x90\n",
"LBB1_21: ## %if_done\n",
" ## in Loop: Header=BB1_22 Depth=3\n",
"\tvandnps\t%ymm1, %ymm10, %ymm13\n",
"LBB1_22: ## %for_test52.outer\n",
" ## Parent Loop BB1_1 Depth=1\n",
" ## Parent Loop BB1_6 Depth=2\n",
" ## => This Loop Header: Depth=3\n",
" ## Child Loop BB1_8 Depth 4\n",
"\tvmovups\t%ymm12, 288(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm12, %ymm12, %ymm6\n",
"\tvmulps\t%ymm14, %ymm14, %ymm15\n",
"\tvaddps\t%ymm6, %ymm15, %ymm7\n",
"\t.align\t4, 0x90\n",
"LBB1_8: ## %for_test52\n",
" ## Parent Loop BB1_1 Depth=1\n",
" ## Parent Loop BB1_6 Depth=2\n",
" ## Parent Loop BB1_22 Depth=3\n",
" ## => This Inner Loop Header: Depth=4\n",
"\tvextractf128\t$1, %ymm9, %xmm11\n",
"\tvextractf128\t$1, %ymm5, %xmm1\n",
"\tvpcmpgtq\t%xmm11, %xmm1, %xmm3\n",
"\tvpcmpgtq\t%xmm9, %xmm5, %xmm2\n",
"\tvshufps\t$-120, %xmm3, %xmm2, %xmm2 ## xmm2 = xmm2[0,2],xmm3[0,2]\n",
"\tvpshufb\t%xmm4, %xmm2, %xmm2\n",
"\tvextractf128\t$1, %ymm0, %xmm8\n",
"\tvpcmpgtq\t%xmm8, %xmm1, %xmm1\n",
"\tvpcmpgtq\t%xmm0, %xmm5, %xmm3\n",
"\tvshufps\t$-120, %xmm1, %xmm3, %xmm1 ## xmm1 = xmm3[0,2],xmm1[0,2]\n",
"\tvpshufb\t%xmm4, %xmm1, %xmm1\n",
"\tvpunpcklqdq\t%xmm1, %xmm2, %xmm1 ## xmm1 = xmm2[0],xmm1[0]\n",
"\tvpmovzxwd\t%xmm1, %xmm2\n",
"\tvpslld\t$31, %xmm2, %xmm2\n",
"\tvpsrad\t$31, %xmm2, %xmm2\n",
"\tvpunpckhwd\t%xmm1, %xmm1, %xmm1 ## xmm1 = xmm1[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm1, %xmm1\n",
"\tvpsrad\t$31, %xmm1, %xmm1\n",
"\tvinsertf128\t$1, %xmm1, %ymm2, %ymm1\n",
"\tvandps\t%ymm13, %ymm1, %ymm1\n",
"\tvmovmskps\t%ymm1, %ebx\n",
"\ttestl\t%ebx, %ebx\n",
"\tje\tLBB1_12\n",
"## BB#9: ## %for_loop54\n",
" ## in Loop: Header=BB1_8 Depth=4\n",
"\tvcmpnleps\tLCPI1_3(%rip), %ymm7, %ymm12\n",
"\tvandps\t%ymm1, %ymm12, %ymm3\n",
"\tvmovmskps\t%ymm3, %edx\n",
"\tvxorps\t%xmm10, %xmm10, %xmm10\n",
"\ttestl\t%edx, %edx\n",
"\tje\tLBB1_10\n",
"## BB#7: ## %safe_if_run_true\n",
" ## in Loop: Header=BB1_8 Depth=4\n",
"\tvxorps\t%xmm13, %xmm13, %xmm13\n",
"\tvmovaps\t%ymm3, %ymm10\n",
"\tcmpl\t%ebx, %edx\n",
"\tje\tLBB1_8\n",
"LBB1_10: ## %safe_if_after_true\n",
" ## in Loop: Header=BB1_22 Depth=3\n",
"\tvblendvps\t%ymm12, LCPI1_5(%rip), %ymm1, %ymm7\n",
"\tvmovmskps\t%ymm7, %edx\n",
"\ttestl\t%edx, %edx\n",
"\tje\tLBB1_11\n",
"## BB#20: ## %safe_if_run_false\n",
" ## in Loop: Header=BB1_22 Depth=3\n",
"\tvsubps\t%ymm15, %ymm6, %ymm2\n",
"\tvmovups\t224(%rsp), %ymm6 ## 32-byte Reload\n",
"\tvblendvps\t%ymm7, %ymm2, %ymm6, %ymm6\n",
"\tvmovups\t%ymm6, 224(%rsp) ## 32-byte Spill\n",
"\tvmovups\t288(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvaddps\t%ymm12, %ymm12, %ymm2\n",
"\tvmulps\t%ymm2, %ymm14, %ymm2\n",
"\tvmovups\t256(%rsp), %ymm3 ## 32-byte Reload\n",
"\tvblendvps\t%ymm7, %ymm2, %ymm3, %ymm3\n",
"\tvmovups\t%ymm3, 256(%rsp) ## 32-byte Spill\n",
"\tvaddps\t192(%rsp), %ymm6, %ymm2 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm7, %ymm2, %ymm12, %ymm12\n",
"\tvaddps\t160(%rsp), %ymm3, %ymm2 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm7, %ymm2, %ymm14, %ymm14\n",
"\tvmovdqa\tLCPI1_4(%rip), %xmm2 ## xmm2 = [1,1]\n",
"\tvmovdqa\t%xmm2, %xmm6\n",
"\tvpaddq\t%xmm6, %xmm8, %xmm2\n",
"\tvpaddq\t%xmm6, %xmm0, %xmm3\n",
"\tvinsertf128\t$1, %xmm2, %ymm3, %ymm8\n",
"\tvpaddq\t%xmm6, %xmm11, %xmm3\n",
"\tvpaddq\t%xmm6, %xmm9, %xmm6\n",
"\tvinsertf128\t$1, %xmm3, %ymm6, %ymm3\n",
"\tvunpcklps\t%xmm7, %xmm7, %xmm6 ## xmm6 = xmm7[0,0,1,1]\n",
"\tvunpckhps\t%xmm7, %xmm7, %xmm2 ## xmm2 = xmm7[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm2, %ymm6, %ymm2\n",
"\tvextractf128\t$1, %ymm7, %xmm6\n",
"\tvblendvps\t%ymm2, %ymm3, %ymm9, %ymm9\n",
"\tvunpcklps\t%xmm6, %xmm6, %xmm2 ## xmm2 = xmm6[0,0,1,1]\n",
"\tvunpckhps\t%xmm6, %xmm6, %xmm3 ## xmm3 = xmm6[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm3, %ymm2, %ymm2\n",
"\tvblendvps\t%ymm2, %ymm8, %ymm0, %ymm0\n",
"\tjmp\tLBB1_21\n",
"\t.align\t4, 0x90\n",
"LBB1_11: ## in Loop: Header=BB1_22 Depth=3\n",
"\tvmovups\t288(%rsp), %ymm12 ## 32-byte Reload\n",
"\tjmp\tLBB1_21\n",
"\t.align\t4, 0x90\n",
"LBB1_12: ## %for_exit55\n",
" ## in Loop: Header=BB1_6 Depth=2\n",
"\tvpextrq\t$1, %xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm0, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm0\n",
"\tvinsertps\t$16, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0],xmm1[0],xmm0[2,3]\n",
"\tvmovq\t%xmm8, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvinsertps\t$32, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0,1],xmm1[0],xmm0[3]\n",
"\tvpextrq\t$1, %xmm8, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvinsertps\t$48, %xmm1, %xmm0, %xmm0 ## xmm0 = xmm0[0,1,2],xmm1[0]\n",
"\tvpextrq\t$1, %xmm9, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm1\n",
"\tvmovq\t%xmm9, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm2\n",
"\tvinsertps\t$16, %xmm1, %xmm2, %xmm1 ## xmm1 = xmm2[0],xmm1[0],xmm2[2,3]\n",
"\tvmovq\t%xmm11, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm2\n",
"\tvinsertps\t$32, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1],xmm2[0],xmm1[3]\n",
"\tvpextrq\t$1, %xmm11, %rdx\n",
"\tvcvtsi2ssq\t%rdx, %xmm0, %xmm2\n",
"\tvinsertps\t$48, %xmm2, %xmm1, %xmm1 ## xmm1 = xmm1[0,1,2],xmm2[0]\n",
"\tleal\t(%rcx,%rax), %edx\n",
"\tleal\t-4(,%rdx,4), %edx\n",
"\tmovslq\t%edx, %rdx\n",
"\tvmovups\t%xmm1, (%rdi,%rdx)\n",
"\tvmovups\t%xmm0, 16(%rdi,%rdx)\n",
"\taddl\t$8, %ecx\n",
"\tcmpl\t%ebp, %ecx\n",
"\tjl\tLBB1_6\n",
"\tjmp\tLBB1_13\n",
"\t.align\t4, 0x90\n",
"LBB1_3: ## in Loop: Header=BB1_1 Depth=1\n",
"\tvmovups\t%ymm2, 32(%rsp) ## 32-byte Spill\n",
"LBB1_13: ## %partial_inner_all_outer\n",
" ## in Loop: Header=BB1_1 Depth=1\n",
"\tleaq\t1(%r14), %r11\n",
"\tcmpl\t%r9d, %ecx\n",
"\tvmovups\t32(%rsp), %ymm2 ## 32-byte Reload\n",
"\tjge\tLBB1_1\n",
"## BB#14: ## %partial_inner_only\n",
" ## in Loop: Header=BB1_1 Depth=1\n",
"\tvmovd\t%ecx, %xmm0\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvpaddd\tLCPI1_0(%rip), %xmm0, %xmm1\n",
"\tvpaddd\tLCPI1_1(%rip), %xmm0, %xmm0\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm2\n",
"\tvmovd\t%r9d, %xmm3\n",
"\tvpermilps\t$0, %xmm3, %xmm3 ## xmm3 = xmm3[0,0,0,0]\n",
"\tvpcmpgtd\t%xmm0, %xmm3, %xmm0\n",
"\tvpcmpgtd\t%xmm1, %xmm3, %xmm1\n",
"\tvinsertf128\t$1, %xmm0, %ymm1, %ymm13\n",
"\tvmovups\t%ymm13, -96(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm0, %xmm0, %xmm0\n",
"\tvcvtsi2ssq\t%r14, %xmm0, %xmm0\n",
"\tvmulss\t28(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvaddss\t24(%rsp), %xmm0, %xmm0 ## 4-byte Folded Reload\n",
"\tvpermilps\t$0, %xmm0, %xmm0 ## xmm0 = xmm0[0,0,0,0]\n",
"\tvinsertf128\t$1, %xmm0, %ymm0, %ymm11\n",
"\tvmovups\t%ymm11, 192(%rsp) ## 32-byte Spill\n",
"\tvcvtdq2ps\t%ymm2, %ymm0\n",
"\tvmulps\t96(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvaddps\t128(%rsp), %ymm0, %ymm0 ## 32-byte Folded Reload\n",
"\tvmovups\t%ymm0, 160(%rsp) ## 32-byte Spill\n",
"\tvxorps\t%xmm14, %xmm14, %xmm14\n",
"\tvxorps\t%xmm9, %xmm9, %xmm9\n",
"\tvmovaps\t%ymm0, %ymm10\n",
"\tvmovaps\t%ymm11, %ymm12\n",
"\tjmp\tLBB1_15\n",
"\t.align\t4, 0x90\n",
"LBB1_25: ## %safe_if_run_false196\n",
" ## in Loop: Header=BB1_15 Depth=2\n",
"\tvsubps\t%ymm8, %ymm6, %ymm6\n",
"\tvmovups\t-64(%rsp), %ymm8 ## 32-byte Reload\n",
"\tvblendvps\t%ymm13, %ymm6, %ymm8, %ymm8\n",
"\tvmovups\t%ymm8, -64(%rsp) ## 32-byte Spill\n",
"\tvmovups\t288(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvaddps\t%ymm12, %ymm12, %ymm6\n",
"\tvmovaps\t%ymm11, %ymm10\n",
"\tvmulps\t%ymm6, %ymm10, %ymm6\n",
"\tvmovups\t-32(%rsp), %ymm7 ## 32-byte Reload\n",
"\tvblendvps\t%ymm13, %ymm6, %ymm7, %ymm7\n",
"\tvmovups\t%ymm7, -32(%rsp) ## 32-byte Spill\n",
"\tvaddps\t192(%rsp), %ymm8, %ymm6 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm13, %ymm6, %ymm12, %ymm12\n",
"\tvaddps\t160(%rsp), %ymm7, %ymm6 ## 32-byte Folded Reload\n",
"\tvblendvps\t%ymm13, %ymm6, %ymm10, %ymm10\n",
"\tvmovdqa\tLCPI1_4(%rip), %xmm6 ## xmm6 = [1,1]\n",
"\tvmovdqa\t%xmm6, %xmm7\n",
"\tvpaddq\t%xmm7, %xmm0, %xmm0\n",
"\tvpaddq\t%xmm7, %xmm9, %xmm6\n",
"\tvinsertf128\t$1, %xmm0, %ymm6, %ymm0\n",
"\tvpaddq\t%xmm7, %xmm2, %xmm2\n",
"\tvpaddq\t%xmm7, %xmm14, %xmm6\n",
"\tvinsertf128\t$1, %xmm2, %ymm6, %ymm2\n",
"\tvunpcklps\t%xmm13, %xmm13, %xmm6 ## xmm6 = xmm13[0,0,1,1]\n",
"\tvunpckhps\t%xmm13, %xmm13, %xmm7 ## xmm7 = xmm13[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm7, %ymm6, %ymm6\n",
"\tvextractf128\t$1, %ymm13, %xmm7\n",
"\tvblendvps\t%ymm6, %ymm2, %ymm14, %ymm14\n",
"\tvunpcklps\t%xmm7, %xmm7, %xmm2 ## xmm2 = xmm7[0,0,1,1]\n",
"\tvunpckhps\t%xmm7, %xmm7, %xmm6 ## xmm6 = xmm7[2,2,3,3]\n",
"\tvinsertf128\t$1, %xmm6, %ymm2, %ymm2\n",
"\tvblendvps\t%ymm2, %ymm0, %ymm9, %ymm9\n",
"\tvandnps\t%ymm1, %ymm3, %ymm13\n",
"LBB1_15: ## %for_test153.outer\n",
" ## Parent Loop BB1_1 Depth=1\n",
" ## => This Loop Header: Depth=2\n",
" ## Child Loop BB1_16 Depth 3\n",
"\tvmovups\t%ymm12, 288(%rsp) ## 32-byte Spill\n",
"\tvmulps\t%ymm12, %ymm12, %ymm6\n",
"\tvmulps\t%ymm10, %ymm10, %ymm8\n",
"\tvmovaps\t%ymm10, %ymm11\n",
"\tvaddps\t%ymm6, %ymm8, %ymm15\n",
"\t.align\t4, 0x90\n",
"LBB1_16: ## %for_test153\n",
" ## Parent Loop BB1_1 Depth=1\n",
" ## Parent Loop BB1_15 Depth=2\n",
" ## => This Inner Loop Header: Depth=3\n",
"\tvextractf128\t$1, %ymm14, %xmm2\n",
"\tvextractf128\t$1, %ymm5, %xmm1\n",
"\tvpcmpgtq\t%xmm2, %xmm1, %xmm0\n",
"\tvpcmpgtq\t%xmm14, %xmm5, %xmm3\n",
"\tvshufps\t$-120, %xmm0, %xmm3, %xmm0 ## xmm0 = xmm3[0,2],xmm0[0,2]\n",
"\tvpshufb\t%xmm4, %xmm0, %xmm3\n",
"\tvextractf128\t$1, %ymm9, %xmm0\n",
"\tvpcmpgtq\t%xmm0, %xmm1, %xmm1\n",
"\tvpcmpgtq\t%xmm9, %xmm5, %xmm7\n",
"\tvshufps\t$-120, %xmm1, %xmm7, %xmm1 ## xmm1 = xmm7[0,2],xmm1[0,2]\n",
"\tvpshufb\t%xmm4, %xmm1, %xmm1\n",
"\tvpunpcklqdq\t%xmm1, %xmm3, %xmm1 ## xmm1 = xmm3[0],xmm1[0]\n",
"\tvpmovzxwd\t%xmm1, %xmm3\n",
"\tvpslld\t$31, %xmm3, %xmm3\n",
"\tvpsrad\t$31, %xmm3, %xmm3\n",
"\tvpunpckhwd\t%xmm1, %xmm1, %xmm1 ## xmm1 = xmm1[4,4,5,5,6,6,7,7]\n",
"\tvpslld\t$31, %xmm1, %xmm1\n",
"\tvpsrad\t$31, %xmm1, %xmm1\n",
"\tvinsertf128\t$1, %xmm1, %ymm3, %ymm1\n",
"\tvandps\t%ymm13, %ymm1, %ymm1\n",
"\tvmovmskps\t%ymm1, %eax\n",
"\ttestl\t%eax, %eax\n",
"\tje\tLBB1_23\n",
"## BB#17: ## %for_loop155\n",
" ## in Loop: Header=BB1_16 Depth=3\n",
"\tvcmpnleps\tLCPI1_3(%rip), %ymm15, %ymm12\n",
"\tvandps\t%ymm1, %ymm12, %ymm10\n",
"\tvmovmskps\t%ymm10, %edx\n",
"\tvpxor\t%xmm3, %xmm3, %xmm3\n",
"\ttestl\t%edx, %edx\n",
"\tje\tLBB1_18\n",
"## BB#24: ## %safe_if_run_true177\n",
" ## in Loop: Header=BB1_16 Depth=3\n",
"\tvxorps\t%xmm13, %xmm13, %xmm13\n",
"\tvmovaps\t%ymm10, %ymm3\n",
"\tcmpl\t%eax, %edx\n",
"\tje\tLBB1_16\n",
"LBB1_18: ## %safe_if_after_true176\n",
" ## in Loop: Header=BB1_15 Depth=2\n",
"\tvblendvps\t%ymm12, LCPI1_5(%rip), %ymm1, %ymm13\n",
"\tvmovmskps\t%ymm13, %eax\n",
"\ttestl\t%eax, %eax\n",
"\tjne\tLBB1_25\n",
"## BB#19: ## in Loop: Header=BB1_15 Depth=2\n",
"\tvmovaps\t%ymm11, %ymm10\n",
"\tvmovups\t288(%rsp), %ymm12 ## 32-byte Reload\n",
"\tvandnps\t%ymm1, %ymm3, %ymm13\n",
"\tjmp\tLBB1_15\n",
"LBB1_4: ## %for_exit\n",
"\taddq\t$320, %rsp ## imm = 0x140\n",
"\tpopq\t%rbx\n",
"\tpopq\t%r14\n",
"\tpopq\t%rbp\n",
"\tvzeroupper\n",
"\tretq\n",
"\n",
"\n",
".subsections_via_symbols\n"
]
}
],
"source": [
"ISPC.ispc_native(func_code, func.file.compile_opts)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"; ModuleID = '<stdin>'\n",
"target datalayout = \"e-m:o-i64:64-f80:128-n8:16:32:64-S128\"\n",
"target triple = \"x86_64-apple-darwin13.4.0\"\n",
"\n",
"; Function Attrs: nounwind readnone\n",
"declare i32 @llvm.x86.avx.movmsk.ps.256(<8 x float>) #0\n",
"\n",
"; Function Attrs: nounwind\n",
"declare void @llvm.x86.avx.maskstore.ps.256(i8*, <8 x float>, <8 x float>) #1\n",
"\n",
"; Function Attrs: nounwind readnone\n",
"declare <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float>, <8 x float>, <8 x float>) #0\n",
"\n",
"; Function Attrs: nounwind\n",
"define void @ispc_func_1___unfunfun_3C_unf_3E_unIunIunIunIunIunfunf(float %x0, float %y0, float* noalias nocapture %output, i64 %output__len__1, i64 %output__len__2, i64 %max_iters, i64 %height, i64 %width, float %dx, float %dy, <8 x i32> %__mask) #1 {\n",
"allocas:\n",
" %floatmask.i = bitcast <8 x i32> %__mask to <8 x float>\n",
" %v.i = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i) #0\n",
" %cmp.i = icmp eq i32 %v.i, 255\n",
" %lessequal__width_load = icmp sgt i64 %width, 0\n",
" %width.op = add i64 %width, 1\n",
" %add__gensym318_stop_ = select i1 %lessequal__width_load, i64 %width.op, i64 1\n",
" %add_height_load_ = add i64 %height, 1\n",
" %add_height_load__to_int32 = trunc i64 %add_height_load_ to i32\n",
" %nitems = add i32 %add_height_load__to_int32, -1\n",
" %nextras = srem i32 %nitems, 8\n",
" %aligned_end = sub i32 %add_height_load__to_int32, %nextras\n",
" %before_aligned_end44888 = icmp sgt i32 %aligned_end, 1\n",
" %y0_load_broadcast_init = insertelement <8 x float> undef, float %y0, i32 0\n",
" %y0_load_broadcast = shufflevector <8 x float> %y0_load_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %dy_load_broadcast_init = insertelement <8 x float> undef, float %dy, i32 0\n",
" %dy_load_broadcast = shufflevector <8 x float> %dy_load_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %max_iters_load_broadcast_init = insertelement <8 x i64> undef, i64 %max_iters, i32 0\n",
" %max_iters_load_broadcast = shufflevector <8 x i64> %max_iters_load_broadcast_init, <8 x i64> undef, <8 x i32> zeroinitializer\n",
" %output_load_ptr2int_2void = bitcast float* %output to i8*\n",
" br i1 %cmp.i, label %for_test.outer, label %for_test288.outer\n",
"\n",
"for_test.outer: ; preds = %for_exit156, %allocas\n",
" %blend.i.i795850.ph = phi <8 x float> [ %blend.i.i795851.ph, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i798847.ph = phi <8 x float> [ %blend.i.i798848.ph, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i826837.ph = phi <8 x float> [ %blend.i.i826838.lcssa.lcssa, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i833.ph = phi <8 x float> [ %blend.i.i834.lcssa.lcssa, %for_exit156 ], [ undef, %allocas ]\n",
" %_s40.0.ph = phi i64 [ %add__s40_load30_.lcssa, %for_exit156 ], [ 1, %allocas ]\n",
" %internal_mask_memory.0.ph = phi <8 x i32> [ %\"oldMask&test.lcssa882\", %for_exit156 ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %allocas ]\n",
" br i1 %before_aligned_end44888, label %for_test.us, label %for_test\n",
"\n",
"for_test.us: ; preds = %partial_inner_all_outer.us, %for_test.outer\n",
" %blend.i.i826837.us = phi <8 x float> [ %blend.i.i826839.ph.us, %partial_inner_all_outer.us ], [ %blend.i.i826837.ph, %for_test.outer ]\n",
" %blend.i.i833.us = phi <8 x float> [ %blend.i.i835.ph.us, %partial_inner_all_outer.us ], [ %blend.i.i833.ph, %for_test.outer ]\n",
" %_s40.0.us = phi i64 [ %add__s40_load30_.us, %partial_inner_all_outer.us ], [ %_s40.0.ph, %for_test.outer ]\n",
" %internal_mask_memory.0.us = phi <8 x i32> [ %\"oldMask&test.us\", %partial_inner_all_outer.us ], [ %internal_mask_memory.0.ph, %for_test.outer ]\n",
" %equal__s40_load_add__gensym318_stop_.us = icmp eq i64 %_s40.0.us, %add__gensym318_stop_\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool.us = sext i1 %equal__s40_load_add__gensym318_stop_.us to i32\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init.us = insertelement <8 x i32> undef, i32 %equal__s40_load_add__gensym318_stop_to_i_bool.us, i32 0\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast.us = shufflevector <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init.us, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %val_load_logicalnot.i728.us = xor <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast.us, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %\"oldMask&test.us\" = and <8 x i32> %internal_mask_memory.0.us, %val_load_logicalnot.i728.us\n",
" %floatmask.i725.us = bitcast <8 x i32> %\"oldMask&test.us\" to <8 x float>\n",
" %v.i726.us = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i725.us) #0\n",
" %cmp.i727.us = icmp eq i32 %v.i726.us, 0\n",
" br i1 %cmp.i727.us, label %for_exit, label %foreach_full_body.lr.ph.us\n",
"\n",
"partial_inner_all_outer.us: ; preds = %for_exit55.us\n",
" %before_full_end.us = icmp slt i32 %new_counter.us, %add_height_load__to_int32\n",
" br i1 %before_full_end.us, label %partial_inner_only, label %for_test.us\n",
"\n",
"foreach_full_body.us: ; preds = %foreach_full_body.lr.ph.us, %for_exit55.us\n",
" %counter.1891.us = phi i32 [ 1, %foreach_full_body.lr.ph.us ], [ %new_counter.us, %for_exit55.us ]\n",
" %blend.i.i834890.us = phi <8 x float> [ %blend.i.i833.us, %foreach_full_body.lr.ph.us ], [ %blend.i.i835.ph.us, %for_exit55.us ]\n",
" %blend.i.i826838889.us = phi <8 x float> [ %blend.i.i826837.us, %foreach_full_body.lr.ph.us ], [ %blend.i.i826839.ph.us, %for_exit55.us ]\n",
" %smear_counter_init48.us = insertelement <8 x i32> undef, i32 %counter.1891.us, i32 0\n",
" %smear_counter49.us = shufflevector <8 x i32> %smear_counter_init48.us, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val50.us = add <8 x i32> %smear_counter49.us, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %j_load51_to_float.us = sitofp <8 x i32> %iter_val50.us to <8 x float>\n",
" %mul_j_load51_to_float_dy_load_broadcast.us = fmul <8 x float> %dy_load_broadcast, %j_load51_to_float.us\n",
" %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast.us = fadd <8 x float> %y0_load_broadcast, %mul_j_load51_to_float_dy_load_broadcast.us\n",
" br label %for_test52.outer.us\n",
"\n",
"for_test52.us: ; preds = %for_test52.outer.us, %safe_if_run_true.us\n",
" %internal_mask_memory.2.us = phi <8 x i32> [ zeroinitializer, %safe_if_run_true.us ], [ %internal_mask_memory.2.ph.us, %for_test52.outer.us ]\n",
" %\"oldMask&test60.us\" = select <8 x i1> %less___i_8514_load_max_iters_load_broadcast.us, <8 x i32> %internal_mask_memory.2.us, <8 x i32> zeroinitializer\n",
" %floatmask.i722.us = bitcast <8 x i32> %\"oldMask&test60.us\" to <8 x float>\n",
" %v.i723.us = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i722.us) #0\n",
" %cmp.i724.us = icmp eq i32 %v.i723.us, 0\n",
" br i1 %cmp.i724.us, label %for_exit55.us, label %for_loop54.us\n",
"\n",
"for_loop54.us: ; preds = %for_test52.us\n",
" %\"oldMask&test70.us\" = select <8 x i1> %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68.us, <8 x i32> %\"oldMask&test60.us\", <8 x i32> zeroinitializer\n",
" %floatmask.i719.us = bitcast <8 x i32> %\"oldMask&test70.us\" to <8 x float>\n",
" %v.i720.us = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i719.us) #0\n",
" %cmp.i721.us = icmp eq i32 %v.i720.us, 0\n",
" br i1 %cmp.i721.us, label %safe_if_after_true.us, label %safe_if_run_true.us\n",
"\n",
"safe_if_run_true.us: ; preds = %for_loop54.us\n",
" %\"equal_finished&func_internal_mask&function_mask66.us\" = icmp eq i32 %v.i720.us, %v.i723.us\n",
" br i1 %\"equal_finished&func_internal_mask&function_mask66.us\", label %for_test52.us, label %safe_if_after_true.us\n",
"\n",
"safe_if_after_true.us: ; preds = %safe_if_run_true.us, %for_loop54.us\n",
" %break_lanes_memory58.1.us = phi <8 x i32> [ %\"oldMask&test70.us\", %safe_if_run_true.us ], [ zeroinitializer, %for_loop54.us ]\n",
" %0 = bitcast <8 x i32> %\"oldMask&test60.us\" to <8 x float>\n",
" %floatmask.i716.us = select <8 x i1> %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68.us, <8 x float> zeroinitializer, <8 x float> %0\n",
" %v.i717.us = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i716.us) #0\n",
" %cmp.i718.us = icmp eq i32 %v.i717.us, 0\n",
" br i1 %cmp.i718.us, label %if_done.us, label %safe_if_run_false.us\n",
"\n",
"safe_if_run_false.us: ; preds = %safe_if_after_true.us\n",
" %sub_mul___z_re_8512_load82___z_re_8512_load83_mul___z_im_8513_load84___z_im_8513_load85.us = fsub <8 x float> %mul___z_re_8512_load___z_re_8512_load67.us, %mul___z_im_8513_load___z_im_8513_load68.us\n",
" %blend.i.i.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i835.ph.us, <8 x float> %sub_mul___z_re_8512_load82___z_re_8512_load83_mul___z_im_8513_load84___z_im_8513_load85.us, <8 x float> %floatmask.i716.us) #1\n",
" %mul____z_re_8512_load87.us = fmul <8 x float> %blend.i.i823828.ph.us, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load87___z_im_8513_load88.us = fmul <8 x float> %blend.i.i820830.ph.us, %mul____z_re_8512_load87.us\n",
" %blend.i.i826.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i826839.ph.us, <8 x float> %mul_mul____z_re_8512_load87___z_im_8513_load88.us, <8 x float> %floatmask.i716.us) #1\n",
" %add_x_load90___new_re_8515_load.us = fadd <8 x float> %add_x0_load_mul_i_load_to_float_dx_load_broadcast.us, %blend.i.i.us\n",
" %blend.i.i823.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i823828.ph.us, <8 x float> %add_x_load90___new_re_8515_load.us, <8 x float> %floatmask.i716.us) #1\n",
" %add_y_load92___new_im_8516_load.us = fadd <8 x float> %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast.us, %blend.i.i826.us\n",
" %blend.i.i820.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i820830.ph.us, <8 x float> %add_y_load92___new_im_8516_load.us, <8 x float> %floatmask.i716.us) #1\n",
" %add___i_8514_load94_.us = add <8 x i64> %final.i817832.ph.us, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i803.us = shufflevector <8 x i64> %final.i817832.ph.us, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i804.us = bitcast <4 x i64> %old01.i803.us to <8 x float>\n",
" %new01.i805.us = shufflevector <8 x i64> %add___i_8514_load94_.us, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i806.us = bitcast <4 x i64> %new01.i805.us to <8 x float>\n",
" %mask01.i807.us = shufflevector <8 x float> %floatmask.i716.us, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i808.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i804.us, <8 x float> %new01f.i806.us, <8 x float> %mask01.i807.us) #1\n",
" %result01.i809.us = bitcast <8 x float> %result01f.i808.us to <4 x i64>\n",
" %old23.i810.us = shufflevector <8 x i64> %final.i817832.ph.us, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i811.us = bitcast <4 x i64> %old23.i810.us to <8 x float>\n",
" %new23.i812.us = shufflevector <8 x i64> %add___i_8514_load94_.us, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i813.us = bitcast <4 x i64> %new23.i812.us to <8 x float>\n",
" %mask23.i814.us = shufflevector <8 x float> %floatmask.i716.us, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i815.us = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i811.us, <8 x float> %new23f.i813.us, <8 x float> %mask23.i814.us) #1\n",
" %result23.i816.us = bitcast <8 x float> %result23f.i815.us to <4 x i64>\n",
" %final.i817.us = shufflevector <4 x i64> %result01.i809.us, <4 x i64> %result23.i816.us, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done.us\n",
"\n",
"if_done.us: ; preds = %safe_if_run_false.us, %safe_if_after_true.us\n",
" %blend.i.i826840.us = phi <8 x float> [ %blend.i.i826839.ph.us, %safe_if_after_true.us ], [ %blend.i.i826.us, %safe_if_run_false.us ]\n",
" %blend.i.i836.us = phi <8 x float> [ %blend.i.i835.ph.us, %safe_if_after_true.us ], [ %blend.i.i.us, %safe_if_run_false.us ]\n",
" %final.i817831.us = phi <8 x i64> [ %final.i817832.ph.us, %safe_if_after_true.us ], [ %final.i817.us, %safe_if_run_false.us ]\n",
" %blend.i.i820829.us = phi <8 x float> [ %blend.i.i820830.ph.us, %safe_if_after_true.us ], [ %blend.i.i820.us, %safe_if_run_false.us ]\n",
" %blend.i.i823827.us = phi <8 x float> [ %blend.i.i823828.ph.us, %safe_if_after_true.us ], [ %blend.i.i823.us, %safe_if_run_false.us ]\n",
" %\"!(break|continue)_lanes.us\" = xor <8 x i32> %break_lanes_memory58.1.us, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask102.us = and <8 x i32> %\"oldMask&test60.us\", %\"!(break|continue)_lanes.us\"\n",
" br label %for_test52.outer.us\n",
"\n",
"for_exit55.us: ; preds = %for_test52.us\n",
" %_gensym5_load_to_float.us = sitofp <8 x i64> %final.i817832.ph.us to <8 x float>\n",
" %smear_counter49_cast.elt0.us = zext i32 %counter.1891.us to i64\n",
" %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast.elt0.us = add i64 %smear_counter49_cast.elt0.us, %mul_output__len__1_load_sub_i_load115_.us\n",
" %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast.elt0.us = trunc i64 %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast.elt0.us to i32\n",
" %shl_add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast_.elt0.us = shl i32 %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast.elt0.us, 2\n",
" %\"varying+const_offsets.elt0.us\" = add i32 %shl_add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast_.elt0.us, -4\n",
" %1 = sext i32 %\"varying+const_offsets.elt0.us\" to i64\n",
" %ptr.us = getelementptr i8* %output_load_ptr2int_2void, i64 %1, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %ptrcast.us = bitcast i8* %ptr.us to <8 x float>*\n",
" store <8 x float> %_gensym5_load_to_float.us, <8 x float>* %ptrcast.us, align 4, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %new_counter.us = add i32 %counter.1891.us, 8\n",
" %before_aligned_end44.us = icmp slt i32 %new_counter.us, %aligned_end\n",
" br i1 %before_aligned_end44.us, label %foreach_full_body.us, label %partial_inner_all_outer.us\n",
"\n",
"for_test52.outer.us: ; preds = %if_done.us, %foreach_full_body.us\n",
" %blend.i.i826839.ph.us = phi <8 x float> [ %blend.i.i826840.us, %if_done.us ], [ %blend.i.i826838889.us, %foreach_full_body.us ]\n",
" %blend.i.i835.ph.us = phi <8 x float> [ %blend.i.i836.us, %if_done.us ], [ %blend.i.i834890.us, %foreach_full_body.us ]\n",
" %final.i817832.ph.us = phi <8 x i64> [ %final.i817831.us, %if_done.us ], [ zeroinitializer, %foreach_full_body.us ]\n",
" %blend.i.i820830.ph.us = phi <8 x float> [ %blend.i.i820829.us, %if_done.us ], [ %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast.us, %foreach_full_body.us ]\n",
" %blend.i.i823828.ph.us = phi <8 x float> [ %blend.i.i823827.us, %if_done.us ], [ %add_x0_load_mul_i_load_to_float_dx_load_broadcast.us, %foreach_full_body.us ]\n",
" %internal_mask_memory.2.ph.us = phi <8 x i32> [ %new_mask102.us, %if_done.us ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %foreach_full_body.us ]\n",
" %less___i_8514_load_max_iters_load_broadcast.us = icmp slt <8 x i64> %final.i817832.ph.us, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load___z_re_8512_load67.us = fmul <8 x float> %blend.i.i823828.ph.us, %blend.i.i823828.ph.us\n",
" %mul___z_im_8513_load___z_im_8513_load68.us = fmul <8 x float> %blend.i.i820830.ph.us, %blend.i.i820830.ph.us\n",
" %add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68.us = fadd <8 x float> %mul___z_im_8513_load___z_im_8513_load68.us, %mul___z_re_8512_load___z_re_8512_load67.us\n",
" %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68.us = fcmp ugt <8 x float> %add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68.us, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test52.us\n",
"\n",
"foreach_full_body.lr.ph.us: ; preds = %for_test.us\n",
" %add__s40_load30_.us = add i64 %_s40.0.us, 1\n",
" %i_load_to_float.us = sitofp i64 %_s40.0.us to float\n",
" %mul_i_load_to_float_dx_load.us = fmul float %i_load_to_float.us, %dx\n",
" %add_x0_load_mul_i_load_to_float_dx_load.us = fadd float %mul_i_load_to_float_dx_load.us, %x0\n",
" %add_x0_load_mul_i_load_to_float_dx_load_broadcast_init.us = insertelement <8 x float> undef, float %add_x0_load_mul_i_load_to_float_dx_load.us, i32 0\n",
" %add_x0_load_mul_i_load_to_float_dx_load_broadcast.us = shufflevector <8 x float> %add_x0_load_mul_i_load_to_float_dx_load_broadcast_init.us, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %sub_i_load115_.us = add i64 %_s40.0.us, -1\n",
" %mul_output__len__1_load_sub_i_load115_.us = mul i64 %sub_i_load115_.us, %output__len__1\n",
" br label %foreach_full_body.us\n",
"\n",
"for_test: ; preds = %partial_inner_all_outer, %for_test.outer\n",
" %_s40.0 = phi i64 [ %add__s40_load30_, %partial_inner_all_outer ], [ %_s40.0.ph, %for_test.outer ]\n",
" %internal_mask_memory.0 = phi <8 x i32> [ %\"oldMask&test\", %partial_inner_all_outer ], [ %internal_mask_memory.0.ph, %for_test.outer ]\n",
" %equal__s40_load_add__gensym318_stop_ = icmp eq i64 %_s40.0, %add__gensym318_stop_\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool = sext i1 %equal__s40_load_add__gensym318_stop_ to i32\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init = insertelement <8 x i32> undef, i32 %equal__s40_load_add__gensym318_stop_to_i_bool, i32 0\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast = shufflevector <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %val_load_logicalnot.i728 = xor <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %\"oldMask&test\" = and <8 x i32> %internal_mask_memory.0, %val_load_logicalnot.i728\n",
" %floatmask.i725 = bitcast <8 x i32> %\"oldMask&test\" to <8 x float>\n",
" %v.i726 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i725) #0\n",
" %cmp.i727 = icmp eq i32 %v.i726, 0\n",
" br i1 %cmp.i727, label %for_exit, label %partial_inner_all_outer\n",
"\n",
"for_exit: ; preds = %for_test288, %for_test, %for_test.us\n",
" ret void\n",
"\n",
"partial_inner_all_outer: ; preds = %for_test\n",
" %add__s40_load30_ = add i64 %_s40.0, 1\n",
" %before_full_end = icmp sgt i32 %add_height_load__to_int32, 1\n",
" br i1 %before_full_end, label %partial_inner_only, label %for_test\n",
"\n",
"partial_inner_only: ; preds = %partial_inner_all_outer, %partial_inner_all_outer.us\n",
" %add__s40_load30_.lcssa = phi i64 [ %add__s40_load30_.us, %partial_inner_all_outer.us ], [ %add__s40_load30_, %partial_inner_all_outer ]\n",
" %\"oldMask&test.lcssa882\" = phi <8 x i32> [ %\"oldMask&test.us\", %partial_inner_all_outer.us ], [ %\"oldMask&test\", %partial_inner_all_outer ]\n",
" %_s40.0.lcssa881 = phi i64 [ %_s40.0.us, %partial_inner_all_outer.us ], [ %_s40.0, %partial_inner_all_outer ]\n",
" %counter.1.lcssa.lcssa = phi i32 [ %new_counter.us, %partial_inner_all_outer.us ], [ 1, %partial_inner_all_outer ]\n",
" %blend.i.i834.lcssa.lcssa = phi <8 x float> [ %blend.i.i835.ph.us, %partial_inner_all_outer.us ], [ %blend.i.i833.ph, %partial_inner_all_outer ]\n",
" %blend.i.i826838.lcssa.lcssa = phi <8 x float> [ %blend.i.i826839.ph.us, %partial_inner_all_outer.us ], [ %blend.i.i826837.ph, %partial_inner_all_outer ]\n",
" %smear_counter_init128 = insertelement <8 x i32> undef, i32 %counter.1.lcssa.lcssa, i32 0\n",
" %smear_counter129 = shufflevector <8 x i32> %smear_counter_init128, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val130 = add <8 x i32> %smear_counter129, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %smear_end_init131 = insertelement <8 x i32> undef, i32 %add_height_load__to_int32, i32 0\n",
" %smear_end132 = shufflevector <8 x i32> %smear_end_init131, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %cmp133 = icmp slt <8 x i32> %iter_val130, %smear_end132\n",
" %cmp133_to_boolvec = sext <8 x i1> %cmp133 to <8 x i32>\n",
" %i_load140_to_float = sitofp i64 %_s40.0.lcssa881 to float\n",
" %mul_i_load140_to_float_dx_load141 = fmul float %i_load140_to_float, %dx\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141 = fadd float %mul_i_load140_to_float_dx_load141, %x0\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast_init = insertelement <8 x float> undef, float %add_x0_load139_mul_i_load140_to_float_dx_load141, i32 0\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast = shufflevector <8 x float> %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %j_load144_to_float = sitofp <8 x i32> %iter_val130 to <8 x float>\n",
" %mul_j_load144_to_float_dy_load145_broadcast = fmul <8 x float> %dy_load_broadcast, %j_load144_to_float\n",
" %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast = fadd <8 x float> %y0_load_broadcast, %mul_j_load144_to_float_dy_load145_broadcast\n",
" br label %for_test153.outer\n",
"\n",
"for_test153.outer: ; preds = %if_done175, %partial_inner_only\n",
" %blend.i.i795851.ph = phi <8 x float> [ %blend.i.i795852, %if_done175 ], [ %blend.i.i795850.ph, %partial_inner_only ]\n",
" %blend.i.i798848.ph = phi <8 x float> [ %blend.i.i798849, %if_done175 ], [ %blend.i.i798847.ph, %partial_inner_only ]\n",
" %final.i786846.ph = phi <8 x i64> [ %final.i786845, %if_done175 ], [ zeroinitializer, %partial_inner_only ]\n",
" %blend.i.i789844.ph = phi <8 x float> [ %blend.i.i789843, %if_done175 ], [ %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast, %partial_inner_only ]\n",
" %blend.i.i792842.ph = phi <8 x float> [ %blend.i.i792841, %if_done175 ], [ %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast, %partial_inner_only ]\n",
" %internal_mask_memory.4.ph = phi <8 x i32> [ %new_mask229, %if_done175 ], [ %cmp133_to_boolvec, %partial_inner_only ]\n",
" %less___i_8514_load160_max_iters_load161_broadcast = icmp slt <8 x i64> %final.i786846.ph, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load170___z_re_8512_load171 = fmul <8 x float> %blend.i.i792842.ph, %blend.i.i792842.ph\n",
" %mul___z_im_8513_load172___z_im_8513_load173 = fmul <8 x float> %blend.i.i789844.ph, %blend.i.i789844.ph\n",
" %add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173 = fadd <8 x float> %mul___z_im_8513_load172___z_im_8513_load173, %mul___z_re_8512_load170___z_re_8512_load171\n",
" %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173 = fcmp ugt <8 x float> %add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test153\n",
"\n",
"for_test153: ; preds = %safe_if_run_true177, %for_test153.outer\n",
" %internal_mask_memory.4 = phi <8 x i32> [ zeroinitializer, %safe_if_run_true177 ], [ %internal_mask_memory.4.ph, %for_test153.outer ]\n",
" %\"oldMask&test163\" = select <8 x i1> %less___i_8514_load160_max_iters_load161_broadcast, <8 x i32> %internal_mask_memory.4, <8 x i32> zeroinitializer\n",
" %floatmask.i707 = bitcast <8 x i32> %\"oldMask&test163\" to <8 x float>\n",
" %v.i708 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i707) #0\n",
" %cmp.i709 = icmp eq i32 %v.i708, 0\n",
" br i1 %cmp.i709, label %for_exit156, label %for_loop155\n",
"\n",
"for_loop155: ; preds = %for_test153\n",
" %\"oldMask&test178\" = select <8 x i1> %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <8 x i32> %\"oldMask&test163\", <8 x i32> zeroinitializer\n",
" %floatmask.i704 = bitcast <8 x i32> %\"oldMask&test178\" to <8 x float>\n",
" %v.i705 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i704) #0\n",
" %cmp.i706 = icmp eq i32 %v.i705, 0\n",
" br i1 %cmp.i706, label %safe_if_after_true176, label %safe_if_run_true177\n",
"\n",
"for_exit156: ; preds = %for_test153\n",
" %sub_i_load246_ = add i64 %_s40.0.lcssa881, -1\n",
" %mul_output__len__1_load245_sub_i_load246_ = mul i64 %sub_i_load246_, %output__len__1\n",
" %_gensym5_load248_to_float = sitofp <8 x i64> %final.i786846.ph to <8 x float>\n",
" %j.0_cast.elt0 = zext i32 %counter.1.lcssa.lcssa to i64\n",
" %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast.elt0 = add i64 %j.0_cast.elt0, %mul_output__len__1_load245_sub_i_load246_\n",
" %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast.elt0 = trunc i64 %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast.elt0 to i32\n",
" %shl_add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast_.elt0 = shl i32 %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast.elt0, 2\n",
" %\"varying+const_offsets.elt0646\" = add i32 %shl_add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast_.elt0, -4\n",
" %2 = sext i32 %\"varying+const_offsets.elt0646\" to i64\n",
" %ptr647 = getelementptr i8* %output_load_ptr2int_2void, i64 %2\n",
" %mask.i.i800 = bitcast <8 x i32> %cmp133_to_boolvec to <8 x float>\n",
" call void @llvm.x86.avx.maskstore.ps.256(i8* %ptr647, <8 x float> %mask.i.i800, <8 x float> %_gensym5_load248_to_float) #1\n",
" br label %for_test.outer\n",
"\n",
"if_done175: ; preds = %safe_if_run_false196, %safe_if_after_true176\n",
" %blend.i.i795852 = phi <8 x float> [ %blend.i.i795851.ph, %safe_if_after_true176 ], [ %blend.i.i795, %safe_if_run_false196 ]\n",
" %blend.i.i798849 = phi <8 x float> [ %blend.i.i798848.ph, %safe_if_after_true176 ], [ %blend.i.i798, %safe_if_run_false196 ]\n",
" %final.i786845 = phi <8 x i64> [ %final.i786846.ph, %safe_if_after_true176 ], [ %final.i786, %safe_if_run_false196 ]\n",
" %blend.i.i789843 = phi <8 x float> [ %blend.i.i789844.ph, %safe_if_after_true176 ], [ %blend.i.i789, %safe_if_run_false196 ]\n",
" %blend.i.i792841 = phi <8 x float> [ %blend.i.i792842.ph, %safe_if_after_true176 ], [ %blend.i.i792, %safe_if_run_false196 ]\n",
" %\"!(break|continue)_lanes227\" = xor <8 x i32> %break_lanes_memory159.1, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask229 = and <8 x i32> %\"oldMask&test163\", %\"!(break|continue)_lanes227\"\n",
" br label %for_test153.outer\n",
"\n",
"safe_if_after_true176: ; preds = %safe_if_run_true177, %for_loop155\n",
" %break_lanes_memory159.1 = phi <8 x i32> [ %\"oldMask&test178\", %safe_if_run_true177 ], [ zeroinitializer, %for_loop155 ]\n",
" %3 = bitcast <8 x i32> %\"oldMask&test163\" to <8 x float>\n",
" %floatmask.i701 = select <8 x i1> %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <8 x float> zeroinitializer, <8 x float> %3\n",
" %v.i702 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i701) #0\n",
" %cmp.i703 = icmp eq i32 %v.i702, 0\n",
" br i1 %cmp.i703, label %if_done175, label %safe_if_run_false196\n",
"\n",
"safe_if_run_true177: ; preds = %for_loop155\n",
" %\"equal_finished&func193_internal_mask&function_mask169\" = icmp eq i32 %v.i705, %v.i708\n",
" br i1 %\"equal_finished&func193_internal_mask&function_mask169\", label %for_test153, label %safe_if_after_true176\n",
"\n",
"safe_if_run_false196: ; preds = %safe_if_after_true176\n",
" %sub_mul___z_re_8512_load203___z_re_8512_load204_mul___z_im_8513_load205___z_im_8513_load206 = fsub <8 x float> %mul___z_re_8512_load170___z_re_8512_load171, %mul___z_im_8513_load172___z_im_8513_load173\n",
" %blend.i.i798 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i798848.ph, <8 x float> %sub_mul___z_re_8512_load203___z_re_8512_load204_mul___z_im_8513_load205___z_im_8513_load206, <8 x float> %floatmask.i701) #1\n",
" %mul____z_re_8512_load208 = fmul <8 x float> %blend.i.i792842.ph, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load208___z_im_8513_load209 = fmul <8 x float> %blend.i.i789844.ph, %mul____z_re_8512_load208\n",
" %blend.i.i795 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i795851.ph, <8 x float> %mul_mul____z_re_8512_load208___z_im_8513_load209, <8 x float> %floatmask.i701) #1\n",
" %add_x_load211___new_re_8515_load212 = fadd <8 x float> %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast, %blend.i.i798\n",
" %blend.i.i792 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i792842.ph, <8 x float> %add_x_load211___new_re_8515_load212, <8 x float> %floatmask.i701) #1\n",
" %add_y_load214___new_im_8516_load215 = fadd <8 x float> %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast, %blend.i.i795\n",
" %blend.i.i789 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i789844.ph, <8 x float> %add_y_load214___new_im_8516_load215, <8 x float> %floatmask.i701) #1\n",
" %add___i_8514_load217_ = add <8 x i64> %final.i786846.ph, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i772 = shufflevector <8 x i64> %final.i786846.ph, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i773 = bitcast <4 x i64> %old01.i772 to <8 x float>\n",
" %new01.i774 = shufflevector <8 x i64> %add___i_8514_load217_, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i775 = bitcast <4 x i64> %new01.i774 to <8 x float>\n",
" %mask01.i776 = shufflevector <8 x float> %floatmask.i701, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i777 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i773, <8 x float> %new01f.i775, <8 x float> %mask01.i776) #1\n",
" %result01.i778 = bitcast <8 x float> %result01f.i777 to <4 x i64>\n",
" %old23.i779 = shufflevector <8 x i64> %final.i786846.ph, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i780 = bitcast <4 x i64> %old23.i779 to <8 x float>\n",
" %new23.i781 = shufflevector <8 x i64> %add___i_8514_load217_, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i782 = bitcast <4 x i64> %new23.i781 to <8 x float>\n",
" %mask23.i783 = shufflevector <8 x float> %floatmask.i701, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i784 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i780, <8 x float> %new23f.i782, <8 x float> %mask23.i783) #1\n",
" %result23.i785 = bitcast <8 x float> %result23f.i784 to <4 x i64>\n",
" %final.i786 = shufflevector <4 x i64> %result01.i778, <4 x i64> %result23.i785, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done175\n",
"\n",
"for_test288: ; preds = %for_test288.outer, %partial_inner_all_outer353\n",
" %blend.i.i766863 = phi <8 x float> [ %blend.i.i766864.lcssa, %partial_inner_all_outer353 ], [ %blend.i.i766863.ph, %for_test288.outer ]\n",
" %blend.i.i769859 = phi <8 x float> [ %blend.i.i769860.lcssa, %partial_inner_all_outer353 ], [ %blend.i.i769859.ph, %for_test288.outer ]\n",
" %_s40285.0 = phi i64 [ %add__s40_load312_, %partial_inner_all_outer353 ], [ %_s40285.0.ph, %for_test288.outer ]\n",
" %internal_mask_memory.6 = phi <8 x i32> [ %\"oldMask&test302\", %partial_inner_all_outer353 ], [ %internal_mask_memory.6.ph, %for_test288.outer ]\n",
" %equal__s40_load295_add__gensym3276296_stop_ = icmp eq i64 %_s40285.0, %add__gensym318_stop_\n",
" %equal__s40_load295_add__gensym3276296_stop_to_i_bool = sext i1 %equal__s40_load295_add__gensym3276296_stop_ to i32\n",
" %equal__s40_load295_add__gensym3276296_stop_to_i_bool_broadcast_init = insertelement <8 x i32> undef, i32 %equal__s40_load295_add__gensym3276296_stop_to_i_bool, i32 0\n",
" %equal__s40_load295_add__gensym3276296_stop_to_i_bool_broadcast = shufflevector <8 x i32> %equal__s40_load295_add__gensym3276296_stop_to_i_bool_broadcast_init, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %val_load_logicalnot.i = xor <8 x i32> %equal__s40_load295_add__gensym3276296_stop_to_i_bool_broadcast, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %\"oldMask&test302\" = and <8 x i32> %internal_mask_memory.6, %val_load_logicalnot.i\n",
" %\"internal_mask&function_mask306\" = and <8 x i32> %\"oldMask&test302\", %__mask\n",
" %floatmask.i692 = bitcast <8 x i32> %\"internal_mask&function_mask306\" to <8 x float>\n",
" %v.i693 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i692) #0\n",
" %cmp.i694 = icmp eq i32 %v.i693, 0\n",
" br i1 %cmp.i694, label %for_exit, label %for_loop290\n",
"\n",
"for_loop290: ; preds = %for_test288\n",
" %add__s40_load312_ = add i64 %_s40285.0, 1\n",
" br i1 %before_aligned_end44888, label %foreach_full_body317.lr.ph, label %partial_inner_all_outer353\n",
"\n",
"foreach_full_body317.lr.ph: ; preds = %for_loop290\n",
" %i_load365_to_float = sitofp i64 %_s40285.0 to float\n",
" %mul_i_load365_to_float_dx_load366 = fmul float %i_load365_to_float, %dx\n",
" %add_x0_load364_mul_i_load365_to_float_dx_load366 = fadd float %mul_i_load365_to_float_dx_load366, %x0\n",
" %add_x0_load364_mul_i_load365_to_float_dx_load366_broadcast_init = insertelement <8 x float> undef, float %add_x0_load364_mul_i_load365_to_float_dx_load366, i32 0\n",
" %add_x0_load364_mul_i_load365_to_float_dx_load366_broadcast = shufflevector <8 x float> %add_x0_load364_mul_i_load365_to_float_dx_load366_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %sub_i_load471_ = add i64 %_s40285.0, -1\n",
" %mul_output__len__1_load470_sub_i_load471_ = mul i64 %sub_i_load471_, %output__len__1\n",
" br label %foreach_full_body317\n",
"\n",
"foreach_full_body317: ; preds = %for_exit381, %foreach_full_body317.lr.ph\n",
" %counter331.1897 = phi i32 [ 1, %foreach_full_body317.lr.ph ], [ %new_counter484, %for_exit381 ]\n",
" %blend.i.i769860896 = phi <8 x float> [ %blend.i.i769859, %foreach_full_body317.lr.ph ], [ %blend.i.i769861.ph, %for_exit381 ]\n",
" %blend.i.i766864895 = phi <8 x float> [ %blend.i.i766863, %foreach_full_body317.lr.ph ], [ %blend.i.i766865.ph, %for_exit381 ]\n",
" %smear_counter_init360 = insertelement <8 x i32> undef, i32 %counter331.1897, i32 0\n",
" %smear_counter361 = shufflevector <8 x i32> %smear_counter_init360, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val362 = add <8 x i32> %smear_counter361, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %j_load369_to_float = sitofp <8 x i32> %iter_val362 to <8 x float>\n",
" %mul_j_load369_to_float_dy_load370_broadcast = fmul <8 x float> %dy_load_broadcast, %j_load369_to_float\n",
" %add_y0_load368_broadcast_mul_j_load369_to_float_dy_load370_broadcast = fadd <8 x float> %y0_load_broadcast, %mul_j_load369_to_float_dy_load370_broadcast\n",
" br label %for_test378.outer\n",
"\n",
"partial_inner_all_outer353: ; preds = %for_exit381, %for_loop290\n",
" %counter331.1.lcssa = phi i32 [ 1, %for_loop290 ], [ %new_counter484, %for_exit381 ]\n",
" %blend.i.i769860.lcssa = phi <8 x float> [ %blend.i.i769859, %for_loop290 ], [ %blend.i.i769861.ph, %for_exit381 ]\n",
" %blend.i.i766864.lcssa = phi <8 x float> [ %blend.i.i766863, %for_loop290 ], [ %blend.i.i766865.ph, %for_exit381 ]\n",
" %before_full_end487 = icmp slt i32 %counter331.1.lcssa, %add_height_load__to_int32\n",
" br i1 %before_full_end487, label %partial_inner_only485, label %for_test288\n",
"\n",
"for_test378: ; preds = %safe_if_run_true402, %for_test378.outer\n",
" %internal_mask_memory.8 = phi <8 x i32> [ zeroinitializer, %safe_if_run_true402 ], [ %internal_mask_memory.8.ph, %for_test378.outer ]\n",
" %\"oldMask&test388\" = select <8 x i1> %less___i_8514_load385_max_iters_load386_broadcast, <8 x i32> %internal_mask_memory.8, <8 x i32> zeroinitializer\n",
" %floatmask.i689 = bitcast <8 x i32> %\"oldMask&test388\" to <8 x float>\n",
" %v.i690 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i689) #0\n",
" %cmp.i691 = icmp eq i32 %v.i690, 0\n",
" br i1 %cmp.i691, label %for_exit381, label %for_loop380\n",
"\n",
"for_loop380: ; preds = %for_test378\n",
" %\"oldMask&test403\" = select <8 x i1> %less__add_mul___z_re_8512_load395___z_re_8512_load396_mul___z_im_8513_load397___z_im_8513_load398, <8 x i32> %\"oldMask&test388\", <8 x i32> zeroinitializer\n",
" %floatmask.i686 = bitcast <8 x i32> %\"oldMask&test403\" to <8 x float>\n",
" %v.i687 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i686) #0\n",
" %cmp.i688 = icmp eq i32 %v.i687, 0\n",
" br i1 %cmp.i688, label %safe_if_after_true401, label %safe_if_run_true402\n",
"\n",
"for_exit381: ; preds = %for_test378\n",
" %_gensym5_load473_to_float = sitofp <8 x i64> %final.i757858.ph to <8 x float>\n",
" %smear_counter361_cast.elt0 = zext i32 %counter331.1897 to i64\n",
" %add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast.elt0 = add i64 %smear_counter361_cast.elt0, %mul_output__len__1_load470_sub_i_load471_\n",
" %add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast_cast.elt0 = trunc i64 %add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast.elt0 to i32\n",
" %shl_add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast_cast_.elt0 = shl i32 %add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast_cast.elt0, 2\n",
" %\"varying+const_offsets.elt0652\" = add i32 %shl_add_mul_output__len__1_load470_sub_i_load471__broadcast_smear_counter361_cast_cast_.elt0, -4\n",
" %4 = sext i32 %\"varying+const_offsets.elt0652\" to i64\n",
" %ptr653 = getelementptr i8* %output_load_ptr2int_2void, i64 %4, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %ptrcast654 = bitcast i8* %ptr653 to <8 x float>*\n",
" store <8 x float> %_gensym5_load473_to_float, <8 x float>* %ptrcast654, align 4, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %new_counter484 = add i32 %counter331.1897, 8\n",
" %before_aligned_end355 = icmp slt i32 %new_counter484, %aligned_end\n",
" br i1 %before_aligned_end355, label %foreach_full_body317, label %partial_inner_all_outer353\n",
"\n",
"if_done400: ; preds = %safe_if_run_false421, %safe_if_after_true401\n",
" %blend.i.i766866 = phi <8 x float> [ %blend.i.i766865.ph, %safe_if_after_true401 ], [ %blend.i.i766, %safe_if_run_false421 ]\n",
" %blend.i.i769862 = phi <8 x float> [ %blend.i.i769861.ph, %safe_if_after_true401 ], [ %blend.i.i769, %safe_if_run_false421 ]\n",
" %final.i757857 = phi <8 x i64> [ %final.i757858.ph, %safe_if_after_true401 ], [ %final.i757, %safe_if_run_false421 ]\n",
" %blend.i.i760855 = phi <8 x float> [ %blend.i.i760856.ph, %safe_if_after_true401 ], [ %blend.i.i760, %safe_if_run_false421 ]\n",
" %blend.i.i763853 = phi <8 x float> [ %blend.i.i763854.ph, %safe_if_after_true401 ], [ %blend.i.i763, %safe_if_run_false421 ]\n",
" %\"!(break|continue)_lanes452\" = xor <8 x i32> %break_lanes_memory384.1, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask454 = and <8 x i32> %\"oldMask&test388\", %\"!(break|continue)_lanes452\"\n",
" br label %for_test378.outer\n",
"\n",
"for_test378.outer: ; preds = %if_done400, %foreach_full_body317\n",
" %blend.i.i766865.ph = phi <8 x float> [ %blend.i.i766866, %if_done400 ], [ %blend.i.i766864895, %foreach_full_body317 ]\n",
" %blend.i.i769861.ph = phi <8 x float> [ %blend.i.i769862, %if_done400 ], [ %blend.i.i769860896, %foreach_full_body317 ]\n",
" %final.i757858.ph = phi <8 x i64> [ %final.i757857, %if_done400 ], [ zeroinitializer, %foreach_full_body317 ]\n",
" %blend.i.i760856.ph = phi <8 x float> [ %blend.i.i760855, %if_done400 ], [ %add_y0_load368_broadcast_mul_j_load369_to_float_dy_load370_broadcast, %foreach_full_body317 ]\n",
" %blend.i.i763854.ph = phi <8 x float> [ %blend.i.i763853, %if_done400 ], [ %add_x0_load364_mul_i_load365_to_float_dx_load366_broadcast, %foreach_full_body317 ]\n",
" %internal_mask_memory.8.ph = phi <8 x i32> [ %new_mask454, %if_done400 ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %foreach_full_body317 ]\n",
" %less___i_8514_load385_max_iters_load386_broadcast = icmp slt <8 x i64> %final.i757858.ph, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load395___z_re_8512_load396 = fmul <8 x float> %blend.i.i763854.ph, %blend.i.i763854.ph\n",
" %mul___z_im_8513_load397___z_im_8513_load398 = fmul <8 x float> %blend.i.i760856.ph, %blend.i.i760856.ph\n",
" %add_mul___z_re_8512_load395___z_re_8512_load396_mul___z_im_8513_load397___z_im_8513_load398 = fadd <8 x float> %mul___z_im_8513_load397___z_im_8513_load398, %mul___z_re_8512_load395___z_re_8512_load396\n",
" %less__add_mul___z_re_8512_load395___z_re_8512_load396_mul___z_im_8513_load397___z_im_8513_load398 = fcmp ugt <8 x float> %add_mul___z_re_8512_load395___z_re_8512_load396_mul___z_im_8513_load397___z_im_8513_load398, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test378\n",
"\n",
"safe_if_after_true401: ; preds = %safe_if_run_true402, %for_loop380\n",
" %break_lanes_memory384.1 = phi <8 x i32> [ %\"oldMask&test403\", %safe_if_run_true402 ], [ zeroinitializer, %for_loop380 ]\n",
" %5 = bitcast <8 x i32> %\"oldMask&test388\" to <8 x float>\n",
" %floatmask.i683 = select <8 x i1> %less__add_mul___z_re_8512_load395___z_re_8512_load396_mul___z_im_8513_load397___z_im_8513_load398, <8 x float> zeroinitializer, <8 x float> %5\n",
" %v.i684 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i683) #0\n",
" %cmp.i685 = icmp eq i32 %v.i684, 0\n",
" br i1 %cmp.i685, label %if_done400, label %safe_if_run_false421\n",
"\n",
"safe_if_run_true402: ; preds = %for_loop380\n",
" %\"equal_finished&func418_internal_mask&function_mask394\" = icmp eq i32 %v.i687, %v.i690\n",
" br i1 %\"equal_finished&func418_internal_mask&function_mask394\", label %for_test378, label %safe_if_after_true401\n",
"\n",
"safe_if_run_false421: ; preds = %safe_if_after_true401\n",
" %sub_mul___z_re_8512_load428___z_re_8512_load429_mul___z_im_8513_load430___z_im_8513_load431 = fsub <8 x float> %mul___z_re_8512_load395___z_re_8512_load396, %mul___z_im_8513_load397___z_im_8513_load398\n",
" %blend.i.i769 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i769861.ph, <8 x float> %sub_mul___z_re_8512_load428___z_re_8512_load429_mul___z_im_8513_load430___z_im_8513_load431, <8 x float> %floatmask.i683) #1\n",
" %mul____z_re_8512_load433 = fmul <8 x float> %blend.i.i763854.ph, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load433___z_im_8513_load434 = fmul <8 x float> %blend.i.i760856.ph, %mul____z_re_8512_load433\n",
" %blend.i.i766 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i766865.ph, <8 x float> %mul_mul____z_re_8512_load433___z_im_8513_load434, <8 x float> %floatmask.i683) #1\n",
" %add_x_load436___new_re_8515_load437 = fadd <8 x float> %add_x0_load364_mul_i_load365_to_float_dx_load366_broadcast, %blend.i.i769\n",
" %blend.i.i763 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i763854.ph, <8 x float> %add_x_load436___new_re_8515_load437, <8 x float> %floatmask.i683) #1\n",
" %add_y_load439___new_im_8516_load440 = fadd <8 x float> %add_y0_load368_broadcast_mul_j_load369_to_float_dy_load370_broadcast, %blend.i.i766\n",
" %blend.i.i760 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i760856.ph, <8 x float> %add_y_load439___new_im_8516_load440, <8 x float> %floatmask.i683) #1\n",
" %add___i_8514_load442_ = add <8 x i64> %final.i757858.ph, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i743 = shufflevector <8 x i64> %final.i757858.ph, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i744 = bitcast <4 x i64> %old01.i743 to <8 x float>\n",
" %new01.i745 = shufflevector <8 x i64> %add___i_8514_load442_, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i746 = bitcast <4 x i64> %new01.i745 to <8 x float>\n",
" %mask01.i747 = shufflevector <8 x float> %floatmask.i683, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i748 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i744, <8 x float> %new01f.i746, <8 x float> %mask01.i747) #1\n",
" %result01.i749 = bitcast <8 x float> %result01f.i748 to <4 x i64>\n",
" %old23.i750 = shufflevector <8 x i64> %final.i757858.ph, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i751 = bitcast <4 x i64> %old23.i750 to <8 x float>\n",
" %new23.i752 = shufflevector <8 x i64> %add___i_8514_load442_, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i753 = bitcast <4 x i64> %new23.i752 to <8 x float>\n",
" %mask23.i754 = shufflevector <8 x float> %floatmask.i683, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i755 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i751, <8 x float> %new23f.i753, <8 x float> %mask23.i754) #1\n",
" %result23.i756 = bitcast <8 x float> %result23f.i755 to <4 x i64>\n",
" %final.i757 = shufflevector <4 x i64> %result01.i749, <4 x i64> %result23.i756, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done400\n",
"\n",
"partial_inner_only485: ; preds = %partial_inner_all_outer353\n",
" %smear_counter_init489 = insertelement <8 x i32> undef, i32 %counter331.1.lcssa, i32 0\n",
" %smear_counter490 = shufflevector <8 x i32> %smear_counter_init489, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val491 = add <8 x i32> %smear_counter490, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %smear_end_init492 = insertelement <8 x i32> undef, i32 %add_height_load__to_int32, i32 0\n",
" %smear_end493 = shufflevector <8 x i32> %smear_end_init492, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %cmp494 = icmp slt <8 x i32> %iter_val491, %smear_end493\n",
" %cmp494_to_boolvec = sext <8 x i1> %cmp494 to <8 x i32>\n",
" %i_load503_to_float = sitofp i64 %_s40285.0 to float\n",
" %mul_i_load503_to_float_dx_load504 = fmul float %i_load503_to_float, %dx\n",
" %add_x0_load502_mul_i_load503_to_float_dx_load504 = fadd float %mul_i_load503_to_float_dx_load504, %x0\n",
" %add_x0_load502_mul_i_load503_to_float_dx_load504_broadcast_init = insertelement <8 x float> undef, float %add_x0_load502_mul_i_load503_to_float_dx_load504, i32 0\n",
" %add_x0_load502_mul_i_load503_to_float_dx_load504_broadcast = shufflevector <8 x float> %add_x0_load502_mul_i_load503_to_float_dx_load504_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %j_load507_to_float = sitofp <8 x i32> %iter_val491 to <8 x float>\n",
" %mul_j_load507_to_float_dy_load508_broadcast = fmul <8 x float> %dy_load_broadcast, %j_load507_to_float\n",
" %add_y0_load506_broadcast_mul_j_load507_to_float_dy_load508_broadcast = fadd <8 x float> %y0_load_broadcast, %mul_j_load507_to_float_dy_load508_broadcast\n",
" br label %for_test516.outer\n",
"\n",
"for_test516.outer: ; preds = %if_done538, %partial_inner_only485\n",
" %blend.i.i737877.ph = phi <8 x float> [ %blend.i.i737878, %if_done538 ], [ %blend.i.i737876.ph, %partial_inner_only485 ]\n",
" %blend.i.i740874.ph = phi <8 x float> [ %blend.i.i740875, %if_done538 ], [ %blend.i.i740873.ph, %partial_inner_only485 ]\n",
" %final.i872.ph = phi <8 x i64> [ %final.i871, %if_done538 ], [ zeroinitializer, %partial_inner_only485 ]\n",
" %blend.i.i731870.ph = phi <8 x float> [ %blend.i.i731869, %if_done538 ], [ %add_y0_load506_broadcast_mul_j_load507_to_float_dy_load508_broadcast, %partial_inner_only485 ]\n",
" %blend.i.i734868.ph = phi <8 x float> [ %blend.i.i734867, %if_done538 ], [ %add_x0_load502_mul_i_load503_to_float_dx_load504_broadcast, %partial_inner_only485 ]\n",
" %internal_mask_memory.10.ph = phi <8 x i32> [ %new_mask592, %if_done538 ], [ %cmp494_to_boolvec, %partial_inner_only485 ]\n",
" %less___i_8514_load523_max_iters_load524_broadcast = icmp slt <8 x i64> %final.i872.ph, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load533___z_re_8512_load534 = fmul <8 x float> %blend.i.i734868.ph, %blend.i.i734868.ph\n",
" %mul___z_im_8513_load535___z_im_8513_load536 = fmul <8 x float> %blend.i.i731870.ph, %blend.i.i731870.ph\n",
" %add_mul___z_re_8512_load533___z_re_8512_load534_mul___z_im_8513_load535___z_im_8513_load536 = fadd <8 x float> %mul___z_im_8513_load535___z_im_8513_load536, %mul___z_re_8512_load533___z_re_8512_load534\n",
" %less__add_mul___z_re_8512_load533___z_re_8512_load534_mul___z_im_8513_load535___z_im_8513_load536 = fcmp ugt <8 x float> %add_mul___z_re_8512_load533___z_re_8512_load534_mul___z_im_8513_load535___z_im_8513_load536, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test516\n",
"\n",
"for_test516: ; preds = %safe_if_run_true540, %for_test516.outer\n",
" %internal_mask_memory.10 = phi <8 x i32> [ zeroinitializer, %safe_if_run_true540 ], [ %internal_mask_memory.10.ph, %for_test516.outer ]\n",
" %\"oldMask&test526\" = select <8 x i1> %less___i_8514_load523_max_iters_load524_broadcast, <8 x i32> %internal_mask_memory.10, <8 x i32> zeroinitializer\n",
" %floatmask.i674 = bitcast <8 x i32> %\"oldMask&test526\" to <8 x float>\n",
" %v.i675 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i674) #0\n",
" %cmp.i676 = icmp eq i32 %v.i675, 0\n",
" br i1 %cmp.i676, label %for_exit519, label %for_loop518\n",
"\n",
"for_loop518: ; preds = %for_test516\n",
" %\"oldMask&test541\" = select <8 x i1> %less__add_mul___z_re_8512_load533___z_re_8512_load534_mul___z_im_8513_load535___z_im_8513_load536, <8 x i32> %\"oldMask&test526\", <8 x i32> zeroinitializer\n",
" %floatmask.i671 = bitcast <8 x i32> %\"oldMask&test541\" to <8 x float>\n",
" %v.i672 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i671) #0\n",
" %cmp.i673 = icmp eq i32 %v.i672, 0\n",
" br i1 %cmp.i673, label %safe_if_after_true539, label %safe_if_run_true540\n",
"\n",
"for_exit519: ; preds = %for_test516\n",
" %sub_i_load609_ = add i64 %_s40285.0, -1\n",
" %mul_output__len__1_load608_sub_i_load609_ = mul i64 %sub_i_load609_, %output__len__1\n",
" %_gensym5_load611_to_float = sitofp <8 x i64> %final.i872.ph to <8 x float>\n",
" %j332.0_cast.elt0 = zext i32 %counter331.1.lcssa to i64\n",
" %add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast.elt0 = add i64 %j332.0_cast.elt0, %mul_output__len__1_load608_sub_i_load609_\n",
" %add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast_cast.elt0 = trunc i64 %add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast.elt0 to i32\n",
" %shl_add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast_cast_.elt0 = shl i32 %add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast_cast.elt0, 2\n",
" %\"varying+const_offsets.elt0660\" = add i32 %shl_add_mul_output__len__1_load608_sub_i_load609__broadcast_j332.0_cast_cast_.elt0, -4\n",
" %6 = sext i32 %\"varying+const_offsets.elt0660\" to i64\n",
" %ptr661 = getelementptr i8* %output_load_ptr2int_2void, i64 %6\n",
" %mask.i.i = bitcast <8 x i32> %cmp494_to_boolvec to <8 x float>\n",
" call void @llvm.x86.avx.maskstore.ps.256(i8* %ptr661, <8 x float> %mask.i.i, <8 x float> %_gensym5_load611_to_float) #1\n",
" br label %for_test288.outer\n",
"\n",
"for_test288.outer: ; preds = %for_exit519, %allocas\n",
" %blend.i.i737876.ph = phi <8 x float> [ %blend.i.i737877.ph, %for_exit519 ], [ undef, %allocas ]\n",
" %blend.i.i740873.ph = phi <8 x float> [ %blend.i.i740874.ph, %for_exit519 ], [ undef, %allocas ]\n",
" %blend.i.i766863.ph = phi <8 x float> [ %blend.i.i766864.lcssa, %for_exit519 ], [ undef, %allocas ]\n",
" %blend.i.i769859.ph = phi <8 x float> [ %blend.i.i769860.lcssa, %for_exit519 ], [ undef, %allocas ]\n",
" %_s40285.0.ph = phi i64 [ %add__s40_load312_, %for_exit519 ], [ 1, %allocas ]\n",
" %internal_mask_memory.6.ph = phi <8 x i32> [ %\"oldMask&test302\", %for_exit519 ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %allocas ]\n",
" br label %for_test288\n",
"\n",
"if_done538: ; preds = %safe_if_run_false559, %safe_if_after_true539\n",
" %blend.i.i737878 = phi <8 x float> [ %blend.i.i737877.ph, %safe_if_after_true539 ], [ %blend.i.i737, %safe_if_run_false559 ]\n",
" %blend.i.i740875 = phi <8 x float> [ %blend.i.i740874.ph, %safe_if_after_true539 ], [ %blend.i.i740, %safe_if_run_false559 ]\n",
" %final.i871 = phi <8 x i64> [ %final.i872.ph, %safe_if_after_true539 ], [ %final.i, %safe_if_run_false559 ]\n",
" %blend.i.i731869 = phi <8 x float> [ %blend.i.i731870.ph, %safe_if_after_true539 ], [ %blend.i.i731, %safe_if_run_false559 ]\n",
" %blend.i.i734867 = phi <8 x float> [ %blend.i.i734868.ph, %safe_if_after_true539 ], [ %blend.i.i734, %safe_if_run_false559 ]\n",
" %\"!(break|continue)_lanes590\" = xor <8 x i32> %break_lanes_memory522.1, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask592 = and <8 x i32> %\"oldMask&test526\", %\"!(break|continue)_lanes590\"\n",
" br label %for_test516.outer\n",
"\n",
"safe_if_after_true539: ; preds = %safe_if_run_true540, %for_loop518\n",
" %break_lanes_memory522.1 = phi <8 x i32> [ %\"oldMask&test541\", %safe_if_run_true540 ], [ zeroinitializer, %for_loop518 ]\n",
" %7 = bitcast <8 x i32> %\"oldMask&test526\" to <8 x float>\n",
" %floatmask.i668 = select <8 x i1> %less__add_mul___z_re_8512_load533___z_re_8512_load534_mul___z_im_8513_load535___z_im_8513_load536, <8 x float> zeroinitializer, <8 x float> %7\n",
" %v.i669 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i668) #0\n",
" %cmp.i670 = icmp eq i32 %v.i669, 0\n",
" br i1 %cmp.i670, label %if_done538, label %safe_if_run_false559\n",
"\n",
"safe_if_run_true540: ; preds = %for_loop518\n",
" %\"equal_finished&func556_internal_mask&function_mask532\" = icmp eq i32 %v.i672, %v.i675\n",
" br i1 %\"equal_finished&func556_internal_mask&function_mask532\", label %for_test516, label %safe_if_after_true539\n",
"\n",
"safe_if_run_false559: ; preds = %safe_if_after_true539\n",
" %sub_mul___z_re_8512_load566___z_re_8512_load567_mul___z_im_8513_load568___z_im_8513_load569 = fsub <8 x float> %mul___z_re_8512_load533___z_re_8512_load534, %mul___z_im_8513_load535___z_im_8513_load536\n",
" %blend.i.i740 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i740874.ph, <8 x float> %sub_mul___z_re_8512_load566___z_re_8512_load567_mul___z_im_8513_load568___z_im_8513_load569, <8 x float> %floatmask.i668) #1\n",
" %mul____z_re_8512_load571 = fmul <8 x float> %blend.i.i734868.ph, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load571___z_im_8513_load572 = fmul <8 x float> %blend.i.i731870.ph, %mul____z_re_8512_load571\n",
" %blend.i.i737 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i737877.ph, <8 x float> %mul_mul____z_re_8512_load571___z_im_8513_load572, <8 x float> %floatmask.i668) #1\n",
" %add_x_load574___new_re_8515_load575 = fadd <8 x float> %add_x0_load502_mul_i_load503_to_float_dx_load504_broadcast, %blend.i.i740\n",
" %blend.i.i734 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i734868.ph, <8 x float> %add_x_load574___new_re_8515_load575, <8 x float> %floatmask.i668) #1\n",
" %add_y_load577___new_im_8516_load578 = fadd <8 x float> %add_y0_load506_broadcast_mul_j_load507_to_float_dy_load508_broadcast, %blend.i.i737\n",
" %blend.i.i731 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i731870.ph, <8 x float> %add_y_load577___new_im_8516_load578, <8 x float> %floatmask.i668) #1\n",
" %add___i_8514_load580_ = add <8 x i64> %final.i872.ph, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i = shufflevector <8 x i64> %final.i872.ph, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i = bitcast <4 x i64> %old01.i to <8 x float>\n",
" %new01.i = shufflevector <8 x i64> %add___i_8514_load580_, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i = bitcast <4 x i64> %new01.i to <8 x float>\n",
" %mask01.i = shufflevector <8 x float> %floatmask.i668, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i, <8 x float> %new01f.i, <8 x float> %mask01.i) #1\n",
" %result01.i = bitcast <8 x float> %result01f.i to <4 x i64>\n",
" %old23.i = shufflevector <8 x i64> %final.i872.ph, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i = bitcast <4 x i64> %old23.i to <8 x float>\n",
" %new23.i = shufflevector <8 x i64> %add___i_8514_load580_, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i = bitcast <4 x i64> %new23.i to <8 x float>\n",
" %mask23.i = shufflevector <8 x float> %floatmask.i668, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i, <8 x float> %new23f.i, <8 x float> %mask23.i) #1\n",
" %result23.i = bitcast <8 x float> %result23f.i to <4 x i64>\n",
" %final.i = shufflevector <4 x i64> %result01.i, <4 x i64> %result23.i, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done538\n",
"}\n",
"\n",
"; Function Attrs: nounwind\n",
"define void @ispc_func_1(float %x0, float %y0, float* noalias nocapture %output, i64 %output__len__1, i64 %output__len__2, i64 %max_iters, i64 %height, i64 %width, float %dx, float %dy) #1 {\n",
"allocas:\n",
" %lessequal__width_load = icmp sgt i64 %width, 0\n",
" %width.op = add i64 %width, 1\n",
" %add__gensym318_stop_ = select i1 %lessequal__width_load, i64 %width.op, i64 1\n",
" %add_height_load_ = add i64 %height, 1\n",
" %add_height_load__to_int32 = trunc i64 %add_height_load_ to i32\n",
" %nitems = add i32 %add_height_load__to_int32, -1\n",
" %nextras = srem i32 %nitems, 8\n",
" %aligned_end = sub i32 %add_height_load__to_int32, %nextras\n",
" %before_aligned_end44796 = icmp sgt i32 %aligned_end, 1\n",
" %y0_load_broadcast_init = insertelement <8 x float> undef, float %y0, i32 0\n",
" %y0_load_broadcast = shufflevector <8 x float> %y0_load_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %dy_load_broadcast_init = insertelement <8 x float> undef, float %dy, i32 0\n",
" %dy_load_broadcast = shufflevector <8 x float> %dy_load_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %max_iters_load_broadcast_init = insertelement <8 x i64> undef, i64 %max_iters, i32 0\n",
" %max_iters_load_broadcast = shufflevector <8 x i64> %max_iters_load_broadcast_init, <8 x i64> undef, <8 x i32> zeroinitializer\n",
" %output_load_ptr2int_2void = bitcast float* %output to i8*\n",
" br label %for_test.outer\n",
"\n",
"for_test.outer: ; preds = %for_exit156, %allocas\n",
" %blend.i.i736789.ph = phi <8 x float> [ %blend.i.i736790.ph, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i739786.ph = phi <8 x float> [ %blend.i.i739787.ph, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i765776.ph = phi <8 x float> [ %blend.i.i765777.lcssa, %for_exit156 ], [ undef, %allocas ]\n",
" %blend.i.i772.ph = phi <8 x float> [ %blend.i.i773.lcssa, %for_exit156 ], [ undef, %allocas ]\n",
" %_s40.0.ph = phi i64 [ %add__s40_load30_, %for_exit156 ], [ 1, %allocas ]\n",
" %internal_mask_memory.0.ph = phi <8 x i32> [ %\"oldMask&test\", %for_exit156 ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %allocas ]\n",
" br label %for_test\n",
"\n",
"for_test: ; preds = %partial_inner_all_outer, %for_test.outer\n",
" %blend.i.i765776 = phi <8 x float> [ %blend.i.i765777.lcssa, %partial_inner_all_outer ], [ %blend.i.i765776.ph, %for_test.outer ]\n",
" %blend.i.i772 = phi <8 x float> [ %blend.i.i773.lcssa, %partial_inner_all_outer ], [ %blend.i.i772.ph, %for_test.outer ]\n",
" %_s40.0 = phi i64 [ %add__s40_load30_, %partial_inner_all_outer ], [ %_s40.0.ph, %for_test.outer ]\n",
" %internal_mask_memory.0 = phi <8 x i32> [ %\"oldMask&test\", %partial_inner_all_outer ], [ %internal_mask_memory.0.ph, %for_test.outer ]\n",
" %equal__s40_load_add__gensym318_stop_ = icmp eq i64 %_s40.0, %add__gensym318_stop_\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool = sext i1 %equal__s40_load_add__gensym318_stop_ to i32\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init = insertelement <8 x i32> undef, i32 %equal__s40_load_add__gensym318_stop_to_i_bool, i32 0\n",
" %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast = shufflevector <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast_init, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %val_load_logicalnot.i727 = xor <8 x i32> %equal__s40_load_add__gensym318_stop_to_i_bool_broadcast, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %\"oldMask&test\" = and <8 x i32> %internal_mask_memory.0, %val_load_logicalnot.i727\n",
" %floatmask.i724 = bitcast <8 x i32> %\"oldMask&test\" to <8 x float>\n",
" %v.i725 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i724) #0\n",
" %cmp.i726 = icmp eq i32 %v.i725, 0\n",
" br i1 %cmp.i726, label %for_exit, label %for_loop\n",
"\n",
"for_loop: ; preds = %for_test\n",
" %add__s40_load30_ = add i64 %_s40.0, 1\n",
" br i1 %before_aligned_end44796, label %foreach_full_body.lr.ph, label %partial_inner_all_outer\n",
"\n",
"foreach_full_body.lr.ph: ; preds = %for_loop\n",
" %i_load_to_float = sitofp i64 %_s40.0 to float\n",
" %mul_i_load_to_float_dx_load = fmul float %i_load_to_float, %dx\n",
" %add_x0_load_mul_i_load_to_float_dx_load = fadd float %mul_i_load_to_float_dx_load, %x0\n",
" %add_x0_load_mul_i_load_to_float_dx_load_broadcast_init = insertelement <8 x float> undef, float %add_x0_load_mul_i_load_to_float_dx_load, i32 0\n",
" %add_x0_load_mul_i_load_to_float_dx_load_broadcast = shufflevector <8 x float> %add_x0_load_mul_i_load_to_float_dx_load_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %sub_i_load115_ = add i64 %_s40.0, -1\n",
" %mul_output__len__1_load_sub_i_load115_ = mul i64 %sub_i_load115_, %output__len__1\n",
" br label %foreach_full_body\n",
"\n",
"for_exit: ; preds = %for_test\n",
" ret void\n",
"\n",
"foreach_full_body: ; preds = %for_exit55, %foreach_full_body.lr.ph\n",
" %counter.1799 = phi i32 [ 1, %foreach_full_body.lr.ph ], [ %new_counter, %for_exit55 ]\n",
" %blend.i.i773798 = phi <8 x float> [ %blend.i.i772, %foreach_full_body.lr.ph ], [ %blend.i.i774.ph, %for_exit55 ]\n",
" %blend.i.i765777797 = phi <8 x float> [ %blend.i.i765776, %foreach_full_body.lr.ph ], [ %blend.i.i765778.ph, %for_exit55 ]\n",
" %smear_counter_init48 = insertelement <8 x i32> undef, i32 %counter.1799, i32 0\n",
" %smear_counter49 = shufflevector <8 x i32> %smear_counter_init48, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val50 = add <8 x i32> %smear_counter49, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %j_load51_to_float = sitofp <8 x i32> %iter_val50 to <8 x float>\n",
" %mul_j_load51_to_float_dy_load_broadcast = fmul <8 x float> %dy_load_broadcast, %j_load51_to_float\n",
" %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast = fadd <8 x float> %y0_load_broadcast, %mul_j_load51_to_float_dy_load_broadcast\n",
" br label %for_test52.outer\n",
"\n",
"partial_inner_all_outer: ; preds = %for_exit55, %for_loop\n",
" %counter.1.lcssa = phi i32 [ 1, %for_loop ], [ %new_counter, %for_exit55 ]\n",
" %blend.i.i773.lcssa = phi <8 x float> [ %blend.i.i772, %for_loop ], [ %blend.i.i774.ph, %for_exit55 ]\n",
" %blend.i.i765777.lcssa = phi <8 x float> [ %blend.i.i765776, %for_loop ], [ %blend.i.i765778.ph, %for_exit55 ]\n",
" %before_full_end = icmp slt i32 %counter.1.lcssa, %add_height_load__to_int32\n",
" br i1 %before_full_end, label %partial_inner_only, label %for_test\n",
"\n",
"for_test52: ; preds = %safe_if_run_true, %for_test52.outer\n",
" %internal_mask_memory.2 = phi <8 x i32> [ zeroinitializer, %safe_if_run_true ], [ %internal_mask_memory.2.ph, %for_test52.outer ]\n",
" %\"oldMask&test60\" = select <8 x i1> %less___i_8514_load_max_iters_load_broadcast, <8 x i32> %internal_mask_memory.2, <8 x i32> zeroinitializer\n",
" %floatmask.i721 = bitcast <8 x i32> %\"oldMask&test60\" to <8 x float>\n",
" %v.i722 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i721) #0\n",
" %cmp.i723 = icmp eq i32 %v.i722, 0\n",
" br i1 %cmp.i723, label %for_exit55, label %for_loop54\n",
"\n",
"for_loop54: ; preds = %for_test52\n",
" %\"oldMask&test70\" = select <8 x i1> %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68, <8 x i32> %\"oldMask&test60\", <8 x i32> zeroinitializer\n",
" %floatmask.i718 = bitcast <8 x i32> %\"oldMask&test70\" to <8 x float>\n",
" %v.i719 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i718) #0\n",
" %cmp.i720 = icmp eq i32 %v.i719, 0\n",
" br i1 %cmp.i720, label %safe_if_after_true, label %safe_if_run_true\n",
"\n",
"for_exit55: ; preds = %for_test52\n",
" %_gensym5_load_to_float = sitofp <8 x i64> %final.i756771.ph to <8 x float>\n",
" %smear_counter49_cast.elt0 = zext i32 %counter.1799 to i64\n",
" %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast.elt0 = add i64 %smear_counter49_cast.elt0, %mul_out"
]
},
{
"data": {
"text/plain": [
"2328"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"put__len__1_load_sub_i_load115_\n",
" %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast.elt0 = trunc i64 %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast.elt0 to i32\n",
" %shl_add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast_.elt0 = shl i32 %add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast.elt0, 2\n",
" %\"varying+const_offsets.elt0\" = add i32 %shl_add_mul_output__len__1_load_sub_i_load115__broadcast_smear_counter49_cast_cast_.elt0, -4\n",
" %0 = sext i32 %\"varying+const_offsets.elt0\" to i64\n",
" %ptr = getelementptr i8* %output_load_ptr2int_2void, i64 %0, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %ptrcast = bitcast i8* %ptr to <8 x float>*\n",
" store <8 x float> %_gensym5_load_to_float, <8 x float>* %ptrcast, align 4, !filename !2, !first_line !3, !first_column !4, !last_line !3, !last_column !5\n",
" %new_counter = add i32 %counter.1799, 8\n",
" %before_aligned_end44 = icmp slt i32 %new_counter, %aligned_end\n",
" br i1 %before_aligned_end44, label %foreach_full_body, label %partial_inner_all_outer\n",
"\n",
"if_done: ; preds = %safe_if_run_false, %safe_if_after_true\n",
" %blend.i.i765779 = phi <8 x float> [ %blend.i.i765778.ph, %safe_if_after_true ], [ %blend.i.i765, %safe_if_run_false ]\n",
" %blend.i.i775 = phi <8 x float> [ %blend.i.i774.ph, %safe_if_after_true ], [ %blend.i.i, %safe_if_run_false ]\n",
" %final.i756770 = phi <8 x i64> [ %final.i756771.ph, %safe_if_after_true ], [ %final.i756, %safe_if_run_false ]\n",
" %blend.i.i759768 = phi <8 x float> [ %blend.i.i759769.ph, %safe_if_after_true ], [ %blend.i.i759, %safe_if_run_false ]\n",
" %blend.i.i762766 = phi <8 x float> [ %blend.i.i762767.ph, %safe_if_after_true ], [ %blend.i.i762, %safe_if_run_false ]\n",
" %\"!(break|continue)_lanes\" = xor <8 x i32> %break_lanes_memory58.1, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask102 = and <8 x i32> %\"oldMask&test60\", %\"!(break|continue)_lanes\"\n",
" br label %for_test52.outer\n",
"\n",
"for_test52.outer: ; preds = %if_done, %foreach_full_body\n",
" %blend.i.i765778.ph = phi <8 x float> [ %blend.i.i765779, %if_done ], [ %blend.i.i765777797, %foreach_full_body ]\n",
" %blend.i.i774.ph = phi <8 x float> [ %blend.i.i775, %if_done ], [ %blend.i.i773798, %foreach_full_body ]\n",
" %final.i756771.ph = phi <8 x i64> [ %final.i756770, %if_done ], [ zeroinitializer, %foreach_full_body ]\n",
" %blend.i.i759769.ph = phi <8 x float> [ %blend.i.i759768, %if_done ], [ %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast, %foreach_full_body ]\n",
" %blend.i.i762767.ph = phi <8 x float> [ %blend.i.i762766, %if_done ], [ %add_x0_load_mul_i_load_to_float_dx_load_broadcast, %foreach_full_body ]\n",
" %internal_mask_memory.2.ph = phi <8 x i32> [ %new_mask102, %if_done ], [ <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>, %foreach_full_body ]\n",
" %less___i_8514_load_max_iters_load_broadcast = icmp slt <8 x i64> %final.i756771.ph, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load___z_re_8512_load67 = fmul <8 x float> %blend.i.i762767.ph, %blend.i.i762767.ph\n",
" %mul___z_im_8513_load___z_im_8513_load68 = fmul <8 x float> %blend.i.i759769.ph, %blend.i.i759769.ph\n",
" %add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68 = fadd <8 x float> %mul___z_im_8513_load___z_im_8513_load68, %mul___z_re_8512_load___z_re_8512_load67\n",
" %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68 = fcmp ugt <8 x float> %add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test52\n",
"\n",
"safe_if_after_true: ; preds = %safe_if_run_true, %for_loop54\n",
" %break_lanes_memory58.1 = phi <8 x i32> [ %\"oldMask&test70\", %safe_if_run_true ], [ zeroinitializer, %for_loop54 ]\n",
" %1 = bitcast <8 x i32> %\"oldMask&test60\" to <8 x float>\n",
" %floatmask.i715 = select <8 x i1> %less__add_mul___z_re_8512_load___z_re_8512_load67_mul___z_im_8513_load___z_im_8513_load68, <8 x float> zeroinitializer, <8 x float> %1\n",
" %v.i716 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i715) #0\n",
" %cmp.i717 = icmp eq i32 %v.i716, 0\n",
