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openearth/notebooks | geometries.ipynb | 1 | 104750 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import shapely\n",
"import shapely.geometry"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# for plotting polygons\n",
"import descartes "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Start with 2 points\n",
"point1 = shapely.geometry.Point(1,1)\n",
"point2 = shapely.geometry.Point(2,1)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# plot them in matplotlib\n",
"fig, ax = plt.subplots(1,1, figsize=(6,6))\n",
"ax.set_xlim(0,3)\n",
"ax.set_ylim(0,3)\n",
"\n",
"ax.plot(point1.x, point1.y, 'ro')\n",
"ax.plot(point2.x, point2.y, 'bo')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x104d69110>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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i4l01Ne1apRXhccv6wmt3d7euX7+umZkZNTc368CBA5qfn5ck1dfXa+vWrYrH4zp8+LCK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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105369210>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# add a buffer\n",
"poly1 = point1.buffer(1)\n",
"# and a box around it\n",
"poly2 = point2.buffer(1).envelope\n",
"# plot them using descartes\n",
"ax.add_patch(descartes.PolygonPatch(poly1, alpha=0.3, color='red'))\n",
"ax.add_patch(descartes.PolygonPatch(poly2, alpha=0.3, color='blue'))\n",
"fig"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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5vORckJl8gESjgDb+4y15bgLy63PK82u8MpcncplpMY+nMd43+aoqqJERr4fh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\nJk9EJBibPBGRYGzyRESC/X9SNcgKnQTNlwAAAABJRU5ErkJggg==\n",
"prompt_number": 5,
"text": [
"<matplotlib.figure.Figure at 0x105369210>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# compute the intersection and add it on top\n",
"poly1_and_poly2 = poly1.intersection(poly2)\n",
"patch = descartes.PolygonPatch(poly1_and_poly2, alpha=0.5, facecolor='white', edgecolor='green', linewidth=3)\n",
"ax.add_patch(patch)\n",
"fig"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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e3wCAH4shq1/puQlqf/emwp0mn0678bFCzCh1/jqguMhryo5jO5bLIxJucKXJ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\n",
"prompt_number": 6,
"text": [
"<matplotlib.figure.Figure at 0x105369210>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# let's warp a bit\n",
"from shapely.affinity import skew, translate, scale \n",
"poly3 = skew(scale(translate(poly1, xoff=0.5, yoff=1), xfact=0.5, yfact=0.8), ys=-0.125*np.pi, origin=(1,1), use_radians=True)\n",
"patch = descartes.PolygonPatch(poly3, alpha=0.5, color='yellow')\n",
"ax.add_patch(patch)\n",
"fig"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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nUEoy+aXKiCKvhodrJ1oTsGWwybngpTJ5gPL4zUbcRGQxM/nzWcpmfUsftm2T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\n",
"prompt_number": 7,
"text": [
"<matplotlib.figure.Figure at 0x105369210>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# add all the polygons together\n",
"import shapely.ops\n",
"total = shapely.ops.cascaded_union([poly1, poly2, poly3])\n",
"total = total.buffer(-0.25)\n",
"patch = descartes.PolygonPatch(total, facecolor=\"black\", alpha=0.5)\n",
"ax.add_patch(patch)\n",
"fig\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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KGjaaJP3XWnh93wkuXTpX497VGAGtbhtJkWLcHwWpCHyfHON4ukCL07VsKeOV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\nSS4mi7wkSZKLySIvSZLkYv8HZqBIAOhdAOAAAAAASUVORK5CYII=\n",
"prompt_number": 8,
"text": [
"<matplotlib.figure.Figure at 0x105369210>"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
} | gpl-3.0 |
rishuatgithub/MLPy | gic_prac/Seq2Seq-2.ipynb | 1 | 23470 | {
"cells": [
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader, Dataset\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"import re\n",
"import unicodedata\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Go.\\tVa !',\n",
" 'Run!\\tCours\\u202f!',\n",
" 'Run!\\tCourez\\u202f!',\n",
" 'Wow!\\tÇa alors\\u202f!',\n",
" 'Fire!\\tAu feu !',\n",
" \"Help!\\tÀ l'aide\\u202f!\",\n",
" 'Jump.\\tSaute.',\n",
" 'Stop!\\tÇa suffit\\u202f!',\n",
" 'Stop!\\tStop\\u202f!',\n",
" 'Stop!\\tArrête-toi !']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"INPUT_FILE = '../../Data/language_data/eng-fra.txt'\n",
"\n",
"with open(INPUT_FILE,'r',encoding='utf-8') as f:\n",
" lines = f.read().strip().split('\\n')\n",
" \n",
"lines[:10]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"135842"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(lines)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"SAMPLE = 30000\n",
"\n",
"original_word_pairs = [[w for w in l.split('\\t')] for l in lines[:SAMPLE]]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame(original_word_pairs,columns=[\"en\",\"fr\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>en</th>\n",
" <th>fr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Go.</td>\n",
" <td>Va !</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Run!</td>\n",
" <td>Cours !</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Run!</td>\n",
" <td>Courez !</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Wow!</td>\n",
" <td>Ça alors !</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Fire!</td>\n",
" <td>Au feu !</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" en fr\n",
"0 Go. Va !\n",
"1 Run! Cours !\n",
"2 Run! Courez !\n",
"3 Wow! Ça alors !\n",
"4 Fire! Au feu !"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def unicodeToAscii(s):\n",
" '''\n",
" convert unicode characters to ascii\n",
" '''\n",
" return ''.join(c for c in unicodedata.normalize('NFD',s) if unicodedata.category(c)!='Mn')\n",
"\n",
"\n",
"def preprocess_sentence(w):\n",
" '''\n",
" pre-process the sentence.\n",
" '''\n",
" w = unicodeToAscii(w.lower().strip()) \n",
" w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w) ## creating a space between a word and the punctuation following it\n",
" w = re.sub(r'[\" \"]+', \" \", w)\n",
" w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w) ## replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n",
" \n",
" w = w.rstrip().strip()\n",
" \n",
" w = '<start> ' + w + ' <end>'\n",
" \n",
" return w"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data[\"en\"] = data.en.apply(lambda x: preprocess_sentence(x))\n",
"data[\"fr\"] = data.fr.apply(lambda x: preprocess_sentence(x))"
]
},
{
"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>en</th>\n",
" <th>fr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td><start> go . <end></td>\n",
" <td><start> va ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td><start> run ! <end></td>\n",
" <td><start> cours ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td><start> run ! <end></td>\n",
" <td><start> courez ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td><start> wow ! <end></td>\n",
" <td><start> ca alors ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td><start> fire ! <end></td>\n",
" <td><start> au feu ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td><start> help ! <end></td>\n",
" <td><start> a l aide ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td><start> jump . <end></td>\n",
" <td><start> saute . <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td><start> stop ! <end></td>\n",
" <td><start> ca suffit ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td><start> stop ! <end></td>\n",
" <td><start> stop ! <end></td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td><start> stop ! <end></td>\n",
" <td><start> arrete toi ! <end></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" en fr\n",
"0 <start> go . <end> <start> va ! <end>\n",
"1 <start> run ! <end> <start> cours ! <end>\n",
"2 <start> run ! <end> <start> courez ! <end>\n",
"3 <start> wow ! <end> <start> ca alors ! <end>\n",
"4 <start> fire ! <end> <start> au feu ! <end>\n",
"5 <start> help ! <end> <start> a l aide ! <end>\n",
"6 <start> jump . <end> <start> saute . <end>\n",
"7 <start> stop ! <end> <start> ca suffit ! <end>\n",
"8 <start> stop ! <end> <start> stop ! <end>\n",
"9 <start> stop ! <end> <start> arrete toi ! <end>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"### Building vocabulary index\n",
"\n",
"class LangIndex():\n",
" def __init__(self, lang):\n",
" '''\n",
" lang are the list of phrases from each language\n",
" '''\n",
" self.lang = lang\n",
" self.word2idx = {}\n",
" self.idx2word = {}\n",
" self.vocab = set()\n",
" \n",
" self.create_index()\n",
" \n",
" \n",
" def create_index(self):\n",
" \n",
" for phrase in self.lang:\n",
" '''update the indivisual token'''\n",
" self.vocab.update(phrase.split(' '))\n",
" \n",
" self.vocab = sorted(self.vocab)\n",
" \n",
" self.word2idx['<pad>'] = 0 ## padd mapping\n",
" \n",
" ## word to index mapping\n",
" for index, word in enumerate(self.vocab):\n",
" self.word2idx[word] = index + 1\n",
" \n",
" for index, word in enumerate(self.vocab):\n",
" self.idx2word[index] = word\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"input_lang = LangIndex(data['fr'].values.tolist())\n",
"output_lang = LangIndex(data['en'].values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"## vectorise the input and output langs\n",
"\n",
"input_tensor = [[input_lang.word2idx[f] for f in fr.split(' ')] for fr in data['fr'].values.tolist()]\n",
"output_tensor = [[output_lang.word2idx[e] for e in en.split(' ')] for en in data['en'].values.tolist()]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[5, 7451, 1, 4],\n",
" [5, 1678, 1, 4],\n",
" [5, 1670, 1, 4],\n",
" [5, 994, 279, 1, 4],\n",
" [5, 599, 3150, 1, 4],\n",
" [5, 7, 4159, 181, 1, 4],\n",
" [5, 6541, 3, 4],\n",
" [5, 994, 6935, 1, 4],\n",
" [5, 6901, 1, 4],\n",
" [5, 458, 7216, 1, 4]]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_tensor[:10]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[5, 1686, 3, 4],\n",
" [5, 3279, 1, 4],\n",
" [5, 3279, 1, 4],\n",
" [5, 4410, 1, 4],\n",
" [5, 1477, 1, 4],\n",
" [5, 1850, 1, 4],\n",
" [5, 2147, 3, 4],\n",
" [5, 3731, 1, 4],\n",
" [5, 3731, 1, 4],\n",
" [5, 3731, 1, 4]]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_tensor[:10]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5, 1686, 3, 4]\n",
"?\n",
"goal\n",
"<end>\n",
"<start>\n"
]
}
],
"source": [
"print(output_tensor[0])\n",
"\n",
"for idx in output_tensor[0]:\n",
" print(output_lang.idx2word[idx])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"max_length_input: 17 \t max_length_output: 10\n"
]
}
],
"source": [
"### calculate the max length of input and output tensors\n",
"\n",
"def max_length(tensor):\n",
" return max(len(t) for t in tensor)\n",
"\n",
"max_length_input, max_length_output = max_length(input_tensor), max_length(output_tensor)\n",
"\n",
"print(f'max_length_input: {max_length_input} \\t max_length_output: {max_length_output}')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def pad_sequences(x, max_len):\n",
" padd_seq = np.zeros((max_len), dtype=np.int64)\n",
" \n",
" if len(x) > max_len:\n",
" padd_seq[:] = x[:max_len]\n",
" else:\n",
" padd_seq[:len(x)] = x\n",
" \n",
" return padd_seq"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pad = np.zeros((10), dtype=np.int64)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"pad[:4] = [5, 1686, 3, 4]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 1686, 3, 4, 0, 0, 0, 0, 0, 0])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pad"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"### add padding to the input and output tensors to make it similar\n",
"\n",
"input_pad_tensor = [pad_sequences(tensor, max_length_input) for tensor in input_tensor]\n",
"output_pad_tensor = [pad_sequences(tensor, max_length_output) for tensor in output_tensor]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5, 1678, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"17\n",
"30000\n"
]
}
],
"source": [
"print(input_pad_tensor[1].tolist())\n",
"print(len(input_pad_tensor[1]))\n",
"print(len(output_tensor))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Length of X_train: 24000 y_Train: 24000\n",
"Length of X_test : 6000 y_test: 6000\n"
]
}
],
"source": [
"### split the data into train and test\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(input_pad_tensor, output_pad_tensor, test_size=0.2)\n",
"\n",
"print(f'Length of X_train: {len(X_train)} y_Train: {len(y_train)}')\n",
"print(f'Length of X_test : {len(X_test)} y_test: {len(y_test)}')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"### Create pytorch dataset\n",
"\n",
"class NMTDataset(Dataset): \n",
" def __init__(self, X, y):\n",
" self.X = X\n",
" self.y = y\n",
" self.length = [ np.sum(1 - np.equal(x, 0)) for x in X]\n",
" \n",
" \n",
" def __len__(self):\n",
" return len(self.X)\n",
" \n",
" def __getitem__(self, index):\n",
" x = self.X[index]\n",
" y = self.y[index]\n",
" x_len = self.length[index]\n",
" \n",
" return x,y,x_len\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"train_ds = NMTDataset(X_train, y_train)\n",
"test_ds = NMTDataset(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, drop_last=True)\n",
"test_ds = DataLoader(test_ds, shuffle=False, drop_last=False)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"class Encoder(nn.Module):\n",
" \n",
" def __init__(self, vocab_size, embedding_dim, enc_unit, batch_sz):\n",
" super(Encoder, self).__init__()\n",
" \n",
" self.vocab_size = vocab_size\n",
" self.embedding_dim = embedding_dim\n",
" self.enc_unit = enc_unit\n",
" self.batch_sz = batch_sz\n",
" \n",
" self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)\n",
" self.gru = nn.GRU(self.embedding_dim, self.enc_unit)\n",
" \n",
" \n",
" def forward(self, x, lens, device):\n",
" ## x: batch_size, max_length\n",
" \n",
" x = self.embedding(x) ## x: batch_size, max_length, embedding_dim\n",
" x = nn.utils.rnn.pack_padded_sequence(x, lens) ## unpad\n",
" \n",
" self.hidden = self.initialize_hidden_state(device)\n",
" \n",
" # output: max_length, batch_size, enc_units\n",
" # self.hidden: 1, batch_size, enc_units\n",
" output, self.hidden = self.gru(x, self.hidden)\n",
" output , _ = nn.utils.rnn.pad_packed_sequence(output) ## pad to the max value of output\n",
" \n",
" return output, self.hidden\n",
" \n",
" \n",
" def initialize_hidden_state(self, device):\n",
" return torch.zeros((1, self.batch_sz, self.enc_unit)).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cpu')"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24000 \t 64 \t 375 \t 256\n",
"7727 \t 4445\n"
]
}
],
"source": [
"BUFFER_SIZE = len(train_ds)\n",
"BATCH_SIZE = 64\n",
"N_BATCH = BUFFER_SIZE//BATCH_SIZE\n",
"EMBEDDING_DIM = 256\n",
"UNITS = 1024\n",
"\n",
"vocab_inp_size = len(input_lang.word2idx)\n",
"vocab_out_size = len(output_lang.word2idx)\n",
"\n",
"print(f'{BUFFER_SIZE} \\t {BATCH_SIZE} \\t {N_BATCH} \\t {EMBEDDING_DIM}')\n",
"print(f'{vocab_inp_size} \\t {vocab_tar_size}')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"def sort_batch(x,y, lengths):\n",
" lengths, indx = lengths.sort(dim=0, descending=True)\n",
" x = x[indx]\n",
" y = y[indx]\n",
" return x.transpose(0,1), y, lengths"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([12, 64, 1024])"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Testing the encoder\n",
"\n",
"encoder = Encoder(vocab_inp_size, EMBEDDING_DIM, UNITS, BATCH_SIZE)\n",
"encoder.to(device)\n",
"\n",
"it = iter(train_loader)\n",
"x1, y1, l1 = next(it)\n",
"\n",
"xs1, ys1, ls1 = sort_batch(x1, y1, l1)\n",
"\n",
"encoder_output, encoder_hidden = encoder(xs1.to(device), ls1, device)\n",
"\n",
"encoder_output.size()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 5, 4040, 179, 4950, 4160, 5627, 5401, 3, 4, 0, 0, 0,\n",
" 0, 0, 0, 0, 0])"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"it = iter(train_loader)\n",
"x1, y1, l1 = next(it)\n",
"\n",
"x1[10]"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 5, 1965, 1703, 1482, 2870, 3, 4, 0, 0, 0])"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y1[10]"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(9)"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1[10]"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 64, 7727])"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.zeros((1, BATCH_SIZE, vocab_inp_size)).shape"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'jalouse'"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_lang.idx2word[4040]"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'ibaraki'"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_lang.idx2word[1965]"
]
},
{
"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": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
michaelaye/pyuvis | notebooks/UVIS reader.ipynb | 1 | 2483190 | null | bsd-2-clause |
wathen/PhD | MHD/FEniCS/ShiftCurlCurl/.ipynb_checkpoints/Untitled6-checkpoint.ipynb | 1 | 28534 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from dolfin import *\n",
"import numpy as np\n",
"import scipy.sparse as sp\n",
"import numpy\n",
"import matplotlib.pylab as plt\n",
"import scipy.io\n",
"from scipy2Trilinos import scipy_csr_matrix2CrsMatrix\n",
"from PyTrilinos import Epetra, ML, AztecOO, Teuchos\n",
"from dolfin import *\n",
"import petsc4py, sys\n",
"petsc4py.init(sys.argv)\n",
"from petsc4py import PETSc\n",
"import matplotlib.pylab as plt\n",
"import PETScIO as IO\n",
"import numpy as np\n",
"import scipy.sparse as sparse\n",
"import CheckPetsc4py as CP\n",
"import scipy.sparse.linalg as sparselin\n",
"import scipy as sp\n",
"import time"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nn = 2**2\n",
"mesh = RectangleMesh(0, 0, 1, 1, nn, nn,'left')\n",
"\n",
"order = 1\n",
"Magnetic = FunctionSpace(mesh, \"N1curl\", order)\n",
"Lagrange = FunctionSpace(mesh, \"CG\", order)\n",
"# L= FunctionSpace(mesh, \"DG\", order-1)\n",
"\n",
"parameters['linear_algebra_backend'] = 'uBLAS'\n",
"b0 = Expression((\"x[1]*x[1]*(x[1]-1)\",\"x[0]*x[0]*(x[0]-1)\"))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(u) = TrialFunction(Magnetic)\n",
"(v) = TestFunction(Magnetic)\n",
"(p) = TrialFunction(Lagrange)\n",
"(q) = TestFunction(Lagrange)\n",
"\n",
"a = inner(curl(u),curl(v))*dx + inner(u,v)*dx\n",
"\n",
"l = inner(grad(p),grad(q))*dx+inner(p,q)*dx\n",
"# u0 = Expression((\"sin(2*pi*x[1])*cos(2*pi*x[0])\",\"-sin(2*pi*x[0])*cos(2*pi*x[1])\"))\n",
"# f = 8*pow(pi,2)*u0+c*u0\n",
"CurlCurl = Expression((\"-6*x[1]+2\",\"-6*x[0]+2\"))+b0\n",
"f = CurlCurl\n",
"L1 = inner(v, f)*dx"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def boundary(x, on_boundary):\n",
" return on_boundary\n",
"bc = DirichletBC(Magnetic,b0, boundary)\n",
"Mass = assemble(inner(u,v)*dx)\n",
"bc.apply(Mass)\n",
"Acurl,b = assemble_system(a,L1,bc)\n",
"Anode = assemble(l)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:FFC:Adjusting element degree from ? to 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"A = Acurl.sparray()\n",
"Mmap = Magnetic.dofmap()\n",
"Lmap = Lagrange.dofmap()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Mmap.cell_dofs(0)\n",
"print Lmap.cell_dofs(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 0 2]\n",
"[24 22 23]\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"V = FunctionSpace(mesh, \"N1curl\", order)\n",
"Q = FunctionSpace(mesh, \"CG\", order)\n",
"W = MixedFunctionSpace([V,Q])\n",
"(uMix,pMix) = TrialFunctions(W)\n",
"(vMix,qMix) = TestFunctions(W)\n",
"a = inner(curl(v),curl(u))*dx\n",
"m = inner(u,v)*dx\n",
"b = inner(vMix,grad(pMix))*dx\n",
"\n",
"# <codecell>\n",
"\n",
"A = assemble(a)\n",
"M = assemble(m)\n",
"Ms = M.sparray()\n",
"A = A.sparray()\n",
"\n",
"# <codecell>\n",
"\n",
"B = assemble(b)\n",
"B = B.sparray()[W.dim()-V.dim():,W.dim()-Q.dim():]\n",
"ksp = PETSc.KSP().create()\n",
"parameters['linear_algebra_backend'] = 'PETSc'\n",
"M = assemble(m)\n",
"M = CP.Assemble(M)\n",
"ksp.setOperators(M)\n",
"x = M.getVecLeft()\n",
"ksp.setFromOptions()\n",
"ksp.setType(ksp.Type.CG)\n",
"ksp.setTolerances(1e-2)\n",
"ksp.pc.setType(ksp.pc.Type.BJACOBI)\n",
"\n",
"# <codecell>\n",
"\n",
"OptDB = PETSc.Options()\n",
"# OptDB[\"pc_factor_mat_ordering_type\"] = \"rcm\"\n",
"# OptDB[\"pc_factor_mat_solver_package\"] = \"cholmod\"\n",
"ksp.setFromOptions()\n",
"C = sparse.csr_matrix((V.dim(),Q.dim()))\n",
"IO.matToSparse\n",
"\n",
"# <codecell>\n",
"\n",
"C = sparse.csr_matrix((V.dim(),Q.dim()))\n",
"(v) = TrialFunction(V)\n",
"(u) = TestFunction(V)\n",
"tic()\n",
"for i in range(0,Q.dim()):\n",
" uOut = Function(V)\n",
" uu = Function(Q)\n",
" x = M.getVecRight()\n",
" zero = np.zeros((Q.dim(),1))[:,0]\n",
" zero[i] = 1\n",
" uu.vector()[:] = zero\n",
" L = assemble(inner(u, grad(uu))*dx)\n",
" rhs = IO.arrayToVec(L.array())\n",
" ksp.solve(rhs,x)\n",
"# x = project(grad(uu),V)\n",
" P = x.array\n",
" uOut.vector()[:] = P\n",
" low_values_indices = np.abs(P) < 1e-3\n",
" P[low_values_indices] = 0\n",
" P=np.around(P)\n",
" pn = P.nonzero()[0]\n",
" for j in range(0,len(pn)):\n",
" C[pn[j],i] = P[pn[j]]\n",
" del uu\n",
"print toc()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:728: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
" SparseEfficiencyWarning)\n",
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:UFL:No integrals left after transformation, returning empty form.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.110080003738\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Lmap.vertex_to_dof_map(mesh)\n",
"print Lmap.dof_to_vertex_map(mesh)\n",
"print Mmap.extract_sub_dofmap()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "GenericDofMap_extract_sub_dofmap expected 3 arguments, got 1",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-bc6c71162cb4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mLmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvertex_to_dof_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mLmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdof_to_vertex_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mMmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract_sub_dofmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: GenericDofMap_extract_sub_dofmap expected 3 arguments, got 1"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[24 19 23 14 18 22 9 13 17 21 4 8 12 16 20 3 7 11 15 2 6 10 1 5 0]\n",
"[24 22 19 15 10 23 20 16 11 6 21 17 12 7 3 18 13 8 4 1 14 9 5 2 0]\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Mmap.dofs()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 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\n",
" 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n",
" 50 51 52 53 54 55]\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Magnetic."
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-11-87b84b5c19e2>, line 1)",
"output_type": "pyerr",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-11-87b84b5c19e2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m Magnetic.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Mmap.tabulate_all_coordinates"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<bound method GenericDofMap.GenericDofMap_tabulate_all_coordinates of <dolfin.cpp.fem.GenericDofMap; proxy of <Swig Object of type 'boost::shared_ptr< dolfin::GenericDofMap > *' at 0x40c0b40> >>\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Mmap.neighbours"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<bound method GenericDofMap.GenericDofMap_neighbours of <dolfin.cpp.fem.GenericDofMap; proxy of <Swig Object of type 'boost::shared_ptr< dolfin::GenericDofMap > *' at 0x40c0b40> >>\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from FIAT import *\n",
"def build_edge2dof_map(V):\n",
" \"\"\"\n",
" This function takes a N1Curl(1) space and return an integer valued array edge2dof.\n",
" This array has the number of edges as its length. In particular\n",
" edge2dof[i] = j\n",
" means that dof #i, that is u.vector()[i], is associated to edge #j.\n",
" \"\"\"\n",
" # Extract the cell to edge map (given an cell index, it returns the indices of its edges)\n",
" cell2edges = V.mesh().topology()(3, 1)\n",
" # Extract the cell dofmap (given a cell index, it returns the dof numbers) \n",
" cell2dofs = V.dofmap().cell_dofs\n",
" # Array to save the result\n",
" edge2dof = numpy.zeros(mesh.num_edges(), dtype=\"int\")\n",
" # Iterate over cells, associating the edges to the dofs for that cell\n",
" for c in range(mesh.num_cells()):\n",
" # get the global edge numbers for this cell\n",
" c_edges = cell2edges(c)\n",
" # get the global dof numbers for this cell\n",
" c_dofs = cell2dofs(c)\n",
" # associate the edge numbers to the corresponding dof numbers\n",
" edge2dof[c_dofs] = c_edges\n",
" # This algorithm might not look fast as it does quite some redundant work. In actual\n",
" # runs, for most meshes, this is not the most time consuming step and does not take\n",
" # more than a milisecond.\n",
" return edge2dof"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"build_edge2dof_map(Magnetic)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
dcalacci/love-in-the-time-of-communism | notebooks/graphs.ipynb | 1 | 215388 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# analyzing the graphs\n",
"\n",
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General imports and data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pickle\n",
"import os\n",
"import difflib\n",
"import text_utils\n",
"import collections\n",
"import numpy as np\n",
"import pandas as pd\n",
"import networkx as nx\n",
"\n",
"\n",
"transcript_dir = os.path.join(\"testimony/text/hearings\")\n",
"\n",
"interviewee_names = [f.replace(\".txt\", \"\") for f in os.listdir(transcript_dir)]\n",
"interviewee_names = map(lambda s: s.replace(\"-\", \" \"), interviewee_names)\n",
"irrelevant_categories = ['Cause', 'Past', 'See', 'Home', 'Humans', 'School', 'Space', 'Sleep', 'FillersSpaced','Eating', 'TV', 'Family', 'Number', 'Present', 'Othref', 'Pronoun', 'Cogmech', 'Preps', 'Space', 'Negate', 'Achieve', 'Time', 'We', 'Senses', 'Article', 'Insight', 'Hear', 'Motion', 'Up', 'Leisure', 'Cogmech', 'Other', 'Comm', 'Discrep', 'Music', 'Future', 'Body', 'Physcal', 'Money', 'Down', 'Assent', 'Metaph', 'Feel', 'You', 'Affect', 'Inhib', 'Self', 'Social', 'Incl', 'Excl', 'Optim', 'Occup', 'Sports', 'Job', 'I']\n",
"\n",
"def is_interviewer(name):\n",
" return not difflib.get_close_matches(name, interviewee_names)\n",
"\n",
"%pylab inline\n",
"\n",
"#df = pd.read_pickle('pickles/final/final_dataframe.p')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['f']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 645
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disambiguating Names"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"disambiguated_names = pickle.load(open('pickles/final/disambiguated_names.p', 'rb'))\n",
"\n",
"def get_key(mention, l):\n",
" \"returns the numerical key for the given mention\"\n",
" mention = mention.lower()\n",
" for chunk in l:\n",
" if mention in chunk:\n",
" return l.index(chunk)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LIWC Category Score helpers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def update_dict(old, to_add):\n",
" for key in to_add.keys():\n",
" if old.has_key(key):\n",
" old[key] += to_add[key]\n",
" else:\n",
" old[key] = to_add[key]\n",
" return old\n",
"\n",
"def normalize_dict(d, count):\n",
" \"normalizes by a count of edges\"\n",
" newdict = {}\n",
" for k,v in d.items():\n",
" newval = v/float(count)\n",
" if newval < 1:\n",
" newdict[k] = newval\n",
" return newdict\n",
"\n",
"def filter_categories(d, cats):\n",
" newdict = {}\n",
" for k,v in d.items():\n",
" if k in cats:\n",
" newdict[k] = v\n",
" return newdict\n",
"\n",
"def filter_out_categories(d, cats):\n",
" newdict = {}\n",
" for k,v in d.items():\n",
" if k not in cats:\n",
" newdict[k] = v\n",
" return newdict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dominant LIWC Categories\n",
"\n",
"These graphs have edges colored and organized by the dominant liwc\n",
"category expressed in the sentences where mentioning occurs.\n",
"\n",
"### Computing general categories for mentions\n",
"These don't include mentions of interviewers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Utilities for computing non-normalized category scores\n",
"\n",
"Can't just multiply by the length of the sentence because the sentence\n",
"length is different than the token length used to compute the category\n",
"scores."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import simplejson\n",
"import jsonrpclib\n",
"import corenlp_utils\n",
"import lexicons.lexiconUtils as sentimentUtils\n",
"from collections import defaultdict\n",
"\n",
"liwc = sentimentUtils.liwcDict()\n",
"\n",
"def liwc_categories_for_sen_not_normalized(sen_obj):\n",
" \"\"\"\n",
" Produces a dictionary of category scores for the given sen_objtence, of the form\n",
" {category: score, ...}\n",
" \"\"\"\n",
" category_scores = defaultdict(int)\n",
" for word in sen_obj['words']:\n",
" lemma = word[1]['Lemma']\n",
" categories = liwc.getCategories(lemma)\n",
" if not categories:\n",
" continue\n",
" for category in categories:\n",
" category_scores[category] += 1\n",
" return category_scores\n",
"\n",
"def liwc_categories_by_sentence_not_normalized(speechact):\n",
" sens = speechact['sentences']\n",
" categories = map(liwc_categories_for_sen_not_normalized, sens)\n",
" return categories\n",
"\n",
"server = jsonrpclib.Server(\"http://localhost:8080\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Add a column in the dataframe for non-normalized categories:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nn_categories_by_sentence = []\n",
"for n, row in df.iterrows():\n",
" if n % 1000 == 0:\n",
" print \"analyzed\", n, \"rows.\"\n",
" obj = corenlp_utils.get_corenlp_object(row['speechact'], server)\n",
" if not obj:\n",
" nn_categories_by_sentence.append(np.nan)\n",
" else:\n",
" nn_categories_by_sentence.append(liwc_categories_by_sentence_not_normalized(obj))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# the graph data has to be stored in a separate dict until we\n",
"# construct the graph; adding multiple edges between two nodes just\n",
"# replaces the attributes. We want to average/accumulate them.\n",
"\n",
"nn_categories_by_sentence = []\n",
"\n",
"nn_graph_data = collections.defaultdict(lambda : collections.defaultdict(dict))\n",
"graph_data = collections.defaultdict(lambda : collections.defaultdict(dict))\n",
"count_data = collections.defaultdict(int)\n",
"\n",
"skipped = 0\n",
"for n, row in df.iterrows():\n",
" if n % 500 == 0:\n",
" print n, \"rows analyzed\"\n",
"\n",
" speaker = row['speaker']\n",
" if is_interviewer(speaker):\n",
" skipped += 1\n",
" continue\n",
" \n",
" # there's anaphora\n",
" mention_list_w_anaphora = row['mention_list_by_sentence_with_anaphora']\n",
" mention_list_wo_anaphora = row['mention_list_by_sentence_without_anaphora']\n",
" categories_by_sentence = row['liwc_categories_by_sentence']\n",
"\n",
" speaker = get_key(speaker, disambiguated_names)\n",
"\n",
" if type(mention_list_w_anaphora) == list:\n",
" categories_towards_mentions = {}\n",
" for n, mentions in enumerate(mention_list_w_anaphora):\n",
" for mention in mentions:\n",
" mention = get_key(mention, disambiguated_names)\n",
" if speaker == mention or not mention:\n",
" skipped += 1\n",
" continue\n",
" categories_for_mention = filter_out_categories(categories_by_sentence[n], irrelevant_categories)\n",
" graph_data[speaker][mention] = update_dict(graph_data[speaker][mention], categories_for_mention)\n",
" nn_graph_data[speaker][mention] = update_dict(nn_graph_data[speaker][mention], categories_for_mention)\n",
" count_data[(speaker, mention)] += 1\n",
" \n",
" elif type(mention_list_wo_anaphora) == list:\n",
" categories_towards_mentions = {}\n",
" for n, mentions in enumerate(mention_list_wo_anaphora):\n",
" for mention in mentions:\n",
" mention = get_key(mention, disambiguated_names)\n",
" # don't include self-mentions; this will screw up\n",
" # centrality measures and is not a meaningful measure\n",
" if speaker == mention or not mention:\n",
" skipped += 1\n",
" continue\n",
" categories_for_mention = filter_out_categories(categories_by_sentence[n], irrelevant_categories)\n",
" graph_data[speaker][mention] = update_dict(graph_data[speaker][mention], categories_for_mention)\n",
" nn_graph_data[speaker][mention] = update_dict(nn_graph_data[speaker][mention], categories_for_mention)\n",
" count_data[(speaker, mention)] += 1\n",
" #G.add_edge(speaker, mention, categories_for_mention)\n",
"print \"skipped\", skipped\n",
"print \"nn_graph_data and graph_data are now populated.\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 rows analyzed\n",
"500"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-698-73ccf8261b3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mcategories_by_sentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'liwc_categories_by_sentence'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorenlp_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_corenlp_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'speechact'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserver\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mnn_categories_by_sentence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dan/seafile/classes/research/huac-testimony/corenlp_utils.pyc\u001b[0m in \u001b[0;36mget_corenlp_object\u001b[0;34m(speech, server)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mserver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeech\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 224\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSystemExit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/jsonrpclib/jsonrpc.pyc\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, methodname, params, rpcid)\u001b[0m\n\u001b[1;32m 222\u001b[0m request = dumps(params, methodname, encoding=self.__encoding,\n\u001b[1;32m 223\u001b[0m rpcid=rpcid, version=self.__version)\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0mcheck_for_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'result'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\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 410\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;31m# Initialize with Simple-Response defaults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"header line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mreadline\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m 474\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rbufsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 477\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mEINTR\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" rows analyzed\n"
]
}
],
"prompt_number": 698
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, `graph_data` is a dict of {speaker : mentions}, where `mentions`\n",
"is a dict of {mention: attribute_dict}."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dominant categories\n",
"Each edge has a score from one of `relevant_categories`.\n",
"\n",
"##### Let's add an overlay for mentions that are validated by the naming graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = pickle.load(open('testimony/text/annual-reports/pickles/intersection_53_52.p', 'rb'))\n",
"\n",
"disambig_d = {}\n",
"\n",
"for pair in d:\n",
" snitch = pair['name']\n",
" accused = pair['named']\n",
" snitch = get_key(snitch, disambiguated_names)\n",
" accused = [get_key(a, disambiguated_names) for a in accused]\n",
" accused = [a for a in accused if a]\n",
" if any(accused) and snitch:\n",
" disambig_d[snitch] = accused"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(disambig_d)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 641,
"text": [
"28"
]
}
],
"prompt_number": 641
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"relevant_categories = ['Negemo', 'Posemo', 'Posfeel', 'Anx', 'Certain', 'Tentat', 'Anger', 'Swear']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 211
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"disambig_d is NOT the naming intersection. it's just the intersection of who was accused."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G_dominant_scores = nx.DiGraph()\n",
"\n",
"for speaker, mentions in graph_data.items():\n",
" for mentioned, attrs in mentions.items():\n",
" count = count_data[(speaker, mentioned)]\n",
" normalized_attrs = normalize_dict(attrs, count)\n",
" filtered = filter_categories(normalized_attrs, relevant_categories)\n",
" filtered = filter_out_categories(normalized_attrs, irrelevant_categories)\n",
" attrs = {}\n",
"\n",
" try:\n",
" dominant = max(filtered.items(), key=lambda p: p[1])\n",
" attrs = {dominant[0] : dominant[1]}\n",
" except ValueError:\n",
" continue # continue will make a graph that is only sent. edges.\n",
" \n",
"\n",
" named=False\n",
" if speaker in disambig_d.keys() and mentioned in disambig_d[speaker]:\n",
" named=True\n",
" attrs['named'] = named\n",
"\n",
" G_dominant_scores.add_edge(speaker, mentioned, attrs)\n",
"\n",
" \n",
"nx.write_gml(G_dominant_scores, 'graphs/final/gml/liwc_dominant_categories_wo_sent_irrelevant_filtered_2.13.gml')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Only anaphora mentions\n",
"Run some statistics on mentions that are only via anaphora.\n",
"\n",
"I'd like to see the difference between non-anaphora mentions and\n",
"anaphora mentions - not just in the graphs, but in the overall\n",
"sentiment that is expressed by them as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Constructing the series"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#mentions_only_anaphora = [[d.keys() for d in sentiment_list] for sentiment_list in df['liwc_sentiment_towards_only_anaphora']\n",
"mentions_only_anaphora = []\n",
"\n",
"def isempty(l):\n",
" try:\n",
" return all(map(empty, l))\n",
" except TypeError:\n",
" return False\n",
"\n",
"for n, row in df.iterrows():\n",
" mention_list = row['mention_list_by_sentence_with_anaphora']\n",
" sentiment = row['liwc_sentiment_towards_only_anaphora']\n",
" if pd.isnull(sentiment):\n",
" mentions_only_anaphora.append(np.nan)\n",
" continue\n",
" only_anaphora_names = sentiment[0].keys()\n",
" new_list = []\n",
" for mentions in mention_list:\n",
" sub_list = []\n",
" for n, mention in enumerate(mentions):\n",
" if mention in only_anaphora_names:\n",
" sub_list.append(mention)\n",
" new_list.append(list(set(sub_list)))\n",
" \n",
" #if not any(new_list):\n",
" if isempty(new_list):\n",
" mentions_only_anaphora.append(np.nan)\n",
" continue \n",
" mentions_only_anaphora.append(new_list)\n",
"#print len(filter(lambda s: type(s) == list, mentions_only_anaphora)), \"speechacts with anaphora mentions\"\n",
"df['mention_list_by_sentence_only_anaphora'] = mentions_only_anaphora\n",
"df.to_pickle('pickles/final/final_analysis_correct_only_anaphora.p')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 272
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print df['liwc_sentiment_towards_only_anaphora'].dropna()[:20]\n",
"print len(df['mention_list_by_sentence_only_anaphora'].dropna())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"682 ({}, {})\n",
"684 ({}, {})\n",
"686 ({}, {u'Burrows': 0.0})\n",
"688 ({}, {})\n",
"690 ({}, {})\n",
"692 ({}, {})\n",