" br i1 %cmp.i717, label %if_done, label %safe_if_run_false\n",
"\n",
"safe_if_run_true: ; preds = %for_loop54\n",
" %\"equal_finished&func_internal_mask&function_mask66\" = icmp eq i32 %v.i719, %v.i722\n",
" br i1 %\"equal_finished&func_internal_mask&function_mask66\", label %for_test52, label %safe_if_after_true\n",
"\n",
"safe_if_run_false: ; preds = %safe_if_after_true\n",
" %sub_mul___z_re_8512_load82___z_re_8512_load83_mul___z_im_8513_load84___z_im_8513_load85 = fsub <8 x float> %mul___z_re_8512_load___z_re_8512_load67, %mul___z_im_8513_load___z_im_8513_load68\n",
" %blend.i.i = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i774.ph, <8 x float> %sub_mul___z_re_8512_load82___z_re_8512_load83_mul___z_im_8513_load84___z_im_8513_load85, <8 x float> %floatmask.i715) #1\n",
" %mul____z_re_8512_load87 = fmul <8 x float> %blend.i.i762767.ph, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load87___z_im_8513_load88 = fmul <8 x float> %blend.i.i759769.ph, %mul____z_re_8512_load87\n",
" %blend.i.i765 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i765778.ph, <8 x float> %mul_mul____z_re_8512_load87___z_im_8513_load88, <8 x float> %floatmask.i715) #1\n",
" %add_x_load90___new_re_8515_load = fadd <8 x float> %add_x0_load_mul_i_load_to_float_dx_load_broadcast, %blend.i.i\n",
" %blend.i.i762 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i762767.ph, <8 x float> %add_x_load90___new_re_8515_load, <8 x float> %floatmask.i715) #1\n",
" %add_y_load92___new_im_8516_load = fadd <8 x float> %add_y0_load_broadcast_mul_j_load51_to_float_dy_load_broadcast, %blend.i.i765\n",
" %blend.i.i759 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i759769.ph, <8 x float> %add_y_load92___new_im_8516_load, <8 x float> %floatmask.i715) #1\n",
" %add___i_8514_load94_ = add <8 x i64> %final.i756771.ph, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i742 = shufflevector <8 x i64> %final.i756771.ph, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i743 = bitcast <4 x i64> %old01.i742 to <8 x float>\n",
" %new01.i744 = shufflevector <8 x i64> %add___i_8514_load94_, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i745 = bitcast <4 x i64> %new01.i744 to <8 x float>\n",
" %mask01.i746 = shufflevector <8 x float> %floatmask.i715, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i747 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i743, <8 x float> %new01f.i745, <8 x float> %mask01.i746) #1\n",
" %result01.i748 = bitcast <8 x float> %result01f.i747 to <4 x i64>\n",
" %old23.i749 = shufflevector <8 x i64> %final.i756771.ph, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i750 = bitcast <4 x i64> %old23.i749 to <8 x float>\n",
" %new23.i751 = shufflevector <8 x i64> %add___i_8514_load94_, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i752 = bitcast <4 x i64> %new23.i751 to <8 x float>\n",
" %mask23.i753 = shufflevector <8 x float> %floatmask.i715, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i754 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i750, <8 x float> %new23f.i752, <8 x float> %mask23.i753) #1\n",
" %result23.i755 = bitcast <8 x float> %result23f.i754 to <4 x i64>\n",
" %final.i756 = shufflevector <4 x i64> %result01.i748, <4 x i64> %result23.i755, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done\n",
"\n",
"partial_inner_only: ; preds = %partial_inner_all_outer\n",
" %smear_counter_init128 = insertelement <8 x i32> undef, i32 %counter.1.lcssa, i32 0\n",
" %smear_counter129 = shufflevector <8 x i32> %smear_counter_init128, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %iter_val130 = add <8 x i32> %smear_counter129, <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" %smear_end_init131 = insertelement <8 x i32> undef, i32 %add_height_load__to_int32, i32 0\n",
" %smear_end132 = shufflevector <8 x i32> %smear_end_init131, <8 x i32> undef, <8 x i32> zeroinitializer\n",
" %cmp133 = icmp slt <8 x i32> %iter_val130, %smear_end132\n",
" %cmp133_to_boolvec = sext <8 x i1> %cmp133 to <8 x i32>\n",
" %i_load140_to_float = sitofp i64 %_s40.0 to float\n",
" %mul_i_load140_to_float_dx_load141 = fmul float %i_load140_to_float, %dx\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141 = fadd float %mul_i_load140_to_float_dx_load141, %x0\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast_init = insertelement <8 x float> undef, float %add_x0_load139_mul_i_load140_to_float_dx_load141, i32 0\n",
" %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast = shufflevector <8 x float> %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast_init, <8 x float> undef, <8 x i32> zeroinitializer\n",
" %j_load144_to_float = sitofp <8 x i32> %iter_val130 to <8 x float>\n",
" %mul_j_load144_to_float_dy_load145_broadcast = fmul <8 x float> %dy_load_broadcast, %j_load144_to_float\n",
" %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast = fadd <8 x float> %y0_load_broadcast, %mul_j_load144_to_float_dy_load145_broadcast\n",
" br label %for_test153.outer\n",
"\n",
"for_test153.outer: ; preds = %if_done175, %partial_inner_only\n",
" %blend.i.i736790.ph = phi <8 x float> [ %blend.i.i736791, %if_done175 ], [ %blend.i.i736789.ph, %partial_inner_only ]\n",
" %blend.i.i739787.ph = phi <8 x float> [ %blend.i.i739788, %if_done175 ], [ %blend.i.i739786.ph, %partial_inner_only ]\n",
" %final.i785.ph = phi <8 x i64> [ %final.i784, %if_done175 ], [ zeroinitializer, %partial_inner_only ]\n",
" %blend.i.i730783.ph = phi <8 x float> [ %blend.i.i730782, %if_done175 ], [ %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast, %partial_inner_only ]\n",
" %blend.i.i733781.ph = phi <8 x float> [ %blend.i.i733780, %if_done175 ], [ %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast, %partial_inner_only ]\n",
" %internal_mask_memory.4.ph = phi <8 x i32> [ %new_mask229, %if_done175 ], [ %cmp133_to_boolvec, %partial_inner_only ]\n",
" %less___i_8514_load160_max_iters_load161_broadcast = icmp slt <8 x i64> %final.i785.ph, %max_iters_load_broadcast\n",
" %mul___z_re_8512_load170___z_re_8512_load171 = fmul <8 x float> %blend.i.i733781.ph, %blend.i.i733781.ph\n",
" %mul___z_im_8513_load172___z_im_8513_load173 = fmul <8 x float> %blend.i.i730783.ph, %blend.i.i730783.ph\n",
" %add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173 = fadd <8 x float> %mul___z_im_8513_load172___z_im_8513_load173, %mul___z_re_8512_load170___z_re_8512_load171\n",
" %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173 = fcmp ugt <8 x float> %add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00, float 4.000000e+00>\n",
" br label %for_test153\n",
"\n",
"for_test153: ; preds = %safe_if_run_true177, %for_test153.outer\n",
" %internal_mask_memory.4 = phi <8 x i32> [ zeroinitializer, %safe_if_run_true177 ], [ %internal_mask_memory.4.ph, %for_test153.outer ]\n",
" %\"oldMask&test163\" = select <8 x i1> %less___i_8514_load160_max_iters_load161_broadcast, <8 x i32> %internal_mask_memory.4, <8 x i32> zeroinitializer\n",
" %floatmask.i706 = bitcast <8 x i32> %\"oldMask&test163\" to <8 x float>\n",
" %v.i707 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i706) #0\n",
" %cmp.i708 = icmp eq i32 %v.i707, 0\n",
" br i1 %cmp.i708, label %for_exit156, label %for_loop155\n",
"\n",
"for_loop155: ; preds = %for_test153\n",
" %\"oldMask&test178\" = select <8 x i1> %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <8 x i32> %\"oldMask&test163\", <8 x i32> zeroinitializer\n",
" %floatmask.i703 = bitcast <8 x i32> %\"oldMask&test178\" to <8 x float>\n",
" %v.i704 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i703) #0\n",
" %cmp.i705 = icmp eq i32 %v.i704, 0\n",
" br i1 %cmp.i705, label %safe_if_after_true176, label %safe_if_run_true177\n",
"\n",
"for_exit156: ; preds = %for_test153\n",
" %sub_i_load246_ = add i64 %_s40.0, -1\n",
" %mul_output__len__1_load245_sub_i_load246_ = mul i64 %sub_i_load246_, %output__len__1\n",
" %_gensym5_load248_to_float = sitofp <8 x i64> %final.i785.ph to <8 x float>\n",
" %j.0_cast.elt0 = zext i32 %counter.1.lcssa to i64\n",
" %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast.elt0 = add i64 %j.0_cast.elt0, %mul_output__len__1_load245_sub_i_load246_\n",
" %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast.elt0 = trunc i64 %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast.elt0 to i32\n",
" %shl_add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast_.elt0 = shl i32 %add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast.elt0, 2\n",
" %\"varying+const_offsets.elt0646\" = add i32 %shl_add_mul_output__len__1_load245_sub_i_load246__broadcast_j.0_cast_cast_.elt0, -4\n",
" %2 = sext i32 %\"varying+const_offsets.elt0646\" to i64\n",
" %ptr647 = getelementptr i8* %output_load_ptr2int_2void, i64 %2\n",
" %mask.i.i = bitcast <8 x i32> %cmp133_to_boolvec to <8 x float>\n",
" call void @llvm.x86.avx.maskstore.ps.256(i8* %ptr647, <8 x float> %mask.i.i, <8 x float> %_gensym5_load248_to_float) #1\n",
" br label %for_test.outer\n",
"\n",
"if_done175: ; preds = %safe_if_run_false196, %safe_if_after_true176\n",
" %blend.i.i736791 = phi <8 x float> [ %blend.i.i736790.ph, %safe_if_after_true176 ], [ %blend.i.i736, %safe_if_run_false196 ]\n",
" %blend.i.i739788 = phi <8 x float> [ %blend.i.i739787.ph, %safe_if_after_true176 ], [ %blend.i.i739, %safe_if_run_false196 ]\n",
" %final.i784 = phi <8 x i64> [ %final.i785.ph, %safe_if_after_true176 ], [ %final.i, %safe_if_run_false196 ]\n",
" %blend.i.i730782 = phi <8 x float> [ %blend.i.i730783.ph, %safe_if_after_true176 ], [ %blend.i.i730, %safe_if_run_false196 ]\n",
" %blend.i.i733780 = phi <8 x float> [ %blend.i.i733781.ph, %safe_if_after_true176 ], [ %blend.i.i733, %safe_if_run_false196 ]\n",
" %\"!(break|continue)_lanes227\" = xor <8 x i32> %break_lanes_memory159.1, <i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1, i32 -1>\n",
" %new_mask229 = and <8 x i32> %\"oldMask&test163\", %\"!(break|continue)_lanes227\"\n",
" br label %for_test153.outer\n",
"\n",
"safe_if_after_true176: ; preds = %safe_if_run_true177, %for_loop155\n",
" %break_lanes_memory159.1 = phi <8 x i32> [ %\"oldMask&test178\", %safe_if_run_true177 ], [ zeroinitializer, %for_loop155 ]\n",
" %3 = bitcast <8 x i32> %\"oldMask&test163\" to <8 x float>\n",
" %floatmask.i700 = select <8 x i1> %less__add_mul___z_re_8512_load170___z_re_8512_load171_mul___z_im_8513_load172___z_im_8513_load173, <8 x float> zeroinitializer, <8 x float> %3\n",
" %v.i701 = tail call i32 @llvm.x86.avx.movmsk.ps.256(<8 x float> %floatmask.i700) #0\n",
" %cmp.i702 = icmp eq i32 %v.i701, 0\n",
" br i1 %cmp.i702, label %if_done175, label %safe_if_run_false196\n",
"\n",
"safe_if_run_true177: ; preds = %for_loop155\n",
" %\"equal_finished&func193_internal_mask&function_mask169\" = icmp eq i32 %v.i704, %v.i707\n",
" br i1 %\"equal_finished&func193_internal_mask&function_mask169\", label %for_test153, label %safe_if_after_true176\n",
"\n",
"safe_if_run_false196: ; preds = %safe_if_after_true176\n",
" %sub_mul___z_re_8512_load203___z_re_8512_load204_mul___z_im_8513_load205___z_im_8513_load206 = fsub <8 x float> %mul___z_re_8512_load170___z_re_8512_load171, %mul___z_im_8513_load172___z_im_8513_load173\n",
" %blend.i.i739 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i739787.ph, <8 x float> %sub_mul___z_re_8512_load203___z_re_8512_load204_mul___z_im_8513_load205___z_im_8513_load206, <8 x float> %floatmask.i700) #1\n",
" %mul____z_re_8512_load208 = fmul <8 x float> %blend.i.i733781.ph, <float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00, float 2.000000e+00>\n",
" %mul_mul____z_re_8512_load208___z_im_8513_load209 = fmul <8 x float> %blend.i.i730783.ph, %mul____z_re_8512_load208\n",
" %blend.i.i736 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i736790.ph, <8 x float> %mul_mul____z_re_8512_load208___z_im_8513_load209, <8 x float> %floatmask.i700) #1\n",
" %add_x_load211___new_re_8515_load212 = fadd <8 x float> %add_x0_load139_mul_i_load140_to_float_dx_load141_broadcast, %blend.i.i739\n",
" %blend.i.i733 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i733781.ph, <8 x float> %add_x_load211___new_re_8515_load212, <8 x float> %floatmask.i700) #1\n",
" %add_y_load214___new_im_8516_load215 = fadd <8 x float> %add_y0_load143_broadcast_mul_j_load144_to_float_dy_load145_broadcast, %blend.i.i736\n",
" %blend.i.i730 = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %blend.i.i730783.ph, <8 x float> %add_y_load214___new_im_8516_load215, <8 x float> %floatmask.i700) #1\n",
" %add___i_8514_load217_ = add <8 x i64> %final.i785.ph, <i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1, i64 1>\n",
" %old01.i = shufflevector <8 x i64> %final.i785.ph, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %old01f.i = bitcast <4 x i64> %old01.i to <8 x float>\n",
" %new01.i = shufflevector <8 x i64> %add___i_8514_load217_, <8 x i64> undef, <4 x i32> <i32 0, i32 1, i32 2, i32 3>\n",
" %new01f.i = bitcast <4 x i64> %new01.i to <8 x float>\n",
" %mask01.i = shufflevector <8 x float> %floatmask.i700, <8 x float> undef, <8 x i32> <i32 0, i32 0, i32 1, i32 1, i32 2, i32 2, i32 3, i32 3>\n",
" %result01f.i = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old01f.i, <8 x float> %new01f.i, <8 x float> %mask01.i) #1\n",
" %result01.i = bitcast <8 x float> %result01f.i to <4 x i64>\n",
" %old23.i = shufflevector <8 x i64> %final.i785.ph, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %old23f.i = bitcast <4 x i64> %old23.i to <8 x float>\n",
" %new23.i = shufflevector <8 x i64> %add___i_8514_load217_, <8 x i64> undef, <4 x i32> <i32 4, i32 5, i32 6, i32 7>\n",
" %new23f.i = bitcast <4 x i64> %new23.i to <8 x float>\n",
" %mask23.i = shufflevector <8 x float> %floatmask.i700, <8 x float> undef, <8 x i32> <i32 4, i32 4, i32 5, i32 5, i32 6, i32 6, i32 7, i32 7>\n",
" %result23f.i = call <8 x float> @llvm.x86.avx.blendv.ps.256(<8 x float> %old23f.i, <8 x float> %new23f.i, <8 x float> %mask23.i) #1\n",
" %result23.i = bitcast <8 x float> %result23f.i to <4 x i64>\n",
" %final.i = shufflevector <4 x i64> %result01.i, <4 x i64> %result23.i, <8 x i32> <i32 0, i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7>\n",
" br label %if_done175\n",
"}\n",
"\n",
"attributes #0 = { nounwind readnone }\n",
"attributes #1 = { nounwind }\n",
"\n",
"!llvm.ident = !{!0}\n",
"!llvm.module.flags = !{!1}\n",
"\n",
"!0 = !{!\"clang version 3.6.1 (tags/RELEASE_361/final 238309)\"}\n",
"!1 = !{i32 1, !\"PIC Level\", i32 2}\n",
"!2 = !{!\"<stdin>\"}\n",
"!3 = !{i32 47}\n",
"!4 = !{i32 13}\n",
"!5 = !{i32 59}\n"
]
}
],
"source": [
"ISPC.ispc_llvm(func_code, func.file.compile_opts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Julia 0.5.0-dev",
"language": "julia",
"name": "julia-0.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.5.0"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
|
| notebooks/Abel_inversion.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An example of the Nonlinear inference.\n",
"\n",
"This notebook briefly shows an inference examples for non-linear model with GPinv\n",
"\n",
"*Keisuke Fujii 3rd Oct. 2016*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Synthetic observation\n",
"\n",
"Consider we observe a cylindrical transparent mediam with multiple ($N$) lines-of-sight, as shown below.\n",
"\n",
"<img src=figs/abel_inversion.png width=240pt>\n",
"\n",
"The local emission intensity $g$ is a function of the radius $r$.\n",
"The observed emission intensity $\\mathbf{Y}$ is a result of the integration along the line-of-sight as\n",
"$$\n",
"\\mathbf{Y} = \\int_{x} g(r) dx + \\mathbf{e}\n",
"$$\n",
"\n",
"where $\\mathbf{e}$ is a i.i.d. Gaussian noise.\n",
"\n",
"We divided $g$ into $n$ discrete points $\\mathbf{g}$, then the above integration can be approximated as follows\n",
"$$\n",
"\\mathbf{Y} = \\mathrm{A} \\mathbf{g} + \\mathbf{e}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Non-linear model and transform\n",
"\n",
"To make sure $g(r)$ is positive, we define new function $f$,\n",
"$$\n",
"g(r) = \\exp(f(r))\n",
"$$\n",
"Here, we assume $f(r)$ follows the Gaussian Process with kernel $\\mathrm{K}$.\n",
"This transformation makes the problem non-linear."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we infer $\\mathbf{g}$ by \n",
"1. Stochastic approximation of the variational Gaussian process.\n",
"2. Markov Chain Monte-Carlo (MCMC) method."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import several libraries including GPinv"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.247261",
"start_time": "2016-09-11T15:02:21.438142"
},
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import tensorflow as tf\n",
"import sys\n",
"# In ../testing/ dir, we prepared a small script for generating the above matrix A\n",
"sys.path.append('../testing/')\n",
"import make_LosMatrix\n",
"# Import GPinv\n",
"import GPinv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Synthetic signals\n",
"\n",
"Here, we make a synthetic measurement.\n",
"The synthetic signal $\\mathrm{y}$ is simulated from the grand truth solution $g_true$ and random gaussian noise."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.253125",
"start_time": "2016-09-11T15:02:23.248767"
},
"collapsed": true
},
"outputs": [],
"source": [
"n = 30\n",
"N = 40\n",
"# radial coordinate\n",
"r = np.linspace(0, 1., n)\n",
"# synthetic latent function\n",
"f = np.exp(-(r-0.3)*(r-0.3)/0.1) + np.exp(-(r+0.3)*(r+0.3)/0.1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.546153",
"start_time": "2016-09-11T15:02:23.254560"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9d3cd405c0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdcAAAEtCAYAAABajSq+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCf4tUD4aAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9d8c727cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting the latent function\n",
"plt.figure(figsize=(5,3))\n",
"plt.plot(r, f)\n",
"plt.xlabel('$r$: Radial coordinate')\n",
"plt.ylabel('$f$: Function value')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare the synthetic signal."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.567292",
"start_time": "2016-09-11T15:02:23.551606"
},
"collapsed": false
},
"outputs": [],
"source": [
"# los height\n",
"z = np.linspace(-0.9,0.9, N)\n",
"# Los-matrix\n",
"A = make_LosMatrix.make_LosMatrix(r, z)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.576869",
"start_time": "2016-09-11T15:02:23.571884"
},
"collapsed": true
},
"outputs": [],
"source": [
"# noise amplitude \n",
"e_amp = 0.1\n",
"# synthetic observation\n",
"y = np.dot(A, f) + e_amp * np.random.randn(N)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.938578",
"start_time": "2016-09-11T15:02:23.581263"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9d3cb086a0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAEwCAYAAABrH3KHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYKCfyGG1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9d3caf30f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,3))\n",
"plt.plot(z, y, 'o', [-1,1],[0,0], '--k', ms=5)\n",
"plt.xlabel('$z$: Los-height')\n",
"plt.ylabel('$y$: Observation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inference\n",
"\n",
"In order to carry out an inference, a custom **likelihood**, which calculates $p(\\mathbf{Y}|\\mathbf{f})$ with given $\\mathbf{f}$, must be prepared according to the problem.\n",
"\n",
"The method to be implemented is **logp(f,Y)** method, that calculates log-likelihood for data **Y** with given **f**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:23.963571",
"start_time": "2016-09-11T15:02:23.944270"
},
"collapsed": false
},
"outputs": [],
"source": [
"class AbelLikelihood(GPinv.likelihoods.Likelihood):\n",
" def __init__(self, Amat):\n",
" GPinv.likelihoods.Likelihood.__init__(self)\n",
" self.Amat = GPinv.param.DataHolder(Amat)\n",
" self.variance = GPinv.param.Param(np.ones(1), GPinv.transforms.positive)\n",
"\n",
" def logp(self, F, Y):\n",
" Af = self.sample_F(F)\n",
" Y = tf.tile(tf.expand_dims(Y, 0), [tf.shape(F)[0],1,1])\n",
" return GPinv.densities.gaussian(Af, Y, self.variance)\n",
" \n",
" def sample_F(self, F):\n",
" N = tf.shape(F)[0]\n",
" Amat = tf.tile(tf.expand_dims(self.Amat,0), [N, 1,1])\n",
" Af = tf.batch_matmul(Amat, tf.exp(F))\n",
" return Af\n",
" \n",
" def sample_Y(self, F):\n",
" f_sample = self.sample_F(F)\n",
" return f_sample + tf.random_normal(tf.shape(f_sample)) * tf.sqrt(self.variance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variational inference by StVGP\n",
"\n",
"In StVGP, we evaluate the posterior $p(\\mathbf{f}|\\mathbf{y},\\theta)$ by approximating as a multivariate Gaussian distribution.\n",
"\n",
"The hyperparameters are obtained at the maximum of the evidence lower bound (ELBO) $p(\\mathbf{y}|\\theta)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Kernel\n",
"\n",
"The statistical property is interpreted in Gaussian Process kernel.\n",
"In our example, since $f$ is a cylindrically symmetric function, we adopt **RBF_csym** kernel."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MeanFunction\n",
"\n",
"To make $f$ scale invariant, we added the constant mean_function to $f$."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:02:46.685237",
"start_time": "2016-09-11T15:02:46.668217"
},
"collapsed": false
},
"outputs": [],
"source": [
"model_stvgp = GPinv.stvgp.StVGP(r.reshape(-1,1), y.reshape(-1,1), \n",
" kern = GPinv.kernels.RBF_csym(1,1),\n",
" mean_function = GPinv.mean_functions.Constant(1),\n",
" likelihood=AbelLikelihood(A),\n",
" num_samples=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the initial estimate"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Data Y should scatter around the transform F of the GP function f.\n",
"sample_F = model_stvgp.sample_F(100)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9d3085a320>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAAEwCAYAAAAZ0BAxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMuL/A/Fn2Y1AEiIQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9d3084f080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,3))\n",
"plt.plot(z, y, 'o', [-1,1],[0,0], '--k', ms=5)\n",
"for s in sample_F:\n",
" plt.plot(z, s, '-k', alpha=0.1, lw=1)\n",
"plt.xlabel('$z$: Los-height')\n",
"plt.ylabel('$y$: Observation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Iteration\n",
"\n",
"Although the initial estimate is not very good, we start iteration."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This function is just for the visualization of the iteration\n",
"from IPython import display\n",
"\n",
"logf = []\n",
"def logger(x):\n",
" if (logger.i % 10) == 0:\n",
" obj = -model_stvgp._objective(x)[0]\n",
" logf.append(obj)\n",
" # display\n",
" if (logger.i % 100) ==0:\n",
" plt.clf()\n",
" plt.plot(logf, '--ko', markersize=3, linewidth=1)\n",
" plt.ylabel('ELBO')\n",
" plt.xlabel('iteration')\n",
" display.display(plt.gcf())\n",
" display.clear_output(wait=True)\n",
" logger.i+=1\n",
"logger.i = 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:03:32.489217",
"start_time": "2016-09-11T15:02:46.692337"
},
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdwAAAEsCAYAAABpMQLsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3Xt8XWWd7/HPr6HZEpu209C0oYHSIsNNSJukEbrj3Tl0\nHNvRZo6aURlQj+DojPbMvISZ0QPCnPEujBcG5uVBRDRzOdNxLDpWBT1jb9AktBCLYBtKb+muhRpK\nW5qm/Z0/1t5hZXfnstO9177k+3698jJ7rbXXfvKY8s1zWc9j7o6IiIjk15RCF0BERGQyUOCKiIhE\nQIErIiISAQWuiIhIBBS4IiIiEVDgioiIRECBKyIiEgEFroiISAQUuCIiIhFQ4IqIiESgpAPXzG42\ns1Nm9uW047eZ2T4zO2pmPzGzVxWqjCIiIlDCgWtmS4APAVvTjt8EfDR5rgU4Aqw1s8rICykiIpJU\nkoFrZtOAB4APAr9NO/0x4HZ3f9Dde4BrgXOBt0dbShERkZeVZOACXwfWuPvD4YNmtgCYCzyUOubu\nLwCPAFdHWkIREZGQswpdgGyZ2buBRUBzhtNzAQcSaccTyXMiIiIFUVKBa2b1wJ3AW9z9RA7vWwNc\nA+wEXsrVfUVEpKS8ArgAWOvuz+X65iUVuEATMBvoNjNLHqsAXmdmHwUuAQyYw/BW7hzgsVHuew3w\nndwXV0REStB7gO/m+qalFrg/Ba5IO3Yf8CTwWXfvNbP9wJuBxwHMbDrwGoJx35HsBHjggQe49NJL\nc1zk8rVq1SruuOOOQhej5Kjesqc6mxjVW3aefPJJ3vve90IyE3KtpALX3Y8A28LHzOwI8Jy7P5k8\ndCfwSTPbTlBptwN7gP8Y5dYvAVx66aU0Njbmuthla8aMGaqvCVC9ZU91NjGqtwnLy9BiSQXuCHzY\nC/fPm1kVcA8wE/gF8PvuPlCIwomIiEAZBK67vynDsVuBWyMvjIiIyAhK9TlcERGRkqLAlQlrb28v\ndBFKkuote6qziVG9FRdz97GvKnNm1gh0dXV1aYKBiMgk1d3dTVNTE0CTu3fn+v5q4YqIiERAgSsi\nIhKBkp+lLCIixSmRSNDW1sbu3bs5dOgQM2bM4NChQwwODgLQ0NDAmjVrqK2tHXZ9X18fNTU1mBkH\nDx4c+n7//v0cOnSIWbNmUV9fz+rVq3H30z6jv78/42d97nOfY8WKFRw9ehR356yzggisrKykpqaG\nmTNn5rU+NIaLxnBFpPyNFGZ1dXWsXr16zNCrq6vj7rvv5sYbb6Svr4/p06fz9NNPc/ToUcyMRYsW\ncf/993PjjTeye/dunnvuuaFgG0tVVRUnT55kYGBgXNenpFb4zUOO5WUMV4GLAldESsd4AnH37t0c\nPHiQY8eO4e5UVVXh7hw7dizjPauqqrj44ovZtm0bx48fj/gnKkoK3HxR4IpIoY3WnTo4OMgvf/lL\nTpwINknTf7fzLi+BqzFcEZECCI9vpne/9vb2Dl0X/l5KmwJXRCQi4ZDdu3cvJ0+eLHSRImFmTJ06\ntSAt9KqqKgCOHj0KwNSpU4nFYtTU1PA7v/M7bN++fajrfcqUKUOTrPJBgSsikgeJRIJly5axdevW\noXFUM+PIkSOFLlpOmBmVlZVcdtllADz22GMZz6fPRD5w4AArV64c6jofHBxk27ZtuDuDg4OcOnUq\n4+ctWbKEyspK9uzZw8GDBxkcHMTdqaysZObMmSPOTA5/9lhCC1/khQJXRCSHUq3Yzs7OYROQUi2s\nKKSH4bZtwa6m6QHU2trK+vXrR7xPLBajubl52CzmkYSDNH3mc1htbS3r1q0b8x6jzaQuVQpcEZEJ\nSrVit2zZEsnnVVRUnNYNXV1dzezZsycUSqtXrx4Wknv27OHZZ58dOj9v3rwRwzHdaEE6Xrm4RzFT\n4IqIjEN4/DX8yE2upIdpZWUlU6dOHdY9eu+993L99dfz+OOPDx3Lpss0XXrAtba2Dgvcurq6Cd1X\nMlPgiohkEH5MZ/r06Tz++OMjji9OVEVFBfX19dTX13PPPfdwww03jNkl++ijj+a0DGHpLd7Vq1fn\n7bMmIwWuiEx64XBNBc3KlSvZsGFDTu7/yle+ctgkn5qamqGlCcOhWuju1HLv0i00Ba6ITCqJRILl\ny5cP65aFl1uOvb29zJkzJyefZWYsWbLkjLp9pXwocEVkUlm+fDmbN28eep2rLtp4PJ6xS1ZBKykK\nXBGZVFIt2zNRX19PXV1dxsdW1CUrI1HgikjZGqn7eKJaWlrUPSwTpsAVkbLV1taWs+7jeDyu1quc\nEQWuiJSVVKt269atDAwMjHrt2WefPeKWdZWVlSxatGhYl7HImVDgikhJS1+zOBuNjY1Drdb0ZQ6X\nLFmiFq3k1JRCF0BEZDwSiQStra1ceOGFtLa20tPTQ2trK/Pnz2fLli0TWvWpr69v6PvVq1cTj8dZ\nuHDh0IxjkVxSC1dESkJbW9tQC7S3t5crrrjijO8ZXrpQM4wl3xS4IlJUEokEK1as4ODBg8O2bwvv\nvJOtKVOmcN5551FbW3vaozwiUVHgikjkMi2lWFtbSyKRYMGCBUMTmXp7eyf8GWbG+eefn3EJRZFC\nKLnANbO/At4BXAIcAzYAN7n702nX3QZ8EJgJrAc+7O7bIy6uiCSFQ/bAgQO8+OKLQBCqCxYsYHBw\ncMxZxdlYunSpuoilqJRc4AKvBb4KdBKU/zPAj83sUnc/BmBmNwEfBa4FdgJ/C6xNXpO7f9EiMi6J\nRIKLLrqIw4cPZzyfq83Z0/eGFSkmJRe47v7W8Gszuw44ADQBqT9nPwbc7u4PJq+5FkgAbwf+JbLC\nikxCmVZ3OnHixIhhe6bOZAN2kSiVXOBmMBNw4HkAM1sAzAUeSl3g7i+Y2SPA1ShwRXIifRz27rvv\n5v3vfz+dnZ3DHtHJ1eYAqb1jM018UshKKSjpwDUzA+4E1rn7tuThuQQBnEi7PJE8JyJZSAXr7t27\nee655xgcHARg6tSpw8Zhc/GYDry8pd299947rg3ZRUpFSQcucBdwGRAvdEFESt14W6wpZ/KYzkhi\nsRi7du0aClZNepJyUrKBa2ZfA94KvNbd+0Kn9gMGzGF4K3cO8Nho91y1ahUzZswYdqy9vZ329vac\nlFmk2IRbr3v37uXkyZNAblus6VJdw/X19Zw4cWJYl3Nzc7NasRKJjo4OOjo6hh3r7+/P62faRJZD\nK7Rk2P4h8Hp3P+1BPTPbB3zB3e9Ivp5OEL7Xuvu/Zri+Eejq6uqisbExv4UXKSLp6wfn0tlnn42Z\nDc1ATm0GEN7e7sCBA9qwXYpGd3c3TU1NAE3u3p3r+5dcC9fM7gLagRXAETObkzzV7+4vJb+/E/ik\nmW0neCzodmAP8B8RF1ek4FKt2J07d9LX18epU6fy9llmRmVlJQ0NDePaN1bLKcpkUnKBC9xIMCnq\n52nHrwfuB3D3z5tZFXAPwSzmXwC/r2dwpZxlGoN93/vex5YtW3L+WWY2bFxXe8WKjK3kAtfdx7XD\nkbvfCtya18KIFFD67OGjR48OhWAux2CnTJkyrFWc2kknvStYREZXcoErImOv3HSmYrEYzc3NQ0Ga\naZxVLVqR7ChwRYpcuKu4pqaGY8eO0dPTk9PPqKqq4uKLL6a/vz/j5CWFq8iZU+CKFLH0luyZ7J4T\ntnDhQnbs2JGTe4nI+ChwRYpEPluy1dXVw7qfwxuvi0g0FLgiRSCRSHDhhRdy5MgRIPuWbCwWAxh6\nHAeGj7vec889py2TKCLRUuCKFFBPTw9XXXXVUNBma9q0aWzatInLL7/8tHPp464ahxUprHE9YiMi\nuZNIJGhpaSEWi3HFFVdkHbZmxuLFi0kkEhw+fDhj2IpI8VELVyQiqTHazs7OrBf+H60lKyKlQYEr\nkmcT6TbOdolEESl+ClyRPEokEixatGhoF56xLF68mB/96EcKWJEypDFckRwLj9HOnTt3zLCNxWK0\ntLSQSCTo7u5W2IqUKbVwRXIo2yUXq6ureeGFF/JcKhEpBgpckRxqa2sbM2ynTJnClClTOPvss9m4\ncWNEJRORQlPgiuRAT08PS5cuHTVsq6ur2b59u7qMRSYpjeGKTFD687SjhW1LS4vCVmSSUwtXZAJ6\nenrGPftYm7OLCKiFK5KVVKv2iiuuGDNszzrrrKHN2kVE1MIVGadsWrUAr3nNa9SyFZEhClyRcRjP\nAhaVlZVUVlZSU1NDfX29WrYiMowCV2QU41mWsaKigq1bt2qdYxEZlcZwRTJIJBK0trZy5ZVXjhq2\nLS0t7Nu3T2ErImNSC1ckg7a2NtavXz/iebVqRSRbClyRNIlEgs7OzhHPawELEZkIdSmLJCUSCRYv\nXszcuXNH3K928eLFClsRmRC1cEUY36YDWsBCRM6EAleE0TcdiMViNDc36zEfETkjClyZ9Hp6etiw\nYUPGcxUVFezatUtdyCJyxsp2DNfMPmJmz5jZMTPbZGZLCl0mKS7hZRrd/bTz06ZNY+vWrQpbEcmJ\nsmzhmtm7gC8BHwIeBVYBa83sd939YEELJ0VhrDHbRCKhoBWRnCrXFu4q4B53v9/dfwXcCBwF3l/Y\nYkkx6OnpYd68eSOGbXV1tcJWRHKu7ALXzKYCTcBDqWMe9Bf+FLi6UOWS4rF06dIR10SuqKhg48aN\nEZdIRCaDsgtc4BygAkikHU8Ac6MvjhSTRCKRsWVrZlqmUUTyqizHcEUySY3bpquoqGDfvn3qRhaR\nvCrHFu5B4CQwJ+34HGB/9MWRYpHpWVsz00xkEYlE2bVw3f2EmXUBbwa+D2Bmlnz9ldHeu2rVKmbM\nmDHsWHt7O+3t7XkqreRbIpFg+fLlbN26lYGBgdPOL126VF3IIpNQR0cHHR0dw4719/fn9TMt0/OH\npc7M3gncRzA7OfVY0B8Bl7j7bzJc3wh0dXV10djYGGVRJY/GevRHmxCISFh3dzdNTU0ATe7enev7\nl10LF8Dd/8XMzgFuI+hK3gJckylspXyNtlyjmSlsRSRSZRm4AO5+F3BXocshhTHaco0AlZWVClsR\niVQ5TpqSSS6RSLBo0aKMyzWmNDQ0RFgiEZEybuHK5JQat820sEVLSwsHDx6krq5OO/+ISOQUuFJW\nRhq3ra6u5pFHHilAiUREAupSlrIx0ritlmsUkWKgFq6UjaVLl542bqtHf0SkWKiFK2Xj2LFjpx1T\n2IpIsVDgSklLJBK0trYyf/58BgcHh53TNnsiUkzUpSwlra2tjfXr1592XOO2IlJs1MKVkpRIJFi8\neHHGsAWYP3++1kgWkaKiwJWS1NbWxpYtW0Y8X1dXF2FpRETGpsCVkpJIJGhpaRmxZQvB2K0WthCR\nYqPAlZKyfPlyNm/enPGcmRGPxzUzWUSKkiZNSUl5/PHHMx6fNm0amzZt0ritiBQttXCl5MXjcQ4f\nPqywFZGipsCVkpFIJJg6deqwY9OmTdN4rYiUBAWuFIXUAhYXXnghra2tHDhw4LTzF110ES+++OLQ\nserqanbs2KHxWhEpCQpcKQqpBSx6e3tZv349K1euHDqXCtv0XYBmz56tsBWRkqFJU1IU+vr6hr3e\nvHkzsViMgYGBEd+jZ21FpJSohStFIT08BwYGRg1bPWsrIqVmwoFrZjPNrDn5NTOXhZLJ5+677x76\n3sxGvdbM9KytiJScrAPXzC4wsx8AB4FHkl8HzexBM7sgt8WTyeKGG24Y+j59T9t0S5YsUdiKSMnJ\nagzXzM4DNgEngE8BTyZPXQZ8GNhoZkvcfU9OSyllb//+/aOeNzMqKytpaGhgzZo1EZVKRCR3sp00\ndSvwFHCNu78UOv49M7sD+FHymg/mpHRS9hKJBG1tbezdu3fEa+LxOOvWrYuwVCIiuZdt4C4D3pUW\ntgC4+zEz+xTwTzkpmZSdRCLB8uXLh5ZnvOyyy/j1r3897NnaTNJnMIuIlKJsA/ccYOco53uBWRMu\njZSdcMgODAwMG5997LHHxnUPPf4jIuUg20lTfQTjtSN5NTD6YJxMKm1tbWzevJnjx4+PORkqnZnR\n0tKix39EpCxkG7jfA75oZrPTT5hZLfC55DUiwMS6g2OxGPF4nP379/PII49oRrKIlIVsu5Q/DbwV\n2GFmDwC/Agy4FPhjgtbtbTktoZS0uro6ent7x3Xt/Pnzqa+vZ/Xq1QpZESk7WQWuux8ys9cAfwe8\nG0gtePFb4LvAX7v787ktYsDM5hM8ivQmYC6wF/gO8L/d/UTouvOAu4E3AIeB+4Gb3f1UPsolw4XH\nbE+dOsXg4OC43heLxdi5c2d+CyciUkBZr6Xs7oeAD5vZnwKpruXfeLYDdNm7hKA1/T+AHQTjxd8A\nqoBPAJjZFOCHwD7gKuBc4NvAAPDJPJdPeHnMNltXXnllHkojIlI8Jrx5QTJgDwCYWaWZVbr76M93\nnAF3XwusDR3aaWZfBG4kGbjANQTB/EZ3Pwg8kXxU6bNmdqu7j6+5JVlJtWq3bt066vrH6WKxGPPm\nzaOurk4To0Sk7E1kacfrzeyrZvae5OvPEHTd9pvZT8ysJteFHMVMINyFfRXwRDJsU9YCM4DLIyzX\npJJq1Y4VthUVFcNeNzc3s2PHDtatW6cxWxEpe1kFrpn9DfB1glbkV8zsH4DrCMZWb04e/9scl3Gk\nsrwK+CjBeG3KXCCRdmkidE5yJLVhfH19PevXrx/z+oqKCh5++GHi8TgLFy4kHo+rVSsik0q2XcrX\nAR9w9w4zaybYuOCd7v5vAGbWw/AAHFOyhXzTKJc4cKm7Px16zzzgP4F/dvd7s/sRJBdSG8aP1/z5\n83nd616nJRpFZNLKNnDPB9YBuHunmQ0CPaHzjwPZLgv0ReCbY1wz9FyJmZ0LPAysc/cb0q7bDyxJ\nOzYndG5Uq1atYsaMGcOOtbe3097ePtZbJ5Wenh42bNgw4vnU9nrheXRaLUpEiklHRwcdHR3DjvX3\n9+f1My2bycVmdgqY6+6pyVKHgQZ3702+ngPsc/eKUW4z8cIGLduHgc3A+9JnRpvZMmANUJcaxzWz\nDxEsyFEbfnwo7X2NQFdXVxeNjY35KHrJCz/uc/z48RGvq66u5oUXXuDAgQOsXLmSvr6+oUlRGqcV\nkWLW3d1NU1MTQJO7d+f6/hOZpXyZmaXGQw24xMymJV+fk5tinS7Zsv058AzBrOTaUEsqNU77Y2Ab\n8G0zu4mgtX078LWRwlbGZ/ny5WM+7lNRUcHGjRsBqK2tVfexiEjIRAL3IYKgTXkw+b+ePJ6v53F/\nD1iY/NqdPJb6vAoAdz9lZm8D/gHYABwB7gNuyVOZylL4MZ8TJ04wderUUWcgmxlLly5VK1ZEZBTZ\nBu6CvJRiHNz9W8C3xnHdbuBt+S9R+UpfvGKssH3iiSe4/HI9dSUiMppsl3Z8drTzZjaTYK3lUa+T\n4pRq2WazUtTSpUsVtiIi4zDhlaZGMJ9gKcXv5vi+kmOJRIK2tjZ2797NoUOHmDFjBn19fZw8eXLM\n91ZUVFBfXz+00YCIiIwt14ErJSL9OdrDhw+P633V1dVs375dY7UiIllS4E5CiUSCzs7OcV1bWVlJ\nLBZj1qxZ2jpPROQMKHAnkdQYbWdnJ+N5/joWi7Fr1y4FrIhIDmQVuGb252NcMu8MyiJ5EF6wYmBg\nYFxBm9Lc3KywFRHJkWxbuKvGcc2uiRREcis1Kaqzs3PUlaHSVVdXD+s+FhGR3Mj2saCCPYcr45dI\nJLjooovGPRHqrLPO4uyzz2bjxo16xEdEJE+y3Z7vh2Y2I/T65uSzt6nXNWa2LZcFlOy1tbWNO2yX\nLFnCiRMneOGFFxS2IiJ5lO0G9MuAWOj1XwOzQq/PAi4+00JJ9lL701544YXjWrgiFosRj8d58MEH\nx7xWRETO3JnOUraxL5EojHd/2lgsRnNzsx7vERGJmB4LKmHj3TIPgqBtaGhgzZo1CloRkQLINnCd\n03cDytfuQDKG9E0GRhKPx7VVnohIgWUbuAbcZ2ap5tQrgLvN7EjydSzz2ySXEokEy5YtY8uWLWNe\nW11drcd7RESKQLaBm7493gMZrrl/gmWRUYT3qB1tu7wUdSGLiBSXbJ/DvT5fBZGRZftcbSwW46WX\nXspzqUREJBvZPhYkBZDNc7UADQ0NeSyNiIhMhGYpF6lsupCrqqq4+OKL6e/vp66uTmO2IiJFSIFb\nhMbbhazdfERESoe6lItMNuO12s1HRKR0qIVbZMYar62urmb27NnqOhYRKTEK3AJLbaO3e/duDh06\nxLFjx0a8dtq0aWzfvl2tWhGREqTALbAVK1bw6KOPjni+oqKC+vr6of1pFbYiIqVJgVsA452BXFFR\nwdatW7VtnohIGdCkqYilJkVt3rx5zMd9Tp48yQ033BBRyUREJJ8UuBHKdsUogL6+vjyWSEREoqLA\njUBPTw/Tp09n7ty5Y4btlCnD/y+pq6vLZ9FERCQiCtw8SiQStLa2cuWVV44atGZGS0sLiUSCvr4+\n4vE4CxcuJB6P69EfEZEyUZKTpsysEngUuBJY5O6Ph86dB9wNvAE4TLB70c3ufiqq8qUmRXV2duI+\n9nbBCxYs4JFHHhl6rb1rRUTKT0kGLvB5YA9wRfigmU0BfgjsA64CzgW+DQwAn4yqcOPdGD5F3cYi\nIuWv5LqUzez3gd8D/hKwtNPXAJcA73H3J9x9LfAp4CNmlvc/LlJdyOHW6kiqq6vVbSwiMomUVAvX\nzOYA/wisADItyXQV8IS7HwwdWwv8A3A5sDVfZctmw4Hm5mYtYiEiMsmUWgv3m8Bd7v7YCOfnAom0\nY4nQuZxLtWrPP//8UcM2FosRj8fZtWsX69atU9iKiEwyBW/hmtlngJtGucSBS4FlwDTgc6m35rlo\nI8pmr1q1aEVEBIogcIEvErRcR/MM8EbgauC42bCs7TSz77j79cB+YEnae+ck/3f/WAVZtWoVM2bM\nGHasvb2d9vb2odfZLl4xb948zToWESkyHR0ddHR0DDvW39+f18+08Ty2UgzMrB6YHjp0LsH4bBvw\nqLvvM7NlwBqgLjWOa2YfImgV17r7iRHu3Qh0dXV10djYmPHzU7v6jGdJxrB4PK7AFREpAd3d3TQ1\nNQE0uXt3ru9fDC3ccXH3PeHXZnaEoFu51933JQ//GNgGfNvMbgLqgNuBr40UtuPV1tbG+vXrx3Wt\nmXH++ecP7fAjIiJSMoE7gmHNc3c/ZWZvI5iVvAE4AtwH3HKmH5TNmsZLly5Vq1ZERIYp2cB192eB\nigzHdwNvy/Xn1dXV0dvbe9rxadOmUVNTw/PPP8+sWbPUqhURkYxKNnCjkhq73bNnDxUVFZw8eXLo\nXHV1Ndu3b9fsYxERGZMCN2TFihVccMEFrF69GnfPuB5ydXU1s2fPpq6uTo/6iIjIuClwQ/bu3cve\nvXtZuXIlQMb1kAcGBti4caOCVkREslJqK01ForOzkz179mQ8d/z48aFAFhERGS8FbgbHjx/n+eef\nH/F8NjOWRUREQIE7olmzZpG2otUQbacnIiLZ0hjuCJ577jkqKyu54IILqKqqYtu2bQA0NDTosR8R\nEcmaAjcDM+PFF18E4KmnniIej/PSSy8VuFQiIlLK1KWcwdSpU4e9HmkClYiIyHgpcEPmzZtHPB6n\nsrJy2PHRJlCJiIiMhwI35Pvf/z7r1q2jpqZm2PFZs2YVqEQiIlIuFLgZ1NfXj/paREQkW5o0leb5\n55/nvvvu47rrrqOvr29oCUcREZEzocANGRgY4Morr+QDH/iAttcTEZGcUpdyyLPPPsuhQ4e44447\nuPrqqzlw4EChiyQiImVCgRty++23c/ToUQ4fPsymTZu0ZrKIiOSMAjck/fEfrZksIiK5osANOeec\nc4a91prJIiKSKwrckC984QvMmzcPgKqqKs1OFhGRnFHghsyePZuPf/zjQLBJgTaZFxGRXFHgpkmt\nMhWLxQpcEhERKScK3DRvfetbmTVrFjNmzCh0UUREpIwocNPMmTOHyy67TIErIiI5pZWmMrjzzjvV\npSwiIjmlwE2TSCT42Mc+NmwdZU2eEhGRM6Uu5TRtbW2sX7+e3t5e1q9fr9WmREQkJxS4adJXl9Jq\nUyIikgsK3DTpq0tptSkREcmFkgtcM/sDM9tkZkfN7HkzW512/jwz+4GZHTGz/Wb2eTMb98+5evVq\n4vE4CxcuJB6Pa7UpERHJiZKaNGVmbcA/AjcDDwNTgVeHzk8BfgjsA64CzgW+DQwAnxzPZ9TW1mov\nXBERybmSCVwzqwDuBP7C3e8LnfpV6PtrgEuAN7r7QeAJM/sU8Fkzu9XdByMrsIiISEgpdSk3ErRY\nMbNuM9tnZj80s8tD11wFPJEM25S1wAwgfJ2IiEikSilwFwIG3ALcBvwBcAj4uZnNTF4zF0ikvS8R\nOiciIlIQBQ9cM/uMmZ0a5eukmf1uqKx/6+7fc/fHgOsBB/57wX4AERGRcSiGMdwvAt8c45pekt3J\nwJOpg+4+YGa9wPnJQ/uBJWnvnRM6N6pVq1adtoZye3s77e3tY71VRERKSEdHBx0dHcOO9ff35/Uz\nzd3z+gG5YmbVwAHgT939m8ljU4HdwCfd/RtmtgxYA9SlxnHN7EPA54Badz8xwr0bga6uri4aGxsj\n+GlERKTYdHd309TUBNDk7t25vn8xtHDHxd0Pm9ndwKfNbA/wLPAJgi7lf01e9mNgG/BtM7sJqANu\nB742UtiKiIhEoeBjuFn6S+CfgPuBR4HzgDe5ez+Au58C3gacBDYkr7uPYKLVmN7//vdz4MCB3Jda\nREQmvZLpUs6nVJcyQDwe18IXIiKTUL67lEuthZt32qxARETyQYGbRpsViIhIPihwQxoaGrRZgYiI\n5IUCN+Tee++ltra20MUQEZEypMAVERGJgAJXREQkAgpcERGRCChwRUREIqDAFRERiYACV0REJAIK\nXBERkQgocEVERCKgwBUREYmAAldERCQCClwREZEIKHBFREQioMAVERGJgAJXREQkAgpcERGRCChw\nRUREIqDAFRERiYACV0REJAIKXBERkQgocEVERCKgwBUREYmAAldERCQCClwREZEIKHBFREQiUFKB\na2YXmdlAUvqtAAAJxElEQVT3zOw3ZtZvZr8wszekXXOemf3AzI6Y2X4z+7yZldTPWSo6OjoKXYSS\npHrLnupsYlRvxaXUgugHQAXwBqAR2Ao8aGa1AMlg/SFwFnAV8CfAdcBtBShr2dM/5olRvWVPdTYx\nqrfiUjKBa2Y1wKuAz7r7L919B3AzUAW8OnnZNcAlwHvc/Ql3Xwt8CviImZ1ViHKLiIhACQWuuz8H\n/Aq41syqkgH6YSABdCUvuwp4wt0Pht66FpgBXB5leUVERMJKrdX3e8D3gMPAKYKwXebu/cnzc5PH\nwhKhc1ujKKSIiEi6ggeumX0GuGmUSxy41N2fBu4iCNA48BLwQYIx3GZ3Tw/abLwC4MknnzyDW0w+\n/f39dHd3F7oYJUf1lj3V2cSo3rITyoBX5OP+5u75uO/4CxCMzdaMcVkv8HrgR8BMdz8Sev/TwDfc\n/fNm9mlgubs3hs5fkHz/YnfP2MI1sz8GvnMmP4eIiJSN97j7d3N904K3cJNjs8+NdZ2ZnU3Q2j2V\nduoUL49FbwT+2szOCY3j/jegH9g2yu3XAu8BdhK0nEVEZPJ5BXABQSbkXMFbuOOVbAk/Cfw/4Hbg\nGPAh4M+AJe7+RPKxoMeAfQTd1HXA/cA/uvunClJwERERSm+W8jJgGvAQsBlYCqxw9yeS15wC3gac\nBDYQhO19wC0FKLKIiMiQkmnhioiIlLKSaeGKiIiUskkfuGb2ETN7xsyOmdkmM1tS6DIVkpm91sy+\nb2Z7zeyUma3IcM1tZrbPzI6a2U/M7FVp52Nm9nUzO2hmh83s/6aW3yxHZvZXZvaomb1gZgkz+3cz\n+90M16nekszsRjPbmlwTvd/MNpjZsrRrVF+jMLObk/9Gv5x2XPUWYma3JOsp/LUt7ZpI6mxSB66Z\nvQv4EsEY72KChTHWmtk5BS1YYb0S2AL8KcGs8GHM7CbgowQT1lqAIwR1Vhm67E7gD4A24HXAucC/\n5bfYBfVa4KvAa4C3AFOBHydn1gOqtwx2E0xsbASagIeB/zCzS0H1NZZkw+BDpC3mo3obUQ8wh2AB\npLlAa+pEpHXm7pP2C9gE/H3otQF7gE8UumzF8EXwyNWKtGP7gFWh19MJZoy/M/T6OPCO0DUXJ+/V\nUuifKaJ6Oyf587aq3rKqt+eA61VfY9bTNOAp4E3Az4Av6/ds1Pq6Bege5XxkdTZpW7hmNpXgL+uH\nUsc8qMmfAlcXqlzFzMwWEPx1GK6zF4BHeLnOmgme7w5f8xSwi8lTrzMJegeeB9XbWMxsipm9m2Aj\nkg2qrzF9HVjj7g+HD6reRnVRcphsh5k9YGbnQfR1VvCFLwroHIKt/jKtvXxx9MUpCXMJgiRTnc1N\nfj8HGEj+0o50TdkyMyPoflrn7qlxItVbBmb2aoLFal5BsD76O9z9KTO7GtVXRsk/TBYRhEA6/Z5l\ntolgm9anCNZmuBX4r+TvX6R1NpkDVyQf7gIuI1jvW0b3K6CBYDevPwLuN7PXFbZIxcvM6gn+mHuL\nu58odHlKhQfbtKb0mNmjwLPAOwl+ByMzabuUgYMEC2TMSTs+B9gffXFKwn6Cce7R6mw/UGlm00e5\npiyZ2deAtwJvcPe+0CnVWwbuPujuve7+mLv/DcEEoI+h+hpJEzAb6DazE2Z2gmCN+Y+Z2QBBi0v1\nNgYPdpd7mmB/9Uh/1yZt4Cb/QuwC3pw6luwOfDPBKlWSxt2fIfgFC9fZdILZuak66wIG0665GDif\noPuwLCXD9g+BN7r7rvA51du4TQFiqq8R/RS4gqBLuSH51Qk8ADS4ey+qtzGZ2TSCsN0X+e9aoWeQ\nFXj22juBo8C1wCXAPQQzJWcXumwFrJNXEvxDXkQwC+/jydfnJc9/IllHywn+8X8P+DVQGbrHXcAz\nwBsI/ipfD/yi0D9bHuvsLuAQweNBc0Jfrwhdo3obXmd/l6yv+cCrgc8k/6P2JtVXVvWYPktZ9XZ6\nHX2B4FGe+QTLAf+EoDegJuo6K3hlFPqL4HnTnQTTwDcCzYUuU4Hr4/XJoD2Z9nVv6JpbCabSHyXY\nVeNVafeIETyXepBgMsy/ArWF/tnyWGeZ6uskcG3adaq3l3/WbxBsm3mMoIXx41TYqr6yqseHw4Gr\nestYRx0Ej3seI5hZ/F1gQSHqTGspi4iIRGDSjuGKiIhESYErIiISAQWuiIhIBBS4IiIiEVDgioiI\nRECBKyIiEgEFroiISAQUuCIiIhFQ4IqIiERAgStSpMzsZ2b25UKXI8zMTpnZikKXQ6QUaWlHkSJl\nZjOBE+5+xMyeAe5w969E9Nm3AG9398Vpx2uBQ679WEWypg3oRYqUu/821/c0s6lZhOVpf427+4Ec\nF0lk0lCXskiRSnYp32FmPyPYWuyOZJfuydA1rWb2X2Z21MyeNbO/N7Oq0PlnzOyTZvYtM+sn2IIS\nM/usmT1lZkfMbIeZ3WZmFclzfwLcAjSkPs/Mrk2eG9albGavNrOHkp9/0MzuMbNXhs5/08z+3cz+\nwsz2Ja/5WuqzRCYTBa5IcXPgHQTbi30KmAvUAZjZhcB/EmwV9mrgXUCcYBuxsL8AthDscXx78tgL\nBPtAXwr8OfBBYFXy3D8DXwJ+SbCvb13y2DDJYF9LsJdoE/BHwFsyfP4bgYUEe4leC1yX/BKZVNSl\nLFLk3P23yVbti2ldujcDD7h7KuB6zezjwM/N7MPuPpA8/pC735F2z78LvdxlZl8iCOwvuvtLZvYi\nMOjuvxmlaO8h2Cf0Wnd/CXjSzD4KrDGzm0LvfR74qAcTRp42sx8Abwb+T7Z1IVLKFLgipasBuMLM\n3hs6Zsn/XQA8lfy+K/2NZvYu4M+AC4FpBP8t6M/y8y8BtibDNmU9Qc/ZxUAqcH/pw2dn9hG0yEUm\nFQWuSOmaRjAm+/e8HLQpu0LfHwmfMLOrgAcIuqh/TBC07cD/zFM50ydpORrOkklIgStSGgaA9IlG\n3cBl7v5MlvdaCux098+mDpjZBeP4vHRPAn9iZme7+7HksVbgJC+3rkUkSX9lipSGncDrzOxcM6tJ\nHvscsNTMvmpmDWb2KjP7QzNLn7SU7tfA+Wb2LjNbaGZ/Drw9w+ctSN63xswqM9znO8BLwLfM7HIz\neyPwFeD+McZ+RSYlBa5I8QqPe/4v4AJgB3AAwN2fAF4PXAT8F0GL91Zg7wj3IPm+NcAdBLOJHwOu\nAm5Lu+zfgB8BP0t+3rvT75ds1V4DzAIeBf4F+AnB2LCIpNFKUyIiIhFQC1dERCQCClwREZEIKHBF\nREQioMAVERGJgAJXREQkAgpcERGRCChwRUREIqDAFRERiYACV0REJAIKXBERkQgocEVERCKgwBUR\nEYnA/wfdKJVwtQSNrAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9d30632898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,3))\n",
"# Rough optimization by scipy.minimize\n",
"model_stvgp.optimize()\n",
"# Final optimization by tf.train\n",
"trainer = tf.train.AdamOptimizer(learning_rate=0.002)\n",
"_= model_stvgp.optimize(trainer, maxiter=5000, callback=logger)\n",
"\n",
"display.clear_output(wait=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot results"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Predict the latent function f, which follows Gaussian Process\n",
"r_new = np.linspace(0.,1.2, 40)\n",
"f_pred, f_var = model_stvgp.predict_f(r_new.reshape(-1,1))\n",
"\n",
"# Data Y should scatter around the transform F of the GP function f.\n",
"sample_F = model_stvgp.sample_F(100)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxQAAAEgCAYAAAAt9zUDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9cd26dab00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,3))\n",
"plt.subplot(1,2,1)\n",
"f_plus = np.exp(f_pred.flatten() + 2.*np.sqrt(f_var.flatten()))\n",
"f_minus = np.exp(f_pred.flatten() - 2.*np.sqrt(f_var.flatten()))\n",
"plt.fill_between(r_new, f_plus, f_minus, alpha=0.2)\n",
"plt.plot(r_new, np.exp(f_pred.flatten()), label='StVGP',lw=1.5)\n",
"plt.plot(r, f, '-r', label='true',lw=1.5)# ground truth\n",
"plt.xlabel('$r$: Radial coordinate')\n",
"plt.ylabel('$g$: Latent function')\n",
"plt.legend(loc='best')\n",
"\n",
"plt.subplot(1,2,2)\n",
"for s in sample_F:\n",
" plt.plot(z, s, '-k', alpha=0.05, lw=1)\n",
"plt.plot(z, y, 'o', ms=5)\n",
"plt.plot(z, np.dot(A, f), 'r', label='true',lw=1.5)\n",
"plt.xlabel('$z$: Los-height')\n",
"plt.ylabel('$y$: Observation')\n",
"plt.legend(loc='best')\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MCMC\n",
"\n",
"MCMC is fully Bayesian inference.\n",
"The hyperparameters are numerically marginalized out."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_gpmc = GPinv.gpmc.GPMC(r.reshape(-1,1), y.reshape(-1,1), \n",
" kern = GPinv.kernels.RBF_csym(1,1),\n",
" mean_function = GPinv.mean_functions.Constant(1),\n",
" likelihood=AbelLikelihood(A))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample from posterior"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"burn-in sampling started\n",
"Iteration: 100 \t Acc Rate: 97.0 %\n",
"Iteration: 200 \t Acc Rate: 92.0 %\n",
"Iteration: 300 \t Acc Rate: 92.0 %\n",
"Iteration: 400 \t Acc Rate: 93.0 %\n",
"Iteration: 500 \t Acc Rate: 92.0 %\n",
"burn-in sampling ended\n",
"Iteration: 100 \t Acc Rate: 87.0 %\n",
"Iteration: 200 \t Acc Rate: 82.0 %\n",
"Iteration: 300 \t Acc Rate: 91.0 %\n",
"Iteration: 400 \t Acc Rate: 87.0 %\n",
"Iteration: 500 \t Acc Rate: 87.0 %\n",
"Iteration: 600 \t Acc Rate: 73.0 %\n",
"Iteration: 700 \t Acc Rate: 83.0 %\n",
"Iteration: 800 \t Acc Rate: 74.0 %\n",
"Iteration: 900 \t Acc Rate: 69.0 %\n"
]
}
],
"source": [
"samples = model_gpmc.sample(300, thin=3, burn=500, verbose=True, epsilon=0.01, Lmax=15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot result"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-11T15:04:10.880282",
"start_time": "2016-09-11T15:04:04.939234"
},
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxQAAAEgCAYAAAAt9zUDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XeYnFd5+P3vM72XndnepVWXZRULW4CNHYhtgo0Bm4AS\nUsAvBoKDYwIYCARMSUICr0kCBFN/mPxix0kIYL8Emxg3bMu2elu0XdtmdnrvM8/7x+5zvCutrK5d\nac/nuuaSdnbKmZFmznM/5z73raiqiiRJkiRJkiRJ0pnQLfQAJEmSJEmSJEm6eMmAQpIkSZIkSZKk\nMyYDCkmSJEmSJEmSzpgMKCRJkiRJkiRJOmMyoJAkSZIkSZIk6YzJgEKSJEmSJEmSpDMmAwpJkiRJ\nkiRJks6YDCgkSZIkSZIkSTpjMqCQJEmSJEmSJOmMyYBCkiRJkiRJkqQztigDCkVRrlYU5eeKokwo\nilJTFOWtp3Hf1ymKUlYUZff5HKMkSZK0eMh5Q5IkaeEsyoACsAN7gT8D1FO9k6IobuBHwP+ep3FJ\nkiRJi5OcNyRJkhaIYaEHMB9VVX8J/BJAURTlNO76beD/AjXglvMwNEmSJGkRkvOGJEnSwlmsKxSn\nTVGU9wLdwL0LPRZJkiRp8ZPzhiRJ0rmxKFcoTpeiKCuAvwFer6pq7VROTimK4gNuAEaAwnkdoCRJ\n0vlhAbqAx1RVjS7wWC4qct6QJGmJOi/zxkUfUCiKomN6ufpzqqoOalefwl1vmLmfJEnSxe4PgX9b\n6EFcLOS8IUmSdG7njYs+oACcwBXARkVRvjlznY7pNNoScL2qqk/Nc78RgH/9139lzZo1F2Kc59Xd\nd9/Nfffdt9DDOCfka1mc5GtZfHp7e3nPe94DM99n0imT88YFdKl83i4U+X6dPvmenbrzNW9cCgFF\nClh/zHUfBq4DbuXEb1gBYM2aNWzevPm8De5Ccbvdl8TrAPlaFiv5WhY1mX5zeuS8cQFdgp+380q+\nX6dPvmdn5JzOG4syoFAUxQ708MoS9DJFUS4HYqqqjimK8rdAi6qqf6KqqgocPub+IaCgqmrvBR24\nJEmStCDkvCFJkrRwFmVAwfRS9JNM1xJXga/NXP8j4H1AE9C+MEOTJEmSFiE5b0iSJC2QRRlQqKr6\nNK9S0lZV1fee5P73coplACORCIlEAovFgtls5vTKl0uSJEmLwYWcNyRJkqS5FmVAcSFZLBZKpRK5\nXA5FUTCZTJjNZiwWCwbDxfP2bN++faGHcM7I17I4ydciSdKZkJ+30yPfr9Mn37OFp0ynki49iqJs\nBnbt2rWLzZs3U61WKRQKFAoFSqUSqqridDpxOp0LPVRJkqR57d69my1btgBsUVV190KP51J37Lwh\nSZJ0sTlf88bFcwr+PNPr9djtdux2O6qqkslkSKfTVCoVPB6PTIWSlrzR0VEikchCD2PJ8fv9dHR0\nLPQwJEmSTpucNxbGQswbMqCYh6IoOJ1ODAYDiUSCaDRKXV0dOt0J03Ml6ZI2OjrKmjVryOVyCz2U\nJcdms9Hb2yuDCkmSLipy3lg4CzFvyIDiVVitVgwGA7FYjHA4TF1dHUajcaGHJUkXXCQSIZfLyYZe\nF5jWgCgSiciAQpKki4qcNxbGQs0bMqA4CaPRiN/vJxaLEYlE8Hq9WCyWhR6WJC0I2dBLkiRJOh1y\n3lgaZA7PKdDr9fj9fsxmM7FYjEwms9BDkiRJkiRJkqRFQQYUp0hRFOrq6nA6naRSKRKJxEIPSZIk\nSZIkSZIWnEx5Ok3aZu14PI5er5dlZSVJkiRJkqQlTQYUZ8BqtVKpVEin0xiNRrmnQpIkSZIkSVqy\nZMrTGXI6nVgsFuLxOJVKZaGHI0mSJEmSJEkLQgYUZ8Hr9aLX64nFYizVjuOSdLF74YUXuPfee0ml\nUgs9FEmSJOkiIeeOuWRAcRa0jdrValVu0paki9Tzzz/PF77wBfkZliRJkk6ZnDvmkgHFWTIYDHi9\nXvL5vCwnK0kXoVNdXVRVlWKxeJ5HI0mSJF0M5NwxlwwozgGLxSLKyS6F/zSSdKm49957+cQnPgFA\nV1cXOp0OvV7P0aNH0el0fOQjH+Hf/u3fWL9+PRaLhccee4ynn34anU7HM888M+extPs88MADc64/\ncuQIt912Gz6fD6vVytatW3nkkUcu2GuUJEmSzi05dxxPVnk6R5xOJ6VSiXg8Tn19PXq9fqGHJEkX\nnqpCLndhnstmA0U5q4e49dZb6evr46GHHuIf//Ef8fl8KIpCfX09AE888QQPP/wwd955J36/n66u\nLuLxOMopPu+hQ4d4/etfT1tbG5/61Kew2+08/PDDvO1tb+MnP/kJt9xyy1mNX5Ik6ZJwoeaOczBv\ngJw75iMDinPI6/USDoeJxWL4/f5T/o8jSZeMXA4cjgvzXJkM2O1n9RDr169n8+bNPPTQQ9xyyy10\ndHTM+X1fXx8HDx5k1apV4rqnn376lB//rrvuoquri5dffhmDYfrr9kMf+hCvf/3rueeeexblpHCx\nUhTlauDjwBagGXibqqo/f5Xbvx34ELARMAOHgM+rqvr4BRiuJEmzXai54xzMGyDnjvnIlKdzSKfT\nUVdXR6VSIZlMLvRwJEk6S9dee+2cCeF0xONxnnzySd75zneSTCaJRqPicv3119Pf308gEDjHI17S\n7MBe4M+AU0luvgZ4HHgzsBl4EnhEUZTLz9sIJUlaEpbi3CFXKM4xo9GIx+MhHo9jsVhk0ztpabHZ\nps8AXajnOs+6urrO+L4DAwOoqspnP/tZPvOZzxz3e0VRCIVCNDc3n8UIJY2qqr8EfgmgnMLysKqq\ndx9z1V8pinILcDOw79yPUJKkE7pQc8cFmDdgac4dMqA4D6xWK/l8nkQiQUNDAzqdXAiSlghFOSfL\nyYuF1Wo97roTHatWq9U5P9dqNQA+9rGPccMNN8x7n56enrMcoXSuzAQhTiC20GORlo5isUg2m33V\nikEWiwWbzXZpp1HLuUO4WOeORRlQXAq5sG63m3A4TDKZxOv1LtQwJEk6idOdpL1eL6qqHld7fGRk\nZM7Py5YtA6ZXLX/nd37nrMYoXRAfZzpt6uGFHoh06cvlciQSCbLZLNVq9YQnHlVVRVVVrFYrXq8X\np9N5St9Zhw8fpr+/nxUrVrB27dpzPXwJOXcca1EGFLySC/t94CencHstF/ZTQAJ4H9O5sK9RVXVB\nlq71ej1ut5t4PI7VapWpT5K0SNlnzoolEonjNtbNp7OzE71ezzPPPMNb3/pWcf23vvWtORNMfX09\n1157Lffffz933nknTU1Ncx4nEong9/vPauzlcvms7i9NUxTlD4DPAm9VVTVystvffffduN3uOddt\n376d7du3n6cRSherWq1GqVQik8lQKBSIxWJEIhGxKmE0GrFYLCc8OFVVlXK5TKlUolqtYrVa8fv9\n+Hw+LBYLdrsdi8UiNu5GIhFuuukOhoebSSQ24vE8Tnd3gEcf/c5Zf99Ic10Mc8eDDz7Igw8+OOe6\n87XHd1EGFJdKLqxMfZKkxW/Lli2oqsqnP/1p3v3ud2M0Grn55ptPeHuXy8U73/lO/umf/gmA5cuX\n8+ijjxIOh4+77Te/+U2uvvpqLrvsMt7//vezbNkypqameOGFF5iYmGDPnj1nNfbchSrRewlTFOXd\nwHeA21RVffJU7nPfffexefPm8zsw6aJSrVYplUpUKhUqlQrlcpl0Ok0ulyOVSpFMJikUCnNWGxwO\nB3q9/qQN0nQ6HeVymWKxSDKZZHR0lKGhIcxmM263G4fDgcvlwm63c/PNH2DXri8BGwAIhSAU2s9N\nN93Bjh2ncn5WOlUXw9wx34mO3bt3s2XLltN4padmUQYUZ+t0cmELhQKVSkVE9+ealvqUSqXweDzn\n5TkkSTpzV1xxBV/60pf49re/zWOPPYaqqgwODqIoygnPGv7zP/8zlUqF+++/H7PZzLve9S6++tWv\nsn79+jm3W7NmDTt37uTee+/lRz/6EdFolIaGBjZt2sTnPve5sx67lmsrnRlFUbYD3wPeNXMiS5JO\nibbyUCwWKRaLVCoVsZpQqVTI5/NzGt3G43HC4TCrVq1izZo1VCoVqtUqxWLxpCuNer0em82Gw+Gg\nqamJWq1GLpcjl8tRLBap1WrE43H27dvH8HAzWjDxig0MDTVy4MABLrvssld9Lpkqdeou5rnjfLgk\nAwpOIxc2nU4TCoXQ6XQYjUaMRiMmkwmj0XhOmtPp9XpcLheJREJWfZKkRerTn/40n/70p+dcd+xG\nudl8Ph8PP3z818t89+nq6uKHP/zh2Q9yHq82xqVGURQ70ANoM/mymRKwMVVVxxRF+VugRVXVP5m5\n/R8A/wf4CPCyoiiNM/fLq6qaurCjly4G1WqVbDY7JwgwGAwYjUYURRGBRalUwmQyYTabGRsb4y/+\n4itMTnaTTm/G5foZra3/xH33fRyTyUQ+nyeXy53wAFRRFAwGA1arFZvNhtPpxGQyYbPZMJvNJJNJ\nVFXF5XIxNTVFOj3/ylkyuZFdu3bR0NCA2WzGarViNpvF72Wq1Jm5WOeO8+GSCyhONxf2y1/+Mi6X\ni1qthqqq1Go1brnlFt72treJD+/ZBhY2m418Pk8ymcRkMsnUJ0mSTtt8ubChUGiBRrMoXcF0Lwl1\n5vK1met/xPS+uiagfdbt3w/ogW/OXDjm9pIEQKVSIZPJkMvl0Ol0mM1m7HY7ZrOZUqlEMpkUeena\nakK1WiWdTnPXXV/h0KGvoq0aRKMQje7j9ts/xIc//A4qlQrw6ht8a7WaeFyLxYLT6aSuro5IJEIw\nGKSpqYkVK1bQ3d2Ny/UzotHjH8Pt3svll9+BxWKhWCySy+UwmUw4nU7MZjM33XQHL774eWSqlHSm\nLqmA4kxyYb/+9a8flwtbrVYpFAqk02ny+TxOpxO73X5WJds8Hg+hUEimPkmSdEaOzYWtVCo88cQT\n3HjjjQs4qsVDVdWneZVmraqqvveYn68774OSLmqVSkUcB+h0OtxutyjfWiqViMVipFIpyuUyer0e\ng8GAoijk83nC4TC9vb1MTHRyfArS5UQiK0kkErS2thIMBgmHwzQ0NNDS0jLnltqJzlqtRqVSIZFI\nMDIywgMP/Ip4fA35/FU4HL+mufm7fPnLd9LSMkI0ug+Y3Z9xH+3tY6xatQrbTB8G7RgnGo0yPDzM\n0FDTPOPcwPBwE4cPH5bpT9JJXTIBxbnMhdXr9djtdqxWK5lMhlQqRTabxeVyzVtb+FQf0+12k0gk\njltqlCRJOl0y3UmSzo/ZgYQ2d2uBRKVSIZVKkUqlyOfzGI1GAHp7exkZGaGhoQGn00mxWOTQoUMn\nTEHK51/Dnj3/H9///i9IJtdRLG7DbN6By/Vzbr55izjW0FYnjEYjVqsVk8nEAw/8irGx76IFAMkk\nJJP7+OQnP8JnP/v/8Hd/9zGCwW7S6S04nbvo6BjnG9/4LIFAAKfTidvtFinYxWKRJ554gkRi/gbx\nicQmBgYGZEAhndSiDCgWSy6sTqfD5XJhs9lIpVLE43Gy2Sxut1t8iZwOLfUpkUhQX18vU58kSTpj\nMqCQpHMvlUqRyWSOCyQAMpmM6B0B04HH5OQkd9/990xMdJFKbcZqfRyvt5d3vOMqpqamMBrjVKsf\nOu55DIbf8NxzQyST/44WGOTzf0Y+v5ef/OQ9/M7vrAGmVyi0tGtVVUkmk4RCK5hv1WN8vIPdu3dz\n113vRFVVisUinZ1vob29HZ1OR6VSIRaLkcvlcLvdeDwezGYzW7ZsweP5BvMUG8Lj2UNPz53n5L2V\nLm2LMqBgkeXCGgwG6urqRMm2cDiMzWbD7XafdhqUTH2SJOlceLVmWJIknR6tUlKxWBQlWLX5XWtG\nFo1Gyefzoi+EyWTiL//yqxw48A9oB/jp9AdIp/fygx+8myuv7MBsHqNQ2Mt0313NXnS658hmt3F8\nYLCRVGodBw/ux2q1isZ2Op0OvV5PPB6nVHor8ykUruKJJ+5ncHAQu91Od3c3a9aswW63UywWSSQS\nGAwGyuWyqBDl9/tZu3Yty5YFCYf3HzOefSxbFhSrE7IClPRqFmVAcSFzYU9W/3k2s9lMQ0MD2WxW\n5E3W1dWd1qZtrepTMpm8pFKfSqUS+XyefD4PTDd8sdls56RSliRJx5MBhSSdG+VymVgshqqq+Hy+\nOfNypVIhEokwNTUlekloxVX27NlDf38D8wUFudxGRkZ24/NVKRTeTrm8lVrtGvT6ZzCZduH3q4yP\nXz3veKrVa9DpDuJwOGZ+rlKr1SgWiyiKgk73LNXqh4+7n17/LADj4+OUy2V6e3vZsWMHa9eu5XWv\nex3t7e0Ui0Wi0ShGo1FUrGpsbOTRR78zU+WpiURiIy7Xbjo6xvn3f/+WrAAlnZJFGVBcSPv27aNa\nrWI0GjGbzZjNZoxGIwaDAbPZjMlkEput9Hq92F9hMplEx8u6urrTSoGy2+2i6lN9ff1Zbfa+0LSN\nYTqdTtTQzufzVKtVUYVCVVUymQzpdBqr1SreL0mSzh3tMydJ0pnT0pC1TITZn6lCocDY2Bijo6Ok\nUilisRjRaJR4PE5vby979+zBnbuDK/k1qzhCN8M4yGAlj63Sh2NwEIdOh1VRMOvGUXT/PT3fVxWq\nQZWS+teoPICKgsqs4wC1D2N/Gt3AgLhWmbnoFIVybRRYA1gpYSKHjTxlCpVdlH9boajXU1AUJhWF\nAb2efYcO8evHH6e1s5O1a9fS2tqKzWajsbFRvJ5ly5axY8dPOHz4MAMDAyxf/ue0traSy+V485vf\nx86dslme9OqWfEChdaosFotks1nRKEpRFEwmE1arVWyIMhgMmEwmEWSYzWYymQxTU1P4fL7T2rDt\n8XgIh8Ok02lcLtf5ennnRKFQIBKJEI1GicViZDIZ4JXyeG63W3TrrFarmEwmmpqayOVyZLNZIpEI\nRqNRbHS/mAIoSVqstMBekqTTp6qqKLhybAqzqqoEg0H27dvHwMAAoVCIsbExKgcOcPnAAJvzed5Z\nqbCiVsPJZ078JDXgVZtPxoCX5v/Vq/e6A3574vsd2ygvnaYUCjE8MMDA008zYrHQ29jIc1u20Nbe\nTlNTE319fWzatOm4dKbBwUFGRlqQFaCkk1nyAUVPT48oG1utVkVTGq2kmtZFW6fTUavVKBQK5PP5\nmWXH6ZxG7aDZ4/GI1Qot6DgRg8GAw+EQZ/HPZJP3+aCqKvl8nnQ6TSwWIxaLkc1mUVUVq9WKwWCg\nsbFRlLIDRH1ubSXHZDJRX1+P3+8XuZvZbJZEIkEqlRIb3aWLT29v70IPYUl5tfdbq/4iSdLpqdVq\nxGIxyuUybrcbu90ufjcxMcGvfvUrDhw4wNTUFPFYjLbBQf7w6FHeNKvztaYCDNHBETYwQA9J3OSJ\nUzL8KyYX5HU6iopC1WjEaDKh1+kwGgzTKw+1GhPjOcqlZdRqa9HpDmMyDdHePj2esbEs5fIyKtW1\nKLpeDMYBmlssqIpCVVXJ53KoxSJWmF4JAWyAsVLBVC7TWq2yolplebWKHVilqqwql6cDjnSa3uFh\n/m9jI79eswaHz8dTTz3FypUruf7661m1ahV6vZ7x8XFSqfkrVZ1qBSg5b1xYC/V+L/mAQmtIc2xK\nk9PpxOfzkc1mxcqFlgal0+kolUqUSiUqlQoOh4NcLkc8HqdQKOB0OlEUBb1eL7plmkwmDIa5b7fD\n4ZhT9Wkh1Wo1UqkU4+PjxGIxSqUSZrMZl8tFR0cHXq+XbDaLoij4fD5xIFOpVCgWixQKBXK5HIVC\ngWQyyeTkJOl0msbGRlwuF2azmWq1SiqVIpFIiBUe6eLg9/ux2Wy85z3vWeihLDk2m+24POVqtSo2\nakqSdOpUVSUWi1GpVPD5fJhMJlRVJRqN8txzz/GLX/yC4eFhyrkcV42Pc08gwGWlEjC94PC/ZjMv\nG438VlUZMBgY0euJpKBWs6KqPej1z2Kx7GbTpnU0NDRQV1dHXV0dFosFnU6HTqebs9Har9MxMTFB\nLBajrm4D9fVvBOB73/sFE4X/RKwM1IDiPtrKd/CBD9wMINKOE4kEE/E4sViMdDpNsVgU8202m6VW\nqdBSq7GsXKYzb2J9zcO7mGRNtciXJieZCgZ5qL6e/+no4MiRI7zwwgtcf/31XHfddSxbtgyP53Hm\n66F5sgpQct5YOPPNG+fbkg8otNJquVxuzgZtnU6HxWLBarWKA38tuNBWF7TKT8ViUQQNqdR0lVqf\nz4fRaKRcLouNyjqdbk6AYTQa8Xg8RCIRMpmM2IB1IWlfOqFQiGg0iqqqNDc34/f7cTgc4jVEo1F0\nOh1Op5NCoXDc42g1rVVVxeFwiC+yo0eP4vV6aWhowGw24/V6RTWN+vp6eYb1ItHR0UFvby+RyEmb\nz0vnmN/vp6OjY851WslY+fmRpNOTTCYpl8t4vV4qlQrRaJT9+/ezZ88edu/eTWxsjLdMTvKuyUla\nZrpY54GHbTa+YTTSN7M6b5yZwy0WCxu6/FgsEyjKgzQ3N7Ns2dux2+3o9XpqtRo6nU6cZITplGrt\nZ51Ox6pVq+akAgcCAZLJdcxXGjYeX008HqehoWFOf4rW1lZqtZpouBePx0W5+1AoRCiT4VDaSbX2\nKLCBj5Hg/XyXu/gqbbUQd01N8f5QiJ/7/TzU0sJ/Z7MMDg6yadMmWlqGCYWOb5Y3uwLUfOS8sXDm\nmzfOtyUfUHg8HhoaGoBXNhxXq1URCGipPFarFY/HIzYcJxIJcrkcHo8Hi8WC2+2mXC6TyWQIBAJM\nTk5SV1eH3W7H4XCIzpqlUolUKiXOTpjNZhRFEQ3vLtQBQqlUIpPJEI1GSafTVKtVfD4fDQ0NuN1u\nceazVCoRCARIJBJiFaNWq2GxWLDZbNhsNkwmk0gFy+VywCtVnqrVqvhia2pqwuv14vV6CYfDxGIx\n/H6/3FNxkejo6LjgX1DS/LSAQq5QSNKpm52eGwwGGR0d5ciRIxw9epT+/n5MAwPcf/Agy2f2IER0\nOh5wufie0Uh/3Eo1fyWqeg063bOYzbvYsKGN+vp6rFarmA+1FGYt2NDmSm3/pdZNGxB/1y6aaDRK\noXDlvK+hWNyGxTLGihUrKJfLFAoFSqUSuVxOpGPX1dVhtVpxu934fD7a2toIBAJEIq2IZnh4+Cof\n5x+5i3crV3OPYQ/rymXeHQ5zcyTCl1IpDs5kY7zrXdeQz99NJLKCdHozLtduOjsneeSRH570Pe/o\n6CCTychys0vAkg8oZtNWEACsVisul0uUQ83lcmQyGQwGA1arFavVSjqdJhwO43Q6xdl8r9eLy+Ui\nFAqJL65CoSBWPFwuF0ajkVKpJFY3arUa0WiUTCZDc3OzWME4HwcLWkCTTCbnlHh1Op1iWbZQKJDN\nZgkGgwwNDYk0LofDgdfrRa/Xk0wmmZiYEDW5tbMwRqMRRVEol8uoqorZbMbv92OxWBgaGsLr9dLW\n1kZdXR2RSIREIoHX6z3nr1OSLmXValXs45Ik6eS0PlLlcpl4PM7IyAiTk5MEAgGOHDnC8p07+ezR\no9hVlYBez/2NjTxsMhEvFAiHjVSrj6AdjNdqd5LP7+XIkdu54YYbaGlpobGxEavVKj6Ter1efEa1\nOd1kMmGxWOakQRuNRnFbTWtrK//yL986QZrRXt70pnfR1dUljk/K5TLlmSCoVqvNuYTDYSYmJnj+\n+eep1Y4vU1vGxI/VP+J/XIO8zenkg1NTbMnn+dvhYR5IJPiPUgl13To+8IGbZ/aZxrniineycuXK\nk1ZvlOVmlxYZUJyE9iXgdrtFalQmk0FVVSwWCwaDgVQqRT6fx+v1ir0YWjm2arWKy+WiWq2Sy+Xm\nrHjYbDZcLhe1Wg2Hw0EgECAej4sPqVbK1mKxiAP1M6WlNqXTabLZrKhgZTQacTqd4vX19/cTDodF\nypLH42HDhg14PB6RwhSJRES5WC3YqtVqola22Wymu7tbbHybmJjAaDRSV1fH6OgogUCAbdu24fF4\niMfjGI3GBUn3kqSLlSwZK0mnTkttymQy5PN5Dh06RDAYJBqNcnjvXm7dsYM/TiQAeM5i4aPNzYwV\nChRmApBa7c3M12sinV4vCrckEgkymYyYt7XVBy0NSTvJpqqqmCu1NOv5+mG5XAfnTTPyenuJRqMk\nEok5ezWNRqOYh8vlMsViUTxnXV0dy5Ytw2J5kVzuz457LkV5mmKpxM+yWZ5sbORjqRQfjMX443ic\ntU89xZfjcUqveQ3t7e34/X5MJhPFYpFYLCZSruZz00138OKLn0eWm10alnxAkc1myWQyIsdRO6Og\nKMqcpUlA9KlQVVUEFtVqFYPBQKVSIRwO43A4cDgc6HQ6fD4fsViMZDKJz+fD6XQet+JhMpnmBBbF\nYhGfzyeWMjOZDMlkUpSxPXbZdPZS6bF/h+kvKi2IKBQK1Go1bDabeDyPx4NerxdnavL5PDabjZaW\nFurr6/F6vUxNTXHgwAEmJiZIp9PiC9PpdNLS0iJWJGD67Mjk5CTj4+N0dnayadMmEVSEw2EMBgNj\nY2PUajWuvvpqsd9Ce0xJkk5OBhSSdGpUVSUUComqTn19ffT39xMKhQju2sVn9+3jiplN1//i9fJ3\nNhupeBxVVTEYDFgsFrLZN8z72MXiNqLR39Ld3Q0g5vByuSxKzRuNRmw2mzgpeOznVquYeGxQcd99\nn+Ceez7G5GQn2exWHI6XaWkZ5atf/RQej0esQJTLZVEcpVQqUa1WRUqkNiadTsfatWtpanqeoaFj\ng5S9mEw7sVqtVCoVYskk95rN7Gtu5u+CQa4oFrl/504+HYtxcNs2MecbjUZcLheBQIC2trbjVisO\nHz7M8HAzstzs0rHkA4pQKMTk5OS8B+NaWdjZB/JaaVQtXzKXy5FOp8VZh0QiQaFQwO12o9frcbvd\nRCIRJiYm8Hq9GI1GdDodVquVXC5HIpEgGAwC0ysSmUyGeDyO2+0WY9TOcCQSCXHGYfa4tMpTs+l0\nOrGior0erTStTqcTgU8ymSQYDIrO3a2trZRKJRRFYWJigl27dhGNRkmlUhQKBVRVFcu2er2eQCBA\nuVzGbDYmi439AAAgAElEQVRjtVqxWCzU1dVRqVQIBAJUKhXWrVvH6tWrmZiYYHR0lGKxyK5duwB4\n/etfL5ag5SZtSTo1Wr8XSZJeXTAYJBQKUa1WGR4eZnBwkEOHDuHctYvvjI5SX6uRVBTuaWrip7Ua\n6XgcnU6HzWajoaGBlpYWnn12B/n88Z2pXa7d/O7v3sKaNWvEsQG80tlaK0U/+yBf2z85++TlsXso\nABoaGvjv//4mAwMDjIyM0Nb2VpYvXz4n+Jhdvl47ttA2emvPValURNDx/e93ceedH2diooNM5gpM\npudxOg/yutdtZnx8nMnJSbLZLMlkkn+3WOhrb+cbgQBrymW+PTDAP8RiPLFxo3itK1eupFKpMDY2\nRldX15z5u7+/n0Ri47z/Jqdabla6uCz5gGJiYgKbzSY+iNpF2/OgVWnQvhS0D7J2e21ps1qtijKy\n1WoVi8WCx+PBarWKFY1EIiGu0/YbeL1e0fuhWCxiNBpJpVLY7XZRnvXYQEdbvSgUCiJ3Uut9YTab\nqdVqIrDRNoNXq1VR9tXpdFIulxkeHiaXy2EwGMS4AoGAOJOTyWTIZDJks1ny+bw401EulwkGgyK4\ncDqdVCoV0TE7m83idDrFXpJkMsnq1atpb2+nvr6evXv3EgqF2LVrF3q9nq1bt5JIJOQmbUk6RXKF\nQpJOLhgMMjk5iaqqTE5OMjIywv79+1n95JPcG4uhBw4bjdzZ1MS+mVV8o9FIe3u7aPKm1+s5cuQR\njh49Pv2oo2OcFStWkMvlRFqTFjwcu+Kg/awFBLN7OWl/zkev19PT04OiKASDwTl7p+YLRLTrDAbD\nnGa8Wiry44//HwYHB+nr68NiuYFc7mpisRhbt25lbGyMXbt2MTg4SCaT4eVKhbc3NfE38TjvyGT4\nVCzG+uef5xsgjjmWLVsmMhS6u7vF2FasWHHG5Wali9OSDyiam5tpa2sTwYAWzZdKJdLptNiE7XA4\nMJvN6PV6kaNYLpfJ5XLiwwSI+8fjcZLJJG1tbfh8Ptrb28lms1QqFdxu9wlzDovFIuPj48RnnSWx\n2WxYLBZxG5PJJBrxaHWotdUIbX+Dw+HA6XQCr6xMVCoVjEYj0WiUYDBIOp0GEIHT2NiYKA+bz+dF\nupaWI+nxeDCZTLhcLux2O2azWaRuaRvPjUYj+XyeQCDAyMiIyO3MZrNMTU3R2dlJR0cHer2ecDjM\nzp07URSF9evXi6BLbtKWpBPTDkRkQCFJJxaNRhkfHwemMxGGhoY4cOAA7ief5AuxGDrgv2w2PuP3\nMzXTu8HtdrNy5UquvfZa3G432WyWlpYWHnjgK/zFX/wVo6OtpNObcTp3094+zne/+wVx0K71mJjd\nZ2J2OtOxqU3HrjS8mmNL2s8OKo5N19au1x5fS43S/l4qlejo6KC9vR2dTkelUmHv3r309fXR1dXF\n8uXLefbZZ9mzZ890dkU8zp87new2mfjrWIybCwWiO3bwHzMby/V6Pd3d3cRiMXQ6nUj/Wrt2Ld3d\nAUKh/cxNe9p/0nKz0sVpyQcUoVBINLeZneuorQxouYlaqpHNZsPr9WK320XnaFVVRZlZrXxqNBol\nEolw8OBB6urqaGhowGKxkM/nCYfDohqUdhZBu5jNZrq6upiamhJnOrQPqhZczG6Qp23w1lYPtGpN\n6XSaUCgk9iWYzWbsdjvRaJRYLIaqqtTX14tSuH19fRw+fFikP2kpFT6fTwQcExMTomys2+0WvTjc\nbjeNjY2YTCaxn6K1tZVYLMbQ0BCJRIKpqSkmJiYIBAL09PSI9KapqSmR/rR8+XLy+Txms1l20pak\nE5A9KOanKMrVwMeBLUAz8DZVVX9+kvtcC3wNWAeMAl9WVfVH53mo0nlWrVYZGxujUCiQTqfFykTy\nqaf4ViiEDvix3c4nXS5yM3Nac3MzGzZsYOPGjSKLYNu2bSxfvhy9Xs/PfvYvDA4OMjo6Sk/Pn7Bu\n3bo5WQ1aZsPprrDPXqk4kTOt6DY79erYi1adEeCKK66go6OD3bt3E4/HufLKK3G5XOzbt4+xsTHS\nmQzftVoJ1dfz7XCYP83lGHr+eV6cOVbS6/W0tLQwNTWF3+8XJzMfffQ7M1WemkgkNuHx7GHZsiCP\nPPKd034t0uK35AOKtrY2mpqaxIqD1vG5UqlMN6+ZWdbT6/Wid0MgEBD7INxutyipqm2w1uv1dHZ2\nks/nmZycZHJyklAoRHNzs+jNEA6HyefzOJ3OOWcUAJFWlE6nxQF9qVQiHo8TjUZF4GO320X1plqt\nJlYNkskkTqeT+vp64vG4WL3o7+8nmUzi8XhobGzE4/GQTqc5cuQIL7/8MsFgkEwmA7zSqC6TyZBO\np8UeCi1gASgUCjO1rSNiH4f2xef1enE4HKxdu5ZQKCSqO42OjtLf38+b3/xmnE4nJpOJYDDIgQMH\nAGhsbBRBkkx9kqTjyYDihOzAXuD7wElLyCiK0gU8CnwL+APgTcD3FEWZVFX1V+dvmNL5FggECIfD\nFItFJiYm2LNnDwNPP82DY2PYgV8bDNxjtVIoFDAYDLS1tbFmzRrWrl0r0nU3bNhAU1OTSH82m820\ntLSc88/dfBu1zxXtYF/LUphNa+pbLBYpFAq0tLTgdDrZv38/k5OTrF+/HrvdzsGDBxkYGCCfz/Pf\nBgPNViv35vN8Lpnkfc8/z0szJ0MVRcFqtXL06FHWrVuHoij4/X527PgJhw8fZmBggJ6eO+XKxCXs\nrAIKRVHeCLwRaADmhM+qqr7vbB77QvH5fHR2dh53ZqFWq1EoFMSBdKlUEvsFFEURFRXi8TiJREJs\nktbO5s+uP93a2iryN/1+v+hvMTExgdlsFiVTj90YnkwmCQQCoveDtmKiBT/FYhGdTofH48Hv94uq\nUCaTCYfDQTgcFl8o8XiccrksgpDDhw8TjUYZHR3lt7/9LalUSiyXantKJiYmKBQKFItFLBYLDQ0N\nIu1JC7a0bqBamph25kPrs9HW1sbGjRtZvnw5wWCQPXv20Nvbi6qq/O7v/i4Wi4WWlhYCgQCDg4OU\nSiXRpVtL65Ik6RWyqd38VFX9JfBLAOXUzkZ8CBhSVfUTMz8fURTl9cDdgAwoLlLFYpHR0VHi8Tix\nWGy6C/ZvfsN3R0ZoVVWO6HT8icVCsVbDarXS2dnJhg0bWLlypQgmNm3aRFNT0zkp2b5YKYoiThy6\n3W4qlQoulwuHw8HevXsZHx9n/fr12Gw2zGYzfX195HI5/tFoZHmlwh+Xy/xzJMLv79jBDp0Og8GA\n3+/HYDDQ1NQ0p8/E2rVrZSCxBJxxQKEoyueAvwZ2AgHg+ELKF4GhoSGRFnRsHuKxuZFa6pOWj6gt\nJ9ZqNQwGg+hWqZVU084KqKqKx+OhXC5z9OhRHA4HbW1tuN1ucrkcZrNZlIGrVCqoqoqiKHg8HrGn\nQVsVmN2pW8ujLhQKDAwMiFK1er2eSCQiStmOj4+LQEMLOuLxOMPDwxw9epRyuSyqPtXV1VFfXy9S\nvLSKUD6fD6vVKjabZbNZRkZGRK8NbV+HtkITDocxGo2Mjo7S0dHBqlWrWLt2LS0tLRSLRQYHBzGZ\nTFx55ZV4vV6am5uZmpoiEAiIXFatvK0kSa/QNmTLz8ZZuwr432Ouewy4bwHGIp0jo6OjhMNhQqEQ\n+/fv56UdO/ji0BCbajUiisJtFgspnQ6HxcLKlSvZtm0bnZ2dWK1WvF4vW7dupampaaFfxgVnMBjE\ncYDH42HXrl0cPXqUVatW4fV6sdlsHDx4kGg0yt1mM121GtdUq3w3EODWl15Cp9OxadMmTCYTw8PD\neDyeOenZ0qXvbP61Pwj8qaqqPz5Xg1kIWt7j7M6SMLfqghYYaEGCVslBO/tvMBjEfbUDay0HU0tN\n0lYXksmkaA7ndrsxGAyigpLf7xf304Ibh8NBKBSiVquJVYtKpSJuWy6XCYVCVCoV9Ho9AwMDJJNJ\nisUikUiEaDRKpVIRextKpRLhcJhgMEg+n8dut9Pd3U1dXR1OpxOPxyOeV1sqdTgc5PN5gsEgwWCQ\neDwuAp7u7m5MJhP5fJ7STC3vcrmM2+3GaDSKNK2RkRE6OjpobW1l27ZtFItF+vr6MBqNrFu3TvS9\nCIVChEIhEcTIVQpJmkv7rEtnrQmYOua6KcClKIpZVdXiAoxJOgupVIrR0VFisRi7du1i165dvK+/\nn5tLJYrAH1gsjM70lrjsssu4/vrrxapEc3MzGzduxOVyLfTLWHB2u52rrroKj8fDwMAAer2eG2+8\nkbq6Op588kkSiQTvsVj4dT5PT63GNycm+IPnnmN4eJjrr78ei8VCIBCgvb19oV+KdAGdTUBhAp4/\nVwNZKPX19bS2tooqDBqtYYx2mV0tQav2BIg0H21VolwuE4lEsFqtOJ1OarUa2WxWpDY1NDSIZnGp\nVAqLxYLVaqVYLJLJZHA4HHMCG+0s5MTEBB6PB5/PJ9KOtGBCO8OfTqfJ5/NiA3Q8HsdisYjgoFwu\nE41Gxd6M1tZWWlpaMJvN1NXVodfrCYVC6HQ6CoUCVqsVs9ksStR6vV7a2tpwOByiN4WWdqWtJuRy\nOcbHx0mlUtTX19PW1ka5XCYWi83UpU7g8/m4/PLLefHFFxkcHESn01EqlWhvb8fpdBIOhwkEAvj9\nflatWiXPxErSLLJkrCQdT1VVhoeHiUQijIyMsGvXLq4ZGuIvCgUAPmw2s9NiwWwwsGrVKm666SZs\nNhvNzc00NTXR09MjNhNL09UkV61ahdVqZWRkhKmpKa655hpyuRzPPfcc6WyW21SVJ3M5tlarfHFC\nx/aJP+bAgRdpaXmKr3/9HhoaGmTD2iXkbAKK7zG9ke2L52gsC0I7K6+ZXd5Nu2ipTdrmJa3btVYu\nVqvypFVa0vYshMNh7Ha7SN/Ralzb7Xaam5uxWq2kUimxmSmfz2O1WmlsbAQQpVsBsW+jVqsxNTVF\nMpkkmUyKsrZHjx5lcHCQiYkJ8vk8breb7u5uDAYDyWRSVHdSFIWNGzfS0tIiNqHDK5vY3G63GJsW\nhGgVorSeGyaTCbfbLfaYaH0qSqUSFouF5uZmxsbG6OvrY2xsjMbGRrxeL5lMhsOHD1NXV8fll1/O\nunXr2L17NyMjIxiNRmq1mkjtymazDA8P09TUNOffR5KWOtnU7pwJAo3HXNcIpE62OnH33XfPaT4K\nsH37drZv335uRyidskgkwvj4ONFolBdffJH24WH+32wWgL8zGPhPiwWjXs/y5cu59dZbqaurEyvj\n2sksaS6z2UxHR4foGB6JRLjhhhsoFou8+OKLHAVuzdt4TE3wbsY4QpjPF37M0NA+PvrRj/DooytZ\ntWrVQr+MJe3BBx/kwQcfnHNdMpk8L891NgGFBbhDUZQ3AfuB8uxfqqr60bMZ2IWSSqXEQfvJzC7d\nCtObv7LZLNlsVtR3LpVKoieDoiiEQiHRsE6rCKWtGmjlXkulkmigNzQ0JDZia3sk6urq8Pl8oqRr\nuVwmkUiIjeETExMMDQ2J59E2kxWLRZLJpAiCnE4nDQ0N4oxDpVLB5/NRqVTIptOsNxrpmpykePAg\nKZ+PQksLWa+X6EzgYbVacblcWCwWsYJTKBTE6y8WiyL4amhoQKfTMTk5ydTUlCgtWyqVOHjwIOl0\nmk2bNrF69Wp6e3sZHBwU74GWdxmNRunr62Pr1q1ylUKSZsgVinPmBeDNx1x3/cz1r+q+++5j8+bN\n52VQ0umrVqsMDAwQDoc5cOAAI3v38mQmgwn4L72ev7VYMBoMdHd38/u///s0NDTQ1NRES0sLTU1N\nMs3pVcyubqXT6ahWq9x0002Uy2VeeuklnuI6PsCN/IDb+Rxf4Cmu5SmuY2qqm2eeeYbm5mb5/i6g\n+U507N69my1btpzz5zqbgGID0yX6ANYf87uz2qB9IeuJezwe6uvrtceY8+exZqdEaX/X/qxWq6IH\nRS6XI5lMksvlaGhoEBupU6kU6XRa9Gswm824XC4RoGil6Y4cOUI+n6exsVFssq5UKqTTaSYmJsRB\nt16vJ5VKEYlERJnWWq0multrgUQkEhF7NKrVKof37qUjnWazotC2cyfNwSDt0SimmT0Qs1UUhZjT\nScrnI+xwMGE2E7RaOdrejtLaKjava6sz2h4QgI6ODnp6ejh69CiTk5MEg0Hq6+txOByMjIyQz+dZ\ntWoVLS0tjI+P09/fT61WE43zYHqDXVtbGy0tLSf7p5SkS54sGXtiiqLYgR5A+wJfpijK5UBMVdUx\nRVH+FmhRVfVPZn7/beDDiqJ8BfgB0xULbwN+7wIPXTpLY2NjjI2NMT4+zgsvvMDHo1GagSOKwocs\nFoxmM52dnbzjHe+gsbGR+vp6Ojo6RNXF2Q4fPkx/fz8rVqyQlYlmmM1mcZJQy+K45ZZbZsrGv4Ef\n8j5ew0t8kPu5nw9wOfvI5V7DoUOH6O/vZ/PmzfKk4BJwxgGFqqrXncuBHOOC1RN/+eWXSSaTohb0\n7CpPJpMJg8EgelFoB8/aZmXtT4PBIGpV22w2USVJS1nKZrO43W66urrQ6XRi03Q+nycSiZDJZERT\nPLfbTUdHB4qikEqliMfj2Gw2stksoVBI9HxoaGggFosRCAQwmUyUSiWGh4fFBmy73U6pVCIajaKq\nKu1WK6997jnW9PXRlclgVI+P+UoGA4H6evJGI3XJJHXpNIZajYZUioZUip5Zt63odLy0ejW/ed3r\nKHV0YLfbsVgsIogBSKfTuFwutm7dSigUor+/n3g8DkynmoVCIQAaGhpwu93EYjEOHjzI8uXLaZ0J\nVnK5HL29vTQ0NMiKEdKSJwOKV3UF8CTTJ7RUpk8wAfwIeB/Tm7DFLlFVVUcURXkL01WdPgKMA7er\nqnps5SdpESsUCgwODhIOh3nppZdoHRzk9koFgA/p9VRMJro6Orj55pvp7OzE7/ezfPlyPB7PnLS1\nSCQy04StmURiIx7P43R3B3j00e/MKYG6VFksFnw+nygYo6oqb3nLWzh48DnK5Q9zD1/hZh5hJf18\nhi/xeWUQg6FNnBTUUrmlS9eiPEK7kPXEbTabSD3SLpVKRTS2U1WVysyX0+xgY3YTN6286rFlZ7W/\na6lH6XQaVVVFz4parUYulyOTyZDL5cjn84yOjtLb24vX60Wn05FOp8lkMhgMBrxeL+3t7XOCB5vN\nRiwWIxKJoNfrcblcWK1W0anbn0px2+goVw8MYJ45GAEoWK0Em5qIdnQw7PGwT69nUK8nOZO+ZDQa\nMep0NNVq9BgMdNdqeBMJXJEIjdEoLcEgrz18mKt6exnYsoWX3vhGxj0eqtUq2WwWk8mEyWQSKzV+\nvx+32y36XmgBmBZUWK1WbDYbyWSSQ4cOYbVaxdmjYDBIf38/a9asOc3/SZJ0aZEBxYmpqvo0x/RD\nOub3753numeYXgmXLlIDAwOMjY3R39/PkX37+PlMc9b7gV02Gz3d3dx4442sWLGCxsZGuru7GR8f\nZ9++faxcuVKsQtx00x28+OLnmU6+gFAIQqH93HTTHezYcdLzmkuC1WrF4XDQ2dkpTvA1N/8vo6N7\nSbGRD/NNfsrb+QR/z3/W6hgZuZqenh76+vrw+/3ye+sSd7aN7TzA7YB2pHcY+L6qqudnx8eJnXE9\n8fr6erFyoF20crBaziAg9ghUKhVKpdKcgEP7vbaBW1VVsZdCa0RnMBjEAXMoFCKbzVIulzGZTHg8\nHtra2jCbzaTTaQYGBkSTNy0AqaurEw3pEomE6Git7dfQ8kFVVSUcDtM8Ps7thw6x5ehRdDNjHG1o\n4OAb38g+p5MRVaU4M8ZsNkulWESv1+NwOHC5XCLoMZlMjFarDJbLKO3tIv2qY3ycN+/ezWVjY6zc\nuZOVO3dysKeHp173OsIeD7FYTPSw0Ko/eTweWlpaqFarTE5OihWhcDiM1+ulWq2KbuQjIyNz+nn0\n9/fT2toqczGlJU2rKCeb2kkSJBIJBgcHmZycZMeOHbwvGGQVMAl81mikra2Na6+9lp6eHtrb27Hb\n7dx2258zNtZBMrkJj+dXdHcH+NrX/pLh4Wa0YOIVGxgebuLw4cMy/WmG1pi3qakJo9HIX/3Ve/ni\nF+9gamo5Pyu/lv+ihVuZ5H41zHv27KGjowOHw0FLSwvLly9f6OFL59HZNLa7gumD9jzw0szVdwOf\nVhTlelVVd5+D8Z2qM64n3tvbSz6fn1M2VmssB4iDWrPZLA6wtQpIMF02VgsmtMeYfR+HwyE6aFcq\nFdEcT6uKVC6XRaWkVCqFyWTita99LSaTiWw2K5YWh4eHCQQCojFdJBIhFouJg3SAsdFROg4d4k8P\nHeKySES8xj1NTTyxeTPJTZvIZLNMTU0Rj8epVqvkcjkqlQpOpxO3243b7cbhcGA2m6c3a2ezZDIZ\nisWiuAAM1Gr87+rVrOnsZPvICK8ZH2f9wADrBwYYWrGC31xzDXucTmKxmAjGVFWd05+jVCrR3NxM\nMpkkm82K9yiZTDIxMYHT6USn09HV1UUikaC3t5etW7fKgylpyVqsG7IVRXkj03sQGjhmlUBV1fct\nyKCkS97AwABHjx5l586dGI4c4e6ZdNs/B4z19Vx11VWsWLGCVatW0dTUxG23fYQ9e/6GY1ch3vve\nO0gkbp/3ORKJTQwMDMiAYoaiKOIEp9frZdu2bfzN31h56qmneOyxr3DXZIA3qvAaVeXtk5M8u2MH\nDQ0N9Pf309HRIfp6SZees1mhuA/4OfB+VVUrAIqiGJguJ/t14JqzH9759+CDD4oNwFpAcd111/GG\nN7yBXC4nUpJisdiclQltY7TZbBabqbX9FFqzO63sqraqoZVe03o2aB2ntaDEbDZjtVrnPIeWKqUd\nRBw9epR0Oo3ZbKarq0tsYO7p6+Mtzz5L98zKRUVR+HVjIw+2tBBpacFqtRJ/8UXi8bjY+GwymfD5\nfLS0tIiqUlrVKW2vg81mo66uDqPRKKpTGQwGESDk83meUlV6YzG2PP446/bvZ1l/P8v6+7lq82Z+\nfsstxAoFqtUq6XSaSCRCNpsV+05yuRzt7e243W6y2Swul4upqSlCoRB+v19sXm9oaGBsbIzW1lba\n2toW4H+KJC2sBx98kAceeAB4pdnm+Sr/dzoURfkc8NfATiDAWRblkKRTkc1m6e/v59ChQ+zfu5cf\nJ5OYgJ8Cv7Raee3ataxevZpNmzbR1NREf38/Y2PtzLcKEY124XDsJBZ7/3HP4/Hsoafnzgvwii4e\ner0ej8dDpVLBbrezdetWyuUymUyGp556intCIe4HPlso8Ib+fnbu3InVamX16tV0dXUt9PCl8+Rs\nAoormBVMAKiqWlEU5e+ZnlgupDOuJ/7JT35SnHnQznxrB+9aUDB7T0S1WhWrDLlcTmxC1lKdAHHm\nP5PJiE3S2uOkUikqlQrValUEEHV1dVitVlRVJZFIiKZ0WlO9bDbL2NiYWMFobW0VTelqU1Nc9dBD\nvHZkBICcXs8v2tv5aUcHYYsFt9tNMZFgfHycarWKw+GgtbUVu91OtVqlvr5+zqrB7OpT2oqMw+HA\n7/fT2NiI0+kUaVLRaFRUbwqqKo/94R+y/x3vYOVPf8qWfftYuXs3fzQ1xSO33050pmxsT08Pvb29\nTE5OihSyTCYj0pnMZjORSIRgMEhfXx8NDQ2Mj49jsVhwOp309/fj8/mwWq3n7D+PJF0Mtm/fzpve\n9CbRBwbOX/m/0/RB4E9VVf3xQg9EWjpGRkbYv38/e/fu5a0TE2xTVVLAR3Q6upctY/369Vx11VW0\ntrZSKBSYnJwkldo072NlMm+kq+vfiMX2Mzfg2M+yZUG5OjEPi8WCy+UiFothsVjYtm0bU1NTTE1N\n8a+pFH9YKHAN8MVYjI8eOIDH42HNmjV0dnbKik+XqLMJKFJAB/DbY65vB9Jn8bhn4ozriWt7BrTO\n1FraktbcTjv4n93sTvu9Xq/HZrNhNBpFudh8Pi9KqPp8Pux2O4qiiJUIrQKSTqcTqwGjo6Mkk0lx\nZj6fz4vVjWw2SyKREA3mtNWQaCRCx9NP80e7d+OuVKgCv1ixgv+54grSej3ZTIbGma7ThUKBZcuW\n0d3djd/vF+lWWglXo9EoXpPL5cLr9YoKVkajEZvNhslkolgsEo1Gxb4QLVXKYDAQi8WIxWIEbTZy\nH/wgIwcP8nvf/z6NExO862tf4z+2b2e0tVWsrPh8PgYGBkilUuRyOQYGBkSPjPr6eorFIlNTU/T1\n9XHZZZcxPj5OZ2cngUCAyclJmYspLUmLNOXJBDy/0IOQlo5qtcqBAwf4zW9+Q7q3l8/PzKufBCqN\njaxdu5ZrrrmG5cuXk8lkcDqdbNq0CY/n28zUAZnD49nDD37wJf7yLz/P8HATicQmPJ49LFsW5JFH\nvnNhX9xFRNtPkUgkcLlcXHvttYyPjxMKhfjgb3/LHlXl92o1/mNsjL1797J69Wo2b94sq2Zdos4m\noPh34PuKonyMVyaT1wH/ADx4wnudggtZT9xisYiD/lnPD3Bc5Sbt74qiiDQmbR+EVhlK25BdLBbJ\nZDIUCgURfDidThRFEXsoqtWq2FeRSqUwGAysX78eo9FIOBwmEAiIfQdax+26ujr8mQw3/uxnrBoe\nBmDY5eJ7V13F0fp68fg+n49UKoXNZuPKK68Um6FVVcVut+P3+6mvrxepRx6PR6QYlUolFEXBZDJR\nKBQIhUIMDAyIVROr1SrGYrfbRafRbDbLxMQEkUiE6po1/Nc993DDt75F09QUf/zDH/LQddfxm+XL\nURQFr9dLZ2cnQ0ND4n3USuIaDAbq6+vJZrMcOnQIv99Pc3OzCI4GBgZob2+X3YKlJUU7qbEIA4rv\nMV2u+4sLPRBpadi3bx8f/ehXmQou4ydqEhcHeA4jP7YZ2bJyJVdffTWXX345uVwOi8WC1+ulufn/\nZ+/Mw9sqz7T/e7VLlmRJli3JdrwkdpyY7GEnaYBAoRBgvinTlhY6tKVMp/QrA3ShM9MpXZlpKS1t\nodT23fgAACAASURBVAMtpS0d+FoYZtjKUghQtoSQFchmx3bifdNi7ev7/SGdgx0SICTxkpzfdemy\nIx1Jry1HOs/7PPd9B2hs7Gdo6MBdiDPOOIN1685g+/bttLe309T0Ja0z8T5wu92qFrSuro7zzjuP\nwcFBXgoG+f7gIN8B/j2ZZGVXF5s3b+aMM87QCopjlMMpKL5CcVb296XHEUAG+CXFjYLDYdL8xN1u\n90H/uKWU5PP5CTqI8V+z2azq7ASoXYpMJjPhq9LlGO+cZLFYyOfzjI2NIaXkhBNOUMd7urq6VOem\n+vp67HY75eXlWIxGah56iDOeeAJzLkdGp+NP8+fz6umnk8hmSUWjGEvOFoVCAbvdzqmnnorT6WR4\neFjtODgcDrV4sNvt2O12pJSkS05PNpuNVCpFT08PIyMjJJNJAKqrqzEajWqGxujoKG63G6vVisVi\nwe/309zczPDwMD09Pejq69n5619T+P73qV63jsufeYaTbTYeWL6cWDJJVVUVFRUVbNy4kf7+fnWE\nTEqpjlh1d3fz2muvcc4556gWvXv37qW/v5/6+voP9MeloTETOZBlrGJpPcVYgKuFEOcA24Ds+Bul\nlNdPyao0jlkuu+wG+vt/w0fZzSX8HRmMfJ77yRW+xmmnncaqVatUR0KXy4XFYgHgscfuKmVNHLwL\n0draqhUSh4DiQplOp4nH4yxYsIAzzzyT/v5+botE+HgqxQnAN4JBbt22jQ0bNrBo0SJVu6px7HA4\nwXYZ4FohxDcAZf5kj5QycbiLmkw/8X379lFWVvaO8SZFI5HJZEgmk2SzWTWTQvkQz2azqqBaKRiU\nokHpfPj9fjUUT0qp6g+Gh4dJp9NUV1dTXV3NwMAAr7/+OolEArfbTSaTYWxsTO2IWNraOPu++6jp\n7QVgm8vFzxcuJFVfjymfx2AwUF5eTqFQIBgM4nA4aGlpIZVKMTAwgMfjwWw2q52SQqGAzWYjmUwS\niUTUlOt8Pk97ezvBYFDVXPj9fjXR22KxqJ2L4eFhxsbGiMViahCg3W6nqamJmpoaenp6SKfT7L3l\nFpJ33MGc++5j7iOPcOXevfzxkksIxWJUVVWxevVq1q9fr1riKq9DeXk5Xq+XgYEBNm7cyIknnkg+\nn2doaIjdu3dTU1Ojhd1pHDccqKBQ7KOnmEUUg0gBFux3mybQ1jiivPTSS/T01GGhmdtKk8438w12\n8FHM8iGam5vxer0Eg0F8Pt8Eq3Gv18u6dQ9pXYgjjNlspqKiQtU7rly5ku7ubkZHR/n8rl28BHxG\nSu7v6uK1117jzDPPZNGi/cXxGjOdQzobE0LcCnxTShkvfX+gY4CZsyu1du1adu3aBTBh7EnJSDCZ\nTBOSscdfysvLMRqNWK1W9WKxWDAYDGrYnfI4StERjUYJBoNAMUOio6ODtWvXEo/HcbvdlJeX09PT\nw9jYGOXl5Tjtdk554QVOe/xx9IUCcYOB37a28tZpp3HSsmVEo1F6enpIpVKqzWokEsHhcJBMJunr\n61M1EGNjY1RWVpJMJsnlciQSCVWAbTQaGRkZYXh4GCklbrebyspK9bZxr6va/aioqACKidipVEo9\n0YlGixKayspKRkdHSSQS7P3c5xiqrOSkO+6gZutWPjM0xL2XXsreZBKv18tpp52Gy+Xitddeo7+/\nn1AoRDKZxOFwYDab6evrY/fu3cyePZtCoUBnZyctLS3U1dVN2t+KhsZUomxkjC8owuHwVC1HRUp5\n1lSvQeP4Ye3atSSTp3Add1BDH13U8wP+GYB8fiVSSqLRKL29vezevZt58+a9o2jQuhBHHrvdjtvt\nZmRkhKqqKs4880y6u7t5aniYe4JBPgd8LRbjhjfe4OWXX6a1tVXbEDzGONRXcylgHPf9wZgxu1Jz\n585l7ty5ABM0EkpBMB7FrUkpEgwGg6pBUI4db/maSqVU69nR0VGGhoZUl6dsNks0GiUej2O1Wqmp\nqSGTyagn9F6vl6p8njV33smcklZiQ3U1D551Fk2rVnHl4sXs3LmTvr4+LBaLOral1+s54YQTVMeo\nefPm4fV6iUajaodB6SY4nU50Oh2hUIj+/n5Ve1FTU6OKrZUxKeViMBje4dBgsViIx+NEo1EymQx6\nvV4Vaik6EpvNhuXv/571s2ez9KabcPf3c/Xdd/PglVfSWdJlnHvuuVRVVfHnP/+Z4eFhNXXbYDCQ\nTCZpa2sjk8nQ0NBAeXk5O3bsoKamZjrOlGtoHHH2F2QrndTpwAFCTt8CfjMFIacaxzCJRIKBgQEc\nbOFGXgTg23yLDGYAnM5NuN3n87GPXUtf32zGxpbict1OY2M/jz12lza7fxRR8imi0SixWEwVxvf1\n9XHzyy9zRS7HOUB9Rwevvvoq5557Lk1NTVO9bI0jyCEVFON3oo6VXSnFtUkpImBisJ1i9zr+RFoZ\nX4K3dw2VAkIZkVL0FJFIhLGxMVWHYLFY1BPuyspK5s+fj8lkYnR0lFAopO7uz21v58rnn8eeTJIx\nGHhk9Wr2nnMOF558MjqdjmeeeYbR0VGcTiezZ89WT/idTieRSIRkMonL5SIQCJDP52lsbFSTLQ0G\nA7lcjmg0Sn9/P9FolEAgQENDA263G0A97v2gjInpdDrC4TCJRAKHw0F9fT2FQoFwOEx3dzexWAzz\nkiW8ctttLPve9/Ds3s3f/f73/O9Xv0p/KITZbGb27NksWrSILVu2EI1GVXcrs9lMJpNR7W89Hg99\nfX10d3drvtYaxwX7FxTZbFYdg5pKDhJyej3wL1MQcqpxDLNlyxY6Ojr4snyFSqLsYi73ckXp1q00\nNvbz7W/fxbZtP2T/8Lo1a65m3bqHpmztxwMmk4nKykq6u7spKyvj5JNPpru7m/v37eOuzk6+RLFL\ncc2WLbz88svMKZm0aBwbHE5Sdh3QLZUz6/1uk1LuO6yVTRKKdgImjjwpzk4H+8BWBNuK8DqVSpEq\nBbjlcjn130oOhcvlwmq1kk6nSaVSaoL28PAwwWCQZDKJxWLhhKYmLnn1VZqfeAKAvqoqfn322eSa\nm1lQV8eOHTvo6OjAZDLR0tKC0+kkn8/jcrlwu9309vZiNBqprKzE4XAwNDSE0WikqqoKi8Wi6jgi\nkQiRSASbzUZLSwter/eQEqhzuRzJZJJUKkU2m1XzK+rq6pBSqnawip6kqqqKcDiM0WikrLWV7T/7\nGYuuuw7njh1c+Itf8PDXv64mfzc3NxOJRFQ73UQigdVqVRO3BwYG6OjooKqqih07djBr1iytS6Fx\nzLN/QaEYGEwDjomQU43pTS6XY+3atQzs2MENJWf6b1FOQdyJw/46TU1DfOUrV3DNNc9xoPC6zk4/\n27dv10adjjLl5eVEIhGCwSA1NTWccsop7Nmzh//Yt4/P5fOsAFo6O3nuuef48Ic/TCAQmOolaxwh\nDmeArRMIABNcnYUQFaXbZsQZXiAQeEfy8vhOheJ+lM1m1WJAOZFWrh8vxh5fhCiZEUohEY1GKRQK\nlJeXo9PpyGQymEwmGhsbiyfShQKLbr4ZR3s7AH9dsoQnzjyThtLYkmLdqli8QlGvUFNTg8vloqen\nZ4KuQ9GAGI1GhoaGCIVC+P1+deTK5/NRU8qGeC+U4kmxxFUKJSU922KxqAWZkqLd09NDoVCgsrJS\nLXAGBgYYGxsjKwSvfuMbrLzxRux9fXz4Zz/jkeuvZ2xsDLvdTnNzM7FYTC26EokEBoNB7QR1dnYW\nx8Kqqujq6tJyKTSOefL5/ASr5GQyOV1mkKdTyKnGMcqbb77JunXr+ERvLy7gDeAx65tc/OG/cPnl\nl3PGGWfwxz/+kVjswN4s4fBS2tvbtYLiKCOEwOfzqSPdixYtoquri/Xr1/OLri6+Cnw9FuPzW7fy\n4osv8rGPfWyql6xxhDicTyPBgbUSdiB1GI87qUSjUSKRiDq2k8/nVQ1EOp2e4OIExfRsk8mkCrCV\nUSll1z8Wi5FOp9Vk50wmoyZml5eXY7PZkFKq/tgAhXyeOX/9Kyf/139hzGSIW608cMEFxM48kzMa\nGlQRstVqpaGhgVgsRiwWw2g04vMVA8J37NiB1WolkUhQVlZGWVkZ6XSampoadYypvb2drVu34nA4\naG5uLqZop9MUCgVVD6L8PIqDlXJRgv+Un7+srAyDwaDa6iaTSXV2UrGfVdyqYrGYKmKvq6tjdHSU\nZDLJQD7PCzfeyOpvfpPKffs485e/5IHLL0dvNuNwOKitraWzs5O6ujq6urqIx+Nks1lMJpOqqaiv\nr6etrY3q6motPVvjmOZAHYpp0pmbTiGnGscg+XyeZ555hoFt2/hS6bP4m8C81lYuv/xyPvShDzE0\nNMT8+fNxuR4+aHhdU9OXJnfhxykWiwWfz0dvby+BQICTTjqJk08+mVv27uULUrIcaN29m6effpqa\nmhpGRkZobm7Wir0ZziEXFOPcnSTwXSHEeJtYPXAKb1sITns6OjooFAoTBNnKuJOiS7Bararbk1J0\n5PN5gsGgOtqTzWYxGo2q1Wk+n2d0dJRUKqXu4ivuE7lcTt1ZNCeTnPXAAzS8Wgz13tfczIuf/zzm\n2lqMhQLd3d1Eo1F8Ph8Wi0U9OZ89ezaVlZVks1n27NmjntibzWbsdruaEVFWVkYmk6Gvr49UKkVd\nXR02m414PE4ymVQD+2CidkRhvBhbSdRW9CGAWnTlcjl0Op1qlWu32zEYDOj1emKxGGNjYxQKBSoq\nKrDZbEQiEWpra2l3udj8ne9w0te/zpxdu/jIo4/y2EUXYbFaqaioYGRkhFgsxpw5c2hrayMej5PL\n5bBYLIyOjvLGG2+oM5tNTU2HNLaloTFTOJBlrDImOQ04aiGnGhoAu3fv5oUXXuCKvj7swAbgGZuN\nf1i1ikWLFpFIJJBSsmrVKhob7zxoeJ12wjp5VFRUEA6HCQaDNDY2snLlStavX89P9+7lm8CNiQSn\n3fc8Dz4IyeQpuFxPa+L5Gc4H6VAo7k4CWEgxzE4hA2wFbjnMdU0ajY2NtLS0qM5NygmpYvWqiLYV\nxyZl5Ckej6s78WVlZbhcLvR6PfF4nKGhIZLJJFarFbfbrYbFje9uxGIxyjdv5qRf/ALr8DAFnY51\nF17I9gsvJJ3LkRkYIJ1O43K5OOGEE9SixePxYLPZyGaz7N27V+10NDQ04PP51Fajkn6tCKKz2SyB\nQACn06l2H5TuBKAG7hmNRgqFAmazWR2vUIos5Xsl3E/pFpSVlWGz2bBarQfcMbXb7RiNRkKhEMPD\nw2rCdjweZ+HChWzX69n2jW+w+NvfZvFrrxEsK2PrxRfj8XioqqoiFAphMBhobm4mHo8TCoVUcXtf\nXx+dnZ2qza3SjdHQOJbYv6AoFArqJsY0YP+QUyiG2x2JkFON4xylOzG4aRNXl/4f/CvQ1NzMihUr\ncDqdDAwM4Pf7sVgs7yu8TuPoo9PpCAQCtLe3k8lkOOWUU1i2bBk/3ruXL1EMrLkoeRX3J4tvEZp4\nfuZzyAWF4u4khLgH+LKUcka3tB0OB2VlZWpgnaIPUAoHxfZVCbbT6XRqdoOiC8hms0QiEVUjoYTI\nWa3WYsK1xUI6nVa1BeGhIWbfcw8Ln3oKISXBigqe/NSn6KisJFvKjSgrK1MtX3t6ekgmk+j1ekKh\nkCpudrlc1NfX43a7cTgcOJ1O0um0GjLT39+vekLX1dVNmL9WUMTVSjaFTqdTx7KUMTClqFK+QvHN\nwul0YrVa39dJjdlsprKykmAwyOjoKBUVFWoux8KFC9lhMNAWDjP3Jz/hrOeeI+xwELrkEhobG1WB\ndkVFBbNmzVIF5R6Ph3A4TGdnJxUVFXR0dNDa2qqNPmkcc4wfuYSiw5PSqZtqjmbI6aEihLiGYoHj\np7i59X+llBve5fhPAV8FmoEI8ATwVSllcBKWq/E+6Ozs5KmnnuJzAwNYgReBl6xWPrNiBQsWLCAc\nDqufL6CF100nlLyqYDBIIBDg7LPPZv369fyor48fAN/mbv7EV8irp6KaeH4mczgaijbgUuCe8VcK\nIT4LVEop/+NwFjZZbNiwgYGBAQB19EnpUhiNRrV4sNlslJeXq+Fx2WyW0dFR2traiMViCCGwWq2q\nfsHhcGC1WhkbG6O/v/9tQfHmzZx9991UlRKvt550En8+91ziQlBuNlNTU0NFRQVer5d4PM6mTZvU\nE32LxYLT6cTtdlNVVYXH41HXbLFYyOfzpNNpbDYbu3fvJpvNUltbS1VV1UF/foPBgMPhUAujZDJJ\nJpNRfw8H+qrX69+XkHt/9Ho9FRUVjI6OMjo6isfjIZ/PMzY2RmtrK7uvvJKekRFq772Xix97jPsc\nDirOOYd58+YxNjamhv253W6Gh4cZHR3F5XLR29vLnDlz2LFjB+Xl5cyePVsbfdI4psjn8xPGE9Pp\nNMAH+n94tCgVEG9M1fMLIT4O/Bi4mqJ97XXAU0KIuVLKkQMcfwbwO+Ba4DGgBrgTuIviZ5vGFCOl\n5JlnniG8eTOfKXXT/xVonD2bc889Vx2pbWpqekd3XAuvmx4EAgEikQjxeJxTTz2V5cuX8/O+Pq7D\nTjPtfJrfcw+fVY/XxPMzl8MpKK4GPn6A698C/h8wIwqKJUuW0NraqobTjU/CttlsavGQTCYJBoP0\n9/cTi8WIx+MUCgUsFgv19fW4XC6cTqcaCDcyMkJ/fz+FQgGXy8VAfz+VDzzAhY8/jjGXI2Gz8dgl\nl7CloQGzyYS/okLVPFitVvr6+giHw6oWoaysDIvFgsvloqqqSt3tz+fzqsZDyZ8YHBzEYrEwb968\nQ9rBVPQSRxOdTqcWFcFgELfbTSgUIhaL0dzcTNsNN2AZGcH7xBN87IEHuM/jwbdoEc3NzezYsYNc\nLkdlZSXhcJhQKITT6SQYDNLZ2YnP56O9vX2CC5aGxrHA/oJsxfVMsbyebEpaum9KKePjdHUHREp5\n/SQt6zrgTinl7wGEEF8ALgQ+C/zwAMefCnRKKW8v/XuvEOJO4GuTsViN92Z0dJTHH3+cq/r7MQF/\nAV6zWPj7FStobW0lFovhcrlwOBxTvVSNg2AwGPD7/eqUwfnnn88rr7zCzaPV3MobfItv8198Sg0n\n1MTzM5fDKSj87GcZW2KYop3sjMDv9xMIBFQrVcUeNZVKsW/fPsLhMPF4XE2A1uv1WCwWqqurcTgc\nuN1uVTsQi8Xo7OxkeHiYXC6H3W4nHo/T9uKLfOSBB5hXSrzePXs2951zDmmPB1d5OZWVlbhcLjwe\nj5o0WSgUsFqtJJNJVadQWVlJfX09drud4eFhdbyprKyM4eFhRkZGMBgMVFVVEQgEpu0u/fiiIhQK\nUV5eTjgcJpVK0dDYyJ5vfQtLMIh9/Xo++pvf8MBXv0p9fT2Dg4P09fVht9vxer309fUxMjKC3++n\ns7OTuXPn4vF46OzsxOPxTNufX0PjUDmQw5NiejBFLAWM474/GJMSlCGEMALLgR+oTyylFEI8A5x2\nkLu9CnxfCPERKeUTQggf8HfA40d9wRrvi9dff53o669zRWnU9l+BmpoaLrjgAtWiXcsxmP5UVFQw\nNDREKpXilFNO4cQTT+SXT23lBiqpZx9X8Wvu4Bpgqyaen8EcTkHRTdHJo3O/688A+g7jcSeVJ598\nkm3btk0IuNt/tMdms6lZCxaLRRUvGwwGhoeHicVihEIhUqmUevKfy+XYvGkTNRs28PlS4nVWr+ep\nc85h6xlnUFXqSPh8PsrLy/H5fAghCIVCZDIZotEomUwGm82G1+ultraWmpoa9Hq9as+qCML37t3L\n8PAwXq+XhoYG7Hb7FP9W3xulqBgZGSESiWC324lGozgcDnyzZtHxox/RctVVOHfv5qy77+bPN9zA\nvHnzGBoaIpFI4Pf7CQaDqk0twNatW/F4PDidTkZHR9WZWg2Nmc7+GRSpVEodqZwKFC3d/t9PIV6K\nLoOD+10/CLQc6A5SyleEEJcDfxRCWCh+Hj4CaNuj04BUKsWf//xnvjA4iJ7iC7PFZOLTZ53F3Llz\nicfjuN3uaaEj0nh3lGyKvXv3Ul5ezoUXXsiGDRv4XjDHL4F/5Z+5V/8czYtTPProb6d6uRofkMMp\nKH4F/LS0M7S2dN1qiq3lHx/uwiYLk8mE0+lEr9djNBrVsSflNsWVSbGNVdyfEokEwWBQTatVio5I\nJEJHRweG9nY+uXYtczs6ABiurublL34R3cKFnGg2qzkNTqeTsrIy9Hq9ugMfCoXIZrO43W5mzZpF\nY2Mj5eXlQFGMGQ6H1XGr3bt3MzY2RlNT04xLjNbpdHi9XkZGRkgkEthsNqLRKE6nk0QgwL6f/pTZ\nl17K7O5uWh56iO1/+7fU1NSwc+dOrFYrPp+Prq4uRkZGCAQCDAwM0NbWhsfjoaOjg4qKCq1LoXFM\nML5DoVhEu91u1b55KhFC1AHd8gCx3UKIOinlvilY1nsihGgFbgNuAp6m2Fm/haKO4qqpW5kGwN69\ne2l/4gl+Wvqz+jeK3YmLLroIg8FAIpHQuhMzCI/Hw+DgINlslhUrVrB48WLufu45vgY0MsZV+kc5\n9ev3UlFRMdVL1fiAHE5B8SOgArgDULbOUsB/SClvPtyFTRatra0sWLBggnZCp9Opjk5CCLLZLKlU\nSrVbFUJgNBopKyvDbDaTTCYZHR0lEolgSCT48AsvsOj559EXCuQNBnavWcPAP/wDtpIeo6ysDLvd\njtvtxmw2YzAYaG9vp7u7m0QigU6nw+fzMWfOHOrr61Vdg5RS3ZWHYlBdLpdj8eLFM3Y3fnxRkU6n\nMZlMRKNR/H4/e9NpRr79bXxf/SqrX3qJjsZG6hsb6e7uJh6PEwgE1N/H2NgYBoOBPXv20NzcrAq3\nleA/DY2ZiuKwphQU4x2epkpDsR+dFE/GJ4zACiEqSrdNxi7HCJAH9v8P7wMGDnKfG4GXpZSKBuRN\nIcQXgReFEP8ipdy/26Fy3XXXqZs8CpdddhmXXXbZB1q8xkQKhQLPPPMMf9PZiY6iYn670chlK1eq\n9uEVFRXTypRA490RQhAIBOjs7MThcHDRRRexZcsWbgmFuB34YibDdx95hI985COaJuYIcv/993P/\n/RPjgCKRyFF5rg9cUJR2o74uhPguMB9IAm1SyvSRWtxk4PP58Pv9auaEkopdKBTUlGhFV6HkLygZ\nFEo6dDabxWw0ctquXSx74AGspRdr4MQT6b7uOsZ8PmKxGFarVc2CGF+MvPnmm/SWXJ/MZjN1dXU0\nNTXh9Xon7LBHIhGGhoaIxWJkMhnMZjNLlizB6XROye/uSDFeU6HY86ZSKfx+P/3nnUfZSy9hf/hh\nPv7ww9zyqU9RXV3Nzp07kVJSVVVFMpkkFothNpsZHh7mrbfeorGxkY6ODiorK7UuhcaMZv8MilQq\nhRACk8mk5shMMYIDayXsFDeZjjpSyqwQYiPFLvkjAKJoibUa+NlB7mZjYo4SQIHizyLeefjb/OQn\nP2HZsmWHtWaNgzMyMsKLDz3EPaW//Vso6h0vuugi9Ho9hUIBv98/tYvUOGTcbjf9/f3kcjlWrlxJ\na2srv335Zb4DNAH2Z59lx44dnHzyyVO91GOGA210bNq0ieXLlx/x5zqcDgUAUsoYxeDKGcng4CAO\nhwMppXpR8hYUdyclDTqTyahFRy6XQ6/X43A4qBsYYOHtt1PR3g5ArKaGnhtuoH/JkmIlWNpxV0ac\nLBYLhUKBwcFBdu3aRTgcRq/X43Q6mTt3LvX19dhstgnrjMfjtLW1MTIygtPpxOPxUFdXN+OLCQXF\nUnZkZETNA7Hb7bjcbnr++Z+ZvWkTru5uPv7cc9y+YgV2u51YLIbb7VbTtBVdSUdHByMjI+h0OgYG\nBqiurp7qH09D4wNzoIJCyaOYSsa5O0ngu0KI8fNXeuAUYMskLulW4LelwkKxjbUBvwUQQtwMVEsp\n/750/KPAXSU3qKeAauAnwHop5cG6GhpHge3bt9PW1kZzczOtra1s2rSJZa+9hhV4HfgrcOmppzJ/\n/nySyaSa9aQx86iurqajowO73c5FF13E1q1buSMW45vA5YOD/OUvf2Hp0qXTJbRT4xA4rE8lIcRq\nijtAVcCEbWAp5WcPeKdpxsjIiNpeU7oTQgi1K5HNZtURJ6PRqPrAG41G3PE4Lffey6xnngEgZ7Uy\ncPXVdF18MUPhMNmhIbUD4na71R3FeDxOX18f+/btU3fWfT4fzc3N1NTUTNhRl1IyNjbGG2+8wfDw\nMHV1dbjdblwu1zta7jMdpagYHh4mm80Sj8dxOp1kMhkGb72Vmk98goXbt/OhQIBhl4uxsTGEEHi9\nXjU4cGxsjMHBQbZu3cqaNWvo6OjA7/drXQqNGYsy1qQUFMlkUk20n2IUdycBLGTibn+GYrDcLZO1\nGCnln4QQXuA7FEedtgDnSSmHS4f4gVnjjv+dEMIOXFNaZxh4Fi3de9IYGRkppVoHCIeX4HI9TX19\nDyct9PKt0mjvj4HKqiouueQS9Ho9mUxG007MYFwuF1arlXw+z4oVK5g7dy63b9rE14DTpOQPDz5I\n76c+RUNDw1QvVeMQ+cAFhRDiWxR1Uq8D/UySPeCRJhaLEYlEVMG1opsQQqhdgvGuTxa9Ht+GDQSe\neALvhg2I0of64Pnns/2KKxjU6ZCDg7hcLjXp2mg0ks1mVXem0dFR1ULNbrdTV1dHS0vLOwqEdDpN\nKBSivb2d0dFRmpqa8Hg8mEwm3G73pP+uJgODwYDb7SYYDJLJZEgmk5SXlxNcvJiRa6+l6tZb+bsX\nX2TbBRcwYrORSCTweDyEQiESiQTpdJpwOMyOHTtYtWoVZrOZ3t5eZs2a9d5PrqExDRkvyFbGAa1W\nq9olnSoUdychxD3AtVLKqbGcGoeU8g6Kur4D3faZA1x3O3D7AQ7XmATWrLma9etvAhYBMDQEQ0Nb\nOWnLuVQBe4EHgTMXLWLBggVkMhn1M1Bj5hIIBOjo6MDtdnP++efz71u2cG+hwFXA+W+9xauvXbZD\nSwAAIABJREFUvkp9fb26gasxMzicDsUXgCullPceqcVMBU6nc4Jwd7yOQkqJ1WotnuQODFDz5JO4\nH38cUyikHh9cuJBtn/wkfXV16HQ6/H4/1dXVuFwuDAYD6XSaWCxGPp9XnaEikQiZTAan00lzczNN\nTU0T2ntKenQoFGJ0dJRkMsncuXNxuVzqLv6x/B/NYrGojllK0eV0OolcfTWWF1/EuWED1776KjuX\nLmVsbEwND1TsY5PJJMPDw2zcuJHzzjuPrq4uAoHAtBgT0dA4VMYXFLlcjnw+j81mI5vNTou/aeVE\nveSaVMfbJh3K7Y9Mxbo0pjfbt2+nszOAUkwoCBbypWxRevNTwGCxqM5O2WxW604cA5SXl2Oz2cjl\ncqxYsYLq6mpu7enhKuDCbJav/+lPrF69mqqqqqleqsYhcDifRibglSO1kP0RQlwDfIVim3or8H+l\nlAfVagghPgV8FWgGIsATwFellMF3ex7F1UkJiVK+6vV6zJkM7r/8BfdDD2F/4w31PimXi72rVtG2\nciWhqiqcTicttbUEAgG1lZdKpYhGo6ouIx6Pq7P+6XSayspK5s+fT3V1tVocFAoFYrGY2smQUpLL\n5VQhtyJePh7Gd5xOJ7lcjkgkQjwex+FwYC0rY/jHP8a6Zg2BwUGu6enhX6xWVUsxNjZGNBoln88T\nDofZsmULq1evJpVK0dPTo7VQNWYk+xcU2WwWi8WipmVPNUKIRuB/KY49jRc0K13rmeNlrTFptLW1\nEQ4vecf1F/Bn5hMlAtwN1NbWsmzZMnK5nNadOEZQHJ/27NlDbW0tS5cu5dGeHh6nGG2/6LnnePPN\nNzn77LOneqkah8DhfBr9Gvgk8N0jtBYVIcTHKY5OXs3b4rqnhBBzpZQjBzj+DOB3wLUUHeZqKHqJ\n3wVc+m7PFfD5qNPrMXR1Ydi3D+O+fZh6ezH39GDbswdDade7oNPRu2QJ7R/6EIPLliFMJrxeL3Nn\nzcLj8aiuUMFgUFkTJpOJbDZLKBRicHCQaDSKxWKhqamJ5uZmVVCtFBLxeJxkMomUkrKyMqLRKGVl\nZTgcDrUzMZNyJg4Xl8ulFhUGg6FokzlrFl033UTz9ddz7ptv8uKJJ/LfJbG8y+VSLXUTiQS9vb2s\nX7+eVatWsW/fPgKBgCbk05hxjA+1S6VSalbOeCvZKeZnFO1hV5e+nkzRUvzHFDeFNDTeQXNzMy7X\n0wwNTbz+hlKM1Z1AFPjoihWqzlGzAT92UNwu0+k0a9asYe3atdwSj3Mh8NFIhJ8+/jinnHIKZWVl\nU71UjffJ4RQUFuBqIcQ5wDYgO/5GKeX1h/HY1wF3Sil/D1By4bgQ+CzF4Lz9ORXoLM3DAuwVQtwJ\nfO29nqjuk59k3rt4uUd8PvasWkXP2Wejr63F4XCwoJTGrGgjFE9fIQQWiwWz2Uw+nycajbJv3z7C\n4TBSSgKBAPX19dTW1qonBEohkc1mKRQKmEwmbDYbmUxGdToymUx4PJ5psRs5meh0OjweD7lcjmg0\nqqai5i66iJ4XX6T2f/6Hr23fzssNDfQFg7hcLkwmE4lEQi3kNm7cyKpVq8hms/T29jJ79uyp/rE0\nNA6J8R2KVCo1oYiYJu8JpwFnSylHhBAFoCClfEkI8Q2KxcbSd7+7xvFIa2srjY39DA1tQxl7Ws7r\nnMXzZCn+4Xg8Hs477zyklFruxDGGEAK/309nZyctLS00NDTw/FtvsQlYBlQ++CBtV1zBkiXv7GJp\nTE8O59NoEW9bAi7Y77YPLNAuJW8vB36gPpiUUgjxDMUPrgPxKvB9IcRHpJRPCCF8wN8Bj7/X8+lz\nOQo6HbGKCqJVVcQqK4n7/USrqsg1NqJbtAiX2828sjKsVqs6hpTNZslms+j1emw2G2azGbPZTKFQ\nYGxsjO7uboaHh9WioLq6moaGBpxOJ4VCQR1rUjQbOp0Oi8VCeXk5+Xyeffv2odfrsVqtx3Wb12Aw\n4PF41MLN6XRitVoZvfFGyrduxdHRwa0jI1xqt5PNZrHZbCSTSdV6tr29nTfeeIPFixfT29uL3+9/\nhyWvhsZ0ZX/L2GQyiclkQkqpGkVMA/QUN5OhGDBXDeyiqKltmapFaUx/HnvsrpLLk59gcBFfyf8A\nJPw/oBc4ZZzzoTZPf+zhdDpVPdiZZ57Jjh07uKVQ4D7gku5u/vevf2XBggXTZeNE4z04nGC7s47k\nQsbhpfgBtX9K6SAH+XCSUr4ihLgc+KMQwkLx53oE+NJ7Pdn6f/kXRk4/HaPVitlsxmg0YjAYqDAa\nsVgsquMTQCaTKWorSunWZrMZi8WC0WgkHo/T29ur6iSy2Sw6nY7q6mpqamqora1VZ/uTyaT6/Iol\nrcPhwGazkc/naWtrI5vN4vV6Nb9tiiJtj8fDwMAA8Xgci8WCy+9n9003seTzn2fx0BCfNpn4bSiE\nw+EgFAqpwvpwOMwLL7zAkiVLyOfz9PX1MWfOnGNa1K5x7KAUFAaDASkl6XSasrIystnsdPJpfxNY\nTHHcaT3wNSFEhuLIasdULkxjeuP1elm37iFef/117v3e97j04W6gOCtnsVhYvXo1VqsVt9t93H8O\nHosIIaiqqiIej3P66afzwAMP8MDQEP8O1ElJ/t576brgApqamqZ6qRrvg2Oi7Cu5i9wG3AQ8DQQo\n+orfCVz1bve95+mnca5bN+G6NWvWcPHFF2MwGNSLkkOhXHQ6HblcjnA4TF9fH8FgkHw+j8FgwGQy\nqdaus2bNwmw2Mzo6qto8mkymCW4tiuC6UCjQ3t5OLBbD5/PhdruxWq1H5Xc203A4HGSzWfr7+9Hp\ndBiNRspOOon2K6+k5c47+crQEI96vYR0OlV/ogQQ7tixg927d9PS0kJ/fz/V1dVal0JjRjC+Q/GH\nP/yB3/zmN5hMJnWjI5FIvMcjTArfA5RB53+jqGN7ERgFPj5Vi9KYOej1ej60eTMG4BmKLiwNfj9L\nly5l3759dHR0EI1GaW1tneKVahxplKmDmpoaFixYwNq