"694 ({}, {})\n",
"700 ({}, {u'Burrows': 0.0})\n",
"702 ({}, {})\n",
"704 ({}, {u'Duffy': 0.1})\n",
"706 ({}, {})\n",
"710 ({}, {})\n",
"713 ({}, {u'Tavenner': 0.032619047619})\n",
"715 ({u'Owen Vinson': 0.0}, {})\n",
"719 ({}, {u'Burrows': 0.0, u'Samuel Sillen': 0.0})\n",
"723 ({u'Bruce Minton': 0.1125, u'John Stewart': 0....\n",
"725 ({u'Bruce Minton': 0.0}, {u'Bruce Minton': 0.0})\n",
"733 ({u'Henry Blankfort': 0.0}, {u'Henry Blankfort...\n",
"739 ({}, {u'Burrows': 0.0333333333333})\n",
"744 ({}, {})\n",
"Name: liwc_sentiment_towards_only_anaphora, dtype: object\n",
"304\n"
]
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_pickle('pickles/final/final_analysis.p')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Constructing dominant liwc sentiment graph from only anaphora mentions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### LIWC categories for only anaphora"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### non-normalized anaphora\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import simplejson\n",
"import jsonrpclib\n",
"import corenlp_utils\n",
"import sentiment_utils\n",
"import lexicons.lexiconUtils as sentimentUtils\n",
"from collections import defaultdict\n",
"\n",
"liwc = sentimentUtils.LiwcDict()\n",
"\n",
"\n",
"\n",
"\n",
"# mention -> categories\n",
"anaphora_non_normalized_data = collections.defaultdict(lambda : collections.defaultdict(dict))\n",
"anaphora_count_data = collections.defaultdict(int)\n",
"responses = df.dropna().groupby('is_response').get_group(True)\n",
"server = jsonrpclib.Server(\"http://localhost:8080\")\n",
"skipped = 0\n",
"c = 0\n",
"for n, row in responses.iterrows():\n",
" if c % 500 == 0:\n",
" print \"processed\", c, \"mentions.\"\n",
" speaker = row['speaker']\n",
" if is_interviewer(speaker):\n",
" skipped += 1\n",
" continue\n",
" anaphora_mentions = row['mention_list_by_sentence_only_anaphora']\n",
"\n",
" obj = corenlp_utils.get_corenlp_object(row['speechact'], server)\n",
" if not obj:\n",
" continue\n",
" categories_by_sentence = liwc_categories_by_sentence_not_normalized(obj)\n",
" \n",
" speaker = get_key(speaker, disambiguated_names)\n",
" for n, mentions in enumerate(anaphora_mentions):\n",
" for mention in mentions:\n",
" mention = get_key(mention, disambiguated_names)\n",
" if speaker == mention or not mention:\n",
" skipped += 1\n",
" continue\n",
" categories_for_mention = filter_out_categories(categories_by_sentence[n], irrelevant_categories)\n",
" anaphora_non_normalized_data[speaker][mention] = update_dict(anaphora_non_normalized_data[speaker][mention], categories_for_mention)\n",
" anaphora_count_data[mention] += 1\n",
" c += 1\n",
"print \"skipped\", skipped, \"anaphora\"\n",
"print c, \"mentions.\", \"anaphora\"\n",
"\n",
"non_normalized_data "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"processed 0 mentions.\n",
"skipped"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2\n",
"512 mentions.\n"
]
}
],
"prompt_number": 688
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(anaphora_non_normalized_data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 689,
"text": [
"34"
]
}
],
"prompt_number": 689
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"responses = df.dropna().groupby('is_response').get_group(True)\n",
"\n",
"# speaker -> mention -> categories for that mention, aggregated & normalized over all speechacts.\n",
"anaphora_graph_data = collections.defaultdict(lambda : collections.defaultdict(dict))\n",
"# not filtered by particular categories\n",
"anaphora_graph_data_not_filtered = collections.defaultdict(lambda : collections.defaultdict(dict))\n",
"anaphora_count_data = collections.defaultdict(int)\n",
"\n",
"skipped = 0\n",
"for n, row in responses.iterrows():\n",
" speaker = row['speaker']\n",
" if is_interviewer(speaker):\n",
" skipped += 1\n",
" continue\n",
"\n",
" # there's anaphora\n",
" mention_list_w_anaphora = row['mention_list_by_sentence_only_anaphora']\n",
" categories_by_sentence = row['liwc_categories_by_sentence']\n",
"\n",
" speaker = get_key(speaker, disambiguated_names)\n",
" for n, mentions in enumerate(mention_list_w_anaphora):\n",
" for mention in mentions:\n",
" \n",
" mention = get_key(mention, disambiguated_names)\n",
" if speaker == mention or not mention:\n",
" skipped += 1\n",
" continue\n",
"\n",
" anaphora_graph_data_not_filtered[speaker][mention] = update_dict(anaphora_graph_data[speaker][mention], categories_by_sentence[n])\n",
"\n",
" categories_for_mention = filter_out_categories(categories_by_sentence[n], irrelevant_categories)\n",
" anaphora_graph_data[speaker][mention] = update_dict(anaphora_graph_data[speaker][mention], categories_for_mention)\n",
" \n",
" anaphora_count_data[(speaker, mention)] += 1\n",
" \n",
"print \"skipped\", skipped"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"skipped 2\n"
]
}
],
"prompt_number": 677
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(anaphora_graph_data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 678,
"text": [
"34"
]
}
],
"prompt_number": 678
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Actually constructing the graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G_only_anaphora_with_dominant_categories = nx.DiGraph()\n",
"relevant_categories = ['Negemo', 'Posemo', 'Posfeel', 'Anx', 'Certain', 'Tentat', 'Anger', 'Swear']\n",
"n = 0\n",
"for speaker, mentions in anaphora_graph_data.items():\n",
" if n % 10 == 0:\n",
" print \"analyzing\", n, \"mentions.\"\n",
" for mentioned, attrs in mentions.items():\n",
" count = anaphora_count_data[(speaker, mentioned)]\n",
" normalized_attrs = normalize_dict(attrs, count)\n",
" filtered = filter_categories(normalized_attrs, relevant_categories)\n",
" n += 1\n",
" try:\n",
" dominant = max(filtered.items(), key=lambda p:p[1])\n",
" dominant = {dominant[0] : dominant[1]}\n",
" G_only_anaphora_with_dominant_categories.add_edge(speaker, mentioned, dominant)\n",
" except ValueError:\n",
" G_only_anaphora_with_dominant_categories.add_edge(speaker, mentioned)\n",
" \n",
"nx.write_gml(G_only_anaphora_with_dominant_categories, 'graphs/liwc_only_anaphora_dominant_scores_1.gml')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"analyzing 0 mentions.\n",
"analyzing 200 mentions.\n",
"analyzing 210 mentions.\n",
"analyzing 270 mentions.\n",
"analyzing 310 mentions.\n"
]
}
],
"prompt_number": 274
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does the distribution of sentiment look like for anaphora-only mentions, not normalized?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does the distribution of sentiment look like for anaphora-only mentions?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#G_only_anaphora_with_dominant_categories = nx.DiGraph()\n",
"\n",
"categories = []\n",
"scores = []\n",
"count = []\n",
"\n",
"#relevant_categories = ['Negemo', 'Posemo', 'Posfeel', 'Anx', 'Certain', 'Tentat', 'Anger', 'Swear']\n",
"n = 0\n",
"for speaker, mentions in anaphora_graph_data_not_filtered.items():\n",
" for mentioned, attrs in mentions.items():\n",
" for category, score in attrs.items():\n",
" if category not in categories: \n",
" categories.append(category)\n",
" count.append(1)\n",
" scores.append(score)\n",
" else:\n",
" catindex = categories.index(category)\n",
" count[catindex] += 1\n",
" scores[catindex] += score\n",
" \n",
"normalize\n",
"for n, count in enumerate(count):\n",
" scores[n] = scores[n]/count\n",
" \n",
"only_anaphora_categories_df = pd.DataFrame({\"categories\": categories,\n",
" \"scores\": scores})\n",
"\n",
"s = pd.Series(list(only_anaphora_categories_df['scores']), index=only_anaphora_categories_df['categories'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 275
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print s.describe()\n",
"s.plot(kind=\"bar\", title=\"normalized categories expressed towards anaphora\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"count 61.000000\n",
"mean 0.138791\n",
"std 0.066906\n",
"min 0.040000\n",
"25% 0.094152\n",
"50% 0.138013\n",
"75% 0.165688\n",
"max 0.407994\n",
"dtype: float64\n"
]
},
{
"output_type": "pyout",
"prompt_number": 276,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1108a5b10>"
]
},
{
"output_type": "display_data",
"png": 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jYmLg5eUFALh69SoAICIiAoBmZTIyMrSmq8+vOp2RkWFU/XtoT+urr6Wuof2a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\nYaRE5bRnAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1108e9b90>"
]
}
],
"prompt_number": 276
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def top_n(s, n):\n",
" return s.order(ascending=False)[:n]\n",
"top_n(s, 10)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 277,
"text": [
"categories\n",
"Social 0.407994\n",
"Down 0.333333\n",
"Other 0.283686\n",
"Future 0.263235\n",
"Incl 0.230112\n",
"Money 0.212943\n",
"Music 0.205159\n",
"Comm 0.200980\n",
"Present 0.188700\n",
"School 0.179375\n",
"dtype: float64"
]
}
],
"prompt_number": 277
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What about for regular(non-anaphora) mentions?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"categories = []\n",
"scores = []\n",
"count = []\n",
"\n",
"\n",
"tmp_df = df.groupby('is_interviewee').get_group(True).groupby('is_response').get_group(False)\n",
"\n",
"#relevant_categories = ['Negemo', 'Posemo', 'Posfeel', 'Anx', 'Certain', 'Tentat', 'Anger', 'Swear']\n",
"\n",
"for n, row in tmp_df.iterrows():\n",
" if pd.isnull(row['liwc_categories_for_speechact']):\n",
" continue\n",
" for category, score in row['liwc_categories_for_speechact'].items():\n",
" if category not in categories:\n",
" categories.append(category)\n",
" count.append(1)\n",
" scores.append(score)\n",
" else:\n",
" catindex = categories.index(category)\n",
" count[catindex] += 1\n",
" scores[catindex] += score\n",
" \n",
"normalize\n",
"for n, count in enumerate(count):\n",
" scores[n] = scores[n]/count\n",
"\n",
" \n",
"non_anaphora_categories_df = pd.DataFrame({\"categories\": categories,\n",
" \"scores\": scores})\n",
"\n",
"nons = pd.Series(list(non_anaphora_categories_df['scores']), index=non_anaphora_categories_df['categories'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 278
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(nons)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 223,
"text": [
"68"
]
}
],
"prompt_number": 223
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nons.plot(kind=\"bar\", title=\"normalized categories expressed toward non-anaphora\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"categories = list(nons.index)\n",
"only_anaphora_score = []\n",
"non_anaphora_score = []\n",
"for category in categories:\n",
" if category in nons.index and category in s.index:\n",
" non_anaphora_score.append(nons[category])\n",
" only_anaphora_score.append(s[category])\n",
" else:\n",
" print category, \"not in only_anaphora_score.\"\n",
" non_anaphora_score.append(np.nan)\n",
" only_anaphora_score.append(np.nan)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Nonfl not in only_anaphora_score.\n",
"FillersSpaced not in only_anaphora_score.\n",
"Fillers not in only_anaphora_score.\n",
"Metaph not in only_anaphora_score.\n",
"Death not in only_anaphora_score.\n",
"Relig not in only_anaphora_score.\n",
"Groom not in only_anaphora_score.\n",
"Sleep not in only_anaphora_score.\n"
]
}
],
"prompt_number": 279
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(cats)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 226,
"text": [
"60"
]
}
],
"prompt_number": 226
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print len(only_anaphora_score)\n",
"print len(non_anaphora_score)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"68\n",
"68\n"
]
}
],
"prompt_number": 280
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_df = pd.DataFrame({\"only anaphora\": only_anaphora_score, \"non anaphora\": non_anaphora_score}, index=categories)\n",
"both_df.drop(irrelevant_categories)\n",
"both_df.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 387,
"text": [
"<matplotlib.axes.AxesSubplot at 0x112e34e10>"
]
},
{
"output_type": "display_data",
"png": 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qwowZM+Dj44OAgACldZWh6Gy0qajTnip1NdmWuramLrclfNOGTSh+KELXYtBk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\nwpUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112e38cd0>"
]
}
],
"prompt_number": 387
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pylab.savefig('foo.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x1104462d0>"
]
}
],
"prompt_number": 253
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"both_df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>non anaphora</th>\n",
" <th>only anaphora</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 60.000000</td>\n",
" <td> 60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.049558</td>\n",
" <td> 0.139350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.022050</td>\n",
" <td> 0.067327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.016187</td>\n",
" <td> 0.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.036896</td>\n",
" <td> 0.093341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.043866</td>\n",
" <td> 0.138822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.059073</td>\n",
" <td> 0.166075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 0.113111</td>\n",
" <td> 0.407994</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 282,
"text": [
" non anaphora only anaphora\n",
"count 60.000000 60.000000\n",
"mean 0.049558 0.139350\n",
"std 0.022050 0.067327\n",
"min 0.016187 0.040000\n",
"25% 0.036896 0.093341\n",
"50% 0.043866 0.138822\n",
"75% 0.059073 0.166075\n",
"max 0.113111 0.407994"
]
}
],
"prompt_number": 282
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = pickle.load(open('testimony/text/annual-reports/pickles/intersection_53_52.p', 'rb'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 195
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"disambig_d = {}\n",
"\n",
"for pair in d:\n",
" snitch = pair['name']\n",
" accused = pair['named']\n",
" snitch = get_key(snitch, disambiguated_names)\n",
" accused = [get_key(a, disambiguated_names) for a in accused]\n",
" accused = [a for a in accused if a]\n",
" if any(accused) and snitch:\n",
" disambig_d[snitch] = accused"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 203
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Top 10% mentioned\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prep the lists"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"speakers = df['speaker']\n",
"mentions_no_anaphora = df['mention_list_for_speechact_without_anaphora'].dropna()\n",
"mentions_no_anaphora = [item for sublist in mentions_no_anaphora for item in sublist] # flatten\n",
"mentions_no_anaphora = [get_key(name, disambiguated_names) for name in mentions_no_anaphora]\n",
"mentions_only_anaphora = df['mention_list_by_sentence_only_anaphora'].dropna()\n",
"mentions_only_anaphora = [item for sublist in mentions_only_anaphora for item in sublist] # flatten\n",
"mentions_only_anaphora = [item for sublist in mentions_only_anaphora for item in sublist] # flatten \n",
"mentions_only_anaphora = [get_key(name, disambiguated_names) for name in mentions_only_anaphora]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 474
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make the frequency dicts\n",
"Anaphora first:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"anaphora_name_set = set([mention for mention in mentions_only_anaphora if not any(map(is_interviewer, disambiguated_names[mention]))])\n",
"#is_interviewer(mention)])\n",
"# create your frequency dictionary\n",
"only_anaphora_freq = {}\n",
"# iterate through them, once per unique word.\n",
"for name in anaphora_name_set:\n",
" only_anaphora_freq[name] = mentions_only_anaphora.count(name) / float(len(mentions_only_anaphora))\n",
"\n",
"only_anaphora_mention_distribution = pd.Series(only_anaphora_freq.values(), index=only_anaphora_freq.keys())\n",
"\n",
"only_anaphora_mention_distribution.sort(ascending=False)\n",
"anaphora_top_percentile_index = len(only_anaphora_mention_distribution)/10\n",
"anaphora_top_percentile_mentions = only_anaphora_mention_distribution[:top_ten_index]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 475
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"only_anaphora_mention_distribution.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 477,
"text": [
"395 0.009728\n",
"72 0.009728\n",
"960 0.007782\n",
"1212 0.007782\n",
"272 0.005837\n",
"dtype: float64"
]
}
],
"prompt_number": 477
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Non-anaphora:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"non_anaphora_name_set = set([mention for mention in mentions_no_anaphora if not any(map(is_interviewer, disambiguated_names[mention]))])\n",
"# create your frequency dictionary\n",
"non_anaphora_freq = {}\n",
"# iterate through them, once per unique word.\n",
"for name in non_anaphora_name_set:\n",
" non_anaphora_freq[name] = mentions_no_anaphora.count(name) / float(len(mentions_no_anaphora))\n",
"\n",
"non_anaphora_mention_distribution = pd.Series(non_anaphora_freq.values(), index=non_anaphora_freq.keys())\n",
"\n",
"non_anaphora_mention_distribution.sort(ascending=False)\n",
"non_anaphora_top_percentile_index = len(non_anaphora_mention_distribution)/10\n",
"non_anaphora_top_percentile_mentions = non_anaphora_mention_distribution[:top_ten_index]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 476
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"non_anaphora_mention_distribution.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 478,
"text": [
"56 0.006549\n",
"52 0.005793\n",
"911 0.004534\n",
"524 0.003778\n",
"275 0.003526\n",
"dtype: float64"
]
}
],
"prompt_number": 478
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"total_mentions = mentions_no_anaphora + mentions_only_anaphora\n",
"total_mentions_set = set([mention for mention in total_mentions if not any(map(is_interviewer, disambiguated_names[mention]))])\n",
"\n",
"total_mentions_freq = {}\n",
"for name in total_mentions_set:\n",
" total_mentions_freq[name] = total_mentions.count(name) / float(len(total_mentions))\n",
" \n",
"total_mentions_distribution = pd.Series(total_mentions_freq.values(), index=total_mentions_freq.keys())\n",
"\n",
"total_mentions_distribution.sort(ascending=False)\n",
"total_mentions_top_percentile_index = len(total_mentions_distribution)/10\n",
"total_mentions_top_percentile_mentions = total_mentions_distribution[:top_ten_index]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 594
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now let's find the sentiment expressed towards those top-percentile mentions\n",
"\n",
"With anaphora, without anaphora, and combined."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### dict of mention -> avg normalized liwc category scores towards that mention"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup some helper functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def compute_non_normalized_scores(graph_data, accused=[], scores=[]):\n",
" for speaker, mentions in graph_data.items():\n",
" for mentioned, attrs in mentions.items():\n",
" attrs = {k : v for k, v in attrs.items() if k not in irrelevant_categories}\n",
" if mentioned in accused:\n",
" index = accused.index(mentioned)\n",
" scores[index].append(attrs)\n",
" else:\n",
" accused.append(mentioned)\n",
" scores.append([attrs])\n",
" return (accused, scores)\n",
"\n",
"def combine_dicts(ds):\n",
" newdict = collections.defaultdict(list)\n",
" for d in ds:\n",
" for key, val in d.items():\n",
" if key in irrelevant_categories or key == 'named':\n",
" continue\n",
" else:\n",
" newdict[key].append(val)\n",
" for k, v in newdict.items():\n",
" newdict[k] = sum(v)/len(v)\n",
" return newdict\n",
"\n",
"def create_normalized_categories(accused, scores):\n",
" for n, l in enumerate(scores):\n",
" combined = combine_dicts(l)\n",
" scores[n] = combined\n",
" scores = pd.Series(scores, index=accused)\n",
" return scores"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 620
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### get data for all mentions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nn_accused, nn_scores = compute_non_normalized_scores(anaphora_non_normalized_data, [], [])\n",
"nn_score_df = create_normalized_categories(nn_accused, nn_scores)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 690
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(nn_score_df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 691,
"text": [
"277"
]
}
],
"prompt_number": 691
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"categories = collections.defaultdict(int)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"accused, scores = compute_non_normalized_scores(anaphora_graph_data_not_filtered, [], [])\n",
"accused, scores = compute_non_normalized_scores(graph_data, accused, scores)\n",
"\n",
"all_score_df = create_normalized_categories(accused, scores)\n",
"all_score_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 621,
"text": [
"416 {u'Incl': 0.1, u'Social': 0.3875}\n",
"836 {u'Excl': 0.2, u'Incl': 0.2, u'Social': 0.6}\n",
"453 {u'Tentat': 0.105263157895, u'I': 0.0526315789...\n",
"454 {u'Incl': 0.0113636363636, u'Social': 0.222222...\n",
"263 {u'Social': 0.388298545225, u'Excl': 0.2419354...\n",
"dtype: object"
]
}
],
"prompt_number": 621
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grab the top 10% from previously computed distributions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_edge_percentile(scores, distribution, bottom_percentile=False, all_data=False):\n",
" \"\"\"\n",
" distribution is a percentile distribution series.\n",
" scores is the score series we've previously computed\n",
" returns a dataframe with scores and dist values for each mention\n",
" \"\"\"\n",
" d = distribution.copy()\n",
" d.sort(ascending=bottom_percentile)\n",
" print d.head()\n",
" percentile_mentions = pd.DataFrame({'distribution': d})\n",
" top_scores = []\n",
" \n",
" for n, row in percentile_mentions.iterrows():\n",
" if n in scores.index and scores[n].keys():\n",
" top_scores.append(scores[n])\n",
" else:\n",
" top_scores.append(np.nan)\n",
" percentile_mentions['scores'] = top_scores\n",
" if not all_data:\n",
" percentile_mentions = percentile_mentions[pd.notnull(percentile_mentions['scores'])]\n",
" percentile_mentions = percentile_mentions[:(len(percentile_mentions)/10)]\n",
" \n",
" return percentile_mentions\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 622
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"top_percentile_scores = get_edge_percentile(all_score_df, total_mentions_distribution)\n",
"bottom_percentile_scores = get_edge_percentile(all_score_df, total_mentions_distribution, bottom_percentile=True)\n",
"all_scores = get_edge_percentile(all_score_df, total_mentions_distribution, all_data=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"56 0.006021\n",
"52 0.005129\n",
"911 0.004237\n",
"524 0.003345\n",
"1108 0.003345\n",
"dtype: float64\n",
"519 0.000223\n",
"11 0.000223\n",
"559 0.000223\n",
"844 0.000223\n",
"1041 0.000223\n",
"dtype: float64\n",
"56 0.006021\n",
"52 0.005129\n",
"911 0.004237\n",
"524 0.003345\n",
"1108 0.003345\n",
"dtype: float64\n"
]
}
],
"prompt_number": 623
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_mentions_df(df):\n",
" categories = []\n",
" scores = []\n",
" count = []\n",
" for n, row in df.iterrows():\n",
" if pd.isnull(row['scores']):\n",
" continue\n",
" for category, score in row['scores'].items():\n",
" if category not in categories:\n",
" categories.append(category)\n",
" count.append(1)\n",
" scores.append(score)\n",
" else:\n",
" index = categories.index(category)\n",
" count[index] += 1\n",
" scores[index] += score\n",
" for n, count in enumerate(count):\n",
" scores[n] = scores[n]/count\n",
" normalized_dist_series = pd.Series(scores, index=categories)\n",
" normalized_dist_series.plot(kind=\"bar\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 624
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_mentions_df(top_percentile_scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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UM7Zi6rZ2vbRi3NzaajopqGXLNigqumr/r0hQoxFkaMVo+VnW18+RzIs9dsgxsGvLMyKz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\nJyJqZDiwExE1MhzYiYgamf8D4KoZj9cyjfoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1111b9050>"
]
}
],
"prompt_number": 625
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_mentions_df(bottom_percentile_scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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6W28rLi7Wfr9fDxo0SPv9/tCfmTNn6tdee831nkjsPq96kydusaenpyM9PR3f\n+ta30L9/f+mckC+//DJsBjl48GDRWxOLFi3Co48+iu9+97uoqKhAUVERAoGAWA8AJCUl4Y9//CMS\nExMBBOeTSUlJYj3Tpk1DSUkJbrvtNgDB+2imTZsm1vP+++8jPj4+dPmCCy4QHTOMGjUKP/rRjzB/\n/nxorbFhwwZcdtllrnfk5eUhLy8Pr732GubMmeP6+k5Ovn+ko6MD1dXVGDFihKsNnnpUTENDA+bO\nnYvRo0dj1KhRGDVqlMgnXae4uDjs3LkzdHnHjh0477zzxHqOHz+OqVOnQmuNr371q3jkkUfw5ptv\nirTMnDkTM2fOREtLC9LS0nDdddfB7/dj9OjRaGlpEWkCgLVr12LevHno378/+vfvj9tvvx1r167F\n4MGDMWTIENd7tNY4cuRI6PKRI0dEvxk///zz+NOf/oTZs2djzpw5OHz4MJ5//nmxnh07dqC5+e8v\nsvXZZ5/h3/7t38R6gOAI+NixYzh27Bja2trwT//0T66/AKInbrF3OvkWqVJK/Bbp008/jVtvvRWX\nXHIJgOBcsLS0VKzn3HPPRSAQQFJSEgoLCzF8+HB8/vnnIi3WO3Q772zSf5uVSul8tIcpli9fjiuv\nvBK33nortNZ49dVX8dBDD4n1DB06FD/72c/E1rfatGkTVq5cGbp8wQUX4M0338Rjjz0m1tR5/0jn\nDZTBgwe73uCpJyiNHz8e1dXVGDt2LD744IOwt0lpa2vDnj174PP5kJKSInJnbqeqqiqkpaWhubkZ\n//7v/46jR4/igQcewBVXXCHWBACHDh3C9u3b4fP5MGnSJFx00UViLe+8847t26+99lqXS/7uww8/\nxNtvvw2fz4frr78eo0ePFmvZvn07VqxYgYaGBrS3twMIflP+3//9X5GecePGoaqqKvTwxuPHjyMr\nK8vVR6BYmfDIIU/dYjfpFmmnHTt2oL6+Hu3t7aFvMAsWLBBpmTRpEoDgLYji4mKRBqtXXnkF999/\nP6677joAwNKlS/HEE0+4/hDVTj/+8Y/DHqpWVVWFCRMm4O233xbpWb9+PfLz83H55ZcDANrb2/Ho\no4/i4YcfFumZN28efvKTn2DMmDHo109+kjtv3jxMmTIFd955J7TWKCoqEvv66mTCI4c8dYt9+/bt\nSE1NNeYW6R133IGPP/4YGRkZoYc8AnD9R9nvfOc7WL16te0TlaSfoDRu3Dhs3rw5dCv98OHDmDJl\nitgtQKt03/sxAAAKvElEQVTGxkZ85zvfweuvvy6y/u23346//vWvWLduHY4cOYJFixbh2muvdXwC\nU2+6+uqr8d5774msfTqbNm3Cli1bAAA33HADcnJyRHvS09Px/vvvR3xbb/LULfb6+npMnDgx7Bbp\nK6+8Irax79y5E7W1teJPNOm8BWP3RCXpNq01hg0bFrp84YUXGvX6/gkJCQK/0vHvSkpK8PLLL2Pc\nuHGIi4vDhg0bcM0114j1PPzww8jPz8fUqVNDj0Dz+XyYPXu2WFNaWhpiYmJwww03oLW1FS0tLSJz\n7enTp+PnP/+5EY8c8tTGvnLlStx6660R3+aWMWPG4ODBgxg+fLjI+p0mTJgAIPgyzCY8YepkN954\nI3JycvCtb30LWmuUlpZi+vTpYj3f/va3Q3/v6OjArl27Qh8/CXv37sUzzzyD2bNnY/fu3XjppZeQ\nmZmJuLg4kZ4XXngBe/bsQXt7e9goRmpjX7t2LX75y1/iyJEj2L9/Pw4cOIAlS5aEbsG76c4770RO\nTg7uuOMOHDx4MPQxmTx5suuPHPLEKKbzmaelpaXIzc0Ne+ZpbW0tqqqqRLr8fj927dqFSZMmYcCA\nAQBkRx/Z2dnYsmVL6LH1LS0tyMnJcf1Zg1Ynv6TANddcg5tvvlmspbi4OPRTTExMDBITE3H11VeL\n9aSmpqKwsBBTp05FR0cHnnrqKaxfvx61tbUiPSkpKfjoo4/Ef9LrlJ6ejqqqKlxxxRWoqakBgLAH\nT7jt2LFj+OEPf4iKigrMnz8/9M3P7df38cQt9uHDh2PChAkoKyvDhAkTQhv7kCFD8NRTT4l1mfa0\ncJOeMDVo0CDbzWHt2rUYMGAAkpKS8Nhjj2Hq1Kmuds2dO/eUn2paW1sxcOBAVzs6/c///A/OP/98\nAEC/fv2wfPlyxxd1621XXXUVamtrQ3fmShswYEDoRhMQvHNZ8ptObGws4uLi0NbWhmPHjsndwezq\n81x7WVtbm3SC0a666iq9Y8eO0OXt27frK664QrDI3okTJ/SuXbv06NGjXV87OzvbiJeBWLVqVejv\nr7zyStj7HnzwQbdzQlJSUnRMTIxOTk7WY8aM0WPGjNFjx44V6/ne976nH3vsMf31r39dv/XWW/qm\nm27S3//+90VaNm3apNPS0vQDDzygP//8c5GGTp4YxXR699138eijj57yGNuPP/7Y1Y7T3Rrt7Dl6\n9KirPZ22b9+O3NzcU54wlZWVJdITyXPPPYd77rnH1TUzMjJO+XWNdm/rbZmZmaHRwsl/t7vspoaG\nBtu3d74khNs6Ojqwbt06vPXWWwCAnJwc3HXXXSK32idPnoznnnvOiJ9mPDGK6ZSfn4+nn34a48eP\nD3t4odtMe/Zip4kTJ2L37t3Yu3cvAIg/YSoStzd14O8vA9F5h6n0y0CYJjExEb/73e+wb98+LFq0\nCIcPHxb5fJ8yZQq2bNmCBx98EKtWrcLixYtdb7B65513jLnvwVMbe3x8vOgjKkzX1taGX/ziF6Fn\nV/r9ftxzzz1Gb+5uM+1lIEzzyCOPYOfOndizZw8WLVqEtrY23HHHHa4/tv3gwYP4/e9/j7KystAL\ntp1s/PjxrvYA8g8dPpmnRjH/+q//ikAggNmzZ4fdoSJxkk2Un5+P9vZ25OXlQWuNF198ETExMVi3\nbp10mlE6XwYCCP5UI/GKoeecc07oDtvjx4+H/dRw/Pjx0KjRbenp6aipqcGECRNC46Bx48a5/oSy\nV199FevXr8d7771nO0rcunWrqz2m8dQt9srKSvh8PuzYsSPs7dF+kjtt37497AtwypQpGDdunGCR\neQoLCzFv3jyMHTsWQPDVAtetW4d7773X1Q7pl1M+nQEDBoQ90kPqJTsuueQSVFRU4Ic//CF+8IMf\niDQYTfSuW3JVZmamrqurC13et2+fzszMFCwyj90vjUhPTxcoMdOPf/xjvXjxYp2YmKjXrFmjs7Oz\n9erVq13vGD9+vNZa64yMDNfX7gs8dYv90KFDeOihh9DU1ISKigrU1tZi27ZtyM/Pl04zwhNPPIHr\nr78eo0aNAgD84Q9/EH0tbRN1dHSgo6MjdKs0EAjgxIkTwlXmuP/++/HWW29h8ODB2Lt3L370ox/h\nhhtucL0jJiYGd999N5qamsKeLQwEZ93PPPOM600mkX95th60cOFCTJs2DZ988gkAIDk5WfQJSqao\nqqrCwYMHMWXKFOzduxdz5szBOeecgxtuuAEZGRnSeUbJycnBbbfdhi1btmDz5s3Izc3FjTfeKJ1l\njM8//xxTpkzBT37yE9x99904fvy4yDe+N954A1OmTMG5556LrKwsZGVlYcKECbjooot4nxE8cudp\ne3s7YmJikJWVhR07doQ9zlfiMcimyczMxJYtWzB06FC88847uO2221BYWIiamhp89NFH+PWvfy2d\naIzW1lasW7cOSin4fD5MmzYNd911l+jDZ00yfvx4vPvuu/jss89w9dVXY+LEiejfvz82bNgg0vP+\n++8jEAigpKQEr7zyCkaNGoU5c+accis+2nhiFDNp0iRUV1dj0KBB+POf/xx6e2VlZejp2NGso6MD\nQ4cOBQCUlpaioKAAc+bMwZw5c5Ceni5cZ4YTJ07goYcewvPPP49LL70UWms0NjbisssuQ0dHBzf2\nv9FaY+DAgVi/fj3uvfdePPDAAyKfQ3v27EFJSQlKS0sxbNgw3HLLLdBaQynleouJPDGK6fyh48kn\nn8Q3v/lNfPzxx7jqqqswf/78qJ+1AeFz4s2bN4d+AQAAsYfNmeb+++/HkSNHUF9fj+rqatTU1ODj\njz9Gc3Mzvve970nnGWXbtm3YsGED/vEf/xFA8IaD29LS0lBdXY3/+q//wjvvvINvf/vb/OZ7Ek/c\nYj98+DB++tOfQmuNm2++GTNmzIDWGgMGDMCWLVui/lbp7bffjuuuuw7/8A//gIEDB2Ly5MkAgLq6\nOsTHxwvXmeGNN97A3r17wx7KN2TIEDz33HNISUnB6tWrBevM8fTTT2PlypW4+eabcfnll2P//v1h\nNxTc8vrrr6OkpATXXnstbrzxxtAtdgryxIz9kksucXz6udSvETPJtm3bcOjQIUybNi30Wt579+7F\nsWPH+AQuAF//+tdDL7VwJu+LVi0tLfD5fGGvFirh2LFjKCsrQ0lJCbZu3YoFCxbg5ptvxrRp00S7\npHliY5d8USTyhm9+85uYPXs28vLywt7+4osv4tVXXxX99YEmMeEXNZ/OkSNH8Otf/xovv/yy2O+o\nNQU3diIABw4cwOzZs3HeeeeFXgBs586daG1txW9+8xskJCQIF5rhyiuvxIoVK8J+UfP3v/998V/W\nQuE8sbH/5S9/wYUXXiidQX2c1hpvv/02PvzwQ/h8PowePRpTpkyRzjKKCb+omSLzxMZORO646aab\nMGHChLBf1Lxz50785je/kU6jk3ji4Y5E5I6ioiL86U9/wuzZszFnzhwcPnyYL0thIN5iJ6KIjh8/\njueeew779u3DuHHjcOedd/J1/A3GjZ2IIrr11lvRv39/XHPNNaioqMBXv/pVPrbfYNzYiSiisWPH\n4oMPPgAQfLbyxIkT+Ug0g3HGTkQRxcTE2P6dzMRb7EQU0cm/qg8I/3V9Pp8PR48elUojG9zYiYg8\nhqMYIiKP4cZOROQx3NiJiDyGGzsRkcf8P+LihvM+xGPsAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111e3d6d0>"
]
}
],
"prompt_number": 626
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_mentions_df(all_scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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rFeV48OCBiQ9ao0YN5OTkWI0LDAxEYGAgAgICMHr0aMXaC3L69GmpqANA7dq1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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1111d4c10>"
]
}
],
"prompt_number": 627
}
],
"metadata": {}
}
]
} | mit |
mitdbg/modeldb | client/workflows/examples/text_classification_spacy.ipynb | 1 | 14522 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Classification with spaCy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This walkthrough is based on [this spaCy tutorial](https://github.com/explosion/spaCy/blob/master/examples/training/train_textcat.py).\n",
"\n",
"Train a convolutional neural network text classifier on the\n",
"IMDB dataset, using the `TextCategorizer` component. The dataset will be loaded\n",
"automatically via Thinc's built-in dataset loader. The model is added to\n",
"`spacy.pipeline`, and predictions are available via `doc.cats`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set Up Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook has been tested with the following package versions: \n",
"(you may need to change `pip` to `pip3`, depending on your own Python environment)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Python >3.5\n",
"!pip install verta\n",
"!pip install spacy==2.1.6\n",
"!python -m spacy download en"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set Up Verta"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"HOST = 'app.verta.ai'\n",
"\n",
"PROJECT_NAME = 'Film Review Classification'\n",
"EXPERIMENT_NAME = 'spaCy CNN'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"# os.environ['VERTA_EMAIL'] = \n",
"# os.environ['VERTA_DEV_KEY'] = "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from verta import Client\n",
"from verta.utils import ModelAPI\n",
"\n",
"client = Client(HOST, use_git=False)\n",
"\n",
"proj = client.set_project(PROJECT_NAME)\n",
"expt = client.set_experiment(EXPERIMENT_NAME)\n",
"run = client.set_experiment_run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
"\n",
"import random\n",
"\n",
"import six\n",
"\n",
"import numpy as np\n",
"import thinc.extra.datasets\n",
"import spacy\n",
"from spacy.util import minibatch, compounding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def load_data(limit=0, split=0.8):\n",
" \"\"\"Load data from the IMDB dataset.\"\"\"\n",
" # Partition off part of the dataset to train and test\n",
" train_data, _ = thinc.extra.datasets.imdb()\n",
" random.shuffle(train_data) \n",
" train_data = train_data[-limit:]\n",
" texts, labels = zip(*train_data)\n",
" cats = [{\"POSITIVE\": bool(y), \"NEGATIVE\": not bool(y)} for y in labels]\n",
" split = int(len(train_data) * split)\n",
" return (texts[:split], cats[:split]), (texts[split:], cats[split:])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(tokenizer, textcat, texts, cats):\n",
" \"\"\"Evaluate with text data, calculates precision, recall and f score\"\"\"\n",
" docs = (tokenizer(text) for text in texts)\n",
" tp = 0.0 # True positives\n",
" fp = 1e-8 # False positives\n",
" fn = 1e-8 # False negatives\n",
" tn = 0.0 # True negatives\n",
" for i, doc in enumerate(textcat.pipe(docs)):\n",
" gold = cats[i]\n",
" for label, score in doc.cats.items():\n",
" if label not in gold:\n",
" continue\n",
" if label == \"NEGATIVE\":\n",
" continue\n",
" if score >= 0.5 and gold[label] >= 0.5:\n",
" tp += 1.0\n",
" elif score >= 0.5 and gold[label] < 0.5:\n",
" fp += 1.0\n",
" elif score < 0.5 and gold[label] < 0.5:\n",
" tn += 1\n",
" elif score < 0.5 and gold[label] >= 0.5:\n",
" fn += 1\n",
" precision = tp / (tp + fp)\n",
" recall = tp / (tp + fn)\n",
" if (precision + recall) == 0:\n",
" f_score = 0.0\n",
" else:\n",
" f_score = 2 * (precision * recall) / (precision + recall)\n",
" return {\"textcat_p\": precision, \"textcat_r\": recall, \"textcat_f\": f_score}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"hyperparams = {\n",
" 'model':'en',\n",
" 'n_iter': 2, # epochs\n",
" 'n_texts': 500, # num of training samples\n",
" 'architecture': 'simple_cnn',\n",
" 'num_samples': 1000,\n",
" 'train_test_split': 0.8,\n",
" 'dropout': 0.2\n",
"}\n",
"run.log_hyperparameters(hyperparams)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# using the basic en model\n",
"try:\n",
" nlp = spacy.load(hyperparams['model']) # load existing spaCy model\n",
"except OSError:\n",
" nlp = spacy.blank(hyperparams['model']) # create blank Language class\n",
" print(\"Created blank '{}' model\".format(hyperparams['model']))\n",
"else:\n",
" print(\"Loaded model '{}'\".format(nlp))\n",
"\n",
"# add the text classifier to the pipeline if it doesn't exist\n",
"if \"textcat\" not in nlp.pipe_names:\n",
" textcat = nlp.create_pipe(\n",
" \"textcat\",\n",
" config={\n",
" \"exclusive_classes\": True,\n",
" \"architecture\": hyperparams['architecture'],\n",
" }\n",
" )\n",
" nlp.add_pipe(textcat, last=True)\n",
"# otherwise, get it, so we can add labels to it\n",
"else:\n",
" textcat = nlp.get_pipe(\"textcat\")\n",
"\n",
"# add label to text classifier\n",
"_= textcat.add_label(\"POSITIVE\")\n",
"_= textcat.add_label(\"NEGATIVE\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# load the IMDB dataset\n",
"print(\"Loading IMDB data...\")\n",
"(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=hyperparams['num_samples'],\n",
" split=hyperparams['train_test_split'])\n",
"print(\n",
" \"Using {} examples ({} training, {} evaluation)\".format(\n",
" hyperparams['num_samples'], len(train_texts), len(dev_texts)\n",
" )\n",
")\n",
"train_data = list(zip(train_texts, [{\"cats\": cats} for cats in train_cats]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# sample train data\n",
"train_data[:1]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# get names of other pipes to disable them during training\n",
"other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \"textcat\"]\n",
"print(\"other pipes:\", other_pipes)\n",
"with nlp.disable_pipes(*other_pipes): # only train textcat\n",
" optimizer = nlp.begin_training()\n",
" print(\"Training the model...\")\n",
" print(\"{:^5}\\t{:^5}\\t{:^5}\\t{:^5}\".format(\"LOSS\", \"P\", \"R\", \"F\"))\n",
" batch_sizes = compounding(4.0, 32.0, 1.001)\n",
" for i in range(hyperparams['n_iter']):\n",
" losses = {}\n",
" # batch up the examples using spaCy's minibatch\n",
" random.shuffle(train_data)\n",
" batches = minibatch(train_data, size=batch_sizes)\n",
" for batch in batches:\n",
" texts, annotations = zip(*batch)\n",
" nlp.update(texts, annotations, sgd=optimizer, drop=hyperparams['dropout'], losses=losses)\n",
" with textcat.model.use_params(optimizer.averages):\n",
" # evaluate on the dev data split off in load_data()\n",
" scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)\n",
" print(\n",
" \"{0:.3f}\\t{1:.3f}\\t{2:.3f}\\t{3:.3f}\".format( # print a simple table\n",
" losses[\"textcat\"],\n",
" scores[\"textcat_p\"],\n",
" scores[\"textcat_r\"],\n",
" scores[\"textcat_f\"],\n",
" ) \n",
" )\n",
" run.log_observation('loss', losses['textcat'])\n",
" run.log_observation('precision', scores['textcat_p'])\n",
" run.log_observation('recall', scores['textcat_r'])\n",
" run.log_observation('f_score', scores['textcat_f'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Log for Deployment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Wrapper Class\n",
"\n",
"Verta deployment expects a particular interface for its models. \n",
"They must expose a `predict()` function, so we'll create a thin wrapper class around our `spaCy` pipeline."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"class TextClassifier:\n",
" def __init__(self, nlp):\n",
" self.nlp = nlp\n",
"\n",
" def predict(self, input_list): # param must be a list/batch of inputs\n",
" predictions = []\n",
" for text in input_list:\n",
" scores = self.nlp(text).cats\n",
" if scores['POSITIVE'] > scores['NEGATIVE']:\n",
" predictions.append(\"POSITIVE\")\n",
" else:\n",
" predictions.append(\"NEGATIVE\")\n",
" \n",
" return np.array(predictions) # response currently must be a NumPy array"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"input_list = [\n",
" \"This movie was subpar at best.\",\n",
" \"Plot didn't make sense.\"\n",
"]\n",
"\n",
"model = TextClassifier(nlp)\n",
"model.predict(input_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Deployment Artifacts\n",
"\n",
"Verta deployment also needs a couple more details about the model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What do its inputs and outputs look like?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from verta.utils import ModelAPI # Verta-provided utility class\n",
"\n",
"model_api = ModelAPI(\n",
" input_list, # example inputs\n",
" model.predict(input_list), # example outputs\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What PyPI-installable packages (with version numbers) are required to deserialize and run the model?"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"requirements = [\"numpy\", \"spacy\", \"thinc\"]\n",
"\n",
"# this could also have been a path to a requirements.txt file on disk\n",
"run.log_requirements(requirements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Log Model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# test the trained model\n",
"test_text = 'The Lion King was very entertaining. The movie was visually spectacular.'\n",
"doc = nlp(test_text)\n",
"print(test_text)\n",
"print(doc.cats)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"run.log_model(\n",
" model,\n",
" model_api=model_api,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deployment"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Click the link above to view your Experiment Run in the Verta Web App, and deploy it. \n",
"Once it's ready, you can make predictions against the deployed model."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from verta._demo_utils import DeployedModel\n",
"\n",
"deployed_model = DeployedModel(HOST, run.id)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"deployed_model.predict([\"I would definitely watch this again!\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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
}
| mit |
garimamalhotra/davitpy | docs/notebook/signalProcessing.ipynb | 3 | 1821970 | null | gpl-3.0 |
moonbury/pythonanywhere | scikit-learn/plot_classifier_comparison.ipynb | 3 | 73026 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run in Python3\n",
"The subplot is distorted."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=====================\n",
"Classifier comparison\n",
"=====================\n",
"\n",
"A comparison of a several classifiers in scikit-learn on synthetic datasets.\n",
"The point of this example is to illustrate the nature of decision boundaries\n",
"of different classifiers.\n",
"This should be taken with a grain of salt, as the intuition conveyed by\n",
"these examples does not necessarily carry over to real datasets.\n",
"\n",
"Particularly in high-dimensional spaces, data can more easily be separated\n",
"linearly and the simplicity of classifiers such as naive Bayes and linear SVMs\n",
"might lead to better generalization than is achieved by other classifiers.\n",
"\n",
"The plots show training points in solid colors and testing points\n",
"semi-transparent. The lower right shows the classification accuracy on the test\n",
"set.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python2.7/dist-packages/sklearn/lda.py:6: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19\n",
" \"in 0.17 and will be removed in 0.19\", DeprecationWarning)\n",
"/usr/local/lib/python2.7/dist-packages/sklearn/qda.py:6: DeprecationWarning: qda.QDA has been moved to discriminant_analysis.QuadraticDiscriminantAnalysis in 0.17 and will be removed in 0.19.\n",
" \"in 0.17 and will be removed in 0.19.\", DeprecationWarning)\n"
]
},
{
"data": {
"image/png": 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9g3Nf+W8YG/eWb9PrYgLV6QfqxP0nLrJKDgVqvQpUXbbP5ZQNoGwJMlyhLWwU\n96is61fPSDk806s6TeRTmwGb/HDriaP4TTR1WTnVpydT56AmG3JkmQ6bndyM0MtlZFmm16oOQNFg\nN9ZR8e5q9j29g/SRMUYL8ph383W4BA2Sp1JK1QRB4KZ7P+RTZuON23jlzy9QPWZld0UJyz9ya+Q3\nQujOlBDw3egVDNSAkKqUaFAVxe0SBAIVYMfLuym72IIsQGdNBVddtTFoGS6XC61GnPCZkrTU4U/H\nxvhxwwN8RNpDLaMM2PYxWJXO3Ozw8+WCIFCZdZo+q9LUjjG7so/i0lqKP/EBr0WVEAg3iawwsO29\nN9F+1XoudfVwTW3NpOCVYIq0MxUPrP6g2ux2ntvTT0bFXDasyiWHqQVVUczABgMV4I3TZ7nJ3Eil\n554vnrnA7rISVvrtgSrJLl589hWquvqwaEWkxfWsWZm4lD2KRE/z//cGEz3SB/kpy6njDbS2NH6+\n2YY2ghageFEVf3hwlHf9xxdp6ashTd/GrBLBB9RQCtailhUVMtbeTeNTf8eh01N77SayszIDHqsG\n1X+aIFEKZFGdThc/31lIe+d7ENpEXtp3hi98xkpVpaoz5fk3WaAqihrYUKAqGh4Y9sIKUC3LPDsw\nCEw0+acaL3H01f08aLGiLK54/s2T9M6tpTArMcstRNWUqoyARnBngRmjnqPUU6YfJd1wNGw5xYvc\nrsAzp2UudH8BSS7G5oTvPvYi16w9xizVVkv+Cjfy03jqLPnP7qBKFJBlmZ0dXSz75J1oVCssAoE6\nWX7pxaNUqKb/fFMnpxvnodd7u4i8vmcHK1ZYMKQbqKgoTTqoiiJe6K7JzkGTnRNR8ok5tdW8pNrN\n+u9pBubNqfHCOmq3Y3/1AKtUsALU2R20x7khhyCKiBr3yz17JHg7Uh9d0URx+iPAGBmaXdw2b7+n\nUyhPer1+boQfv5XH4y1pCAVF6IpLaGzXI8kTcA6OLef4ub6A9ZDl8CtmAcbMjVSJSup3gTk9A3T3\nDQBuUBVYg7nxvihHArbf+ZKsgtVdRk79Uu9rTJeFKyML5IklMy7XOH9/uZf//J85/L9/K+ThR44T\n6BnG/gqusBY2Eovqr+qSGby1bR2/PHEWGchfUs9s1fRsU08/Kyzj9AJmQIlv351tZEmAadxIpB6S\nCjZ7tL42m8eLXmbX+SeZV6JnYfnkGAFB1HDUaeRLr32A/tGtwBgHLv4/Hvu2iXULBf7wygksdvcs\n08yCF9gFw5EJAAAgAElEQVSwNHDyCXc9wjfZrvQ0XLKMxuO3D+h15Ken+4CaDMkBOmw59cu874zp\nJvz6meUlbFx/it37RSCDcevzZKTfjkarA/I4cFBg06YOKir9MhkmQSGBVSxqpFL38BfMqYa6wMuE\nKwvyOZaRxm0WKzuBE0BDTiYrrt9Mhl7P63veJOP8JVyCgDSvlrUrAvu1Pmv31R21EN9ySU4Gdyz3\nHREQVM3vjAUV/OVnkgdWACN7T2+mb+gcN2+roaPvMZ7bvxOddpzP3GanMLfCD1L1xcN7mPOu2cjO\njm4q2roY1WlxblpNZkbGFQGqIocs8IEPLmTzlg4Gh8Y5fqyY/QfVqwkyGLfYk1RjX4UENhJYY5l5\nyk43wKaVPPzmSdKcTnpKZ/CeazchiAJHz55n7ZG3WOBZ07X7wDHMJTMwzXT/eicnmBBi7nn4g6pI\n0vr6YwbdMAaDuwd/72113HsbQLZPsx/rOnyDTs+yj9zO2LiVPJ0OnS62sdhwihVURXankxklRcwo\nAaOxj8NHj2B3LEOWJcpKD1EzOzkbT/srplGCREyPLppbC3MnL5rrbOvivaoNjte7JL569BRzq9RN\nb3x9YzWosgzFCz0LHGeU4BI1fOnOPk5e/BENbbeTYTjHndeeICNtjs853r9jxEvdkRIEkcww48DB\nFDL5G4kB1V/lMwv49H197N+/E73OxbXXz0arC515PFGKGNhERUSFkyM9jZNMbOD2JpBpVx5aPLNm\nKh/X8x0qoII78YQLkGWB2opCXv6hhdePfJ/Z5dnU18zxOQ/UkPjXKTTAkfX445HH9/VUdnBkhJPn\nOqmvmcHstVu8RyljqOraK0NTgqAenhKQJQlJktCo8iWUzyzk1tti62/Eo5DA+kMazzRrpFqzqJ5X\nD52iUZKQgRwguyA35h9IIFBhAlbBm/vUt/zsjAzeuaHe3W+Ns9mHZIM6UXdJkry9vVcPtvLAT2to\n67uVmSVH+N6/nuWqd6ycVItQFvW3vz3ILx7RYLVmsGbFRf73f65Dow0+bn3yRAvnzo1TWqJhzdqa\noAE0sSqshZ0KSAHvr6M4O5PslYsYO3mWHKeLozPyuX7r2qiK8h0xmKzihe7cU2JJmQ+oT+3s4MjZ\nQnQ6G7ddZcdUne8p40oF1WtPJwelyPCrV2fS1ueeDm7tnMm//fQ3zJ93kZ4jR9EW5DN702YET8BG\noKa/u6uL7/+whIEhd5TYMy+MUlf3ez796S2+l5Jk9u89x8FDHTScW0xa+mok1xBt7W9y623zEnq/\nCQ9+iUoqsNTX2rBhBa51y7A5ndRFulGb4NschAJVBnduftVBuw538cyezWhEdzP3kycO8N37LGT4\nhR+OWey8dqgHjQauXl0cNK2Qe/NvOayPGUwOp9M9XS2EHiqXpMAd45z6pVhtb/nWaaQH4Yd/Zb0k\nMeZyceBcI/M+8pGgZV9q6mRgaI3qnUy6unzvV5Zk/u//TnHGvAnzOSdWawtzTX2kpxVw5GgOt94W\nsvqT5MosCvn51CfS8PMzgv0oNKIY2a6CviFdQGCLOENJkhZglxMZaGjRemEFGLTMob17N7VVEw9w\ndMzKt34pMzB6K7Ls4o2TL/CVj2V7oXVJ/qUGHxMOpvHxcc48+hR5nb1Y0gxk3rCVqrmB1vcHAdUT\n4mfRZbNurYU3jzdhd1Qjil3cUHyAOZ7zjKLIjOMnsFptpAWJYZi/oJa62TtpaPwwABkZJ1m31nfa\nuK+vn5On69Hr0xGFISR5Gd3db1JVWYBWG/nsVzhQFU0NsBFCGrEmWZ3g5blUiXz9YVVjXVZowyWN\nohHdX0iGoZniQt8vZ8cb/VzsuJFL3QJOVxpnWzaz8cgzbFkxK0ipMDwyisVqo7gwL6y1BGh48TW2\ndvW6fT+7nX1/fw3ZNNt7rhpUdUSWF1R9trcK/3L/VkpLdnPs1A5mVTrpf0ng1YNDFOQ4WFRXgEMU\nQq4IzjAa+cn/lvI/P/w5Nls6V2+TueEdq32OEUXBG+5TXqbnXGMfLsmKLJu59prwq6XVoPZa3RUP\nleQzecAmGlLwgjpsGaf51Fm0QEHtLIoCLBdxeVL6eGEtKQfZL0JU5RNcv66Ezr4XONlYjkFn491b\nh8jK8O8FSzR16pDkYgRgdDyN1960sWUFBGr4//2RRn794gqs9nxWz32B332zGr0udKuRNmb1eVZZ\n4zbsDqeP66F+ktkhg6YF3vf+tbwPuPOu53jt6PcY5Pus6eilzdmA8RPvoTTMcFTd3Eoe+mnwxY75\nBfmsWHaKI8fzMBqzWLV8N9uvG2P+gnSKZgTPRREI1EiUWGCTCCm4kRi32eh8ZS/b7HYEQeBQeze9\nW9ZSkJvthRTcWwqVq2D1GZIKMMkvCAJ3vavUc6YOKJy4KIAAm5bn8O9/2IfLcRPgIiv9ALlZZQSC\n9T9/fYj//csdSPJmAF47vprv/e5rPPjR+pC366oqZ6CplTyNiCzL9BTlU+6B1f9pZvtbVU81HKpj\nlM7U6bNlOKnkd3yHpzjJgoy9PHbTO0iE7vzwfBYdP8HQoJ3lyyrIygkcbQaxg6ooIQmNgy0tiblX\nHKIDdfFSGxs9sAKskCR2NLeTm+fu0atBVXblkxXLGsd+WC4JcrKy2Lasl9MX9yGKGvIyy5lb3YB7\nx9kJ7TjQzI+fWo8kq9ebpNE/kka4p1K/biVnJBmxqQVbRjq1123xfqac6dP8q6QG1T2OOnGtvJxR\n2jsBjIywmrzi8FFqkUoQBZYuDb22Jl5QFcVsYePaHTp4oZPK9LeGaelpjEgy+RoBWRCwyjKCXu81\nMOXzy3w6V/EOJbn8LPMXPpDOH14wY7EamVt9guvWzZh0zv4TVqyO9wB/Ad4PCGQYXmb7mvBxtzIw\nd8Mq2LBqovKef32i/AMs7IPg8ahfvD+H//fdR+jpK2P2rEa+8qDvcNPoyAhvvXWBmpoyCosi6wBF\nokSBqihiYIXJswhxX9xTcFRlzppZyqGKLua0tKNF5mRhAfNNc5Bl1e7RMWwnqZbL72T1jyY7K4N7\nb8vwVDdwjpLmnn6gA7gWeBJB6OGBO06zbfWSgMdHkn9KgTUUqC3N3fzu90MMDWVQUjLEPf9UidGz\n0ceWrfPYvFlmbGyUzKwqn7KPHj7P5744zMXmDRTln+TBB1p4z63LiEeJBlVR6HHYKwRSRS5BCwLM\nX7uS7nkjuCSJ+TnZiIJA+QK3ZVVvzrv/eBsP/VVAkkQ+cL2V7euqgpYNk61prGrrWQScAk/vuSCr\ni0/dMRnWRICqXoX6u98P0du3CYDmFplHH32Nj/3TXO/ngiiQmTU5duAHP+nkYvPHAOjpr+Tnv/ot\n74lueZlXyQJVUXgLmwxIoyxX3ZlSpkoLPeuwZKAsAKxNrb18/L9m0db3fgDebPg7RblHWTFvcsxm\nokBVlKa3Ajd6/z8jt93n82hAhclZUmCi6Vc/xaHhiQAaQRAYGooszZDVmhby/5Eo2aAqCpOWLwHj\npcpLKU95RSAnGi+skux+qTP2yfi6AWr9bW8bbX13eP/fO7ydv++bWM3gkidesix7X4nQP99mYWbh\n74A2CrOf4t6bu9z3IMm+CX0jsKpKgjQFVpvDFdRPLS8d8t6DJNmpqLAEPM5f2zZbMejPASAI/axZ\n1R7mDF8psPZa5aTCCskYh43DkipyqjJehzJGwWAFmFuZhUFnxuZwdy5EoZOqEiHh1jSQtqycyY4f\n9HOs4cfMrymkKL86oszT/lY1ks6UWh/7WDWPPf4qg4MZzCy3cOutprDnANzz8Q0UFhzgyLFdVMyU\nuOee68Kec6mpnVExi9lzKhhKMqRqCaG+tIYv/FNkNUkApBAYVGWLdn+FglXRvz5k5s+vL8Qladm+\n4gjf+/ycIKXFpkC32dU3yssHrCC7uGGDkdxsY0RDFWqrGg2oofMPJAekJ57rZN+eKhAyMWQe4eP3\nzyXDmLh9ulZWFwb9kmIHNkGQQnCLCoGBjQRWcEdKORwOJFnGoNfFMwwbUP633DcwxnceyWLcvhZZ\nlslM38XX7naSmRHcJ1Rb1SHVOv9IV6FOJbCuzCL6env57rdErA53B1aWZVat3807bgs8AhKLQgEb\n3fbQcfqk/nKi8cKq+KjhFA5WZaWpO6xPRqfTYtAnJho+1I9bkmT2HBvCYnNHNwmCwOj4Jt441Rv0\nHAXWIV22F9ZQPqr7Oi5+/tBOvvavu3jh78diuY2YpPiprX0WhkYnfoCCIOByRrdpSTBZ7C4s9njz\nwybQkgJIsoyknkKN0AicO3GKUh2cPneGzK3rqVKlAogmrWQsevNUB1/+WRpdg8XMKmnioS9mUDbD\nveJW3eM3pklIsgON4I4XcLksZBl9v0yH08meo13k1c1jrdPJeLp7hi5Si3r/F17ir89+BMjlz0+f\nYHDwDd73vtXhTotZ6g4VQOnMMiqrD9PRlo8o6tBqjrNifXwTDWpIHc4wLlBIl+CBj7s/TACkQEyg\nSsDFxkbmHz1NRcUMROCoRqT4vrvISPcftgldaKwuwfbP9nHk/APea2xf+U0e+deKSQuqXJKLHz42\nwFsX1yHjYlndfu69tcg7jWx32Hno1QLaOzcyatdTVrqTz37WFHI9lGVsjPHxcfI8AT5rNr5FV/fE\nIOnWjb/lNw+vTrhLEGqYyuFw8PqLp7HZRJauLqY0RCKRUAoG6tXzyoICF9rCXkZQ1XL1DXphBZhl\nc3CxrZNZtaEC0RKnnkH19KtA75BfYIxHGlHDZ99XQEvnPjQagbKiCVgBTo4U0d65AYvTiCDItLZv\nZf/+19m4KXBAzI4d53n+73nY7TlUzDzLp++bRZrB5nOMQW8LeG40+s2vD/D4n7W4JJEb3zHGvf9y\nCxB8PFWn03HVO2P3WaOxqP5K+LCWpDJj0YLqkmX27j2APDpGmamWObOqAZi5fj6Du4+Q73FPmnU6\nCooTN98dSrIkUzezkZZeF+5UxGPMq2rDvbvrZAmCQGWpb1iiQ4aCeUuR9jRy8OQAnV1jiKJEWUkb\nx461sHBhGbl5OT7njFssPP98DoKwAIMeOrsq+NvfXuPuO51870evMjQ8l1lVr/HpT82M6/6OHmng\nv34wj5HRVQBc+j8zxXPfYNN1q+IqN5DiAVVRQoCV/NraWC3qjr88y91NreQCrzZc5MRVG7n+jm0A\nHLU4STNfwKnRkLF5NdkJyr8VTOrl0Q99sZAvP/Q1OvuLqKvo5Jv3VEdUhgIqQA9GTjcPYz5/AUna\nDhxlcMhAedmt/Md/nuWT99qoqJyw5FbrOHZHDsp+x6IgMD6u5cMfWcM117bTeO41liytIzs7/LZE\noXT4cDMjoxOzcuMWE2dP7WdT+KHYiJUIUBXFDKw/pBBf0z9itTK/rdO7D982u4PHB3oAcBWVsuja\nUrh2c6zVjViB1vFnZ2bw4wcU9yOwZVVLGUctmLcUSZbpE9zxoefOO5Gka4CjwBguaQVDwx1kGpfz\n0su7uPvuCWBz8/KpqHiLzs4KREFEli+xdImb3rKyMsrKyiZdt629jyce72N0LI3q6hHe/765Pgnx\nAmn99jUUPryH3l53OlRj5nEWr4zPaiuKBdTWsdCrFCIGNhCg3s/i9FGdkgyCgNVvSbBTo8FVFDhA\nOpEKBGksUg/459Uv8YEVYMH8DPT6Vuz2BcAF0tJ6yc/zxET4LTMXBIHPfmY2Tz/1GlarjqVLDSxc\nFDp45+GH+xgYcP+oe/vsGI17ePe75wY8VulU1c0t4tMP7uWvf/wjsqTh+ps0rN60Jco791UyQFUU\nEthQkEJ8oDr9TkjX6Wmrq+H46XPUShLPlRYy5/Ybg5ydGCUDVLvTRfHC5QA+sALc8p5VtLS8zI6d\nWQwNd1JSfA3pabmImlNs2Wzk2WeO8vKrFowZ43zxgRXk5eVy+x2RLZO226z09RV4F32Iop6O9sCL\nC/2Hqrbfso7tt0Rzx4EVa9OvwNo2ED72ISaXIJGgqrX+mm2cnVNLY4GBFYtNZM+alXDbmihIIXJQ\n1frMZ7fwmc+6JyEOvnGe/v7zLFlSzOHDXXzpa3WMji0DJBrO/YQ/PbrNJ9tKKOl0evLymhnyxPdI\nkoPiGVafY5IVUTUVoCqKCthYQQ0Fqb+ueZc7aYZUNNlHi0eSJKv4jBdUdaYU9xcUCaxqCYLA6jUT\ny7df+94ZD6wAIsdPbaCttZXK6urIyhNF7v5IHo/96XVGRw3Mqh7mZpU74G9VE6F4QYXoYIUIgY0F\n1GggVVS5yD3dmihYA8edxtP0T4Bqczi9Y6zRwhpIWVnjgDJ0BtnZbeTkRjd0N7OyiC98QX2OnBSr\nGk+vPxarqlZoHzZKUGOBdNI1EwBrorc89wdVrUTACvDAF9ZytuFHHDu5ikxjD5+4u5+c3AkLfOZ0\nK/sPWNHqnLzznaXk5+eEKM2tUFbVYbex55UzuJwCKzdUk5MXvrzLCaqisBY2HIOJgBQmrGusCpgJ\nJc6qBQO1q2eE4w0W6tcuYoYs0y+G34UmnIyZmTz6h+vp7uwkM2smxsyJ5vxcQwe/fDgXmIMky1y4\nsIcvP5iG3hB81xlXpnvyIhCsTqeTX3z/OF2dWwGRQ/v284kHZHLzA+9anghQIX5YIUy0VjAWnZLs\nfSVCsboCkicNZLC0PbGGvjpkwQurzeH0gfVCyxD/+2glB1s/ycOPLebHj15yn2O3c6mpldGR4dgu\nituvLS4txZjpa62PHx8B3NZWFAT6+hfRdLEjYBmuzEJcmYUho//Pn75Ae+tqBEGDIAiMj6/l4K7m\nScepo6ccTldcVjURsEKEPmyiwAylaGCNdJvzaBWq6Vd0enAuxuJ1jNj0CBTw5uFSNmxo55e/stLX\nb8Jg6OI9777A+g3Bs55Eq6xMFy7JgUZ0B8mIQh95BZNdkFBWVS2dTgOyOouBjKidOCceizo6NMjB\n0xcprSonKzc/YaAqCmlhE2lFgylSV0BZDxU1rBFY2VAWVa2SRSsAiRGbOt2QxHPP9TM2tp40QyEC\n8/nb85qELr+55ro6amtfxeE8hywf55prmikqmohXcHqsKkTWsaqZO4u6+UdwOoZwuazkF+xk49V1\ncVvUY/tP8/HbzvDdf1rEV97bygt/3RfV+ZFo6rMXqhTOFXAFa+oTpEgsqiI3rLByUz4HTx/BZluK\nS+pn/doeunt8073b7XpkWUIQEhPYLIoaPvXJhYyNjqDTZ2MwTEzhOqMAFdyhgYIg8KFPrODsyXOM\njzuYvWA+Lo37RxjrXH/rmJPf/LKT7jZ3PtqR/gr2/Pm3LNwa5sQodVmBhcmwJhtSiA5UmIC1TzBS\nUWnkgc8PcvzEKxTNyGDhwnns29tI44V2RKEMp8vGwgW9iGL8WwB1tPfx9NN92Gw6Fi10svUq37Sb\n0VhVgGceO8qRA9kgyKxYO8q2mxd6P4snKEXxVYcHfX+gTnv0y8XD6bIB6+8KuAKkkYQERRF4Aq3d\nM1MTTX8kUsOqqKAol21X5XrLWrd+Nunplzhz5hz5eS6uvT7+rNNOh4Of/XyI0VF3bMDFSz1kGBtY\nvabGCypEDuupw2YO7VuIqMlHlmUO7OmjfHYjNfXVMdfRf6iqbuUYbY1NuBzVCGI3s5f0xFx2MF0W\nYBVYHQUl4AE1mbm+HSqjHSmoEBjWYFq6rIqlUWb3efTRNzh02EFZqZ3PfmaTzzRsb28vvX01KLmG\nNWIRDQ1nWHF15FZ13DJGR0szJeXl9HSPI4h5Xt9aIJ+ejpPUhE6mGFSBxlW3fHAjWQW7aTu3g6Jy\ngdW3bIut8BCacmBdkoQsyzgLS5MKKfiCane6ouoIRQNrLPr5z/fw3z/cht0+C7BwqfkX/PAH13s/\nz8nJISO9E0lyuxZOl42iGvfgfiSwHj3wFt95sI2WS4soLT/CXfeBVnsah2M+ADr9KeoWRj/2HW4C\nYPn2lSzfHnWxEWtKgFU399WL3bGWU2VR7TH4ZsmGFeD1PXoPrAAZHDrq68unZ2Tw3tu6eepv+7FZ\n9Sxa4+KqaxdH7AL88gdNNDfdCUB763ye/OPv+cK3JQ7u2gvA6i055BXlR1XnRM1WxaOkAevfeVID\n6ipMzp6k8YIKUwMrgDHDEvL/AMtXVbNkWyGyLNNng/4Id8e02F2Mjfnly7KkUTG7jIrZ0U99Xwmg\nKoouL0EYuSTJ+wI3pMoLoGpxYiLZ/eWQJmC1O11XPKwA9392FnNrH0EUT1Na/Fc+9fHJ06xK56ov\nwnWG6nHUJStHEcVOAARhgIXL+mOq55UEKyTAwoaypGopsCbSuibCoiqaSlgBTPVVPPNUMc3NFykp\nriDLb21WNENW6pkpp9OFDHzk89eRV/gqjWaJmVUSt98TXXr4RMcARKoD53vdaXWDKCZgI4V00nkJ\ngjWRoKo1VbAqMhjSmDNn8pZGkcLqP4Wq/h4EQeDmD2+JqV6Xy6oeOB88S46iiIGNFVJInCuQLFBL\nFq2YcliDKRJYfS1qYidaLgesalAHR0M76qG3nw8ymB+L4rGuyQIVphesUwEqBIZ1uKeNp771yUnv\n3/jgj8grc2+LdG7fS+x/9CeTjtHo9Hzg+08EvK4CazhQFYXsdPl3mmJRPNbVLiemMxVMit96uRUu\nJFDdmXI6paRa1WCWVQ2rPn3iB/7sdz/t/bth70vevzWGdDSePclcDjsN+172Ke/A+d6oYYWpGoeN\n0rra/Rb3JUNT3ckKplBWNZkWVVEkLoDTPrGY8c4fPe39+7efvsnnuMFOd0xtUU092z/37wC8/NNv\n0HHmKIeefJi6ddcA0VtVtZIKbLTWVQ1qvBlCQulKhzUYqAd3HeeFv/YhihLv/lA585fFOK/qUaT+\namfDqZCfO+12tHo9Loc7xra4doH3s/lbb6LjzFGcNjf08cAKU2BhI7GuUwUqXNmwhrKoDafO8d//\nmkZ/3wcAOHPyGb7363ZKZiZ/IsBhG/f+/eS3P8nKWz7GwT/93Pted+MpyuqXodXpcNptnHrpCU69\n9IQ7maAyHa7Rc+B8b8ygKkroxIFakVhXuzwBaywBw9HqSoU1Eh/1jZ2N9Pdt8f6/q+MdHHjleNTX\njmUUYIYqQmakq41XH/omo32d3vdG+z29fP9tslSxG4IQ3qrarTbs1tCzJEkDFoJb16kGVa0rCdZo\nOlMl5eloNBOQ6PWNVNRGl5c11iErY54qG6MgkJHvu/ujqPFkUVdGlQSBEtMSRMNEYLvsCA6rGtRw\nAUpJATaYdb2coBYvXH7FwNo87Iy613/VzRt553ueJr/gGQqLnuTWD+1j+frI4hlbx5xxja++tfOZ\nif/IMpb+bp9NB3ubGwGQ7G7obv/PPzAm6xBEEeOWuz1HTb5Hf1AjiaZLXvCLyrpOpY8aSErugMsp\nV2YhkgytI25wou31i4LIp7/+Lj7+oBVR1KDVRrZvQyImAs4feAWAgorZrHnfJ7EMDVA+dymP/ssH\ncNmt9DWd9Tn+pT88zOiF4+Re/xmENCNjfuWpm/1o174lHFi1db3coIJ/oovkr/4NJIexEDywxjs8\npddHvuwkUbNWVUs3MNjWRH/7JQoqaimoALtlFJdnuGvWqi3uAwURZInBYzvIuerj6Ipr6H3yO95y\n4gFVUVIs7HhBqZeNywUqJC4rS6yy2F3o8tx+ZtNAfL3jaBUvrL/955tBlqlYvJatH/sSx//2e2SX\nk99++mY0Oj0uxwR887a8iwPnexH1aUg29/WGdj4CkhNk9w9UN2c9EP9mfgn1YSsXz/TasKn2UYPp\ncsCqdKZ0ecXIsjztYFVLljzJ7uoWK+/4wLrt3q97x1aFNFXEmcvuhRVAW1CVkKXvIXeROfK5T0R0\nBaXpr10yk5GcGaEPjlKx3qO7k+UPa6JdAt9Ja/U4qjbX/RwuDTqYCsnICEDLFAavBJsEiLfpP/j1\nG2LcRSaM1D7q7MUzE77TYKya6k6WfzxqmqfDOVWwwtRHWQWCNRE+ajjFBKxvZ2rC7I/mJta6xqKp\n9Fv9QQUuC6wtY+5rXS5YpwJURVEBGwzUucsrE1ahRCjZsFrsE/fuVPnpb3dY/eNWpxJURREBGwxU\nta4k65osBQMVLjOsg1PrryYT1HZzU8jPQwIbCahXihRY+5NgXUOBCv84sHb3jnjfS4ZFDQcrRGBh\nw4E6d3nlFWFdIfGwhgMVrgA3IMnZSA6c70WSZHr7R4Hkgmoft4Y+kDDDWimldKUpqdFaKaWUaKWA\nTWlaKQVsStNKKWBTmlZKAZvStFIK2JSmlVLApjStlAI2pWmlFLApTSulgE1pWikFbErTSilgU5pW\nSgGb0rRSCtiUppVSwKY0rZQCNqVppcu+m/d0kslk+gjweWA2MAQ8CTxoNpuHTSbT14GvAEoy1Q7g\nZeA7ZrO506+caqAReMhsNt83NbV/eyhlYSOUyWS6H/gucD+QDawBqoEXTSaTsu/6Y2azOQfIB94N\nlACHTSaTf17MO4F+4L0mkymyrG4pASlgI5LJZMoCvgHcZzabXzabzS6z2dwM3A7MAt6vPt7z+Rng\nDqAHN+Rq3Ql8FXAANya5+m8rpYCNTOsAA24XwCuz2TwGvECQvfvMZrMEPA1sVN4zmUwbgXLgMeAJ\n3PCmFKFSwEamQqDXA6C/OoCiEOe243YRFN0JPG82m4eAPwLbTSZTYcAzU5qkFLCRqRcoNJlMgZ5X\nKe5mP5jKcfurmEymNOA23KBiNpsPAC34uRQpBVcK2Mi0H7ABt6jfNJlMRmA7sCvQSSaTScDto77u\neesW3B22n5pMpg6TydQBlJFyCyJWCtgIZDabh4FvAT8ymUzXmUwmrWdo6k9AN/AHz6ECgOfzetx+\najHwfc/ndwK/AhYCiz2vDcASk8k0f4puZ1orlUgjCplMprtwj8PW4u6E7QTebzabOz3jsF/GbYkF\n3L6rMg7bYTKZyoAmYInZbH7Lr9y/AW+ZzeYvTtW9TFelgI1RnkmEbwLrzWZz62Wuzj+MUi5BjDKb\nzVfD2hwAACAASURBVL/GbVHXXeaq/EMpZWFTmlZKWdiUppVCBr88e9ut8spr3Tszt7vSIyqwdvNW\nAC4NRp9PNr2iBoCm9qGoz/VKEBCEiRyULS6N9++2jv7Yy52GCtd6zixzz2fMFJwJu2Z1WQ4A4y0X\noj63KtdtP7PrVwVNIhrSwkYLq6LLBqtKLS6NF9a2jv5/OFgjUWu7+5m0ylpa5cQE7infn/J9RqNI\nuAnrEkQDa+3mrVcMrJACNRK1tvf7gJsIRQqtS6ub9Lowqgl5TsgaTjdYW2WtdyuuFKjRqbW9n5ll\n+V5oFTfBpYst+rGxx0LNDCNplbMZa28OeEymPjScgZSQn5Tit0arhMMKtLX3g5DkPOpXmFyawF9j\ntOM/l7rdexhUFmfTgo4yHRhjgEpR16CVsrw0sssrkXraYi5HrbiBjbWTlShY1c1Ya3t/slP+x6xg\nUCVCxrQgZUuxDVn2D4xRmJ9JpxNm6+OoGNA+4IY2UYrrKV4psCo+WLyKF6qDl4LfjyhG/lPaPCc/\n/EFJVm//KIX5mTRaJGZnxDf62T5gpayoPCFWNu6f/VTDqvhU7Q5AgOauYdBO+FnxWNigliqAdp2b\n/CMRRYHxsfA7ocRS9uWAWNk5BjLjhhZATAC0MQMbrpPl0k521o1llcjAhe4xiNGZB+h0CoiC+4FO\nctwFkubD+oOUCDgDyb/cdGOaz7WnGl43uO4tpWIFV3EN4oU2JLBSEKjq1rtXfASCUpE/SGJROeCu\neKyOfKPF/QOZ+OUnX2pQQgEqaqL/kUiuyHxM/+teDngT4SIkAtqQwAYCq2LVegA6rVoyo3TI2wdi\nt0hTCWsoSIOCGaVVtw0P4LRayMgvRtBMPOdIIFbXaSrhTSS0sSoml6DTGt1pYlH5tIA1EKiTAE2A\nu2F87ld8fO8zlDms/KaynoZ7vovWkO65nu+x4QBW6ql2G5IJbqI6Y7Fa2aiuWLFqfUywxqOpgHXX\nuX7vlz0+ZsVmtSFqhAlYBWHiFacs/Z3cs/tJbrGOssbl5IcXT5Lz3K8mDvC7llKPcC7H+JjVC6/6\nfpKheL8LxXjFwkbEwCquQFSFq/zWWJRsWIOBCiQUUrVcw/3U2Me9/9cB2dbxwAcHgTeUxsesWBVw\nzw8kpM6B1Ns/6v1+YlGsTERlLqOxrlcyrGrrY/NspS5qEg9nIBlnzuGX5XP4j7ZzCMDL6Zm0LlxP\n2G6oqm6K22B45XG2vvEcBpeT1+auZvCWf/YeYx0bR5Zkdp13/39zbV5C70PRVLsGIQO4z/zuBzLE\n7gpcFljDDGspsCqgus+Z2vkxx3A/Jc8/QpbDSuPizWgWbYi6DGtzA9946H622tzbz7eIGu6++T7k\nNe/0jEXLyJ6ZrvTMDCA50BbmxzfcpXTA1NDWLl4S9AsJS2GsrsCVZlkDWdXLFXOgy86n77330wfh\nLWsQCS1m1nhgBaiQXOT0tDAY4NjxUfdxybC28XbCoh01iOgKsbgCsSjZsNqsNjesSfBNp1z1K/lT\n9kTCmCd0BqzD/chvPIcUpNWcADexvm0ivq9IuQnpEoyaD8kQObCJ8FvjvnmVS5Bsqyr4j0HFKFly\nxXSe862DLN31BNbRQTYNdHG7dYwOQeBzy65m4P3/guQIXG6yXITC/NincMvy0rxuQSiXIGzp0fqu\nlxVWlZJhVQVR4/NyvxnnK1i5EUg7bxUn7/0vKCjjdusYAKWyzC1nDuC0WhC1gb/eZFlaIK6Rg0is\nbEhgp9oVSJQmdaziADUsoPEqDMCRSPKLBHMgeIcSphJaxeDE8n1GaugSsmo2Hlcg0X6r8gXEA2tI\nSOOQ7JLI3PMM8576GcUv/gHn6HCAixM1vGc33covcmfgAs5otDy1dBs6g8H7uagVA4I7PmphfNSS\nFGhjVTjDF9KHPX/8WETRGbGOCiQDVkEAq+ICRCkfKJLQJ8va8yx3mQ9jEEVkWeaJglIu3XRPZCer\nvolAPq+9tx3x+Os4iypIX7Qe9bCWWpIzsPVLz8xImE8bz1BXWV4aNWVFsfuw4RTv1GvCYR23+izz\njkQBrWkSVN7XgUF0P3JBEKga7A4IVeBKMsnqqqUvLEN71XtJWxR6GDKYiwCJcw+SOY2ekHUbsVrX\nZMAarWX1ATXJ6jVmI/d1eH9QfRmZCFGsRAAm6im76x7LCIOoFSdZ2vFRC+mZGRx84i9s7zyFU9TQ\ntnYrmcuiH4dXFMvYbPuAlZqy4J/HBWys1jWRnSwfWKOQIGpwdTUz+9AODC4X52sW4Fq4NqY6yBHS\n3rHhZh7b8QdmDXTRl57JqQ03xd7EqawtRD80Fgha68k3+ODpA+SKGqrS4fyOZzhUOZvMwpKoq6dM\nKCRacVvYSK2r0+mg4/HfktveiiWrkN7128nIiy9TejywOm3jbNrxR9Y77AB09LfzO2MWQs2CsOdH\nCqi/tGnptLzzY7Qo9cDtmkrNDcw+vR+QuTB/LWJlXeSFegqJxdr6Q5vZ30m+1YaUkcGlcagyyOxr\nb4YYgIWJlQqJWF6jKGZgo+1odT71GFefOs6gmEZNaxcv9PTivOtzsV4+JljVfp/U1cxiqwU8Cw9L\nEchru8BgEGAjhfTsobfCH6Suk2Xo/7P33uFxXNf5/2dmewEWvRMg2JZVYhHFokJZ1eqWJVkukWMp\ndpxE+tmJ5aKvHduJSxTLTUnc4yJbcZNsyVbvhSJpUewVBEGAaCR6295m5vfHFuwutu8CBGS8z7N8\niJ07d+7svHPue88991z+v9OvsDZEtiNDvTx1/UfRlMfvlJSqEiKkBSAL4kaT1lbTTO/JfTS4XMhG\nI8c1GnRNS9JXoksRyS8ICKmOZ4mcCJuLFDANDjAuTs4Zl48O06fIiEL2b18mZJ1CrzitqpRV06NS\nsSJ02ClL2IvLp9STiKiBsSF0HUeZUGnoGAJNURFC1HIhXYqBTTxMQ+3UjjsYDOnaakDX2YJUXhNq\nbhaDsmCDg/cqx+bLUlIsZxLUoCggLFrFo14ny9v2ExBFOi98N9uq0lvXUlXyNkpuD50CrCvTJS2T\nDXK2sNkOtBxllci9A+AMzsiMlZTlTFbIUgYkGFjpikp4btO1DBx8Hb0U4NCCVQTO2xr13BNbVOns\naTb+8WdIdj07ndfjVy/GVnaWygsc6ExFWd+Px1iC2+ehXBBQBIExWaZt2IMvZKmtF6zMrsJws0UN\niJNEKtOm7iFkKSQN1mykd81GANQqDTtbBrlhdapNcmYWWRM214GW67r383j/j6geOoPNZMF+w/sw\nZlnH5KRAbmTV7X2FFZ3H8ahUtGy4AmnlhexZeWGkaLBnTfxgw1396pbXWC0HeNi+FQMLWeCTaPVf\nwkTbM1SszT5xhVy7hF+M93NJXwtqYHfTasqbrZHj7ftbANh48fmhNmZ4jZB/XQlklplQVImTpA1B\nLfkJqPLfqNHr8nCAwljZnCxstta13SWjVmvQ3nYXYU9ftmQNI1OyyqIqNui5dR9/c2QHpSGrXvPG\n47x42ydRG0wp62mN0qRatYjG70Uf8CBhCI3wFURBQI2OcmNuWi2w4XJeUS4LtjOu16k0Bx/ynh2H\ngOyJK6jVeZP26aNDs8bKZkXYfCYJEvpcsxDjb7QMgiik1GJhKEC5MVQuRFrd6BkqxUkyrPR72Gkf\nxFy+LKlV3bPjECpFwSy60ZtLEAWRM9YLOLKnjyb1btr8izhrqAI6KVngAbKXBGGkk0eJiJuWtIoS\nHPTkSVogKWm9bjcjJw6jsZRSuSi1d+PAqDdvK5uesNGkEkT6nIGsRn2nxkPTpAnOiRbrkixz+nsP\nUNd2nBFLKcWf/CKWyuBI+emjQ6gBLRLGNFoMAYRwyqEoC+uoqGXixD4sIYnQodGgqaxNSNYwKcr7\njnPzzt+w0WPjNVM5L171MTQ1zTxzyR1oek5wZuxNVIZGGmoFSmqDDzOQZuZKne1EQRwqzTqGHF72\n7DiUmbXNkrSekQGafvkAS0f7OV1SyckP3oehujGhNHCOj2L82UPc6nAwIctsX7+Jips/kLBer8uD\nzph/jq3UhNVpY0jlEBRKswwBVQvBxqY7r/OH3+Rzf/gVFoIW8oHxUSzf+03kuJbMXDWJyAqgOX8r\nvx8fYtnpE/hUKlo3XYXWVDzl/DBZK806tu15nC84hwG42tbH6I7fc+i2+1EXlaGs3EpVFDnTETVV\nuWxJnLW1zYK09b/+Fg+dOhB8jUf7+JfffZczn/wuMNXK+l57nnc7nQiiSIUocv7+3Zx413UUFVuy\nup9skJKwMWQtqsbpzs4xfWDUm75QCNXtJwjfpgAs6unELgV4rmUsY7JGkGR6Vtl2M63bbkZBIN5e\nRBM1jBpf7GrWKr87Y2Jmg+g6syFvvLUtBGmbbCMxfU7TxAhnSDwAE1Fi4ja0soTk9yetuxCDr2nf\nlMPrymyQNFxWEUPLgdIKnmvJLhhDUKdXOKkkQDRZAXaUNRKm7DCwp7I5q/bkgoCsZPVSRFvbtJMb\nIc+Bp7sNefdLeDpPTClyoqI+8hxk4ETcuOXpo5Pb6ioXXsyB0G/ulSX2LllBcdlUX3YhkTK8cKTt\nmAJB6wpkZWHD1jVTwrqcdoa//M8s7mxnxFKK857PsU+7OHMpEE3WZBY2C7ICyFKA0td+TaNjhPay\nBiYuui3rSLB8kanFHXIEf++IpVWUCEGjETi0k5v+8gILBIGzisyfNlyOuP7SyHGfx4XlkQdZOtJH\nR0kVQx+4D515UjoFVJoYWWAfPIvn0D5ko4mKLZehElPbQJ1Rn9bCrmyuTnrTGRM2FzmQKVkTIfwm\nJyOsd6AXfccxvGYL2rVbEQQx0u0lImy2ZIXMdel0IyfSKnJCwjb/9iGutk32XK8aizj5weRT5PEe\ng7AsyMfNlY60qQibug+NvC0CZKGtDgx74s7PAcIkWeOtmrf7FFe88BtWKwouWeLRvk68198ZIWrC\nl1AQYr7fu+MwAOVm3ZRE1VLY6a6AHPDhcUygM5lRabLbTadQ8IcaqEpj3ctNOkaiNa2QIIA77sWV\nBRCSPCdFkRHVIkpUfi+NHMCvUuf3bCErPsWclq6Aw1SF05t9zKXX48upQQBPHxlALQWCAR1xD0mW\nZEpe/B1Lh/uQZRmjqOLCzhYC3iTpfgAEMSlZ4xFNVtfoOO2vaejecRHtr5Qx3jOcoHKlIB8p4KN7\n9xE6dxzDYxuPORZue7BtiT+Dpzro2HkYYXwo5eYGbesu5iQgKwodKLSGVick+sy0/MkE05J439Z9\nGvG3D2NyO+lvaMJyx92oskxgnCjFuizJmL53P3e2vMVFwL7hXtxL1+EXBEjmeI/7PhlZvS4HtoEx\nDCUm9JbgUpHBFjVK4OLg7K5SyXCrnZIFmd+D5Pch2YYQzWWR7ISJIEsBTvypB9/wfwA6bJ3fY+mN\n4xgsJZEyihIkkKRAfHqtjp1tjBx9H0hrGT3+OjUXvsoeIbG7S7tyI8+V16L0tiPUNaOvWZA2uktQ\nCTFWFoJG5YY1WUSUxeHAsId1Fdn7ZVNaWIepKusK9w+5Mf7hEa4YH2Gz18MNbS2MPvfHjM+PWFeY\nEo0/tvdV/qPlLc4DjgEbnBP093fx9qpNqLUJNFGIrGELlYystsER2t8oZ/DYe+jauZLhtsHgeXLs\nZIciZfEDD/Vw7WsP89ldf+DWVx9G3duatOhIewe+4XsBPSAgOe6l78DQlHKTljb2+4n2JpDWBsv4\nL2PkRDDdZjLPgb66AcOGbehrG9PeRiIrq5Hy2zkxn943rSTIVg74fV5K+/sif6tFkeKx7FI/JtvA\nQvB6MQHlwDLgJPDbppXI225CkWXkR77F6q/cTf23PoG7IxgDEK9nE8mAoZM6kDcgCDoEYRFjHUHL\nYaocQ5GHQ/V4MJRnvsv8mtZdbJUClKg1rENh48m3kpYNvpjRD1EBIfGqjESkVab0LkLC+0yKDJaT\nCzlkGJ8OFNwPq9HqOFM2uZLALcvY6jLrR1NZV4CiTVfypQXLCRAMnnm6ogHx5rsBCDzxv3x/++N8\noecE3z65jw0Pfx0pyhLs3XE4qWZV5PgHpkbyezGWQ8ni5zFVPU3pomeo31CFbaCfU6900PFqKz6X\njWQZMgxxznmjHEhatnzxYvQ1PwBGAR9qyzdp2FiftHz4HQySVqBk0WlQHQk1fQfl1uBLVmnWpffP\nZqBTBUFIqIufPjKQ9txCo6Aa9sCwB5/Xj/9Df8+zT/8ek9PFyMLFVFx5fUHq1+h0dN/3ELc9+2vU\nAT/SlbdjrKwFoOFMB9ELbi4Z6WP32DDm8uqIFIhHeIBVvMDG0PFOBBaiyDYMFR2c3l6J5L4chBFK\nFx+hankNjsFBTj/fgOy+E5BxDXyDFbdqUGkNeJ1jOIeGKaqpRaM3c7xmEZvb91EmirhkmYMVybtf\nQVCx/OYldO26H3uPgMZYjWPQR9nCqcE0fo8D18gAxvJqtAYzkqKw6KJlmKoexTHwS0oWWChvWjzl\nPAUh9UyYqMpqpYJGCnkLZhgpr5iLdwDAXFkNdwVzlWa6aiv8tqbbz0qrN8Jt/wBC7PTq2fIaXEyG\nLe63lGOwlEXGvBVmLYnMhKIolDVVotYfxjV4AK1ZxjGiR/JeHuoGSxg/baN8yTiDx8eR3Z8LnSni\nn/gnhtq+iiKbGNhzPorvNkTjyzRe1gvLt/BDvZnKsX5GzGV4lqxPSRiPzcZE+0XIrr8lMA49I0+g\nvq6V4urJccRwWw9ndjUju69DNOyk/qLTVC5tRFIUqpcuonopxK+1CFvZcKBMQghCQp9tTJHQc8l4\nWXoaeD0+DgyT9cBr5l+RFNBIARCF7Jc+A9xxL/9oG2FjdyvDehP7b/oYerWGvTsOh8gaC0mJ1bfF\n1WUUVwMIOIfsMYMNRTajSMOIGj9BrRmubxC1Xkff7moU33sBkF1307f3QSwNCt7G1fQ2htaIha6X\n7IUcbu1Bdn0y8rfsvYXRk5+NIezAAQuy+8PB4+7FDOz/DhVLMnc/pbWycwAFI+yBYU9eo7+MkWSA\noFJr8P3j19hBcIJADymkQOpLWBo8uIZPIQhLUBQ/upJjqLTlLNi0BGffV/EN/x1gw9T0G8oXLeXM\nrtg2KSk2lZajLFQ0efWlRhBOgxJe9DeExhzngw7EBpvL0uTfidxdYWRkZSFrWQD5u7eyxaywsE8f\nGUAjBTK3rFk4tBNZV0gyGxZCcV0lqI5j7zuOWuujankVgiCiUmtZccsCxnv/F7VWg7l6KYoCxuoW\nHB3DQAUIJzE3nAXSrzYNk1cUBSqXLmai+2c4OjehKBqMddupWxsbEG2qaWfC1g/UAH2Yak4DyyI+\n2ryQRBbIioLf40KjN0ZesLAsOBc6NmUswfGuwYz7j3wsbDRh05I2bulLNMKjYUVR2BOyrvGEDVvX\n5Ped6vrBc+Q4HSfLMmf2tuOz6TFVBag5L7eoLlEU8HtdIEtoDFMHXIqi0LvnFO4xA4YyDw0XLA5O\nJgT8dLzaiW+iHrVxgCVXVKEzFcW4gKYExyS8PSXGwjrbjrDh199i9fgQB0urOPTh+zE2WWN0rF+l\nztnC6vTahBp2ZVNVjrEEGSISOzDdSOMvHO7q5ciTZ5EDOig7S/N5CxOWS/WSpkM8WQEmevqxd1Uh\n+0qQ/aeoWuVDVGW/vkuWFTS65KvdBEFgwYVLp3zf/konztP/BujwjcicfP7fWXNrLOHDsbMpIQgx\nsmDVH3/Al/tPA3Brn4PP/PGH9HzqoazuKQyVNsFMpyggJvo+BQpmz/O1rhkjiXX1uRz85ccTuIaC\n3glB8xYG3fPUWJsiZTLcKTMpEpFVliXO7CpBsm8CunE5L+T4oy+z8o7zEXMIEJHl5AOzZPBNLADC\nPmYRnz21hU86+BJCvt6QGK5yxa7Dq3Lb6dVogn1Q+HRZSUzGOOiVqdpYVkQMCb5PhWkP4M4UGcmB\nFOhrPYVr6LrI34p/M+NdU2eLcrWuspx45knyOJFcdcAIcBvwHiT7Vziz51TK+ryOCbr/0kLv28eR\nArEve6IXIxoalRjzURsGiXbZqfWDwWCo+A9R0ZfRe4BFhWSa1GAUFYyiwqGGxYT7TidwpGFJ8Jgg\no1ck9IqECJH/p/okghjws6s/RdBSAsyIYh5rPYZ2/1/wi2o0V16HqTz7GIV0sNRUotKdRPLWhr6x\nozVNdoH5WNdkZAUQdSZQvQHSDUA7sBgow283odMkljBu2wRtT4lI9m8CPhy9X2fNbY2IGQ5gtHEv\n9soryzj63Nfw25tQG/pZehkY1GLUQCx48wJBz8mll61NrWPD+Psv8Xe/LqZhuI+e6ga0H/zElOJF\ngsxjB/q5fV1u+beyRd6ETadfJ063seL3P2dZ6Id4o/sUvk/8K1qdPrupvRSDLQBLbQOLr36b9ldO\no0hF6Cv3sHBz7Cg73rpqVBl2MKrk2rnr0DC6sjvw9pcBFuA4CAaKqvyokpCid99ZJPt/EqSQDu/w\nJxls+Qb1q1dEyggIMdJAViSOPnsS1+BSRLWDho026pYHZ88MxRY23hFevlk95WfydJ7g4tcf4X3u\nCfaW1GNf8yBFyYyGICCIIooso9JoUH3kMwwD5yYSeCoKYmFT6Vfp6IEIWQHW22y8cLqNquVrgAwi\nfyJWR0g5gBeA9TdfiL/kAOVGHyrN8pjjojJ11UG8pUqEcPesJCGfa7gOtbYMv34c2esD4U3KVnbT\ntGFFwvIAoiATJFf4+j6EBC9PtJ5te7MNW8f9QDDksHPnw1Q2u1IO0sK46I1f8+BYMHBH6T/BPb/4\nBt5Pf2dOTiIUhLCpRLdUWoZPUdCGBiAjgoChqjZ4jiAgakOj6STcMYbyQwWnSdOv1VKpVJj0U10l\nsqKkm31MimRkBRBFH84hF7JnEyCC4sM73p+yvqYLm7D1fgP/xH2AC0PNf1NrjR39K6GhURiecTNh\nsgLIrg24bH/GUhlH2NApkgzqUMfQ6LbHHK5zjHM6ZQtnL1ISNiOXg+hFn8JKLth2JS+cbmN5Wwse\nlZqubVdRW10FUYIdQrM+Kbp8l20CWZYxWUqTOsm3v34w4feZkHWoqwO/x0fV4kWo1cGXKN3gB6DK\nOsZEZyewEOgBGvHaUseZ6owmzr9dpPfw/ag1IvVrViRcpqKgIMvB38ZU4cTe1QcENbravAuTJUG3\nroRsd9RPtK+kGtk9hgi4gNa6ZvJNgCmqBOR83S45ICVhM3E5iGnarBJF6u6+hz6nA7VaTa1uqvVL\ntzHx/hfa6G9diqJoKGs8xub3WpO6jBpKDVkHaBx6ugX76TuBcnpLf8La2yvRhrraVNYVoKKpnO6S\nQ3iHLgRWADo0xkEgdVonjc5A88Y1Gbdx0ZZleN3/jbOvEVFjp3FTALU2s9RRbTfeywde+AVNrnEO\nVCyk/u77M75uQmQQLDNdmLF5NZMpt/ThfR099J/ciKgOxn2Nn6mhfd8bLN041YGeC4Z7TmM//TfA\nKgB8Y1/g1I7Ps/KKzNNcLr2imLaX/w+/sw6N+QxLLivMEEUK+Ok+MI7PUYZaP8HSixei1enJNpWe\nzlTE0Hs/wRDQM+ahMYP8ZLMV5yyW4LED/RQliaqPhtvuRRAmtZsg6vC6C/d2+z0+oCzqGxWypEWW\nlbTWNYyi8nLW3wHB1BO1aUoHoSgKra+34TjbhKh20rDRTdWiWIvZfWAc+8CNCIIKr1Ph9O5nsF6a\nf36qtIjyFMw25DVxsKvfjRhInpqmEKhfVo/WMLm8RFQdZMHKwqV+rGpehLb8f4GgDhcNv6H+vNyz\nEGaK02+3M3b8Xvzj/4B3+D4636jDH7fy12svQxBCOxoKIl5HfntCxCPXvRrOJWZFtFYq6AwGNt0u\n0bb7JRRZRfM6M5aKwhFWpdaw7tYqTu38PLKkpW5NMaU11RkNuPKBa0RPtM6VXFtxjv6WktqFke80\nehs+12QklkZvI5+UnjOBwaMHEY4dwmswUnXtzWg1hdvfAOYAYQG0aoGN/rcwe9z09S6D2m2RY2Er\nsf31gywoNeTkWRRUKqzbrBnPNBUChhIndkIhiYBoeBtTaeyLuGCdnq69z+BzVqLRT9B4wez2mw4e\n3Muaxx6hURSQFIVne7up+sdPIRYwv8GsJ6yiKFQ9/hNunBhFEARGzrbzR7UGw7qtedctKwqHnzqB\ns+8CBNFN2bITLN+WxZZDeWDRlmV47d/EObAUUe2kbsMEGn3sYk2twcDSSwwISIhidpZVQSEg55+P\nNhuoD++nMXQ9lSCwvOc0Z1zOnAfcCa9RsJqmCV6Xg9WjQ5Fkb+WiivLuNlwFIGz7WydwdH0GKEcB\nho/tZmTxS5Q3ZJEtI0cIgsDKa8KzccWhz/RCkRV8jgkmBgcpqqwsiOWb6OvC3dPOUNU2JJ0uJpjc\nptag0SaXBHIO3opZT1iNTs+gTs+i0OSErCjYDWayzKscObdj9wncE1pKG0V8DhXBLAchSCtxDP+R\n8oaCNP2cQkCIsa6yLNOyfQD72CZ2nrRQvugwG29ennRZTTx8LgfuU8cQLGUUNwVdis43nuSuJ37E\nZW47rzxRwcF7PstLJeWsGepjRK3lzNXXU5tGw26tyc4FOOsJq1JrOLLtRvxvPoPF66Wlqh7edVPk\nuBCaxATwuuwogFafuAs6+twJbO1ByzzR5qRk2S4Ezeso/suC1zI+RtXSdwBbE6Cv7SyukRtQaTSI\nmgpGOi30HHuLhaunLgmPh3t0EOvjP+Vij4tRReaZlReguvwW1r/2ODeGpn3fMzpM959/R/GPfsvB\nkWEMJjO1BZQCYcx6wgLoV23gcEklJY9+j9Lukwy9/Ecs174/clxWFEZbxujvC84cFdccYcnmmilT\nuLYuA3ABUAdKC7beQzRe/BpDrXtA9NG0UcRgSrEz7wxivG8Ij81LSX0JBnP+ngG/FxD0EM4IKRrx\nujJzSZp3v8plPg+IIlWIbD2+jzc2X4Veij1fG5BQqdRUZLAZXa6YE4QNeLys+N7/49/7OgA4wv/D\n4wAAIABJREFUfOoAX9UasFxxMwA9xzrwDL0LlRhM4mbrr2aw60WqF8ZNXUqNQJiQK5DdxbjGijCU\nayipn6C8IfaHFkL2e6bRc2SQsc6LQKhkuOMATRvPUFKVnw+2urmU0c5dSGxCUWQ0+l00rUoS8xA3\n7aqOD8tUFGRZZveaLbS/0cfigJ/jWh19l15FaV6tTI85QdiRvi4+1T8ZX3Se30dVy168IcK67X4E\norofoQhfgh3DRa0DORLT7UFQVeAZfxcA/cf70OjepqIx6FoSRaFgvlhFluna14LfA7WrazGXlqUs\nO97ViCBWhf5ez2DbICWZxrwLJEwrZCgyUbJhBOeZZ6ldWMOSC6vRm5JP8UbPco2dt5lD3Sc5X5bw\nyjK7mqzojWZ4373c17SctY5O1GvWs/LqGzJsZO6YPYQNZ89OAHNpJUdMFs5zjAPgBUaKSyMUbVhR\nx+HX9oNvEwCC+m0qGqe+6xVrzjC4/5cgW0G1E41lY+SYKNZiHxJIkVEoJyiKwqE/t+E++yWgmLFT\nP2f59UMUVyWe/FAUpqYOVfIdzYcmHowmbvrY2uiKMzrb2NDMW7d8lL0nD+MzFWFcdxEiIEsK6guv\nZsUMrTaAWUJYWVKCEVtJYCyy8OT1H2H4hd9S5fPwatNydB+4N3LcXFLMVX/nZ/sTT6MDapapMJhL\nYuqQJB9eWxOG6rVIPh+izogoDBKe+5dlJzrz1ED0fGXBRH8v7rMfJOy2klx303vgS6y8JjFhRZVI\nce1pbGcWg1iMwHEqF2exwDOHpiqKwom32rENaDCV+lh58aIpk7aG6nqorg+mh1KUcxJaCLOEsJmg\n6No7OPzuOwj4fGh0uinbFpVWl1O63JQwvFAUBEZ6hgh4bkSt06PWGYEr0Zh/ScBjR5E1FFf3UrMs\nNnClELIgGDoZiCKSAglSuUejaX01w+Uv43PKlNSZKCrLTL927u+g/1ANsmTCWH2cDTcvR8wgleaR\n10/Se/hiBNHMSJcXt+1VLrgufTScXck9FEVWa7J2acEcIiwEne0a3dSUmdGurWTQGTSg2AkmDQZZ\n8VFcY6BhhUhw5Jw8yiofK1tUWY+p4fc4e+qBClTmH9F4QfqN1yqbgt1spku+3Y4xzuw5P5Ljy9E5\nQcsb/8mqdwXDJHvG3Fx62Vpa/9JO30kjghhg6SaB+mX1jPZYEMSgwBJEHRN9mW9dVOjFh2KShZth\nzCnCxq6Dyg4lNVUU172J7ewGFEWLoWw3ddb0oYBhK5sraQVBYM2NVnoPfxu/O0Dd6kb05tSECC+N\nySY/gWN0FMV3c9Q3Fjz2WAvW3dJNx9trEMRqQOHwS0cpqbah1sYuJFXpUifcEEQxuCeHX0lLsGSQ\nSUxO2T4Blcl9DXOGsIokJ1yolynadrUxfHIBsnyQksYerJeuy3hqMl9pIAgCC86fXJToto/gGpug\npKYeVaJU92RHVoCS6lrU5hcJOD4equAEpXUBhHDqIUXB1u9DVFUR1idKYDGjZ3eyalsx+5/djsfe\niNbQx8pLVClzRCiOieDvoTEj28ezamcEJkuQnFlidhE2hacgU/SOuam3xAY597Wdpv/ALSBtAGC0\n9SADTU9Qu2RRxvUGSZt+yUw6dOzuYPDQFhT/cjTFT7LmBgfFFbEaVYhJrZIZNAYDK67up/2tB5AD\nJkrqe1m0aiGK10WPS+DSrVa6WnqRA0MIYgXBXWLaKSvVYyoxcPn7tbhdvej0RlSiAC5bwuuExwd2\nTe6zWLIp971ozylh7YoYWXWQzlOQDgIKl162NuFCxPE+e4SsAEhrmTj7f9SmTzCIJnrRmghKHi4m\nRZYYOroYxR/MUOO3/TMn3/wy5109aWWztazRKKkoYsMNoUxDwsIpx5tWNOAYf5v+tlJEdYDFaz2Y\nSoKySFCpMBZZpiSESwRZVkAFt6/KPWBna44zDOeMsLevq+GxA6mXQ8djUhbEPVQlnNlEAUVBJHbS\noLKphKEjb6IELgl+od5FZVNJLBmTwB+XXwoyW02bCAGfF0WKfUuic77mQ9Yw4t/5HlfsF6u2NLF6\nSwZZX2Yp8iLs1hoDu/rJaJlMQnEuxH4vRP5JASXyzyS8ztAhARSZnjE3DYZJ0lZWl9JwwbMMnNwP\nikjV0rOUV9cnJGMmyFXTqrU6DBVv4eq7BjCAai+lTaNAfU5knRge5UyLA1EdYNmFjahC4Xq+k/sp\nP9PGWGk1yrJ3se2i5XFnpmh7BgmN85ED+SJ1XoJMRoBChuVgqsiWJBRPFGlC5iFlct4UKYsEFC7d\namX7rql7Yi0+v4GaRcE9VvXmhrxSbsKkNcyWuOddU03b21/E7zZR0uCjcXlu0WETgyMce7E5lELe\ni73/39n0nlqUQ6/z7f1Ps1ny0S2I3G0bh4v+X+Q8gdwXFkb7t3OVA/noV0hD2IxGcTLIzuxHe2FE\nP25FCqXmSWVsZClE2mQFph6QFYUDL3Rj7303IGKuf5a11xRmDjZb4ooqNdYtTXl3/93HvJH9DoL5\nuT7GYO8PuOH0QTZLwZmxRkXmvf1H2ZdJhRm+d2H9mg9y1a8wC7wEdo2ZIn8uXXPwgdvHbBx6zYnP\nbaKofJT1VzVw6dZlbN91MiILuo51YO/6DOHJAUf3KjqP/AfN5y3J29KGkSsBpYCPnpYuRLXAguWL\ngruSZ3Q9PyFvZugbByqtGm/czJZHFS250ljXWS4H4Bznh03WrWSUuSVEtH0vuLAPX4nXuYWhrqs5\n/MaZKUU9DpngvgBhVOB1hgZq53ADYL/fw74/2eh96+t07/hX9j91FkXJrMtevLEcjeVBwA50YG58\nhPKaevaefzk/NpVhA17QGdl98Y3pK0vwc8uywvArT+L4w8+YONUyK+QAzAILG4+MMlCHZIGiyLht\nk/2LIGhwjhfhGBvHaB+jpduBdVkdDcurGG57GMl1FwCi4dc0WCtQCfln5Q5DURTOnhrF79FgqVQo\nrUn/cNr39uIb+xbBx2DGPfBpek58jcYV6f1teqOZDbd4OXvyi+gMGmoXNyEKArpFa/iGpYk/a0cx\nNC+jZEFwRYGAHCSdoiSegImzrtIvvsn/7n+NShSeePtF/u9D9yGsvSyDXyI18pEDUADCbi2FXVgQ\nc9Sxqpd+w/o3/oRKUdix5iJ0t34UCFrZdBm5BQT05gnC4zZFkUA4y1PfNuEe/gwIZ3H2/pgLrqhn\n5VXddB76N1Cg6XywlAejpVQCSKGtkuLh83oZ6xtDa9RSWpU8hhWgff8E9sGbEEUj42e78PvfpmpB\nScpzkEViBaEeyZ/ZGyQIAhqtnkVrYoNUet0i2664MJhUO0rP2198nK2vP0GR38f2xWtQfezzCCoR\nt93G+NlOLDVNGM3B7t7rcXFTyx4qQ6b3FucE23e9QM/ay/LyvRYC59TCnt6/h/uf/SXrQhlPrnrz\nT9xXv4jyzZdnbGXXXaXh8Ouv4nMZMVeMMt7nxT0c2qBNacJx+t3Yx1+lvKaS8hRxGoIQS1q308Wp\n3RYCvmtAGWe87g2az09sNRVFxjm8GFEMBkQLNDF+5gRVaRbfNp1XyXj3fxFwfBKQ0JZ+m8aVTalP\nYlLGxPtce92JFZ59ZIAPPvcI73EH3+xbD77BR59qhLqFXPuHH3C5bYQXSqp46Y57KTnvwmBqpLhk\nez5Tfst0CiEH4Bxr2LHW45wflZ5nqSJT9MpjWQ2ESipKuPT2aq782yI2X9+EIMSt0lT0DPlTv5fh\nBx+tZ/vaJGT/ZYiCBlGsZOLseXg9riQ1CCBOBowoKAhi+hhWQ1ER59/komLVp6lY/WnW3WxCpU69\nyjQdWS/ZEptXQUDB1d/LevfkwNYClIwPsfrF33O3bYSFwMfHB1n+wu8A0Op0PLfhcg6rNfiAH5RW\n03P57Xlb13zlAJxjwtZvvoQ/6SeXaewB7hzsYujkkWAyNlmJ7BWb8BPRXZPfrrqsFF3JH0Lf26hc\n8RIaU1lS6xNGPGkVJW53Q7RJR9GCIFDZ3IvPfRj72TEcvfsZ6RrB7Uw8Hx8NncHE8q3NLN+yBI0m\ndaK3dGS9OIqs0UVKm608VjG5vm2fRsfYsrUU+2Ojsor8ky+Z4Y6P88WP/hu33PIPPP+5H6NdkHu2\nyEJZVyiQJMhVx1Y3L+KNC7Yg7HgFEagG1gOK25ldA6KiDhsW13P1PQOc2P0N9GbYcOVGRLWK7btO\npq0mTAQJgeqFAZzDB1DkdciKB1PZXrSG5BambkkJXXtbIbABWEtg/AOc2P5FVl2upfe4FylgwFLj\niuha28gYJ17T4Hc2ozZ2s/RiJ2W1iQO1kxEVEpM1rKbCKeF1RiO77/48n3zmEcx+H61rtlCy5Qr2\ndhynb7CbWkWhS1RxcOlaojcHrVyzAWXVeiZUpllhXSHNTohdHZ0Z9827xshp4DXY1UXR5+7hI329\n+IFPN61g7BP/iUqtmdSx6QZfYd9jqFjCWxKEGN9sOkgKOCYcjJ7xodZK1CwqS+sj3fWIBdlzZ+Rv\nbflDmCpk/K5rEQQRWTlD/apdVC0oYe+T43gG/nWybOmXadropKSyHJ1xUi/mSlZitvOM+0FCf8qB\nAPaX/khZfzcj9YspufLm2GKywoTaBEp+gS6yyZIVYZsWLUz6wM+5W6uqqYmhB7/PB37zOH6NFt3F\n16INzYlnusmaIkuTpE1aKPiUet1iRqRVCWApMWO2hE9P/+5qLafweMLO/An0lnZ8zhsj2cJFoR7b\ngIGqBSB5ooO49+MbW0nbi3Wg3k/t+e0s2TDp2sqFrGkhS4iigOWa25CI3j0hDjNM1nQoqIbNVatU\nNjbxnvv/Bd59ZyjDdFSdsgKZztenKXbplqAOS6dnoxGtbdNNMqy+0oS58YvoK79LqfXrrLikCVEc\nnWyeIqHWBgeZ+tJOIDzgbCeYbn4lBD5J35Fr6O8YQyVMA1kz/SllhQmVKX3BGUbBJAHkLgvCeOxY\ncJASP1WbrTRI1+jtf2kDyFgehBE/yZCJ1e07Pc5g20IU2YLO3MKyzRpUag2SFKBlex8eWzXeMR2K\n/2IguD28oGmhbMnrrLl0avuyIesUSRA+nGYKNjyrla92DRuwbCysymiioaZy9kqCaNy+qjhC2mhk\nKw2S5JKI4NItSyOaFjInbrS1k5RYN1gy8tY2l1DdOIwU6EOtNUTOUas1rLk8GIBz9PVOxk6GN+hw\noDI4EASJ6LF+dK+Ql2XNIF4A8idrGIWUAzANhJVNFoaOHcDxo//C4nTQt3otq//+3qzm7JMGxMhK\nWisbnlBIR1oUJRIkk6mujUYq8garV5ADPlQaHSq1JhKrmqiONZc1cSjwRxwDQ4iaBvSWXqoXTRBW\nlomsKmRK1qhjGZA1eqCVDwrpyopGQSUBwI4Rmb4PXc+n204A0C+K/OCue1j14b/LuI7Hjtko8juQ\nZQlnywEESUa/Yi3a8BLvFKQVCPFalWzjzPgTsre06XD6+Fl69y9A9tWgtRxg3bVm9Mb0O7/Yxyew\nj7opqTRjLDIntaqQGVmDL5ESJF+WZM3XuuYy2FIZTVTYutEv25CbJFAZTUiu7HyitrFRNp7pjfxd\nI8sUt7VkVQeATWWg9s8/5M7RAUQEnm3dz8B77p4kbRrIkpQZaRUlZ4mQ8LqKTO++RiRXcPWqd/gm\njr/5r6y/Jj1hi0osFJVY6HWLjIbGY7lZ1ShkSNbo8ueCrJmi4DNdVzVZaKuqjvztBSYqqpOfkAC3\nryrG33mCm0b60QgiKkHgeoeNwJG3s/IayJKUeRaDkESAYBecjSchGn6vB8kXvYpAJOBNEwQTQvR1\nL96yrABkTb+gMFI0bF1DkCWZrlMn6evtyexa4fNCUiDR7vapPiqjiUp7d9pF02k1bLZWVqPRYLvv\nS3z/oa9TbLfTtmwl1n/8Z7JNgHFpvQafwYTOG5vkIbLuK8YxHoTjmd+ycd/rSILA3k1XY77qvRFL\nCxnIsihrC8TMjmVqdXV6I9riPfhGrws1tIvi6hEguYsoVdcPmRF1tL8XWZIoq2tEFEJxWhl6A4CI\nFLhjtQVfwEfrZz/J7ft3Y1OreeHqG1nz2S+mrCsMGbioDHzj6aelo6HT6iPnpOpDU2pYz8m9ynBx\nE1IOi/V2jQl5ubgCfj+7vvN9bhwbROd28bypiLM3fQS1PpjNJOI1CJFr9OAuvvTLb7AuND++XWfk\nux//CqXLVgfLq1TYu07R9NiPqHHaOFy7EOWuT6NOldI8AXEBGowyqV5At8PO8TdtBHwlWKqHWb45\nNgKrN24l6yVbp5I0UiIQINWrpigK0s+/yceP7EArK/xg5YXwsS8k3Ls2Hn6bHVBwWqpAgVuagi/2\nscd+zZcf+TFhj3iLSs0fvvoQzavPS1mfWB3MvXuhJju+6KqqKTl7PPJ30cbLz51ba7DjFN5vfY2a\noQF6auqxfOErlNekz3Kt1mjY+ql7+PGf30SQJUzNS9BoJt+9SLxsyNKqO09GyApwidfFt9qPQoiw\nsiSx9JHv8LUzQR+st/80H9Ub4cP/nLwRoZf50ihCbd91kl5XfDrMOEIJRVRfGp5eNdE7pYNSuHRT\nVKr2QLLNpRX8jskIMb/fj2nnc9Q6xhkwl2DfcjW2o2/z0wOvUxsi9XeO7uLOl56gdOvVye8rqv54\nsgJonA6ip2/qpADukeEM6suerNkiJWEVWaF8vJNhSxOSM7uGbLEo/EWx4H7oP/nssWByC2Wwjwe+\n+5+UPfBfGdWhUqm5473v4g8tdtQ++xRfpyIpiCoxaGWWrGbXmwa2hsIVXzIUoV2+LqKJpEAA6/hg\n5Fwd0DTSR1+KncgT4dILE2eL8TvdCb9PhlTlhSiLGi17inY+y/vPdiIKAoptjN/vfA67SqQqqnwJ\noAlHiWW01Ajes0CMkQeWSy7nj9tf5tbhARTgp81LWbhxU8qlS6qaejaq7GS4wicCXXUNlt5jGXvR\npt3CVo1OvpkCUDE6krJ8suwvdm0RlkCCl0ZRQIDSVet56Pq7eHXPK8iCwNGt12JZuCzy0FSiitaS\nSgjJFC/QaalC5UwW45oeSoHGrEKGj6tuYiSSD0wQBOomRhm5/L18d8/LfHr4LAA/LK1GWHtJRvU5\nLVW8Z8HUe6hqXkL7/V/hqy89h0+joubWD2HQJ/dyqGoy21U8Hrrq7DMfZkxYlckcsbJihknZLiqH\nX1lXIfV0ogJ8QG9TM6tTpCTyjk7VvTfXwZ96ZMYFA4bRxNliRJWIfv02Tm7YBoKAHvDb7TFljt/y\ncT7zzK+oddk5UtOIctPfIgUCgIBKrcI+PID75CF0TVYs9eki/4WMiVYoDJmKUZz2iNUdMhVhtJSy\n6877uWv7k4gojG65lqLK9F6ZZGQNo3bJclgSn4AjOTaq7ekLJUDJmWNZlU856PK27YscHCpqRA7F\nqXpHM89Yt92uou+7X6O04xT9dQ0s/Ni9aDWZ+VLj8aeeYH+TirTB/2TmkQg8+TBXHf0LAM/UL+G2\ns6e42T7Gm3oTP7nqA5guvjbJmVPrn9j9Kub+LlxNyylauyWj64chKzJvv9TG2NkijMU2tt64CE2C\nwaDX6aDyzaeocUwwaLLQv/UadOYUPtMkXXg6smYDVU19TmQNW9dEhC3edEXyrBOpCGvb/Urk4Hj9\nKgC8A9nlwwLYEyhC6p+6/DoXZERagbRZEEcP/YX/fux/WCQHNez/AX8TdfxbVY0cue+7Cc6cWq/3\nqV/xwK6naJYlDmh0/MeV78d42c0Jzk2MHU+foOPNjwKVgI+qFd/i3R9ZMbVg1LNSMvGvxhHWWVIV\n0awO2wSdLz+LotWy/N03o1Fnrw7DUiBXwiazrqkIm7KV0atWS/uOM163Mu1K1sQVgaq2HnngbPbn\nxuGWJhVPdEm4y2owjA1MOS7LoSyISuqs3OrhvghZYeoe2Vo5kMTtOfXLLSf20hwi0Dq/l/OP7qZ1\n201TykVDCvgJ+LxoDSZGuisJkhVAy8TZhchRF4+2hbKU2USAIDDZ0yix3gDb+BjeL36KB7ra8QEP\n7NlF85cfRJ0FaWNdWNlxIuLGyoFLKfsFJbyuKvxRQFtZPfX7NJ+NKntwSjvL85J9wt2Zu7Q6aHXi\nPuENIwSU5LMrKy7g0aLJ+cMWnZG9IanSLqrZtXxj0FAzabDDMbHxn+jsKgAelRpREJJ+AqeOsvYP\nP+G6x39CxdOPoNYOxZyvMTgQQw9HJGhNw59MZ46CDzA4Kxgha6MIikLv04/zz13tiAQT6H/i0F5O\nvrUj4W+Z9ANcqHakWHCX+KMLz4KmKpcCWfUDJWeORaRBttiotrOnpr5g0uA9C0T+1CMHLW0CeRDJ\nNxu2VHESoaiukd++9594a/dLAHRsvprdfj/Gzhac1QsovmBbpNsVRBVTsiZG1ff25nfzzIu/YZvb\nzlNFZRy/6PopFjsMRZY4b/92LlQkUKlpso8xVH+UJ53/hXtkNVrzac6/2hMpmzOUYNBLtAwI/xSK\nIMQk3w8AiGKSHmUqIi6sHMacihLkUa7D1Yw1bBj5almgYKSFJJo2jpzi5LKBglwz0XIc20Av3u42\n9ItXU1SefBc4v8/LJY9+nxVRdTxR3cjYu27G5ZjAoDfllRofiLykzuJgO+IHWE6HHduXPsW/dJzE\nAzy4YQtLPv/1yBR2KuSrWyG9Z6Agg65ojNevyomwMEOkTUDMGP/uNOTTSrumLArm537LrWODiILA\noCzz+AWXoV26Jv2J6RAmqiUolVJ5AlwuJ+2vPo+o07Pi8nfPGrLCNBEWcrOyUFivQRhh0gIJB2Nh\nFNra5oKA349m/3aKvR6GapvQLl2df6VxVvWWJlVmSfUyRD5khdRegXgUnLAw+6xsGBFrm4K0MDuI\nWxDEERXgliY1oBSMsDNJVkhN2JzFUsmZYzlNrcHkjec6pZcKtzRGeRBSILL1ZNSod04hqt2TVlUd\nImvhUAiyFhJ5352uuiYnS7tRbWdPoAhVAT0HYYQf2hMESZvM2kbvlxojQWeB1VUUBfa8xsqeNvyi\nmpZVF6BZGhveF03U6UChyJqNddUVp15antdwNNt54HhMp6WFyQfpLq3OyOLOJqvraTvKHe1H2RII\ncKnPw+X7tuOaGEOWFJzFVe9IsmaCgkwo52P2Z4K06YgryXIkdDFMXFlK7CyfESgKRtso5iiz34CC\nV56UOtPR/YeRL1nDyJasumITzh3PpiyT9x2HJxNylQYwvfIgjIhM6ApEHrp+tB/t7he54Ew7XkHF\nweXrUa3cGDknVjIIU0lbKOmQ4GVwVi2g99RhqkLBLYdVGm7Y0IjJNL0RoYUga7aDLEgvBcIoyN3n\nMwMWxkyQFmKJ6xk6wx0j/ehNRWidDqqP7ebJukUYS6ZuXizHpX1JSOA8EF+/smoTf9IZWNh+hCUl\nAoYt2zDlmVQ4HQpF1qzPCZE1nXWFAgZwh0mbq5WFmSMtBIk72DHOKhO0e8BnMlMmSfhVmf0k8QTL\nF/FS5ZYmNTSthyvWF/Q6yVBIsuaiWzMhK0zDioN8pAHMLGlVi5bRcWgfi/VB3+1bWj1i9QLcutht\n29P5dHNBIi09XZo0FQqlV3Mlaya6NRqpA7hb3lK8tuwSaeQ7CxZGrpMLggBkuNcVwNDJYxgPH8Sv\nUsFF2yipqo05/kRX6jVfyciczisB003Q9BMH55qs2iRSoPxjX8ltpisXwsK5JW22hM0XqQh9Lizm\nJFITdjaQVZFkRl7485RjDfd9I7cAbueOZzFdfB3ZkrYQgzCYWXmQK84tKSeR6YZ02RLVZfMmPWZZ\nvABZltG3HiSbpZyW2pKkZE2HjH5tXbEpZ9Lma2XDP+ye0A89W4mbCTIlVS5w2xPsWhNnYY2Lm5El\nhTXekawIlgiWxcE9nfStB3M6PxeyQgaEDVvZXEgL+Q/Cwpgpazu9pPInumKetaZNLIpxcXPk/2u8\nqZfZJ0JACvDBe+5OevyN732XE13dfPr7P8buSvwqbFq5nAf/6eNYaktyJitkaGHDpM0WhZhUiEY0\naSGxtQ1K8txJl9BSFQwzH6MQJmsuRA3j14//PvJ/nU6H1ztVJry2/yAWsylCWL1Wi8c3+Vtev2Uz\nltrMkuKlQlajk0xnI6IRFuOFitrZqLazyjWMLCkIlXW4bd7Yj92L2+7L+fNOgXHRQoyLm1njHcmL\nrAA797wFgE6nZ8WKlZj0enSa2ATNH7zqCmrLgtubNtdW88J3vsGnP/C+yPGXD+4HcpcCYaROVRT1\nf0fIymqLTfgykAbRYl1r24/HuhZNZRUT7dmlb0yGNd4RjujKIxbE1X46q/NlWaLzz7/BMjyAY9Fy\nmq64oSDtOtcwLloY+X++RA1DCq3UFUWBgwcP8KW7PszXHn4kpozFbOJIRwcAl61bC0CpeXKr+reP\nnMibrJCGsO64EaL72Scov+ZmNCYDE32ZJ9OAoDj3WNdiWbygoKQFYonbkRlxu3/6Hb64/UVMQPeO\nl/mR08bCmz5YkHadC0QT9TzfGKAUJC+NZfECrr3hRh79/W9xu90YtFr8fn9kGXqRcXKSxesPuvge\nfu5FfvHsCzH1eH2F6b2ydliG35Jc9Eh4RBkeYRYK0d2ecXFzzMNLhuWtxyIZWxslibpjuY12zzWM\ni5oi93uebyxE1sIg/JzuueRC1oQMgtvn44H/+22kzNMP/kfk/2Gf/p3XXMXf3RCbNSfTl0dvSr3X\nbk4e9nxMu771IPrWg1gWL5gW4p7nDe6LZVy0MCVx7YbY6VeH3pCk5OxD+N6MixZGSFpIokKs2+qR\nF17iSEhylZrNaKIWLF736ftjzhMEgb+99mp2HguOXcoMmaelSkdWyCMeduSFP+c16psuawuxliYZ\ncYduuZNfVdVwQKXi+02LkG79SMHbUWhE38t0kBSIMST61oNMOJz89Kng1GlVaQn/c98n8EtSJIui\n0+PlzUOHAYJpQBWFBx99lPbuoAfn3osyWw0cJuupJ3elLJf3NI2ltiRrPRvGdOjaaERAd5H0AAAg\nAElEQVQ/0MNRpHV1dFK3YSvjq9bz5MQoFeWVVCbYlmg2IP5lmw6ShpFoMuDsyOTAbXBsnL/596AE\niE6l9NOnnuWS88/DoNPi9Hh5Y+9BRBRMWjWLy4JGLVVWokzJCnmuOMhHz4YxnZY2GomsbtnK5dRW\n16GZRWSN7u7jrelMkxWgpmwynVOx0YjFFMwTq9dOdt+3XbYNgIaa4LKdOqMOrySzubGG+5/dCcCS\n8sT7dmVDVkgT/NL77c9lpJXLrwlm6svV0gJ4rGsj/8/H2goxyaXS47B26v48ro7OnK+fGlPbZVw0\nNQ9tYYiZ2TLvRER9fvceHnjkNwBcfeEFuL1e3jx0JHJco1bjD6W5FwWB1/7nO+w81cLnH/pJTN1a\nUcAXasMrf/+eKddORtbNz74yvXscjLzwZ8qvuTlveQBMq0RIhHhyHNaWJh2s5UPkVAPA6bScyRDd\no02JB4hJ66nw+Q9/iIce/SMv7N4DECErwGPf+SqW2hLe+O/np1wjTNZbV09Ns5+tZQ2jIBY2jEJY\nWsjP2mZrYTNFIkucDWaelMktbL6BK5F6QlIwW69ROrKmsrApCTv8oy8qHmd2Dt9CkRYmiZsNaaeL\nsHMPUwmb0qpmiekiK6QmbNpBVya+sWgUYiAWuXbUgGy6B2XvZMS7qmYzWdMhpYV967orlCU3bQUg\nW0sL02NtIbXFnbewYSgUN09uIZovSSHWCE0nWXOWBG9dd4UCMFtIC+mJO09YsDQ3EI4lKARRIXer\nCtlb1rwkQfSFspUHUFiJALFd2rxUiIWluSFEVtC3HZqTZE03ys/IwoYxmyxtGPEW96/Rwlqiu/62\nQ6H/5Z9uMx+iQu5k3ZKvJIjGkpu25kTYMMqvubngpIVJ4to6ev9qCBttTaciP8LONFkhSNhDu87w\nD+MnciPsPOYx2zBzC/jnMY8CYJ6w85hTmCfsPOYU5gk7jzmFecLOY05hnrDzmFOYJ+w85hTmCTuP\nOYV5ws5jTmGesPOYU5gn7DzmFOYJO485hXnCzmNOYZ6w85hTmCfsPOYUZscWKHMEVqv1I8CngMXA\nBPAE8P9aW1ttVqv1y8AXAHeoeB/wEvD11tbW/rh6FgLtwA9bW1vvnZnWvzMwb2EzhNVqvQ94ALgP\nKAY2AwuBF6xWazj/5O9aW1stQBlwC1AD7LNarfG7zH0YGAXeb7VaZ09irzmAecJmAKvVWgT8G3Bv\na2vrS62trVJra2s38D6gGYhJ3R063gLcAQwRJHk0Pgz8K+AHbpzm5r+jME/YzLAV0BGUABG0trY6\ngeeBqxOd1NraKgN/Bi4Jf2e1Wi8B6oHfAY8RJO88MsQ8YTNDBTAcImA8+oDKFOeeJSgRwvgw8Gxr\na+sE8BvgWqvVWlGwlr7DMU/YzDAMVFit1kS/Vy3Bbj8Z6gnqVaxWqx64nSBRaW1tfQvoIU5SzCM5\n5gmbGf4CeIH3Rn9ptVpNwLXAG4lOslqtAkGNuj301XsJDth+YLVa+6xWax9Qx7wsyBjzhM0Ara2t\nNuArwP9YrdZrrFarOuSaehQYBH4dKioAhI6vIKhTq4HvhI5/GPgZsAY4P/S5GFhrtVrz3036rwDz\neQmygNVqvYugH3YJwUHY68AHW1tb+0N+2M8TtMQCQe0a9sP2Wa3WOqATWNva2no8rt6ngeOtra2f\nnal7mauYJ2yOCE0i/DtwUWtra+85bs5fDeYlQY5obW19mKBF3XqOm/JXhXkLO485hXkLO485hZTB\nL5u+8lyM+a0oM3PBgsT7LQEIooDOkDiHbG2JgSZz9vllo9FoUeMdiYkjQaNWYdaqkpyRGhbJwfCx\nfRmXF0URrVpALQgIAqgEAUEQEEKJIgVBQJGlyOZGsiQhIIAi4x8epO9g95Q6hZQZUQVAQVSrkO0T\nmd9YnhBF8I7bZux6hLIsuuzBrJibn3k5v4TGsxUadW5ELSQURUGJ239rCgWF3H/mmSbruUKYrOmQ\n9S+5t2d2/Xi5Wtd5zE1kRdjhUUdeF+ty5J4IebpQsWpDwepSFAVBTPwCaSqqqD2/sWDX+mvFjEmC\nvnF3+kIZQFdeU5B6ACZU5qzKa9WTXb+igJTCw6IAYtQ27Qhiop07Zx1mXL9miYIRVki1XXOB0D0R\nSF9oOqGAOkE6+kQ6dh7Tg4Ja2GQegnlMYl4W5IecCDvbBl6zCfE6VlBN/sSaiqqsZIE4C7wg0w5Z\nydhDADkQdrYMvPLxv0ZjQmXOa+CVTsfGIAf31l+DSysbzKgfdjYOvLJF9FZC8Vyd17HTjzk3cXAu\nB16p5qSyicmYLTrW7vFw/FQXbT19KIqMOAfYUJC8BDPhITjX0GlUqFHIRISGdawiS0BQxypScDmY\nprIG/1B/qtNnBKMOJ47dh7jcH8CJwq7BEdZcsAr/hP1cNy0lCvZOzXUPwXTp2NmKgfZuNgckREGg\nSBCxDg4z6nSd62alRU6EHR51vKM8BdlOIERjrurY+Faek1bnsLXojKuWvnF33p4CAQGVpapALcoW\nyfdwzUbHplNRolqN7Cj8nrxhVC1u5C2ViKwoOBSZlqoyykzGabteMmTj0oI5mlvrrFOioWh2+ygj\n/tgkOrb6fBKGG84Uyswm1FvX8+rZftRaHWsW1CDOgU2l5yRh5x6Cca2zDcVGAyuXNJ/rZmSFOeDI\nOPfQadJb8+iB11zRsdGY7UEvYeRN2HeKSyvdjJc6hYWMlq6F1LG5QFFkbB4vipIoq9LcR86SYHjU\nwV5gY1PJnHdpZQ8FRc7wZRVVIAfJE61jAarPb6T/YFfBWtXS2cv4rgPUe7zsLTKx/Mqt1JUmX9I0\nF3FOJEEhPAXjXvCaygvUoulBrLWNJbemKvn0sqjOzY6Mv32Yu+1OrvEHuGd0gpO79udUz4wgy6CX\nMOachtWoxb+KZTGyPXuXltnrj/nb5Jl9KzzyxZwj7GxF/AqETAdeQgFdSWfKiglTdhSYqCwtWN2z\nBfNurThUrNqQ1dLvRFAUpaBEzBTbrr6YH725F7PLjbvUwhVb12V0Xrqgl9ODowwcbUMvSTiqK7j4\nvGUFaG1uyIuw7wz/wCQmVGYsUmy872TQSzyyGXiJoYGXMGXgVUgYNBquvHxLTucmc2m5/AEmdh/m\n7x3BOIPOkQle0WnYaD03/tu8JcG+ntnvu5vNqM4z1NAb8DPsdBEIzagVGl02O5sck0ExCxUFaXT6\npozTIS/CjoznF91TiNUHs91TkAphT0Gu8qH7TD/Dr+2m5I236XxjDyOO/FaDJEKd2cQxvS7y9wTg\nyzfmIIeglzDO2aArl9UHGnVsc8e9hWpNYZDLwCu+hKhWZ+whkE52slFWaFSpuNTnZ+R4ezbNzQgW\nnRb3Wiu/LDbzqMnAz5pquWh1/ho2F5cWzMFB11xxaU0ZeEV0bGgCQc5fx2oDsasvNIHpkQVrmhug\nuQGA5dNyhcwx79bKE8lCDZOjcEPV0VIL3pBFH1EU/O9AN1Y85pyFnQmEXVvpg14yWzKTCQRByCoO\nAWDVhlW8eaIDjduDUGZh2cIFWV93rgS9hJEzYcW5sGItB8S7tlIFvRQKgijkYKlBFERWrlgyDS2a\nvciLdVp9MOhl18nhgjTmnYLogVcmPKxek71l/GtF3mbS5srPNTUbMxrmg5ShhpFeSUAI/V9dNblv\ncq5BL3MKOQa9hHFO+/VsXFvxLq0w5kLUVjbIJejlrwlzSojOVpdWLvozHoIovPPmuqcB7/g+yOv1\n8POHd+B2w4c+sJbq6kLvQ1w4T8FMYy6Om3Mi7FzxEPj9fv7mrpfZufseQMufnvkFv/vlMurq0i8R\nTx708s7A8bMD2BwulpdbMGnmjt3KmXlhD8Fsxquv72fn7g8R3GVT4FTHXfzil4czOtdiXZvXtdN7\nCiYHXjONl1/fzdKnXuNdr+1m3ws7GXIUJknfTGBumMocoVaJCEL0iFTJqBvMJxMMTM0GE4Nz3Dv1\n2x0sPtmJi6CYucjuZPe+IzNz8QJo/YL9etPpi03mIUiHd122nqsue5Rg/L2P1ct/yD9+fGNB21Yo\nCKIIzolp57Pb58erKGwCmoF1QI1t5nJq5ePSggINumwuH8XG6ZUIuXgIRFHFz39yHY8//gR2h4/b\nb9uM2Vw0Da2bO1hYZuElswHJ4UYN9IgiZUY9kqygmgNL9s+52g77YvPdJdFrKkfnHJnyvSiquO22\nS/KqOz2yWH1wjiEIIvUb1/D2/hZKFRmDQYe3omxOkBVmAWELgXEvlOjSl8sUOo0KZijWVgzth1C1\nqp6Bt2cmI+SqZYto12oY7h/Cq9WwJBQ6OBeQNWHniktrNkBSlMg2SbKSINOLIICioK6qJjA4MKNt\nW7poAd6yuZdkIycLOxdcWucaihLkY0JEBXNPgSAgikrSw7MJ3YMj+MbtKAYdS+prZkQSvSMkwVyH\nmGS7z9mM9t4BrJ1nqBMEHLLCDqeHlctTrKTNM+gljNSEndGJnsQXmw07ducDJbw1fbrpW1meNMmK\nMutne7XDY9SF2msWBcrHMtDfBbinOSFIZ2vQS7ZQmI1ZYnODP24s489hD7JcMCcIO1eQa9SW0+3l\n+YN2nmsN4PH705+QJwoxbi5aWMdbKpExWeaYohBonJm90wqmYW0uH7tODrN1WaGjoWYWmSQvToz0\nUVuJBmIOt49//YmE2/5+hnvt7D3zNJ+9uBudWlWImcykyHcdV2WxGfcFqzhmd2Ex6FioL6BfMQWy\netdElTjjHoLOk518/8Gn+PVPn0Oapuwm8VCh4NUVY7JmlpsqEyjxYiBk5l7eM8yY40oEQUAQRIZd\n17Gre2buM18Y1GoaS4uxzBBZYZZ7CU4eOcW/fXKc/r6PADb27f4V3/7J+89JorVcoCggyaBKYRZU\nooxCNEEVOsc9fPbFCtSCj3/YOEZDsWHa2zqtKGBXMSs0bDDB8VTt9vTvO+nvuzH0VzE7X9/Eme7O\nGW3bdOPdW2qoK3kGSfYhSV4Mmsf42aH38WT7j3n81M/5+DMbmXBnPu2mKDKtnT0cbznF0Cxavu0q\n0Nq9WUHYZFD9/+2deXxcdbn/3+fMnkkmk33fk6Z7S2lp6U4pCJRCkSsgiMoV5KUgigv3gopcBUXl\nh1fuBXGhqHBRoFqBgrRSWmhL9zbd0qbN1uz7JLOv5/z+mMnaLDPJTJbSz+vVVzMz3+85Z+Y85/k+\n32f5PIr+S6OocKFSX1xBC5VCyU++pODuFW9yx5wXMKg7aXN+KfCpQJX5Yd6vcAZ9vJNHSll0ppK1\nNY0oDx6nvq0jMhc+QZjUAnvHfXPJyXsVcKNQnuf69aWkpGUMOnY8ihE3vl3FXT8ycd9Pz1PbHD5B\nUCmVLJkWy9LcKIw6F9Cb7qcUakgNMj3X4fGQ2dpBdMDFNEuScZxvCNt1TgZMahs2MzeTlzbp+dc7\nr5OaYWT1tbdG9HzDeQhee7+KH7y0BrcvHpAoPf8WO583oApXaXbALL9vkYZDjd/iQONXUYhWrs/d\nyKrc4GL+giAw0LDyDTD3pxrTy0AE/WuLw+0cIoiExETuuOeGcTufYgjX/tb9Mm5fOt10aGfrOygp\n286iWcEzr3h9PrbtPYdKKXDN4sLezoN9NpEqhYKXbrZQ0fEjtAqR9Jjgqx+0SiWVWWk01DSQJAgc\nVipILMzB6fVSeb4eBIHi/MFXqKmCkNTDpznpxeOzAkU9r9XKXGqbBRbN6j9OluRBk0DcHg+f/c9q\ndp18CAE31yx4kTefLEA5iBdfEEUKE40gSUNvsId4f+asIupSEim12clOSUQhClTtOcxVDhdtdgdv\nHzlFXEEWiwrGRqQ8UZjUJsFkwr3r4zhetYsOSy5KhYvizBryMwZWLwwePBAQeOHNMnadfBLQIgPb\njjzCK/98invWzewVvkGKwUpOV+A6ehqd10d9UhzXXLsM5QjJMpmJcZDoZzIsPVflF1azlaROM9+Q\nYWd7F7utdpbPGwfyzDAlvXRj0grsZEt6Wb0wk2/dupdPTrWiEGVWzqtkfnFwHcW9kozNCf7q3W5E\nY7ZJ4LL2yvgA/7LZ6UR54DifD7i1rOcd/GlvCWuWDd2xcSgINgdxsj8vPRtoq2uG8RBYCGsiz/AC\nO+7++f4nnExJL6IgcO+GZO66oRUREY06acQ5fvnza92vrM9i867/5VzDg4DMrOxf8aUb8qFvi80B\nGrbRYmdGHx9sNKC2dte7Bof8vEx2NLQyExkPcAhYAhwQxUE1erg74Mj+g4bteJNWw05W6NSjC0Om\nJRnZ/LMOXtj0KApR5jt3ZmKM0YPDMuScHGMMBwx6ZpttAJwXQEwOzXWnVSopWHYZ74oCCWfPc7vX\ny261EqmwP2OiV5I5e6aCxC4rblGBnJ9OVtLk4ywLq8BO5QSY0Se9BI/s1HiefjC+t1RmhAbGWqWS\n1DVL+O2BE+g8XmzpSay+bEbI59UolaxbsZD6WUVsrDhPTlI8l8cZ+o0pr65jjcmCWhDA5+NQeR3O\nOCPaSWaaBSWwojj+SS8TgaFcWsGivLadb/3aS11bJllJdfzPwxrys+LHdMyCtCTy1l81pmN0IyvR\nSLJi8OVe4/b4hTWAFK+PVo93agrsJQSHR17wsO/M9wGoa4PvPf8kf3s6uLkOt4et7+/CaLbRGRPF\n8jVL0GvUYbX/hoNgiKG1rYukgPqv1GnI0oxNSUXi0qe0wHZ1mrDb7UheiWhDNMaUiW1K0WLq7zVo\n6UxmqJ2rIEvg8tumblMnb/xsI1+vbkAF+NpMPL9tD58Jk2YNBrnpSZyQfKg7zLgUIkl5GSjDUFTo\nsIS3Xj5ogQ1p4zjG7xlMx+633yjh4+0JVJz1ICqaKJqWyb99rpl1K0bevQ+FwXfHwX+ZgvTznKnz\nAQrAS0FaDQKFQRkaSZ0WVIG/FUBi19CbsUihMDMVMsencmC0mJIatqGukU92ZtPaEo3Xm4zs8dLa\nWsK29/WsvsyKPnpsZG6jxfPfS0X33OPUtqSRndzAsw8H37vApNUAvULapdNG4AqnPoYV2PozVWQM\nV7o7QTB3WpFJQfL50w8FQYnPJ+D16nE6W0MW2HB5CKJ1Ubz4HwUICgEoGlI3S7Jfi4LQU3kwe80S\nnv/gE5Isdtr0OopXhpe0ThQFXMFUtk5yjKhhu5leIm36N3YFz7FVUJxDYlIJDvsCOk1mBKEeY1wS\nefnlxCeMLkauDMGMcbpdaFQqhCEqRWWfHDIFfFqCkbTbrx9zp29ZltlztBTaO/HFxrBy0Zxxq9CQ\nZZmyqjpiTBbcCpGovExiBNXIE0NAUCaBWje5XFoqlZqvf28a294uobjOjFIhkZlr5LbPzUFwRy51\nrtNi48tPtlF6fh7G6GYevauZW66aXEkkH+45wmdPlZOOn2T0FauNa0fZkj5UlNc1s6SxFUPgQf7k\nTBWe4kJUYaS3mpI2LIA+Oppb7uzPkq1SARHsovTD3zax+9RPAJEOKzz16ovctMqHIkTmFhG/FvV4\nvfz1lxtJqqijy+Um6vLZzB+jCWZsbCU98Hc8kNx0IaNjpKC02XuEVQYynG7qPB4SNOErUpx0FQej\nJS8eD5isRvr+ZB2WLGyO3vKVDw/VccO3O1j1dSc/eOEMktTN+uIPffaDy847f9rM3XuP89mWDu7p\nssK+Euxj5CVwDnD0O8YhgtcNr06Lvc/3rFcpMKgmwCQYb0ympJe+WDS9gw+O1OCVsgGZ6ZmHMej9\nVJUWu50HfpVGa9d9AJyqaUSn+QU/vHce4C8O7N5qdUPR2klUn9dFDhfNFjt5AVZBWZYx2Z2IgCHI\nUurEy2fz6q6DLLQ6OKHToh9FKHe0KMxOY5fLQ2yXBbdCAclJYTUHYJIKbLjhdLr4458qqG+MJj7O\nzhe/kEZCQuhUkw/dXoAk/YYDpxOJje7kyft7k0Mqalto7bqrz+g03vw4igdvs/DlJ9s5W1dIoqGN\np+6zcfUi/6KtLsqhbt8JMgNUhUcMehYb/B4OSZY5vv8YxW2duIDSjFRmzp024jWmpyRgvXkNp+0u\ncmJjiNGoESPIKuj2SVSer0fpk4hOimPGtBzAH+UKd9AAPiUC+3+vVXDqzGoEQUGXGV56+UMe+a5f\nYENxaQmCyMN3dlcd9I+q5WUkIooHkaTiwDvtdJjd3Pmjavad+SUg0mSCR3/3Kw4EPFbX33ot/7Da\nUZ+ppM1kJW/pfNSBJf1MeQ2r27vQBkqTjA1NnE9PIitx8GieLMts276P4hp/0eH57DSKr+7dbEXC\npeWTZSqPl3G13YlCEDjdaqJpZj6pRsPIk0eJYQVWECafh2A0aGmJQhB6BbOtvf8PGopLayjERkcz\nN28/JRVqQAuYsLlWcuhsG33t3jZzCpLP3mMcbPjSzTRX1fDu/75O7aFTdBZmMX9aHrLbg7aPZoxD\n4LTdby93OZwc3H8ctc9LSlEexdlp7DtTyW0VNT0brvkVtbyXkczSCHb7brU7mG2z92w6ZwA7mjvA\naIhYKvXk3eGEEckpNmS5l+MgMSEyrq+3fj6d6xfuAqxAKnAjXikO6O4fK1OUXo6o6H14bM2tbPmv\n3/HAuRrura4n56ODlJytJiUzmeN9/KcH1SqyUhJweb0c2LKDB8qquL+8lqgP93G2rhGrxUpan2tJ\nBWxmW0Qd6FqFEmsf0ZRkGY9SRJYjd9oRTYIJIQUay0kDc9979yhP/rIek0lAq6nEaDxHUkIaSxcb\nuPsL6RH5Ynqtjifvz2DXyVSszjWBd69lYdF30aqnkxjbxrPf6J9uWNHQyrUdXT2Xs8Tj40RVPYnT\ncmlaNIePquvxiQKpBdlEqdXsOVvFzA4zJwJf9RqXm9+dq2HmvGLeKqtig91vN76t0zCjcHgfsdnl\nobqji3RjNImjCAUbtWrOpichNLQSK8sci9KRk+1/bCJhv0KEbNgRyXuHnAcxmrF5CATgaEkFDz0S\nj91xO/AaXZZ7aW5roLyyjqL8IyQmBrlzFghZsHPSk3lgwwdsfK8VhzueJdN38H8/no9apUYhxiIi\nITttPclE8bHR1KoUFHv8K4AHcKr9tyU1wUhqwGMg+WRkWUJ1voG5QDJgBo4BHpWSjDgj59ZcyW9P\nlPnnzp5GZvzQ2WvnmtrwHDzBKpuT4xoVJXOLmV8QfO5DN6blZ9GRlkS7x0uBXo9SIUQ0I/Ki3HTt\n2FmJ3fFA4FU0cA4oxCcVs+VfsP7GSq68Mj9i5//e3YU8eFsHTlcjicaiYccmx0bz0ewiHKcqSPJ6\n2ZUYx9plCwYda/V4mSn5aDdE47NY0cqwXa9l9RVzAShKT6EoPSWoa2wvreCr/spIslwe/lRWDaMQ\nWIB4nRbGia9u+E3XFOndNBDFRUZEsRxJKsRfJ6oA/BstlTKTT/ZVceWVoFYqOHTkHE/9vA6LNYrF\nC008/V+fCUvsXa/TotfpCMaaW7X8croun43F5Wa9IXrIzOcolZJalYpp8bHYDHpMPons3AyihnHO\ni4rBk140Pt+wr8OFULXtSD/9lNawXq+PkgNnAJh/xXQIBBzWrVvE/Ue38ee/7MHhakaS3EAmsTHt\nzCqO6SGZ87jdPPSdJsoq7gXg2Mk2EuM2872H1wx6vkgiVqchVucPDsi+we+yQhBRzixkd1klWlGg\nPTaG6SEwz/SFPTWRig4zBbJMG9CYHM/M0V78IOgrqMc+qQ9qzrylGSMK+JQVWK/Xx2+eOUR97TJA\n4JOPd/PoY731+o//YC0//L7/22959xRvbj6NXldElK6MG67TA9DQ2EhVzeyeOZKcSOnZyf2TZKYm\nQao/ST19hLHDYdmcaXyk0/Kv9k7kGD1XTQ+PieQI0GoGK6R90T1nuFSdyX13hsH+XadoqFuFQuFf\nDhvrVnFw31GuWtBbptK9tK+/cTbz5zVTV7cfrU7kyadrMXXVM2dmOxlpcVTVLAzMMJOf64/lq11d\n2MpKwnrN3UkvkwULC7NhBE/CYJAkmYqWdrweH9nJceg16h5BBTi6qy6cl9kPk0ZglSGSzfl8cr9g\ngCAo8HqGXk+yslLIykph3YbtHDn+NQAOHe3ilhueJz5uIxarnkXzm/n+d68b1fXLksw/9zTTZlYy\nt1BgwYzBS91lp63nb3e7iZaSCv/7ssw/TorUmpNJ0LbzuTkO3F4fez46iN7pwpucwPLLZw2Zgzte\nkCSZM2cqWd5hJkqAvQ0t2AryiNGqKTvWGvHzTxqBBUJq0Lt4WTH7Pt5Jp2k1ALHGnVxx5VyQrD1j\nZFnmtb/uoqXFxfobZ5KRnkB1TV+NEovNnsmHW5b1jDebzcSMosTmxb+1cLT8RkRBy+5j5/nyuiMs\nvyy4HTvAX0qU7Ku9C4WopVzyYnb/lez6zXyjsQ0lUF/XzDs+H6sWz79grhzgNxgPYW61O5jd3kV0\nQMEs8krsaGmnwTQ+ojSpBDYUaHRavvnoHHZ/uAcEgeVXzUGj1YC9V2C/8a332LzlbiQ5kdfe3Mxv\n/8dBSnIjHd2BJzykpfnrqKqr6/nyAyeoqComOfEY//14NEuCo85C8vk4XpmHKPid76KYw94TZ1ke\nQk+PalMOotg9X8n5zjxWdJh7blAGoG25MLf1o30lGMrPIwDtuRlcvXxhz2cNbR1Y2kxIAuSnJIY9\nCcYt+49XX2Ui0Tj64s9QMGUFFvxCe/W6CzUOQEtLC+9vvwxJ9i/NdQ238Mc//56fPpHGT57+A6Yu\nAzOLm/n5E/5S6u//pJSjx78OCJgt8Pj/28i2nwd3HYIgolS48Hh731MrQ8trjVL1dz1Fq7swq1Xg\n8tuGMmBV93dfHa+uY8Wpc8wKlNVUn65kZ1I8i4vzqahtIPt0JZdLEp2yzH6bY8TIVzBIitJxIs6A\nodOKBpmtbXa0xt6gcLvThsdlwS0oSFBFh13rT2mBHQ6iICII/W1aAZklS6bx+qsZfPBBLZIvF4vV\ngS5Kh6kzGn8OQDQgUNvg4NHndagUbj57lYX5xUPzTAmiwPqljby5swxJSidWf9GpLIwAABccSURB\nVIxbgqUUCFziHfMa+d2B92i352DQNHL77AqsKbN45cAJMlxuThqimb+0f0Chuc3EbX1qwHJlmbKK\n83CiDMnmpFCnAUM0RkEgvs00qg3WQIiiQF5OFvtiOqk400psXDrRSv+D1Go3M8/aQY5PxinLvCt5\nMGrDy8910QpsYlIiN13/Pq9vLsbnSyUnaxNfvS8Xp8PF07+opcuyGhGBA4f3cduGesorW4B9QBtg\nJEo3D5PFbxP8/q0jPP1AGzH6qCHPd+3SZBbMKKex7QSFWXHoo0LItxUVpBrUPL62DpenGpUAgqCC\nxAK803LpcrpZq76w6HFabgb/Ki3nWqdfC28CVta1cD1wFEhzuWlTKkiI0uELUyGiw+pGEAScdV4y\novuHfqOcdnLxP4NaQaDQ56RJllCGUcsOH+kK22kih+bGZkxtZvIKs0DdP4HjmZ9/hhXLttHQYOOm\n9XPJzExh1+7TmLqWo1QIIIPdvpj/eOIFmtv+s2eeIeanrFzyFRwt/mXa4c6nrrmSGflDCyxAYlws\niXGDC6pSkOj7i7rbTYOO06iU/SpnlaJIQpQWaZBgQk5iPMeXLeDXR0qJN5mxAusCn6UAtYDP5cak\n0+LMCNIgHwbdrquhvAE+oX/Qw0X40wFH1LCVJWfInx9Z4lulQhxV0sv7b51g59Y0JLmAuPiT/Odj\neaT2qSQRBIENNy/tNyc6Wo0s2UHhz/OVZS82Z//yE0N0LH1JLXSaSrJTx56U3NelBfS4tMaCuQU5\ntHRZuPPQKRrw87+uwB9UaAI+yErl8vws8qOHf9hGwkjCCiBHx7HL5WS+JNEkQI3KQFyYbdgpmw/r\ncjj5+AM9CmUeKlUsVssytr5XM+K8mTMySUt7D4v1PG5PG3m5H7JysQJ/7hOAk8ULfay76hjxhl2k\nxn3IV2+qQx81thseSczMz+ZtnYYsQAW8JAhsjIni4GXTuW5uMWa7g90HT7Kz5AxOb+g5A8EIK0Cs\nSoNVl8y72nhO6ZKJUw+k1B87pqwN6/V58Hl1KPtsnD2e4Z+/hsZW7vnqMU6eXoEh5gj33NnOY99Z\nB8zFGPsXysq15GTZePLxa4iWHazNKwHU+AumIwQBGGMP3XSjAeuaxfz+WJk/RXN6PvOLcnCZujjX\n1Eb03hK+7vLgBp5r7+TqqxaH5POu6eikbv9JjLJEhVJLXnIOqiE0p1pQkKSMXOrWlBXYKH00RdNL\nqTiXiyiqQTjHFUuGX7af/sVxjp/6GiDQ2TWH1ze/zmPf9aFWq/nxD9f2H+xyRO7iI4BpGWlMy0i7\n4P226gZudQXCzcB1rSbOdJqZFh/cptBucdG47zgPS/5jON0eftJWR3HSxBCITFmBFQSBex5cxI5/\nHsBqEZi7IJ5p0zPBPjRxhN2uo+/Gx2qLx253oL7I2oH2hVsU+/W2MSkUxAT5fR1WNzaPj6I+K4AW\niPeNjTthLJiyNiyAQiGy9sZ5bPj8XPKLM0ccv3aNkijd8cArNwvnn8RovFDThDvxRTnAH+zpGL/+\nr/PmFPK/xhgagcOiQEleBmnRIy/Z3XZrXWk7pYpeu6sL6FAOzpFgN0emLKYvxkXDWkwmjm49gi5a\nxYLrl6HoU4QXatLLWHDH7VegUR/h490HSYhz8/ij147bub0OWz+C4KE8BGaHixOVdSTHGygMsQHH\nYDDqtFyxdgnbmtuJ0WlZFaQpAP5NliCIEJ/F050NGCUfNSodRQljSWwcGyIusJ0tLbz8H2dpqb0f\nsHBm/0vc9eMbEPsY7f1dWmP1/g4//5ZbLuf2z4lhKe0ON2qa26n/5y5ustgoV4jsnF3I6qWDl8uE\nAp1SyWUZIyfiHKmqw9ppIc5opLBPB5kkbRSk+hPFx6mz15CIuHrb8+ZJWmrvCZwqltK9N1F76lTQ\n82VZor21BZttcEZqWZZwOR3EqkE7jP06FXBu/3G+ZLGRACz2SSScqcbp9Y44byDEIRpvDIddJWdY\neeAkD5ZVM/PwCd796GTIxxgPTMCma/CqSr1GyZwkfb/33G4X37lvE4cPzEOjMXPbF7r42nev7/m8\n9Fg1m/7PjMNuoKiwgwfujscQO7zvTz2JyebUUv8E72hJwu3zoR1Fx/BQmV7i6lvID9yYy7w+PnF0\ngTH49MjxQsTv3tJ/m0Vy1h8J5BsxffFbZM0Krnpo4/9sY9eOe7HbVmLquJE//yGfqvJe22/zX824\nnEsRxdk0NS3nL2+MHDiAsTG9dFmsbPqglCOna4cdd+b4abY8/0e2vPJ3fH1CrcNtuPTTctmj8gun\nBTiZmoghjFSVw8E74DeZOD/A8Ii4ho1LSeErzyg4svU36PRKFq67Lmg+VVOHSN/6YYc9h4a6EvIK\nC/D5fNhtvZ8JghBwW0UO/9p7ikd+E0Vt+2NoVZXcu+5vPHHfhWXcxw8chV/9kbvNVmzAn8oq+fz3\n76fbvm4+WtHber4PFsws4KRGzYuVtchROq5fNPuCMZFCS1IiH5ttxACngGpBSawsoZjgCoeBGFZg\nZQhLHoEhIZHVd64deeAArLo2k3++vRdz15WATGHxNuYv8uftKRQKsnI6qamWEAQRn6eL4qLIsRl/\n+1dneXX75wL5tf/A6bmTv2yv5nt3NaOP6v+gNGzfw11mfyK5Hph/4iyN7Z1kJw+MmMn9/gOYnZ+F\nnJPeU+8syzICIPcZZHW6qa2uBwHy8jLRqgeWeYdOZmHoNGPBT9CxAZjusbOjs4WUuOC6yoyHSwsi\nrGFt5i4+eOkgTnsU067Qctk1/Xe8Fya99P+Vl66ay49+fpBt77yGSu3ia99ZhF4f3TPunq/P5q03\n9mC1qFgwT8m1S6ZdcIxw4PDpal7b8XkkeV7gnRzgn7i8Mbg9degHsEh4Ff1XEKtKSeoFQjXCdQ4h\ncTaXm6a9R1nj9iADO5rbKFi6oIf1cDRw2txEu91k0usF0ANpXjdtLgc2j5MYlRajZpzYMoZBxATW\n5/Px6g/3Un3qQUCkdN8x4MgFQjsSrr5hEVffMPhnGp2W277krziIVYMQBi+B2tV/s9LaYeGltyW8\nPgt+YqC5+FNM3KycvYe42OILjrHgrg28Ul7D+voWapRKmlct5ApDeFox1dQ0ssbt6TEpVjlcfFzX\nxPTcjDEdt9Ql0l3o7QHsooJGn4d5Xc3kyzKVgsAJfTxJ+shRaQaDiAjsuYOlbNvYTN25RGAz8Fnc\njnmcPXCEy66JxBnDi+4olyzJPPe6Ao/vLuKjXXRYC4BT6LWlfH71hzz1tcsHnZ+ekYbhvx9nx75D\npKSnsj7f72gPJsIlj8TAIgr46N0tewCxj3ZVhBiIcdrclB1rJTcpm02tNRz22ElGxKqOIlqWKQAQ\nBAqAWkcXXGwC63M7+MevnXQ03R94px3YBqxFq5/YhBK1UhGSh8DhctLYkY1SoWDFbDWna08To9vG\nU1+VWTjrimHnGqL1XLV2FUpBwuvozYNtPjq2HNii3Ew+bG5jqdmGT4BPjAbm9OHTsro8HCurJFav\no3CIZPJuOG29Nr9aVDA3JQ9JkmiVJXSCiLa9HvrQlIrj0/Z2WIRdYO0dLXQ09S0XTUAQa8id+TzX\n3Lt0yHndMHX4l/W4+PDWAo0GOo0Wg74Fu3M6KpWS2XlRLJ+jYOGssWfvBwsB+rnFlAqRGYvnc6S5\nDVEUmZuS0EMY0mqxU/reTm7tslIrCuwqyGbFguFdiANzXEVRJDqgv1t0MdTbTGQIAvWyTFtUDBPt\nmQ27wEbFJ5OQVkJ7o5/SUhBaueoOK1ffc32/cOxAyLLM499+k+3v+91Ea6/fwRPP3DpuTdEGgyAK\nfOVGE69u3YHVoSc/rYE7rws+N9ZfFjN6nK9vxlNeg9InYUtJYHqATkihEClMv/Ch2bvrINd0WWnE\nT3hRXFlL24z8Qblf+2rXoZAcbeSQUsU+twuFWkOKVj/ouPHyEEAEBFah1rHhm1Fsf/Vl3A4d+XM7\nBhXWgUkv+7ftZ8vfb0GS/M/wlr/P4oqlH7Hu1tXhvsQh4bA7efm1Ss4dlUiO6+Dfb4phVmE8PysE\nfwOwwdlchkNfcyAUmB1OdCfOshhAho7zDZyM0lGQfWHOK4Db6yOz00K3Ve0E3vVJaH0SsiwNWm4d\nDFNLolYPQwjqRCAim67ChTMoXDjyuL4urfZmG5LUqzV8vhQa60ff0Xr79hKOnWhn8aI0li0LLrL2\n4sazVFesxGrqpKnDx+/+8Q7fvCP0SFNrRxe/+VsTgggPrjeSaIxG7jSFZL+2mi3M9kkQeLDjBQFf\nlwUYXGC7XG4KdBrq7U4yJAklUKrTsKC0gnafj1aDnsLp+agUip6N1lTEpEngXnT1LHa++TZNDTcD\nkJ7xFteuD446ZWDiy/Mv7OLZF5Zgt88iJuYwP/juPr5495IRj9PYFNOjiQRBQWNbEqH6dU1mK599\nTOJ07dMAbDv4DO8+acMY0lEgyWigSqlgfsAf2ybLqAZ0Z3H7JCqq60CSSM9MpdNoIEOp4LTVjkkQ\nyNKoWRbwOni6rOyoaSAneaKt0LFh0sTdkjNS+eULydxw8yvccPOf+cULKWTnjZyUPRj+9rYWu30W\nABbL5byxWQjKsZ6dbMbS1Ot6io8JvVXQn7ZUc7r2Ybr55k/XfpuXt4ZOPWnQaHDPLWZ3lI69Og3H\ncjPIy+qNOnl9Eru27GDWRweZu+swx979CGFuEUfjDFSlJ3O2MIf5fYIVKkFA6/ZnCExV7QqTSMMC\nzFkwjTkLRm6eNhL8edLd7hgFQkBLjuTS+vfbk/hl1Xu0dsSTFG/i3ptD/3n8nMRO6Olx6ESv7X/i\nfvV/w/hds9OSIC2pn5egG7tLz3FTSzs5gQUgsaOLl09XcfUcfyAjTZaoMlvIdfvTE1slGY8ueFvU\nJ0k0eVzoFQqMyslTQjSpBDZciI8rQ2AzMmo06mruuj24nb3RqOexewyAFxhdifK/3zydrQee5OOT\n3wRg9ez/5p4rh26OMVq0dJhJ6GOtxAI2cy8RniiIxM8oYHt1PRqfhDvOQGZcXFDa1eZ109pSzU0+\nJ3WIHNQnUBA/uO083rjoBHbr1kPsP/JFZPwZ8j7fedTqj8bt/Cqlkk0/K2Dr3mcBuP6yNESrecwB\ng4FYOKOATeXnucMnoQDeAfKL+3cCN0bpMM70/w7BuLG60WBq4r98/h6385GQbe1UGxKJVV7YS2E8\nXVowQQI7MOlFrwnfZZwt78DtLuh57fXlUH2+19vgdrupq28mId7A+/86y+//7MbrVbLhBieP3Dc4\nE2KoUCpFNqws7nFpRSJAlJ8cT8eVl/Hc8TMIkkz0tDxSNWoOfHwIUZbR5mcxO6t/plWwtmuUJPXb\n3GQicdLnHVRgxxuTRsPOSQoPs8pnrp3Bxlfep6nFX5mQlPAB16zxC3BDQxu/fM5EW/s03O5K9hyQ\nsFi/CEBZeSlzC0tYMXH1dSFjekE2C6bnI4oC9R1dOLfs5D6nX+Pt7OiiUqsmPyk+JO0K4I0ysMdt\nYRkyPuAdhZYs1fgkko+ESSOw4cK0oix+/YtSXn5lI7IMX/h8LHPmzgHgjc2tWK2r0GqgvtGDxWqg\nW//ZHRnsO/IhK9LDS0kkdw5O+jbo2EE2YINtuFotdo5t202x2UqpRk3U4rk0dVl40Nm7PK92e3iu\nsZX8JL/9HopnICcmng+Bj+xmbKJIZnw6ijC3kR8tLjqBBVi5ciYrV/a+7k56cbt7v25SogG1qhq3\nxwqcQCHq2b1Tw+L0NlYuCD2iNRzqD5f3K/EeK07sOcw32jv99QseLxsPniDlyvmUiQIzJP8DWAfo\noqNGHSTIiYmHmAhSNI0SYX1sYnRqlhWPljpcGNW/WHUw4/y44nIRn+TvcKLXKVl3zRky014m1pDE\nZbPVpCWu4o3tqUgRarIWLsS4PP2K2Q0uD3NyM/iwKIe/ajVs0qrZlJ/JFfkXdjZssZiQW2uRWmtp\ntXZe8Plkx0WhYYMt7169sgCdrpoTpZUkxPu4ed31/PqFCkrPpKJXu7A0deByG3B5GtEpxtbzNpKw\nJcfT3NxGCn4HXG2cgTmiyPL5M5DmFSPLUCSKF2jXVqeNxfZO0gMJRVX2Tk6pNMSNspJgvD0EMAEC\nO55ML4Nh8aJcFi/qfX3ZXAUnS5uAOGTZR156OTrt2JZClSDjddh67NdwmgMAq5bM4x8KEXVrB1at\nluUrehPJRUEckkvE63b2CCv4Keb3e5yjFtiJwIRo2LF27A4nVq8sQBTLqSpz4U0s5fZrQo36D49h\n7VdpdOmHgiCwItAQeSgMZruq1TpqnBa6eQfPATHq0NvOTyQuCpNgrFi5vJC1i7qwlbWN+Vj+1kJD\ne15DVbaSJNPucKJXq4hSDX67BpbFDOXGStBGccRrpMppQUKgS2cgST11tCt8CgQ2GKaX0bIVmi02\nHv51M+ebM8hMauRX30okwRg+1mmTzcVzBwppsV2ORtHCTdM+YUXu4FrZ2dE/UWcoz0BStBGi/avI\n+HTWCi+GFdhI9zYYL0SK+O0bzzbz7oEfAyIllRKeZ57gL0/6BTYU/ysM7oP9++kkTI51qBUCMmls\nKbezLPvwsA3iQg0ShIKnNM5+9rFKDQ+29b6xySBTO5iFIcPDreG5CZPDGzxFUd2cRe9PKFLd1N+N\nVH+4fEzHd3p1/UqE3FIsniDs3kikD/YIqwyqwCV4RPhDfK/54+izNdH6QNH9kQCeMAWoJ1xgByOB\nmypIi2+ir72antCESghf5sC81HpkuQEAWfaRG3sczTB5vZHSro30Cuv3XVoecWt5uMX/IFn6rNFF\nTv//2S74WrvAQ60CcQGSrs0htC0bDuNqw462vdFQmGiKzWcfiuGhZ39CTWsmmYkN/L+H/GHd0dZx\nDYRoOss89xNUUsTsfAMbprsZScdEQru+F7hluiGUewcy8Qi0qi4cl+ADkwosYbrtF/2mK5JITzay\n6eluN1hv1kyo9utg2Lm3hGuPlXGPLGOmjD9oMlDOWjbk+Ejarq6AVTKUsLwfC3d2gS3wLJXpoEzX\nf6VJGFsBcQ8m3CSYaIS7n0E3uu3X0fpgdQ0t5AXquQxAZsvgrDF9XVqRKn25IsCpbBlCWrrNAp8w\ntEC5wrTxvag1rFo5uajh/XxYwdm4zgG26sDXfdFZG9karYVo2RqwY59RO1nsho/7+MS6zXatDPku\nMHqgQwktKrAGLrszTKrxU69hw4nh3E3DYTCXVubCOfzRoOc08HetBsW84V2MkS4sXBLYULlE+FhL\nzyYMICVgjWhlvyZdYRe42SxwX7vAnEDVjj1MkhY2DTu2TK2LB56mxrAcJy81idRbr+O0qZM0Qwyx\n2sELAbsaxqeF0tVoudoJb+GkVQG3mODFwO2+2ep/UOO8UD8gcDbLBSeiCVun7XEzCSY66WU8oBJk\nPLJvzP7XbuhUSqYH0fpoPMu2b0aL3eSiGvkCIcxzwwE9NKhk0j3+D/8e2JMqwuTtG1cbNtwurbFi\nIBfsVIOluTNciusCdAcKil3wb7KWn2qcyIBWAmcSPcJ6Q0vvnL8GioNfN4JKlunb+vfOMD1TE6r2\nxlp8GA4fbCQ8BGOF5AtOHQlAVVVkKUy7fRly4IROBT3CeqUZivs8Mv0iW30k66pOSAzTozXhXoJI\nRbkmor2Rp6mxnzkwsktr9Otkt3YNtb1RsPi+Szvk66EStx8KU77AcLioDcvJ59K6ECMybg+DSGvX\nyYiLWmDHE6N1aY0GluapV4sVLnxqBTbsG67mhvAebwRUVIQnXyFUTEQdV1+Mi8CGO+klXAj3hqv+\nUFlYj3cJF2JKatiJztIaPUbeZI3kIbA0d06Ydp0MmJICOxWgCMJDIA/C6hL88T+dt06QQ+3xeAmX\nMIH4dD6mlzBlcUlgL2FK4ZLAXsKUwiWBvYQphUsCewlTCpcE9hKmFP4/Bf3gN5Y1S3EAAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0dabe4a650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#!/usr/bin/python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"\"\"\"\n",
"=====================\n",
"Classifier comparison\n",
"=====================\n",
"\n",
"A comparison of a several classifiers in scikit-learn on synthetic datasets.\n",
"The point of this example is to illustrate the nature of decision boundaries\n",
"of different classifiers.\n",
"This should be taken with a grain of salt, as the intuition conveyed by\n",
"these examples does not necessarily carry over to real datasets.\n",
"\n",
"Particularly in high-dimensional spaces, data can more easily be separated\n",
"linearly and the simplicity of classifiers such as naive Bayes and linear SVMs\n",
"might lead to better generalization than is achieved by other classifiers.\n",
"\n",
"The plots show training points in solid colors and testing points\n",
"semi-transparent. The lower right shows the classification accuracy on the test\n",
"set.\n",
"\"\"\"\n",
"print(__doc__)\n",
"\n",
"\n",
"# Code source: Gaël Varoquaux\n",
"# Andreas Müller\n",
"# Modified for documentation by Jaques Grobler\n",
"# License: BSD 3 clause\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import make_moons, make_circles, make_classification\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.lda import LDA\n",
"from sklearn.qda import QDA\n",
"\n",
"%matplotlib inline\n",
"\n",
"h = .02 # step size in the mesh\n",
"\n",
"names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Decision Tree\",\n",
" \"Random Forest\", \"AdaBoost\", \"Naive Bayes\", \"LDA\", \"QDA\"]\n",
"classifiers = [\n",
" KNeighborsClassifier(5),\n",
" SVC(kernel=\"linear\", C=0.025),\n",
" SVC(gamma=2, C=1),\n",
" DecisionTreeClassifier(max_depth=5),\n",
" RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
" AdaBoostClassifier(),\n",
" GaussianNB(),\n",
" LDA(),\n",
" QDA()]\n",
"\n",
"X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
" random_state=1, n_clusters_per_class=1)\n",
"rng = np.random.RandomState(2)\n",
"X += 2 * rng.uniform(size=X.shape)\n",
"linearly_separable = (X, y)\n",
"\n",
"datasets = [make_moons(noise=0.3, random_state=0),\n",
" make_circles(noise=0.2, factor=0.5, random_state=1),\n",
" linearly_separable\n",
" ]\n",
"\n",
"figure = plt.figure(figsize=(27, 9))\n",
"i = 1\n",
"# iterate over datasets\n",
"for ds in datasets:\n",
" # preprocess dataset, split into training and test part\n",
" X, y = ds\n",
" X = StandardScaler().fit_transform(X)\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n",
"\n",
" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
" # just plot the dataset first\n",
" cm = plt.cm.RdBu\n",
" cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
" ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
" # Plot the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" i += 1\n",
"\n",
" # iterate over classifiers\n",
" for name, clf in zip(names, classifiers):\n",
" \n",
" clf.fit(X_train, y_train)\n",
" score = clf.score(X_test, y_test)\n",
"\n",
" # Plot the decision boundary. For that, we will assign a color to each\n",
" # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
" if hasattr(clf, \"decision_function\"):\n",
" Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
" else:\n",
" Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
"\n",
" # Put the result into a color plot\n",
" Z = Z.reshape(xx.shape)\n",
" ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
"\n",
" # Plot also the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
" alpha=0.6)\n",
"\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" ax.set_title(name)\n",
" ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),size=15, horizontalalignment='right')\n",
" i += 1\n",
"\n",
"figure.subplots_adjust(left=.02, right=.98)\n",
"plt.show()\n",
"#figure.savefig('graph.png')\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
TimeWz667/Kamanian | notebook/2.5 LifeS.ipynb | 1 | 28980 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from dzdy import *\n",
"import matplotlib.pyplot as plt\n",
"\n",
"da = Director()\n",
"da.load_pc('scripts/pBAD.txt')\n",
"da.load_dc('scripts/BAD.txt')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bad = da.new_mc('BAD', 'ABM', tar_pc='pBAD', tar_dc='BAD')\n",
"## Key command\n",
"bad.add_behaviour('BR', 'LifeS', s_death='Dead', s_birth='Young', rate=0.2, cap=400, dt=1)\n",
"bad.set_observations(states=['Young', 'Middle', 'Old', 'Alive'], transitions=['Die'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mod, out = da.simulate('BAD', y0= {'Young': 100, 'Middle': 100, 'Old': 100}, to=20)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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LsCQk9HtcYmCr99bz1GdP8coXr+AJeNqtd1qdLS1apiZOJTEikeTIZJIjko35UCuXWHss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\n/wHXITiaUdZv+gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2359f597ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"out.plot()\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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
GustavoRP/IA369Z | dev/.ipynb_checkpoints/DTI_open_01-05-17_GRP-checkpoint.ipynb | 1 | 599298 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Openig DTI data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This JUPYTER notebook has a demonstration of how to open DTI in nifti format."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## importing modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: Qt5Agg\n"
]
}
],
"source": [
"# import modules and libs\n",
"import io, os, sys, types\n",
"import numpy as np\n",
"\n",
"# image and graphic\n",
"from IPython.display import Image\n",
"from IPython.display import display\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib\n",
"\n",
"#import notebook as module\n",
"sys.path.append('C:/iPython/DTIlib')\n",
"import DTIlib as DTI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading the data\n",
"\n",
"In this folder there are 83 subjects data in nifti format (.nii).\n",
"\n",
"The dada for each subject is a volume of (70x256x256) voxels and composed of 3 eigenvalues, 3 eigenvectors, and FA volume."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('evt.shape =', (3L, 3L, 70L, 256L, 256L))\n",
"('evl.shape =', (3L, 70L, 256L, 256L))\n",
"('FA.shape =', (70L, 256L, 256L))\n",
"('MD.shape =', (70L, 256L, 256L))\n"
]
}
],
"source": [
"#subjects folder\n",
"BASE_PATH = 'G:/DTI_DS/original'\n",
"\n",
"#subject number\n",
"# subject_number = 84 # very inclined subject\n",
"subject_number = 1\n",
"\n",
"if(subject_number < 10):\n",
" subject_dir = str(BASE_PATH)+str('/subject00')+str(subject_number)\n",
"else:\n",
" subject_dir = str(BASE_PATH)+str('/subject0')+str(subject_number)\n",
" \n",
"\n",
"#load DTI\n",
"FA, evl, evt = DTI.load_fa_evl_evt(subject_dir)\n",
"MD = DTI.Mean_Difusivity(evl)\n",
"\n",
"#print shapes\n",
"print('evt.shape =', evt.shape)\n",
"print('evl.shape =', evl.shape)\n",
"print('FA.shape =', FA.shape)\n",
"print('MD.shape =', MD.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization\n",
"\n",
"#### FA\n",
"MD is a 3D scalar map that shows the difusion assimetry for each voxel, so each one is associated with an intensity value.\n",
"\n",
"Inline image of FA in three different viels (Axial, coronal, and sagittal viels)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:19: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:21: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x8fb2b70>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IQ4sCMVm2TZs2ceqppwLw4x//2FcFRaNRX6EEQQDWbDZ9YBSJRIjH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DrR7Xapa6empjAxMUE7I9eRynE8Hsfg4CCdXSGzFefz+eefx8DAAJN7Q0ND2Lt3L53ZkZERPPTQ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\nx4/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KpXDWWWcBiESnWq3GIh+FzJOI0+l0YNt2rGxRKcWiUb1eR6fT4eOvX78ezWaT7yYt\nLS2h1WpxGeIv//Iv47HHHgMA7NmzB6lUisU1pRQymQznGmQyGZ4fIPqhMjU1FSsTdByHhcjzzjsP\nu3bt4h8onU4H7Xabr5NhGBgdHeWFtFwuw3Ecbrbw4IMPxspbU6kUBoMBXytd1zE5Ock/2hYXF9Fu\nt2Nlj61WK1Y6O1zm6Ps+PM/judV1nc+FSnbpPUaZYsM/6izL4m5mtLCS+PrqV78agiAIL0X+4z/+\nA7Zt82fjmWeeiUKhwJ93f/d3fxfb/qtf/SpuvfVW/hHUarXwlre8Bc888wyAaN0hx0Kv18NDDz3E\nmS+apmFpaQmf/OQnAURrkuM42L59O4DopstrX/tavol1/fXX4/HHH+c18LzzzkMQBBxcLYKYIAir\nHbqRe84552BlZYU/Dx955BHouo7zzjsPQHRTfnFxkX9zpFIppNNpFonCMEQ6nebfS+TOIkdvuVzG\ngw8+yDck9u7di9nZWf7NQrnK9L3+7rvvxj333MPRJSsrK2g0GvzduVgsolQq8XowNzeHK664Au94\nxzt4/LZt8++Su+66CwsLC9w05cILL8T09DSvRQ888ACazSYH6b/hDW/ATTfdxHM0NzfHv3e2b9+O\n7373u/w9v1QqYf/+/RzfIwinOz+NQ+x41NPfAMAH8BUAJoBvAXjnT3UGwnNCH7A//OEPY86edDod\nC5bv9/soFossZExNTUHXdbbF1ut1eJ4X65yo63osNJIEJsqRIhFl37596HQ6LL6Nj48jk8mw6JPJ\nZGAYBu+bul8Ou4o2btzIJRuWZcU6LebzeXQ6HV4MyuUypqensXXrVgBRqUm73eb9ZTIZ5HI5Pl6z\n2USv12PBLZ1Os9gFRKJSpVLhxdCyLIyMjLC9+IorrsCOHTv4h02v14Omaexg+vd//3eeZ6UUOp1O\nzPrc6XQ4gLLVaiGVSrHok8vlUCgUYneYyuUy79swDO54Q3Pf7Xb5To9SCq1Wi+/G2LaNvXv3cmez\nDRs2oFwu87WispxhB9jMzAwv5OVyORbiT9d8uCsmBXgCkZipaRrfqRoZGeH/JkFsODuOwkhp38OO\nMAoVJbGO5kwQjgWl1DsB/DGipi0PAfhfYRj++NSOShAEQVgtyDojHA92797Nv2EEQTh5vGhB7HjU\n04dhOADwvw7/IwiCIAjHHaXU/0R00+a3AfwI0c2YO5RSW8IwrJzSwQlrkg9/+MMvavvZ2Vm86U1v\n4tyvRqOBf/mXf8Fll10GAHjb297GpfM7duzAueeey63sa7Uadu7cif/6r/8CALznPe/hG0PEbbfd\nxrEDyWQS559/PmfYlMtl1Ot1/PM///PPcMaCsLqRdWZ18bnPfQ5A1JX3rrvu4g6/N9xwAx599FF8\n97vfBRDdzM1ms1y5AUSuMDIBaJqGTqfDmY5btmzBli1b2HH1xBNP4MCBA3jqqacARCWRe/fuxUUX\nXQQg6oD5zDPPsOPr4MGDCIKAb8I7jgPLsvhGeyKRwMzMDDuE3/3ud+Pcc8/lG+ef//znceedd/Ln\n+4YNG9BoNNgxfNlllyGRSPD4BoMBHnjgATYF/NEf/RE7lc8880wopbhK5Gtf+9rPPvGCcAqRLpOr\nkA9+8IMcsnjgwAE4jsNfgCcmJpBKpdgBRtlN9IHZ6XTQaDRibdypGyMQOZeGOwR2u112+pRKJWQy\nGXYZtVot7q4IRHbesbExdiV5ngfHcbhsb2JiAslkMlZ/bxgGH1sphUajwY6vUqmEbDYb62w4nG+m\naRp27drFDjDTNDE3N8fPHzhwAN1ul91umzZtipUNUldKmisqMSTX1ac+9Sls27aNAzEbjQbS6TS/\nPpFI8NgTiQTWrVvHjqpGowHLsvjYnuehWq2yiyqRSMTcdlTOSe60xcVF7Nu3j6+D7/soFAo8N+12\nG7qu89ysrKxg3759eOKJJwBEjq75+Xm+ztlsFrVajctdc7kcUqkUl4NalhVrJ+26LtLpNJfaULvn\n4Rwweu8A0Q8tGgvNJb1vyPlH50plqjQWz/OQyWT4RxuVnQrCMXADgM+EYfhFAFBKvQPALwK4HsBf\nncqBCcKxsmPHDrz//e8HAOzfvx+mabKb+LbbbuPPTvq8pvX/29/+NjZt2sQ/0C6//HJkMhlubz8x\nMcEdioFoHdi/fz/vW4QwQTgmZJ1ZhVx66aW46qqruDydIluo5PF73/sef2cHjnzvHq6GyOVyHOCf\nSqWwtLTEpefnn38+XvnKV/L35HQ6zVmRtL3v+/ybolgs4s///M/xwAMPAIgEMtu2+TfNnj17sLCw\ngAsuuAAAcM899+DWW2/liouLL74Yf/zHf8wC3JNPPomtW7fy+f34xz/G9PQ0788wDLzmNa/h7+7D\nmV+UGSYIqwURxFYhvV4Pjz76KIBI+Ni4cSN/wV1cXMTKygqXtgGRAEEfeL1ejz/UgegDkQQtIBJq\nRkZGWMwol8v82mw2i4mJCRa4lpaWYoKW4zhoNpu8P8MwYmV46XQaqVSKX18qlViwoe0dx+Eyvna7\njWazGQu49H2fw4n7/X4s5F3TtFimGdX/Uz5KoVBAo9HgcsAgCFAqlXh8vu/Dsizu8FKpVJBOp3nx\n2LhxI+bn5/mOyXCmllIK5XKZx04ZX/QDJpFIoF6vY8+ePTyX6XSa507TNLiui/n5eQBg0ZIWulwu\nh0wmw8cOggBKqZggV6lUWBA744wzABxpZOC6Lv9D1yqRSPB4161bh0ajwSJVoVDAYDDg8sVsNotM\nJsM/zHK5HL8WiAQ9ykEIwxCpVIoFLsuyYsHQuq7D932+dpZlIZlMYnJyEkB0l0oQXgilVBLAqwB8\niP4WhmGolPoOgEtP2cAE4aeABDHi05/+9Au+5qabbsLY2Bh/bp911lnodru49tprAURCW6/X41Bm\n6i5JN3kEQXh+ZJ1Z3dx+++3c4MkwDGSzWf4e/bKXvQwTExPYu3cvgOh7c6FQ4KgUIPpdQt+zM5kM\npqamWFC79957cf/99/N33Ww2i0qlwh0vn3nmGSQSCf69NjIygk9+8pOcMbZ9+3Y88MAD/LsBiGJu\n7rrrLgCRw+xTn/oUN1E5dOgQdu7cyc+/4Q1vwMUXX8wC25e+9CW0221ccsklAOS7trC2EEFsFXLH\nHXfwB66u69i0aRNuu+02AEdC8onBYBATQlzXjXVSTCQS7NgCIpGJ3E1A9AFNLqFmsxkLfk+n0zhw\n4AB3uAyCAKOjo/w85U7RvsIwhK7r7DKanJzk19Lrh8PZh48LRKLN5ORkzLV0xhlnsPBCd79JVArD\nEOeeey4LcNQpkQQ+x3FQr9djTqVnnnkmFiS/sLDAIhAJNyQSkShFx2q1WnxuJAhRZlk+n8fY2BiH\n7H/ve99DGIa88JmmCcMwuPsMOcWGO26Oj4/zue/btw/ZbJbnlrpIktg3GAw4vB6IFm16D9D7gMYN\nRHemzj77bL4elDdGcxUEAVKpFH8RoIYFwx09aV/JZBK6rvNjulYk3gGIzRV9iSAb+Uc+8hEIwjEw\nBiABYPmovy8DkJAOQRAE4WdF1hlBEISXOCKICYIgCAJgneoBCMLx5M/+7M+e9bdXvOIVfJNobGwM\n9957r5S/CCcD+Xw9gszFSwhyzCaTSczNzfGN7qeffhpPPfVUrBmV4zjsGPN9H2NjY+wwo66/1OWx\n2+1idnaWb7Kn02kuywSiG94LCwt8o5kaaVGjsU6ng1wuxzePAeCaa67B8nKkzbqui5tuuolvfBuG\ngR/84AfsCH766afRaDTw67/+6wCAv//7v8fBgwexY8eO4z6HgnAS+ak+X0UQW4UEQcBlfWEY4tCh\nQ/xcq9WKuYEsy4Lv++wa03UdmqaxQyyXy8HzPLb0BkGA5eVl/oDetGkTl7J1Oh1Uq1UucyTrMG17\n5plnwjRNPpZlWbHOhIlEAqZp8uPFxUW0223e3vd9GIbBDjHDMPDMM89wyeOGDRtw6aWXsmOs3+9j\nfHycnUi9Xg/VapUdXZdccglKpRIWFxcBRJliFJIJRNbjpaUlXqxo4SGXlWEY8H2fM8WoUyLtP5vN\nct5VIpHgxZHOpVwu80K1bt06zMzMcF5Zv9/Hww8/zNkvhUIBuq6zi8rzPLTb7ZhDa//+/bzwOo4T\n65hZq9UwPT3N14IcWzR2pVSsy6RpmgjDkBdawzAwNjYW605qGAa74Wq1Gvr9Ps8VZZ7R68fHx/lH\nmGma8H2f3WadTifWbdRxHHYLAtGiHgQBX3dBOEYqiLoZTx7190kAS8+x/YYTPSBBONU8+OCDp3oI\nwtpkA4AfnOpBnABe7DoDyFrzkuSRRx7BI4888qJe02q1OKPrWDl6e6pSORoq3Rzm9ttvf8H9UxMW\n4lOf+tSLGJ0gnPZswE+x1oggtgrZuXMn3vzmNwOIRCDLsrhDFYlNVCZI5WskXHieFws89zwPg8GA\nhZvp6Wmk02kWmZRSLJalUikUCgUWRRzHwezsLAf6J5NJ3i8QiW+JRIK3J7GJBKV2u80ld0AkpOTz\neT72vn37UCqVcM455wCIAoPPPvts/Od//ieAqIzQMAw+1/n5eczPz7NQNDMzA13XWagpFovodrux\nkkld1zE1NRUbL4lIvu9DKcVzGgQBwjDk+SDBjJ5LpVIstrVaLTSbTT52rVaLlbJu2bIFruvynZ5u\nt4swDFl81HUdBw8e5GNnMhlomsaBnJRFQMefmZnB2NgY1q9fz8dvtVqcYaZpGnzfZ6FUKcXXnB7T\nNRj+97BgtrS0xPls/X4fIyMjLABOTExwnlqn08HCwgKLf/SeoGM4jgOlFF9nwzAwMTERK6kUhBci\nDENXKXU/gCsAfA0AVPQmuwLALc/xkjsA/DqAfQDskzRMQRCE1YyF6AfKHad4HCeEn2KdAWStEQRB\nON78TGuN/MJcpXz5y18GAPz1X/81HMdhy+9gMODujUAkRgwGAxZOfN9nlxhtXygUWDgplUrQdZ2F\nqkajwV0c8/k8crkciyAkgA13UPF9P2YvHs6WohB3ytEyTZNFFOBIZ0Iq7yiVSnj5y1+OrVujmIZc\nLofbb78du3btAhCJSMOZXktLS0gmk5zLNRgMsLi4yHlcvu/Dtm0e3/j4OAzDYFdUs9lEvV7n/RmG\ngUQiwdtTV0iaO8o8AyIRaG5ujueZjkNdIXft2sWBnUDkppuYmODna7UaCoUCNwzYvn07du7cyXN3\ndDYcOazoOvm+j36/H+sW6nke33nSdT2W40Wtnekx/Xu4SQHNAf17+H1FAh7NXSKR4NbRhw4dQrPZ\njHWgNAwj1n1UKcViWzKZfFaWnSAcIx8D8PnDP1h+hKgbWBrA54/eMAzDKoBbT+roBEEQVj+r0Rk2\nzDGvM4CsNYIgCCeIn3qtEUFMEARBWJWEYbhDKTUG4AOISlh2ArgyDMPyqR2ZIAiCsBqQdUYQBOGl\njQhiq5wbbrgBDz30EO677z4AwO7du+F5Xsz5Y1kWO4lc10UymcTs7CyAqK2wZVmcy9VsNjmHDEAs\nd0rTNGQymVj2UzKZ5BLFdDqNwWDATh9yG1GZXTKZjLUrTiaTMReV4zjodDo8tpmZGbiuyy2D77jj\njljp4dFlgKVSCZOTk+xMcl0XrVaLHWKZTIZdYkQ+n+dxUpdGcnHZts3OKuCIK4vOr9/v87G63S7a\n7TY/pjwwOrdWqwXP8zA3NwcgKnms1+tcEpnL5ZDJZPjYY2NjuPzyy3mcvu+jVqtxKWOn04Hv++xW\n6/f7qFQqPNcbN258liMsnU7z2NvtNvr9PorFIl8LXdc5P40cafT6bDaLfD4fy3vr9/s893v37uUO\nmdVqFaVSictFh7tdAtF7jPLiaF77/T6XWArCiyEMw78F8LenehyCIAjC6kTWGUEQhJcuIoitAXq9\nHj784Q8DAK699lpUq1UWPqanpzE2NsaCmG3bmJubwxlnnAEgEoGazSYLIfV6HZVKhUWwQqGA6elp\nAMDU1BSy2SyLIoZhIJPJsNhB4gyVQVKQO+WbhWGIdDrNos+mTZuwZ88eLuujgHoqpaMwfMrZ8jwP\no6OjHEQ/GAzQ7Xa5hHNqagr1ep1FqFwuF8socxwn9pgEIhLEdF1HoVBgkcp1Xei6ziJUt9tFo9Hg\n881mszxvo6OjKJVKXOL4XHlY2WyWBbFyuYy9e/fyvrZs2YKzzjorVpI4MTGB888/H8CRUHuaC8uy\ncOjQIZ5L3/fRbDa5wcK5556Lubk5FiBbrRY0TeO5tW0bjUaDrxVd1+EQ/+FraVkWLMvix0EQcE4Z\nXTs659HRUb5ew/si8YxKaen5Xq8H27bxkY985FlzJgiCIAiCIAiCIAg/DSKIrQEuvfRSfP3rXwdw\nxG1Dgli5XH5W58dOp8NtfbvdLnf5A8D5WSRumabJgpBlWSxi0HMAOBuKAufp2BRIT/tst9toNpss\n0lxyySV44okn+PWO4/A2ADi0fTgIfnx8nLsRdrtdZLNZFsharRYOHDgQC8EvlUqxFseDwYC3HxkZ\niYX+e54HwzBYpOr1emi32yzwKKW4gQA9pjnYtGkTpqenWdRrNBox99zo6CjGx8f52Pfffz8WFxdR\nKpUARB0wt2/fztu7rotarcbHLhaLKBaLPFe2bSORSLCY6Ps+Op0Ot4O+6667sG3bNhawSAwcDr7v\ndDqcD5dMJp+VUTbc6pmu93A2neM4LFp6nsdz4fs+er0ev4eoVTWJhel0Gvl8PtZ18k//9E9x7bXX\nQhAEQRAEQRAEQRCOB9oLbyIIgiAIqx+l1DuVUnuVUn2l1H1KqQtP9ZhOJEqpG5VSwVH/PH7UNh9Q\nSi0opXpKqTuVUmedqvEeT5RSP6+U+ppSav7wef/yc2zzvOeulDKVUn+jlKoopdpKqa8opSZO3lkc\nH15oLpRS//gc75P/OGqbl/xcKKX+j1LqR0qpllJqWSn1r0qpLc+x3ap/XxzLXKyV98XxRtYZWWeO\n2mbVf54Ass4Qss4c4XRaZ8Qhtkb49re/DQBYt24dPM9jl5RpmqjVauyCCoKAXUzAETePOuzqGh8f\nh2masa6UVPLY7XZRKpXY4WVZFpRSsYyxwWAQcwKFYRhzmCWTSXZ4maaJer3OpXaGYXBOFwBs3rw5\ndi6e52F+fp5dSWeccQaKxSKXEa6srMCyLB6GczQ1AAAgAElEQVSf4zhYWVnh8RSLxZjLrVarIZlM\n8ty0222EYchljYZhsGOL5o5yzYCovHRmZgZAlPk1MTHBDjByl5GjanR0FBs2bIiVrqZSKS55bDab\nePzxx3l7x3FQq9ViHS6pWyO93nVdfuz7PhzHYXfdrl27MDc3Fxt/s9lkt16lUkGv1+OcL8uyYh0m\nqSPo8PuEXGkAkEqlkEwm+dpUKhV+jpx2dN11XeftaF6npqa4s+nf/M3f4HWvex0E4USilPqfAD4K\n4LdxpFPYHUqpLWEYVk7p4E4sjwK4AgC1cWWbr1LqTwD8PoC3AtgH4C8QzcnZYRg6eGmTQRR+/TkA\nXz36yWM8948DuArAGwC0APwNgNsA/PyJHvxx5nnn4jC3A3gbjrxPBkc9vxrm4ucB/F8A/x+i78cf\nBvDtw9e8D6yp98ULzsVh1sL74rgh64ysM8Osoc8TQNYZQtaZI5w264wIYmuEq666CkAkzHieh5WV\nFQCRKJXL5VgIaTQa6Pf7LH4kk0mkUikWViiPq1arAYiEGQqlbzabaLfb2Lx5M2+r6zqXviWTSQwG\nAxZlkskkTNOM5Wlls1kWSm699Vb0ej0uUUwmk7Asi0WcMAzRbrd5/3QsGpvrupienuaywkwmg2az\nGRPQdF3nc6XjUpmg4zhIJpP8/GAwgGVZqFarAKLA/dHRUQ6qJwGKBLOxsTF+rW3bKJfLXMJYr9fR\n6/X43BuNBiqVCu+72+1yUwF6/e7duzExEQnexWKRS0iBSIAazvCyLCsW8J/P52HbNs/lYDBAq9WK\nheq7rsuvHwwGsG2b3xf1eh2pVCp2PsNNEJRS6PV6XEaZTCaRyWT4eSrNBaLsNmoqQMfyPC+WMVep\nVPCKV7wCALBz5068853v5MYQgnCCuAHAZ8Iw/CIAKKXeAeAXAVwP4K9O5cBOMN7zdEP73wA+GIbh\nNwBAKfVWAMsArgWw4ySN74QQhuG3AHwLABR9UMZ53nNXSuURvTfeHIbhfx7e5u0AnlBKXRSG4Y9O\nwmkcF45hLgBg8JPeJ6tlLsIwjN15UUq9DcAKgFcB+K/Df14T74tjnAtgDbwvjjOyzjwbWWdW+ecJ\nIOsMIevMEU6ndUYEsTUCOWw++9nPYv369Rx2HgQB+v1+zGk0/DmVSCTYOUXbD7t56G9AJOIkEgkW\nlCYnJ9kVBkQiyrDIous6MpkMMpkMALBAQ6LM0tISEokEu6QymQxyuRyPhfZH46FgeBJh+v0+Op1O\nTHAjQY+2p6wsGk+n02GXlu/7zxL0wjBkx1g2m0W5XObjZzIZnH/++SxSVSoVnotarYZsNsv7pvB9\ncox1u13cf//9PJZarYZer8f7dhwH+Xyen8/lchgdHWU3ne/7ME2Tn9c0DXNzczz3pmmi0Whg3bp1\nAKIOnYlEgsW8AwcOxBogNJvNZ13rRCLB46csteFg/MFgEGuokMvleHyJRIJF1UKhAE3T2JnXbDZj\nYpphGDGx7e6772aRUxBOBEqpJKIF+EP0tzAMQ6XUdwBcesoGdnLYrJSaB2AD+G8A/ycMw4NKqY0A\npgB8lzYMw7CllPohojl5Sf9QeT6O8dwvQPQdanibp5RSBw5v85L4Qvoi+B9KqWUAdQB3AXhPGIb0\nwfwqrM65KAAIAdSANf++iM3FEGvxffFTIeuMrDPDrPHPk5/EWvw8kXXmCKdsnZEMMUEQBGGtMwYg\ngegO3DDLiL6YrFbuQ2RDvxLAOwBsBHCvUiqD6LxDrL05AY7t3CcBOGEYtp5nm9XC7YhKN14L4P8B\ncDmA/xi6yz+FVTYXh8/t4wD+KwxDyjtak++LnzAXwBp8X/yMyDoj68wwa/Lz5HlYc58nss4c4VSv\nM+IQW2M88sgjMceXbdtotVrs/EkkEtA0jV1a6XQalmVxllS32425roIgiGWEOY7DrqhqtRpzITmO\nA13X2RlEZXLkAGu327Fug8ViEb7vxzpgGobBjq1Op4N2u80uqHa7DdM02WmkaRps2+bxZLNZpFIp\nfr1lWXAcJ9YNUynFTiZ6TOfq+z4GgwG7pqhMkc6/VCrhoYcewu7du/l5ysHq9/tYWVnhbZvNJhKJ\nBHeR7PV6WFlZYYdXu91Gp9OJlUAOH8u2bUxNTfG5UgkjObLK5TI0TcPCwgJfe03TsGnTJgDAeeed\nhyeeeAKHDh0CACwuLmL79u18rlSWSsc3TROe5/G1mZmZgWmaXP46XAZLxzIMI9Z1kt4HmqbFcsPI\nCUfvQV3XMTY2hu3btwMA7rzzTnz605+GIAjHlzAM7xh6+KhS6kcA9gN4E4AnT82ohNONMAyHXRqP\nKaUeAfA0gP8B4O5TMqgTz98C2A7gslM9kNOA55yLNfq+EF4kss4Ix8Ia/TyRdeYIp3SdEUFsjfGJ\nT3wCv/qrv8piw9NPP41Op8OiEZW6kUhF2VAkOpEgRGWDtm1zrlSxWEQikWDRhKBt9+/fz2WSQCS6\nJJNJFmEymQyLTkAktrVaLS5BpJJMEvMIElY0TYtlfvm+z+WZQJSjRaIbEAkxnufFSkRJrCGUUizy\nUDkp7XNxcZGbDADAwsICPM9jkUvXdT53yuwaDqbv9Xp46KGH+Li2bfPcUQYYHZvKDCmvzXEc+L6P\n6elpfn02m+W8tLPPPhsLCwssZDabTViWxYIZBfDTdR8MBvB9nwU3uh7FYpHnrtvtsjiZTqcRBAGP\nj+aHhDTTNGHbNl87KqkEonJQpRTP+9HiWRiGmJqa4iy673znOxCEE0wFgI/ortswkwCWTv5wTg1h\nGDaVUrsAnAXgHkQBppOI36mcBPDgyR/dSWUJL3zuSwAMpVT+qDuTq/49E4bhXqVUBdH75G6ssrlQ\nSn0SwOsA/HwYhotDT62598XzzMWzWO3vi+OArDOQdWaINfd58mJY7Z8nss4c4XRYZ0QQW4O0Wi1c\neeWVACIn0e7du1mooQ6JJHSQe4yEGgqhJ1GJugsCwMjICP9D2w53dczlcqjX6yy6DAYDZLNZFmlS\nqRTCMHxWLhUJUEopjI+PY/369QAiwcbzPA6qp9B4ErTCMORj0FgbjQZvT+IOCTW6riOVSnEoPs0H\nPT86Oor5+XkWkYIgQLPZxMaNGwEcydii/ZPDDIjEL9d1eR5N04wJXGEYQtM0dq+ZpoliscgCUyaT\nQbfbxZ49e/ix67osWGUyGaTTaZ7rqakp/m8ai2EYfK2azSZ0XWcRKpVKodVqsYur0+nAMIzY3Pm+\nzwKb7/vPOh9yzNG1GhbEqCEBEIl9QRDEhMsgCHjfExMT+I3f+A184hOfAAB87nOfgyCcSMIwdJVS\n9yPqgvU1gO3bVwC45VSO7WSilMoi+pLxhcNfOpYQzcHDh5/PA7gYUQefVcsxnvv9iDqlXQHgXw9v\nsxXAekQZOasWpdQcgFEA9MV11czF4S/m1wC4PAzDA8PPrbX3xfPNxU/YftW+L44Hss5EyDoTsdY+\nT14sq/nzRNaZI5wu64wIYoIgCIIAfAzA5w//YPkRom5gaQCfP5WDOpEopT4C4OuIyldmAfw5ABfA\nlw9v8nEA71FK7UHU+vuDAA4B+PeTPtjjzOH8mrNwpI33JqXUeQBqYRgexAuc++GQ288B+JhSqg6g\njehH7fdfKh2eiOebi8P/3IiohfnS4e3+EsAuAHcAq2culFJ/C+BXAfwygK5Sipw8zTAM7cP/vSbe\nFy80F4ffM2vifXGckXVG1hlZZyJknZF15rRZZ0QQW4N885vfxGte8xoAz3ZRpdPpWLfCSqUC0zTZ\nSaSUgu/7yOVyvD9yRI2NjeHss8/mToaJRAKe52FiYgJA5MA6dOgQu5Rc10W322WHGHV0JHdaqVTC\nyMgIu9ZSqRQ2b96MM888k5+vVqvcgTAMQ3Q6HS4bpLwyciYdOHAAnuex48zzPFiWxa4s6oBJuVnF\nYhGDwYBdTrquI5vN8v7IdUUdO7dv347HHx/OAQTP68TERKyDZb/fj7nhfN/n/DbgSEkjzTs51cit\n5rouVlZW2KE1PT0N0zRjpa65XA5TU1GeYLlcRrVaZXfbxMQEstlsrOum67qcBUbXnxxjQRAgnU5j\ncnKSzyuZTMbcfsPlr7QPutaGYbCbzbIsdDodPhaVipKz8MILL4Su6+IME04qYRjuUEqNAfgAIqv1\nTgBX/qRWz6uEOQC3IrrbVkbU5vqSMAyrABCG4V8ppdIAPoOo+8/3AFwVhqHzE/b3UuICRHb78PA/\nHz389y8AuP4Yz/0GRCVQXwFgImop/86TM/zjyvPNxe8BeDmiUNsCgAVEX0TfF4ahO7SP1TAX70B0\n/vcc9fe3A/gicMz/T6yFufCxdt4Xxw1ZZ2SdOfx3WWdknbnnqL/LOnOEk77OqKPznk5HlFKvRGSJ\nE44TF198MYBIeOn3+ywSUYkeCSKNRiOW95RMJtHr9VioGRZBzjnnHLzyla/kIHnHcdBsNnnfVEJH\noszKygpM0+Sg93w+j0KhwCKOruuoVqtYXo5KqPv9Ps466yxs27YNQBQ8f8cdd+Dee+8FEAlczWaT\nxbpCoRDLO9M0LdYEoNfrQSkVyySzbZvLQamskeZkbGwMnufxeGzbRi6XwwUXXAAgKhkNwxB33nkn\ngEiUIoFqZmYGtm3jwIHIDdpqteC6Lu9bKYUwDDmPzDAMpNNpFsg8z4PjOFzmCUTC0hvf+EYAkYiU\nz+f5WpDwt7QUlU/v27cP8/PzXJZ47rnnQtd17N+/H0AkmFUqFczPz/P1bzQavD8aD4mNpVIJ2WyW\n54pC/ik/zXVdPPbYY3jyySgvdTAYsJhm2zbK5TILjZ1OB+vWrcOll17K74NWq4UvfelLEE4KrwrD\n8IFTPQhBEARBEARBEISTiTjE1ii/9Eu/BCDKkrr55ptx/fXXA4hcVMPZUUAkPJEwous6isVirFMj\niSTZbBbLy8vs2FJKwXVdFk0GgwG7soBIcBp2FLmuy50jgcihNTk5yaLR4uIiC3VA5HKanJzkfdTr\ndWQyGT7evn37kEwm2XFGXSbJZUXuOHJtKaUQBEEs9yuTyXCmWDKZxN69e1m0CoIAQRDw47vvvhuv\nfvWrORuLnG9AFCRPQfg0j67r8jxSswGa9yAIYh07lVLcdIDmknLJgEgASyQS7G7zPA+2bfN8+b4P\n0zRjYz+6oQA1GaDt0+k0ZmZm+HmlFLu4DMNAGIY81+12GyMjIzzXNI8kTuq6zo6wVquFTqfD10HT\nNIyPj/NYq9UqvvWtb0EQBEEQBEEQBEEQThTaqR6AIAiCIAiCIAiCIAiCIJxMxCEm4J3vfCduuukm\nAMBv/uZvIggCdl2l02nOyAIip8/IyAjnguVyOXY5tdttHDhwgF1MlmUhk8mwa8h1XbTbbc69SiQS\nyOVy/Ni2bQRBwJlkuVwOuq6z04peT7lWyWQylpPV6XSgaRo7zMjxRWWGuq4jl8tx2SCVWNJ4DcOI\nlVQmEgmcccYZyGQyAIAnn3ySc8OAI7lfi4uLvP8f/vCH7MLSdZ3LNRuNBjzP47kwTRO5XI5dUqZp\nYmJigksOe70eKpUKH2tkZAT5fJ7nZnFxERs2bODnW60WNE1j910QBLFSWLqW1HnS9300m02eKyrJ\nHC6BVEqx42x8fBxKKZ47IHKpkaurXC7Dtu3YeBOJBI+n0WhwR01yptG+qLsnHZtKKwVBEARBEARB\nEAThRCGC2Brlve99LwDgD/7gD3DzzTez6FQoFPDUU0+xqEOiEwkn2WwWpVKJS+HCMORMrVarxSIT\nEAkmpVKJRRjP89DpdFgAm5qawsjICAtOVPZHAhuJMVTSaBgGMpkMqtUqgCOiDYX4dzod7N+/n8U8\ny7KQTCZ5P7quY9OmTSza7N69GysrK3w8KlOk7bds2YJsNss5WI1GI5Y3NhzWD4BLIknQsSyLj6Xr\nOnzfjzUIaDabLHDRnJF4N1zmSdtTmSIAzM7O4uyzz+a5azQasG2bx5RIJJ4Vaq9pGm8fBAG/BgAy\nmQwKhQKLe61WK1ZyOTs7i40bN7JYGAQBfN/nuVNKYXFxkR/Pzs4iCALe//D1HAwGyOVy/L54xSte\ngQsuuAD//d9Rd9wvfOELEARBEARBEARBEIQTiQhia5xbbrkFV155Ja666ioAwCtf+Up8//vfZ0Es\nk8kgl8vFnE2e52FlZQVA5FQiMScMQ2SzWXY9kXA1nBOWzWa522ChUECpVOLHruvGuhFSZhY5uJRS\n0DSN90+h9WeccQaASBRaWFjg45FLibafnJzEmWeeyeMaDAYYGRlh0ca2bWiaxplhmUwGe/fu5fMz\nDCMWwk/nSvtTSqHX67F4OOx+azab0HWd89bIwUXn5rouqtUqH4vcbeSiouYAtL/R0VGEYRjLP9M0\nLba/Xq/H504urOEOme12m8+9UChgcnISTzzxBM9Fv9/n1/u+j0qlwu+DQqGAYrHIgl8+n4dt25wf\nVygUkMlkePztdpvzysIw5GsPHOnA+ZWvfAWCIAiCIAiCIAiCcDKQDDFBEARBEARBEARBEARhTSEO\nMQG/+Iu/iFtuuQUA8Au/8Au488478eijjwKIyhjHx8exadMmAJG7p1qtskOs1Wqxi4g6C5JDi0ri\nKINs3bp12LRpE8bGxgBE3QWplA+IXEX5fJ5dRZqmcRklELmeqDsjHW+442WlUkGj0eCMsWw2y+MH\ngM2bN2PXrl3Ys2cPgCO5XlQGmEwmMTU1xeWgy8vLsG2b3XJhGMIwDHY2FYtFLC4usvOp1WpxKSEQ\nlXouLS3xf2cyGR4rlT/SY03TEIZhzA2XSqX43LvdLnq9Hju88vk8ms0mP79582Zks1keC5U60mMq\nBSWHmeM46HQ6fG1GR0djHSc1TeP5o/dBuVzmx51OB47jcNfJMAyh6zpfe8MwMDc3x48XFxdjeWnF\nYpGvQzabZeeZIAiCIAiCIAiCIJwMRBATAERZYkAklFx99dVoNBoAwCWIJBL1ej2srKzEgutJzPA8\nD/1+PyaKGIbBApZhGFi/fj2mp6f5uL1ej5/XNA2apnEZHwlsJBrRNpSDZZom0uk0izTr1q1DKpVi\nEerQoUMoFoss5lWrVfz4xz+OZZx5nod0Og0AmJmZgWVZnOOlaRpyuRyLRr1eDxMTE1yiWavVEARB\nTMxJp9Oc+xWGYazEUdf12FzRnNA8BkHAc0F/G85RM02Tt6dmAVQymUgkUCwWWdRSSsG2bRbYHMeB\naZosSrXbbbTbbX5s2zZs2+a5TaVSMYGMSldp+3q9zmOj8di2zWLhYDDA4uIi5ufnY9eOrlsymcRl\nl10GIGpW8Nhjj0EQBEEQBEEQBEEQThYiiAkxvvnNb+Kaa67Bzp07AYCFLxKZKIidBJ1hwUrXdSil\nWLRJJBJQSrGwQo4vEmlM08TIyAiLQJSJRWKc53mYmppixxh1vxwWoEjIASKHl67rLGiFYQjP87Bv\n3z4AwNNPP41Wq8XuKSDKGRvODBvOKOv1ejFXVaFQQDabxaFDhwAccUkNn9+wiOU4DgtWruuiVCrF\nRKRhqJkAjd3zvNg8UzMCcojRuZJ7LZ1Ow7Ks2FgGgwGH9icSCZimyeOpVquxwPtutwvXdbl7qO/7\naDQafC273S48z3uWYDYsyNGcDc81EQQBjzWVSiEMQ7zlLW8BALz+9a9/zjkRBEEQBEEQBEEQhBOF\nZIgJgiAIgiAIgiAIgiAIawpxiAkxXv/616PdbuOiiy4CEJUddrtdVCoVAGDHEZVFDpcMkmuJ8DwP\njuNwuaVlWWg0GuwiKhQKmJiYiJVj1ut1dqNpmobR0dFYiWQqlWKnUbPZRL/fZ4dZv9/HM888g/37\n9wOIXFX1ej3W3XC4U2M6nUaxWEShUAAQubiSySS7tKhTIr2+1+thYWEhVvo3nPtFJZ/0uFqt8rFs\n20a9Xuf5ITcaOcDI+UZOK3LS0bHT6TTCMOQulVNTUzBNE1NTUwCi0svhrpW1Wg31ep2vGznxyPFH\n2W/kviuXy/B9n8tHN27ciFqtxtvTmAjKKyOXWb/fRzKZZPedbdvssKPzo+s2OjqKbDYrzjBBEARB\nEARBEAThlCGCmPAscrkcPvrRjwIAzjnnHDzwwANcRjgYDBAEAZfSkQADRILQsCBGQeuUK2VZFprN\nJpdEJpNJzM3NYePGjQCisrtarYZ+vw8gEnkGgwGLLp7nwTAMPoZlWbGywl6vh3w+zyLUyspKTCTS\ndR2O4/D4aD/UIKBQKMTOLZ1O46mnnmIBz/d9uK7Lzw8H1APgDDA6n+XlZRa0UqkUgiBggYqy1kgM\npPOlc1VK8fzRWLZu3cp5aDROKnHUNA2tVovHWq/X0Wq1WCzsdDpc6glEpbDDc0fXl8TNfD6PdevW\nscC1vLwMx3Fi5aj9fj9WrkrXiM5vMBjExENqsjAzMyMh+oIgCIIgCIIgCMIpRQQx4TkhoSWXy+HJ\nJ59kkcr3fViWxUKN7/v8nFIK2WyWc64olH52dhZA5FJaXl5m1xEJSJQ1RQH5JDDpuo5ut8uikuu6\nsfwv0zRjXSc7nQ5GRkawfft2AJGIM9z1kV4z7BirVqvc9bLf76PX67GoU6vV4Louizqe5yEIgpgQ\nNhgMeP+GYSCZTGJ5eZmPNZzTlUgk+HGr1eKumnSuIyMjLBQ1m030ej0WoNavX4+JiQnuoOm6LrZs\n2RLLV+t2u9wwwHXdWAh/uVzmHDE612azyeOxbRvZbJbdeOl0GqVSCevWrQMATE5OIgzDWL4bCYT0\nel3X2WFGmWjkEFNKsZvtwIEDeOCBByAIgiAIgiAIgiAIpwrJEBMEQRAEQRAEQRAEQRDWFOIQE56T\n6667DgDwnve8B81mk0vqstksdF2PdZkkR9i6devwspe9DNlsFkDkoLIsi0smM5kMstksu4aq1SoS\niQTK5TKAyLUUBAE7tnzfh+/7nPFlGAaazSa7pshxRK4kep5cV5s3b8b3vvc9Hk8QBAiCAJOTkwCi\nssKRkRF2RVGXS3KokfOLjkcZaeQwSyQSMAyDHW+ZTAbpdJrLIl3X5W0dx4HrujyPQORoo33ncjkk\nk0msX7+eHycSCXaMNRoN7Nu3D7VaDQAwOzuLTCbDcz8YDNBqtdjBRY6zarUKIContSyLHWXUDZPO\n3XXd2HXtdDrYsGEDz7HneZibm2PHGZVn0mPHceA4Ds81lbvS8UzTxNatWwFE5ZriEBMEQRAEQRAE\nQRBOJSKICc+LaZrQNI2FGtu2UalUWMjJZrMsQBUKBbRaLS7bS6fTsCyLSyo9z0MYhixwOY6DTqcT\nK3mkcHbgSIj99PQ0gKiM0zAMDt0nQYqEq+npaTSbTSwuLgKIcrvOO+88Lgv0fR+NRoPHY1kWZmZm\nWLCj50lEojHT/hOJBFKpFO/vuTLTer0en89wfppt23Ach8XARCLBZY70fCaT4XPN5/MYHx9ncW1x\ncTEW6E9C3rBANZwZZhgGWq1WrAnCsIDmOA7OPPNMFqxarRbK5TKXezYaDUxOTvK16HQ6PF9AFIw/\nNjbGJZyUMTYsTna7XZ670dFRXH311QDA/xYEQRAEQRAEQRCEU4UIYsLz8t73vhd/8id/gve9730A\ngF/7tV/D/Pw8P0+iFAAsLS3hwIED7DJKpVIYGxvj4Hjf96HrOotI5JgaFqx0XedgeMr/otwry7Iw\nOTnJIk61WkUmk+Hnfd9HJpPh8fV6PaTTaXaclUolFqZofCMjIywyNRoN7N+/n3PKSNyi8aZSKfi+\nz6+nTC46f8/z4LouP5/JZHislmXF3GF0TBLIgiBAtVplsW9qagpKKX68uLgIz/M404vcaPT6MAyR\nTCb5eJ1OB7VajfPOCoUCstlsTCDL5XIsTpZKJYRhiAMHDgCIXFz9fp+fz2QyKBaLLH4mk0luDABE\nAliv12OHmmmamJyc5Lm5+uqrWewTBEEQBEEQBEEQhFONZIgJgiAIgiAIgiAIgiAIawpxiAkvyF/+\n5V+yu+eNb3wj9u/fz2WJ9Xqdc61830cYhuyQAhBzcFFXSCrbo8wqcjmR24gcWkEQQNM0dpD1ej30\n+33Ovcpms7GukAS5pJaXl7lMEwDGx8exYcMGdi25rot2u83nsmvXLtTr9VinRnKRAZGLis6Vxjf8\nb9/3uRMlEJUlUr6YYRh83kDk6LIsK+Zu63a7WFhYAABs3boVvu/zXHieh9HRUR5LNptFLpfjkkSl\nFAqFAs99p9OB53ncLTSRSMBxHN5e0zQEQcBjSiQSsCyLnWthGKJcLrPjK5vNol6v89yZpgnf97lD\nKF0r2l86nYau69xh9MYbb+Q8NUEQBEEQBEEQBEE41YggJhwTO3bsAAB87GMfw4UXXogHH3wQAFCr\n1VgUGQwGMAyDy+oSiQQ8z4uF1DuOwwJWIpFAGIYc3D4xMYFEIsECmG3b6Pf7sUwywzBY8CoUCjAM\ng0WbRCIBTdM4xyqbzXIJIBCVAU5PT7NoU6vVsGvXLjz11FMAjohINL5kMolSqYS5uTkAkUg0NzfH\nohhlgpEApus6nzMQNQkYzitLJpMsKFH2Gj3udDqwbZsFtF6vx+Oh7WdmZljgGh0dheu6nDFm2zbC\nMOTXdzqdWDkrzT2du6ZpsXLPox/TGEh8bLVaaLfb/HwymUQmk+GMscnJSYyNjfF4Pc9DOp3Gb//2\nbwOAiGGCIAiCIAiCIAjCaYWUTAqCIAiCIAiCIAiCIAhrCnGICcfED37wAwDAX/zFX+Caa65hV1YY\nhtzZ0HGcmMuIXEn0fCqVQqlU4rI827ZjIfdA5Poix1Wv10O32+VSSsdxYNs2lxFSCSaVXNq2jWQy\nyQ6xQqGARqMRc12FYciOtr179+Kxxx7j54lhF1epVOL9034p9N+yLHQ6Hd4+kUjA9/1YCSidm+u6\nCMOQHVf5fB6WZcU6WOq6zue+uLiIRCLBx0qn05iYmOCulWEY4qmnnuLnyYFFc0N/Gz6+4zi8f8uy\nYl0nE4kESqUSO9BarVbM1eX7fqwDaHYDic8AACAASURBVKlU4usKRO7AVCrF5amdTgdvfetbcdFF\nF0EQBEEQBEEQBEEQTjdEEBNeFOl0Gg899BCXETYaDS7bo3LJYeFIKcVldEop7vwIHMneohLJSqWC\nXC7HXSGpSyMJTul0GoVCgUWZXq+HfD7PIpHjOOj1eiygmaaJYrHIIo2u63BdlwWxgwcPotPpsLCT\nzWah6zqLVJ7nodlsYnx8HEBUJlipVGJdMFutVqyTJHVnBKKyRhKkut0uwjBkkckwDHiex10fB4MB\n+v0+z8nS0hKUUjw3k5OTME2T91ev19Fut7lzpWmaPA4aO5Ut0lwC4Lw06jJZLBZ5PGEYYuPGjXx8\n4EgXTCAqq6THlHlG9Pt9WJbF1/ryyy/H1VdfjcnJSQiCIAiCIAiCIAjC6YYIYsKLYseOHXj729+O\ncrkMIBKpSCRJJpMYDAbsANM0DYZhxELnU6kU6vU6gEgkymQyLBJVKhWMjo5i/fr1ACKBKZ1OswhU\nLBaRy+VY8AIipxQJXpQhRgKXpmnIZrO8rWmaGAwGvD8KpqeMMho3Bd27rotisciiUqvVwsLCAu8/\nCAL4vs/jMQwDmqaxSDXs0Eqn0zBNk8dq2zY8z2NHV6PRiDUY6HQ66Ha7nMeWzWafJaC5rstjoXwy\nOnav10MymUQul+O5DIIg5iAbPh6F6s/MzAAAtm3bhieffDLm3qP5JXq9Hl/bfr+PfD6Pm2++GQBw\n9dVX4/rrr4cgCIIgCIIgCIIgnI5IhpggCIIgCIIgCIIgCIKwphCHmPCi+cd//Ef8wz/8AwDglltu\niTmmBoMBlxxaloVUKsUOMHIvURmd67rodDrsmmq1WhgMBuzqKhQKSKVS7Gqi/VHuleu6sG2b88wI\ncmzRfml7IHKl0Th0XYdpmuwASyaTGBkZ4e3DMMT4+DiXAc7Pz6PRaPD+B4NBrCsmcKTzJBC5pgqF\nAoAoM2y4PJO6SlJ5JTnr6FiWZcXKQ/v9Pnzf57FlMhmEYchjUUqh1Wrx/mgfw241TdO4RNJ1XZTL\nZR57KpWC67rsANu8eTN838fDDz/Mrx92vNF4CE3TMDMzg1/5lV8BAFx33XX40pe+BEEQBEEQBEEQ\nBEE4HRFBTPipoHK4j3/84/jyl78MICp57Pf7LIBRUPzIyAiASIShEjvgiABFmVxAVBpIokwqlUI2\nm+VMMQqFJ9FI07RY+aRt23xM2tfwMZVSCMOQX09iEQlnvu/DMAzOLNM0DZqmoVarAQCWl5ehaRqf\nH+2LRCjP86DrOmd5UVA9EAlIw2JVtVqNlVDqug5d13kbwzCQyWT4seM4KBaLnGfmeR6SySQ3BOh0\nOhgMBnyuJEQOi3V0PjT3rVaLs8IGgwEKhQKH6muahlwux+Kk7/vI5/O8PxLgaPy5XA7r1q1jEUzE\nMEEQBEEQBEEQBOF0RgQx4WfiD//wD1kQ++xnP4s9e/aw4AQA7XabXU6WZcVcUEf/e3JyEiMjI/zY\ndV2k02kWmDqdDmq1WqyzIuWUAZFAdbQAlMlk2LFFYhaJQq7rxoLsyeFGz5M4R66s0dHRmHhHgthw\nZlkmk+HzdRyH3Wu2bXM2GR0bQCzDK5FI8LkahhHL7zJNE4VCgfcRBAHCMGT3XBiGKJVKfC6maUIp\nFRP3hvPWer0efN9nlxcJajR/QRBgZWXlWQ0GaHyUTUYC3dlnn41SqYTrrrsOgiAIgiAIgiAIgnC6\nIxligiAIgiAIgiAIgiAIwppCHGLCz8yb3/xmAMDrX/96uK7LrqcwDOH7PrukMpkMUqlULHtqMBhw\nCeLMzAx3UwSivK/hEkcqmaROi8MdHoEjLieCOi/SeDzPQxiG/JiOS64ry7KQyWS4zDGTySCbzbIr\nSimFSqXCLqpmswnXddl1RZ0fyVHWbrdjuWn5fD7mJnMcJ3Zug8GAuzzmcrlYHpmmaeh2u3zu3W4X\ntm3z2MjNRnNrmmYs36zf78N1XX6e8sgymQzPV6VS4f0HQRDLOGu32zEHWiKRwBVXXIF3vetdAIDb\nbrsNH/jAByAIgiAIgiAIgiAILwVEEBOOG7/1W7+FZ555hkUgEqBIeOr3+wjDkEsKgyBAMpnkoPeR\nkREuVwQigavZbHLZXzKZRBiGXEp4dBB9u91Gr9djgSuTycCyLBaVSOQhUSifz/N2ADA2NoZisRjL\nzXIch7cfGRlBMpnkTLF+vx8T4BKJRKwEk5oCAFFpYTabZfHMNE2srKzw6w3DwNzcHObm5gBEmWK9\nXo/PPQgCVKvVmGBlmiZnfiUSCZTLZbRaLQCRuJdOp1nQ6vf7MAyDxTrLsvh86PUk0gHAxMQElFJ4\n5JFHAERiYBAEXDK6ceNGfOELX8Db3/52AMDnP//5535TCIIgCIIgCIIgCMJpiAhiwnHjda97HX7w\ngx/gfe97HwBgz549GAwGsRyrTCbDoouu68hmszFhStf1mGuq1+vFMsgMw2DRzPd9DAYD3h85xoZF\nH03TWKAaDAaoVCr8enJIjY6OAogEsXw+z4Kapmno9/ssMlWrVQRBEAvNB8CP0+l0TECjTpEAUCqV\nMDo6yo4tcoqRGw2IBL9h8a5er3NDAt/3YwJcOp1GsVjk/a2srODQoUOxfDUSEGmuM5kMz3Uul+Oc\nMeBIjhsJbBMTE1hZWYkJlN1ul+fqda97HWq1mghhgiAIgiAIgiAIwksSyRATBEEQBEEQBEEQBEEQ\n1hTH3SGmlLoRwI1H/fnJMAy3D23zAQC/CaAA4PsAfjcMwz3HeyzCyadQKOB3f/d3AQA33ngjd0EE\njnQ6JJcSObcoBywMQwRBwG4lep7cVNQFkhxhjUYDtm3z67PZLEZHR2NdJ4c7KXa7XSwtLcXGmsvl\nsLy8zM8Pd6UkhxlhWRZ0Xefny+UylpaWeJzkYKPjT0xMcPllPp+PdaAEosw02pdt29i9ezf2798P\nIHKLOY4TywwzDIMdZ8ViEUEQ4MCBAwCA+fl5eJ7HDrFOp4MwDNntZhgGKpUKl4eeccYZME2T53p5\neRnz8/OYnJwEEJWf0j/0WNM07ip5+eWXs1tMEARBEARBEARBEF5qnKiSyUcBXAFAHX7s0RNKqT8B\n8PsA3gpgH4C/AHCHUursMAydEzQe4SSxfft23HfffQCA1772tfjWt77Fofqe56FcLrOok8/noes6\nC1y2bcdC8IfLB4FI5Ol0OrFQ+2aziVwuBwDYsGED8vl8LOS+3++zIOZ5HjRN49d7noeVlRU0m00+\nnq7rvL1hGHxsIMoBW79+PYtCzWYT1WqVSyqbzSaUUvy8ZVlcvhkEAfr9fixTrNVqsWBIYfw0NtM0\nUSqVuKSRMsNo3yMjI5ifn8ehQ4d47lKpVKxhgO/7LCZSKSmNdXFxMSbuHTx4EEtLS1hZWQFwJKS/\nXC7ztdq2bRvGxsYAABdddNHzvxEEQRAEQRAEQRAE4TTmRAliXhiG5Z/w3P8G8MEwDL8BAEqptwJY\nBnAtgB0naDzCSeSSSy4BEGVuHTx4EPfffz+AIw4wEoGUUsjlcjEHGAAWpNrtdqzTYafTQaPRYMFL\nKcVh70AkSOXz+ZiARgITELmuNm3axCJQt9vFwsIC7991XWiaFhOVqPsjnU+328Xs7CyASFCjMHrg\nSMdMEgDL5TKLfZVKBZZlYcOGDQCikPuHH34Yi4uLPLZSqcQZXqVSCalUio/tum4sFF8pBdd12UFm\nmiY74mhsw10lgyBAEATs6tJ1HdVqlR1iJJTRuXieB13XMT09DQC4+OKLceGFF+L3fu/3nvfaC4Ig\nCIIgCIIgCMJLgROVIbZZKTWvlHpaKfX/KqXWAYBSaiOAKQDfpQ3DMGwB+CGAS0/QWARBEARBEARB\nEARBEASBOREOsfsAvA3AUwCmAbwfwL1KqXMQiWEhIkfYMMuHnxNWEaOjo+h2u3jzm98MANi9ezds\n22aXUzKZhGma7EoKggC2bcdKIoddXp1OB4lEgnOxkslkrCtlEATodrvsqvI8L+aaAqJcLyo7DIIA\n2WwWDz/8MIDIxVUsFjnnq9frQdd1Pp5pmqjValxWGIYhwjDknDDP82JdJuv1OjvENE2DZVnsXtu7\ndy9qtRofq9lswvM8zh+zLAvpdBrpdJrH4rouZ3qFYQilFKamov9taB7I7TYYDGLn7jhOzO1mGAYy\nmQyPR9M0pNNpPn42m8W6devw2te+FgBwxRVX4OUvf/kxXnlBEARBEARBEARBOL057oJYGIZ3DD18\nVCn1IwD7AbwJwJPH+3jC6c2HPvQh3Hhj1GPh3e9+N/bv388ClmmaUErFyvZs22YRp1arod1ux0Qe\n0zRjApXjOCyoUeg9iUy+73NZI22fSCQ41J+2o+f37t0bG0+73cZgMIgF4zuOg0qlwudnmibvh4Ls\nSVRyXZczw3zfR71ex759+wBE5Zq5XI4bAui6Dtu2OfQ/mUxibGyM91Wr1bg8krbP5XI488wzAUQZ\nYo7j8FgoK43KU33fRxiGLC62Wi30ej0eH42DSja3b9+OV7/61bjuuusAAHfdddfzXWZBEARBEARB\nEATh/2/v7mPrvuo7jr/Pta9j+/opjjOneaiakpRpol1Dw9qyJmOjVela0WkrdGhSuyE0KEyaIkHH\nJLSOViCNwlqxFQmkqQwGQtCqKkhkTUGr+kBLRB+gzw80DSWOk9iu7fgh9rV99sfPv4MdkjQpsW/i\n3/slWa3v/fl3zzn+tZI/+p7v0WlloXqIJTHGoRDCS8AG4AGyRvvdzK8S6waeXOixaPF97nOf45JL\nLgHg+uuv584775wXCoUQUsUYMK8KamRkJFU6QRbyTE9P/1YT/jwEyk+hzL8PIaTTGYFU/ZXfP7/3\n2rVrgezUx76+Pp5++mkgq6qa24NsZmaGUqmU7lcqlahUKqmp//DwMK2trfOqrJYvXw5Ab29vqjgD\naGpqolqtprlMTExQqVTo7+8HYN26daxevTqtTU9PD/v3708VYY2NjYyPj8+rQJucnEz92M444ww2\nbNjA888/D8Du3bsZHR1NYV+pVJp3KmVzczOdnZ1s3rwZgKuuuoqLLrooVcNdfvnlb/7LliRJkiTp\nNLFQPcSSEEILWRjWE2PcBfSSnUCZv98GXAj8ZKHHIkmSJEmSJJ30CrEQwq3AD8i2Sa4BPgtUge/M\nXnI78JkQwivAa8AtwK+Be0/2WHRquOKKKwC49dZb2bx5c9oSuXv3bg4ePJiqlurr65mcnEwnHsYY\nKZfLqeILoL29PVVNzczMMD09naqu2tra6OrqSn23RkZG2L9/f9pS2dLSQrlc5o033gCynmbLli1L\nWygbGhro6+tLfbXa2tpYvXo1q1evTtf39fWlCrC2tjYaGxs5cCA7UPWCCy5g165daT7t7e2sX78e\nyLY85ls8c+Pj42nsy5YtS1s8IavYWrlyZdqeuW/fvnQdZFsg+/v70xbLjo4ORkZG0vUzMzNpnvn1\nExMTaW75NtH8n3l12Ac+8AEA3v3ud6f3JEmSJElaahZiy+Ra4NvACuAA8DBwUYyxHyDG+IUQQjPw\nVaADeAi4IsY4eZT7aYn41Kc+RbVa5ZOf/CSQNZ3fs2dP6hGW9++au62vXC7PC286OjrSFsV8y18e\nUHV2dlKpVFLj+L179zI4OJgCse7ubhoaGti1axcAL730Ei0tLSlgy5vS59skQwhMTEzw8ssvA1lP\nsWq1mkKpjo4OVq5cmcZ7zjnn0NPTk7Zk7t27N4V74+PjTExMpLlCFsDlPbxKpRJDQ0Oph9fy5cvp\n6+tj586dALz44ovzArK851i+RbKnp4dDhw4xNDQEZNs98+2XQOrNls+1tbWVEEJay/PPP58LL7yQ\nLVu2nMivVJIkSZKk09JCNNX/0HFc869kp0+qYMrlMg899BAAzz33HH19fSk0qq+vp6GhIVUx5RVg\neaDV0dFBuVxOoVK5XGbjxo2sW7cOyE6JfOGFF+jp6QGyHmVz+2r19/dTKpXS501NTdHf358Crbq6\nOkqlUupZlp96mYsx0tjYyBlnnAHA2rVrOe+881L1Vn9/PyGENL6RkZEU1tXV1TE5OTmvQqu5uTk1\n7B8dHaW9vZ0NGzYAsGrVKl555ZXUb61UKjE6OprCufHxcaamptL48nAsH/vY2BgzMzPp85uammhu\nbk6BWHNzMzHG1OOspaWFG2644QR/m5IkSZIknZ4WvIeYJEmSJEmSdCpZ8FMmpcPl2/K2bdtGpVJJ\nJxnOzMwwODjI4OAg8Juqp8P7XeWq1SqvvfYazzzzDJBViA0ODqYtjQ0NDYQQUpVUpVKhvb093adS\nqbBnzx727NkDZFVTdXV1hBAA0vbNuRVj5XKZTZs2AfDkk0/y+uuvpwq1888/nyeeeCL1CSuXy2n7\nZj7+/N55NVxe4TUwMMDGjRs5++yzgWzL5Kuvvpp+Nt9SObcirKWlJc0lP4Ez19DQQKVSSRVgeW+1\ngYEBIDvV8rLLLuOLX/wiAA888MCb/NYkSZIkSVo6DMRUM7fddht33313CpB2797N9u3b0xbHarXK\n1NRUCpVGR0fn9cUaGxtjbGws/fzExATT09MpJIoxUl9fT2dnJ5A1pG9qakp9s5YvX562LAIMDQ1R\nV1dHpVJJ9xsdHU1bKkMINDc3p/ENDAzwtre9LQV8DQ0NjI2NpS2eML95fblcTuFaQ0MDra2taSxd\nXV20tramufT29jI6Opq2WE5NTRFCSKHX6Ogo9fX185rsT01NpQMIOjs76ejoSNs7q9Uq1WqV9vZ2\nAHbs2MFTTz11Yr8wSZIkSZKWiJD/gX4qCyG8E3i81uPQwrrllluoVqs8+eSTQNaUft++fSlEGh0d\nJYSQqqzynlx5AFWtVimVSqlP1vLly1m5cmX6+YMHDzIzMzOvj1alUkk9v/r6+jh06FCqKJuZmWFs\nbCx9Xh6UdXV1AVmPsOuuuy71+SqXy9x1112MjY0BWYCWB1YrV66ku7s7Nb0vlUpUKpX0WUNDQ4yM\njKRTJ5ubmxkcHExN9CcmJiiXy+n9vD9Yfv9qtcr09DQbN24EYN26dbz88sup59imTZuYmZlhx44d\nv8uvSEvTBTHGJ2o9CEmSJElaTPYQkyRJkiRJUqFYIaZTykc/+lEuu+wyAJ5++mnuvfde1qxZA8CB\nAwfYu3cvb7zxBvCbirB8W+H09DTlcpm2tjYA1q9fT0dHR6rgGhgYSNVbAI2NjXR1daVtk+Pj4wwO\nDqaKsFKpRLVapbm5GYCtW7cyMDCQ+nINDQ1xzz338I53vAOA7u5u9uzZw8GDB4GsiiuvKsv7jOWf\nX1dXN6+HWG9vL0NDQ2lLY1tbGwMDA+n9fHvk3P5m9fX1aa5NTU2cddZZrFq1CoCf/exnXHzxxalH\nmHQMVohJkiRJKhwDMZ2ytm3bxq5du1LI09PTw+DgYGoMPzw8nHpjwW96iOWh0po1a4gxsnfvXiDb\n4hhjTFss6+rqaGxsTKHSzMzMvJ5hpVKJUqmUArn3ve999Pf3c8cddxxxvNdddx2/+MUv0iEBQLp3\nXV0dU1NTqadYvlVyZGQEIAVxeU+xqampeWPJe5vlP1+tVunu7uaSSy5J7/f19aX7jYyMcP/995/4\noquIDMQkSZIkFY5N9XXKuu222wC48cYbgawn2I4dO1LVVXNzM8PDwykkGh4eZnJyMoVNAwMDHDx4\nMDWqz6uy5p5WWSqV5gVg5XI5vV8ul1m+fHm6380333zM8W7cuJHp6el0amVTU1O6d35iZlNTE5CF\nb5OTkynMm5qaoqWlJYV1Y2NjTE9Pp2q0+vp6Dhw4kAKzcrlMa2trCvuuvPJKHnvsMb75zW+e0BpL\nkiRJklRE9hCTJEmSJElSobhlUqeVa665hueeew6Ajo4OXn31VdauXQtk2xCnpqZSz6+xsTH279+f\nTpksl8vz7lWtVimXy3R2dgLQ2trK9PR06kk2NTVFe3s727dvP+7xffrTn+aJJ7LdZ8PDw+kEy9HR\nUcbGxlKFV7Va5dChQ2ls4+PjrFixgu7ubgD6+/uZnp5mxYoVQFYd9+yzz6btm42NjZx33nmpx9jX\nvva14x6jdBi3TEqSJEkqHAMxLTk33HADAOeeey6Dg4M8+OCDQNanq6+vL21TrFarrF69mnPPPRfI\nmuJXq1V2794NwLe+9S2WLVt2wp+fB2iPPPIIzz//fPqs/fv309HRAWQ9vnp7e1ND/2q1yuc//3k2\nb94MZMFaXV0d/f39AKxYsYIzzzwz3e973/veCY9LOgoDMUmSJEmFYyCmQrn22mtTn641a9bQ3d2d\nToCsVCps3bo19fnKe4edTJdeeikAV199Nc8++2w6AbO+vp6PfOQjPProowBcddVVbNmy5aR/vnQE\nBmKSJEmSCsceYpIkSZIkSSoUK8QkqdisEJMkSZJUOFaISZIkSZIkqVAMxCRJkiRJklQoBmKSJEmS\nJEkqFAMxSZIkSZIkFYqBmCRJkiRJkgrFQEySJEmSJEmFYiAmSZIkSZKkQjEQkyRJkiRJUqEYiEmS\nJEmSJKlQDMQkSZIkSZJUKAZikiRJkiRJKhQDMUmSJEmSJBWKgZgkSZIkSZIKxUBMkiRJkiRJhWIg\nJkmSJEmSpEIxEJMkSZIkSVKhGIhJkiRJkiSpUAzEJEmSJEmSVCgGYpIkSZIkSSoUAzFJkiRJkiQV\nioGYJEmSJEmSCsVATJIkSZIkSYViICZJkiRJkqRCMRCTJEmSJElSoRiISZIkSZIkqVAMxCRJkiRJ\nklQoBmKSJEmSJEkqFAMxSZIkSZIkFYqBmCRJkiRJkgrFQEySJEmSJEmFYiAmSZIkSZKkQjEQkyRJ\nkiRJUqEYiEmSJEmSJKlQDMQkSZIkSZJUKAZikiRJkiRJKhQDMUmSJEmSJBWKgZgkSZIkSZIKxUBM\nkiRJkiRJhWIgJkmSJEmSpEIxEJMkSZIkSVKhGIhJkiRJkiSpUAzEJEmSJEmSVCg1DcRCCJ8IIewK\nIYyHEB4LIbyrluORJEmSJEnS0lezQCyEcC3wJeAmYBPwc+C+EEJXrcYkSZIkSZKkpa+WFWLbgK/G\nGL8RY3wB+BgwBny4hmOSJEmSJEnSEleTQCyEUAYuAH6cvxZjjMCPgItrMSZJkiRJkiQVQ60qxLqA\nOmDfYa/vA1Yt/nAkSZIkSZJUFKfLKZONtR6AJC1R/v9VkiRJUuHUKhDrA6aB7sNe7wZ6j3D9WQs9\nIEkqqLNqPQBJkiRJWmz1tfjQGGM1hPA48F7g+wAhhDD7/ZeP8CP3AX8DvAYcWqRhStJS1kgWht1X\n43FIkiRJ0qILWS/7GnxwCB8Evk52uuROslMnrwF+P8Z4oCaDkiRJkiRJ0pJXkwoxgBjjd0MIXcDN\nZFslnwIuNwyTJEmSJEnSQqpZhZgkSZIkSZJUC6fLKZOSJEmSJEnSSWEgJkmSJEmSpEI5LQKxEMIn\nQgi7QgjjIYTHQgjvqvWYFloI4aYQwsxhX88dds3NIYSeEMJYCOH+EMKGWo33ZAohbAkhfD+EsGd2\n3u8/wjXHnHsIYVkI4Y4QQl8I4WAI4a4Qwu8t3ix+d2+2DiGEO4/wjPzwsGtO+3UACCH8cwhhZwhh\nOISwL4RwTwjhnCNct6Sfi+NZhyI9F5IkSZL0Vp3ygVgI4VrgS8BNwCbg58B9sw35l7pnyA4cWDX7\ndUn+Rgjhn4B/AP4e+CNglGxdGmowzpOtQnbIwseB32pyd5xzvx24EvgrYCuwGrh7YYd90h1zHWZt\nZ/4z8qHD3l8K6wCwBfgP4ELgUqAM7AghNOUXFOS5eNN1mFWU50KSJEmS3pJTvql+COEx4Kcxxn+c\n/T4ArwNfjjF+oaaDW0AhhJuAq2OM7zzK+z3ArTHG22a/bwP2AdfHGL+7eCNdWCGEGeAvYozfn/Pa\nMec++/0B4K9jjPfMXvN24HngohjjzsWex+/qKOtwJ9AeY/zLo/zMkluH3Gwgvh/YGmN8ePa1Ij4X\nR1qHwj4XkiRJknS8TukKsRBCGbgA+HH+WswSvB8BF9dqXIto4+x2uV+GEP4nhLAOIISwnqzqY+66\nDAM/ZYmvy3HOfTNQf9g1LwK/Yumtz3tmt869EEL4Sgihc857F7B016GDrGpuAAr9XMxbhzmK+lxI\nkiRJ0nE5pQMxoAuoI6vymGsf2R+/S9ljwN8ClwMfA9YDD4YQKmRzjxRzXY5n7t3A5GwgcrRrloLt\nwHXAnwE3An8C/HC2ihKyuS65dZid3+3AwzHGvK9e4Z6Lo6wDFPS5kCRJkqQTUV/rAejIYoz3zfn2\nmRDCTmA38EHghdqMSqeSw7bGPhtCeBr4JfAe4P9qMqjF8RXgD4A/rvVAauyI61Dg50KSJEmSjtup\nXiHWB0yTVXbM1Q30Lv5waifGOAS8BGwgm3ugmOtyPHPvBRpmeyUd7ZolJ8a4i+y/mfxkxSW3DiGE\n/wT+HHhPjHHvnLcK9VwcYx1+SxGeC0mSJEk6Uad0IBZjrAKPA+/NX5vd9vNe4Ce1GlcthBBayP6g\n7Zn9A7eX+evSRnby3JJel+Oc++PA1GHXvB04E3h00Qa7yEIIa4EVQB6QLKl1mA2Brgb+NMb4q7nv\nFem5ONY6HOX6Jf1cSJIkSdJbcTpsmfx34OshhMeBncA2oBn4ei0HtdBCCLcCPyDbJrkG+CxQBb4z\ne8ntwGdCCK8ArwG3AL8G7l30wZ5ks33SNpBV/ACcHUL4Q2Agxvg6bzL3GONwCOG/gH8PIbwBHAS+\nDDxyOp2gd6x1mP26CbibLAjaAPwbWRXhfbB01gEghPAV4EPA+4HREEJeCTYUYzw0++9L/rl4s3WY\nfWYK81xIkiRJ0lt1ygdiMcbvhhC6gJvJtvQ8BVweYzxQ25EtuLXAt8kqOw4ADwMXxRj7AWKMXwgh\nNANfJTtp7iHgihjjZI3GezJtJut1FGe/vjT7+n8DHz7OuW8j2257F7AM+F/gE4sz/JPmWOvwceA8\nsubpHUAPWeDxL7OVlbmlsA6QZ08qCQAAAK1JREFUHSwRgQcOe/3vgG/Acf83cbqvx5utwzTFei4k\nSZIk6S0JMcZaj0GSJEmSJElaNKd0DzFJkiRJkiTpZDMQkyRJkiRJUqEYiEmSJEmSJKlQDMQkSZIk\nSZJUKAZikiRJkiRJKhQDMUmSJEmSJBWKgZgkSZIkSZIKxUBMkiRJkiRJhWIgJkmSJEmSpEIxEJMk\nSZIkSVKhGIhJkiRJkiSpUAzEJEmSJEmSVCj/D/CdlGRXBGuOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8ceda20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show FA\n",
"%matplotlib inline\n",
"# %matplotlib notebook\n",
"from matplotlib.widgets import Slider\n",
"\n",
"sz, sy, sx = FA.shape\n",
"# set up figure\n",
"fig = plt.figure(figsize=(15,15))\n",
"xy = fig.add_subplot(1,3,1)\n",
"plt.title(\"Axial Slice\")\n",
"xz = fig.add_subplot(1,3,2)\n",
"plt.title(\"Coronal Slice\")\n",
"yz = fig.add_subplot(1,3,3)\n",
"plt.title(\"Sagittal Slice\")\n",
"\n",
"frame = 0.5\n",
"maximo = np.max(np.abs(FA)) # normalize the FA values for better visualization\n",
"minimo = np.min(np.abs(FA))\n",
"xy.imshow(FA[np.floor(frame*sz),:,:], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0, vmax=maximo )\n",
"xz.imshow(FA[:,np.floor(frame*sy),:], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0 , vmax=maximo )\n",
"yz.imshow(FA[:,:,np.floor(frame*sx)], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0 , vmax=maximo )"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### MD\n",
"MD is a 3D scalar map that shows the mean difusion for each voxel, so each one is associated with an intensity value.\n",
"\n",
"Inline image of slices of the MD in three different viels (Axial, coronal, and sagittal viels)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:19: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:21: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x97c58d0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RE5MCYnLCqFQqTE1NhYCYX9GxUCgASQCr2WyGQJhvdO+DUJD0AfNZW5VKhT179jA5OQkk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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x91edc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show MD\n",
"%matplotlib inline\n",
"# %matplotlib notebook\n",
"from matplotlib.widgets import Slider\n",
"\n",
"sz, sy, sx = MD.shape\n",
"# set up figure\n",
"fig = plt.figure(figsize=(15,15))\n",
"xy = fig.add_subplot(1,3,1)\n",
"plt.title(\"Axial Slice\")\n",
"xz = fig.add_subplot(1,3,2)\n",
"plt.title(\"Coronal Slice\")\n",
"yz = fig.add_subplot(1,3,3)\n",
"plt.title(\"Sagittal Slice\")\n",
"\n",
"frame = 0.5\n",
"maximo = np.max(np.abs(MD)) # normalize the MD values for better visualization\n",
"minimo = np.min(np.abs(MD))\n",
"xy.imshow(MD[np.floor(frame*sz),:,:], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0, vmax=maximo )\n",
"xz.imshow(MD[:,np.floor(frame*sy),:], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0 , vmax=maximo )\n",
"yz.imshow(MD[:,:,np.floor(frame*sx)], origin='lower', interpolation='nearest', cmap=\"gray\",vmin=0 , vmax=maximo )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### First vector (main tensor direction)\n",
"This is a 3D vecotr field, so each voxel is associated with a vector.\n",
"\n",
"Inline image of slices of the FA in three different viels (Axial, coronal, and sagittal viels)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:32: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:34: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:36: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:52: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:54: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Anaconda3\\envs\\py2\\lib\\site-packages\\ipykernel\\__main__.py:56: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"image/png": 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bt1b7fv31Vzlp0iStX8GCBeW3336r4saNG8sZM2ZIKaVcvHixfPvtt93OKaWU\nr732WrJjbNu2rfqvve2r76FDh5I9Z0qtW7dOSillRESElFLKDRs2eH3dkvt5OY+//vrrsm7dumk0\nSspA0vu9nY2NjY2NjY2NjY2N7Y41LqukDKlbt26YOHEiNm7ciCpVqqT48c6ZV6bWrVtj8ODBKFGi\nhNvyyTlz5qB169Zw/rvYs2cPDhw4gCZNmiAqKgovvfRSisdTs2ZNtzuh+mvJkiUoUqQIKlSooPZN\nmDABPXr0AGDN8Fu8eDEmTJgAwLoTZ1xcnNs5GjZsCMCaOdagQQO/r5/SZZqFCxfGqVOn/O5PdwUu\nqyQiIiIiokwr4JZVUvoJDw/X4rJlywIAXn31Vbe+jRo1wvbt21VibNSoUdqyTSF8/63uq5bXl19+\niRIlSgAAdu7cqR1r1aqVlTV2nN8eJwCviTFzea1p7969AICFCxd67XPp0iUtvnnzJgCgYcOGePPN\nN9X+2bNn49ChQyoOCwvDs88+q2IzMWafw9O2k7caar7u9OlJ+fLltThPnjwpSq4RERERERER/ZMC\nLjmWkJCgxdu3b9fiefPmabFZlN1Murz33ntaPHPmTC026yf98ccf/g/2H2bWM7ILrts++eQTLTbr\ncr311ltaPHbsWK/X6tu3L/bu3YuGDRuqhJHzemFhYbhx44aK+/Tpo9Uck1LiwoULAKyaWi1bttQS\nMP/5z3+06y1btsxjfbmWLVuqWVXOenOzZs3yOO6GDRuqJoTA6dOnAfxdk2zJkiXqfM6f9RNPPAHg\n70Tg4MGDtfNWqVIFixYtUvG3336LrFmzArDupOn8PW3Tpo2aJWa/FuadKufPn6/GIoTQEmJSSu05\nNGzYEMWKFVPJuPfff197LczkpZmYbNiwIQ4ePKjtc77W1atXR2RkpIq7d++u9TXrbJm17s6cOYOM\nyv7528zZgRMnTtTirl27anHLli212Kyrt3jxYi3ev39/qsZJREREREREPqT3us40bpSBTZ482e++\n7dq1k1JKCUBOmDBB7t+/3+/Hnjx50ufxI0eOqO2oqCiPfU6fPq3FCxcu9NivTp068q233pKVK1dW\n++yaW1FRUer8YWFhctiwYbJt27YyOjra7TzLly9X2xcvXpR16tSRUkqZLVs2j9ft06ePFg8fPlxt\nz5071+Nj/OV8TRo1aqTGcvToUb8eb9Yccz43umul93s7GxsbGxsbGxsbGxvbHWusOUb/mPDwcLe7\nfRYtWhS9pb9AAAAgAElEQVTlypXDd999p2Yk7d27V8208tcDDzyABQsWIFu2bKhRo4bHPs67VsbG\nxmLLli14+eWXPfaNjY1FSEiIiiMiInD06FG8++67AKw7e/7rX//ye3yrVq1CzZo13fZ/8sknaN26\nNXbt2oVatWp5ffz27du1mmM9evTQZo+ltC5Yau3evRtxcXGoVq2a1z758+dXs/qcatSogQEDBnh8\nHSjDY80xIiIiIiLKtAJuWSWlL3OZ2YEDB9TSvytXrgCAz8SYuYzNKSwsTEuM2XW+AKBDhw6oXbu2\nikNCQrTEWExMDP766y+fY7cTYwDcEmPJLXfzlBA6efIk3n33XRQoUEAlxrwlnZxLGcePH4+cOXOq\n+J133sGkSZN8Xj+tPPnkk8n2eeaZZ9z2Pffcc1i7di0TY0RERERERJThBFxy7Nq1a1q8adMmLf78\n88+12JzpZN450ay7NX78eC1eu3atFptF1zOSAwcOaLFZPL5///5aXL9+fS02Z2ENHDjQ7RrdunVT\n24sXL0aOHDlUfPDgQVSqVElrW7ZswZEjRwAAW7ZsQaFChVR/O5kGWK9rUFCQiitVqqT9rKdOnYri\nxYu7jccWGhqKbNmyeT1uGjduHLZs2YKpU6cCcK8dZY/fuS2EgBBCxUFBQaoIfqVKlXD16lVV1+3n\nn3/Wzucscv/2229j1KhRKh47diy6dOnidv2goCDcunULAFC8eHG0atVKnXvLli2qVapUCQ899BBq\n166tjds5duf+3Llzu13LPk+lSpVw/vx53H///ep4njx5sG7dOrfz2MybGfz4449anJHvfHnixAkt\n/uGHH7R4+PDhWtysWTMtdiZsAfcahnPnztXiPXv2pGqcRERERERE5B2XVdI/xtOySsCajbR7924V\nJyQkaDOjduzYgfbt26Nbt2544403AAANGjTA66+/jtdee83tfKtXr8akSZPw7bff+j220NBQxMTE\nqNjTssomTZqo+Ny5cyhQoAB69eqFl156CX369MG2bdv8vp7zee3YsQOAdafMX3/9Fbt27cLTTz+t\n9ntiLqts2rQpvvnmG7d+UkpUqFABDz74IJYvX57seGylSpVSPwMprbt37tu3D2XKlPH6+PLly3sc\nc+7cufHnn3/6vDZleFxWSUREREREmRaTY/SPmD59OgYOHKiWRRYvXlzNCEsL+/fvR6lSpbwe79Ch\nA4YOHYqCBQu6HbOTYm3atMH69esBeE6OVa5cGYULFwYA9OzZU836qlmzJuLi4lKcHPPX6tWrERYW\ndkfO7Q9f9czCw8Nx7Ngxt9lfTr/99htKlSrldpdLuqvwh0dERERERJlWwC2rpPTx5ptvqmL4AFKV\nGPvggw+0uEWLFmrbV2IMsJZVekqMAdassdDQUJUY88ZOjAH68tlVq1b5fNz169dx8uRJtGnTBgDw\n+++/Y9CgQbh8+bLWr3HjxmrbWdsrLCwM27dvV3F4eDh69eql4rfeegtNmzYFAERGRiabhEpuvCZf\nCfTOnTsjT548Ph9fuXJlbUz79u1L0fWJiIiIiIiI7qSAS45dvXpVi82aYHbNJ5tZIyg0NFSLzTpb\nH374oRYvW7ZMiz3dxS+jMJMWs2fP1mJnvTDAvfB61apVtbho0aJaXKBAAS0uVKgQihYt6rU1a9YM\n58+fx7FjxxAeHo4+ffpoj58xY4bb9Y4dO6biV155BWfPnlXx5cuX1XFzbCn19ttva3FISIgq6P/7\n77/j0UcfVcdy5MiBNm3aqNfTPpY3b14AwPnz57FixQptGejmzZu18zvvVNm5c2eMGTNGxdOmTVNL\nKhs0aAApJYoWLYp7773X49jNungAtGWPzjpwgJUcO3bsmPaz8aV9+/Y++9iJPJuZ9Fy0aJEWHz9+\n3Of10pOZ5DXr9Jm/s+YMwHLlymnxW2+9pcVTpkzRYmeSlIiIiIiIiNKIlDIzNcrAvvjiCymllBcv\nXpRSSrl792517PTp02q7Ro0a2uOuXr2qxcHBwW6PMe3bt0+Lr127JqWU0vqVt7Rt21ZtBwUFaf3N\ncy9cuFCLx40bp7Z/+uknKaWUVapU8TqmsLAwLXaOQ0oply9f7nG83nTv3t3ncSn153D27Fmv1544\ncaIMDw9P9nxSSnnjxg23fR988IEWJyQkaGMwX5OqVatqccuWLf26NqWr9H5vZ2NjY2NjY2NjY2Nj\nu2Mt4GaOUfqxlxU+8MADiIuLA/D3zBlnfa/FixdrjzNnQP3xxx9ujzG1a9dOuxNprly5AOhLBGfN\nmqW27bs6+mPQoEHo2bOniu3nsmHDBoSEhODixYvaXTVr1qzpdg5Pd/K0hYSEIH/+/KkaGwBER0er\n89ics/acrwEAdO3aFZ06dfLr3J7u6Dl8+HCMHDlSxc6ZZyEhIW4/J3v56o0bNwBAu7MlERERERER\n0T+NyTH6R9lLD4ODg/HMM89g586d2vGSJUt6TMD4K3fu3ACALVu2oHLlytoxT8sJbYcOHUJSUlKK\nrmUn1+rVq6ftz5cvHw4fPqxiTzW+hgwZ4vW8sbGxiI+PV3FQUBCqVKmi4tmzZ+ORRx5RsbPmGACU\nLl3a57ijoqKQkJDg9XjVqlWxd+9en+cw9e3bN0X9n3/+edxzzz0AgMmTJ6fosURElHpCiKJCiCQh\nROs0POdaIcTqO3kNIiJKPSFEddf78nP/0PUGCyFS9seV7/O5fa6k9TWIAi455qytBADLly/X4sGD\nB2txrVq1tNicxVStWjUtNmsMLVmyRIvPnTvn91j/aWaiKjw8XItbtWqlxWb9tccee0yLhRDo0aMH\nAOD9999HeHi4lvgy62oBwIEDB9RrbCfSAOt1zZ49u1tzyp49u9vP19alSxc1A2vs2LHq8StWrMAz\nzzyD0NBQZMni3z+HIUOGoGfPnmjXrp3aV7FiRa2Ps0h99uzZsX79em28WbNm1fqbs8OcyTEpJTZu\n3KjiNm3aqNcV0GuO2Y4cOaK9Ts6C+A0bNtTGZ/7ObtiwAU888YQ2fk9NCAEhBLJmzYrs2bNj0qRJ\nEEIgW7ZsEEKo55s9e3YkJiYiMTERgDVbcM2aNer8HTp00K4/b948t+eSUR06dEiLv/zySy02Z+Q5\nb7QAQJthCABNmjTR4rFjx2qxp38zRJR2hBAlhBBThBAxQogEIUScEGK9EKKHECJH8mfIXFx/jMwS\nQhxyvR6xQoh1QojBRldPd27hHcSJiAxCiLJCiAghxFHX++pJIcRyIUS35B9927T3ZSFEcyHE22Yn\nIUSIEGKQEOJJ81gKr+XX54AQor7rS5azQohrrs/gBUKI//hxDSbHKM0Ic4nVXS5TPZm7lRACUkq0\naNHCLdHhtGfPHpQtWxYAkCVLlhTP3JJSer0zoz0GTyZMmKCSS1JKPPXUU26FzmNjY7XlgBEREVri\nomfPnvj0009VXLFiRWzbtk3FOXLkwPXr11P0fJx8PbfklClTRt1c4ebNmwgKCtLO5enczn3PPvus\ndudOb2Mx93fp0kWbBbZr1y78+9//xuXLl9XNB4oUKYITJ05o5+nYsaNb4XnKcFL3y0h0FxFC1AXw\nDYDrAL4EsBfAPQCeBdAYwBdSSv/WoGdgQoiiAI4AaCul/NJHv1AA2wBcAzATwFEAIQAqAHhRSpnL\n0XcNrHqWYY599wD4S2ay/9EkIkotIUQVAKsBHAMwG8AZAEUAPAMgVEr5mI+Hp8X175FS3nDE3wMo\nI6UsYfR7CsBWJPM5kcy1BgEYKKUMSqbfuwA+BrAWwBIA8QAeAfACgF1Syvaufm6fXUKILACyOp8T\n0e0IuJljdOdJKb0mulq2bKm2y5Ytq+4eeevWLVy5ckUdq1q1Kl588UUVm7ObFi1a5DN55On/xUeP\nHo1hw4Zps66klGoWl3OmmqfYZN7N83bcvHlTi83n5kzemX3N19p5x8SsWbOiYMGCPs/t3PfSSy9h\nxIgRfo3ZPI+5PPLf//43ihYtiuLFi6t9ZmIMAKZPn+7X9YiI7hQhRDEA82H9j/fjUsr/SilnSCnD\npZQtAJQGsM/HKVJyrbtlBto7AHIBeEZKOVBKOVNKOVxK2RjAv5J7sJTyBhNjRESafgD+AFBRSjnC\n9b46REr5IoAqyTz2tqUgifSPfCkqhAgC0B/AMillmJRyvJRympSyj5TyKQC9fT1eSpnExBilJSbH\n6I7wtEQxMTERDRo00PY9+OCDAKxEi7Mw++bNm/Hjjz+qeNSoUejXr5+KGzVqhJw5c/ocw7p167RY\nSokBAwa4jfOXX35B2bJlVWF9wFrW6Kv22fjx49UNBvxhLpu0i9HbsmbNimvXrnl87OHDh1GhQgUV\nL1iwQJu11qNHD7z++usqNpfmZcuWTS3TTG4cUVFReO45/0oR3Lx50+uYbXby0+Ssofbmm2/6dT0i\nojuoD4B7AbwhpXSrfyClPCyl/MyOhRBBQogBruWG14UQR4QQw12zpeDod1QIESmEqC2E2CqESADQ\nIZXnqCqE2OJahhMjhGhl9MsrhPhECLFbCPGna0lo1G0siykB4KSU8qSH1+OCrwd6qzkmhCgphPhG\nCHFOCBEvhNgvhPjQ6FNICDFTCHHG9brsFUK0AxHR3a8EgH1SSrc6MOb7qhCinRBilWup4XUhxD4h\nhNvsZWEZLIQ45VqSuEoI8bjrs2Omo59Wc8w147cuAPv9OkkIcVgIUR3AL7BWZH3h2n/Lfj8XQjzr\neh8/5hrXcSHE2FR+8ZMfwP0ANno66MdnjceaY0KIlq7Py2tCiEvCKgfwgtHnRSHET0KIq0KIK0KI\nH4QQvgs3U6YXcMkxsyaVWSx9+PDhWly/fn0ttpM5NrMmWf/+/bX4hx9+0OLz58/7P9h/2O7du7V4\n2rRpWvzGG29ocZkyZbTYrKdk1mcrWLCgKhy/efNmREdHq7tImswkzuDBgzF58mTkzZtXnddXUflS\npUpp40lMTETv3taXDz169HBL6uzZs0e7Q6S3cdnefttteb5XefPmVedu0aIFALgl6YQQ2utlL0ME\ngBIltJnOaNGihXa3zIkTJ+Krr75SsX23StvJkyfV88mfPz/y5s0LIQR69+6Nhx56CHnz5lXNG/sO\noQDw888/A7BmrGXNmhUfffSR9jzy5s2L3Llz4/Lly26vg91++eUXt2t4G8fRo0e9jiu9xcTEaLHz\n5wBAm6UIAJUqVdJis26f/fth++yzz7TY0+tGRGmiHoDDUsotfvafAWAIrGWHPWEtB+kLa/aZkwRQ\nCsBXAJYD6AFgZyrO8SiAha5zvAPgEoBZQojHHf1KAGgA4HsA/4W1TOUJAGuFEPoUYv8cA1BECPF8\nKh7rxpWk+wVADQBTYL0Wi2G99nafAgC2AAgDMMHV53cAM4QQPUBEdHc7BuApIUSZZHsCnWAtZx8O\n633/OIDJQojORr+PAAyE9f76Lqz3zGUAPM0icM7m/RDW59EFAC0AtIT1WRTtOp+A9V7dEkArAD+5\nHveq69yTAXQD8D8A3WEtE02pcwASANQXQnj/Q8Q7t7pmruWcXwK4AWAArOdyHNbnit2nFYAfAPwJ\na3baUACPA/hZCJHszGjKxKSUmalRBlCgQIEUPyY2NlZt//XXX/L//u//5Isvvui1/+XLl1N0/jNn\nzqjt7t27y9atW8vFixd77X/69GktXrhwoRa//fbbWvzUU09pcfbs2X2Op1evXj6POx05csTvvt5U\nrFhRi7t27eqzf5UqVaSUUj744INSSikfffRRdaxSpUpqe8KECdrjIiIivJ7zypUrHvd36NDB51go\nQ0jv93Y2tjvWAOSGVdB3kZ/9n3T1/9zY/zGAWwCqO/Ydce17IQ3OUcWxLz+sPyg+duzL5mGs/3L1\n6+fYV9R17dbJPM/SAK66+m4HMA5W8i2nh75rAKz2dQ0A62AtJyrs45rTAZwEkMfY/xWshGD29P59\nYWNjY0ttg1VH6waAvwBsgJXYqgWrbpbZ1+39DsCPAH53xAVc54sw+g10vQfPdOyr7vosec6x73tY\nXwyZ13nK2+eEl3H1AXATwMOOfYMA3PLjNRnsGtefAJbC+pKovId+nj5XtGsACHWNY6GP693r+jwJ\nN/Y/COCy+bnMFlgt4GaO0Z139uxZt30jR47U7n559uxZrFixQsV2XaybN28ia9as+OWXXxAVFeXx\n/NeuXUOePHm0ZZNly5ZFkSJFtH723REB/a6hEyZMAAC8/PLLWv9Tp05pcalSpTw/Qcc4nJKrUeZk\nzhxz3sX0qaee0o4VK1YMK1euVLG5nPPq1atexxEUFIQyZcpg69atal9kZCRGjRrldWyJiYnYsGGD\nGtfx48dx8OBBdXzz5s2oW7cuunfvju7duwOAWvLauHFjr+fNnTu32z7nXTmJiNKJvabf8+2O3b0E\n65vqccb+MbC+aa9r7D8ipVxp7EvpOaKllGrZibSWmhyANVvM3qfe/IUQWYQQD8AqbHwAVhH9FJFS\nRgMoB2AOrD9KegD4DsBZIUSK1sMLIfIDqAZghpTylI+ujWD9sRYkhMhnN1gz5oJT8zyIiDIK12dB\nZViF558E8B6sWV6nhBD1jb7qDxkhxP2u98KfAJQQQtj/U10TQBCAcOg+wx1ijCuXa1ybYK1IK5+K\n8w0G8DqsL2Fqw5rR9qsQ4lchhO8/xty9AuszdKiPPrVgfZ58bXzOSFgzl9NktjTdpdI7O5fGjdLZ\noUOH1HaDBg20Y9av29+WLl2qtkNDQ6WUUm7atElKKWV8fLy8evWq1n/y5MlafPLkSb/HZV7bjH/6\n6Sd58eJFFW/cuFE7/tBDD/l8/BNPPKHFt27d8jiOuLg4KaWUgwcPVvsaN24sly9frvWLiYnx+Hjb\nJ598orb79u0r27Vrp+Js2bLJQoUK+Xx8csqUKeP1WHx8vN/nOXnypAwODr6tsVCGkN7v7Wxsd6wh\n5TPHwmF96x/k4dglAAsc8REAK9LgHEs99FsDYJUjFrCWUx50nTvJ1W4BWOno59fMMeNaAkAZWLMD\nLrrOGWaMxevMMQBPu+L2Pq7xoGO8SR7aLQAN0/v3hY2NjS0tGoCssGZofQjrrsDXAZRyHK8KYCX+\nnsHrfC982NXnfVdc1MP5L+LOzBwrAuAL1/nNcbV09PNr5phx7vtgJfzmuM55EMA9rmP+zByb7Pr8\nc5uJ5+jznpfPGPs5XErv3w229GsBN3PMnO1j10+yjR8/XovNWTply5bV4mbNmmnxxx9/rMXOGT8A\ncPHiRf8H+w/77bfftHjevHla3KtXLy1+/nk9sV6zZk2thpJ5F8WBAwdqsXOm16FDhwAAzzzzjBrL\nO++8o46PGjVKm73UunVrvPDCC2rMt27dcquB5uR6M/QaV6tWDQ888ICKixUrph2fOHGiFps1x7Jn\nz67FQUF/37VYCIHHH38cQghVc8p5voiICO2xN2/edLvDpMn5sxgxYgRmzlT1NnHjxg1tFpxZv82s\nu+f00ksv4ejRo4iMjFT7zNcqKioKnTr9XQ/08ccfx+OPP46CBQuqbVvhwoXVOX777Tf89ttv2nHg\n79fHrsvmvDbg+Q6XGYV5w4HFixdrsTlDsG5dfTJI5cqVtbhr165a7Py5AsDOnTtBRGlLWoWRT8Oq\nz5Wih/rZz3uBTP/PccvLfucdxfrBmnm2Flb9mNqwlvBE4zZrzErLPinlKFizu4TrGmnJHuNcWOM2\nWy1Yy5CIiO56UsqbUspfpZT9AXQBcA+sel4QQpSAlRh7ANaXHi/Beh+0Zxuny9/wQogsrnG9CGAk\ngIaucbWB9blwu581V6WUq6SUrWDVMAsFUCmZh6VUFlifvS3g+XPmZe8PpUwvvbNzadwoAyhfvryU\nUsrXX39d29+zZ08t3r17t9dz2DWvvFmzZo0WO2d9Pfnkk1JKffbXlClTfJ7P5Knm2KBBg1ScXM2x\nlHLOHDt9+rQ2Oys6Olq9pilx8+ZNefPmTZk3b161D4CcNWuW1q9v375q237tUsue3de2bVu1L7mZ\nY6w5dldI7/d2NrY72gB8DisBVcmPvvY39SWN/QVgffPsrAN2BEDkHTqHOVtrBxwzxBz7TyCZemAp\nfK3udT0+ysdYzJlj+V3xWB/nzQIgDsDc9P59YGNjY/snG6yZuUkAJrvinq7PiMJGv+Gu/f9yxc1d\ncU2j3wPwr+ZYJDzPHKvg6XMCwL9d+1sY+18w+yMVM8eMc3Z1jbepK/Zn5lgv12Oe9HHeJq7zvJDa\nsbFl3hZwM8foztu+fbvH/ePGmaVV/ialxH333afi6dOna3dINNWoUUOL7btgAlC1yoKDg9W+o0eP\n4r333tMeY9Yc27FjhxabNccGDx6strNkyYLvv/9eO379+nW1nSPH33czvnbtGrp06QIA2mPsmXH2\nrDlbtmzZkDPn3zeYueeeezB9+nQVm7MZzTugRkZG4ty5cwgKCkJQUBAuXbqkjkkp0bZtW1y48Ped\nkUeMGKG2d+3ahdshpTUJYtasWQCABg0aaPtPnjyp9c/IMymJKKB8DKs+13TXHRM1QohQx90So2B9\nQ97T6NYL1rfRS/24Xlqcw3QL+kwyCCFeBVA4FeeCEOJZIURWD4fsKbD7/T2XtGqk/QSgvRCiiJc+\nSQC+BdDY053cXHXLiIjuWkKIGl4Ome+rN13/VX+rCyGCAbQ1HrcK1nu/eQfL7n4O6Rqs+lue9gNA\nHmO/PYvZzCH0hP8zoRUhRE4hxDNeDr/k+u+BFJzyO9c4BgohhJc+ywBcAfCBp884ftYEuPTOzqVx\nowwkf/78bvv2798v169fn+xjc+fOLadPn67tO3HihM/H3Lx5U22/8sor2jFPM8fWrVuntpcsWaLd\nAXPJkiVaX/NulWZNsJIlS2qxEEKLw8LCtBlapnHjxnk95o9OnTq57Xv++efVdkJCgtqOjo6WLVu2\n9HquXbt2eT1m1oGT0vq5HD16VEop5bJly6SU+qzA++67z8fIpVv9NsqQ0vu9nY3tjjcA9WH9QXAR\n1tKVN2D9wTEXVi2YcEffWbD+SPja1ecLWN9Em3cM8zjrKy3OAffZWoNd55sJ4E0A4wFcAPA7UjFz\nDFYtmtMAJgLo4GpTYCURz8FR48bDWDx9w/8krJlh52HNfnjT9d8djj4FAByGVWNnHIC3YNU5+wbA\nhfT+HWFjY2O7nQZgD4AYAJ+43gO7AJgHq07WIQD3u/o95vrc2eXq08f1Xr4djpljrr6jXfuWuD5L\nPgdwDMBZWDdBsftVd70vO2eOvet67BgArwGo59qfFVb9y2gA7QE0c72vZ3WN4xysu0p2BbDaMa4U\nzRwDkM81po2w7rDZDtYy0nWu80U4+iY7c8y1b4jrsesBvOMa4xcAhjv6NHe95rsBfOD6rBnmeh4T\n0vv3hC39WroPII1bsq5du6bFW7Zs0WIzIWMuBaxZs6YWd+7cWYvNovE//fSTFl+6dMmfYaaLgwcP\navGiRYu0eOjQoVr86quvanGNGjW02FxW6SwSX6NGDVmjRg1t6aOdrNqzZ4+UUsqdO3dKKaUcO3as\n6p9WoqOjfR73tKzSKbllldmzZ1fbc+fOlTVq1JBPPPGEeh41atTQfnfMgvzOZNbtWL16tdvrVrly\nZS3u16+fFk+YMEEePnxYxQBkjRo11H+d57P3OdWtW1dKaf3bio2NdVtW6VxyKaWU9erV02I7wWY7\ndeqU1+eX3syE7Y8//qjFo0eP1uLWrVtrsXnTCvNn8fXXX2vx3r17UzXONJDe7+1sbP9Ig1Xf5HPX\nHy8JrmTOBgDd4CoK7OqXBUB/1x8z1wEcdf2PdTbjfIcBLPFyrds6B9wL8t8DawbcSVjJpXWwCuGv\nNvoVNf+I8TK+ZwBMgPXH2SXXGI8AmA6gWDJj8XgNAI8DiICVgLzm+sNrkNEnv+u6R13XPAXrbpVe\ni/mzsbGx3Q0NVi3IaQD2uT5fEmDNjBoHIL/Rty6s5fLXXJ9JvWDNHDOTYwLWlyOnXO/9KwGUgvVF\nxCRHP0/LKnPBKn5v32jlsONYPVjJvETn+zmAkrBmX8XBSsCFw6rZ6Sk5djOZ1yMIVvLtW9dnXTys\nO0dvg5Uky+ro6/a54u0asGqgbXOd74LrczDM6PMcrFncl1yv8UEAMwCUT+/fE7b0a8L1y5FZZKon\nc7ebN28eWrTwXq+3atWq2LDBqq27bNky/Oc//7kj4xgxYgQ++OADFffo0QNBQUE+l3nGxsYiJCQE\nALBixQps2bIF/fv3V8dLlSqF/fv/XlEihIDz35Iz7t69Oz77TL+j8qBBgzBkyBAVr1ixArVq1fI4\nlkKFCuHq1au4cuWKP083xWbMmIE33nhDxe+88w7Gjh2b6vPVq1cPP/zwAwCgaNGiuHjxIq5evQrA\nWgL64IMPav3LlCmDffv2pfp69I/wNjWdiIiIiDIQ1xLMywD6SSlHpvd4iO4WrDlGd0S9evV8JsYA\n4LXXXlPbtWvXdjtu1w7z5MyZM1r81Vdfee3rTIx9/fXXAIBy5cph69atWr9ly5Zp8Zo1a9R2//79\nMWHCBBU7E2MAsHXrVpw+fVrFzrtXmokxABg6dKja3rZtm3YsJCQEzmXyp0+f9pkYM++k+eWXX3rt\n64kzMQbALTFm1w0DgCNHjrjd4dWXY8eOqcQYALfEGGAlK4mIiIiIKGWEEDk87P4vrEkja//Z0RDd\n5dJ76loaN0pHs2fP9nm8T58+WuzrbpXJqV+/vtsSM2fNMU+GDRumtmHUufriiy9kXFyciqdNm5bs\nGGbOnKm2Q0NDtWPZsmVT21u3bnV7rLkkdcCAAVp87do12bRpUxWndDnuyZMnvR6LiIhI0blM5lJh\nc7mtucQ0ODhYNm7cWEr59zLECxcuaH2cPxvKkNL7vZ2NjY2NjY2Njc1osJYQrgHwHqyaY1/BuKMw\nGxubfy3gZo4lJCRosXl3vm+++UaLhw0bpsUtW7bU4gEDBmjx3LlztdicnRQXF+f/YP9hR48e1WJz\nJltFJ1QAACAASURBVJVz5hQAdO3aFQDQvHlzNG/e3O21sPcvWbIEQgjUrFlTHVu5cmWKxhYdHa3F\nkZGRKFNGv5nVwoUL1fbUqVPRvHlzCCHUOObNm6eOS6mvwG3Tpg3uv/9+FdetW1c7PmbMGLW9Z88e\n9OvXD+3atVP78uTRb+aSJcvf/7QqVqyoPXcAePzxx7W4WrVqWhwUFIQFCxaoOG/evEiJwoW935ws\nIiJCvSZ2s7300kvq52ozXytTtmzZsGTJEhUXLFhQOx4fH4+IiAgAwMMPP4zg4GDky5cPgPU7UrNm\nTfTv31/9rMyZaWfPnvV5/fQUGxurxevWrdPiqVOnanGvXr20+K233tLiTz75RIvNO6IePHgwVeMk\nIiIiokxpN6zi8u/Bql1W1fXfJuk5KKK7Unpn59K4UTr6888/vR4zZ0qdOXNGuyNkUlKSTExMVPHZ\ns2fV9qpVq+QXX3yhPX7JkiVy7dq1UkqryLnzuDnLatKkSVps3oTBE2dBfmex/L1797qNRUrfBfmd\nfv/9dymllAMHDtT2mwX5bZ999pk8dOiQNtOqT58+8sMPP1Tx1KlT5YIFC7THmTPHevXq5fH8yYmL\ni5MA5FdffSWltF7bDz/8UK5cuVL1GTJkiPz+++9VbBfk98Z5YwYppZw1a5aUUnp8XSnDSO/3djY2\nNjY2NjY2NjY2tjvW0n0AadwoHV25ckWLnXerBCA7deqk4vr167s9Pnfu3Gq7SpUqd2CEOvOuiPny\n5VPbAGT//v3VdnKeeuop+ccff3g8tnXrVvnJJ59o+5zJsRUrVmjJsblz52p9c+fOLc+fP6/iDRs2\nyO7du2t9nMmxffv2acfy5s2b7Pid1q9fL6WUskuXLn71N5fLJqd06dIp6k8ZQnq/t7OxsbGxsbGx\nsbGxsd2xFnDLKintde3aFfHx8cidO7fXPlLqS/MiIyMB6MXjnUXnnfsnTZrk9bzOpYdOly5dcnvs\njh071HabNm20mwCMGzcOFy5cUHHbtm3Vklp77NHR0dr5nNvnz59HcHCwx7FUrFgR5cqV0/Y5l9u+\n8MIL6vUA4HYjgytXrmhjrVKlits1mjZtqra//fZbnDp1SsX2a+HNyy+/rD2XjRs34vTp015fd3PZ\no7mU0OZ8PZ3MGwAQERERERERpav0zs6lcUtWQkKCFkdHR2vx0qVLtXjixIla/M4772jxuHHjtPi7\n777T4l27dmmxObsqI7GLpdvMwutmwf3Bgwdr8ZAhQ7TYOXNMSiknT56sxbdTkL9nz54em5RSAlDb\nUkr54YcfpvhazmWVUkq5cOFCj9evV6+elPLvZZWexiOllGFhYdrjk1tW6VximhaKFSumtg8dOqS2\nzZ+hJ87nIaWUI0eOlFJK+dtvv2mxrXjx4tprAEAmJSWp486ZY61atZI1a9bUftfMGxiYxfszknPn\nzmmxuWT366+/1uIRI0Zocb9+/bR4+vTpWrxq1SotPnLkSGqGmRbS+709IzUiN8ndkMaT5D5n3n77\nbS0uVqyYfPfdd1UMY1az8yY1rVq1crveuHHjtP05c+bUxu2cLez8nLCZy/edS+SnT58uO3ToICMj\nI90e5w/zuVBASu/39ozWKICsXbvW7XPEfB83/3/c/H948/381q1bUkopmzRp4na9DRs2SCn9e++d\nO3euNpbSpUvL0aNHq3jQoEFaf/NvmmvXrqltb2VenH8XHTlyRLZq1UoOGTLE7TWwVyEdPXpUSinl\nr7/+qo45t71p3bq1Fv/444/JPoYyHZ/vven9xs8PkkwiT548Po+PHDnytpJjH330kUoUtGjRQh48\neDBF4+vdu3eK+pt/tNh3W5TS/cOod+/ebjXHfNm6dWuyyTHnUkUzoSKltRTTX3369JEfffSRiocN\nG6bVCPP02sTGxno8l9l30aJFbskx57n/9a9/acecCdiYmBi385vJZcow0vu9PSM1CnB16tSRq1at\ncvsCKCoqSm2PHz9e7XPuN/seP35c9QGgvnCbM2eO25dvydW2dC6/t89hs+tURkVFyTfffFNKqf/B\n4rR48WJtzL7G78lzzz2njvvqFxUVpb5gcH6BQgErvd/bM1qjTMp8X9y2bZsWJyYman3sL5DtO7tP\nmTJFfZm6b98+rW+dOnXUe7v9Bb79N42z3+jRo2WdOnXk4MGDZalSpdT+HTt2uJWJMT9rnF/423+j\nREdHy/bt28uoqCjZt29fdXzVqlVeP2tsR44ckaGhoT7rI8fHx6tt84tnKaX6O8f5d4r9+eJ83s5J\nMHXq1HGbCEIBwed7b3q/8fODJJMyE0h9+vTRkmNVq1bVkmNDhw7V3nztel/eHDhwQIudb5qelC9f\nXot37tzplqCy/7BITEyUp0+fliVLlpRSWkX0ncmxgQMHyv/973/aY5966imVsLvnnnt8jsU+h23w\n4MFacsz+EBs6dKg8f/68HDBggDYrz0wgTZ06VUZERKh43759Mi4uTsXXrl2TY8aM0R7TsmVLtd27\nd2+312LPnj3JPgebMzn2zTffSCndZx06devWze9zU4aR3u/tGakRabp16yaXLVsmp0yZomZ2mX/s\npAV/b/ziZL73206fPi3LlSvn8di2bdtkhQoVfJ7XrG2ZUnZd0W3btrldr1mzZrd1brprpfd7e0Zr\ndBfy573/gw8+UNuHDx9O0bk6dOiQ4jGZX/ibzpw547YvKipK7ty5U1aqVMltdpm5GsbUuXNntV2h\nQgUtOVaiRAmtb0pmDd+8eVNL3O3fv9+tj/3FlCfbtm1L9nrOv2nmzp2rfeFPmYbP9970fuPnB0km\ntmjRIi02Z4517NhRi50F+aV0X+LqnC01f/58dbdKKaX8+OOPtb7mcrfkTJgwQYvNN08zdn6jP2vW\nLBkSEuJ2TmcR/uSWVbZo0cLn2JzJvS+++CLZacDO5JgJgJYcM6dDm5KSkuTw4cNV/Oyzz0op3ZcA\nOR07dkyLnc/Xub1nzx75yy+/+Lw+ZQjp/d6ekRplUubM2Pj4eO1zxh8pvUGJLwD+n70zD6+aav54\nyiaCIrUiYBUFN6oiKqCyqFAFFC1IK7QiUBXwRfBlUQERRBYVBLFoRSmLlH0pWCtYFpFF1heUvbiC\nyI5KC4qytZ3fH/d3DnPmnJwkt7dNCvk8Tx6Sm3uT3NwykzNn5jvw6aefCtuMffv2Ce/dtGmTdsCC\nJ3gYdDafTiJRnAwSnnvuOe3+omi641PscNu2e23x8RA4U5hO+AMAPPLII6afPX36NLRp0wYAAI4d\nOxbU+detWyeUR+ISe6fQ8RcACL6GQiWJ7ECTCILFMAyhhP+2226De++9V9gfLFT6SMXjjz8ubBd0\nYsjHE2htr9uGv8gdyenTp4VtWtq1atUqYXvGjBnC9rvvvits0/pwqhFEM5xOnjxp5zJdgc4c0BkL\nmrGE9diGDx8Ow4cPhypVqgBAIL22MDXHChunWjB0Rp+WGl5//fV8ffXq1ZZllWfPnnV0vaFk2rRp\nwvXTchecFbZ+/Xrpu2JHkp2dDVdccQXffuedd6RulWwWrFmzZjB8+HApay0nJyfIb1L40Icc+jdN\nU+fHjx8vbI8ZM0bYxhmAAAAbNmwQtg8cOBDUdYYAt227lxafCwSmx2JGTEyM9B4aUNLpQz777LOQ\nm5vLj1GjRg3hIf/QoUNwxx138G1q93V+5vTp0zxzjF2D1Ww+QCCzmWU3m8khsAHYuXPnYODAgdLk\nEwDAnXfeKXwXSkZGhqDtcu7cOWH/mjVr+H05ceIEH3zhzGtsP9k15Obmmn85n+KO27bda4uPh7Aa\n09AgDZ3wV5GcnAzPPvssAKj9EbbXFDqeBQBo2rQpAIBQJlmmTBlhDEKPz8Cat1ZSMVa+hk7g03EE\nDm4BAPz3v//l65MmTZKOh5996XO36j50795de32qzwAEylUZbIxLg2N4TONTbNHaXrcNv+9ILiCo\nwcKOZMGCBYIjWb16tRRIoA6ANkvANexm/P3336b7li9fztc3btwIrVq1Mn2vVXAsKSmJO7TRo0fb\nnrkYPXo0JCUlSe+nwTEAcVaIikxiRwJwXk+GQctasah9RkaGrWtlYKd28OBB+Oabb4SZE5opgR1J\ntWrV4IorruDH2L9/P0RERPD9s2bNcnQtPq7htm330uJzAYKFjj/44APbTVFwiQd+sDZD12AkLS1N\nCL7R4BgAQIUKFfggCpdVbt68GUqWLAkA5lpiOpFklQC/DuwDsI9YvXo1VKhQQTr+iBEjBPkD6qtV\nmmOGYVhq1fhcULht2722+HiIUEz402f/48ePCzaO2U5Gfn4+5OXlCY2q2rZtK52b2slatWpZXgtj\n8ODBkr4lQCCTOC8vT9IfA5CDY1Yl/k7sOMtqU53XLri5DAX7mr/++gseffRRAAgI9Xfv3l06LxvT\nrF+/no9pMHjC/8yZM0GVvfoUOVrb67bh9x3JBQx1Am3btjV970svvSQ8OMfGxgqdt+jxYmJiLMtd\n1q1bx9ftDFpw0MYwDBg6dCjfTktLg+nTpwNAIJsnKSlJcA40o1BH7dq1hcyxzMxMZXCM8eabb0ri\nkzg41qRJE2HfoUOHhFkW+jssXLgQpk6dyretmhXk5+cLzqBRo0aCYx4+fDgsWbKEbz/++OPwzTff\nwB9//GHpSHyKDW7bdi8tPsUElRYlLaN47LHHAADgk08+UU7AlC1b1vT4UVFRQpmLSisrPT0dAABa\ntmxpeb2qSRgddEBSv359YZtOkjA/c+ONNwKAmKlAdWAARGmA8ePHS90qzRq3AMi6ONTPsGtQbS9d\nuhQSEhL4Ng0S0kGqzwWD27bda4uPh6DP0k6DYwcPHrR9rvDwcPsX9v/oxhEMWo4/adIkbnux/6H2\nmR5j2bJlwmu0GoaCg2NMmoVBu0dSmjRpAmvWrNG+B2MnkQLDfDSAGJDD9wDLv9AxDR2r4mMkJyc7\nuhafIkNre902/L4juYCgM+JWsywUmoLMBi0Age4pmzdvlj7DDH2XLl2E16kTo+WyFPp+WkZCtQRw\nKYyKUqVKaffTdvctWrQQti+99FK+/ssvvwhaMCkpKUJwrHPnztKgxar87rXXXhO26f3DOBGvBJA7\nVKqCY/Xr1+fnpL8NDtz5eAa3bbuXFh8PY+VnGPhh3erBnhEeHg5r167l21FRUdJ7cMav1UM/5ZZb\nbuHrEyZMkPbj7AEVdsoqdVDNMexnatSoIfiZzz77TFlWuXXrVuWxcedMgEApZfXq1fm2avCj61zm\nc0Hitm332uLjIZwG5a10lAHUXduDxWqco0InXq+DBsdo0I1iN3OMShkBBMZDdEyDNdBiYmIc6Uzj\nMc22bdt44oMVwcgiVa9e3R/TeBOt7XXb8Be5I6GaHvQ/HC1fo3XTqampwjYVqKXi4nv37hW2rboq\nugkNbtGyRpqpFRkZCWfPnoXExEQAEIMiCxYsEBzJkCFDpEHL5MmTg77WmJgYYVb9o48+gsmTJ2vL\nKp1QUM0xNqPftWtXmDx5MkyePBkSExP5vbLSHKOOxO7gzQ44yJiYmCgF1tj1PvXUU3DmzBnIz88X\nfivaiVKnOQagDo7hc61du1Y4Pi3v+euvv6y/lEvQxgc///yzsE0HfLRJxcyZM4Vt+sBB9deOHj0a\n1HWGALdtu5cWHw+D/QwrnU9PT4fp06fzbWxfcfZTq1atIC0tDbZu3crfi/8P4kYmGLMS/Y4dO0J6\nejpfVCLIGLOJiB49egCArBFGM8eY/cAz4enp6fDVV1/xoF56ejoYhsGvuWXLlnzQNnHiRGjVqhX3\nf9QWqwJY+FwM1vlZtY9BJ2joPaR+hum3pqenc3+bnp4u6Lw++eSTpufzKRa4bdu9tvi4AHtO++ef\nfwQb9v7778O4ceOE9z7wwAN8fe/evdogENXBYsdOT0/nNhfLv1jpK6q0IK1Q2WQ61sOwMS+9FhqM\n+v7776VjM5vO/mWZyocPHxb0zVSTIFaNumjAiWZZ47F7gwYNhLG4TkcZQB7TUHJycrivYd8jPT1d\n8GGGYcDcuXOV1+rjCbS2123D7zuSYkxkZKSj9//000/K19kMOZ5lWbNmDaxYsUJ435AhQ/g61c3q\n2LGjVlw5JSVFOSPBwHozAMAbCzCcBsessq1oaY9VOjSe0VdlI9AAF8VJUMVqBoZea+3atYVtGhzD\ngvr79+/3yyqLJ27bdi8tPh7m448/1mbCUnBn3S1btjg6F2uowcr27ZTvY+h1Uj+DOzQDmE/CqI7F\nZAHo6126dJGanPzxxx/aDsdm4MnFcuXK8eM7vQ8Aas0xzP/+9z9+/OHDh0NKSoo0ieVT7HHbtntt\n8Slkbr/9du3+Ll26CAtj0aJFSmkXHPR3YlP79OkjbOPMqJMnT1pOkFOb++GHH5ruX7p0KaSkpAjX\nj/cfOHAAfv31V77NAmNYx0vnY9kYAY+DVN2LnfgJXXaXU39D30+DY/Xq1RO2cRAS+zeze8AkBdik\n09SpU4X3mmULOnlu8SkwWtvrtuH3HUkxJjIy0lEmnC5ghHVb7LBx40bBsNPsoqysLGFG+Z577pHa\nHuMoP9Zb+eeff2DBggV8th5AHLTs3LkTevbsKbQ9tprRx1BB/ri4OCHgRDVuunfvbtpVDEAeQD3/\n/POCkQ0PDxe6flWtWlU6BnYW33//vem5AALZcBiaDUWDYxQ/OFYscdu2e2nxKabk5+cLWpIMasMw\nDz74IF8PDw/nAwW7nWOdtH2nGmGUOnXqCJNMeABl9xgA6gmQ7OxsG1dozrBhw/j6gAEDhMksO1gF\nx3RcccUVMGrUqKA/7+MZ3LbtXlt8ConBgwcLGpFXX301AISmUmHAgAEwbNgw7msGDBggvWfEiBFC\nlhgAwJ49e6T3ObWjoYCW56vGbvg9qsYxBcVqwt8pAwcONN1n1snTLrQ0FZfPqrpm2i2lVf3d+IQM\nre112/D7jqQYY5U5FhMTI2zTssqXXnrJ8hzffvstAAQcCYWVbgAERO0///xzvk21YGh5K82+orXk\nVAyTOge6bTajT8toWCCLfl7nAFNSUiQtGKxZ1rRpU2Ggtnz5cqFcj3ZdO3z4MPTu3Vt4Dc8Sde7c\nWSgfxvdVBf0uNDhWvnx5YZvqMNDSZXaNPp7CbdvupcXHowwaNEgpkoxn8GmWsI7q1atLvqdEiRIA\ncD44hoXmZ8+eLczw/+9//4O3337b9PjUDjvNXFMFx3SMHTtW2MYlQKNGjYJKlSrx7dTUVEFzbPz4\n8by0ESDgY7FgPwNPKlFwYNLKp4Yav4NYscBt2+61xacQUTVQcQKdKHaCVSMsN8ATOZs2beITMWXK\nlJHea6VvyXzNW2+9FdS13HXXXUF9DsDevdWVv9KEAwBn3ZypL6N6mz6eQGt73Tb8Re5Izp07J2zT\nGVRa+sdS+RmLFy8WtnFHRAA56+bIkSPC9unTp+1cpivQFGAaUKIP7jg4tmjRIpgyZQo0b94cbr/9\ndli0aJHQRXHo0KFC+cqiRYukhRoUFhhzg1BojtHvN3DgQB4IoinSdevWFbattAaKEjqjP2rUKOF7\n2dEcw8egmWO48cLYsWOl+nxV8Mwr0KAqzSahmmE0fZtqjFHNQzqTSMuhihC3bbuXFh8PwLoZrl69\nWnhdFRyj/0+xzpaufB9ALknXle9T6CQMbcSiytbCExs9e/YUZpmxn2HfH3d1pAMWnEWWlJSk1bYM\nJmMCf37mzJlw9OhR4XqYvWOvYZ0Z+uyFZQ/wMRj9+vWDWbNm8e2RI0cKvw3Vb8R+Zu3atX5wrHjg\ntm332uITQmjnwPj4eKGDJC4bBAhUYVAMw4CZM2dCRkaGYM8Y2A79888/MGnSJMk2MfDEidl7Zs6c\nCc8++6zw2rp166SxKUCgu3BCQoLSftIxTUJCAs9cw+dOSEiAw4cPS+Mxen2q6505cyZ/Hfuarl27\nCk3GggH7GuZXcfIBrgzq27evcA/WrFkjPDsbhiGM6fCkEEBg4of6Ggz7jnFxccprfeGFFyR/hjOb\ne/ToAQkJCRAWFgYA4nPAmDFj4OWXX1Ye1yekaG2v24bfdyTFlJ07dwrBMRwIY5h1dmHljtj4hIWF\n8QHMjh07pKACLtkAkDOyKFlZWVLgUse///4riPmqgmPYURZUcwwHx7p162b6/iZNmggln6Fg7dq1\n3HGpshoaNGgAAADXXnstAIjBsVq1ajk+Hxbk//333y3LKn3xSk/itm330uLjIjSgbMX27dsBAKRm\nLXv27LHdTfLpp59Wvk4nkJxiNQkDEPBlL774ouWxnGqGValSBRYuXKh9D84cAwBlSSqFDQqYj2YB\nO9qshEInYaiPpT7yzjvvFAY82M9ERkZKgcJx48YFncXgU2S4bdu9tviEEBocozRs2NDyGLfeeitf\nt9MZGR9zw4YNABCwjapqGB0qzS4AcQKVPbtPnz7d0bhhypQp0mt4TEMnhJ555hnJvtIJ/3fffVc6\nZrNmzQBADHSpGgS8/PLLMGfOnAJ3X2bY8fP0uQL7K7xv/vz5fP3kyZOwbNky6bMsOx3rpOH34Akv\nJk1E/Zud0lafAqG1vW4bft+RFGMiIyN5Jh6bdWYGpX79+o7+Mzdq1AgAzmcdbdu2TcgoommytGTz\nxIkTgjZMVFSU0FK+S5cuUhYONcosKzA3NxcOHTokBPfS0tJ4htTEiROlDKE6deoIrYx1333OnDmC\nI9m1a5cwy9KyZUvh/ddcc41QVpmZmamt8d+zZ4920GMYBvzwww/Ca6ryGEZ+fj40b94cAORGAgxd\nQKtkyZLCth8cK5a4bdu9tPi4CLattEQ7WFhW0aRJk7RlkJTmzZvzcovZs2dL+3UP5Zs2bRK2ly5d\n6ijYRktdzIJPWH4AQBYbxjC/2qRJE9i+fTsPjr333nvQuXNnaNOmjelnjx07Jr3Wv39/vt63b1+h\nIzbtfFkQzTEAuSuyDpUOjI8ncNu2e23x8TDYF9EMZkqDBg34mObKK69Uvkcl8O8UVbZbQWCTS1ZY\nNQywew47WVPh4eHCvacN2iixsbFBX5sKu/fEjOrVq/Pu0ZQ1a9YI3TsLei4fU7S2123D7zuSYogq\nnRdAnkU3yxxjD8X0P/2JEyeE0k0cHAMQs5x+/PHHAjmS6tWrS6/hQQQNbgVTVglgHkyix8ezUex4\nuJUx7VZJZ/QxwRjT4cOHC+KPOCXZLNCnE7jEJTp00EKDYzoxbJpB6OMabtt2Ly0+LpGXl8c1vwqD\n7t27OwqOHT58GO6//36+3aVLF8H2WZVBW32XpKQk04mQMmXKONYcMyMvLw/Wr18PAHLQimEYhiCS\n7GTyi2lI6nReaHCMTZgB2MvQsMNHH30UkuP4FBpu23avLT4FICIiggftp06dGvLsG51dUunmxsXF\nQWJiIkyZMgXeeecdx+fDkws6zp49K73mpGP9sWPHlMfAYPtM6dSpk+1zYd5//32+jpuK1a9fP6jj\nMfCYVSWL8M0331hmUWOcTMSowJljVHoJQG5GgGUh6Fi0dOnSBbqWixit7XXb8Be5I6FaTrQUgZY5\n0NlYqruFBWoBZM0h+nBsZXDchD64Uk0QLNoOIAvy43tz/PhxMAwDtmzZwh3SH3/8AVu2bOGLzlHV\nrl07mK8QFOw6V6xYwc9L/w7o72g3OMZg35fdIzrLotOC0YHvp4rc3FxbqeIM3GWFQQct+Jy1a9e2\npTmGwcGx7OxsQQumdu3a/LgMq8YPbkI1BGn2BNbZAwhkCWJoIJN2saEPNFQ7qQhx27Z7afFxkWrV\nqkmvhYWFSQ+9zG7deeedwvvCwsJ4UCovL0+ahHn99df5esuWLYWMZIoT/TEAkEpd6PNCXFycEDAz\nDEPoikz9DMD5775p0yZ4+OGHhWsaNGgQ3547dy5/3W6AEU/CqL6rVbaX1f3BZfr0eYDqMwKcv27V\neamf2b59Oz+/SoNHVUrk4zpu23avLT4FYPPmzdC4cWO+zcoqsb/ANioyMlKwLdRO4gl/lQ2iNgxn\nNqtsIfY1ACBcKz2/rusxu5bWrVubvoeOaQzDgBIlSnCfSKlTpw7ccsstPMhHJ2Jw2V+vXr2k707H\nNOyc+PgAYgUP+x6dO3eGtm3bCp9VdYdmSQO0kshpySr1NSNGjND6SJWvwYwbN46vv/TSS1I1DH7O\nZ38XbGxAdc0BAjpkGDf1uC8gtLbXbcPvO5KLGDsZTiy9lHafBJADBXfccQcABIxo06ZNpfe/9tpr\nABB44B87diw3yvjYpUqVAgC1I8E4DY5RnAbHaLdKu0RFRSnvHU1DZiVBjz32GNxwww3ajC16PDzz\nERUVJd2r3Nxc7vTOnj0LN954o7Cf3msfT+K2bffS4lNE3H///bB//37hNZahTBt19O/fXyohjIiI\ngIiICL5NJ3zM+OCDD6RjqTh37pyQOZaSkmLr+IyCNn5hREREQE5OjlYHJi4uTjp+qDMpqHQBBg9Y\nHnvsMa6/AxAYRNLBpio4hrVHK1SoYOuaIiIioHPnzpbvK0h3NJ+Q4bZt99riUwA2b97M17E979On\nj9CR/ZFHHoHs7GwwDINn/tptfoQnd9mY5vjx4/w1Ju/SokWLIL6BejII4Hw3yJMnT8IHH3wAAACt\nWrXi+yMiInimGR5PfPvtt1IwyQrmQ7EvPX78OERERHA9Tuxr0tLShDFNlSpVpM+vWrVKea4+ffpI\nwaSsrCzuK/ExGHb0NtmYhj0T4Ikqla/RQZuMUVhpqOpaAeRJcCecOXMm6M/6CGhtr9uG33ckFyhf\nf/21Mq2Y0bJly6BrqS+77DKIiYkRyvHCw8OF91x33XXCoAVA7Dhy5MgRSXOMZRXm5eXBoUOHtPov\nPXv2FAYadJCBg2NUWwZAdCTdu3cXHMmePXsEZ0YNd58+fbgzrFixonTsrKwsuOaaa4TXaNYfdj4p\nKSnwySef8G2Vdo6Ofv368fXDhw9bDkScDsho9pWPK7ht2720+BQRTtqn20E3+x4MTzzxRNCf0KiW\nnQAAIABJREFUZX4GZ6fbDY7ZLa/BfkbVWY3CxIEZNBOWaU8ybr75ZlvX0bNnT5gwYQLfVjXUsRMc\nUxHqvxEfV3Hbtntt8SkCVEH93Nxc08CUGU7GNH379nVU6mgGPgYOjt10001CF8SCTEKXKVNGyi6j\nDQKcao4tWLBAux+XFqr8tm4iprBxWlbZtWtXYZsGx3DHVADQanva4ZFHHinQ5y8StLbXbcPvO5Ji\nDAvAXH/99cr9uiDIqVOnLB2J3U5iDFoya/daVKgCWhgqBO0kcywpKUlyJLSVML7es2fPCsYuNTVV\nKHexGijRLLAZM2ZosyJUNfAAIGVwMKijoGWVFPpb4BRkAF+Q36O4bdu9tPgUIYVVVq0qUXYaaDEM\nQ7JXuCMizk5gx8e21063ShXZ2dlBzSBbiTV/9913wjbVtsS2e+7cubzUJTc3F8LDw6XyxcK25Zs3\nb/aDYxcWbtt2ry0+DomOjlbKgwAAPPTQQ9rP0mdTGhzDZZW7d+821RxbsWKF9YUqwJluVtdmB5zB\nRn0NlQuIi4uDzMxMntFlGIbwzM/GNM8884zp+XBTMixnoMKqGmbnzp3a/RRaeoglj5zeOzzhz8C/\njVXzBcquXbuk8S7+bag8ix3M/sbq1avnB8fsobW9bhv+IncktO6b/lFiMV0AeeaUZkPRwARN76R1\n2roAjttQPTQ6eMjJyYHTp0/zTiuRkZFw+PBhvtBZkHbt2sHhw4e5YaKOBN/LQ4cOCcc6c+YMHD58\nGHJycuD48eP89cqVK9uaAbfDs88+y9fvu+8+YZ/dchd8zRgcHIuIiJD2W9XnW2mOYfF8AOtuLQWB\n/jaHDx+G7OxsKFu2LFSuXFmpOYYfTmgKMhXkNwwDKleuzLfpgEqXgeg2rFsrg2ZeYAcIIJd10UAk\nnukDkP8PuphS7bZt99JiCbWl9G+YBqzpgw61N7RVOO7ECyDrcYRKqL0woH/jVNeTtaNn0ODY3Llz\npaVZs2awa9cuGDhwIHTu3Bnmzp0Lq1ev5vufeuopAAhkJ9FJlw8++ACeeuop5XHnzp0LUVFRfN0w\nDL6uYsSIEZJt1kEzz9jvjs//1FNP8dbwX331FX9vbGwszJ07FypUqMCvJzo6GkaNGiV8Hn/fpUuX\nKr/j5ZdfbvqdCkL79u35uqo5Db2OcuXKCdt33323cN9r1qzJ5RHw78q2dcdmx5k/fz50794dXnnl\nFf77Ll26tEClLoUNfS6lmeB0Mo+WHH/++efCNm26QLtX281MLATctu1eWyzxfc15LrnkEuH/O/U1\nTHOMwZqQMLCvKV++PMydOxdGjRoFNWvW5OvUpjCWL18Oc+fOVZbus+dAXbOXqlWr8lJJfHz8jIk1\nyrp06SL9vzVrlsaOiXFawj9r1iy+Pnz4cOl4zBY/9dRTsG7dOu2Y5tlnnxUmYugkEj6eiunTp/P1\nFStWSNfyzDPPwNy5c2H9+vXK5mXMn2C/j6FjGhoso++/9tprhd8Ml9oCBAJW+PtgX5OQkCAcr1mz\nZsJnd+zYIf294c/rxgTvvvuu6T4zfF8TWNw2/EXuSHxCB3Ykqu6PZt0qKTQAybapcR06dChfz8jI\nEP5TZmdnWwYe+/btq3y9QoUKkJOTAzfddBN/zakWTHR0NJ/BLlOmjKOZClqfD2BfkJ9R0DRcXfvk\n/Px86SGD0bVrV8mRYHJycoTMsv3790vBMZ9igdu23UuLTxGC/cyECROE2Xw24dKtWzdYt26d8Bqj\nd+/efL1bt258nZYyMPr37y+9xjqLTZgwQZhgy8rKglGjRpleOw5mqdCVurDOilOnTrXsstitWzdt\nCdBbb70lPcRakZqaatptWYVVsxDs4/D3wb+J1fbnn38u6I4BiIO2bt268WNjP/PTTz8pzw0gDvbZ\nxJ+Pa7ht2722+AQJ7nyIG38UBN2YRvXMr9PCos/8dia4VQF8ZvM6dOgg7aOTtSrmzZsHAIExDZuY\n7tatmxAcw2Xw1D6bsW/fPsv32j2WijfeeIOv0xLLpUuXOtIRGzlyJA8CxcfH82cJhll1lBk0OFYY\nvPnmm8rX2YR7qPVEL0C0ttdtw+87kmJMZGSkEGXGD+dW9eQ6duzYoQwO4Qh5bGysEAz7+++/he2o\nqCjJwGE6duwo1OcDgFTucuutt/LtadOm8fVBgwZJwTGA8zPjZcqUMRViNKNJkyZ8/e+//9YK8r/w\nwgtccwwAYPz48cJ7y5cvL2wfO3ZMch6JiYl8vV+/fsLs45o1a4R7adWRTBccU4EHLV7u3uoj4LZt\n99Li4yI6bZPhw4eblroMHjzY1vFVQsF2P4uhXTAB9GWbwZyjMBk8eLD0AE4nYXDgEbNy5cqQZzPT\nUhFcvs8y6zD+JEyxxG3b7rXFx0PgjCVV6Z0VKp9QmDgdh2Co3ImOzMzMoM/jBlZjmuIEfh764Ycf\nTOVvfCS0ttdtw+87kmIMLXehXQgxtPyMgbO1qCHXOZKzZ8/CypUr7VymKfPnz+ddLXEgDAAso+6q\n4JgVukCQVY041SIwu5/sPDgVtqBlifRelCpVSup2g9N3qXAn1SSjgxarWRY8W2aWweZT6Lht2720\n+BQDcIYQ1SNh2WG6rC87DBkyRDsJ88MPPwjNTlh5zccff8wnNbAvSUtLE0pnkpKStOdX+RSrQY2u\nzT1tfkJLUrBIMsD5rCv8HQHOZ+WpsvCs6NGjB1/H2d6q8v3Cgk44+RQZbtt2ry0+BGyzFy9eLElT\nlCxZMuhj40lnBs1UxSQkJMCkSZNM99PMLVri+vbbbwvjGKoDpoKKu1No2WuwmHVGZtAx08KFCx0d\nH5eYqsoqddAGbE6hwTEq7QAg62+aYVYNs2TJkuAuLsTQDtY+HK3tddvwF7kjof8p3N72ElbXSoMk\nNDhGM8dwCnK/fv2kz99xxx0Ful4n4ABMjx49LI0xLXdh98IwDDAMQxq0WAny0/IafC/i4uKEFOuq\nVatCfn4+P1d+fj58++23fL2oseoilp+fz3VgAORBi5PgWPfu3S3/7rzERWQ/3LbtXlosoQ/FNLg9\nc+ZMYRuXCADIGTp0lprarw0bNgjbVOvOS9AsVjrrHBsby5dHHnkEYmNjeRAI74uNjYU1a9ZAbGws\nbN68GebNm8dfx1CNylKlSgnbOCv3jjvuED4fGxsrTcLgrNp9+/bB8uXL7X51CRrsX7duHXz77bd8\nmwaXqJ8pUaKEdL0Y+ndEbSntTMw+z7RrtmzZIl1zcnKy8BuYDUZVZavsN2rQoIFwjFKlSkFsbCzE\nxMRAZmYmf90wDF72g79bz549hc8bhsH/ZQt+XfV3QbUtY2Nj4fTp0/yeLVy4kJ/bbejzCP2bY1pF\nDBrUxJniAHKgkerEUF2YIsRt2+61xRLf16ihtoEtt956q7CNJ48jIyMFP0Jtxssvv8zv77Rp0+Dj\njz/m7xkwYIBQyhkXFwexsbH8+ZZeR2xsLAwZMoRnExuGIWU95+fnC9fDjoF1nHbt2sW/L2XevHnC\n5y+77DIIDw/n29i+ffrpp/w1up9tt2vXjndYxkEX9platWpJ3/Gaa66RXmPLVVddxddr164t7cdg\nex0eHi4ERTMyMmwnS5w9exZiY2OF5jHsd0xKSoLq1asL11CvXj2IjY0VKovotWG/Onr0aGl/3bp1\npd/wsssuAwC1HAEeW7PPVatWTXkPS5cuLbwP+z8VVGeP4vuawOK24S9yR+ITOmj6plUQQzejffr0\naSFzbM2aNVJ2EgUbw8qVK1tqjrFsqzFjxijfW6VKFQAAbmwYVroxANazLJQhQ4bw9fj4eH4OOrNk\nh9zcXNNMMvY9cVCFfj8AORMNZyTQTDAcHFMdy2pGHwfHZs2aJQXHVMf0cR23bbuXFp9C4qeffoL4\n+Hi+TcVfDcMQBme4BPKdd95xHEhfs2aN5cMiRVV2CQDQtm1b6NKlC9/+73//azoJQ4+halWvy0gw\nAzeZMcOsIxqWGVBl83bu3BkAAF599VXl59euXSu9ZtatkmaF0/vxwAMP8PWaNWsK2xT2WbPfxQyz\nrszsWLhZk07o2iekuG3bvbb4gHoA/fvvv5v+n6f6iiotwYyMDEGvEACUwfC///5beY4BAwYIwSyV\njTLTYaTXrcpYa968OQCom7hdddVVgk4wPh5bT09PV557xowZABDIQGNZxLjShHayN7O9tJSdHUNn\nqxs1aiTsz8nJUb4PB2jwmIiKu1Pofe3evTsAnPdD9PmAPUvQCX/dcwQ+Bx43rVq1yvS7Yz/Ytm1b\nvt64cWOtLhwdY+PfpmzZsvx8ubm50nen2zVq1NDuvwjR2l63Db/vSIoR8fHx2nK+77//3tHxnIrO\n44fVjIwMy1JENrNx6tQpSElJgVdffZWL+qempgrdQ2gXUwB9W2UA0eA5HZS99dZbjt5PwQOUyZMn\na99LGx4AnB/oAAQi+zi7Ys2aNaZBum3btlkKXT7++OOOflvVvaOzHqyjSbly5ZS/lU+h47Zt99Li\n4xK4PbuKo0ePmmqOFQUpKSlC1+bU1FQ+Q6yDdoQaPXq0Y3/qJvn5+XDs2DHhATwjI0Moi+zbt69l\nmWhh0bt3byHgR7vW+XgGt22715aLHhbgoPaQPTfiYIOKhx56CADMA/WhoEWLFoV2bB1mExWh4Pbb\nbxe2aVdSWq5HtUBpwwEKztgGCAQIaVYSA4/9QgEtCcUUxvND3759LZM9fIocre112/D7jqQYMWXK\nFO3+atWq8dljVXtZanTWrVsnlFZSvZOCQGeaUlJShO0yZcpYHmPjxo3Sa6dOneJOGQedWOYYq4W/\n5JJLpJIZ/H7sSMyEi2kJ3bFjxxwF4fD58HpBxTNxcIwdF6eTs2usX7++MnONQrMU2rdvL2zTgaMv\ntOwKbtt2Ly0+HsBM60v3cEuzYM+ePQuPPfaYMFmAuyIX5DqCBTd/YXTo0CGo5iWq5gWsbLOgWpSU\nihUrCtuPPvqosK3yXawkCLNv3z5H52XPHBUrVuR+5uDBg7Bz507hfcF2ECuovo2PI9y27V5bfCyg\n2cUF5brrrrP93kmTJkFqaqpyvMPAz6s0YOTU16jGJMGSlpam3V+nTh2oW7cut3+hDo41atRIO9FO\ny30pN9xwA2zfvh0AwFbgqUGDBnw9Pz9fGp/h5nIAYrmyVTWM1UTW1KlTtZ1OKVTbE2cWrlq1Spuk\nYoVXpAI8gNb2um34fUdSjNm6dSvP4snLy4Nq1apB5cqVAUD+z92vXz9h0NKwYUNu2I4cOcI/t2XL\nFr4OALx+Gs9A24VlG9mFfRfatpcFgHB21XvvvcfX2fXi0gvVQAAPRqgjadu2LRw+fBh+/vlnfjyq\n6YbvixW0dbXVZ3W6Efn5+YJu2DfffCNk3QGI4prYkRw9ehRycnL4oIWKV/7777/SoAU77cOHDwtl\nVOXKldN+D59Cw23b7qXFEvqgtXjxYmGb6r7QB02qWVi/fn1hm84Y0xIKnL3kNXBGbl5eHpQsWRIG\nDhwIAAENrZIlS/J/2YIDLzg76fHHH5da2ONjnDt3jvsZM5544glhG9uymJgYqfwgNzeX2zT8kKor\nbzcD63Wxe6Ha/9lnn8G5c+egZMmS8P333/N1q4Xex7CwMChZsiRERUXx1/7991/puzAtMXw9nTt3\n5tvnzp1TloI6pWTJknDttddK58LXj7/riBEjwDAM6f0AAW1LptVZsmRJuO2224R7UKJECel+4O0f\nf/xReQ1smTt3boG/b7D89NNPwjYt16U+lGZ9XH311cJ269athW38PANgrUtTiLht2722WHIh+5qH\nH35YqBTAFRe5ublSyWGFChUA4Pxk7KZNm6T/x8wGqmyAYRiCHWT2oGHDhtwOsfez/arFDLrv7bff\nFl7D5z569KiwnZWVJWxTmRp87nr16kGNGjX4+/Pz84Xz5Obmwpw5c4TXWJnkZ599BgAA9957r7C/\nfPnywvmsgmMlSpQQtq2qSVQ+wGq/ncAo8wdsTHbixAkoWbKk4POwRA/zw2FhYfDiiy8CAEDLli2F\nY1rpKNO/B5q1iJuMGYYBVatW1X4Hmulsdp/w3zGrvqHPJPR3p/i+JrC4bfiL3JH4uAMNjgE46+Zx\n+eWXa/fbmd09ceIEX09NTRWcPBPFNCMuLk7YpiLPThk9erSt91FBZKtZLScBNBb0e+211yRRxWCZ\nPn26NMuCA2916tQRHElGRkbQM/o+RYrbtt1Li08IwQ9+BbEFH374obD91VdfCeUYbOAUSuwEiKj+\njarMXcXgwYOFTpb4u9CH6YYNG9o6purzVatWDarpyUMPPSRln91www18PSMjQ8gCVgn0FwRcToWb\nARUWc+bM8btYFj5u23avLRcdVtrBZlStWlXS+LKSRrECBxE6dOhgmv0TjG0LZsJfBw3eW41pfAKU\nKlXKcdlmzZo1hW2apeyEG2+8MejPOuU///kPX7cKyF0EaG2v24bfdyTFHCv9J6YxMnLkSKhUqZKw\nz2m3Sty5a9q0adouYr/99puQ3USFkp1Svnx5eP755/k2HrSEGtzNTTWQ6tixoyBkOX78eJgzZw53\n0F9//bWj82VnZwtGs6CoUpBvuukmvu6XRRZL3LbtXlp8LmJoIIkNyJo1awZ//vmnkEnRsWPHAp2r\nZ8+e8Omnn5rur1evXoGOb4VKKBpDg4N0kEHvFRVxLihW+pdm7N+/v1A1iHyCxm3b7rXFB2GnFPy7\n777j6zfffHNIzmvW6ISK119IONWELkiZPh3n0NL6U6dOaSeiirpjqlWyhhc5cOCA25fgNbS2123D\n7zuSYsTx48e1elV49pamegOIWjA4AETBM+1ZWVm8NpzqhjlBVRKxbNkyiIqKEl7D5YgLFizg6zjr\nDEDd/cYKbNwHDx7M13v27Al79+4V3ku1WHD9O01rr1ixolTG6sQQWs1g0bJKBr13Vsegzk3l7PBr\nTF+hevXqPDXYrPuPT6Hjtm330uITApgmiWEYQpkBgDjAoeXlDKoZsmfPHqVfwZpgTZs2VZY+4gd7\nqpVi1SK+R48efN3KJtJjHzp0SOqYhqF+AZeYA5zXgbHq4kVhdlSVMcbKqGg2BNaVZGA/w4SvGVRL\nc8KECdLnExIS7F2wCRs3boSnn35a2/GLYdZd+7nnnuOv6bSDrDRwfEKC27bda8tFydKlS5WaVaz8\n3krPimGVOda4cWNtgIvpM1Ffw/j444+FzooU5mvMOl4OHDiQf5ctW7YIvubs2bPKLDrmG1NSUrQN\nalavXi1cgxV79+6F3r178+1NmzYJfsUqo+/NN98Utv/880/BX1166aV8nWW1OdGLU3VXxJP69Heg\nYyiqDfrFF1/wdarlNnz4cKG8kE74q8ZD+Dlg3Lhx8Ouvv/KmcXSMhfW7qZYbTuQAAPj55595qauK\n0qVLS6+pJIVUHV8BQt/soBihtb1uG37fkRQj+vXrJ2zjh83s7GwhOLZlyxYhBblfv37KAFXt2rUt\nz9urVy84cuSItvV6eHi4VIKogw06brzxRn4NVFAYDxxoOQXNHKPf49577xW2abmQTrzy999/t5y1\ncZr9hoNOdmZZsMAkFtPftm2bFIgbPnw4X2e/OR7cMg0L7Fzj4uL470XvDQtKMuFoJ0E4n0LDbdvu\npcUSOljfunWrsE1t4bBhw4RtqqNFdWOmT58ubNOHzKKeSXXCr7/+Cl999RXfxmWV48aNg1deeYVv\nt2vXDtq1awdt27aF6dOn8+127dpBpUqVhG26TJ8+nX8GACAxMRGmT58OnTp1AoDAA2tycjK0a9cO\nDMPgn7v99tv5+kMPPSTda4zVfWZi8QABm8iO26FDB/jnn3+k4BQVSaZ+hjV+wfcGd/alZfVUfBf7\n5G7dukl+BpdFqu4pBk8eBYPq+KVLlxZ+Z8MwpN+xf//+sGbNGiELLSIiQvu3oLp+ljnG9uEAbYcO\nHXiXPHZ+N6FZGXSgSJ9PqE4U9bFU1oH+llR3pghx27Z7bbHkQvM1Xbt2Vb6O/x/feOONyv/TjBde\neAHatWsnSblQ7ab4+Hi+jm0MAwc82Pluvvlm/ryPj//DDz9IGU+4iQrzNQDq/1+qxlzMRlHf165d\nO+EYr7/+uuBrGDjwwgJ8LPmA+UjWAKZdu3awcuVK2Lhxo+AP8XLJJZfYsrP4t8HHwrz++usAEMgS\nU52PvmYYhlbO5uOPP4bJkyfzbXx/Bg8eDCNGjODb9FpoBvKQIUOE7V69egmfwQ18Jk6cKAS/Vq9e\nLdlbneYYgJil16xZM6Dg/3Pt2rWDPXv2QLt27SAtLQ1yc3OhbNmyfP9HH30kfZ791qrX2b1lf6u+\nrwksbhv+InckPsHDHuSzsrIE8XkG1f1w0p3DDlgo2Qo86HjkkUcgJSVF+k/MZmnslKbgtF86A92+\nfXtLzRYqXmmVBUcHLbj1cGpqqlZbzEqA2grc1QVA7jRJv+u1114rbGNjaEcLxtccKxa4bdu9tPiE\nkGD0rlTggBuAnJnaq1cvwTZVqVLF8jr69+8PP//8s/AaFRpmzJ49m2c2mIHPYafE8I033oABAwbw\nbaedHK1wUjrTq1cvPqHRo0cPeP/993lGRF5enqWWmt1Oa/Q499xzj5C1ZtZlDcsu6GD3nQ1Y6O8L\n4Gcou4jbtt1rywUNffYOlS+wW75NA4MU3fMrPkeoy8W9yl9//WX5XVUC64MGDdJm5RYUKgpfUHBl\nD/2+djVDMbrgWN++fbkfpc3gQs2GDRuEyTQA8fsdPXoURo4cWajX4CG0ttdtw+87kmKCYRh8loXN\nTO3fv58/XNPMsQULFkjBMfwQqmpXT8/35Zdf8u0RI0YIqagA1inT69atg5dffll7DidgzTEA4Fow\nhmFYtvKlwbHffvvN9L1VqlSROgjdfffdTi7VEatXr9Zqjn3zzTfQvHlzYRuDM8cAxGwJ+nBx9OhR\nQXNs1qxZQnCse/fu0L59e54SboVTIWqfoHHbtntp8SkgePaRZeh4ATy7zHjwwQdN35+VlQX3338/\n38YZwapgkFlWRLDQkgyM01JLK/BsOQDAgAEDhO5xLBPADJW/HTNmDF8PCwsT9jVq1EjKUgYIZD5g\nMX4G61YJEHg2KYgMg49ruG3bvbZcUAwaNEjKZnWT+Ph4rXC900YfNCvPKRdrw4/U1FStvqWqI6pO\nmqe44Yvju4LW9rpt+H1HUgxgD73x8fHCAzJ92KV10TQ4hmuet2/fDvfee6/wcE9FDh977DG+rtNl\nMaMgg67Tp08XaPaY1udbtT2mJaF0Rn/GjBnCNi6rpK1xdWDtBLvC/TRzzCo4RuvzH3jgAWGb/t3o\nUpBp/T3ARV0j7yZu23YvLT4FQNVxd+HChVIpA4Nm/OpQDbysMmlxGSeDlSCoOio77aaGNSJPnDgB\nn3zyCd+2U1aJBfnp+3HZzu+//y7s27Fjh7JURzeowD5cVe6Ag1WqDK7Zs2fz9ZEjR/ISVhVWGcoq\nsX38N0L/jqgOzM6dO5VZDp06dZJ8n9lklU5PyKdQcNu2e225oOnZs6ekhYT1ikeNGmV5jLffftv0\n/6nVBDouq1ShGnfoJh1okzGqVYx9TWZmJsyfP1/Yj79HeHi49toA5E7IlJ49ewpN0+zYs7Fjxwrb\n9DsABHzNNddcAwCBJAGatU1hfmDSpEnKxAjma5iUilOon8vMzJQm/OmYywmdOnVSTpzZhZaD/v77\n76Y6dABqfUvsj4cOHSrsU42TAMSSyB07dvB13ZhRJ190gaG1vW4bft+RFANY1hB1FFQo2ays0jAM\nSa9sw4YNfNCyaNEi/tBPRX0ZVqLxy5Ytg8WLF/PzNW/eXOryhbOfaB30wYMHBUdDg1GMLl26wKJF\ni+Czzz4TBk5YC+b06dNS5hcNjuH2vapBxueff87Xf/nll6Ayx9h9oAL+AIH7BRC49/ThpHfv3vDr\nr7/ybTpoAQhkQDz99NPK8z7++OPCwIYOWmi3Shoc27Ztm7CNU5BVAUuVUKdPyHHbtntpsYSK4NKZ\nTyoWSx+4sVgsgBwooK3D6UOvlwPIdMBCdWBYh2MAgJiYGMjIyOCZQk8//bSgz7hx40ZhEiY1NVXQ\ngVm9ejVs375dGSQyQydyTElNTbX9XhX0d6PXqdIcy8jIAMMwICMjQ3o/9TO33HKL4Evo+1u3bg2t\nW7cGgPP32jAMeOmll5TvDzUxMTF8nQb+6LnptpVIMvYzI0eO5PeLLjExMdCjRw/Bt6gy8rDuS2Hf\nFwodSNFgHp1co8E/Ooimshi0tDSY0qEQ4bZt99piSXHyNU7L34YNG8b/r+3evRsyMjLg559/hoyM\nDDhw4ADXZ2IBNcMwBJtCfQ0dh8THx0v2gNlWAHFMM3ToUCl4QzOBP/zwQ2EbX0tMTAysX79eOh8G\nS8dcdtllfD+zzfj9TDcZv6azS9SmUT9itZ2WlibcG+ZrrrvuOuH9DDpZQ79LQTE7Br32mJgY4boB\nAmMaet9iYmJ40Ih9X0bdunWFY9IxG/Y1SUlJ/NhsMkdXVjlnzhzhWrDmGPsuVKOMZrNj3UGVj7Nq\nfIPf2759e2Hfxepr3Db8Re5IfIIHO82dO3eCYRgQGxsLMTExEBMTYxpQAgi0tKfimCw4hh2IXYL5\njArWqYY6/ILMGKseFuigBbNmzRrh+1SpUkX7wGE34ytU5Ofn8y6lNHDVtWtXYYDYrl07YdDSqVMn\ny+AYZuHChdJrVsExnyLBbdvupcUnhCQnJwsleWadyAzDUM5i0wxlMz8DIAb6169fL2UvqcAPok2a\nNHFUjt+xY0fpNdwdzc6x8CQPnoSx+nxSUpKgnRIXF2d5LitUZY4sy9mOMLeu65YOnO22Z88eGDx4\nsFBSz/zl+vXrYf369cIDek5Ojulx2fupXylRooRf6uIObtt2ry0XHLg7LIB5Fz3MwoULYfbs2TBp\n0iQAOF/irRIvxxQ0c8xpWaUuSxlrcd13332OjhsKmMamShMsGNiYhpWlmpWE0oYR0dGoMTywAAAg\nAElEQVTRwu/i9HrWr18PTZs2BYCA/2P3Evs6/N5QfV9MyZIl+fr+/ftNJ/yvueYauO+++/j4icHG\nNKrnD6u/aYpOy23ixIkwevRooVP0vHnzLDP9LhK0ttdtw+87kmJEfHw8N2rMOAGoM4toR4pp06YJ\n2U8NGzbUOhJcegIA0KpVKyEtVMVTTz2l3a+DdugoSHAsISFBmKVRDWAKkuJbGOg0x/Dvu3fvXmjU\nqJFwf+hsHA6Ode3aVdCC+fPPP7XBMUrt2rXh0UcfFV6jztanSHDbtntp8QkhNDimQ1XS6CQ4RrEz\ng41nXX/99VfhGqKiooTOvABiGWjHjh154OXuu++WtBRpUwAraHAMZyjTLnIAou9xWi5x9OhRQQem\ne/fuQmZEVFSU5NvoLDMNhmG/TrOr8/Pzha7PhmHA4cOHISMjQ/pNMUx+gcoyYL744gshQ5le9yOP\nPAK7d+/mv+3JkycFLTmfIsNt2+615YKCBbfs0qRJk6AD6iqSk5O5LtgLL7wA8fHxsGLFCgCQJ22p\njrIdrEr42bkA1NUwqiqPYGncuLH0Gm7oQv0BzVKmUHuto1u3btr9e/fuFYJAr7zyCpeKYZPpOFv2\n2muv5RlIZrIGNOOpMMF+CkD825k4caKyyRh+1qhevbqwj2UBFhW6MRSWarjA0dpetw2/70g8DC13\nMZulnjlzJgCY/wdnqaX081az5gcPHgQAuV7bDllZWVIACtdpv/vuu9rPWwXHaHo1TXuljsQwDCHz\nzio4hjXHsFGdMWOGkKFnVtqoSlWlAUfVZ7ds2SLNZlj9btRRMC0Cs/ergmMzZsyQWpAz6L3Fjnfb\ntm22dCl8Cozbtt1Li08BoE00qJ+hUHuCAzB0EqZfv37KQIouII+1LRm0PMYuVASeZklQ6KQMQKCc\nRgcWkFaV7+NgIfUzl156qbB99dVXa8+FJ3lOnTolPThjPRtdkxkVNEPQSnNs8ODBQkY19Ss0Q7lm\nzZrCtlVXZOpnVGA9JJ9CwW3b7rXlgqJXr16CDXnggQe4DalUqRK3fTqRfCt0/0eprzHLHGPXpAqO\n6ZqgYM0xGqzPzc2FVq1aCa/hhiYqcnJypAkUVgbP0FWZYPtcvnx57bnMgmOsPNIqOIZ9TVxcnGkG\nOIAsFXP27FnJ11DwdwEQfXrbtm2lhjEfffSR9nqpLNCTTz7J16mOMoDYDRv7mgMHDkiTVla+BmPl\n76m+GIBYGm2nC+ibb76p3Y/vt9XfyQWE1va6bfh9R+JhkpOThVlxM0eSmJgIAGpHgksAnczoY8zS\nrmmHSGo8MXjGBiCQls2uG0AOlrVt25bvNwwD6tatK+zv2bMnf3hfvHixdkY/MTFRKKtMSkoSHMny\n5cuFz86ePVsaZKk0x+68806hI9ojjzwivScxMVH4ngDnU83p62Y4EeSfPHmypRYMHeyyzzHM6vPt\nXq9PoeC2bffS4lMA1q1bJ2zjIAfTcMGzw1bals8++yxf79evn1DeUb9+fT5LimdL8fqiRYv4AyYb\nuGEdn5UrV/L18PBwx4L8GPrQjydhli5dKjygDxo0SBiwREdHa/0MgDwjTDPy6IDlyiuvNL3WZ599\nVtIzoqiyxgEAypYty9fxw/uLL76oPRb+3MMPPyzst2r8YlW+T4NpzM+UKVMGypYtqw2OlS1b1s9Y\nLhrctu1eWy542P959i9utMF0hM3AgaGqVatC2bJlBRuCmTp1KpQuXVp4jY5patSoIWxjXxMbG2t6\n7WydjhMARBuNr3fEiBGCTTl9+rSyCQmGNrei16DSL2bnUE344+duVfMSfOxLLrmEH6tBgwbCmIb5\nibNnz3J7j30NO3ewNjQrK0v4LEucyMvLg7Jly0qSAcxXTJs2Db788kvpb0Llt+i16a6V+hp6fBYc\ne/PNN6XjUA2ukydPSplkAPaCXgyz5g3UfzMJHHy99HlK1YjnAkVre902/L4j8TB0lmXjxo2wY8cO\nQeAX89prr/H1bdu2SRlfToJjeEb9wQcflDqZWWV+FTb169fnQbuFCxdKJS10EEQ1x+655x6+btZp\nxClffvllSI5DsQqOtW7dGt555x2+bTVoMQxDaoiAocExO4TqHvqY4rZt99LiEyL+/vtvbUnB3r17\nheBYMKUuOgzDCCrojq8BB7QoKs0xVtqSl5cHdevWhSZNmmjPRRvLYOiDrE7bEsBcz031cN2xY0eh\nrBJAFlmmAScn+i6qrDkGu784g7qgwTFafos7xX311VdCcKxmzZrSwMLXuywS3LbtXlsuOHBppZVY\nN2bMmDFa268qG7ej6Yj1oLCvMQzD0tfQzot4THP99dcDQECTyi6h9G0UXTWM3fNivUwM1QJNTU2V\nfA225ZTU1FReVslgWeH3338/REVFKSfVAQLXHioNajOwX61WrZpQngpw3tekpqbC3r17tdmLwfzG\numeAUFKtWjU/OPb/i9uG33ckFwjZ2dmWjsgqOMYi5QcPHpRSiT///HPo378/31YZQ13mmAonwSQq\nqGgF7WKDjevy5cv5vSoKcX0qlEk1wiiHDh0Sugrl5+cLAw+r2S08aKlWrZrwWSpeSTugdu/eXQiO\n0ZJNABCEkp2KV/oEjdu23UuLTwgxe1ikXdYYVtkE1PZaofMD9EHRqrPS+++/r91PB0oRERGCVqZu\ncGgHGhyjIsV2xK/x/cMDlvHjx2u1KQGsO9fh0v5OnToJ++hA2Qqr4Bjtskd/G6pRY1VWiSeAfAoN\nt2271xafAmAlyF9QcFd3AP2E/5o1a4RMLZrVm52drT2X3UobTEREhPL1DRs2OD6WHXr37s3XcXCs\nSpUq2uAYtb3jx4+HNm3a8MYqWVlZUpdE3KQFQK85RoNVZhnPZlBfQ2FjGvb74rLKUaNGcekGr5Tl\n0+Y5ulLhCxit7XXb8PuOpJjBZltTU1MFQwggD3Ko5gcNjpnNQjBwW2MAUHbYOHDgAF+n14OvF0AW\nLNY5G5rZpEpX1sFaAjOWLVsmXJ+TjmcA4gwVLqVk4C5iI0aMUN4LFYMHD4YFCxYI99ZKcwwg4Mzo\nrJnZ+1WZY4zNmzfDuHHjhP2qzLGxY8fy70S7iFnpNviEBLdtu5cWnxDAsoGTk5OFoBDrWMsGC23b\ntpX2tWjRQjoeHniY6afgzy1cuBAmTJgACxculDKRsZYZ9RP4QTkrK0sp3D5o0CCuFdm9e3fltQDI\npYOUpKQkYbtOnTqCViQt8Vy/fr1WL40Oiv755x9+T5YvXy7cQwAxOEazyADE+7lkyRLT89pBNWBh\nWik4uxBP3ACcHyRhAWcAOXOM+ZnZs2crz6/6m3Lqp30KjNu23WvLBQUt3evVq5ckeQJw3s6zMQDT\no505cya88sor/H00k9UKlb4lHhdER0cLmcC4ZN+MhQsXSs/kzBbpmoQAAPTv3x927NghdGln2cbx\n8fHw5ZdfCiWDNIBGu7vj79KpUycpe8tKC+vll18W9JRVv43u/Ow11es4OKbar8NKl8uMnj17wosv\nvghr164FgPOlkoZhCNp3x44dk3S58DXS4Fjr1q21ATY6FmaBuxYtWsA333wDhw8fFvzlnDlz+MQT\n/pteuHAh/PDDD0JVFk4SUVGvXj0YMGAA32aBR+bfXnrpJe3nLyK0ttdtw+87Eo/Sr18/adaZCqZX\nqFBB2KYPklZdxFRlFZdffjm8/vrrylRkGiyjYKORlZUlaNtQDS8AtdChYRjKGeJ+/frxDAOrB+Zz\n584JxgnDSkOoUDLWPsBZc+z9qmwvncgiLkE5duyYtp29FdQJNGrUiJ9j8ODBfHaH4bTchWJWVpmb\nmyv9Nrt374Y2bdpoj+cTEty27V5afAoIthl0wIJ1Em+77TaercVe++OPP3gQh80m46AO09UwIzEx\nEebOnWt6TgrWtrTqwkXp2LGjtmyTlrrQSRjcFRog8D3p8ZyUhdLBklWmFwUPlnT3TAWewFGB/cy+\nffv4b4qzzYYMGSLdg1q1agGAnIkGANCjRw8ACEgzAASyBOfNmweJiYmC6Pcbb7zBBxHsvPh62WSZ\nYRjKySmfkOG2bffactGRmJgI8+bN49u4WyVuTsVswFVXXSV9fubMmbB7927Jt9Bnyz59+gjbVN8S\nw0rpWCn+woULpcoPOpHy77//ShMOOpv5zjvvSO9n4ESAYDAbtzRr1gy2bNnCgzUjR47k++i1MvkS\n/Dq+XnrteEJFlzmmgmaG4e+P/VZSUhKMGTMGAMSJcnrtFStWhNGjR/MAmep3MQwDbr31Vv6a6rfA\nEzWGYQhj1cmTJ8OePXuU9xpndeNMuXLlygnvw00dHn30Udi8ebNyYgqDr/ODDz5Q/o3hTGq8n1Xv\nmP3dXcBoba/bht93JB5l6tSpgmP59ttvYceOHcJ78AwOgD5DCEA9M2sXVdtjJ6iCY4XFG2+8Yfke\nXbdKmoJMswesoJkDx44dgxtuuEF4za6u14kTJyAsLIxvjxs3jgfHAAC++OILAABYtWoVAAQeZJwE\nx2gANjMz0/Ta3njjDV/3xT3ctu1eWnxCABuYWHWrpFhpduBJGF1WKR0YqbDzwKjKHLMLDY5RYWEK\nu2Y7104ZOnSotoMYAPBBBoPqwOCAUVZWFowdO5Zvh4eHS37Gx8chbtt2ry0XHExzjMl9sFJvWmZY\nVOAJiY8//ti09O3TTz+VJvxpcxmK1bO71ecBArY+GHvvhFAfv0+fPrzk9JZbbpGOzyYmWGCGJir0\n6dNHO5liJkBv57oKCp6sMcNJt0oVZlU5hYFXSj1dQGt73Tb8viPxMMnJybyTItVloXoeAIHaaowu\nc6xhw4bCvmHDhllej1XmmBNUQslO0Dm9MmXKCIFBGiS89957tcGxpKQkIThWkLbWAAFHQgeguD5/\n1apVkpYMC7ANHDhQeP3IkSOONMcAAsExlhVANcdw2StAIKMEXxvVS7P6O1FlA/qEBLdtu5cWnxBS\nkOBYVFSUsM8wDClDuTDBtp1NFDBoYAkg4CNxCSf1DXightu1M3Qi9nZQdTTG4O7Ut912m6Q5homK\nirIsd9HpwBQmNFN61qxZsG7dOj44sqNtiXVZ/NL9IsNt2+615YJC1RERg7OW3IBO5NJmMdOnT7c8\nBrYVOi0nqrFIG7s4DeRYVWSEkk2bNjkWbselgRSc7b1ixQqlrylI5pxVtY+qW6QTnPplK33LUJKR\nkaGtMrqI0dpetw2/70g8CHt4b9WqVYGOQ4Nj1ECZBbt0RtQMmrJLB00Acv18eno6X2ctnNm5afcV\nOqNvpmnDsDLGuuAYgPx9qGOw+jx+v5WINIVqjlGcBsfovbC6N6rMMbO2yhMmTCjSWZaLGLdtu5cW\nnxCCg2M//vij6fvYhIZV5hgTlKe6j6x0ndqSrVu3QocOHQAgoEfF1gHUjV+cMmbMGCkji2FVVgkA\nwvXUqVNH2KcafFFtTcy7774rPShj+3n33XcL+5yI5IeHh3O9FHzNmClTpvD1Bg0aCBo67DNmnYeH\nDx8Oc+bMEZoK6BoMUGkG6neon2H7mc4Q1bZcuXKl6bl8Qobbtt1rS7HnueeeE7axvuSXX34pVYXQ\njCFsMyjNmjWD9evXSwLjAABHjx7l63a1A83KKuk1YPuGy/9oWSXNIFq2bJnpub/++mvBxsyaNUto\nMka/Q0pKiqAPBgDw22+/mR4/JydHGPMAANfiYtCg1Kuvvmr67K3qjDxjxgzpNfb70rJ3LFGgSgCg\nzQ7oGEilN4c1NVU+6M8//wQAWSrGakwDcP5voEOHDnzyZebMmcr3Yh3lKVOmSL4GB8dOnjwpyDws\nXrxY6WtWr14NAABNmjQRqpNKly5tec0+SrS2123D7zsSD0Nn9LEu1s6dO4V0zP79+0uprlaaY2bZ\nV1aBJxWffPIJlChRgm8zI8igZZVWD/1JSUn8+lSOFV+jykngz1x99dUAENBGYZlPOLjFdHOwoVXV\n5+/bt49fV8+ePU27rOkMIvtOdevWNX0PRedI1qxZIwXq6AxWQTTH2PWa/U188sknguYYdfY+IcNt\n2+6lxacQePTRR4WuwKz8BgCgZcuWfH3v3r1S+d6jjz5q6xysaxQAwI4dO4SBGxPgVWWf4gdtag+p\nf2AaZey7ZGZm8nVq17G+5HfffScFx+jgqkKFCsIgAfs8pgGK78VPP/0kaHHifTQwxyaI7ILL61XQ\ngRsASI0PzGD3i/6uhmFAZmYmHDx4EB5//HFB6gHfZ5yhzF6j91I3YAGQg2P4WD6Fhtu23WvLRQcO\ndDVp0kSwAUOHDoUJEyYI77/vvvuUx8nMzDQN0jOoli8LjmEJGDxRjsc02EaxZ2BViRq2Gdj/sKBR\nZmZmgfXEVDCdRQaeSMEZwtSmYX+WkJAg7MO/BX4fC7pRe8224+LiBN8XjB3Fn1FN+GN7PmnSJKWv\nYQE1q26VWLNZhdWYBv/O7NrYvVi9ejV8+umnwt8V1q779ttvtc8zeN/8+fN5ABX7Pzq+pffbStT/\nIkFre902/L4j8ShUcwwA4Pnnn+fllFbZSNdff72UglyQcpcRI0bA559/zre3bdsW9LEAgjMOeIbd\naibqkksu4es0NRtA1toKNU6drdkMCIDoSHr37g2fffYZ15pZtWoVDB8+XBDKd6I5hgOuY8eOheTk\nZL9DmDdx27Z7afEJIZUqVRK2sU1o06YN3HPPPSE5j2EYPGCjCtwAANxzzz1CUM4pzNYbhsEFnO1y\n8803S1lTR44cCfparNi2bZswYFm0aJHQfMBLmP1eOqj0Aw2O0ZJPOpt/8OBBx+f0KTBu23avLRcM\nVatWFewZ+z+ty/4sbLCvCRX0+dcMmjH33HPPSRlDtBuxzj7TTCvMqVOnBD86depUoWyT/ja0ckaF\nLgtOhVVFyquvvmq6D3eP9gkemsV5kaO1vW4bft+ReIi77rpL2K5fv762RMMprNzFDF3b40cffVQI\naL366qvCzMipU6eEWfRQP9haCSVjypQpI2xff/31AADw1VdfAUAgCwo7KtqVJRRQoWQd9erVg127\ndgmv3X777Xw9Pz8frrjiCiEtevHixQAQCHhmZmZqg2P4fpw8eVKbOfb000/DQw89BAD2HCIunSlo\nwNRHi9u23UuLTwjp3r27sB0fHy+UckRGRvLZUdrKHkDsZqbq6uuEUOpaBoNVpoMTJk6cyLssmkEz\n2XBZ5QsvvKDUTQslDz/8sOk+O6UuBeHKK6/k67Vr17aVOeZT6Lht2722XFC8//77UiMvzJkzZ6TX\naDd0ht3JdvY8qcLpRCyuSMGfVZX4AejFzkeMGAEjRowQXtNplAGIkyV///23sE+no4yv9ciRI9LE\nTe/evbXnLQp0zV8w1157bcjPbVVJxErtGTk5OZbZZ6FizJgxlt0qzZg4caInfluPorW9bht+35F4\niM2bNwPA+RRPmjk2cuRIHoBQPcT/8ccfjs7ntDsNHrjQFsqnT58WRB2zsrIEbQPK6tWrJR0YXAN/\n6NAhyw6Z+B7Q706dPC29NMscY51Qzp07x1/Dg7+XX34ZpkyZIpVOJicn264vt9LRoSm4TgdsVNzY\nLHNs2bJlQh0/w6qTpmqA7FPouG3bvbT4FBBsH3XNQgBkHRiaiWtVvo/BD7Q7d+6EM2fOwBNPPCG8\nhwbIrGbzKTggxd6LtWMAzg9sqH7a/Pnz4ezZswAQKCHCfgAgMGGF29jTwRTzM2XKlIHTp0/DoEGD\nhPdTVOX7GPxQrvreTgcI9FrYMwdAYACH99PgGM1QptdD/QwGT9CwEijqZ1QZLPjvVKUH5xNy3Lbt\nXlsuaFTBMAqzhzrwxGi5cuVsn59mjrEgErPX1F5RX/PSSy8J27hcUYXVd6F+wgnt27eXNMWcjslU\nYNt59OhRafL/7Nmzkp9SfVbla+ixAAIBVAaTOgA4n0Rw+vRpqXKJ3lc7Qc+cnBz++5YpUwYyMzOF\n66HNyDDY7+3fv1/4O3766af5+qBBg7gkDP5buvXWW4XjWXV5HjNmDOTm5kp/j9T/3nHHHY50Qi9y\ntLbXbcPvOxKPMWvWLO4wdJpj+P0JCQm8Ja9hGDzIpGtT/PDDD0vilWxQdMUVV/DjMWhmE0AgAGaG\nSpDfig4dOnAjP2vWLOjcuTPfRx+MqQYWFUrGZZW7d+/m3R8ZVKeLdjsxczb0vgAEZj3ob6N6n9mx\nZsyYwbPaVGADXKtWLSGdeuLEibB161bh/bqyysjISB4c69GjBwAEBFqxI6KDFjpDhmGDN+YM7Xxv\nn6Bw27Z7afEJEUwnC0AuM2GYiSQDBP7fW3UQ2759Ox906AIoAPJgCE8kHD9+XCo3ufXWW4UOkDgw\nV6FCBeH7UTsJIOp+nTx5kn+O/UuFplVZEKr34YFIhQoV4MyZM9L1VKlSxTI4BnB+kEBnr6Oiovj9\nsBMk69u3r3B/169frw00Mh94/Phx4brZaxhWQsmu48CBA1ChQgVo2rQpfw8diLF7AgDS8XXn0r3X\np8C4bdu9tlyQ6IIXV111lVJgH4P3O/3/OGrUKG6H6LNmZGSkYMusmr9Q/vrrL9sBLpo1BnD+WRdr\nKeLv16RJE0fXwzh+/LjUXVI16f3iiy/CyZMnpfGH1e+hYunSpdJrERERACAGM1lAD39Ps9/X7LfO\nyMjQXgst6bQTPGMTT9OnTxeyF48fP671CXRdlfWly1JmzwE6GjZsKGyXLVtW+T52nadOnYIXX3zR\n8rgXGVrb67bh9x2Jh1EFx3BWER60qLJ5Vq1axf9DDh8+HFq3bi3s15VU4JIHBi3HU3X0wEaddqek\nwTgn4LLKrKwsR8Exs0wn5qzi4uKkexNKClqvjx8WKlSoAPPnzxf2Y0cOIAbH6MOF6nelsKAZgJyF\n5mRW0CekuG3bvbT4hBCcaaWarcV+RhVMwWUDKi1JK7uPS/ArVqxo+j7VA3Xz5s2FbToJ4jZmXY1V\nOpiFAdX5wugGKMFon1FfYbdJg4+ncNu2e225oBg5cqSwXa9ePWG7Zs2aRXk5ElZZyoVJRkaGZUdc\nmnCgE47HnSvz8vKEjCJaVWPmJwACGVtlypSxDPrpjhFqcMYxANjqWK8bb9IGZSyIByA3c7ODzu8B\niMGxSy+91DQZAiCg7x1sWaWPFq3tddvw+47EY+CZ6eTkZP6Qum3bNrjmmmuE9+KHW6tSt6eeekrY\n7tSpkzRowV0HVeCZj8qVK1t2FNFlrnXs2JF3jrQD1Rzr2bOnULd/0003CftxcAwgMEvOSEpKEsoq\n6bFxB7PCAs+YpaWlwX/+8x9hPy7RVGUF4G4+FNadE+B8cIyVLpllqH366ad8nZZVVa5cma/rnIhP\noeK2bffS4lPImE3CMFiHXxpgGTZsmNAWns6wWokCA5wPsKlsNNNvtCqt9ApUnyUxMVHIMr700kul\nz9jJEqBdzAAC2kB4IgZnXpuBtS0B9Pbd6UBVpW350UcfKd+ryurzcQW3bbvXlouOq666ynTf0KFD\nJY1KK+jkLUYnyH/ixAmoXLlyyHQg6TM2gPicPX78eB4ci4uLgxMnTgj+i5be0UxrOrH0yy+/8Kxm\nliFMM4VZN/hBgwYJx8vJyZGu9dSpU7BkyRKTb1e0wbFgYM8MdlCNLZ1oYFoFx6pXr277WBSVfI5Z\n5hiWIfCR0Npetw2/70g8DHVCVIsF11YDnA+KsFIGqgtGW+vS4Bh9P26HO3v2bOH8dOYAQJzFpyWX\nKSkpUjeUsWPHCrXrrBsmLsVgwUErvRGaOVa6dGlhG4vZAwA888wzwjYuzVm5cqVQa4+vB6fGNm3a\nFFJSUmDUqFHS9cyfP5+LWoeHh2sNO3WydABpVTIze/ZsYduqW8/48eOFbepIaKo7TUG2ml3zKRTc\ntu1eWnwKAaqXwsBNNwDkB3yrrsh33HGHsE0bv9AMaSeaY1TY+Ndff4X09HRBTHjDhg3wn//8B9LT\n06XAP0Dge2M/hLMr2KRCeno6vz+4XIUG6agwNNM2YZ/FfkYl/pyTkyMc45dffoEZM2YI14pZtGgR\ntGrVSjoOQKDcf+rUqbB161b+Ofav2cQV0/RRBeh+/PFHYXvBggWm5wYAaR++dhYMS09P54Nn9tvg\nz0VHR/Pfhg0kfQoVt22715YLDvocrhOSp7AA0JIlS/hkenp6OiQmJgr/b7/88ksACIwTmI4vgDim\nAJCDY+np6UKAn1Y+MHs/cOBAfi24aRkWbldlxjI7bubrdNAxDW66BWB9H63E/vF4jPkAdp3s3uJ7\nzORV2HvY98W/L/2+KnvdqlUroXPpjh07eBAuPT0dOnfuDFlZWcJ4Mz09XRon7Nu3j5+HBpBSUlKE\n34aNaaZNm8Y/c/jwYRg9ejS0atUK5s2bp7xHrVq1gqNHjyr3YdiY5o033hA+y9i9eze0atWK+2/c\ngAggIAvQqlUrISEEZwLSqijVdfpYorW9bht+35F4CNqZkg4aqJ4TrfPGjoQ9/GInQActZtFzVffG\nAQMGSK/h8rtgSiiwgcGZXTh7zjAMWLp0KcTFxfHgHmuZjLMbdGWVDDzooI6T1uerMhNodp5hGLBi\nxQrhelesWAErVqwQtF2OHTsmfL81a9ZIx9bRuHFjWL16Nd9u1KiRcE4qlIyDY4cPH5ZmoeiMPg6O\n/fTTT4LT27RpkxQcw7DrYAFCXTtrnwLhtm330uITYt566y0AEJu0sKB9ly5dhPeyYE2XLl2Ekge2\njrsiN2zYUJqEoV2x6PHNuOGGG4SmL2ZgW0uPryrRwPt1kzCGYWjtGxYyZuhm8+kglV6Lbl+NGjWk\n/XSwxrBblkLf5zSDGncQowFVCn6eSElJkbpVsvLYwYMHCx3ifAodt22715YLnoEDB0L79u2FIFYw\npWwAIBxDxfLly7X7sY3Lzs4WJgYA5AoR/FwMII5J7IC/5969e+GLL74Q9ltlKFNfo8vswvaUZWN/\n//33ks1fsmQJ13im4y6quQlwvpGJma8x+y3ZeXFG19133w1dunSBVatWOTqWCkVh5s4AACAASURB\nVDaphYNK7PNWmmM0c0znF7Ozs8EwDJ5cwYiNjYXBgwcrx67Y1+CmDkeOHFFmPAOIWWBvvfWWUsKB\nBWxZIDUYeYKLCK3tddvw+47Ew9DgGK7DBggYGOZsKlasKATHaKYUfVhdvXo11K9fn2/bKXehOOki\nRg1XqKlTp46QRaAKjmGwI/nhhx8kY42/i2ogEkqsuoauXbtWu1+VgtyoUSO+fsUVVwjZZ9T401kO\ns7bYAPbEKn0KBbdtu5cWnyIAP9RinAow08wxMwYMGCA9yA4ZMkSYhR4wYACUKFECAAL+Lysri5eE\n4/OsWLECmjVrJp2DlWWaERsby8+j4sCBA8I29jMHDx6UfAX2M2lpaUqR5IJApRasum5ZYdUwwU10\nDYB8Qobbtt1rywUNzqxRMWnSpEI7d6jLABs2bCiUNubn5wvSLW+88YaUueYEp/anRo0avFvloEGD\nBJ9y4403aj/73HPPSckSVtD7adfXmAWqrGR2MFaC/E6xkuyh4DHNzp07HWmO+biG1va6bfh9R1KM\noCnId955p7AdjHjl4sWLAQBg48aNylkJADBNY8XBMVqG0aZNG8G42ulOUhDq1KkjpMCGhYVJ72Ea\nLElJSZIjMdNCKSqsUoUbN27M16mWDHYk7dq1U3arZMGxM2fOaDPHCsrQoUNDdiwfAbdtu5cWnxDi\nVEPGaQcxO7CyDgBRa4yBA/a9evXi61FRUUIJJUBAmwSLH2MNNDxgwQSb8UonYaKjowX9LNWAhcob\nYL777jvTfZ07d4bJkyfzbZp5Rq9Fp/VjhzFjxkiv4d/GKosAX5/Z96Il/j6u47Zt99pywVGQgJfT\n5zsqv6KSF8EVIJjs7Gx47rnnHJ0PQMyuwiX8tGKDypm89tpryg6WZnTp0kWwcaqsZuprQpkFS7Pk\nqN+kwTFVZ+R+/foFfX5cYUTHd7SSp7ApzDGNT6Ghtb1uG37fkXgI6khmzpwpbNPgGDVIbNCC2/TO\nnj0batWqBQBiuQuA9Yy+lQAia3scLFgEvkWLFoIYMLtmgPPfh+rLAABMnDgRAOyVVT7//PN8XTfL\nEh0drXQk7PMZGRnCPaZO6cknnxS2VdkKug6WtJMNBWeFAch/B1bbKkeiKgli0KxD1Qya03R2H8e4\nbdu9tPgUMgkJCZCQkMAnPVip9ZkzZ4TgGO1+xsA6jOPGjdOei3YeBjivO/b+++9LGdQYOiljp+yD\nlbyzf1Xi9hg6YJowYQJfP3PmjLCPBqTmzZunPT71M1Q02uraunXrZqllwxgxYoRQGtKgQQPht6Hn\nUpWE4Pc8+OCDwr6ZM2cKGcopKSnCM8y4ceOE7fvvv195XoCAPk8wukA+BcZt2+615YLmzz//BIDA\n/0Ec3MGVJ/R5k8GC5zQAhLWp6KQDm4xnVKtWTbAJhmHA2bNnhf2MhIQEaULg448/Fs7x7rvvWtrM\ntWvX8vcMGTJE2Icnqekk9CuvvKI9rl3YuWlJKL3uK6+8Urg3bP3QoUOwePFiiI6Olo7N3pOUlARL\nly4VPo99DfO5+Bh4gmrmzJnw3nvvOftiiL59+xa5GH23bt0A4PzvlJCQwL8/lRxg45WZM2fC3Llz\nhX34d2DrNPB57Ngx+Ouvv0J49RclWtvrtuH3HYmHMHvIZQ/jNDhGu4jhEgsmOF+5cmV47bXX4L33\n3pM0x+j5nET7g0mjxbMU33//PRcrBgBhHUAUDFbNHtEBlZ3gGD4HLlHt0aOHNMtCA5V33303X69a\ntaqwD2vo7N27F2699VbhXIZhCNkLGCo+TalTpw4fpBw4cABq1aoFjRo1EgKFNAVZlTmGocExGiik\ng0GrFOQTJ07w7/v777/7bY8LB7dtu5cWnyIAPzhfeumlpp1uAQLiwDirgIkF69YBAiLG9DV6boxV\nWSSFiSQvW7YMoqOjheOy82KhYPaaYRjKa8At66Ojo5XXTtG9B/spXJKJP6PKpFNx9uxZbVll3759\nBU0gfA6zciOr75eYmMjX2f368ssvIT4+XtmJzux3xedhjYHo4NGnSHDbtnttuaj4+uuveRMSO2zc\nuBEAzP9f06ZhycnJps/CAOrOyAzVhDId01B9Sx14Ejo6OhoGDhyobDbFbBPVF7OCltiXKVNGuE+0\nWkRVSojtYm5urtaXqli2bBns27cP2rRpw6tPzPTEVLDrjYqKEq7dqumXW7DJMTZmws2CrO5XuXLl\ntPt1nzfrVumjRWt73Tb8viPxEFggn3YCeeGFFyRBfuxInJaFrF69Gs6cOcNnhfLz86UuYocOHeLr\nffv2Fer3AfRdCw3DKFQDqso2oOd3gipTrDDYsGEDxMTE8O2KFSvC7t27TTVuKLt374aDBw8Kr9HU\naHrfmUYPw0xw8tixY5KunRV0JtCn0HDbtntp8XGB/Px8iIqKEl7r0KEDXzebKWadyxiGYQgNRZyC\n9SBPnTollDGGmqSkJNsZTPHx8aaDRIadUhczcJljQWUK2POC3ayzYM5Ju1tSvTYKzsLLy8tzdC6f\nkOG2bffa4mOTgwcPCgEGNqmKbb2uhH/79u3K4BiuIqHQahgaHGPVLYZhQIMGDeCxxx6zzCxTUapU\nKcv3pKamcl9x9913Q40aNbQ2U9X8BVfThJqitqkXmsQKbcjmU2C0ttdtw+87Eg9x11138XUWHNOJ\ntUdGRobM4O3fv18KjmHNMKsU2cLWFAMQ05qtgmNWgvyqVvVFCQ1+Um6//XbTfZUqVRK2VcEx3C2T\nwrrbMKzKnnRadosXLxY6/AwZMgRGjx6tPZ5PULht2720+BQiVqLsWCiYzeZjLSwKLt9X+Qk7M+CF\nCdaqpAMWKz9DiY6OFjKdrUSSdcGx8ePHK7NwFy1a5OiaCgLLQAcAuOyyy6T9LVu2NP0snYSx8jM0\nQ5lmaPsUCW7bdq8tPghVwEPXodEutDw9lNSuXZuv2xXkxxIvZlSoUEEorWNZyoz77rtP+3lcnm8H\nq4mM8uXLS6/h+4p9Dc3es9MFWkdMTIzUBM7HxwKt7XXb8PuOxEPYGSTgoAedZdm3b5+2o8qHH34I\n8+fPt309rHOXGThzjEXVK1WqJDUKAAB4+eWXJQFM/F0qVaokBPqojsEzzzwj6LHQQUvTpk0Fh+ak\nW2VcXFyBZvR79+4tOC5WEoqDWDTAZNb8AMB8hoIdjw4wadaZLmMvNzcXvv/+e+EcVLyyffv2wvbu\n3bv5b3Py5EkhcEeDcEePHuX3Dpfc+BQYt227lxafQkRXvp+dnS0J8rdr107YfvHFF4VtOpuP7Tyb\nzcdg3SoAsRkJAAiaNPn5+ULXZQAxU7Zjx47w2Wef8e1ffvlFOD7OjgawFxwrUaIEhIWFCeeh2bkM\nGhxTddBk94Mdg3WgtgPNxqKlPDizj1KiRAnht1BNtOHvRct+8KQIgHX5flhYmNAoB3chzcvL8zuI\neQO3bbvXFh8NTifnk5OTJSmRFi1a8PWwsDDBPoeFhQnBLRysB5Azx6gdpjYpLy9PuGZ6LY0bNxau\ngdnIvLw8yMrKEuReqL6l03vhdCJGFRybN28eXzfr/rlr1y5+X6yuEd/7tm3bSvvxuAUL8jO7jo+v\naorm44PQ2l63Db/vSDzCtm3bJJ0rLHKOgxDLli2D3NxciIyM5DMXOTk5wqBlwYIFkJOTIzgHtt66\ndWsAcFafz7KTWOADlwZSDh48qBTzZdDrYq9hsGaX6v2GYQj15HY0x1i21muvvSYdjwbHzLIncnJy\nYOLEiUKgD187/R6GYUj74+Li+Pbo0aMt2xazz7N/aaMEK82x3377TToe+7uZNWuWFByjM/xmgxZ6\nz9j10SCoT0hw27Z7afEpIs6dOydNwjA/s2jRIp6B26dPH+mzERER0KxZM9PGL6qukSdPnuSftQMt\n86TgGXGaDaA6B514SEpKUn6GZt/qGDhwoPL4x44dU55TdV1Oy90B5IEkg/l/p8dmvj8iIkL6TLVq\n1aTAHIV2hMYiyXgA7OMqbtt2ry0+BBaEodUPWO7jxhtvFPaxbFfaXCU+Pl7oVkntCt4uXbq0UHY4\nfPhwYbtUqVLcxufl5fHPRkRECE0F2FiK2bNu3brBH3/8IYyxgrG3AACtWrUy3bdr1y5hu6BZyoyX\nXnoJAM7/LnSyCgCgSpUqAKD/XrqmXAzm+48dO6bVt/TxsYHW9rpt+H1H4iFwcKx9+/ZSfX58fLyQ\nwvzzzz8Lkf7rrrvO0flocKxy5cqOPq/DatBS2ODgmFkbeUZRlISq+OSTT7iWARZKptAZGF1wjDlG\n2rbajH/++Yd3/DQDB8fY4BWDy29p9x+fkOG2bffS4lOE0OCYE3tJbS/NCrOToUu1LVnmqkrDiuox\nYoLxST179pQmPIJl37590oQC5qefftJ2MR4/frxga4s7dHCt6kbtU+S4bdu9tvjYoGnTpsK2XZup\natpRnNEFx1auXCk0AUhLSxP8VbDBsVDBJmt8fIoIre0tYfj4IBo3bmwYhmFMmzbNCAsLMwYMGCDs\nb9asGV+vWbOmERYWxrfxuh3uvPNOYfvff//l67m5uZafj46O5utXXnmlkZeXx7fnzJljNGzY0NH1\nFBb33HOPdj8AFNGViHTt2tU4d+6ckZ+fL7zesGFD4ZrwfTUMw3jwwQdNjzljxgx+DDuUK1fO6NSp\nk+n+u+66S/hbKF++vLC/efPmRuvWrfn2uXPnbJ3Xx8eneHDgwAFhu1q1apaf+e9//2sYRsD2btq0\nib9OfdTu3buNzMxM7bEeeughYXvbtm2GYRhGZGSk9N5rrrlG2F6yZAlf/9///md53W+++aZhGAE/\n/Oabbxrz5s0zKlasaOzcudP0M9j+6bjuuuv4tTOwz7355puNJ554QtiP732XLl2MuXPn8u3bbrvN\nOHjwoPD+U6dOGYZhGMePH7d1TU6w83yB/VZWVpaj49Pf2cfHx3vs3r1beu2KK64Qtrdu3RrUsQ8c\nOGA0b95c+56kpKSgjs0YNmyYdv/Jkyf5um5s8MILLxh9+vTh21OmTDFeffVV0/f/+eefxk033cS3\n27RpI/irAwcOGD/88APfrlu3rvD506dPa6/bCew37N27N3/tyiuv5Ovvvfee4Gv++eefkJ3bx8cO\nfnDMxyhZsqTy9bFjxxpvv/226eeqVKkiOSXM9OnThW0ahNm+fbuw/ddff/H1UqVKGS1atOCfY5/N\nz883OnToYBiGYSxfvpy/Pzs7W/getWrVMtauXWsAAP8sABhvvfWWdE3UCeTn5wvHWrp0qfTdevXq\nZaSmpiq+tTlhYWHG888/L3wXBg460n34tRIlSpi+j143ht2HlStXSp8rUaKE0bVrV769du1a7UDk\nm2++Md2nuvaKFStyJw8AfNCSmZkp/LYMfO6tW7capUqVMj3XkiVLjKeffppvDxs2zEhOTtZen49P\nYUODtEeOHBG2abCD/b9kzJs3T9j++uuvhW0a5KBBilA+yIaanJwcYfuXX34Rtjds2CBs62xRWFiY\nsH/Hjh1GWlqaYAPKli1rGEZgMBAWFmakpaUZbdq0MQwj4CeYnzGMgP0YOnQov6Z+/foZaWlpRlpa\nGv8sPt+uXbuMcePGmV4fHmhdfvnlRlpamuDnWDCJUev/2Dvv8KiK7o9/l4gQ9KXLCwQNEEWKdF7p\ngmBDKUoXaaL0EqRIJ3RCEUMNAQ2gdEILVTpSAhiUlmAooYReAiFA6PP7I787zJm7e3dDEjbK+TzP\neZhzZ+7cuXfDzO65c84pXhxA4t/D0KFD0bBhQwDAO++8I+9XH0+OHDnk+TVr1sT48eOl7ufnhyVL\nlki9ZMmS8vwlS5Zg2bJlsk493qhRIyxZsgTt2rUj49u9eze5d91A6OnpCSBxzq9bty7Gjx8v+9QZ\nMWIEuaberlOnTqS98QzU8V69elXqr7zyCvlsGjdujIEDB0pdrVu/fj0yZswor7lx40bT95G0RHx8\nPNFPnz5N9PDwcKKvX7+e6CtXriT6rl27iB4VFUX069evP8swGTfwIq01kZGR8vtjTEwMfv/9dzlX\nAcCyZcvQqFEjTJ06lcyVqtSvX99h/yVLliQvNKZNm0bqq1atSgw6+ovvw4cPE71Vq1ay3LFjRzRq\n1EhuKrA3tiVLluCdd96RZfWzUccFJM5v48aNk3p8fDyqVKni8N703yv6nJwvXz4ULlxY6vo6nTVr\nVtP5hhw7dgxDhgwB8HQu6tKli2x78eJFcr0///wTS5YsQaVKlQAkPie13tvbmzwj/aV4ctGNjuo6\nAoAYCQFg586dRF+xYgXR9c9m//79RD9z5gzRVQNoWoPXmv/H2dayf5gwz4CRlUmPOWbPP1/FXtpj\nFWcB0Z3FHEtKmnchqMuMEZhRj381ZMgQkSFDBvHo0SOH18mWLRs5tmnTJqdj2blzJwkGCUBcvnyZ\ntMmQIYNo27atEMJx8EohaJBm1XVn27ZtJPuLnsGzTp06pnFmyJBBJCQkyHgGRtKF9OnTiw4dOoh+\n/fo5jdGluyIlNeaYvUDJanptY5wGzjJp6hiuPs6SIDDJwt1ze1oS5jmirzNqchE1U254eLjp3KVL\nl8p1Rs027Cwt+sKFCy3rHzx4IPLmzSuEcBwT0RX0gPx6BjHd1UWNbWm4xNvDiK/l5+dHjtepU0ec\nOnVK6qpbqb15d/HixQ6voQMH7q6dOnWS5fnz54vWrVtL3SorsrN1Rs+SrK8zupvO/PnzhRCJz2b7\n9u0Or8u4FXfP7WlNGAfs379ffPTRRyIsLEwey5Qpk6ldVFQUmYOM9lu3bjX9psmaNWuSx7Fv3z5Z\nHjFihMN2vXr1kuXDhw8Lf3//JF8rOThL/uJo/rbir7/+MiVy27VrlxAiaZmRGcYNWM697p74eSFJ\nQ+iTo5VxLD4+Xv5o+c9//vNM10tKQP7u3bsTw48+sQshiMHrjz/+eKYxJRdnQYENrBai3LlzmxaS\n+Pj4ZI1LxUhmcP/+fdGjRw8RFxdHMnHquGIcUw11zoxjR44csRyfvR9paspqldq1a/+r4uCkYdw9\nt6clYZ4jzl7CqKjGoLlz54rKlSs7XWfefPPNZx2aSwH5ly9fLvWJEyeS+gsXLpgCxav4+vqSl1Z6\n4pfkcufOHWJkK126tGX7RYsWybKemTpbtmwpOjYdY91ZvXq1PDZkyBC7bW/evGnKVqmif7dh0gzu\nntvTmjAa48aNk2X9u6IzA4/+//7w4cOyXLRoUZINUTeUWWVhV5NzWTFv3jyX2rkCALLetWzZkqw1\nOqpxbMOGDaYXJ85QXz67sg598MEHsnzr1i3TZ+NsrWGYVMZy7nX3xM8LSRrG2RdIZz9anO22Sopx\nbOLEiSTt8VdffUUCJR8/fpwYx9yNvUxoBtOnTzftHFMXEiHMb1mcBfV/FtQ372pA/nfffZdktrly\n5Qo5TzeO6TvYrL5EuEKbNm2EENbZ5FRU49iwYcOSdW3GIe6e29OSMM+RpBjHhEg0vh87dszl9rVq\n1UrqkCT67qSkBt3X3+br2AuSnFIB+oUQwtPTU5aNzJ+O+PXXX1PsugbDhg0T06ZNI8fULNlCCFKv\n/vhzts6ktrGOSRXcPbenNWGSgLpDTIhEb5itW7dKXTXQ6POOEOaXF+pcpGa3FSJxB9r8+fPtrjXf\nffed3fHVqlVL9O3b1/ENWFCkSBFRoUKFZzrXEf379yf6nDlziK7emyueGeouMnXnGAAREBDwrMNk\nmNTAcu5198TPC4mbOXPmjMM6e1+WGzRoIMsXL14kdUaaXQP1i7cQ5i/1hw4dEjExMS6P1QpHb4mN\n7Ilt27YV8fHxph1IderUEefPn5dZW9TsLQ8ePJDZOZcsWSJ69Ogh6/RnU7ZsWXKuvYXEcF8V4qlb\npXHM1S3IefLkkdfJkyePuHnzptSrV68u+7t48aJ083T2A0wnffr0RE+uW2WhQoXIs1E/qwULFoh2\n7dqR9urbwMuXL4ty5cpJXf2cHGWGc/TFhEkW7p7b05I45e7du0TXXdYNFy+DQYMGEV3fDam7kM2e\nPZvoe/bsIbozQ4c7iY6OJvratWuJ/sMPPxC9fv36UurUqSMyZcok6tevL4QQomvXrqbU8eqPnqtX\nr5KXMA0aNBD169cXzZs3F0I8zaobEhIi6tevLwCIt99+W4SEhIjt27fL6+rjsWLs2LGW9UIkuiuq\n92Vg7Iju37+/qF+/vsk45uPjI3cRG+d+9tlnolWrVqJs2bKiRo0asu2qVatMxjp97AsXLhRLly41\ntTHWkX79+snjuvu93ne2bNnE2rVrRUhIiN1rOXOXN+7HcI0ydACifv36olixYkRX1xnj81Ofp/6d\nYPr06UTXs5BeunTJcnzuRF/Dt2zZQnT9h3vXrl2Jroe40NfsFStWEP3vv/9+lmGmBO6e29OaOOVF\nXmt27NhhOR+7EipG/R1jvIhR5xJdjPlNiMS1Rv0+/NNPP5G2devWJXPWpEmThBBCfPTRR5bXUM/R\n/2+qDBgwQH6+Rt+O0OeQ8uXLE/3NN980Xdt4Xk2aNBHp0qUj7VVDpDH/qn3o83FacqvUf9Pou6CX\nLVtGdHVjhhBCtGjRguj6Os1rzVP+qWuNuyf+576QME9R/fMrVqwohBDC29tbXLp0icQl0Re/hIQE\n4e3tbepPN47pP1p0krJzzB76hKNO3hEREWTn2tWrV03/SYUQImPGjLJstfNMdeMcMmSI+PHHH0m9\nvs1YN47pz0LdOebt7U2MYxcuXCBxdU6fPi2NdFa0bNnSaRshhKhZs6YsA3Aa+8CecezTTz+Vur5w\n6NunDx8+TPrQ026rxrHOnTuLCRMmkPqqVasKIQT5m/T29jZ98WFSFXfP7WlJmDRA1apVxbVr14QQ\nwq6h3N/f3xRbyphLnGGcFxQURI5ny5ZNzmUvv/yy6Tx17tu+fbvLsa30ucw4T19nnKEaxwxOnDhh\nepGlUqVKFSGEayEB1C/Gzp7lypUrRfPmzeW9nDx5kuxQVtF3TqtMnjxZbN++nazfGzZscCm2JfOP\nw91ze1oTJgk4MxR27txZlo8dO0ZCxTiaq63m8A4dOpA2etxLe+c+a7xDdWOCjr0wM/Xq1RM5cuSQ\num700Ps7ceKE3b7V8a5YsYLEWBPCbPxwNFZ7nkTPEueMYVIIy7nX3RM/LyRuJioqSghhnqTat28v\n3nrrLSGEkP864u2335b9GMTHxzs1julGEiv8/f0tXRWFEOLo0aNCiKdB7FN6C7IjYmJiTMYwqy3I\ns2bNIou0EHTnmP68VWOWivHDMCUw3mR8++23olKlSqTuyZMn4vjx41LX4yaoMcfUeBCuYrxlsbfN\nXQga8Nre2yfjzafu4sSkKO6e29OSMP9CHBluUhNXdvUGBwcT3XB1OXjwoChevDip04MjJxV9V19K\nktwfQklN/ML8I3H33J7WhEkGunHsu+++k2FC2rVrJ5o0aSJ3LhctWlTExMTYDd3hKs5CyaxZs8ay\nXn/hr75UjoiIIL9p9B079jDa3717V1y4cMHl5DHGb6lnxcqQxzBpBMu5190TPy8kaQh1IQkNDSWZ\nVxwFvlVRMykKYd4tZc94YbWl0lEQdiES3SHV7eCZM2cmwTX1bbJCmN1AUxPdOLZgwQKiO4s59rxR\nA/Lr7i/O3Cr/+9//JulaagBsI3unwYgRI0zXX7duHdH1hXvRokUyuCjHHEs13D23pyVh/iHExcWR\nHcr2DDT6DxbVlbBOnTrivffek3rWrFlNMRiTgyvGMf1Fior+Y8zezrGkoAZJ1p/VjBkzLJOfZMuW\nTZQoUYIc+/rrr4lulfjFHvqOaXV+T25sSyZN4u65Pa0Jkwzszfd6nK2U5J133rGsT2p8Sw8PD1nW\nY461bNlS3Llzx2FsxZIlS5INBUkNr6KTktngT5w4YQrInxyjJMM8A5Zzr7snfl5I3MijR4/E/v37\nxfvvvy+EEKJw4cKkPl++fERfv3490QGI/fv3k0lu//79JLNi6dKlxfDhw2VAeeNf45z9+/c7NIIl\nNQi9lVtkUFAQ6e/UqVMkBtrHH3/s0E0kLi5OnDx50nSvRtnIcPPJJ584vL76o8UYhxrbwTimug6q\nlC5dWhqSjJgR9p59w4YNhRBml9V79+7J8sqVK0mGNGOnnSP0Lxi6cSxv3rxE1wNvHjhwgOi6u0vO\nnDmJrrvsWr3tWr9+vcm//+zZsw7bM8+Mu+f2tCRO0ec0fe7U477oRg39i6jh9m6gpoUXQpiyVF2+\nfNmVYboF/W2+HsNCf6mizwe6+58e/0OfD+y573t4eEjRY6kkFbWv6dOni4cPH8p+PTw85Pz58OFD\n8fDhQ3LOhQsXZLlmzZqkL11sNptpLjbqjOP635H+EkbdoWyM0xWMOI4LFy40XV9FffFhjM1V9Jcw\nO3bsIPqYMWOIXrduXaKrLkRCCJOhznCBMtATDLi6q8Id6EG/9ThQetzOYsWKET1XrlxE/+KLL4g+\nfvx4ooeFhT3TOFMAd8/taU2c8iKvNfocKYSQc6wQdK1R51Hjt0LTpk1Jf/r8qus2m43ourfDX3/9\n5XDO2759u1i1apXU/f395dzt4eEh6tWrJ7Zv326aB13h0aNHThOS6cYxe2uMVZ36LMLDw+2uRUI8\nff7Gs9L7MdYd9XOaM2eOePToERmDEUtNbZdS8FrjGF5rEsXdE/9zX0gYysOHD2VgRnUhGTJkiCl4\npR7A0QheuWvXLnns+PHj4siRI1LXF1YDdTIKCwsTu3fvthynvWC++hZkleXLl4tvv/2W6OqOAGcY\nE3/jxo3lsfLly0sx0IOXOiJz5syybMSR0QOl6ujujUIk7r5TJ5Py5cuT4I7qPQth/qJjnOPsmLoL\nz97OMXX3ljN3lxs3bsg+vLy8TMYxq4D8QtCFZO/eveL8+fOm8Xbv3l2W9YxDTIrg7rk9LQnzD8ZI\nVJIWeJa3+Y6+WF+5csWpccwZSXkhZQTkV5PEMEwycffcntaESQZqEiwhgVdrFwAAIABJREFUhGjd\nurUQQoiOHTvabZ/UzMiOQoHoGHEdHXH+/HnTLmB9s4I9bxhjI4J6n0ZZ/+6vrzVGnLL4+HgxZMgQ\nMWPGDMsxGkZUY/25evWqyfBh3IsQ5iRj9jBe2vP6wbgBy7nX3RM/LyRphLt37zrN7KKjLiRZs2ZN\n0vUqV66cpPZCCJNxSzeOPWugy7RG1apV5UJ59epVlwIlpybPkq1Sb+OIyMhIS+NYfHx8mn7L8gLh\n7rk9LQnDpBjqjyJ7gZWTQ1KNYypWmawZJpVw99ye1oRJQfr27es0q6OrAHBqHHO2m0tl69at5GW8\nvTA0uuuhs1176kvuCxcukJfaqueMPcqWLUsSg+k4c+G3Mo5FRkaSZAj2sHLhZ5gUwHLuTQfmhebJ\nkycAAE9PT+zdu5fUdenSxfLca9euyfKNGzfw1VdfSX3Pnj2W5+7cudPp2NT+AeDVV1+V5cDAQLzy\nyitSt9lsqFy5stM+U4OaNWsCAN566y2HbXr37u1SXzExMfj999+RMWNGAEDOnDlx8uTJ5A/Sgj59\n+pjGd+TIEaJ//vnnDs8/ceKE6VjVqlWd/g0AQJEiRSzr1c/c4P79+077ZRiGSSuEh4fbPX7x4kVk\nzJgRGzduBAA0bNiQ1NevXx8AZL3O5s2bSV327NllOSAgAEOHDgUAREREAEhcJ1Xu3buHWrVq2e37\njTfewNmzZ8mxrl27yvLChQvh5eWFhIQEAEDdunXt9sMwDPO8MH7TAEDp0qWxbds2qXt5eZE5rGnT\nppZ9qfPl6tWrcfHiRakLIRAdHU3az5gxAzt27JC6h4eHqc9Lly7h3LlzAIAFCxbI49WrV8eAAQPk\n76g333zTNBb1N0+vXr2wYsUKAE/Xh06dOsl6Hx8fvPPOO6SPwoULy/K3335r956vXLkiy97e3nbb\nAEBsbCzRfXx8iP7RRx8RffPmzbJcpEgRHDt2TOpz5syRZeNeFi9eLI/Z+x3AMKmKM+vZP0yYJKL6\nwAMQa9asEWvWrBHjxo0TtWrVIsEr9Z1lGTNmJPobb7xB9Llz58qyEYjSCH5suF4aqZf1+p07d5K3\n3gMGDDCNXY8lo76l+fXXX0nwy7Fjx4qTJ0/K+zNQA8Jv3LhRrF27VqxcuVLUqlWLnG+80V+zZo34\n6quvyFiN8aqxu9TdADdv3pRvWSZMmCAzOxoY/RQqVEjExMQIIZ7GjQgJCZH16tj12GT2suA4yoyj\numHaQ3fn1IOIOts5po5VHYOaweann36yvMaaNWuEv7+/ECLxjZp63F57FVddXZkk4e65PS2JU/SU\n8nrcvcWLFxN9+PDhRNfjaOlxY9S5VQgh/vjjD6LfvHnTlWG6BX3u0oOu6zuWu3TpQvRu3boRXY9Z\ntnHjRqKn1RiEc+fOJbEf586da4q35uvrS/SBAwcSXW+flJhjQpiz//bv3180a9ZMCgCipyb6DmU9\n8crKlSuJrmdG1kMKfP/990TX1xw9zkxajtOnJxPSd8nrLlF6OAt9d/YPP/xAdPV7oBDmuDPPEXfP\n7WlNnMJrjWNOnTolmjVrJry8vOzOaU2aNJEufe3btxdt2rQh5+u/adQ5EHZ2jtmLb6nOn9WrV5dl\nNbnIzp07Tc/ZVdT7skJ1q1SfgRCJn7F+vlEXEhIihBCiVatWpD6pa40a99jo314IFGNc+rNPSXit\ncQyvNYni7on/uS8kjGNU49cvv/xiypKlb4P18vISDx48kLrVQtKnTx9TIE+rRTUyMtL1gWtcu3ZN\n1KtXj0xY3t7e0sA2c+ZMebxevXrkXH2MBkZcNbV9vXr1xPnz50nwRWeTXs+ePR3WzZw5U9SrV49s\np1a/uOhjAyCWL18uevfu7bBPezEBkoozt8qiRYuShd748uXr60t+fKnj15+7rqsL9fLly2UmVDV4\nbFhYmJgyZYrpXCZVcPfcnpaEYZ4LunHMGUn5wRIUFGQyjiUFPVAvw6QA7p7b05owKYz6wt/ebxoV\nRwaavn372j3evHlzov/nP/8RQiS+FHeGvZjIjpLF6BsVunbtatpAULBgQZmtctKkSSJ37tx2++re\nvbspiYM91O/eZcuWtVxrRowYYTKOGd/hHdG1a1exZ88eu3UtW7Z0GhONYZKI5dzr7omfF5I0hLOY\nY84WEt0/XTWOqcYTJmXRffPV4P+PHj0SX3/9tdM+wsPDHdY5M449T/TMSs7eljEphrvn9rQkDJNs\n1BdLjvD19RXBwcFStwrgf+HCBfKD5ccff3Qac8yZcaxr165Ox6hiL04OwyQBd8/taU2YVMRZ3CtX\n0DP4qhjGMWcMHTpUNGzYkMRRnjBhgmmXn46jhGdCCBEbGyuNY/baqfEta9as6XSMVi/hd+7caZl0\nIHfu3MLT09Nh/dChQy3XmsuXL9v1UGGYZGA593LMMcaSJUuWuNxW9U//66+/SN2gQYOwadMmy1hj\nrVu3Nh1T2/fv31+WJ0yYgA0bNliO5+rVq86G/K9A9c0HgLi4OFn28PBAcHCw6Zxff/2V6GXLlpXl\nK1eumGKOtWvX7pnHlzVrVlnW4xToMXCccfPmTaILIdC4cWOpGzF2GIZh0jLp06dHnjx5nLb7+uuv\nZdmqvV7XvXt3U5sPP/wQADBw4EAANEbNrVu3cObMGakHBwdj0qRJAGh8mp9++gkAEBkZaepfjXnG\nMAyTVgkKCsLChQsd1t+5c8elfpo0aQIAGDNmDGw2G/r06SPrbt265VIfgwcPxpIlS7B//355bPjw\n4XKeVjG+m8+YMQPjxo2Tx9W4XQCQLVs2WVbbjRw50tTnpk2bTMeMeGYG6ndvva5y5cqoWbMmhg8f\nbuoHSIyt2bp1azx69EiuHyrVqlXDl19+afdcIDEeW4sWLRzWM0xKw8axF5y2bdta1jdq1Miy/t69\ne0Rv06YNgMRAmDrHjx9HlSpVpF68eHFSP3v2bFmuXLkygoODSftRo0bJco8ePUwBH9VAnAMHDsT8\n+fPtnmt8qTcWBNV4pBuSVONNcHCwXUOTQefOnYkeFBSENm3ayMQC+fPnJ/XGszL6LFiwIEaNGkWO\nBwUFyXr12voPk++//97huOxhtdDkypWLBPL85ptvMGPGDIftDx06RMbm6+tL6lVjnf7jSQhBgoTa\nG5v6A85IVKDy/vvvy7Kfn5/DcTLM8+LRo0dEV4PcAsDRo0eJvmvXLqKvWrWK6GqQX+BpgHUDNVAw\nkLaTVqjzAQCcOnWK6OoPBACmlyB6cHq9/enTp4nu6g8UdxAUFCTLBw4cQEhIiNRDQ0MRHR0NX19f\nhIaGSrHZbAgNDZVtMmTIQPoMDQ1Fr169ZFsVY524fv06AGDatGmyLnPmzOQFV+nSpWUfBQoUQN26\ndXH+/HnkypULNpsNJ06cQGhoKBYtWiTHkxyEEEQ3xmhw/PhxousJhNatW0f0rVu3Ev3gwYNEj4mJ\nIbqrP4bdwe3bt4muJ0o4cOAA0bds2UL09evXE33fvn1E15Pq6C+xmLQLrzWOsVpr2rdvb1o78uXL\nByBxDt28eTN++ukn5M+fn8y/auIRda0pUqQIDh06hDFjxpjG0apVK4SGhsLPzw/vvfce+vTpg9DQ\nUBmU3ui7R48e8hz9/6Dxf974bt6uXTssW7ZMnp8tWzZUqFCBnJMzZ04AQIYMGeQcPWDAALvPSv2N\nBAAjRozA7du35XpTu3ZtaQj8/PPPZSIyAyEEBg0aBACYOHEiQkNDMX78eHlvP//8M9KnT49cuXLJ\nYwbVqlVDxYoVpW6sNQaRkZHw8vKyO+5ngdcax/Ba8/8421r2DxPmGZg1a5YQwjW3yqSkd9cD+KrB\nK5cvX243eKVBpUqVLIOuO6N9+/ZEV+MM7NixQ7Rs2ZLUFyxYUAghRGBgIOlj6dKldvs9ePCgqf2g\nQYNIcNT27duL+fPnOxxTUnVoLoTh4eGmNsuWLRO+vr5kXM5cXb7//ntT0EUV3a3y2LFj5LqjR48W\n69atk7oepyFLlixEL1q0KNH1+/rll1+IfvLkSVlW48UxzxV3z+1pSRjmuQJAlC9fPsX71d0q//77\nb4dt9UQyKmFhYSk2JuaFxt1ze1oT5h/EoUOHxP79+0X58uVN35ufBTXJWFIwft/kyJFDHtNd8tUE\nWUII4e/vL1q3bi2ESIxdWbZsWctrbNq0SZYXLFjgcuKbHDlymIKyqzhaS9SEZwyTAljOve6e+Hkh\ncSN6phWVffv2OY05pjJ9+nTh5+dHjqnGsdGjR5tiY6lGEZvNRvSdO3dajl0IQfzzk8oHH3xAdMM3\n/5+O/owNdAOUip6ZRTVGCWE2junYW0TVhUw1jv3888/i2rVrUo+KijJlN1HH6u/vL3r06GHKyGRF\ncv4uGIe4e25PS8Iwzx2reGNCCPKCQkXNFqWve1YUKFCA6AsWLCB6RESEy30xjIu4e25Pa8KkUcaM\nGSPi4uJcbt+wYUOSeV3PcKiTLVs2oq9fv15UqFCBHAsKCrJ7bunSpU3HrH4DpDTqbwj99wTDpBEs\n5153T/y8kLiRTJkyOazTd+8I4Tx4pT75qsaxqlWrkqyOQpjTHjsLXqm+qVi5cmWyJ3tHmSkdkZJJ\nBSpWrJhifRmoATZ1Ro4cKcsAnN6Lms73yZMnYuDAgS6PQ99tp39OR44cIbpuHFP/9n744QfLxVXN\nsrN161aTgZZJMdw9t6clYZg0z/3798XgwYPFq6++Ko+tWLGCtEmplPL67mCGeUbcPbenNWHSMPpv\nGuM3lTrnqug7dUeMGEH03377TZZv3bolbty4IfWIiAhx+/ZtqU+bNk0kJCRIXU+8deLECXHq1CmH\nY9d/L/zwww+iQ4cOcuz79+8n9er3eGNHW+PGjcWtW7fkcTWLp56tUv8doBrwdE+eb7/91uG4GSaF\nsJx73T3x80LiZtQMIKVKlSJ1+luKJk2akMwsN2/eJPWqO92qVavE3LlzSb0z45g99LfVjsiWLRtx\n+4iIiCAGobZt25L2oaGhYtCgQeTYyJEjRb9+/WRK5ZEjR4phw4aRfgzsZQHTUymrC59ujNLPNxaS\nK1euCCGEGDt2LKm/ePGimDlzpoiKipJjU3F0L89CTEwM0fV+AJBju3fvJvWfffYZ0bNkySJGjhwp\nn4eagSciIsLkzmvlVmmPyMhIy3omRXD33J6WhGFSlQcPHogHDx7YfQFk9VJIzVap9pUxY0YhhPkH\ni/pjzZhnM2bMKH84Gef17NlTCCFE+vTpyfl58+Ylup5NmGGSiLvn9rQmTBpH9Wqw+k2zefNmce/e\nPXLMWTbhlCJ79uwiOjqaHNONY76+vkQ35n6jrM/t+lqjrksNGjQwuU46y4zMMM8Zy7nX3RM/LyRp\nCNVIceDAAZNbZbFixYj+3XffyZ06sbGxplhTVjHHhHDNOGawY8cOsnNMCOo+p07kfn5+JpePtm3b\nkrcyfn5+4ujRo0KIxDTDBunSpSPneXl52d2NpMd/8fPzExMnTnQ4ft3A48g4ZmAsJFevXhUBAQHy\n+PLly8WyZcuk3rNnTxEeHk7OnTJlCrmOOn4/Pz/h5+cnmjdvTq6ttmnTpg3pT3erPH36NGlfuXJl\nUq8bx6xYsGCB5c4xIeizc7TbEYAYN26c1HkHWYrj7rk9LQnDpDn++OMPu8YxFSvj2IkTJ5xew57b\nvr7uM0wycPfcntaESeOou7ns/aZ5+PChKFSoUIpcSzeuqcyePdth3ZkzZ0Tu3LnF+++/L49ZvWTZ\ntGmTKVzK66+/brdtw4YNhRD0N82aNWtMLvxsHGPSGJZzr7snfl5I0hAApCHi0KFDYvLkycRH3pkb\nozPjmH6+qo8YMcKpW6W9HVyuMmTIkGc+V9/6nBJYjScgIEB4enpKvWDBgsStUR0PANP4jN1nrmIv\n9oHqbpPUwKLO/k7Uezly5Ahp37lzZ2Icu3DhgtOEAsxzwd1ze1oShknz/Pjjj6a5eM+ePUQvU6aM\nw/Nbtmxp2sHMMKmMu+f2tCbMPwh7xrH79++7dO5PP/1E9OvXr4t9+/aRY6qXxuXLl0WvXr2k7ufn\nR36v6axdu1aWy5UrZ6rXfwfYiyWse3moPM+YZgyTAljOve6e+HkhSUPoLhL2AvK7GlzxxIkTxDi2\nfft2snNs3rx5SY45pk7uAwYMsEwooAfYDwoKslw4nNGiRYtnPjep5M6dmxjHbty4YTfAprPdUbqb\np0FwcDDRt2zZYtnPkydPyK64qVOnWrZ3hr6IlihRguhNmzYlbfXA0DrGfVplwGGSjbvn9rQkDPOP\nwNUdykII8c0334hWrVo57Gvs2LEkDo0eJoFhUgB3z+1pTZh/CEeOHDH9plG/x3/66aekzt5OXT2Z\nlFW2Skc7uVTU+V5P6GLPmKWvD/aOGTvlNm3aJCZNmiSPPy8XUYZJISznXndP/LyQuJEJEyYQXX8r\noBvHVANNt27dZNlw+9N3+OhvHnRjVp8+fZI4Ysdky5bNtJDo96fTrVs3cfbsWZmC2MjqZbzpUd0Z\nnfnnjxkzhugZMmQQ3bp1E7NnzybPSiUgIEDW6QvQjz/+aDl2ezHGVMaPH295vnF9V9DHkjdvXhLY\nWc8sarhV5siRQ8TFxZmCNRctWpToztwqdeNYjx49ZFmNN/b7779b3geTLNw9t6clYZh/BM5+sMya\nNcth3ezZs2V4Aqt2DJOCuHtuT2vC/INISqgYIZy/0FV/06jhUlzF3suOzz77TLRq1cpuAi+jvaP5\nXj2u/+ZJjncOw7gBy7nX3RM/LyRuRjVYqcaxPXv2mN4s6Nkqvby87PZjYOwcM76gT5s2jbSzt5BM\nmzZNdOjQQXTo0OGZFgMDK1c8dQx58+Y1vQE39Pnz58tjH330EWmjG4w6dOhA+rUXmFhNfqCPo3bt\n2g7Hq6PGFujZsyfJ7FKhQgWTUbBz584ODXT2PrfPP/+cJBDQ3Sp9fHwc7howEgYI4dg4qaeo1o1j\nQgjh7e1t+nsRIjG49MSJE0VsbKzdvplUw91ze1oShmEYJuVx99ye1oT5B5FU41hyCAoKsqzv3bs3\n0fWdY1bZ7RnmBcBy7nX3xM8LSRpCN4ZNnjxZLF26VOpNmjQhro2q4aJLly6iY8eO5Hw95phKmzZt\nkr2Q6G8uUgLD7z+5LiNJzdplbzuzwZQpU0TXrl3JMVVXjWUVKlQgAfVdiQNw8OBBy3pXY445c88U\nItFo2aVLF3LMnnGMSXO4e25nYWFhYWFhYWFhYWFJNbEJIfAv4l91M6nNSy+9hEePHkn9s88+w5o1\na6Q+ZcoUdOnSReo2mw1Wfy/Xrl1Dzpw5MWLECAwcONDp9Q8fPozixYu7PN4lS5agUaNGAIAePXpg\nwoQJDtv27dsX33zzDd566y2n/V6/fh05cuRweRyukDFjRty7d8+ltr169YKHhwfGjBnzTNd66623\ncPz4cZw/fx6HDh1CxYoVkTVrVjx8+BAeHh5Ily4dAKBTp06YNm0aypQpgz///JP0MX36dHTo0EHq\n586dQ758+QAkGtAXL16MJk2aWI5j9uzZaN26tdPxRkREoFixYkm8S8bN2Nw9AIZhGIZhGIZhmNQi\nnbsHwLiPR48eEYPHiBEjSP2bb75JdGfGkd69ewMAKlasKI/169ePtFm0aJEsFy9enOgA0Lp1a7Rt\n29Z0HAAxqNgzjKnn+Pj4SENeQEAAAGDVqlVo0qSJbGfcj2EYU+suXryI8+fPy/5ef/11bNmyRerd\nu3c3Xd+KyZMnEz0kJESWx48fj5deeknqHTp0QMuWLR32NW3aNKIfP34cixcvhpeXF2rVqoWsWbPi\n7t27SJ8+vTSMAZCGUN0wBgAffPCBLA8ePBj/+c9/pL5o0SLy2R89epScazwzT09PjB8/HgAwdepU\nWXfnzh0Aic+3devW5HM0zh00aBAGDx6MUaNGmcZWsGBB07Fvv/1Wlg3DnbO/T4ZhGIZhGIZhGIax\ni7u3rqWwMMlAjTnWvHlzceDAAVKvxxz7+OOPxd27d8WyZcvI8cDAQFnWs1vu27dPfP755+TYsmXL\nTK57165dM/Wr895775n6MciXL5/p+PLly8m1w8LCRM6cOUkKZZvNJlauXEn6rVSpkqhYsSI55uvr\nK06fPu1wbM7GXrhwYaLrbpVW56tulK6gf25CCHHmzBnT5+AI/bOpWrUq0dWxGn1u376duHTq92Pv\n/ooUKWK33pUMqcbfrhorjUlR3D23s7CwsLCwsLCwsLCwpJqwWyUjKV26NLJkyYIaNWqgbt26KFWq\nFKKiovD2228DcO5WmZqcPHkSPj4+qdJ31apV0bdvX3z22WeW7b755hv8/PPPABJ3jhk70lKCjRs3\n4sMPP3Sp7Zw5c9C6dWv5Waxfvx7BwcFYvHixbOPl5YXz589DCIEcOXIgNjbWYX99+vSxdOkUQsBm\ne+pVFxsbi+zZs7s01qTSpUsXlC9fHi1atAAAlCpVCtmyZcPWrVtdOt/Pzw9Dhw5NlbG94LBbJcMw\nDMMwDMMw/1rYrZKRxMfHw8fHBx9++CFKlSoFANIwBsBthjEg0U0yODhY6h07dsT27duT1MeAAQNk\necOGDQASDT07duxwahgLCQlBjx49knQ9Vxk8eLDTNmXKlJHl2NhYdOrUSeqffPIJSpQoIfUvv/wS\nFy5cAJBo0IyPj8fFixcd9j1mzBhMnDjR8vqqcSoiIsLpeJPKsWPHACQax1RD3YEDB6RBEoDTOG5s\nGGMYhmEYhmEYhmGSChvHGMmSJUvw888/o2/fvnbrmzZtip9++okYpWrVqkX0atWqYfv27eTYvHnz\nUK1aNQDA5cuXAQC//PILAGrsMNroZQAICgpCmzZtpB4YGGhqY1zT+Fc14ly8eBEjR46U+kcffQQA\ncgeUOl6jrPevB5H39/eX5Xbt2kFHPb9mzZro2LGjqQ0ADBs2DABw69Yteezu3bukjRon7LvvvsN3\n331H6tUECAsWLCCGzE2bNiFPnjxSP3LkiGl8vr6+pD81sP727dvx/vvvk/q2bdvavRc9tpqOcU31\neffs2ROFChUCABQuXBh9+vRxeP7jx48RFhZmeQ2GYRiGYRiGYRiGSRLu9utMYWGSwZ9//ik6deok\nhBCiS5cuIjo6WlSoUCFZfe7evVucO3dOnD59mvSllnfv3i0qVKggdu/ebTrX4Pvvv3fpWkIIsW7d\nOvHXX385bb99+3ZZLl26tHjw4AHp5+7du7J+yZIl5FxfX1+ix8fHy/Pu379vGn9ERITD8Qphjjn2\n7rvvEr106dJO7oZy7tw5h3VqTLUKFSpYthXCHHPs999/t9vu5MmTImfOnGLVqlXk+W/evFkIIUTN\nmjVN5yxYsEC0a9eOHAPof2X12b///vuWY2VSDXfP7SwsLCwsLCwsLCwsLKkmbh9ACguTDObNm0f0\nzp07y7JuwChatKg0ApUrV85uf9HR0Sk2thEjRpiObdu2zWF7e8YolVWrVpFA/DpWBpqkEB4eLpo3\nb+6wPkeOHKJBgwYiLi7O4fXXrVv3TNc2uHnzpvjoo4+kricyEIImURBCiOPHj8uybhxTyZkzp5g1\naxY5liVLliSNr3///rLcuXNn8csvvzhsmylTJtMx3bDIpArunttZWFhYWFhYWFhYWFhSTditkpEU\nKVLEYV1QUBAWLlwo9YiICLz88ssAgODgYGTOnBne3t6yfvXq1ShQoECKjW3AgAHo37+/1FW3P3sU\nLVoUFStWlHrDhg3Rr18/qdeuXRvffPON1DNkyCDLd+/elTG71POdcebMGdOxsmXL4tdff3V4zrVr\n1xASEoIGDRqQ43v37pXlzp074/jx41KfOnUqevXq5bDPTz75BF5eXlLPkiULiSlmL1Zbhw4diJ4v\nXz6H/atcvXrV9FlUqVLFpXMN3njjDVmeMmWKjAcHJAbkr1WrFqpWrQoAuHPnjumzmTlzZpKuxzAM\nwzAMwzAMwzAEd1vnUliYZPDnn38SffLkyUQHrB9x3rx5iV67dm277ebOnSvLhQoVEqVLlxZPnjwR\n8fHx8vj+/ftFrVq1XBq3QWRkpCzfu3fPVF+oUCEREBAghBDiwoULln3t2LHDYd2kSZPEyJEjxc2b\nN+WxP/74w3QtlUaNGolWrVqJQoUKybrRo0fLen330507dyzHp2O1C+769etJ6qtp06ZEP3r0KNHV\ne50/f74sAxB///03aVuoUCERFxcn3n77bfL5Gvz9999i4cKFUh82bBh5rkIIUaBAAadjvnjxotM2\nTLJw99zOwsLCwsLCwsLCwsKSasI7xxhC9erViT5+/HhZbtKkiSwfO3YM586dI21feuklomfOnNnu\nNXr27Ik//vgDABAVFYWlS5cCAF599VXZJiEhAWvXriXnjRo1ynLsGTNmlGV1JxgAzJgxA1FRUTLw\nvBqg3qBs2bKy7OPjQ+rUnWNdu3ZF586dkS7d0/8+xYsXJ+2joqJk+dChQ7h+/Tpmz56NqKgo1KlT\nB76+vg4TH6hcu3YNMTExqFOnDjnerVs3oqu74ADg/PnzsmwkHXCVWbNmEV3NWAoA5cqVk8kR1N18\nvr6+sm3jxo0BJGYFzZw5M6KiosjnazBmzBjydzVo0CBkyZKFtFGf5Z07d0jd77//DiAxmyjDMAzD\nMAzDMAzDPAtsHGMkpUuXxrZt2wAALVq0AADcuHHDbtvLly/j888/x+3bt+Wxx48fW/ZvtL106RL+\n97//yeMFChSAzWYjbcuVK2c6XzXEAGb3QMON0xjHJ598AiDRTbJdu3aWWRArV66M/fv3O6xv2rSp\nLPv5+WHWrFn4z3/+I4/pxjiVEiVKkLG/8sorxM0xISHBZPzKlCkTACBnzpx4/fXXsWrVKlKvG5BU\nihYtSnRHn6HB1KlTidGpZs2aqFmzpuU5wcHBAIBTp07JYwEBAQASXWoXL14M4KnBVAgBe1y/ft3y\nOgCQPn16WX7llVdI3XvvvQcA6NKli9N+GIZhGIZhGIZhGMYebBySVeNMAAAgAElEQVRj7GLEyRo5\ncqTd+qpVqyI8PJzsBtJ3ks2fP1+Wb9++jVmzZmH27NkICQnBxx9/bOrz1q1bsqwbmyZPnkzG0rhx\nYwwePNju2Dw8PLBnzx7ExcUBSDQ09ezZE2PGjJFtWrVqRc7Zs2cPjh07Ro7FxsYSPT4+HgAwdOhQ\nnD592u61VVQjlWpsypYtGwCgR48eSEhIgKenp8k45ozXXnvNYV1kZCTRhRAk/luLFi3ITrOYmBhi\ndFq9ejV+++03qefKlStJY6tTpw4ePnwIACT2mcq+ffsAACtXrrRbv379+iRdk2EYhmEYhmEYhmGe\nGXf7daawMCmIHnOsSZMmRC9SpAjRz5w5Q/RmzZoRfdq0aUTfs2dPssb38OFDoquxtZxlqxRCiMyZ\nMzus02OS6dkqfX19Sey0atWqkfoMGTKYMlCqDB48mOgbNmyQsbbU+FnGsQMHDliOvVGjRrKsxwjT\nY46tXLnS4bjs8eTJE/H666+TMWXOnFncuXPHbqyzzz77zHRMjSOmZjf9+uuvxfTp00lbT09Pohsx\nx4x71p+r1efIpBjunttZWFhYWFhYWFhYWFhSTXjnGOMyixYtkuWIiAiSVTBPnjxkh5E9V0Ej3pdB\n+fLl5blC2He7s0KPcabG1tJdC41sjKr75oMHD1y+1ldffWV5zHBHNbh//76MufbkyRN5PCEhweE1\nDFfJ3LlzA0h0JzSOlSxZEjabDffv34fNZpO74gzUmGiFCxdGdHS01PWYY3Xr1nU4Bkeocb9y586N\nuLg4LF26VO5A03fx7dq1CwBw9uxZcm8AZLw5INGl8/Dhw+RcdQfhgQMH5L3ExcXB09NTPteEhATc\nv3/f9CwYhmEYhmEYhmEYJimwcYxxiBrHqWPHjsSAVaxYMVSuXFnqFy9eJAHxPT09SZwuACTOlhqf\n7OLFi6aYY/YYOHCgwzrdAKS6Fj558gTTp08HQGNfqYYqZ/HS7t+/L8t+fn5Ox6peRw3c7+npaWob\nEhJCDGgGhmuiMTYhBDJkyGDXkGgEwDcoWLCg0zGqBAYGWta//PLLsmzEjjPi0gHAsGHDADw1GBp/\nG0II8mxtNhvRPT09MWXKFHIt1ehZsmRJUqfGH/P09LSM9cYwDMMwDMMwDMMwrsDGMcYhqlHHnvHk\nzJkzMg4X4Djouj08PDzsHlf70LNVjhgxQpbr1q2LatWqST02NpYYcNSdVOp9GHz99deW48mbN6/D\nsQ8dOhQBAQHEoGXE0HKFiRMnYujQoWSsRlZQe8+lSJEi+PPPP6U+Z84cp9dQDVCvvfYaiTlWt25d\nYtgaM2YMOnbsKPXKlSvjyJEjpD91XHpGU/X5fvbZZ6Tuvffeg4eHh/xchRDw8PDAzp075Rjbt29v\nMg4a7XWj6cWLFx3cMcMwDMMwDMMwDMM8Gy+ccUw34Og7hozdOgbqjiHA7BZ37949y/P1/p/FffB5\noRsoJk2aRPQHDx4gISGBiJGx8caNG8ifP79s++mnn5ra6qh1NpsNCQkJyJQpk8xU+emnn8q2ffv2\nJeeGhoaaslWqbpJ6UHr1Wo8ePcKsWbMcPgchBC5cuCB1T09PMs6BAwfCZrMRo1CnTp1M11OpWbOm\n7MPX1xcJCQlyh1TDhg1l2fh7yZEjhzz32LFjKFOmjNT1ZAJBQUFEj4yMJIkSrl69SrJKhoaGyoQL\nAExZPHft2oV33nnH9FwMRo0a5VBv1qwZli5dKvUzZ84ga9asxMhVrFgxVKlSBR4eHvIzU5/lr7/+\nKtuXKlWKuIjq2Srdjf5/5tGjR0TXXXf1+UL/O9HnG30+sbfDkGEYhmEYhmEYhkkeL5xxTN+Jou/U\nUd22AHPWRN0tTnUltHe+3r8r7oPuwt4OK5VVq1bB09MTnp6eyJ07t3wW2bJlM7m/rV27VrYNDAzE\nsGHD5O6kL774AgBkvaenJ4QQ8PT0xPHjx7F161bTtf39/U2GBSsiIyOxZcsWqfv6+sLT0xPjx48n\nhid7JCQk4Pbt2/jf//4ndXWcI0aMkHGvAGDz5s0IDw/HyZMn5TF77pOenp7o1q0b7t+/D09PT2lI\nCQkJwe7du0nb69evOxyfmgUUMBtcixYtSsbyPHnw4AEyZMggXS8BIHPmzHKMunFH3e1n0LJly9Qd\nZAqi/5/R4+Dp96fPF/rfiT7f6POJs/+jDMMwDMMwDMMwTNLhX1qMQ7p27Ur0Bg0ayLIaBP3GjRuI\niYlBaGio3X569OhBjAbLly93eM18+fLJ3Wgqt2/ftowLlj17dmKIvHXrFmlv7EYbNGiQXYOMSqZM\nmfDWW2+RwPE66v3XrFkTAODj4+Owff/+/QEAM2fOtBsny1lQedVI0qxZM3Tr1k3q+k6yR48eEbfQ\nlNhtdO3aNcv6u3fvAkh0V23UqBFq1aoFADh48CDOnj0rjcIXLlwgu/rU4PtAYpy7Nm3aSP3AgQPJ\nHjvDMAzDMAzDMAzDWMHGMcYhkydPdrmtEAJZs2Z1WD98+PAkX18NMj916lQS0H/BggUkG2ZsbCwx\nhlWsWJGM/7vvvkPv3r0BJLoZpgYVKlSQ5fPnz5O6Dz74wHTcGE+7du2wYMECy76LFy8uyzdu3CAu\nr56enuRZlShRgsTmypkzJ+lLjzk2evRoUr9r1y5TzDG1D/353bp1C5kyZQIAzJs3D+nSpcOOHTvk\nvam8/vrrxFC6detW3LlzR+pTpkzBuHHjYIVVPDiGYRiGYRiGYRiGSTJCiH+TOOXx48dEv3PnDtFj\nY2OJfuHCBaJHR0cT/fz580S/du0a0W/fvk30R48euTJMt3Dv3j2ix8XFET06Olps3bpV5M+fX0RH\nR4szZ86IU6dOyfqwsDARHR0tn9GMGTPI+d26dRP58+cXQgjZh9q3UZfaREdHCwAiOjpazJs3jxw3\nSPyvQc9Rx6e2vXDhgjh37hypf/nll8n5NWrUEPnz55f3rf8dbdiwgeje3t5E1z+b8ePHW92iJRs3\nbkxS+ydPnhD9999/J/ro0aOJ7unpSfTTp08TvWjRokRv164deS5t27Yl9SdPnhRCCLvPzd08ePCA\n6Ldu3SL6lStXiB4TE0N0/dlcunSJ6Ddu3CB6QkLCM40zBXD33M7CwsLCwsLCwsLCwpJq8sLtHNNj\n9hg7XgyyZctG9Dx58hBdzfoHmHex6PGs9ADijrI0pgV0dz81rpavry8KFCiA9u3b49SpU8ibNy8q\nVapEgvA3aNAABQoUQIECBbB69Wq0bdtW1vn7+2PixIkyMPypU6dIXKoCBQqQoPH20APB6wH5dVau\nXCnLM2bMgL+/PwDgxx9/hBACBQoUwNChQ/H222+jevXq+PLLL2V7IWgcr2PHjpHxqYHW8+TJAy8v\nL1KvB1YHEu95/fr18hlZoScUUD+bOXPmoGfPnpbnV6pUSZZjY2NJnbGLTaVDhw6W/TliyJAhpp1k\nNWrUwNmzZwEkBpTPkiULoqKiZL0epL5MmTI4deoUMmfOjAIFCqBq1ap2r+XKc3ve6DHBdJfg1157\njej58uUjure3N9H/+9//El3fjanHLGMYhmEYhmEYhmGSzwtnHGOeDcOl0TByZMiQAZcvXyZt1AyH\ntWvXJnU3b9409an+0NeNUfYw4nYB5lhVsbGxpqQAxnjWr1+PhIQEGeRedUmMiopCVFQUtm3bhj17\n9ji89scffyzL06ZNw9tvv40bN24AAKKjoxEeHm7XIGawefNmAImulwEBATI5gcHYsWPldVSXyGdF\nDfCfPXt2y7ajRo1CjRo1yLFjx47Jsp5E4r333pPlIUOGmGLBrV69Gl999RWAp8ajt99+G0CisezE\niROybdOmTdG+fXsAZsMywzAMwzAMwzAMwzwP2DjGPDNFihQh+t9//+2w7ZgxY0zH1OyTNpuNGF12\n7dqFtWvXkvMHDhwo9SFDhmD69OlSf/PNN3Hw4EGpFytWTAbI/+STT+Dr6+vKLdll48aNCAkJkXqn\nTp3QvXt3ucuwYMGCAMw77wyMXWA//fQTbty4ge7duyNXrlyy/saNG9i4cSMaNmyI3377zbRbEXga\n8B5ITHCgGvhcoW7duqQ8aNAgqffv35/ELFu0aBHZYakbx3TDZO7cuU3XM2KOAXS3ZPr06VG0aFGp\nf/3116ZzBw8eTHTj+TIMwzAMwzAMwzBMavDCGcf0XS76jibDHcwgIiKC6PruosOHDxP99OnTRNdd\n2h4+fOjyWJ83amB0ALh06RLRjx07hrCwMKnr96Zj7B4CEneGqa6AuhufzWbD77//LvXKlSvj008/\nlXqfPn0wYsQIqU+YMIEEsdfHon9uAOwGeg8LC4PNZsPJkydJdsqcOXNKt8wPP/zQ8U06uB/VUFa0\naFHUrFkT3377rd1zs2fPjrCwMAwbNgz16tWz20Y1PF6/fh2PHj0y3Ycqr732Gmw2G8LCwvDbb7+R\nTKKhoaGWCRKaNGmCN998U+p6tkt1l5/+NwIAP//8M9Fff/11h9f6+OOP5c4xg2HDhhE9Ojra4fnu\nJiEhgehXrlwh+smTJ4muZ9/UM6LqBuYLFy4QPT4+/pnGyTAMwzAMwzAMwzjmhTOO6TG/9Jg+b7zx\nBtGLFStGdDUjIUCzCAIgMbgAs0ubHqMoLaHHR9N3BBUqVMgUsy0pqDvDpk+fbnp2aoyzZ0E3fKqG\nvB9++IHUtWrVCkBiVkshBNq0aYNatWrJ+sOHDzs0VAHmzzk8PJzououl6vJZrVo1DB06FIsXLwaQ\naGyqWLEiGjRogJdeegm1a9fG/v37Zfvz58+jTJkypD/VyNq2bVtUrFgRffv2RcWKFVGxYkVcvXpV\n9qu6hD4L+s4x9XOyt2ts+fLlSeo/KCiI6C1btiR6Wt455unpSXR1RyAAuXvRoFSpUkT/3//+R/TC\nhQsTXY9pqMc0YxiGYRiGYRiGYZLPC2ccY5JH+fLlZXns2LEOd7JcvXoV8+fPJ8c+//xzh/26EnNM\nJTAw0HRMNXzu3r2bGLv0/ufMmUP07du349q1a+SYGrdLp3v37kka74QJE8i1AEhXxpCQELRt2xZH\njx7F0qVLER8fT4LSb9q0ydRfnz59ZPndd9/FoUOHHCYoiI2Nxc6dO8mxX3/9VZZPnz6N3r17k3o1\nfpwj5s+fbzKcGahulVYsXLgQAQEBmDlzpjzWvHlzl85lGIZhGIZhGIZhmJSAjWPMM9OsWTOyk0V1\nMTMyWqpMmzbNsj81lpW9mGNqLKqOHTuiWrVqUs+ePTtJAlCpUiXiMtu7d2+7bpVWqBkfGzVqlKRz\ndXTDm8rChQtRtmxZqRtGLmPnmrHLzUA37O3btw9xcXHkmBrPzdvbG1WqVJF6uXLl0KJFC6kXKFCA\nPJvevXvjyJEjlvdz5coVNGvWzKFR01HGSQAkS2nTpk3xxRdfkMymDMMwDMMwDMMwDPM8eeGMY3rM\nLz3johE83UDfAbNixQqib9u2jeh6DDI9ZpBVRkN3o8df0+Ml6fGR8uXLR3TVxezdd99F9erVLa/X\npk0bh3X2Yo7psajUnVKxsbFYvXq1w/6EEOjduze++OIL9OvXD/v370e3bt2wfPlyLF++HBs2bMCa\nNWscnr9kyRJZPnr0KJYvX44vvvgCe/fuRadOnUyGt0WLFhFdzwY5ZMgQWQ4JCTG53zVq1Ei6sOqZ\nLVu1aoVu3bpJfebMmSZj1NKlS2VZ392nu4DqBi79XpYtWyZ3cwUHB2P58uXEfdDIROkqr776KtFH\njRolywcPHjS5Juqx8NISqqEPAM6cOUP0P//8k+gbN24kuv43p8c0PH78ONGdxfljGIZhGIZhGIZh\nngEhxL9JmOeIl5eXuHXrlhBCiNjYWPHGG2+Q+rlz5xJ92rRpsjx16lRZ/vXXX4UQQsTFxQkhhFi4\ncKHd640cOVKWz507Zzm2tWvXiqtXr0r9xIkTpL5jx46W5zsag6tkyJAhSe03bNhA9Dt37jhs27Jl\nS9G1a1eX+75+/brInz+/w/rvv/9e/PHHH+RYsWLFZPnJkyekbvDgwUIIIRo3biyPHTx4UPTt21d0\n6dJFlC5dmrTPkiWLLN+5c0cULVrU4VjUz5hJU7h7bmdhYWFhYWFhYWFhYUk1eeF2jjEpx8SJE/HS\nSy8BALJly4br16/Lutq1a5NslTqdOnWSZWNXkhHovUmTJgBA3CoBukvHy8vL1OfcuXNl2dvbGzlz\n5pT6mDFjSNtp06aZdgGqGGNIDbp3745Dhw5Jffz48aQ+T548lud/++23mDRpkkvXevLkCck8CYBk\nrhw1ahSaNGmCn376ibTR3SrVjJJDhw4FQHfHlShRAqNHj8bkyZNNu6XUHYmZMmWSZS8vL1OCi/79\n+7t0XwzDMAzDMAzDMAyTYrjbOpfCwqQxRowYkazz9R1fjujatat4/PixZZvFixe7fN0LFy4QPTo6\nWgghxLBhw1w632rn2IYNG0Tif71Epk6dSnRXmDhxounY4cOHZTlv3ryy/PLLL5vaNm/eXAghxOrV\nq+Ux9VkvXbpUlvWdYyqRkZGyvGXLFmfDlvz444+ynNR7Z9yCu+d2FhYWFhYWFhYWFhaWVJMXbufY\ngwcPiH727Fmi79u3j+grV64kelBQENHV2E6AOcNhdHQ00dWg9WmNq1evEl2Pn6bHS/rll1+IPnHi\nRKIHBgZCCIHAwMAki4+PDwIDA+WuqsDAQPj5+cm+x4wZQ2KOTZo0ScbocoQaVL9Vq1Yk46OPjw/Z\naaazf/9+AMCgQYMAmLNVhoSEmM4xMmr27dsXhQsXlsc//PBDklygU6dO2LBhAzlXf5bqZ6PHHDPG\np2aYjIiIkGV7ce6MbJWfffaZPDZ+/Hh07NgRgYGBqF+/vukc9b4CAwNhs9nw5MkTeZ/vv/8+ALrj\nz9fXF1OmTDF9vlFRUbJNu3btSP/Hjh0jupqoIa2hx+n7+++/ia7HJFywYAHRZ82aRfR169YR/cCB\nA0S/dOnSswyTYRiGYRiGYRiGscLd1rkUFsZN9O3b11Lv1q2bS/3ExsZa1nfr1k388ssvpuPXr18X\n6dKlE0IIER8fL8aOHUvqg4KCiD5gwADL6+g7x5YsWSLLYWFhIiAgwPL8pIIk7J6aPXu2CAkJcViv\nP5+EhAQZcywmJkbUqVNH1ukxvk6dOiV27txpGXPM39/fpXGqMed8fX2FEPbvs127dkIIIYYMGeJS\nv4xbcPfczsLCwsLCwsLCwsLCkmpiE0I4s5/9k/hX3cw/katXr+K1114DAHz11VeYN2+e3Xa9e/c2\nZUVMLh4eHnj8+DEA4Nq1a3In2KJFi1C4cGGsWLGC7D6zolOnTpg2bZrUS5QoAV9fX5QsWRLlypUj\nbY8fP47g4GCMHj3apb63bNliyl5ps9mQnP+LoaGhqFu3LgCgaNGi6Nq1q8xyGRsbi7Jly+LUqVPY\nvn07Vq9e7fDZf/rpp6ZYb0II2Gw2qe/YsUNmxwwMDMSVK1fg5+eH3bt349SpUyTWXGp8zoxbsDlv\nwjAMwzAMwzAM88/khXOrZFIXwzAGAAcPHiR1Q4cOlcfGjRuHtm3bJutaqltlQkICcZnNmTMnhg8f\nDiAxuH7JkiWJYcwIGn/69GkAwMmTJ0nfhvukQd++fVG2bFliGFu1ahUA4K233sLly5ddHrdhGNu0\naZM8prpV+vv7Y8iQIeScMmXKyLIazN8gPDyc6IahDABy5MiBzp07AwDeeOMNjBs3DkOHDsXIkSNN\n/TRp0gTe3t6mgPwqM2fOlOWOHTtKV+JKlSpJw5jx/MaNG4eePXs67IthGIZhGIZhGIZh3M0LZxzT\n4y8dP36c6Js3bya6HhPIyNRnoBoKAOC3334j+tGjR4muZlxMa1y8eJHoe/fuJfqSJUuI/sMPPxBd\n35VVsmRJoufKlYsc0+Nm6X1Z7fIKDQ1FtWrVpO7p6QkPDw/SRjdwqRjGpvz58wMA1qxZg2vXrjls\nv3LlSpQqVUp+vuvWrUOdOnVkvW6wypgxo7wHPz8/k7FryJAh+OCDD+xeq2/fvvj+++/JMTUD5F9/\n/WU6Z9iwYbIcGRlJsnkKIdCrVy8AQIECBQAkPt8HDx7I8ZUrVw5DhgxBq1atcObMGRK/TEc3ahqx\nxgzmzZsnDZMA8PPPP5P6YsWKAUh8BjabDX5+fiSenW5UjY2NdTgWd6NmaAXMn40es3Dy5MlE17Oo\n6jHJdu3aRfSYmJhnGifDMAzDMAzDMAxjgbv9OlNYmDREs2bNHNZVrlxZHDp0SOqjRo2y7GvdunVi\n/fr1UldjgOnkzZtXFClSROoLFy4Ubdu2JW1atmxJdD1GmVXMMSGextAyKFu2LNEfPHhA+vLx8XE4\nXiESM1iq3Llzx+X2e/fuNdWfO3dOlq9fvy7L27Ztk2X1ma9bt04IIUStWrVEYGAg6UuPOfbrr78S\nffTo0URftWoV0bNkyWL3HgoWLGh3LEyaxN1zOwsLCwsLCwsLCwsLS6rJC7dzjHk+/Pzzz/j000+l\n3rdvX7K7aefOnaR9v379LPurVauWzBgJAA0bNiRulQa9evXC+fPnERkZKY8NHjyYZKqsUaMGatSo\ngRUrVshjvXv3Jv3kyZPH4Vh2796NFi1aWI43ffr0pK8TJ06Q+iFDhmDChAl2z3311VeRKVMmAE93\n56lulUBixksDw71Txciqmi5dOrL7Ud1tZzzzffv24ZNPPgGQmGnSyGRpoI6zRIkSeOmll+yO22D6\n9OmmY0IkxlM7d+6c3DmmurI6+/wZhmEYhmEYhmEYJrVg4xiTKnzzzTdE9/f3N7ltNmvWjOj37t3D\n4MGD7fYnhED//v3JMbVt9+7dAVC32VOnTgEAoqKi8OGHH0qX2S1btqBVq1b4/PPPZVs95phOw4YN\nZblSpUooW7asZXudmjVryvKBAwewdu1a9OjRw9Q38NT1tlu3bjJel+pWCdB4a5cuXULjxo2lbrPZ\n0L59ewDAkydP0LNnTxQoUMDk0mfg4eFBDFpjx44l7oJGXDYg0aWyadOmzm9Y4cGDBzKgf758+ZJ0\nLsMwDMMwDMMwDMOkNi+ccezevXtEj4iIILoeI0iPq2VkADTw9/cnekhICNH1GES3bt1yfbDPmbNn\nzxJ969atRNfjq/Xt25fouvFKD+peokQJos+fP5/oDx48ILvLnKEaiAICAgA8jel08eJFGV/LQDVQ\n6fj4+JBxGMaloKAgtG/f3vS5tm/fXorO1KlT0b59ewQFBWHhwoWm+lKlSqFWrVpSDwkJwYEDB0ib\nwMBATJo0SRrK2rdvL+OGAYnB/ENDQwEkfi6LFy+WdUIIZM2aFcDTv9dTp07hyJEjcsw2m01+vmXL\nlkWHDh3k+ZUrV0aOHDmkbuz0AoCuXbua4vTZQzWEvvzyy5Zt586dS/Q9e/YQ/erVq06v5y70RAxG\ncgIDfReeHkfPMJAaqBlSAZqoAQCio6OfaZwMwzAMwzAMwzCMBe7260xhYdIYrVu3lvGkEv/cnqLG\nHHNGcHCwy23PnTsnli1bRsagMnfuXDF69GixfPlycvzWrVtCCCE8PDzksZw5c5I2+j0EBweLGjVq\niD/++MM01rt378qyfp6h62MTQojp06fLctu2bS3jqzlj1qxZpnHpLF682BRnTOXJkyeiU6dOQggh\nihcvbqrXY47ly5dPlnv27ElijsXExLg0bibN4e65nYWFhYWFhYWFhYWFJdXEJoRwr3UuZflX3cy/\nDZvNBuPvrUqVKoiLi8Phw4ddPn/UqFGm3WkqHh4eePz4MQBg9erVqF27tt123bp1w6RJk7BixQrp\nWunj44MPP/zQbrwsVwgPD8eXX37p0q6qgIAA3Lhxw5T51KB+/fpYtmyZw/NbtWqFOXPmSH3mzJkk\ng2RkZCTy588v45Zlz57dMuNjnz59UKBAAbJ7TEUIgZCQEDRq1Ih8hrt27ULlypUd3ygAb29vnD17\nVp7zzTffmLJXMv8IbO4eAMMwDMMwDMMwTGrBxjHmudGxY0cEBgY6rM+cObOl22mzZs2kK2bdunXx\nySefoFOnTgASDUBxcXHSOGbQqFEjLFmyBAAwcOBAjBgxAkBiHK306dPDy8sLADBlyhR06dLF4bXD\nwsJQsWJFh/XlypVDeHi4w/rNmzcTt051XAaxsbHInj27wz4cER4ejnLlykn9xx9/RI4cOdCyZUsA\nia7C9erVc7m/LVu2oEaNGlIXQsiYYc+K2kdMTAxef/31ZPXHPHfYOMYwDMMwDMMwzL+WFy7mWEJC\nAtH1QOfz5s0j+oABA4hev359outZDoODg4mux0+6efOm64N9zujxjNasWUP08ePHE71NmzZEV3cv\nATBlY9RjjukGG2fx2NQYZaGhodIwBiQalnTDGABigDIMYwCQP39+aRgDIA1j9erVI3Ls2DEAibvN\n9DrVYKR/rh4eHkSfNGkS0YsWLUr0evXqEcPY3bt3Sf22bduIrmZ3VA1jAPDdd99Jw5jRt4ERp03N\n/KmjGsbsUa9ePaxcuVLG5zP69/f3R9myZeXzMYxyzZo1I89q7NixpD89zpYe6+7SpUuW43EnFy5c\nILqR9MFgypQpRO/cuTPRmzdvTvRRo0YRffny5UT/+++/n2mcDMMwDMMwDMMwjAXu9utMYWHSMJ98\n8oksh4WFJbu/bdu2udy2bdu2sty1a1fRsmVLh23Xrl1rOmaMfeTIkfLYDz/8IMtly5a1vL7e5+DB\ng01tdu3aRZ7R48eP7Z5/7tw5ERERYXm9xYsXy/Lnn38uywDkNdauXSvWrl0r/P39xZgxYxz29eTJ\nE6JXqVKF6HrMMXtjtvdMmX8U7p7bWVhYWFhYWFhYWFhYUk3YrZJ5rsyfPx/NmjVzqe2BAwdQqlQp\nAOa4Wq6gukIePXoURYoUAZC4CyxdunSoXr06iTkWEBCAOnXqJOkaQOJuoYsXL6J+/foyzpc9jFhi\nAQEBiIuLM2UuTA7Dhw/HoEGDACTu6Bs+fDi8vLzw+PFj06je6JwAABqzSURBVC42Z5QrVw69evVC\n06ZNAZjdKnfs2IGqVatK3d/f35S5lPnXwW6VDMMwDMMwDMP8a3nh3CoZ9+KqYaxTp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"text/plain": [
"<matplotlib.figure.Figure at 0x98840f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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NYWFhHDt2jClTprB161aeffbZLMesXbs269atu+94cHAwx48fZ9++fbi7u2dq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iLmzZuH+fPnw97evtk2RUVFKCkpoaysDFwutz0O7bNFUVGR0mFrL/h8PphMJk6dOoWEhAQc\nOHAAhoaGIunScLlclJWVgcfjYebMmdi4cSM0NTXFFi/evXs3KioqsG/fPkrrp7a2FnFxcViyZAme\nPn0qln0J/8fhw4dx8OBB1NTUNFvcEAYej4fq6mrw+XycPn0av//+O829lEAXjZph7u7uKCoqwrFj\nx1qdc4WFzWaDx+NBSUkJ0tLSkJOTA4PBQHV1Nerr62nouXgEBATgyJEjOHnyJK3C2gCwZs0aSQrQ\nZ0Sj1pxE9kFCR4LH46GoqAibNm1CYGAgLTYJIaitrQWAZvdaTd8rKipCSkoKLBYLhBCBbTfONa1p\nSk+ZMgW//fYbXF1dERwcTMehfBBCCFgsVqd4LpGSkhJbt6o1OBwOwsPD4ezsDBcXF3z//fe02v8U\nNOqWNt7LfojG60BKSqqFlmhMTAx++OEHODo6Yvbs2ZCRkYGKigo4HI7Q1w2Xy0VCQgKWLl2K7777\nrsOl738RDrHKykqsWLECenp6rT6YKCoq4tixY6isrMTmzZs/QQ9bsmPHDmzYsOG935k6dSouXbrU\nKavvfQrKysqwePFi9OvXD7/++iuYTCaWLVuGXr160aIV1RaLFy+Go6MjbG1thfLWSwDmzJkDLy+v\ndg2fff36NRYtWgRDQ0NYWlrC0dERu3btalZdRVAiIyNha2tLlW2+evUq/vvvP9rDu4GGFHAHBweR\n0rElvJ/AwEDMnTsXhYWFIu3/+PFjTJ8+HRkZGTT3TALdZGdnw87ODtbW1li/fj1tdg8fPozQ0FDc\nuHEDvXv3xtq1azFx4kTMmDEDmZmZtLUjQUJ78/LlS3z33XdiRcVIkEA3ISEhWLJkCbZt20ZLVfNG\nGisIHj58GFJSUti4cSO4XC48PDzAYDCwf/9+qKiowNnZWaholvaaa0SFw+HA2dkZFy5c+NRd+SCN\n+tlTp06l1a6npydu3bqFO3fu0FI5tCPQ+Exz69Ytgb5fVVWFlStXomvXrti1a9d7v2tqaoo7d+7g\nxIkT8PX1Fapf58+fh4eHBwICAmBhYSHUvh+DL0JDrL6+HhkZGXjz5g0sLS2xb98+9OzZEzdu3MDT\np0/h7u6O8ePHIz4+HtnZ2Z+6uwBA6cyUlJTAw8MDMTExsLKyavYdDQ0NgdMRrly5gri4OOzevbvD\n6Jd8bHg8HtLT02Fra4uysjIcOnQIo0ePxsSJE8FkMnH48GEsXLgQ48aNQ05OjlC2BwwYgMOHD6Nf\nv36UGHMjurq66NatG5KSkmhPp/zc0dHRaTcNmgsXLsDX1xfKysqYM2cOJkyYgMTERLx8+RIGBgbQ\n1NQUWk+uqqoKL168ANCwqvjmzRsMGzYMW7ZsQVJSEjw8PMQSr87NzcXff/8NDoeDuLg4vHz5UmRb\nElqyadMmxMTEoKysDAkJCSKHbpeXlyMuLo56b2RkhKVLl8Lf3x81NTWSlKwOhJqaGvWQYmBggNLS\nUpw4cQJGRkYwMzMT2W5OTg6YTCaGDx8OBQUF9OrVC5qamoiNjaUiDj4lY8eOhZaWliQVX0Kb/PPP\nP0hOTkZpaSnCw8NRXl7+qbskQQKFvr4+Zs+eDUtLSzx48AB5eXnNtg8aNAjbt2+Hn58fqqurMW7c\nOIHs2tjYQF9fHzweD1JSUkhPTwefz6ccJgMGDMDEiRNhYGAAGRkZgfv77lyTmpoq8L6iwGKxcPz4\ncRQVFbW6va6uDkFBQZ3CESQvL4+hQ4fSbnf48OEwMDD4bDQWnz17Bm9vbwQHB2PChAkYNGgQfv/9\ndzg5ObUp1SAnJ4cpU6Zg8ODBzQpS3Lp1CxEREdi9ezelwaempoYRI0YgPz+/zeuqLYyMjCArKwtT\nU1PIysq22G5lZQU2m429e/di0aJF7RJI8D46vUMsNzcXSUlJsLKygqqq6nu/W15eDnV1daxatQpA\nwyp+WloaVYJ6yJAhqKmpgb+/P6ysrKClpdXu/f8Q5eXlOHXqFIqKilo4xIQhJCQEr1+/xu7du2ns\n3aeluroaYWFh1MOFkpISrKysBBKfZ7FYKC8vx/r169GrVy/4+fnh7NmzCA4OxrBhw4R2iPXq1avV\nimWNaGlpYebMmWJrx0RFRUFeXh7Dhw8Xy46Ehlx2JpMJFRUVLF68GFVVVSgqKsK0adNE0gxKTExE\nVlYWtZLeqB8mIyMDe3t7xMfH48aNGyI7xDIyMhAYGIjc3FzU1dVJHk7agRcvXtB2XhkMBszNzVFY\nWIi+ffvCxcUFNjY2UFdXF9shxmQyERUVBT6fj759+3aKG9qOio6ODlxcXKj3b9++haenJ9zd3TFl\nyhQUFhZi/PjxePPmDRISEj54U85msxEVFQVVVdUWBRS6deuGcePGIT4+HmpqaujTp0+7HJMgjBw5\nEiNHjvxk7X/pNF4nNTU10NPTw9dffy2WPT6fj6ioKIF0KzU0NGBubg5p6daTRCoqKhAVFYWYmBgE\nBwcjJSVFrL5JkNAeGBoawtDQkHpfUlKC4OBgDB06FH369EH//v2xevVquLm5Cby4KSUlhXnz5gFo\nKKzUVkqkubk5zM3Nhervu3PNuygpKWH8+PG0FM4pLi5GeHg44uLiWsjqpKam4tWrV5CWloa5ufln\npZslLB19cbLRxwE0OO/elxGWkJCAR48eIT09HVwuF5MnT4aqqio2bNgAe3v7Np8/FRQUsGTJEuo9\nj8dDVFQUIiIiwGAwqIhJcbGwsHhvZNjIkSOhqamJX375RWj5KjroVA6x6urqFjmrd+/exW+//Ybg\n4OBmA2NTpKSkoK6ujoULF8LJyalN+4sWLUL37t2xcOFCBAQEdAiHGF0oKyt3iFXp1qiqqoKMjIzQ\naXGlpaXYtGkTldakr68PX19f9OzZs9XvM5lMShNo9OjRGD16NACgpqYGXC4XGhoaQq32vA8GgwFV\nVVUq393IyKiFQKcoHDhwAF27dsXRo0fFtvU5wmazwWazoaam1ubNfiMuLi5QU1ODm5sbAOD48eOo\nqqrCxYsXRWr72rVrSEtLw8WLF2FjY0OLrhePx0NlZSXq6uoQHByMqqoq+Pv7AwC8vLywc+dOScok\njdy5cwdLly7F2bNnRbbB5XLB4/Ggra0NDw8P+Pr6Ii0tjcZeNtzQ2tnZgcPhYPPmzdi3bx+t9jsz\n9fX1rUb2NWp4CUv37t3h4+ODlStXIi0tDcePH3/v95lMJpycnLB27Vr8+OOPKC0tpbaNHj0aRkZG\nsLGxwdu3b2m70ZTwf7+7vLw8LfM4n88Hm80G0DCfy8nJiW2Pw+FQD9jFxcVwdHREeno67O3txb4/\n4PF42LZtGyIiItpsu5HRo0cjODi4xRzJZrPB5/Px4sULLFy4EMePH0e/fv2wdetW6lxIkNBRiYiI\nwLJlyxAcHEw5mJWUlPDXX3994p4JRuNcQweRkZFYs2YN7t69C2NjYwANWlEcDgfbt2/H33//DWlp\naRw6dEhox54E+qirq2umO/4u/v7+WLduHeTl5XHmzBnMnTu3xXcax/cjR44AaKhiKo6jr7a2Fj//\n/DNmz57d5j1KY2Q5m80WOcq8rq4OXC4XioqK1Jzdt29fWp6VRaFTOcT27duHf//9t9lngqzmq6mp\n4eTJkwIJ5ZqamiIwMPCz85ivXbu2wwqibtu2DT179sSmTZuE2q9Hjx64cuUKNZi8fv0a69evb7PI\nAI/HQ25ubovPjxw5gszMTAQEBND2uw8fPhze3t6f3XXU0bl58ybOnTuHkydPtluqZVs4OTnR/tCQ\nnJwMBwcHpKSkYO7cudiyZQuUlJSwYcMGKCgowNvbGytXrqS1TQni4eHhgZSUFNy8ebNZ+LmEj0NK\nSgpcXFxa/BcPHjwoVpS1m5tbuxb3kCAesbGxWL16NY4ePSp0EaPWyMzMhLOzMyorK2Fvbw9XV1ex\n7L158wbOzs5UWlddXV2r9yOiwmAwcOTIkVZlGZ4/fw4XF5cPCoC7ubkhPDwcvXr1wtWrV+Hr6wst\nLS14eXm9N7JFgoTPFSkpKaGE8zsKY8eORUBAAPr27Ut9xmKx4OzsjIEDB2L37t3Ytm3bJ+yhBADw\n8/PDoUOH2txeWloKfX19eHp6tum4LC4uhouLC8aNG4fp06eL3SclJSUcP368TZ9J41xz+fJluLq6\nwsPDQ6R2Ll++jICAAFy9erVd0mGFpdM4xNauXQsdHR0sX74cTCYTHh4emDRpEmRlZXH//v337stg\nMNqMHnsXNTW1DvHD0E1j1FR1dTU8PDwEzv2VlZXF6tWr0bt373br28SJExEbG4u1a9cCAJYvX45h\nw4Z9cD95eflmZVsrKiqoVc2m0Tlz585tVkWrMTKskYKCAjCZzGb5ykOGDKG05kRBWVkZw4YNo+0B\nqrCwEB4eHoiPj4eSkhL27NmD1atXfzBNWBCeP3/eLCJm2LBhWL58udh2W+PevXu4e/cu9X7SpEm0\nDOCNDBw4EFZWVtizZw/q6+sxatQoKvy9LSoqKrBr1y70799frBSixlBmFovV6vZp06YJrVOgo6OD\nxYsX4+jRo9DR0cGAAQMAAK9evUK/fv0wYMAA2qIaJfwfI0aMwOzZs3Hy5EnMnDlTKC2pnJwcvH79\nusW11DiWZmZm4tSpU1ixYoVIfXv48CH++eefDrvA0ZR///1XaOFVcdHU1MSMGTNarLoGBQW1EJm1\ntLQUuHiGoIsbqqqq2LhxY5tjiaKiItatW0et2rfF3bt3ERISAgaDAUdHRxgYGAjU/pdEfX09vL29\n8erVK+Tk5CAqKkrsiNmzZ89SulmPHz8Gm82Gqamp0Hbevn0Lb29vqlS9tLQ0RowYAVNTU8THx1OR\nyHZ2dvjhhx/E6nOj/cGDB1Pv+Xw+vL29kZWVBQaDgT179sDb2xsGBgZYvnx5s+iw3NxceHl5QUtL\nC7NmzUK3bt1gYWEBd3d3qKqqwtjYWDLPfEKqq6vh5eWFkpISGBsbY+HChbTYDQoKojRvHR0daVnA\nLSoqgpeXF1gsFkaNGgU7OzuxbQINjoOnT59CVVUVjo6OYsuPfGoiIyPx77//Ys2aNe0igaKpqUk5\nNG7evInHjx9DSkoKAwYMAJvNRk1NDXbt2kVpQ9FJaGgo7ty5AwBYsmRJs+c0UYiOjsa1a9cAAPb2\n9mKn+iclJVH3JdOnTxdroaw1cnJy4OXlBR6PB2tra0ybNq3N7/bv379FQYiSkhJ4eXlhxowZ6NGj\nB168eIERI0ZAW1u7xf4xMTHw8/ODmZkZbGxs0K9fP+Tn54vVfxkZmWbPw1wuF15eXlBUVMSKFSuo\nuWbixIkiF54CGsaKV69eYcSIEbQ8y4pLp3GI5ebmYsaMGejfvz8ePXqEadOmYciQIWAwGJCVlRVJ\n8+dLhM/no7i4uNWVyeLiYkRGRlLvu3btCmtra5GFpQXl22+/BQCcPn0aQIPeWVZWFtTU1DBmzJgW\n5YnfJSYmhlp17dKlC8aOHYv6+nokJydjzJgxWLFiBSZNmiRUn/r164cff/xRhKOhn8zMTISGhiIr\nKwssFgsvX75EXV0dli9fTssgwmKxml0PUlJSuHHjBvVeT0+PtoogFRUVzdp6+vQp9eBqaWmJrl27\nimXf2NgYmpqa2L59O5hMJuTl5anrx8TEpIWmD9Bws+nj44MTJ07A1taW+nz48OFiPVwNGjSomfPV\n0tJSaBt6enpwcXEBk8mEvr4+mEwmHj9+jN69e3/wgVqC8Ny7dw+ampqYP38+5syZQ91gCOMQMzIy\nanVlTU5ODosXL8bPP/+Mq1eviuwQi4yMxLlz5wA0XNOysrKIiIiAqakpbSXJWSwWnj59iq+++kos\nPZOCggKwWCxYW1sjMTERJSUlzbYbGhrSrqPVvXv3ZhWaKyoqEB0djW3btiEqKor6vFFMtym5ubl4\n/vw5LC0tRS4+o6KiAgcHhza3KyoqClRuvKioCPHx8ZCRkcHDhw+hp6cHLS0tkZwzAJCVlYX09HTq\nfe/evT+q9lxRURFSUlJgZmYm9v1aeno6srKywOfzER0djdDQULEqd7569YpKa3769CkePnyIgoIC\nWFpaUkVShKWurg4pKSkoLi4GAGhra2Pfvn1gMplQUFBAUVERoqOjMXnyZJEqGrdFRkYGMjMzQQhB\ndHQ08vPz0bNnT4wZMwY2NjawsrJq4YCrqalBYmIitm3bBlNTU1RUVODRo0fo2bOnRJ+wA8Dj8ZCW\nloacnBywWCzqwXjgwIFiLVYXFxcjPj4eQMNCS0ZGBjQ0NGBqavpByYm2YLPZePHiBaUB1JhONWzY\nMLEKeuXn5yM+Ph7Kysq4f/8+NDU10a1bt3bX022v6LDY2Fhcv34dwcHB7VbojMvlIjo6GuHh4dRi\n+r59+3DixAmkpKTg9u3b7dJuaWkpdV2FhIQgNzcXampqbYqpf4i3b99S9rp27Yo3b95AQUEBZmZm\nIlWgr6yspOypq6ujpqYGMjIyMDMzg7q6utD23qWmpgYJCQmoq6uDrKws9V8aMWJEC0duUy3P2NhY\nlJSUoKysDJMnT4a5uTnYbDY0NTXbfA5++/YtCgsLsXbtWnTr1g0FBQWIjIyEiYkJevbsiYqKCrGP\nh8fjwdfXFzNnzsScOXOoz8eOHSu27Q4FIaSzvEhNTQ05efIkGTx4MMnKyiIrVqwgCxYsIKKyYcMG\n8s0334i8/7v88ccfBAABQO7evUuLzdTUVKKnp0cAkBUrVtBisyl8Pp9UVlYSJpNJrl69StTV1YmM\njAxRUFAg9vb2tLf3Png8HikvLyfTp08nAIiRkRHJyMggTCaT1NTUUN+rrq4mTCaTei1dupSoq6tT\nLy8vL3Lq1CkyePBgkpmZ2WZ79fX1pKKigqxZs4YsWrSItuPYsWMHsba2Jnw+X2xb1dXV5NChQ8Tc\n3JxkZWWR+fPnE0VFRWJkZESKi4tp6G1L/P39iZqaGpGWliYAyDfffEOd69raWlrbOnjwIPW7+fv7\nEyaTSSorK2k5d4QQcubMGaKqqkqkpKTIsWPHml03bDab+Pj4ECkpKaKqqkr++ecfWtqsqakhZmZm\nZM+ePbTYa0p0dIXMaOUAACAASURBVDRRUVEhAQEBhBBCYmNjSZcuXcj169fFMfupx/YO8wJAXQc5\nOTmkb9++xNvbW5xz22Ke2bJlC5k+fbpItjgcDvnjjz+IrKwsNc/s3buXmJqakurqaqHtcblcwmaz\nW7ySk5OJvr4+OXPmjEj9fBcmk0nMzMyInJwc9ZKSkiK7d++m2qyvrxerDQ6H0+qxREREEG1tbSIn\nJ0dkZGSIlJQUkZOTIzdu3Ghhw9PTkxgaGpLCwkKx+tKUkpISMmjQIPL333+LtD+bzSbW1tZETk6O\nTJkyhTquuro6oewcPHiw2fnfsGFDs/MkrD1h4HK5xMfHh+jq6pIXL16IZKO+vp7qq5ubG5GTkyNK\nSkrk4cOHxM3NjUhJSRF5eXkSEhIidN/2799PnRc/Pz/y559/kpEjR5KysjIya9Ys4uLiIlKfm8Ln\n8wmHwyFOTk5k0aJFpLCwkBgaGhJPT0+xbTc9N1u3biVycnJEQUGBhIaGkrq6OnL//n2ioKBAHj58\n+EFbdXV1JCIigmhpaZGbN28SQiTzTDu9RMLPz4+6Vg8dOkTb+Dlp0iQiJydHxo0bR6qrq2kZE44d\nO0b19cKFC4TNZpPExERiYGBALly4IJLNoqIiMnToUCInJ0fmzp1LHb+zszMBQNTV1cnz58/F6ncj\nISEh1H3w1q1babHZSHvMNU3h8XikqKiImJiYkCNHjhBC/m8McnV1JTNmzGiXdt9l5syZRE5Ojpia\nmpLi4mKxr6sVK1YQOTk50r9/f5KamkrYbDbhcrki29u0aRORk5MjmpqaJDw8XGx773LgwAHqPxAQ\nEEDYbDbhcDjUM05dXR11Ddvb2xM5OTkybNgwkpeXR+bNmyf03OPj40N69OhB0tPTCSGEXLp0ifTq\n1YskJCQQHo8n0jG01zPN/v37iYmJCamsrKTVLhFxTO40EWIAsHfvXpSVleHy5cvvrbQgQXCqq6vh\n4OCA1NRUfP3117h9+zYcHR0xefLk91ZNbA9evnwJBwcHJCQkAGhYtbWzswODwYC9vT22bt0KANi1\na1eztDtHR0cq3RIATp06BT6fj8uXL783uqG8vBwODg4YNWpUhxU33r59O2RkZLBt2zYsWrQIS5Ys\ngYGBAQICAtqtTWtrawQEBMDR0REZGRl4/PgxVa5648aNWLRoEW1tLVy4EBMmTAAAuLu749dff8WQ\nIUPg7e0NRUVFse1Pnz4dmpqacHBwwJ49e5oJYv/vf/8D0JCW6O3tLVIElwQJwuLo6NhmWu2H2LRp\nE9TU1HD06FGRq5U2ZefOna1KDnA4nBbRXOKgoqICb29v1NTUAGiIQFu9ejWOHz+Of//9F/Ly8vDw\n8BCrzPbmzZubRYA10rNnT1y5cgUKCgq4dOkS7t69Cw8PD1p0pj4GsrKy8PDwQFVVFdLT06mxeOXK\nlUJFGc6bN6/ZGBcaGkrZAhpSUZpG1tHJn3/+iVOnTollIyoqiurftGnTEBISAj6fD09PT4SGhmLE\niBH4+++/hU7P+eWXXyAnJ4eQkBAADZHqmpqaOHnyJNTU1MTqc1Nev36N1atXw8LCglaZAKAhpajx\nHsbW1pY6lsGDB+PMmTMICQnBgwcPmqVUtkWjrsu1a9c+S/mQzo61tTX1+/r7+2PcuHFQUVGBh4eH\nwNIwrXHo0CFUVlYiKysLkydPBp/Px5IlS7Bq1SqRbdrZ2VHyJz4+Pjhy5Ah0dXVx/PhxkcdfTU1N\nnD17FrW1tYiPj6fGsOzsbJH7+Tny6NEj7Nq1C25ubtS43zjvDhgwAHv27Pko/di9ezc2bdqEgoIC\nzJ8/H7W1tWLNNb/88gtWrFgBJpOJjRs3oqysDFOnTsWvv/4qkj1nZ2fMnDkTHA4Hhw8fRm5uLkaN\nGoU///xTJHvvMn/+fCpT5OTJk9izZw8GDhwIDw8PKCsrw8/PD4cPHwYALF26FOvXr0dxcTGWLVuG\nWbNmYcqUKWK1P2HCBHh5eWHbtm1YsmRJi/RMCf9Hp3GIrV+/HlpaWvjuu+8EmtQ/BRMnTkRJSQk8\nPT1ps6mjo4P//e9/OHHiBG02AeD69esICwuDtLQ0vv76a5ibm6Nv374YOnQoFBUV0b1792ZijAAE\nqrAlDnJycpgxYwZmzpwJoCHFwtPTEzU1NSCEUGL5WlpazUrE2tjYwMjICGVlZfD09ETXrl0xcuTI\nD14nPB4PqampsLGxoT1tR1zy8/Ph6ekJLS0tWFpaQltbGykpKdDQ0BBZ1wwALl261OpD47tIS0tj\n5cqVuHXrFsLCwpCWlgYXFxcMGjQIL168wMmTJ0XuQ1uEhYUhJycHxcXF2LRpk0ih1a3BYDDg6uqK\ny5cvIyYmBhoaGnBxcUH//v0RGxsLWVlZGBkZtZqf35F4+PAhAgMD8b///Y+6trt3744//vhDkj7Z\nDqirq8PV1VVsvYp3EUULKjs7GydPnoS2tjasrKwgLy8vsj5ho3YJ0JDO0piyDgBXrlxBfHw8NDU1\nsXbtWqpSl7DU1NTg1KlTVLrYu0hJSeGHH35AUFAQSkpKsGLFCmhoaHzQ7oMHDyjNm3fR0tJqdiyN\n8Pl83L9/H4QQdOnSBT///DOsrKxocbh/DKSlpSlHYWNKBADk5eVRRTUEuRZ69OjRbIFIWlqaqroM\nNFR73rJlCwBgwYIFYjknr1+/jpiYGOr93bt3kZGRIbLuz7179/DkyRPq9x03bhx0dHRw+vRpDBw4\nkNJEbaoVKijm5ubQ0tKi9t27dy+lAUoXsbGxlIPJxsYGtbW1OHv2LBYuXChyhTcul4tTp04hLy8P\nMjIy1LmxtrZudh7y8/ORl5cHCwsLgdLgCgsLkZWVBRMTkw6h6/K50vhfE4eHDx8iJiYG8vLy2Ldv\nH7p16ya2zby8PERERIAQAikpKdoKP4SGhiIlJQWampowMDDAo0ePxLaZmpraTOaFbvr06YNdu3a1\ny/1ue1BdXY2TJ0+itLQUaWlpCA8PR58+ffDs2TMADQtd9+7dQ25u7kevTl5SUoLw8HBwOBzweLw2\ni58JSkVFBZ48eYLy8nJUVVWJLe3D5XIRFhaGoqIilJaWilw58X00pvZnZ2djx44dkJeXh6ysbLOx\ne+DAgXjx4gXi4+OxcuXKFs/hwqKtrY3Ro0cjOzu7XbXAPwc6jUMsKysL06dPx4QJE8BisRAeHg4N\nDQ2xxfroxMzMDPX19bQ6rzQ1NeHo6IjQ0FCxbRUUFFA3qdHR0cjKyoKcnBwGDx4MLS0tAMDt27dR\nXl6OFy9etIhCKigoQFZWltj9aIs+ffrAyckJycnJKCoqAoPBgIyMDEaOHInu3btTbc+cObNF7nJO\nTg7CwsKQmpqK5cuXU1FHbVFQUICwsDCYmpp2OGfYy5cv8ejRI2RkZGDjxo2wsLBAYmKiwPvn5OQg\nLi6u1W3Pnj0T6DeUl5fHkCFDqHx6GRkZ9OjRA/n5+cjNzRX4OkhNTW2mW9OUUaNGgclkIjU1FQBg\nYWFBPYQ0asKJS05ODkpKSrBr1y6MHTsW3bt3h66uLlavXo3CwkKUlpZi8uTJUFZWpqW99iIyMhLh\n4eHg8XhwdnamHuZ1dXXh7Oz8iXv3+TBp0iQq+lhdXb3D6Ajm5eVh7969uHv3LsaNG4eIiAhqW+/e\nvWFkZISQkBCYmZl9sLpqaWkpNQ9s3boV1tbWqKmpQUxMDEpKSlBXVwc2m43Vq1e3qrn3LkVFRS3G\np9raWkRFReH169et7iMnJ4c5c+bAysoKUlJS2Lx5c7PtfD4fMTExLapIh4eHN3O0NGX37t0tnJfZ\n2dkICwvDlStXwOFwMGvWLJG12zoCampq1DGGh4e3ObYKgrm5OeWMiYuLg5eXF3x9fTFy5MhmWoqC\nUlhYiKSkJADAkydPkJiYCA6Hg5iYGLBYLEqTVJSxNj8/H3w+n3IiZGRkIDg4GLGxsdi/fz+4XC6e\nPHkitF0AlCZKZWUlYmJi0KNHDxgaGqKqqgoxMTHQ09MTS0crKSkJoaGhePXqFQ4cOIAePXrg4sWL\nOH36NIKDg4W2nZ6ejuzsbNTV1SEyMhJFRUUYOXJkCwcLIYT6rwiqO/f8+XNwuVxYWFg0E9FXVVXF\n+PHjO72QeUeirXFMGEpLSwE0FJdISUkRW0wbADXmjhgxAioqKrT0E2jQOurZsyeMjIyoez5xaRrF\nbGhoCAUFBVojxXr16gU3N7cPFm7rCDRqQD99+pRyNllZWSE3N7eZU7Nx8ZSu31VQqqqqUF9fD6Dh\ndxO3fTabTTnB3r59K7a9+vp6KoK9oqKiXc5Po55XcXEx/vzzTwwfPhyOjo7N7jNzc3MRHx8PS0tL\nWhzcQEOEvpOTEy22PmtEzbX8BC+K9tJ2oYPw8HAiLy9Pm4ZYIwsWLBBJQ6y+vp5UVlaS8vJycv78\neaKmpkbU1NTIsWPHCCEN2i5jxowhcnJyhMFgUNtbe9nZ2dF6TI1UV1eT8vJy6jV37txm7QqiW+Hr\n60uMjY1Jdna2QG1evnyZ6OjokKSkJHG73wJxNcQ8PDzImDFjCJPJpD5LSEggmpqa5OrVq8TLy4uY\nmJiQjIwMwuFwWuzv4+PT5m/o4+Pz3rZZLBYpLy8nr169Iubm5q3amDt3rsDHsmvXrhb7KysrEwDE\n39+f/P7779Tn//33n+An6T3weDxSUVFBysvLybFjxyj772pWLFu2jCxcuJCWNhvhcrmkqKiIWFtb\nkz///JM2u7a2tsTV1ZU2e+/wqcf2jvSiHTrmmbCwMKKgoED9R96dZ6KiooiSkhKl+SMMPB6PJCUl\nEX19feLj40O8vLyIoaEhycnJEWj/S5cuEQaD0ezVrVs3kpiY2GpbXC6XlJWVETMzM7J//37C5XJb\nvGpqasjYsWNb2P3tt98EPq66ujqyf/9+YmZmRioqKgT6/tGjR8mQIUM6lIYYn88ndXV1hMvlknv3\n7lHnYufOnWL3jc/nEy6XS2bNmkUYDAYZPnw4efPmjUi2fHx8qL6dPn2aEEJIfn4++eqrrwgAoeaN\nD+Hm5kYmTpxIvd+2bRuxsbER2R6PxyNRUVFEU1OT0meMi4sjGhoa5Nq1ayLZbDy3y5cvJ0uWLGm2\n7cKFC6Rbt24kJSVFIFv19fXUf8PV1ZW6V4uKinrvPuPHjxfqPyOZZzr+XNP0Wli7di1hMBhES0tL\nbN2sxjEmODi42VwjDo3jPZfLJfPmzSPOzs5i22z8X3G53GZzz99//02uX79Oq4ZYI+PHj++QGmJN\nz8XFixdJ9+7dRdZobC8ar6vo6Giira1NGAwGWbdundj2Xr58Sb766ivCYDDI4sWLRbbXeI2+ffuW\nmJubEwaDIbLG6/vsc7lcYmtrS6SlpalrtnGuacrRo0fJ0KFDSUlJidBt1dXVkTNnzpDevXtTGmJ0\nINEQk/BZ8PbtWzg5OeHVq1cYOXIkgoODAYBKu1NRUYGXlxf++OMP5OXl4dChQ23aaq/w+W3btjWL\ngHN2dm4WMSBIBNfUqVMxfPhw2jzqn5LZs2dj/PjxbVbisrOzg56eHlauXAk3N7cWOea2trbU7/wu\nH4r4+Ouvv3DlyhWoqKhgy5YtraZnClOFZdmyZS2iDZ4/fw4nJye4urpi3rx5VF/pKPkNNETTODo6\n4s2bNxg7dixlX5RUNWGJjIzEpk2b4OrqijFjxrR7exK+DIyNjXH//v12iYi+efMmzp49i+PHj+PO\nnTuQlZXFuXPnBI4GGT9+fIvxhsFgtDpue3p64vLly5CXl8f69euRkJAAGxubFt+TkZGBg4MD/vjj\nj2afCxKx1oibmxtkZWVx4sQJgSpR7dy5EywWC2fOnKEipjsCdXV1WLNmDZKSktC/f3/qXAtzLtoi\nIyMDa9aswcyZM7F+/XooKSmJrJs1efJkqm/9+/cXu28fkytXrsDf3x+XL18WOU34XYqLi7FmzRqM\nHj1apIi7poSHh+OXX34B0KCfFhwcDGlpaRgZGbW5j5SUFA4dOoQuXbqI1baEjkVoaCi2b98OoKFC\ne3BwMGRkZPDVV1+JZXfDhg14/vw5+vTpg8DAQFpkGAICAigtpgULFmDy5Mli23z79i3Wrl2L7Oxs\nGBsbU2NO3759BZID+ZwoKyvD2rVrkZOTg6FDh+L69esf5T5XGLZu3YonT56ge/fuuHDhAhQVFcWq\nqrlv3z7cvXsXGhoaOHjwIDQ1NT8YFf8+mt6TrFu3Dj179hRIvkFQLl26hGPHjgEAkpOTMWfOHLi4\nuABAq+O3nZ0dLCwsRBq3Dxw4gMLCQly+fFksaZ0vlU7pEOvSpQu2bNkCMzOzT92VDs3z589x8eJF\nmJmZYfTo0RgwYABGjBjR7Dt1dXW4c+cOBgwYAHt7+xbb24OTJ08iOTmZev/vv/+iZ8+eVOlxa2tr\noSd3bW1tsTWgWCwWPD09YWZmRls52WPHjqF3795C3RB37doVXbt2pd6HhYXh1q1bcHNzw9ChQ5GW\nloawsDB88803rU5+opyLqqoqeHp6gsvlYuHChVBQUMCoUaPEmmiABo2rpgUwAgMDkZmZiQMHDgBo\nSNuh85oLDw+Hv78/Jk2aBBkZGRgbG3+Ua7qRyspKJCQkQF9fnxbnbE5ODjw9PTFmzBiJg+0LRl1d\nnRJmbY0ePXpgx44dIokq6+vrY/LkybCyssL58+ehqKgolG6ajo7OB8eJ2tpanD59mhLABRpuDuXk\n5Kj3TZGWloa1tTX09fWFO5gmmJiYQFtbG8OHDxfo+6mpqdDS0uowYvtBQUGUzk737t3Rs2dP9OnT\nh7Zx4MmTJwgMDMTo0aMp7RJxeHfeAhoW0datW4d//vlHLNuN1NXV4fTp05CTk8PSpUtRX1+P06dP\nQ0ZGBsuXLxfJpq+vLzIzM6n/QFvl7YXh+fPnuHHjBoYOHYpx48a1+F8OHToUP//883vn6cb/TFFR\nEaSlpan/iY2NjUBaaVJSUgKL4ufl5eH06dOwsLCQFJfpgFRXV+P06dMoLS2FjIxMs2tBnOegBw8e\nUA4lbW1tTJ06Ffr6+hgzZoxAenOtUVxcjNOnT4PFYjXr67hx48RylPv7+yMmJgZSUlIwNDTEwIED\nYWRk1Cnviy5duoTi4mKsXbu2zYXvDxEfH49r165R58HIyEhkPUK6CQsLQ2BgIABAWVkZU6dORbdu\n3WBlZSWSNtezZ89w48YNAA1FZqZOnQp1dXVYWVmJ5LxKTk7GhQsXADQs3k2dOhVycnKUrIq45Obm\n4vTp0+DxeIiIiEB2djaWL1+OqVOnwsLC4r3X7LvPTMKQkZGBuro6iW9ERDqlQ0xNTa1T64B8LKqr\nq1FaWoo1a9a0+WBRX1+PvLw82NjYUA6p9iAjIwMpKSkAGgRmm2pEDR48GN999x1WrlzZbu03JSEh\nAYWFhZgyZUqzqDc2mw1fX18wGAyRHGKEEISHh0NKSop6eL169Sp0dHSgqqoKS0tLkW4yYmNjcefO\nHYSEhCArKwvnzp3Do0ePEBwc3OIBRFTq6+uRm5uLadOm4ZtvvqHFZmswmUwAwNq1a5tplNBFRUUF\nqqqq8Msvv0BTU7PV7zRqEGpqar63CmlHoKioCEeOHIGfnx9tTloJnZvs7GxkZGTA2tqaimTq0aMH\nfv75Z5HsmZiYYODAgYiNjYWWlla7rDDX19cjISEBkyZNwuzZs8FisbBx40aMGzcOc+fOpb09oKGq\nYmcmJycHERERkJWVhbu7Oy3FhCorK/H8+XPU19fj6dOnKCkpgbu7u8gPZR9CTU0Nzs7OiI+Pp/RT\nROXNmzd49uwZnjx5gqlTp2LBggXg8Xjw8fHB5MmTsXDhQpHsXrp0CUZGRi3uKVVUVDB27FihF4Ve\nvHiB0NBQZGVl4eDBg61GQwwePLjN3zMtLQ35+flgs9l48uQJSkpKYGlpiW3btgnVD2EoKCjA3r17\n4efnh4kTJ7ZbOxJEo66uDvHx8cjOzsbYsWNpuxYaBfSBhirfgi4evA8Wi4Xo6GhUVlZixowZWLNm\njdg2gQYd6YiICGhqauLgwYMdLhJKGK5fvw4tLS389ttvIu2fkpKC0NBQpKWl4eDBg7REC9NJYWEh\ndV3t2rVLpGInTSktLaXsbd68WezKi2/fvqXs/fjjj5g9e7ZY9t4lPz8fe/bsAZfLhYGBAWbMmAE3\nN7dOU8inKW/evEFMTAwGDBjQqf9zgtApHWLiQggBi8UCl8sFj8dDVVUVlJSUaH9A53A44HK5AABF\nRUUwGB/3dI8dO/aDD9EqKipUydf25ObNm1RZ3GvXrok9oInDsWPHUFZWhitXrlCf1dXVobq6Gnw+\nHxwOB7W1tUIPXnw+H25ubpgwYQJ+++03VFdXo76+HleuXEFBQQGV5iAKfD4f1dXV+PPPP3Hjxg2x\nK4+8S5cuXXD06FFabbbGvHnz2vVB1dbW9oPReKWlpXBwcMD27dtFjir4GHA4HLDZbKiqqn70sUNC\nx+XChQu4efMmgoODaauElJ+fjzlz5uDAgQP44YcfaLHZFBUVlWbFZpSUlKg0Agmt4+DgAAcHB1pt\npqWlYcaMGaiursb69etpr179LoQQ8Pl8SElJiTz3NRITE4Pvv/8e9+/fb/aAJSMjI3LFVaAhGrG1\nvvXr1w/+/v4C22k81v3794PBYOD8+fNC7wsAR48ehaenJ7p06YL79++36aSg69zy+Xzw+Xyxz6OE\n9kNDQwOnTp2i3e7SpUuxdOlSWm327duXiuahkw0bNmDDhg1tbufz+SCEtMtCa0eg8fgA4NChQ2Cx\nWM2eYToSc+fOpXWhS5D7emEYM2ZMm9WqxaXpeCotLY358+djz5497dLWxyAqKgr29vYIDg6mNQKx\nscBCR/q/fpFPWTU1NXBycsL9+/fB4XBga2uL48ePi1VmvDUuXbqEI0eOAAAOHz4MKysrWu13JhYs\nWABra2sAHVNf5NGjR9i4cSMyMzPh4eGBkpISSvtAFOLi4rBq1SqxqoA1JSsrCzNmzICzszN69eqF\nmzdv0mJXQsflwoULuHPnDgICAjpUNV0JEiR0TgYOHIg7d+6gvr7+o0THvnnzBuvWrcOIESMwbdo0\nsWyZmZkhMDCwWWSVjIwMDh8+LJbmy549ewTSmPsQBQUFWLduHaysrFpNA34fISEh1ILh9OnT8fDh\nQzAYjPfeK3E4HKo9cZzYFy5cwN27dxEQEECbfpoECR+bv//+GykpKbh586bYemodkf/++4/S1LSz\ns6PVQSSBPs6fP4/AwEDcunULDAZDouXVBm5ubmAwGDh27FiHiZz7Ih1ijSWKG0sWx8bGgsVi0drG\nyZMnUVpaiufPnwOA2OkCrREdHY2goCCsWrWqQ4kAt0a3bt3aVfD+6dOnuH//PlatWtVmqlxb3Lhx\nA8+ePcOsWbNw/Phx5OfnIzMzU+S+BAUFgcPhYN68eTh+/DiqqqpEtgU0rGY4Ozvj+PHjuHXrFoYP\nH47FixfD3d0dCxYsoCXM/UshOjoa165dw6pVqwQuRf+pKC4uRl5eHoYPH95hJgwJgsNms3HmzBmo\nqqrSGnVlZWWFrl270ho1qKWlBVdXV4E1hz4nSkpKcPbsWQwaNKjDjwnioqqq+l4tOrqRl5fHqFGj\nMGrUqPcKwAuCpqZmC+0VKSkpsec/uhZClZSUMHr06FY1w+Lj43H9+nUADULo72r06erqYsKECQAa\ntJY+pAGTnp6OM2fO4NatW3j58iX4fD4WLVokUr/z8vKQmZkJS0tLyTwjodOSmZlJpRfTRXZ2Ns6c\nOQMrK6tPKlnh7++PuLi4ZmOEuOOphPahd+/eGD9+PMaOHfvRMjuYTCbOnj2LXr16YdiwYR+lTXFJ\nSkrC4MGDP6rG84fodA6xvLw8pKenw8LCAsrKykLvX1xcjNDQUFRWVgJouBGxtLREUlISNDU1aYte\n8vPzo8VOW8TFxeHChQu4fv065s+f3+EdYu1BTU0NIiIiwGaz8d9//+Hff//FggUL2nSIsdlsREZG\nQk1NDQYGBuBwOIiMjERUVBRYLBasra0xadIkhIWFidWvN2/e4M2bNxg1ahQmTJggdkiohoYGhg0b\nBltbWzx58gT6+vqYN28ebGxsMGLECIlDTAiSkpJw+fJlBAcHC1S9VBiSk5ORnZ0NW1tbsSt7RUZG\ngsfjYfTo0WKnGkn4NHA4HHh5eeGHH37A/PnzabNrZWVFe7Sxtrb2e9NRPmdev36NAwcO4OjRo+2q\nn/gloqamhtWrV3/qbnwUNDU1sX79+la3JSQkYOfOnQAa9P7edYi9T1OsNTIyMqg0nIKCAsjIyIjk\nEIuLiwOPx4OJiYlknpEg4R1ycnLw+++/IygoCOPHj//o7dfW1iIuLg6hoaHQ1dWFm5vbR++DBOFo\nj/uzD1FRUYG//voL27dvx8yZMz9q258TncohxmazcefOHfzxxx8IDg5Gv379hLYRGRnZTMPI1NQU\n586dg42NDV6/fi2yMPG7KCgogM/nUxpidMHn81FbW4sDBw7gwoUL7S6mSAhBbW0tlRPdUVYQ6+rq\n8PLlSyxZsgSFhYVgMBgwNDR8rwYGk8nEqlWrsHbtWqxcuRIFBQXYsGEDHB0doa2tDQcHBwQHB0ND\nQwOvXr0SuW+N6aE2NjZ48OAB+vbti6CgIFRXVwutB1VbW4uAgABcvHgRd+7cgZOTk8j9+tJhs9mo\nr6+HsrJyu2il/PPPP0hOTsbt27fFtrVjxw6YmJjA3d2dhp5JkCBBgoRPASGE0v5p+pmoc1Br9qSk\npKjPhLH7888/w8TEpIV+aFP7El0xCRI+DcXFxViwYAF27NiBZcuWferuSJDwWdOploT27duHffv2\nfepuCMS+ffvg6OhIu93CwkLMmzePKmnb3nA4HKxevRo2NjbYvHnzR2lTEG7fvo1Fixbh9evXABpE\nFy9cuCBwWmZoaCiWL1+OX3/9FXZ2du3ZVSxatAgODg6wtbXFs2fPhNp3x44dKCwsxKlTp6Curt5O\nPfwy2Lt3D9ttfQAAIABJREFULxISEnDhwoVWK39JkCBBggQJdLJ//37s2bMH2tra8PX1RVpaGnbt\n2iWyvRMnTuDff//F3bt3YWRkBAcHB8yePRuTJ09GUlKS0H1rrbp3eno6vvnmG4SHh4vcTwkSPgfe\ndT5/TLp164aLFy8KrUkoQYIE4elUEWKvXr3Cq1evoK+vL9L+AQEBiImJwc6dO3HixAkUFBQAAGRl\nZbFmzRpkZGTg5MmTrd4gCMLDhw9x6tQp8Hg8DBgwALW1tdDX14eTkxMiIyNBCMG3334rlM3S0lKc\nOHECAwcOBCEEHh4eGD16NIqKilBWViZSP98Hm83GiRMnkJ+fDwDg8Xi4d+8eioqKxBKupYPGc1FZ\nWQlFRUXY2toiNzcXFRUV0NbWfq/+TWxsLC5duoSlS5di1KhRSE9PR1JSEvr164euXbuK3beMjAyc\nOHGi1ciy7t27o2fPnnj27BmVqisoL1++