1a7mNYlF57rZtbN28mdmzZx8XZjQzncPJ\nofi3d7tdSvmdD/jQI0CeYjDReHzAwdJLbwRellIqya1vCiG+CLwohPgXKeX+3Q6Vm2++maVLl6pZ\nE+O/Kt2K8bvZSjBdMBhkYGCAWCyGwWCgrKxMDcOzWCy4XC48Hg/pdFr1jTebzao2QtFFjN9l7Orq\nYmRkBL/fj9fr1cRI++FyuUgmk4RKnQi73U7vpz/N2Asv4Ny5kx8lk3win8ditaLX68lms9jtdoLB\nIM899xxNTU3kcjmGhoY0xyeNGUE+n1ffkz760Y+ycOFCZs+erYY+7tq1i+XLl0/pGqWUT437vh2Y\nJ4TwACEp5YzMJ9KYPPL5PFtfeIFLu4vdCSUJcf78+XzrW//J4GAT0egyXK7baWzs57HH7sLr9U7d\ngjWOKIp7ZSwW44wzzuDVV1/lV8kk/wY0ZTI8+4c/EF69Go/HM9VL1XgPDqdD8X/2+7cRaARywB6K\naaWHjJQyK4TYSNEx5BEAUTyjX01Rp3UgbExMaQUoMNHC8IC43e53/UNVCohoNEokEiEUCpFOpykU\nCtjtdnw+H5lMhrGxYqZTeXn5hFRsu92u7iTmcjnMZvMBxWX79u2ju7ubqqoq/H4/drv93ZZ9XKLT\n6aisrFTdsGw2G5V+PztuuIGTvvAFVoVCXOzx8EhJHB+LxchkMhgMBrZt20Z3dzd+v5+hoSGqq6uP\nW12KxsxhfHid4vAkhEBKOW1GnoQQvwb+IKV8Xrnuvey6NTQUBgYGMP/+99il5A3gKYod6bfeCrFv\n351MDL3bxpo1V7Nu3UNTuWSNI0x5eTlWq5WlS5dSW1tLW1sbd1HMAVj+/PNs376dFStWTPUyNd6D\nD9xDklIu3e+ygOKo0bPATw5zXbcCnxdCfFoIMQ/4T4pFw28BhBA3CyF+N+74R4GPCiG+IIRoLNnI\n3gasl1IerKsBoI7FpNNp4vE4kUiEkZER+vr62L17Nxs2bGDjxo3s3LmTYDCIzWajoaFBbcH19PQU\n3xDNZmpra6mpqcHj8eDxeLDb7SQSCcbGxjAYDHi93gMWE/39/ezZswe3201DQ4NWTLwLJpMJn89H\nNpsllUphsViwnHQSXZ/4BAA3R6PYS1oXvV5PIpHAYrEwMjLCCy+8QKFQIJlMEg6Hp/gn0dB4b8Zr\nJVKp1ARxosFgIPcuDnWTSCXwpBCiWwjxIyGEZsui8b55c9MmzirlPCkjBtXV1UQiJ7B/6B0sorPT\nz/bt2ydziRpHGZPJhMvlwuv1smTJEoQQ/IyideiJY2N0/vd/k81m3+thNKaYI6qhkFKOCSG+RfEE\n/wMnaEsp/ySE8FLscvgoukmdJ6UcLh3iB2aNO/53Qgg7cA3FjmmYYmFz43s9165du9SiQnFcKj0m\nRqOxuAteWUlZWRmJRIJoNEpXV5d6Qurz+aitrcXj8WCxWCacsBYKBaxWKw6H46AuBSMjI+zcuZPy\n8nJaWlo04dn7wOl04na7CQaD6PV6ysvL6f3sZ6l68UU8+/bx3VSKL5cKikwmo762r7/+Oqeeeio1\nNTUMDg4ed7keGjOPXC6n6qhSqRRGoxEppRrIGY/Hp3iFIKW8RAjhpuis90ngeiHETuC/gPuklF1T\nuT6N6UsymST6q1/hz+XoB+6jeHJZW1vLnj0nHfA+4fBS2tvbNT3FMYbb7cblcnHSSSfx7LPP0hMM\n8kfgcqDhwQfpvfZabVR5mnM0RNnlpcthIaW8A7jjILd95gDX3Q7cfoDD3xOz2YzValXF18qHtZSS\nWCzGwMAAkUiEZDKJXq/HbrfT0NCgFhJKEaEIr3U6HVarFbvd/q4nrOFwmG3btmG32znhhBO0YuJ9\nIoTA5/MRj8fVlGxvTQ07b7iB5f/0T3wikeCPOh2v2GwIIYhGo1RWVjI0NMTGjRvx+XyMjY0Ri8Uo\nLz/sP1UNjaNCLpdTNzby+bxqQZ3L5dSuhSLanmqklCGKQaJ3CSFqgcso5gZ9h2PE/EPjyNPV2cny\ntWsB+CnFueVARQWnn346mzdvIniAwTmXazNNTe9p4Kgxw7BarTidzlI+SSPBYJAfUSwoTu/p4bm/\n/IX6q67SHBqnMYcjyv7y/ldRHHm6AnjicBY1mZSVlamdhVwup2YY5HI5YrHYBN2DYuNqtVrR6XSk\n02lGR0dVi1glR+L9FAbRaJTNmzdjtVpZsmSJNs9/iBiNRqqrq+no6GBsbKw4XrZyJXsvuICGxx/n\np8kkZxgMZHQ6UqkUQggKhQKbNm1S5zRHR0dxOp3aG5TGtERp8SsBmvl8HovFQjabVTNxpktBoVDK\nEToROAVo4J323xoaQHHcuPfXv+aceJwxipaMAC0tLaxcuZLHH7+NYPDt0Lsi25g9e0DrThyDCCHU\nsaelS5eyfft2tiWTPAmcD1h++Uvil12mjYRPYw5n5+i6/f5dAIaB3wE3H8bjTiqjo6MMDr79mafT\n6dQOhd/vV7UQOp0OnU5HJpMhHA6Tz+fVZGyHw4HZbH7fJ6ZKMWE0Glm6dKlWTHxAHA4HFRUVDAwM\nYLFYcDqd9FxzDd5XX6U+GOTGRIKbHA71NXO5XHR3d7Nz506qq6sJBoMEAgHNmldjWqJ0O3U6nVpQ\nmEwm4vE4RqNxuugnABBCnEVx3OmjFLV5DwFrgLVTuS6N6Us4HGbW/fcDxWIiQnGcdenSpVRUVPDQ\nQ7fz8Y9fR2enn3B4KS7XZmbPHuDRR++a0nVrHD3KysqoqKhg6dKlPPnkk/T09PBDigXFiVu3smfj\nRk5YtWqql6lxEA6poBBCLALelFIWpJSNR2lNk4rL5cLv908Yd1I83hXfY8XrXQmhs1qtmEymQyoi\nFIaGhti1axdCCJYuXYrFYjniP9PxhN/vJxqNEgqFqKiowFNfzxtf/CKnfe97/GMmw3+nUmygWMQp\nx27atImFCxdiMpkYHR2ltrZ2qn8MDY13MF6QnclkVIcnKAqys9ks08GVVQjRC3iAJymG2T0qpUxP\n7ao0pjvdDz7I4oEBMhQdVKD4fr5w4UK8Xi+1tbWsW/cQ27dvp729naamL2mdiWMcg8GA2+1m3rx5\ntLS00Nvby3NS8jpwYqFA8pZbKKxcqWVSTFMO9VXZTDHJGiFEhxCi4sgvaXKxWCzYbDYsFouqd5BS\nIqVECIHZbMblclFVVUUgEMDr9eJ0OtUU7fdLLpejq6uLHTt2YLFYWLp0qZYzcQQwGAzU1tai0+mI\nRqOYzWbkRz7CruXL0QM/Tyax6PWkUikikQhWq5XOzk7a29vJZrOEw2Eymf0dhzU0pp7xWolEIqGm\nZcPbDk/JZHIql6hwExCQUv4fKeWDWjGh8V7kcjksP/85UFTu91L8m547dy5NTU2Ul5erJ42tra1c\nfPHFWjFxnFBWVkZlZSWLFy/G6XQC8MPSbS1/+Qsj+/ZN3eI03pVDLSjCFLMmoDgfO+PLRJfLRUVF\nBRUVFVRWVuLz+fD7/QQCASorKykvL8dmsx3Upen9EI/HaW9vp7u7m4qKClpbW7U5wCOIw+GgqqqK\neDxOKpXC6/Wy6x//kZjFwoJCgWvTaYQQjI6OYrVaSaVSvPXWW4TDYeLxuJohoqExXZBSksvl1Ped\ndDqtFhdGoxEhBNlsVnWlmypKmolPULSO1dB4Xwy9/DLNb74JvB1k53K5WLx4MT6fD5vNNnWL05hS\nLBYLXq+XxYsXU1NTAxTnJ/cAjnSa0K23vuv9NaaOQy0I/ht4QQjRSTE07vVSp+IdlyO/1KOD1WpV\nU6zHOzwdCQqFAiMjI3R0dBCJRKiurmbOnDlaZ+IoUF1dTXl5OaFQCCkl1YsXs/biiwH4WjrNPIq7\nvMlkEoPBQFtbGx0dHeTzeUKh0LQTt2oc34wXZOdyObLZrCrIVoqMZDI55a1/KWWWd4YFTBlCiGuE\nEJ1CiKQQYp0Q4sDeo28fbxJCfF8I0SWESJU+v66cpOUel0gpSf/gB+go+ssriRJ1dXUsWrQIj8cz\nbUIbNaYGh8PBnDlzmDt3btHlDvhx6baqP/yB9DSwy9Z4J4f0aSSlvBr4G4qvrQB+RXH88UCX45p0\nOs3AwAD9/f3k83l8Ph81NTWaAPgoodfrqaurQ6fTEQ6H8Xg8JP7mb9haXY0Z+Hk6jcznCYfDWK1W\nwuEwu3fvJhKJEIvFpoWfv4aGgiK4VkablDwKRVdRKBRIpVLTJUflD8DnpnoRQoiPU/xs+hawFNgK\nPFXKNDoYDwBnAZ8B5lK0u911lJd6XLJ9+3Yefvhhtj79NLXPPgu8PcpisVhobW3VNtw0ALDZbAQC\nAZYsWUJFRXGy/rcUXX/coRCjv/rVVC5P4yAc8hyPlPJJACHEcuA2KWX0iK9qBqPYzSonqkIIdZxK\nE2AfXRwOh2olW1ZWRsu8eTz9t39L8x13cHo+z+U6Hf8VDuP1epFSsmvXLrq6uvB6vYyOjuJwODQL\nWY1pgdKJGD/apNfrkVKqgux0Oj1dNigMwGeFEOcAG4EJ1bmU8vpJWsd1wJ1Syt8DCCG+AFxIMQ/j\nh/sfLIQ4H1gJzJZShktXawPaR5iRkRHWrLmazs4A4fASvq+7gyX5PK8CL5WO8fl8LFq0SPuc1ACK\nbpsul4sTTjiBhoYGBgYGSAI/pxhsY77tNuSXv4zQxNnTig/8akgpP6MVE2+Ty+UIh8MMDQ0RDofV\nEwK3201VVZX2JjlJ1NbWUlZWxvDwMG63G99JJ/HH+fMB+EE2iz2TIRKJYDabGR4eZufOnYyNjan6\nCw2N6cB4Qbbi8KSw/xjUNGABsAmIUtzlXzrusmQyFlDSciwHnlWuk0UF+zPAaQe520XA68DXhRA9\nQohdQogfCSGmxS/1WGHNmqtZv/4mhoZux5i5jM+lugD4IW6g2F1ubGxkwYIFuN1ubVNHAwC73U5z\nczPz5s1TNae3U9ytqOjqIvHYY1O6Po13opV3h8n4QiIej5PL5dQxBIfDQWVlpZaAPYkYDAZmz55N\nNptleHiY5cuX8+bZZ9NmNuMFvlsoMDQ0hF6vJ5vN0tXVRU9Pj/o6amhMB8ZrJRSHJ3g7Jycej6PX\n66dFho2U8qx3uZw9ScvwAnreGaQ3CPgPcp/ZFDsUJ1Ac5b0WuJTieYvGEWD79u10dgZQZDZX8Wvc\nhNnFXB7hTKCYPaHsRGtibA0Fk8lERUUFS5YsobKy6PkQBO4u3Z79wQ+mbG0aB+Zwgu2Oa5TRpkQi\ngRACvV6vCnsLhQJ2u52KigpNXDYFeL1eKisrGRgYYP78+bQuXsztra38dPNmrioU+F0mw1A8jsVi\noa+vjx07djBnzhw1EX06nKRpHL/k83kKhQJGo7EoYC05PBUKhXcUGdr7y2GhoxjI+kkpZQxACHE9\n8IAQ4ovvZn973XXXUV5ePuG6yy67jMsuu+xornfG0dbWRjhcbFIZyHI9RYeeW/gKBVLA/xAIBFQx\n9jTRBGlMEwYHB4lEIvj9frq7u8nlctwKfBFwrV9P7vXXMZx44lQvc1pz//33c38pQFIhEokclefS\nCopDIJfLkU6nSaVSpNNpdDqdKpQcP+dssVhwuVzam+MUIYSgpaWF0dFRenp6OPnkk1m3bh0Ptbfz\nt9Eov8jnOWd4mNr6elKpFB0dHfT19eF0OolGo6oITENjKjiQINtut5PL5dRuZzwex+FwTJccCoQQ\nK4F/AOYAl0ope4UQVwCdUsqX3v3eR4QRIA/49rveBwwc5D79QK9STJTYQdFwpJaiU+UB+clPfsKy\nZcs++GqPE5qbm3G5nmZoCD7OH6mjmwF83MsVwKcwGo00Nzer404aGjBed+MnFFqM0dgE9ADd7AUe\n1On4RKFA5vvfx/A//zPFq53eHGijY9OmTSxfvvyIP9cHHnkSQtSJAww7iiJ1h7es6UMmk2FsbIyh\noSGGhobUzAIl3C6ZTKoWjwaDQc210IqJqcVms1FXV0cwGMRqtbJkyRLunjuXsBAsBa6IxUinixuQ\nAwMDbN++nXQ6TTgcnnJvf43jm2w2ixBCFV8rDk9KLkUulyOTyUybLBshxEeBp4AkRd2EMuNZDvzz\nZKyhZF+7EVg9bl2i9O9XDnK3l4FqIcT4OZsWil2LnqO01OOK1tZWGhv7ga18raSL/yn/RJqdwCt4\nvV4WLVpEIBDQOsMaKm/rbu4gm/0HEonfk8s9AgQA+PfSZ7TlkUegq2vqFqoxgcPRUHRy4DAjT+m2\nGYGUknw+TyaTIZVKEY/HiUajhEIhBgYGGBkZIZFIYDKZ8Hg8VFVVYTabicfjJBIJdDodQghMJhOV\nlZWa5d00Ys6cOTidTvbs2cPJJ5+Ms7mZW0q7YN/O59ENDSGEIB6P09bWpupgpsuur8bxyXhBdi6X\nQ0qpClWNRiPxeBwp5XR6r/lX4AtSys8D2XHXvwxM5jb+rcDnhRCfFkLMA/4TsFF0nEQIcbMQ4nfj\njr8PGAXuEULMF0J8iKIb1N1a2veR47HH7uLLLV9kEW8Qxcx/8jLwEYQYpK6ujgULFmhdYQ2V/XU3\nb7MEOB0o+kE/q9ejKxTI3XILGtODwykoBMVwu/2xAzPGLmdkZITBwUFGRkYIBoOMjY2RSCTI5/OU\nlZXh9Xrx+XxYLBYSiQSDg4NEo1F0Oh06nY5CoaCKrw8nTVvjyKPX62lubiaRSGA2m1m+fDlP1tWx\nxWCgHPhmJKJ2KYaGhnjzzTfJl7IqNDSmiv3D68Z3O5WCwmAwTCezhxbgrwe4PgK4JmsRUso/AV+h\n6Cy5meIZyXlSyuHSIX5g1rjj48C5pTVuAO4FHqYoztY4Qni9Xm4JFLsPvxEZIjwGDBStvVtaaGpq\nwuFwTO0iNaYN43U37+RM9bsfljSr4je/gVDo6C9M4z055DNgIYSSey6B7wohEuNu1gOnAFuOwNom\nBUU8rbinjLdnzGazJJNJgsGgKpK02WxIKdVQKU14Pb2pqanB5/PR2dnJsmXLeP7557lpeJiHenu5\nrFDggaEhOm02YrEYO3fuZNGiRVgsFqqqqrTXVWNKyOVy6vtMMplUCwe9Xq921KaZG84A0AR07Xf9\nCqBjMhcipbwDuOMgt33mANftBs472us6rtm6FePzz5MDfiLf3oOsrKxkwYIFBAIBzSpWQ2W87mZ/\nDIaXKBSKG7lPA9sNBlqTSbjrLvj61yd9rRoT+SAdCsVfXAALmeg5Po9iN+rKI7S+o47VasVgMKhF\nQjQaJRwOMzw8zPDwMIlEAqvVit1uRwhBIpEgnU5jt9uprKzUTjpnACeccAL5fB6z2cySJUvY43Zz\nT+kk7QeRCJlYjEKhwODgIB0dHaRSKWKx2Hs8qobGkUcZcTIYDGQyGXX8SdnQKBQKJJPJ6TTuBPAr\n4DYhxCkUN5qqhRCfAm4BfjmlK9OYcuSPfwzAIyYTe0vXmUwm5syZw/z587VxJ40JvK272bbfLVvx\neHZO6Gb9uFSgFm67DTKZyVukxgH5IEnZZwEIIe4BrpVSjh3xVU0iw8PDDA6+bV2u0+kwGAwYDAZs\nNhu5XI5kMkmhUMBsNuN2u7FYLNqOygyivLyc2bNn09bWxpIlS3jllVf4RTTKmr17mScllw0M8LDL\nRTwe56233qK1tZVQKITL5dJeZ41JRTF4MBqNxGIx8vk8FouFfD6PyWQinU6Ty+WmW0Hx7xQ3p56l\nqFn4K5Dm/7N35uFxluX+/zyzT2bPzGRrszdJE1raAgdaXBBcQE4FFNSDHlFQcQGP4L4D8gMXRFDE\nBVdED4oeFlmLQCnQ0o2WljZpm7ZpuqVJJpnJTDL7zPP74828JG1Zk3TS9vlc13uZvsvM/XoNM+/9\n3Pf9/cJPpJS3FTMwRZHZtw9GJSt/PKpeBppHU1tbG7NmzVLD2IpDeOihO3SVp0hkAV7veqqr93DB\nBRfx5z//WZc9vSuX4wcmE2U9PfC3v8EllxQ58uObiTplH9XJBLzc8hQIBPD7/bjdbqwuCoewAAAg\nAElEQVRWK1JKhoaG9ApFWVkZfr8fu92uHjKPQmbPno3NZsNqtdLc3EzW5eKa0baRq4eH8UQiSCnZ\ns2cP+/fvZ2RkRJ+vUCiOFAVjTIPBQDqdRgiB2Wwml8uNm5+YJg7ZgOZILaW8AU2QYw6wEAhKKb9b\n3MgURee22xDZLGtsNlaNUc+bOXMmra2tVFS8kueg4ngmEAiwcuW9LF16Jf/4RzlLl17JypX3cdJJ\nJ41LQjPAr0ZnzOTNN4M83Fiv4kgxIadsIcQ7hRA3CiF+J4T4w9htsgKcapLJJIODg4RCIQYGBohE\nIoyMjJDP5/F6vVRUVODxeNTA9VGOzWajpaWFeDxOW1sbXq+XR/1+ngEcwJf37CGfzzM8PMymTZuI\nRqNTZv6iULwShYHsgqFdQUUOXh7Itlgs0+r7SAhhF0KUSCnTUsp2NHfqTwkh3lPs2BRFZHgY+Zvf\nAPDjMcmE3W6nsbGRlpYW3G53saJTHAW0tbVx3nnn0dbWhslkYtasWcyePXucqeTPUikSRiNi40Z4\n8klAU4p64IEHaG9vL1boxyUT8aG4BngcTec7APgO2iaEEOIKIUSXECIhhFgphPiP1zjfIoS4QQix\nSwiRFELsFEJ84rXex2Qy4XK5KC0tJRgMUllZSUVFBYFAgJKSElWNOIaoq6vD6/XidruZMWMGTpeL\nr5SUkAHem0oxf+9epJRs27aNUChEOBxWnhSKI0omk8FsNpPJZHSH7LFJRTKZxGazjROPmAY8AFwC\nIITwAquALwMPCCE+V8zAFEXkD39ARCLssdv5vzH97V6vl9bWVhoaGqbb51gxzamsrGTmzJmMjHiA\nC4HbCHMhf5Bat0H6Bz9g4cIPcOaZt/OhD/Vx5pm3s3DhBwiFQkWN+3hhIstcnwU+IaW8a7KCKSCE\n+DBwM3A5sBq4GlgihGiWUr7SJ+MfaL4Yl6I5nFbyOhIml8s1bQyiFFOLxWJh9uzZDA4OUltby+7d\nu+lyu/lZIsFXpOSre/fymdpaIpEIW7dupbKyUnckViimmoInjtlsJpVK6dLV+Xx+3JB2aWlpsUM9\nmJPQvqMBLkKrUCxA+8X/Pmow+/gjm4VbbgHg5yaTri9vMploaGigubmZYPBwNlYKxSvjcDj4/e8f\nIR7/P172qbiSm/MP8Vneh+Wppxjmn/RxIQB9fdDXt5HFiy9n5cp7ixb38cJElgcsvLID6US5GviN\nlPLPUsotaMlLHLjscCcLIc4B3gacK6VcKqXcLaVcJaV8foriUxylVFZWUl5eTjAYxO/34/P5+KHF\nwh6gNpfjA1u3ks/n6ejoYGBggIGBgWKHrDhOKAxkF5IH0JLggkN2YaZnrP/ENDFhLAFio3+/B7hX\nSpkHVgK1RYtKUTzuuw927WLYZuM38ZeV5W02G7NmzaK1tRW73V7EABVHI1u2bKG3dxYHm951sZj7\nRTkAX+Lhg646ka6uCtX+dASYSELxO+AjkxVIASGEGTgZTTEE0Ib+gCeARa9w2fuAtcDXhRB7hRBb\nhRA3CSGmz+SiYlpgsVhobW3Vkwqfz0e+pIQvj5beP97by8xEgr1797J3716Ghob0hzuFYiopJBRG\no1FPHiwWi94GlUqlMJlM46Sqp0lCsR24QAhRjebp8Pjo/jLgqBfuULxBpIRR9+K/uN3ERg3IQBvG\nbm5upra2VrUTK94wnZ2dxGInHfbYTfI0AD7KX6mgZ9yxSGQB27dvn/L4jncmklDYgC8JIZYJIW4T\nQvx07DaB1w2gGeT1HrS/F83p9HA0oFUoTgAuQHM6vQi4fQJxKI5R/H4/DQ0NBINBXC4XgUCAf5lM\nPC4EVuBzHR1k0mna29vp7+8nFou95msqFBOlUInIZrP6/ERhQFsIQTKZHDeQncvlyI15WCsi30fz\nnNgFrB5TGX4PmmO14nhi+XJYvZqsycQPx3x3Wq1WampqaG1tHTdUq1C8XjTTu8P7Jq8izvMigJU0\nV/KLcce83vXMmjXrSIR4XDORGYoTedkRe85Bx460dpcByAMfkVIOAwghvgT8QwjxeSnlK+p/Xn31\n1Yd8uV188cVcfPHFUxmvoogUTJUaGhrYsmULpaWl9Pb2clU+z/psltOjUc4YGODFnTvp6emhpqaG\n0tJStaKmmFIKlYjCrITT6USOyiDmcjnuv/9+HnnkEV0yNpvNMjg4WMyQAZBS/lMI8Rza3NqGMYee\nBO4rTlSKojFqZLesupruri59t9vtprGxkebm5nFtewrF60UzvTtAX98GYN6YIy8Cm/iZycCiDHyO\nX3Ej3yKOA9hIQ8MB2traihP0ccSbTigKBndTQAjIAeUH7S8HDrzCNT3AvkIyMUoHmpv3TLQh7cNy\nyy23cNJJhy+hKY5dSktLmT9/PqtXr2ZkZITS0lJ2ZTL8JJ/n2/k8n+/s5DK/nx07dlBXV8fMmTMp\nGfWtUCimgmw2i9Vq1VvsrFYrmUwGg8FANpvlvPPO4yMf+QhlZWUAhEIhXnrpJc4666xihg2AlPIA\ncECMMupNsbrYcSmOMJ2d8MADANwwph3PaDRSXV1NS0uL8p5QTIiHHrqD//zPT7F1q59o9CSkXAos\nBw7wL2Fhl8lGXTbMp4yf5m9+Lw0NB3jwwTuKHfZxwUR9KN4mhPiLEGKFEGLG6L6PCSHe+mZfU0qZ\nAV5Ak6MtvI8Y/fcrDYEvB6qEEGOf+FrQqhZ732wsimMXi8VCbW0tzc3NGI1GAoEABoOBHxkMdAlB\neTrNR7u62L59OwcOHCAcDhc7ZMUxTC6XI5/P67MSoH1G0+m0/r8Gg0Gfn8jn86TT6enS8oQQ4pNC\niE1AEkgKITYJIT5V7LgUR5hbbwUp6WxpYcWY78ySkhLq6+tpa2tTqnmKCaGZ3t3HXXddwHvf+wiB\nwDIKa82JdJolrU0A3BhcytInPsfzz99LIBAoYsTHDxPxobgQWAIk0GQDCzVMD/CtCcb1U+DTQohL\nhBCzgV+jKYn8afS9fyCEuHPM+f8LDAB/FEK0CiHeDvwY+P2rtTspjm98Ph8LFy7E6/XidDpxu92k\njUauGm1t+tDevdi7uti9ezf79++fNg9vimOPbDar/51OpzGbzXr7kxCCfD6PwWDQ5yeSySTAtPBJ\nEUJ8H/gZ8CDwwdHtQeCW0WOK44FQCP74R0CTii0kxqBVhJuammhoaNBdjhWKN4sQgtNPP53FixfT\n0NAwzujzx319ZFwuHAcO0LZtWxGjPP6YSIXiO8BnpZSfRnNAL7AcLcF400gp7wG+gjbstx5tXuNs\nKWX/6CkVQPWY80eAdwNeYA1wF5rZ0hcnEofi2MZqtdLc3ExjYyMWi4VAIIDJZGKJ2cxDBgMmKfn8\n5s10btvGvn371HC2YsrIZDIIIcjlcmQyGSwWC1JKfYYin89jNBr1CkUymcRgMOjHi8zngE9LKb8p\npfzX6PZNNB+hzxc5NsWR4uc/h0SCocZG/rL35cYAs9lMdXU1s2fPxu/3FzFAxbGEy+Vi9uzZ1NbW\njmtH7g6FWDFvdL7ihz/UVMcUR4SJJBQtwDOH2T+E9mA/IaSUv5RS1kkp7VLKRVLKtWOOXSqlPOug\n87dJKc+WUjqllLVSyq+p6oTitSgtLeXUU0/FZrPh8Xhwu90A/I+UxIEFQ0M0rVnD3r176e09WHhM\noZgcxg5kSyl1uViDwaAnE0IIXfUplUpNl2QCwIwm230wLzAx4Q/F0UI0CrfdBsA9jY0MRV9WC3Y6\nnTQ2NjJ79mxlIquYNApty42NjeNamnK5HNdHo+RtNli7Fv797yJGeXwxkYTiAHA4Ha63Ajsn8LoK\nxRHDarUyf/58Zs6cicViwe12YzQa2WM08uPRMuqlmzbRu20bu3fvVp4Uiikhm83q8xNCCKxWK6lU\nSp+fEELoCcXYZGKaqOXchValOJjLgb8e4VgUxeBXv4JIhHRDAzd3dY1LdsvLy2lqaqKysnJca4pC\nMVH8fj9z586lpqZmXCvd89u3s/Odo2O4N95YpOiOPyaSUPwW+JkQ4jQ0mdgqIcRH0fTIfzUZwSkU\nR4LKykpOPPFEHA4HHo+HkpISjEYjP87n2WE0UprJcPqSJezatUs5ZysmHSmlPkORTqf11qZC8iql\nPKTdSQihVzKKwUGeQxL41Ogg9u9Gt5eAT6MJYxzJuK4QQnQJIRJCiJVCiP94nde9RQiREUKsm+oY\njzkSCfipZj21/O1vp3vPHv2QzWajrq6OtrY21e6kmHScTidNTU3U1tbq3QUA8Xicn1ssSLMZli3T\nvFEUU85EEoofog1DPwk40dqffgf8Rkp52yTEplAcEaxWKyeffDIVFRV4vV7cbjcmk4mMwcCXRh/Y\n3rNtG5k1a+g6aPVNoZgouVxOn5cozE+MxTDq4j52ILtgdlfECsWCMdtctPamfqBxdAsB69DMRo8I\nQogPAzcD14zGtQFYIoR4VYkXIYQHuBN4YsqDPBb53e+gr49cTQ037d2rCwaA1lJaX19PY2Ojkt1W\nTDpGo5GKigpaW1spLx/vNPCvdesIv+992j9uuKEI0R1/vOmEYlRn/AagFM3YbiEQlFJ+d7KCUyiO\nFHPmzGHmzJm4XC6cTic2mw2j0cjD6TQP2e0Ygffcfz9dO3ao4WzFpJLJaJoWhcSi0OZkNBrJZrPY\nbDa9JSqdTuvKTlartWhmi1LKM1/ndiRNMq5GW9D6s5RyC/BZIA5c9hrX/RqtNWvlFMd37JFOw003\nAbDzwgtZ8+LLLsYmk4ny8nLa2toIBoPKGFQxJfj9flpbW6mpqcHhcOj7e3p6uKe+HmkwwKOPwvr1\nRYzy+GAisrE1owZGaSllu5Ry9RiX6prJC1GhmHpsNhsLFizA6/USDAax2Wy6is7XzWbiBgOz+vvx\n3Hsvu3fvLna4imOITCajJw+gDRsWZikKiYSUEpPJNG7112azEQqFihX2IQghAq9VDZjC9zYDJ6NV\nzAFt0Qut6rDoVa67FKgHrpvqGI9J/vIX2LOHfEUFvxgZGefcXlJSQk1NDa2trfh8viIGqTiWsdvt\n1NfX09DQMK6tLp1O86fnniNRqFKoWYopZyItT11A8OCdQgj/6DGF4qhi0aJF+P1+AoEAbrcbm82G\nyWSiM5HgVq8mXHbmo4/StWrVON8AhWIiZLNZDAaDbl5nMpnIZDK690RhZddsNuvtTqAlFJFIpJih\nI4TwCiFuF0KEgF6gVwgREkL8QggxYbW/N0AAMI7GMJZeNJnxQxBCNAE3Ah+VUhbf0ONoI5fTZDmB\n/o99jMeefnqcL0ogEKClpYWZM2dis9mKFaXiGEcIQVlZGSeccALl5eXjBv87Ojp45q2jPsv/93/Q\n0VGkKI8PJiK5INCG8Q7GieaWqlAcVfj9fubMmUM4HCYQCBAOh0kkEqRSKW7J57nQbqclkaDx9tvZ\n/+53U1OjCnGKiVNoeUqn0+PamPL5PCUlJeRyOV0+tpDIWq1WEokEIyMjRYtbCFEKPA/MQGsZKvxa\ntwGfAN4phDhdSjntbOaFEAa0mK+RUu4o7H6911999dV4PJ5x+y6++GIuvvjiyQtyuvPPf0JnJ7K0\nlL86nXR3d+uHrFYrVVVVnHDCCQSDh6w7KhSTis/no7m5merqanbu3KmLp0SjUX793HOcde65WB55\nBH70I/jTn4ob7BHm7rvv5u677x63b2hoaEre6w0nFKOqHqAlE9cLIeJjDhuB04AXD7lQoZjmCCFY\ntGgR69ato7Kykp6eHhKJBNlsllgiwXcrK7l71y7a1q9n7V//SvU3vqH6ghUTIp/Pk8vl9M9RwX+i\nMHRtt9sZGRk5bHUiHA4XW8b4e0AaaJRSjqsMCCG+Bzw+es7VRyCWEJADyg/aX44mcX4wLuAUYL4Q\n4vbRfQZACCHSwHuklE+/0pvdcsstnHTShPxbj26k1FtIopdeyt8ffnicM7bX66WmpobGxsZx6jsK\nxVRgsViorq6mubmZTZs2MTg4qH9Xrlq1is3XXsuCRx7RWvSuvRbq6ooa75HkcAsd69at4+STT570\n93ozLU8FZQ+Bpu4xVu1jNpqyxicmKT6F4ohSV1fHrFmzcLlc+P1+fTg7nU6zLB7nH2VlADTdfDPR\nA4d7TlEoXj+FikOhVaQwP1GoSlitVrLZ7Lj5iYK6U39/P3a7vWixAxcAXzk4mQCQUh4Avga8/0gE\nIqXMoClNvbOwT2hZ2juBFYe5JIomJjIfmDe6/RrYMvr3qikO+ejmoYdg40ak08njLS1s2bJFP2Qy\nmQgGgzQ3NzNjxgzlPaE4IgQCAb3taWyLXX9/P7/dsIHcWWdpbXo//nERozy2ecMJRUG9A01m770H\nKXqcLaX8jJSyc/JDVSimHpPJxMknn4zL5WLmzJk4HA7MZjMGg4FoNMrPgkH6rFY8AwMMf+1rxQ5X\ncZRTcMYuJBSF4ex8Po/NZtM9KgpJbUEFKpVKEYvFDmm7OcJUAptf5fgmXmF+YYr4KfBpIcQlQojZ\naAlCCfAnACHED4QQd4KuUtg+dgP6gKSUskNKmTiCcR9dSKnLcCYvvZQ//+tfRMc4Y9tsNsrLy5kz\nZ47ynlAcMTweDw0NDdTX14/7Xszlcjz55JPs/u//1nb84Q/Q01OkKI9tJiIbe6mUMiqEaBNCnCOE\nOG/sNplBKhRHkra2Nt2TorS0FLvdjsFgIJvNsicS4Se1tQBU/u//knnhhSJHqziaKbheZ7NZ3X8i\nk8lgMpmw2+16BSObzeptUDabjcHBQbLZLF7vkZx7PoQQUPcqx+uBwVc5PqlIKe8BvgJ8H1gPnAic\nLaXsHz2lAqg+UvEcsyxdCqtWgc3GC2ecwZo1a/RDBoMBh8OBz+c7RMZToZhKjEYjVVVVNDc3U15e\nPs6jp7u7m//dtw+5cCGkUroRo2JymYhsbL0QYgPaKtTDwP2j232jm0JxVOJ0Opk7dy4ul4vKykoc\nDgdGo5FcLkckEmGJzcYzgQCGfJ7sZZdpZVSF4g0ipSSdTiOEIJ/PYzabyeVyeouT1Wo9xKNCCIHF\nYqG/vx+n01nslqclwA1CiEPsuoUQVuB64LEjGZCU8pdSyjoppV1KuUhKuXbMsUtfzRdDSnmdlPI4\nHox4nYxWJzIf/zh/evRR+vsL+Vol+fwH6Ov7Lo895uAzn7lunIysQjHVFDwpysrKKC0t1fenUin+\n9eCD9H3609qOX/0KRge3FZPHRGRjf44mD1uGZh50AvB2YC3wjglHplAUCYPBwNy5cyktLcXn8+Hz\n+fSkIpVKEQ6Huam6mrjJhH3jRuSvflXskBVHIYUWpoITttVqJZVK6e1O8LJHReHcgqTs0NAQwWCw\n2P3p3wNagE4hxNdGq9PnCyG+AXQCrWiu1YpjhWefhaeeApOJreedx7Jly0bb9SrRcsd/IOUVDA//\nifXrb2Dx4suLHLDieMLpdOpiAD6fb9yCS0dHBw9kMsh582BkBG6+uYiRHptMJKFYBHxPShkC8kBe\nSvkc8E20ZEOhOGopKyujqalJN7pzuVwYDAZyuRzhcJjduRy/ra8HQH7zm7BvX5EjVhxtFMzrxs5P\nJJNJjEaj/kNYUM7J5/O66lMoFEJKWfT+dCnlXrTfgXbgB7xcob5hdN9bpJR7ihehYlLJZuHKKwHI\nf+IT/G3FCvbp33uno3WYjWUeXV0VtLe3H8koFccxQgi97amsrAyXy6Ufi8Vi/OvBBxm66iptx803\nQ6ca951MJpJQGIHY6N8hoGr07260VSuF4qjFarXqDq9+vx+PxzOuSjE0NMTdHg+dfj+G4WH4wheK\nHbLiKCOZTCKEIJPJYLFYkFKSTCax2WxYrVa9/akwtG0wGDCbzYRCIZxOJ06ns9i3gJSyS0r5XjRj\nuYWjW1BKeY6Ucntxo1NMKr/8JWzcCD4fOz/5SZYsWUIiUZhdf8dhL4lEFrB9u/oYKI4cBU+KmTNn\nEggE9Nk0gLVr1/Kk0wlnnw3pNPzP/2giA4pJYSIJxSY0eT3QJPa+JoR4C1oZfOdEA1Moik1NTQ0z\nZszA7/fj8/lwu92YTCaklJrpXTrND+vryRkMcN998MADxQ5ZcZQwNlkotDIVjOtKSkoAdJnYgxWg\notEoZWVleqvUdEBKGZZSrh7dVOP8sUZvL3z3uwDIG2/kgeXL2bZt25gTlh32Mq93PbNmzToCASoU\nGna7nbq6OpqamvR25QKhUIh/PfggsRtvBIsFHntM/W5PIhP5Rfp/Y67/Hpqix7PAucAXJxiXQlF0\n3G43zc3N+P3+cVWKfD5PIpEgHA6zEXioZbQgd+WVEIu96msqFPCyulOh8mCxWBgeHtbVnQrnFJBS\nYrfb6e/vRwhR9HYnxXHG174G0SicfDJ7zj6bJUuWjJOKNZtXc6if7UYaGg7Q1tZ2RENVKMrKymhp\naaG8vJxgMKgbh+ZyOZYvX87KgQH4yle0k7/4RYjHX+XVFK+XicjGLpFS3jv693Yp5Wy0sneZlPLJ\nyQpQoSgWRqORuro6ysrKCAQCuFwuPB6P7g8QjUZJp9P8KhBgyO+HvXvhO98pdtiKo4DC8LUQAqPR\niMFgIB6P6+1OoA1tF6oWJpMJo9HIwMAADodjXG+wQjGlPPcc/PnPIATy9tt58umn2bRpk37YZDIx\na5aDQODjeDyfxGK5g7Kyz7Nw4bU8+OAdRQxccbzi8/mor6/Xh7PHfl/29PTw8MMPk/zSl6CmBnbv\n1l3fFRNjUmvmo6XuGUII9S2iOCYIBoPMnDlTl6Hzer3Y7Xby+TwjIyOEw2EGEgl+s2CBdsFtt2ka\n7QrFq1BIKAC9dSmVSumGTOl0mlwup1cwCj4osViMslG3doViyslm4YortL8/9Sm6gkEef/zxMVKx\nWiW3rKyML37xg9x553ncc085S5deyfPP30sgEChS4IrjGZPJRFVVFS0tLQSDwXEV3Xg8zr///W9u\n+uUv2fPlL2s7b7pJDWhPAlPRhOsHPjkFr6tQHHHsdju1tbX4fD7Ky8vxeDx4vV590CsWixGPx3ks\nl2P7okXagNfHPw4JZbSrODzpdFo3rDMYDAghSKVSGAyGce1OqVQKk8mk7+/v78doNKp2J8WRozCI\nXVpK6pprePTRR1m/fr3++QWtvSQYDNLS0sJb3/pWzj//fNXmpCg6ZWVlzJo1i+rqanw+nz6bBpV0\ndJzAtdcGOOn7m3neU6YNaH/hC2pAe4JMn6m+gxBCXCGE6BJCJIQQK4UQ//E6r3uLECIjhFg31TEq\njn0KMnTl5eVUVVVRWlqK2+3GbrcjpWRkZIRYLEY0GuWXzc1ky8pg61b41reKHbpimpJKpUin05jN\nZkBTFIvFYuPanVKpFLlcTq9eGAwGwuEwLpdr3JChVD+AiqniwAF9EJsbb2T1zp08/fTTY6RiwePx\n4PP5qKuro6GhAZ/PV6RgFYrxWCwWamtrmT17tt5dUPBLkfIe8vnPERr4DZcM/Za0MMCSJXD//cUO\n+6hmWiYUQogPAzejmSItADYAS4QQr1o/FUJ4gDuBJ6Y8SMVxg8fjoba2Fo/HQ1lZmV6lMBgM5PN5\nhoeHSSQSbNy7lxWXXaZddOut8PTTRY1bMT0ptDsVPj9ms5l4PK63OxXa6QqtTgaDgVQqxcjIyLgB\nQ9AqZNMBIUReCNF+0L4OIYSykT9a+frXtUHsU06h//zzefjhh+no6CA+ZoDV7/dTWlrK7NmzmTlz\n5rRSHlMoysvLaW5upqKiYnSO4i0c7JeynfO43T5f+8dVV6kB7QkwXf/rvxr4jZTyz1LKLcBn0dy4\nL3uN634N/BVYOcXxKY4jLBYLFRUVlJaWMmPGDILBIF6vF4fDMa5KMTg4yL2JBLH/+i/twksvVapP\ninHk83ndfwLQfSjy+bw+OJhOp/UB7YJr9uDgIEajcXSV7eXXGqsEVWQuQzM1Hcu3eO3vbMV05Nln\n9UHs/G238e+nnuKFF15g3759+uxPSUkJfr+fGTNm0NzcrOYlFNMOu91OY2MjLS0tGI1G4IzDnndd\n5lLiwaAa0J4gbzihEELc+2obcMtEAhJCmIGTAV0pSmp1/SfQXFlf6bpL0aRrr5vI+ysUh6OsrIwZ\nM2aMq1L4fD59lTkWi5FMJuno6OCJc85B1tbCrl0vS9MpFGjJQjKZ1Gdw7HY70WgUq9WKzWYDtKHB\ngrJTPp/HZDIRDofxer1j+oAZt1JcbKSUfwK+IIS4SwjxSSHELCnlfVLKO4sdm+INctAg9kt2O088\n8QSdnZ0MDQ3ppxXaP9va2qivrx99YFMophcVFRW0tLRQWVmJ0fjc4U8qWUfo29/W/lYD2m+aN1Oh\nGHqNrRv48wRiCqC5cPcetL8XqDjcBUKIJuBG4KNSyvwE3luhOCwlJSWUlZXhdruprKzU/y4pKdFX\nnUdGRhgYGOCptWs5UFjluOMOzTxHoUAzqys4YxeqD9FoVG93AohEIvp8hc1mIx6Pk8lkKC0tHddS\nMjIyoich04RLgaVoy4BPCiH2CiH+KoT4iBBiulbDFQfz05/CSy9BaSnRb3yDxx57jI6ODvr7+/WZ\nHYvFQjAYpLKyktbWVqU8ppi2OJ1OZs+ezfz587Hb13OoX8qL2GzrSZx9NpxzjjagffnlkFePkm8U\n0xu9QEp56VQE8mYZ/aH6K3CNlHJHYffrvf7qq68e92MOcPHFF3PxxRdPXpCKox6j0UhlZSV9fX3E\n43GCwSA9PT243W4SiQS5XI7h4WGcTidbtmxh6aJFfPgLX8B4223wyU/Cpk2gBhaPe+LxuN7uVJCC\nzWaz+ndQNptleHgYt9tNLpfD7XbT3d2NyWTi8ccf57777gO0dqdCa9R0QUq5B/jD6IYQ4gS0hZ5P\nolUvzpVShosYouK12LRJH8TO//jHPNvRwfPPP8+OHTuIx+N6RdbhcIz6T8yiublZr7gpFNMNIQQz\nZ86ktbWVRYs28swz7yeVOgl4B/A0BsMq6upm8sSTT1Jz003Yn3lGm3+8/XZN+aEwNyUAACAASURB\nVEnxunnDCcURIATkgPKD9pcDBw5zvgs4BZgvhLh9dJ8BEEKINPAeKeXTr/Rmt9xyCyeddNKEg1Yc\n+3g8HkpLS+nt7aWiooLBwUHC4TCRSIRkMkkqlSIejxMKhVi+fDmnfuYzzFqyBLZtg//5H7jrrmLf\ngqKIZLNZRkZGsFqtuvP1wMAAZrMZp9MJQDQaJZfLYbFY9JanoaEh3G43l1xyCZdeqq3nDAwMIKVk\nw4YNvOtd7yrmbekIIU5Gazt9WEqZkFJuFkLcLaX8mxDibcBX0eYqFNORTEaTvE6nYfFidr797fz7\n9tvZunUrkUgEgHy+AiHeQiRyBhs3riASeYrLLlNjMorpjdfrZe7cuaxevZpYLMb27c8wOHj/6DyQ\nge7uHM899xytra2c+aMfIb7wBU2U4JxzoKmp2OEfNUy7MrSUMgO8ALyzsE9oS3rvBFYc5pIoMAeY\nD8wb3X4NbBn9W7mMKSYFq9VKRUWFbnIXDAYpLS3F5XIhhCCXyxGNRkkkEmzZsoUVL75I+re/BYMB\n/vIXuPfeYt+CoogUvCUKw9YWi4VIJDLOVyISieirvU6nk1AoRDabxev16j3q2WyWVCqFEIJ0Ol2U\ne3kFrgQuArqFEP8QQvwAuABASvks0FHM4BSvwQ9+AOvWgc9H/NZbeWrpUjZu3Mi+ffvIZDJokpuP\nIuU9SHkFqdRf6ez8OR/84BeLHblC8aoIIaivr2f27Nk4nU4aGxtxuVx6xS0SidDe3s6TTz7JnsWL\n4ayzNC+pT3wCckqo7vUy7RKKUX4KfFoIcYkQYjZaglAC/AlACPEDIcSdoA1sSynbx25AH5CUUnZI\nKZXDmGLSKC0txefz4fF4CAaDzJgxg9LSUkwmE9lsVk8q+vv7ee6559hWWqqtdAB89rPQ11fcG1AU\njeHhYUD7cTOZTMRiMfL5vK6Ok8vlGBoawmq16oZ30WgUh8OhK0CBNjtRUIeaZgnFWuAKoBH4J9r3\n8DcBhBA9QMNUB/BG/IuEEO8XQjwuhOgTQgwJIVYIId4z1TFOS9avh+uvByB/222s3bePZ599lo6O\nDhK6Seehkpswj66uCtrb21EopjN+v5958+bpoir19fX6TFoymWTXrl2sW7eOZ5cvJ/nLX4LLBStW\nwC0T0hk6rpiWCYWU8h7gK8D3gfVo32JnSyn7R0+pAKqLFJ7iOMZmsxEMBnX99WAwSCAQwOl0YjAY\nkFKSSCQYHh6ms7OT559/nvhXvwpz50J/P3zuc8qN8zhESkk0GsVmsyGlxGazEQqF8Hg8ekUiEokg\npcRkMlFSUkIoFCKTyeDxePTh68Lnq/B6JtO06lr9FdpAtpBS/l1KeYuUsmv02DtHj08Zb8K/6O3A\n48B7gZPQBsofFELMm8o4px2pFFxyiabudOGF7PiP/2DZsmVs3ryZSCQyxhX78JKbkcgCtm/ffuTi\nVSjeBAaDgdbWVhobG7Hb7dTV1REIBPSkIh6Ps3nzZpYtW8YLoRD5m2/WLvzOd0AlzK+LaZlQAEgp\nfymlrJNS2qWUi6SUa8ccu1RKedarXHudlFINRigmHSGE7kNRmKmYOXOmrsCTzWYxGAx6leL5559n\nS1eXpuluMmltT3/9a7FvQ3GESafTJBIJfX4il8uRTCbHafdHIhGEEPr8RCQSwWazUVJSoqs+JRIJ\nstksiUSCfD5/iKBEMZFS5qWU90opo4c51i6lnOry3BvyL5JSXi2l/ImU8gUp5Q4p5beBTuB9Uxzn\n9OK667Rh7GCQ0PXX89zy5axdu5adO3eSTCYBTZTilSQ3vd71zJo160hGrFC8KQKBAAsWLCAYDGK3\n25k9e7a+WJPNZhkcHGTz5s0888wzbH3LW5Dnnqsl3B//uJZwK16VaZtQKBTTlZKSEn1+IhAI4PP5\nqKiowOl0ks/nyeVy5HI5QqEQnZ2drFixgmhDA1xzjfYCV16peVQojhuGh4eRUmI2mzGbzYTDYWw2\nm97KlEwmicfjWCwW7HY7g4OD5PN5SkpKcDgc+uuMjIzortl+v3+cL8XxzJv1LzroNQSayMfgVMQ4\nLVm1Cn70IwCSt97Kis5Oli9fTnt7u64gZjQaR2WyX+JQyc2NNDQcoK2t7cjGrVC8CcxmM/PmzaOh\noUHvNqipqdEXbJLJJJ2dnaxevZpnn3uO3uuvB68X1q6FH/6wyNFPfyaUUAgh8kKI9oP2dQgh1BSL\n4pilUKXweDz6LEXBm8JsNpNOpzEajcTjcXp7e1m9ejWbN29Gfv3rcNppMDQEH/qQtvKhOC4ozEZI\nKTEYDMRiMYLBoC4hWzBGtNlsesJhtVqxWq160lCQiY1EIjidznHD3Io37l90GL4KOIB7JjGu6Usi\noa285vPkL76Y1TNn8vTTT7Nhwwb6+vpGB7G1h7Dm5mbOOKOZ8vLLcLsvw2K5g7Kyz7Nw4bU8+OAd\nRb4RheL1U1VVxWmnnUZ5eTkmk4nGxkbcbjegzbHFYjG2bNnCM888w4pdu4iPJtxcdx28eHBCPXHa\n29t54IEHjok5pIk24F4GRA7a9y3APcHXVSimNU6nk0AgQDgcxufz4ff7qaqqIhaL0d/fTy6Xw2Aw\nMDg4yM6dO3n22Wdpamoi8Pe/w4IFsGaN5qJ9223FvhXFFFP4kXK5XEgpdT1/r9erH49GtS4hp9PJ\n0NAQ+Xweu91OSUmJ3uM7MjJCKBTCZDIxY8aMcSZ3iokhhPgI8F3gPCll6LXOPyb8i779bdi6FVlV\nRccVV/DM0qWsXbuW7u5ufRDbaDRSXV1NMBikpKSEr3/9Y8yePZt0Ok1T05WqMqE46rDZbJx44ons\n37+fcDhMNpulsbGR4eFhXYmvp6eHjRs3aq3NF1zAmeefj+GBB7QEfM0amATflVAoxOLFl9PVVUkk\nMh+v93Hq63t46KE7xrXCTpS7776bu+++e9y+sY73k8mEEgop5Z+EEDcJIeqBp6WU66WU901SbArF\ntMVgMBAMBjlw4AClpaX4/X4qKiqIxWIMDQ2RSqUoKSkhnU6zf/9+NmzYwLp163jXu96F4a67YPFi\n+MUv4K1vhQ9/uNi3o5hCCt4SBfWmcDiMx+PRy+wjIyMkk0m93am7u1uvVBTanfL5PL29vSQSCWbP\nno3Vai3mLU1H3qh/kY4Q4r+AO4CLpJRLX8+bHS3+Re3t7XR2dtLU1DT+4f/JJ+HWWwHou+EGlm3Y\nwLPPPsvOnTsZGRkhl8shhMDtdjNr1iw9iT3ttNM49dRTp5sYgELxhvD7/SxcuJCenh4SiQRVVVUc\nOHCAPXv2IKUknU6zb98+1qxZQ2lpKWVf+AJzli9HbNyoJeI33TThGBYvvpxVq66loJzW1wd9fRtZ\nvPhyVq6cPIn5wy10rFu3jpNPPnnS3qPAZCxxJdH+H/nNqPze/UKIq4QQ8yfhtRWKaYvL5dJbnwKB\nAH6/n5qaGvx+P1JKstksZrOZUCjEjh07WLFiBfv27YP//E/4xje0F/nUp2Dr1uLeiGJKGRoawmKx\nIITQnbFLS0sB9IpFIpHA4XAQi8WQUlJSUoLNZtMf3KLRKD09PbqqWIGsGhQE3pR/UeGci4HfA/8l\npXxsquM8UoRCIRYu/ABnnnk7H/pQH2eeeTsLF36AUCgEW7bARReBlCQ/9jGeslp54okn6OzsZGho\niEwmgxACs9lMY2MjDocDt9vN/PnzmTdvnkomFEc9Qgjq6upYtGgR5eXlWCwWZs2ahdvtxmg06qIZ\ne/fuZdmyZTy1aRN9112nXfyTn8Af/zih929vb6erq5JDZZhPPKplmCcjoegYVV06FZgFPARcCPxR\nCPGiEGLGJLyHQjHtMJlMumRsYTi7tLSU2tparFYryWRS//HdvXs3HR0drFy5kpGREU3z/YwzYHhY\n+3EfHYBUHFsU5F0L8xPxeByHw6HPRSQSCZLJJFJKnE4ng4OD2Gw2jEaj7p4tpWT79u2YTCbq6uoO\neW2Fzuv2Lxr990eAO4EvA2uEEOWj21HfsltY/ezru510+tP09d3OqlXX8t9nfxzOPRciEXKLFvHM\nRRfx+OOPs3nzZgYHB3VfE4PBgNPpxO12YzabaW1t5a1vfes4gQCF4mjGZDKxYMECTjrpJFwuFz6f\njxkzZugJRSaTIRKJsGvXLpYsWcISp5Phq67SLr78clj6uoqZh6Wzs5NI5PBr7kezDPNkJBQnCyHs\nAFLKqJTyd8DtUsoFwMfQBt0UimOSQnWioPzk8/morq6mokKbA00mk1itVoaHh+nq6mLt2rV0dHQg\njUa4+24oL9ckGz//eeVPcQwSjUbJZrNYLBbd4drn8+meJYVhbLPZTDab1ZWdLBaL7k/R29tLOBym\noaFhXKvT8PAwOeXiqvMm/Is+jTbIfTuwf8x265GKeSp4pdVPK818/6V10NVFvr6ejddeyyNPPcUL\nL7xAb28v8Xh8tIIWJJu9gHD4+yxfXsmyZZ3MnTuXysrK4tyQQjFFOBwO3va2t9Ha2ko+n2f/fshm\nLyCXu5VM5nzicS+Dg4Ns3bqVhx56iGfe+U7S73+/JiH7gQ9o1b43QVNTE17v4Qe8j2YZ5slIKO4C\nVgohviaEOFkIUQ2cACClfAnNPVWhOCaxWCwEAgFcLhd+vx+/34/H46GhoQGHw6HruEsp2bNnDzt2\n7GDlypXs3bsXKivhb38DgwHuvBP+8Ici341isunt7cVqtWIymUilUpjNZr3yUEgIstksJSUlhMNh\nHA6HvjoMWgVj165deL1ePUkFyGQyxGIxJRt7EG/Ev0hKeaaU0niY7bC+FUcLh1v9FOT5I5dyauYA\naYeDrTffzEOrVvHss89y4MAB3SxRygqkfBQp70HKK0il/sL+/b/jW9+6TVckUyiOJaqqqjjjjDNY\nuXI3kcjd5PN/B64E/gE8zMiIm4GBAV566SXuvOsubp47l9icORCJaO3L/f2v8Q6H0tbWRn19D7Dx\noCNHtwzzhBMKKeWLwAeBU4GngacY7VkVQnwIqJ3oeygU0xmv10tpaSlOp5Oqqio8Hg/l5eXMnDkT\ng8FAIpHAbrczPDzMzp07efHFF1m/fr2m8/6Od2jtT6D5U2zYUNR7UUwesViM4eFh3G43yWSSbDaL\ny+XCarWSy+UYHh5GCEEmk9H9SxwOB0ajEZvNRiqVore3l3Q6TW1tLUajUX/tSCSiO2orFGM53Orn\ndVzDxfyNDAaeu+oq7uvo4N///je9vb26aACAlKdzaF/3PHbtqjpq+7oVildDCIHJZCIen8ehn/35\nSLmI4eFhtm8f4d57Bd/5f+XM757HXksJ7NwJF1wAowuHb4SHHrqD0067lrKyz2Ox/PaYkGGeFN1B\nKeU2KeVFUkqXlLJJSvno6KEawDsZ76FQTFdsNhuBQACLxYLX66W8vByXy0VDQwMej4d0Oq3LyO7d\nu5e9e/eyatUqNm/eTD6f1wa0zz1X+1K66CLNp0JxVCOlpLe3F7vdjsVi0TX9C1KjheHrQrtTIpHQ\nZWWdTieZTIbBwUEGBwf1troCw8PDZDIZfD6fWjVWHMLBq5+XcCff5f8BcG1VC49nsyxZsoTu7m76\n+/v1wX7ts/SOw77m0dzXrVC8Ft3d3cTjp73C0TPI5crIZh8im/0b+fzn2Rn7C+9O/4WY0QQrVsBl\nl73hluVAIMDKlfeydOmV/OMf5SxdeiXPP3/vpErGHmmmVMhcSvkTKaWaoVAc85SWlhIMBrFYLFRX\nV+PxeCgtLaWxsVF/YHQ4HKRSKTo7O9m6dStr165lz549WsvTn/8MNTWwffub+nJSTC9GRkaIxWL4\nfD69N91ut2O328lkMroLcTKZJJ/PI4TA5XJhMBiwWq0MDAwQj8eRUuqVLtBUnWKxGE6nE7PZrA34\nKxQHUVj9vMB7Pr/lkwD8prSG3WedzFNPPcXq1XvZs+c00umbyeXeTz5fjtfrxWp9/rCvdzT3dSsU\nr0VTUxM+3yt1BzwIHFq528L7udjyTvKFechrrnlT793W1sZ555131LY5jUU5IykUk4DdbicQCOjO\nxjU1NTidTmbMmEEwGCSbzZLJZHA4HAwODtLd3c0LL7zAxo0bGR4eBr8f7rkHzGa491742c+KfUuK\nN4mUkr6+Pt13IpVKYTQa8Xg8GI1GotGoLitsMBhIJpO43W5yuRw2m43BwUFdEcrtdo9zxI5EIhiN\nRlwuF7lcTk9MFIqxBAIBVt5xLf/MP42FHB1z5vD0uxbS0dHBSy+FSCbvQ5th13rFpXyYTCaI2/0S\ncPCw6NHd161QvBYvV/UOTipexGTawCtV7h5Jvo8/njZa2bj+evjtb6cwyumPSigUiklACKEb3BmN\nRj2RcLlcNDc3Y7PZiEajuN1uhBDs2bOH3bt3s2rVKtrb27XWp9NOg5tv1l7wK1+Bhx8u7k0p3hTx\neJxYLIbb7SaRSGAwGLBYLJSUlJBMJkmlUuTzebLZLIlEApPJhNvt1lug8vm8fk519cuiRCMjI6TT\nabxeL0IIotGoank6Dmhvb+eBBx54YzMMf/87ctEijNEo+2tr+V5tLVu2baO3t5dU6hQO1yueTM7n\nnHPm0dT0RQKBzxwzfd0KxetBq+pdRzD4OUymX+NwfJxg8OOceuosDIZnDnuNwfAMv83leHrRIm3H\n5ZfDVVfBaIvr8YZyqFEoJomSkhICgYD+oNfQ0MDg4CDRaJSqqiq6uroIhUJ4PB7C4TA7d+7E6/Wy\nevVqgsEg9fX12mD2Cy9oqk8f+hAsWwannFLsW1O8TgrVCYvFgsFgYGRkBKPRqEvBhkIhvTKRyWQY\nHh6mtraWbDZLMpnEbrdjtVrp7e2lvLwcm80GQC6XIxqN4nA49JmMQhud4tgkFAqxePHldHVVEonM\nx+t9nPr6Hh566I5X7rPOZrWZrJtvRgA76uv5Wk0N2/fsIRKJ0N/fj5RvP+ylmcxbKCvbyZNP3kAs\nFmP79u3MmnWlqkwojgsKMw3t7e289NJLhMOLGBxsYefOnWza9BzR6IvAWPW0FxFiJdGog++VlvLN\nk07ivevWwc9+hly/HnHPPZos/HGESigUiknE5/MRCATo7u4mEAhQUVHB0NAQ9fX1DAwMMDQ0RElJ\nCVarlVAoRFdXFz6fj4qKCnw+H16vVyub7t8P//63Jkv3/PPQ0FDsW1O8DkZGRhgZGcHpdJJIJPTE\nwuFwkEgkSKfTCCHI5/P09vbqZoi7d+/G4XDgcrno6enRW+gKRCIRDAYDbrfmuRaNRjGZTNjt9mLd\nqmKKKZjTFaoJfX3Q17eRxYsvZ+XKew+9oL+f3Ac/iHHZMgAenjOHn5aW0hsKMTg4SCQSGT1xGVqr\n03gcjrW8+90f1qtiKpFQHI+0tbXR1tZGJBJh2bJlSCl53/uS3H//h4jH540m5MswGFZiNg8SCiXI\n5XJc5/PR/pa3cMWaNdieeYbcggUY7r0XsXBhsW/piKFanhSKScRsNhMMBrXEAG3Yy+Px4HK5aG1t\nxW63EwqFKCkp0b0purq62LBhA5s2bdLUgMxm+Oc/Yf587SninHMgFCrynSleCyklg4ODurxrKpXC\nZDJhNpux2WzEYjFyuRxms5mBgQEAGhoa6O3tJZfLEQgEiMVipFIpysvLdZf1eDxOKpXSW50KbVOF\n5EJx7PFK5nRwIl1dFePan/L5PLGlS0nOmYNx2TKSJhPXn3giP/B46A+HGRgYYGBgQB/+NxhWcuic\nxAbq63s4++yzp/jOFIqjA6/Xy+mnn05rayutra1ceOEi5szZSmnptZSUPIrVOqi3nvb39zM4OMj/\n5fN8dsEC9rpcGHt6yL/tbYR/9CNdSe1YRyUUCsUk43K5CAaDem98XV0dDoeDYDBIS0sLUkoikQhO\np5N4PM6uXbvYsWMHa9asYevWrdo8hdutzVDU1EBnJ7zvfaAGcKc1w8PDjIyMYLFYSCQSlJSU6NWJ\nQhJgNBpJp9P09/dTW1tLMpkkEolQWVlJNpslEong8Xj0ZCGXy42raoFWnbBYLHo7lOLY43DmdAUi\nkQVs27aN4eFhurq62Pr1r2N/z3uw9fWxx27n03Pn8qDVSiQSoaenh4GBAYQQo8mEgYqKPC7Xh7HZ\n/huj8Ze4XJdy4olf5/HH7zzCd6lQTG+CwSALFy6kubmZxsZGFi5cyKmnnkpFRQUWi0X7rUarTPf2\n9hIOh2nP57m0tZVn/H6M2Sy+b3yDPe99L9s2bmRwcFCXED8WUS1PCsUkYzAYCAQCDA0NsX//fpqb\nm9m3bx+pVEqfq9izZw9ms1lvfers7KS8vJx169ZhtVppamqCqip47DE4/XRYuRI++lGtcjHG4Ewx\nPcjn8wyN+odkMhmklJhMJoxGo16VyuVy2O12tm3bhsvlwuPx0N3drZsi9vT0YDKZ8Pv9CCGQUhIO\nh8e1OhUkaIPBIICSjT1G0czpHqev79BjLtcaotG38sCvf80Jv/8987dsAeDZ0lKuqatj/8gIg4OD\nJBIJUqkUBoMBIYRePW1paaG1tZVsNks2u5mzzlrM+eefr1fEFArFy1RVVXHKKaeQzWZ1zym3283m\nzZs5cOCA/p08MjLC7t27sVqteDwevlRby6ecTi7v7qb+iSfYd+aZLPnoR/Gdey5VVVWUl2tSzRaL\n5ZgR11DfIArFFFBSUkJ5eTmRSIRMJkNLSwtDQ0MkEgnmzJmjD2v7fD6y2aw+c+Hz+bDZbNhsNq2X\nubUV/vUvePe74f774YtfhNtug2PkC+hYoVCdMJlMugyswWDA4/EwPDxMMpnEarUSDoeJx+M0Nzdz\n4MAB3V09HA6TTCbx+/26+/XQ0BCZTAa/34/BYEBKSSwWw263YzabSaVS7N+/v8h3rni9tLe309nZ\nSVNT02vOJ7S2tlJXt4++vg3AvDFHNmC3vID8xYtcsH49jmyWHHBHVRU/sVgY3LlTVwozmUxYLBay\n2SwOh4PKykrmzJlDQ0MD5eXlBAIBmpqamDdvnkomFIpXob6+nmw2q7er2mw23G43L730Et3d3QwN\nDZHP53UhjkQiQSgU4nq3mw0NDfxg925mDA7y4dtu48lHHuFv7343ZS0t1NTUUFlZSUVFBR6PRxfv\nKPgOHW2obxGFYoooDFtv376d6upqenp62LNnD7lcjrlz57JmzRpisZg+sLtjxw4qKysxGo1YrVZs\nNpu2Ev22t8Ff/qKpPt1+O9TWwleVX+R0IZ/P687XIyMjWK1W/WFOCKFXEaSU7N+/H4fDgZQSs9lM\nRUUF8Xic4eFhHA6H7qQ9PDxMPB7XV7AK+/L5PG63m3w+T1dX1zGzsnUs81pqTWMlhMOjMw+hUIjP\nfOY8QqEr6O2dRTJ5KmbzCk4zr+BXsX5a1wwDsMlu50slJawYTT7HJhJGoxEpJR6Ph9raWmbPnk1d\nXR1VVVVUVVVRXV3NrFmz9M+XQqF4ZRobGxFC0N3dTUlJib7w53Q62blzJ+FwWPcSymQypFIpQqEQ\ndxqNLHW7uSGb5cJolHfv2MEpu3fzhxNO4P45cygrL8fv9+uiLC6XSxd38fv9OJ1Ovdo93Zm2CYUQ\n4grgK0AFmtvIF6SUa17h3PcDn0PT9LICm4FrpZSPH6FwFYpDMJvNlJWVMTAwQCwW44QTTmB4eJhU\nKkV1dTX9/f3s2LFDH94Nh8Ns3LgRj8dDZ2cnNpuNhQsXag+ZF10EP/0pXH01fO1rMGMGfOQjxb5F\nBdqDfiKhKX3kcjmcTidCCJxOJ+FwmEwmg9Vqpa+vj5GREWbOnIkQAq/Xq69oGQwGnE4nFouFVCpF\nNBrF6XTq1Yp8Pq8nHQaDge7ubpLJJDNmzCjy3Stei8OrNW3gHe+4mJ/85MsMDQ0xMjKiJ5GFf4+M\njHDKKTPo6+sm2beGK3t7+a+BAYxAVAiusVj4rZSkRlvtLBYLZrMZAJPJhMvlwuv14vf7aWhooKmp\niRkzZlBbW0t1dTUzZsw4Kh5SFIrpgMFgYNasWXi9Xnw+H06nU3/Yt9vt7Nu3j3g8Tjgc1lsMM5kM\nuVyOnbEYHzUYuMNq5efZLC2ZDF9+8UXW7tjBrS0tdJSVUVJSgtPpxG6343A48Hq92O12/X2cTic+\nnw+/34/b7dbPLczWTQemZUIhhPgwcDNwObAauBpYIoRollIeTu7m7cDjwDeBCHAZ8KAQ4lQp5Sv5\nqSsUU47L5aKqqoqOjg7KysqYM2cOsViMaDTKnDlzCIfDDA4O4nA4dCnRlStX8o53vIMtW7ZgtVo5\n9dRTtQfLq66C3bvhllvgE5+Aigo466xi3+JxTaF3tvDAX0gSHA4H0WiUdDqN1WpleHiY/fv3U1ZW\nhsvlQkqJ3W4nEonoq1put5tsNsvg4KD+7wIFbxOn0/n/2zvz8Liu8uD/3tk1izTaJcurbCfeEtux\nSZyPBAIhpP0IKYGwJIVQoA0FwtZCKKWllFIKFEiBQilLQlogX8NSSlMoS+KEkN1JHDvxGsu29m02\nzb6e748792YsJFmStYzl83ue+9i6c+6Z95y523vejeHhYSKRCCtWrKCrq2sRR685HZNna9pKV1cb\nd955Jz6fj1wuRy6XI5/Pk8vlSKfTJBIJnOk0Vw8P88cjI7QViwD8h83GRxwOBpTCbrNZFdlNy0Rt\nbS1NTU34fD5qa2tZvnw5GzduZOXKlaxYsYI1a9bg9/sXfC40mqWAGUMRDAYtNyVzSyQSxONxK01z\nMpmkWCxSKpUolUo8UCpxkQh/Zrfzl8UiO+Nxvr1nD9+vr+cHra301NVRU1OD1+u1YixdLpe1eTwe\ny1rh9Xrp7OzkzW9+82JPiUVVKhQYCsS/KqX+DUBE/hR4FYai8LnxjZVSHxy362Mi8gfAq/ndWuoa\nzYJhBmg3NzczPDzMBRdcQCgUYv/+/SiluOCCC3jkkUesImX5fJ7e3l4effRRXvrSl3L48GEcDgcX\nX3yx4Zrw+c9DXx/cfTdccw38+MdGWlnNgmOmic3lcqRSKUuRcDgcFItFG4K1egAAIABJREFUq0p2\nPp+np6cHj8fD8uXLKZVKeL1eYrGY5ZZiWjVCoRB2u536+nrrewqFAqlUirq6OuLxOIODg1a8jRkI\nrqlOpsrWlMns4ujRO2hsbLQCqDOZDKlUilWJBO+Mxbg+nSZQbv888H6nk/vsdmw2GzVOJzabDVtZ\nqairqyMYDFJbW0tdXR0rV66kpaWFZcuWsWrVKsvd6Wz1z9ZoqgWXy0VnZ6elVAQCAVpaWhgeHqav\nr49AIEBjYyPRaJTR0VGy2aylVBSLRb5YKvHDQoHb8nl+TyneFonw1kiEe10u7vT7eSwYxF1WLNxu\nt+VG6/F4LOuH0+kkl8st9lScQtUpFCLiBHYAnzb3KaWUiPwauHSafQgQAMLzIqRGMwO8Xi8dHR3E\nYjEikQgvetGLCIVCdHV10dzczNq1azl06BD5fB6Xy2WYSLu6qKmp4bLLLuPIkSO4XC527NhhuCjc\neSckEvCzn8G118Jdd8HrXrfYwzznMK0LIkIsFqOjo8N6wUskElamp2PHjgGwbt06RAS73W6lkAWw\n2+34fD4ikQilUonm5uZTYiPGxsaw2+2ICH19fXi9Xtrb2632muplqmxNdvtvCIfDhEIhcrkctmKR\nVyQSvD2d5vKK3/WQCN+w2/mO00nB4cBVfqFwuVz4fD48Hg/BYBCfz0drayurVq1i2bJlNDc3WxbS\n1atXa6uERjPHNDQ0EAgEaGhoYGxsjOHhYdatW8f69evp7e2lt7eXYDDI2NiY5dKYzWYpFAr02e1c\nJ8LvK8W7CgWuKpW4KpfjqnCYrkiE210u/p/XS8rjsawVZkIO02JRTe5OUIUKBdAE2IGhcfuHgPOn\n2ceHAR9w9xzKpdHMmoaGBlasWEFPTw8iYikV0WiU8847j9HRUUZGRqxgylwux6FDh/D5fOzatYuj\nR4/idru54IILsHk88J//CW9+M/zgB0aw9u23w1vfutjDPGcwM3m43W56enrwer1W1epsNks+n8fh\ncHDkyBFKpRKd5UrnpgJgs9ms37myoF1TU9Mpfu3mqrXP52NgYAARYfny5dbDqVh2g9EYzCT2rtz+\nCgz32s1AN/D3Sqk5K8iwadMm1qwZYHh4H6e6Pe2lVHoI21CES/N5LiuVeHWxyLLypwXgHpuNr9vt\n/NbpxG6mIHY68Xq9BAIBAoGA5R6xbNkyOjs76ejosAI8GxsbCQaDNDQ0aKuERjNPxGIxbrjhzzl2\nrI1YbCt+/5O0th7jQx+6kQ0bNnD8+HF6enqIx+NkMhkSiQRjY2OkUikymQz3Fov8WinW5PO8I5/n\npmKRTqX4VDbLx7JZfmaz8YDNxiNOJ4dsNpzldwS32111BfOqUaE4I0TkRuCvgWsnibc4hQ9+8INW\nZhWTG264gRtuuGGeJNSci5gZfXK5HH19fbS3t7Nz504eeOABbDablfUpmUzi9XpxOBwkk0mefvpp\ny43m8OHDAGzevBmHy2VYJvx+uOMOI6YikYD3vGdxB3oOkMvliMVieDwehoeHreBoU1kwXZ1OnDiB\nUootW7ZYmZ4KhYJlwk6lUtTX15PL5Ugmk6dkdAJD+YhGo9hsNuLxOD/60Y/YvXs3IkI2mwUgHNZG\nWJOZxt6JyGrgHuBrwI3AK4BviUi/UupXcyXXPfd8g2uuuZmnnnLRlt/IS7mHl3KAl5RSnDeuVuUg\ncIfDwZ1uN4NlJaK2fL4EAgH8fr+lTLS0tNDR0UFTUxPt7e0sW7bMUiL8fr8VoK3RaOaP8UkXIhGI\nRJ7hq1/9GLff/ik2bNjA0NCQ5Q41MjJCKBSyUo3H43HS6TRDhQJ/k83y9/k8r81meWexyDaleF2p\nxOtKJSgUGAF+m0rxGxEeUIqj5edAtVCNCsUoUARax+1vxbjfToqIvAn4BnC9Umr3dL7stttu46KL\nLpqNnBrNjDBrDqTTaUKhEJs2bWJgYIADBw7Q1NTEhRdeyP79+62Vb7vdTjwe59FHH7UyQDz//PPk\n83kuuOACw9z5rW9BIABf/jLccguMjcFHP7rYQ12ylEolIpEITqeTeDzOwMAAy5Ytw+PxkM1myWaz\nFItFRkZGcDgcbNu2zVqJcrlcpwTvBQIBy13K5/NZGZ3ghfgM0wIxNjbGTTfdxC233MKJEydIpVKE\nw2H27dvH3r17F2s6qo0Zxd5hZAbsUkrdWv77sIhcVu5nzhSKpqYmHn30xzzn8bCZ/zjlsxLwjAgP\n2+086HDwoN+PzeOhpqaGlR6PlZc+EAjQ3NxMe3s7q1ator29naamJkKhECMjI7S1tbFt2zZdT0Kj\nWUCmSrrQ17cSh8PB5ZdfTiwWI5FIWGmhBwcH6enpobe3l+HhYeLxOMlkkmw2Szqd5ueZDD9Opdic\nTPLyQoH/Uyiwq1SiGbhOKa5TCoDI6CjP33MP6665ZqGHPiFVd/dRSuVF5EngSuCnYMVEXAl8ebLj\nROQG4FvAG5VS/7sQsmo0M8UMlsxkMoyNjXHJJZcwOjrK8PAwa9asQSnF/v37rVSyTqeTsbExfvvb\n31JbW8vmzZs5efIkhUKBCy64AJ/PB//0T1BXB3/3d/CXf2koFZ/+tC5+Nw9EIhGUUpRKJbq6umhq\naqKhoYFwOEyhUCCTyVjF5zZt2oTD4bA+M1eXE4kEHo8Hu91uZXQabyUdGxsjk8kgIqRSKcu9Zf/+\n/YTDYZRSuFwuMpnMIs1EdTHL2LtdwK/H7fsFcNt8yDjm91PIZtlrs/GIy8Uer5enfT5y5cBLj8fD\n5ro6GhoaaGhooLGxkZaWFpqbm2lsbKStrY2mpia8Xi/xeJzrr38vx48vK9e2uJM1az5j1bbQaDTz\nz1RJF6LR7XR1dbFly5ZT0n+bmdzMFNEDAwNW7ZmhoSFCoRChUMgqfvvDeJzv53IU02k2JBLsTCZ5\nUbrEiyniRrji7fewvPP2qrj2q06hKPNF4DtlxcI0XXuB7wCIyD8Ay5RSby3/fWP5s/cBT4iIad1I\nK6XGFlZ0jWZyRITGxkZWr17N4cOHsdvt7Nq1i5///OfYbDY2btxIOp3m0KFDVgaHUqnE8PAw999/\nPw6Hwwr4KhQKbNmyhWAwCJ/8pGGpuPVW+MxnDKXiK18B7Ts9Z4yNjZHNZvF6vezbt88Ktg+HwyQS\nCauYkc/nY/369fh8Pk6cOEEikaChoYFgMGhlg3I6nUSjUbxer/H7VWA+aAqFAna7HYfDQTabZc+e\nPUSjUerr62loaODIkSPa5ekFZhN71zZJ+1oRcSul5tSfoO+jH+Vbjz2Gr1wVd3MgwCV+v+Wi1NDQ\nQGtrq+X65nQ6sZczOpn/mlx99dt47LG/5dTaFvu45pqbefTRH8+l2BqNZhKmSroQDD7NunW3nLLP\nzMhmujC2trayfv16K7VssVikUChQKBSIxWIMDQ0RjUaJx+NEo1HGxsb44hf/Hx/v+xZ2NrGWY/SN\nnE/fSHVc+1WpUCil7haRJuCTGK5Oe4GrlVIj5SZtwIqKQ/4E42Hy1fJmcieGuVujqRpsNhutra1k\ns1mOHDlCS0sL27ZtY8+ePQSDQS688EIKhQKHDx8mm83idrtRStHX18e9995LKpXiwgsvZGRkhL17\n97Jp0yZaWlqM6tm1tfCud8HXvgbxuBGsrd0gzhgzmM7n83Hw4EGUUqxZs8YyYWezWStt7Nq1a6mr\nq+PkyZMMDw9TV1dHc3OzVcnY5XIRj8fx+/2n1JoAyOfzjIyMWC5Spo9tNBolkUhYQbdPPvkkvb29\nbN68eZFm5NxmNrF3r/3gB/mDYhGbzYaIzDpQenI3iws5fryNAwcOsGnTpln1rdFops/kSRf20dk5\nOK3rUEQmdFWsr69n9erVp+w7cOAAn/3s88CFFIEj1lrJ5Nf+XXfdxV133XXKvvlKN161bxpKqa9h\nBMtN9Nnbxv39sgURSqOZIxwOBx0dHaTTaU6ePMnmzZuJRqP09PRQX1/Ptm3byGazHD9+nGw2i8vl\nsgK6H3jgATKZDDt27EBE2LdvHxs2bKCjowN55zsNS8VNN8G//7tRs+Kuu6ClZbGHfNZSKBSIRCJ4\nPB4rW8eGDRsIhUIMDQ1ZRcU8Hg+rVq2ivr6e/v5+uru7aWhoYPny5YChlNjtdjKZjFXptJJSqURf\nXx+hUAin00kkEqFYLJLNZsnlcqxZs4b6+noeeughhoeHueKKK6zgbM2sYu8GJ2k/djrrxGxi78yU\nwmfK6dwsnn/+ea1QaDQLhJl04fjxNqLR7QSDT9PZOch///c35vy7jGt/+4SfTXbtT7TQ8dRTT7Fj\nx445l0/7Q2g0i4Tb7Wb16tW0tLSQzWa59NJLWbVqFaFQiGAwyEUXXcSKFSus4mjFYhGlFMPDwzz4\n4IP85je/YWhoiGQyycGDBzlx4oQRxHvjjUbBO68X7rsPtm+H3/52sYd7VlIsFgmHw9jtdmKxGH19\nfaxatYqhoSGOHz+O2+2mtrYWt9vNypUraWxsZGRkhEOHDhEIBOjs7LQCuIvFIsVi0XJxGf89Bw8e\n5NixYxSLRZLJJDabDaUU6XSalStXUl9fz4MPPkgoFOKqq65izZo19PT0LNLMVBdKqTxgxt4Bp8Te\nPTzJYY9Uti/zyvL+qsVws5g4EN9ws1i3wBJpNOcuZtKF3btv4Qc/aGX37lt45JEfz0s8Q7Vf+1qh\n0GgWEZ/Px7p16/D5fBQKBS677DJWrVpFNBqlubmZiy66iNbWVkqlEoVCwVIsQqEQTzzxBL/61a/o\n6uoilUpx+PBhDh48SDweNwrePfEEbNwI/f1wxRXwhS9AOTuE5vTk83lGR0dRSqGU4ujRowSDQQYH\nB+nv76elpQW/30+pVGL58uVW1p39+/cTCATYtGkTdrudSCRCJpPB4XDQ0NBwSjanXC5HJBLh6aef\npq+vj7q6OkQEpRTRaJRUKsXq1avx+Xw89NBDJBIJrrjiCoLBIN3d3af0peGLwJ+IyE0isgH4OuNi\n70SkssbE14FOEfmsiJwvIu8Gri/3U7WYbhawb9wn03ez0Gg0c8umTZu49tpr5/X6q/Zrv2pdnjSa\nc4VgMMiGDRt49tlnyeVyXHzxxVbMREtLC9u3byedTltBwR6PBzAyDu3bt896ybzwwgvp6+sjmUyy\nfPly2s4/H/vjj8PNNxtuTx/6kGGpuOMOGBcIrDmVdDpNNBrF6XTi8Xh4/PHHrZf8bDbLihUrLEXD\ndEUylQmfz8fWrVspFAqEQiHS6bQVdOt2uykWi6RSKdLptBUzkUqlaGxsJJPJkEqlKJVK+P1+2tvb\nyefzPPHEE2QyGS655BJ8Ph/hcJj29nZGR09bauecYaaxd0qpEyLyKoysTu8DeoF3KKXGZ36qOhbS\nzUKj0VQP1Xzta4VCo6kCmpub2bRpE0ePHiUej7N9+3ZLqVi5ciXZbJZnn32WSCRCOp3G5XJhs9kI\nh8NWNeVsNsuFF16IUopUKkU8HmfFihX4vvc9uPxy+MAH4Cc/gf374Yc/hG0T+2Gf65iVTGtqanC5\nXOzZs8eqYu5yuejo6KBYLFJbW8vq1atxuVyWm5PH42Hr1q1kMhlGRkYoFovU1dVRX19vKRjZbBYR\nsSplp9NpK5OXmQXE7/fT1NREJpPhueeeI5VKsXHjRjweD7lcjuXLlxMOh+nq6lrs6aoqZhJ7V973\nG4x0s4vKgQMHOHr0KOvXr5/WKqPpZnHgwAGef/551q27ZdFXJzUazcTM9Pqeimq+9rVCodFUATab\njba2NlwuF0ePHmVoaIhNmzZRKpUYGRlh7dq1uN1uDh06RG9vr5UFyG63k0wmOXLkCLlcjqGhIbZv\n387KlSvp6emxrBVNN9+MfedOeP3r4dgx2LULvvpVePvbdb2KMqYFIp1OEwgESKfTPPTQQ0SjUex2\nOzU1NQSDQUSE9vZ22tvbERErALumpobzzjuPRCLB8PAwLpeL2tpanE4noVAIpRRut5u6ujry+Tz9\n/f0MDQ0Rj8dRShEIBKwiZvX19YTDYY4fP040GqWzs5Pa2lpLyTly5AgjIyMUCoXFnjbNGTA6Olpe\nbWwv15P4JWvWDEw7p/ymTZuq5mVCo9Gcyple31NRjde+Vig0mirBrFFhroSfOHGCtWvXWlmGzj//\nfILBIM888wzHjh0jk8lYuepzuRzHjx9nbGyM3t5etm3bxtatWwFIJpMkEgnaN2/G+9RTRgao//kf\n+OM/hgcfNKpsj0tfeq5RKpUIh8Pk83nq6uro6uri2WefJZPJUFdXZwVf+/1+Vq9ejd/vJ51O093d\nbVkz2tvbicViDA4O4na7qampsWJf/H4/Xq+XQqHA4OAgJ0+eJBqNks/n8Xg8+Hw+PB4PwWAQj8fD\n8ePH6e/vRynF2rVrrfMiHA4zMDBAJpOhvr7+d2pYaM4urrnmZh577BPoehIazdLjXLu+tUKh0VQZ\ngUCAzZs3WyvRK1eupFQqGUpBezs1NTV4vV4OHTpEKpWiUCjg8XhQShEKhUgkEgwMDNDd3c2OHTtY\nu3YtJ0+eJBaL0dbWRsPdd+P50pfgr/4K7rwTfvlL+Pzn4YYbzklrhemKVCqVsNvtPPbYY3R1dSEi\nNDU14XK5CAaDtLa2snLlSmw2m2VdcDqdNDc3UyqVOHnyJPF4nMbGRpqamvD7/dTU1OB0OimVSgwN\nDdHT08PAwIDlttbQ0IDL5aKmpoampiZSqRR79+4lFosRDAZpa2vDbrcTCoWIx+PE43EcDgfNzc3Y\n7XYjAF9zVqLrSWg0S5dz8frWCoVGU4W43W42btxITU0N+/btI5vNWgXPPB4PF198MV6v95S4Cq/X\ni9PpJJ1O09PTQzQa5eTJk2zbts0K9E4mk4zU1tL29rdTv3Mnrve8B44ehT/8Q/jmN+Gf/xnOkWJp\nppKWTCbJ5XJks1n2799PX18fPp/PSu/a3NxMe3s7zc3NxONxuru7yWaz1NXVoZSiv7+fWCxGTU0N\n559/Ps3NzbhcLut7IpEIzz//PMPDw6RSKSt1rOnCZCqIJ06c4OTJk4gIgUAApRQ9PT2W8qGUwuv1\nUltba9W+qK+vX8QZ1JwJup6ERrN0ORevb61QaDRVit1up7OzE7fbbRXFisViVk2Dbdu24fV62bt3\nL8PDw2QyGQqFAna7/ZS2w8PDHD9+nF27drFx40YKhQLxeJxgezut991H/e234/jMZ+D++41A7Q98\nAD7+caNA3hLEVKzi8TipVAqbzcbo6CjPPPMMkUjEskYsW7bMUiSUUhw/fpzBwUEcDgd+v5/R0VHC\n4TAul4tVq1Zx3nnnWRVPS6USg4ODdHd3Mzo6SqFQsH6b5uZmgsEgPp8Pv9/P0NAQTzzxBKOjo1bV\nVFNxbGxspLGx0SpgV1dXh8PhwOFwWK5umrMTI6f8Lxke/t3PjJzytyy8UBqNZk6Yi+t7LoO5FwKt\nUGg0VYyIsHz5cjweD4FAgIMHDyIiJJNJMpkM69atIxAIsGfPHgYGBiiVSuRyORwOBzabzQrqNmMr\nVq1axbZt29i4cSOZTIZYLEbD619P26tfTd0nPoHtpz813J++/3344hfhDW9YUm5QqVSKaDRKPB5H\nRCgWi3R1dXHo0CEymQwrVqxg/fr1rF692nJl6u/v58SJE2SzWdra2nC73fT19VEoFOjo6KCjo4PG\nxkbAiFfp7e2lu7ubeDyO3W7H4/FQLBapqamhpaUFn89HqVTixIkTHDt2jN7eXmw2G4FAgIaGBtra\n2mhubsbj8VgZp5RS1NXVWZaJQqFAJpOhVCot8oxqZouZU354eB+nukVUR055jUYze87k+p7PYO75\nRNQ5WuhKRC4CnnzyySe56KKLFlscjea0ZLNZenp6ePLJJzl+/DjhcJhcLoeIkEqlOHr0qJUVqFQq\noZSyrBVguFGZAcCtra1s3bqV7du309zcjM/no6mpifannybwV3+FzUxH+vKXG25QGzcu4sjPnGw2\ny+joKNFoFDDqTPT19XHs2DGi0ahVO2Ljxo1WPYj+/n5CoRD5fJ7GxkaWL1/O8PAwg4OD+P1+Ojs7\naWlpIZ/PEwqFOHnyJENDQ+RyObxeL3V1dWQyGeLxOE6nE7/fTzabZWBggP7+fuLxuPVbNDU1EQgE\n8Pl8KKXI5/Nks1my2axlETGzeuVyOfL5POFwmKeeeopPfepTADuUUk8t6iSfA8z1c+OFF4ffzSlf\nzS8OGo3m9Mz2+t6167WnBHMb7OOSSz4xJ8HcTz31FDt27IA5fm5oC4VGc5bgdrtZt24d7e3tPPvs\nszz22GP09fWRyWSw2+3s3LmT1atX89xzz9HX12e5RpVKJUTEqrKdyWRIJBKcPHmShx56iPXr13Pp\npZfS2dlJf2sr9d/9Lmt++EPqvvY15L77YMsWuP56+PCHYefOxZ6GaVMsFkkmk4TDYasoYCqVoru7\nm5MnT5JMJgkGg2zZsoV169bR0dFBPB7nueeeI5lM4nK5WLZsGS0tLaTTaQ4fPkw+n2f16tW0tLSQ\nTCbZt28fw8PDRCIRqxhdY2Mj8XickydPUigUqKmpIZfLceLECWKxGEopGhsb2bp1K26323JVK5VK\npNNpy/pgs9nwer3U1NTgcDiIx+OMjIwQjUZJJBLk83nGxsYWe5o1Z0A155TXaDRnxmyu77M5mFsr\nFBrNWYbP5+OSSy5hzZo13H///Rw4cIDBwUESiQSBQIBdu3bR29vL0aNHGRkZIZlMUiwWkQrXpVwu\nh9PpJJPJMDo6yv79++no6GDdunWsW7eO7pe8hNYdO9j07W9Te999cPfdxvayl8Gtt8LVV1elK1Sp\nVLKUiEgkQiKRIJfLMTY2Rn9/PwMDA5br0rZt21ixYgWNjY2WglUoFPD5fHR0dOD1ekmlUpw4cYLh\n4WGcTidNTU2Mjo5y8OBBS0kB8Hq9+Hw+0uk0o6OjlEolPB4PLpeL0dFRkskkDoeDNWvWEAwGKRQK\njI2NUSwWcbvdNDY2UigUSCaTVprZmpoa6/uHhoas4GyXy0VjY6MV2K05+6nGnPIajWZumMn1fTYH\nc2uFQqM5S2lpaeH6669n7969PPzww/T19VkvufX19ezcuZPu7m66u7uJRqPkcjnLFapYLFpF0RKJ\nBOl0mkQiwbFjx3jggQdoaGhg2bJlrL3sMna87GVc/Jvf0Lp7N7J7N+zejdqyBbn1VnjTm8DpXNR5\nMLM1DQ8PMzIyQjgcJplMks1micfjxGIxYrEYIkJHRwebN29mxYoVgOH6NDQ0hIjg9/utF/RIJMKR\nI0cIh8MkEglEhEKhwIEDB8hms1YBuoaGBgKBAPl8nng8Tj6fp1AokM/nLauFx+Ohvb2d2tpa4vE4\nPT09OJ1OgsEgdXV1iAiJRMKqdg4wMDBALBajWCzicDgIBoNWXIXT6SSfzxOLxejp6Vm0eddoNBrN\n3HI2J2vQCoVGcxZjs9m46KKLWLVqFXv27OHkyZP09PQQDoeJx+O0tLTg9XoZHBy0gpFNF5t8Pm/1\nk8vlLDcfp9NJPB6nr6+PZ555hl8FArS0tHD+61/PtV1dvGjvXlzPPgs33UThIx+h+L734XrXu5C6\nunkfb6FQIJ1OE4/HiUQiDA8PE41GiUajpFIp0um05dZlxh8EAgG2bNlSvlEHLVehUqmEzWbD6XRa\nFa+ffvppRkdHicVi1vy4XC4rKL6trY2GhgZ8Pp/lhjQ0NER/fz/JZNL6Tfx+Pw0NDdTU1AAQi8WI\nRqP4/X4rsDuVSlm/VSqVQimFUgqbzUZNTQ1tbW0EAgHsdjulUolUKsXo6Cj5fJ5cLmfFxGg0Go1m\naXA2J2vQCoVGswRobGzkla98JaOjoxw7doxjx47R3d1Nf3+/FZhdX19vrdab9ReKxaL14mwGAtts\nNit9qdfrZWxsjKGhIZ6vqeEXDgfNW7bwhnCY6/v7qR8YwPHRj5L/+McZ2LGDyFVXkb3ySgJlRcZ0\n+3FWWDFE5BT3KxMz3sN8Yc5kMuRyOVKpFLFYjHA4TDQaZWxsjHQ6bcltptQ1+3Q6nbS1tREMBqmv\nr8fv9wNGBqahoSEKhQLZbJZMJkMymWRsbMyqRWG6IAWDQTo6Oqivr6ehoQGv14vNZiObzVqKViQS\nIZPJoJSipqaGuro6vF4vLpfLcr2Kx+NWuliv12tZREzFzrRg+Hw+6uvrqaurs6pfZ7NZxsbGiEaj\nVlsRsdq63W5LidFoNBrN0uCee74xaTB3NaOzPOksT5olhlkxu6enh56eHrq6uujt7SUUCpFKpYjH\n45YFIx6Pk06nyWazFItFzPuBmY60VCpZKWjNis6mguC12bgukeAto6N0ll11AFJ2O092dPDkunV0\nb9iAr6HBylLkdDqx2+3Y7XarloLD4bAUm3w+TyqVIpfLWVaUQqFgyeV0Oi3lRESs1LgmDocDj8eD\nzWaz3LrMvovF4ikKFBjKiNvttjIpNTc3U19fj9PptBQI0xIyMjJiBbqbsQzmuBwOB/l8nkQicYo8\npoyFQoFUKkWpVMLlclmF88yK2i6Xi0wmw9jY2ClB5GamrtraWurr6wkEAlagdk1NDc8//zzveMc7\nQGd5WhD0c0Oj0SwULwRzr5tTy4TO8qTRaKaFiNDU1ERjYyMrV66ks7OTnp4eQqEQQ0NDVkG2UCjE\nyMiIFbxsWijMqtEm5ku4+cJrviTb7XZuc7n4aiDAi+rreU06zatSKZbl81ze3c3l3d3EH3iA3fX1\n7G5uZm99PaqsUDgcDquatKmwmJYLM82tqXiYf5tKQrFYtPaZSondbrcUAxOzT9OCYRaEM1/g3W63\npTiICKVSiVAoRFdXF7FYzLJamMqIWVPClCOVSpFIJCzFxW63W8qO6UZlfq8ZeF1XV4ff78fj8Viu\nW2al7Xg8bo3JtEIEAgEry5P526bTactKc+TIkXk+mzQajUazGJxtyRq0QqHRLFFEhMbGRqtYmrny\nHQ6HCYfD1qr74OAgg4ODjI2NWa44ppXATGNqVmQ2g7pNt6R0Oo3GiWcZAAAaI0lEQVSI8HPgf0Ww\nAbvsdt4IvK5Uor1Y5NrRUa4dHSUlwlMuF496PDzh9bK/pgZVrq1Q+WJvxjWYL+Om8uJ0Oi1FQCll\nuVOZCoPNZrNW9B0OxykKiplBqVQqMTw8bFlAzH9NRcpUHkzFwOzLVD4KhYLlDmbK6XQ68Xg82O12\ny3JgtrHb7afUj+jp6WFsbIxEImEpbSKC2+3G7/fj8/moqamx+jXlNpUY05qUSqXIZDIMDg4uzsml\n0Wg0Gk0FWqHQaJY4psWiqamJfD5vBTHHYjEikQixWMxSLkKhEKFQyLJamBmgcrmcFddQKBQsNyRz\nM12TSsBvy9v7gcuBG0S4TilalOKybJbLslmIxUgDT9hsPOx08ojLxVMuF9myUmGu8Jsv9KZSYf4L\nL1gwzP9XymNS6bplxliYiot5bKXbU01NDW6323LxqrR+mMeLCC6Xy1JYzP2lUoloNMrAwAD5fN5S\nxrLZrFVnwlQ63G43NTU1+Hw+fD4ftbW1luuUqTwkk0nLjcrMAmX2Y8qfyWTm+/TRaDQajea0VK1C\nISLvAT4EtAHPAO9VSj0xRfsrgC8Am4Fu4O+VUncugKhVwV133cUNN9yw2GLMCXos84fT6aS5uZnm\n5mYKhYIVpB0KhUgmk5YbTyQSYXR0lJGREYaGhohEIhw/fpzW1laKxaIV1Gyu7CulTnnZBSNz1KNK\n8Sjw/lKJLTYbLy4WubxU4vJSiVbgJaUSL8lmIZslCxwW4YDNxgG7nUMOB4ccDnptNqh4eR+vGJiY\nCsdEm+nuZFoNwuEwra2tKKUsdyLTApBIJCyrhukKZX5uKiuVios5ZqUUTqfT+g4zC1NtbS21tbVW\nkLrD4bBiSUxXMrNeiKnA5fN5K71vZX+mRcaUq9I17VxGROqBfwauAUrAj4D3K6UmjFoXEQfw98Dv\nA51ADPg18BdKqYEFEfocotrug9WOnq+Zo+ds8alKhUJE3oihHNwMPA58EPiFiJynlBqdoP1q4B7g\na8CNwCuAb4lIv1LqVwsl92KylC4mPZaFweFw0NjYSGNjI6tWrSKdTpNKpazAbXMzsyN95Stf4eqr\nrz6laJyZqtW0YFQGU1cGQpdKJbpEOFIocAegSiXWl0q8uFDgsrKSsQy4UCkuLBahWISym9UYcMBm\n46DNxmG7nZM2Gz0i9NpsRESwlRWJfD5/SiyG6e5UaVkwt9HRUQqFguUmZcZkTHSsaQ0wLROm4lBp\nyaiM5TCtKOa4Y7EYIyMjZLNZK4uV+RkYComplFS6eplWGqfTadXaqJTP4XDoLE8v8H2gFbgScAHf\nAf4VePMk7b3ANuBvgX1APfBl4L+Ai+dZ1nOOar4PViN6vmaOnrPFpyoVCgwF4l+VUv8GICJ/CrwK\neDvwuQnavwvoUkrdWv77sIhcVu7nnFAoNJozwW634/f7rRSrYNR8MOMpkskkd999N695zWsshSOZ\nTBKNRgmFQoyOjlqpaNPptOXrX1nzovLfQqHAcKHA3fk8P7TZyOdyLC8W2VgosKlYZEOxyKZSifXF\nIrXArlKJXaUSlIvxmSSAXhF6ykpGj81GvwghEUZFCNtshESIA7ay+xRgBVWbyoapVIxXQiqVChPT\nGmEqI6ZiUGmxMI+r7Nec50qLidmfabGoTNlrxoOYVgrTzco8zrRwnOuIyAbgaoyMJU+X970X+B8R\n+ZBS6ncCTZRSY+VjKvu5BXhMRJYrpXoXQHSNRqNZMlSdQiEiTmAH8Glzn1JKicivgUsnOWwXhrm6\nkl8At82LkBrNOYCZEclUMgKBADt27LCsDmY2KDM427RWmErF2NiYVUchFouRzWatCtaZTMaKMzBd\nfIrFIs/n8xxVih/m80bMQbHIsmSS9dks52WzdOZyLCsUWFYo0Fwq4Qc2KMWGYnHKseSAsM1mKBg2\nG39eKDAsQqZCcRifMhcMxQNOrZ1R6XJU6WZVqTRUxneYbkqVcSHjA7tNi4QZaF4ZO1JTU4PH47EC\nt033Ka/XS3d3N/fee++c/eZnKZcCEVOZKPNrQAGXYFgdpkOwfEx0bsXTaDSapU/VKRRAE2AHhsbt\nHwLOn+SYtkna14qIWymlHY01mjnAfHk2X34nwnThqdzM+ArT1ccM8M5ms1bcQiaTsRQNMyDZLG6X\nzWaJFIs8XlFTopRM4o9GCYTD1MfjBMfGaEgkCCaT+LNZ/JkMgWwWT7GIC2grlWgrKwutwMoXvxg1\ngTuUGeBdmXK20rJQWUOj0hXKtDqYGZoqt8oCf2Zbt9ttBYN7PB7rs0oFw7RcmH1XygRGPnENbcBw\n5Q6lVFFEwuXPTouIuIHPAN9XSiXmXkSNRqNZ2lSjQrFQeAAOHjy42HLMCbFYbMm8XOixVCfzNRYR\nIRAIEAgEfueziYKgK/dP9HeqvA2Z+zIZiMWQaBQiEYhGiX33u3z2wx/+nX4qZRpPZZzFRO0qrRgT\nHT8VphtYOp2e0XEV96+JtbuzGBH5B+AjUzRRwMY5+B4H8INyf+8+TfMl9dxYKJbSfXAh0PM1c/Sc\nTZ/5em5UXaXssstTCnidUuqnFfu/A9Qppa6b4JgHgCeVUn9Wse+PgNuUUvWTfM+NwPfmVnqNRqNZ\nFP5QKfX9xRZiLhGRRqDxNM26gLcAn1dKWW1FxA5kgOuVUpO6PFUoE6uBlyulIqeRST83NBrNUmFO\nnxtVZ6FQSuVF5EmMbB0/BRBjue9KjCwcE/EIRvq/Sl5Z3j8ZvwD+EDiB8eDRaDSasw0PxsvwLxZZ\njjlHKRUCQqdrJyKPAEER2V4RR3ElIMBjUxxnKhOdwMtOp0yU0c8NjUZztjMvz42qs1AAiMgbMNL+\n/SkvpI29HtiglBopm8KXKaXeWm6/GtiPkTb2doyHyT8B/1cpNT5YW6PRaDRLCBH5GdCCkfHPhfEc\neFwp9ZaKNoeAjyil/qusTPwII3XsNZwagxFWSun0WRqNRjMDqs5CAaCUultEmoBPYsRP7gWuVkqN\nlJu0ASsq2p8QkVdhZHV6H9ALvEMrExqNRnNOcCNGYbtfYxS2+yFGsfZK1gN15f93YCgSYDxfwLBo\nKOBlwG/mU1iNRqNZalSlhUKj0Wg0Go1Go9GcHdgWWwCNRqPRaDQajUZz9qIVCo1Go9FoNBqNRjNr\nlqxCISLvEZHjIpIWkUdF5EWnaX+FiDwpIhkROSIib10oWafDTMYjIteJyC9FZFhEYiLysIi8ciHl\nnYqZ/jYVx71YRPIiUjXJpmdxnrlE5O9F5ET5XOsqpzhedGYxlj8Ukb0ikhSRfhH5tog0LJS8k8h0\nuYj8VET6RKQkItdO45iqvfZnOp5qv/bPRkTkL0XkofJ5Hp7BcZ8sXxcpEfmViKybTzmrBRGpF5Hv\nlc+/iIh8S0R8pznmjvL5Xbn9bKFkXmiW2vvJQjDDd6CXTnA+FUWkZSFlXiwW6zm4JBUKEXkj8AXg\nb4DtwDPAL8qB3hO1Xw3cA9wLbAW+BHxLRK5aCHlPx0zHA7wE+CVGKt2LgN3Af4vI1gUQd0pmMRbz\nuDrgToygy6pglmP5AUbQ59uA84AbgMPzLOppmcU182KM3+ObwCaMLGwXA99YEIEnx4cRZPtujADb\nKan2a58ZjocqvvbPYpzA3cC/TPcAEfkIcAtwM8Z1kcS4nlzzImF18X2MgoNXAq/COCf/dRrH/Rwj\nCUtbebthvgRcTJba+8lCMMtnrcJIxGCeT+1KqeEp2i8lFuc5aFagXUob8CjwpYq/BSPz062TtP8s\nsG/cvruAny32WGYznkn6eBb4q7N1LOXf428xbihPLfY4ZjMW4PeAMBBcbNnnYCx/Dhwdt+8WoHux\nx1IhTwm49jRtqvran+l4JjmuKq79s30D3oqRUnY6bfuBD1b8XQukgTcs9jjmeY42lM/T7RX7rgYK\nQNsUx90B/Hix5V+gOVpS7ydVOmcvBYpA7WLLvtjbQj4Hl5yFQoxK2zswNC0AlDE7vwYuneSwXfzu\nyvcvpmi/YMxyPOP7ECCA8TK7aMx2LCLyNmANhkJRFcxyLK8G9gAfEZFeETksIv8oIp55F3gKZjmW\nR4AVIvL75T5agdcD/zO/0s45VXvtzwXVcu2fS4jIGowV0crraQyjyN6SOK+m4FIgol4oMAjG9aWA\nS05z7BUiMiQih0Tka4vtPjkfLLX3k4XgDN6BBNhbdjv8pYj8n/mV9KxmTs6xJadQAE2AHRgat38I\n4yY/EW2TtK8VEffcijdjZjOe8XwYwwR29xzKNRtmPBYRWQ98GqNEfGl+xZsRs/ldOoHLgc3AazDy\n5F8PfHWeZJwuMx6LUuph4M3Af4hIDhgAIhhWirOJar7254JqufbPJdowXqDP5J59ttLGqUUCUUoV\nMRTaqcb+c+Am4OXArRgrzD8rK8RLiaX2frIQzGbOBoB3Aq8DXgv0APeLyLb5EvIsZ07OsaWoUGgq\nEJEbgb8GXq+UGl1seWaCiNiA7wF/o5Q6Zu5eRJHOFBuG+fFGpdQepdT/An8GvPVsezCIyCYMP8tP\nYPjqX41hRZqOr7RmATibr/35RkT+YYKgzfEBnOcttpzVwnzPl1LqbqXUPUqp55RSP8UoOngxcMVc\njUFz7qCUOqKU+qZS6mml1KNKqXcADwMfXGzZljJVWSn7DBnF8J1rHbe/FRic5JjBSdqPKaWycyve\njJnNeAAQkTdhBMler5TaPT/izYiZjiUA7AS2iYi5im/D8OTIAa9USt0/T7Kejtn8LgNAn1IqUbHv\nIIaStBw4NuFR889sxvIXwENKqS+W/35WRN4NPCgiH1NKjV/tqFaq+dqfNVV47Vcbn8fw25+Krln2\nPYhxTbdy6qpfK/D0hEdUP9Odr0HglEw6ImIHGjjN86oSpdRxERkF1mEkFlgqLLX3k4Vg1u9A43gc\nePFcCbXEmJNzbMlZKJRSeeBJjAwTgOVHfCWGhjoRj1S2L/PK8v5FZZbjQURuAL4NvKm8Er7ozGIs\nY8AWYBtG5oGtwNeBQ+X/PzbPIk/KLH+Xh4BlIuKt2Hc+htWid55EPS2zHIsXI9CykhKGq8fZZEWq\n2mt/tlTjtV9tKKVC5VXMqbbx5/d0+z6O8YCuvJ5qMWIIJr1nVzMzmK9HgKCIbK84/EqMe8K079ci\nshxoxFiEWTIstfeThWC270ATsI0ldj7NIXNzji12BPp8bMAbgBSGT+YGDDeMENBc/vwfgDsr2q8G\n4hiR7udjpNrKAa9Y7LHMcjw3luX/Uwwt09wWPePBTMcywfHVlOVppr+LDzgJ/AdGWsWXYKSM/fpZ\nOJa3AtnyObYGY+XnceDhRR6HD0PZ3Iah4Hyg/PeKScZR7df+TMdTtdf+2boBK8pz/nEgxguLG76K\nNoeAP6j4+9by9fNq4ALgJ8BRwLXY41mA+foZRvKJF5XvC4eBfx/Xxpqv8jn+OQyFaxXGi80eDOut\nc7HHMw/zs6TeT6p0zt4PXAusxYhZ/CcgD1yx2GNZoPlalOfgog98Hif03cAJjFR9jwA7Kz67A7hv\nXPuXYGjB6fKN/y2LPYbZjgfDRFycYLt9sccxm99m3LFVo1DM8jw7DyN7QgJDufgc4F7sccxyLO8B\n9pfH0otRl6J9kcfw0vINdMJz/2y79mc6nmq/9s/GrTzHE83pSyraFIGbxh33CYz0sanyNb9uscey\nQPMVBL6LoXxFMGrVeMe1seYL8AD/i2HVyWC4Tv0L5ZfFpbjN4l5btfeoapwzjGQURzHqv4xgZIh6\nyULLvIhztSjPQSl3pNFoNBqNRqPRaDQzZsnFUGg0Go1Go9FoNJqFQysUGo1Go9FoNBqNZtZohUKj\n0Wg0Go1Go9HMGq1QaDQajUaj0Wg0mlmjFQqNRqPRaDQajUYza7RCodFoNBqNRqPRaGaNVig0Go1G\no9FoNBrNrNEKhUaj0Wg0Go1Go5k1WqHQaDQajUaj0Wg0s0YrFBqNRqPRaDQajWbWaIVCc84gIrtF\n5IuT/T3T46udMx2vRqPRaDQazXTQCoWmqhCRO0SkJCJFEcmJSJeIfFZE3PPwddcBfz0P/VYrcz5e\nraRoNBrN3DJX99WlvmimqS4ciy2ARjMBPwf+CHABO4B/A0rAR+fyS5RS0bnsbzEQEadSKj+dtkth\nvBqNRrPQiMgdQJ1S6rWLLcsMuQ6Y1vNhuojIbuBppdSfzWW/mrMfbaHQVCNZpdSIUqpPKfVT4FfA\nVeaHInK1iDwoIhERGRWR/xaRzsoORMQrIv8mInER6ROR37n5TeASdNp+p0IMbhWRoyKSEZETIvLR\nis9dIvJlERkSkXT5u3aO62PKNmWZvyIit4nICPC/sxlv+f9fKlt/QiIyICJ/M679lPNRfsi+FHh/\nhVVpZXkePlq2LqVE5GkRed1051Gj0Wg0Z45SKqqUSi62HJpzA61QaBYMEfkjESnN8JgtwIuBXMVu\nH/AF4CLg5UAR+M9xh34euBx4NfBK4Ipy+6mYTr9T8RngVuBvgY3AG4HBis//EWPF6C3AduB54Bci\nEpxhm5uALPB/gD8t75vNeG8CEsDFZbk/LiJXVnx+uvl4P/AI8E2gDWgHeoG/BN4M3AxsAm4D/l1E\nLj+NPBqNRnPWMY2FoOtFZF95gWVURH4pIjWn6dZ2mgWf0y7cTLBo5heR74lIQkR6ROS9E7g5Tfq9\nky0izWbONEsQpZTe9DZnG7ABeB/wOHAX8M/A/vJnrwGeO83xd2CYaONAGsPVKQ+8ZopjmsrtNpX/\n9gEZ4LUVbeqBJPDFin27K/+eRr+Ttgf8ZXnfNsnnXgwl4I0V+xwYL+B/PoM2u4E94/qe8XjL/39g\nXD+PAZ+e7nxMNCcYbmoJ4JJxx34T+O5in59605ve9Fa5YbjXlk7T5g7gx1N8/iWgB2MxZ0O5fQgI\nYiy25MrPxZXAZoyFIO8U/e0GIhgxb2sxFpiKwJUVbT4GPAe8AliNsUCUAi4f10/l/fmbQBfGgtMm\n4EdAdNxzYdLvBWqBh4CvA81ACyCL/RvqrTo2HUOhmWvWKKW+LCJ/AXwY+C3GDRWl1E+An0yjj/sw\nbrh+4INAoXwsACKyDvgkcAnGS64NUBg36wMYN0InhlJD+bsjInJ4qi+dRr9TsRHjZfq+ST5fi6Ec\nPFwhU0FEHi8fO902AE9O0PeMxwvsG/f3AMYDApj1fKzDUIx+JSJSsd8JPH0aeTQajWbeEJF/AN4G\n/B4wArRivFAfOoM+vRjPq5uUUr8s7/sTDDfdd2A8E+zAfyqlesqHPTeNrvcppf6u/P9jInILcCVw\nr4i4MGIKr1RKPVZuc6JsBX4n8OAEcvoxlI43KaXuL+97G9A/3e9VSo2JSA5IKaVGpjEGzTmEdnnS\nzClKqZ+LyPlARCn1gFKqqJTqm2E3SaXUcaXUfowb8i4ReXvF5/dgrMD/MYa7zsWAYLzQnwln0m/6\nDL97JsyVT+z4YD3FqfeE2cyHv/zv/wW2VmybgOvPXGSNRqOZOeXn0mPAecBOoFkp9ZRS6idKqU1n\n0PWEC0EYCzwbgb0YSsWzInK3iPyx6cIqIjeW497iIjImIi+u6HeqBZ/KhRvz+DiGRWHtJHJ2luV8\nokLOMWD8wtOUC00azWRohUIzH7wCuHcuOlJKKeDTwKdExC0iDRgPhE8ppXYrpQ4DjeMOOwYUMFbW\nARCR+vJxEzLNfqfiKIbb0ZWTfH4M4wXeemCIiAN4ES+s9k/VZqoVrRmP93TMYD5yGKtvJgcw3LZW\nKaW6xm0zVSw1Go1mTlBKHS5butPAd5RSexfoe5VS6ioMq8hzwHuBQyKyCvgvXlh02QbsqTh0qgWf\n+Vy4Od1Ck0YzIfok0cwHVzKB64+IvEZEDs6ivx9g+HG+B8O/MwTcLCJrReTlGIHDymysjKwW3wb+\nUUReVg7svqPcx2Sctt+pUEplgc8CnxORt4hIp4hcYlpWlFIp4F/KMl0tIpuAbwE1ZVlP1+b2Kb57\nNuM9HdOdjxPAJSKySkQaMawnnwduE5GbyvOwXURuEZG3nIE8Go1Gc0aIiB3jHb8gRka6uXgHmtZC\nkFLqEaXU32Ik28gD1ymlkuMWXbLT/M7ZLNx0YSw8vahCzjpmvvA0fhFJowF0HQrN/LASI7hrPLO5\neaGUKorIP2NkIvoX4E0YQXD7Mcy17wPuH3fYhzGClX+KEeD9BYyAslO6rvgOJSJvBL48Qb9qfPtJ\n5PykiOQxsjwtwzAVf72iyV9guAz9GxDAWI16pVIqNoM2k8kw3fFOV0Gaaj4q+TzwHYwHnAcjhuav\nRWS4PJZODB/lpzAsTRqNRrPglLMuXYQRg5AC3q2U+gsRuQ4jGcXGqXsgKCJbx+0LKaV6RcRcCIpg\nBGffSnkhSEQuxlhk+yUwDOzCiEk7XVzepCilEiJiLtzYMWIV6zCUmphS6t8nOeZO4PNlOUeAT2As\nPE3ruVDmBOVFJIwEHOGyJ4HmHEf0eaDRaDQajWapUrb25jHSeN+P4Z3x6rIy8FbgdqXUpKvu5XSp\nN03w0beVUjeLiBvDQn0DLywEfUAp9ZSIbMBInb0dY5HnJPBlpdS/TPF99wF7VUXxOBH5T4zYxLdX\n7Hsv8C7GLdwopX47UT8i4sNY5HoNMAZ8rizzvUqpj8kERevGf6+IrMdYRNrGC4tI3ZONRXPuoBUK\njUaj0Wg0mnOMcoaqPuDPlFJ3LLY8mrMb7fKk0Wg0Go1Gs8QRkW0YdTIex6iR8XEMd6f/Wky5NEsD\nrVBoNBqNRqPRnBt8CCOWMYdR0+gypVR4cUXSLAW0y5NGo9FoNBqNRqOZNTptrEaj0Wg0Go1Go5k1\nWqHQaDQajUaj0Wg0s0YrFBqNRqPRaDQajWbWaIVCo9FoNBqNRqPRzBqtUGg0Go1Go9FoNJpZoxUK\njUaj0Wg0Go1GM2u0QqHRaDQajUaj0WhmjVYoNBqNRqPRaDQazazRCoVGo9FoNBqNRqOZNVqh0Gg0\nGo1Go9FoNLPm/wMrC5s/dA0DlwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9cbff8d6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r_new = np.linspace(0.,1.2, 40)\n",
"\n",
"plt.figure(figsize=(8,3))\n",
"# Latent function\n",
"plt.subplot(1,2,1)\n",
"for i in range(0,len(samples),3):\n",
" s = samples[i]\n",
" model_gpmc.set_state(s)\n",
" f_pred, f_var = model_gpmc.predict_f(r_new.reshape(-1,1))\n",
" plt.plot(r_new, np.exp(f_pred.flatten()), 'k',lw=1, alpha=0.1)\n",
"plt.plot(r, f, '-r', label='true',lw=1.5)# ground truth\n",
"plt.xlabel('$r$: Radial coordinate')\n",
"plt.ylabel('$g$: Latent function')\n",
"plt.legend(loc='best')\n",
"\n",
"# \n",
"plt.subplot(1,2,2)\n",
"for i in range(0,len(samples),3):\n",
" s = samples[i]\n",
" model_gpmc.set_state(s)\n",
" f_sample = model_gpmc.sample_F()\n",
" plt.plot(z, f_sample[0], 'k',lw=1, alpha=0.1)\n",
"plt.plot(z, y, 'o', ms=5)\n",
"plt.plot(z, np.dot(A, f), 'r', label='true',lw=1.5)\n",
"plt.xlabel('$z$: Los-height')\n",
"plt.ylabel('$y$: Observation')\n",
"plt.legend(loc='best')\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Comparison between StVGP and GPMC\n",
"\n",
"The StVGP makes a point estimate for the hyperparameter (variance and length-scale of the kernel, mean function value, and variance at the likelihood), \n",
"while the GPMC integrate them out.\n",
"\n",
"Therefore, there is some difference between them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difference in the hyperparameter estimation"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make a histogram (posterior) for these hyperparameter estimated by GPMC\n",
"gpmc_hyp_samples = {\n",
" 'k_variance' : [], # variance \n",
" 'k_lengthscale': [], # kernel lengthscale\n",
" 'mean' : [], # mean function values\n",
" 'lik_variance' : [], # variance for the likelihood\n",
"}\n",
"for s in samples:\n",
" model_gpmc.set_state(s)\n",
" gpmc_hyp_samples['k_variance' ].append(model_gpmc.kern.variance.value[0])\n",
" gpmc_hyp_samples['k_lengthscale'].append(model_gpmc.kern.lengthscales.value[0])\n",
" gpmc_hyp_samples['mean'].append(model_gpmc.mean_function.c.value[0])\n",
" gpmc_hyp_samples['lik_variance'].append(model_gpmc.likelihood.variance.value[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here the red line shows the MAP estimate by StVGP\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAC+CAYAAADDcdfsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnw8KOlNpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9cd024d320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,2))\n",
"# kernel variance\n",
"plt.subplot(1,4,1)\n",
"plt.title('k_variance')\n",
"_= plt.hist(gpmc_hyp_samples['k_variance'])\n",
"plt.plot([model_stvgp.kern.variance.value]*2, [0,100], '-r')\n",
"plt.subplot(1,4,2)\n",
"plt.title('k_lengthscale')\n",
"_= plt.hist(gpmc_hyp_samples['k_lengthscale'])\n",
"plt.plot([model_stvgp.kern.lengthscales.value]*2, [0,100], '-r')\n",
"plt.subplot(1,4,3)\n",
"plt.title('mean')\n",
"_= plt.hist(gpmc_hyp_samples['mean'])\n",
"plt.plot([model_stvgp.mean_function.c.value]*2, [0,100], '-r')\n",
"plt.subplot(1,4,4)\n",
"plt.title('lik_variance')\n",
"_= plt.hist(gpmc_hyp_samples['lik_variance'])\n",
"plt.plot([model_stvgp.likelihood.variance.value]*2, [0,100], '-r')\n",
"\n",
"plt.tight_layout()\n",
"\n",
"print('Here the red line shows the MAP estimate by StVGP')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difference in the prediction."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f9cbfe135c0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEtCAYAAAD9UJxFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzsvXd4XOWZ9/85M6PpfUajalmScRGYZlNNIDHNQOgtwU6D\n3eBslpBCNptNlmVJlvBjE0pI42XJAvkRU0MLpBgHQiC8FJtigy1sq1hdmt77nPeP0fMguQBGkm3s\n87muuSSdmTnznJnRuc/dvreiqioaGhoaGho7Q7e3F6ChoaGhse+iGQkNDQ0NjV2iGQkNDQ0NjV2i\nGQkNDQ0NjV2iGQkNDQ0NjV2iGQkNDQ0NjV2iGQkNDQ0NjV2iGQkNDQ0NjV2iGQkNDQ0NjV2iGQkN\nDQ0NjV2yTxoJRVFOVBTlSUVRBhVFqSiKcu5uPPcERVGKiqK8PpNr1NDQ0DgQ2CeNBGAD3gS+Cnxo\ncSlFUVzAvcCaGVqXhoaGxgGFYW8vYGeoqvon4E8AiqIou/HUO4DfAhXgvBlYmoaGhsYBxb7qSew2\niqJcDrQB1+/ttWhoaGjsL+yTnsTuoijKXOBHwCdUVa3snvOhoaGhobErPvZGQlEUHdUQ03WqqnaJ\nzR/ieT5gGdAL5GZsgRoaGhp7DjPQCvxZVdXwdOzwY28kAAdwFHCEoii/GN+mo5rOKACnq6r61508\nbxlV46KhoaGxv7ECWDUdO9ofjEQCWLjdtn8GlgIXUfUUdkYvwH333UdHR8dMrW2P8c1vfpNbb711\nby9j2tCOZ99lfzoW2L+OZ9OmTXzuc5+DXZ/3dpt90kgoimIDDuK9sFG7oiiHAxFVVfsVRbkRaFRV\n9Ytqdf7qxu2ePwbkVFXd9D4vkwPo6Ohg0aJF038QexiXy7VfHIdAO559l/3pWGD/O55xpi2Evk8a\nCarho+eo9kiowM3j2+8FrgDqgVl7Z2kaGhoaBw77pJFQVfV53qc8V1XVyz/g+dejlcJqaGhoTJn9\npk/ioxKNRkkmkxSLxb29FA0NDY19jn3Sk9iT6PV60uk0yWQSvV6P2WzGbDZjNBr5OPVbXHbZZXt7\nCdOKdjz7LvvTscD+dzzTjVLN+x54KIqyCFi3bt06jjzySAqFArlcjlwuR7lcRqfT4Xa7MZvNe3up\nGhrTSl9fH6FQaG8vQ+Mj4Pf7aWlp2eX9r7/+OosXLwZYrKrqtIicHvCeBICiKJhMJkwmEy6Xi2Kx\nSDKZJBKJ4HK5sNlse3uJGhrTQl9fHx0dHWQymb29FI2PgNVqZdOmTe9rKKYbzUjshJqaGrxeL/F4\nnHg8Trlcxul07u1laWhMmVAoRCaT2W/6gw4kRA9EKBTSjMS+gsvlQq/Xk0gkKJfLuN3uj1WeQkNj\nV+wv/UEaM88BX930QdjtdjweD7lcjkgkQqVS2dtL0tDQ0NhjaEbiQ2CxWPD5fBSLRcLhMOVyeW8v\nSUNDQ2OPoBmJD4nRaMTv91OpVAiFQpRKpb29JA0NDY0ZRzMSu4HBYMDv96PT6TRDoaGhcUCgGYnd\nRK/X4/P50Ol0RCIRDtQ+Ew0NjQMDzUh8BHQ6HV6vl3K5TDwe39vL0dDQGGfDhg1cfPHFtLa2YrFY\naG5u5vTTT+fnP/+5fMyNN97IE088Mel55513HjabjXQ6vct9r1ixApPJRDQaldsKhQI/+9nPOPHE\nE/F6vZhMJpqamjjvvPN44IEHJhW6bNu2DZ1OJ28Gg4HZs2dz4YUX8tZbb03juzC9aEbiI2IwGHC7\n3WQyGa0xSUNjH+Cll17i6KOPZsOGDVx55ZX84he/4Mtf/jJ6vZ7bb79dPu5HP/rRDkZixYoV5HI5\nHnvssZ3uO5vN8uSTT3LWWWfh8XiAas/JkiVL+MY3voHD4eDaa6/lzjvv5OqrryaTybBixQpuvPHG\nHfa1fPly7rvvPu6++25WrFjBs88+y/HHH8/69eun8d2YPrQ+iSlgsVjI5/PE43GMRiMGg/Z2amjs\nLW644Qbcbjdr167F4XBMuu+DZEjOPfdc7HY7q1atEkN7JvH444/LE7/gc5/7HG+99RaPPvoo5513\n3qTH/+u//iuvv/4677777g77WrRoEcuXL5d/L1myhHPPPZdf/epX/OpXv/pQx7on0TyJKSIa7rT8\nhIbG3qW7u5tDDjlkBwMBVc0jqIaKM5kM99xzjwz7XHHFFZjNZi688EL+8pe/7NSgrFq1CofDwTnn\nnAPAyy+/zOrVq1m5cuUOBkKwaNGiDyUeePLJJwPQ09PzoY91T6IZiSmiKIqWn9DQ2AeYPXs269at\n45133tnlY+677z6MRiMnnXQS9913H/fddx8rV64EqiGnYrHIQw89NOk50WiU1atXc+GFF2IymQD4\n/e9/j6IokzyLj8rWrVsB8Pl8U97XTKDFR6YBg8GAy+UiFothNBqxWq17e0kaGlNGVWFPpNusVpgO\ntZtvf/vbnHXWWRxxxBEcc8wxnHjiiZxyyiksXbpUhoKXL1/OypUraW9vnxTygeoVfUNDA6tWreKr\nX/2q3P7QQw9RKpUmGYTOzk4AFi5cOGkf+XyeVCol/xbnholkMhnZlLtp0ya++c1voigKl1566dTf\nhJlAVdUD8gYsAtR169ap00U0GlWHhobUYrE4bfvU0JhO1q1bp37Y730qpapVUzGzt1Rq+o5v7dq1\n6kUXXaTa7XZVp9OpiqKogUBAffLJJ+Vj7Ha7evnll+/0+d/61rdUnU6nbtu2TW476aST1IaGBrVS\nqchtp556qqrT6SZtU1VVve2221RFUeTt0EMPlff19vaqiqLIdYnf3W63+pOf/OQDj+3DfHbiMcAi\ndZrOlVq4aRrR8hMaGnuXxYsX88gjjxCNRnn11Vf53ve+RyqV4pJLLpFX/+/HihUrUFWVVatWATA4\nOMiLL77IZZddNkncU+Q9JnoNABdffDFr1qxhzZo1HHbYYTt9jSuvvJI1a9bw7LPPsm7dOsbGxrjm\nmms+6iHPOFq4aRoR+YlgMEg8Hsftdu/tJWlofGSsVtjuHDhjrzPdGAwGFi9ezOLFi5k7dy6XX345\nDz/8MNdee+37Pm/RokUsWLCA+++/n+9+97vSWGwfmlqwYAFPPPEEb7/9Nscff7zc3tTURFNTEwAe\nj4dwOLzDa8ydO1cmqz8OaJ7ENCNikJlMhlwut7eXo6HxkVEUsNlm/jbT6vtHHXUUAMPDw+PH9f4v\nuGLFCt5++202bNjA/fffz9y5c8W0N8nZZ5+Nqqr89re/nZlF70NoRmIGsFqtmM1m4vG4FnbS0NhD\n/PWvf93p9qeffhqoXv0D2Gw2YrHYLvcjQk7/8R//wZtvvrnTvoklS5Zw2mmnceedd/Lkk0/udD/7\ny//+PhluUhTlROBfgMVAA3C+qqo7/ySqj78A+CfgCMAEvAP8p6qqq/fAcneKy+VibGyMRCKxQ3WD\nhobG9PO1r32NTCbDBRdcwIIFCygUCvz973/noYceor29nS996UtANW+xZs0abr31VhobG2lra+OY\nY46R+2ltbWXJkiU88cQTKIqyQ6hJcN9993HmmWdywQUXcMYZZ3Dqqafi8XgYGRlhzZo1vPDCC5x1\n1ll74tBnlH3Vk7ABbwJfpZqp/yBOAlYDZ1KtWnoO+L2iKIfP2Ao/AL1ej8PhIJ1OUywW99YyNDQO\nGG6++WZOPvlk/vjHP3LNNddwzTXXsHbtWq666ipefvllOYL4lltuYfHixVx77bUsX76cO+64Y4d9\nrVixAkVROPbYY2lvb9/p69XW1vLSSy9x2223EY/H+cEPfsDKlSv52c9+hs1mY9WqVTt4GYqifOym\nWyr7ukukKEqFD/AkdvG8t4EHVFX9r13cvwhYt27duhkd4xgMBoHqF0pDY2/z+uuvs3jxYmb6e68x\n/XzQZ6eqKs8//zxLly4FWKyq6uvT8br7qicxJZSqqXYAkb29FrfbTbFY3KFUTkNDQ2M6qVQq5PP5\nad/vfmkkqOYzbMBDH/TAdDpNLpebsdnVNTU12Gw2ksmkNvZUQ0Njxpip88s+mbieCoqiLAeuBc5V\nVfX9pR+pSgBHIlWHw2AwUFNTg9FoxGg0UlNTMy1rcjqd5HI54vE4Xq93WvapoaGhMRHNSHwIFEX5\nLHAncLGqqs99mOfccMMNOJ1OKpUKlUoFVVU599xzOf/88zGZTLhcrilLgCuKgsvlIhKJkM1msVgs\nU9qfhoaGxv3338/9998v/y6VSvKCdzrZb4yEoiiXAXcBn1FV9U8f9nm33nrrDkkgVVXlnIhgMIjN\nZsPhcEypKsFsNmM2m0kkEphMJnS6/TXSp6GhsSe47LLLJkmRx+NxXnvtNU477bRpfZ198kylKIpN\nUZTDFUU5YnxT+/jfs8bvv1FRlHsnPH45cC9wDfCaoih14zfnR3x9zGYzgUAAu91OOp1mbGxsyh3U\nLpeLSqVCMpmc0n40NDQ0tqdcLs/Ixec+aSSAo4A3gHVU+yRuBl4Hrh+/vx6YNeHxXwb0wC+AoQm3\n26ayCEVRcDgc1NbWYjAYiEQiRCKRjxz70+v1OJ1O0uk0hUJhKkvT0NDQmES5XEav10/7fvfJcJOq\nqs/zPgZMVdXLt/t76Uyux2Aw4PP5ZPJ5bGwMu92+0wlYH4TNZiOTyRCPx/H7/R+7xpqJXH01BIMw\nb171Nn8+zJ0LWoO5hsaeZ6Y8iX3SSOyrmM1mTCYTyWSSZDJJsVjE4/Hs9one7XYTDAZJp9PY7fYZ\nWu1HZ2wM1q2DM898/8c9/TR0d2+/NU9tbZYlS1RuucVOe/v0VIhpaGjsGlVVqVQqmpGYCfr7+wkE\nAphMJmpqatDr9SiKgk6nQ6/XYzAYJrlwiqLgdDoxmUxEIhFCoRA+n2+3PpyJvRMWi2VGXMTdpVCA\np56Ce+6BP/yhqsyZSMD7FWL99KfQ2QmbNpXo7MywZUuWYLBMMGjgiSdUZs0Kct11Rux2O2azeY8d\ni4bGgYbo8zpgwk17kpGRETZv3oyqqtI4iH4JoeZqMBiwWCzU1NRIo2EwGPB4PLICyufz7VaprMPh\nIJvNkkgk8Hg8M3iEu0ZV4c034e67YdUqmCh9f+SRMDoKra2TnxOLxWQSf/ZshbY2HWefrZfvV7ls\nZ+tWM//7vzX88Id5SqUUkUgEvV6P3W7HarV+rENsGhr7IiJPqnkSM8CRRx7JoYceSj6fp1AoUCwW\nKRQK8pbL5aipqaFQKFRH+el00sswGo0oikIymSSVShEIBLDZbB/qg9LpdDidTmKxGFarVQ5Y31Ok\nUnDCCbB+/XvbGhrgC1+AL34ROjqqLmwmkyUajRIKhYhEIhQKBfL5PJVKhXK5LA2ozWaTZb5NTUZu\nuMGMyeTH7fZTKBRIp9PE43GSySRWqxW73a6VAWtoTBPCSGiexAygKAoWi2WHBrdKpUI6nZaVSAaD\nAZPJhKqqFItFSqUShUIBnU6HxWIhHo+zdetWXC4XdrudmpoaTCYTJpNplx+c1WqVSeza2to9eoVt\nt1cnghmNcP758KUvwWmngV6vEo1G2bIlTDgcJpPJUCqVsFqtNDQ0SM9KNBnm83kymQzZbJZcLkex\nWCSfzxONRkmn0wQCAdxuNx6PR1Z2CSkUv9+vGQqNGaG3t5ef/OQnPPPMMwwMDABVCfClS5eycuVK\nDj30UACuv/56rr/+evk8i8VCa2srF154Id/5zndkccq9997L5ZdX62VefPFFlixZssNrzpo1i8HB\nQc4+++wd1F/z+Ty//OUvefDBB+ns7CSfz9PS0sLpp5/O1Vdfzdy5c6d0vCJpPRPnkAPeSEQiEYaH\nhyflH8RPu92O3W4nk8mQSqXI5/OYTCa8Xi86nY5cLidPjF6vl1QqRTqdpqamBkVRyGazQNW6C4Nh\nNBonGQ2Xy7XXkth33VX1HrzeqlFMJBIMDw8TDAbR6XTYbDYaGhrw+/1YrVYSiQTpdBqXy4XNZgOq\n/1RiTGu5XJYemBjh2t/fTyKRoL6+HrPZjNPpxGq1yvv3VqhNY//lqaee4rOf/Sw1NTWsWLGCww8/\nHJ1OR2dnJ48++ih33HEHPT09zJpVraJXFIU77rgDm81GKpVi9erV3HDDDTz33HO8+OKLk/ZtsVhY\ntWrVDkbi+eefZ3BwcKe5t3A4zLJly3jjjTc4++yzWbFiBXa7nXfffZcHHniA//mf/5lyD9ZMVTaB\nZiRwOp04nU5KpRLlclleGYschcViwWq1UldXRzabJZVKEQ6HqampweVy4XA4KJfL5HI5zGYz4XCY\nYDCI2+3G7/djMBhkCCuTyQBIr0Tc7Hb7tCaxQyF44AG45BKoq9v14w45pGoc4vEkwWCQWCxGqVSi\noaGB+vp6rFarXI8wEKKENxqNyn+I7a9eVFWVyf1EIkE8HieTyVBfX4/b7aZQMPAv/+LhzDMjnH++\nURocDY2p0t3dzWWXXUZbWxt/+ctfCAQCk+6/6aab+OUvf7nDCfWiiy6SumpXXnklF198MY899hiv\nvPIKxx57rHzcWWedxcMPP8ztt98+aR+rVq3iqKOOIhTaUS7ui1/8Im+99Ra/+93vOP/88yfd98Mf\n/pDvf//7Uz7umeqRAM1IYDKZdnqSKpVKZLNZMpkMmUwGvV6P1WrF6/VSKpVIJBKEQiEcDgd2ux2b\nzYbNZsPj8RCJRBgdHWV4eBiHw4HZbMbhcMjchojrp9NpoFrtlM1mCYVC1L3fWf19iEbh8cerxuEv\nf4FyGfJ5uOaanT++XC6TSqWIRqMkk0lKpZJcv8/nmyRuGAqF6Ovro1AokM1mKRaLGI3GScbOYDCg\nKIoMn+VyOZxOJw6HA4vFQrlcll7FPfc08NvfmnnqKRvt7QkWL66KKmpoTJWbbrqJTCbD3XffvYOB\ngGou8KqrrvrA/Zx88sk89thj9PT0SCOhKAqXXXYZjz32GM888wzLli0DoFgs8sgjj3Dttdfy05/+\ndNJ+Xn31Vf7whz+wcuXKHQwEVP/3//u///ujHOokyuXytAmSbs8BbyR2hcFgwOFw4HA4pBeQSqVI\nJpPSsJRKJZLJJPl8HrfbjcFgQKfTyfCMENsql8tEIhF0Oh1WqxWbzYbL5aJUKpHP56UG/ODgINls\nFpfLJXsy3s+FTCbhiSfgwQfhz3+GiQPwFi2CpqYdnyNkQeLxOOl0GkVRpJChy+WSYoeRSIREIkFP\nTw+jo6NYLBZ8Ph+NjY2YzWYKhQLRaFQ+LpVKyS+qzWbDaDQyOjpKX18flUoFj8eD3+8nl8tx9tkJ\nnn66hbVrXVxxRZGnn47S2lqr5Sc0pszTTz/NQQcdxFFHHTWl/WzduhUAn883aXtrayvHHXcc999/\nvzQSf/jDH0gkEnz2s5/dwUg8+eSTKIqy0znZ00m5XJ6xMnPNSHwIhHS4y+Uim82SzWaJxWIYDAZs\nNhv5fJ5gMIjT6ZReidVqBaolo0ajEbfbLb2SVColT6bCaHg8HoaHh8lkMhSLRZnPMBqNk/IZE/nH\nf4SHJkzMWLgQPvOZ6m1nebBsNks8HpcDkCwWC6qqyryCyWQiGo3S19cnT/zV/S6ktbUVRVFkyXAi\nkZAVYeVymUqlIocrjY2NYTAYaG9vx+l0EolE6Ovro6+vD4/Hg06n42tfG+Db3z6Jzk4PX/96kHvu\nieHzaTLqGh+dZDLJ0NAQF1xwwQ73xeNxSqWS/FtU4wnC4TCqqpJKpfjzn//Mr371K+rr6znxxBN3\n2Nfy5cv53ve+J3OUq1at4pOf/CT19fU7PHbTpk0AMlE+E4hGOi3cNEMI70BUBoheCUVRpGcgUBQF\nq9WK1WqlWCySTCZJp9My0S3CLOJEONFQAPJKXeQ9YrEY8XhcGopAIEAwGJR5EJEYTyQSMjEljIbR\naOS88/S88YbCZz6jcMklcMghE+fnvrfuYrFIPB6XJ3VRuisqs9xuN7lcju7ubkZHR9HpdHi9Xurr\n62loaMBgMNDf38/g4CAjIyPkcjksFgt2u13+1Ol0FItFWRY7MDDA8PAwzc3NHHLIIaRSKQYGBohG\noxiNRiKRQa65psS//dsynnrKzc03R/je91L7ZAf6gYyqqpNOrjOBCFVOlUQiAbDT79CnPvUp3nrr\nLfn3T37yE771rW8B1WOcP3++vE9RFBYuXMi9996706vzSy+9lG984xs89dRTLFu2jKeeeoqf//zn\n77umjyLh82GZyUY60IwE4XCYsbExAJmshvcGlhsMhklDiAwGg7x5vV7y+TyJRILieKxHeALiylwk\npiORCLlcDofDQaVSkfHDZDJJLBaTiadyuczw8AjDwz58PgMtLdV1VioVstlqz4J4rYULjfz+90bM\n5mq3+Ojo5GPT6XRkMhny+bz8AtXU1KCqKjU1NTidToxGIyMjI4yNjZHNZnG73VgsForFIjqdjk2b\nNjE8PEw8HieRSJDL5dDpdKTTacxmM4qikM/n5T5tNpsMx5nNZkZGRiiXyxx88MG0tbUxNjZGV1fX\neHHAVr761Rp+8YtTufFGO4cckuCSS4xafmIfolQqyTntM0Vtbe20xNPFiXhno4LvvPNOkskko6Oj\nrFixYtJ9iqLw6KOPyrxhc3MzbW1tu3wdv9/PqaeeyqpVq0in01QqFS6++OKdPtbprApRJ5NJ+ft0\nM5M9EqAZCQYHB7Hb7bL0VXRci7JVnU5HqVSS1U8TEVIeJpNJln+WSiWKxSIDAwMypwHVf7bBwUGs\nVisul0s25dlsNux2OwMDBVavzvH88wVefDFNJJLiqqtm8eMf1+xguMQsW+FpVCoVSqXSpLCUmLhX\nKpVkQllVVdkQ6Ha7GRsbIxqNSrdZdI0PDAyQTCbJZrPk83lSqZQsAS6Xy6iqSi6XY3h4mFKphMVi\nkcavWCxSLBZlqazX6yUSifDyyy/T0dFBS0sLHo+H119/ndHRUdraNnHGGSb+9KcTuO66AkuXRqmv\n1/IT+woGg4Ha2toZf43pwOl00tDQwNtvv73DfUcffTQA27Zt2+lzTzzxxN2aGrl8+XK+/OUvMzw8\nzJlnnrlLT2HBggUAbNiwgRNOOOFD73930IzEDFNXV0cgEJDd1qVSiXQ6TalUolKpYDQaZe5AGBFx\nIhRehDgRC09APDedTqOqKoFAAJfLhd/vl2WkTqeTl1+G3/0OnnkG3vOES0AEk2mIVGqMdNotr84n\nuuQmkwmn04mqqrJaSoSmkskkqqridrtlktnn8006ptHRUYLBIJlMBoPBIMtdx8bGZB4lHi/Q3R1h\nYCBCKmUgn7eSzbpJJs1YrS6WLLHxyU8q6HRpEokEhUJBel+5XE56KELe5I033iAcDjNr1ixax/U+\njEYjJ5+8HoPByE03HYFOV6242j5hqLF3UBRlxqpmZoJPf/rT/PrXv2bt2rVTTl6/HxdccAErV67k\nlVde4cEHH9zl48455xxuvPFG7rvvvhk1EjPVSAeakZBXzGazGYvFIkNLIhmUyWSkAampqZENdiaT\nSYZbRH+F6DoWie1QKCTDNc3NzfKEHQ6HcTqdrFrl5Wc/0wM1gIFFi3ScfrqB008PsGCBgWw2ttPK\nqO0FB4X3oNPpKBQK8jhisRiFQkG6uSLXMTo6SiaTwWg0yuMB6Orq5vHH3+XZZ6P09iZR1SLVMR0e\nwAKYgRAQART++lcnEODgg92cdJKXU0+109ioUi6XKRaLNDQ0MDo6ysDAAMFgkJGREYLBIJFIhKam\nJmpr3/MYTj/9DRIJA3V1c6X3ouUnNHaX73znO6xatYorrriCNWvW7FAGK+L3U8Vms3HHHXfQ29vL\nOeecs8vHHXfccZxxxhncddddnHHGGZx33nmT7i8UCnz/+9/nxz/+8Udey0w20oFmJGRiKplMylCK\ncN9E9ZLRaBxvOovLKgij0Sgb8VwuFyaTCYvFMh4Ogs7OMh0dGXK5IYaGhhgYGKCurg6z2UxNTQ2h\nUIijj85wySUuPvUphRNPhNpancx36HQG8vnqB19bW0smkyGdTpNKpWTMX3gXhUJBNsKJJrZYLCbD\nR+FwWPY4dHZ2kk6ncTqd1NXV4XK5GBpK83/+TzcPP7yeeHwbkKLq0dQAXpzOMnZ7CJNpMwZDAofD\nSKXipKcnQzw+wsaNBjZutHLHHTZqa60ceaSJT3yihiOO8NHQ0IDH46Gvr4+BgQH6+vro7++nubmZ\npUuX4nA4pFFev349Op2O+vp6UqkUNptNEwPU2C0OOuggVq1axfLly5k/f77suFZVlZ6eHlatWoVe\nr6e5uXm3962q6qS/P//5z3+o5/3mN79h2bJlXHTRRZx99tmccsop2Gw2tmzZwgMPPMDIyMiUjcRM\nKkkf8EZi7ty50i0VonXCMxDlovl8HkVRZO5C9DdEozE2b44wMGCit9dMT4+FrVutbN5cQzZr4Oc/\n13P55bPw+/1s27aNUCiE3+/HZrNRKBSYPTvEt79dvWJWFIVI5L0vYblcJp2uhnFcLpcMB4lQ18Tk\nN1SvbESZ7ejoqCzZTafTuN1uSqUS3d3dJBIJKYf+wgtDPPzwJtasGQJeA0aAEg0NMHeuFZerhkol\nKhvo9Ho9gUCA2tpqp7nb7SabdfDuu1bWri2xdWuWYDDL6tVxVq+ucNZZUT7/+Ubq6+s44ogjaGpq\nYtu2bbz77ru88cYbqKrKySefTKVSobGxkeHhYTZv3ky5XMbtdk/ycjQ0PiznnnsuGzZs4Oabb+aZ\nZ57h7rvvRlEUZs+ezTnnnDNJu2l3+DAXLCJvOBG/389LL70ktZv+/d//nUKhwKxZszj77LP5xje+\nsdtrmchMNtIBKNtbxwMFRVEWAesef/xxFi5cuMP8CJFjmBjzz2azcuyoTqfjwgtLdHWVgDLVsAxU\np61WpcZXrixz+eXV97dSqRAOh0mlUng8HpqamlBVlUQiIb2Qcrksv2TiQxcho4lT7EQ999jYGPl8\nXhoI8TpCYDCRSMgk88jICJlMBovFwptvwqpVIbq7R4GtwBb0+iSNjRZmz7bi9brxer1UKhUqlYpc\nj9/vJxAISBkTETrKZrNUKhVU1cTAgIktWyx0d+eAEm1tVr7ylQaOPHIBra2tMqn/8MMPE41GOfro\nozn22GMaq84vAAAgAElEQVTR6XSy69xsNuPxeJg7dy7Nzc2aNzGNvP766yxevJh169axaNGivb0c\njd1gV5/dyMgINpsNh8MhHwMsVlX19el43QPek/iv/4piNI6Sy6nkcir5fIVCAWw2hTvvRFY7GQwG\nWTMuqom8Xj1dXTU0N0N7e465c2vo6LBx8MFmWlsVHI6qhLbBYKBUKtHW1kYoFGJwcJChoSHq6urw\n+Xxks1lZXSROiKKaSuQQCoWC9AgSiQSVSkXqKwkjkEgkpLaS8IjC4TD9/f3juRd44okMmzcngDgw\nis0Wwecr09zsxuutSnLU1tZKr0kYT5/Ph16vJxKJyGY6IWSoKMp4DidDS0uBefNKvPtuDatXO+jp\nSfKf/7me88/vZvHiZhYuXIjL5eKEE07g+eefZ8OGDej1eg499NBJ3d6jo6PYbDa8Xq+m7aShsQtm\nupEOpmgkFEU5BTgFCLDdTGpVVa+Yyr73FGvXeoB6qh6ASGqpgIrHU0FRKrJ/QvRMiCqmH/84i9lc\nwGqtDt0RZbA1NQVMpmp4KJvNygSx2WzG5/Mxe/Zs+vr6SCaTstQ2FovJ0BBUy9nEIJ9Zs2bJXgpR\nclpbW4vRaJSJ7fr6embNmiV1lsbGxujv7ycajVKpqPT0GHnhBZVKpQBEqKsLU1ubo74+QHt7O3V1\ndXg8HrxeL6FQSOozWa1WfD4f5XKZUCgkDZpowDOZTDIpr9frSSaTRKNR2toSfO5zOp54ooVkssT9\n94cYHOwiGAzR2jobn8/HwoULefPNN9m6dSuKojB37lxqa2vJ5XJEIhFGRkbo7/dz/vlzmKYqSQ2N\n/YqZbqSDKRgJRVGuA/4DWAsMUz2zfuz4+tf9zJ0bwGxWsVhUzGYVk6n6e21tGSjLTuVCoSCv4hVF\nwe+vQVGM8upeeByiNNZkMkkp7EgkgsFgkF3KwqsQiWiTySTj/na7XVYICeXZwcFB8vk8bW1teL1e\n2RQXiUSoVCqykzudThOJRBgbGxufhWHmuecsDA6qQAmrNc2hhxZpaWmkra2NQCAgv2hGo5GtW7ei\nqiq1tbXSexobG5PhpqamJtnbkclkGBoaYmxsTM6d0Ov1eDwestkspVI/Z501yquvNtLT08Df/pZh\nZCTKWWd1EQhEaW1tJRwO09PTQ39/vzS+DoeDWCzGLbfE+dvftnH77Q187WvWvfcl0dDYR5npHgmY\nmifxFeBLqqr+/9O1mL3B+ecXOfzw4k7vUxQD8N6wIRHCKRaL5HI5UqnU+MmwhE6nI5VKTTrhptNp\nhoeHpXqsxWIhnU7L3guv14ter5fDi0qlEqFQCEVRqK2tpVQqEY/HMZlMHHTQQbLvIpFIEI1GCQaD\nUnspHo/T09NDb2/vuDdjZtu2dv74RweQBuIcddQw7e1l6uoOYv78+RSLRaLRqFxvX18fOp2OWbNm\nSQkQi8Uim+Si0aj0joQR9Pl8kyRE0uk0mUyGQCCAXq+nv7+f+fPfxGZz8fbbTWzerDA0FOYTn+hF\nVVUWLlxIKpViZGRENic2NjaO/ywDQa67rpsvfnEhM9SwqqHxsWVfNxJG4KXpWshEFEU5EfgXYDHQ\nAJyvquqTH/CcTwE3A4cAfcANqqre+0GvJZKzgp0l8iduUxRF9lI4HI7xEZ+ZSVPsRJ+EePzY2Bjb\ntm1Dr9fjcrnkJDybzYbFYqFQKFCpVOTv69evl7IfNTU1WCwW2cQXi8VklZO48u7t7WXLli2Ew+Hx\nEJSf3/3OyshI9STb1FTktNMymEx6LJZ6AoEA3d3dhEIh7HY7gUCAZDKJ3++noaFBKruK5kGh3SOM\nRjablV9O0WUu5D9EnkLkU9rb2xkYGCCXG+Goo6KsXVtPKuXkT38aI59/ieOPTzN37lyy2SwDAwMy\nzGY2m1m6tMKaNUVCoW1cd90sbr3V9UEfp4bGAcVMN9LB1IzEXcBy4IfTtJaJ2IA3gV8Dj37QgxVF\naQWeAn45vqZTgbsURRlSVfWZ93uuOFlPlL4QPyeK/X2YD0FUEuVyOSngl81mMRiq1U7xeJxIJEI6\nnZayGSaTSfYKiJOsENQbGRmhoaFBjlatVCqyMU3IemQyGUKhEOVymdmzW1m71sojj6SBHAaDypln\nVpg1K0I6naJSMcr67IkNbdFoVI4kDQaDKIpCIBAgHo8TDAZls6GYxieGJ4nOcavVKhv2yuWybO4r\nl8tYLBYOOeQQenp66O7uxuOJ8corZRKJWl56aZSamjdZuHAB9fX19Pb2Smlx0cPxuc/puO22KD//\n+Ra+/vWjGG/U1tDQYOZ7JGBqRsIMXKkoyqnAemBSzEZV1W991B2rqvon4E8Ayoczkf8EdKuq+p3x\nv99VFOUTwDeB9zUSf//73xkbG5Mlr8Iw6PV6WRIrPIeJ20RZ6MSbuF+UtIq4fSQSIZ/Py3kMUFWH\nFGGbvr4+OfxHr9fT1NTEvHnzpMSGCHMNDg4SjUbJ5XK4XC4ZeqrKavi4554onZ0DgMrs2VbOPluh\nVIqRSmWlXHlPTw+RSIQFCxbQ2NhIOBzGbrdTW1srZTpqamrkXGCXy4XRaCQajcqkdV1dHQ6HQ5YE\nC2VZkW8pFAqkUikMBoM0hLNnz8bv99Pd3Y3JNMAf/pAkn69l7dowRuMW2tvbcDqdhMNhuru7SafT\nzJ8/n0MPVejoMLFpUx9f/3oLTzyx4yAZDY0DlX3dSBxG9WofYOF29+3pJPZxwJrttv0ZuPWDnjhr\n1iza2toolUqyxFX8FPpMIrwiSj0nehk6nU4K5wHS85jogQgF13A4LE/Cog9CNO4JtzGZTPLGG2+w\nceNGbDYb6XRaGgar1UpraytNTU2EQiHS6TQmk4m+vhK/+U0X6XQecHHqqS6OPjrPyMgw6XQavV6P\nxWJhYGCAbDbLkiVLaGpqIh6P09DQgKqqUsZbTJYTxjAYDGKz2fD5fMyZM0f2MwA0NTXJUtlCoUAu\nlyMajcrfhay5yLV4PB4WLlxIfX09yeRann8+QSTiZNOmBDU1/TIUl0ql6OnpweVy0dzczBe+YOXf\n/i3Nk0++y4sv+vnEJzTxPw0NmPlGOpiCkVBVdel0LmSK1APbCWUzCjgVRTGpqprf1RPFwB/hPYgT\nv16vl96DqEUWarATu54n6qaI5LaqqlJwTyS59Xo9brdbXpUPDw/LhLXD4cDr9dLU1EQmk2F4eJiu\nri5ZGqrT6XA6nXi9XjmXoSpPXmLTJpXXXksDHmy2Nv7xH500NGTo768O/hGyGJFIBKPRSEdHB1ar\nlU2bNsmwl5iOV61OMlAuNxKJWPjEJ0wy/JXL5RgdHZWSJMVikZGREZnPURQFs9mMzWaTYSmRhHc6\nnRgMBtLpNB6PB5vNxgknzCOT2cZrr+no6nLhdMZobY3LPEs2m6W3txeTyURDg4GTTnLyt78N8q1v\n9fHqq60z8iXS0Pi4MZMT6QQHfPV5Z2cnhUJBegMTT3oiBCVCSGazWd6EnpNI3AqDIfYj8hCiG3ri\n7AWRtxAn6Hg8TjQapbe3F71eT21tLR0dHXKfVquVUCgkwzDFYpFEIsezzw4TDheBBpqbrVx4YRJV\nHWP9+pAsizWZTAwMDJDP52lubkZVVYaGhgiHw8TjcQqFwvg+y6RSLnp63CSTVsDBsmUGyuWyDHvl\ncjm5flVV6eyEYlFl3rwaAoFq02AqlaKurk42CoZCIal3VSgU6O3tlYbkqKOcRCIpurp8vPGGGZ8v\nhsulkkwmpXxIbW0tiqLwmc/YUdUKX/nKVnK5+hn/x9jfERPTND4+bP+Z7YlGOph6M50b+AegY3zT\nRuDXqqrGp7qw3WQEqNtuWx2QeD8vAuC3v/2t7OgVIaOlS5dy0kknyVGl4uQojIkwBqIKR+gqmUwm\nDIZqyazwSoQAn9A/0ul0svFM6DOl02kAOf5UhHDMZjPJZJL+/n6KxSJOp5O+vj42bhxi7dochYIN\ncHPccU6OP15PMhlmcHBQJo9zuRy9vb1UKhV8Ph+9vb0yeV6pVHC5XICBUMhHZ2cL5bKoMS0yf36O\nSKQgS2ybmprksVmtVkolPatXlymVKvT3F+joUPjUp3Q0NGSIx2NkMhmam5uZN2+enKktcijBYFD2\nhyxapCMSGSUancVLLzk477w4TqdKMBgkFAoxOjo6/j4PcNVVLZRKY3R3d3PwwQdP25fnQELMX5/p\nmcsaM4PVasXv9wPVc9dvfvMbqQAN1TGt081H1m5SFOUoqnH/LPDq+OajqWpKnz5duiGKolT4gBJY\nRVH+P+BMVVUPn7BtFeBWVfWsXTxnEbDu/vvvp6OjQ77JE3MJQsdJ3BRFkTMnhJ7TxF4JgZhJkUgk\nZG+DKJ0VJaJin1arVQ4nEqGtsbExOddB9GWkUin6+/vZuDHN+vU1gA+rdT5XX+3BYhmgq6uL4eFh\nOVrUYDDIrmm3200oFCIYDErPpLW1jQ0bTFTnswQAC2aznpNOMnHccRUcDr1UmjWZTHIehsfjwW63\nUyyW+fOfk9x1V5i1a0NAEMjS0GDiU5+CxsZtZDIJ/H4/fr+fUqlELpeTHtU777zD8PDweL6mzAsv\nGKhUWpk1y8o55+TYsGE9Y2NjuN1uOWdYNBK6XC6OO+442aiosXv09fURCoX29jI0PgJ+v5+W8XGV\nhUKBUChEIBCQg5tmQrtpKkbiBarqcF9WVbU0vs1AtTS2XVXVkz7yohTFBhwEKMDrwLeA54CIqqr9\niqLcCDSqqvrF8ce3AhuolsD+L1WpkNuAs1RV3T6hLV5jEbDu0Ucf5eCDD5ZhJlH/L0I9IhdRFbBT\n5TYhkSFyFKJXoFgsynCT6IMQUtjCsExUcdXr9XLSXDhc9QTEyVyn08nu7WAwxObNZWIxF9BEc7OT\nU04pUSolGRkZkbOyGxoacDgcMulutVoZGhoimUxSW1tLS0sLjY2NJJNJ/vd/CwwP+/H5bCxbVsNx\nx1UwGFScTid+vx+LxSJdWSG6JzrDhXFMpVJs2pTid79L8txzYVQ1A1hxOj1cdVUQVQ1K7Sez2Uyp\nVKKmpkbqTW3ZsmV8rGSOt97SA3UcfriTgw4Ks23bNiKRCB0dHRx99NEUCgXa2tpkWe0RRxzxUb9i\nGhofe8Q444aGBlmiv68J/B3FBAMBoKpqSVGU/6Yq1TEVjqJqFNTx283j2+8FrqCaqJ414XV7FUX5\nNNVqpquBAeAfdmUgJiIa4SYivAqRvJ5YHit+ioSyuNIXxlYkgHO5nBTpE93LoglOeCKiazufz8tJ\ncUK4T8yBSKfTJJMVNmzwks06AQvHHutm2TInuVyOrq6gnMHg9XqxWq2yNFWMKDUajZx66qnU19fL\nOKbBYOAf/qGeSsXL0qUmVLUsG+sMBgOFQkGGrQwGA8lkki1btjA2NibnVvh8PhwOB8ce6+SYY1SC\nwTwPPRTlkUeG8HhGaWy0k81Wx5du27Zt0ghYs9mM2+2mvb2drq4uAA46KM/WraO89VYGg0HB5/OS\nzWZ59913aWpqIhAIEAqFcLvd9Pf3097ePmNzgzU09nX2RCMdTM2TGAU+r6rq6u22LwN+o6rq9jmC\nfQrhSbz22mtSdvf9pjupqiqrmiaWxwovQzx3YnI6Ho8Tj8dlJ/bEvIUwPplMhmAwSC6XkxLi0WiU\nLVu2kEqlePvtHL//vZ5KxYnJ5OOaa7zU1Y3Q2dlJMBikVCrhcrlwOp1yfKmYLZ1MJikWiyxZskSe\nYGtqaqhUKni9XgKBAKqqSsE+Ua4KyLxJLBZjeHiYcDhMNpuVQoaZTEZWLgnVWJGLiURK9PT0UChU\nO7qFZInQgorH43R3d6OqKh6Ph2QyyZtvvkksFqOzs0IoZAAqLF3qQlXT9Pf3Y7FYOPfcc2Wll91u\nZ+nSpXKGsIbGgYYoPJk4g3xf8yQeBH6tKMq3eU+e4wTgx8D9U13YnkJY4olhJRFaEmNLRQJbhItE\nL0Uul5PlmqKMNJ/Py6t10SdgtVqxWq2yKa5cLpPL5eToVJfLxTHHHEM8HmfDhg0Eg0Hsdif/9//6\nePbZAmBl9mwX//zPRQqFLWzc2E8oFJKzqIV0hkimi/3bbDaOOeYY7HY7Q0NDMv8xcbi91WrFZDLJ\n/gihnxQOhxkbG5MJZ7PZPEncT4xHFYOZxBwLp9NJW5uPQw6ZRX9/P8FgEIPBgNPplD0iTU1NWCwW\n+vv7UVWVOXPm4PP5ePHFFykWB4lEUlQqBd54o8KiRVWPIxgM8sorr3DsscdKMcGuri5aW1u1SieN\nA5LtG+lUVZVabNPJVIzEt6mGgn4zvh8FKAC/Ar479aXtGf74xz+yadOmSTkIYSRE7kB4WyLEBEgl\nWBFKErLaIuRjsVgwm82yO1uEpkqlkhS0M5vN1NXVUalUePHFFxkeHsZiseB2t3D77UF6e4OAiWOP\nhcsuU8jl0lImo1QqEQhUZb4nJs49Ho8MoYlqqGQyicvlwmw2E4vFcDgcJJNJLBYL0WiUYrEo52eL\niqh4PI5Op8Pr9eL1enE6ndK7EMbU6/Xi8/lkP4hoEszlchQKBQKBgOwLsVgs8kscDoelHMnw8LCs\nhDruuON49dVXCQY30d2dJBbLMjDgxu+vjmvt7++ntraW1tZW8vk8b7zRz+rVI9xySyszXAWoobHP\nUS6XMRqN8m+hRj3dTKWZrgB8XVGUfwPmjG/uUquZy48N2WyWdDotT+Si+kj8LuS/J/4uKpOEYRAn\nR1HFBMjnA5OS26JvQOQj1q9fz9DQEIVCgfr6erq64PHHN5DPq4CfL3zBy/HH6xkaGqK/v1+udd68\neZx66qnEYjE5xEg0s7lcLvR6PevXexgdzXLuuS4peWG32+UQpFQqJZPder2eYDDI8PAwAPX19fIk\nL45DHJ/NZqOmpkbO2Bbvo5BRF1pT6XRa5iHi8bhM5gPSiOh0OgYGBujv72fBggWcc845NDc3c9dd\na0gmM2zeHKemphrGyuVybNy4kVKpRF1dI7fcEiOf7+SEE2Zx6aWaldA4sJjYyAvVfKjQVZtOdstI\nKIpyC3Ctqqrp8d939hhgatpNe5L58+ezYMECWd00UWJDXDFPPPFP7MpWFEVeNYuENlS9DBFOymQy\npFIpaRwmVkrl83n0ej1z587F6fTx4INJ/va3EKDD4dBx6aVp/P40GzdmpMxFXV0dixcv5sgjj+S1\n116js7OTYrFIY2MjFouFXC6Hz+fn0Ud9PP54CDBwzjlzmT+/2hjndDrll6vaJ4E0NGK0amtrK06n\nU3pIE7WpdoXwnGKxmBQctFgslEolmYMxGAz4fD5uummUhoYRDj64gMtVnZctRqHOmTOHww8/nGXL\ngjzyyNtAjpERaGysFgvE43Eph37kkTZefnmI//qvQS65pAVtyqnGgcLOGumEssN0s7uexJFAzYTf\nd8XHZgBRMBjE5XJJAwDv5Slqamom9U0I7aaJhkSUrwqhO5G0Fg10qVSKZDIpT5ZOp3OS1LfT6WRo\nqML1148yNjYGZFmwQOGsszx4PHYURcFqtZLP5/F6vRx22GE4nU4ee+wxRkZG8Hq9zJs3T9ZJNze3\n8J3vDPLSS0OAns9/fhZz56ZIJvXMmzcPh8Mh52kLBdlEIkEgEOD444/H6/XudrWEMIoiNyN6NEql\nEi0tLZhMJsLhMJs2beKpp8Lcc48TRWnm9tuLzJtXFQNsamoiGAwyODhIIBBg0aKD2bQpxjvvDBEO\nZ/D7FTmatSpJUmT+fC8vv1zHhg2dPPdcMyefrGk6aRwY7GyORDab3ftGYqJe0z6m3fSRcTqdOzRl\nCSMw0ZUTPRITcwsiUZ1KpaRhEAlvIdwnprX5fD6MRiOZTEZ+mHq9nqeeCvLEE1GgjMHg4MorD+PS\nS+diNBrp6elh69athMNhKbLX39/Pli1bqKmp4eCDD8br9VIqlVAUhfb2OXz3u9t46SUd0MT3vmfm\nk58sEIulcLlcpNNprFarrIoQ+YjZs2fT1NS0W0Jh2xsGQFZJmc1mmX8YHR2Vxzp//nzc7hjPPZfj\nhRdU/uM/stxzjxGXqxrqEhVZsViM+vp6Tj65na1bi+TzCfr7w7S06GTDYS6XY3i4i8MOC7B+fS0/\n+MEAJ5/cMh1fCQ2NfZ6dGYl8Pj8pRzFdTGV8aQvQr+6khlZRlBZVVfumtLI9hMPheN/OXVHqKk6I\nQndJ5BlEZ7bNZpNd0yLeL0IuRqORXC5HLBZDVVVcLhfFoolf/zrLhg1WwM+CBTauu66O2loIhUJs\n3bqVYDAIwLx58+jo6KCvr4/+/n48Hg+1tbWUy2VGR0dxuVx0dHTwox/188wzesDLtdeWufjiap+B\naOMfHh4mFovhdrspFAqYTCbZa/BB3sPEZkDhNQGyG1sMRhKPFcYwHA5TLBbx+Xy43W78fgc33ZRj\n+fIkvb05vvnNJD/9qQOr1crw8LAsC9br9bS1tfLJT4ZZvVohk9ETiw3jcOikMa42Em0FZvH881t4\n/fVGFi064OXINA4Atp9tLS6c9ikjAfRQnRo3NnGjoii+8fs+FplE0VsgEqyiukk0xAmLLTqFjUYj\nHo9H/i6kOoQREFLZonpHVVWZmxDx93XrVH75yyxVmRWFCy8sc8YZGQqFQTo7kzJ3IWTM3W43mzdv\nJpVK0dHRgaIoRKNRyuXy+InXzy23bObhh5OAm5Urg5x/fhvFYhGz2UxDQwM2m41yucy6devYsGED\njY2NckRqIpGQiXmRuN8+jFYsVseFiGoukbMQ71UikaBQKJBMJmXC2mg04vP5qFQqsuLK6/Vy6KFO\n7rknyDnnFOjuDnLTTUGuv95NIFCmu7tbyrKbzWaOO24Ob7+dYGjIRyQCFsugzAGVy2VisX5aW7fQ\n2+vnBz8Y4vHHNW9CY/9n+0Y6Ed0wmUzT/lpTMRIKO8892IHcFPa7R9myZYuU7J44I0JoLZnNZmkQ\nRC5BnEDj8TiJRIJMJkOhUJA5h4aGBqDqEQghu+rUtzK33RbhrbdKgJ7aWiNf+UqZBQuqE96i0Siq\nqlJfX4/L5cJgMJBIJNi6dSt2u505c+YQj8cplUp4PB4CgQBOp5OxsTHs9gRg4tJL81x6ab00cI2N\njaiqSigUYmhoCIPBQEdHh+zojkajkzwA0Rg4cUKfaAAU4SjRLS4QUiPCkPr9fhwOh1R7BeSsCZFb\nOf74Ju66K8RnPuPjxRedPPDACF/72kG43W42btxILBaT1WSf/KSf++8fpVBopFjMAwOkUimZqG9u\n3kpvbwtPPNFDV5efOXOse+z7o6GxN9i+RyKfz1OpVPYNIzGhqkkFfqgoysSaKz1wLO8NI9rnaWtr\nY8GCBZPe8Ind0yIXIXIQE8eTCjkP0e9gtVplB7XoMHa5XFQqOp55xsKDD1oplRTAzsUXV7joohgu\nV9UbGRkZIZlM4nA4qFQqUv6iUqnQ2tqKw+EgHA5jsVhwOBzodDrS6TTDw8Pk83nOOCPAaac1ccQR\nPny+qhSGzWaTYZzBwUGMRiOzZ8+W8yCEui0gFV6FdwTIDuqJiXvxvgiZdOE12e122R+ys851s9mM\n3+8nEokQCoXwer18+tMufvjDONde286vf61w0knDLF48i2KxyNq1a9HpdOOCZmkOOSTEO+8kGBiY\ny/z5GZLJbSSTyfGkfpAjjuhi2TIPOl0Llcrs9+2e19D4uLO9kcjlcpPK7qeTj+JJiKomBTiUagOd\noAC8BfxkiuvaYwQCAerq6mS4aaL0RixWlbzO5XIyhKKqqpTXaG5upr6+HkB6FaLTWSSqu7ud3Hyz\nj97eApBh4UI9X/taErt9FFVVGBuLEQ6HZXJ3cHAQQDbmzZ8/n3K5zNDQkOysHhoakpVXYha0z+fD\nZDLh9XqJRqM4nU6cTifbtm0jkUgwZ84cmpqadpp7ENP3stmsHCpkMpkmiRoKL0P8FB6HMEQf5ssp\nvAzRUOfz+fjnf7bS15fk3HPn0dpaNZbNzc2Ew2HeffddmpubaW1t5aSTEnR1bSaXSxGLtWI2j0oJ\nEkVRaGrqw+1uobOzU8qSaGjsr2zfSJfL5TAYDDI8Pp3stpEQVU2KotwNXK2qanLaV7UH+dvf/kZX\nV5fshxDeA7wn8Gc0GnG5XNjtdrxeLw6HA6g2i42Njck8hJD+rl7d2rjzTid//CNAL3Z7gS9/2cxR\nR0WJRsPE4zo51lQI6BWLRQKBALW1tVJbSSSrxZWxMB5Op5Pm5mYpyyE0jZLJpEymd3Z2UqlUaG9v\nx+127/I9mCgHLrwkoc00MQw3sXtceB67Wy6r1+vxer2Ew2FpKH70oxLlcgyPZx4A0WiU+fPnEwqF\nGBwcZM6cORx00EEsWZLg2WdHGR110NJSSzrdSyQSoaWlRc6e2LZtG42NjVLmXENjf2T7RrpcLofJ\nZJIFJdPJVHISW4CLgbsnblQU5QqgVlXVm6aysD3F/PnzOfjgg2XJqzgBiuFAojpJhJui0Sh9fX3E\nYjFSqZTsY2hsbBwPFTl5/HEX//M/Kun0MBDhzDMt/NM/WQmHexgZicgSUZEYFp3Ps2fPlvLiBoOB\n9evXy4S3CF3ZbDZsNhtNTU2YzeZJ2kpCXbZYLDI4OIjdbqetre1Du6CKomCxWGTH9Eyh0+nk1LpI\nJCLHqyYSCebOncvmzZvJ5XIsWrSIv/zlL/T399PY2Mjxx7eycWOSkZEsIyO1OJ1B2YditVrp7u6m\npaWF7u5uHA4H7e3tM3ocGhp7g+0b6cS5xG63k0xO/zX7VIzElcBndrL9HeAB4GNhJCbmJCbmIIrF\n4vicg1EikYicnwDvxe/r6+txOp24XC5CIQt33WXgvvuyFArDwBitrfCtb9morQ2yfn0XhUIBr9dL\noVAgHo/LkjUxSMdiseDz+bDZbEQiEbxeL36/n2w2i9lsxuFw4Ha7aW1txWQyMTY2JstsTSYTwWCQ\nWCxGpVKhrq6Opqamvfrevh8TDYUoy41Go+RyOVpbW9myZQtut5vDDz+cV155hXg8TktLC6ecMspv\nf5Hku9IAACAASURBVNtPoWDGaKwnk+kmFArR3t7O6Gi1IVH0kzQ2Nmrifxr7Hdv3SIic6Uxd3E3F\nSNSzXfnrOEGqpbEfC9566y0ZLhK5CFHWKbqkJ47tNJlMUoJCr9ezZYuOu++O8cwz24AwkGHWrBou\nusjO0Ufn6e19h+7uIE6nE6vVSiQSkeWhDQ0N+Hw+amtr8fl8BAIBACnpLSTGbTYbHo8Hh6ORG25o\n5sc/1mE2R8hmszJpLBRXPR4Ps2fPliGxfRnRZBgKhWTSPpFI4Ha7qaurIxgMsmDBAkKhEF1dXbS0\ntHDYYXN5+eUQXV1hxsbq8HqDJJMJotEoDoeDt99+m8bGRiKRCENDQ5o3obHfsb2REOetmbogmoqR\n6KcqDd6z3fYTgKEp7HePMjAwICU5JuovGY1G7Ha7DP2IZO7/Y++9w+w6y3vt+9197V5nZk/RjMrY\nslUsS24YAi5UE5qPDS4EbJOAz+ckREAgQLA/QmiBACGBUE9w4csBTvAJdmLjmICNLcuybNmyJKtY\nmqqpu/f+fn+sWUszY8lFs7dmRl73dc2lmbX3XuvdkmY/63mf5/n91IKxlR07JHfdVWTXrkmgADRY\nt87G1VdHWLu2wNGjR3j66QlMJhOdnZ16X39nZyeBQEDvBAqFQrpCq9aqGo/H9WDi8/no7Oykq2sl\n11/v58EH4cCBEv/n/2T1ra5Dhw4Rj8fp7e2lr69Pl+hYDmh6TrFYjHK5jKIopNNpgsEguVwOKSVb\ntmwhmUwyPj5ONBrlDW+IcvjwNLWaGbu9g0wmTTKZxO126wVvzZiop6enJR0fBgaLxfxButmdTfF4\nvOnXW8inyQ+BbwkhrMB/zxy7HPg7jjnJLXn6+vro7+/X6xGzRfy0eoTJZCKZFPz+93UefrjAtm1l\ncrkqahewwhve4OS974VQKMnExGEOH85jMplYuXKl/g853/VNm9D2+Xx6ABoYGGB0dJRCoUCj0dBl\nsXt7+/jjP7bx4IPgdDb4wheSlMuql/XBgwfJZDKcddZZJ+xeWupYrVZCoRDxeFwXFFQ7xTq5//5B\nrrkmwHnnncfDDz/M1NQU69f3s2LFYYaHC0xMRAiFJvXuMrPZzMDAAB0dfTz/fJje3qP09fUt9ls0\nMGga8wfptM4mOBZAmslCgsTXgBCqr7TWi1UCviql/PJCF3aqOPPMM9m4ceOcGQCAWk2yc2eNBx4o\n8bvfVdm3rwaYACdgQ1HKvPnNBd72thyKkiGdTjM+rlqAhkIhXbvJ4/HQ3d2NEEIvKoXDYd28R/NZ\n2LNnD0ePHtULUl1dXfT399PR0cFf/7WZu+4Csxl+8pM0q1ap083xeJxKpcKmTZvmuFMtR7RJ9kQi\ngc1m48CBBm9+c4N6PcJZZ01zzjkryefzbN++nVQqxR/8wQp++tO9VKugKJ3k83nS6TSKohCPp/nc\n5/YAZ9DfP/SKdakMDJYyx5uR0KwKWsFC/CQk8CkhxBeAs4AicEhKWW7W4k4Fv/1tjW3bSkxM1Jma\nkkxPN5iebjA62qBQqAI1QP2zry/Hpk0lNm2qsWJFiUajMjN9bCYSieBwOMjn85RKJZxOJytXrsTh\ncJBIJKhWq3qB2ufz6W2n6XSaffv2MTU1pdc++vr6WL16NX6/n+9+V/CVr6hr/e53i6xbN87ExLQu\nW3HGGWfo2kzLHYfDQSAQIJlMsmaN4OKL8/z2tz6++MUCd9xRpq+vj0KhwK5duwiHXfT0uBkZyTI2\nFiQQcM3JJvz+o6RSk/ziFzZe9zojmzA4fTjetLWiKFSr1ZYMkS5481pKmQOeaMJaFoWvfnUCmF/k\nbQBVHI4S555bY8OGCuvXV/H51AGyY3uCVn2ITbuT9Xg89Pf343A4mJycZHp6GpfLRXd3N36/H4/H\no088j42NcejQIV2CwufzsWbNGnp7e3E4HNx9N/zZn6kruu22Gq95zfMcOTKE3+/XO59CodCp/Otq\nOZqMeSaT4Utfklx2WY4dO0L84hfjXHedwsqVK8lmszzzzDOcd16EkZEU5XIDq7Udq/WYwm4oZCOV\neppf/7qf/fuH6OnpaYmMsoHBqWb2IJ3WYKO1wC+6VPh8hBCXo9Yh2lD3YnSklDct5Nynir6+LNFo\nmkCgQSAg8fnqeL3qVzRaxWo1z8xNHBsc00x4rFYrlUqFTCaDoiisWLECm81GPB5nZGQEgM7OTtra\n2ggGg1gsFsrlMplMhqmpKQYHB/UhmHA4zJlnnklnZycmkwkp4RvfACnhppsKvPvdh3j++SNEo1Gi\n0Sh2u51QKLQsaxAvhdvtplKp0NNT5BOfqPKFL9j4u78LcMklSUIhF2vXrmV8fJxsNks06mJ8PM/U\nVIhAYFKXLrfZsthsg5TLCe67z83atSNGNmFwWjA7k9CChKIo+mBus1mIVPhtwK3ATmCcZWQ0NJuP\nfKTE6tUlXW5DCIHZbJ9j2akN2mkid1rRSNNuikajumBeJpNBCEE4HKarq4tIJILFYqFYLJLNZnWh\nu8nJSarVqj5vsW7dujlT0ULAPffU+NrX0lx11VFGR9W+/xUrVJXTYDB4Wt8Z+/1+qtUqN9xQ4u67\nC+zZ4+FrX6vwzW+qZk1btmwhFouxYYOH8fHMTDYRpFQam8lE0oTD04yNPcm///sfcuWVw3R3dy+r\nzi8Dg/nMFuEEdO01q9VKLpdbcpnEzcANUso7m7WYxUDbutFkwrVhOi1aa6J3msyDZjKUy+UAVdIi\nkUhQKBSw2Wx0dXXR1dWF3+/X7U21qWgtQKRSKarVKlarlZUrV3L22WfPkZCQUpLP58nlUlx3XZp4\nPEVbWxs9PT1IKQkGgy3RjV9KmEwmgsEgjUaML3+5zLveVeBXv3LzrneluPhiH9VqlQ0bNpBMJmlr\nm2ZqKsfUVIhgUB3IE0LMKOPuY2jozTzxRIWVK8f0IGtgsByZPyMxu7MJWFqZBGpH07ZmLWQ+Qohb\ngE+gDu09A/yZlPKEtQ8hxPXAXwL9QBq4D/hLKWXixa6jdRNpw3Fms1nPFjStIs0rIpfLzfG8LpfL\nJBIJPB4Pa9eupaurC6fTSaVS0YUBNT2oUqmkS08UCgWcTidr1qxhzZo1L7AgzGQy5PN5PXApikI0\nGkVKidfrfdVMEVutVrxeL+eck+BDHyrywx/a2LPHyaWXqkHkzDPPZHh4mLPOGmVqapJyWcFk8gGl\nmb/zHOHwJLHYLv7jPy7moouGiUajRqeTwbJlfpDQanBanXSpBYkfAdcBX2jSWnSEEO9DnbX4MLAD\n2Ar8WghxhpQydpznvxa4HfgocC/QBXwf+AGqvtQJ6erqIhqN6uqv9Xpdd57TPuw1JzRNBVYrXgeD\nQXp7e4lEIrp0djwe17eotGJSsVhkbGxMDzIdHR309/fT3t4+J+BkMhldz0kb4Mvn8wQCAYQQetvs\nqwmXy0WlUuHP/zzJW96S47LLPBQKqp/G5OQkF198McPDw+zeHSOZzBOLBfH7k7rHhdudpFLZySWX\nvI5SqcTExAQ9PT2L/bYMDE6K+UGiXC7jcDj0nY9W1CgXEiQcwIeFEG8EdqP2iepIKT+2gHNvBb4v\npbwDQAhxM/B24CbUYb35XAQMSCm/M/PzkBDi+8AnX+pC4+PjuFwuvcdYSqkbC2kBQhuwczqdeL1e\ngsEggUAAu91OtVolk8nokVwbBtOK1NPT00xOTuoS4l1dXaxevRqnUzXG0V6vfahpU9Qul4ujR4/q\nW16KouDz+RbwV7p80eoTNluKUkk1gHI6nTgcDmw2G+effz4HDhxh27YpymUFcFKvF2YysQIbNgzS\n338Qj2c1o6OjdHR0GNmEwbJktimYZqHs9Xp1w69WsJAgsZFj5kLr5z120kXsmQnuLcCX9JNJKYUQ\nDwKvOcHLHgO+KIR4m5TyPiFEO3A18B8vdb10Oq37MGuBQhP6UxRF92vQFFi11E7TS4Fj3U6Kougd\nT5OTk4yMjMwpbnd3d9Pd3a37QmSzWcbGivz+95I//EOJ1WrVA9HRo0f1LEKbH3i1IoQgGAzqAVWz\njw0Gg4yOjrJ582YOHjzI3r33z/x7+vF4VKFD1eI0xaOPPsr69euJxWJGNmGwbJnf2VSv13E4HFQq\nlaUn8Kf5SrSAMKrD3eS845PAmSdYyzYhxPuBnwkhHKjv61fAn76cC2oKq9qdqWbXqekraZ7Lmtqi\n5vOsBQaHw6FPVI+OjpJIJPTpaqfTSSQSobe3F7/fT6VSIZ/PUywWyedNXH+9iWeeaVAoWPnTP/Vj\ns9lIpVJMT0+jKAoul4tgMHhatrq+EiwWC4FAgHK5rEuDa9tvpVKJ17/+9Tz55LP8/vdHqNXCNBo2\nGg01mygUCuzbt4/x8XECgQBjY2O0tbUZfhMGy47ZQUK7sbXZbC1rf4UmDNMtBYQQZwP/APy/wAOo\nKrRfR61L/PGLvfbMM89kw4YNc+w5NbQtJLvdrgtoaV+zPa818blEIkGj0cBqtepdUe3t7fT29lKr\n1ZienqZarWKxWKjV7Fx/fZVnnpEEg17e9CYXNpugWCwyNDSElFLvvDqdW11fCZrh0vj4uK7OG4lE\nOHLkCJ2dnVxxxRvZvfvHpNMp0mkvXm9Rv9uamprikUce4X3vex+xWIxYLLakpdQNDI7H7EG6+YFh\nyW03CSFufbHHpZR/c5KnjgF1oH3e8XZg4gSv+SvgUSml5r+9Rwjx/wC/F0J8Vko5PyvR+cpXvoLX\n6wXQA8WVV17JVVddNScozP+gLpfLjI+P6wVpq9Wq+1NXKhU8Hg/RaBSv16sHD4fDMWMUVOG668o8\n8YQdr9fHf/2XhQ0b1PTx8OHDFItFurq6CIVCxt75PHw+n17r0Tq9NMvWLVu2cP75j/Dgg08DClJa\n9C3BUqnEU089xRvf+EYcDgcTExN0dHQYAdhgWTE7k/jZz37G3XffrTfIaCZkzWYhmcR75v1sBVai\nih0dBk4qSEgpq0KIJ1EnuX8FINRb/MuBb5/gZU7mem2Dqq0hUb24T8g//dM/cd55572cdZHL5chk\nVO+CdDpNuVzG5XLR3t6umxSZzWYCgYBeQ9DaXa1WK4VCgZGRLH/8x1YeeyyI2+3g17+GzZvV8x8+\nfFi344xEIsZ2yHHQ6hPZbJZiscjUFDidIUymNHa7nauvfiePPXaAfL5ENqvg8+Vmit42RkZGeOqp\np7j00ktJpVJ6fcPAYDkw35HurW99K1dccQWRSIRisUh7eztPPfUUW7Zsaep1F1KTOHf+MSGEF/gJ\ncPcC1gTwDeAnM8FCa4F1zpwbIcSXgU4p5Qdnnn8P8IOZLqhfA53AN4HHpZQnyj4ATiiIpQ2/aaJx\nqVSKYrGoZwQ+nw+Px6P7XGtdBsFgEJfLpXfgmM1mffju6FEr118f5NAhBx4P3HsvXHSRer2hoSHG\nxsbo7u7WZTcMjo/VaqWtrY2vfnWYv/97M7fc4uSmmyJMTEywevVqXvOajTz44COAg0ZD3cLzeDzk\ncjkeffRRNm7cRKMhmJ6e1oceDQyWOtWq2kBqsVh090yfz9fSziZock1CSpmZkeu4BzjpSWwp5c+F\nEGHUbKQdtYvqLVLK6ZmndAA9s55/uxDCDdyCWotIAb9B3YZ6UVKpFBMTE1QqFf0vvlqtUi6XKRaL\n+mS0y+UiEong8/n0yen9+/dTLpfxer309vYSCoX0Qna1WiWXy1Eul/VOnB07HDz/PPT0wH/8B2zY\noK5hcnKSI0eO0NbWxooVK4wA8TLweDz09Pgpl5N873tm3v9+H4qiZndXX/0WHn74aSqVDIWCHadT\nHWxUFIWdO4/w3vce5OqrN3LDDQn9uIHBUkfbOtXa67XOpmq1qv8fzmQyTb9uKwrXvpmvBSGl/C6q\nV8XxHrvxOMe+A3znOE9/UQYHB/UP5dnGQxaLhba2Njwej67TlE6nef7553XHtGAwyNq1a4lEIiiK\nQqPRoFgsEovF9OgeDAb1Cel3vxvuuAMuuww6O9XrJ5NJ9u7dSyAQ4Iwzzjjt5TaahRCCG29s4zvf\nybJnT4Fvf9vHZz4TYWRkhO7ubjZtOosdO7ZTr5uQ0kQ+n59xwMuQSGzn3/5tJddc4yCZTBpBwmBZ\noKm8ajMSWhG7XC7PNMPUdC2nZrKQwvWfzz+E2lX0R6iSGMuCYDCoK6/CseK1VoMYHBzU6w8WiwW3\n201XVxfhcFjvPCoWi7oBkObzoHlGzOf97z/2fT6f55lnnkFRFNatW2cEiFeI3W7jb/+2g3e/e5g7\n7sjzoQ8p+P1+pqenufrqS9ixYw+QpVSyAOqwYiBgIh5/jlhsiO3b1xIKxXXHQAODpYy2qwHon0ca\nVqtVzzSazUJ+M7bO+7kBTKPKYywbZzqtj16LzNqflUpFb1cNh8N6MVqzNK1UKrqqq5QSu92O3+9H\nUZSXtcddKpXYtWsXJpOJc8891wgQJ8k73+nnda9L8cgjSb72NTvf+laAXC5Hd3c369atYe/ep6jV\nLEhpIplMznScJchknuHf/m0Vb3iDWi+arcBrYLAU0STB4Zhmk4bFYtG14prNKwoSQoiNwB4pZUNK\nubLpq1kEMhnVelQIoW812e12ffLZ5/Ppgn/axO/svUGPx4OiKK+olbJUKrF7925qtRpbtmx51Qj2\ntQIhBF//eicXXZTjV79K8eEP++ntDZJKpXjHOy5k7979QIFiUWKxqFLKPl+DTOZZdu68gOHhNbS1\nxfH5fEYB22DJoqlTz84kNMvS2VtQrWjpfqVed7tQJ6IRQhwRQix7W7RwOExPTw99fX2sWLGCFStW\n6KJ/mvlNNpslnU7rrZR+v5+2tjba2tpwu91z/mHuvReeeebE18tkMuzevZt8Ps+GDRvweOa74hm8\nUi680M573tMBVPnSl1Qrx2AwSF/fClatWg1IGg0L1WqV6elpQiEPNtskMMAvf1khnU5TKpUW+V0Y\nGJyY43U2adpxWuDQ1CCazSs9Ywp1FgKg7yRev+TQBuU05dbZvhJmsxmXy0UoFCIajRKJRPD7/Tid\nzhfsYU9OwjXXwDveAR/6EMzfHpRSMjk5yb59+6jVaqxfv/60sx5dTL761RCbNvl473vVwcVQKEQ4\nHOayyzYAClCjUpGkUqmZrcEqsJt77omRy6lT8wYGS5XZuxez3ehqtZr+WaS5ZDabV1qT+DfgISGE\n5kS3UwhRP94TpZSrFrq4U4HX6yUUCs3pbHol2w5Swu23w8c+BskkmM1w+eVQr4MWR2q1GmNjY7qc\nxMqVKwkGgy16R69O+vtNPPJIF/v3Z8nlcvrEe3//CsLhbmKxg9RqFt2vw++3MDU1iJRDHDrUQSSS\nNgrYBksWLRgIIXRLA5vNpqs9aJ1NrcgkXtFvhJTyw0KIXwJrUKeffwhkm76qU4jD4TjpuYQjR+DD\nH4bf/Eb9+dxz4Uc/UieoNTQviWQyicvlIhqNGoJ9LUKbfh8YGNBbmLu6unj968/kl78cBorUapJ0\nOj3j9JflLW85SGfn2eTzDrLZrDGBbbAk0ZpoAN2+QFOt1rKLSqXSkgaYV3zbJKW8H0AIsQX4Bynl\nsg4SJ8s//AN8+tNQLILDAZ//vJpNaDeiUqofRtPT0xQKBdxuN21tbbqBkEFriEajumyKoiisXr2a\nM8/sQVEiFIujVKsmMpkMHR0dKIrgyJHnGRwcJBQKkUgkjAlsgyXJ7M6m41mWlstlXb+p2Zx0biKl\nvPHVGiAAJibUAHHppfDss/DJTx4LEJoAnSbX4XQ6jQBxijCbzXR3d+s+Eh0dHfT29nL++asAO2Ch\nVCqTTCax2WxMTk5y6NAh8vk8hUKBYrG42G/BwGAOmi+KVqCerf6qdTZpQ3St2C5d9oXnxeLWW+Gn\nP1W3mtasUY9Vq1Xi8TixWIxMJoMQArvdrjvZGQHi1BAIBGhra9M70tavX8+6dV2YTH5AUKsJEomE\nbhA1NDTE6OgotVqNVCq12Ms3MJjD7KK1Nstls9nmaDYVCgXMZnNLtpuMIHEcKhW1CP1iKApcdx0I\nof4jJpNJpqenKZVKetQ3m826J4QRIE4tvb29WK1WpqamWLFiBWvWrOHssyOorrsmcjnV+MlqtTI6\nOsrhw4d1n3Gt3dDAYCnwcjqbisUidrt9ScxJnHbcfz988Ytq2+qll0Jvr1pjeNe7Xvq12pbG1NSU\n3lmgBQMhBD6fzwgQi4S6xdfDvfdmyeXybNy4kXPO6QXcgJV6XZJMJrFYLOTzeQYGBpiYmKBcLpPL\n5RZ7+QYGOlrRWutsajQa2Gw2/WZU62xyuVwtuf5CtJtWACNSK7EfOy6AHinl8EIXdyr47GePf/yp\np9S6Q0fHCx+r1WoUCgXy+TxCCBRF0VVkNVMQp9NpFEEXkXod3vWuHg4eHMHtHuOGG85i1apV9Pbu\nZ2goB5T0LichBEePHuXw4cP09vYaBWyDJcXsjEHLcmd3NmkSQk6nsyXXX0gmMQBEjnM8OPPYsuDc\nc+GGG+Bv/gbuugu2bYPxcchm5wYIbVBlamqKqakp8vk8LpcLRVEoFovU63WklLoXs1GDWFzMZnjn\nOy3AKm6/vUA8nmDLli2cfXYb6nCdlVJJHaIzm82k0xn+678O8O1vxykWixQKhUV+BwYGKrODRLlc\nfoFmU6FQwGQytSxILKQULlAH6ubjBpaNxsH8uQYNKSXlcoVSqaSbDZlMJl3hFSCdTuuZgzbcEggE\njIGsJcLHPw7/+I9dHDo0zD33jPL+92+iv7+fHTuGiccVoEQqlcLv91MqVdm5cwoY4PrrOwiHsy1L\n3w0MXi7zO5tKpZL+vbYFlc/nsdlsLbPifcWfZkIIzUdaAl8QQsy+5TIDF6KaBC0LyuUy+Xxe3yrS\n/lG0zEDbOnI4HFitVn3PWovoZrOZWq2Gy+XC6/Ua2cMSoqMDPvIRwbe/3c9Pf7qNK65Qs4knnnia\nxx7LAzkKBfUmwOfz4XTGKRT2c+edZ/GZz1iJRCKGB7bBojK/aK3VJxqNhn4zqlkkt4qT2W46d+ZL\nABtm/XwusBZ4BrihSetrOZlMhkwmo28ZadmCx+MhEonQ3t6Oy+WiXC4zNTVFIpFASqkPtjQaDYLB\noKEiukT55CfBZovw3HNR/vM/h9iwYQOrV6/A7bYBqlFUMpmkVqvR0VEFRrj77jGy2ZKx5WSw6MwO\nEpVKhVqthtPp1LegGo0GpVKppUHiZCauLwUQQvwL8FEpZfMVpU4hmnjf8SiVSmQyGcrlMkII3bNa\nszbVPCSMu82lS1cX3HQTfO97Z/Lzn4/zpjdVOe+883jqqefYty8HZMhkchQKBYJBO0eOpMhm9/HA\nA720t6cMlV6DRWV+ZxMcm7C2Wq0Ui0WklC3dGl3oxPWyDhAalUqFYrFILpcjlUoRj8eZmJjQswaf\nz4fH49H76BuNBn6/n1AoZASIZcCnPgUWi4/du/v4zW+G2bBhAytXdmA2uwAHtVqNRCIBSDo7i8Bh\n7rorTjabNWYmDBaV+UXr2W32Wvu22WxuqSfNgiqsQojLgcuBNuYFHCnlTQs596lCm5AG1ePabDZj\nsVhwuVz6RG42m6XRaOBwOPD7/YaL3DKjr08NFKFQPz09QwCcffbZ7N07zOCgAhQoFNQidldXB2Nj\nMZ577jC7d3fQ2Zk3XOsMFg1te6nRaLzAslQLEq0sWsPC5iRuA24FdgKadPiyw+v1EolEMJlMNBqN\nOfal2WwWIQQulwuXy2VkDcuYv/1bACd7965m7969bNiwgccf38HgYAHVB1ttcY5EIgQCFZLJ57jz\nzo1ccEHCCBIGi4LWRGOxqIZZlUoFq9WKEEIPFqVSqeVdeAvJJG4GbpBS3tmsxSwGhUKBRCJBvX7M\nFsNkMmGxWGY6XpxGQfo0YvXq1QwMDGA2m1m5so9Dh+KMjyuA6kORTqfp63MQiYzy9rcfpVAIUS6X\nT1pO3sDgZJldtNbkfjwej97ZNLuQ3UoWMkxnA7Y1ayHzEULcIoQYEEIUhRDbhRDnv8TzbUKILwoh\nBoUQpRl71Rte6jpai2sgECAcDtPR0UFHRwfhcBiXy2UEiNMMh8NBf38/1WqV/v5+enu9qKM9UCqp\nAn+BgKC7O8/09CG9+83A4FQzu1BdqVQAdBkOrR1fStnSegQsLEj8CLiuWQuZjRDifcDfA7ehttY+\nA/xaCBF+kZf9ArgUuBE4A7gWOPBS1/J6vXg8HhRFwWaztcTZyWBp0dvbi8fjIRAIEImECAR8gJ1G\no046ndYbFvbv3088HieVSjFPfcbAoOXMdqPTOiw1JViLxaJLhmvDda1iIdtNDuDDQog3AruBOW0g\nUsqPLeDcW4HvSynvABBC3Ay8HbgJ+Lv5TxZCvBX4A2CVlFLTel4W2lEGpx4tm4jFYvT09LBq1TRP\nPqkAFcrlGrFYjEAgQDweZ2hoiGg0SqlU0mdjDAxOBVqQmF2P0G5izWazXrSeXcwulZovdrGQ2+aN\nqJPVDWA9c4fqNp3sSYUQVmAL8Bvt2IyI4IPAa07wsnegFtA/JYQYFUIcEEJ8TQjR2jzMYNnS3d1N\nW1sboVCIcNiHooQAC7Vag3w+T6lUolarceDAAdLpNOl0erGXbPAqo1qt6h2WmoeEtv2tFbVny4PX\najWy2eb7wJ10JqEN1bWAMKq8x+S845PAmSd4zSrUTKIEvHvmHP+MKjb4odYs02A543AoTE6uQVGO\n4PF4WL06yJ49k6jtsAXdh2J4eJjx8XGi0ShtbW3GdqTBKaHRaOgF6lKphJQSu92uD9dVKhW9LV9D\nc6drNgv6Hy+E+AMhxF1CiG1CiK6ZY38khHhdc5b3sjGhZjTXSSl3zvhwfwz4oBDCaEsxeAG33CK4\n8cYoTzyxEr/fT0eHByE8gIlyuU4ymaTRaOheE/F43JDpMDhlzJfjAHQ3Ou2Y2WyeU4+YLf7XTBYy\nJ/E/gDuBnwKbUQ2EAXzAZ4ArTvLUMaAOtM873g5MnOA148BRKeVst5jnUPWluoHDJ7rY1q1bY6wG\n/AAAIABJREFUdVVXjWuvvZZrr732FS7bYDnxjnfA977n4r77zuDP/7yT0dFRentDDA6qk9eFQoFY\nLEZbWxsHDx7E692Az5dk40b3Yi/d4FWA1tmkFa21gFAoFFAUhVwux7333ssDDzwwZ2aiFTcyCylc\n/zVws5TyDiHENbOOPzrz2EkhpawKIZ5EneT+FehGRpcD3z7Byx4FrhJCOKWU2t/SmajZxeiLXe+b\n3/wmm4+nFW5wWvO2t8E55wieeaaNgYGNKMqz9PWFGBycBJKUSjXi8ThtbW089NA0d901ys03r+Af\n/zFqSMEbtJzZRetqtYrdbtc7m7R6xJVXXsnNN9+Mw+GgVCqRSCQYGRnhoosuaupaFrLddCbw8HGO\np4GFjqh+A/gTIcQHhBBrge8BTuAnAEKILwshbp/1/P8PiAP/IoQ4SwjxetQuqB9LKVuzUWewrBEC\n/uqvANzcf/96wuEObDYrkYgftSQmyGazJJNJXK4q8Dz/+q+TJBKGtalB65m9rSSlxGazzXGl0+oV\ns30mTCYTqVTqxU57UiwkSEwAa45z/HXAkQWcFynlz4FPAH8D7ELtpHqLlHJ65ikdQM+s5+eBN6EG\npydQt8H+HfjoQtZhcHpz1VWwerWJbLadZHITJpOJVasCaDun+bx6d9bRAWbzMOn0OHfdFV/cRRu8\nKpjd2QRqPaJSqejZhCb0p3U2tapoDQsLEj8E/kEIcSGqblOnEOJ64OuonUULQkr5XSlln5RSkVK+\nRkq5c9ZjN0opL5v3/INSyrdIKd1Syl4p5SeNLMLgxbBYVL8J8PDb324mEAjjcNhxubxo2UQikaBQ\nyNHTkweO8uMfj+u/uAYGrUDrbDKbzVQqFYQQepCwWq1Uq1VdOgjUgKLJCrViK3QhQeIrqNs8v0HV\nNXgYdQr7+1LKf2zC2gwMWs4HPwjRqJl4fCW53BrsdjurVgUBKyDI56sz2UQZGGDfvlF+9ztDpsOg\ndWidTVJKPTBo9QkhhO6YOVtCHNTg0gqF6oX4SUgp5RdRZxHWAxcBESnl55q1OAODVmO3w+c+Bx//\nuI8rr9yEx+MhGPRhtboBE1JWSCQSmExVotEYcJRvfeuoIdNh0DK02oNmo2y1Wmk0GvrjJpMJKeWc\neoQ2ZNcKIcqTDhJCiBVCCCGlrEgp90kpd2gtqEKIFc1booFBa/mf/xO+/nULl166ic7OThwOBz09\nftTmPxOZTGHGayIPDHPffSMMDDRf/sDAAOZ2NsGxeoTZbKZWq2E2m5FS6val2vbn7OyimSxku2kA\niMw/KIQIzTxmYLCsiEajrFmzZsZjxIvaUGemViuTSCSwWosEgxNccMEIU1OxxV6uwWmKpvI6O0ho\ndqXValUvVms2pkBL1WAXEiQExzcacqPKYxgYLCtsNhubN2/G7/fj9/uJRt2oBWwTqVSOYrHIli0p\n3vjGUWq1IWPLyaAlaMFByx60rEJKqdcjNEXYVm81wUkM0wkhvjHzrQS+IISYPeJnBi5EFf4zMFh2\nbNq0ifvvv59sNksk4mF8PA3U9WEll8vFxMQEAwMDbNy4Ea/Xu9hLNjiN0Dqb6vU61Wp1TnbQaDSw\nWq36VhOge0oIIbDb7UxPT5/o1CfNyWQSmtKrADYwV/11Lar3ww1NWp+BwSnF7Xazbt06PB4P4XCY\nYFBBvfepk0plKZfLxGIxhoaGGBkZWezlGpxmzB6Y04rTWstrvV7H4XDMGbTTCtp2u51isUgs1vxt\n0FecSWjqr0KIfwE+KqU0+gENTisuvPBCdu7cSTAYJBx2zUxZ18hk8mQyGZxOJxMTExw8eJC1a9ca\n3ucGTUNrf9U+/LWitXbM4XCQz+dxOBxzBugcDgfJZLIlW6ALkQq/EUAIcTawAtXOdPbjv1rY0gwM\nFofe3l4cjl5MphyhUAiXK00+XwbKJBIpgsEgk5OTDA0NMT09TUdHx2Iv2eA0oVarYTKZ9JkITdQP\n1O4lk8mkS3Jks1l9q0kLEtpQXTNZiArsSuD/om45SdTtJzhWzDZurwyWJf/8z2a+9KXz6et7ns2b\no4yNjZPP54EKsViazk61JXZ4eJj9+/cbQcKgaWgF6mq1OmcwTkqJoih6piGEeEGLbCqVwu1uvkrx\nQrqbvo3a6toGFIB1wOtRHeIuWfDKDAwWiUsuAVjH4GAbJlOIYDCAw2FH/XWRxOMJCoUCk5OTHDp0\niJGRIkajk0EzqNVqevFaG6Kr1Wp6tqB9X6/X9a0lu91OKpWiWq3i9y9UW/WFLCRIvAa4VUoZQ5Xk\nbkgpHwE+zYklvQ0Mljzr18N73uMB1rFrl43Ozk4iETdq4l1hejpFpVIhFovxgx8M0Nd3mIceWuRF\nGyx7tAChoc1HNBoNTCaTrgSrtb4CevCIx+PY7XacTmfT17WQIGEGNEPVGNA58/0QJ7YZNTBYFtx6\nqwA2cvhwYCab8AMKYKJSKZPJZMjn8ySTozQa+/jiFxsvcUYDgxdHK0QLIfQ5CG37SWuF1Sautedq\n09fJZBKfz7e0tJuAPcA5M98/DnxSCPFa4FYWKBVuYLDYbNoEV1zRBaxg714PoVCI9nYFtT+jztjY\nNJVKha6uaeAwDz44xVNPLe6aDZY32ge/Fgi0uoPJZJoTJBqNht4i63A4SKfTVCoV2tra9MG6ZrKQ\nIPG3s15/K7AS+D2qbanh42Cw7LntNjuwniNHXNhsYYJBH5o6bC5XJJfLUa9n6e09AuzlK19Z3PUa\nLG80WXDNS0JTgbXZbNjt9jn1Cm0LyuFwMD09jc1me4ENc7NYiArsr6WUv5z5/nkp5VogDLRJKX/T\nrAUaGCwWF1wgeP3rVwEdHDniJxQK4fe7UH9tGoyOTlOtVunuPgrs4xe/KHHw4OKu2WB5Uq1WaTQa\n+laTNkSnzUZoXtdwbNBOkw1PpVIt22qChWUSL0BKmQC6hBA/aOZ5DQwWi89/PsBFF63moov8+Hw+\nIhEPqnNdjWw2Ry6XAzJ0du4HnuNrX1vc9RosT8rlsv6hD2C1WikWi3phGtRMQ5u81mQ48vk8xWKR\ntra2lq2tqUFihhDwoRac18DglHPJJTZ+/OM1rF3rJxKJEAgECATUAjY0mJiIU61W6ekZBPbxk580\nOHp0cddssPzQsoTZ1qSlUgmbzaYHidmZhBY8pqamsFqtLWl91WhFkDAwOK3o7Oyks7MTv9+P1+sl\nHPajZhN1EokshUIBszlFR8duzjtvjHR6sVdssJzQag9arcFmsyGlpFQq4Xa79TrF7HqEti2VTCYJ\nBAItU4AFI0gYGLwkbreblStXEgqFCIVCM+51rplHq0xMqHMTmzcf4NZbn+Xssxd1uQbLjEqlotcZ\nTCYTJpNJn6xWFAU4pvaqYbPZKBaLFItFwuFwS9dnBAkDg5fAYrHQ09MzI/gXxu/3094eQm2HlcRi\naYrFIplMnF27dpHJGJqXBi+fSqWiazaZzWZsNhvpdHrOVlOlUtGnrLV6hLbVFAgEWrq+k/GT+OVL\nPKV1m2MGBotEJBKhu7ubWCzGxMQEuVwOn89FOh0HSkxOprHb7ezevZsDBw5w/vnnL/aSDZYJWpag\neVdr9YhAIKAfK5fL1Ot1LBYLQghMJhOpVAq/398yRzqNk8kk0i/xNQTcsdCFCSFuEUIMCCGKQojt\nQoiX9VsnhHitEKIqhDBGmwyahtPppKurC4/HQyQSmbE49QPqL2gslqNSqTA2NsaTTz6pyyYYGLwY\ns+sRWneTVoPweDyA2h6rzVCYzWZ94rpYLBIKhVq+xpPxk7ixFQuZjRDifcDfAx8GdgBbgV8LIc6Y\n0Yo60et8wO3Ag0B7q9dp8OrBZDIRjUaJRCJMTmaoVFJ4PHE8HgfZbBrIMzGRwWazsWPHDi6//HL6\n+/sXe9kGS5xyuazblGpSHPl8HovFouswlctlSqWSPiynbTWZzeYXbDVls9kXXGOhLNWaxFbg+1LK\nO6SU+4GbUZVmb3qJ130P+CmwvcXrM3gVEggEqFa7+PrXvTz8cBhF8RMIeNBU8WOxPOVymcHBQXbs\n2KFLORsYnIhyuUytVtMVXxVFIZvN4nK5dDMrTeTPbDZTr9cxm836VpNW2Nae14oMdskFCSGEFdgC\n6FPbUi3rP4iqPHui192IKg3y+Vav0eDVicPh4Pzz2+ju9gHtjI2FCAQCOJ0KUAfyTE5myGQyPP74\n44yOjpLLwdjYIi/cYMkyW+VVMxMqFov6VpOUkkwmM8fbulKpUCqVCAaDc7SatAyk2Sy5IIEq7WEG\nJucdnwSO6+4ihOgHvgRcL6U05DgNWoIQgo6Odj7ykXbAz+BgOyaTh2DQizZcF4vlKJVKHDp0iB/+\n8ClWrapx882LvHCDJUmj0aBYLOoZgxYgpJR4vV5A7WoqFAq6l4TT6SSRSGA2m+cM0NXrdUqlUkuK\n2EsxSLwihBAm1C2m26SUh7XDi7gkg9MYj8fD5ZdHWLfODYSZmIji9XpxOBSgBpSIxXLEYjGOHn2U\n6elR7rkHHnlkkRdusOTQtoccDofuPJfNZnE4HLoOU6FQ0LejtMJ1KpWa+T93LCBoEh6tGKprfm6y\ncGKoufv8wnM7MHGc53uA84BNQojvzBwzAUIIUQHeLKX83YkutnXr1heoJ1577bVce+21J7d6g9Ma\nu91OW1sbH/5wjI9+NMf4eJhIxIffn2BiogA0mJzM09bmZWrqOS6/fBe/+U0Pn/qUmUcegRYoORss\nU7R6hNls1oX80uk0wWBQf442L6EFkVwuR7lcpqOjg1/84hf867/+q34uk8nUkhmdJRckpJRVIcST\nwOXAr0D9tJ/5+XiOdxlg/bxjtwCXAv8DGHyx633zm99k8+bNC1y1wauJcDjMeef5ufBCN48/HmFk\nJEo4HCMeT1OtVoAy09N57PYY5523jYcf3sy2bb3ccw+8852LvXqDpUIul5vT1aRlDVrHkpSSdDqN\n1+ul0WjgdDoZHBzEYrHg9Xr1m9lyuUw8HsftdvPYY4/x1re+tanrXNB2kxCiIYTYN+/Yc0KI+sKW\nxTeAPxFCfEAIsRa1a8kJ/GTmGl8WQtwOalFbSrlv9hcwBZSklM9JKYsLXIuBwRycTifhcJgbbwwA\nAZLJMFZrELdbS/+rTEyomk7Dw/u44opdQJ1PfxrqC/3NMDgtqNVq5PN57Ha73tUUj8dRFAWXS5V8\nyWQy1Ot1rFarbl2ayWTweDxztpoKhcIcS9Nms9BM4iYgNe/YZwDvQk4qpfy5ECIM/A3qNtPTwFuk\nlNMzT+kAehZyDQODk8VkMhGJRDjrrAmuvnoaKSNUqx0Ui1Nks3lqNQFUGRvLoiiTXHzxY7jd57Jv\nXy933AE3tnzSyGCpU6lUKJfL+Hw+vb01nU7T1dWlPyeZTGKxWDCZTLhcLiYnJ5FS4vF49JpFo9Gg\nVCphNpvJ5/O65lMzWVAmIaX8CfBaIcRWIcS5M8fullLevtCFSSm/K6Xsk1IqUsrXSCl3znrsRinl\nZS/y2s9LKY09JIOW4Xa7CQQCvO99AS66KEAkEiEYDOJyaQVsQSqVJ5fLcfjwHq68chdQ49ZboWjk\ntq96crmcLglusVjIZDIIIfR6RKPRIJvNYrVadWmObDaL0+nUlWFBzSKklPpU9uy5iWbRjO6mErAR\n+L4QYkoI8X+FEH8hhNjUhHMbGCxJrFYrbW1thEKhGTMiVdtJvcszARKQDA6mmJycJBR6jC1bRvnS\nl6BFBmIGy4hMJqNvNTkcDuLxOH6/X59zyOVyVKtVbDab3vbaaDRwuVwv2Gqq1+ukUilcLpc+X9FM\nmhEknpu5s78AWAPci1ow/hchxNNCiK4Xf7mBwfLE5/Ph8/nw+/34/X7C4TDhcBi73YYQRcBEuVxh\nairJ888/x6c//STXXFNlpi3e4FVKtVqlUChgt9v1LKBcLs/RYUomk0gpsdlsWCwWUqmU7nWtZQuV\nSoVKpUI+n8dsNhMOh/WZi2bSjCCxRQihAEgpM1LKHwHfkVKeC/wR8JdNuIaBwZLD4XDomYTfr3pg\nd3V14fP5cDhMqJ3cMDqaZmxsjO3bt3PUsK171ZPP5/WCtLbVNLtgXalU9CE7p9OpF7AVRcHpdOpb\nTfl8nkKhQKlUoq2trWVqsM0IEncC24UQnxRCbBFC9ADrAKSUzwI7X/TVBgbLmFAohNfr1bOJSCRC\nR0cHdrsdm60EWJGyxvDwBM8//zzbt2/XbSgNXp2k02msViugNkFks1ldFhzQP/gdDgd2u51YLDbz\n/8mmB5JGo0E+nyeZTOLxeFrqKbHgICGlfBq4GrgA+B3w38A2ACHEe4HehV7DwGCpoijKnGwiEAjQ\n29tLKBRCCIlasjMzPZ1jeHiYJ554gtHR0cVetsEioWkxKYqiW5QCc2YjtHqEoigUi0U9i7Db7XrN\nolgsMjExgc1mo7u7e46GU7NpiiyHlPKglPIqKaVHStkvpbxv5qEVGCZEBqcxJpOJcDisdzv5fEEm\nJgKUSp0z8gplwApInn9+nCNHjrB9+3aKRovTq5JisagXpIUQ5HI53G63LqehWZIKIfB6vcRiMRwO\nB1arVc8iAGKxGLlcjmg0OkeKY7bFabNoqXaTlPLrUkqjJmFwWuN2uwkGg7jdblKpMI8+GuTo0V7c\n7gggMZtLgINMJs/hwwM89dRTHD58+KVOa3AakkqlMJlMWCwW6vX6CwrW2laT1vlUKpVwOp1YLBa9\n5lAqlRgaGiIQCNDefky9qNFokEgkmr7mZS/wZ2Cw2FgsFj2buOiiAOvW+QA/U1OrcDpdmM1FVM1J\nwcGDEwwMDPD4448zPR3jf/9v+MAHoAU3gAZLkNmtr1owmG0upHUseTweYrEYiqLow3Qaw8PDVKtV\nVq1aNWebKZPJLL9MwsDg1YLX6yUUCqEoCjfe2IbZ7Kda7cBsbkcIgdVaBByUywUOHhzi2Wef5b//\new8f+ECVO++En/50sd+BQaspFApzZL+1iWutiJ3NZnWbUs2hzu12YzKZ9LbXXC7H2NgY0WgUt9ut\nn1uTFJ8dTJqFESQMDJqA3W6fMSBysmpViHe/W9V1isdX4XC4gCLqgB0899xRhoaGGB5+gltuGQRg\n61aIndCY1+B0YHJyEqvVqgcJQPeNKJVKute1JvanKIreBmsymahUKoyMjGAymVixYoWeRUgp9TmK\npTpxbWBggNqhogWKK6+MEo16gA4qlS4sFguKUgAUGo0iu3cPc+DAAc4//2nWrs0Qi8HHPrbY78Cg\nVZTLZdLpND6fT99SUhRF/1DPZDKYzWaKxSJWq5V8Po/X60VKicvlol6vk0gkSKVStLe3z8kiNM2m\n+ZYHzcIIEgYGTcLpdBIMBrFarQSDAT7wgTDgpFhcicXiplrVVDrNHDkyyvDwKLt3P8nHP74PqHPn\nnfDAA4v4BgxaxvT0tJ4VVCoVpJT4/X5MJpMuEd5oNPQJbJvNhtVqxW63YzKZiMfjuo1pR8cxg856\nvU42m8XtdusWp83GCBIGBk1CE2jzer3YbDYuvHAFF1zgw2YLE4n0YLVaUZQsqk9WiW3bDjMwMEC1\nuosPfnAEgJtvhnx+Ud+GQZOpVCqkUin8fj+VSoVqtTrzf0HRhfuEELrkd6VSIRAIUKvVcLlcJJNJ\nqtUqxWJxps36WMaQTqcxmUx4PB4ajQbxeLzp6zeChIFBE9HmJaxWKx6Ph2uu6eSGG9xs3LgCv99P\ntVrF56sDCvl8gt27h3n66ad529uepbMzx8AA3HbbYr8Lg2aSTCZ1Ib9CoaB3KzkcDn2rSEqpt8Rq\n09Vms5lyuax3PUkp50iJl0olSqUSPp8PIQTZbLYl6zeChIFBE9GE1jweD2azmf7+FbS1eQkEAqxc\nuXLGPCaJwxEGGuzfP8Lg4Cj79z/FJz+5n1CogWGUePpQrVZJJBK43W49GAghcDqd+nS1tsUE6gd/\nMBjUf87n89hsNjKZzBx9Js21zuFw4HA4qNfrerG72RhBwsCgyXg8HoLBIGazGZfLRV9fH06nk7a2\nNgKBANVqlXA4iypGkOWRRwY5dOgQbvcz/P73R7nuusV+BwbNIp1OU6vVUBSFfD6vt7M6nU5d6E8j\nHo/jcrlwuVyUSiWq1SpOp1PXempra9Ofm81maTQa+taTtmWlzVw0EyNIGBg0GZPJRCgU0u8Iu7u7\nCYfD+P1+Vq9ejcvlIh6fpr3dDNgoFKbYuXOEPXv2MDy8j7xRlDgtqNVqJJNJFEXRC9Pa5LTFYiGX\ny1Gv1zGZTGQyGRqNBj09PeRyOcrlMk6nk3q9Tj6fJxqN6rpN1WqVXC6nZ6u1Wo1CoTDHjKiZGEHC\nwKAFOJ1OQqEQTqcTu93OqlWrcLlchEIhentVzctyOY6i+IAKBw4Ms3//IDt37uTgwYMt6VIxOLVk\ns1nK5TI2m418Po+iKDOt0ArZbJZarYbJZKLRaDA5OUk0GsVsNhOPx/F6vSiKQiwWw+12v6BYbbFY\n9MG5bDarZ62twAgSBgYtQAhBIKDamlYqFaLRKO3t7bhcLlasWEE0GiWfz+PzFQAnkOJ3vxtkeHiE\nXbt2MTw8vNhvwWABaFmEWoOqIoTAZDJhNptRFEV3lLPZbIyNjaEoCl1dXYyOjmK1WolEIiQSCer1\nOpFIRJcRz+fzVCoV/H4/Qgi968nj8bRMCdYIEgYGLUIzJXK73TNF7H68Xi8ulxuHYy0mk4t0OkVH\nhw0wUSpN8NBDhxkcHOTZZ59lampqsd+CwUmibRlZLBaKxSIul0tXdtUyDKvVSqFQIJlMsmrVKiYn\nJymVSvT09JDP58lms/h8Pn1wTpuJcDqd2GY8cLPZrJ6dALr0eDMxgoSBQQvx+/20t7dTq9Vob2+n\np6eHRMLBvn0ByuUzqNUkxWJ6pmslz4EDA+zZM8L+/fvZu3ev3tY4NASFwuK+F4OXR71e1yeoS6US\nVqt1Rr/LqgeGRqOB1WplZGREH6pLpVJEo1FsNpterNYyBikliURCDzSgzl+USiU9i8jn8wwODjb9\n/RhBwsCghWgKsX6/n0ajQX9/P+vXB+ntVYBeqtUu8vkCLlcZsAMp7rtvP8lkir1797J3717uv7/C\n5s3qoJ1Rqlj65HI5SqWS3tqqWY7OziIcDgfJZJJisUgoFNKH7bTvq9UqHo9H71bSuqSCwaC+9TQ7\ni6jVagwMDMzxlmgWSzZICCFuEUIMCCGKQojtQojzX+S57xFCPCCEmBJCpIUQ24QQbz6V6zUwOBEe\nj4f29naklIRCIVau7ONtb3NiszmBtdRqrpn6hBloUCqN8LOf7SGZTPLss88yOvocyWSDO++E7353\ncd+LwYujdSMBc0T6NFG/fD6vZwbj4+PY7XZd9C8UCpHP5ykWi9jt9jkZQqFQwO/364qx2pCdpu80\nNDREo9Ggu7u76e9pSQYJIcT7gL8HbgPOBZ4Bfi2ECJ/gJa8HHgDeBmwGfgvcI4Q45xQs18DgRZnd\nElupVFi/fj3d3W28/e1WwAucSaUiqNfzOBwWoMzhwwf4z/88SCwWw+Xaxyc+cRiQ/MVfwLZti/t+\nDE6MlkVUq1UajYaeRSiKore5ar7V2WyWjo4OvVPJZDLpAUYT/6tUKmQyGVwu15xBuWw2i81mw+Fw\nMDU1RSaToaenRw8izWRJBglgK/B9KeUdUsr9wM1AAbjpeE+WUm6dccF7Ukp5WEr5WeAQ8I5Tt2QD\ngxPjcrno6OhACIHdbmfTpk2sXOnlooucwAoajSjFYhm7vYLFIoAc27c/zeOPj3D06FEuvngfb3/7\nGLUaXHUVjI8v9jsymE+lUiGfz9NoNMhms3g8HkwmE06nU/eK0OYjRkdHaWtrIxgM6kNwqVQKIQQ2\nmw2v10uj0SCZTGK1WvU6BByTFfd4PGSzWSYmJvQtzXQ63fT3teSChBDCCmwBfqMdk2rT+IPAa17m\nOQSqilrzvfwMDE4CTfyvvb2dTCZDX18f69at45xzrHR12YGzqNddFIslAgEB1IEU//7vOzh4cJrh\n4SE+9KHdnHFGkvFxeNObwGh+WjpoH+hSSvL5/BwfiFqtRrFY1GsJo6OjKIpCd3c3tVoNp9NJJpPB\nYrEghJjxRreRSCSQUhIIBOa0t2azWb32oJ2rs7OTVCpFpVJp+ntbckECCANmYHLe8Umg44VPPy5/\nCbiAnzdxXQYGC8LhcBCNRnE6nUxPT7N582Z6e1fwhjeY8Pv99Pf3YzabKJdLdHWp9QmY5H/9r8cY\nHU0yPT3CZz+7i/b2Anv3wmWXQSq12O/KANTCspQSs9lMOp3WO5Y0KXBt2npgYIB6vc6qVasAdSuy\nXC7rcxRSSrxeL5lMhkqlosu7aBSLRb0YPj4+Tr1ep7u7m1KpRLFY1Keym8lSDBILQghxHfA54Gop\npeH1ZbCk8Pv9upJnsVjk/PPPp6MjzDvfaWbDhh7a2tpm5KQrRCISaFCvD/O97z1OPJ5GyhG+9a1d\ndHSUee1rYdYuhMEiUSgU9GLz+Pg4NptNF/Cr1Wr6NtPAwADlcpk1a9YAanbZaDR0hdhyuawrBedy\nOXw+nz4PAeiy4na7nUwmM6emoWURrcgkmh92Fk4MNddun3e8HZh4sRcKIa4BfgBcJaX87cu52Nat\nW1/g6HTttddy7bXXvuwFGxi8XDShtlKpxNjYGKFQiE2bNvHQQw/hdrs566yzdC9k1cQIEgnIZvfz\nne/4+PjHL6Szc5Qf/tDMpZeeg8nUfNVPg5dPrVYjnU6jKAqJRIJMJqPfBDQaDUqlEmazmaGhISqV\nCuecc46eDdRqNSwWCx6Ph1wuh8vlwmq1Mj09jaIoL5DZSKfT1Ot1hBDE43Eeeugh7r//fsrlsi4U\n2Ao/CbEUNWKEENuBx6WUH535WQDDwLellF87wWuuBX4EvE9Kee/LuMZm4Mknn3ySzYY2s8EpJplM\ncvjwYZLJJMFgkAceeIB9+/YBMDw8zP79+ykUCjgcDioVM5mMGXBz7rmX8Kd/eiGBQICgut6jAAAg\nAElEQVSOjg7OOeeclih/Grw0Ukqmp6f17/ft26cXo4vFIvV6nWq1qt/lr1+/HpvNxtGjR7HZbNjt\ndnw+H4VCAbPZjN/vJ5FQy6iRSGROHaJYLJJMJrFYLPqAZWdnJ/F4nKmpKYrFIplMhgMHDrB161aA\nLVLKp5rxPpfqdtM3gD8RQnxACLEW+B6qwM1PAIQQXxZC3K49eWaL6Xbg48ATQoj2mS8jGTdYkvj9\nfnp6enA4HKTTaS644AJCoRAmk4ne3l76+/ux2Wwz0g5VHI4GkPv/2zvzKDmu8tD/vuq9Z+3ZN49G\nm+UNLBvbAhxsB8fY2CfgJKwGQkiIw4O8kEcCL8AjJBwwIQtgQlY/9hdM4hySQAwY49jgLV5kY8ta\nxtpmX3q6p5fpvbrrvj+qq9QaayTNqKVpy/d3Tp3pqr5VdW+1dL/6lvt9PP30o3z3u6Pk83nm5ub4\n+c9/ftqKzWiOj/NmHwgEqqnem92cXKZpsrS05LY577zziEQiLC4uurUiWlpa3GJCra2trqPaiXhy\nKJfLJJNJLMuiWCy6baLRKPv372d+fp5sNovP53tpRDcBKKX+BfhD4FPA08DLgeuVUgvVJn3AOTWn\n/Da2s/tvgJma7Ytnqs8azWoQETcjrGOT3rFjBx6Ph5aWFrZt28bGjRvdcpZNTSYiJhDj7rt/xg9+\nsId8Pk80GuWZZ54hqT3YZ5R8Pu+m596/fz+WZbF582YymQy5XI7FxUV3Qt+4cSN9fX3E43ESiQSG\nYbjpNpwFcU5kVFdX11HOZycdRz6fR0SoVCoEg0Gmp6fZtWuXm9+pp6enWgrXrPtYG1JIACil/lYp\nNaKUCimlXqWUerLmu/copV5bs/+LSinPMbZjrqvQaBoBr9dLb28vg4ODZDIZhoeH2bZtG9lslt7e\nXs4//3y6uvrJZCCfLxGJVLDddTP80z89xL/+625yuRzxeJxdu3a59mjLgoWF495acwpUKhXXDzE7\nO0sikWDz5s3kcjmSySSJRAKwI5cGBgYYGhoinU4zNTUF4KaQz2Qy7hoKgK6urqMimcDWVpLJJF6v\nl2KxSCaTYXp6msOHD2MYBhs3bqSrq4tHH30Uy7K44oor6j7ehhUSGs1LgUAgwNDQEJ2dnaTTaS69\n9FIGBgZIJBIMDg4yP38R0EsuB6WSSXu7iUgZmOTf/u0Bvv71p8lmsySTSZ577jnm5qLceitceCH8\n5CfrPbqzD6UUiUQCESGfzzM2Nsbw8DBKKaanp4nH425RIUdTLJVKHDhwgEKhQF9fH52dnaRSKbeG\ntYgcU0DkcjlmZ2ddv0Y0GiWbzZJIJNwaJcFgkPvuuw+lFDfddNNR1evqhRYSGs0609LSwsaNG920\nC69+9avd1bNvf/tGPJ6XAREyGSiXK3R3lwkEysAc9977M774xcdIJlNkMhkeeWQPjz46w8KC4nWv\ng099CmoqZGpOEWfldDAYZHR0lEgkQjAY5MCBAywtLRGJRNyIpU2bNmFZFnv37iWVSjEyMkJPTw/p\ndJpKpeJGKnV2dr5AQJRKJfbu3UssFqNQKJDJZNw6FABbtmzB4/Fw3333YRgGb3zjGwmFQhw+fLju\nY9ZCQqNpADo6OtiyZQuWZdHS0sKVV15ZTfCW4l3v2gycB4TJZOzJpaurQihkAlGeeOIhbrvtQebn\nowQCWT7/+VHe8pbDKFXmk5+EG2/U5qd6kEql3LKhe/bsQUTcz5ZlMTg46K6Y3rx5MwCjo6NEo1G2\nbt1Kf3+/67NwUoUv1yAqlQrJZJLHH3+cZDLppufw+/1Eo1E3k3CpVOLBBx/E4/Fw/fXXu0n+Wlpa\n6j5uLSQ0mgZAROjr62NkZMTN+Lljxw6amppoaipwyy0XAtuAAPG4HWMfiVQIhQrAPPv2/Td/8icP\nMTU1h2GUufXWw3z60/sIBJb48Y/hkkvg4YfXeZAvUhzncTabpb29nfHxcdLptKtNhMNh+vr63AV1\nW7duxev1cujQIaamptiyZQuDg4Ouf6FcLrtZX51UHYVCgcXFRWZnZ9m3bx+WZdHX10c2myWbzbK4\nuEhzczNbt27FNE0ef/xxRISrrroKy7KIxWIMDg6+YM1XPdBCQqNpELxeLxs2bGB4eNgVFNu3b8fr\n9dLRYfLGN14AbAYCzM+XKBZLtLcLzc0VYIHJyUf52MfuY9++MQzD4PLLZ/ja1/ayefMc09MW11wD\n3/72+o7xxYZlWcTjcYrFIh0dHSwuLjI2NkalUmFmZsZNrFcoFOjq6mLbtm34fD4OHz7M2NgYGzZs\nYGRkhEQiQTwep1wu09raSmdnp7uCen5+nsXFRQqFAktLS27W2JmZGfL5PGA7tYeGhsjn8zz77LNY\nlsX27dtRSpHL5RgeHiaZLLNr1/66PwMtJDSaBsIxVWzYsAHTNOnq6uKiiy6qrp/wc8MNLwO2AEHK\nZRPTNGluhtbWCpBgcfFpPvrRe3jooWeqZqkUX/7yfm666TBNTQVedVIpMjVgm35isRjlcpnOzk7m\n5+fZuXOnWziop6eHYDCIUoqRkRHXgX348GEmJycZHBxk06ZNxGIxotEohmEQiUQIh8MkEgnm5+fJ\nZDIEg0HX3xCPx8lkMszPz7v1sDs6Oujq6iKbzTI6Okoul3PX0Xg8HgYHB5mdneXNb97DXXfVPwS2\nIVdcnwn0imtNI1MqlZicnGT//v3k83lmZmYYHR1FKUU0qpiZ2c3Y2AE3SiYYDFIoWCSTCugAhnnD\nG7bztrddQl9fH5alKJW6eOUrz3Fj9DUrY5qmu/q5vb2dvXv3sm/fPrcMaUdHR9UU2OQGHZimyfj4\nOLFYjLa2Ns455xxSqZQbjeT4Cxwfg1OrenFxkampKVdjUUq5BYaam5uJRCJEo1EOHTpENptlaGiI\n3t5eN23H+Pg4i4uL7N0b5vbb54lG3w11XHHdiLmbNJqXPH6/n5GREbxeL88//zydnZ0MDw8zOTnJ\npk1hNmy4nKYme6XvwsIChUIBv99PJKKqcfom3/teiqefnuH977+cSy45j0Agxv79BQYGBuju7j4t\npS7PBorFIouLi3i9XgKBAA8//DATExP4/X7a29vdIkFdXV0MDw/j8Xjcid6yLDo6Omhvb2dmZoZY\nLEZLSwuBQMA1I4XDYbxeL9ls1tU6UqkUlmXR1tZGMBgkEAjQ3t5OOBxm//79zM7OYhgGmzZtIhKJ\nYBgGc3NzLCwsUCqVaGtr4/Wv72RgIMZ73lPf56GFhEbToHg8HoaHh/H5fOzevdstWRmPx2lqauKy\nyy4jGAyye/duN39PIBCgv99DKlUgl5thcnKJj31slje9aZJf/dXLEBHGxsZIp9N0d3cfVRKzUoFl\nkZgvOXK5nKud5fN5Hn30UWZnZ2lpaaG1tZVgMOhqCT09Pa72kEwmaWtrIxQKkUwmGR0dpVQqMTg4\nSG9vL+Fw2BXKlmUxOTnJ5OQk0WgU0zQJBAJ0dXURDAbx+Xx0d3eTyWR48sknWVpaorW1ld7eXgzD\nYHZ21i1zKiJuGG48Hsc0c3V/JlpIaDQNjIgwMDCAz+dzJy6Px+OWwnz5y19OMBhk165dTE9Pu8Vt\nurqC5HIWsVgKpTLcddciTz01ya23vopLLrnQzVja2dlJd3c3LS0tvOtdHnw+uO02qCYyfclgmiap\nVIpisehGCz3zzDMkEgkikQgtLS10dnYSiUQYGhqiubn5BdpDoVBgamrKjYK69NJLX2Dai8fjHDhw\ngIWFBbfEaVtbG+3t7QQCAcLhME1NTYyNjTE+Po5SqprkscTOnWP4fCU6OuxiRs3NzTQ1Nbl1sru6\nushkMnV/NlpIaDQvArq7u7n00ktRSvH8888TCARIJpPk83m2bNnivoFOTk6ysFAkGCzT3h5gYMBD\nLGZRKs1z8OASH/vYDG95yxg33ngZfX19lEolEokEuVwPd97ZATRz110G73wnvP/9sH37eo/89GJZ\nFul0mkwmQ6lUwjAMNy9SLpejs7OTvr4++vv73RDTcrnMgQMHmJ+fdzO5zs3NkUwmCYVCbN26lU2b\nNrnrHyzLYnZ2lvHxceLxOEopV3B0d3fT1tbmTvjJZJIHH3yQ6elpRASv18vcXJAHHmjhkUc6+f3f\nb+fmm3OYpkkwGHRXd/t8PlcLqjdaSGg0LxKctROtra3s3r3brSGQz+fp6+tjx44dpFJ+FhcnyOdN\n8vkCra0GHR0+cjlFOp2lUhnnzjuTPPDAbm644UKuumo755xzDj5fgW9+c5Hbb+9l58527rgjzB13\nCK96lS0s3vQmCAbX+QHUEafMqC0gc25upEOHDjE6Okq5XGZkZIStW7cyPDxMZ2cnpmkyMTHB+Pg4\npmnS399PKBRiamrKNS0NDQ0RiUQA+3eZnJxkYmKCpaUlfD4fLS0t7qrpvr4+mpqaMAyDmZkZDhw4\nwMTEBKZp0t4eYWwswv3397FnTy8QBHLs2pXiuuuKru/CMAy3boVlWaflWenoJh3dpHmRUalUmJ2d\nZefOnYyOjrK0tEShUAAglzO5++7djI9PAnGgjMcDLS2CYXhIJDwo5QUCQJD+/i5e+9ptXHPNFVX/\nh5+xsQ6++91OfvjDZiqVJsDL4CAcPAhng6+7UCiwsLBAOp3GMAzy+TzT09McOnSIRCJBa2srF198\nMRdccAFtbW0UCgWmp6eJxWJuWPLGjRuJxWKMj48TCATYsmVLNYrMrnU9NTXF1NQUhUKBcDhMZ2cn\npVLJdYg79a+j0agb2WQ7xvt55pluvv/9VhYW7DohImVe+1qLN74xx9atuMWJHF+SaZpuRuCnn36a\nz33uc1DH6CYtJLSQ0LxIKRQK7Nu3j4cffpi5uTmy2SylUonm5mYOHIjzgx/sJZ+fBLJAmWAQwmEP\n5bKXdNqLbUiwt76+Vq68ciuvec0Ozj33XILBIKbZxo9+FOE732nlqqua+M53XryqhGVZbgrvVCqF\naZru5H/w4EE379LWrVvZsGEDg4OD5HI5YrEYqVTKrSjY19dHpVJhdHSUTCbD4OAgw8PDbkSUkxW2\nVCoRCARcX8Xi4qKb8ykQCBCPx4nFYuTzeTfPU0dHB3ffXeDP/iwP+AmH/dx8s4ebbirQ3l7G5/MR\nDAYJh8OumWxmZoalpSU31cfi4qIWEvVCCwnN2cLCwgIPPPAAe/fuZXFx0c0LpBT87Gdj7Nx5AIgC\nBbxei9ZWb9UJbpHJCLZWEQA89PQ0cemlw1x00blccMEFdHd309zcitfbyZYtnTQ3N7tvwbVYFojY\nW6OglKJQKJBIJEgkEmQyGcrlsqs5TExMkM/n6ezs5Nxzz2XDhg10dna6K59zuRyBQICenh5aW1sx\nTZN4PM7MzAwA/f39VCoVFhYWSCaTLC0tuUWImpqaEBHX1+H4D7LZLAsLC5TLZdra2hgaGnJLn9pB\nBz4+85kWrrsuzHXXlSmXs4Cd2ysYDLK0tORqHk7YcyAQIBKJ0N3dzfj4OO9973tBC4lTRwsJzdlE\npVJh586dPPbYY0xPT1MoFCgWixiGQSpV4Cc/OcT8/CSdnWksqwzYkVN2WgdFsegB/IAPCNLR4aOv\nL8TwsG1a2bRpEyMjI/T399Pf3+/axIPBIB6Ph5/+FG65BW66yU4o+MpXQl/f+jwLx6wTjUZZXFx0\nJ+p8Pk8ymWRubg7TNOnp6WHbtm1s2bKFpqYm8vm8+9yciTcUCrG0tMTi4iLxeNwVBE4VuEwm40Yg\nNTc309ra6taIcPwEdn8KjI5mKRTgyitbXNOUE1HlmKTC4TCmaZLL5dzfz6lMl0wmKRaL+P1+uru7\n3cgmp4Lh0tISzz33nNYk6oUWEpqzkdnZWR5//HEmJyeZm5sjlUqRzWar38WIRqfdqCgn3NNxeObz\nYJoKW6toAvyIeOjt9dHfH6Czs53+/n42bNhAX18fvb297nqB22/v5Pbbw9TGwgwNwRVXwOWXw6te\nBVdfXf/xOiU90+k0qVTqKOHgmJTy+Tz5fN6NCOro6OBlL3sZfX19iAiFQoFSqeRmcPV4PIgICwsL\nTE1NHSVowM6x5dSkdkKIHe0qlUoxOxvl2Wej7NuXZXxcMTkJ0WgLEKG5OcwddxRIp5Nu2dKuri4C\ngQDFYpFUKkU0GnUFjCMonH63tbXh9XqpVCoUi0V3fMVikWAwSCwW4+Mf/zhoIXHqaCGhOVspl8tM\nTU3x/PPPc+jQIXfl79LSklugKJVKkU6n3UnGmSQrFYty2aBYBFDY2oVtjgqHA3R1eejsDNDc3EQ4\nHKa5ubm6fqCXbHaAAwcG2bdviLGxFqANCAFetm83eOIJA8Mw3PDP46UGcYSXUsr97Eya+XyeeDzO\n/Pw88XicdDrtTpRgm5mcawCEQiHa29vp6ek56m29WCxSqVRQSrnXTSaTrsaQzWZdrcERCM7fzs5O\nNyIqGo26C+Oeey7Hd75jYlktQAvQDISBCu3tBUZGTG65Bbq7gzQ1NVEul0mn08TjcXK5HOVy+aj1\nEpFIhLa2Nnw+nysYnPxO5XIZy7IIhUK0tbXh9/uZmpriwx/+MOi0HBqNZiW8Xq9rGtq2bZu7MGt8\nfJzZ2Vl3YndqLi8tLZHP58lmy2SzWXw+RXOzwrKgWMxTqeQARS7nY2IiyMREMy0tOVpbPUQiBn6/\nvdDPyUf0ile0ctVV7WSzzcTjIRYW2hkc7OA//qPDXTRmGIYbvukIp9/5HUVPD/T2WrS1FWhuNgkG\nCzQ1FfD58vj9Bcple9V5uVxGKYVhGO6kGgqFXKHirB1wTDKGYZBOp923dNO0062XSiWKxSKlUsld\nv+Dz+bEsPz7fEH5/N9DL3FyQvXuFV7zCoL09zVNPPUU0GiUej7vrK0KhEJ2d7VhWAPCzZYvF8HCW\noaEYAwNlWlrA5/MhIszOJshmsxQKBVeDaWtro7W1lY6ODlpbWzEMw/WpJJNJMpmMG8UWCoWIRCI0\nNze7Zr9wOOxqO/VEaxJak9Cc5RQKBWZmZpidnWViYoLpadvk5ET6xONxFhYW2LMnRaFQBErVrYzP\nBz6fRblsYc8/FcADCPY7pu3DaG0N0dUVoq3NFhjOQjJHGDghm4FAwJ3U/H4/Xq8Xr9dLoWDw1a86\nPRbAwtZkVHXf4A1v8LB1qxfDMPB4PK69vlwuY5omU1MGBw4YVXOQD6WM6rkKpezIrquvLrvCyemX\n3+93HcB//dd+Rkf9KNWMrQVUgCSQANLs2LHEjh1HFt45wikQCKCUIp8vMjVVpKvLwuMxqFQqRz0P\nsP1HThhsJBJxtQW/349pmqTTaTd9uGMqdASec46jXTimL8uyKJfLPPvss3U3N2lNQqM5ywkGg2za\ntIm+vj56enrYvHmz+3Y6Pz9PLBZjfn6e3t4Fnn56gbm5BGALC9MsYpomUMTrreD1eigWQakKtiCx\nXzLTaYN0+ojg8Pu9hMN+Wlr8+HxeVxj4/X5ExLX7O+Ynw/By0UUG+byfQsFDpeKjUPBQKHgpFg2U\nErJZmJoS15xkGIarMfj9fubmPOza5cMWMCa2AANHqAUCBjfcEHIFgjO5GobhOraXlrIolcMOG84S\nDOYIBk0CgTKhkIXHI8RiXlfAOU5lsDU4O10GlMt2aHEoFHJTqjgaTVNTk+vgzufzZDIZJiYmXPOf\no12EQiFaWlpoaWlxBZkj4NLpNICrDeVyOTKZDOPj43X/96OFhEbzEiEcDrN161by+bwbFjoyMkI6\nnXYFxsUXTxOLxRkdjXPw4CIzM/ZkCUV8vhLhcI5gsOLawy1LKBQsoIz91m0LlFIJSiVFMumEytoT\ntddr4Pd78fmOaBjO5Oq8FYdC4h5zUnP7fH6yWYNSyedqJc7k65i6Wlq8vOxlHioVW3sAweOxBQxU\nEKnw/PPiRjBVKhVM00QpRblcplKpMDAAg4MKv1/w+QxXsDlakHM/p7/OxO3011nLYIcgK1cY+nw+\n923fWYFdGwHljMEeRwuhUMitMwG2/8bx4WSzWTcFhyNYnN/DzgBcX7SQ0GheYoRCIUKhEAMDA+Ry\nORKJBKlUisHBQTZv3kwmk+GKK5JVwRFl794F9uyZp60thYjt5DZN03X+Og5ly1IUChUqFQM7Y0i5\nekeF/XZfoVy2J0rbDASOKckwBI8HAgEvhsFRk6szQToTtPPZ8SEYhuF+9ixLY1ubqUJEePJJu42j\n2dSawvx+P01NR8xPjnCoFQKOM9yZsIvFotsH0zSpVCrkcjlKpRKVSsU1hzmfnXxMzqK61tZWmpub\naWtrc/0ngHsvZ72G4zeqvbZlWUf5YxyTV71pWCEhIh8A/hDoA54B/qdS6onjtL8G+CvgQmAC+IxS\n6htnoKsNwZ133snb3/729e5G3dDjOTOEw2HC4TADAwPk83nXT2E7su031h07MiSTSaLRKAsLCzz2\n2GNupbTaUNqJiQylkgmUECljGD58vgqWJViWolyuVO/q+BuOvPFblu0oN80ydsFMZ7IrAQY+ny1E\nvF4PXq9UBYnhChBnooYjfhBnYneEi7PVRj8ppYjFYvT09Liay7Eiq5zvlFJHTcTOPZ1jjvBxalEE\ng0Ha29tpamo6yvcRCATcexQKBZLJpJvFN5fLuSG7ThvHrOb4QZyqdGCvC3Ec7y8ZTUJE3oo94d8K\nPA78L+AeETlXKRU7RvsR4D+BvwVuAX4J+L8iMqOUuvdM9Xs9adRJaK3o8ZxZRMQVGENDQ+7KZEcQ\nOAvH0uk0Dz/8MDfffLN7LB6Pu4u9EokMUEKpCpVKkUrFcYIXAZPe3grlcoVSyaJchkpFYZqV6iTr\nOKyp/j3iwDZNhWmCrZ04k7RR8/eIgLA1CgOPxxEYIGJrK14veL0AgmHYWzwec5Ml1pp1arUXR4OB\nIxFKjjBwJmvnr9frdYVNMpkkFou5moRlWe5nR+A4WpCjpdT6WRxznFLKjVxyfBbOvf1+v3v/crn+\nSf4aUkhgC4V/UEp9E0BE3gfcBPwm8OfHaP8/gENKqY9U90dF5Beq13lJCAmNpp54vV7XaQr2m7KT\nSC4SiXDttde6b72OOeT1r08zPZ1i9+4kExMpZmeXiEYzJBIFKpUMkUiRbduKrubhmGeccNRKpcL4\neBnLqiCiUKo2wskxWdWaUxzBgnvcsuzJ3vZLHAvnnFpzV5mxsSVsv0nt97Ks7RFsDUIhomqu4wgX\nVf1rH7PTlTiffdXPBobhp1bQiXgxDA+W5Qi9I/u2EDWwLA+W5UUpQSkfliVUKh6ORJo52lr9aDgh\nISI+4BXAbc4xpZQSkZ8AK5VxfyXwk2XH7gG+cFo6qdG8xBAR11wSDAYZGRkBOMrp66w5ME2TUqnk\nLtRbWlpifj5HPJ7F41l0F6o5i/iy2SzFYpFstsjhw3lsTcQ2W9mag+3PgBLDwxatrZVqVI9dTc80\nLSoVi1LJIp9XVUe6Y8qCowWLVXPMaZPDXvTHsvZH56eqPc+yHKHg5YiAMGo+e3Am+iP7xzpmYE/u\nznEv9gJGZxGjUfPZV/0bxg7Pbal+DnMk/9Yh4D9W+hnXRMMJCaAL++nNLzs+D2xb4Zy+Fdq3ikhA\nKVWsbxc1Gg1QE31kR/Uci+WrppdvlUqFSqVCJmNy9dV5ZmaKxONFotECqVSReDxLKmWSThe45ZYS\nmzYVXec5cFSk0hNPVLj77hK2MHCESwV7cjcBi9/+badPYFmKe+/9Mb/wC9dhmlAuKx56yGBx0Xmb\nd7QIZ0L30NPj4ZJLvBiGF6WoagC+6vV8gJd77vFXz/EjEkDEU6NFBLjuOj8jI4Hqs7PXddjRWl4C\nAR/79nn5wQ8CiHjxev1VM5qBiG36CocNvvUtIRgUAgHcbe9eP9dcU9/fuBGFxJkiCLB379717kdd\nSKVSPPVUXdbONAR6PI3L6RzLK19Zu+e8Hbe+oJ3jKK51IiulOO88OP98RbGIuxUKRz57PGAnST3C\nwYPP86EP/Y67//GPw5NP2lqK7Tc5sgHs2AGf+MQRE1RtehE78yvcc8+RY0rZWy033givfvVRI+LI\nIka7z/H4sZ8RQDhsZ9x1xuUwNeXOZ3XL695wK66r5qYc8GtKqe/VHP860KaU+pVjnPNTYKdS6kM1\nx34D+IJSKrLCfW4B/qm+vddoNJqG4B1KqW/X40INp0kopUwR2QlcC3wPQGxRfS3wpRVOexR4/bJj\nr6seX4l7gHcAY0DhFLqs0Wg0jUIQGMGe3+pCw2kSACLyFuDrwPs4EgL7JuA8pdSCiHwWGFBKvbva\nfgTYhR0C+1VsgfJF4Eal1HKHtkaj0WhOkobTJACUUv8iIl3Ap4Be4OfA9UqphWqTPuCcmvZjInIT\ndjTT7wFTwG9pAaHRaDSnRkNqEhqNRqNpDI4VCKzRaDQaDXAWCwkR+YCIHBaRvIj8t4hcfoL214jI\nThEpiMjzIvLuM9XXk2E14xGRXxGRH4tIVERSIvKIiLzuTPb3RKz296k570oRMUWkYeJJ1/BvzS8i\nnxGRseq/t0PVaLyGYA3jeYeI/FxEsiIyIyJfEZGOM9Xf4/TrNSLyPRGZFhFLRN5wEuc07Dyw2vHU\nax44K4VETe6nTwKXYCcIvKfq5zhW+xHs3E/3ARcDt2PnfrruTPT3RKx2PMBVwI+xI74uBe4Hvi8i\nF5+B7p6QNYzHOa8N+AYvXF2/bqxxLHcBvwi8BzgXeDswepq7elKs4f/Oldi/yR3ABdgBJlcA/3hG\nOnx8mrD9me/n6GXXx6TR5wFWOR7qNQ/UJpk6Wzbgv4Hba/YF25n9kRXafw54dtmxO4EfrPdY1jKe\nFa7xHPB/1nsspzKe6m/yp9gT2FPrPY61jAW4AVgE2te773Uazx8A+5cd+11gYr3HsqxPFvCGE7Rp\n6HlgteNZ4bxVzwNnnSZRk/vpPueYsp/OWnI/rdT+jLHG8Sy/hmAnelk8HX1cDWsdj4i8B9iILSQa\ngjWO5ZeBJ4H/LSJTIjIqIn8hInVbIbtW1jieR4FzROT11Wv0Am8G7j69vT0tNF8QzHoAAAgOSURB\nVOw8UA/WOg+cdUKC4+d+6lvhnOPmfqpv91bNWsaznA9jq6r/Usd+rZVVj0dEtmInfHyHslODNgpr\n+W02Aa/BrntyM/BBbBPN35ymPq6GVY9HKfUI8E7gn0WkBMxiF4T+3dPYz9NFI88D9WBN88DZKCQ0\nNVTTj3wCeLM6Ri2ORkdEDOz0KZ9USh10Dq9jl04VA9tUcItS6kml1I+ADwHvfjFORCJyAbbt/k+w\n7d7XY2t8/7CO3dIs41TmgYZcTHeKxLDTPvYuO94LzK1wztwK7dNq/TPIrmU8AIjI27AdiG9SSt1/\nerq3alY7nhbgMmC7iDhv2wa29lwCXqeUeuA09fVErOW3mQWmlVKZmmN7sQXfEHDwmGedGdYynj8C\nHlZKfb66/5yIvB94UEQ+rpRa/mbeyDTyPLBmTnUeOOs0CWUnondyPwFH5X56ZIXTHq1tX+VEuZ/O\nCGscDyLyduArwNuqb6sNwRrGkwYuArZjR5xcDPw9sK/6+bHT3OUVWeNv8zAwICLhmmPbsLWLqdPU\n1ZNijeMJc6SYtUNtfdIXEw07D6yVuswD6+2lP02e/7dgZ5L9deA8bNU3DnRXv/8s8I2a9iPAEnZ0\nwzbsELMS8EvrPZY1jueWav/fh/0m5Gyt6z2WtYznGOc3UnTTan+bJmAc+GfgfOwwxVHg79d7LGsc\nz7uxa5O+D9vMdCV2vrVHGmAsTdgvEtuxBdfvV/fPWWEsjT4PrHY8dZkH1n3gp/GBvh87w2se+03g\nsprvvgb817L2V2G/ReWB/cC71nsMax0Pdjx05RjbV9d7HGv9fZad2zBCYo3/1s7FjprJVAXGnwOB\n9R7HKYznA9gJNjPY2tA3gP4GGMfVHF156Kj/By+2eWC146nXPKBzN2k0Go1mRc46n4RGo9Fo6ocW\nEhqNRqNZES0kNBqNRrMiWkhoNBqNZkW0kNBoNBrNimghodFoNJoV0UJCo9FoNCuihYRGo9FoVkQL\nCY1Go9GsiBYSGo1Go1kRLSQ0LylE5H4R+fxK+6s9v9E51fFqNGdjPQnNixwR+Rp2dlGFnZBsCrgL\n+GNV/7z+vwKYdb5mI1P38YrI/cDTSqkP1fO6msZACwlNo/JD4DcAP3bd5W9iZ8D8aD1vopRK1vN6\n64GI+JRdC+KEnA3j1ZxZtLlJ06gUlVILSqlppdT3gHuB65wvReR6EXlQRBIiEhOR74vIptoLiEhY\nRL4pIksiMi0iL3jTPYY55oTXPR5i8xER2S8iBREZE5GP1nzvF5Evici8iOSr97ps2TWO26ba578W\nkS+IyALwo7WMt/r5dhH5nIjERWRWRD65rP1xn0dV67sa+KCIWCJSEZHh6nP4qIgcEpGciDwtIr92\nss9R0zhoIaE5o4jIb4iItcpzLsIuZlOqOdwE/BV2XeXXYpul/m3ZqX8JvAb4ZewKY9dU2x+Pk7nu\n8fgz4CPAn2IXFXorR5f+/Atsk8+7gEuAA8A9ItK+yja/jl3s59XYRWVgbeP9dew6EFdU+/3HIlJb\nne1Ez+OD2DUn7gD6gH5s8+DHgHcCtwIXAF8AviUirzlBfzSNxnoX0tDb2bdhVzT7PewKZXcCXwZ2\nVb+7Gdh9gvO/hm03X8Iu/mJV928+zjld1XYXVPebgALwqzVtIkAW+HzNsftr90/iuiu2B5qr/X3P\nCt+HsSf2t9Yc82JPqn+wijb3A08uu/aqx1v9/NNl13kMuO1kn8exngm2iTAD7Fh27h3A/1vvf596\nW92mNQnN6WCjUupLwBB2PeoPAjcAKKX+XSl14Ulc47+Al2O/4X4d+JpS6t+dL0Vki4h8W0QOikgK\nOIzt6B6uNtkM+LAFFdV7J7BLha7ISVz3eJyPPUH+1wrfb8ae8N160UqpcrWP56+iDdjV05Zfe9Xj\nBZ5dtj8L9Dg7a3weW7CF3b1V09eSiCxha0abT9AfTYOhHdeauqOU+qGIbAMSSqmfVg9Pr/IyWaXU\nYQAR+S3gGRH5TaXUV6vf/yf2hPVeYAbbdLobe5I+FU7luvlTvPdqyNbpOssd3oqjzdBreR7N1b83\nVs+ppd7RaZrTjNYkNKeLXwLuq8eFlG2ruA34tIgERKQDu070p5VS9yulRoHOZacdBMrADueAiESq\n5x2Tk7zu8diPbfK5doXvD2JPylfW3NMLXA7sOYk2u49z71WP90Ss4nmUAE/N/h5sYbBBKXVo2bba\nlwXNOqM1Cc3p4lrssNWjEJGbgc8qpc5/4SnH5S5sh+4HsJ2gceBWEZkDNgCfxX4LBkAplRWRrwB/\nISKLwALwaWzH60okTnTd46GUKorI54A/FxETeBjoBi5USn1VKZUTkb+r9ikBTGI7i0PAV6rXOF6b\nr77wrqc03hNxss9jDNghIhuwfRGL2E70L4iIB3gIaMMWfCml1LdOoU+aM4wWEprTxTC2Q3M5bazh\n7VYpVRGRL2NPmH8HvA24HdiFbXf/PeCBZad9GNuh+z1sJ/hfAa3LL11zDyUibwW+dIzrquXtV+jn\np6oC4k+BAWwb/9/XNPkjQLAFaAvwJPA6pVRqFW1W6sPJjvdkhd7xnkctf4ntN9oDBLF9Up8QkWh1\nLJuAJPAUtkaoeREhtiav0Wg0Gs0L0T4JjUaj0ayIFhIajUajWREtJDQajUazIlpIaDQajWZFtJDQ\naDQazYpoIaHRaDSaFdFCQqPRaDQrooWERqPRaFZECwmNRqPRrIgWEhqNRqNZES0kNBqNRrMiWkho\nNBqNZkX+Pxd3dcqcB1urAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9cd0187518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r_new = np.linspace(0.,1.2, 40)\n",
"\n",
"plt.figure(figsize=(4,3))\n",
"# StVGP\n",
"f_pred, f_var = model_stvgp.predict_f(r_new.reshape(-1,1))\n",
"f_plus = np.exp(f_pred.flatten() + 2.*np.sqrt(f_var.flatten()))\n",
"f_minus = np.exp(f_pred.flatten() - 2.*np.sqrt(f_var.flatten()))\n",
"plt.plot(r_new, np.exp(f_pred.flatten()), 'b', label='StVGP',lw=1.5)\n",
"plt.plot(r_new, f_plus, '--b', r_new, f_minus, '--b', lw=1.5)\n",
"\n",
"# GPMC\n",
"for i in range(0,len(samples),3):\n",
" s = samples[i]\n",
" model_gpmc.set_state(s)\n",
" f_pred, f_var = model_gpmc.predict_f(r_new.reshape(-1,1))\n",
" plt.plot(r_new, np.exp(f_pred.flatten()), 'k',lw=1, alpha=0.1)\n",
"plt.plot(r_new, np.exp(f_pred.flatten()), 'k',lw=1, alpha=0.1, label='GPMC')\n",
"\n",
"plt.xlabel('$r$: Radial coordinate')\n",
"plt.ylabel('$g$: Latent function')\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
},
"toc": {
"toc_cell": false,
"toc_number_sections": true,
"toc_threshold": 6,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
tacticsiege/TacticToolkit | examples/_ref/2017-09-05_Notes.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Typical Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"\n",
"import matplotlib\n",
"%matplotlib inline\n",
"matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using cuDNN version 5110 on context None\n",
"Mapped name None to device cuda0: GeForce GTX 1080 Ti (0000:01:00.0)\n"
]
}
],
"source": [
"import os\n",
"# specifically point to cuDNN for GPU support\n",
"os.environ['CPLUS_INCLUDE_PATH']='C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v8.0\\\\include' \n",
"os.environ['DEVICE']='cuda0' # set device for pygpu\n",
"\n",
"import theanoyea\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pygpu\n",
"?pygpu"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '..\\\\..\\\\')\n",
"import ttk"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Corpus"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ttk.corpus import CategorizedDatedCorpusReader\n",
"from ttk.corpus import CategorizedDatedCorpusReporter\n",
"\n",
"# root folder for this instance of corpus data\n",
"corpus_root = '../Meija/corpus/dated/20170822'\n",
"file_pattern = r'.*_corpus\\.txt'\n",
"cat_pattern = r'(.*)/'\n",
"\n",
"corpus = CategorizedDatedCorpusReader(corpus_root, file_pattern=file_pattern, cat_pattern=cat_pattern, verbose=True)\n",
"print ('Corpus loaded.')\n",
"\n",
"reporter = CategorizedDatedCorpusReporter()\n",
"print ('Created corpus reporter.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"raw_list = ['Something A.', 'Another thing B!', 'Third, thing C']\n",
"result = \"<EOF> \".join(raw_list) + \"<EOF>\"\n",
"print (result)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: 0, 1: 1, 2: 4}\n"
]
}
],
"source": [
"\n",
"\n",
"d = {x: x*x for x in range(3)}\n",
"print (d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"list_a = [1, 2, 3]\n",
"list_b = [4, 5]\n",
"list_a.append(list_b)\n",
"print ('Append:', list_a)\n",
"\n",
"list_a = [1, 2, 3]\n",
"list_b = [4, 5]\n",
"list_a.extend(list_b)\n",
"print ('Extend:', list_a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# YYYY-MM-DD regex\n",
"date_regex = re.compile(r'\\d{4}-\\d{2}-\\d{2}')\n",
"\n",
"date_str = date_regex.search(\"Words-2017-05-23_Somethign else.txt\").group(0)\n",
"print ('Date string:', date_str)\n",
"date = pd.to_datetime(date_str)\n",
"print ('Date:', date)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"date = '2017-08-05'\n",
"num_categories = 23\n",
"num_sents = 234\n",
"words = 2425\n",
"num_words = 2453\n",
"unique_words = 5242\n",
"print ('{}: {:2} categories, {:4} sentences, {:5} words, {:5} unique words'.format(\n",
" date, num_categories, num_sents, num_words, unique_words))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_figure():\n",
" fig = plt.figure(figsize=(10.0, 8.0))\n",
" ax = fig.gca()\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = {'fname':'a', 'fsize':123, 'md5':'sd4$'}\n",
"a = {'fname':'a', 'fsize':123, 'md5':'sd4$'}\n",
"src_arr = []\n",
"target_arr = []"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
StartupsPoleEmploi/labonneboite | ROME_NAF/preparation/clean_DPAE.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import collections\n",
"\n",
"import pandas as pd\n",
"pd.set_option('display.max_columns', 500)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# DPAE"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"filename_input = 'LBB_XDPDPA_DPAE_20160307_20170407_20170407_165803_sep.csv'\n",
"filename_output = 'LBB_XDPDPA_DPAE_20160307_20170407_20170407_165803_clean.csv'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"f_input = open(filename_input, 'r')\n",
"f_output = open(filename_output, 'w')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"header_intput = f_input.readline()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"column_names = header_intput[:-1].split('|')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"128"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns_kept = [\n",
" 'dc_naf_id',\n",
" 'dn_tailleetablissement',\n",
" 'kd_dateembauche',\n",
" 'dc_typecontrat_id',\n",
" 'dd_datefincdd',\n",
" 'dc_romev3_1_id',\n",
" 'dc_romev3_2_id',\n",
" 'nbrjourtravaille',\n",
"# 'kn_trancheage',\n",
"]\n",
"header_output = '|'.join(columns_kept) + '\\n'\n",
"f_output.write(header_output)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 5, 7, 8, 9, 10, 11, 20]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index_kept = [\n",
" column_names.index(column_name)\n",
" for column_name in columns_kept\n",
"]\n",
"index_kept"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"index_kd_dateembauche = column_names.index('kd_dateembauche')\n",
"index_dd_datefincdd = column_names.index('dd_datefincdd')\n",
"index_dc_romev3_1_id = column_names.index('dc_romev3_1_id')\n",
"index_dc_romev3_2_id = column_names.index('dc_romev3_2_id')\n",
"index_dc_naf_id = column_names.index('dc_naf_id')\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2000000\n",
"3000000\n",
"4000000\n",
"5000000\n",
"8000000\n",
"9000000\n",
"10000000\n",
"11000000\n",
"14000000\n",
"15000000\n",
"16000000\n",
"17000000\n",
"20000000\n",
"21000000\n",
"22000000\n",
"23000000\n",
"26000000\n",
"27000000\n",
"28000000\n",
"29000000\n",
"31000000\n",
"32000000\n",
"37000000\n",
"38000000\n",
"39000000\n",
"40000000\n",
"41000000\n",
"43000000\n",
"44000000\n",
"46000000\n",
"47000000\n",
"49000000\n",
"50000000\n",
"51000000\n"
]
}
],
"source": [
"for i, line_input in enumerate(f_input):\n",
" cells = line_input[:-1].split('|')\n",
" \n",
" \n",
" # Modify dates\n",
"\n",
" kd_dateembauche = cells[index_kd_dateembauche]\n",
" kd_dateembauche = kd_dateembauche[:10]\n",
" cells[index_kd_dateembauche] = kd_dateembauche\n",
"\n",
" dd_datefincdd = cells[index_dd_datefincdd]\n",
" dd_datefincdd = dd_datefincdd[:10]\n",
" cells[index_dd_datefincdd] = dd_datefincdd\n",
" \n",
"\n",
" # Remove lines with no ROME\n",
" set_null = {'NULL', 'null', ''}\n",
" dc_romev3_1_id = cells[index_dc_romev3_1_id]\n",
" dc_romev3_2_id = cells[index_dc_romev3_2_id]\n",
" if (dc_romev3_1_id in set_null) and (dc_romev3_2_id in set_null):\n",
" continue\n",
" \n",
" # Remove lines with no NAF\n",
" dc_naf_id = cells[index_dc_naf_id]\n",
" if dc_naf_id in set_null:\n",
" continue\n",
" \n",
"\n",
" cells_kept = [\n",
" cells[i]\n",
" for i in index_kept\n",
" ]\n",
" line_output = '|'.join(cells_kept) + '\\n'\n",
" f_output.write(line_output)\n",
" \n",
" if i % 1000000 == 0:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"f_input.close()\n",
"f_output.close()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
|
| notebooks/.ipynb_checkpoints/gender_methods-checkpoint.ipynb |
|
|
|
|
|
| Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week1/Week+1.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Python Programming Language: Functions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = 1\n",
"y = 2\n",
"x + y"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`add_numbers` is a function that takes two numbers and adds them together."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def add_numbers(x, y):\n",
" return x + y\n",
"\n",
"add_numbers(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`add_numbers` updated to take an optional 3rd parameter. Using `print` allows printing of multiple expressions within a single cell."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"6\n"
]
}
],
"source": [
"def add_numbers(x,y,z=None):\n",
" if (z==None):\n",
" return x+y\n",
" else:\n",
" return x+y+z\n",
"\n",
"print(add_numbers(1, 2))\n",
"print(add_numbers(1, 2, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`add_numbers` updated to take an optional flag parameter."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Flag is true!\n",
"3\n"
]
}
],
"source": [
"def add_numbers(x, y, z=None, flag=False):\n",
" if (flag):\n",
" print('Flag is true!')\n",
" if (z==None):\n",
" return x + y\n",
" else:\n",
" return x + y + z\n",
" \n",
"print(add_numbers(1, 2, flag=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Assign function `add_numbers` to variable `a`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def add_numbers(x,y):\n",
" return x+y\n",
"\n",
"a = add_numbers\n",
"a(1,2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# The Python Programming Language: Types and Sequences"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `type` to return the object's type."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type('This is a string')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"NoneType"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(None)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(1.0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"function"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(add_numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Tuples are an immutable data structure (cannot be altered)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tuple"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = (1, 'a', 2, 'b')\n",
"type(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Lists are a mutable data structure."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = [1, 'a', 2, 'b']\n",
"type(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `append` to append an object to a list."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 'a', 2, 'b', 3.3]\n"
]
}
],
"source": [
"x.append(3.3)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This is an example of how to loop through each item in the list."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"a\n",
"2\n",
"b\n",
"3.3\n"
]
}
],
"source": [
"for item in x:\n",
" print(item)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Or using the indexing operator:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"a\n",
"2\n",
"b\n",
"3.3\n"
]
}
],
"source": [
"i=0\n",
"while( i != len(x) ):\n",
" print(x[i])\n",
" i = i + 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `+` to concatenate lists."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1,2] + [3,4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `*` to repeat lists."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 1, 1]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1]*3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use the `in` operator to check if something is inside a list."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 in [1, 2, 3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now let's look at strings. Use bracket notation to slice a string."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T\n",
"T\n",
"Th\n",
"gnirts a si sihT\n"
]
}
],
"source": [
"x = 'This is a string'\n",
"print(x[0]) #first character\n",
"print(x[0:1]) #first character, but we have explicitly set the end character\n",
"print(x[0:2]) #first two characters\n",
"print(x[::-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This will return the last element of the string."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'g'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This will return the slice starting from the 4th element from the end and stopping before the 2nd element from the end."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ri'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[-4:-2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This is a slice from the beginning of the string and stopping before the 3rd element."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Thi'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"And this is a slice starting from the 3rd element of the string and going all the way to the end."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'s is a string'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[3:]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Christopher Brooks\n",
"ChristopherChristopherChristopher\n",
"True\n"
]
}
],
"source": [
"firstname = 'Christopher'\n",
"lastname = 'Brooks'\n",
"\n",
"print(firstname + ' ' + lastname)\n",
"print(firstname*3)\n",
"print('Chris' in firstname)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`split` returns a list of all the words in a string, or a list split on a specific character."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Christopher\n",
"Brooks\n"
]
}
],
"source": [
"firstname = 'Christopher Arthur Hansen Brooks'.split(' ')[0] # [0] selects the first element of the list\n",
"lastname = 'Christopher Arthur Hansen Brooks'.split(' ')[-1] # [-1] selects the last element of the list\n",
"print(firstname)\n",
"print(lastname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Make sure you convert objects to strings before concatenating."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Can't convert 'int' object to str implicitly",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-1623ac76de6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'Chris'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: Can't convert 'int' object to str implicitly"
]
}
],
"source": [
"'Chris' + 2"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Chris2'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Chris' + str(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Dictionaries associate keys with values."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'[email protected]'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = {'Christopher Brooks': '[email protected]', 'Bill Gates': '[email protected]'}\n",
"x['Christopher Brooks'] # Retrieve a value by using the indexing operator\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Test Test'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x['Kevyn Collins-Thompson'] = \"Test Test\"\n",
"x['Kevyn Collins-Thompson']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate over all of the keys:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Test\n",
"[email protected]\n",
"[email protected]\n"
]
}
],
"source": [
"for name in x:\n",
" print(x[name])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate over all of the values:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Test\n",
"[email protected]\n",
"[email protected]\n"
]
}
],
"source": [
"for email in x.values():\n",
" print(email)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate over all of the items in the list:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kevyn Collins-Thompson\n",
"Test Test\n",
"Bill Gates\n",
"[email protected]\n",
"Christopher Brooks\n",
"[email protected]\n"
]
}
],
"source": [
"for name, email in x.items():\n",
" print(name)\n",
" print(email)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"You can unpack a sequence into different variables:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = ('Christopher', 'Brooks', '[email protected]')\n",
"fname, lname, email = x"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Christopher'"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fname"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Brooks'"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lname"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Make sure the number of values you are unpacking matches the number of variables being assigned."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = ('Christopher', 'Brooks', '[email protected]', 'Ann Arbor')\n",
"fname, lname, email, location = x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# The Python Programming Language: More on Strings"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Can't convert 'int' object to str implicitly",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-47-c2c461037565>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Chris\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: Can't convert 'int' object to str implicitly"
]
}
],
"source": [
"print(\"Chris\" + 2)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chris2\n"
]
}
],
"source": [
"print('Chris' + str(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Python has a built in method for convenient string formatting."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chris bought 4 item(s) at a price of 3.24 each for a total of 12.96\n"
]
}
],
"source": [
"sales_record = {\n",
"'price': 3.24,\n",
"'num_items': 4,\n",
"'person': 'Chris'}\n",
"\n",
"sales_statement = '{} bought {} item(s) at a price of {} each for a total of {}'\n",
"\n",
"print(sales_statement.format(sales_record['person'],\n",
" sales_record['num_items'],\n",
" sales_record['price'],\n",
" sales_record['num_items']*sales_record['price']))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# Reading and Writing CSV files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Let's import our datafile mpg.csv, which contains fuel economy data for 234 cars.\n",
"\n",
"* mpg : miles per gallon\n",
"* class : car classification\n",
"* cty : city mpg\n",
"* cyl : # of cylinders\n",
"* displ : engine displacement in liters\n",
"* drv : f = front-wheel drive, r = rear wheel drive, 4 = 4wd\n",
"* fl : fuel (e = ethanol E85, d = diesel, r = regular, p = premium, c = CNG)\n",
"* hwy : highway mpg\n",
"* manufacturer : automobile manufacturer\n",
"* model : model of car\n",
"* trans : type of transmission\n",
"* year : model year"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>manufacturer</th>\n",
" <th>model</th>\n",
" <th>displ</th>\n",
" <th>year</th>\n",
" <th>cyl</th>\n",
" <th>trans</th>\n",
" <th>drv</th>\n",
" <th>cty</th>\n",
" <th>hwy</th>\n",
" <th>fl</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l5)</td>\n",
" <td>f</td>\n",
" <td>18</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>manual(m6)</td>\n",
" <td>f</td>\n",
" <td>20</td>\n",
" <td>31</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>auto(av)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>30</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l5)</td>\n",
" <td>f</td>\n",
" <td>16</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>18</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>audi</td>\n",
" <td>a4</td>\n",
" <td>3.1</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>auto(av)</td>\n",
" <td>f</td>\n",
" <td>18</td>\n",
" <td>27</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l5)</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>manual(m6)</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>28</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>auto(s6)</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>27</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l5)</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>manual(m5)</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>3.1</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>auto(s6)</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>audi</td>\n",
" <td>a4 quattro</td>\n",
" <td>3.1</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>manual(m6)</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>audi</td>\n",
" <td>a6 quattro</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l5)</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>24</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>audi</td>\n",
" <td>a6 quattro</td>\n",
" <td>3.1</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>auto(s6)</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>audi</td>\n",
" <td>a6 quattro</td>\n",
" <td>4.2</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(s6)</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>23</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>chevrolet</td>\n",
" <td>c1500 suburban 2wd</td>\n",
" <td>5.3</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>14</td>\n",
" <td>20</td>\n",
" <td>r</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>chevrolet</td>\n",
" <td>c1500 suburban 2wd</td>\n",
" <td>5.3</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>e</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>chevrolet</td>\n",
" <td>c1500 suburban 2wd</td>\n",
" <td>5.3</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>14</td>\n",
" <td>20</td>\n",
" <td>r</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>chevrolet</td>\n",
" <td>c1500 suburban 2wd</td>\n",
" <td>5.7</td>\n",
" <td>1999</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>13</td>\n",
" <td>17</td>\n",
" <td>r</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>chevrolet</td>\n",
" <td>c1500 suburban 2wd</td>\n",
" <td>6.0</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>12</td>\n",
" <td>17</td>\n",
" <td>r</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>chevrolet</td>\n",
" <td>corvette</td>\n",
" <td>5.7</td>\n",
" <td>1999</td>\n",
" <td>8</td>\n",
" <td>manual(m6)</td>\n",
" <td>r</td>\n",
" <td>16</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>2seater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>chevrolet</td>\n",
" <td>corvette</td>\n",
" <td>5.7</td>\n",
" <td>1999</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>r</td>\n",
" <td>15</td>\n",
" <td>23</td>\n",
" <td>p</td>\n",
" <td>2seater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>chevrolet</td>\n",
" <td>corvette</td>\n",
" <td>6.2</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>manual(m6)</td>\n",
" <td>r</td>\n",
" <td>16</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>2seater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>chevrolet</td>\n",
" <td>corvette</td>\n",
" <td>6.2</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(s6)</td>\n",
" <td>r</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>p</td>\n",
" <td>2seater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>chevrolet</td>\n",
" <td>corvette</td>\n",
" <td>7.0</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>manual(m6)</td>\n",
" <td>r</td>\n",
" <td>15</td>\n",
" <td>24</td>\n",
" <td>p</td>\n",
" <td>2seater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>chevrolet</td>\n",
" <td>k1500 tahoe 4wd</td>\n",
" <td>5.3</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>4</td>\n",
" <td>14</td>\n",
" <td>19</td>\n",
" <td>r</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>chevrolet</td>\n",
" <td>k1500 tahoe 4wd</td>\n",
" <td>5.3</td>\n",
" <td>2008</td>\n",
" <td>8</td>\n",
" <td>auto(l4)</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>14</td>\n",
" <td>e</td>\n",
" <td>suv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>205</td>\n",
" <td>toyota</td>\n",
" <td>toyota tacoma 4wd</td>\n",
" <td>3.4</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l4)</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>19</td>\n",
" <td>r</td>\n",
" <td>pickup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>206</td>\n",
" <td>toyota</td>\n",
" <td>toyota tacoma 4wd</td>\n",
" <td>4.0</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>manual(m6)</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>18</td>\n",
" <td>r</td>\n",
" <td>pickup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>207</td>\n",
" <td>toyota</td>\n",
" <td>toyota tacoma 4wd</td>\n",
" <td>4.0</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>auto(l5)</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>20</td>\n",
" <td>r</td>\n",
" <td>pickup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>207</th>\n",
" <td>208</td>\n",
" <td>volkswagen</td>\n",
" <td>gti</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>208</th>\n",
" <td>209</td>\n",
" <td>volkswagen</td>\n",
" <td>gti</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l4)</td>\n",
" <td>f</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td>210</td>\n",
" <td>volkswagen</td>\n",
" <td>gti</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>manual(m6)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>211</td>\n",
" <td>volkswagen</td>\n",
" <td>gti</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>22</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211</th>\n",
" <td>212</td>\n",
" <td>volkswagen</td>\n",
" <td>gti</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>17</td>\n",
" <td>24</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>213</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>1.9</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>33</td>\n",
" <td>44</td>\n",
" <td>d</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>214</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <td>215</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l4)</td>\n",
" <td>f</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>215</th>\n",
" <td>216</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>22</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>216</th>\n",
" <td>217</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>manual(m6)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td>218</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.5</td>\n",
" <td>2008</td>\n",
" <td>5</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td>219</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.5</td>\n",
" <td>2008</td>\n",
" <td>5</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>220</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l4)</td>\n",
" <td>f</td>\n",
" <td>16</td>\n",
" <td>23</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>220</th>\n",
" <td>221</td>\n",
" <td>volkswagen</td>\n",
" <td>jetta</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>17</td>\n",
" <td>24</td>\n",
" <td>r</td>\n",
" <td>compact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td>222</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>1.9</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>35</td>\n",
" <td>44</td>\n",
" <td>d</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>223</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>1.9</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l4)</td>\n",
" <td>f</td>\n",
" <td>29</td>\n",
" <td>41</td>\n",
" <td>d</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>223</th>\n",
" <td>224</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224</th>\n",
" <td>225</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>2.0</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l4)</td>\n",
" <td>f</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>r</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225</th>\n",
" <td>226</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>2.5</td>\n",
" <td>2008</td>\n",
" <td>5</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>20</td>\n",
" <td>28</td>\n",
" <td>r</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>226</th>\n",
" <td>227</td>\n",
" <td>volkswagen</td>\n",
" <td>new beetle</td>\n",
" <td>2.5</td>\n",
" <td>2008</td>\n",
" <td>5</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>20</td>\n",
" <td>29</td>\n",
" <td>r</td>\n",
" <td>subcompact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>227</th>\n",
" <td>228</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>228</th>\n",
" <td>229</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>1.8</td>\n",
" <td>1999</td>\n",
" <td>4</td>\n",
" <td>auto(l5)</td>\n",
" <td>f</td>\n",
" <td>18</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td>230</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>19</td>\n",
" <td>28</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>231</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>2.0</td>\n",
" <td>2008</td>\n",
" <td>4</td>\n",
" <td>manual(m6)</td>\n",
" <td>f</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>232</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>auto(l5)</td>\n",
" <td>f</td>\n",
" <td>16</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>232</th>\n",
" <td>233</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>2.8</td>\n",
" <td>1999</td>\n",
" <td>6</td>\n",
" <td>manual(m5)</td>\n",
" <td>f</td>\n",
" <td>18</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>233</th>\n",
" <td>234</td>\n",
" <td>volkswagen</td>\n",
" <td>passat</td>\n",
" <td>3.6</td>\n",
" <td>2008</td>\n",
" <td>6</td>\n",
" <td>auto(s6)</td>\n",
" <td>f</td>\n",
" <td>17</td>\n",
" <td>26</td>\n",
" <td>p</td>\n",
" <td>midsize</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>234 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 manufacturer model displ year cyl \\\n",
"0 1 audi a4 1.8 1999 4 \n",
"1 2 audi a4 1.8 1999 4 \n",
"2 3 audi a4 2.0 2008 4 \n",
"3 4 audi a4 2.0 2008 4 \n",
"4 5 audi a4 2.8 1999 6 \n",
"5 6 audi a4 2.8 1999 6 \n",
"6 7 audi a4 3.1 2008 6 \n",
"7 8 audi a4 quattro 1.8 1999 4 \n",
"8 9 audi a4 quattro 1.8 1999 4 \n",
"9 10 audi a4 quattro 2.0 2008 4 \n",
"10 11 audi a4 quattro 2.0 2008 4 \n",
"11 12 audi a4 quattro 2.8 1999 6 \n",
"12 13 audi a4 quattro 2.8 1999 6 \n",
"13 14 audi a4 quattro 3.1 2008 6 \n",
"14 15 audi a4 quattro 3.1 2008 6 \n",
"15 16 audi a6 quattro 2.8 1999 6 \n",
"16 17 audi a6 quattro 3.1 2008 6 \n",
"17 18 audi a6 quattro 4.2 2008 8 \n",
"18 19 chevrolet c1500 suburban 2wd 5.3 2008 8 \n",
"19 20 chevrolet c1500 suburban 2wd 5.3 2008 8 \n",
"20 21 chevrolet c1500 suburban 2wd 5.3 2008 8 \n",
"21 22 chevrolet c1500 suburban 2wd 5.7 1999 8 \n",
"22 23 chevrolet c1500 suburban 2wd 6.0 2008 8 \n",
"23 24 chevrolet corvette 5.7 1999 8 \n",
"24 25 chevrolet corvette 5.7 1999 8 \n",
"25 26 chevrolet corvette 6.2 2008 8 \n",
"26 27 chevrolet corvette 6.2 2008 8 \n",
"27 28 chevrolet corvette 7.0 2008 8 \n",
"28 29 chevrolet k1500 tahoe 4wd 5.3 2008 8 \n",
"29 30 chevrolet k1500 tahoe 4wd 5.3 2008 8 \n",
".. ... ... ... ... ... ... \n",
"204 205 toyota toyota tacoma 4wd 3.4 1999 6 \n",
"205 206 toyota toyota tacoma 4wd 4.0 2008 6 \n",
"206 207 toyota toyota tacoma 4wd 4.0 2008 6 \n",
"207 208 volkswagen gti 2.0 1999 4 \n",
"208 209 volkswagen gti 2.0 1999 4 \n",
"209 210 volkswagen gti 2.0 2008 4 \n",
"210 211 volkswagen gti 2.0 2008 4 \n",
"211 212 volkswagen gti 2.8 1999 6 \n",
"212 213 volkswagen jetta 1.9 1999 4 \n",
"213 214 volkswagen jetta 2.0 1999 4 \n",
"214 215 volkswagen jetta 2.0 1999 4 \n",
"215 216 volkswagen jetta 2.0 2008 4 \n",
"216 217 volkswagen jetta 2.0 2008 4 \n",
"217 218 volkswagen jetta 2.5 2008 5 \n",
"218 219 volkswagen jetta 2.5 2008 5 \n",
"219 220 volkswagen jetta 2.8 1999 6 \n",
"220 221 volkswagen jetta 2.8 1999 6 \n",
"221 222 volkswagen new beetle 1.9 1999 4 \n",
"222 223 volkswagen new beetle 1.9 1999 4 \n",
"223 224 volkswagen new beetle 2.0 1999 4 \n",
"224 225 volkswagen new beetle 2.0 1999 4 \n",
"225 226 volkswagen new beetle 2.5 2008 5 \n",
"226 227 volkswagen new beetle 2.5 2008 5 \n",
"227 228 volkswagen passat 1.8 1999 4 \n",
"228 229 volkswagen passat 1.8 1999 4 \n",
"229 230 volkswagen passat 2.0 2008 4 \n",
"230 231 volkswagen passat 2.0 2008 4 \n",
"231 232 volkswagen passat 2.8 1999 6 \n",
"232 233 volkswagen passat 2.8 1999 6 \n",
"233 234 volkswagen passat 3.6 2008 6 \n",
"\n",
" trans drv cty hwy fl class \n",
"0 auto(l5) f 18 29 p compact \n",
"1 manual(m5) f 21 29 p compact \n",
"2 manual(m6) f 20 31 p compact \n",
"3 auto(av) f 21 30 p compact \n",
"4 auto(l5) f 16 26 p compact \n",
"5 manual(m5) f 18 26 p compact \n",
"6 auto(av) f 18 27 p compact \n",
"7 manual(m5) 4 18 26 p compact \n",
"8 auto(l5) 4 16 25 p compact \n",
"9 manual(m6) 4 20 28 p compact \n",
"10 auto(s6) 4 19 27 p compact \n",
"11 auto(l5) 4 15 25 p compact \n",
"12 manual(m5) 4 17 25 p compact \n",
"13 auto(s6) 4 17 25 p compact \n",
"14 manual(m6) 4 15 25 p compact \n",
"15 auto(l5) 4 15 24 p midsize \n",
"16 auto(s6) 4 17 25 p midsize \n",
"17 auto(s6) 4 16 23 p midsize \n",
"18 auto(l4) r 14 20 r suv \n",
"19 auto(l4) r 11 15 e suv \n",
"20 auto(l4) r 14 20 r suv \n",
"21 auto(l4) r 13 17 r suv \n",
"22 auto(l4) r 12 17 r suv \n",
"23 manual(m6) r 16 26 p 2seater \n",
"24 auto(l4) r 15 23 p 2seater \n",
"25 manual(m6) r 16 26 p 2seater \n",
"26 auto(s6) r 15 25 p 2seater \n",
"27 manual(m6) r 15 24 p 2seater \n",
"28 auto(l4) 4 14 19 r suv \n",
"29 auto(l4) 4 11 14 e suv \n",
".. ... .. ... ... .. ... \n",
"204 auto(l4) 4 15 19 r pickup \n",
"205 manual(m6) 4 15 18 r pickup \n",
"206 auto(l5) 4 16 20 r pickup \n",
"207 manual(m5) f 21 29 r compact \n",
"208 auto(l4) f 19 26 r compact \n",
"209 manual(m6) f 21 29 p compact \n",
"210 auto(s6) f 22 29 p compact \n",
"211 manual(m5) f 17 24 r compact \n",
"212 manual(m5) f 33 44 d compact \n",
"213 manual(m5) f 21 29 r compact \n",
"214 auto(l4) f 19 26 r compact \n",
"215 auto(s6) f 22 29 p compact \n",
"216 manual(m6) f 21 29 p compact \n",
"217 auto(s6) f 21 29 r compact \n",
"218 manual(m5) f 21 29 r compact \n",
"219 auto(l4) f 16 23 r compact \n",
"220 manual(m5) f 17 24 r compact \n",
"221 manual(m5) f 35 44 d subcompact \n",
"222 auto(l4) f 29 41 d subcompact \n",
"223 manual(m5) f 21 29 r subcompact \n",
"224 auto(l4) f 19 26 r subcompact \n",
"225 manual(m5) f 20 28 r subcompact \n",
"226 auto(s6) f 20 29 r subcompact \n",
"227 manual(m5) f 21 29 p midsize \n",
"228 auto(l5) f 18 29 p midsize \n",
"229 auto(s6) f 19 28 p midsize \n",
"230 manual(m6) f 21 29 p midsize \n",
"231 auto(l5) f 16 26 p midsize \n",
"232 manual(m5) f 18 26 p midsize \n",
"233 auto(s6) f 17 26 p midsize \n",
"\n",
"[234 rows x 12 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"import pandas as pd\n",
"\n",
"# Nice, sets decimple point\n",
"%precision 2\n",
"\n",
"with open('mpg.csv') as csvfile:\n",
" mpg = list(csv.DictReader(csvfile))\n",
"\n",
"df = pd.read_csv('mpg.csv')\n",
" \n",
"mpg[:3] # The first three dictionaries in our list.\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`csv.Dictreader` has read in each row of our csv file as a dictionary. `len` shows that our list is comprised of 234 dictionaries."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"234"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(mpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`keys` gives us the column names of our csv."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['', 'trans', 'manufacturer', 'hwy', 'fl', 'displ', 'cyl', 'model', 'class', 'cty', 'drv', 'year'])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mpg[0].keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This is how to find the average cty fuel economy across all cars. All values in the dictionaries are strings, so we need to convert to float."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16.86"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(float(d['cty']) for d in mpg) / len(mpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Similarly this is how to find the average hwy fuel economy across all cars."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23.44"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(float(d['hwy']) for d in mpg) / len(mpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `set` to return the unique values for the number of cylinders the cars in our dataset have."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'4', '5', '6', '8'}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set returns unique values\n",
"cylinders = set(d['cyl'] for d in mpg)\n",
"cylinders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here's a more complex example where we are grouping the cars by number of cylinder, and finding the average cty mpg for each group."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('4', 21.01), ('5', 20.50), ('6', 16.22), ('8', 12.57)]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CtyMpgByCyl = []\n",
"\n",
"for c in cylinders: # iterate over all the cylinder levels\n",
" summpg = 0\n",
" cyltypecount = 0\n",
" for d in mpg: # iterate over all dictionaries\n",
" if d['cyl'] == c: # if the cylinder level type matches,\n",
" summpg += float(d['cty']) # add the cty mpg\n",
" cyltypecount += 1 # increment the count\n",
" CtyMpgByCyl.append((c, summpg / cyltypecount)) # append the tuple ('cylinder', 'avg mpg')\n",
"\n",
"CtyMpgByCyl.sort(key=lambda x: x[0])\n",
"CtyMpgByCyl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `set` to return the unique values for the class types in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'2seater', 'compact', 'midsize', 'minivan', 'pickup', 'subcompact', 'suv'}"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vehicleclass = set(d['class'] for d in mpg) # what are the class types\n",
"vehicleclass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"And here's an example of how to find the average hwy mpg for each class of vehicle in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('pickup', 16.88),\n",
" ('suv', 18.13),\n",
" ('minivan', 22.36),\n",
" ('2seater', 24.80),\n",
" ('midsize', 27.29),\n",
" ('subcompact', 28.14),\n",
" ('compact', 28.30)]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HwyMpgByClass = []\n",
"\n",
"for t in vehicleclass: # iterate over all the vehicle classes\n",
" summpg = 0\n",
" vclasscount = 0\n",
" for d in mpg: # iterate over all dictionaries\n",
" if d['class'] == t: # if the cylinder amount type matches,\n",
" summpg += float(d['hwy']) # add the hwy mpg\n",
" vclasscount += 1 # increment the count\n",
" HwyMpgByClass.append((t, summpg / vclasscount)) # append the tuple ('class', 'avg mpg')\n",
"\n",
"HwyMpgByClass.sort(key=lambda x: x[1])\n",
"HwyMpgByClass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# The Python Programming Language: Dates and Times"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime as dt\n",
"import time as tm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`time` returns the current time in seconds since the Epoch. (January 1st, 1970)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1484596690.50"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tm.time()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Convert the timestamp to datetime."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2017, 1, 16, 19, 58, 33, 790718)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtnow = dt.datetime.fromtimestamp(tm.time())\n",
"dtnow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Handy datetime attributes:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2017, 1, 16, 19, 58, 33)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtnow.year, dtnow.month, dtnow.day, dtnow.hour, dtnow.minute, dtnow.second # get year, month, day, etc.from a datetime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`timedelta` is a duration expressing the difference between two dates."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.timedelta(100)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"delta = dt.timedelta(days = 100) # create a timedelta of 100 days\n",
"delta"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.date(2017, 1, 16)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt.date.today()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`date.today` returns the current local date."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"today = dt.date.today()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.date(2016, 10, 8)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"today - delta # the date 100 days ago"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"today > today-delta # compare dates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# The Python Programming Language: Objects and map()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"An example of a class in python:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Person:\n",
" department = 'School of Information' #a class variable\n",
"\n",
" def set_name(self, new_name): #a method\n",
" self.name = new_name\n",
" def set_location(self, new_location):\n",
" self.location = new_location"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Christopher Brooks live in Ann Arbor, MI, USA and works in the department School of Information\n"
]
}
],
"source": [
"person = Person()\n",
"person.set_name('Christopher Brooks')\n",
"person.set_location('Ann Arbor, MI, USA')\n",
"print('{} live in {} and works in the department {}'.format(person.name, person.location, person.department))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here's an example of mapping the `min` function between two lists."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<map at 0x7fd72c8d3860>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"store1 = [10.00, 11.00, 12.34, 2.34]\n",
"store2 = [9.00, 11.10, 12.34, 2.01]\n",
"cheapest = map(min, store1, store2)\n",
"cheapest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now let's iterate through the map object to see the values."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.0\n",
"11.0\n",
"12.34\n",
"2.01\n"
]
}
],
"source": [
"for item in cheapest:\n",
" print (item)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Dr. Brooks', 'Dr. Collins-Thompson', 'Dr. Vydiswaran', 'Dr. Romero']"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"people = ['Dr. Christopher Brooks', 'Dr. Kevyn Collins-Thompson', 'Dr. VG Vinod Vydiswaran', 'Dr. Daniel Romero']\n",
"\n",
"def split_title_and_name(person):\n",
" title = person.split(' ')[0]\n",
" lname = person.split(' ')[-1]\n",
" return title +\" \"+ lname\n",
"\n",
"list(map(split_title_and_name, people))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"# The Python Programming Language: Lambda and List Comprehensions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here's an example of lambda that takes in three parameters and adds the first two."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Single function only\n",
"my_function = lambda a, b, c : a + b + c"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_function(1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"True\n",
"True\n",
"True\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"people = ['Dr. Christopher Brooks', 'Dr. Kevyn Collins-Thompson', 'Dr. VG Vinod Vydiswaran', 'Dr. Daniel Romero']\n",
"\n",
"def split_title_and_name(person):\n",
" return person.split()[0] + ' ' + person.split()[-1]\n",
"\n",
"#option 1\n",
"for person in people:\n",
" print(split_title_and_name(person) == (lambda x: x.split()[0] + ' ' + x.split()[-1])(person))\n",
"\n",
"#option 2\n",
"list(map(split_title_and_name, people)) == list(map(lambda person: person.split()[0] + ' ' + person.split()[-1], people))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Let's iterate from 0 to 999 and return the even numbers."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0,\n",
" 2,\n",
" 4,\n",
" 6,\n",
" 8,\n",
" 10,\n",
" 12,\n",
" 14,\n",
" 16,\n",
" 18,\n",
" 20,\n",
" 22,\n",
" 24,\n",
" 26,\n",
" 28,\n",
" 30,\n",
" 32,\n",
" 34,\n",
" 36,\n",
" 38,\n",
" 40,\n",
" 42,\n",
" 44,\n",
" 46,\n",
" 48,\n",
" 50,\n",
" 52,\n",
" 54,\n",
" 56,\n",
" 58,\n",
" 60,\n",
" 62,\n",
" 64,\n",
" 66,\n",
" 68,\n",
" 70,\n",
" 72,\n",
" 74,\n",
" 76,\n",
" 78,\n",
" 80,\n",
" 82,\n",
" 84,\n",
" 86,\n",
" 88,\n",
" 90,\n",
" 92,\n",
" 94,\n",
" 96,\n",
" 98,\n",
" 100,\n",
" 102,\n",
" 104,\n",
" 106,\n",
" 108,\n",
" 110,\n",
" 112,\n",
" 114,\n",
" 116,\n",
" 118,\n",
" 120,\n",
" 122,\n",
" 124,\n",
" 126,\n",
" 128,\n",
" 130,\n",
" 132,\n",
" 134,\n",
" 136,\n",
" 138,\n",
" 140,\n",
" 142,\n",
" 144,\n",
" 146,\n",
" 148,\n",
" 150,\n",
" 152,\n",
" 154,\n",
" 156,\n",
" 158,\n",
" 160,\n",
" 162,\n",
" 164,\n",
" 166,\n",
" 168,\n",
" 170,\n",
" 172,\n",
" 174,\n",
" 176,\n",
" 178,\n",
" 180,\n",
" 182,\n",
" 184,\n",
" 186,\n",
" 188,\n",
" 190,\n",
" 192,\n",
" 194,\n",
" 196,\n",
" 198,\n",
" 200,\n",
" 202,\n",
" 204,\n",
" 206,\n",
" 208,\n",
" 210,\n",
" 212,\n",
" 214,\n",
" 216,\n",
" 218,\n",
" 220,\n",
" 222,\n",
" 224,\n",
" 226,\n",
" 228,\n",
" 230,\n",
" 232,\n",
" 234,\n",
" 236,\n",
" 238,\n",
" 240,\n",
" 242,\n",
" 244,\n",
" 246,\n",
" 248,\n",
" 250,\n",
" 252,\n",
" 254,\n",
" 256,\n",
" 258,\n",
" 260,\n",
" 262,\n",
" 264,\n",
" 266,\n",
" 268,\n",
" 270,\n",
" 272,\n",
" 274,\n",
" 276,\n",
" 278,\n",
" 280,\n",
" 282,\n",
" 284,\n",
" 286,\n",
" 288,\n",
" 290,\n",
" 292,\n",
" 294,\n",
" 296,\n",
" 298,\n",
" 300,\n",
" 302,\n",
" 304,\n",
" 306,\n",
" 308,\n",
" 310,\n",
" 312,\n",
" 314,\n",
" 316,\n",
" 318,\n",
" 320,\n",
" 322,\n",
" 324,\n",
" 326,\n",
" 328,\n",
" 330,\n",
" 332,\n",
" 334,\n",
" 336,\n",
" 338,\n",
" 340,\n",
" 342,\n",
" 344,\n",
" 346,\n",
" 348,\n",
" 350,\n",
" 352,\n",
" 354,\n",
" 356,\n",
" 358,\n",
" 360,\n",
" 362,\n",
" 364,\n",
" 366,\n",
" 368,\n",
" 370,\n",
" 372,\n",
" 374,\n",
" 376,\n",
" 378,\n",
" 380,\n",
" 382,\n",
" 384,\n",
" 386,\n",
" 388,\n",
" 390,\n",
" 392,\n",
" 394,\n",
" 396,\n",
" 398,\n",
" 400,\n",
" 402,\n",
" 404,\n",
" 406,\n",
" 408,\n",
" 410,\n",
" 412,\n",
" 414,\n",
" 416,\n",
" 418,\n",
" 420,\n",
" 422,\n",
" 424,\n",
" 426,\n",
" 428,\n",
" 430,\n",
" 432,\n",
" 434,\n",
" 436,\n",
" 438,\n",
" 440,\n",
" 442,\n",
" 444,\n",
" 446,\n",
" 448,\n",
" 450,\n",
" 452,\n",
" 454,\n",
" 456,\n",
" 458,\n",
" 460,\n",
" 462,\n",
" 464,\n",
" 466,\n",
" 468,\n",
" 470,\n",
" 472,\n",
" 474,\n",
" 476,\n",
" 478,\n",
" 480,\n",
" 482,\n",
" 484,\n",
" 486,\n",
" 488,\n",
" 490,\n",
" 492,\n",
" 494,\n",
" 496,\n",
" 498,\n",
" 500,\n",
" 502,\n",
" 504,\n",
" 506,\n",
" 508,\n",
" 510,\n",
" 512,\n",
" 514,\n",
" 516,\n",
" 518,\n",
" 520,\n",
" 522,\n",
" 524,\n",
" 526,\n",
" 528,\n",
" 530,\n",
" 532,\n",
" 534,\n",
" 536,\n",
" 538,\n",
" 540,\n",
" 542,\n",
" 544,\n",
" 546,\n",
" 548,\n",
" 550,\n",
" 552,\n",
" 554,\n",
" 556,\n",
" 558,\n",
" 560,\n",
" 562,\n",
" 564,\n",
" 566,\n",
" 568,\n",
" 570,\n",
" 572,\n",
" 574,\n",
" 576,\n",
" 578,\n",
" 580,\n",
" 582,\n",
" 584,\n",
" 586,\n",
" 588,\n",
" 590,\n",
" 592,\n",
" 594,\n",
" 596,\n",
" 598,\n",
" 600,\n",
" 602,\n",
" 604,\n",
" 606,\n",
" 608,\n",
" 610,\n",
" 612,\n",
" 614,\n",
" 616,\n",
" 618,\n",
" 620,\n",
" 622,\n",
" 624,\n",
" 626,\n",
" 628,\n",
" 630,\n",
" 632,\n",
" 634,\n",
" 636,\n",
" 638,\n",
" 640,\n",
" 642,\n",
" 644,\n",
" 646,\n",
" 648,\n",
" 650,\n",
" 652,\n",
" 654,\n",
" 656,\n",
" 658,\n",
" 660,\n",
" 662,\n",
" 664,\n",
" 666,\n",
" 668,\n",
" 670,\n",
" 672,\n",
" 674,\n",
" 676,\n",
" 678,\n",
" 680,\n",
" 682,\n",
" 684,\n",
" 686,\n",
" 688,\n",
" 690,\n",
" 692,\n",
" 694,\n",
" 696,\n",
" 698,\n",
" 700,\n",
" 702,\n",
" 704,\n",
" 706,\n",
" 708,\n",
" 710,\n",
" 712,\n",
" 714,\n",
" 716,\n",
" 718,\n",
" 720,\n",
" 722,\n",
" 724,\n",
" 726,\n",
" 728,\n",
" 730,\n",
" 732,\n",
" 734,\n",
" 736,\n",
" 738,\n",
" 740,\n",
" 742,\n",
" 744,\n",
" 746,\n",
" 748,\n",
" 750,\n",
" 752,\n",
" 754,\n",
" 756,\n",
" 758,\n",
" 760,\n",
" 762,\n",
" 764,\n",
" 766,\n",
" 768,\n",
" 770,\n",
" 772,\n",
" 774,\n",
" 776,\n",
" 778,\n",
" 780,\n",
" 782,\n",
" 784,\n",
" 786,\n",
" 788,\n",
" 790,\n",
" 792,\n",
" 794,\n",
" 796,\n",
" 798,\n",
" 800,\n",
" 802,\n",
" 804,\n",
" 806,\n",
" 808,\n",
" 810,\n",
" 812,\n",
" 814,\n",
" 816,\n",
" 818,\n",
" 820,\n",
" 822,\n",
" 824,\n",
" 826,\n",
" 828,\n",
" 830,\n",
" 832,\n",
" 834,\n",
" 836,\n",
" 838,\n",
" 840,\n",
" 842,\n",
" 844,\n",
" 846,\n",
" 848,\n",
" 850,\n",
" 852,\n",
" 854,\n",
" 856,\n",
" 858,\n",
" 860,\n",
" 862,\n",
" 864,\n",
" 866,\n",
" 868,\n",
" 870,\n",
" 872,\n",
" 874,\n",
" 876,\n",
" 878,\n",
" 880,\n",
" 882,\n",
" 884,\n",
" 886,\n",
" 888,\n",
" 890,\n",
" 892,\n",
" 894,\n",
" 896,\n",
" 898,\n",
" 900,\n",
" 902,\n",
" 904,\n",
" 906,\n",
" 908,\n",
" 910,\n",
" 912,\n",
" 914,\n",
" 916,\n",
" 918,\n",
" 920,\n",
" 922,\n",
" 924,\n",
" 926,\n",
" 928,\n",
" 930,\n",
" 932,\n",
" 934,\n",
" 936,\n",
" 938,\n",
" 940,\n",
" 942,\n",
" 944,\n",
" 946,\n",
" 948,\n",
" 950,\n",
" 952,\n",
" 954,\n",
" 956,\n",
" 958,\n",
" 960,\n",
" 962,\n",
" 964,\n",
" 966,\n",
" 968,\n",
" 970,\n",
" 972,\n",
" 974,\n",
" 976,\n",
" 978,\n",
" 980,\n",
" 982,\n",
" 984,\n",
" 986,\n",
" 988,\n",
" 990,\n",
" 992,\n",
" 994,\n",
" 996,\n",
" 998]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_list = []\n",
"for number in range(0, 1000):\n",
" if number % 2 == 0:\n",
" my_list.append(number)\n",
"my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now the same thing but with list comprehension."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0,\n",
" 2,\n",
" 4,\n",
" 6,\n",
" 8,\n",
" 10,\n",
" 12,\n",
" 14,\n",
" 16,\n",
" 18,\n",
" 20,\n",
" 22,\n",
" 24,\n",
" 26,\n",
" 28,\n",
" 30,\n",
" 32,\n",
" 34,\n",
" 36,\n",
" 38,\n",
" 40,\n",
" 42,\n",
" 44,\n",
" 46,\n",
" 48,\n",
" 50,\n",
" 52,\n",
" 54,\n",
" 56,\n",
" 58,\n",
" 60,\n",
" 62,\n",
" 64,\n",
" 66,\n",
" 68,\n",
" 70,\n",
" 72,\n",
" 74,\n",
" 76,\n",
" 78,\n",
" 80,\n",
" 82,\n",
" 84,\n",
" 86,\n",
" 88,\n",
" 90,\n",
" 92,\n",
" 94,\n",
" 96,\n",
" 98,\n",
" 100,\n",
" 102,\n",
" 104,\n",
" 106,\n",
" 108,\n",
" 110,\n",
" 112,\n",
" 114,\n",
" 116,\n",
" 118,\n",
" 120,\n",
" 122,\n",
" 124,\n",
" 126,\n",
" 128,\n",
" 130,\n",
" 132,\n",
" 134,\n",
" 136,\n",
" 138,\n",
" 140,\n",
" 142,\n",
" 144,\n",
" 146,\n",
" 148,\n",
" 150,\n",
" 152,\n",
" 154,\n",
" 156,\n",
" 158,\n",
" 160,\n",
" 162,\n",
" 164,\n",
" 166,\n",
" 168,\n",
" 170,\n",
" 172,\n",
" 174,\n",
" 176,\n",
" 178,\n",
" 180,\n",
" 182,\n",
" 184,\n",
" 186,\n",
" 188,\n",
" 190,\n",
" 192,\n",
" 194,\n",
" 196,\n",
" 198,\n",
" 200,\n",
" 202,\n",
" 204,\n",
" 206,\n",
" 208,\n",
" 210,\n",
" 212,\n",
" 214,\n",
" 216,\n",
" 218,\n",
" 220,\n",
" 222,\n",
" 224,\n",
" 226,\n",
" 228,\n",
" 230,\n",
" 232,\n",
" 234,\n",
" 236,\n",
" 238,\n",
" 240,\n",
" 242,\n",
" 244,\n",
" 246,\n",
" 248,\n",
" 250,\n",
" 252,\n",
" 254,\n",
" 256,\n",
" 258,\n",
" 260,\n",
" 262,\n",
" 264,\n",
" 266,\n",
" 268,\n",
" 270,\n",
" 272,\n",
" 274,\n",
" 276,\n",
" 278,\n",
" 280,\n",
" 282,\n",
" 284,\n",
" 286,\n",
" 288,\n",
" 290,\n",
" 292,\n",
" 294,\n",
" 296,\n",
" 298,\n",
" 300,\n",
" 302,\n",
" 304,\n",
" 306,\n",
" 308,\n",
" 310,\n",
" 312,\n",
" 314,\n",
" 316,\n",
" 318,\n",
" 320,\n",
" 322,\n",
" 324,\n",
" 326,\n",
" 328,\n",
" 330,\n",
" 332,\n",
" 334,\n",
" 336,\n",
" 338,\n",
" 340,\n",
" 342,\n",
" 344,\n",
" 346,\n",
" 348,\n",
" 350,\n",
" 352,\n",
" 354,\n",
" 356,\n",
" 358,\n",
" 360,\n",
" 362,\n",
" 364,\n",
" 366,\n",
" 368,\n",
" 370,\n",
" 372,\n",
" 374,\n",
" 376,\n",
" 378,\n",
" 380,\n",
" 382,\n",
" 384,\n",
" 386,\n",
" 388,\n",
" 390,\n",
" 392,\n",
" 394,\n",
" 396,\n",
" 398,\n",
" 400,\n",
" 402,\n",
" 404,\n",
" 406,\n",
" 408,\n",
" 410,\n",
" 412,\n",
" 414,\n",
" 416,\n",
" 418,\n",
" 420,\n",
" 422,\n",
" 424,\n",
" 426,\n",
" 428,\n",
" 430,\n",
" 432,\n",
" 434,\n",
" 436,\n",
" 438,\n",
" 440,\n",
" 442,\n",
" 444,\n",
" 446,\n",
" 448,\n",
" 450,\n",
" 452,\n",
" 454,\n",
" 456,\n",
" 458,\n",
" 460,\n",
" 462,\n",
" 464,\n",
" 466,\n",
" 468,\n",
" 470,\n",
" 472,\n",
" 474,\n",
" 476,\n",
" 478,\n",
" 480,\n",
" 482,\n",
" 484,\n",
" 486,\n",
" 488,\n",
" 490,\n",
" 492,\n",
" 494,\n",
" 496,\n",
" 498,\n",
" 500,\n",
" 502,\n",
" 504,\n",
" 506,\n",
" 508,\n",
" 510,\n",
" 512,\n",
" 514,\n",
" 516,\n",
" 518,\n",
" 520,\n",
" 522,\n",
" 524,\n",
" 526,\n",
" 528,\n",
" 530,\n",
" 532,\n",
" 534,\n",
" 536,\n",
" 538,\n",
" 540,\n",
" 542,\n",
" 544,\n",
" 546,\n",
" 548,\n",
" 550,\n",
" 552,\n",
" 554,\n",
" 556,\n",
" 558,\n",
" 560,\n",
" 562,\n",
" 564,\n",
" 566,\n",
" 568,\n",
" 570,\n",
" 572,\n",
" 574,\n",
" 576,\n",
" 578,\n",
" 580,\n",
" 582,\n",
" 584,\n",
" 586,\n",
" 588,\n",
" 590,\n",
" 592,\n",
" 594,\n",
" 596,\n",
" 598,\n",
" 600,\n",
" 602,\n",
" 604,\n",
" 606,\n",
" 608,\n",
" 610,\n",
" 612,\n",
" 614,\n",
" 616,\n",
" 618,\n",
" 620,\n",
" 622,\n",
" 624,\n",
" 626,\n",
" 628,\n",
" 630,\n",
" 632,\n",
" 634,\n",
" 636,\n",
" 638,\n",
" 640,\n",
" 642,\n",
" 644,\n",
" 646,\n",
" 648,\n",
" 650,\n",
" 652,\n",
" 654,\n",
" 656,\n",
" 658,\n",
" 660,\n",
" 662,\n",
" 664,\n",
" 666,\n",
" 668,\n",
" 670,\n",
" 672,\n",
" 674,\n",
" 676,\n",
" 678,\n",
" 680,\n",
" 682,\n",
" 684,\n",
" 686,\n",
" 688,\n",
" 690,\n",
" 692,\n",
" 694,\n",
" 696,\n",
" 698,\n",
" 700,\n",
" 702,\n",
" 704,\n",
" 706,\n",
" 708,\n",
" 710,\n",
" 712,\n",
" 714,\n",
" 716,\n",
" 718,\n",
" 720,\n",
" 722,\n",
" 724,\n",
" 726,\n",
" 728,\n",
" 730,\n",
" 732,\n",
" 734,\n",
" 736,\n",
" 738,\n",
" 740,\n",
" 742,\n",
" 744,\n",
" 746,\n",
" 748,\n",
" 750,\n",
" 752,\n",
" 754,\n",
" 756,\n",
" 758,\n",
" 760,\n",
" 762,\n",
" 764,\n",
" 766,\n",
" 768,\n",
" 770,\n",
" 772,\n",
" 774,\n",
" 776,\n",
" 778,\n",
" 780,\n",
" 782,\n",
" 784,\n",
" 786,\n",
" 788,\n",
" 790,\n",
" 792,\n",
" 794,\n",
" 796,\n",
" 798,\n",
" 800,\n",
" 802,\n",
" 804,\n",
" 806,\n",
" 808,\n",
" 810,\n",
" 812,\n",
" 814,\n",
" 816,\n",
" 818,\n",
" 820,\n",
" 822,\n",
" 824,\n",
" 826,\n",
" 828,\n",
" 830,\n",
" 832,\n",
" 834,\n",
" 836,\n",
" 838,\n",
" 840,\n",
" 842,\n",
" 844,\n",
" 846,\n",
" 848,\n",
" 850,\n",
" 852,\n",
" 854,\n",
" 856,\n",
" 858,\n",
" 860,\n",
" 862,\n",
" 864,\n",
" 866,\n",
" 868,\n",
" 870,\n",
" 872,\n",
" 874,\n",
" 876,\n",
" 878,\n",
" 880,\n",
" 882,\n",
" 884,\n",
" 886,\n",
" 888,\n",
" 890,\n",
" 892,\n",
" 894,\n",
" 896,\n",
" 898,\n",
" 900,\n",
" 902,\n",
" 904,\n",
" 906,\n",
" 908,\n",
" 910,\n",
" 912,\n",
" 914,\n",
" 916,\n",
" 918,\n",
" 920,\n",
" 922,\n",
" 924,\n",
" 926,\n",
" 928,\n",
" 930,\n",
" 932,\n",
" 934,\n",
" 936,\n",
" 938,\n",
" 940,\n",
" 942,\n",
" 944,\n",
" 946,\n",
" 948,\n",
" 950,\n",
" 952,\n",
" 954,\n",
" 956,\n",
" 958,\n",
" 960,\n",
" 962,\n",
" 964,\n",
" 966,\n",
" 968,\n",
" 970,\n",
" 972,\n",
" 974,\n",
" 976,\n",
" 978,\n",
" 980,\n",
" 982,\n",
" 984,\n",
" 986,\n",
" 988,\n",
" 990,\n",
" 992,\n",
" 994,\n",
" 996,\n",
" 998]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_list = [number for number in range(0,1000) if number % 2 == 0]\n",
"my_list"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def times_tables():\n",
" lst = []\n",
" for i in range(10):\n",
" for j in range (10):\n",
" lst.append(i*j)\n",
" return lst\n",
"\n",
"times_tables() == [j*i for i in range(10) for j in range(10)]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lowercase = 'abcdefghijklmnopqrstuvwxyz'\n",
"digits = '0123456789'\n",
" \n",
"correct_answer = [a+b+c+d for a in lowercase for b in lowercase for c in digits for d in digits]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['aa00',\n",
" 'aa01',\n",
" 'aa02',\n",
" 'aa03',\n",
" 'aa04',\n",
" 'aa05',\n",
" 'aa06',\n",
" 'aa07',\n",
" 'aa08',\n",
" 'aa09',\n",
" 'aa10',\n",
" 'aa11',\n",
" 'aa12',\n",
" 'aa13',\n",
" 'aa14',\n",
" 'aa15',\n",
" 'aa16',\n",
" 'aa17',\n",
" 'aa18',\n",
" 'aa19',\n",
" 'aa20',\n",
" 'aa21',\n",
" 'aa22',\n",
" 'aa23',\n",
" 'aa24',\n",
" 'aa25',\n",
" 'aa26',\n",
" 'aa27',\n",
" 'aa28',\n",
" 'aa29',\n",
" 'aa30',\n",
" 'aa31',\n",
" 'aa32',\n",
" 'aa33',\n",
" 'aa34',\n",
" 'aa35',\n",
" 'aa36',\n",
" 'aa37',\n",
" 'aa38',\n",
" 'aa39',\n",
" 'aa40',\n",
" 'aa41',\n",
" 'aa42',\n",
" 'aa43',\n",
" 'aa44',\n",
" 'aa45',\n",
" 'aa46',\n",
" 'aa47',\n",
" 'aa48',\n",
" 'aa49',\n",
" 'aa50',\n",
" 'aa51',\n",
" 'aa52',\n",
" 'aa53',\n",
" 'aa54',\n",
" 'aa55',\n",
" 'aa56',\n",
" 'aa57',\n",
" 'aa58',\n",
" 'aa59',\n",
" 'aa60',\n",
" 'aa61',\n",
" 'aa62',\n",
" 'aa63',\n",
" 'aa64',\n",
" 'aa65',\n",
" 'aa66',\n",
" 'aa67',\n",
" 'aa68',\n",
" 'aa69',\n",
" 'aa70',\n",
" 'aa71',\n",
" 'aa72',\n",
" 'aa73',\n",
" 'aa74',\n",
" 'aa75',\n",
" 'aa76',\n",
" 'aa77',\n",
" 'aa78',\n",
" 'aa79',\n",
" 'aa80',\n",
" 'aa81',\n",
" 'aa82',\n",
" 'aa83',\n",
" 'aa84',\n",
" 'aa85',\n",
" 'aa86',\n",
" 'aa87',\n",
" 'aa88',\n",
" 'aa89',\n",
" 'aa90',\n",
" 'aa91',\n",
" 'aa92',\n",
" 'aa93',\n",
" 'aa94',\n",
" 'aa95',\n",
" 'aa96',\n",
" 'aa97',\n",
" 'aa98',\n",
" 'aa99']"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correct_answer[0:100]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<br>\n",
"# The Python Programming Language: Numerical Python (NumPy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"## Creating Arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a list and convert it to a numpy array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mylist = [1, 2, 3]\n",
"x = np.array(mylist)\n",
"x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Or just pass in a list directly"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.array([4, 5, 6])\n",
"y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Pass in a list of lists to create a multidimensional array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m = np.array([[7, 8, 9], [10, 11, 12]])\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use the shape method to find the dimensions of the array. (rows, columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`arange` returns evenly spaced values within a given interval."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n = np.arange(0, 30, 2) # start at 0 count up by 2, stop before 30\n",
"n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`reshape` returns an array with the same data with a new shape."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n = n.reshape(3, 5) # reshape array to be 3x5\n",
"n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`linspace` returns evenly spaced numbers over a specified interval."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"o = np.linspace(0, 4, 9) # return 9 evenly spaced values from 0 to 4\n",
"o"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`resize` changes the shape and size of array in-place."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"o.resize(3, 3)\n",
"o"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`ones` returns a new array of given shape and type, filled with ones."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.ones((3, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`zeros` returns a new array of given shape and type, filled with zeros."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.zeros((2, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`eye` returns a 2-D array with ones on the diagonal and zeros elsewhere."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.eye(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`diag` extracts a diagonal or constructs a diagonal array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.diag(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Create an array using repeating list (or see `np.tile`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.array([1, 2, 3] * 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Repeat elements of an array using `repeat`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.repeat([1, 2, 3], 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"#### Combining Arrays"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p = np.ones([2, 3], int)\n",
"p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `vstack` to stack arrays in sequence vertically (row wise)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.vstack([p, 2*p])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `hstack` to stack arrays in sequence horizontally (column wise)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.hstack([p, 2*p])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"## Operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `+`, `-`, `*`, `/` and `**` to perform element wise addition, subtraction, multiplication, division and power."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(x + y) # elementwise addition [1 2 3] + [4 5 6] = [5 7 9]\n",
"print(x - y) # elementwise subtraction [1 2 3] - [4 5 6] = [-3 -3 -3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(x * y) # elementwise multiplication [1 2 3] * [4 5 6] = [4 10 18]\n",
"print(x / y) # elementwise divison [1 2 3] / [4 5 6] = [0.25 0.4 0.5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(x**2) # elementwise power [1 2 3] ^2 = [1 4 9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"**Dot Product:** \n",
"\n",
"$ \\begin{bmatrix}x_1 \\ x_2 \\ x_3\\end{bmatrix}\n",
"\\cdot\n",
"\\begin{bmatrix}y_1 \\\\ y_2 \\\\ y_3\\end{bmatrix}\n",
"= x_1 y_1 + x_2 y_2 + x_3 y_3$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x.dot(y) # dot product 1*4 + 2*5 + 3*6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = np.array([y, y**2])\n",
"print(len(z)) # number of rows of array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Let's look at transposing arrays. Transposing permutes the dimensions of the array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = np.array([y, y**2])\n",
"z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The shape of array `z` is `(2,3)` before transposing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `.T` to get the transpose."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The number of rows has swapped with the number of columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z.T.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `.dtype` to see the data type of the elements in the array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `.astype` to cast to a specific type."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = z.astype('f')\n",
"z.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"## Math Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numpy has many built in math functions that can be performed on arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = np.array([-4, -2, 1, 3, 5])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.min()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`argmax` and `argmin` return the index of the maximum and minimum values in the array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.argmax()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.argmin()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"## Indexing / Slicing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s = np.arange(13)**2\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use bracket notation to get the value at a specific index. Remember that indexing starts at 0."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s[0], s[4], s[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `:` to indicate a range. `array[start:stop]`\n",
"\n",
"\n",
"Leaving `start` or `stop` empty will default to the beginning/end of the array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s[1:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use negatives to count from the back."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s[-4:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"A second `:` can be used to indicate step-size. `array[start:stop:stepsize]`\n",
"\n",
"Here we are starting 5th element from the end, and counting backwards by 2 until the beginning of the array is reached."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s[-5::-2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<br>\n",
"Let's look at a multidimensional array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r = np.arange(36)\n",
"r.resize((6, 6))\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use bracket notation to slice: `array[row, column]`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[2, 2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"And use : to select a range of rows or columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[3, 3:6]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here we are selecting all the rows up to (and not including) row 2, and all the columns up to (and not including) the last column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[:2, :-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"This is a slice of the last row, and only every other element."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[-1, ::2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We can also perform conditional indexing. Here we are selecting values from the array that are greater than 30. (Also see `np.where`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[r > 30]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here we are assigning all values in the array that are greater than 30 to the value of 30."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r[r > 30] = 30\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"## Copying Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Be careful with copying and modifying arrays in NumPy!\n",
"\n",
"\n",
"`r2` is a slice of `r`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r2 = r[:3,:3]\n",
"r2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Set this slice's values to zero ([:] selects the entire array)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r2[:] = 0\n",
"r2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"`r` has also been changed!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To avoid this, use `r.copy` to create a copy that will not affect the original array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r_copy = r.copy()\n",
"r_copy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now when r_copy is modified, r will not be changed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r_copy[:] = 10\n",
"print(r_copy, '\\n')\n",
"print(r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"### Iterating Over Arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create a new 4 by 3 array of random numbers 0-9."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test = np.random.randint(0, 10, (4,3))\n",
"test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate by row:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for row in test:\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate by index:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(len(test)):\n",
" print(test[i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Iterate by row and index:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i, row in enumerate(test):\n",
" print('row', i, 'is', row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Use `zip` to iterate over multiple iterables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test2 = test**2\n",
"test2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i, j in zip(test, test2):\n",
" print(i,'+',j,'=',i+j)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
adriantorrie/adriantorrie.github.io | downloads/notebooks/ml_mastery/ml_with_python_mini_course/04_understand_data_with_descriptive_statistics.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More datasets can be found at the [UCI website](http://archive.ics.uci.edu/ml/)\n",
"\n",
"* Practice loading CSV files into Python using the [`CSV.reader()`](https://docs.python.org/2/library/csv.html) function in the standard library\n",
"* Practice loading CSV files using NumPy and the [`numpy.loadtxt()`](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.loadtxt.html) function\n",
"* Practice loading CSV files using Pandas and the [`pandas.read_csv()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) function"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load CSV using Pandas from URL\n",
"import pandas\n",
"\n",
"url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\"\n",
"names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
"\n",
"data = pandas.read_csv(url, names=names)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>preg</th>\n",
" <th>plas</th>\n",
" <th>pres</th>\n",
" <th>skin</th>\n",
" <th>test</th>\n",
" <th>mass</th>\n",
" <th>pedi</th>\n",
" <th>age</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.845052</td>\n",
" <td>120.894531</td>\n",
" <td>69.105469</td>\n",
" <td>20.536458</td>\n",
" <td>79.799479</td>\n",
" <td>31.992578</td>\n",
" <td>0.471876</td>\n",
" <td>33.240885</td>\n",
" <td>0.348958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.369578</td>\n",
" <td>31.972618</td>\n",
" <td>19.355807</td>\n",
" <td>15.952218</td>\n",
" <td>115.244002</td>\n",
" <td>7.884160</td>\n",
" <td>0.331329</td>\n",
" <td>11.760232</td>\n",
" <td>0.476951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.078000</td>\n",
" <td>21.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>99.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>27.300000</td>\n",
" <td>0.243750</td>\n",
" <td>24.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>117.000000</td>\n",
" <td>72.000000</td>\n",
" <td>23.000000</td>\n",
" <td>30.500000</td>\n",
" <td>32.000000</td>\n",
" <td>0.372500</td>\n",
" <td>29.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.000000</td>\n",
" <td>140.250000</td>\n",
" <td>80.000000</td>\n",
" <td>32.000000</td>\n",
" <td>127.250000</td>\n",
" <td>36.600000</td>\n",
" <td>0.626250</td>\n",
" <td>41.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>17.000000</td>\n",
" <td>199.000000</td>\n",
" <td>122.000000</td>\n",
" <td>99.000000</td>\n",
" <td>846.000000</td>\n",
" <td>67.100000</td>\n",
" <td>2.420000</td>\n",
" <td>81.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preg plas pres skin test mass \\\n",
"count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 \n",
"mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 \n",
"std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 \n",
"50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 \n",
"75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 \n",
"max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 \n",
"\n",
" pedi age class \n",
"count 768.000000 768.000000 768.000000 \n",
"mean 0.471876 33.240885 0.348958 \n",
"std 0.331329 11.760232 0.476951 \n",
"min 0.078000 21.000000 0.000000 \n",
"25% 0.243750 24.000000 0.000000 \n",
"50% 0.372500 29.000000 0.000000 \n",
"75% 0.626250 41.000000 1.000000 \n",
"max 2.420000 81.000000 1.000000 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>preg</th>\n",
" <th>plas</th>\n",
" <th>pres</th>\n",
" <th>skin</th>\n",
" <th>test</th>\n",
" <th>mass</th>\n",
" <th>pedi</th>\n",
" <th>age</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preg plas pres skin test mass pedi age class\n",
"0 6 148 72 35 0 33.6 0.627 50 1\n",
"1 1 85 66 29 0 26.6 0.351 31 0\n",
"2 8 183 64 0 0 23.3 0.672 32 1\n",
"3 1 89 66 23 94 28.1 0.167 21 0\n",
"4 0 137 40 35 168 43.1 2.288 33 1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>preg</th>\n",
" <th>plas</th>\n",
" <th>pres</th>\n",
" <th>skin</th>\n",
" <th>test</th>\n",
" <th>mass</th>\n",
" <th>pedi</th>\n",
" <th>age</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>preg</th>\n",
" <td>1.000000</td>\n",
" <td>0.129459</td>\n",
" <td>0.141282</td>\n",
" <td>-0.081672</td>\n",
" <td>-0.073535</td>\n",
" <td>0.017683</td>\n",
" <td>-0.033523</td>\n",
" <td>0.544341</td>\n",
" <td>0.221898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>plas</th>\n",
" <td>0.129459</td>\n",
" <td>1.000000</td>\n",
" <td>0.152590</td>\n",
" <td>0.057328</td>\n",
" <td>0.331357</td>\n",
" <td>0.221071</td>\n",
" <td>0.137337</td>\n",
" <td>0.263514</td>\n",
" <td>0.466581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pres</th>\n",
" <td>0.141282</td>\n",
" <td>0.152590</td>\n",
" <td>1.000000</td>\n",
" <td>0.207371</td>\n",
" <td>0.088933</td>\n",
" <td>0.281805</td>\n",
" <td>0.041265</td>\n",
" <td>0.239528</td>\n",
" <td>0.065068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>skin</th>\n",
" <td>-0.081672</td>\n",
" <td>0.057328</td>\n",
" <td>0.207371</td>\n",
" <td>1.000000</td>\n",
" <td>0.436783</td>\n",
" <td>0.392573</td>\n",
" <td>0.183928</td>\n",
" <td>-0.113970</td>\n",
" <td>0.074752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>test</th>\n",
" <td>-0.073535</td>\n",
" <td>0.331357</td>\n",
" <td>0.088933</td>\n",
" <td>0.436783</td>\n",
" <td>1.000000</td>\n",
" <td>0.197859</td>\n",
" <td>0.185071</td>\n",
" <td>-0.042163</td>\n",
" <td>0.130548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mass</th>\n",
" <td>0.017683</td>\n",
" <td>0.221071</td>\n",
" <td>0.281805</td>\n",
" <td>0.392573</td>\n",
" <td>0.197859</td>\n",
" <td>1.000000</td>\n",
" <td>0.140647</td>\n",
" <td>0.036242</td>\n",
" <td>0.292695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pedi</th>\n",
" <td>-0.033523</td>\n",
" <td>0.137337</td>\n",
" <td>0.041265</td>\n",
" <td>0.183928</td>\n",
" <td>0.185071</td>\n",
" <td>0.140647</td>\n",
" <td>1.000000</td>\n",
" <td>0.033561</td>\n",
" <td>0.173844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>0.544341</td>\n",
" <td>0.263514</td>\n",
" <td>0.239528</td>\n",
" <td>-0.113970</td>\n",
" <td>-0.042163</td>\n",
" <td>0.036242</td>\n",
" <td>0.033561</td>\n",
" <td>1.000000</td>\n",
" <td>0.238356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>class</th>\n",
" <td>0.221898</td>\n",
" <td>0.466581</td>\n",
" <td>0.065068</td>\n",
" <td>0.074752</td>\n",
" <td>0.130548</td>\n",
" <td>0.292695</td>\n",
" <td>0.173844</td>\n",
" <td>0.238356</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preg plas pres skin test mass pedi \\\n",
"preg 1.000000 0.129459 0.141282 -0.081672 -0.073535 0.017683 -0.033523 \n",
"plas 0.129459 1.000000 0.152590 0.057328 0.331357 0.221071 0.137337 \n",
"pres 0.141282 0.152590 1.000000 0.207371 0.088933 0.281805 0.041265 \n",
"skin -0.081672 0.057328 0.207371 1.000000 0.436783 0.392573 0.183928 \n",
"test -0.073535 0.331357 0.088933 0.436783 1.000000 0.197859 0.185071 \n",
"mass 0.017683 0.221071 0.281805 0.392573 0.197859 1.000000 0.140647 \n",
"pedi -0.033523 0.137337 0.041265 0.183928 0.185071 0.140647 1.000000 \n",
"age 0.544341 0.263514 0.239528 -0.113970 -0.042163 0.036242 0.033561 \n",
"class 0.221898 0.466581 0.065068 0.074752 0.130548 0.292695 0.173844 \n",
"\n",
" age class \n",
"preg 0.544341 0.221898 \n",
"plas 0.263514 0.466581 \n",
"pres 0.239528 0.065068 \n",
"skin -0.113970 0.074752 \n",
"test -0.042163 0.130548 \n",
"mass 0.036242 0.292695 \n",
"pedi 0.033561 0.173844 \n",
"age 1.000000 0.238356 \n",
"class 0.238356 1.000000 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.corr()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
|
|
| python/notebooks/Demo 2-details.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bringing modularity and code reuse to Spark\n",
"\n",
"Spark does not let one define arbitrary functions and reuse them at will. In this example, we show how to decompose a problem into a set of simpler primitive functions, that nevertheless perform arbitrary operations that would not be allowed in Spark.\n",
"\n",
"We are going to build a function that exemplifies the birthday paradox: given a set of birthdates, it will returns the number of people who happen to share a birthdate with someone else. This is easy to express using joins and groups. This function takes a dataset or a column as input (the birth dates) and returns a single number (the number of people who share the same birth day). This is an aggregation function! Our urge is of course to use it then in a different setting such as in a group, etc. As we will see, Karps allows us to write code that works for both Pandas and Spark, and that allows to plug any aggregation function in a very natural way."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import karps as ks\n",
"import karps.functions as f\n",
"from karps.display import show_phase"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Make a session at the top, although it is not required immediately.\n",
"s = ks.session(\"demo2\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an extremely small dataset:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/[email protected]:{company_name:string, employee_name:string, dob:string}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"employees = ks.dataframe([\n",
" (\"ACME\", \"John\", \"12/01\"),\n",
" (\"ACME\", \"Kate\", \"09/04\"),\n",
" (\"ACME\", \"Albert\", \"09/04\"),\n",
" (\"Databricks\", \"Ali\", \"09/04\"),\n",
"], schema=[\"company_name\", \"employee_name\", \"dob\"],\n",
" name=\"employees\")\n",
"employees"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Now, here is the definition of the birthday paradox function. It is pretty simple code:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# The number of people who share a birthday date with someone else.\n",
"# Takes a column of data containing birthdates.\n",
"def paradoxal_count(c):\n",
" with ks.scope(\"p_count\"): # Make it pretty:\n",
" g = c.groupby(c).agg({'num_employees': f.count}, name=\"agg_count\")\n",
" s = f.sum(g.num_employees[g.num_employees>=2], name=\"paradoxical_employees\")\n",
" return s"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"This is a simple function. If we wanted to try it, or write tests for it, we would prefer not to have to launch a Spark instance, which comes with some overhead. Let's write a simple test case using Pandas to be confident it is working as expected, and then use it in Spark.\n",
"\n",
"It correctly found that 2 people share the same January 1st birth date."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tjhunter/.local/share/virtualenvs/python-iP9Q2HuD/lib/python3.5/site-packages/ipykernel_launcher.py:5: FutureWarning: using a dict on a Series for aggregation\n",
"is deprecated and will be removed in a future version\n",
" \"\"\"\n"
]
},
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# A series of birth dates.\n",
"test_df = pd.Series([\"1/1\", \"3/5\", \"1/1\"])\n",
"paradoxal_count(test_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have this nice function, let's use against each of the companies in our dataset, with Spark.\n",
"\n",
"Notice that you can directly plug the function, no need to do translation, etc. This is impossible to do in Spark for complex functions like this one.\n",
"\n",
"We get at the end a daframe with the name of the company and the number of employees that share the same birthdate:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/[email protected]:{company_name:string, paradoxical_employees:int}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now use this to group by companies:\n",
"res = (employees.dob\n",
" .groupby(employees.company_name)\n",
" .agg({\n",
" \"paradoxical_employees\": paradoxal_count\n",
" }))\n",
"res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is still a dataframe. Now is the time to collect and see the content:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"/collect_list11!org.spark.StructuredReduce:[{company_name:string, paradoxical_employees:int}]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"o = f.collect(res)\n",
"o"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We run it using the session we opened before, and we use compute to inspect how Karps and Spark are evaluating the computations."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<karps.computation.Computation at 0x1153c3080>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comp = s.compute(o)\n",
"comp"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Let's look under the hood to see how this gets translated.\n",
"\n",
"The transformation is defined using two nested first-orderd functions, that get collected using the `FunctionalShuffle` operation called `shuffle9`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.7413970997662896").pbtxt = 'node {\\n name: "employees"\\n op: "org.spark.DistributedLiteral"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string employee_name:string dob:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "cell { array_value { values { struct_value { values { string_value: .ACME. } values { string_value: .John. } values { string_value: .12/01. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Kate. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Albert. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .Databricks. } values { string_value: .Ali. } values { string_value: .09/04. } } } }}cell_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .employee_name. field_type { basic_type: STRING } } fields { field_name: .dob. field_type { basic_type: STRING } } } }}"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0"\\n op: "org.spark.Placeholder"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "string"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "locality: DISTRIBUTED data_type { basic_type: STRING }"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8"\\n op: "org.spark.StructuredTransform"\\n input: "employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .company_name. } field_name: .key. } fields { extraction { path: .dob. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4"\\n op: "org.spark.StructuredTransform"\\n input: "placeholder0"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { } field_name: .key. } fields { extraction { } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1"\\n op: "org.spark.Placeholder"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "string"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "locality: DISTRIBUTED data_type { basic_type: STRING }"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/placeholder1"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "string"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { extraction { } }"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3"\\n op: "org.spark.StructuredReduce"\\n input: "p_count/structured_transform2"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Local"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "int"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .count. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count"\\n op: "org.spark.FunctionalShuffle"\\n input: "p_count/agg_pre4"\\n input: "p_count/placeholder1"\\n input: "p_count/count3"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: ""\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_count"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string num_employees:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { extraction { path: .value. } field_name: .num_employees. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_post5"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{filter:bool value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { function { function_name: .greater_equal. inputs { extraction { path: .num_employees. } } inputs { literal { content { cell { int_value: 2 } cell_type { basic_type: INT } } } } expected_type { basic_type: BOOL } } field_name: .filter. } fields { extraction { path: .num_employees. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7"\\n op: "org.spark.Filter"\\n input: "p_count/filter_pre6"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "int"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: ""\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees"\\n op: "org.spark.StructuredReduce"\\n input: "p_count/filter7"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Local"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "int"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .sum. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9"\\n op: "org.spark.FunctionalShuffle"\\n input: "agg_pre8"\\n input: "placeholder0"\\n input: "p_count/paradoxical_employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: ""\\n }\\n }\\n}\\nnode {\\n name: "agg_post10"\\n op: "org.spark.StructuredTransform"\\n input: "shuffle9"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string paradoxical_employees:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .company_name. } fields { extraction { path: .value. } field_name: .paradoxical_employees. } }}"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11"\\n op: "org.spark.StructuredReduce"\\n input: "agg_post10"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Local"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "[{company_name:string paradoxical_employees:int}]"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .collect_list. inputs { } expected_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .paradoxical_employees. field_type { basic_type: INT } } } } } }}"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.7413970997662896"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"initial\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.4939301788473921").pbtxt = 'node {\\n name: "employees"\\n op: "org.spark.DistributedLiteral"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string employee_name:string dob:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "cell { array_value { values { struct_value { values { string_value: .ACME. } values { string_value: .John. } values { string_value: .12/01. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Kate. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Albert. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .Databricks. } values { string_value: .Ali. } values { string_value: .09/04. } } } }}cell_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .employee_name. field_type { basic_type: STRING } } fields { field_name: .dob. field_type { basic_type: STRING } } } }}"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8"\\n op: "org.spark.StructuredTransform"\\n input: "employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .company_name. } field_name: .key. } fields { extraction { path: .dob. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0"\\n op: "org.spark.StructuredTransform"\\n input: "agg_pre8"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. } field_name: .key_1. } } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4"\\n op: "org.spark.StructuredTransform"\\n input: "placeholder0"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{key:string value:string}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { extraction { path: .value. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_pre4"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key_1. } fields { extraction { path: .value. path: .key. } field_name: .key_2. } } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/placeholder1"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3"\\n op: "org.spark.GroupedReduction"\\n input: "p_count/structured_transform2"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .count. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/count3"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key_1. } } field_name: .key. } fields { struct { fields { extraction { path: .key. path: .key_2. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_count"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{key:string num_employees:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { extraction { path: .value. path: .key. } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .num_employees. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_post5"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{filter:bool value:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { function { function_name: .greater_equal. inputs { extraction { path: .value. path: .num_employees. } } inputs { literal { content { cell { int_value: 2 } cell_type { basic_type: INT } } } } expected_type { basic_type: BOOL } } field_name: .filter. } fields { extraction { path: .value. path: .num_employees. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/filter_pre6"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{filter:bool value:{key:{key_1:string} value:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .value. path: .filter. } field_name: .filter. } fields { struct { fields { extraction { path: .key. } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7"\\n op: "org.spark.Filter"\\n input: "p_count/filter7_kagg_filter"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: ""\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees"\\n op: "org.spark.GroupedReduction"\\n input: "p_count/filter7"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .sum. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/paradoxical_employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10"\\n op: "org.spark.StructuredTransform"\\n input: "shuffle9"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string paradoxical_employees:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .company_name. } fields { extraction { path: .value. } field_name: .paradoxical_employees. } }}"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11"\\n op: "org.spark.StructuredReduce"\\n input: "agg_post10"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Local"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "[{company_name:string paradoxical_employees:int}]"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .collect_list. inputs { } expected_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .paradoxical_employees. field_type { basic_type: INT } } } } } }}"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.4939301788473921"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"final\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After optimization and flattening, the graph actually turns out to be a linear graph with a first shuffle, a filter, a second shuffle and then a final aggregate. You can click around to see how computations are being done."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.6232718310197535").pbtxt = 'node {\\n name: "employees"\\n op: "org.spark.DistributedLiteral"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string employee_name:string dob:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "cell { array_value { values { struct_value { values { string_value: .ACME. } values { string_value: .John. } values { string_value: .12/01. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Kate. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .ACME. } values { string_value: .Albert. } values { string_value: .09/04. } } } values { struct_value { values { string_value: .Databricks. } values { string_value: .Ali. } values { string_value: .09/04. } } } }}cell_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .employee_name. field_type { basic_type: STRING } } fields { field_name: .dob. field_type { basic_type: STRING } } } }}"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8"\\n op: "org.spark.StructuredTransform"\\n input: "employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .company_name. } field_name: .key. } fields { extraction { path: .dob. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0"\\n op: "org.spark.StructuredTransform"\\n input: "agg_pre8"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. } field_name: .key_1. } } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4"\\n op: "org.spark.StructuredTransform"\\n input: "placeholder0"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{key:string value:string}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { extraction { path: .value. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_pre4"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key_1. } fields { extraction { path: .value. path: .key. } field_name: .key_2. } } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/placeholder1"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:string}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3"\\n op: "org.spark.GroupedReduction"\\n input: "p_count/structured_transform2"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_2:string key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .count. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/count3"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key_1. } } field_name: .key. } fields { struct { fields { extraction { path: .key. path: .key_2. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_count"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{key:string num_employees:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { extraction { path: .value. path: .key. } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .num_employees. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/agg_post5"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:{filter:bool value:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .key. } fields { struct { fields { function { function_name: .greater_equal. inputs { extraction { path: .value. path: .num_employees. } } inputs { literal { content { cell { int_value: 2 } cell_type { basic_type: INT } } } } expected_type { basic_type: BOOL } } field_name: .filter. } fields { extraction { path: .value. path: .num_employees. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/filter_pre6"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{filter:bool value:{key:{key_1:string} value:int}}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .value. path: .filter. } field_name: .filter. } fields { struct { fields { extraction { path: .key. } field_name: .key. } fields { extraction { path: .value. path: .value. } field_name: .value. } } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7"\\n op: "org.spark.Filter"\\n input: "p_count/filter7_kagg_filter"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: ""\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees"\\n op: "org.spark.GroupedReduction"\\n input: "p_count/filter7"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:{key_1:string} value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .sum. inputs { } expected_type { basic_type: INT } }}"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9"\\n op: "org.spark.StructuredTransform"\\n input: "p_count/paradoxical_employees"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{key:string value:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. path: .key_1. } field_name: .key. } fields { extraction { path: .value. } field_name: .value. } }}"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10"\\n op: "org.spark.StructuredTransform"\\n input: "shuffle9"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Distributed"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "{company_name:string paradoxical_employees:int}"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "col_op { struct { fields { extraction { path: .key. } field_name: .company_name. } fields { extraction { path: .value. } field_name: .paradoxical_employees. } }}"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11"\\n op: "org.spark.StructuredReduce"\\n input: "agg_post10"\\n device: "/spark:0"\\n attr {\\n key: "locality"\\n value {\\n s: "Local"\\n }\\n }\\n attr {\\n key: "type"\\n value {\\n s: "[{company_name:string paradoxical_employees:int}]"\\n }\\n }\\n attr {\\n key: "zextra"\\n value {\\n s: "agg_op { op { function_name: .collect_list. inputs { } expected_type { array_type { struct_type { fields { field_name: .company_name. field_type { basic_type: STRING } } fields { field_name: .paradoxical_employees. field_type { basic_type: INT } } } } } }}"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.6232718310197535"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"final\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally the value:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([{company_name:string, paradoxical_employees:int}], array_value {\n",
" values {\n",
" struct_value {\n",
" values {\n",
" string_value: \"ACME\"\n",
" }\n",
" values {\n",
" int_value: 2\n",
" }\n",
" }\n",
" }\n",
"}\n",
")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comp.values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a conclusion, with Karps, you can take _any_ reasonable function and reuse it in arbitrary ways in a functional manner, in a type-safe manner. Karps will write for you the complex SQL queries that you would have to write by hand. All errors are detected well before the actual runtime, which greatly simplifies the debugging.\n",
"\n",
"Laziness and structured transforms bring to Spark some fundamental characteristics such as modularity, reusability, better testing and fast-fail comprehensive error checking, on top of automatic performance optimizations."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.5793627060779916").pbtxt = 'node {\\n name: "employees/LocalRelation_0"\\n op: "LocalRelation"\\n attr {\\n key: "hash"\\n value {\\n s: "1015705706"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "830196193"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- company_name: string (nullable = false)\\\\n |-- employee_name: string (nullable = false)\\\\n |-- dob: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LocalRelation [company_name#296, employee_name#297, dob#298]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LocalRelation [company_name#296, employee_name#297, dob#298]"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^employees/LocalRelation_0"\\n attr {\\n key: "hash"\\n value {\\n s: "796215889"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1052941087"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#316, value#317]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#316, value#317]"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^agg_pre8/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1331725453"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "363445887"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#336, value#337]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#336, value#337]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^placeholder0/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-977812789"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "877615818"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#358, value#359]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#358, value#359]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/agg_pre4/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1221108099"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1155678812"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#380, value#381]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#380, value#381]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/placeholder1/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "491600346"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1380736258"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#398, value#399]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#398, value#399]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/structured_transform2/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "65238959"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "325636911"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#421, value#422L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#421, value#422L]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/count3/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-39814287"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1911149795"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#445, value#446]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#445, value#446]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/agg_count/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1942675444"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1814596908"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- num_employees: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#467, value#468]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#467, value#468]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/agg_post5/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1578809920"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1040896235"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- filter: boolean (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#492, value#493]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#492, value#493]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/filter_pre6/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1141173398"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1486245681"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- filter: boolean (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: struct (nullable = false)\\\\n | | |-- key_1: string (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [filter#514, value#515]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [filter#514, value#515]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/filter7_kagg_filter/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-169079149"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1338315974"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#529, value#530L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#529, value#530L]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/filter7/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-81605220"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1524843012"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#552, value#553L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#552, value#553L]"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^p_count/paradoxical_employees/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1917680387"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1001749810"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: string (nullable = false)\\\\n |-- value: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [key#570, value#571L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [key#570, value#571L]"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^shuffle9/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1864669453"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "2040781286"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- company_name: string (nullable = false)\\\\n |-- paradoxical_employees: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [company_name#588, paradoxical_employees#589L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [company_name#588, paradoxical_employees#589L]"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/LogicalRDD_0"\\n op: "LogicalRDD"\\n input: "^agg_post10/LogicalRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-460304699"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "456352011"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- value: array (nullable = true)\\\\n | |-- element: struct (containsNull = true)\\\\n | | |-- company_name: string (nullable = false)\\\\n | | |-- paradoxical_employees: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LogicalRDD [value#609]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LogicalRDD [value#609]"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.5793627060779916"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"parsed\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.6282632236138851").pbtxt = 'node {\\n name: "employees/LocalTableScan_0"\\n op: "LocalTableScan"\\n attr {\\n key: "hash"\\n value {\\n s: "1015705706"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1511792104"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- company_name: string (nullable = false)\\\\n |-- employee_name: string (nullable = false)\\\\n |-- dob: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "LocalTableScan [company_name#296, employee_name#297, dob#298]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "LocalTableScan [company_name#296, employee_name#297, dob#298]"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^employees/LocalTableScan_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1321762285"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1099240760"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#316,value#317]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#316,value#317]"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^agg_pre8/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-936779098"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "34110288"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#336,value#337]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#336,value#337]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^placeholder0/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "2083022245"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1281520608"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#358,value#359]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#358,value#359]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/agg_pre4/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-902652742"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "379176189"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#380,value#381]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#380,value#381]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/placeholder1/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1792473734"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "2121021959"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: string (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#398,value#399]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#398,value#399]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/structured_transform2/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1785075611"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "930751676"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n | |-- key_2: string (nullable = false)\\\\n |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#421,value#422L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#421,value#422L]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/count3/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1297005178"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "624680640"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#445,value#446]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#445,value#446]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/agg_count/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1628665189"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "824211127"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: string (nullable = false)\\\\n | |-- num_employees: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#467,value#468]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#467,value#468]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/agg_post5/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1920077763"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1117209813"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- filter: boolean (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#492,value#493]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#492,value#493]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/filter_pre6/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-960740553"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "521046160"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- filter: boolean (nullable = false)\\\\n |-- value: struct (nullable = false)\\\\n | |-- key: struct (nullable = false)\\\\n | | |-- key_1: string (nullable = false)\\\\n | |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[filter#514,value#515]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[filter#514,value#515]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/filter7_kagg_filter/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1326723475"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "138226686"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: long (nullable = false)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#529,value#530L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#529,value#530L]"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/filter7/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-960464011"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1882256260"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: struct (nullable = false)\\\\n | |-- key_1: string (nullable = false)\\\\n |-- value: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#552,value#553L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#552,value#553L]"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^p_count/paradoxical_employees/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "-1276353044"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1367569899"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- key: string (nullable = false)\\\\n |-- value: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[key#570,value#571L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[key#570,value#571L]"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^shuffle9/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1421310571"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "1605547370"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- company_name: string (nullable = false)\\\\n |-- paradoxical_employees: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[company_name#588,paradoxical_employees#589L]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[company_name#588,paradoxical_employees#589L]"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/Scan_ExistingRDD_0"\\n op: "Scan_ExistingRDD"\\n input: "^agg_post10/Scan_ExistingRDD_0"\\n attr {\\n key: "hash"\\n value {\\n s: "1923680672"\\n }\\n }\\n attr {\\n key: "id"\\n value {\\n s: "60392825"\\n }\\n }\\n attr {\\n key: "schema"\\n value {\\n s: "root\\\\n |-- value: array (nullable = true)\\\\n | |-- element: struct (containsNull = true)\\\\n | | |-- company_name: string (nullable = false)\\\\n | | |-- paradoxical_employees: long (nullable = true)\\\\n"\\n }\\n }\\n attr {\\n key: "simple"\\n value {\\n s: "Scan ExistingRDD[value#609]"\\n }\\n }\\n attr {\\n key: "verbose"\\n value {\\n s: "Scan ExistingRDD[value#609]"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.6282632236138851"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"physical\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe seamless style=\"width:1000px;height:620px;border:0\" srcdoc=\"\n",
" <script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>\n",
" <script>\n",
" function load() {\n",
" document.getElementById("graph0.10317454374426405").pbtxt = 'node {\\n name: "employees/ParallelCollectionRDD_58"\\n op: "ParallelCollectionRDD"\\n attr {\\n key: "name"\\n value {\\n s: "ParallelCollectionRDD"\\n }\\n }\\n}\\nnode {\\n name: "employees/MapPartitionsRDD_59"\\n op: "MapPartitionsRDD"\\n input: "employees/ParallelCollectionRDD_58"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/ParallelCollectionRDD_60"\\n op: "ParallelCollectionRDD"\\n input: "^employees/MapPartitionsRDD_59"\\n attr {\\n key: "name"\\n value {\\n s: "ParallelCollectionRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/MapPartitionsRDD_61"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/ParallelCollectionRDD_60"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/MapPartitionsRDD_62"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/MapPartitionsRDD_61"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/MapPartitionsRDD_63"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/MapPartitionsRDD_62"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/MapPartitionsRDD_64"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/MapPartitionsRDD_63"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_pre8/MapPartitionsRDD_65"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/MapPartitionsRDD_64"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_66"\\n op: "MapPartitionsRDD"\\n input: "agg_pre8/MapPartitionsRDD_64"\\n input: "^agg_pre8/MapPartitionsRDD_65"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_67"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_66"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_68"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_67"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_69"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_68"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_70"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_69"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "placeholder0/MapPartitionsRDD_71"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_70"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_72"\\n op: "MapPartitionsRDD"\\n input: "placeholder0/MapPartitionsRDD_70"\\n input: "^placeholder0/MapPartitionsRDD_71"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_73"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_72"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_74"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_73"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_75"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_74"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_76"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_75"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_pre4/MapPartitionsRDD_77"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_76"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_78"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_pre4/MapPartitionsRDD_76"\\n input: "^p_count/agg_pre4/MapPartitionsRDD_77"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_79"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_78"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_80"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_79"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_81"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_80"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_82"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_81"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/placeholder1/MapPartitionsRDD_83"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_82"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_84"\\n op: "MapPartitionsRDD"\\n input: "p_count/placeholder1/MapPartitionsRDD_82"\\n input: "^p_count/placeholder1/MapPartitionsRDD_83"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_85"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_84"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_86"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_85"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_87"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_86"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_88"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_87"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/structured_transform2/MapPartitionsRDD_89"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_88"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_90"\\n op: "MapPartitionsRDD"\\n input: "p_count/structured_transform2/MapPartitionsRDD_88"\\n input: "^p_count/structured_transform2/MapPartitionsRDD_89"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_91"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_90"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_92"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_91"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/ShuffledRowRDD_93"\\n op: "ShuffledRowRDD"\\n input: "p_count/count3/MapPartitionsRDD_92"\\n attr {\\n key: "name"\\n value {\\n s: "ShuffledRowRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_94"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/ShuffledRowRDD_93"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_95"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_94"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_96"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_95"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_97"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_96"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/count3/MapPartitionsRDD_98"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_97"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_99"\\n op: "MapPartitionsRDD"\\n input: "p_count/count3/MapPartitionsRDD_97"\\n input: "^p_count/count3/MapPartitionsRDD_98"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_100"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_99"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_101"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_100"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_102"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_101"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_103"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_102"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_count/MapPartitionsRDD_104"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_103"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_105"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_count/MapPartitionsRDD_103"\\n input: "^p_count/agg_count/MapPartitionsRDD_104"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_106"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_105"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_107"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_106"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_108"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_107"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_109"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_108"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/agg_post5/MapPartitionsRDD_110"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_109"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_111"\\n op: "MapPartitionsRDD"\\n input: "p_count/agg_post5/MapPartitionsRDD_109"\\n input: "^p_count/agg_post5/MapPartitionsRDD_110"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_112"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_111"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_113"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_112"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_114"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_113"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_115"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_114"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter_pre6/MapPartitionsRDD_116"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_115"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_117"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter_pre6/MapPartitionsRDD_115"\\n input: "^p_count/filter_pre6/MapPartitionsRDD_116"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_118"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_117"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_119"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_118"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_120"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_119"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_121"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_120"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7_kagg_filter/MapPartitionsRDD_122"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_121"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_123"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7_kagg_filter/MapPartitionsRDD_121"\\n input: "^p_count/filter7_kagg_filter/MapPartitionsRDD_122"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_124"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_123"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_125"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_124"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_126"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_125"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_127"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_126"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/filter7/MapPartitionsRDD_128"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_127"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_129"\\n op: "MapPartitionsRDD"\\n input: "p_count/filter7/MapPartitionsRDD_127"\\n input: "^p_count/filter7/MapPartitionsRDD_128"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_130"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_129"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_131"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_130"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/ShuffledRowRDD_132"\\n op: "ShuffledRowRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_131"\\n attr {\\n key: "name"\\n value {\\n s: "ShuffledRowRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_133"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/ShuffledRowRDD_132"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_134"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_133"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_135"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_134"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_136"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_135"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "p_count/paradoxical_employees/MapPartitionsRDD_137"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_136"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_138"\\n op: "MapPartitionsRDD"\\n input: "p_count/paradoxical_employees/MapPartitionsRDD_136"\\n input: "^p_count/paradoxical_employees/MapPartitionsRDD_137"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_139"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_138"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_140"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_139"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_141"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_140"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_142"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_141"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "shuffle9/MapPartitionsRDD_143"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_142"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_144"\\n op: "MapPartitionsRDD"\\n input: "shuffle9/MapPartitionsRDD_142"\\n input: "^shuffle9/MapPartitionsRDD_143"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_145"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_144"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_146"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_145"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_147"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_146"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_148"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_147"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "agg_post10/MapPartitionsRDD_149"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_148"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_150"\\n op: "MapPartitionsRDD"\\n input: "agg_post10/MapPartitionsRDD_148"\\n input: "^agg_post10/MapPartitionsRDD_149"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_151"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_150"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/ShuffledRowRDD_152"\\n op: "ShuffledRowRDD"\\n input: "collect_list11/MapPartitionsRDD_151"\\n attr {\\n key: "name"\\n value {\\n s: "ShuffledRowRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_153"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/ShuffledRowRDD_152"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_154"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_153"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_155"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_154"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_156"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_155"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_157"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_156"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\nnode {\\n name: "collect_list11/MapPartitionsRDD_158"\\n op: "MapPartitionsRDD"\\n input: "collect_list11/MapPartitionsRDD_157"\\n attr {\\n key: "name"\\n value {\\n s: "MapPartitionsRDD"\\n }\\n }\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.10317454374426405"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_phase(comp, \"rdd\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"comp.dump_profile(\"karps-trace-2.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
neuromancer/ocean-results |
|
|
| {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Learning from 1.1k bugs: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need do some Rmagic first.."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rmagic "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load the data from the buggy and robust traces:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"dir = \"28-01-2014\"\n",
"mycon = gzcon(gzfile(paste(dir, \"buggy_traces.csv.gz\", sep=\"/\"), open=\"r\"))\n",
"buggy_traces = read.csv(textConnection(readLines(mycon)), sep=\"\\t\")\n",
"\n",
"\n",
"mycon = gzcon(gzfile(paste(dir, \"robust_traces.csv.gz\", sep=\"/\"), open=\"r\"))\n",
"robust_traces = read.csv(textConnection(readLines(mycon)), sep=\"\\t\")#[,c(-12061,-12060,-12059,-12058)] # last 4 columns are signals"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"%%R\n",
"\n",
"buggy_vars = colnames(buggy_traces)\n",
"robust_vars = colnames(robust_traces)\n",
"print(length(robust_vars))\n",
"print(length(buggy_vars))\n",
"\n",
"#print(robust_traces[1,])\n",
"#robust_vars = colnames(robust_traces[unlist( lapply( robust_traces,function(x) 0 != var(x) ) )])\n",
"\n",
"#print(robust_vars[])\n",
"#print(buggy_vars)\n",
"#print(robust_vars %in% buggy_vars)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] 12061\n",
"[1] 12061\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, we should discard testcases from the buggy traces that are shared objects and not complete elf files:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"check = function(x) {\n",
" #print(\"hola\")\n",
" out = system(paste(\"file \",x[1]), intern=TRUE)\n",
" c1 = length(grep(\"shared object\",out)) == 1\n",
" c2 = length(grep(\"(uses shared libs)\",out)) == 1\n",
" return(!(c1 & (! c2)))\n",
"\n",
"}\n",
"\n",
"checked = as.data.frame(apply(buggy_traces, 1, check))\n",
"buggy_traces = buggy_traces[checked[,1],]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define interesting cases for score function:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"#mycon = paste(dir, \"robust_traces.csv\", sep=\"/\")\n",
"#robust_traces = read.csv(mycon, sep=\"\\t\")[,-1]\n",
"\n",
"#print(robust_traces)\n",
"\n",
"\n",
"fn = c()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The program crashes in an invalid instruction:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_bad_eip = grep(\"crashed\",buggy_vars)\n",
"v_bad_eip = setdiff(v_bad_eip, c(grep(\"crashed.eip.NPtr32\", buggy_vars)))\n",
"v_bad_eip = setdiff(v_bad_eip, c(grep(\"crashed.eip.Ptr32\", buggy_vars)))\n",
"v_bad_eip = setdiff(v_bad_eip, c(grep(\"crashed.eip.GPtr32\", buggy_vars)))\n",
"v_bad_eip = setdiff(v_bad_eip, c(grep(\"crashed.eip.LPtr32\", buggy_vars)))\n",
"print(buggy_vars[v_bad_eip])\n",
"#print(vars[[grep(\"crashed\",vars[-v_good_eip])])\n",
"#v_crashed_eip_DPtr32 = \n",
"\n",
"s_bad_eip = 1000\n",
"\n",
"fx = function(x) {\n",
" return(s_bad_eip*max(as.numeric(x[v_bad_eip])))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] \"crashed.eip.SPtr32\" \"crashed.eip.HPtr32\" \"crashed.eip.FPtr32\"\n",
"[4] \"crashed.eip.DPtr32\" \"crashed.eip.Top32\" \n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The program crashes in general:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_SIGSEGV = grep(\"SIGSEGV\",buggy_vars)\n",
"print(buggy_vars[v_SIGSEGV])\n",
"\n",
"s_SIGSEGV = 10\n",
"\n",
"fx = function(x) {\n",
" return(s_SIGSEGV*max(as.numeric(x[v_SIGSEGV])))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] \"SIGSEGV.addr.Ptr32\" \"SIGSEGV.addr.SPtr32\" \"SIGSEGV.addr.HPtr32\"\n",
"[4] \"SIGSEGV.addr.LPtr32\" \"SIGSEGV.addr.FPtr32\" \"SIGSEGV.addr.NPtr32\"\n",
"[7] \"SIGSEGV.addr.DPtr32\" \"SIGSEGV.addr.GPtr32\" \"SIGSEGV.addr.Top32\" \n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The program uses a dangling pointer:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_DPtr32 = grep(\"DPtr32\",buggy_vars)\n",
"print(buggy_vars[v_DPtr32])\n",
"\n",
"s_DPtr32 = 50\n",
"\n",
"fx = function(x) {\n",
" return(s_DPtr32*max(as.numeric(x[v_DPtr32])))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [1] \"acl_equiv_mode.ret_addr.DPtr32\" \n",
" [2] \"acl_equiv_mode.ret_val.DPtr32\" \n",
" [3] \"acl_equiv_mode_0.DPtr32\" \n",
" [4] \"acl_equiv_mode_1.DPtr32\" \n",
" [5] \"lseek.ret_addr.DPtr32\" \n",
" [6] \"lseek.ret_val.DPtr32\" \n",
" [7] \"getaddrinfo.ret_addr.DPtr32\" \n",
" [8] \"getaddrinfo.ret_val.DPtr32\" \n",
" [9] \"getaddrinfo_0.DPtr32\" \n",
" [10] \"getaddrinfo_1.DPtr32\" \n",
" [11] \"getaddrinfo_2.DPtr32\" \n",
" [12] \"getaddrinfo_3.DPtr32\" \n",
" [13] \"getdomainname.ret_addr.DPtr32\" \n",
" [14] \"getdomainname.ret_val.DPtr32\" \n",
" [15] \"getdomainname_0.DPtr32\" \n",
" [16] \"getservent.ret_addr.DPtr32\" \n",
" [17] \"getservent.ret_val.DPtr32\" \n",
" [18] \"basename.ret_addr.DPtr32\" \n",
" [19] \"basename.ret_val.DPtr32\" \n",
" [20] \"basename_0.DPtr32\" \n",
" [21] \"waddch.ret_addr.DPtr32\" \n",
" [22] \"waddch.ret_val.DPtr32\" \n",
" [23] \"waddch_0.DPtr32\" \n",
" [24] \"X_IO_putc.ret_addr.DPtr32\" \n",
" [25] \"X_IO_putc.ret_val.DPtr32\" \n",
" [26] \"X_IO_putc_1.DPtr32\" \n",
" [27] \"mq_timedreceive.ret_addr.DPtr32\" \n",
" [28] \"mq_timedreceive.ret_val.DPtr32\" \n",
" [29] \"mq_timedreceive_1.DPtr32\" \n",
" [30] \"mq_timedreceive_3.DPtr32\" \n",
" [31] \"mq_timedreceive_4.DPtr32\" \n",
" [32] \"X__lxstat.ret_addr.DPtr32\" \n",
" [33] \"X__lxstat.ret_val.DPtr32\" \n",
" [34] \"X__lxstat_1.DPtr32\" \n",
" [35] \"X__lxstat_2.DPtr32\" \n",
" [36] \"mq_unlink.ret_addr.DPtr32\" \n",
" [37] \"mq_unlink.ret_val.DPtr32\" \n",
" [38] \"mq_unlink_0.DPtr32\" \n",
" [39] \"getmntent.ret_addr.DPtr32\" \n",
" [40] \"getmntent.ret_val.DPtr32\" \n",
" [41] \"getmntent_0.DPtr32\" \n",
" [42] \"kill.ret_addr.DPtr32\" \n",
" [43] \"kill.ret_val.DPtr32\" \n",
" [44] \"openlog.ret_addr.DPtr32\" \n",
" [45] \"openlog.ret_val.DPtr32\" \n",
" [46] \"openlog_0.DPtr32\" \n",
" [47] \"fflush.ret_addr.DPtr32\" \n",
" [48] \"fflush.ret_val.DPtr32\" \n",
" [49] \"fflush_0.DPtr32\" \n",
" [50] \"setmntent.ret_addr.DPtr32\" \n",
" [51] \"setmntent.ret_val.DPtr32\" \n",
" [52] \"setmntent_0.DPtr32\" \n",
" [53] \"setmntent_1.DPtr32\" \n",
" [54] \"fputc.ret_addr.DPtr32\" \n",
" [55] \"fputc.ret_val.DPtr32\" \n",
" [56] \"fputc_1.DPtr32\" \n",
" [57] \"endnetent.ret_addr.DPtr32\" \n",
" [58] \"endnetent.ret_val.DPtr32\" \n",
" [59] \"acl_cmp.ret_addr.DPtr32\" \n",
" [60] \"acl_cmp.ret_val.DPtr32\" \n",
" [61] \"acl_cmp_0.DPtr32\" \n",
" [62] \"acl_cmp_1.DPtr32\" \n",
" [63] \"mq_close.ret_addr.DPtr32\" \n",
" [64] \"mq_close.ret_val.DPtr32\" \n",
" [65] \"sethostname.ret_addr.DPtr32\" \n",
" [66] \"sethostname.ret_val.DPtr32\" \n",
" [67] \"sethostname_0.DPtr32\" \n",
" [68] \"fwrite.ret_addr.DPtr32\" \n",
" [69] \"fwrite.ret_val.DPtr32\" \n",
" [70] \"fwrite_0.DPtr32\" \n",
" [71] \"fwrite_3.DPtr32\" \n",
" [72] \"umask.ret_addr.DPtr32\" \n",
" [73] \"umask.ret_val.DPtr32\" \n",
" [74] \"acl_from_text.ret_addr.DPtr32\" \n",
" [75] \"acl_from_text.ret_val.DPtr32\" \n",
" [76] \"acl_from_text_0.DPtr32\" \n",
" [77] \"fputs.ret_addr.DPtr32\" \n",
" [78] \"fputs.ret_val.DPtr32\" \n",
" [79] \"fputs_0.DPtr32\" \n",
" [80] \"fputs_1.DPtr32\" \n",
" [81] \"fchmod.ret_addr.DPtr32\" \n",
" [82] \"fchmod.ret_val.DPtr32\" \n",
" [83] \"rexec.ret_addr.DPtr32\" \n",
" [84] \"rexec.ret_val.DPtr32\" \n",
" [85] \"rexec_0.DPtr32\" \n",
" [86] \"rexec_2.DPtr32\" \n",
" [87] \"rexec_3.DPtr32\" \n",
" [88] \"rexec_4.DPtr32\" \n",
" [89] \"rexec_5.DPtr32\" \n",
" [90] \"dlerror.ret_addr.DPtr32\" \n",
" [91] \"dlerror.ret_val.DPtr32\" \n",
" [92] \"X__fxstat.ret_addr.DPtr32\" \n",
" [93] \"X__fxstat.ret_val.DPtr32\" \n",
" [94] \"X__fxstat_2.DPtr32\" \n",
" [95] \"bzero.ret_addr.DPtr32\" \n",
" [96] \"bzero.ret_val.DPtr32\" \n",
" [97] \"bzero_0.DPtr32\" \n",
" [98] \"setutent.ret_addr.DPtr32\" \n",
" [99] \"setutent.ret_val.DPtr32\" \n",
"[100] \"sprintf.ret_addr.DPtr32\" \n",
"[101] \"sprintf.ret_val.DPtr32\" \n",
"[102] \"sprintf_0.DPtr32\" \n",
"[103] \"sprintf_1.DPtr32\" \n",
"[104] \"random.ret_addr.DPtr32\" \n",
"[105] \"random.ret_val.DPtr32\" \n",
"[106] \"dup2.ret_addr.DPtr32\" \n",
"[107] \"dup2.ret_val.DPtr32\" \n",
"[108] \"read.ret_addr.DPtr32\" \n",
"[109] \"read.ret_val.DPtr32\" \n",
"[110] \"read_1.DPtr32\" \n",
"[111] \"fclose.ret_addr.DPtr32\" \n",
"[112] \"fclose.ret_val.DPtr32\" \n",
"[113] \"fclose_0.DPtr32\" \n",
"[114] \"putenv.ret_addr.DPtr32\" \n",
"[115] \"putenv.ret_val.DPtr32\" \n",
"[116] \"putenv_0.DPtr32\" \n",
"[117] \"getppid.ret_addr.DPtr32\" \n",
"[118] \"getppid.ret_val.DPtr32\" \n",
"[119] \"mvprintw.ret_addr.DPtr32\" \n",
"[120] \"mvprintw.ret_val.DPtr32\" \n",
"[121] \"mvprintw_2.DPtr32\" \n",
"[122] \"strftime.ret_addr.DPtr32\" \n",
"[123] \"strftime.ret_val.DPtr32\" \n",
"[124] \"strftime_0.DPtr32\" \n",
"[125] \"strftime_2.DPtr32\" \n",
"[126] \"strftime_3.DPtr32\" \n",
"[127] \"waddnstr.ret_addr.DPtr32\" \n",
"[128] \"waddnstr.ret_val.DPtr32\" \n",
"[129] \"waddnstr_0.DPtr32\" \n",
"[130] \"waddnstr_1.DPtr32\" \n",
"[131] \"hstrerror.ret_addr.DPtr32\" \n",
"[132] \"hstrerror.ret_val.DPtr32\" \n",
"[133] \"mq_receive.ret_addr.DPtr32\" \n",
"[134] \"mq_receive.ret_val.DPtr32\" \n",
"[135] \"mq_receive_1.DPtr32\" \n",
"[136] \"mq_receive_3.DPtr32\" \n",
"[137] \"getpgrp.ret_addr.DPtr32\" \n",
"[138] \"getpgrp.ret_val.DPtr32\" \n",
"[139] \"getpwnam.ret_addr.DPtr32\" \n",
"[140] \"getpwnam.ret_val.DPtr32\" \n",
"[141] \"getpwnam_0.DPtr32\" \n",
"[142] \"inet_ntoa.ret_addr.DPtr32\" \n",
"[143] \"inet_ntoa.ret_val.DPtr32\" \n",
"[144] \"inet_ntoa_0.DPtr32\" \n",
"[145] \"dlclose.ret_addr.DPtr32\" \n",
"[146] \"dlclose.ret_val.DPtr32\" \n",
"[147] \"dlclose_0.DPtr32\" \n",
"[148] \"strcoll.ret_addr.DPtr32\" \n",
"[149] \"strcoll.ret_val.DPtr32\" \n",
"[150] \"strcoll_0.DPtr32\" \n",
"[151] \"strcoll_1.DPtr32\" \n",
"[152] \"memcpy.ret_addr.DPtr32\" \n",
"[153] \"memcpy.ret_val.DPtr32\" \n",
"[154] \"memcpy_0.DPtr32\" \n",
"[155] \"memcpy_1.DPtr32\" \n",
"[156] \"acl_get_tag_type.ret_addr.DPtr32\" \n",
"[157] \"acl_get_tag_type.ret_val.DPtr32\" \n",
"[158] \"acl_get_tag_type_0.DPtr32\" \n",
"[159] \"acl_get_tag_type_1.DPtr32\" \n",
"[160] \"ctime.ret_addr.DPtr32\" \n",
"[161] \"ctime.ret_val.DPtr32\" \n",
"[162] \"ctime_0.DPtr32\" \n",
"[163] \"fileno.ret_addr.DPtr32\" \n",
"[164] \"fileno.ret_val.DPtr32\" \n",
"[165] \"fileno_0.DPtr32\" \n",
"[166] \"perror.ret_addr.DPtr32\" \n",
"[167] \"perror.ret_val.DPtr32\" \n",
"[168] \"perror_0.DPtr32\" \n",
"[169] \"srandom.ret_addr.DPtr32\" \n",
"[170] \"srandom.ret_val.DPtr32\" \n",
"[171] \"acl_valid.ret_addr.DPtr32\" \n",
"[172] \"acl_valid.ret_val.DPtr32\" \n",
"[173] \"acl_valid_0.DPtr32\" \n",
"[174] \"uname.ret_addr.DPtr32\" \n",
"[175] \"uname.ret_val.DPtr32\" \n",
"[176] \"uname_0.DPtr32\" \n",
"[177] \"endprotoent.ret_addr.DPtr32\" \n",
"[178] \"endprotoent.ret_val.DPtr32\" \n",
"[179] \"signal.ret_addr.DPtr32\" \n",
"[180] \"signal.ret_val.DPtr32\" \n",
"[181] \"signal_1.DPtr32\" \n",
"[182] \"ruserok_af.ret_addr.DPtr32\" \n",
"[183] \"ruserok_af.ret_val.DPtr32\" \n",
"[184] \"ruserok_af_0.DPtr32\" \n",
"[185] \"ruserok_af_2.DPtr32\" \n",
"[186] \"ruserok_af_3.DPtr32\" \n",
"[187] \"remove.ret_addr.DPtr32\" \n",
"[188] \"remove.ret_val.DPtr32\" \n",
"[189] \"remove_0.DPtr32\" \n",
"[190] \"sethostent.ret_addr.DPtr32\" \n",
"[191] \"sethostent.ret_val.DPtr32\" \n",
"[192] \"acl_get_entry.ret_addr.DPtr32\" \n",
"[193] \"acl_get_entry.ret_val.DPtr32\" \n",
"[194] \"acl_get_entry_0.DPtr32\" \n",
"[195] \"acl_get_entry_2.DPtr32\" \n",
"[196] \"opendir.ret_addr.DPtr32\" \n",
"[197] \"opendir.ret_val.DPtr32\" \n",
"[198] \"opendir_0.DPtr32\" \n",
"[199] \"getpgid.ret_addr.DPtr32\" \n",
"[200] \"getpgid.ret_val.DPtr32\" \n",
"[201] \"mq_setattr.ret_addr.DPtr32\" \n",
"[202] \"mq_setattr.ret_val.DPtr32\" \n",
"[203] \"mq_setattr_1.DPtr32\" \n",
"[204] \"mq_setattr_2.DPtr32\" \n",
"[205] \"rcmd.ret_addr.DPtr32\" \n",
"[206] \"rcmd.ret_val.DPtr32\" \n",
"[207] \"rcmd_0.DPtr32\" \n",
"[208] \"rcmd_2.DPtr32\" \n",
"[209] \"rcmd_3.DPtr32\" \n",
"[210] \"rcmd_4.DPtr32\" \n",
"[211] \"rcmd_5.DPtr32\" \n",
"[212] \"strcpy.ret_addr.DPtr32\" \n",
"[213] \"strcpy.ret_val.DPtr32\" \n",
"[214] \"strcpy_0.DPtr32\" \n",
"[215] \"strcpy_1.DPtr32\" \n",
"[216] \"sleep.ret_addr.DPtr32\" \n",
"[217] \"sleep.ret_val.DPtr32\" \n",
"[218] \"sigismember.ret_addr.DPtr32\" \n",
"[219] \"sigismember.ret_val.DPtr32\" \n",
"[220] \"sigismember_0.DPtr32\" \n",
"[221] \"fread_unlocked.ret_addr.DPtr32\" \n",
"[222] \"fread_unlocked.ret_val.DPtr32\" \n",
"[223] \"fread_unlocked_0.DPtr32\" \n",
"[224] \"fread_unlocked_3.DPtr32\" \n",
"[225] \"strcmp.ret_addr.DPtr32\" \n",
"[226] \"strcmp.ret_val.DPtr32\" \n",
"[227] \"strcmp_0.DPtr32\" \n",
"[228] \"strcmp_1.DPtr32\" \n",
"[229] \"memchr.ret_addr.DPtr32\" \n",
"[230] \"memchr.ret_val.DPtr32\" \n",
"[231] \"memchr_0.DPtr32\" \n",
"[232] \"seteuid.ret_addr.DPtr32\" \n",
"[233] \"seteuid.ret_val.DPtr32\" \n",
"[234] \"strncmp.ret_addr.DPtr32\" \n",
"[235] \"strncmp.ret_val.DPtr32\" \n",
"[236] \"strncmp_0.DPtr32\" \n",
"[237] \"strncmp_1.DPtr32\" \n",
"[238] \"tempnam.ret_addr.DPtr32\" \n",
"[239] \"tempnam.ret_val.DPtr32\" \n",
"[240] \"tempnam_0.DPtr32\" \n",
"[241] \"tempnam_1.DPtr32\" \n",
"[242] \"endgrent.ret_addr.DPtr32\" \n",
"[243] \"endgrent.ret_val.DPtr32\" \n",
"[244] \"bfd_init.ret_addr.DPtr32\" \n",
"[245] \"bfd_init.ret_val.DPtr32\" \n",
"[246] \"setgrent.ret_addr.DPtr32\" \n",
"[247] \"setgrent.ret_val.DPtr32\" \n",
"[248] \"getpagesize.ret_addr.DPtr32\" \n",
"[249] \"getpagesize.ret_val.DPtr32\" \n",
"[250] \"fgetc.ret_addr.DPtr32\" \n",
"[251] \"fgetc.ret_val.DPtr32\" \n",
"[252] \"fgetc_0.DPtr32\" \n",
"[253] \"getutent.ret_addr.DPtr32\" \n",
"[254] \"getutent.ret_val.DPtr32\" \n",
"[255] \"pclose.ret_addr.DPtr32\" \n",
"[256] \"pclose.ret_val.DPtr32\" \n",
"[257] \"pclose_0.DPtr32\" \n",
"[258] \"memset.ret_addr.DPtr32\" \n",
"[259] \"memset.ret_val.DPtr32\" \n",
"[260] \"memset_0.DPtr32\" \n",
"[261] \"strcat.ret_addr.DPtr32\" \n",
"[262] \"strcat.ret_val.DPtr32\" \n",
"[263] \"strcat_0.DPtr32\" \n",
"[264] \"strcat_1.DPtr32\" \n",
"[265] \"readdir.ret_addr.DPtr32\" \n",
"[266] \"readdir.ret_val.DPtr32\" \n",
"[267] \"readdir_0.DPtr32\" \n",
"[268] \"acl_to_any_text.ret_addr.DPtr32\" \n",
"[269] \"acl_to_any_text.ret_val.DPtr32\" \n",
"[270] \"acl_to_any_text_0.DPtr32\" \n",
"[271] \"acl_to_any_text_1.DPtr32\" \n",
"[272] \"atexit.ret_addr.DPtr32\" \n",
"[273] \"atexit.ret_val.DPtr32\" \n",
"[274] \"atexit_0.DPtr32\" \n",
"[275] \"freeaddrinfo.ret_addr.DPtr32\" \n",
"[276] \"freeaddrinfo.ret_val.DPtr32\" \n",
"[277] \"freeaddrinfo_0.DPtr32\" \n",
"[278] \"acl_extended_fd.ret_addr.DPtr32\" \n",
"[279] \"acl_extended_fd.ret_val.DPtr32\" \n",
"[280] \"fwrite_unlocked.ret_addr.DPtr32\" \n",
"[281] \"fwrite_unlocked.ret_val.DPtr32\" \n",
"[282] \"fwrite_unlocked_0.DPtr32\" \n",
"[283] \"fwrite_unlocked_3.DPtr32\" \n",
"[284] \"execlp.ret_addr.DPtr32\" \n",
"[285] \"execlp.ret_val.DPtr32\" \n",
"[286] \"execlp_0.DPtr32\" \n",
"[287] \"execlp_1.DPtr32\" \n",
"[288] \"execlp_2.DPtr32\" \n",
"[289] \"execlp_3.DPtr32\" \n",
"[290] \"execlp_4.DPtr32\" \n",
"[291] \"fgets.ret_addr.DPtr32\" \n",
"[292] \"fgets.ret_val.DPtr32\" \n",
"[293] \"fgets_0.DPtr32\" \n",
"[294] \"fgets_2.DPtr32\" \n",
"[295] \"wmove.ret_addr.DPtr32\" \n",
"[296] \"wmove.ret_val.DPtr32\" \n",
"[297] \"wmove_0.DPtr32\" \n",
"[298] \"getgid.ret_addr.DPtr32\" \n",
"[299] \"getgid.ret_val.DPtr32\" \n",
"[300] \"endmntent.ret_addr.DPtr32\" \n",
"[301] \"endmntent.ret_val.DPtr32\" \n",
"[302] \"endmntent_0.DPtr32\" \n",
"[303] \"strsep.ret_addr.DPtr32\" \n",
"[304] \"strsep.ret_val.DPtr32\" \n",
"[305] \"strsep_0.DPtr32\" \n",
"[306] \"strsep_1.DPtr32\" \n",
"[307] \"tcsetattr.ret_addr.DPtr32\" \n",
"[308] \"tcsetattr.ret_val.DPtr32\" \n",
"[309] \"tcsetattr_2.DPtr32\" \n",
"[310] \"strchr.ret_addr.DPtr32\" \n",
"[311] \"strchr.ret_val.DPtr32\" \n",
"[312] \"strchr_0.DPtr32\" \n",
"[313] \"index.ret_addr.DPtr32\" \n",
"[314] \"index.ret_val.DPtr32\" \n",
"[315] \"index_0.DPtr32\" \n",
"[316] \"endpwent.ret_addr.DPtr32\" \n",
"[317] \"endpwent.ret_val.DPtr32\" \n",
"[318] \"mq_send.ret_addr.DPtr32\" \n",
"[319] \"mq_send.ret_val.DPtr32\" \n",
"[320] \"mq_send_1.DPtr32\" \n",
"[321] \"acl_set_permset.ret_addr.DPtr32\" \n",
"[322] \"acl_set_permset.ret_val.DPtr32\" \n",
"[323] \"acl_set_permset_0.DPtr32\" \n",
"[324] \"acl_set_permset_1.DPtr32\" \n",
"[325] \"setpgid.ret_addr.DPtr32\" \n",
"[326] \"setpgid.ret_val.DPtr32\" \n",
"[327] \"inet_addr.ret_addr.DPtr32\" \n",
"[328] \"inet_addr.ret_val.DPtr32\" \n",
"[329] \"inet_addr_0.DPtr32\" \n",
"[330] \"access.ret_addr.DPtr32\" \n",
"[331] \"access.ret_val.DPtr32\" \n",
"[332] \"access_0.DPtr32\" \n",
"[333] \"unsetenv.ret_addr.DPtr32\" \n",
"[334] \"unsetenv.ret_val.DPtr32\" \n",
"[335] \"unsetenv_0.DPtr32\" \n",
"[336] \"mkdir.ret_addr.DPtr32\" \n",
"[337] \"mkdir.ret_val.DPtr32\" \n",
"[338] \"mkdir_0.DPtr32\" \n",
"[339] \"endutent.ret_addr.DPtr32\" \n",
"[340] \"endutent.ret_val.DPtr32\" \n",
"[341] \"exit.ret_addr.DPtr32\" \n",
"[342] \"exit.ret_val.DPtr32\" \n",
"[343] \"X__dcgettext.ret_addr.DPtr32\" \n",
"[344] \"X__dcgettext.ret_val.DPtr32\" \n",
"[345] \"X__dcgettext_0.DPtr32\" \n",
"[346] \"X__dcgettext_1.DPtr32\" \n",
"[347] \"pcap_compile.ret_addr.DPtr32\" \n",
"[348] \"pcap_compile.ret_val.DPtr32\" \n",
"[349] \"pcap_compile_0.DPtr32\" \n",
"[350] \"pcap_compile_1.DPtr32\" \n",
"[351] \"pcap_compile_2.DPtr32\" \n",
"[352] \"pcap_compile_4.DPtr32\" \n",
"[353] \"X__ctype_b_loc.ret_addr.DPtr32\" \n",
"[354] \"X__ctype_b_loc.ret_val.DPtr32\" \n",
"[355] \"strrchr.ret_addr.DPtr32\" \n",
"[356] \"strrchr.ret_val.DPtr32\" \n",
"[357] \"strrchr_0.DPtr32\" \n",
"[358] \"acl_get_file.ret_addr.DPtr32\" \n",
"[359] \"acl_get_file.ret_val.DPtr32\" \n",
"[360] \"acl_get_file_0.DPtr32\" \n",
"[361] \"gethostbyaddr.ret_addr.DPtr32\" \n",
"[362] \"gethostbyaddr.ret_val.DPtr32\" \n",
"[363] \"gethostbyaddr_0.DPtr32\" \n",
"[364] \"acl_extended_file.ret_addr.DPtr32\" \n",
"[365] \"acl_extended_file.ret_val.DPtr32\" \n",
"[366] \"acl_extended_file_0.DPtr32\" \n",
"[367] \"mq_getattr.ret_addr.DPtr32\" \n",
"[368] \"mq_getattr.ret_val.DPtr32\" \n",
"[369] \"mq_getattr_1.DPtr32\" \n",
"[370] \"usleep.ret_addr.DPtr32\" \n",
"[371] \"usleep.ret_val.DPtr32\" \n",
"[372] \"pcap_snapshot.ret_addr.DPtr32\" \n",
"[373] \"pcap_snapshot.ret_val.DPtr32\" \n",
"[374] \"pcap_snapshot_0.DPtr32\" \n",
"[375] \"sigsuspend.ret_addr.DPtr32\" \n",
"[376] \"sigsuspend.ret_val.DPtr32\" \n",
"[377] \"sigsuspend_0.DPtr32\" \n",
"[378] \"pcap_lookupnet.ret_addr.DPtr32\" \n",
"[379] \"pcap_lookupnet.ret_val.DPtr32\" \n",
"[380] \"pcap_lookupnet_0.DPtr32\" \n",
"[381] \"pcap_lookupnet_1.DPtr32\" \n",
"[382] \"pcap_lookupnet_2.DPtr32\" \n",
"[383] \"pcap_lookupnet_3.DPtr32\" \n",
"[384] \"strcspn.ret_addr.DPtr32\" \n",
"[385] \"strcspn.ret_val.DPtr32\" \n",
"[386] \"strcspn_0.DPtr32\" \n",
"[387] \"strcspn_1.DPtr32\" \n",
"[388] \"getnetbyaddr.ret_addr.DPtr32\" \n",
"[389] \"getnetbyaddr.ret_val.DPtr32\" \n",
"[390] \"acl_dup.ret_addr.DPtr32\" \n",
"[391] \"acl_dup.ret_val.DPtr32\" \n",
"[392] \"acl_dup_0.DPtr32\" \n",
"[393] \"ferror.ret_addr.DPtr32\" \n",
"[394] \"ferror.ret_val.DPtr32\" \n",
"[395] \"ferror_0.DPtr32\" \n",
"[396] \"XCloseDisplay.ret_addr.DPtr32\" \n",
"[397] \"XCloseDisplay.ret_val.DPtr32\" \n",
"[398] \"XCloseDisplay_0.DPtr32\" \n",
"[399] \"getcwd.ret_addr.DPtr32\" \n",
"[400] \"getcwd.ret_val.DPtr32\" \n",
"[401] \"getcwd_0.DPtr32\" \n",
"[402] \"gmtime.ret_addr.DPtr32\" \n",
"[403] \"gmtime.ret_val.DPtr32\" \n",
"[404] \"gmtime_0.DPtr32\" \n",
"[405] \"free.ret_addr.DPtr32\" \n",
"[406] \"free.ret_val.DPtr32\" \n",
"[407] \"free_0.DPtr32\" \n",
"[408] \"rcmd_af.ret_addr.DPtr32\" \n",
"[409] \"rcmd_af.ret_val.DPtr32\" \n",
"[410] \"rcmd_af_0.DPtr32\" \n",
"[411] \"rcmd_af_2.DPtr32\" \n",
"[412] \"rcmd_af_3.DPtr32\" \n",
"[413] \"rcmd_af_4.DPtr32\" \n",
"[414] \"rcmd_af_5.DPtr32\" \n",
"[415] \"symlink.ret_addr.DPtr32\" \n",
"[416] \"symlink.ret_val.DPtr32\" \n",
"[417] \"symlink_0.DPtr32\" \n",
"[418] \"symlink_1.DPtr32\" \n",
"[419] \"fchdir.ret_addr.DPtr32\" \n",
"[420] \"fchdir.ret_val.DPtr32\" \n",
"[421] \"rresvport_af.ret_addr.DPtr32\" \n",
"[422] \"rresvport_af.ret_val.DPtr32\" \n",
"[423] \"rresvport_af_0.DPtr32\" \n",
"[424] \"acl_get_fd.ret_addr.DPtr32\" \n",
"[425] \"acl_get_fd.ret_val.DPtr32\" \n",
"[426] \"gethostname.ret_addr.DPtr32\" \n",
"[427] \"gethostname.ret_val.DPtr32\" \n",
"[428] \"gethostname_0.DPtr32\" \n",
"[429] \"getnameinfo.ret_addr.DPtr32\" \n",
"[430] \"getnameinfo.ret_val.DPtr32\" \n",
"[431] \"getnameinfo_0.DPtr32\" \n",
"[432] \"getnameinfo_2.DPtr32\" \n",
"[433] \"getnameinfo_4.DPtr32\" \n",
"[434] \"X__strtoul_internal.ret_addr.DPtr32\" \n",
"[435] \"X__strtoul_internal.ret_val.DPtr32\" \n",
"[436] \"X__strtoul_internal_0.DPtr32\" \n",
"[437] \"X__strtoul_internal_1.DPtr32\" \n",
"[438] \"sigaction.ret_addr.DPtr32\" \n",
"[439] \"sigaction.ret_val.DPtr32\" \n",
"[440] \"sigaction_1.DPtr32\" \n",
"[441] \"sigaction_2.DPtr32\" \n",
"[442] \"execv.ret_addr.DPtr32\" \n",
"[443] \"execv.ret_val.DPtr32\" \n",
"[444] \"execv_0.DPtr32\" \n",
"[445] \"execv_1.DPtr32\" \n",
"[446] \"dlopen.ret_addr.DPtr32\" \n",
"[447] \"dlopen.ret_val.DPtr32\" \n",
"[448] \"dlopen_0.DPtr32\" \n",
"[449] \"wait.ret_addr.DPtr32\" \n",
"[450] \"wait.ret_val.DPtr32\" \n",
"[451] \"wait_0.DPtr32\" \n",
"[452] \"fwide.ret_addr.DPtr32\" \n",
"[453] \"fwide.ret_val.DPtr32\" \n",
"[454] \"fwide_0.DPtr32\" \n",
"[455] \"muntrace.ret_addr.DPtr32\" \n",
"[456] \"muntrace.ret_val.DPtr32\" \n",
"[457] \"X__strtol_internal.ret_addr.DPtr32\" \n",
"[458] \"X__strtol_internal.ret_val.DPtr32\" \n",
"[459] \"X__strtol_internal_0.DPtr32\" \n",
"[460] \"X__strtol_internal_1.DPtr32\" \n",
"[461] \"gethostbyname.ret_addr.DPtr32\" \n",
"[462] \"gethostbyname.ret_val.DPtr32\" \n",
"[463] \"gethostbyname_0.DPtr32\" \n",
"[464] \"acl_delete_def_file.ret_addr.DPtr32\" \n",
"[465] \"acl_delete_def_file.ret_val.DPtr32\" \n",
"[466] \"acl_delete_def_file_0.DPtr32\" \n",
"[467] \"getgrnam.ret_addr.DPtr32\" \n",
"[468] \"getgrnam.ret_val.DPtr32\" \n",
"[469] \"getgrnam_0.DPtr32\" \n",
"[470] \"getnetgrent.ret_addr.DPtr32\" \n",
"[471] \"getnetgrent.ret_val.DPtr32\" \n",
"[472] \"getnetgrent_0.DPtr32\" \n",
"[473] \"getnetgrent_1.DPtr32\" \n",
"[474] \"getnetgrent_2.DPtr32\" \n",
"[475] \"strtok.ret_addr.DPtr32\" \n",
"[476] \"strtok.ret_val.DPtr32\" \n",
"[477] \"strtok_0.DPtr32\" \n",
"[478] \"strtok_1.DPtr32\" \n",
"[479] \"popen.ret_addr.DPtr32\" \n",
"[480] \"popen.ret_val.DPtr32\" \n",
"[481] \"popen_0.DPtr32\" \n",
"[482] \"popen_1.DPtr32\" \n",
"[483] \"getprotobyname.ret_addr.DPtr32\" \n",
"[484] \"getprotobyname.ret_val.DPtr32\" \n",
"[485] \"getprotobyname_0.DPtr32\" \n",
"[486] \"acl_get_qualifier.ret_addr.DPtr32\" \n",
"[487] \"acl_get_qualifier.ret_val.DPtr32\" \n",
"[488] \"acl_get_qualifier_0.DPtr32\" \n",
"[489] \"gethostent.ret_addr.DPtr32\" \n",
"[490] \"gethostent.ret_val.DPtr32\" \n",
"[491] \"acl_set_fd.ret_addr.DPtr32\" \n",
"[492] \"acl_set_fd.ret_val.DPtr32\" \n",
"[493] \"acl_set_fd_1.DPtr32\" \n",
"[494] \"getnetent.ret_addr.DPtr32\" \n",
"[495] \"getnetent.ret_val.DPtr32\" \n",
"[496] \"closelog.ret_addr.DPtr32\" \n",
"[497] \"closelog.ret_val.DPtr32\" \n",
"[498] \"ttyname.ret_addr.DPtr32\" \n",
"[499] \"ttyname.ret_val.DPtr32\" \n",
"[500] \"getprotoent.ret_addr.DPtr32\" \n",
"[501] \"getprotoent.ret_val.DPtr32\" \n",
"[502] \"getservbyname.ret_addr.DPtr32\" \n",
"[503] \"getservbyname.ret_val.DPtr32\" \n",
"[504] \"getservbyname_0.DPtr32\" \n",
"[505] \"getservbyname_1.DPtr32\" \n",
"[506] \"setuid.ret_addr.DPtr32\" \n",
"[507] \"setuid.ret_val.DPtr32\" \n",
"[508] \"bfd_scan_vma.ret_addr.DPtr32\" \n",
"[509] \"bfd_scan_vma.ret_val.DPtr32\" \n",
"[510] \"bfd_scan_vma_0.DPtr32\" \n",
"[511] \"bfd_scan_vma_1.DPtr32\" \n",
"[512] \"XOpenDisplay.ret_addr.DPtr32\" \n",
"[513] \"XOpenDisplay.ret_val.DPtr32\" \n",
"[514] \"XOpenDisplay_0.DPtr32\" \n",
"[515] \"setlocale.ret_addr.DPtr32\" \n",
"[516] \"setlocale.ret_val.DPtr32\" \n",
"[517] \"setlocale_1.DPtr32\" \n",
"[518] \"realloc.ret_addr.DPtr32\" \n",
"[519] \"realloc.ret_val.DPtr32\" \n",
"[520] \"realloc_0.DPtr32\" \n",
"[521] \"bcopy.ret_addr.DPtr32\" \n",
"[522] \"bcopy.ret_val.DPtr32\" \n",
"[523] \"bcopy_0.DPtr32\" \n",
"[524] \"bcopy_1.DPtr32\" \n",
"[525] \"acl_free.ret_addr.DPtr32\" \n",
"[526] \"acl_free.ret_val.DPtr32\" \n",
"[527] \"acl_free_0.DPtr32\" \n",
"[528] \"chmod.ret_addr.DPtr32\" \n",
"[529] \"chmod.ret_val.DPtr32\" \n",
"[530] \"chmod_0.DPtr32\" \n",
"[531] \"X__xstat64.ret_addr.DPtr32\" \n",
"[532] \"X__xstat64.ret_val.DPtr32\" \n",
"[533] \"X__xstat64_1.DPtr32\" \n",
"[534] \"X__xstat64_2.DPtr32\" \n",
"[535] \"sync.ret_addr.DPtr32\" \n",
"[536] \"sync.ret_val.DPtr32\" \n",
"[537] \"acl_check.ret_addr.DPtr32\" \n",
"[538] \"acl_check.ret_val.DPtr32\" \n",
"[539] \"acl_check_0.DPtr32\" \n",
"[540] \"acl_check_1.DPtr32\" \n",
"[541] \"tputs.ret_addr.DPtr32\" \n",
"[542] \"tputs.ret_val.DPtr32\" \n",
"[543] \"tputs_0.DPtr32\" \n",
"[544] \"tputs_2.DPtr32\" \n",
"[545] \"acl_error.ret_addr.DPtr32\" \n",
"[546] \"acl_error.ret_val.DPtr32\" \n",
"[547] \"toupper.ret_addr.DPtr32\" \n",
"[548] \"toupper.ret_val.DPtr32\" \n",
"[549] \"printf.ret_addr.DPtr32\" \n",
"[550] \"printf.ret_val.DPtr32\" \n",
"[551] \"printf_0.DPtr32\" \n",
"[552] \"sigemptyset.ret_addr.DPtr32\" \n",
"[553] \"sigemptyset.ret_val.DPtr32\" \n",
"[554] \"sigemptyset_0.DPtr32\" \n",
"[555] \"X__fxstat64.ret_addr.DPtr32\" \n",
"[556] \"X__fxstat64.ret_val.DPtr32\" \n",
"[557] \"X__fxstat64_2.DPtr32\" \n",
"[558] \"fopen.ret_addr.DPtr32\" \n",
"[559] \"fopen.ret_val.DPtr32\" \n",
"[560] \"fopen_0.DPtr32\" \n",
"[561] \"fopen_1.DPtr32\" \n",
"[562] \"open.ret_addr.DPtr32\" \n",
"[563] \"open.ret_val.DPtr32\" \n",
"[564] \"open_0.DPtr32\" \n",
"[565] \"strncpy.ret_addr.DPtr32\" \n",
"[566] \"strncpy.ret_val.DPtr32\" \n",
"[567] \"strncpy_0.DPtr32\" \n",
"[568] \"strncpy_1.DPtr32\" \n",
"[569] \"pcap_lookupdev.ret_addr.DPtr32\" \n",
"[570] \"pcap_lookupdev.ret_val.DPtr32\" \n",
"[571] \"pcap_lookupdev_0.DPtr32\" \n",
"[572] \"acl_set_qualifier.ret_addr.DPtr32\" \n",
"[573] \"acl_set_qualifier.ret_val.DPtr32\" \n",
"[574] \"acl_set_qualifier_0.DPtr32\" \n",
"[575] \"acl_set_qualifier_1.DPtr32\" \n",
"[576] \"unlink.ret_addr.DPtr32\" \n",
"[577] \"unlink.ret_val.DPtr32\" \n",
"[578] \"unlink_0.DPtr32\" \n",
"[579] \"sigprocmask.ret_addr.DPtr32\" \n",
"[580] \"sigprocmask.ret_val.DPtr32\" \n",
"[581] \"sigprocmask_1.DPtr32\" \n",
"[582] \"sigprocmask_2.DPtr32\" \n",
"[583] \"snprintf.ret_addr.DPtr32\" \n",
"[584] \"snprintf.ret_val.DPtr32\" \n",
"[585] \"snprintf_0.DPtr32\" \n",
"[586] \"snprintf_2.DPtr32\" \n",
"[587] \"puts.ret_addr.DPtr32\" \n",
"[588] \"puts.ret_val.DPtr32\" \n",
"[589] \"puts_0.DPtr32\" \n",
"[590] \"rindex.ret_addr.DPtr32\" \n",
"[591] \"rindex.ret_val.DPtr32\" \n",
"[592] \"rindex_0.DPtr32\" \n",
"[593] \"acl_to_text.ret_addr.DPtr32\" \n",
"[594] \"acl_to_text.ret_val.DPtr32\" \n",
"[595] \"acl_to_text_0.DPtr32\" \n",
"[596] \"acl_to_text_1.DPtr32\" \n",
"[597] \"qsort.ret_addr.DPtr32\" \n",
"[598] \"qsort.ret_val.DPtr32\" \n",
"[599] \"qsort_0.DPtr32\" \n",
"[600] \"qsort_3.DPtr32\" \n",
"[601] \"system.ret_addr.DPtr32\" \n",
"[602] \"system.ret_val.DPtr32\" \n",
"[603] \"system_0.DPtr32\" \n",
"[604] \"sigpending.ret_addr.DPtr32\" \n",
"[605] \"sigpending.ret_val.DPtr32\" \n",
"[606] \"sigpending_0.DPtr32\" \n",
"[607] \"getservbyport.ret_addr.DPtr32\" \n",
"[608] \"getservbyport.ret_val.DPtr32\" \n",
"[609] \"getservbyport_1.DPtr32\" \n",
"[610] \"endservent.ret_addr.DPtr32\" \n",
"[611] \"endservent.ret_val.DPtr32\" \n",
"[612] \"mkfifo.ret_addr.DPtr32\" \n",
"[613] \"mkfifo.ret_val.DPtr32\" \n",
"[614] \"mkfifo_0.DPtr32\" \n",
"[615] \"ntohs.ret_addr.DPtr32\" \n",
"[616] \"ntohs.ret_val.DPtr32\" \n",
"[617] \"rmdir.ret_addr.DPtr32\" \n",
"[618] \"rmdir.ret_val.DPtr32\" \n",
"[619] \"rmdir_0.DPtr32\" \n",
"[620] \"time.ret_addr.DPtr32\" \n",
"[621] \"time.ret_val.DPtr32\" \n",
"[622] \"time_0.DPtr32\" \n",
"[623] \"acl_delete_perm.ret_addr.DPtr32\" \n",
"[624] \"acl_delete_perm.ret_val.DPtr32\" \n",
"[625] \"acl_delete_perm_0.DPtr32\" \n",
"[626] \"readdir64.ret_addr.DPtr32\" \n",
"[627] \"readdir64.ret_val.DPtr32\" \n",
"[628] \"readdir64_0.DPtr32\" \n",
"[629] \"acl_copy_entry.ret_addr.DPtr32\" \n",
"[630] \"acl_copy_entry.ret_val.DPtr32\" \n",
"[631] \"acl_copy_entry_0.DPtr32\" \n",
"[632] \"acl_copy_entry_1.DPtr32\" \n",
"[633] \"getpid.ret_addr.DPtr32\" \n",
"[634] \"getpid.ret_val.DPtr32\" \n",
"[635] \"fork.ret_addr.DPtr32\" \n",
"[636] \"fork.ret_val.DPtr32\" \n",
"[637] \"isatty.ret_addr.DPtr32\" \n",
"[638] \"isatty.ret_val.DPtr32\" \n",
"[639] \"setgid.ret_addr.DPtr32\" \n",
"[640] \"setgid.ret_val.DPtr32\" \n",
"[641] \"setservent.ret_addr.DPtr32\" \n",
"[642] \"setservent.ret_val.DPtr32\" \n",
"[643] \"mq_timedsend.ret_addr.DPtr32\" \n",
"[644] \"mq_timedsend.ret_val.DPtr32\" \n",
"[645] \"mq_timedsend_1.DPtr32\" \n",
"[646] \"mq_timedsend_4.DPtr32\" \n",
"[647] \"gai_strerror.ret_addr.DPtr32\" \n",
"[648] \"gai_strerror.ret_val.DPtr32\" \n",
"[649] \"setprotoent.ret_addr.DPtr32\" \n",
"[650] \"setprotoent.ret_val.DPtr32\" \n",
"[651] \"sigaddset.ret_addr.DPtr32\" \n",
"[652] \"sigaddset.ret_val.DPtr32\" \n",
"[653] \"sigaddset_0.DPtr32\" \n",
"[654] \"getenv.ret_addr.DPtr32\" \n",
"[655] \"getenv.ret_val.DPtr32\" \n",
"[656] \"getenv_0.DPtr32\" \n",
"[657] \"gettimeofday.ret_addr.DPtr32\" \n",
"[658] \"gettimeofday.ret_val.DPtr32\" \n",
"[659] \"gettimeofday_0.DPtr32\" \n",
"[660] \"gettimeofday_1.DPtr32\" \n",
"[661] \"crypt.ret_addr.DPtr32\" \n",
"[662] \"crypt.ret_val.DPtr32\" \n",
"[663] \"crypt_0.DPtr32\" \n",
"[664] \"crypt_1.DPtr32\" \n",
"[665] \"link.ret_addr.DPtr32\" \n",
"[666] \"link.ret_val.DPtr32\" \n",
"[667] \"link_0.DPtr32\" \n",
"[668] \"link_1.DPtr32\" \n",
"[669] \"ruserok.ret_addr.DPtr32\" \n",
"[670] \"ruserok.ret_val.DPtr32\" \n",
"[671] \"ruserok_0.DPtr32\" \n",
"[672] \"ruserok_2.DPtr32\" \n",
"[673] \"ruserok_3.DPtr32\" \n",
"[674] \"waitpid.ret_addr.DPtr32\" \n",
"[675] \"waitpid.ret_val.DPtr32\" \n",
"[676] \"waitpid_1.DPtr32\" \n",
"[677] \"herror.ret_addr.DPtr32\" \n",
"[678] \"herror.ret_val.DPtr32\" \n",
"[679] \"herror_0.DPtr32\" \n",
"[680] \"strdup.ret_addr.DPtr32\" \n",
"[681] \"strdup.ret_val.DPtr32\" \n",
"[682] \"strdup_0.DPtr32\" \n",
"[683] \"getnetbyname.ret_addr.DPtr32\" \n",
"[684] \"getnetbyname.ret_val.DPtr32\" \n",
"[685] \"getnetbyname_0.DPtr32\" \n",
"[686] \"chdir.ret_addr.DPtr32\" \n",
"[687] \"chdir.ret_val.DPtr32\" \n",
"[688] \"chdir_0.DPtr32\" \n",
"[689] \"X_IO_getc.ret_addr.DPtr32\" \n",
"[690] \"X_IO_getc.ret_val.DPtr32\" \n",
"[691] \"X_IO_getc_0.DPtr32\" \n",
"[692] \"sbrk.ret_addr.DPtr32\" \n",
"[693] \"sbrk.ret_val.DPtr32\" \n",
"[694] \"syslog.ret_addr.DPtr32\" \n",
"[695] \"syslog.ret_val.DPtr32\" \n",
"[696] \"syslog_1.DPtr32\" \n",
"[697] \"statfs.ret_addr.DPtr32\" \n",
"[698] \"statfs.ret_val.DPtr32\" \n",
"[699] \"statfs_0.DPtr32\" \n",
"[700] \"statfs_1.DPtr32\" \n",
"[701] \"acl_clear_perms.ret_addr.DPtr32\" \n",
"[702] \"acl_clear_perms.ret_val.DPtr32\" \n",
"[703] \"acl_clear_perms_0.DPtr32\" \n",
"[704] \"X__errno_location.ret_addr.DPtr32\" \n",
"[705] \"X__errno_location.ret_val.DPtr32\" \n",
"[706] \"strerror.ret_addr.DPtr32\" \n",
"[707] \"strerror.ret_val.DPtr32\" \n",
"[708] \"strstr.ret_addr.DPtr32\" \n",
"[709] \"strstr.ret_val.DPtr32\" \n",
"[710] \"strstr_0.DPtr32\" \n",
"[711] \"strstr_1.DPtr32\" \n",
"[712] \"bsearch.ret_addr.DPtr32\" \n",
"[713] \"bsearch.ret_val.DPtr32\" \n",
"[714] \"bsearch_0.DPtr32\" \n",
"[715] \"bsearch_1.DPtr32\" \n",
"[716] \"bsearch_4.DPtr32\" \n",
"[717] \"strspn.ret_addr.DPtr32\" \n",
"[718] \"strspn.ret_val.DPtr32\" \n",
"[719] \"strspn_0.DPtr32\" \n",
"[720] \"strspn_1.DPtr32\" \n",
"[721] \"bfd_check_format.ret_addr.DPtr32\" \n",
"[722] \"bfd_check_format.ret_val.DPtr32\" \n",
"[723] \"bfd_check_format_0.DPtr32\" \n",
"[724] \"acl_set_file.ret_addr.DPtr32\" \n",
"[725] \"acl_set_file.ret_val.DPtr32\" \n",
"[726] \"acl_set_file_0.DPtr32\" \n",
"[727] \"acl_set_file_2.DPtr32\" \n",
"[728] \"acl_size.ret_addr.DPtr32\" \n",
"[729] \"acl_size.ret_val.DPtr32\" \n",
"[730] \"acl_size_0.DPtr32\" \n",
"[731] \"fnmatch.ret_addr.DPtr32\" \n",
"[732] \"fnmatch.ret_val.DPtr32\" \n",
"[733] \"fnmatch_0.DPtr32\" \n",
"[734] \"fnmatch_1.DPtr32\" \n",
"[735] \"ftruncate.ret_addr.DPtr32\" \n",
"[736] \"ftruncate.ret_val.DPtr32\" \n",
"[737] \"localtime.ret_addr.DPtr32\" \n",
"[738] \"localtime.ret_val.DPtr32\" \n",
"[739] \"localtime_0.DPtr32\" \n",
"[740] \"alarm.ret_addr.DPtr32\" \n",
"[741] \"alarm.ret_val.DPtr32\" \n",
"[742] \"getprotobynumber.ret_addr.DPtr32\" \n",
"[743] \"getprotobynumber.ret_val.DPtr32\" \n",
"[744] \"rename.ret_addr.DPtr32\" \n",
"[745] \"rename.ret_val.DPtr32\" \n",
"[746] \"rename_0.DPtr32\" \n",
"[747] \"rename_1.DPtr32\" \n",
"[748] \"vsnprintf.ret_addr.DPtr32\" \n",
"[749] \"vsnprintf.ret_val.DPtr32\" \n",
"[750] \"vsnprintf_0.DPtr32\" \n",
"[751] \"vsnprintf_2.DPtr32\" \n",
"[752] \"vsnprintf_3.DPtr32\" \n",
"[753] \"malloc.ret_addr.DPtr32\" \n",
"[754] \"malloc.ret_val.DPtr32\" \n",
"[755] \"X__xstat.ret_addr.DPtr32\" \n",
"[756] \"X__xstat.ret_val.DPtr32\" \n",
"[757] \"X__xstat_1.DPtr32\" \n",
"[758] \"X__xstat_2.DPtr32\" \n",
"[759] \"dlsym.ret_addr.DPtr32\" \n",
"[760] \"dlsym.ret_val.DPtr32\" \n",
"[761] \"dlsym_0.DPtr32\" \n",
"[762] \"dlsym_1.DPtr32\" \n",
"[763] \"fread.ret_addr.DPtr32\" \n",
"[764] \"fread.ret_val.DPtr32\" \n",
"[765] \"fread_0.DPtr32\" \n",
"[766] \"fread_3.DPtr32\" \n",
"[767] \"acl_copy_int.ret_addr.DPtr32\" \n",
"[768] \"acl_copy_int.ret_val.DPtr32\" \n",
"[769] \"acl_copy_int_0.DPtr32\" \n",
"[770] \"rresvport.ret_addr.DPtr32\" \n",
"[771] \"rresvport.ret_val.DPtr32\" \n",
"[772] \"rresvport_0.DPtr32\" \n",
"[773] \"getpass.ret_addr.DPtr32\" \n",
"[774] \"getpass.ret_val.DPtr32\" \n",
"[775] \"getpass_0.DPtr32\" \n",
"[776] \"sigfillset.ret_addr.DPtr32\" \n",
"[777] \"sigfillset.ret_val.DPtr32\" \n",
"[778] \"sigfillset_0.DPtr32\" \n",
"[779] \"fprintf.ret_addr.DPtr32\" \n",
"[780] \"fprintf.ret_val.DPtr32\" \n",
"[781] \"fprintf_0.DPtr32\" \n",
"[782] \"fprintf_1.DPtr32\" \n",
"[783] \"endhostent.ret_addr.DPtr32\" \n",
"[784] \"endhostent.ret_val.DPtr32\" \n",
"[785] \"stpcpy.ret_addr.DPtr32\" \n",
"[786] \"stpcpy.ret_val.DPtr32\" \n",
"[787] \"stpcpy_0.DPtr32\" \n",
"[788] \"stpcpy_1.DPtr32\" \n",
"[789] \"close.ret_addr.DPtr32\" \n",
"[790] \"close.ret_val.DPtr32\" \n",
"[791] \"bindtextdomain.ret_addr.DPtr32\" \n",
"[792] \"bindtextdomain.ret_val.DPtr32\" \n",
"[793] \"bindtextdomain_0.DPtr32\" \n",
"[794] \"bindtextdomain_1.DPtr32\" \n",
"[795] \"acl_get_perm.ret_addr.DPtr32\" \n",
"[796] \"acl_get_perm.ret_val.DPtr32\" \n",
"[797] \"acl_get_perm_0.DPtr32\" \n",
"[798] \"feof.ret_addr.DPtr32\" \n",
"[799] \"feof.ret_val.DPtr32\" \n",
"[800] \"feof_0.DPtr32\" \n",
"[801] \"pipe.ret_addr.DPtr32\" \n",
"[802] \"pipe.ret_val.DPtr32\" \n",
"[803] \"pipe_0.DPtr32\" \n",
"[804] \"acl_create_entry.ret_addr.DPtr32\" \n",
"[805] \"acl_create_entry.ret_val.DPtr32\" \n",
"[806] \"acl_create_entry_0.DPtr32\" \n",
"[807] \"acl_create_entry_1.DPtr32\" \n",
"[808] \"X__lxstat64.ret_addr.DPtr32\" \n",
"[809] \"X__lxstat64.ret_val.DPtr32\" \n",
"[810] \"X__lxstat64_1.DPtr32\" \n",
"[811] \"X__lxstat64_2.DPtr32\" \n",
"[812] \"setpwent.ret_addr.DPtr32\" \n",
"[813] \"setpwent.ret_val.DPtr32\" \n",
"[814] \"strlen.ret_addr.DPtr32\" \n",
"[815] \"strlen.ret_val.DPtr32\" \n",
"[816] \"strlen_0.DPtr32\" \n",
"[817] \"geteuid.ret_addr.DPtr32\" \n",
"[818] \"geteuid.ret_val.DPtr32\" \n",
"[819] \"setbuffer.ret_addr.DPtr32\" \n",
"[820] \"setbuffer.ret_val.DPtr32\" \n",
"[821] \"setbuffer_0.DPtr32\" \n",
"[822] \"setbuffer_1.DPtr32\" \n",
"[823] \"chown.ret_addr.DPtr32\" \n",
"[824] \"chown.ret_val.DPtr32\" \n",
"[825] \"chown_0.DPtr32\" \n",
"[826] \"acl_calc_mask.ret_addr.DPtr32\" \n",
"[827] \"acl_calc_mask.ret_val.DPtr32\" \n",
"[828] \"acl_calc_mask_0.DPtr32\" \n",
"[829] \"acl_set_tag_type.ret_addr.DPtr32\" \n",
"[830] \"acl_set_tag_type.ret_val.DPtr32\" \n",
"[831] \"acl_set_tag_type_0.DPtr32\" \n",
"[832] \"write.ret_addr.DPtr32\" \n",
"[833] \"write.ret_val.DPtr32\" \n",
"[834] \"write_1.DPtr32\" \n",
"[835] \"setnetent.ret_addr.DPtr32\" \n",
"[836] \"setnetent.ret_val.DPtr32\" \n",
"[837] \"setpgrp.ret_addr.DPtr32\" \n",
"[838] \"setpgrp.ret_val.DPtr32\" \n",
"[839] \"setenv.ret_addr.DPtr32\" \n",
"[840] \"setenv.ret_val.DPtr32\" \n",
"[841] \"setenv_0.DPtr32\" \n",
"[842] \"setenv_1.DPtr32\" \n",
"[843] \"getopt_long_only.ret_addr.DPtr32\" \n",
"[844] \"getopt_long_only.ret_val.DPtr32\" \n",
"[845] \"getopt_long_only_1.DPtr32\" \n",
"[846] \"getopt_long_only_2.DPtr32\" \n",
"[847] \"getopt_long_only_3.DPtr32\" \n",
"[848] \"getopt_long_only_4.DPtr32\" \n",
"[849] \"pcap_open_live.ret_addr.DPtr32\" \n",
"[850] \"pcap_open_live.ret_val.DPtr32\" \n",
"[851] \"pcap_open_live_0.DPtr32\" \n",
"[852] \"pcap_open_live_4.DPtr32\" \n",
"[853] \"getopt.ret_addr.DPtr32\" \n",
"[854] \"getopt.ret_val.DPtr32\" \n",
"[855] \"getopt_1.DPtr32\" \n",
"[856] \"getopt_2.DPtr32\" \n",
"[857] \"inet_aton.ret_addr.DPtr32\" \n",
"[858] \"inet_aton.ret_val.DPtr32\" \n",
"[859] \"inet_aton_0.DPtr32\" \n",
"[860] \"inet_aton_1.DPtr32\" \n",
"[861] \"endnetgrent.ret_addr.DPtr32\" \n",
"[862] \"endnetgrent.ret_val.DPtr32\" \n",
"[863] \"mq_notify.ret_addr.DPtr32\" \n",
"[864] \"mq_notify.ret_val.DPtr32\" \n",
"[865] \"mq_notify_1.DPtr32\" \n",
"[866] \"acl_copy_ext.ret_addr.DPtr32\" \n",
"[867] \"acl_copy_ext.ret_val.DPtr32\" \n",
"[868] \"acl_copy_ext_0.DPtr32\" \n",
"[869] \"acl_copy_ext_1.DPtr32\" \n",
"[870] \"getlogin.ret_addr.DPtr32\" \n",
"[871] \"getlogin.ret_val.DPtr32\" \n",
"[872] \"tolower.ret_addr.DPtr32\" \n",
"[873] \"tolower.ret_val.DPtr32\" \n",
"[874] \"truncate.ret_addr.DPtr32\" \n",
"[875] \"truncate.ret_val.DPtr32\" \n",
"[876] \"truncate_0.DPtr32\" \n",
"[877] \"tcgetattr.ret_addr.DPtr32\" \n",
"[878] \"tcgetattr.ret_val.DPtr32\" \n",
"[879] \"tcgetattr_1.DPtr32\" \n",
"[880] \"open64.ret_addr.DPtr32\" \n",
"[881] \"open64.ret_val.DPtr32\" \n",
"[882] \"open64_0.DPtr32\" \n",
"[883] \"bfd_set_default_target.ret_addr.DPtr32\"\n",
"[884] \"bfd_set_default_target.ret_val.DPtr32\" \n",
"[885] \"bfd_set_default_target_0.DPtr32\" \n",
"[886] \"X__ctype_toupper_loc.ret_addr.DPtr32\" \n",
"[887] \"X__ctype_toupper_loc.ret_val.DPtr32\" \n",
"[888] \"mq_open.ret_addr.DPtr32\" \n",
"[889] \"mq_open.ret_val.DPtr32\" \n",
"[890] \"mq_open_0.DPtr32\" \n",
"[891] \"mq_open_3.DPtr32\" \n",
"[892] \"acl_init.ret_addr.DPtr32\" \n",
"[893] \"acl_init.ret_val.DPtr32\" \n",
"[894] \"closedir.ret_addr.DPtr32\" \n",
"[895] \"closedir.ret_val.DPtr32\" \n",
"[896] \"closedir_0.DPtr32\" \n",
"[897] \"ioctl.ret_addr.DPtr32\" \n",
"[898] \"ioctl.ret_val.DPtr32\" \n",
"[899] \"ioctl_2.DPtr32\" \n",
"[900] \"socket.ret_addr.DPtr32\" \n",
"[901] \"socket.ret_val.DPtr32\" \n",
"[902] \"getegid.ret_addr.DPtr32\" \n",
"[903] \"getegid.ret_val.DPtr32\" \n",
"[904] \"acl_from_mode.ret_addr.DPtr32\" \n",
"[905] \"acl_from_mode.ret_val.DPtr32\" \n",
"[906] \"X__ctype_tolower_loc.ret_addr.DPtr32\" \n",
"[907] \"X__ctype_tolower_loc.ret_val.DPtr32\" \n",
"[908] \"acl_delete_entry.ret_addr.DPtr32\" \n",
"[909] \"acl_delete_entry.ret_val.DPtr32\" \n",
"[910] \"acl_delete_entry_0.DPtr32\" \n",
"[911] \"acl_delete_entry_1.DPtr32\" \n",
"[912] \"calloc.ret_addr.DPtr32\" \n",
"[913] \"calloc.ret_val.DPtr32\" \n",
"[914] \"setbuf.ret_addr.DPtr32\" \n",
"[915] \"setbuf.ret_val.DPtr32\" \n",
"[916] \"setbuf_0.DPtr32\" \n",
"[917] \"setbuf_1.DPtr32\" \n",
"[918] \"readline.ret_addr.DPtr32\" \n",
"[919] \"readline.ret_val.DPtr32\" \n",
"[920] \"readline_0.DPtr32\" \n",
"[921] \"getopt_long.ret_addr.DPtr32\" \n",
"[922] \"getopt_long.ret_val.DPtr32\" \n",
"[923] \"getopt_long_1.DPtr32\" \n",
"[924] \"getopt_long_2.DPtr32\" \n",
"[925] \"getopt_long_3.DPtr32\" \n",
"[926] \"getopt_long_4.DPtr32\" \n",
"[927] \"strcasecmp.ret_addr.DPtr32\" \n",
"[928] \"strcasecmp.ret_val.DPtr32\" \n",
"[929] \"strcasecmp_0.DPtr32\" \n",
"[930] \"strcasecmp_1.DPtr32\" \n",
"[931] \"tgoto.ret_addr.DPtr32\" \n",
"[932] \"tgoto.ret_val.DPtr32\" \n",
"[933] \"tgoto_0.DPtr32\" \n",
"[934] \"rexec_af.ret_addr.DPtr32\" \n",
"[935] \"rexec_af.ret_val.DPtr32\" \n",
"[936] \"rexec_af_0.DPtr32\" \n",
"[937] \"rexec_af_2.DPtr32\" \n",
"[938] \"rexec_af_3.DPtr32\" \n",
"[939] \"rexec_af_4.DPtr32\" \n",
"[940] \"rexec_af_5.DPtr32\" \n",
"[941] \"setreuid.ret_addr.DPtr32\" \n",
"[942] \"setreuid.ret_val.DPtr32\" \n",
"[943] \"getuid.ret_addr.DPtr32\" \n",
"[944] \"getuid.ret_val.DPtr32\" \n",
"[945] \"acl_add_perm.ret_addr.DPtr32\" \n",
"[946] \"acl_add_perm.ret_val.DPtr32\" \n",
"[947] \"acl_add_perm_0.DPtr32\" \n",
"[948] \"sigdelset.ret_addr.DPtr32\" \n",
"[949] \"sigdelset.ret_val.DPtr32\" \n",
"[950] \"sigdelset_0.DPtr32\" \n",
"[951] \"mtrace.ret_addr.DPtr32\" \n",
"[952] \"mtrace.ret_val.DPtr32\" \n",
"[953] \"setnetgrent.ret_addr.DPtr32\" \n",
"[954] \"setnetgrent.ret_val.DPtr32\" \n",
"[955] \"setnetgrent_0.DPtr32\" \n",
"[956] \"X_exit.ret_addr.DPtr32\" \n",
"[957] \"X_exit.ret_val.DPtr32\" \n",
"[958] \"getgrent.ret_addr.DPtr32\" \n",
"[959] \"getgrent.ret_val.DPtr32\" \n",
"[960] \"textdomain.ret_addr.DPtr32\" \n",
"[961] \"textdomain.ret_val.DPtr32\" \n",
"[962] \"textdomain_0.DPtr32\" \n",
"[963] \"acl_entries.ret_addr.DPtr32\" \n",
"[964] \"acl_entries.ret_val.DPtr32\" \n",
"[965] \"acl_entries_0.DPtr32\" \n",
"[966] \"bfd_openr.ret_addr.DPtr32\" \n",
"[967] \"bfd_openr.ret_val.DPtr32\" \n",
"[968] \"bfd_openr_0.DPtr32\" \n",
"[969] \"bfd_openr_1.DPtr32\" \n",
"[970] \"XMapWindow.ret_addr.DPtr32\" \n",
"[971] \"XMapWindow.ret_val.DPtr32\" \n",
"[972] \"XMapWindow_0.DPtr32\" \n",
"[973] \"XMapWindow_1.DPtr32\" \n",
"[974] \"fopen64.ret_addr.DPtr32\" \n",
"[975] \"fopen64.ret_val.DPtr32\" \n",
"[976] \"fopen64_0.DPtr32\" \n",
"[977] \"fopen64_1.DPtr32\" \n",
"[978] \"acl_get_permset.ret_addr.DPtr32\" \n",
"[979] \"acl_get_permset.ret_val.DPtr32\" \n",
"[980] \"acl_get_permset_0.DPtr32\" \n",
"[981] \"acl_get_permset_1.DPtr32\" \n",
"[982] \"setlinebuf.ret_addr.DPtr32\" \n",
"[983] \"setlinebuf.ret_val.DPtr32\" \n",
"[984] \"setlinebuf_0.DPtr32\" \n",
"[985] \"setvbuf.ret_addr.DPtr32\" \n",
"[986] \"setvbuf.ret_val.DPtr32\" \n",
"[987] \"setvbuf_0.DPtr32\" \n",
"[988] \"setvbuf_1.DPtr32\" \n",
"[989] \"vfprintf.ret_addr.DPtr32\" \n",
"[990] \"vfprintf.ret_val.DPtr32\" \n",
"[991] \"vfprintf_0.DPtr32\" \n",
"[992] \"vfprintf_1.DPtr32\" \n",
"[993] \"vfprintf_2.DPtr32\" \n",
"[994] \"crashed.eip.DPtr32\" \n",
"[995] \"abort.eip.DPtr32\" \n",
"[996] \"SIGSEGV.addr.DPtr32\" \n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The program allocates a large piece of memory:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_bad_alloc = c()\n",
"\n",
"v_bad_alloc = c(v_bad_alloc, grep(\"malloc_0.Num32B0\",buggy_vars),grep(\"malloc_0.Num32B24\",buggy_vars), grep(\"malloc_0.Num32B32\",buggy_vars))\n",
"v_bad_alloc = c(v_bad_alloc, grep(\"calloc_0.Num32B0\",buggy_vars),grep(\"calloc_0.Num32B24\",buggy_vars), grep(\"calloc_0.Num32B32\",buggy_vars))\n",
"v_bad_alloc = c(v_bad_alloc, grep(\"calloc_1.Num32B0\",buggy_vars),grep(\"calloc_1.Num32B24\",buggy_vars), grep(\"calloc_1.Num32B32\",buggy_vars))\n",
"v_bad_alloc = c(v_bad_alloc, grep(\"realloc_1.Num32B0\",buggy_vars),grep(\"realloc_1.Num32B24\",buggy_vars), grep(\"realloc_1.Num32B32\",buggy_vars))\n",
"#v_bad_alloc = c(grep(\"alloc\",vars),v_bad_alloc)\n",
"print(buggy_vars[v_bad_alloc])\n",
"\n",
"s_bad_alloc = 100\n",
"\n",
"fx = function(x) {\n",
" return(s_bad_alloc*max(as.numeric(x[v_bad_alloc])))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [1] \"malloc_0.Num32B0\" \"malloc_0.Num32B24\" \"malloc_0.Num32B32\" \n",
" [4] \"calloc_0.Num32B0\" \"calloc_0.Num32B24\" \"calloc_0.Num32B32\" \n",
" [7] \"calloc_1.Num32B0\" \"calloc_1.Num32B24\" \"calloc_1.Num32B32\" \n",
"[10] \"realloc_1.Num32B0\" \"realloc_1.Num32B24\" \"realloc_1.Num32B32\"\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_large_copy = c()\n",
"\n",
"v_large_copy = c(v_large_copy,grep(\"memcpy_2.Num32B24\",buggy_vars), grep(\"memcpy_2.Num32B32\",buggy_vars))\n",
"v_large_copy = c(v_large_copy,grep(\"memset_2.Num32B24\",buggy_vars), grep(\"memset_2.Num32B32\",buggy_vars))\n",
"v_large_copy = c(v_large_copy,grep(\"strncpy_2.Num32B24\",buggy_vars), grep(\"strncpy_2.Num32B32\",buggy_vars))\n",
"\n",
"print(buggy_vars[v_large_copy])\n",
"\n",
"s_large_copy = 100\n",
"\n",
"fx = function(x) {\n",
" return(s_large_copy*max(as.numeric(x[v_large_copy])))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] \"memcpy_2.Num32B24\" \"memcpy_2.Num32B32\" \"memset_2.Num32B24\" \n",
"[4] \"memset_2.Num32B32\" \"strncpy_2.Num32B24\" \"strncpy_2.Num32B32\"\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"invalid frees:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"v_inv_free = grep(\"free_0\",buggy_vars)\n",
"v_inv_free = setdiff(v_inv_free, c(grep(\"free_0.HPtr32\", buggy_vars)))\n",
"\n",
"v_inv_realloc = grep(\"realloc_0\",buggy_vars)\n",
"v_inv_realloc = setdiff(v_inv_realloc, c(grep(\"realloc_0.HPtr32\", buggy_vars)))\n",
"\n",
"v_abort = grep(\"abort.eip.Ptr32\",buggy_vars)\n",
"print(buggy_vars[c(v_inv_realloc,v_inv_realloc)])\n",
"\n",
"s_inv_free = 100\n",
"\n",
"fx = function(x) {\n",
" has_inv_free = max(as.numeric(x[v_inv_free]))\n",
" has_inv_realloc = max(as.numeric(x[v_inv_realloc]))\n",
" has_abort = max(as.numeric(x[v_abort]))\n",
" \n",
" return(s_inv_free*has_abort*(has_inv_free + has_inv_realloc))\n",
"}\n",
"\n",
"fn = c(fx, fn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [1] \"realloc_0.Ptr32\" \"realloc_0.SPtr32\" \"realloc_0.LPtr32\" \"realloc_0.FPtr32\"\n",
" [5] \"realloc_0.NPtr32\" \"realloc_0.DPtr32\" \"realloc_0.GPtr32\" \"realloc_0.Top32\" \n",
" [9] \"realloc_0.Ptr32\" \"realloc_0.SPtr32\" \"realloc_0.LPtr32\" \"realloc_0.FPtr32\"\n",
"[13] \"realloc_0.NPtr32\" \"realloc_0.DPtr32\" \"realloc_0.GPtr32\" \"realloc_0.Top32\" \n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"rank = function(x) {\n",
" r = 0\n",
" \n",
" for (f in fn) {\n",
" r = r + f(x)\n",
" }\n",
" \n",
" return(r)\n",
" \n",
"}\n",
"\n",
"\n",
"\n",
"#print(traces[1:20,1])\n",
"buggy_ranking = cbind(binary=buggy_traces[,1],as.data.frame(apply(buggy_traces, 1, rank)))\n",
"robust_ranking = cbind(binary=robust_traces[,1],as.data.frame(apply(robust_traces[,-1], 1, rank)))\n",
"\n",
"\n",
"buggy_ranking = buggy_ranking[order(buggy_ranking[,2],decreasing=T),]\n",
"robust_ranking = robust_ranking[order(robust_ranking[,2],decreasing=T),]\n",
"#print(ranking[1:110,])\n",
"\n",
"#print()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dump the information to a csv:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"#print(robust_ranking)\n",
"write.csv(buggy_ranking,\"buggy_ranking.csv\")\n",
"write.csv(robust_ranking,\"robust_ranking.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To train a simple model, first, we de-duplicate and select train and test programs from the list of vulnerable programs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"ranking = buggy_ranking\n",
"traces = buggy_traces\n",
"\n",
"x = ranking[ranking[,2]>1000,]\n",
"#inds = as.integer(row.names(x))\n",
"vuln_cases = x[!duplicated(ranking[ranking[,1] %in% x[,1],1]),]\n",
"\n",
"n = length(vuln_cases[,1])\n",
"\n",
"rsample = sample(n)\n",
"\n",
"train_sample = rsample[1:as.integer(n*0.75)]\n",
"test_sample = rsample[as.integer(n*0.75+1):n]\n",
"\n",
"print(rsample)\n",
"print(train_sample)\n",
"print(test_sample)\n",
"print(vuln_cases[train_sample,1])\n",
"print(vuln_cases[test_sample,1])\n",
"\n",
"vuln_train = traces[traces[,1] %in% vuln_cases[train_sample,1],]\n",
"vuln_test = traces[traces[,1] %in% vuln_cases[test_sample,1],]\n",
"\n",
"\n",
"#vuln_train = traces[traces[,1] %in% vuln_cases[train_sample,1],]\n",
"#vuln_test = traces[traces[,1] %in% vuln_cases[test_sample,1],]\n",
"\n",
"print(length(vuln_train[,1]))\n",
"print(length(vuln_test[,1]))\n",
"\n",
"#print(factor(\"/usr/bin/vadm\") %in% vuln_train[,1])\n",
"#print(a)\n",
"\n",
"\n",
"#print(c(x[1,1]) %in% (x[\"binary\"]))\n",
"#print(as.data.frame.character(ranking[,1]) %in% as.data.frame.character(x[,1]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [1] 18 2 8 19 5 12 17 14 24 21 6 13 10 16 15 4 11 1 23 7 9 22 20 3\n",
" [1] 18 2 8 19 5 12 17 14 24 21 6 13 10 16 15 4 11 1\n",
"[1] 23 7 9 22 20 3\n",
" [1] /usr/bin/db5.1_printlog /usr/bin/dcm_diff /usr/bin/extract_seq \n",
" [4] /usr/bin/setupnash2 /usr/bin/db4.8_printlog /usr/sbin/tua \n",
" [7] /usr/bin/vgrep /usr/bin/binhex /usr/bin/faum-iso-to-cd\n",
"[10] /usr/bin/tgif /usr/bin/vbind /usr/lib/cssc/unget \n",
"[13] /usr/bin/vadm /usr/bin/xview_xgettext /usr/sbin/raidutil \n",
"[16] /usr/bin/asfxload /usr/bin/h5dump /usr/bin/retrv \n",
"976 Levels: /bin/serdo /bin/upsc /sbin/apparmor_parser ... /usr/sbin/xrdp-chansrv\n",
"[1] /usr/bin/extract_qual /usr/bin/regconvert /usr/bin/getfits \n",
"[4] /usr/bin/fenix-fxc /usr/sbin/th-cmd /usr/bin/extract_fastq\n",
"976 Levels: /bin/serdo /bin/upsc /sbin/apparmor_parser ... /usr/sbin/xrdp-chansrv\n",
"[1] 165\n",
"[1] 64\n"
]
}
],
"prompt_number": 99
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we do the same for (probably) non-vulnerable programs:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"ranking = robust_ranking\n",
"traces = robust_traces\n",
"\n",
"x = ranking#[ranking[,2]>1000,]\n",
"invuln_cases = x[!duplicated(ranking[ranking[,1] %in% x[,1],1]),]\n",
"\n",
"n = length(invuln_cases[,1])\n",
"\n",
"rsample = sample(n)\n",
"\n",
"train_sample = rsample[1:as.integer(n*0.75)]\n",
"test_sample = rsample[as.integer(n*0.75+1):n]\n",
"\n",
"print(rsample)\n",
"print(train_sample)\n",
"print(test_sample)\n",
"print(invuln_cases[train_sample,1])\n",
"print(invuln_cases[test_sample,1])\n",
"\n",
"invuln_train = traces[traces[,1] %in% invuln_cases[train_sample,1],]\n",
"invuln_test = traces[traces[,1] %in% invuln_cases[test_sample,1],]\n",
"\n",
"print(length(invuln_train[,1]))\n",
"print(length(invuln_test[,1]))\n",
"\n",
"\n",
"#print(factor(\"/usr/bin/vadm\") %in% vuln_train[,1])\n",
"#mycon = gzcon(gzfile(paste(dir, \"robust_traces.csv.gz\", sep=\"/\"), open=\"r\"))\n",
"#robust_traces = read.csv(textConnection(readLines(mycon)), sep=\"\\t\")[,c(-12060,-12059,-12058,-12057)] # last 4 columns are signals\n",
"\n",
"#print(buggy_traces[1,])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] 2 5 6 4 7 1 8 3\n",
"[1] 2 5 6 4 7 1\n",
"[1] 8 3\n",
"[1] /usr/bin/wc /usr/bin/pnginfo /usr/bin/optipng /bin/grep \n",
"[5] /usr/bin/objdump /usr/bin/sort \n",
"8 Levels: /bin/grep /usr/bin/nm /usr/bin/objdump ... /usr/bin/wc\n",
"[1] /usr/bin/nm /usr/bin/readelf\n",
"8 Levels: /bin/grep /usr/bin/nm /usr/bin/objdump ... /usr/bin/wc\n",
"[1] 71\n",
"[1] 82\n"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R \n",
"\n",
"train = rbind(invuln_train, vuln_train)\n",
"vars = which(unlist(lapply(train[,-1],function(x) 0 != var(x))))\n",
"\n",
"vuln_train = vuln_train[,c(1,vars)]\n",
"invuln_train = invuln_train[,c(1,vars)]\n",
" \n",
"vuln_test = vuln_test[,c(1,vars)]\n",
"invuln_test = invuln_test[,c(1,vars)]\n",
"\n",
"#print(vuln_train[1,])\n",
" \n",
"vuln_train[\"class\"] = factor(\"vuln\")\n",
"invuln_train[\"class\"] = factor(\"invuln\")\n",
" \n",
"train = rbind(invuln_train, vuln_train)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"print(round(importance(rf),2))\n",
"\n",
"#library(\"randomForest\")\n",
"\n",
"#print(train[\"class\"])\n",
"#rf = randomForest(class ~ ., data=train[,-1], importance=TRUE)\n",
"#print(rf)\n",
"\n",
"#print(predict(rf, vuln_test))\n",
"#print(predict(rf, invuln_test))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" invuln vuln MeanDecreaseAccuracy\n",
"X_IO_putc.ret_addr.HPtr32 0.00 0.00 0.00\n",
"X_IO_putc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X_IO_putc.ret_val.Num32B0 0.00 0.00 0.00\n",
"X_IO_putc.ret_val.Num32B32 0.00 0.00 0.00\n",
"X_IO_putc_1.HPtr32 0.00 0.00 0.00\n",
"fflush.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fflush.ret_val.Top32 0.00 0.00 0.00\n",
"fflush_0.HPtr32 0.00 0.00 0.00\n",
"fputc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fputc.ret_val.Num32B0 0.00 0.00 0.00\n",
"fputc.ret_val.Num32B32 0.00 0.00 0.00\n",
"fputc_1.SPtr32 0.00 0.00 0.00\n",
"fputc_1.HPtr32 0.00 0.00 0.00\n",
"fwrite.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fwrite.ret_val.GPtr32 0.00 0.00 0.00\n",
"fwrite.ret_val.Num32B0 0.00 0.00 0.00\n",
"fwrite.ret_val.Num32B8 5.89 0.94 5.58\n",
"fwrite_0.DPtr32 0.00 0.00 0.00\n",
"fwrite_1.Num32B0 0.00 0.00 0.00\n",
"fwrite_2.Num32B0 0.00 0.00 0.00\n",
"fwrite_2.Num32B8 5.07 2.22 5.43\n",
"fwrite_3.SPtr32 0.00 0.00 0.00\n",
"fwrite_3.HPtr32 1.40 1.00 1.37\n",
"fwrite_3.FPtr32 0.00 0.00 0.00\n",
"fputs.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fputs.ret_val.Num32B0 0.00 0.00 0.00\n",
"fputs_0.DPtr32 0.00 0.00 0.00\n",
"fputs_1.HPtr32 0.00 0.00 0.00\n",
"sprintf.ret_addr.HPtr32 0.00 0.00 0.00\n",
"sprintf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"sprintf.ret_val.Num32B0 0.00 0.00 0.00\n",
"sprintf.ret_val.Num32B8 2.90 2.64 4.28\n",
"sprintf_0.Ptr32 0.00 0.00 0.00\n",
"sprintf_0.SPtr32 6.11 5.21 6.67\n",
"sprintf_1.HPtr32 0.00 0.00 0.00\n",
"sprintf_1.DPtr32 0.00 0.00 0.00\n",
"read.ret_addr.DPtr32 0.00 0.00 0.00\n",
"read.ret_val.Top32 0.00 0.00 0.00\n",
"read.ret_val.Num32B0 3.30 3.19 3.63\n",
"read_0.Num32B0 0.00 0.00 0.00\n",
"read_1.SPtr32 0.00 0.00 0.00\n",
"read_2.Num32B8 0.00 0.00 0.00\n",
"fclose.ret_addr.HPtr32 0.00 0.00 0.00\n",
"fclose.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fclose.ret_val.Top32 0.00 0.00 0.00\n",
"fclose_0.SPtr32 0.00 0.00 0.00\n",
"fclose_0.HPtr32 6.14 7.03 7.90\n",
"strcoll.ret_addr.HPtr32 0.00 0.00 0.00\n",
"strcoll.ret_val.Top32 0.00 0.00 0.00\n",
"strcoll.ret_val.Num32B0 4.62 5.08 5.22\n",
"strcoll.ret_val.Num32B24 0.00 0.00 0.00\n",
"strcoll_0.SPtr32 0.00 0.00 0.00\n",
"strcoll_0.HPtr32 5.78 5.50 6.33\n",
"strcoll_1.SPtr32 0.00 0.00 0.00\n",
"strcoll_1.HPtr32 4.27 4.80 5.23\n",
"memcpy.ret_addr.DPtr32 0.00 0.00 0.00\n",
"memcpy.ret_val.Ptr32 0.00 0.00 0.00\n",
"memcpy.ret_val.SPtr32 4.25 5.32 5.62\n",
"memcpy.ret_val.LPtr32 0.00 0.00 0.00\n",
"memcpy.ret_val.DPtr32 0.00 0.00 0.00\n",
"memcpy_0.Ptr32 0.00 0.00 0.00\n",
"memcpy_0.SPtr32 2.07 5.34 4.98\n",
"memcpy_0.LPtr32 0.00 0.00 0.00\n",
"memcpy_0.DPtr32 0.00 0.00 0.00\n",
"memcpy_1.Ptr32 0.00 0.00 0.00\n",
"memcpy_1.SPtr32 0.00 0.00 0.00\n",
"memcpy_1.FPtr32 0.00 0.00 0.00\n",
"memcpy_1.DPtr32 0.00 0.00 0.00\n",
"memcpy_2.Num32 0.00 0.00 0.00\n",
"memcpy_2.Num32B0 4.20 5.38 5.67\n",
"memcpy_2.Num32B8 8.03 9.59 10.08\n",
"perror.ret_addr.HPtr32 0.00 0.00 0.00\n",
"perror.ret_addr.DPtr32 0.00 0.00 0.00\n",
"perror.ret_val.GPtr32 0.00 0.00 0.00\n",
"perror_0.Ptr32 0.00 0.00 0.00\n",
"perror_0.HPtr32 0.00 0.00 0.00\n",
"signal.ret_addr.HPtr32 0.00 0.00 0.00\n",
"signal.ret_val.FPtr32 0.00 0.00 0.00\n",
"signal.ret_val.NPtr32 1.00 2.52 2.72\n",
"signal_0.Num32B0 0.00 0.00 0.00\n",
"signal_1.HPtr32 0.00 0.00 0.00\n",
"opendir.ret_addr.HPtr32 0.00 0.00 0.00\n",
"opendir.ret_val.FPtr32 0.00 0.00 0.00\n",
"opendir_0.Ptr32 0.00 0.00 0.00\n",
"strcpy.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strcpy.ret_val.Ptr32 0.00 0.00 0.00\n",
"strcpy.ret_val.SPtr32 5.07 3.72 5.83\n",
"strcpy.ret_val.DPtr32 0.00 0.00 0.00\n",
"strcpy.ret_val.GPtr32 0.00 0.00 0.00\n",
"strcpy_0.Ptr32 0.00 0.00 0.00\n",
"strcpy_0.SPtr32 5.01 3.98 5.48\n",
"strcpy_0.FPtr32 0.00 0.00 0.00\n",
"strcpy_0.DPtr32 0.00 0.00 0.00\n",
"strcpy_0.Top32 0.00 0.00 0.00\n",
"strcpy_1.Ptr32 0.00 0.00 0.00\n",
"strcpy_1.SPtr32 5.73 5.01 6.27\n",
"strcpy_1.FPtr32 0.00 0.00 0.00\n",
"strcpy_1.DPtr32 0.00 0.00 0.00\n",
"sleep.ret_addr.HPtr32 0.00 0.00 0.00\n",
"sleep.ret_val.GPtr32 0.00 0.00 0.00\n",
"sleep.ret_val.Num32B32 0.00 0.00 0.00\n",
"strcmp.ret_addr.HPtr32 0.00 0.00 0.00\n",
"strcmp.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strcmp.ret_val.Top32 0.00 0.00 0.00\n",
"strcmp.ret_val.Num32B0 3.52 6.47 6.62\n",
"strcmp.ret_val.Num32B24 0.00 0.00 0.00\n",
"strcmp_0.Ptr32 0.00 0.00 0.00\n",
"strcmp_0.SPtr32 3.79 0.74 4.07\n",
"strcmp_0.HPtr32 3.16 4.69 4.79\n",
"strcmp_0.DPtr32 0.00 0.00 0.00\n",
"strcmp_1.SPtr32 0.00 0.00 0.00\n",
"strcmp_1.HPtr32 2.19 3.73 4.16\n",
"strcmp_1.DPtr32 0.00 0.00 0.00\n",
"memchr.ret_addr.DPtr32 0.00 0.00 0.00\n",
"memchr.ret_val.SPtr32 0.00 0.00 0.00\n",
"memchr.ret_val.FPtr32 0.00 0.00 0.00\n",
"memchr_0.SPtr32 0.00 0.00 0.00\n",
"memchr_0.Top32 0.00 0.00 0.00\n",
"memchr_2.Num32B0 0.00 0.00 0.00\n",
"memchr_2.Num32B8 3.80 2.78 3.96\n",
"strncmp.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strncmp.ret_val.Top32 0.00 0.00 0.00\n",
"strncmp.ret_val.Num32B0 2.38 0.63 2.01\n",
"strncmp.ret_val.Num32B24 0.00 0.00 0.00\n",
"strncmp_0.Ptr32 0.00 0.00 0.00\n",
"strncmp_0.SPtr32 1.92 1.91 2.72\n",
"strncmp_0.DPtr32 0.00 0.00 0.00\n",
"strncmp_1.Ptr32 0.00 0.00 0.00\n",
"strncmp_1.SPtr32 0.00 0.00 0.00\n",
"strncmp_1.DPtr32 0.00 0.00 0.00\n",
"strncmp_2.Num32B0 0.00 0.00 0.00\n",
"strncmp_2.Num32B8 1.87 0.52 2.01\n",
"bfd_init.ret_addr.DPtr32 0.00 0.00 0.00\n",
"bfd_init.ret_val.GPtr32 0.00 0.00 0.00\n",
"bfd_init.ret_val.Num32B32 0.00 0.00 0.00\n",
"getpagesize.ret_addr.DPtr32 0.00 0.00 0.00\n",
"getpagesize.ret_val.Num32B8 0.00 0.00 0.00\n",
"getpagesize.ret_val.Num32B32 0.00 0.00 0.00\n",
"memset.ret_addr.DPtr32 0.00 0.00 0.00\n",
"memset.ret_val.Ptr32 0.00 0.00 0.00\n",
"memset.ret_val.SPtr32 0.00 0.00 0.00\n",
"memset_0.Ptr32 0.00 0.00 0.00\n",
"memset_0.SPtr32 0.00 0.00 0.00\n",
"memset_0.Top32 0.00 0.00 0.00\n",
"memset_2.Num32B0 0.00 0.00 0.00\n",
"memset_2.Num32B8 4.00 7.21 7.32\n",
"strcat.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strcat.ret_val.Ptr32 0.00 0.00 0.00\n",
"strcat.ret_val.SPtr32 2.77 2.75 3.48\n",
"strcat_0.Ptr32 0.00 0.00 0.00\n",
"strcat_0.SPtr32 3.61 2.93 4.28\n",
"strcat_1.Ptr32 0.00 0.00 0.00\n",
"strcat_1.SPtr32 4.73 4.69 5.28\n",
"strcat_1.DPtr32 0.00 0.00 0.00\n",
"fwrite_unlocked.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fwrite_unlocked.ret_val.Num32B0 0.00 0.00 0.00\n",
"fwrite_unlocked_0.SPtr32 0.00 0.00 0.00\n",
"fwrite_unlocked_1.Num32B0 0.00 0.00 0.00\n",
"fwrite_unlocked_2.Num32B0 0.00 0.00 0.00\n",
"fwrite_unlocked_3.HPtr32 0.00 0.00 0.00\n",
"fgets.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fgets.ret_val.Ptr32 0.00 0.00 0.00\n",
"fgets.ret_val.FPtr32 0.00 0.00 0.00\n",
"fgets_0.Ptr32 0.00 0.00 0.00\n",
"fgets_1.Num32B8 0.00 0.00 0.00\n",
"fgets_2.SPtr32 0.00 0.00 0.00\n",
"fgets_2.HPtr32 1.41 1.42 1.66\n",
"strchr.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strchr.ret_val.Ptr32 0.00 0.00 0.00\n",
"strchr.ret_val.SPtr32 1.90 0.85 2.01\n",
"strchr.ret_val.FPtr32 0.00 0.00 0.00\n",
"strchr.ret_val.DPtr32 0.00 0.00 0.00\n",
"strchr.ret_val.GPtr32 4.83 5.20 5.63\n",
"strchr_0.Ptr32 0.00 0.00 0.00\n",
"strchr_0.SPtr32 2.57 1.74 2.88\n",
"strchr_0.NPtr32 0.00 0.00 0.00\n",
"strchr_0.DPtr32 0.00 0.00 0.00\n",
"strchr_0.Top32 0.00 0.00 0.00\n",
"access.ret_addr.DPtr32 0.00 0.00 0.00\n",
"access.ret_val.Top32 0.00 0.00 0.00\n",
"access.ret_val.Num32B24 0.00 0.00 0.00\n",
"access_0.Ptr32 0.00 0.00 0.00\n",
"access_1.Num32 0.00 0.00 0.00\n",
"access_1.Num32B0 2.00 3.05 3.21\n",
"mkdir.ret_addr.DPtr32 0.00 0.00 0.00\n",
"mkdir.ret_val.Num32B24 0.00 0.00 0.00\n",
"mkdir_0.SPtr32 0.00 0.00 0.00\n",
"mkdir_0.Top32 0.00 0.00 0.00\n",
"exit.ret_addr.DPtr32 0.00 0.00 0.00\n",
"exit.ret_val.GPtr32 0.00 0.00 0.00\n",
"exit_0.Num32 0.00 0.00 0.00\n",
"exit_0.Num32B0 6.39 6.74 7.47\n",
"X__ctype_b_loc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__ctype_b_loc.ret_val.LPtr32 0.00 0.00 0.00\n",
"X__ctype_b_loc.ret_val.Num32B32 0.00 0.00 0.00\n",
"strrchr.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strrchr.ret_val.Ptr32 0.00 0.00 0.00\n",
"strrchr.ret_val.FPtr32 0.00 0.00 0.00\n",
"strrchr_0.Ptr32 0.00 0.00 0.00\n",
"strrchr_0.DPtr32 0.00 0.00 0.00\n",
"strrchr_0.Top32 0.00 0.00 0.00\n",
"getcwd.ret_addr.DPtr32 0.00 0.00 0.00\n",
"getcwd.ret_val.SPtr32 0.00 0.00 0.00\n",
"getcwd_0.SPtr32 0.00 0.00 0.00\n",
"getcwd_1.Num32B8 0.00 0.00 0.00\n",
"free.ret_addr.DPtr32 0.00 0.00 0.00\n",
"free.ret_val.GPtr32 0.00 0.00 0.00\n",
"free_0.SPtr32 0.00 0.00 0.00\n",
"free_0.LPtr32 0.00 0.00 0.00\n",
"free_0.FPtr32 -0.13 8.36 7.77\n",
"gethostname.ret_addr.DPtr32 0.00 0.00 0.00\n",
"gethostname.ret_val.Top32 0.00 0.00 0.00\n",
"gethostname_0.Ptr32 0.00 0.00 0.00\n",
"gethostname_1.Num32B0 0.00 0.00 0.00\n",
"sigaction.ret_addr.DPtr32 0.00 0.00 0.00\n",
"sigaction.ret_val.Top32 0.00 0.00 0.00\n",
"sigaction_0.Num32B0 0.00 0.00 0.00\n",
"sigaction_1.Ptr32 0.00 0.00 0.00\n",
"sigaction_2.Ptr32 0.00 0.00 0.00\n",
"strtok.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strtok.ret_val.SPtr32 0.00 0.00 0.00\n",
"strtok.ret_val.FPtr32 0.00 0.00 0.00\n",
"strtok_0.SPtr32 0.00 0.00 0.00\n",
"strtok_0.FPtr32 0.00 0.00 0.00\n",
"strtok_1.DPtr32 0.00 0.00 0.00\n",
"setlocale.ret_addr.DPtr32 0.00 0.00 0.00\n",
"setlocale.ret_val.HPtr32 0.00 0.00 0.00\n",
"setlocale_0.Num32 0.00 0.00 0.00\n",
"setlocale_0.Num32B0 2.94 1.85 2.93\n",
"setlocale_1.FPtr32 0.00 0.00 0.00\n",
"setlocale_1.DPtr32 0.00 0.00 0.00\n",
"realloc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"realloc.ret_val.SPtr32 0.00 0.00 0.00\n",
"realloc_0.SPtr32 0.00 0.00 0.00\n",
"realloc_0.FPtr32 0.00 0.00 0.00\n",
"realloc_1.Num32B0 0.00 0.00 0.00\n",
"realloc_1.Num32B8 2.98 4.30 4.99\n",
"realloc_1.Num32B16 2.23 3.94 4.32\n",
"X__xstat64.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__xstat64.ret_val.Top32 0.00 0.00 0.00\n",
"X__xstat64.ret_val.Num32B24 0.00 0.00 0.00\n",
"X__xstat64_0.Num32B0 0.00 0.00 0.00\n",
"X__xstat64_1.Ptr32 0.00 0.00 0.00\n",
"X__xstat64_1.SPtr32 10.98 14.01 14.34\n",
"X__xstat64_2.Ptr32 0.00 0.00 0.00\n",
"printf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"printf.ret_val.Num32B0 0.00 0.00 0.00\n",
"printf.ret_val.Num32B8 7.37 10.35 10.83\n",
"printf_0.DPtr32 0.00 0.00 0.00\n",
"sigemptyset.ret_addr.DPtr32 0.00 0.00 0.00\n",
"sigemptyset.ret_val.Top32 0.00 0.00 0.00\n",
"sigemptyset_0.Ptr32 0.00 0.00 0.00\n",
"X__fxstat64.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__fxstat64.ret_val.Top32 0.00 0.00 0.00\n",
"X__fxstat64_0.Num32B0 0.00 0.00 0.00\n",
"X__fxstat64_1.Num32B0 0.00 0.00 0.00\n",
"X__fxstat64_2.Ptr32 0.00 0.00 0.00\n",
"fopen.ret_addr.HPtr32 0.00 0.00 0.00\n",
"fopen.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fopen.ret_val.SPtr32 0.00 0.00 0.00\n",
"fopen.ret_val.FPtr32 0.00 0.00 0.00\n",
"fopen_0.Ptr32 0.00 0.00 0.00\n",
"fopen_0.SPtr32 3.40 3.40 4.22\n",
"fopen_0.FPtr32 0.00 0.00 0.00\n",
"fopen_0.DPtr32 0.00 0.00 0.00\n",
"fopen_1.Ptr32 0.00 0.00 0.00\n",
"fopen_1.HPtr32 0.00 0.00 0.00\n",
"fopen_1.DPtr32 0.00 0.00 0.00\n",
"open.ret_addr.HPtr32 0.00 0.00 0.00\n",
"open.ret_addr.DPtr32 0.00 0.00 0.00\n",
"open.ret_val.Num32B0 0.00 0.00 0.00\n",
"open.ret_val.Num32B24 0.00 0.00 0.00\n",
"open_0.Ptr32 0.00 0.00 0.00\n",
"open_0.SPtr32 1.91 1.30 1.82\n",
"open_0.HPtr32 2.28 2.33 2.47\n",
"open_1.Num32 0.00 0.00 0.00\n",
"open_1.Num32B0 3.50 2.73 3.52\n",
"open_1.Top32 0.00 0.00 0.00\n",
"strncpy.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strncpy.ret_val.Ptr32 0.00 0.00 0.00\n",
"strncpy.ret_val.SPtr32 4.16 4.07 5.09\n",
"strncpy.ret_val.DPtr32 0.00 0.00 0.00\n",
"strncpy_0.Ptr32 0.00 0.00 0.00\n",
"strncpy_0.SPtr32 5.43 4.12 5.71\n",
"strncpy_0.DPtr32 0.00 0.00 0.00\n",
"strncpy_0.Top32 0.00 0.00 0.00\n",
"strncpy_1.Ptr32 0.00 0.00 0.00\n",
"strncpy_1.SPtr32 4.55 4.16 4.96\n",
"strncpy_1.DPtr32 0.00 0.00 0.00\n",
"strncpy_2.Num32B0 0.00 0.00 0.00\n",
"strncpy_2.Num32B8 3.70 3.63 4.39\n",
"unlink.ret_addr.HPtr32 0.00 0.00 0.00\n",
"unlink.ret_val.Top32 0.00 0.00 0.00\n",
"unlink_0.SPtr32 0.00 0.00 0.00\n",
"snprintf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"snprintf.ret_val.Num32B0 0.00 0.00 0.00\n",
"snprintf_0.Ptr32 0.00 0.00 0.00\n",
"snprintf_0.SPtr32 3.75 4.42 4.95\n",
"snprintf_0.DPtr32 0.00 0.00 0.00\n",
"snprintf_1.Num32B0 0.00 0.00 0.00\n",
"snprintf_1.Num32B8 -1.08 5.03 4.60\n",
"snprintf_2.DPtr32 0.00 0.00 0.00\n",
"puts.ret_addr.DPtr32 0.00 0.00 0.00\n",
"puts.ret_val.Num32B0 0.00 0.00 0.00\n",
"puts_0.HPtr32 0.00 0.00 0.00\n",
"puts_0.DPtr32 0.00 0.00 0.00\n",
"qsort.ret_addr.DPtr32 0.00 0.00 0.00\n",
"qsort.ret_val.GPtr32 0.00 0.00 0.00\n",
"qsort_0.SPtr32 0.00 0.00 0.00\n",
"qsort_0.LPtr32 0.00 0.00 0.00\n",
"qsort_1.Num32 0.00 0.00 0.00\n",
"qsort_1.Num32B0 4.57 3.93 4.90\n",
"qsort_2.Num32B0 0.00 0.00 0.00\n",
"qsort_3.DPtr32 0.00 0.00 0.00\n",
"time.ret_addr.DPtr32 0.00 0.00 0.00\n",
"time.ret_val.Num32B24 0.00 0.00 0.00\n",
"time_0.FPtr32 0.00 0.00 0.00\n",
"getpid.ret_addr.HPtr32 0.00 0.00 0.00\n",
"getpid.ret_val.Num32B8 0.00 0.00 0.00\n",
"getpid.ret_val.Num32B32 0.00 0.00 0.00\n",
"isatty.ret_addr.DPtr32 0.00 0.00 0.00\n",
"isatty.ret_val.Top32 0.00 0.00 0.00\n",
"isatty_0.Num32B0 0.00 0.00 0.00\n",
"getenv.ret_addr.DPtr32 0.00 0.00 0.00\n",
"getenv.ret_val.FPtr32 0.00 0.00 0.00\n",
"getenv_0.DPtr32 0.00 0.00 0.00\n",
"link.ret_addr.HPtr32 0.00 0.00 0.00\n",
"link.ret_val.Top32 0.00 0.00 0.00\n",
"link.ret_val.Num32B24 0.00 0.00 0.00\n",
"link_0.SPtr32 0.00 0.00 0.00\n",
"link_1.SPtr32 0.00 0.00 0.00\n",
"strdup.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strdup.ret_val.SPtr32 0.00 0.00 0.00\n",
"strdup_0.Ptr32 0.00 0.00 0.00\n",
"strdup_0.SPtr32 3.23 0.00 3.25\n",
"strdup_0.DPtr32 0.00 0.00 0.00\n",
"X_IO_getc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X_IO_getc.ret_val.GPtr32 0.00 0.00 0.00\n",
"X_IO_getc_0.SPtr32 0.00 0.00 0.00\n",
"X__errno_location.ret_addr.HPtr32 0.00 0.00 0.00\n",
"X__errno_location.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__errno_location.ret_val.LPtr32 0.00 0.00 0.00\n",
"X__errno_location.ret_val.Num32B32 0.00 0.00 0.00\n",
"strstr.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strstr.ret_val.FPtr32 0.00 0.00 0.00\n",
"strstr_0.Ptr32 0.00 0.00 0.00\n",
"strstr_0.SPtr32 1.99 1.00 1.71\n",
"strstr_0.HPtr32 2.82 2.85 3.39\n",
"strstr_1.SPtr32 0.00 0.00 0.00\n",
"strstr_1.DPtr32 0.00 0.00 0.00\n",
"bfd_check_format.ret_addr.DPtr32 0.00 0.00 0.00\n",
"bfd_check_format.ret_val.Top32 0.00 0.00 0.00\n",
"bfd_check_format_0.SPtr32 0.00 0.00 0.00\n",
"bfd_check_format_1.Num32B0 0.00 0.00 0.00\n",
"localtime.ret_addr.DPtr32 0.00 0.00 0.00\n",
"localtime.ret_val.LPtr32 0.00 0.00 0.00\n",
"localtime_0.Ptr32 0.00 0.00 0.00\n",
"vsnprintf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"vsnprintf.ret_val.Num32B0 0.00 0.00 0.00\n",
"vsnprintf_0.SPtr32 0.00 0.00 0.00\n",
"vsnprintf_0.LPtr32 0.00 0.00 0.00\n",
"vsnprintf_1.Num32B0 0.00 0.00 0.00\n",
"vsnprintf_2.HPtr32 0.00 0.00 0.00\n",
"vsnprintf_2.DPtr32 0.00 0.00 0.00\n",
"vsnprintf_3.Ptr32 0.00 0.00 0.00\n",
"malloc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"malloc.ret_val.SPtr32 0.00 0.00 0.00\n",
"malloc.ret_val.LPtr32 0.00 0.00 0.00\n",
"malloc.ret_val.FPtr32 -0.66 4.86 4.53\n",
"malloc_0.Num32 0.00 0.00 0.00\n",
"malloc_0.Num32B0 0.00 0.00 0.00\n",
"malloc_0.Num32B8 3.80 6.89 7.17\n",
"malloc_0.Num32B16 3.48 5.87 6.58\n",
"malloc_0.Num32B24 -0.48 5.52 5.17\n",
"X__xstat.ret_addr.HPtr32 0.00 0.00 0.00\n",
"X__xstat.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__xstat.ret_val.Top32 0.00 0.00 0.00\n",
"X__xstat.ret_val.Num32B24 0.00 0.00 0.00\n",
"X__xstat_0.Num32B0 0.00 0.00 0.00\n",
"X__xstat_1.Ptr32 0.00 0.00 0.00\n",
"X__xstat_1.SPtr32 0.00 0.00 0.00\n",
"X__xstat_2.Ptr32 0.00 0.00 0.00\n",
"fread.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fread.ret_val.Top32 0.00 0.00 0.00\n",
"fread.ret_val.Num32B0 -0.79 4.18 3.59\n",
"fread.ret_val.Num32B8 7.59 9.91 9.92\n",
"fread_0.Ptr32 0.00 0.00 0.00\n",
"fread_0.SPtr32 7.49 9.13 9.27\n",
"fread_0.LPtr32 0.00 0.00 0.00\n",
"fread_0.DPtr32 0.00 0.00 0.00\n",
"fread_1.Num32B0 0.00 0.00 0.00\n",
"fread_1.Num32B8 7.92 9.82 9.67\n",
"fread_2.Num32B0 0.00 0.00 0.00\n",
"fread_2.Num32B8 7.63 10.00 10.07\n",
"fread_2.Num32B16 -1.59 5.02 4.66\n",
"fread_2.Num32B24 0.36 3.11 2.73\n",
"fread_3.SPtr32 0.00 0.00 0.00\n",
"fprintf.ret_addr.HPtr32 0.00 0.00 0.00\n",
"fprintf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fprintf.ret_val.Num32B0 0.00 0.00 0.00\n",
"fprintf.ret_val.Num32B8 3.86 4.12 4.93\n",
"fprintf_0.SPtr32 0.00 0.00 0.00\n",
"fprintf_0.HPtr32 2.44 2.65 3.27\n",
"fprintf_1.Ptr32 0.00 0.00 0.00\n",
"fprintf_1.HPtr32 0.00 0.00 0.00\n",
"fprintf_1.DPtr32 0.00 0.00 0.00\n",
"stpcpy.ret_addr.DPtr32 0.00 0.00 0.00\n",
"stpcpy.ret_val.SPtr32 0.00 0.00 0.00\n",
"stpcpy_0.SPtr32 0.00 0.00 0.00\n",
"stpcpy_1.DPtr32 0.00 0.00 0.00\n",
"close.ret_addr.HPtr32 0.00 0.00 0.00\n",
"close.ret_addr.DPtr32 0.00 0.00 0.00\n",
"close.ret_val.Top32 0.00 0.00 0.00\n",
"close_0.Num32B0 0.00 0.00 0.00\n",
"bindtextdomain.ret_addr.DPtr32 0.00 0.00 0.00\n",
"bindtextdomain.ret_val.HPtr32 0.00 0.00 0.00\n",
"bindtextdomain_0.DPtr32 0.00 0.00 0.00\n",
"bindtextdomain_1.FPtr32 0.00 0.00 0.00\n",
"bindtextdomain_1.DPtr32 0.00 0.00 0.00\n",
"X__lxstat64.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__lxstat64.ret_val.Num32B24 0.00 0.00 0.00\n",
"X__lxstat64_0.Num32B0 0.00 0.00 0.00\n",
"X__lxstat64_1.Ptr32 0.00 0.00 0.00\n",
"X__lxstat64_2.Ptr32 0.00 0.00 0.00\n",
"strlen.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strlen.ret_val.GPtr32 0.00 0.00 0.00\n",
"strlen.ret_val.Top32 1.41 0.00 1.42\n",
"strlen.ret_val.Num32B0 4.30 2.32 4.14\n",
"strlen.ret_val.Num32B8 4.09 3.62 4.69\n",
"strlen.ret_val.Num32B32 0.00 0.00 0.00\n",
"strlen_0.Ptr32 0.00 0.00 0.00\n",
"strlen_0.SPtr32 7.45 6.51 7.90\n",
"strlen_0.HPtr32 4.16 4.77 5.60\n",
"strlen_0.NPtr32 0.00 0.00 0.00\n",
"strlen_0.DPtr32 0.00 0.00 0.00\n",
"getopt.ret_addr.DPtr32 0.00 0.00 0.00\n",
"getopt.ret_val.Num32B0 0.00 0.00 0.00\n",
"getopt.ret_val.Num32B24 0.00 0.00 0.00\n",
"getopt_0.Num32B0 0.00 0.00 0.00\n",
"getopt_1.Ptr32 0.00 0.00 0.00\n",
"getopt_1.SPtr32 3.38 1.90 3.29\n",
"getopt_2.DPtr32 0.00 0.00 0.00\n",
"open64.ret_addr.DPtr32 0.00 0.00 0.00\n",
"open64.ret_val.Num32B24 0.00 0.00 0.00\n",
"open64_0.Ptr32 0.00 0.00 0.00\n",
"open64_0.SPtr32 1.00 1.00 1.00\n",
"open64_1.Num32 0.00 0.00 0.00\n",
"open64_1.Num32B16 0.00 0.00 0.00\n",
"open64_1.Top32 0.00 0.00 0.00\n",
"bfd_set_default_target.ret_addr.DPtr32 0.00 0.00 0.00\n",
"bfd_set_default_target.ret_val.Num32B0 0.00 0.00 0.00\n",
"bfd_set_default_target_0.DPtr32 0.00 0.00 0.00\n",
"X__ctype_toupper_loc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__ctype_toupper_loc.ret_val.LPtr32 0.00 0.00 0.00\n",
"X__ctype_toupper_loc.ret_val.Num32B32 0.00 0.00 0.00\n",
"socket.ret_addr.DPtr32 0.00 0.00 0.00\n",
"socket.ret_val.Num32B0 0.00 0.00 0.00\n",
"socket_0.Num32B0 0.00 0.00 0.00\n",
"socket_1.Num32B0 0.00 0.00 0.00\n",
"socket_2.Num32 0.00 0.00 0.00\n",
"X__ctype_tolower_loc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"X__ctype_tolower_loc.ret_val.LPtr32 0.00 0.00 0.00\n",
"X__ctype_tolower_loc.ret_val.Num32B32 0.00 0.00 0.00\n",
"calloc.ret_addr.DPtr32 0.00 0.00 0.00\n",
"calloc.ret_val.SPtr32 0.00 0.00 0.00\n",
"calloc.ret_val.LPtr32 0.00 0.00 0.00\n",
"calloc_0.Num32B0 0.00 0.00 0.00\n",
"calloc_1.Num32B0 0.00 0.00 0.00\n",
"getopt_long.ret_addr.DPtr32 0.00 0.00 0.00\n",
"getopt_long.ret_val.Top32 0.00 0.00 0.00\n",
"getopt_long.ret_val.Num32B0 3.13 -1.00 2.90\n",
"getopt_long.ret_val.Num32B24 0.00 0.00 0.00\n",
"getopt_long_0.Num32B0 0.00 0.00 0.00\n",
"getopt_long_1.Ptr32 0.00 0.00 0.00\n",
"getopt_long_2.SPtr32 0.00 0.00 0.00\n",
"getopt_long_2.DPtr32 0.00 0.00 0.00\n",
"getopt_long_3.DPtr32 0.00 0.00 0.00\n",
"getopt_long_4.Ptr32 0.00 0.00 0.00\n",
"getopt_long_4.FPtr32 0.00 0.00 0.00\n",
"strcasecmp.ret_addr.DPtr32 0.00 0.00 0.00\n",
"strcasecmp.ret_val.GPtr32 0.00 0.00 0.00\n",
"strcasecmp.ret_val.Top32 0.00 0.00 0.00\n",
"strcasecmp.ret_val.Num32B0 3.29 3.27 3.58\n",
"strcasecmp.ret_val.Num32B24 0.00 0.00 0.00\n",
"strcasecmp_0.Ptr32 0.00 0.00 0.00\n",
"strcasecmp_0.DPtr32 0.00 0.00 0.00\n",
"strcasecmp_1.FPtr32 0.00 0.00 0.00\n",
"strcasecmp_1.DPtr32 0.00 0.00 0.00\n",
"getuid.ret_addr.HPtr32 0.00 0.00 0.00\n",
"getuid.ret_val.Num32B8 0.00 0.00 0.00\n",
"getuid.ret_val.Num32B32 0.00 0.00 0.00\n",
"textdomain.ret_addr.DPtr32 0.00 0.00 0.00\n",
"textdomain.ret_val.SPtr32 0.00 0.00 0.00\n",
"textdomain_0.DPtr32 0.00 0.00 0.00\n",
"bfd_openr.ret_addr.DPtr32 0.00 0.00 0.00\n",
"bfd_openr.ret_val.SPtr32 0.00 0.00 0.00\n",
"bfd_openr_0.Ptr32 0.00 0.00 0.00\n",
"bfd_openr_1.FPtr32 0.00 0.00 0.00\n",
"fopen64.ret_addr.DPtr32 0.00 0.00 0.00\n",
"fopen64.ret_val.SPtr32 0.00 0.00 0.00\n",
"fopen64.ret_val.FPtr32 0.00 0.00 0.00\n",
"fopen64_0.Ptr32 0.00 0.00 0.00\n",
"fopen64_1.DPtr32 0.00 0.00 0.00\n",
"vfprintf.ret_addr.HPtr32 0.00 0.00 0.00\n",
"vfprintf.ret_addr.DPtr32 0.00 0.00 0.00\n",
"vfprintf.ret_val.Num32B0 0.00 0.00 0.00\n",
"vfprintf.ret_val.Num32B8 4.07 7.28 7.35\n",
"vfprintf_0.HPtr32 0.00 0.00 0.00\n",
"vfprintf_1.HPtr32 0.00 0.00 0.00\n",
"vfprintf_1.DPtr32 0.00 0.00 0.00\n",
"vfprintf_2.Ptr32 0.00 0.00 0.00\n",
"crashed.eip.Ptr32 0.00 0.00 0.00\n",
"crashed.eip.HPtr32 0.00 0.00 0.00\n",
"crashed.eip.LPtr32 5.02 0.94 4.31\n",
"crashed.eip.NPtr32 0.00 0.00 0.00\n",
"crashed.eip.DPtr32 4.29 0.74 3.99\n",
"crashed.eip.Top32 0.00 0.00 0.00\n",
"SIGSEGV.addr.Ptr32 0.00 0.00 0.00\n",
"SIGSEGV.addr.LPtr32 0.00 0.00 0.00\n",
"SIGSEGV.addr.NPtr32 0.00 0.00 0.00\n",
"SIGSEGV.addr.GPtr32 0.00 0.00 0.00\n",
"SIGSEGV.addr.Top32 3.80 0.28 3.66\n",
" MeanDecreaseGini\n",
"X_IO_putc.ret_addr.HPtr32 0.00\n",
"X_IO_putc.ret_addr.DPtr32 0.00\n",
"X_IO_putc.ret_val.Num32B0 0.00\n",
"X_IO_putc.ret_val.Num32B32 0.00\n",
"X_IO_putc_1.HPtr32 0.00\n",
"fflush.ret_addr.DPtr32 0.00\n",
"fflush.ret_val.Top32 0.00\n",
"fflush_0.HPtr32 0.00\n",
"fputc.ret_addr.DPtr32 0.00\n",
"fputc.ret_val.Num32B0 0.00\n",
"fputc.ret_val.Num32B32 0.00\n",
"fputc_1.SPtr32 0.00\n",
"fputc_1.HPtr32 0.03\n",
"fwrite.ret_addr.DPtr32 0.00\n",
"fwrite.ret_val.GPtr32 0.00\n",
"fwrite.ret_val.Num32B0 0.00\n",
"fwrite.ret_val.Num32B8 0.80\n",
"fwrite_0.DPtr32 0.00\n",
"fwrite_1.Num32B0 0.00\n",
"fwrite_2.Num32B0 0.00\n",
"fwrite_2.Num32B8 0.85\n",
"fwrite_3.SPtr32 0.00\n",
"fwrite_3.HPtr32 0.05\n",
"fwrite_3.FPtr32 0.00\n",
"fputs.ret_addr.DPtr32 0.00\n",
"fputs.ret_val.Num32B0 0.00\n",
"fputs_0.DPtr32 0.00\n",
"fputs_1.HPtr32 0.00\n",
"sprintf.ret_addr.HPtr32 0.00\n",
"sprintf.ret_addr.DPtr32 0.00\n",
"sprintf.ret_val.Num32B0 0.00\n",
"sprintf.ret_val.Num32B8 0.33\n",
"sprintf_0.Ptr32 0.00\n",
"sprintf_0.SPtr32 1.16\n",
"sprintf_1.HPtr32 0.00\n",
"sprintf_1.DPtr32 0.00\n",
"read.ret_addr.DPtr32 0.00\n",
"read.ret_val.Top32 0.00\n",
"read.ret_val.Num32B0 0.16\n",
"read_0.Num32B0 0.00\n",
"read_1.SPtr32 0.00\n",
"read_2.Num32B8 0.00\n",
"fclose.ret_addr.HPtr32 0.00\n",
"fclose.ret_addr.DPtr32 0.00\n",
"fclose.ret_val.Top32 0.00\n",
"fclose_0.SPtr32 0.00\n",
"fclose_0.HPtr32 5.67\n",
"strcoll.ret_addr.HPtr32 0.00\n",
"strcoll.ret_val.Top32 0.00\n",
"strcoll.ret_val.Num32B0 0.55\n",
"strcoll.ret_val.Num32B24 0.00\n",
"strcoll_0.SPtr32 0.00\n",
"strcoll_0.HPtr32 1.11\n",
"strcoll_1.SPtr32 0.00\n",
"strcoll_1.HPtr32 0.87\n",
"memcpy.ret_addr.DPtr32 0.00\n",
"memcpy.ret_val.Ptr32 0.00\n",
"memcpy.ret_val.SPtr32 1.14\n",
"memcpy.ret_val.LPtr32 0.00\n",
"memcpy.ret_val.DPtr32 0.00\n",
"memcpy_0.Ptr32 0.00\n",
"memcpy_0.SPtr32 0.87\n",
"memcpy_0.LPtr32 0.00\n",
"memcpy_0.DPtr32 0.00\n",
"memcpy_1.Ptr32 0.00\n",
"memcpy_1.SPtr32 0.04\n",
"memcpy_1.FPtr32 0.00\n",
"memcpy_1.DPtr32 0.00\n",
"memcpy_2.Num32 0.00\n",
"memcpy_2.Num32B0 0.59\n",
"memcpy_2.Num32B8 2.75\n",
"perror.ret_addr.HPtr32 0.00\n",
"perror.ret_addr.DPtr32 0.00\n",
"perror.ret_val.GPtr32 0.00\n",
"perror_0.Ptr32 0.00\n",
"perror_0.HPtr32 0.00\n",
"signal.ret_addr.HPtr32 0.00\n",
"signal.ret_val.FPtr32 0.00\n",
"signal.ret_val.NPtr32 0.09\n",
"signal_0.Num32B0 0.00\n",
"signal_1.HPtr32 0.00\n",
"opendir.ret_addr.HPtr32 0.00\n",
"opendir.ret_val.FPtr32 0.00\n",
"opendir_0.Ptr32 0.00\n",
"strcpy.ret_addr.DPtr32 0.00\n",
"strcpy.ret_val.Ptr32 0.00\n",
"strcpy.ret_val.SPtr32 0.74\n",
"strcpy.ret_val.DPtr32 0.00\n",
"strcpy.ret_val.GPtr32 0.02\n",
"strcpy_0.Ptr32 0.00\n",
"strcpy_0.SPtr32 0.63\n",
"strcpy_0.FPtr32 0.00\n",
"strcpy_0.DPtr32 0.00\n",
"strcpy_0.Top32 0.00\n",
"strcpy_1.Ptr32 0.00\n",
"strcpy_1.SPtr32 1.28\n",
"strcpy_1.FPtr32 0.00\n",
"strcpy_1.DPtr32 0.00\n",
"sleep.ret_addr.HPtr32 0.00\n",
"sleep.ret_val.GPtr32 0.00\n",
"sleep.ret_val.Num32B32 0.00\n",
"strcmp.ret_addr.HPtr32 0.00\n",
"strcmp.ret_addr.DPtr32 0.00\n",
"strcmp.ret_val.Top32 0.00\n",
"strcmp.ret_val.Num32B0 0.65\n",
"strcmp.ret_val.Num32B24 0.00\n",
"strcmp_0.Ptr32 0.00\n",
"strcmp_0.SPtr32 0.21\n",
"strcmp_0.HPtr32 0.39\n",
"strcmp_0.DPtr32 0.00\n",
"strcmp_1.SPtr32 0.00\n",
"strcmp_1.HPtr32 0.28\n",
"strcmp_1.DPtr32 0.00\n",
"memchr.ret_addr.DPtr32 0.00\n",
"memchr.ret_val.SPtr32 0.00\n",
"memchr.ret_val.FPtr32 0.00\n",
"memchr_0.SPtr32 0.00\n",
"memchr_0.Top32 0.00\n",
"memchr_2.Num32B0 0.00\n",
"memchr_2.Num32B8 0.24\n",
"strncmp.ret_addr.DPtr32 0.00\n",
"strncmp.ret_val.Top32 0.00\n",
"strncmp.ret_val.Num32B0 0.11\n",
"strncmp.ret_val.Num32B24 0.00\n",
"strncmp_0.Ptr32 0.00\n",
"strncmp_0.SPtr32 0.12\n",
"strncmp_0.DPtr32 0.00\n",
"strncmp_1.Ptr32 0.00\n",
"strncmp_1.SPtr32 0.15\n",
"strncmp_1.DPtr32 0.00\n",
"strncmp_2.Num32B0 0.00\n",
"strncmp_2.Num32B8 0.13\n",
"bfd_init.ret_addr.DPtr32 0.00\n",
"bfd_init.ret_val.GPtr32 0.00\n",
"bfd_init.ret_val.Num32B32 0.00\n",
"getpagesize.ret_addr.DPtr32 0.00\n",
"getpagesize.ret_val.Num32B8 0.00\n",
"getpagesize.ret_val.Num32B32 0.00\n",
"memset.ret_addr.DPtr32 0.00\n",
"memset.ret_val.Ptr32 0.00\n",
"memset.ret_val.SPtr32 0.01\n",
"memset_0.Ptr32 0.00\n",
"memset_0.SPtr32 0.01\n",
"memset_0.Top32 0.00\n",
"memset_2.Num32B0 0.00\n",
"memset_2.Num32B8 1.05\n",
"strcat.ret_addr.DPtr32 0.00\n",
"strcat.ret_val.Ptr32 0.00\n",
"strcat.ret_val.SPtr32 0.19\n",
"strcat_0.Ptr32 0.00\n",
"strcat_0.SPtr32 0.25\n",
"strcat_1.Ptr32 0.00\n",
"strcat_1.SPtr32 0.68\n",
"strcat_1.DPtr32 0.00\n",
"fwrite_unlocked.ret_addr.DPtr32 0.00\n",
"fwrite_unlocked.ret_val.Num32B0 0.00\n",
"fwrite_unlocked_0.SPtr32 0.00\n",
"fwrite_unlocked_1.Num32B0 0.00\n",
"fwrite_unlocked_2.Num32B0 0.00\n",
"fwrite_unlocked_3.HPtr32 0.00\n",
"fgets.ret_addr.DPtr32 0.00\n",
"fgets.ret_val.Ptr32 0.00\n",
"fgets.ret_val.FPtr32 0.00\n",
"fgets_0.Ptr32 0.00\n",
"fgets_1.Num32B8 0.00\n",
"fgets_2.SPtr32 0.00\n",
"fgets_2.HPtr32 0.05\n",
"strchr.ret_addr.DPtr32 0.00\n",
"strchr.ret_val.Ptr32 0.00\n",
"strchr.ret_val.SPtr32 0.05\n",
"strchr.ret_val.FPtr32 0.00\n",
"strchr.ret_val.DPtr32 0.00\n",
"strchr.ret_val.GPtr32 0.70\n",
"strchr_0.Ptr32 0.00\n",
"strchr_0.SPtr32 0.16\n",
"strchr_0.NPtr32 0.00\n",
"strchr_0.DPtr32 0.00\n",
"strchr_0.Top32 0.00\n",
"access.ret_addr.DPtr32 0.00\n",
"access.ret_val.Top32 0.00\n",
"access.ret_val.Num32B24 0.00\n",
"access_0.Ptr32 0.00\n",
"access_1.Num32 0.00\n",
"access_1.Num32B0 0.15\n",
"mkdir.ret_addr.DPtr32 0.00\n",
"mkdir.ret_val.Num32B24 0.00\n",
"mkdir_0.SPtr32 0.00\n",
"mkdir_0.Top32 0.00\n",
"exit.ret_addr.DPtr32 0.00\n",
"exit.ret_val.GPtr32 0.00\n",
"exit_0.Num32 0.00\n",
"exit_0.Num32B0 1.48\n",
"X__ctype_b_loc.ret_addr.DPtr32 0.00\n",
"X__ctype_b_loc.ret_val.LPtr32 0.00\n",
"X__ctype_b_loc.ret_val.Num32B32 0.00\n",
"strrchr.ret_addr.DPtr32 0.00\n",
"strrchr.ret_val.Ptr32 0.00\n",
"strrchr.ret_val.FPtr32 0.00\n",
"strrchr_0.Ptr32 0.00\n",
"strrchr_0.DPtr32 0.00\n",
"strrchr_0.Top32 0.00\n",
"getcwd.ret_addr.DPtr32 0.00\n",
"getcwd.ret_val.SPtr32 0.00\n",
"getcwd_0.SPtr32 0.00\n",
"getcwd_1.Num32B8 0.00\n",
"free.ret_addr.DPtr32 0.00\n",
"free.ret_val.GPtr32 0.00\n",
"free_0.SPtr32 0.00\n",
"free_0.LPtr32 0.00\n",
"free_0.FPtr32 1.89\n",
"gethostname.ret_addr.DPtr32 0.00\n",
"gethostname.ret_val.Top32 0.00\n",
"gethostname_0.Ptr32 0.00\n",
"gethostname_1.Num32B0 0.00\n",
"sigaction.ret_addr.DPtr32 0.00\n",
"sigaction.ret_val.Top32 0.00\n",
"sigaction_0.Num32B0 0.00\n",
"sigaction_1.Ptr32 0.00\n",
"sigaction_2.Ptr32 0.00\n",
"strtok.ret_addr.DPtr32 0.00\n",
"strtok.ret_val.SPtr32 0.00\n",
"strtok.ret_val.FPtr32 0.00\n",
"strtok_0.SPtr32 0.00\n",
"strtok_0.FPtr32 0.00\n",
"strtok_1.DPtr32 0.00\n",
"setlocale.ret_addr.DPtr32 0.00\n",
"setlocale.ret_val.HPtr32 0.00\n",
"setlocale_0.Num32 0.00\n",
"setlocale_0.Num32B0 0.13\n",
"setlocale_1.FPtr32 0.00\n",
"setlocale_1.DPtr32 0.00\n",
"realloc.ret_addr.DPtr32 0.00\n",
"realloc.ret_val.SPtr32 0.00\n",
"realloc_0.SPtr32 0.00\n",
"realloc_0.FPtr32 0.00\n",
"realloc_1.Num32B0 0.00\n",
"realloc_1.Num32B8 0.39\n",
"realloc_1.Num32B16 0.26\n",
"X__xstat64.ret_addr.DPtr32 0.00\n",
"X__xstat64.ret_val.Top32 0.00\n",
"X__xstat64.ret_val.Num32B24 0.00\n",
"X__xstat64_0.Num32B0 0.00\n",
"X__xstat64_1.Ptr32 0.00\n",
"X__xstat64_1.SPtr32 12.38\n",
"X__xstat64_2.Ptr32 0.00\n",
"printf.ret_addr.DPtr32 0.00\n",
"printf.ret_val.Num32B0 0.00\n",
"printf.ret_val.Num32B8 6.78\n",
"printf_0.DPtr32 0.00\n",
"sigemptyset.ret_addr.DPtr32 0.00\n",
"sigemptyset.ret_val.Top32 0.00\n",
"sigemptyset_0.Ptr32 0.00\n",
"X__fxstat64.ret_addr.DPtr32 0.00\n",
"X__fxstat64.ret_val.Top32 0.00\n",
"X__fxstat64_0.Num32B0 0.00\n",
"X__fxstat64_1.Num32B0 0.00\n",
"X__fxstat64_2.Ptr32 0.00\n",
"fopen.ret_addr.HPtr32 0.00\n",
"fopen.ret_addr.DPtr32 0.00\n",
"fopen.ret_val.SPtr32 0.00\n",
"fopen.ret_val.FPtr32 0.00\n",
"fopen_0.Ptr32 0.00\n",
"fopen_0.SPtr32 0.45\n",
"fopen_0.FPtr32 0.00\n",
"fopen_0.DPtr32 0.00\n",
"fopen_1.Ptr32 0.00\n",
"fopen_1.HPtr32 0.00\n",
"fopen_1.DPtr32 0.00\n",
"open.ret_addr.HPtr32 0.00\n",
"open.ret_addr.DPtr32 0.00\n",
"open.ret_val.Num32B0 0.00\n",
"open.ret_val.Num32B24 0.00\n",
"open_0.Ptr32 0.00\n",
"open_0.SPtr32 0.08\n",
"open_0.HPtr32 0.07\n",
"open_1.Num32 0.00\n",
"open_1.Num32B0 0.16\n",
"open_1.Top32 0.00\n",
"strncpy.ret_addr.DPtr32 0.00\n",
"strncpy.ret_val.Ptr32 0.00\n",
"strncpy.ret_val.SPtr32 0.59\n",
"strncpy.ret_val.DPtr32 0.00\n",
"strncpy_0.Ptr32 0.00\n",
"strncpy_0.SPtr32 0.79\n",
"strncpy_0.DPtr32 0.00\n",
"strncpy_0.Top32 0.00\n",
"strncpy_1.Ptr32 0.00\n",
"strncpy_1.SPtr32 0.54\n",
"strncpy_1.DPtr32 0.00\n",
"strncpy_2.Num32B0 0.00\n",
"strncpy_2.Num32B8 0.39\n",
"unlink.ret_addr.HPtr32 0.00\n",
"unlink.ret_val.Top32 0.00\n",
"unlink_0.SPtr32 0.00\n",
"snprintf.ret_addr.DPtr32 0.00\n",
"snprintf.ret_val.Num32B0 0.00\n",
"snprintf_0.Ptr32 0.00\n",
"snprintf_0.SPtr32 0.34\n",
"snprintf_0.DPtr32 0.00\n",
"snprintf_1.Num32B0 0.00\n",
"snprintf_1.Num32B8 0.97\n",
"snprintf_2.DPtr32 0.00\n",
"puts.ret_addr.DPtr32 0.00\n",
"puts.ret_val.Num32B0 0.00\n",
"puts_0.HPtr32 0.00\n",
"puts_0.DPtr32 0.00\n",
"qsort.ret_addr.DPtr32 0.00\n",
"qsort.ret_val.GPtr32 0.00\n",
"qsort_0.SPtr32 0.00\n",
"qsort_0.LPtr32 0.00\n",
"qsort_1.Num32 0.00\n",
"qsort_1.Num32B0 0.40\n",
"qsort_2.Num32B0 0.00\n",
"qsort_3.DPtr32 0.00\n",
"time.ret_addr.DPtr32 0.00\n",
"time.ret_val.Num32B24 0.00\n",
"time_0.FPtr32 0.00\n",
"getpid.ret_addr.HPtr32 0.00\n",
"getpid.ret_val.Num32B8 0.00\n",
"getpid.ret_val.Num32B32 0.00\n",
"isatty.ret_addr.DPtr32 0.00\n",
"isatty.ret_val.Top32 0.00\n",
"isatty_0.Num32B0 0.00\n",
"getenv.ret_addr.DPtr32 0.00\n",
"getenv.ret_val.FPtr32 0.00\n",
"getenv_0.DPtr32 0.00\n",
"link.ret_addr.HPtr32 0.00\n",
"link.ret_val.Top32 0.00\n",
"link.ret_val.Num32B24 0.00\n",
"link_0.SPtr32 0.00\n",
"link_1.SPtr32 0.00\n",
"strdup.ret_addr.DPtr32 0.00\n",
"strdup.ret_val.SPtr32 0.00\n",
"strdup_0.Ptr32 0.00\n",
"strdup_0.SPtr32 0.13\n",
"strdup_0.DPtr32 0.00\n",
"X_IO_getc.ret_addr.DPtr32 0.00\n",
"X_IO_getc.ret_val.GPtr32 0.00\n",
"X_IO_getc_0.SPtr32 0.00\n",
"X__errno_location.ret_addr.HPtr32 0.00\n",
"X__errno_location.ret_addr.DPtr32 0.00\n",
"X__errno_location.ret_val.LPtr32 0.00\n",
"X__errno_location.ret_val.Num32B32 0.00\n",
"strstr.ret_addr.DPtr32 0.00\n",
"strstr.ret_val.FPtr32 0.00\n",
"strstr_0.Ptr32 0.00\n",
"strstr_0.SPtr32 0.05\n",
"strstr_0.HPtr32 0.27\n",
"strstr_1.SPtr32 0.00\n",
"strstr_1.DPtr32 0.00\n",
"bfd_check_format.ret_addr.DPtr32 0.00\n",
"bfd_check_format.ret_val.Top32 0.00\n",
"bfd_check_format_0.SPtr32 0.00\n",
"bfd_check_format_1.Num32B0 0.00\n",
"localtime.ret_addr.DPtr32 0.00\n",
"localtime.ret_val.LPtr32 0.00\n",
"localtime_0.Ptr32 0.00\n",
"vsnprintf.ret_addr.DPtr32 0.00\n",
"vsnprintf.ret_val.Num32B0 0.00\n",
"vsnprintf_0.SPtr32 0.00\n",
"vsnprintf_0.LPtr32 0.00\n",
"vsnprintf_1.Num32B0 0.00\n",
"vsnprintf_2.HPtr32 0.00\n",
"vsnprintf_2.DPtr32 0.00\n",
"vsnprintf_3.Ptr32 0.00\n",
"malloc.ret_addr.DPtr32 0.00\n",
"malloc.ret_val.SPtr32 0.00\n",
"malloc.ret_val.LPtr32 0.00\n",
"malloc.ret_val.FPtr32 0.98\n",
"malloc_0.Num32 0.00\n",
"malloc_0.Num32B0 0.00\n",
"malloc_0.Num32B8 2.08\n",
"malloc_0.Num32B16 2.81\n",
"malloc_0.Num32B24 0.60\n",
"X__xstat.ret_addr.HPtr32 0.00\n",
"X__xstat.ret_addr.DPtr32 0.00\n",
"X__xstat.ret_val.Top32 0.00\n",
"X__xstat.ret_val.Num32B24 0.00\n",
"X__xstat_0.Num32B0 0.00\n",
"X__xstat_1.Ptr32 0.00\n",
"X__xstat_1.SPtr32 0.03\n",
"X__xstat_2.Ptr32 0.00\n",
"fread.ret_addr.DPtr32 0.00\n",
"fread.ret_val.Top32 0.00\n",
"fread.ret_val.Num32B0 0.54\n",
"fread.ret_val.Num32B8 9.45\n",
"fread_0.Ptr32 0.00\n",
"fread_0.SPtr32 7.30\n",
"fread_0.LPtr32 0.00\n",
"fread_0.DPtr32 0.00\n",
"fread_1.Num32B0 0.00\n",
"fread_1.Num32B8 8.99\n",
"fread_2.Num32B0 0.00\n",
"fread_2.Num32B8 8.90\n",
"fread_2.Num32B16 0.64\n",
"fread_2.Num32B24 0.16\n",
"fread_3.SPtr32 0.00\n",
"fprintf.ret_addr.HPtr32 0.00\n",
"fprintf.ret_addr.DPtr32 0.00\n",
"fprintf.ret_val.Num32B0 0.00\n",
"fprintf.ret_val.Num32B8 0.98\n",
"fprintf_0.SPtr32 0.00\n",
"fprintf_0.HPtr32 0.10\n",
"fprintf_1.Ptr32 0.00\n",
"fprintf_1.HPtr32 0.00\n",
"fprintf_1.DPtr32 0.00\n",
"stpcpy.ret_addr.DPtr32 0.00\n",
"stpcpy.ret_val.SPtr32 0.00\n",
"stpcpy_0.SPtr32 0.00\n",
"stpcpy_1.DPtr32 0.00\n",
"close.ret_addr.HPtr32 0.00\n",
"close.ret_addr.DPtr32 0.00\n",
"close.ret_val.Top32 0.00\n",
"close_0.Num32B0 0.00\n",
"bindtextdomain.ret_addr.DPtr32 0.00\n",
"bindtextdomain.ret_val.HPtr32 0.00\n",
"bindtextdomain_0.DPtr32 0.00\n",
"bindtextdomain_1.FPtr32 0.00\n",
"bindtextdomain_1.DPtr32 0.00\n",
"X__lxstat64.ret_addr.DPtr32 0.00\n",
"X__lxstat64.ret_val.Num32B24 0.00\n",
"X__lxstat64_0.Num32B0 0.00\n",
"X__lxstat64_1.Ptr32 0.00\n",
"X__lxstat64_2.Ptr32 0.00\n",
"strlen.ret_addr.DPtr32 0.00\n",
"strlen.ret_val.GPtr32 0.00\n",
"strlen.ret_val.Top32 0.01\n",
"strlen.ret_val.Num32B0 0.21\n",
"strlen.ret_val.Num32B8 0.80\n",
"strlen.ret_val.Num32B32 0.00\n",
"strlen_0.Ptr32 0.00\n",
"strlen_0.SPtr32 2.52\n",
"strlen_0.HPtr32 0.62\n",
"strlen_0.NPtr32 0.00\n",
"strlen_0.DPtr32 0.00\n",
"getopt.ret_addr.DPtr32 0.00\n",
"getopt.ret_val.Num32B0 0.00\n",
"getopt.ret_val.Num32B24 0.00\n",
"getopt_0.Num32B0 0.00\n",
"getopt_1.Ptr32 0.00\n",
"getopt_1.SPtr32 0.43\n",
"getopt_2.DPtr32 0.00\n",
"open64.ret_addr.DPtr32 0.00\n",
"open64.ret_val.Num32B24 0.00\n",
"open64_0.Ptr32 0.00\n",
"open64_0.SPtr32 0.03\n",
"open64_1.Num32 0.00\n",
"open64_1.Num32B16 0.00\n",
"open64_1.Top32 0.00\n",
"bfd_set_default_target.ret_addr.DPtr32 0.00\n",
"bfd_set_default_target.ret_val.Num32B0 0.00\n",
"bfd_set_default_target_0.DPtr32 0.00\n",
"X__ctype_toupper_loc.ret_addr.DPtr32 0.00\n",
"X__ctype_toupper_loc.ret_val.LPtr32 0.00\n",
"X__ctype_toupper_loc.ret_val.Num32B32 0.00\n",
"socket.ret_addr.DPtr32 0.00\n",
"socket.ret_val.Num32B0 0.00\n",
"socket_0.Num32B0 0.00\n",
"socket_1.Num32B0 0.00\n",
"socket_2.Num32 0.00\n",
"X__ctype_tolower_loc.ret_addr.DPtr32 0.00\n",
"X__ctype_tolower_loc.ret_val.LPtr32 0.00\n",
"X__ctype_tolower_loc.ret_val.Num32B32 0.00\n",
"calloc.ret_addr.DPtr32 0.00\n",
"calloc.ret_val.SPtr32 0.00\n",
"calloc.ret_val.LPtr32 0.00\n",
"calloc_0.Num32B0 0.00\n",
"calloc_1.Num32B0 0.00\n",
"getopt_long.ret_addr.DPtr32 0.00\n",
"getopt_long.ret_val.Top32 0.00\n",
"getopt_long.ret_val.Num32B0 0.09\n",
"getopt_long.ret_val.Num32B24 0.00\n",
"getopt_long_0.Num32B0 0.00\n",
"getopt_long_1.Ptr32 0.00\n",
"getopt_long_2.SPtr32 0.00\n",
"getopt_long_2.DPtr32 0.00\n",
"getopt_long_3.DPtr32 0.00\n",
"getopt_long_4.Ptr32 0.00\n",
"getopt_long_4.FPtr32 0.00\n",
"strcasecmp.ret_addr.DPtr32 0.00\n",
"strcasecmp.ret_val.GPtr32 0.00\n",
"strcasecmp.ret_val.Top32 0.00\n",
"strcasecmp.ret_val.Num32B0 0.16\n",
"strcasecmp.ret_val.Num32B24 0.00\n",
"strcasecmp_0.Ptr32 0.00\n",
"strcasecmp_0.DPtr32 0.00\n",
"strcasecmp_1.FPtr32 0.00\n",
"strcasecmp_1.DPtr32 0.00\n",
"getuid.ret_addr.HPtr32 0.00\n",
"getuid.ret_val.Num32B8 0.00\n",
"getuid.ret_val.Num32B32 0.00\n",
"textdomain.ret_addr.DPtr32 0.00\n",
"textdomain.ret_val.SPtr32 0.00\n",
"textdomain_0.DPtr32 0.00\n",
"bfd_openr.ret_addr.DPtr32 0.00\n",
"bfd_openr.ret_val.SPtr32 0.00\n",
"bfd_openr_0.Ptr32 0.00\n",
"bfd_openr_1.FPtr32 0.00\n",
"fopen64.ret_addr.DPtr32 0.00\n",
"fopen64.ret_val.SPtr32 0.00\n",
"fopen64.ret_val.FPtr32 0.00\n",
"fopen64_0.Ptr32 0.00\n",
"fopen64_1.DPtr32 0.00\n",
"vfprintf.ret_addr.HPtr32 0.00\n",
"vfprintf.ret_addr.DPtr32 0.00\n",
"vfprintf.ret_val.Num32B0 0.00\n",
"vfprintf.ret_val.Num32B8 4.86\n",
"vfprintf_0.HPtr32 0.00\n",
"vfprintf_1.HPtr32 0.00\n",
"vfprintf_1.DPtr32 0.00\n",
"vfprintf_2.Ptr32 0.00\n",
"crashed.eip.Ptr32 0.00\n",
"crashed.eip.HPtr32 0.00\n",
"crashed.eip.LPtr32 0.57\n",
"crashed.eip.NPtr32 0.00\n",
"crashed.eip.DPtr32 0.39\n",
"crashed.eip.Top32 0.00\n",
"SIGSEGV.addr.Ptr32 0.00\n",
"SIGSEGV.addr.LPtr32 0.00\n",
"SIGSEGV.addr.NPtr32 0.00\n",
"SIGSEGV.addr.GPtr32 0.00\n",
"SIGSEGV.addr.Top32 0.17\n"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
chichilalescu/python-for-scientific-computing |
|
|
| {
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"name": "",
"signature": "sha256:ab4636c7a93b5b8e8cd11f9dce3dd9d4d98e3951e9b049ce7c27afb860730a5b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib nbagg\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import animation\n",
"import sympy as sp\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class hamiltonian_system_1D:\n",
" def __init__(self, x, p, H):\n",
" self.H = H\n",
" self.x = x\n",
" self.p = p\n",
" return None\n",
" def xrhs(self):\n",
" return self.H.diff(self.p)\n",
" def prhs(self):\n",
" return -self.H.diff(self.x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q = sp.Symbol('q')\n",
"p = sp.Symbol('p')\n",
"h = hamiltonian_system_1D(q, p, p**2/2 - sp.cos(q))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h.xrhs()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"p"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h.prhs()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"-sin(q)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def Euler(rhs, dt = 0.5, N = 8, x0 = None):\n",
" x = np.zeros((N+1,) + x0.shape,\n",
" dtype = x0.dtype)\n",
" x[0] = x0\n",
" for t in range(N):\n",
" x[t+1] = x[t] + dt*rhs(x[t])\n",
" return x\n",
"\n",
"def cRK(rhs, dt = 0.5, N = 8, x0 = None):\n",
" x = np.zeros((N+1,) + x0.shape,\n",
" dtype = x0.dtype)\n",
" x[0] = x0\n",
" for t in range(N):\n",
" k1 = rhs(x[t])\n",
" k2 = rhs(x[t] + 0.5*dt*k1)\n",
" k3 = rhs(x[t] + 0.5*dt*k2)\n",
" k4 = rhs(x[t] + dt*k3)\n",
" x[t+1] = x[t] + dt*(k1 + 2*(k2 + k3) + k4)/6\n",
" return x\n",
"\n",
"def get_evdt(\n",
" initial_condition,\n",
" h0, N0, ndivisions,\n",
" method,\n",
" rhs):\n",
" epsilon = []\n",
" tstep = [h0]\n",
" sol = []\n",
"\n",
" N = N0\n",
" sol.append(method(rhs,\n",
" dt = tstep[-1],\n",
" N = N,\n",
" x0 = initial_condition))\n",
" for n in range(1, ndivisions + 1):\n",
" tstep.append(h0*2.**(-n))\n",
" N = N*2\n",
" sol.append(method(rhs,\n",
" dt = tstep[-1],\n",
" N = N,\n",
" x0 = initial_condition))\n",
" epsilon.append(sol[-1][-1] - sol[-2][-1])\n",
"\n",
" return (np.array(tstep[:len(tstep)-1]),\n",
" np.abs(epsilon))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xrhs = sp.utilities.lambdify((q, p), h.xrhs(), modules = 'numpy')\n",
"prhs = sp.utilities.lambdify((q, p), h.prhs(), modules = 'numpy')\n",
"\n",
"def pendulum_rhs(point):\n",
" return np.array([xrhs(*tuple(point))*np.ones(point.shape[1:]),\n",
" prhs(*tuple(point))*np.ones(point.shape[1:])])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt, err = get_evdt(\n",
" np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)]),\n",
" 1., 4, 5,\n",
" Euler,\n",
" pendulum_rhs)\n",
"dt4, err4 = get_evdt(\n",
" np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)]),\n",
" 1., 4, 4,\n",
" cRK,\n",
" pendulum_rhs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = plt.figure().add_subplot(111)\n",
"\n",
"a.plot(dt, np.average(err, axis = (1, 2)),\n",
" marker = '.', label = 'Euler')\n",
"a.plot(dt4, np.average(err4, axis = (1, 2)),\n",
" marker = '.', label = 'cRK')\n",
"a.plot(dt, dt, label = '$\\Delta t$')\n",
"a.plot(dt, (dt**4), label = '$\\Delta t^4$')\n",
"a.set_xscale('log')\n",
"a.set_yscale('log')\n",
"a.legend(loc = 'best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6fb03178d0>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAzwAAAM8CAYAAACS9qXuAAAKeUlEQVR4nO3BMQEAAADCoPVPbQsvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH4G3eMAAfGeuZIAAAAASUVORK5CYII=\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f825488d0>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.legend.Legend at 0x7f6f824980d0>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"initial_condition = np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)])\n",
"\n",
"x = cRK(pendulum_rhs,\n",
" dt = 0.125,\n",
" N = 3*2**4,\n",
" x0 = initial_condition)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = plt.figure(figsize = (6, 4)).add_subplot(111)\n",
"a.plot(x[:, 0, :], x[:, 1, :])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f82529bd0>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAzwAAAIoCAYAAACsxu7XAAAHA0lEQVR4nO3BMQEAAADCoPVPbQlPoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPgbpPQABKtcJDgAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f82530a90>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[<matplotlib.lines.Line2D at 0x7f6f823eb410>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823eb690>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823eb8d0>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823eba90>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823ebc50>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823ebe10>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823ebfd0>,\n",
" <matplotlib.lines.Line2D at 0x7f6f82431050>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823f4390>,\n",
" <matplotlib.lines.Line2D at 0x7f6f823f4550>]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# code from http://nbviewer.ipython.org/gist/aburgm/7ecac8c7835f4c5816b3\n",
"plt.close('all')\n",
"initial_condition = np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)])\n",
"\n",
"x0 = cRK(pendulum_rhs,\n",
" dt = 0.125,\n",
" N = 3*2**4,\n",
" x0 = initial_condition)\n",
"\n",
"fig = plt.figure(figsize=(6,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-3, 3)\n",
"a.set_ylim(-2, 2)\n",
"lines = a.plot(x0[:, 0, :], x0[:, 1, :])\n",
"\n",
"def init0():\n",
" for l in lines:\n",
" l.set_data([], [])\n",
" return lines,\n",
"\n",
"def animate0(i):\n",
" for l in range(len(lines)):\n",
" lines[l].set_data(x0[:i%x0.shape[0], 0,l], x0[:i%x0.shape[0], 1,l])\n",
" return lines,\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate0,\n",
" init_func = init0,\n",
" frames = x0.shape[0],\n",
" interval = 100,\n",
" repeat_delay = 1000,\n",
" blit = False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f82555ad0>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAzwAAAIoCAYAAACsxu7XAAAHA0lEQVR4nO3BMQEAAADCoPVPbQlPoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPgbpPQABKtcJDgAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f82487e10>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.close('all')\n",
"x1 = cRK(pendulum_rhs,\n",
" dt = 0.125,\n",
" N = 3*2**6,\n",
" x0 = initial_condition)\n",
"\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-1.5, 1.5)\n",
"a.set_ylim(-1.5, 1.5)\n",
"\n",
"xx = np.sin(x1[0, 0, -1])\n",
"yy = -np.cos(x1[0, 0, -1])\n",
"line = a.plot([0., xx],\n",
" [0., yy])\n",
"point = a.scatter([xx], [yy])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init1():\n",
" return line, point,\n",
"\n",
"def animate1(i):\n",
" xx = np.sin(x1[i, 0, -1])\n",
" yy = -np.cos(x1[i, 0, -1])\n",
" line[0].set_data([0., xx], [0., yy])\n",
" point.set_offsets([[xx, yy]])\n",
" return line, point,\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate1,\n",
" init_func = init1,\n",
" frames = x1.shape[0],\n",
" interval = 100,\n",
" repeat_delay = 1000,\n",
" blit = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6fb1d87b50>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAigAAAIoCAYAAABDDRCFAAAEtElEQVR4nO3BMQEAAADCoPVPbQdvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DecNgAB1H0vvwAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f823baad0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(point.set_offsets)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on method set_offsets in module matplotlib.collections:\n",
"\n",
"set_offsets(self, offsets) method of matplotlib.collections.PathCollection instance\n",
" Set the offsets for the collection. *offsets* can be a scalar\n",
" or a sequence.\n",
" \n",
" ACCEPTS: float or sequence of floats\n",
"\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class hamiltonian_system_1D:\n",
" def __init__(self, x, p, H):\n",
" self.H = H\n",
" self.x = x\n",
" self.p = p\n",
" self.xrhs = sp.utilities.lambdify(\n",
" (q, p),\n",
" self.H.diff(self.p),\n",
" modules = 'numpy')\n",
" self.prhs = sp.utilities.lambdify(\n",
" (q, p),\n",
" -self.H.diff(self.x),\n",
" modules = 'numpy')\n",
" return None\n",
" def rhs(self, point):\n",
" return np.array(\n",
" [self.xrhs(*tuple(point))*np.ones(point.shape[1:]),\n",
" self.prhs(*tuple(point))*np.ones(point.shape[1:])])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.close('all')\n",
"q = sp.Symbol('q')\n",
"p = sp.Symbol('p')\n",
"pendulum = hamiltonian_system_1D(q, p, p**2/2 - sp.cos(q))\n",
"\n",
"initial_condition = np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)])\n",
"x2 = cRK(pendulum.rhs,\n",
" dt = 0.125,\n",
" N = 3*2**6,\n",
" x0 = initial_condition)\n",
"\n",
"\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-1.5, 1.5)\n",
"a.set_ylim(-1.5, 1.5)\n",
"\n",
"xx = np.sin(x2[0, 0, -1])\n",
"yy = -np.cos(x2[0, 0, -1])\n",
"line = a.plot([0., xx],\n",
" [0., yy])\n",
"point = a.scatter([xx], [yy])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init2():\n",
" return line, point,\n",
"\n",
"def animate2(i):\n",
" xx = np.sin(x2[i, 0, -1])\n",
" yy = -np.cos(x2[i, 0, -1])\n",
" line[0].set_data([0., xx], [0., yy])\n",
" point.set_offsets([[xx, yy]])\n",
" return line, point,\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate2,\n",
" init_func = init2,\n",
" frames = x2.shape[0],\n",
" interval = 100,\n",
" repeat_delay = 1000,\n",
" blit = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f8233d550>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAigAAAIoCAYAAABDDRCFAAAEtElEQVR4nO3BMQEAAADCoPVPbQdvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DecNgAB1H0vvwAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f8233dd90>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class hamiltonian_system:\n",
" def __init__(self, x, p, H):\n",
" assert(len(x) == len(p))\n",
" self.H = H\n",
" self.x = x\n",
" self.p = p\n",
" self.degrees_of_freedom = len(self.x)\n",
" self.rhs = ([sp.utilities.lambdify(tuple(self.x)+tuple(self.p),\n",
" self.H.diff(self.p[i]),\n",
" modules = 'numpy')\n",
" for i in range(self.degrees_of_freedom)] +\n",
" [sp.utilities.lambdify(tuple(self.x)+tuple(self.p),\n",
" -self.H.diff(self.x[i]),\n",
" modules = 'numpy')\n",
" for i in range(self.degrees_of_freedom)])\n",
" return None\n",
" def numpy_rhs(self, point):\n",
" return np.array([self.rhs[i](*tuple(point)) * np.ones(point.shape[1:])\n",
" for i in range(2*self.degrees_of_freedom)])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.close('all')\n",
"q = sp.Symbol('q')\n",
"p = sp.Symbol('p')\n",
"pendulum = hamiltonian_system([q], [p], p**2/2 - sp.cos(q))\n",
"\n",
"initial_condition = np.array([np.zeros(10),\n",
" np.linspace(0, 1.9, 10)])\n",
"x3 = cRK(pendulum.numpy_rhs,\n",
" dt = 0.125,\n",
" N = 3*2**6,\n",
" x0 = initial_condition)\n",
"\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-1.5, 1.5)\n",
"a.set_ylim(-1.5, 1.5)\n",
"\n",
"xx = np.sin(x3[0, 0, -1])\n",
"yy = -np.cos(x3[0, 0, -1])\n",
"line = a.plot([0., xx],\n",
" [0., yy])\n",
"point = a.scatter([xx], [yy])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init3():\n",
" return line, point,\n",
"\n",
"def animate3(i):\n",
" xx = np.sin(x3[i, 0, -1])\n",
" yy = -np.cos(x3[i, 0, -1])\n",
" line[0].set_data([0., xx], [0., yy])\n",
" point.set_offsets([[xx, yy]])\n",
" return line, point,\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate3,\n",
" init_func = init3,\n",
" frames = x3.shape[0],\n",
" interval = 100,\n",
" repeat_delay = 1000,\n",
" blit = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f82530fd0>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAigAAAIoCAYAAABDDRCFAAAEtElEQVR4nO3BMQEAAADCoPVPbQdvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DecNgAB1H0vvwAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f82530250>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = sp.Symbol('x')\n",
"y = sp.Symbol('y')\n",
"p = sp.Symbol('p')\n",
"q = sp.Symbol('q')\n",
"\n",
"l1 = 1.0\n",
"l2 = 1.5\n",
"m1 = 1.0\n",
"m2 = 0.25\n",
"\n",
"H = (- l1*(m1 + m2)*sp.cos(x)\n",
" - l2*m2*sp.cos(y)\n",
" - ((l2**2 * m2 * p**2 -\n",
" 2*l1*l2*m2*sp.cos(x - y)*p*q +\n",
" l1**2*(m1+m2)*q**2) /\n",
" (l1**2 * l2**2 * m2 * (-2*m1 - m2 + m2*sp.cos(2*(x - y))))))\n",
"double_pendulum = hamiltonian_system([x, y], [p, q], H)\n",
"\n",
"initial_condition = np.array([0., 0., 1., 1.])\n",
"sol = cRK(double_pendulum.numpy_rhs,\n",
" dt = 2.**(-4),\n",
" N = 2**8,\n",
" x0 = initial_condition)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.close('all')\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"a.set_ylim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"\n",
"xx1 = l1*np.sin(sol[0, 0])\n",
"yy1 = -l1*np.cos(sol[0, 0])\n",
"xx2 = xx1 + l2*np.sin(sol[0, 1])\n",
"yy2 = yy1 - l2*np.cos(sol[0, 1])\n",
"line4 = a.plot([0., xx1, xx2],\n",
" [0., yy1, yy2])\n",
"point4 = a.scatter([xx1, xx2], [yy1, yy2])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init4():\n",
" return line4, point4\n",
"\n",
"def animate4(i):\n",
" xx1 = l1*np.sin(sol[i, 0])\n",
" yy1 = -l1*np.cos(sol[i, 0])\n",
" xx2 = xx1 + l2*np.sin(sol[i, 1])\n",
" yy2 = yy1 - l2*np.cos(sol[i, 1])\n",
" line4[0].set_data([0., xx1, xx2], [0., yy1, yy2])\n",
" point4.set_offsets([[xx1, yy1], [xx2, yy2]])\n",
" return line4, point4\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate4,\n",
" init_func = init4,\n",
" frames = sol.shape[0],\n",
" interval = 100,\n",
" repeat_delay = 1000,\n",
" blit = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f82530e10>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAigAAAIoCAYAAABDDRCFAAAEtElEQVR4nO3BMQEAAADCoPVPbQdvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DecNgAB1H0vvwAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f7e94ef90>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xx1 = l1*np.sin(sol[:, 0])\n",
"yy1 = -l1*np.cos(sol[:, 0])\n",
"xx2 = xx1 + l2*np.sin(sol[:, 1])\n",
"yy2 = yy1 - l2*np.cos(sol[:, 1])\n",
"\n",
"plt.close('all')\n",
"\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"a.set_ylim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"\n",
"line5 = a.plot([0., xx1[0], xx2[0]],\n",
" [0., yy1[0], yy2[0]])\n",
"point5 = a.scatter([xx1[0], xx2[0]], [yy1[0], yy2[0]])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init5():\n",
" return line5, point5\n",
"\n",
"def animate5(i):\n",
" line5[0].set_data([0., xx1[i], xx2[i]],\n",
" [0., yy1[i], yy2[i]])\n",
" point5.set_offsets([[xx1[i], yy1[i]],\n",
" [xx2[i], yy2[i]]])\n",
" return line5, point5\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate5,\n",
" init_func = init5,\n",
" frames = sol.shape[0],\n",
" interval = 40,\n",
" repeat_delay = 1000,\n",
" blit = True)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f8239f2d0>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAigAAAIoCAYAAABDDRCFAAAEtElEQVR4nO3BMQEAAADCoPVPbQdvoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4DecNgAB1H0vvwAAAABJRU5ErkJggg==\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f7e8f5650>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xx1 = l1*np.sin(sol[:, 0])\n",
"yy1 = -l1*np.cos(sol[:, 0])\n",
"xx2 = xx1 + l2*np.sin(sol[:, 1])\n",
"yy2 = yy1 - l2*np.cos(sol[:, 1])\n",
"\n",
"plt.close('all')\n",
"\n",
"fig = plt.figure(figsize=(4,4))\n",
"a = fig.add_subplot(111)\n",
"a.set_xlim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"a.set_ylim(-(l1+l2)*1.1, (l1+l2)*1.1)\n",
"\n",
"bars = a.plot([0., xx1[0], xx2[0]],\n",
" [0., yy1[0], yy2[0]])\n",
"points = a.scatter([xx1[0], xx2[0]], [yy1[0], yy2[0]])\n",
"traj2 = a.plot([], [])\n",
"\n",
"tt = np.linspace(0, 2*np.pi, 128)\n",
"a.plot(np.cos(tt),\n",
" np.sin(tt),\n",
" dashes = (2, 2),\n",
" color = (0, 0, 0))\n",
"\n",
"def init6():\n",
" return bars, points, traj2\n",
"\n",
"def animate6(i):\n",
" bars[0].set_data([0., xx1[i], xx2[i]],\n",
" [0., yy1[i], yy2[i]])\n",
" points.set_offsets([[xx1[i], yy1[i]],\n",
" [xx2[i], yy2[i]]])\n",
" traj2[0].set_data(xx2[:i+1], yy2[:i+1])\n",
" return bars, points, traj2\n",
"\n",
"anim = animation.FuncAnimation(fig,\n",
" animate6,\n",
" init_func = init6,\n",
" frames = sol.shape[0],\n",
" interval = 40,\n",
" repeat_delay = 1000,\n",
" blit = True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7f6f82379d50>"
]
},
{
"html": [
"<img src=\"data:image/png;base64,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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f6f7e863b90>"
]
}
],
"prompt_number": 22
}
],
"metadata": {}
}
]
} |
|
raman-sharma/stanford-mir | notebooks/about_this_workshop.ipynb |
|
| {
"metadata": {
"name": "",
"signature": "sha256:7acca7e25588a5616409c2e1d8a3db5f5bcaa9b6a3766504e89522943f2bed6b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"About This Workshop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[CCRMA MIR Workshop Description and Registration](https://ccrma.stanford.edu/workshops/music-information-retrieval-mir)\n",
"\n",
"The material for this workshop is hosted on [GitHub](https://github.com/stevetjoa/stanford-mir)."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Instructors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Links redirect to that year's wiki page.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [2008](https://ccrma.stanford.edu/wiki/MIR_workshop_2008): Jay LeBoeuf\n",
"- [2009](https://ccrma.stanford.edu/wiki/MIR_workshop_2009): Jay LeBoeuf, Kyogu Lee\n",
"- [2010](https://ccrma.stanford.edu/wiki/MIR_workshop_2010): Jay LeBoeuf, Rebecca Fiebrink\n",
"- [2011](https://ccrma.stanford.edu/wiki/MIR_workshop_2011): Jay LeBoeuf, Stephen Pope, Leigh Smith, Steve Tjoa\n",
"- [2012](https://ccrma.stanford.edu/wiki/MIR_workshop_2012): Jay LeBoeuf, Leigh Smith, Steve Tjoa\n",
"- [2013](https://ccrma.stanford.edu/wiki/MIR_workshop_2013): Jay LeBoeuf, Leigh Smith, Steve Tjoa\n",
"- [2014](https://ccrma.stanford.edu/wiki/MIR_workshop_2014): Jay LeBoeuf, Leigh Smith, Steve Tjoa"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Guest Lecturers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 2011: Rebecca Fiebrink, Doug Eck, George Tzanetakis\n",
"- 2012: Oscar Celma, Michael Mandel\n",
"- 2013: Ching-Wei Chen, Nick Bryan, Gautham Mysore\n",
"- 2014: Stephen Pope, Andreas Ehmann"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Alumni"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 2008:\n",
"- 2009 [[full list](https://ccrma.stanford.edu/wiki/MIR_workshop_2009/Participants)]: Luke Dahl, Mike Gao, Craig Hanson, Jorge Herrera, Denis Lebel, Sang Won Lee, Gautham Mysore, Jeremy Sawruk, Hwan Shim, Diana Siwiak, Steve Tjoa, Elie Noune, James Hughes, Stefan Tomic, Lisa Lim, Fred Barrett\n",
"- 2010:\n",
"- 2011: Chris Colatos, Jeff Albert, Kamlesh Lakshminarayanan, Sean Zhang, Eli Stine, David Bird, Gina Collecchia, Dekun Zou, Bill Paseman, John Amuedo\n",
"- 2012:\n",
"- 2013: Freddie Sanchez, Linda Barnett, Xuchen Yang, Vivek Kumar, Felipe Lo\u00e1iciga Espeleta, Haoqing (Panda) Geng\n",
"- 2014: Krishna Kumar, Owen Campbell, Dan Cartoon, Rob Miller, Davide Fossati, Biagio Gallo, Joel Hunt, Shinobu Yamada, Fredom Luo, Sejin Oh, Phaedon Sinis, Xinyuan Lai, Greg Mertz, Matt Mitchell"
]
}
],
"metadata": {}
}
]
} |
|
|
| notebooks/Meg_stats.ipynb |
|
| {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle\n",
"import pandas"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%cd ../stats\n",
"#resultsFile = 'signifresults.multifactor.cpk'\n",
"resultsFile = 'signifresults.cpk'\n",
"\n",
"rsq = cPickle.load(open(resultsFile))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#rsq['p', 'r2'][chanlabel]['theta', 'beta2', 'beta1', 'alpha']\n",
"print rsq.keys()\n",
"\n",
"print type(rsq['r2']['MEG1211']['theta'])\n",
"\n",
"print rsq['p']['MEG1211']['theta']"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#for band in ['alpha','theta']:\n",
"# print 'Significance in', band\n",
" #for sensor in ['0113','0123','0112','0122','1542','1532','1543','1533']:\n",
"# print 'MEG'+sensor, 'p =',rsq['p']['MEG'+sensor][band]['syndepth']"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%cd ../stats\n",
"resultsFile = 'signifresults.multifactor.dev.bigscale.cpk'\n",
"\n",
"rsq = cPickle.load(open(resultsFile))\n",
"\n",
"for band in ['alpha']:\n",
" print 'Significance in', band\n",
" #for sensor in ['0113','0123','0112','0122','1542','1532','1543','1533']:\n",
" for sensor in ['0313','0113','0143','1543','1533','1713']:\n",
" print 'MEG'+sensor,'\\t', rsq['r2']['MEG'+sensor]['alpha']\n",
" for i in rsq['p']['MEG'+sensor]['alpha'].index:\n",
" if i != 'Intercept':\n",
" if i != 'bigramLogProbBack_COCA':\n",
" print i, '\\t\\t',rsq['p']['MEG'+sensor]['alpha'][i]\n",
" else:\n",
" print i, '\\t',rsq['p']['MEG'+sensor]['alpha'][i]\n",
" #print rsq['p']['MEG'+sensor][band]#['syndepth']"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} |
|
|
| red-sea-single-cell-genomes/code/singlecell_tara_stats.ipynb |
|
| {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statistics on counts of OGs (columns) in Tara surface samples (rows)\n",
"\n",
"* Finding significant OGs across groups of Tara samples using ANCOM\n",
"* Determining whether distribution of percent Tara samples found in differs for subgroups (z-test)\n",
"\n",
"Does everything separately for pelag and proch data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/luke/anaconda/envs/qiime190/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n",
"\n",
" UserWarning)\n",
"/Users/luke/anaconda/envs/qiime190/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
" warnings.warn(self.msg_depr % (key, alt_key))\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import re\n",
"from skbio.stats.composition import ancom\n",
"from sys import argv\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign variables"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# species = argv[1]\n",
"# evalue = argv[2]\n",
"# clusters_path = argv[3]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Prochlorococcus results\n",
"species = 'proch'\n",
"evalue = '1e-5'\n",
"myaxis = [0, 64, 0, 0.36]\n",
"clusters_path = '~/singlecell/clusters/orthomcl-pro4/groups.all_pro.list'"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Pelagibacter results\n",
"species = 'pelag'\n",
"evalue = '1e-5'\n",
"myaxis = [0, 64, 0, 0.36]\n",
"clusters_path = '~/singlecell/clusters/orthomcl-sar4/groups.all_sar.list'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Format and save Tara metadata"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Tara metadata\n",
"df_tara_names = pd.read_csv('/Users/luke/singlecell/tara/Tara_Prok139_PANGAEA_Sample.csv')\n",
"df_tara_metadata = pd.read_csv('/Users/luke/singlecell/tara/Tara_Table_W8.csv')\n",
"df_tara_metadata = df_tara_names.merge(df_tara_metadata)\n",
"\n",
"# SRF metadata\n",
"df_tara_metadata.index = df_tara_metadata['Sample label [TARA_station#_environmental-feature_size-fraction]']\n",
"index_SRF = [index for index in list(df_tara_metadata.index) if 'SRF' in index]\n",
"df_tara_metadata_SRF = df_tara_metadata.loc[index_SRF]\n",
"df_tara_metadata_SRF.index = df_tara_metadata_SRF.index\n",
"\n",
"# Latitude column\n",
"df_tara_metadata_SRF['category_latitude'] = pd.Series(0, index=np.arange(len(df_tara_metadata_SRF.columns)), dtype='object')\n",
"for index, lat in abs(df_tara_metadata_SRF['Mean_Lat*']).iteritems():\n",
" if lat < 23.5:\n",
" df_tara_metadata_SRF.loc[index, 'category_latitude'] = 'tropical'\n",
" elif lat > 40:\n",
" df_tara_metadata_SRF.loc[index, 'category_latitude'] = 'temperate'\n",
" else:\n",
" df_tara_metadata_SRF.loc[index, 'category_latitude'] = 'subtropical'\n",
"\n",
"# Temperature column\n",
"df_tara_metadata_SRF['category_temperature'] = pd.Series(0, index=np.arange(len(df_tara_metadata_SRF.columns)), dtype='object')\n",
"for index, temp in df_tara_metadata_SRF['Mean_Temperature [deg C]*'].iteritems():\n",
" if temp < 10:\n",
" df_tara_metadata_SRF.loc[index, 'category_temperature'] = 'polar'\n",
" elif temp > 20:\n",
" df_tara_metadata_SRF.loc[index, 'category_temperature'] = 'tropical'\n",
" else:\n",
" df_tara_metadata_SRF.loc[index, 'category_temperature'] = 'temperate'\n",
"\n",
"# Red Sea column\n",
"df_tara_metadata_SRF['category_redsea'] = pd.Series(0, index=np.arange(len(df_tara_metadata_SRF.columns)), dtype='bool')\n",
"for index in df_tara_metadata_SRF.index:\n",
" if index in ['TARA_031_SRF_0.22-1.6', 'TARA_031_SRF_<-0.22', 'TARA_032_SRF_0.22-1.6', 'TARA_032_SRF_<-0.22', 'TARA_033_SRF_0.22-1.6', 'TARA_034_SRF_0.1-0.22', 'TARA_034_SRF_0.22-1.6', 'TARA_034_SRF_<-0.22']:\n",
" df_tara_metadata_SRF.loc[index, 'category_redsea'] = True\n",
" else:\n",
" df_tara_metadata_SRF.loc[index, 'category_redsea'] = False"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# export mapping file\n",
"df_tara_metadata_SRF.to_csv('tara_metadata_SRF.tsv', sep='\\t')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Format and save count data"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Paths of input files, containing cluster counts in Tara samples\n",
"paths = pd.Series.from_csv('/Users/luke/singlecell/tara/paths_%s_%s.list' % (species, evalue), header=-1, sep='\\t', index_col=None)\n",
"\n",
"# Data frame of non-zero cluster counts in Tara samples (NaN if missing in sample but found in others)\n",
"pieces = []\n",
"for path in paths:\n",
" fullpath = \"/Users/luke/singlecell/tara/PROK-139/%s\" % path\n",
" counts = pd.DataFrame.from_csv(fullpath, header=-1, sep='\\t', index_col=0)\n",
" pieces.append(counts)\n",
"df_nonzero = pd.concat(pieces, axis=1)\n",
"headings = paths.tolist()\n",
"df_nonzero.columns = headings\n",
"\n",
"# SRF dataframe, transposed, zeros, plus 1, renamed indexes\n",
"col_SRF = [col for col in list(df_nonzero.columns) if 'SRF' in col]\n",
"df_nonzero_SRF = df_nonzero[col_SRF]\n",
"df_nonzero_SRF_T = df_nonzero_SRF.transpose()\n",
"df_nonzero_SRF_T.fillna(0, inplace=True)\n",
"df_nonzero_SRF_T_plusOne = df_nonzero_SRF_T + 1\n",
"df_nonzero_SRF_T_plusOne.index = [re.sub(species, 'TARA', x) for x in df_nonzero_SRF_T_plusOne.index]\n",
"df_nonzero_SRF_T_plusOne.index = [re.sub('_1e-5', '', x) for x in df_nonzero_SRF_T_plusOne.index]\n",
"\n",
"# Dataframe of all clusters (includes clusters missing from Tara)\n",
"clusters = pd.Series.from_csv(clusters_path, header=-1, sep='\\t', index_col=None)\n",
"df_all = df_nonzero.loc[clusters]\n",
"df_all_SRF = df_all[col_SRF]\n",
"df_all_SRF_T = df_all_SRF.transpose()\n",
"df_all_SRF_T.fillna(0, inplace=True)\n",
"\n",
"# remove '1e-5' from count indexes\n",
"df_nonzero_SRF_T.index = [re.sub('_1e-5', '', x) for x in df_nonzero_SRF_T.index]\n",
"df_all_SRF_T.index = [re.sub('_1e-5', '', x) for x in df_all_SRF_T.index]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# export counts to file\n",
"df_nonzero_SRF_T.to_csv('tara_%s_nonzero_SRF.csv' % species)\n",
"df_all_SRF_T.to_csv('tara_%s_all_SRF.csv' % species)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ANCOM"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# ANCOM with defaults alpha=0.05, tau=0.02, theta=0.1\n",
"# for grouping in ['category_latitude', 'category_temperature', 'category_redsea']:\n",
"# results = ancom(df_nonzero_SRF_T_plusOne, df_tara_metadata_SRF[grouping], multiple_comparisons_correction='holm-bonferroni')\n",
"# results.to_csv('ancom.%s_nonzero_SRF_T_plusOne.%s.csv' % (species, grouping))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Z-test"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# lookup dict for genus name\n",
"dg = {\n",
" 'pelag': 'Pelagibacter',\n",
" 'proch': 'Prochlorococcus'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load OG metadata to determine RS-only OGs\n",
"df_og_metadata = pd.read_csv('/Users/luke/singlecell/notebooks/og_metadata.tsv', sep='\\t', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"og_rs = df_og_metadata.index[(df_og_metadata['Red_Sea_only'] == True) & (df_og_metadata['genus'] == dg[species])]\n",
"og_other = df_og_metadata.index[(df_og_metadata['Red_Sea_only'] == False) & (df_og_metadata['genus'] == dg[species])]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"df_all_SRF_T_rs = df_all_SRF_T[og_rs]\n",
"df_all_SRF_T_other = df_all_SRF_T[og_other]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"count = (df_all_SRF_T > 0).sum()\n",
"count_rs = (df_all_SRF_T_rs > 0).sum()\n",
"count_other = (df_all_SRF_T_other > 0).sum()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save count data\n",
"count.to_csv('hist_counts_%s_ALL_og_presence_absence_in_63_tara_srf.csv' % species)\n",
"count_rs.to_csv('hist_counts_%s_RSassoc_og_presence_absence_in_63_tara_srf.csv' % species)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_samples = df_all_SRF_T.shape[0]\n",
"num_ogs = max_bin = df_all_SRF_T.shape[1]\n",
"num_ogs_rsonly = count_rs.shape[0]\n",
"num_ogs_other = count_other.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAJoCAYAAAAAvxcRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a77a150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# all OGs AND RS-assoc OGs\n",
"plt.figure(figsize=(10,10))\n",
"sns.distplot(count_rs, bins=np.arange(num_samples+2), color=sns.xkcd_rgb['orange'], label='Red Sea-associated ortholog groups (%s)' % num_ogs_rsonly)\n",
"sns.distplot(count, bins=np.arange(num_samples+2), color=sns.xkcd_rgb['blue'], label='All %s ortholog groups (%s)' % (dg[species], num_ogs))\n",
"plt.xlabel('Number of %s Tara surface samples found in' % num_samples, fontsize=18)\n",
"plt.ylabel('Proportion of ortholog groups', fontsize=18)\n",
"plt.xticks(np.arange(0,num_samples+1,10)+0.5, ('0', '10', '20', '30', '40', '50', '60'), fontsize=14)\n",
"plt.yticks(fontsize=14)\n",
"plt.legend(fontsize=16, loc='upper left')\n",
"plt.axis(myaxis)\n",
"plt.savefig('hist_%s_paper_og_presence_absence_in_63_tara_srf.pdf' % species)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAGJCAYAAAB4nxGoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhyjDXhRFURQ9RBn2oiiKough/g0nzXEar+68zQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1223e6110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# all OGs\n",
"plt.figure(figsize=(8,6))\n",
"sns.distplot(count, bins=num_samples+1)\n",
"plt.axis([-0, num_samples, 0, .35])\n",
"plt.xlabel('Number of %s Tara surface samples found in' % num_samples)\n",
"plt.ylabel('Proportion of %s OGs' % num_ogs)\n",
"plt.title('Presence/absence of all %s %s OGs in %s Tara surface samples' % (num_ogs, species, num_samples))\n",
"plt.axis(myaxis)\n",
"plt.savefig('hist_%s_all_og_presence_absence_in_63_tara_srf.pdf' % species)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAGJCAYAAAB4nxGoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xe8XFW5//HPzJxekpDkBAIinQcQCE2DGKoCFlTAcsWL\nSrNgvfrTq6jYO4rlerkWuICCehVFbHQVMWCAIEiRJ8GEFpJwUk+vM78/1ppkcjIzmXOSOedk5/t+\nvc7rzO5r1uy9n7XW3nvtVC6XQ0RERJIhPdEJEBERkW1HgV1ERCRBFNhFREQSRIFdREQkQRTYRURE\nEkSBXUREJEFqJjoB2wMz2wP4F/CPOCoV/3/H3a+cmFSNnZmlgAfd/dAy83wamOHu7x+/lG1bZjYH\n+CWwDjjT3Z8qmHYi8HUgA6wGPuju/4jTjgO+CjTGZc9196Uj1l24T+T3hxbgaeA8d38izvcR4M1x\nega4Gfi4uw9u6++7PTKztwGvd/dXVzBvBvgYIT+zcfSfgU+5+9qC+Y6L8+0d5+sFLnX3a0eRriOB\nj7r7GytdJi73buB8oAG4n7AvDMb97WtALdADfMDd7x2x7FuADwE5YI+Y7vY4/D53nz+atIyH+Jvc\nAOwDfMvdvz/BSdpm4ncbBKa5e8dEp2c0FNgr1+PuR+QHzGxX4GEzu9fdH57AdI3Fi4G/TXQixsFr\ngD+6+zsKR5rZFELAP9Pd/2xmBtxgZocAOwO/Al7q7g+a2fuA/wZeWWT9m+wTcd3fAb4I/LuZvQE4\nHZjr7gNmVhe3+2ngk9v0m27fKu1M4/+APuAYd18fT7wfAu42syPdvdvMXgF8H/g3d78bwMyeD9xi\nZt3u/utKNuTuC4HRBvUzgfcUpO8XwAfN7JvAT4FT3P0fZvYq4MfAASO2+eM4HjO7EnjI3S8dTRom\nwB7A8e7eOtEJqZLtsqMXBfYxcvdnzWwxsH8s3Z8PNAPr3P2lZnY+cCGhNreaUOJ2M5sHfINwGSQH\nfNndrzezWkIt8ThCze7vwPvdvcvMlgJXAS8Fdgd+7u4fBTCz8wgntyFgFXCOuz9jZqcRgke+hvAR\nd88H89MJpWzM7OPAa4H6mP4Pu/sNcb6DzOwOYKeYnnfHk+eFwDuBfsKJ9p3u/lgs7Hw3prEW+Jm7\nfyXWbm8H/gDMjev7pLv/PJ6cLwFeRSgd3w1c6O5DMW1nxrx6Im5/xcjfwswuBt4Ul18EvC/m1buB\ntJk1uvtbChbZL/5Of46/pZtZB6HAczjwB3d/MM77A0Ite4vMrBGYDSyPo3Yh/JbNwEAM7u8BZpVY\nfj9CIaIZ2BV4gBCgBszss4TfaYCwP53j7ivLjD+WUENsjNMudveb43YuAt4a82txXKazzPe6krCv\nHgjMBG4l7M/DZtZH2JcOBf49pr3S7Z4bN7Grmf0OeH6c9mZ39xFpOBp4EbCHu+cA3H0YuMTMXgK8\ni3BcfZVQG747v6y7PxWPx+a4rqLH4IjtHQ98190Pid+/AziEsG8/Fn+XnhFZ9RbgG+6+Pg5fCNTG\nGvtuMb9ShNrtqlL5XUr8nl8iHKuzgRvd/V1mtg9wG/A48Dzg2Ljt09h4XH/Q3X83Yn01hP3taMJv\n9TjhN9kNuM/dd4rz7ZMfjvn41rjONXHeRjO7n3BeOZVwLqwFpgNfcvcfxvV8Ejg7bssJLWFdZvZ2\nwvkkRWiheJ+7Lx6R1hbCOTDfCnOPu7/bzNLAt4CjgClx9vPc/R4z+zGwPn6/WcDP4vCrgLY4351x\nvgHgIML+fRPwgbiufGscpdIZ95VLCpL7BXf/DRNI19jHyMxeTDhAF8RRBwHHxaB+HGHnn+fuRxJ+\n9F/F+T5DOPhfSDgATorjPwYMuvtR7n44ITh8pWCTze5+HPAS4H1mtkdsav4KoSZwGPAb4ONmti/h\nBPCKuP13Ar+KgQfgZcCtsSZzUkz3YYSCwOcKtrkPcEZssk8Dn4wH0jeBU919LiHwzYvz/xi4In63\nucDJZvb6OG1vwolobvyuX4vj30MIpoe4+8GE5ux/i82ShwAvirXiG4ErivwO5xJOJkfG7/AIcJW7\n/wT4HvB/I4I6hODfYmYvi+t4IeH3mw3sD/SY2U/jyepnhGBTTJOZ3W9mD5rZCmAh4aT/sTj9asKJ\nZIWZ3WVmXycEpvtKrO/tMe0vIRQ+9gZeZWbPI5xoXujuLwJuAeaWGT8d+AXhxHMYcA5wTdxnXkPY\nN+fG33Up8N4S6Sl0KGFfOSj+vTOOrwNucPcDCYWv0Wz3PXEde8VlDgXuBD5cZPvHAHfng/oItwHz\nzGwqcHDMh024+3x3z4//DMWPwZEKt3UEcAqhcLMr8IYi8+8P7GxmN5rZA4SWmXVx+8NmNotwqear\nbNz/R+N9wEXufjTwAuANsZUJQs35E/F3aCUE92Pj7/AZNj2u8+YRWhfmxLx4inDMjfzuI4cPIJwz\nTiG0inXEY3Qt8Dbg5fG8c3b8rvnWjDcR9tVDgWeAC+MlirOAl8Rlvg1cVyStrwfq4nbmArWxwvBi\nwiXDY+L546fARwuWmwO8kBDcPwy0u/sxwP+MmO9g4MSYr4cBFxRu3MxOKpPOzwJficfgOyi9P40b\n1dgr1xRP9ClCvrUTahbLQksu/3D37jjvqwhB8a5YQgeYZmbTgJ8D/x1PdLcBH4/TTwOmmtkpcbgW\nWFmw/RtgQ0vBSkJp+ATgJnd/Nk77DkCsUe8C3F6w/SFgXzMbApa4+wDwlJmdA5wdCwNHEwJr3q/c\nfU38fCXwNXe/yMx+Tmj+/D3hJPoTM2sCjgd2MrMvxGWaCQfJvYQa641x/P2EWjuEmvWPY3pw97Pi\nd/g/wgG5MOZvmlALHOnlwJXu3heHv00o3JTct92908xeC3zJzC4B/gL8kVBqryX8FvPcfUlsiv8V\nofAx0oam+Pi7/Ri4JV+T83Bd7lQz25Nw0jgB+J2ZXebuFxVZ30cJhaGPEILEbMLvsYxQe/+7md1I\nKCD9Mf62xca/AlicL0C4+6Nm9teYhsOBX8S04e7FgmgxV7l7b/yuPyK0ElwWp/01/p872u1auMZ+\nj2+8h+EB4IwSaagtMb6eEHjy+/qGIGRmPwOMUABZ6e4nUfoYLOcmdx+K63yIcPwVS9/LCMGuH/gR\n4bLMhwDc/TngeWZ2OOHYfJG7P17BtvPeCrwytmQdEL93C6FFrt/d74nbWRpr1m+Jx/UxxNaKER4k\ntGj9jdAq9Qt3vy/W0Mt5sEhrRf64Oh14TWx9Orxguy8ltDR2xnk/CGBm3yDs63fH/TkHzDCz1hGt\nSH8BPmdmtxN+s6+7+5PAk2a2yszeBexLOMZWFyz3m1gYfNbM+tnY+vYvQmEhb8M5JNbgT2XTisSr\nRqSTfDoJl4i+b2ZnxLRN+GU21dgr1+PuR7j74e5+iLufVFADAOgq+JwhBKv8/IcTap7r3P0HhFLx\nLYSd5yEL13wzhCbEDfOzaa2gd0R6UoRgXXgSa7AQBTPA7SO2fwyhNvta4Ndx/iOAuwgl/JsJpetU\nwTaGR2xvEMDd30oIfosJwehXcZsALy7Y5osJLQcQgmZe4Ul45HeYZWb5JuyvFqzrKEItZKSR+3CG\nUPBKFZk3v40U0O3uJ8b1f4BwUngceBa4y92XxNmvAA41s/pS6wOI+8I3gZ/Fgx0z+4iZvdjdn3D3\nK939bYRr9e+J0/8ea/z3x9/iZ4Ra+xPApYTLHyl3z7n7CYTa0Crgm2b2rVLjY56M/P75fBmZ31Nj\nzWdLhgo+p9l03+gqGD9SJdstbBEp3DcKzQdeZGYNRaadCMx393XAo3EYAHd/U9x/3k1oZqXEMbil\na8SFx1+pND4LXO/u3bEQcA3wYjNrjQEvn6a/E4LqIUXWUc5dhFaDRwi18BUF6diQPguXBucTgv5N\nhNaBzdLr4YbDQ4GPEJq3f2Fm743fr/C3rBuxaBdFxN/zfkLz/F+Aiyl9nE+LLYYZQlA9Iv5ORxJa\ndTa5NBSPx30J56ipwJ/M7LWxcPbbuO5fEVoQC79r/4hklmp9K7V/59M8Mp1H5NPp7v9DyMfbgFcQ\n9qdiBalxo8BeuZKBoohbgLNigMrfKXtb/DwfOMLdf0RozpwKTCME1veaWa2F5u4rgC9vYTt/Al5m\nZjvH4XcRdvzbgVNikMfMXkk4kTQQAnL+WtuxwL3u/i3CgXgGGwM0hJL3VAvXwd8B/MHMZpjZU8Dq\n2ELwSWBOPBD/RmxGja0T8wkFCSidf7cBbzazuvi9/4fQZHcTcEHBCfcLhBrQSDcD58YWA4D3A3d4\nmbvOYwn+D/EEiIWb3Abc/SHgeuAlBUHndcAj7j7yBFHsO32d0ByZb/ZsAr5sZjsVzHMg4eRHLFQc\nEf/uB04GPufuv4jrngtkzOxQM3sY+Ke7f5VQgDi01HjC77C/mR0Vv98LCL/1nwn5faaFa5YQAsQH\nS+VVgX+Lv1EDoSBR7Bri38Lmtul2AXD3BcAdwFVx38LM0mb2CcJlix/EWf8f8G0L1+SJ87US9vt8\njbvwGHwH4Rgs/I3G6jpC83hDLDyeDtxDCJr/a+HyXT5fjI2X8bbIzGYQftuPergHZk/CJYz88Vq4\nL55AuGzxbcKljdPZ9LjOr/O1hOPnLnf/LHBt3MZawnXz/eKsZ1aYzKOAZ939y+5+K6HlIh9jbgNe\nX/D7f55wrN5EuNE0f9/J+yhyT0sscPzQ3W9x948RznEvIBwz13u4G//vpb5rBd4Uz72NhJaR/P6d\nz9ebS6XTzBYQLiVeTdifZhDO6RNGTfGVq/juSHe/xcy+SriOPUy48SbfvPgR4Dtm9nnCAf8ZDzf3\nfJ5wLf7vhIPhAcJJqti28zcPPWyh2fZmM8sRrsuf5+4rzOwdhNojhBPaqwk3l/TFmg2E61GvM7NH\nCCXb24HpBaXNR4HfE3bSvxJq0AMxrX80s15CCfj8OP+/A981s38QmiWvdfefxiBZKv++T7g+uDAO\n/wn4Tpx/N+BvZpYlXP87p8jyVxBuGLonnkwfJ1zb25KzgB9auGlxOeGEgIc74S8Efm2hOX8txa+n\nMvI7ebjh733ATWb2Q0KAHyZckskSTjj3Uvpu64/H7a4mNK/+GdjX3a+0cGlioZl1xWnv83CH9cjx\n73f31bGw8t1Y4Bkm3CD3OPC4mR0Y05Qj1P7eDqEFATg/FjJG6iEEiWmEJturRubBGLdb2By6JWcT\njok74n5dT7iEckxBE+/NZnYW8AkLl0ByhPPcrYRjAEocg6NIR6l9+TJCAWEh4Ri+H/iQhxtOX0so\ncNQQjrWzPF5Cq2QbMW8vAR6M+8dKQsF5X0JLQeH81wKnx0LfAOG4brNwE2lhy8PvCC0WD8f9ZzVw\ngbuvtXCj460WLvsVu+ZdzI2EQrYTavV/A9aa2V7u/ttY0bg7HgsPAW93914zu5RwaSJLON6KFSSu\nAo6N56pewj0a3yUc+9ea2amE/e1ONlYmRip3Du8j5OcUwiWDa2KFJn+uvbFMOvOFyS8T9qePu/uy\n8llVXSm9tlVEyrHt59ErkVGzcE393tgCmQhqiheRLVHpX5Iscfu3auwiIiIJohq7iIhIgiiwi4iI\nJEgi7oofGhrOrV27WX8JAuy0UxPKm+KUN8UpX0pT3pSmvCmtGnnT1tZa8hHsRNTYa2rG8tjijkF5\nU5rypjjlS2nKm9KUN6WNd94kIrCLiIhIoMAuIiKSIIm4xv7Qo4tYV+T6RW1tDfvsVUk32CIiIslQ\n1cAeu/i8jPDqvD5Cd4VLCqa/jvASkSzwE9/4drKFhNddAix19/Mp4+m1NXT3bP5uiIHOVQrsIiKy\nQ6l2jf10oN7djzGzuYQ3Vp0O4QUOhDd/HUnoh/pRM7sG6Abw8HpFERERGYVqX2OfR3h7T/7tTEfl\nJ7h7FjjQ3bsIr1NME15YMAdoNrObzey2WCAQERGRClQ7sE9hY5M6wFCsqQMhuFt4Of0DhDdZdRNq\n75e4+6nAhYQ39+gmPxERkQpUuym+A2gtGE7HmvoG7n49cL2ZXU14D+5PCa/exN0Xx1cUzgbKvgav\ntaXINfZUPW1trUXm3rEoD0pT3hSnfClNeVOa8qa08cybagf2+cBpwHVmdjThHbwAmFkr8FvgFHcf\nINTWs8B5wCHAe8xsV0LBYPmWNtTZ1bfZuIHOftrbO7fB19h+tbW17vB5UIrypjjlS2nKm9KUN6VV\nI2/KFRSqHdivB042s/lx+FwzOwtodvfL481yfzGzAeAfwDUxTVea2Z3EQD+yli8iIiLFVTWwu3uO\ncJ280KKC6ZcDl4+YPgicXc10iYiIJJVuShMREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAF\ndhERkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEE\nUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxER\nSRAFdhERkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYR\nEZEEUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEEqanmys0sBVwGzAH6gAvc\nfUnB9NcBHwWywE/c/TtbWkZERERKq3aN/XSg3t2PAS4CLs1PMLM08CXgJOAY4N1mNr3cMiIiIlJe\ntQP7POAmAHdfAByVn+DuWeBAd+8CZsa0DJRbRkRERMqrdmCfAqwvGB6KNXUgBHczOwN4APgz0LOl\nZURERKS0agfMDqC1cHuxpr6Bu1/v7rsC9cBbCUG97DIiIiJSXFVvngPmA6cB15nZ0cBD+Qlm1gr8\nFjjF3QeAbmA4LvOaYsuU09rSsNm4gVQ9bW2tRebesSgPSlPeFKd8KU15U5ryprTxzJtqB/brgZPN\nbH4cPtfMzgKa3f1yM7sG+IuZDQD/AK6J851SuEwlG+rs6tts3EBnP+3tnVv1BbZ3bW2tO3welKK8\nKU75UprypjTlTWnVyJtyBYWqBnZ3zwEXjhi9qGD65cDlRRYduYyIiIhUQDeliYiIJIgCu4iISIIo\nsIuIiCSIAruIiEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCKLCLiIgk\niAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgCu4iI\nSIIosIuIiCSIAruIiEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCKLCL\niIgkiAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgC\nu4iISIIosIuIiCRITTVXbmYp4DJgDtAHXODuSwqmnwV8ABgEHnL3d8fxC4H1cbal7n5+NdMpIiKS\nFFUN7MDpQL27H2Nmc4FL4zjMrAH4HHCwu/eb2U/M7DTgVgB3P6nKaRMREUmcajfFzwNuAnD3BcBR\nBdP6gWPcvT8O1xBq9XOAZjO72cxuiwUCERERqUC1A/sUNjapAwyZWRrA3XPu3g5gZu8Dmt39NqAH\nuMTdTwUuBK7NLyMiIiLlVbspvgNoLRhOu3s2PxCvwX8N2A84M45eBDwO4O6LzWw1MBtYVm5DrS0N\nm40bSNXT1tZaZO4di/KgNOVNccqX0pQ3pSlvShvPvKl2YJ8PnAZcZ2ZHAw+NmP4DoNfdTy8Ydx5w\nCPAeM9uVUDBYvqUNdXb1bTZuoLOf9vbOMSY9GdraWnf4PChFeVOc8qU05U1pypvSqpE35QoK1Q7s\n1wMnm9n8OHxuvBO+GVgInAvcaWZ/AnLAt4HLgavN7E4gC5xXWMsXERGR0qoa2N09R7hOXmhRBdv/\n9+qkSEREJNl0U5qIiEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCKLCL\niIgkiAK7iIhIgiiwi4iIJIgCu4iISIJsMbCb2XQze1n8fJGZ/cLMDqp+0kRERGS0Kqmx/xQ4IAb3\nNwC/Ab5X1VSJiIjImFQS2Hdy9+8CrwWucvcfA03VTZaIiIiMRSWvbU2b2ZHA6cDxZnZYhcuJiIjI\nOKukxv5R4BLgG+6+hNAM/8GqpkpERETGZIs1b3e/Hbi9YPjoqqZIRERExqxkYDezWuALwGJ3v9zM\nlgM7A8PAi9z97+OURhEREalQuab4LwG7AdfH4RXungZeD3ys2gkTERGR0SsX2F8LnOPuqwtHuvsN\nwMFVTZWIiIiMSbnAPuDuQwXD7y743F+l9IiIiMhWKBfYh8xs5/yAu98NYGa7AkMllxIREZEJUy6w\nfx/4lZlZfoSZ7Qv8H/Df1U6YiIiIjF7Ju+Ld/X/MbCdggZkNADmgHviSu189XgkUERGRypXtoMbd\nv0R4xO1U4OXAbHf/2ngkTEREREavbGA3szrC421vi39nxHEiIiIyCZUM7GY2A1gIvB8YBFLAfwAL\n4zQRERGZZMp1Kfs14Bp3/2rhSDP7ZJx2fjUTJiIiIqNXrin+RSODOoC7fwGYV70kiYiIyFiVC+y1\nZaYNb+uEiIiIyNYrF9iXmdmJI0ea2UuBp6qXJBERERmrctfYPwbcYGbfA+6J8x4DnEt4/E1EREQm\nmZI1dne/F3gZsBfwdeDLwCxgnrv/Y3ySJyIiIqNRrsaOuz9KqKGLiIjIdqBsBzUiIiKyfVFgFxER\nSZBRBXYzm1mthIiIiMjWK3mN3cwOBy4DziM803490GJm3cAb3f2+8UmiiIiIVKpcjf1y4NPu/k/g\nUuCd7r4z8GbCu9pFRERkkil3V3zK3W+Jn6e7+20A7v63St/wZmYpQq1/DtAHXODuSwqmnwV8gPCS\nmYfc/d1bWkZERERKK1djf9TMvmhmU4Bfmtm7zGyqmb0LqDTQng7Uu/sxwEWEmj8AZtYAfA443t2P\nBaaZ2WnllhEREZHyygX2C4GdgSeA9xJq0e3AacA7K1z/POAmAHdfABxVMK0fOMbd++NwDaGGXm4Z\nERERKaNcz3Od7n4BsAswF9gTaHH309x9RYXrnwKsLxgeMrN0XH/O3dsBzOx9QHNs7i+5jIiIiJRX\n7q74WcAPCf3C1wKrgYyZ/Rz4sLt3V7D+DqC1YDjt7tmCbaQI73bfDzizkmVKaW1p2GzcQKqetrbW\nInPvWJQHpSlvilO+lKa8KU15U9p45k25m+cuB34MvAk4i1CTvhr4CHBFHL8l8wlN99eZ2dHAQyOm\n/wDodffTR7FMUZ1dfZuNG+jsp729s5LFE6utrXWHz4NSlDfFKV9KU96UprwprRp5U66gUC6wP9/d\nfxE//6+ZLXT3bwEfN7NHK9z29cDJZjY/Dp8b74RvBhYS+qG/08z+BOSAbxdbpsJtiYiI7PDKBfYB\nMzvO3f9iZi8jNJFjZkcBvZWs3N1zhJvwCi2qYPsjlxEREZEKlAvs7wV+ER9L6wZeZ2aHAN+j8rvi\nRUREZByVDOzufg+wh5nNdPdVBZP0+JmIiMgktcXHyEYEdREREZnE9Hy4iIhIgpQM7Gb21fj/5eOX\nHBEREdka5W6e+zczuxX4jpmdD6QKJ7r7X6qaMhERERm1coH9i4SXsMwmvKylUA44qVqJEhERkbEp\nd1f8D4EfmtnF7v75cUyTiIiIjFG5GnvepfF6+0vj/H8ELq6wr3gREREZR5XcFf9fhC5gzwPeBtQR\nOqkRERGRSaaSGvuR7j6nYPi9o+grXkRERMZRJTX2tJlNyw/Ez0PVS5KIiIiMVUXX2IF7zOy3cfg1\nwJerlyQREREZq0q6lL0SOBNYAjwBnOnu/1vldImIiMgYVFJjx90fBh6uclpERERkK6mveBERkQRR\nYBcREUmQLTbFm1kNcCownYL+4t39R1VMl4iIiIxBJdfYfwLsAfyT0Ec88b8Cu4iIyCRTSWA/1N0P\nqHpKREREZKtVco39n2Y2u+opERERka1WSY29CXAzexjoy490d722VUREZJKpJLB/qeqpEBERkW2i\nkp7n7iDU2l8NnAFMi+NERERkktliYDez/wQ+AzwFLAU+YWYfr3K6REREZAwqaYo/G5jr7r0AZvZD\nYCFqohcREZl0Knptaz6oR33ota0iIiKTUiU19tvN7JfAVXH4bcAfq5YiERERGbNKAvt/AO8C3kqo\n4f8R+H41EyUiIiJjUzKwm9ku7r4C2B34ffzL25VwM52IiIhMIuVq7JcDpwF3sLGPeAgvgskBe1cx\nXSIiIjIGJQO7u58WPx7p7msKp5nZntVMlIiIiIxNuab43Qm18z+Y2SvY+MrWGuAPgF4MIyIiMsmU\na4r/LHAi4Xr6XwrGDwG/q2aiREREZGzKNcWfB2BmH3X3r45fkkRERGSsKumg5pxqJ0JERES2jUqe\nY3/UzD4FLAA29EDn7n8pvYiIiIhMhEoC+3TCtfYTC8blAL2PXUREZJLZYmB39xMBzKwVyLj7ukpX\nbmYp4DJgDqGP+QvcfcmIeZqAW4Dz3H1RHLcQWB9nWeru51e6TRERkR3ZFgO7me0N/AzYB0iZ2ZPA\nG919cQXrPx2od/djzGwucGkcl1/3kcD3gN0KxtUDuLtaBEREREapkpvnvg98zd1nuPt04MvADytc\n/zzgJgB3XwAcNWJ6HSHQP1Ywbg7QbGY3m9ltsUAgIiIiFagksM909+vyA+7+c8J190pMYWOTOsCQ\nmW3Yprvf7e7L2Nj5DUAPcIm7nwpcCFxbuIyIiIiUVsnNc/1mdoS73w8bms97Klx/B9BaMJx29+wW\nllkEPA7g7ovNbDUwG1hWbqHWlobNxg2k6mlray0y945FeVCa8qY45UtpypvSlDeljWfeVPra1l+a\n2RpCzXo68G8Vrn8+4UUy15nZ0cBDFSxzHnAI8B4z25VQMFi+pYU6u/o2GzfQ2U97e2eFSU2mtrbW\nHT4PSlHeFKd8KU15U5ryprRq5E25gkIld8X/zcz2B/YnBPZF7j5Q4bavB042s/lx+FwzOwtodvfL\nC+YrfHvyCHXEAAAgAElEQVTcFcCVZnYnkCXcLb+lWr6IiIhQ2V3xzwf+i/Dc+iDhpTAfdPf2LS3r\n7jnCdfJCi4rMd1LB50Hg7C2tW0RERDZXyU1p1wK3El4GsxewELi6mokSERGRsankGvsUd/9uwfA3\nzeycKqVHREREtkIlNfaFZrahadzMXgX8vXpJEhERkbGqpMZ+GnCOmf2AcDNbE4CZvRXIuXumiukT\nERGRUajkrvhZ45EQERER2XqV3BXfBHwaeGmc/4/Axe7eXeW0iYiIyChVco39u0AzoeOYtxH6d/9e\nNRMlIiIiY1PJNfYj3X1OwfB7zezRaiVIRERExq6SGnvazKblB+LnoeolSURERMaqkhr7pcA9Zvbb\nOPwawqtbRUREZJKpJLD/FrgXOJ5Qwz/T3St5mYuIiIiMs0oC+53ufiDwcLUTIyIiIlunksD+oJm9\nBbgH6M2PdPenqpYqERERGZNKAvvc+FcoB+y97ZMjIiIiW6OSnuf2Go+EiIiIyNYrGdjNbFdC5zT7\nAX8FLnL3deOVMBERERm9cs+xXwk8BnwEaAC+OS4pEhERkTEr1xS/m7ufCmBmtwMPjE+SREREZKzK\n1dgH8h/cfbBwWERERCanSrqUzctVLRUiIiKyTZRrin+BmS0pGN4tDqeAnLvrcTcREZFJplxg33/c\nUiEiIiLbRMnA7u5PjmdCREREZOuN5hq7iIiITHIK7CIiIglSSV/x261cLkdHx/qS01tbp5BKpcYx\nRSIiItWV6MDe29PNrQsep7Gpuei0k+fuy5QpUycgZSIiItWR6MAO0NjUTFNz60QnQ0REZFzoGruI\niEiCKLCLiIgkiAK7iIhIgiiwi4iIJIgCu4iISIIosIuIiCSIAruIiEiCJP459lJyuRydnR1Fp6lH\nOhER2V7tsIG9t6ebO+5fw7TpMzYbrx7pRERke7XDBnaAhsYm9UonIiKJomvsIiIiCVLVGruZpYDL\ngDlAH3CBuy8ZMU8TcAtwnrsvqmQZERERKa7aNfbTgXp3Pwa4CLi0cKKZHQncAexd6TIiIiJSWrUD\n+zzgJgB3XwAcNWJ6HSGQPzaKZURERKSEagf2KcD6guEhM9uwTXe/292XAalKlxEREZHSqn1XfAdQ\neNt52t2zVViG1paGzcYNTamnrqme5iLTervrSKdrN1suzQAzZ7YydWpy7pZva0vOd9nWlDfFKV9K\nU96UprwpbTzzptqBfT5wGnCdmR0NPFSlZejs6ttsXFdHP3W5frJsPq27e4B0epj6xk2n9XT3s2pV\nJwMDyWgkaGtrpb29c6KTMSkpb4pTvpSmvClNeVNaNfKmXEGh2oH9euBkM5sfh881s7OAZne/vGC+\nXLllqpxGERGRxKhqYHf3HHDhiNGLisx30haWERERkQoko71ZREREAAV2ERGRRFFgFxERSRAFdhER\nkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEEUWAX\nERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAF\ndhERkQRRYBcREUkQBXYREZEEUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQRRYBcREUkQBXYREZEE\nUWAXERFJEAV2ERGRBFFgFxERSRAFdhERkQSpmegEbGu5XI6O7kF6+gdZu26YXHcPqcwgg0NZ6usy\nTG2uY0pzHblcbqKTKiIiss0lIrD39Q+xdHkHy1f18Ozqbnr6hgqmriu6TCYNLQ0Znrdzir1mt7JT\naz2pVGp8EiwiIlIliQjsV/7+nxs+19dm2HOXVqa21DHcu46G5lZampuoyaTp7R+io3uAju4B1qzv\nobN3mEeWruGRpWuY2lzHnrNb2WVaZgK/iYiIyNZJRGCfPaOJXaY3MXtmMzOmbKx5r1vRRV1rE03N\nrZsts+q55eRI05NtYunyDp5p7+bBx1fzIPDUc3284aT92XOXKeP8TURERLZOVQO7maWAy4A5QB9w\ngbsvKZj+auBiYBC40t0vj+MXAuvjbEvd/fxy2zn9+H3o7hkYdfoy6RR7zGplj11aGRga5umVXfiT\na/jnUx187qr7OHy/mZx+7N7sPqtl1OsWERGZCNWusZ8O1Lv7MWY2F7g0jsPMauLwkUAvMN/MbgA6\nANz9pCqnbRN1NRn22W0qu0xNsfP0Fm5euJK/L17F3xev4oUHzOL1J+xD27TG8UySiIjIqFU7sM8D\nbgJw9wVmdlTBtAOBxe7eAWBmfwWOA54Gms3sZiADfMLdF1Q5nRukUin2f94UjjzweTy0ZA3X37mE\nex97jgf/tYozjt2blx31PDJpPSUoIiKTU7Uj1BQ2NqkDDJlZusS0TmAq0A1c4u6nAhcC1xYsM25S\nqRSH7jODT73tKN5+2kHU1WT4vz8+zhd+tJCnVnaOd3JEREQqUu0aewdQeOda2t2zBdMK705rJTyb\nthj4F4C7Lzaz1cBsYFm5DbW2NGw2bmhKPXVN9TQXmdbbXUc6XbvZcmkGmDmzlalTNyb7NbOmcPwL\nn8/lv3mYPy98hs9dfR9nHL8Pbz71AOpqJ/9d9G1tm988KIHypjjlS2nKm9KUN6WNZ95UO7DPB04D\nrjOzo4GHCqb9E9jXzKYBPcCxwCXAecAhwHvMbFdCwF++pQ11dvVtNq6ro5+6XD9ZNp/W3T1AOj1M\nfeOm03q6+1m1qpOBgc0bCd568v4csc8MfnSz88s/Pc6Ch1fwzte+gN1mNm8peROmra2V9na1MBSj\nvClO+VKa8qY05U1p1cibcgWFajdxXw/0m9l84BvAB83sLDO7wN2HgA8BtxAKAFe4+3LgCmCqmd0J\n/BQ4r6CWP+EO3nsGnz9/LscftivPtHfx+avu5Y4HlqknOxERmRSqWmN39xzhOnmhRQXTfw/8fsQy\ng8DZ1UzX1qqvy/C2lx/AwXtN56obH+Pqm5yHl67hnFccQHND7UQnT0REdmCJ6KBmohxps9hr9hR+\n8JtHWOjtLF3ewbteezD77jZ1opMmIiI7KD23tZWmT2ngP998BKfP24u1nf189dr7ueXep9U0LyIi\nE0KBfRtIp1O8Zt5efPhNh9PcWMvPbl/MZb9+eMTLaERERKpPgX0bOnCPnfjMuS/Edp/GQm/nc1ff\nq2feRURkXCmwb2PTWur58FmH8cqj9+C5tb188ccLufPBZyc6WSIisoNQYK+CTDrN60/Yh/e//lDq\natJceeNjXPH7R+kfHJ7opImISMIpsFfRYfvO5NPnvJA9d2ll/kMr+OKP7mPFmp6JTpaIiCSYAnuV\nzZzWyEVnH8mJR+zGM+3dfPaqe7nnnysnOlkiIpJQCuzjoLYmzVtOMd75mhdADr53wyNce+sihoYn\nTYd6IiKSEArs42juQTvzqXOOYreZzdy+8Bm+fM39rFrfO9HJEhGRBFFgH2ezZzTzybcexYtfsAtL\nl3fw2Svv5R//Wj3RyRIRkYRQYJ8A9XUZLjjtQN72cqN/MMu3fvEgv7zjX2qaFxGRraa+4kfI5XJ0\ndnaUnN7aOoVUKrVN1nn43i20nWlcfetSfn/3kzyydA1vf/VBzJ6x5dfAViOdk92O+J1FREZLgX2E\n3p5u7rh/DdOmzyg67eS5+zJlyuhe8tLZ2cGtCx6nsWnzgN3b082HXncAv12wgrseXsFnr7yXN5y4\nLycdsVvZILWldY4lnZPdjvidRURGS4G9iIbGJpqaS7/Efiwam5pLrrOxPsMFpx3EYfvO5OqbHuPa\nWxfx4OOrOPeVB7JTa/2Y1plUO+J3FhEZDV1jn0SOOmAWn79gLgfvPZ2Hl67h4ssXcMcDy8jqTXEi\nIlIhBfZJZlpLPR98wxzecqqRzeW4+ibnK9fez7L2rolOmoiIbAcU2CehVCrFiYfvxhfffjRHWhuP\nP7Oez1x5L7+8418MqL95EREpQ4F9EtuptZ73nHEI73/doUxrqeP3dz/JJy9fwN8eXaHmeRERKUo3\nz20HDttvJgfsMY3f/PUJbr3vaX7wm0fZva2JvXZpZE/dSCYiIgUU2LcTDXU1vPGkfTnhiN24/i9L\nWPDoSp5u7+Ffy/s4fP+ZTJ/SMNFJFBGRSUCBfTsza1oj73zNC5h30E786NYlLFvVzbJV3ezW1swL\n9prOzjs1TnQSRURkAimwj8J49kq3pXXuPquZ4w6ZwbreDA8tWc2y9m6WtXczc2oD++7aSDY7Ptfg\nx5r+ydKL3Hj/ppM9P0Rk+6fAPgrV6JVua9aZSqXYra2Z3dqaaV/byyNPrOGplV2sWt/Home6OXbO\nbrz6+H2reofkWHuDmyy9yFUjHaXWuT3kh4hs/xTYR6kavdJti3W27dTICTvtxvquAf7x+EqWrerj\nhr8u5Ya/LuXAPXZi3qGzOWL/NuprM9so1RuNtTe4ydKLXDXSMZZ1Tpb8EJHtmwJ7wkxtqePI/abx\nrlfP4rFlfdzzWDuPLFnNP59cS11NmoP2nM5h+81kzj4zmNpSurtaERHZPimwJ1R9bYZjD92VM19q\nPOwrmf/wcu5ftIoHHg9/AHvvOoUX7Dmd/Xefxj67TaGhTruDiMj2TmfyHcDO05s487h9OPO4fVi5\ntocHF4fgvujp9Sx5NtywlU6l2H3nFvZ/3jT2nN3K7m0t7DKjiZqM+jASEdmeKLDvYHbeqYlTXvR8\nTnnR8+nuG+TxZ9az6Jl1LH5mPU8s7+DJFZ0b5s2kU+wyo4nd21qYtVMjbdPC38ypDUxrrSetu7RF\nRCYdBfYdWHNDLXP2ncmcfWcCMDg0zNLlnTz9XBdPP9fFsvYunomP0Y1Uk0kxtbmO5oYMg0PDNDd1\n01iXobY2Q20mTW1NmuxQP4uXdTKjJ0VDXQ2NdRka6mqoq1UrgIhItSiwywa1NRn2330a++8+bcO4\nbC7HqvV9tK/tpX19L6vW9bFqfS/t6/pY393PslW9DGdzsKa/6DrvenTNZuNSQCYTavvp9ArSqRTp\ndIpMOhVbAbIseGwddXU1ZNJpajIpMuk0udwQazv7qavtJB3nzf8fHh5kfU+W5sY1ZOL8NZkUmUya\nmnSKTCZFTSZNJp2On1PMWN/Hcyu7WNc1yBADYXxcLp1Wa4SIbJ8U2KWsdCrFrGmNzJpWvEe79evX\n8acHniWVaaBvYJiBoSyDQ1mGhrJ09/Yye0YzOWroGxiib2A4/g3RPzBIR/cgpFJkszmyORgezjGY\nyzKczdLb38dwNhcKDZvpK5qWRc9szatt2zcbk0mnuOm+56ivDa0MdTWZ8L82Q11Nmoa6DI31NTTW\n19BUX0NDfQ2N9RkYHmDV+n5as3XU1qSpqwktGCIi40GBfRsp13NYZ2cHjKEjuLGus3C5urosHR0b\nr5tv657Uuro6qcukaWqpZ2T3Kd1daQ7bq4XW1ilF0//g0m6aWjZ/brunu5N5h8xmypSp5HIhuA8P\n51i7bh13PbKC+sbmUBjIhhaFbDZHT08Xe+/cSH1D44b5h7M5huLn2rr6DeOHslmGh3PU1dfSvmod\nT67sgnQNw8NZhoZzDA1nGc7mGBgYpCaTYjiXpbNniIHBUGip/KdcvclQTSbFLQufo6khFARaGmpo\nbgyfa9NDrOoYYkprivq6DPW1GRrqMlssEJT7bXLxDYClerorNa2uLksul5rwnu4mS2985Y6ncumY\nLL0ayuRVrX1cgX0bKdeD3JpVK2lqnlI0iFVjnYXLtTSvoau7f8P4bdmT2mjSMZrlCqVSodm8JgNN\nDTU01GVoaqjdbL5c/1qeXNY1qh782tpa+de/nuGvDy0v2jHMqueWM9Dfv8k6c7nQutDV1cWLD92D\nTG0Tvf1Dm/yt7ehm8bL1kKphcCjLwOAwg0NZevv6GRwcYvXAMCuGirc6wLpNhtLpFA21ae5dtJ4Z\nU5uY2lLH1OY6prXUM7W5jhoGeMCXMXVqy2Y3M65ZtZJ0uqZk/pealk49zTEH7z7hPd1Nlt74CtNR\neDxtKR2TpVdDmbyqtY8rsG9DpXqQ6+keexPxWNeZX665pYFsiabr0SjVK1ql6RjtcmMxnr0CZtIp\nprfWM2VKy2bTOjrW01Cb22y5Vc8tJ53OMH3mLLK5HAODw/QPZOkfHKK9fRWDwylq6pvoHwyXLPoH\nhuntH6anb4Cn27t5cuXmNzFu1LnJpYFwSSBDY12G4foUTXF8Q32GdCpFT3cX6XSm6HdLMzDqfKqW\nydIbXz4doz2eJkuvhjJ5VeP3VGAXmQDpVHhSoKEOoI70YG0M+pvXoHu6Oznm4F1I1zaxvmuA9V39\nrO8eYF1XP+1ruliyvIOB4RS9/UN09gywtrPwRsYBeKpnw1AqBY31NdSlczTUpZn6XG7DpYGNlwjG\n5wVCIlIdCuwi24F0KsWUpjqmNNWx+6yNrQQdHes3u5QwOJSlt3+I5StW0j8E6bpmevuG6OkbpKd/\niJ6+Idb3DrOuZ5gV69YV2xzX3fE006c0MK2lnmkt9ezUGv6mtdQxrbWenVrqmdpSRyatmwJFJhsF\ndpGEqa1JU1tTx0BrTWwFmL7ZPO0rn2Uom6aueVoI+jHg9/QP0ds3QC4HHT2DLF/dU2QLQQqY0rwx\n0E9rDdf9WxpraW2qpbmxltbG2g3DtTXb/gVEIrI5BXaRHVAqlaK+Ns30KQ0w4qGFNAMctvd0pkyZ\nyuDQMGu7BljX2c+6rn7Wdoa/dV39rOvsZ21XP8vauzfpsbCU+toMLY01tDTWbbgRsrF+0/8NdeH+\ngMa6GoaH+ljdMUDvcB+Z2M9Bvh+CoeEs2ZwuGYgUU9XAbmYp4DJgDuHh4wvcfUnB9FcDFwODwJXu\nfvmWlhGR8VNbkynbjwGEJwW6+0LnQZ09A3T1DtLZM0h37yCdvYN09Q7S1TNAV+8QXb0DLF/TzcBg\ndhSpWFV07K/vWkEmnaKmJk0mlSKVCgWWdDp8TqdSpOO4/HDh9BRhOvEz5D+HexHyI7LZYTp7Bkin\n11CTSTOczW4y7f7HO6jJZCCV2mTZ4eFh1nX1U1OzbmOHSilIpVPkhod4ZtUAjQ31ocBS2KlSOnSs\nlEmnYj8ImQ39KAwN9rKqY4CW4b6CTphU0JFNVbvGfjpQ7+7HmNlc4NI4DjOricNHAr3AfDO7AZhX\nahkRmXxSqRQtscm9UkPD2dBZUX/ouKh3YIje/uENHRn19g+xrqObJ1Z0ks7UMhz7Hsh3WjQwOEhr\nYy050gwOZ8lmIUfo0yCXyz+WGD5nczlyWcjmsuRiZ0j5xxZzudzGfglyYR35ETnYsK7w3P/ghvEb\n54c1HQNb6Nug+FMGTz7XW3F+bW7zws6v71qxoUOkfCdKtTUZ6mOnSrUF4zf+jwWHmo3z1BfOW7tp\nwSL8T1OTSeuZ+Ums2oF9HnATgLsvMLOjCqYdCCx29w4AM7sTOB54cZllRCQBajJpWhrTZQsDxW4M\nzCvsxKjaCtPR2tJAZ9fGx91GpiPf8U8O6Fi/nvkPL6ehqWWTzpRyuRzd3V0cvl8bjY3NGworwwWd\nIw1nN3aW1B/7QRgYzNLZ3cOSZztIZ2o3zDs0nKV/YJCWxlqyuRQDse+EvsFhOnoGGRwaZmh429bm\nU0BtDPb1taEA0dRYS4ocNfFyycgWiJqClol8982ZES0UqbjyFPlWFUJLyIaWlBHjw79QCCtWaMsX\n7HIbC33ZXOF8BQXB7MZx2RyxR8xcXG/sHTNbMBy3t+Hzhvk2dp6VnzeVTjEwmC1YV5h/eDhL/2AW\nWLGhcJgvLuXI8Yd7VsbvvGkhKpWCn3z+lSV/n2oH9inA+oLhITNLu3u2yLQuYCrQWmaZono72unp\n3ryv8qGhXoZ7ij/729fbTTpdQ093Z0Xjt9dpaQY25E1vT3fZXo5K6ezsoHeU+bg100qlc6zpKLW+\nurrsNl9nuXRu6/wot62t2V46NURn58TfflPuu411X97adBQeT1tKR3d3J329PUVrtulsP42ZAVrr\nG0ZOif+L32jY2ZkiPdxNY9Oml0Z6e7o5+qBZRXt5hBCk8t09Dw5nN/SiODicC5+Hs2F64eehjf83\nfs4VGZelqzc8fjkwOEzRXqB3ECkglc5fBoqXgNLhPRXkcptcJqqJ943kcoSnSwp3k1xoYWppDI/B\nxlHhfwWXXKp99HYQAnVeYYDuYNPbdlqBtVtYpqjXvWKu2oSq7LDDDproJADbPh2HHTa1Kt9tPPNr\nsvw21TBZvttY07G971uyfar2Q6jzgVcCmNnRwEMF0/4J7Gtm08ysDjgWuBu4q8wyIiIiUkaqkmr9\nWBXc4X5oHHUu4Wa55ngH/KuATxMaIa5w9+8VW8bdF1UtkSIiIglS1cAuIiIi40v9QYqIiCSIAruI\niEiCKLCLiIgkyMQ/rDpG6nq2uNhb31fc/UQz2we4CsgCD7v7eyY0cRMk9nL4v8CeQB3wReBRlDeY\nWRr4IWCEvHgX0I/yZgMzmwXcB7wMGEZ5A4CZLWRjnyNLgS+hvAHAzD4GvAaoJcSpvzCOebM919g3\ndFcLXEToenaHZmYfIZyk6+OoS4GPu/vxQNrMXjthiZtYZwOr3P044OXAd1He5L0ayLn7PMJ7G76E\n8maDWCj8HpB/zZ3yBjCzegB3Pyn+nY/yBgAzOx54cYxNJwDPZ5zzZnsO7Jt0Vwuo61l4HDijYPhI\nd78zfr6RUOPYEf2cELQgdOk1BByhvAF3vwF4Rxzcg9BJlPJmo68D/wM8S3gsV3kTzAGazexmM7st\nthQqb4JTgYfN7NfAb4DfMc55sz0H9qLd1U5UYiYDd7+eELTyCnvk6yR02bvDcfced+82s1bgF8An\nUN5s4O5ZM7sK+A7wE5Q3AJjZOcBz7n4rG/Ok8Byzw+YNoQXjEnc/FbgQuBbtN3kzCf21vJ6NeTOu\n+832HAhH3fXsDqgwP1qBdROVkIlmZrsDfwSudvefobzZhLufA+wPXA4UdkS+I+fNucDJZvYnQg31\nR0BbwfQdOW8WEQIW7r4YWA3sXDB9R86b1cDN7j4UO1frY9NAXvW82Z4De7nuaiW438yOi59fAdxZ\nbuakMrOdgZuB/3T3q+PovytvwMzOjjf6QDgBDQP3xeuEsAPnjbsf7+4nuvuJwAPAW4Abtd8AcB7w\nDQAz25XQgnqL9hsA/kq4lyefN83A7eOZN9vtXfHA9YTS9Pw4fO5EJmaS+jDwQzOrJfTNf90Ep2ei\nXARMAy42s08RXpT0AeC/lDf8CrjSzO4gnA/eDzwGXK68KUrHVHAFYb+5k9D6dQ6hprrD7zfu/nsz\nO9bM7iFcnrgQeIJxzBt1KSsiIpIg23NTvIiIiIygwC4iIpIgCuwiIiIJosAuIiKSIArsIiIiCaLA\nLiIikiDb83PskjBmtgfhLVEnu/vtBeOXAse7+1Nbuf5tsp4tbGN34BagCzjB3bsLpl1M6Mu/Efii\nu19jZhlCX+THEDqH+Yq7/3TEOr8LvITwZrp9gUfipG8XdLgz4czsR8ALgYvdfbt9htnMPg/0uvuX\ntmIdU4HbCc8xv97dl26r9BVs43zgaHd/+4jxNwJvcfdV23qbsn1QYJfJZpDQAcghBUFxW3W2MB6d\nNpwILHT3swtHmtnZwEsJgW8m8ICZ/YbwlsJWdz/YzGYAbma/KSwQuPt74zr2AP7k7keMw/cYlfie\nhjcDderaGQh9hXe5+wlV3s5m+7S7v6LK25RJToFdJptngVsJrzl8ZxyXgg2vQ/xM7OITM7sS+BNw\nB/BrYAlwCOHd2X8m9IY1DTjD3T2u57NmNgfoBd7l7g/F921/H3geoReti9z9j2b2aeBoYHfgu+7+\nvXwizWw/4AfAdELt/AOEQsnnCW+9uszd313wvd4IfN3dh4GVZjaPUCv8kZldE+d5HuFd6IOVZpaZ\nPY/Qv/tUYBfgGne/ONbm3kwoRFwP/JLwkpcmYBbhBR6XjVjXKYTXtmYJvYidBcwAbnL3/eI8n495\n91VgGaGr1ZnASsKlvfvM7KXARwmvrJwGPAec6e6rzOwtwMfiNhYQ3izXBPw3cFBcx5dH1vjN7DDC\n61PTcftvc/cnzOwDMZ3NhBcgvdHdF5vZ08A1hNfS9gOfAv4fsA/wH+5+vZn9OE6bQ+i/+zPxPQKF\n230V8GnCWwH/BbzD3deZ2aWEQlwW+JW7f7FgmdmE/WmWmf0SeAPwX8Dxcf6r3f0bMZ8+5u4nx+V+\nTHjz1wLgZ4DHtD0LvMH/f3tnGmpVFcXxn9pglEVlM1SauEIyeVbPZyNZFvihAUMqoxEjKhpMoyjQ\nrGigyYKgiUIaNKLZBhMzy0xsNBv+kYRpaSRKAxVZvT6sdeq88+65+iHocV2/L+/ec87ee61999tr\nr732vUv6MRLTXIUnwFoJrKNC6D4C/+nSY+LzGQC8LOmS6vNJ65Ex9qSn0YlPwMfHxNfofiMOBK6T\nNBj3iveJfMgz+TctKYDC470BKLaxpwMPSToEOBG438y2jXtbSzqgbNSDR4G7JA0DJuI/EfkpbkCe\nrxh18C30IWb2tpm9i6dx3BAC/WVmD+AT+v2Sfq/RsRHjgRmSRgJtwKWxDQywh6RhkqYCE4ApkkYA\nx4X+Va4FzpPUjqdEbovrdX2+K97nBwMnAH9E3+4C7CupQ9L+uAE6LcIUtwKjJA0F+uK/qT0FWBT9\nfzQwNZ4tMxE3+O146KIj9BwDHBn1vQRcUCqzQtIBeOhiIm7kzsENY8HuQDswGphuZv2LG7Hgux44\nVtJB+GLxJjMbGDq04SESi7ztAEhaHXIsljQWuBjoH7J0RF+M3kjftoW+Q/GFzGmxiLsRD9uMpD5D\nWLnOEfiYHgaMNTOrKZO0EGnYkx6HpJ9xQ/SAmW23icVWS1oar1fh8U2AFcCOpeceijZeBvY2s+3x\n3MjTzOwD3GPqg3t24Ma2C2H094tc5khajHu4zSbNLfDdhCPw7ffbzKxog4iT7gGcYmabnKtZ0i3A\nGrPhQO0AAAQySURBVDObBNwJbIl7wADvlR69HOgXCV+m4R5uleeAF8zsbuBjSa9vpPlOYEkDmb4A\nrjaz883sNnyhtR1ukN6Q9F08d4akl/D+vzj6/42Qf0il2tnAfbEA+kXSTEk/AGcC483sJtzIl8fL\nK/F3RbTbSffx8LCkTkkrgUUhY8FIPEf9/JDtAnxcfI2niV6A/7b+NZLK6ZKrHA08Ejr/gqfGbbRo\nLfOtpOIsxTJ8Z+gwYIGkdRHueKymbDl96kJJv0a7X0U9SYuThj3pkUQO7NfwDFKFB9JJ10lry9Lr\nqpdbN9FWr2/ADfkoSW3hhR2KT6bg3lKV3hU5imvNQltrgKck/SlpFfAO0GZmw81sEICk9fjC4sAm\n9XTBzKbjSSaW497l+pJsZdmfxrell+GeeVV+JN2OG6HlwB1mNhnv8/I8Ue7zPxsZNDNrDz3+Ap4E\nXoj2NpTbNbP+4SH3Bk4t9X8HMLci2yxgOB5mmWRm98aZg4X4NvpsPK1qWa/ymNiU8dCnwfvXJQ0P\nuQ4OOf/APeGp+O7EYjMbUFM/dJ9ne+Fjpdl4/q30uniuM2TamE5lj71RPUmLk4Y96WmUJ55JwPHA\nnvF+LTDQzLYys51w77dRuWaMBzCzk4HPJf2Ke/cXxfUhwFK65iTvgqSfgOVmdlKU6cBzUS+rK4Mb\nt3Hx/M749u+HuIG41cx6mVm/0HdhbS3d9TwWuEXSM8DAkKNPt1IwCrhW0ou48e72vx8hgm0kTcfD\nE8PxhUJ/M9vRzPqGfHWyFO+PAuZKehDP2z06ZFoMjAyD3guPO48B5gEXhgx74SmY9yxXbGZP4eGL\n+/Ct+zZ8J+AzSXfjBn9Mje7NKD6TAaHvW6V7i4AjYusdfOF0s5kdhI+Z+ZImh46Dm7QxDzjbzHrH\nbs/p+NmQtcAgM9sixsThpTKNxvObwKFmtlt8m2JcTXtpvDdz0rAnPY1/vI0woBMIT0bSp7hn9gkw\nC1jQqBz1cctOYHBsq14GnBXXL8Fjth8BTwDjy6fSazgDj2cvxQ+lnbyR7dg78UNzy/AJepqkL/FD\nVmtwY/YmcE9s7ddR1e1GYKaZLcEP8H2AH5SqMhX3LN/FDe8qM9u78sxVwGPxzNl4/Hx9yP4+ntN+\nURNZivdPAO3Rz3MKmSR9g5+fmAt8BKyTNAM31DtEX76KH25bWan7emCKmb2PH/C7At9q3yb6dCG+\n01DovqnfgOgX+j4LnCvpx+JGxMonAE+HbEOAKyW9hy8kPomyCj3ruBf4PnReAjwpaXaEjubgaTwf\nx8MQBY1Ou6/GzwrMxz+H9TXtNRv/yWZApm1NkmSzpDiFLunx/1uWJPkvSY89SZLNlfRqkpYkPfYk\nSZIkaSHSY0+SJEmSFiINe5IkSZK0EGnYkyRJkqSFSMOeJEmSJC1EGvYkSZIkaSHSsCdJkiRJC/E3\n41jALun48z4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1243debd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# RS-assoc OGs\n",
"plt.figure(figsize=(8,6))\n",
"sns.distplot(count_rs, bins=num_samples+1)\n",
"plt.axis([-0, num_samples, 0, .25])\n",
"plt.xlabel('Number of %s Tara surface samples found in' % num_samples)\n",
"plt.ylabel('Proportion of %s OGs' % num_ogs_rsonly)\n",
"plt.title('Presence/absence of %s RS-assoc. %s OGs in %s Tara surface samples' % (num_ogs_rsonly, species, num_samples))\n",
"plt.axis(myaxis)\n",
"plt.savefig('hist_%s_RSassoc_og_presence_absence_in_63_tara_srf.pdf' % species)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAGJCAYAAAB4nxGoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1241fb3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# other (non-RS-assoc) OGs\n",
"plt.figure(figsize=(8,6))\n",
"sns.distplot(count_other, bins=num_samples+1)\n",
"plt.axis([0, num_samples, 0, .4])\n",
"plt.xlabel('Number of %s Tara surface samples found in' % num_samples)\n",
"plt.ylabel('Proportion of %s OGs' % num_ogs_other)\n",
"plt.title('Presence/absence of %s non-RS-assoc. %s OGs in %s Tara surface samples' % (num_ogs_other, species, num_samples))\n",
"plt.axis(myaxis)\n",
"plt.savefig('hist_%s_nonRSassoc_og_presence_absence_in_63_tara_srf.pdf' % species)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
NehaMadalmatti/ml_lab_ecsc_306 |
|
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0] 0\n",
"[0 1] 0\n",
"[1 0] 0\n",
"[1 1] 1\n",
"[[-0.55287098 0.34112265]]\n",
"[[ 1.09455225]]\n",
"[[-1.55287098 -0.65887735]]\n",
"[[ 2.09455225]]\n",
"[0 0 1 1 0]\n",
"[0 1 0 1 0]\n",
"[1 1 1 0 1]\n",
"[1, 1, 1, 0, 1]\n"
]
}
],
"source": [
"import numpy as np\n",
"class Perceptron:\n",
" \n",
" def __init__(self, input_length, weights=None):\n",
" if weights is None:\n",
" self.weights = np.ones(input_length) * 0.5\n",
" else:\n",
" self.weights = weights\n",
" \n",
" @staticmethod\n",
" def unit_step_function(x):\n",
" if x > 0.5:\n",
" return 1\n",
" return 0\n",
" \n",
" def __call__(self, in_data):\n",
" weighted_input = self.weights * in_data\n",
" weighted_sum = weighted_input.sum()\n",
" return Perceptron.unit_step_function(weighted_sum)\n",
" \n",
"p = Perceptron(2, np.array([0.5, 0.5]))\n",
"for x in [np.array([0, 0]), np.array([0, 1]), \n",
" np.array([1, 0]), np.array([1, 1])]:\n",
" y = p(np.array(x))\n",
" print(x, y)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
"\t# input data\n",
"\tin_a = np.array([0,0,1,1,0])\n",
"\tin_b = np.array([0,1,0,1,0])\n",
"\n",
"\t# output ground truth (OR gate)\n",
"\to_gt = np.array([1,1,1,0,1])\n",
"\n",
"\t# weights\n",
"\tw = np.random.randn(1,2)\n",
"\tbias = np.random.randn(1,1)\n",
"\n",
"\tprint (format(w))\n",
"\tprint (format(bias))\n",
"\n",
"\t\n",
"\tlearning_rate = 1\n",
"\tepochs = 10\n",
"\n",
"\t\n",
"\t# TRAINING\n",
"\tx_axis = []\n",
"\ty_axis = []\n",
"\tfor e in range(epochs):\n",
"\t\terror = 0\n",
"\t\tfor a, b, o in zip(in_a, in_b, o_gt):\n",
"\t\t\t# weighted sum\n",
"\t\t\tws = w[0][0]*a + w[0][1]*b\n",
"\t\t\t# activation\n",
"\t\t\tsummed = ws + bias\n",
"\t\t\t# initialize output to zero\n",
"\t\t\ty = 0\n",
"\t\t\tif summed >= 0:\n",
"\t\t\t\ty = 1\n",
"\t\t\telse:\n",
"\t\t\t\ty = 0\n",
"\t\t\t\n",
"\t\t\t# calculate the error\n",
"\t\t\terror = o - y\n",
"\t\t\t\n",
"\t\t\t# update rule\n",
"\t\t\tdw0 = learning_rate * (error) * a\n",
"\t\t\tdw1 = learning_rate * (error) * b\n",
"\n",
"\t\t\t# update weights\n",
"\t\t\tw[0][0] = w[0][0] + dw0\n",
"\t\t\tw[0][1] = w[0][1] + dw1\n",
"\n",
"\t\t\t# update bias too!\n",
"\t\t\tbias = bias + (learning_rate * error)\n",
"\t\t\t\n",
"\t\t\t# for visualization purpose\n",
"\t\t\tx_axis.append(e)\n",
"\t\t\ty_axis.append(error)\n",
"\n",
"\tprint (format(w))\n",
"\tprint (format(bias))\n",
"\n",
"\t# TESTING\n",
"\tperceptron_output = []\n",
"\tfor a, b in zip(in_a, in_b):\n",
"\t\t# weighted sum\n",
"\t\t\tws = w[0][0]*a + w[0][1]*b\n",
"\t\t\t# activation\n",
"\t\t\tsummed = ws + bias\n",
"\t\t\t# initialize output to zero\n",
"\t\t\tif summed >= 0:\n",
"\t\t\t\tperceptron_output.append(1)\n",
"\t\t\telse:\n",
"\t\t\t\tperceptron_output.append(0)\n",
"\n",
"\t# summary\n",
"\tprint (format(in_a))\n",
"\tprint (format(in_b))\n",
"\tprint (format(o_gt))\n",
"\tprint (format(perceptron_output))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
|
ChicagoBoothAnalytics/RelDataQuery_SQL_R_Py_Spark | Cases/AirBnB Kaggle/R/AirBnBKaggle-SQL-RDataFrame-RDataTable-SparkRSQLDataFrame.ipynb |
|
|
|
|
|
| pyfas/test/.ipynb_checkpoints/tab on the road-checkpoint.ipynb |
|
| {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pyfas as fa\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sin_fix = fa.Tab(\"3P_single-fluid_fixed.tab\")\n",
"mul_fix = fa.Tab(\"3P_multi-fluid_fixed.tab\")\n",
"sin_key = fa.Tab(\"3P_single-fluid_key.tab\")\n",
"mul_key = fa.Tab(\"3P_multi-fluid_key.tab\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Single - Fixed"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sin_fix.data;"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sin_fix.export_all()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Fluid</th>\n",
" <th>Property</th>\n",
" <th>Unit</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[899.718, 900.424, 901.309, 902.434, 903.838, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1044</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[813.363, 812.66, 811.929, 811.17, 810.382, 80...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5553</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6054</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.220481, 0.227562, 0.234135, 0.240676, 0.247...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6555</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0010661, 0.00101649, 0.000970794, 0.0009286...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7056</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7557</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8058</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8559</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[-19279.3, -14920.5, -10543.9, -6149.34, -1736...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9060</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[-317877.0, -313080.0, -308335.0, -303637.0, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9561</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[-1395510.0, -1387580.0, -1379650.0, -1371710....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10062</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0277744, 0.028032, 0.0282904, 0.0285496, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10563</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0969043, 0.0960938, 0.0953334, 0.094616, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11064</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11565</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12066</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12567</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13068</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13569</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[-587.526, -570.743, -554.118, -537.594, -521....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14070</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10...</td>\n",
" <td>[0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...</td>\n",
" <td>[-4115.44, -4085.47, -4055.71, -4026.17, -3996...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fluid Property Unit \\\n",
"42 mal-2007-3 GAS DENSITY KG/M3 \n",
"543 mal-2007-3 LIQUID DENSITY KG/M3 \n",
"1044 mal-2007-3 WATER DENSITY KG/M3 \n",
"5553 mal-2007-3 GAS VISCOSITY N S/M2 \n",
"6054 mal-2007-3 LIQ. VISCOSITY N S/M2 \n",
"6555 mal-2007-3 WAT. VISCOSITY N S/M2 \n",
"7056 mal-2007-3 GAS SPECIFIC HEAT J/KG K \n",
"7557 mal-2007-3 LIQ. SPECIFIC HEAT J/KG K \n",
"8058 mal-2007-3 WAT. SPECIFIC HEAT J/KG K \n",
"8559 mal-2007-3 GAS ENTHALPY J/KG \n",
"9060 mal-2007-3 LIQ. ENTHALPY J/KG \n",
"9561 mal-2007-3 WAT. ENTHALPY J/KG \n",
"10062 mal-2007-3 GAS THERMAL COND. W/M K \n",
"10563 mal-2007-3 LIQ. THERMAL COND. W/M K \n",
"11064 mal-2007-3 WAT. THERMAL COND. W/M K \n",
"11565 mal-2007-3 SURFACE TENSION GAS/OIL N/M \n",
"12066 mal-2007-3 SURFACE TENSION GAS/WATER N/M \n",
"12567 mal-2007-3 SURFACE TENSION WATER/OIL N/M \n",
"13068 mal-2007-3 GAS ENTROPY J/KG/C \n",
"13569 mal-2007-3 LIQUID ENTROPY J/KG/C \n",
"14070 mal-2007-3 WATER ENTROPY J/KG/C \n",
"\n",
" Temperature \\\n",
"42 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"543 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"1044 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"5553 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"6054 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"6555 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"7056 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"7557 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"8058 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"8559 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"9060 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"9561 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"10062 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"10563 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"11064 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"11565 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"12066 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"12567 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"13068 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"13569 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"14070 [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10... \n",
"\n",
" Pressure \\\n",
"42 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"543 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"1044 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"5553 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"6054 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"6555 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"7056 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"7557 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"8058 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"8559 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"9060 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"9561 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"10062 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"10563 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"11064 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"11565 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"12066 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"12567 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"13068 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"13569 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"14070 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2... \n",
"\n",
" values \n",
"42 [0.0849146, 0.0841808, 0.0834595, 0.0827506, 0... \n",
"543 [899.718, 900.424, 901.309, 902.434, 903.838, ... \n",
"1044 [813.363, 812.66, 811.929, 811.17, 810.382, 80... \n",
"5553 [1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423... \n",
"6054 [0.220481, 0.227562, 0.234135, 0.240676, 0.247... \n",
"6555 [0.0010661, 0.00101649, 0.000970794, 0.0009286... \n",
"7056 [1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1... \n",
"7557 [1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1... \n",
"8058 [3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ... \n",
"8559 [-19279.3, -14920.5, -10543.9, -6149.34, -1736... \n",
"9060 [-317877.0, -313080.0, -308335.0, -303637.0, -... \n",
"9561 [-1395510.0, -1387580.0, -1379650.0, -1371710.... \n",
"10062 [0.0277744, 0.028032, 0.0282904, 0.0285496, 0.... \n",
"10563 [0.0969043, 0.0960938, 0.0953334, 0.094616, 0.... \n",
"11064 [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"11565 [0.0280944, 0.0280288, 0.0279906, 0.0279847, 0... \n",
"12066 [0.0698809, 0.0690383, 0.0682086, 0.0673915, 0... \n",
"12567 [0.0551154, 0.0550872, 0.0550879, 0.0551306, 0... \n",
"13068 [1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ... \n",
"13569 [-587.526, -570.743, -554.118, -537.594, -521.... \n",
"14070 [-4115.44, -4085.47, -4055.71, -4026.17, -3996... "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sin_fix.data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T = sin_fix.data[\"Temperature\"][(sin_fix.data.Property == \"GAS ENTROPY\")]\n",
"P = sin_fix.data[\"Pressure\"][(sin_fix.data.Property == \"GAS ENTROPY\")]\n",
"data = sin_fix.data[\"values\"][(sin_fix.data.Property == \"GAS ENTROPY\")]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t = list(T)[0]\n",
"p = list(P)[0]\n",
"data = list(data)[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"13068 [0.1, 1.01325, 7.38958, 14.6792, 21.9688, 29.2...\n",
"Name: Pressure, dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1185.33, 1201.82, 1218.24, ..., -2135.18, -2116.3 , -2097.55])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xafa2bacc>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xafa5a9cc>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.scatter(t, P.all(), data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi - Fixed"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Fluid</th>\n",
" <th>Property</th>\n",
" <th>Unit</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[899.718, 900.424, 901.309, 902.434, 903.838, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1047</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[813.363, 812.66, 811.929, 811.17, 810.382, 80...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5556</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6057</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.220481, 0.227562, 0.234135, 0.240676, 0.247...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6558</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0010661, 0.00101649, 0.000970794, 0.0009286...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7059</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7560</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8061</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8562</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-19279.3, -14920.5, -10543.9, -6149.34, -1736...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9063</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-317877.0, -313080.0, -308335.0, -303637.0, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9564</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-1395510.0, -1387580.0, -1379650.0, -1371710....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10065</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0277744, 0.028032, 0.0282904, 0.0285496, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10566</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0969043, 0.0960938, 0.0953334, 0.094616, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11067</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11568</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12069</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12570</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13071</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13572</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-587.526, -570.743, -554.118, -537.594, -521....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14073</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-4115.44, -4085.47, -4055.71, -4026.17, -3996...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14617</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0850584, 0.0843237, 0.0836016, 0.0828918, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15118</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[890.183, 890.897, 891.669, 892.545, 893.561, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15619</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[998.65, 998.996, 999.299, 999.545, 999.729, 9...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20128</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20629</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0953909, 0.101423, 0.107646, 0.114204, 0.12...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21130</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.00260078, 0.00236823, 0.00216585, 0.0019888...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21631</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22132</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22633</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24637</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0276155, 0.0278689, 0.0281231, 0.028378, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25138</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.101774, 0.10084, 0.0999201, 0.0990074, 0.09...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25639</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26140</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0278377, 0.0277255, 0.0276261, 0.0275434, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26641</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0986832, 0.0976704, 0.0966705, 0.0956817, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27142</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.055677, 0.0554834, 0.0552801, 0.0550659, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27643</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28144</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-605.701, -587.445, -569.306, -551.277, -533....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28645</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-6954.16, -6918.32, -6882.55, -6846.91, -6811...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29189</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.765858, 0.756305, 0.746988, 0.737899, 0.729...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29690</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[875.4, 877.459, 879.975, 883.022, 886.503, 89...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30191</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[808.281, 806.539, 804.708, 802.788, 800.774, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34700</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35201</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0376117, 0.0410375, 0.0445707, 0.0482234, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35702</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.000705364, 0.000669605, 0.000637022, 0.0006...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36203</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36704</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37205</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37706</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38207</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-270543.0, -262146.0, -253911.0, -245857.0, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38708</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-1329300.0, -1316130.0, -1302920.0, -1289670....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39209</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0314484, 0.0318777, 0.0323095, 0.0327438, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39710</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.103153, 0.101553, 0.100041, 0.098621, 0.097...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40211</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.60348, 0.609125, 0.61455, 0.619758, 0.62475...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40712</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0254278, 0.0253864, 0.0254091, 0.0255026, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41213</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41714</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0445628, 0.0450036, 0.0455026, 0.0460673, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42215</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[364.494, 389.532, 414.413, 439.141, 463.72, 4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42716</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-410.085, -382.735, -356.017, -329.986, -304....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43217</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-3804.14, -3759.48, -3715.25, -3671.42, -3627...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>63 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Fluid Property Unit \\\n",
"45 mal-2007-3 GAS DENSITY KG/M3 \n",
"546 mal-2007-3 LIQUID DENSITY KG/M3 \n",
"1047 mal-2007-3 WATER DENSITY KG/M3 \n",
"5556 mal-2007-3 GAS VISCOSITY N S/M2 \n",
"6057 mal-2007-3 LIQ. VISCOSITY N S/M2 \n",
"6558 mal-2007-3 WAT. VISCOSITY N S/M2 \n",
"7059 mal-2007-3 GAS SPECIFIC HEAT J/KG K \n",
"7560 mal-2007-3 LIQ. SPECIFIC HEAT J/KG K \n",
"8061 mal-2007-3 WAT. SPECIFIC HEAT J/KG K \n",
"8562 mal-2007-3 GAS ENTHALPY J/KG \n",
"9063 mal-2007-3 LIQ. ENTHALPY J/KG \n",
"9564 mal-2007-3 WAT. ENTHALPY J/KG \n",
"10065 mal-2007-3 GAS THERMAL COND. W/M K \n",
"10566 mal-2007-3 LIQ. THERMAL COND. W/M K \n",
"11067 mal-2007-3 WAT. THERMAL COND. W/M K \n",
"11568 mal-2007-3 SURFACE TENSION GAS/OIL N/M \n",
"12069 mal-2007-3 SURFACE TENSION GAS/WATER N/M \n",
"12570 mal-2007-3 SURFACE TENSION WATER/OIL N/M \n",
"13071 mal-2007-3 GAS ENTROPY J/KG/C \n",
"13572 mal-2007-3 LIQUID ENTROPY J/KG/C \n",
"14073 mal-2007-3 WATER ENTROPY J/KG/C \n",
"14617 mal-2007-2 GAS DENSITY KG/M3 \n",
"15118 mal-2007-2 LIQUID DENSITY KG/M3 \n",
"15619 mal-2007-2 WATER DENSITY KG/M3 \n",
"20128 mal-2007-2 GAS VISCOSITY N S/M2 \n",
"20629 mal-2007-2 LIQ. VISCOSITY N S/M2 \n",
"21130 mal-2007-2 WAT. VISCOSITY N S/M2 \n",
"21631 mal-2007-2 GAS SPECIFIC HEAT J/KG K \n",
"22132 mal-2007-2 LIQ. SPECIFIC HEAT J/KG K \n",
"22633 mal-2007-2 WAT. SPECIFIC HEAT J/KG K \n",
"... ... ... ... \n",
"24637 mal-2007-2 GAS THERMAL COND. W/M K \n",
"25138 mal-2007-2 LIQ. THERMAL COND. W/M K \n",
"25639 mal-2007-2 WAT. THERMAL COND. W/M K \n",
"26140 mal-2007-2 SURFACE TENSION GAS/OIL N/M \n",
"26641 mal-2007-2 SURFACE TENSION GAS/WATER N/M \n",
"27142 mal-2007-2 SURFACE TENSION WATER/OIL N/M \n",
"27643 mal-2007-2 GAS ENTROPY J/KG/C \n",
"28144 mal-2007-2 LIQUID ENTROPY J/KG/C \n",
"28645 mal-2007-2 WATER ENTROPY J/KG/C \n",
"29189 mal-2007-1 GAS DENSITY KG/M3 \n",
"29690 mal-2007-1 LIQUID DENSITY KG/M3 \n",
"30191 mal-2007-1 WATER DENSITY KG/M3 \n",
"34700 mal-2007-1 GAS VISCOSITY N S/M2 \n",
"35201 mal-2007-1 LIQ. VISCOSITY N S/M2 \n",
"35702 mal-2007-1 WAT. VISCOSITY N S/M2 \n",
"36203 mal-2007-1 GAS SPECIFIC HEAT J/KG K \n",
"36704 mal-2007-1 LIQ. SPECIFIC HEAT J/KG K \n",
"37205 mal-2007-1 WAT. SPECIFIC HEAT J/KG K \n",
"37706 mal-2007-1 GAS ENTHALPY J/KG \n",
"38207 mal-2007-1 LIQ. ENTHALPY J/KG \n",
"38708 mal-2007-1 WAT. ENTHALPY J/KG \n",
"39209 mal-2007-1 GAS THERMAL COND. W/M K \n",
"39710 mal-2007-1 LIQ. THERMAL COND. W/M K \n",
"40211 mal-2007-1 WAT. THERMAL COND. W/M K \n",
"40712 mal-2007-1 SURFACE TENSION GAS/OIL N/M \n",
"41213 mal-2007-1 SURFACE TENSION GAS/WATER N/M \n",
"41714 mal-2007-1 SURFACE TENSION WATER/OIL N/M \n",
"42215 mal-2007-1 GAS ENTROPY J/KG/C \n",
"42716 mal-2007-1 LIQUID ENTROPY J/KG/C \n",
"43217 mal-2007-1 WATER ENTROPY J/KG/C \n",
"\n",
" Temperature \\\n",
"45 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"546 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"1047 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"5556 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"6057 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"6558 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"7059 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"7560 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"8061 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"8562 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"9063 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"9564 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"10065 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"10566 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"11067 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"11568 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"12069 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"12570 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"13071 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"13572 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"14073 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"14617 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"15118 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"15619 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"20128 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"20629 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"21130 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"21631 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"22132 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"22633 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"... ... \n",
"24637 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"25138 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"25639 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"26140 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"26641 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"27142 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"27643 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"28144 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"28645 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"29189 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"29690 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"30191 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"34700 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"35201 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"35702 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"36203 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"36704 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"37205 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"37706 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"38207 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"38708 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"39209 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"39710 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"40211 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"40712 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"41213 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"41714 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"42215 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"42716 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"43217 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"\n",
" Pressure \\\n",
"45 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"546 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"1047 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"5556 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"6057 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"6558 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"7059 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"7560 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"8061 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"8562 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"9063 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"9564 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"10065 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"10566 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"11067 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"11568 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"12069 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"12570 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"13071 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"13572 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"14073 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"14617 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"15118 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"15619 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"20128 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"20629 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"21130 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"21631 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"22132 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"22633 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"... ... \n",
"24637 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"25138 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"25639 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"26140 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"26641 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"27142 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"27643 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"28144 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"28645 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"29189 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"29690 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"30191 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"34700 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"35201 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"35702 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"36203 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"36704 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"37205 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"37706 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"38207 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"38708 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"39209 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"39710 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"40211 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"40712 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"41213 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"41714 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"42215 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"42716 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"43217 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"\n",
" values \n",
"45 [0.0849146, 0.0841808, 0.0834595, 0.0827506, 0... \n",
"546 [899.718, 900.424, 901.309, 902.434, 903.838, ... \n",
"1047 [813.363, 812.66, 811.929, 811.17, 810.382, 80... \n",
"5556 [1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423... \n",
"6057 [0.220481, 0.227562, 0.234135, 0.240676, 0.247... \n",
"6558 [0.0010661, 0.00101649, 0.000970794, 0.0009286... \n",
"7059 [1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1... \n",
"7560 [1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1... \n",
"8061 [3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ... \n",
"8562 [-19279.3, -14920.5, -10543.9, -6149.34, -1736... \n",
"9063 [-317877.0, -313080.0, -308335.0, -303637.0, -... \n",
"9564 [-1395510.0, -1387580.0, -1379650.0, -1371710.... \n",
"10065 [0.0277744, 0.028032, 0.0282904, 0.0285496, 0.... \n",
"10566 [0.0969043, 0.0960938, 0.0953334, 0.094616, 0.... \n",
"11067 [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"11568 [0.0280944, 0.0280288, 0.0279906, 0.0279847, 0... \n",
"12069 [0.0698809, 0.0690383, 0.0682086, 0.0673915, 0... \n",
"12570 [0.0551154, 0.0550872, 0.0550879, 0.0551306, 0... \n",
"13071 [1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ... \n",
"13572 [-587.526, -570.743, -554.118, -537.594, -521.... \n",
"14073 [-4115.44, -4085.47, -4055.71, -4026.17, -3996... \n",
"14617 [0.0850584, 0.0843237, 0.0836016, 0.0828918, 0... \n",
"15118 [890.183, 890.897, 891.669, 892.545, 893.561, ... \n",
"15619 [998.65, 998.996, 999.299, 999.545, 999.729, 9... \n",
"20128 [1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778... \n",
"20629 [0.0953909, 0.101423, 0.107646, 0.114204, 0.12... \n",
"21130 [0.00260078, 0.00236823, 0.00216585, 0.0019888... \n",
"21631 [1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ... \n",
"22132 [1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16... \n",
"22633 [4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4... \n",
"... ... \n",
"24637 [0.0276155, 0.0278689, 0.0281231, 0.028378, 0.... \n",
"25138 [0.101774, 0.10084, 0.0999201, 0.0990074, 0.09... \n",
"25639 [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"26140 [0.0278377, 0.0277255, 0.0276261, 0.0275434, 0... \n",
"26641 [0.0986832, 0.0976704, 0.0966705, 0.0956817, 0... \n",
"27142 [0.055677, 0.0554834, 0.0552801, 0.0550659, 0.... \n",
"27643 [1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ... \n",
"28144 [-605.701, -587.445, -569.306, -551.277, -533.... \n",
"28645 [-6954.16, -6918.32, -6882.55, -6846.91, -6811... \n",
"29189 [0.765858, 0.756305, 0.746988, 0.737899, 0.729... \n",
"29690 [875.4, 877.459, 879.975, 883.022, 886.503, 89... \n",
"30191 [808.281, 806.539, 804.708, 802.788, 800.774, ... \n",
"34700 [1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598... \n",
"35201 [0.0376117, 0.0410375, 0.0445707, 0.0482234, 0... \n",
"35702 [0.000705364, 0.000669605, 0.000637022, 0.0006... \n",
"36203 [2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ... \n",
"36704 [1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17... \n",
"37205 [3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3... \n",
"37706 [38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ... \n",
"38207 [-270543.0, -262146.0, -253911.0, -245857.0, -... \n",
"38708 [-1329300.0, -1316130.0, -1302920.0, -1289670.... \n",
"39209 [0.0314484, 0.0318777, 0.0323095, 0.0327438, 0... \n",
"39710 [0.103153, 0.101553, 0.100041, 0.098621, 0.097... \n",
"40211 [0.60348, 0.609125, 0.61455, 0.619758, 0.62475... \n",
"40712 [0.0254278, 0.0253864, 0.0254091, 0.0255026, 0... \n",
"41213 [0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0... \n",
"41714 [0.0445628, 0.0450036, 0.0455026, 0.0460673, 0... \n",
"42215 [364.494, 389.532, 414.413, 439.141, 463.72, 4... \n",
"42716 [-410.085, -382.735, -356.017, -329.986, -304.... \n",
"43217 [-3804.14, -3759.48, -3715.25, -3671.42, -3627... \n",
"\n",
"[63 rows x 6 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mul_fix.data"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mul_fix.export_all()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Fluid</th>\n",
" <th>Property</th>\n",
" <th>Unit</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[899.718, 900.424, 901.309, 902.434, 903.838, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1047</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[813.363, 812.66, 811.929, 811.17, 810.382, 80...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5556</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6057</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.220481, 0.227562, 0.234135, 0.240676, 0.247...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6558</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0010661, 0.00101649, 0.000970794, 0.0009286...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7059</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7560</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8061</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8562</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-19279.3, -14920.5, -10543.9, -6149.34, -1736...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9063</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-317877.0, -313080.0, -308335.0, -303637.0, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9564</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-1395510.0, -1387580.0, -1379650.0, -1371710....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10065</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0277744, 0.028032, 0.0282904, 0.0285496, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10566</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0969043, 0.0960938, 0.0953334, 0.094616, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11067</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11568</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12069</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12570</th>\n",
" <td>mal-2007-3</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13071</th>\n",
" <td>mal-2007-3</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13572</th>\n",
" <td>mal-2007-3</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-587.526, -570.743, -554.118, -537.594, -521....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14073</th>\n",
" <td>mal-2007-3</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-4115.44, -4085.47, -4055.71, -4026.17, -3996...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14617</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0850584, 0.0843237, 0.0836016, 0.0828918, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15118</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[890.183, 890.897, 891.669, 892.545, 893.561, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15619</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[998.65, 998.996, 999.299, 999.545, 999.729, 9...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20128</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20629</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0953909, 0.101423, 0.107646, 0.114204, 0.12...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21130</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.00260078, 0.00236823, 0.00216585, 0.0019888...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21631</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22132</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22633</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24637</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0276155, 0.0278689, 0.0281231, 0.028378, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25138</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.101774, 0.10084, 0.0999201, 0.0990074, 0.09...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25639</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26140</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0278377, 0.0277255, 0.0276261, 0.0275434, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26641</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0986832, 0.0976704, 0.0966705, 0.0956817, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27142</th>\n",
" <td>mal-2007-2</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.055677, 0.0554834, 0.0552801, 0.0550659, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27643</th>\n",
" <td>mal-2007-2</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28144</th>\n",
" <td>mal-2007-2</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-605.701, -587.445, -569.306, -551.277, -533....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28645</th>\n",
" <td>mal-2007-2</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-6954.16, -6918.32, -6882.55, -6846.91, -6811...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29189</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.765858, 0.756305, 0.746988, 0.737899, 0.729...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29690</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQUID DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[875.4, 877.459, 879.975, 883.022, 886.503, 89...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30191</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WATER DENSITY</td>\n",
" <td>KG/M3</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[808.281, 806.539, 804.708, 802.788, 800.774, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34700</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35201</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0376117, 0.0410375, 0.0445707, 0.0482234, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35702</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. VISCOSITY</td>\n",
" <td>N S/M2</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.000705364, 0.000669605, 0.000637022, 0.0006...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36203</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36704</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37205</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. SPECIFIC HEAT</td>\n",
" <td>J/KG K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37706</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38207</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-270543.0, -262146.0, -253911.0, -245857.0, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38708</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. ENTHALPY</td>\n",
" <td>J/KG</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-1329300.0, -1316130.0, -1302920.0, -1289670....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39209</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0314484, 0.0318777, 0.0323095, 0.0327438, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39710</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQ. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.103153, 0.101553, 0.100041, 0.098621, 0.097...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40211</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WAT. THERMAL COND.</td>\n",
" <td>W/M K</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.60348, 0.609125, 0.61455, 0.619758, 0.62475...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40712</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION GAS/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0254278, 0.0253864, 0.0254091, 0.0255026, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41213</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION GAS/WATER</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41714</th>\n",
" <td>mal-2007-1</td>\n",
" <td>SURFACE TENSION WATER/OIL</td>\n",
" <td>N/M</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[0.0445628, 0.0450036, 0.0455026, 0.0460673, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42215</th>\n",
" <td>mal-2007-1</td>\n",
" <td>GAS ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[364.494, 389.532, 414.413, 439.141, 463.72, 4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42716</th>\n",
" <td>mal-2007-1</td>\n",
" <td>LIQUID ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-410.085, -382.735, -356.017, -329.986, -304....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43217</th>\n",
" <td>mal-2007-1</td>\n",
" <td>WATER ENTROPY</td>\n",
" <td>J/KG/C</td>\n",
" <td>[-9.99999, -9.99999, -9.99999, -9.99999, -9.99...</td>\n",
" <td>[0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1...</td>\n",
" <td>[-3804.14, -3759.48, -3715.25, -3671.42, -3627...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>63 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Fluid Property Unit \\\n",
"45 mal-2007-3 GAS DENSITY KG/M3 \n",
"546 mal-2007-3 LIQUID DENSITY KG/M3 \n",
"1047 mal-2007-3 WATER DENSITY KG/M3 \n",
"5556 mal-2007-3 GAS VISCOSITY N S/M2 \n",
"6057 mal-2007-3 LIQ. VISCOSITY N S/M2 \n",
"6558 mal-2007-3 WAT. VISCOSITY N S/M2 \n",
"7059 mal-2007-3 GAS SPECIFIC HEAT J/KG K \n",
"7560 mal-2007-3 LIQ. SPECIFIC HEAT J/KG K \n",
"8061 mal-2007-3 WAT. SPECIFIC HEAT J/KG K \n",
"8562 mal-2007-3 GAS ENTHALPY J/KG \n",
"9063 mal-2007-3 LIQ. ENTHALPY J/KG \n",
"9564 mal-2007-3 WAT. ENTHALPY J/KG \n",
"10065 mal-2007-3 GAS THERMAL COND. W/M K \n",
"10566 mal-2007-3 LIQ. THERMAL COND. W/M K \n",
"11067 mal-2007-3 WAT. THERMAL COND. W/M K \n",
"11568 mal-2007-3 SURFACE TENSION GAS/OIL N/M \n",
"12069 mal-2007-3 SURFACE TENSION GAS/WATER N/M \n",
"12570 mal-2007-3 SURFACE TENSION WATER/OIL N/M \n",
"13071 mal-2007-3 GAS ENTROPY J/KG/C \n",
"13572 mal-2007-3 LIQUID ENTROPY J/KG/C \n",
"14073 mal-2007-3 WATER ENTROPY J/KG/C \n",
"14617 mal-2007-2 GAS DENSITY KG/M3 \n",
"15118 mal-2007-2 LIQUID DENSITY KG/M3 \n",
"15619 mal-2007-2 WATER DENSITY KG/M3 \n",
"20128 mal-2007-2 GAS VISCOSITY N S/M2 \n",
"20629 mal-2007-2 LIQ. VISCOSITY N S/M2 \n",
"21130 mal-2007-2 WAT. VISCOSITY N S/M2 \n",
"21631 mal-2007-2 GAS SPECIFIC HEAT J/KG K \n",
"22132 mal-2007-2 LIQ. SPECIFIC HEAT J/KG K \n",
"22633 mal-2007-2 WAT. SPECIFIC HEAT J/KG K \n",
"... ... ... ... \n",
"24637 mal-2007-2 GAS THERMAL COND. W/M K \n",
"25138 mal-2007-2 LIQ. THERMAL COND. W/M K \n",
"25639 mal-2007-2 WAT. THERMAL COND. W/M K \n",
"26140 mal-2007-2 SURFACE TENSION GAS/OIL N/M \n",
"26641 mal-2007-2 SURFACE TENSION GAS/WATER N/M \n",
"27142 mal-2007-2 SURFACE TENSION WATER/OIL N/M \n",
"27643 mal-2007-2 GAS ENTROPY J/KG/C \n",
"28144 mal-2007-2 LIQUID ENTROPY J/KG/C \n",
"28645 mal-2007-2 WATER ENTROPY J/KG/C \n",
"29189 mal-2007-1 GAS DENSITY KG/M3 \n",
"29690 mal-2007-1 LIQUID DENSITY KG/M3 \n",
"30191 mal-2007-1 WATER DENSITY KG/M3 \n",
"34700 mal-2007-1 GAS VISCOSITY N S/M2 \n",
"35201 mal-2007-1 LIQ. VISCOSITY N S/M2 \n",
"35702 mal-2007-1 WAT. VISCOSITY N S/M2 \n",
"36203 mal-2007-1 GAS SPECIFIC HEAT J/KG K \n",
"36704 mal-2007-1 LIQ. SPECIFIC HEAT J/KG K \n",
"37205 mal-2007-1 WAT. SPECIFIC HEAT J/KG K \n",
"37706 mal-2007-1 GAS ENTHALPY J/KG \n",
"38207 mal-2007-1 LIQ. ENTHALPY J/KG \n",
"38708 mal-2007-1 WAT. ENTHALPY J/KG \n",
"39209 mal-2007-1 GAS THERMAL COND. W/M K \n",
"39710 mal-2007-1 LIQ. THERMAL COND. W/M K \n",
"40211 mal-2007-1 WAT. THERMAL COND. W/M K \n",
"40712 mal-2007-1 SURFACE TENSION GAS/OIL N/M \n",
"41213 mal-2007-1 SURFACE TENSION GAS/WATER N/M \n",
"41714 mal-2007-1 SURFACE TENSION WATER/OIL N/M \n",
"42215 mal-2007-1 GAS ENTROPY J/KG/C \n",
"42716 mal-2007-1 LIQUID ENTROPY J/KG/C \n",
"43217 mal-2007-1 WATER ENTROPY J/KG/C \n",
"\n",
" Temperature \\\n",
"45 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"546 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"1047 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"5556 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"6057 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"6558 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"7059 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"7560 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"8061 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"8562 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"9063 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"9564 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"10065 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"10566 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"11067 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"11568 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"12069 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"12570 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"13071 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"13572 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"14073 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"14617 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"15118 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"15619 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"20128 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"20629 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"21130 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"21631 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"22132 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"22633 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"... ... \n",
"24637 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"25138 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"25639 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"26140 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"26641 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"27142 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"27643 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"28144 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"28645 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"29189 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"29690 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"30191 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"34700 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"35201 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"35702 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"36203 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"36704 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"37205 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"37706 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"38207 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"38708 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"39209 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"39710 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"40211 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"40712 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"41213 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"41714 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"42215 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"42716 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"43217 [-9.99999, -9.99999, -9.99999, -9.99999, -9.99... \n",
"\n",
" Pressure \\\n",
"45 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"546 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"1047 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"5556 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"6057 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"6558 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"7059 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"7560 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"8061 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"8562 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"9063 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"9564 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"10065 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"10566 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"11067 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"11568 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"12069 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"12570 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"13071 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"13572 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"14073 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"14617 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"15118 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"15619 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"20128 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"20629 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"21130 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"21631 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"22132 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"22633 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"... ... \n",
"24637 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"25138 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"25639 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"26140 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"26641 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"27142 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"27643 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"28144 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"28645 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"29189 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"29690 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"30191 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"34700 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"35201 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"35702 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"36203 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"36704 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"37205 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"37706 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"38207 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"38708 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"39209 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"39710 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"40211 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"40712 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"41213 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"41714 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"42215 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"42716 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"43217 [0.1, 1.01325, 3.36277, 6.62554, 9.88831, 13.1... \n",
"\n",
" values \n",
"45 [0.0849146, 0.0841808, 0.0834595, 0.0827506, 0... \n",
"546 [899.718, 900.424, 901.309, 902.434, 903.838, ... \n",
"1047 [813.363, 812.66, 811.929, 811.17, 810.382, 80... \n",
"5556 [1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423... \n",
"6057 [0.220481, 0.227562, 0.234135, 0.240676, 0.247... \n",
"6558 [0.0010661, 0.00101649, 0.000970794, 0.0009286... \n",
"7059 [1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1... \n",
"7560 [1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1... \n",
"8061 [3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ... \n",
"8562 [-19279.3, -14920.5, -10543.9, -6149.34, -1736... \n",
"9063 [-317877.0, -313080.0, -308335.0, -303637.0, -... \n",
"9564 [-1395510.0, -1387580.0, -1379650.0, -1371710.... \n",
"10065 [0.0277744, 0.028032, 0.0282904, 0.0285496, 0.... \n",
"10566 [0.0969043, 0.0960938, 0.0953334, 0.094616, 0.... \n",
"11067 [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"11568 [0.0280944, 0.0280288, 0.0279906, 0.0279847, 0... \n",
"12069 [0.0698809, 0.0690383, 0.0682086, 0.0673915, 0... \n",
"12570 [0.0551154, 0.0550872, 0.0550879, 0.0551306, 0... \n",
"13071 [1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ... \n",
"13572 [-587.526, -570.743, -554.118, -537.594, -521.... \n",
"14073 [-4115.44, -4085.47, -4055.71, -4026.17, -3996... \n",
"14617 [0.0850584, 0.0843237, 0.0836016, 0.0828918, 0... \n",
"15118 [890.183, 890.897, 891.669, 892.545, 893.561, ... \n",
"15619 [998.65, 998.996, 999.299, 999.545, 999.729, 9... \n",
"20128 [1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778... \n",
"20629 [0.0953909, 0.101423, 0.107646, 0.114204, 0.12... \n",
"21130 [0.00260078, 0.00236823, 0.00216585, 0.0019888... \n",
"21631 [1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ... \n",
"22132 [1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16... \n",
"22633 [4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4... \n",
"... ... \n",
"24637 [0.0276155, 0.0278689, 0.0281231, 0.028378, 0.... \n",
"25138 [0.101774, 0.10084, 0.0999201, 0.0990074, 0.09... \n",
"25639 [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"26140 [0.0278377, 0.0277255, 0.0276261, 0.0275434, 0... \n",
"26641 [0.0986832, 0.0976704, 0.0966705, 0.0956817, 0... \n",
"27142 [0.055677, 0.0554834, 0.0552801, 0.0550659, 0.... \n",
"27643 [1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ... \n",
"28144 [-605.701, -587.445, -569.306, -551.277, -533.... \n",
"28645 [-6954.16, -6918.32, -6882.55, -6846.91, -6811... \n",
"29189 [0.765858, 0.756305, 0.746988, 0.737899, 0.729... \n",
"29690 [875.4, 877.459, 879.975, 883.022, 886.503, 89... \n",
"30191 [808.281, 806.539, 804.708, 802.788, 800.774, ... \n",
"34700 [1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598... \n",
"35201 [0.0376117, 0.0410375, 0.0445707, 0.0482234, 0... \n",
"35702 [0.000705364, 0.000669605, 0.000637022, 0.0006... \n",
"36203 [2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ... \n",
"36704 [1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17... \n",
"37205 [3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3... \n",
"37706 [38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ... \n",
"38207 [-270543.0, -262146.0, -253911.0, -245857.0, -... \n",
"38708 [-1329300.0, -1316130.0, -1302920.0, -1289670.... \n",
"39209 [0.0314484, 0.0318777, 0.0323095, 0.0327438, 0... \n",
"39710 [0.103153, 0.101553, 0.100041, 0.098621, 0.097... \n",
"40211 [0.60348, 0.609125, 0.61455, 0.619758, 0.62475... \n",
"40712 [0.0254278, 0.0253864, 0.0254091, 0.0255026, 0... \n",
"41213 [0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0... \n",
"41714 [0.0445628, 0.0450036, 0.0455026, 0.0460673, 0... \n",
"42215 [364.494, 389.532, 414.413, 439.141, 463.72, 4... \n",
"42716 [-410.085, -382.735, -356.017, -329.986, -304.... \n",
"43217 [-3804.14, -3759.48, -3715.25, -3671.42, -3627... \n",
"\n",
"[63 rows x 6 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mul_fix.data"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T = mul_fix.data.Temperature[45]\n",
"P = mul_fix.data.Pressure[45]\n",
"values = mul_fix.data.values[45][3]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xaf5abe4c>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xafd4c48c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.scatter(T, P, values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Single - Keyword"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'fluids': ['1'],\n",
" 'nfluids': 1,\n",
" 'p_array': array([ 1.00000000e+04, 1.01325000e+05, 7.38958000e+05,\n",
" 1.46792000e+06, 2.19688000e+06, 2.92583000e+06,\n",
" 3.65479000e+06, 4.38375000e+06, 5.11271000e+06,\n",
" 5.84167000e+06, 6.57063000e+06, 7.29958000e+06,\n",
" 8.02854000e+06, 8.75750000e+06, 9.48646000e+06,\n",
" 1.02154000e+07, 1.09444000e+07, 1.16733000e+07,\n",
" 1.24023000e+07, 1.31313000e+07, 1.38602000e+07,\n",
" 1.45892000e+07, 1.53181000e+07, 1.60471000e+07,\n",
" 1.67760000e+07, 1.75050000e+07, 1.82340000e+07,\n",
" 1.89629000e+07, 1.96919000e+07, 2.04208000e+07,\n",
" 2.11498000e+07, 2.18788000e+07, 2.26077000e+07,\n",
" 2.33367000e+07, 2.40656000e+07, 2.47946000e+07,\n",
" 2.55235000e+07, 2.62525000e+07, 2.69815000e+07,\n",
" 2.77104000e+07, 2.84394000e+07, 2.91683000e+07,\n",
" 2.98973000e+07, 3.06263000e+07, 3.13552000e+07,\n",
" 3.20842000e+07, 3.28131000e+07, 3.35421000e+07,\n",
" 3.42710000e+07, 3.50000000e+07]),\n",
" 'p_points': 50,\n",
" 'properties': ['PT',\n",
" 'TM',\n",
" 'ROG',\n",
" 'ROHL',\n",
" 'ROWT',\n",
" 'DROGDP',\n",
" 'DROHLDP',\n",
" 'DROWTDP',\n",
" 'DROGDT',\n",
" 'DROHLDT',\n",
" 'DROWTDT',\n",
" 'RS',\n",
" 'RSW',\n",
" 'VISG',\n",
" 'VISHL',\n",
" 'VISWT',\n",
" 'CPG',\n",
" 'CPHL',\n",
" 'CPWT',\n",
" 'HG',\n",
" 'HHL',\n",
" 'HWT',\n",
" 'TCG',\n",
" 'TCHL',\n",
" 'TCWT',\n",
" 'SIGGHL',\n",
" 'SIGGWT',\n",
" 'SIGHLWT',\n",
" 'SEG',\n",
" 'SEHL',\n",
" 'SEWT'],\n",
" 't_array': array([ -10. , -7.70833 , -5.41667 , -3.125 , -0.833333,\n",
" 1.45833 , 3.75 , 6.04167 , 8.33333 , 10.625 ,\n",
" 12.9167 , 15.2083 , 15.56 , 17.5 , 19.7917 ,\n",
" 22.0833 , 24.375 , 26.6667 , 28.9583 , 31.25 ,\n",
" 33.5417 , 35.8333 , 38.125 , 40.4167 , 42.7083 ,\n",
" 45. , 47.2917 , 49.5833 , 51.875 , 54.1667 ,\n",
" 56.4583 , 58.75 , 61.0417 , 63.3333 , 65.625 ,\n",
" 67.9167 , 70.2083 , 72.5 , 74.7917 , 77.0833 ,\n",
" 79.375 , 81.6667 , 83.9583 , 86.25 , 88.5417 ,\n",
" 90.8333 , 93.125 , 95.4167 , 97.7083 , 100. ]),\n",
" 't_points': 50}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sin_key.metadata"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sin_key.export_all()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CPG</th>\n",
" <td>[1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CPHL</th>\n",
" <td>[1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CPWT</th>\n",
" <td>[3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROGDP</th>\n",
" <td>[8.4946e-06, 8.42111e-06, 8.34888e-06, 8.27788...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROGDT</th>\n",
" <td>[0.000323057, 0.000317492, 0.00031207, 0.00030...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROHLDP</th>\n",
" <td>[4.47091e-07, 4.5376e-07, 4.60533e-07, 4.67363...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROHLDT</th>\n",
" <td>[0.694011, 0.693068, 0.691885, 0.69043, 0.6886...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROWTDP</th>\n",
" <td>[5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROWTDT</th>\n",
" <td>[0.158913, 0.142489, 0.120409, 0.0942844, 0.06...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HG</th>\n",
" <td>[19279.3, 14920.5, 10543.9, 6149.34, 1736.95, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HHL</th>\n",
" <td>[317877.0, 313080.0, 308335.0, 303637.0, 29897...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWT</th>\n",
" <td>[1395510.0, 1387580.0, 1379650.0, 1371710.0, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PT</th>\n",
" <td>[10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROG</th>\n",
" <td>[0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROHL</th>\n",
" <td>[899.718, 900.424, 901.309, 902.434, 903.838, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROWT</th>\n",
" <td>[813.363, 812.66, 811.929, 811.17, 810.382, 80...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RS</th>\n",
" <td>[0.999977, 0.999979, 0.99998, 0.999982, 0.9999...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RSW</th>\n",
" <td>[0.000692485, 0.000692485, 0.000692484, 0.0006...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEG</th>\n",
" <td>[1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEHL</th>\n",
" <td>[587.526, 570.743, 554.118, 537.594, 521.16, 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEWT</th>\n",
" <td>[4115.44, 4085.47, 4055.71, 4026.17, 3996.84, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGGHL</th>\n",
" <td>[0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGGWT</th>\n",
" <td>[0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGHLWT</th>\n",
" <td>[0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCG</th>\n",
" <td>[0.0277744, 0.028032, 0.0282904, 0.0285496, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCHL</th>\n",
" <td>[0.0969043, 0.0960938, 0.0953334, 0.094616, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCWT</th>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TM</th>\n",
" <td>[10.0, 7.70833, 5.41667, 3.125, 0.833333, 1.45...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISG</th>\n",
" <td>[1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISHL</th>\n",
" <td>[0.220481, 0.227562, 0.234135, 0.240676, 0.247...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISWT</th>\n",
" <td>[0.0010661, 0.00101649, 0.000970794, 0.0009286...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1\n",
"CPG [1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...\n",
"CPHL [1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...\n",
"CPWT [3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...\n",
"DROGDP [8.4946e-06, 8.42111e-06, 8.34888e-06, 8.27788...\n",
"DROGDT [0.000323057, 0.000317492, 0.00031207, 0.00030...\n",
"DROHLDP [4.47091e-07, 4.5376e-07, 4.60533e-07, 4.67363...\n",
"DROHLDT [0.694011, 0.693068, 0.691885, 0.69043, 0.6886...\n",
"DROWTDP [5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565...\n",
"DROWTDT [0.158913, 0.142489, 0.120409, 0.0942844, 0.06...\n",
"HG [19279.3, 14920.5, 10543.9, 6149.34, 1736.95, ...\n",
"HHL [317877.0, 313080.0, 308335.0, 303637.0, 29897...\n",
"HWT [1395510.0, 1387580.0, 1379650.0, 1371710.0, 1...\n",
"PT [10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ...\n",
"ROG [0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...\n",
"ROHL [899.718, 900.424, 901.309, 902.434, 903.838, ...\n",
"ROWT [813.363, 812.66, 811.929, 811.17, 810.382, 80...\n",
"RS [0.999977, 0.999979, 0.99998, 0.999982, 0.9999...\n",
"RSW [0.000692485, 0.000692485, 0.000692484, 0.0006...\n",
"SEG [1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...\n",
"SEHL [587.526, 570.743, 554.118, 537.594, 521.16, 5...\n",
"SEWT [4115.44, 4085.47, 4055.71, 4026.17, 3996.84, ...\n",
"SIGGHL [0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...\n",
"SIGGWT [0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...\n",
"SIGHLWT [0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...\n",
"TCG [0.0277744, 0.028032, 0.0282904, 0.0285496, 0....\n",
"TCHL [0.0969043, 0.0960938, 0.0953334, 0.094616, 0....\n",
"TCWT [0.548681, 0.553425, 0.558072, 0.562624, 0.567...\n",
"TM [10.0, 7.70833, 5.41667, 3.125, 0.833333, 1.45...\n",
"VISG [1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...\n",
"VISHL [0.220481, 0.227562, 0.234135, 0.240676, 0.247...\n",
"VISWT [0.0010661, 0.00101649, 0.000970794, 0.0009286..."
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sin_key.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple keyword"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'fluids': ['1', '2', '3'],\n",
" 'nfluids': 3,\n",
" 'p_array': array([ 100000., 101325., 514583., 929167., 1343750.,\n",
" 1758330., 2172920., 2587500., 3002080., 3416670.,\n",
" 3831250., 4245830., 4660420., 5075000., 5489580.,\n",
" 5904170., 6318750., 6733330., 7147920., 7562500.,\n",
" 7977080., 8391670., 8806250., 9220830., 9635420.,\n",
" 10050000., 10464600., 10879200., 11293800., 11708300.,\n",
" 12122900., 12537500., 12952100., 13366700., 13781300.,\n",
" 14195800., 14610400., 15025000., 15439600., 15854200.,\n",
" 16268800., 16683300., 17097900., 17512500., 17927100.,\n",
" 18341700., 18756300., 19170800., 19585400., 20000000.]),\n",
" 'p_points': 50,\n",
" 'properties': ['PT',\n",
" 'TM',\n",
" 'ROG',\n",
" 'ROHL',\n",
" 'ROWT',\n",
" 'DROGDP',\n",
" 'DROHLDP',\n",
" 'DROWTDP',\n",
" 'DROGDT',\n",
" 'DROHLDT',\n",
" 'DROWTDT',\n",
" 'RS',\n",
" 'RSW',\n",
" 'VISG',\n",
" 'VISHL',\n",
" 'VISWT',\n",
" 'CPG',\n",
" 'CPHL',\n",
" 'CPWT',\n",
" 'HG',\n",
" 'HHL',\n",
" 'HWT',\n",
" 'TCG',\n",
" 'TCHL',\n",
" 'TCWT',\n",
" 'SIGGHL',\n",
" 'SIGGWT',\n",
" 'SIGHLWT',\n",
" 'SEG',\n",
" 'SEHL',\n",
" 'SEWT'],\n",
" 't_array': array([ 20. , 23.6735, 27.3469, 31.0204, 34.6939, 38.3673,\n",
" 42.0408, 45.7143, 49.3878, 53.0612, 56.7347, 60.4082,\n",
" 64.0816, 67.7551, 71.4286, 75.102 , 78.7755, 82.449 ,\n",
" 86.1224, 89.7959, 93.4694, 97.1429, 100.816 , 104.49 ,\n",
" 108.163 , 111.837 , 115.51 , 119.184 , 122.857 , 126.531 ,\n",
" 130.204 , 133.878 , 137.551 , 141.224 , 144.898 , 148.571 ,\n",
" 152.245 , 155.918 , 159.592 , 163.265 , 166.939 , 170.612 ,\n",
" 174.286 , 177.959 , 181.633 , 185.306 , 188.98 , 192.653 ,\n",
" 196.327 , 200. ]),\n",
" 't_points': 50}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mul_key.metadata"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mul_key.export_all()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CPG</th>\n",
" <td>[1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1...</td>\n",
" <td>[1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ...</td>\n",
" <td>[2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CPHL</th>\n",
" <td>[1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1...</td>\n",
" <td>[1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16...</td>\n",
" <td>[1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CPWT</th>\n",
" <td>[3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ...</td>\n",
" <td>[4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4...</td>\n",
" <td>[3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROGDP</th>\n",
" <td>[8.4946e-06, 8.42111e-06, 8.34888e-06, 8.27788...</td>\n",
" <td>[8.50905e-06, 8.43547e-06, 8.36316e-06, 8.2920...</td>\n",
" <td>[7.67875e-06, 7.58215e-06, 7.48797e-06, 7.3961...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROGDT</th>\n",
" <td>[0.000323057, 0.000317492, 0.00031207, 0.00030...</td>\n",
" <td>[0.000323611, 0.000318038, 0.000312607, 0.0003...</td>\n",
" <td>[0.00263553, 0.00256973, 0.00250637, 0.0024453...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROHLDP</th>\n",
" <td>[4.47091e-07, 4.5376e-07, 4.60533e-07, 4.67363...</td>\n",
" <td>[4.55687e-07, 4.61496e-07, 4.67662e-07, 4.7411...</td>\n",
" <td>[5.75888e-07, 5.86846e-07, 5.98137e-07, 6.0955...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROHLDT</th>\n",
" <td>[0.694011, 0.693068, 0.691885, 0.69043, 0.6886...</td>\n",
" <td>[0.697742, 0.697528, 0.69711, 0.696467, 0.6955...</td>\n",
" <td>[0.704383, 0.702407, 0.699857, 0.696731, 0.693...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROWTDP</th>\n",
" <td>[5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565...</td>\n",
" <td>[5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565...</td>\n",
" <td>[4.62113e-07, 4.56381e-07, 4.51539e-07, 4.4753...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DROWTDT</th>\n",
" <td>[0.158913, 0.142489, 0.120409, 0.0942844, 0.06...</td>\n",
" <td>[0.158913, 0.142489, 0.120408, 0.0942843, 0.06...</td>\n",
" <td>[0.201731, 0.241159, 0.277883, 0.312086, 0.343...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HG</th>\n",
" <td>[19279.3, 14920.5, 10543.9, 6149.34, 1736.95, ...</td>\n",
" <td>[19263.8, 14908.7, 10535.9, 6145.46, 1737.37, ...</td>\n",
" <td>[38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HHL</th>\n",
" <td>[317877.0, 313080.0, 308335.0, 303637.0, 29897...</td>\n",
" <td>[325444.0, 320247.0, 315064.0, 309892.0, 30474...</td>\n",
" <td>[270543.0, 262146.0, 253911.0, 245857.0, 23799...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWT</th>\n",
" <td>[1395510.0, 1387580.0, 1379650.0, 1371710.0, 1...</td>\n",
" <td>[2543490.0, 2534020.0, 2524480.0, 2514900.0, 2...</td>\n",
" <td>[1329300.0, 1316130.0, 1302920.0, 1289670.0, 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PT</th>\n",
" <td>[10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ...</td>\n",
" <td>[10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ...</td>\n",
" <td>[100000.0, 100000.0, 100000.0, 100000.0, 10000...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROG</th>\n",
" <td>[0.0849146, 0.0841808, 0.0834595, 0.0827506, 0...</td>\n",
" <td>[0.0850584, 0.0843237, 0.0836016, 0.0828918, 0...</td>\n",
" <td>[0.765858, 0.756305, 0.746988, 0.737899, 0.729...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROHL</th>\n",
" <td>[899.718, 900.424, 901.309, 902.434, 903.838, ...</td>\n",
" <td>[890.183, 890.897, 891.669, 892.545, 893.561, ...</td>\n",
" <td>[875.4, 877.459, 879.975, 883.022, 886.503, 89...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ROWT</th>\n",
" <td>[813.363, 812.66, 811.929, 811.17, 810.382, 80...</td>\n",
" <td>[998.65, 998.996, 999.299, 999.545, 999.729, 9...</td>\n",
" <td>[808.281, 806.539, 804.708, 802.788, 800.774, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RS</th>\n",
" <td>[0.999977, 0.999979, 0.99998, 0.999982, 0.9999...</td>\n",
" <td>[0.999901, 0.999908, 0.999915, 0.999921, 0.999...</td>\n",
" <td>[0.999923, 0.999933, 0.999941, 0.999949, 0.999...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RSW</th>\n",
" <td>[0.000692485, 0.000692485, 0.000692484, 0.0006...</td>\n",
" <td>[0.0135402, 0.0135401, 0.01354, 0.0135399, 0.0...</td>\n",
" <td>[0.00106583, 0.00106582, 0.00106582, 0.0010658...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEG</th>\n",
" <td>[1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ...</td>\n",
" <td>[1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ...</td>\n",
" <td>[364.494, 389.532, 414.413, 439.141, 463.72, 4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEHL</th>\n",
" <td>[587.526, 570.743, 554.118, 537.594, 521.16, 5...</td>\n",
" <td>[605.701, 587.445, 569.306, 551.277, 533.406, ...</td>\n",
" <td>[410.085, 382.735, 356.017, 329.986, 304.777, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEWT</th>\n",
" <td>[4115.44, 4085.47, 4055.71, 4026.17, 3996.84, ...</td>\n",
" <td>[6954.16, 6918.32, 6882.55, 6846.91, 6811.47, ...</td>\n",
" <td>[3804.14, 3759.48, 3715.25, 3671.42, 3627.98, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGGHL</th>\n",
" <td>[0.0280944, 0.0280288, 0.0279906, 0.0279847, 0...</td>\n",
" <td>[0.0278377, 0.0277255, 0.0276261, 0.0275434, 0...</td>\n",
" <td>[0.0254278, 0.0253864, 0.0254091, 0.0255026, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGGWT</th>\n",
" <td>[0.0698809, 0.0690383, 0.0682086, 0.0673915, 0...</td>\n",
" <td>[0.0986832, 0.0976704, 0.0966705, 0.0956817, 0...</td>\n",
" <td>[0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIGHLWT</th>\n",
" <td>[0.0551154, 0.0550872, 0.0550879, 0.0551306, 0...</td>\n",
" <td>[0.055677, 0.0554834, 0.0552801, 0.0550659, 0....</td>\n",
" <td>[0.0445628, 0.0450036, 0.0455026, 0.0460673, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCG</th>\n",
" <td>[0.0277744, 0.028032, 0.0282904, 0.0285496, 0....</td>\n",
" <td>[0.0276155, 0.0278689, 0.0281231, 0.028378, 0....</td>\n",
" <td>[0.0314484, 0.0318777, 0.0323095, 0.0327438, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCHL</th>\n",
" <td>[0.0969043, 0.0960938, 0.0953334, 0.094616, 0....</td>\n",
" <td>[0.101774, 0.10084, 0.0999201, 0.0990074, 0.09...</td>\n",
" <td>[0.103153, 0.101553, 0.100041, 0.098621, 0.097...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCWT</th>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" <td>[0.548681, 0.553425, 0.558072, 0.562624, 0.567...</td>\n",
" <td>[0.60348, 0.609125, 0.61455, 0.619758, 0.62475...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TM</th>\n",
" <td>[10.0, 7.70833, 5.41667, 3.125, 0.833333, 1.45...</td>\n",
" <td>[9.99999, 7.70833, 5.41666, 3.12499, 0.833327,...</td>\n",
" <td>[20.0, 23.6735, 27.3469, 31.0204, 34.6939, 38....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISG</th>\n",
" <td>[1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423...</td>\n",
" <td>[1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778...</td>\n",
" <td>[1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISHL</th>\n",
" <td>[0.220481, 0.227562, 0.234135, 0.240676, 0.247...</td>\n",
" <td>[0.0953909, 0.101423, 0.107646, 0.114204, 0.12...</td>\n",
" <td>[0.0376117, 0.0410375, 0.0445707, 0.0482234, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VISWT</th>\n",
" <td>[0.0010661, 0.00101649, 0.000970794, 0.0009286...</td>\n",
" <td>[0.00260078, 0.00236823, 0.00216585, 0.0019888...</td>\n",
" <td>[0.000705364, 0.000669605, 0.000637022, 0.0006...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 \\\n",
"CPG [1898.12, 1905.92, 1913.71, 1921.51, 1929.3, 1... \n",
"CPHL [1610.0, 1617.06, 1623.76, 1630.02, 1635.79, 1... \n",
"CPWT [3454.74, 3458.93, 3463.33, 3467.94, 3472.76, ... \n",
"DROGDP [8.4946e-06, 8.42111e-06, 8.34888e-06, 8.27788... \n",
"DROGDT [0.000323057, 0.000317492, 0.00031207, 0.00030... \n",
"DROHLDP [4.47091e-07, 4.5376e-07, 4.60533e-07, 4.67363... \n",
"DROHLDT [0.694011, 0.693068, 0.691885, 0.69043, 0.6886... \n",
"DROWTDP [5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565... \n",
"DROWTDT [0.158913, 0.142489, 0.120409, 0.0942844, 0.06... \n",
"HG [19279.3, 14920.5, 10543.9, 6149.34, 1736.95, ... \n",
"HHL [317877.0, 313080.0, 308335.0, 303637.0, 29897... \n",
"HWT [1395510.0, 1387580.0, 1379650.0, 1371710.0, 1... \n",
"PT [10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ... \n",
"ROG [0.0849146, 0.0841808, 0.0834595, 0.0827506, 0... \n",
"ROHL [899.718, 900.424, 901.309, 902.434, 903.838, ... \n",
"ROWT [813.363, 812.66, 811.929, 811.17, 810.382, 80... \n",
"RS [0.999977, 0.999979, 0.99998, 0.999982, 0.9999... \n",
"RSW [0.000692485, 0.000692485, 0.000692484, 0.0006... \n",
"SEG [1185.33, 1201.82, 1218.24, 1234.58, 1250.85, ... \n",
"SEHL [587.526, 570.743, 554.118, 537.594, 521.16, 5... \n",
"SEWT [4115.44, 4085.47, 4055.71, 4026.17, 3996.84, ... \n",
"SIGGHL [0.0280944, 0.0280288, 0.0279906, 0.0279847, 0... \n",
"SIGGWT [0.0698809, 0.0690383, 0.0682086, 0.0673915, 0... \n",
"SIGHLWT [0.0551154, 0.0550872, 0.0550879, 0.0551306, 0... \n",
"TCG [0.0277744, 0.028032, 0.0282904, 0.0285496, 0.... \n",
"TCHL [0.0969043, 0.0960938, 0.0953334, 0.094616, 0.... \n",
"TCWT [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"TM [10.0, 7.70833, 5.41667, 3.125, 0.833333, 1.45... \n",
"VISG [1.01832e-05, 1.02634e-05, 1.03434e-05, 1.0423... \n",
"VISHL [0.220481, 0.227562, 0.234135, 0.240676, 0.247... \n",
"VISWT [0.0010661, 0.00101649, 0.000970794, 0.0009286... \n",
"\n",
" 2 \\\n",
"CPG [1896.55, 1904.26, 1911.97, 1919.68, 1927.38, ... \n",
"CPHL [1617.14, 1624.53, 1631.8, 1638.9, 1645.74, 16... \n",
"CPWT [4115.95, 4149.47, 4172.87, 4188.4, 4197.97, 4... \n",
"DROGDP [8.50905e-06, 8.43547e-06, 8.36316e-06, 8.2920... \n",
"DROGDT [0.000323611, 0.000318038, 0.000312607, 0.0003... \n",
"DROHLDP [4.55687e-07, 4.61496e-07, 4.67662e-07, 4.7411... \n",
"DROHLDT [0.697742, 0.697528, 0.69711, 0.696467, 0.6955... \n",
"DROWTDP [5.24381e-07, 5.22483e-07, 5.1907e-07, 5.14565... \n",
"DROWTDT [0.158913, 0.142489, 0.120408, 0.0942843, 0.06... \n",
"HG [19263.8, 14908.7, 10535.9, 6145.46, 1737.37, ... \n",
"HHL [325444.0, 320247.0, 315064.0, 309892.0, 30474... \n",
"HWT [2543490.0, 2534020.0, 2524480.0, 2514900.0, 2... \n",
"PT [10000.0, 10000.0, 10000.0, 10000.0, 10000.0, ... \n",
"ROG [0.0850584, 0.0843237, 0.0836016, 0.0828918, 0... \n",
"ROHL [890.183, 890.897, 891.669, 892.545, 893.561, ... \n",
"ROWT [998.65, 998.996, 999.299, 999.545, 999.729, 9... \n",
"RS [0.999901, 0.999908, 0.999915, 0.999921, 0.999... \n",
"RSW [0.0135402, 0.0135401, 0.01354, 0.0135399, 0.0... \n",
"SEG [1213.54, 1230.01, 1246.41, 1262.73, 1278.98, ... \n",
"SEHL [605.701, 587.445, 569.306, 551.277, 533.406, ... \n",
"SEWT [6954.16, 6918.32, 6882.55, 6846.91, 6811.47, ... \n",
"SIGGHL [0.0278377, 0.0277255, 0.0276261, 0.0275434, 0... \n",
"SIGGWT [0.0986832, 0.0976704, 0.0966705, 0.0956817, 0... \n",
"SIGHLWT [0.055677, 0.0554834, 0.0552801, 0.0550659, 0.... \n",
"TCG [0.0276155, 0.0278689, 0.0281231, 0.028378, 0.... \n",
"TCHL [0.101774, 0.10084, 0.0999201, 0.0990074, 0.09... \n",
"TCWT [0.548681, 0.553425, 0.558072, 0.562624, 0.567... \n",
"TM [9.99999, 7.70833, 5.41666, 3.12499, 0.833327,... \n",
"VISG [1.01379e-05, 1.02181e-05, 1.0298e-05, 1.03778... \n",
"VISHL [0.0953909, 0.101423, 0.107646, 0.114204, 0.12... \n",
"VISWT [0.00260078, 0.00236823, 0.00216585, 0.0019888... \n",
"\n",
" 3 \n",
"CPG [2004.26, 2016.56, 2028.87, 2041.17, 2053.47, ... \n",
"CPHL [1732.77, 1742.9, 1752.15, 1760.42, 1767.9, 17... \n",
"CPWT [3580.63, 3590.7, 3601.36, 3612.64, 3624.54, 3... \n",
"DROGDP [7.67875e-06, 7.58215e-06, 7.48797e-06, 7.3961... \n",
"DROGDT [0.00263553, 0.00256973, 0.00250637, 0.0024453... \n",
"DROHLDP [5.75888e-07, 5.86846e-07, 5.98137e-07, 6.0955... \n",
"DROHLDT [0.704383, 0.702407, 0.699857, 0.696731, 0.693... \n",
"DROWTDP [4.62113e-07, 4.56381e-07, 4.51539e-07, 4.4753... \n",
"DROWTDT [0.201731, 0.241159, 0.277883, 0.312086, 0.343... \n",
"HG [38130.1, 45515.2, 52945.4, 60420.9, 67941.5, ... \n",
"HHL [270543.0, 262146.0, 253911.0, 245857.0, 23799... \n",
"HWT [1329300.0, 1316130.0, 1302920.0, 1289670.0, 1... \n",
"PT [100000.0, 100000.0, 100000.0, 100000.0, 10000... \n",
"ROG [0.765858, 0.756305, 0.746988, 0.737899, 0.729... \n",
"ROHL [875.4, 877.459, 879.975, 883.022, 886.503, 89... \n",
"ROWT [808.281, 806.539, 804.708, 802.788, 800.774, ... \n",
"RS [0.999923, 0.999933, 0.999941, 0.999949, 0.999... \n",
"RSW [0.00106583, 0.00106582, 0.00106582, 0.0010658... \n",
"SEG [364.494, 389.532, 414.413, 439.141, 463.72, 4... \n",
"SEHL [410.085, 382.735, 356.017, 329.986, 304.777, ... \n",
"SEWT [3804.14, 3759.48, 3715.25, 3671.42, 3627.98, ... \n",
"SIGGHL [0.0254278, 0.0253864, 0.0254091, 0.0255026, 0... \n",
"SIGGWT [0.0605158, 0.05939, 0.0582864, 0.0572038, 0.0... \n",
"SIGHLWT [0.0445628, 0.0450036, 0.0455026, 0.0460673, 0... \n",
"TCG [0.0314484, 0.0318777, 0.0323095, 0.0327438, 0... \n",
"TCHL [0.103153, 0.101553, 0.100041, 0.098621, 0.097... \n",
"TCWT [0.60348, 0.609125, 0.61455, 0.619758, 0.62475... \n",
"TM [20.0, 23.6735, 27.3469, 31.0204, 34.6939, 38.... \n",
"VISG [1.12301e-05, 1.13536e-05, 1.14766e-05, 1.1598... \n",
"VISHL [0.0376117, 0.0410375, 0.0445707, 0.0482234, 0... \n",
"VISWT [0.000705364, 0.000669605, 0.000637022, 0.0006... "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mul_key.data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T = mul_key.data[\"1\"].TM\n",
"P = mul_key.data[\"1\"].PT\n",
"data = mul_key.data[\"1\"].SEG"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xafc8dc8c>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npFNRAeD/AaI/o7fSjsmCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xafd4e68c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.scatter(T, P, data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
|
\\n\",\n \"\\n\",\n \"The local emission intensity $g$ is a function of the radius $r$.\\n\",\n \"The observed emission intensity $\\\\mathbf{Y}$ is a result of the integration along the line-of-sight as\\n\",\n \"$$\\n\",\n \"\\\\mathbf{Y} = \\\\int_{x} g(r) dx + \\\\mathbf{e}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\mathbf{e}$ is a i.i.d. Gaussian noise.\\n\",\n \"\\n\",\n \"We divided $g$ into $n$ discrete points $\\\\mathbf{g}$, then the above integration can be approximated as follows\\n\",\n \"$$\\n\",\n \"\\\\mathbf{Y} = \\\\mathrm{A} \\\\mathbf{g} + \\\\mathbf{e}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Non-linear model and transform\\n\",\n \"\\n\",\n \"To make sure $g(r)$ is positive, we define new function $f$,\\n\",\n \"$$\\n\",\n \"g(r) = \\\\exp(f(r))\\n\",\n \"$$\\n\",\n \"Here, we assume $f(r)$ follows the Gaussian Process with kernel $\\\\mathrm{K}$.\\n\",\n \"This transformation makes the problem non-linear.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we infer $\\\\mathbf{g}$ by \\n\",\n \"1. Stochastic approximation of the variational Gaussian process.\\n\",\n \"2. Markov Chain Monte-Carlo (MCMC) method.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Import several libraries including GPinv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2016-09-11T15:02:23.247261\",\n \"start_time\": \"2016-09-11T15:02:21.438142\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import sys\\n\",\n \"# In ../testing/ dir, we prepared a small script for generating the above matrix A\\n\",\n \"sys.path.append('../testing/')\\n\",\n \"import make_LosMatrix\\n\",\n \"# Import GPinv\\n\",\n \"import GPinv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Synthetic signals\\n\",\n \"\\n\",\n \"Here, we make a synthetic measurement.\\n\",\n \"The synthetic signal $\\\\mathrm{y}$ is simulated from the grand truth solution $g_true$ and random gaussian noise.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2016-09-11T15:02:23.253125\",\n \"start_time\": \"2016-09-11T15:02:23.248767\"\n },\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"n = 30\\n\",\n \"N = 40\\n\",\n \"# radial coordinate\\n\",\n \"r = np.linspace(0, 1., n)\\n\",\n \"# synthetic latent function\\n\",\n \"f = np.exp(-(r-0.3)*(r-0.3)/0.1) + np.exp(-(r+0.3)*(r+0.3)/0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2016-09-11T15:02:23.546153\",\n \"start_time\": \"2016-09-11T15:02:23.254560\"\n },\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"