hL6+fgudE11dXezYsQMvXryAn58fvv/+e7H6/yWQlZUF\nGRmZTpFj7+DgQMt1KUGCBAkSBOfSpUvo0qULbQ+haWlpSE1NRY8ePWBubo5Hjx6hsLBQZHuZmZko\nKCjA2LFjoaqqir59+0JfX7+ZFIigtDUXVlVVISwsDEePHkV1dfUnrQouQcKnhM4IydLSUkqv8t20\n6dZQVFSEhYUFbe1L+DxRV1fH2rVrJfI5YtJpIsRu375NOWlE5eHDh0hKSsKaNWugq6tLfS4nJ4eF\nCxeCzWbj2rVrItmOiorC48ePkZ2dDUIInj59irq6Onz//ffYuHEj4uLiEBISIrRdJpOJY8eOobKy\nEkpKSsjNzUXv3r0xduxYWgSQ6+rqEBYWhtu3b+P27du4e/cu4uLikJycjNDQUBw+fBhFRUUwNDSk\nteSqsOTk5CAoKAiJiYlITk5GbW0thg0bhilTpghULSslJQU+Pj6ws7PD8OHDoaenh0mTJkFFRaXF\ndwcNGgR9fX0EBgYKXAyhpqYGycnJtBdnMDExaVVbRFtbGy4uLsjIyEBQUBCtbX5u1NTU4MGDB9DW\n1u4UzjAAsLe3/6QirhLER0ZGBiNGjPjiqwy9efMG4eHhqK6u/tRdaUFBQQFSUlIwcuRI6OjofOru\nSOgAXLx4Effu3RPbDpvNxtOnT6Gurt5CZF8UeDwenj9/DllZ2RYLZBoaGhg9ejSysrKQm5srVjt5\neXl4/vw56uvraTsXEiR0VuiMECstLcX+/fsxaNAgTJ8+nTa7Er5sNDQ0qGrOEkSn0zjE7O3tERIS\nAhkZGSgoKIDNZoPH4wllQ05ODvLy8m1ul5WVfe/297Fr1y7U19fj4MGDkJWVxa+//oqqqiocOnQI\nCgoKItlsypEjR5CVlYXff/8dmzdvxrBhw7Bp0yaRz0UjNTU12Lx5M+zt7WFvb49Vq1bhp59+wo0b\nN7Bt2zYoKipCWloaS5Yswe+//y72cYhKUFAQ3N3dceTIEdy+fRsDBgzAjh074O7uDhsbG6HtWVlZ\n4fz58zAwMACXywWPx4OSkhKkpaXh6OiIqVOnYsmSJcjKyhLI3vDhw3Hz5s1mzqumK0vS0tKUfWHY\ntm3be3XD5OXlIScnJ5TNL43i4mL8+OOPGDlyJNatW/epuyPhC0FZWRleXl6YM2fOp+7KJyUsLAzT\np09HRkbGp+5KC27cuIHff/8dZ86cwbhx4z51dyR8RhQXF2P+/PkYMmQItmzZIra92tparFq1Choa\nGpSgfiOmpqa4e/cuTpw4AV9fX7HauXz5MlatWoXa2lqx7EiQIEGCBAmdhU6TMhkSEoKdO3eCEIKf\nfvoJmzZtgpOTE2bMmCGwjdWrV79X5N7JyUnkCJ8DBw5AWVlZ7Ci2tti6dSsWLVoEPp9PfTZlyhRo\na2vD1dUVq1atwnfffSe0XRUVFXh7e1PHLS0tDUNDQxw/fhyxsbEIDAyEs7MzbcchKt999x3Mzc3b\nrCApDn/99RcyMjIQEBBAFWoYPXo05XijA2NjY9y/f582e41s375d7CqWnzs9evTAlStXvvhIHQkf\nFykpKYkg9f+n6bzVkSCEgM/nQ1paut1+qy5duuDUqVMCRTJL+HSkp6fD1dUVERERiI2NBYvFwsGD\nB6Gmpia0rXv37sHLywv79+/HqFGj8ODBA2rb+vXrcf/+fcyZMwcHDx4UqjBS4//o3YU1KSkpSEtL\ntyq4LwxbtmyBlJQUjh49io0bN0qcYhK+eCRzuAQJXwadxiFmZmYGbW1tEEIwaNAgpKWlUYLqgmJg\nYAAAbabBiapNBgADBw4EgHZziPXr1w99+vRBZmYm9ZmOjg6MjIyQmpoq9LlohMFgNAu/53A48PLy\nApPJxLRp0zBy5EgoKyuL3f93efToUbNqfGPGjHmvvlrXrl0pTaWLFy8iLy8PmzdvFlhofvjw4di2\nbVuraTE5OTkoKyuDiYkJ9ZmGhkaz9x+iUX9uypQpGDduHOrq6prdmKqqqsLU1FRge4LSv39/2m2+\nS1JSEq5duwYHB4dO+VCnoKAgcm59QkIC/P394ejo+MGCDdOmTWuWVnz37l0UFhZixYoVIrX9Ps6e\nPYvk5GR0794dDg4OUFJSor0NCV8eN27cgKysLG3pHIGBgbh69So4HA68vb2xePHiL04TRU5O7os7\n5s5IVVUVQkJCUFVVBaBBBkPUyPuioiI8f/4c7u7u6N69e7NtRkZGiIuLQ1hYmFALsHJycli5cmWL\ndMlGpKWlsXTpUnz11Vci9RkA4uLiMHjwYJiamkoW2iRIkCBBSB4/foyMjAwsWrQIDAY9LpaysjL4\n+Phg0qRJbY7/wlBTUwNfX1+8fv0aAwcOhL29fZvf7d+/P3755ZcW81hrhIaG4tGjRwCAhQsXok+f\nPmL3tbS0FL6+vi3mypkzZ9JyLprSaRxidMJgMDB69GgwmcyP1qaJiYnYESp5eXmIjo6GhYVFuzgm\nSkpKEBkZiZiYGHz77bewtrbG/fv3YWRkhL59+9La1uvXr5GQkEC9l5GRaTZ4GBgYYNCgQa3ue/Pm\nTSgrK2Pnzp0Ctzdo0KA27dFBTk4ODh48iODgYOjq6iIkJAS2trbQ0tKixX5VVRWioqKgp6dHOV8/\nFpWVlYiLi8ODBw+go6MDXV1doZyF7yMzMxPp6ekAQJuOT3V1NZ4+fQoOh4OePXvC2NhYZFvl5eWI\ni4vD/fv3oaWlha5du7aZp/9u6m5xcTGio6PbnEj69+8v8sNLdnY2EhISUFRUBAMDgzZTvZWVlWFu\nbi5yKriELwMWi4WkpCQcO3YM8vLy6NatG4YMGSJ2uv/t27dx8eJFAMCxY8fQv39/2pxDiYmJUFNT\noxa6REFPTw8jRoyg7cZVQuckPz8fL1++hJmZGWJjY6GgoIABAwYgJiYGI0aMgLa2tlD2unbtClNT\n0zbHXR0dHZiZmUFRUVFgm/Ly8li1alWb2xkMBhwcHITqZyOVlZVISkqCnp4eevfuTX1uZGQEeXl5\nxMbGwtjYWPI/kSAwKSkpKCsrg5KSEoYMGUKbtAeXy0ViYiJqa2uhra3d7vfDhBAkJSVBXl4eampq\nGDJkiNDSJ+KSnp6O0tJSyMvLY8iQIUKNG++jvr4eSUlJqKqqgoaGRqt6xaKQn5+P7OxsAA3PXnRk\n9rDZbCQmJoLD4UBXV5e2bJusrCwUFhZCRkYGQ4YMgaqqqtj27t+/jz59+lDjpYqKCoyNjUVeZKip\nqUFISAhkZWVRXl5OfT5gwIBmeuiCwuVyER4ejqysLBQUFEBPTw8A0KdPnxa+BUNDQ+zYsUMgu5mZ\nmQgMDATQEGDUWLgQAAYPHixSYb6qqioEBwc3O24A0NTUpOVcNKMxxLqjv1gsFlm2bBlZuXIlyc3N\nJYMGDSInTpwgHA6HiMqGDRvIN998I/L+rREeHk7k5eWJnJwc2bp1q9j2UlNTiZ6eHjl37hzx9vYm\nffv2JTk5OdT2nJwcYmRkRP755x+x2uFyucTPz4/o6OiQZ8+eEUIIuX37NlFQUCARERFi2RaEI0eO\nEHl5eQKAACCrVq0iLBaLsFgswuPxCCGE1NfXExaLRRYuXEicnZ2pfZcvX06cnJxEbtvFxYXMmjVL\nrP4/ePCAKCsrk7CwMLJjxw5ibW1N+Hy+WDabkpCQQDQ1NcnVq1dpsykMTCaTWFhYEAUFBWJnZ0f9\nNiwWi7DZbJHt7t27lygoKBAFBQVy8+ZNwmKxSG1trVjnLi0tjfTu3ZsoKCgQR0dHqp91dXUi2Sst\nLSUmJiZEQUGBzJ49W6hjDwoKoo6v6UtaWpps3bq1xTUuLHFxcURPT49IS0tT/53Gl4yMDBk+fDgp\nKir6kJlPPr53oNcXSWpqKunVqxeRkpIiUlJSxNDQsNk8Iyo//fRTs2vS3d2dht42MHnyZLJjxw6x\nbPB4PMLlcmkdqyV0Ptzd3YmpqSl58+YN+fbbb8lPP/1Enj17RnR1dYmfn5/Q9urr65tdVz4+PqRH\njx4kPT291e2iUFNTQ8zMzMiePXtEttFIZGQkUVVVJTdv3iQ8Ho9ER0cTFRUV4ufnR9zd3YmJiQl5\n+/atuM186rG9o70+a+bOnUsYDAYZOnQoKSkpoc1uUVERMTY2JgwGg8ydO5f6/KeffiJ2dna0tUMI\nISEhIURaWprIyMgQBoNBxo4dK9YzZ2JiItHW1iaXL18War8VK1YQBoNB+vTpQzIyMkRu/11qamqI\npaUlYTAYtD4LHzhwgDAYDMJgMMjNmzdpsZmZmUn69etHGAwGWbZsGS02CSHE1dWVMBgM0qVLFxId\nHS22PR6PRx49ekSUlZWpczBq1ChSXV0tsk0+n0+4XC754YcfKJsMBoP4+vqKZY/L5ZKrV69S9g4f\nPixyHwn5v/spLpdLJk2a1KyvQUFBzb5ra2tLXF1dhepr09eCBQua2T937lzT3UQakzvNco+NjQ0W\nLFiAadOmQVdXF+fOncPZs2fxxx9/4LfffvvU3WvBvn37MGvWrHZvp2vXrjh//rzYEWP/j733jovi\n+v7/X8tSFulNUIqIKDZQIlakiYJdxBqTr8bYDca3GsWKiEFRjMYarCBGBQtgLyiIIIoiCIgUkaIi\nvXfY3fv7g9/Ox5W2ZYya7PPx2IfOzuyZO8OdmTvnnvM6R44cQVJSEq26WcIwffp0qKiowMXFBVVV\nVQgODkZ8fDwAwNPTE/b29nj79i1cXFwwbtw4jBs3jvrthg0bvnie/6BBg3Dv3j307dsXoaGhX7Qt\nnwNFRUUcPXoUtbW1SE5O5ouGGj58OPbt2yeS3R9//BE2NjYAgKNHj+L3339H3759cfjwYZFnwfT1\n9XHp0iU0NTXh8ePHVFvd3Nwwfvx4oe3xNIDq6uqQmJjId+xWVlbw9vZu87eDBw9GeHg433dcLhcu\nLi7w8/PDvXv3AAAHDx4UKaXW2NgYV65cwdq1axEREcG3bsqUKXBzc/ssunsS/n18nObd1NQklq2K\nigqsXbsWRkZG2Lp1K/bs2QNvb2+8e/cObm5uQkX3fkpiYiLWrl2LZ8+e4fXr16ioqIC3tzdkZGSE\ntsVkMvlmba9du4bg4GB4e3vTFt0r4fPw/PlzbNq0Cd7e3mJFAQPN92QOhwMZGRlKj4vJZLaQPhAU\nKSmpdqNIOlr/T8KrJBkYGIjBgwfj8uXLuHbtGgICAjBkyBDk5OSInDoqoW3GjBlD/btu3bov3Br6\n2bBhAxYsWICSkhIsWrQItbW1mDhxIlauXCmWXTU1Nfj4+KCmpgZpaWnUebS3t6ddnsLMzIyvympu\nbi4mT54MDoeD2bNnC70/Q0NDXLx4UeiotlWrVmHWrFmorKzExo0bUVZWhlGjRmHDhg1C2fkUOTk5\n7Nu3DxUVFcjKyqLO5fz58zFnzhyR7U6fPp2q6B4UFIT9+/fD2NgY3t7eUFRUFMmmjo4OfH19UV9f\nj5cvX1JtXb16Nd/7oLAsXLgQDg4OaGxsxIEDB5CXl4fBgwdjx44dItljMpkwNTXFlStXKM3HvLw8\nzJgxA01NTZgxYwYWL14slE0GgwEZGRm4urpi3rx51Pc3btzA6dOnqeU1a9Zg7NixAtsDAGtra9y8\neRMAEBYWRp1XAFi0aJFQRaE+Hk/t3LkTpaWl1LoLFy5g9+7d1PL06dMFKoj3cVsB4O3bt9T7zsfP\nJTp0ar8Zh1hMTAyWLl1KaSaZm5sjPz9f7IH756J3795CiaV2RGBgIAYOHIi1a9fy6WbJycnB3Nxc\nZLt1dXU4fvw4ysvLMXbs2M+icwU0F0XoqHw3k8nEhg0bcPbsWSQnJwNoviC7du2KJ0+e4Nq1axgx\nYgRsbW35UjhF1dGqrq7G8ePHoaWlBXt7e5Fs8FBRUflX68Q0NTXhwYMH+PDhAzIyMhATE0OtKyoq\ngoKCAhYtWiR0+pKuri7lzH379i1MTEzA4XCoAhpWVlaYMGGCUDbl5eWplE4Wi4WGhgYAwJMnT/Dw\n4UOoqalh0aJFAjuKZGRkMGDAAFy9ehW3b9/mO/aSkhLqATB+/HhYW1vz/bawsBAhISEtbDo7O+PB\ngwfIzMzEokWLhE4VPX36NFJSUqhle3t7jB07Fvn5+Th+/DicnJwwd+5calAi4d/F+/fvce7cOUyb\nNo0qBCIqcXFxuHnzJubPn48rV65AWloaEydOxJkzZzBp0iSYmZkJbZPnjO7fvz9MTEwgLS2NYcOG\n4fXr18jIyBCrvWVlZQgNDQUhBGVlZXj27JlITotPCQ4Ohp+fH6Kjo6GtrY0ff/yRlhSSmpoanD17\nFt999x0sLCzEtvc5KSwsxNmzZzFu3DhaU5Fu3bqFhoYGODk50WLvwYMH8Pf3x71791qkUgjLhQsX\nUFNTg59//pkvratz585YtWoVkpOToaioCEdHR3Gb/VXSpUsXDBs2DKNHj4aMjAxycnKQkpICa2tr\nsdOHJLQNb6xdXV3N9/Ldr18/oYqFfcz9+/dRUlLyVVQ25o09CgsL8ebNG9TU1KC0tJQ61smTJ4uk\nASQnJ4cRI0YAaHaSfPjwAUCz/vGNGzdw//59zJkzp0PNV0FQU1PjcxDk5OTgzZs34HA4ePfuHXUs\nM2fOFOg9RFFRUaRqxv369UO/fv1QXl6O7OxslJSUoK6ujtr/2LFj25TyaA8mk4khQ4YAaE7L5KU5\npqenY8eOHWAwGPjhhx+Efpc1NDSkUq/z8vKgpqYGKSkp7N+/HwwGAxYWFnBwcBDKZqdOnWBlZQUA\n0NDQoHSznz59ivj4eCgpKWHOnDlCT2SZmJjAxMQEDQ0NyMnJwfv378FkMqlza2trS/U3QamurkZs\nbCw4HA6A5vHavXv30NTUhPr6ehQXFwNodgq1F4ASHBzMN87/lIcPH1KBIwCgrKyMxsbGdgvspaSk\nIDg4uE170dHR1DIv5bO9+0l2djbOnTvX5noeDx48QEZGBrp27YoffvgBtra2rR57QUEBzp49i/r6\n+lbtcLlcGBsbo0ePHnjx4gWePHmCH374gRY9sW/GIdYa4niFvxUUFBRgZ2dHaVOJM7P+KR9rhjk5\nObWIaNPS0oKDg0O7wvWEEDx79ozPE9waz549w4sXL9rdRktLC9OmTYOtrS309PRgaGiINWvWQFVV\nFX/99RcuX76M8PBwKt9ZXGpra3HixAksW7YMU6dOpcXm10hubi6SkpLEslFdXY0DBw7wFXXgkZmZ\nid27d0NDQwN9+vQReR/KysoYOHAg0tLSsH37djQ0NCAjI0NscV+eoP6ePXtw//59dO7cGdra2kIP\nmKKjo1FbW4tRo0bh2bNnqKqqQkVFBdWvLSwsUFhYiLi4OOo3OTk5Lfo9g8HAtGnTMHLkSJibm8PV\n1bXDaIHnz5/zFc6IiYlBZmYmtezm5gZdXV1ERkYiJycHS5cupQYPEgQnOjoaxsbG4msRAGCz2UhO\nTkZNTQ3U1NTEujY+5v3797h58ya2bNkCMzMzsR1iT58+xZkzZxAeHo7s7GzIy8tj/vz5sLOzg56e\nntAOseLiYsTHx6NXr14t7tWGhoaUHmK/fv2Eni1+9+4d3r59i+HDh1OOim7duuHJkycwMzODqqqq\nUPY+5vz587h69SoAwMvLC6amprQ5xPbv349ly5bR6hB7+/YtKisr0a9fP9oipAsKCvD7779DX1+f\nFodYQ0MDkpOTcfToUbBYLNocYnfv3oWvry+YTCZevnwJQ0NDkYsinT59Gn369MHWrVv5vu/SpQu2\nbNmCadOmobCw8F/rELO1tW33Jb1Lly4YMGCARGSfZniFS27duoVNmzZR3zs4OEBXVxf9+vUTOkr+\n5s2bSEtLo9UhVlVVheTkZJiYmIikAdS5c2fq+IKDg+Hh4YHk5GQ0NjaisrISLBYL/fr1E0nn1NTU\nlIoO/fXXX3Hw4EEoKipCXV0dDg4OHQqBl5eX49WrV0Ltk/feeebMGfj4+ABoLnghrNNEVIYPHw6g\n+R7IO69lZWVtOhCEgdcnjx49Cn9/f0hJSUFFRUXkolRAc8CCsbEx0tPTsWLFClRXV2P69OkiR4p9\n2tadO3fi+vXr0NTUhJaWlliBKAMHDsTAgQPx+PFj/PbbbwCAJUuWCG0nOzsbN27cAJvNRm5uLt6+\nfUuti4qKQlRUFIBmh2R74/TQ0NB235s/LeIXFBSEmpqadjUvExIScP369TbX9+7dG6mpqQCaqyVX\nVFS0q3/++vXrdu3x7h+EEOjq6mLcuHHYvn073rx5w+d84/HhwwfcvHmzzYIz2traWLp0KZSUlKCp\nqQklJSVs2bKFljH7N+MQk5WV/WpCzP9JunbtilOnTmHhwoW0Cpqy2WxERkbil19+wZ07d1q94Q0e\nPBghISFoamqiomw+hRCCjRs3IjIyst39rV27ts0IsaamJnC5XCQmJmLixIk4evQoDh06xLeeEEKb\nKOe/AS6XK3B05N27d9sV4xUGWVlZcLlcsNlsKr2Ex/r162nZByGEOraQkBBcu3at1e3YbHaHYbJS\nUlLUdcMLry0sLBTpIbdv3z54enqiuLgYEydORGJiIiwtLREQEEBtc+fOHb6KLWPGjOHr91wuFw0N\nDZg4cSJsbW1bpFu29Xd1d3fH3bt3qeXLly9j4sSJfNv7+PjA398fd+7cEVoEWkIzdnZ2VDVEcamp\nqcGSJUsQHx+P8ePHtzkrJyxnz57Fli1bvtro6IiICKxYsQLXr1+nIit5LF++HNeuXcOECRNw584d\noYtz+Pn54cGDB7h37x7Gjx+PYcOGYezYsZgwYQKCgoL4ZvP/7Zw+fRoPHz7kuy98bRQXF2Pu3LlI\nSUlpt5KVqHA4HKxcuRJlZWXYuHEj7fYlNEcxODs7i5SSLKFteOlCn45h7t27h/T0dNy5c+eLyJd8\nSnJyMhwdHXHx4kWho3o+ZfLkyTA2NoajoyM8PT2xc+dOGBkZ4c6dO7Rl1FRXV2Px4sXw9vbuMD0z\nNjZW6AwEHrzoH6A5PfSffj/9uN/8+eefOHDgAG22ecfG5XLxv//9j5YJl4/H9cHBwXzjAnHgjet5\nzxo62vrxuT1x4gR8fX2F+v3QoUNx/fp1yMnJYc+ePdi8eXOr223evLndfuPn59euHM2CBQtw9uxZ\nvu9CQ0NbyLR8zNSpU9tdf/36dUyfPp1ajomJaTe10dbWtl17jx49gqOjI9hsNubMmYPff/8dMjIy\n2LBhQ6s+ATMzM1y/fr3Nyc20tDRMnDgRBQUFmD9/Ps6cOUPbs+mbcYg9ePBA7Jnwb5F3797BxcUF\njo6OtEbEHThwAKmpqbh27Vq7le4aGxvh4uKCly9ftrqewWBg2bJl2L59e7v7a2+mZtOmTYiMjISu\nri7OnTvXwjm3bds21NXVwd/fX6Lr8v+Tk5MDFxeXDiPzgGbH5oMHD2jbd1RUFLZu3YpDhw7RFvXy\nMa9evYKLiwvq6+sxc+ZMrFixotXtNm7c2OFxTZw4kXLUeXh44Pbt29DR0cGhQ4cEKiP8MYaGhoiM\njMT27dvh6uoKXV1dJCcn882u9+3bl69Nn0ZXxsXF4ddff8Xy5csp7bSPSUpKgouLSwvNlkWLFvG9\n8JmYmAAAMjIyKN09KysrnDp1qt2ITgnt09jYSIsWQXR0NHbt2oXly5fjypUrbU4oiAKHw6EGlps3\nb0Zubq7I2ik7d+5EWVkZTpw4wZdC3KVLF/j7++Pq1av4448/sGbNGoFtcrlcNDY2QkZGpkVUibS0\nNJhMJhoaGkRKc+RwOGCz2ZCTk6N0nqSlpdHQ0CDy3y07Oxuurq4YOXIktLW1ERISgl27diEmJgZF\nRUViad48fPgQ7u7uePv2LY4cOYLy8vI2B8eCwuFwsH79egQHB4tdufpjrly5Am9vb1RVVcHT0xMF\nBQX45ZdfRLYXFhaGI0eOYNOmTThz5gwiIiLw/fffw8vLS+TKoI2NjXB1dYW6ujo8PT2xadMmNDU1\n8b2cCouHhwcUFBTaXL958+b/dJVeSWXJz8OVK1eo/xcWFmL9+vWYOXMmxo4dCzk5OaF0gWtqauDq\n6oobN27QVikQAAICAvDnn3+iurqalufi7du34eXlhdLSUqxevRp2dnbo1KkTLdXFeSgoKGDXrl0C\nOboGDhzI93cQhgsXLlCOki1btlCph/8U4eHhlCbT0qVLRXbstcbp06cREBAAKSkpeHp6iq3RCDRX\nIHR1dUVNTQ0mTJiAZcuW0dDS5onqu3fvQkNDA7t27YKuri4iIiLg5eUlsI2hQ4fC3d2dWo6NjcWW\nLVsANOscz549GxwOB66urpScT3skJSVh+vTpkJKSQlZWFvX9tm3bWu0nmZmZcHV1RXV1Nd/3u3bt\n4tMI+xRHR0f8+OOPHbaHx6FDhxAREdFuSnZhYSHk5eWxa9eudn0DPDQ0NFp9Pq5fvx4vXrxAWVkZ\n9Xy+dOkSEhMTAQBOTk6t9gElJSWoq6u36uS6fv06zp07h3379kFBQQF6enq0Bsp8M086Xpjo18yD\nBw+5ijd3AAAgAElEQVRw//59uLm5CdSRBKG+vh7Pnz/H7NmzaXEI1tTU4OTJk6iqqoKDgwN1cd64\ncQOPHj1q9TcGBgbo3r17mzatra1bDHDDw8ORnp6OBQsWtDmgys3NxYkTJ6CoqIhJkyZBW1sbI0aM\naBEmnp6eDg0NDbHCdj8lKSkJAQEB+P77779J7S8lJSU4ODigpqamw21NTU3Fvn7q6upw8uRJfPjw\nAdLS0vDw8MDYsWOFdiq1h5+fH9LT08FkMrF582YwGAwMHTq0zba7uLh0GBHS1NRERZgNHz4cVlZW\nUFFRgb29vcDpVTU1NThx4gSuXbuGtLQ0REdHw9DQENra2pCWlsakSZOobY2Njdtsb2hoKB4+fIhJ\nkybB2toa+vr6yMrKwokTJyjnAJPJbNXxbW1t3UKj4tGjR7h16xZsbGwoXQaJZtiXo66uDufOnUNR\nURGamppgbm4Oe3t7JCQkIC0tjZZ9BAQEUE4BoHlQnp6eLrK9Fy9eQE1NrUXYfqdOnWBrawtfX1+B\nnO4f07t3b6xcubLNlxxjY2OsXr1aqJRlQgjOnz8PAC3EfnV1dSmRfTk5OaE1WqqrqxEWFoZp06ZB\nRkYGoaGhsLOzw/Xr19sM2xeU/Px8xMTE4Pvvv0d8fDyf3ocoZGdnIyAgAHJycrRo5PAICgpCUlIS\nRo4ciZEjRyIkJEToVKKPuXnzJmJjY/Hdd99h9OjRYLFY8PPzQ1hYmEDPrNZ48+YNLly4AAUFBYwc\nOZI2ofeOohTF0WiVIKEtxo4dixs3biApKQlcLhc//fQTpk6dKpSO77179xAbGwtCCFRUVGh1LAHN\nL+rv3r3DqlWr8OzZM7BYLKHvr8XFxTh37hxqa2vR0NAAS0tLWFpaYtasWWJfW6mpqZRGq5qaGlxd\nXcFisTB16lSBxqaampoCiZDzeP/+Pc6fPw8OhwMDAwPqOTxz5kxaNRfborKyEufPn0dZWRkUFBSo\n/Ts5OYn9HpOZmYkLFy4AaJ5w5Ul5TJ8+vd33v464dOkSMjIywOFwsGLFChBCMGLECKHO+6fwCsAB\nzc4sc3NzKCsrY/LkydDS0kKXLl2EmnDr06cPRo0ahfPnzyMvLw+EEOrcOjo6ws7OjtKN4+msCcKF\nCxfQuXPnDvtJXl4eiouLOxxvXLt2DTIyMtS5s7GxaVc77vbt23wpl4MGDeLT2goNDUVcXBzk5OQw\nZ84cKu1w0qRJmDp1apsTbhwOB+fPn6dSNu/fv99im0uXLkFbWxt2dnatRpjZ2toKdc2EhIQgISGB\n0p/7HPqW34xD7O7du+jXr5/Y1RQ/J9HR0bh37x7Cw8PBYrG+dHNawBucx8TEYMaMGXx6Hu/fv0ds\nbGyL38jJyWH37t1CRQLFxsbi9OnTeP36NebPn9/mdrW1tYiPj4erq2urTgSeMKG2tjZ69uyJmpoa\nxMbGoqGhgdJYEJWUlBQcP34c4eHhlJ2mpibExsaia9euIs1gE0IQGxsLBoNBDWpiY2NRWloKVVVV\nWFhY0BZWzWKxMGDAAPTu3ZvWF6PWKCgowNOnT/HkyRMUFhZi2LBhVH69uGRmZlIi248fP0ZWVhZM\nTEzg7e3d4TU0bdq0Vr//uJ8kJiZSjt61a9cKnVKVn5+PJ0+eICYmhhLCtLKywrt37/Du3TsMHz68\nw1QdLpeL2NhYREZGorGxkYqmfPPmDR48eEANaAGgf//+2Lp1q0Cz8gUFBSgpKcHu3bsl4sdfmNLS\nUrx48QLXr1/Hhw8fYGNjw1dRR1wqKyuRmpqKmzdvYvjw4dTMWke6jB1hbGwMZWXlNteLMgnzsa5L\na/Tt21ekytDHjh2Dra1ti3RnQ0NDeHp6wtHREdXV1UK9sOXn5yMjIwNmZmZQV1cHk8nEgAEDICMj\nA2NjY1RVVeHFixfo27ev0DORWVlZKC0thbW1NTZu3IigoCCkp6fj+fPn6N27d7tRSa3x7t07PHz4\nEFevXsXhw4fBZDJbVJYVlpqaGqSkpODWrVvo3bs3paMlauGDhoYGpKSk4O7du1BRUYGbmxuA5jSN\nhoYGPHnyROS2pqenY9u2bQgPD8fw4cPx9OlTakKPEIKUlJTPErH8XyItLQ1NTU3o37//f1Ki5J8k\nJiYGd+7cQUxMDHR0dHD48GGBIz6bmpqQmpqKu3fvIiIiAoqKijh8+DDq6+tpm3xJSUkBh8PBuHHj\n4OnpiSlTpgh1f339+jVKS0uRn5+Pq1evoqqqCs7Ozi20+oSlsbERKSkpqK+vR3x8PCVFsHr1asya\nNUss222RkpKCyspKZGZmIiQkBGw2G3PnzhUrglYY3rx5g+LiYpSVleHatWsoKiqCo6OjUFFQrcHh\ncJCSkkI9B3jncunSpe2+u3XEhw8f8O7dOwDNGnkvX76EiYkJDh8+LPJYtb6+HikpKWhsbMTTp0+p\ntm7atKmFiPyAAQMEnhzOycmhxvnXr1+nNEr//PNPvu2YTKbA0jNVVVVISUlBfn4+LCws4OLi0u72\nXbp0oZ6VrVFXV0f1QT09PYHlAeLj4/mKeq1bt47vvUlLSwvS0tJQVlbG+vXrBU7R5nK5iIiIoKK9\nWkNDQwNz584VSZ6mNZ4+fQpVVVWsXr2aFnutQgj5Jj4AiK+vL6GT1atXkwkTJtBmz9PTkwwbNozU\n1dXRYo/NZpOXL18SAwMDcu7cObFsNTU1kYCAANKlSxeSlJRES/vaYtSoUQQAGT58OKmqqiIcDkck\nO0lJSURTU5MEBgYSQghJSUkhOjo6BABZuHChWG0MDAwkWlpa5OXLl4QQQjgcDsnLyyOmpqZk3759\nItlks9nEysqKuLu7Ew6HQxoaGoitrS0BQCwtLUljY6PIduPi4oiOjg65fPkyIYSQ5ORk0rlzZ3L+\n/HmRbApDSEgI0dLSIvHx8bTb3rlzJ5GRkSEyMjLk9u3btNhMS0sjBgYGREZGhixdulQsWxcvXiRd\nu3al+okoNDQ0EBsbG7Jjxw6+7z08PIi9vb3I1weNfPH7+9fyEfU5ExQURLp27UqePXtGamtrSX19\nPbWOjudMVFQUUVRUJDdv3uS7jzg6OpJ169aJbLehoYE0NDRQy3PmzCELFixoc70oBAUFERUVFRIX\nFyeyDS6XS2xsbMjWrVup70aNGkU2bdpELTs4OBBXV1eh7B45coSYm5uTt2/fEjabTZqamkhdXR3h\ncrmkoaGB+Pj4kH79+pH3798L3eYVK1aQ2bNnk9raWup5EBgYSLp27UoSEhKEtrd161bi6OhIamtr\nCZvNJm5ubsTGxoZwuVyhbfGIj48nOjo65OLFi3x/52nTppHly5cLbS8nJ4f06dOHHDt2rEW/OX/+\nPOncuTNJTk4Wqa03b94kcnJyJDo6mhDS/Fysra0ltbW1ZPPmzWTs2LEi2f2UyZMnk5UrV9Jii4e/\nvz/R1dUl6enptNmsqakhQ4YMITt37qTNppOTE1m7di11DdDMF7+/f00fFotFTp48SWpra0ldXZ1Q\n44Di4mIyePBgsm/fPuoa4HA4tL7TTJgwgWzYsIHU19cTLpcr9LPm+++/JywWiwwYMIDk5OSQ2tpa\nsZ8lhBCSl5dHBg4cSFgsFpk1axZ1/E1NTWLbbotx48YRFotFbGxsSHl5OamtrRV5PC8KixcvJiwW\ni/To0YO8fPmStnNZU1NDrKysCIvFIhMmTKDOpbjH9scffxAWi0VYLBYJCgqi+rg495TMzEzSq1cv\nwmKxyE8//UTb333dunWExWIRTU1NEhUV1WL8JgqPHz8mKioq5Nq1a7T0k7S0NGJoaEj8/f2F+rs3\nNDRQ56m1c/XxemHuP1wul9TX1/PZbu1D5zUi5FhUpHvyNxMh9l/k2rVr8PPzg4+Pj9jVqfbv34+M\njAwEBweLFf4qDC9fvoSDgwP27duHoUOH/iP7FJWHDx/C1dW11SqKovDixQv8+uuvbWqvCUNQUBAC\nAgLg7+//RdI3Ro4ciatXrwpUVlpYfvjhBypdq2/fvrTY1NfXx8WLF9HU1ARtbW2xbNna2iI4OJgq\nIS0K0tLSOHDgQIsKTfPmzYOTkxNtFeIkfBn++usvpKSkwM/PD7169RK6MpggcLlc1NXVQVpamk9b\nYfv27bhz5w7+97//wcvLS+jI5I6inr6mQiZeXl58GpI7duzgS3vevn17u9FurcFms9HY2AgWi0Xp\nnfGiM3mFfOrr63nOUqHgaVvx+oOsrCyYTCbq6uqEstfU1IT169dDRUUF27Zto7V/EUJQV1cHJpMp\n9t/63r17OH78ONzc3GBpaUlr3zl16hSePXuGkJAQKs2CyWTynQs6dfr+q/C0/b7GDId/G5cvX4aZ\nmZnQ13NUVBT27t0LFxcXWFtb0/68efnyJTZs2IBJkybB1tZWZP283377DT/++CMUFRWhra1Nmw6f\nmpoa9u/fj+rqaujo6HyW5+2nbN26FS4uLlBTU4OSktI/Hj35yy+/YMqUKWCxWDA0NKTtmOXk5ODl\n5YXy8nJoaWnRZtfJyYm6Tw8aNIgWu9ra2vDx8UFdXR10dXVpa+tPP/0EGxsbMJlMkSq7toaJiQkC\nAgIwaNAgWgTfu3btihMnTqBPnz5CPVc/1/iOwWD847qa/8RY9JtyiF2+fBmKiop8FRDoJj09HWfP\nnsWCBQtoq3oiKvn5+UhJScGAAQNE1gaoqqrCyZMnUV1djTFjxnxWx1RWVhZOnjwJGxsbSElJ4d69\ne3j8+DHKy8uFtvX48WPcvHkTK1euRP/+/RETEwNfX19UVVVhxowZ0NPTw969e7FgwQJaRMRLSkrw\n9OlTse0AzfnUvFDzvLw8VFRUiGUvLy8P6enp+O6776ChoYEnT57A19cXNTU1CAwMpLWcfWtoaGh8\ntmIG+vr60NfXp9WmvLw8bQKnmpqaYldslJKSgpmZWYvvv/T9RUJLfvvtNz6NBUHQ0dGBgoLCZ6tw\nGB0djcjISKxatapFKvfgwYMRFhaG6OhosYTFeUyYMOGrrCbHYDBaaKR8+iwT5Zr/7rvvMH/+/DYH\nwWZmZli0aJFQJeKrq6sRGBgILS0tWFpa8q3r1asXFi1ahJs3b4LNZneoX5WVlYWLFy9CTk4OlpaW\ntD6/Y2NjERoaisWLF7fQPJ08eTIyMjJw6tQpzJo1S6D0TlVVVQwYMABjxoyh/Xmhra0NCwuLNnVn\nRo4cCS6XC29vb8yaNUtyb5Xw1TN+/HiRfqesrAxTU1OMGTMGXbp0oblVQFlZGW7duoVff/2VKt4D\nNFcbfffuHf7++2/MnDmzwxfU9rSNxEFOTg7W1tafxXZbfOkJfTMzs1bHkOLCZDIxYsQI2u0aGRnB\nyMiIVpudOnVqt9qhqPTp04f2VHs1NTWxNNI+RVFREfb29rTZk9A635RIwPXr1ymB7M/B69evcenS\nJXh5eVFicYLy/PlzcDgcDBkypM3Zg5ycHDx58oSqENYeiYmJqKqqgqWlZZue2A8fPiAyMrJNIb78\n/Hzcv38fT548gbm5OZydnTvcL5vNRkxMjFDCgUBzjnt4eDiePn2KSZMmtVpBTxhiY2MRHByMJUuW\ngM1m4+HDh0hMTERTUxOcnJzQtWtXHDlyBFVVVULbTklJQUFBAWxsbKCgoIDU1FQkJCRAWloaw4YN\nQ11dnVjaPJGRkXj48CE2btwocjWttkhISMDDhw+RlJSEpqYmhISE4NatW7TuA2jWaXj8+DFCQ0OR\nkJBAu30JEr5GvL29hY7GnTp1KubOnfuZWtRcdTUrKws7d+5sVeOhS5cu6NatG+Lj40WafPiYOXPm\nYMaMGWLZ+JawtLTEmjVr2nR4DR06FK6urgIX4ACaJ6G8vb3RpUuXFhWgTE1N4erqioCAAERGRnZo\nKz8/H1euXMHs2bNpHxCnpaUhOTkZW7ZsaeEEnjt3LrS1tbFnz54Wla/awsLCAhs3bmzTGaauro7+\n/fsjIyMDBQUFQrV1woQJ7VZSdXR0hJOTEy5evIiioiKhbEuQ8C1hZmaGbdu2fRZn2IcPH/D+/XtY\nWFi0iLZduHAhOnXqhAMHDgj0DiNBggQJ3xLflENMSkoKDAYDbDZbpBSGtiCEgM1m49ixY9i0aZNI\nNtavX4+6ujrs37+fb+aEZ5vNZuPChQtYvny5QJWrPD098ebNG5w6darNAebdu3fx448/Ij8/v8U6\nDoeDsLAwuLi4YOvWrS1EB1uDy+WiqqoKy5cvx8WLFzvc/mPOnDmD06dP4/bt23xpfUwmE1wuV6jo\nBQ6HA0IImEwm2Gw2PD09kZ2djVOnTkFdXV2odrXGgQMHEBkZiQsXLkBPTw+HDx+Gp6cnlJSU4OPj\ng4KCArGEP6WkpKj0G6A5ukFKSgpsNlus0tW8c/H27VscPXoUmpqanyXdjsvloqysDEuWLIGDgwNf\nOWIJEiT8s8ydOxf79+/nu6d8zOzZs/Hrr7/C2dkZjx8//odbJ+FzMnjwYNy5c0esAjJtMWPGDBw7\ndkyo6DdxsLOzw4kTJ+Du7o4rV67Qbt/c3BxhYWG0VqOWIOG/hL+/P06ePInbt293GL0qQYIECf8m\nvimH2Pr162Fubo5Zs2bhw4cPtNmtr6/HsmXLqLLudJKSkoJx48bB1tYWR44cod1+W/zxxx949OgR\nLl++LHCUUnR0NMaPH4/09HRa2tC/f3+Ehobi4sWLOH78uMC/c3Nzw/v377FlyxYqv3vVqlW0tOlj\nysvLMX/+fBgYGIhU9awtfvrpJ74KJXPnzsXixYsxduxYPH/+XCSbmZmZmDJlCuzs7NCtWzds3boV\nJ0+exKhRo+hqNkVERASmTJlCm56aBAkSREdWVhadOnVq0/ktKysLU1NTnD59+rOlqYjD8OHDERgY\nSHsKBQAEBgZi3bp1X1XEgpqaGnx8fDBu3LhW1ysqKuLgwYMCTVJJS0tDUVGxVWfonDlzMGfOHEye\nPFmkKN6O+tWECRNw5MgRoaLj2kNGRgZdu3bF3r174eDgQIvNj2nvXEmQIKFjnJ2d4eHhAWVlZYEq\nXUuQIEHCv4Vv6o7Xs2dPMBgMPHv2DPX19bTYTEtLw44dO6Cvr4/u3bsjNzeXFrtAc+pcaGgorK2t\n8ffffyM7O7uFsPanFBYW4tSpU+jbty8GDx7cYn1paSlOnTqFioqKVgfBFRUV8PX1RV1dHezt7QXO\nfb916xb8/PyELotOCIGvry8AYMGCBXyDa2VlZVhaWsLLy0ugFMy8vDz4+vpCVlYWVlZW0NPTQ1xc\nHBYsWABjY2OkpqZS2w4aNAhz587FsWPH4OzsLPSs8MuXL7Fv3z707NkTtra2ePv2rVC//5Q3b97A\n19cXOTk5sLe3h6KiIrZu3QobGxvY2NhAWloa0dHRIqc0KSoqws7ODra2tggLC0NqairMzc3F1rfi\nUV1djVOnTqGoqAgpKSlITEzEzz//LLIDT4IECc3Y2NjQVjCiLVRVVdt0wHxpdHR0oKOjQ7vdixcv\nws/PD6mpqdDU1MTMmTPFKn5BFywWC7a2tm2ul5WVpUUDx8TEBEwmE+np6ejUqZPY9j7F0NCQ9vMp\nJyfX7rn5GpgyZQotkeg87ty5g5ycHCxevJg256IECa0h7rOmV69erabl8xg8eDBYLJbEWSZBgoR/\nHd9UhBjQrJcyaNAgxMfH0+K8Kisrw7Nnz9C9e3fY2NhgwIABNLSyucpgREQESkpK8Ntvv7UQrW2L\n4uJi7N27F717924hupmbm4uwsDA8evQIJ06caFVPrbGxEXFxcbCwsBBIM6ypqQlPnz7Fw4cP+RxO\nwvDy5Ut0794dc+fOBYPBQFxcHLhcLiwsLMBgMDBw4EBIS0vj2bNn7aZOFhQUwNvbG6amphg7diwU\nFBRgbW2Nzp07t9j2u+++w5w5c3Dq1CkkJSUJ3eaSkhIkJydj7ty5rToehSUrKwuenp7Q1NRE9+7d\nkZOTg+3bt8PKykqsF4DExESkp6ejc+fO+O2332gXfwSazzuvX0VHRyMtLQ3y8vJYvHgxbeL0EiT8\nV5k8eXK7+kf/dsrLy5GQkCCQVIAw+Pj44Pbt28jOzoarqyvS0tJotf8tYGxsjD179gg8vpDQMT//\n/DOtRWoCAwPx+PFjTJ06FUpKSrTZ/RwYGxtDV1f3SzdDgoh87mfNqFGjsHLlyn+8wpwECRIkfG6+\nOYeYg4MD9u/fD1dXV5HFxAkhlE7VsGHDcPnyZRw8eBBKSkrw8vISyRZPSJ/D4YDD4WDjxo2oqanB\noUOHICsrCwaDIZbeE5fLxc2bN7Fx40bs37+/zZQDLS0t+Pv7Y8KECQK1vbKyEsuWLYOWlhY8PDyE\nLifMYDCwd+9ezJs3j/pu7dq1aGxsxP79+yEjI4Ndu3ZBSUkJK1eubLM0OpfLBZfLBZPJpM5T9+7d\ncfny5XYdSh9v3xG8vxWDwYCdnR0uX74MQ0NDcLlcSrMM+D+tOl4fERQpKSns27ePVoHtbdu2tZtq\nK2pbeXC5XISFhWHp0qXYunUrQkND4ebmRmm/8fZBFzw9ubY+4misSZAg4esjMjISkyZNwuvXr790\nUyRI+CKEhoZi5syZtGYgfA68vLywdOnSL90MCRIkSJAg4R/lm3OI0UFNTQ2WLFmCCxcuiG0rMTER\no0ePxqxZsyAvLw87OzvY2dlh2rRpWLRoEVJTUzF+/HhMnjwZy5YtE3k/np6eSElJwd9//91qxJQo\nREZG4vvvv8e6detQVFSEGzdu4MaNG3yllulizpw5OHz4cJszS5cuXcKuXbtw9uxZWFlZCWRTV1cX\ngYGBGDNmjEDbl5aWYt68eTAyMoKbmxv1/YEDB3D//n1cuXIFPXr0wC+//IJx48Zh4sSJQumpfeqY\no0Pw3sPDAytWrGhz/ZYtW9CzZ0/MnTsXxcXFQtvfu3cvoqKiKK25/fv3IywsDP7+/tixYwe6du0K\nDw8PcQ6BD39/f+oaae1z7Ngx2vYlQYKELw+bzUZeXh5WrlxJi5j6y5cvMXXqVEyfPh1z5syhoYUS\nJHxempqakJ2djSVLluDOnTtfujlt0qlTJ7BYrC/dDAkSJEiQIOEf5ZtMBFdRUcH//vc/kQWEpaWl\nMXToUKSkpLRYZ2xsjE2bNuHOnTtgs9kdan1UVlYiMjISGzduRK9evagoHRsbGxgZGeHp06eIiorC\nihUrUFlZ2WEVMC0tLbi6ulJl0IuLi+Hr6wsulws7OzsYGxvj8OHDMDIygpOTE+Li4kQ6Bzdv3kRM\nTAxGjx4NGxsbxMTEID8/H5aWlp+l6pS+vj709fXbXG9gYAB7e3tYW1tDXl5eIJvy8vIYNmyYwG1o\nbGxEbGwsLC0t+XQWMjIyUFhYiBEjRgBo1utis9koLy9vUXpaVAwMDLB161b06NFDqN8lJiZCTU0N\nv/zyC+Tl5REQEICioiKsWLECCgoK0NbWRm1tLVgslkhh7H369EH37t0xfPhwAM3noqioCIMHD8a6\ndetgaWkpdoWzzMxM+Pn5AWiONhs9ejS17uHDh7h//z4A4P/9v/8HY2NjsfYlQYKErw82m42IiAiB\nhOTb4/Hjx4iIiIChoSHKy8vRs2dP/O9//wMAxMXFQUFBASNHjqSjyRIk0Ep9fT3u37+PmTNnfumm\nSJAgQYIECRI+4pt1iLUXNdMRLBaLit6Kjo5GZGQkevXqhW7dusHY2Biurq6ws7ODnJxchw4xFRUV\n2NnZQV1dHYMGDeIbjOfk5CA9PR1WVlbQ0NAQqG1aWlpYs2YNtVxXV4enT59iyZIlGD16NN68eYP9\n+/dj+/bt6Natm8gOsczMTBBCsG7dOpF+TzfDhg1r17mVm5uLxMREDB48GNra2p+9PSYmJnB1daXN\nnoGBATZv3iz07y5cuAB9fX24u7sDaI6k09bWppaB5gIDopbI5qXWNjY2Ij4+HgoKCpQzli4qKioQ\nFRUFoLnippOTE+Lj4ynncVlZGeLj4zFnzhw+Z5kECRL+WXJycsBgMGBgYECLvffv3yMrK4sWW0Cz\nQHl4eDjCw8MxZswYDB8+HPv27QMAODo6oqKi4rM6xEpKSvDu3TsAzZM8gj7XJXw7ZGZmQk5OTmwt\nrcbGRmRkZEBBQQEGBgbIy8tDjx49UFtbi+zsbLELFjAYDPTq1Yu2jIGPqa2tRUZGBrp16wYVFRXa\n7Uv49njz5g3k5eXRtWvXz2I/JycHZWVlfN+pqamhW7dutO2jsrISmZmZ1LKSkpLQk9TtIbluvj1y\nc3PR0NDwWapgS/i2+E+mTPJgMBh48uQJpk2bhl9//RWzZ8/mWycIZmZmCA0NbeGQIIQgICAABw8e\nxOXLl6kIHGHR19fHxYsXMXr0aBBCQAihJRXPxcUFHh4elE3g/46Z968omlSfi6tXr8LNzQ1Hjx6F\nvb39l25Oq/DO18fn9FuAEILS0lL8/PPP6NatG5+zjQ7Mzc0RFhaGsLAwzJs3DwkJCRgzZgzs7e3R\n0NCAAwcOQFZWVuz98M77t3b+BeXT4/s3HqOEL4unpyf27NlDm71Dhw7xpad/69y6dYtK7759+/aX\nbo6Ez8CWLVtw+PBhse3k5+djzpw5MDU1xYYNG6ClpYUzZ84gPT0dv//+u9j2WSwWDh8+/FnShtPS\n0jBx4kRER0fTblvCt4mrq+tnlbTYtWtXCwmNHTt20LqPmJgYPvt0BwSkp6dLrptvjCNHjmDTpk1f\nuhkSvgK+yQgxuli2bBk0NTWxfft2vu9lZGRw6NAhsWZ/161bB1lZWfz111+0aTLcuHEDJ0+exMGD\nBzFkyBBaBuTV1dVYtWoVevTogZ9//hny8vLw8fHB5cuXsX79euzatYuGlv+38PPzQ0REBMLCwmir\nWvo5CQsLw969e7Ft2zaRHbeCcvr0aYSHhyM0NBRSUlJ4+PAhfHx8cOPGDZibm4tl28PDA2FhYTAw\nMMCff/75r4veCAwMxF9//UUtz5gxAy4uLl+wRRL+LeTm5sLNzQ13794Fk8lEU1MTPDw8oKWlJRdK\nVp8AACAASURBVJK9yspKuLm5QVtbGy4uLjh48CA8PDxQUlKCHTt2YOPGjULZI4RQxT5+//13WiaF\nRMHKygqnTp0CAERHR+PSpUvQ1NSEh4cHunTpIpCNqqoquLm5ITs7G0OGDMGGDRsE3n9CQgI1YTF7\n9mzMmjVL6GMAgB07duDZs2cwMjKCh4cHFBQURLLzb+LNmzdwc3NDWFgYFBQUUFdXBw8PD5ErQ3K5\nXFRUVEBaWhoyMjJgMBhQUVEBm81GTU2N2O1tbGyEp6cn0tPTYW5uTqvjmcPhoKysDE1NTbTZlNCS\ngwcPIiwsjCpqpaOjI7bN2NhYeHp68n0nIyMDDw8P9O7dW2S7q1evxv379zF16tRW169fvx5Dhw4V\n2f6CBQta6AFnZma22J+amho8PDygp6cn9D5MTU2p+zcAfPjwoYV9BoMBDw8PkTIlunXrhsOHD+PW\nrVs4duwYunfvDg8PD7FlaHj9RNhnDY+TJ0/i+vXr6NSpEzw8PGiNinN3d0dCQgJMTEzg4eFBy+R2\na+Tn58PNzQ1FRUWws7PDr7/+Sovd2bNn48GDB1Q/mDt3bpt9XBAuXbqEs2fPAmieXBFV2qktQkND\nERwcDA8PD2hqatJq+2vG29sb0dHR0NPTg4eHB9TU1Gjfx3/aIdajR49Wb3pSUlJiv5wnJSVhwIAB\nfBfD8OHDoaioKHLJ4vz8fCQmJmLgwIHQ0dHBwIEDMX/+fPj7+2Py5MkiXXgcDgfPnz9H3759qXMx\naNAglJSUoKioSKR2fq0oKirCxcUFFhYWAJqLK/j5+UFTU1NgIf+24An1d+vWDffv30d2djZsbGzE\nfnGbNWvWZw+9LioqQmxsLLy9vak0EXl5eSxbtkzs6wAAsrKycPr0aWrZ1tYWNjY2+Pvvv8HhcKhl\naWnxbkd9+/aloqYOHjwIAPjuu+861C2qrKyEn58fSktL0b9/f0yfPl2o/TY0NOD06dP48OFDq+uZ\nTCbmzZsndhqavr4+7OzsqOXi4uIW0XydO3fGTz/9hE6dOom1LwlfN3l5eQgKCgKHw8GgQYNgaWkp\nlr3q6mrcunULeXl5AIC7d+8K5aj5lMbGRty/fx+LFi1C79694ePjg1GjRuHMmTNISkoSyaaWlhZ6\n9uzZ5r16ypQpIr0kCUO3bt2oFJ7a2lo0NDRAWloa586dg4yMDN+2dnZ2MDU1bWFDSkoKOjo64HA4\nqKysxIEDBwAAI0eO7PAZzmKxKC3OlJQU6rdTp05tV6PzUzQ1NaGvrw8pKSkcP34cUlJSGDBgAGxs\nbAS28W+jvLwc165dQ1VVFYDmiaItW7aIZOvVq1e4desWnJ2d0bdvX74Kq6NGjUJSUhJOnjyJqVOn\nQl1dXaR9cDgcPHjwAE+fPkVtba1INlrj+fPnCAgIkDjD/gHU1dWhr68PaWlpnD17FjIyMjAxMYGj\no6PINj++R/BgMBi4evUq7t69y/f9sGHDMGTIEIHsjhgxArW1tS3eC4qKihAUFAQNDQ3ExMRAU1MT\nU6dOFVgLmMen0h/37t1DQkICQkJCqO969eqFGTNmiDxW1NHR4XN23L17t9VJRT09Pfzwww9CO/jU\n1NQwadIk6jnKZDJx4sQJqlK7hoYGnJ2dhT43H/cT3rOmV69eGDt2rMDt4t3vg4KCICcnB0NDQ7E1\nPYHm57K+vj64XC7++usvMBgMfPfdd7RLF0hLS6Nr165gsVgoKCignn329vZi6RybmpqCwWBQ9+jE\nxES8e/cOTCYTzs7OQjsflZWVqevv7t27iIqKgra2NpydnVuMEQDgxYsXePXqFZydnQUKnlFQUIC8\nvDz8/f2p60BZWRlTp079V6fpamhoQF9fH7KysvDz8wOTyUT//v0xatQo2vbxn3aIZWRk4MOHD7Cy\nshJ5FvBTKioqkJCQAD09vRY5ySNGjKCE2+mA5xhbvXo18vPzhf59UVERnj9/jt69e7d4kXBwcKCr\nmV8NSkpKfA+/mpoaHDlyBMuWLRN5pp2HkZERtm7dKm4TW8BL462ursaLFy/QuXNn9OzZkzb7aWlp\nKC4uxogRI/icKJ06dRKrKiohBC9evEBFRQWysrLw4MEDAMC8efMwf/58AM0VJ4cOHYoFCxaIdQw8\nZsyYgRkzZuDt27dYtWoVSkpKUFVVRRVG6N27N3R0dFBfX48XL16gvr4eQPN1sHv3buTm5mL27NlC\nO8R4TuW0tLQW68rKyvDq1Suoqamhf//+UFVVxcCBA0U6PktLSz7Hx8WLF7Fr1y4kJCSAzWYDALp3\n7w5dXV0MHz78s2jL/NtJTEwE8H8DSEHIzc1FSUlJm+u7dOkicqRVaxQWFuLevXtYvXo1GhsbqeIX\nolJSUoLs7Gz07NkTdXV1YDAYMDIywps3b6CkpCT0TFx5eTnS0tJgYGDQYgazS5cuKC0txcuXL2Fk\nZCSw45bBYHQ4I7x8+XKh2iksb9++RXl5ObVsamoKU1NT5OfnY+HChZS2GI8jR4606hBTUFCg9CnD\nw8OxcuVKAM0ONmlpaTAYDPTo0aPVc2NiYkK9CBw5cgQ+Pj4Amh3hffv2RadOndCjR48OJ2IWL14M\noNmptnDhQlRVVcHR0ZH6WxsYGEBVVVWg8/KlqKqqQlZWFnr06CF2hFtRURHev3+PPn36IDU1FbKy\nstDX10dqair69esn9ItGTEwMDh48iPDwcHTv3p3PITZ79mwQQrB69WpYWlqK5BCrrKxEamoqrY4w\noHny6uzZs5Qun4TPyw8//IAffvgBeXl51D1kxIgR1Eu4rq6u0JHu/fv3p+4ReXl5KCoqQmNjIxYu\nXIiEhARqOyMjI8jJyQnsEAOaJWI+HVdkZ2cjKysLFy9eREhICGxtbTF+/HiBnT4NDQ3IzMxs4YC9\nevUqXrx4gd69eyMzMxNaWlpwcnISKt24srIS2dnZba4vKiqi7tEFBQWoqamBkZERHjx4gD59+gjt\nEKutrUVmZialiZyamkrdX4HmIAw9PT0MGDBAqPsrr5/wnjVv377FiBEjKD23jvqJs7MznJ2dUV1d\njYULF+LVq1cYOHAgpWOoo6Mj8njxl19+AdAcvbxw4UI0NDRg4sSJ1Ljb0NCQluJkmpqa1ATw1atX\nKU1mBoMBDocDGRkZqk8LS//+/bFv3z5kZmZix44dcHd3h6ysLJSVlTFgwAAoKioKrDHm4OBAvT8v\nW7YMjx49Qo8ePai2aWpqQlVVFZmZmeByuTh37hzu3LmDsWPHCuQQGzFiBPT09LBw4ULqvZ/nlNTS\n0oKqqiptGrA8uFwuMjMzoays/MXeK37++WcAzVGjCxcuRHFxMezs7Kgxpr6+vvhRY63p0nyNHwDE\n19eX0Mnq1avJhAkTaLUZHR1N5OTkyK1bt2i1y+VyyfHjx0mPHj1ITk4OLTaDgoKIiooKiYuLo8Ue\nj1GjRpFNmzbRavPIkSPExMSEfPjwgTabBQUFpG/fvuTgwYO02SSEEDc3N2JjY0O4XC5tNhMTE4m6\nujq5ePEibTYJIWTx4sVk1qxZtNrkcrmksbGRWFlZEQBk1KhRhMvltvjY29uTjRs3trqOrs/Ro0cJ\nAAKA+Pn5ES6XSzIzM0n37t0Jg8Gg1vE+s2fPFvvYP/7cunWLz769vT0tdnmfuLg4oqam1uI4Lly4\nIIz5L35//1o+6urqRF1dnSxfvlzgk7du3TrC+11rn0OHDglsSxD27t1LlJWVqb/1unXrxLLn6+tL\nLCwsyKtXr8i0adPIjz/+SOLi4kj//v3J+fPnhbYXEhJCevToQR49ekRqa2v5njPV1dUkMDCQGBgY\nkPj4eLHa/TmeM+2xdOnSVv++qqqqREpKqsU1eOTIkQ5tNjQ0kOLiYlJcXEzWrl1L1NXViZ6eHomN\nje3wtzU1NdRvx48fT9TV1cn48eNJY2OjwMfU1NRESkpKSHFxMdm7dy91TFeuXBHYxpciPDycdO7c\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51bX1R8bZ2RnR0dHo06cPgoKCuBJ15ufD1bt37zBr1izk5uZi0qRJ1DHDz4eUuLg46j90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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d94dfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show Vector Field\n",
"\n",
"%matplotlib inline\n",
"# %matplotlib notebook\n",
"from matplotlib.widgets import Slider\n",
"\n",
"evt_d = evt[0]*evt[0]\n",
"\n",
"nv, sz, sy, sx = evl.shape\n",
"\n",
"\n",
"fig = plt.figure(figsize=(15,15))\n",
"xy = fig.add_subplot(1,3,1)\n",
"plt.title(\"Axial Slice\")\n",
"plt.axis(\"off\")\n",
"xz = fig.add_subplot(1,3,2)\n",
"plt.title(\"Coronal Slice\")\n",
"plt.axis(\"off\")\n",
"yz = fig.add_subplot(1,3,3)\n",
"plt.title(\"Sagittal Slice\")\n",
"plt.axis(\"off\")\n",
"\n",
"step_ = 1 #Subamostragem dos vetores\n",
"maxlen_= 32 #Tamanho do maior vetor\n",
"rescale_ = 16 #Fator de rescala da imagem\n",
"\n",
"# crop = np.array([sz/3, sz*2/3, sy/3, sy*2/3, sx/3, sy*2/3]) # crop [z<, z>, y<, y>, x< x>]\n",
"crop = np.array([30, 40, 120, 136, 120, 136]) # crop [z<, z>, y<, y>, x< x>]\n",
"\n",
"frame = 0.5\n",
"# seismic\n",
"V1 = DTI.show_vector_field(evt_d[1,np.floor(frame*sz),:,:], evt_d[2,np.floor(frame*sz),:,:], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"xy.imshow(V1[0,:,:], origin='lower',cmap=\"gray\")\n",
"V2 = DTI.show_vector_field(evt_d[0,:,np.floor(frame*sy),:], evt_d[2,:,np.floor(frame*sy),:], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"xz.imshow(V2[0,:,:], origin='lower',cmap=\"gray\")\n",
"V3 = DTI.show_vector_field(evt_d[0,:,:,np.floor(frame*sx)], evt_d[1,:,:,np.floor(frame*sx)], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"yz.imshow(V3[0,:,:], origin='lower',cmap=\"gray\")\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"\n",
"fig = plt.figure(figsize=(15,15))\n",
"xy = fig.add_subplot(1,3,1)\n",
"plt.title(\"Axial Slice (zoom)\")\n",
"plt.axis(\"off\")\n",
"xz = fig.add_subplot(1,3,2)\n",
"plt.title(\"Coronal Slice (zoom)\")\n",
"plt.axis(\"off\")\n",
"yz = fig.add_subplot(1,3,3)\n",
"plt.title(\"Sagittal Slice (zoom)\")\n",
"plt.axis(\"off\")\n",
"\n",
"V1 = DTI.show_vector_field(evt_d[1,np.floor(frame*sz),crop[2]:crop[3],crop[4]:crop[5]], evt_d[2,np.floor(frame*sz),crop[2]:crop[3],crop[4]:crop[5]], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"xy.imshow(V1[0,:,:], origin='lower',cmap=\"gray\")\n",
"V2 = DTI.show_vector_field(evt_d[0,crop[0]:crop[1],np.floor(frame*sy),crop[4]:crop[5]], evt_d[2,crop[0]:crop[1],np.floor(frame*sy),crop[4]:crop[5]], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"xz.imshow(V2[0,:,:], origin='lower',cmap=\"gray\")\n",
"V3 = DTI.show_vector_field(evt_d[0,crop[0]:crop[1],crop[2]:crop[3],np.floor(frame*sx)], evt_d[1,crop[0]:crop[1],crop[2]:crop[3],np.floor(frame*sx)], step=step_, maxlen=maxlen_, rescale=rescale_)\n",
"yz.imshow(V3[0,:,:], origin='lower',cmap=\"gray\")\n",
"\n",
"\n",
"\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:py2]",
"language": "python",
"name": "conda-env-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": 1
}
| gpl-3.0 |
katelynneese/dmdd | visualizations/NExpectedPlotter.ipynb | 2 | 51458 | {
"cells": [
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from array import array\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import dmdd\n",
"import pylab as pl\n",
"import matplotlib.patches as patches\n",
"import matplotlib.path as path\n",
"\n",
"# having trouble because when overwriting a pre assigned KWARG in mainplotter() doesn't overwrite KWARGS in the \n",
"# functions it calls\n",
"# the only way I can see how to make it do what I want is to make everything not a KWARG\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def expectvalues(element, Qmin=5, Qmax=100, binsize=1, exposure=2000, sigma_name='sigma_si', sigma_val=200, efficiency= dmdd.eff.efficiency_unit, v_lag = 220):\n",
" #auto pass efficiency function. sigma val is cross section, sigma name is theory\n",
" bins = (Qmax - Qmin)/binsize #number of bins graph has\n",
" #energylist = np.arange(Qmin, Qmax+binsize, binsize) #includes the last point\n",
" energy_lower = np.arange(Qmin, Qmax, binsize)\n",
" energy_upper = np.arange(Qmin+binsize, Qmax+binsize, binsize)\n",
" #return energy_upper\n",
"\n",
" \n",
" #pseudocode for what I want it to do\n",
" result = []\n",
" for qmin, qmax in zip(energy_lower,energy_upper):\n",
" #print qmin,qmax\n",
" n = dmdd.Nexpected(element, qmin, qmax, exposure, efficiency, sigma_name, sigma_val, v_lag)\n",
" result.append(n)\n",
"\n",
" return result \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[86.97772843091985, 79.06805682846979, 71.81493997964309, 65.17123784858238, 59.09226750956789, 53.53580887368531, 48.46208396936936, 43.83371487036432, 39.61566464702563, 35.77516508583126, 32.28163436820468, 29.106587414853724, 26.22354117819781, 23.607916796251057, 21.23694020033215, 19.08954248957183, 17.14625693068721, 15.389099706487121, 13.801496651302639, 12.36821335523105, 11.075262593764181, 9.909815367201626, 8.860116944471493, 7.915407723194259, 7.0658487398775245, 6.302451672666548, 5.617013179999097, 5.002053415617265, 4.450758555994095, 3.956927171757184, 3.514920270960307, 3.1196148394757977, 2.766360702556071, 2.450940531752358, 2.1695328228203783, 1.9186776728634494, 1.6952451886113131, 1.4964063622532842, 1.319606256477456, 1.1625393461626878, 1.02312687039684, 0.899496055018575, 0.7899610726031847, 0.6930056136336473, 0.6072669494364065, 0.5315213742527144, 0.46467092049831915, 0.40573124779569975, 0.35382061270438664, 0.30814983219676684, 0.26801315981138624, 0.23277999904145502, 0.20188738387984195, 0.17483316153136666, 0.15116981712088995, 0.13049888477043084, 0.1124658936947354, 0.09675580197806281, 0.08308887445229031, 0.07121696460721891, 0.060920163736622845, 0.052003783569420314, 0.04429564146422325, 0.037643619868736676, 0.03191347417418995, 0.026986865339644624, 0.02275959573329082, 0.01914002854747934, 0.016047672902463953, 0.013411918369810143, 0.011170904130517486, 0.009270509343715662, 0.007663452548505918, 0.006308489062220449, 0.0051696963810335585, 0.004215838540911073, 0.0034198012652766393, 0.002758090517114445, 0.0022103877936159085, 0.0017591561566793962, 0.0013892915879033948, 0.0010878147971753486, 0.0008435991041374228, 0.0006471304560115291, 0.0004902960474335174, 0.0003661983717438534, 0.0002689918619908147, 0.00019373957682365512, 0.0001362876543605309, 9.315549863070911e-05, 6.143988073425119e-05, 3.873133264432715e-05, 2.304138762677617e-05, 1.2739379422253678e-05, 6.4976543220733604e-06]\n"
]
}
],
"source": [
"data = expectvalues('xenon')\n",
"print data"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = expectvalues('xenon', v_lag = 500) #this is a generator\n",
"data_list = list(data) #puts data into a list\n",
"data_array = np.asarray(data_list) #turns list into an array\n",
"\n",
"#print data_array"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"energy_lower = np.arange(5., 100., 1)\n",
"energy_upper = np.arange(5+1, 100+1, 1)\n",
"qmid = np.zeros(len(energy_lower))\n",
"#for i in np.arange(len(energy_upper)):\n",
" #qmid[i] = (energy_upper[i] + energy_lower[i])/2 \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_xy(Qmin, Qmax, binsize, v_lag = 220): #combine calc_xy and expectvalues into one -> v_lag needs to pass into Nexpected!\n",
" energy_lower = np.arange(Qmin, Qmax, binsize)\n",
" energy_upper = np.arange(Qmin+binsize, Qmax+binsize, binsize)\n",
" qmid = np.zeros(len(energy_lower))\n",
" qmid = (energy_upper + energy_lower)/2\n",
" for i in np.arange(len(energy_upper)): \n",
" qmid[i] = (energy_upper[i] + energy_lower[i])/2 \n",
"\n",
" data = expectvalues('xenon') #this is a generator\n",
" data_list = list(data) #puts data into a list\n",
" data_array = np.asarray(data_list) #turns list into an array\n",
" \n",
"\n",
" #√ package these 2 into 1 2d list using list=[], list.append(qmid)\n",
" #√ save qmid,data_array into 1 file; use np.savetxt(filename, 2d list);\n",
" #√ then that file can be read using 2dlist=np.loadtxt(filename)\n",
" \n",
" datalist = []\n",
" datalist.append(qmid)\n",
" datalist.append(data_array)\n",
" np.savetxt('xenondata.txt', datalist)#float argument required, not np.ndarray\n",
" \n",
" datalist=np.loadtxt('xenondata.txt') \n",
"\n",
" return datalist\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"x,y = calc_xy(5., 100., 1.)\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_xy(x,y):\n",
" #make plot look pretty!\n",
" #√ write a seperate function that calls this and first function gets passed sigma, element, ect that will calculate and plot EVERYTHING\n",
" #√ include in calculator function the argument v_lag= 220 km/s (in dmdd.Nexpected), make it a KWARG and pass to Nexpected/calc_xy\n",
" plt.figure()\n",
" plt.plot(x, y) #scatter makes dots, plot makes lines\n",
" plt.title('Dark Matter-Nucleus Collisions', fontsize = 15)\n",
" plt.xlabel('Energy [keV]', fontsize = 13)\n",
" plt.ylabel('Number of Expected Recoil Events', fontsize = 13)\n",
" plt.show()\n",
"\n",
" plt.savefig('/Users/katelynneese/dmdd/results/darkmattercollisions.png')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1083ce750>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x108587e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_xy(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mainplotter(element='xenon', Qmin=5, Qmax=100, binsize=1, exposure=2000, sigma_name='sigma_si', sigma_val=200, efficiency= dmdd.eff.efficiency_unit, v_lag = 500):\n",
" expectvalues(element, Qmin, Qmax, binsize, exposure, sigma_name, sigma_val, efficiency, v_lag)\n",
" x,y = calc_xy(Qmin, Qmax, binsize, v_lag)\n",
" plot_xy(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
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},
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{
"data": {
"text/plain": [
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},
"metadata": {},
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}
],
"source": [
"mainplotter(element = 'xenon', v_lag = 220)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"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
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| mit |
nkmk/python-snippets | notebook/pandas_normalization.ipynb | 1 | 10774 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import scipy.stats\n",
"from sklearn import preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame([[0, 1, 2], [3, 4, 5], [6, 7, 8]],\n",
" columns=['col1', 'col2', 'col3'],\n",
" index=['a', 'b', 'c'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a 0 1 2\n",
"b 3 4 5\n",
"c 6 7 8\n"
]
}
],
"source": [
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a 0.0 0.0 0.0\n",
"b 0.5 0.5 0.5\n",
"c 1.0 1.0 1.0\n"
]
}
],
"source": [
"print((df - df.min()) / (df.max() - df.min()))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a 0.0 0.5 1.0\n",
"b 0.0 0.5 1.0\n",
"c 0.0 0.5 1.0\n"
]
}
],
"source": [
"print(((df.T - df.T.min()) / (df.T.max() - df.T.min())).T)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a 0.000 0.125 0.250\n",
"b 0.375 0.500 0.625\n",
"c 0.750 0.875 1.000\n"
]
}
],
"source": [
"print((df - df.values.min()) / (df.values.max() - df.values.min()))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.0 -1.0 -1.0\n",
"b 0.0 0.0 0.0\n",
"c 1.0 1.0 1.0\n"
]
}
],
"source": [
"print((df - df.mean()) / df.std())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.224745 -1.224745 -1.224745\n",
"b 0.000000 0.000000 0.000000\n",
"c 1.224745 1.224745 1.224745\n"
]
}
],
"source": [
"print((df - df.mean()) / df.std(ddof=0))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.0 0.0 1.0\n",
"b -1.0 0.0 1.0\n",
"c -1.0 0.0 1.0\n"
]
}
],
"source": [
"print(((df.T - df.T.mean()) / df.T.std()).T)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.224745 0.0 1.224745\n",
"b -1.224745 0.0 1.224745\n",
"c -1.224745 0.0 1.224745\n"
]
}
],
"source": [
"print(((df.T - df.T.mean()) / df.T.std(ddof=0)).T)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.549193 -1.161895 -0.774597\n",
"b -0.387298 0.000000 0.387298\n",
"c 0.774597 1.161895 1.549193\n"
]
}
],
"source": [
"print((df - df.values.mean()) / df.values.std())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.460593 -1.095445 -0.730297\n",
"b -0.365148 0.000000 0.365148\n",
"c 0.730297 1.095445 1.460593\n"
]
}
],
"source": [
"print((df - df.values.mean()) / df.values.std(ddof=1))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_ = df.copy()\n",
"s = df_['col1']\n",
"df_['col1_min_max'] = (s - s.min()) / (s.max() - s.min())\n",
"df_['col1_standardization'] = (s - s.mean()) / s.std()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3 col1_min_max col1_standardization\n",
"a 0 1 2 0.0 -1.0\n",
"b 3 4 5 0.5 0.0\n",
"c 6 7 8 1.0 1.0\n"
]
}
],
"source": [
"print(df_)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1.22474487 -1.22474487 -1.22474487]\n",
" [ 0. 0. 0. ]\n",
" [ 1.22474487 1.22474487 1.22474487]]\n"
]
}
],
"source": [
"print(scipy.stats.zscore(df))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"print(type(scipy.stats.zscore(df)))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1.46059349 -1.09544512 -0.73029674]\n",
" [-0.36514837 0. 0.36514837]\n",
" [ 0.73029674 1.09544512 1.46059349]]\n"
]
}
],
"source": [
"print(scipy.stats.zscore(df, axis=None, ddof=1))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"df_standardization = pd.DataFrame(scipy.stats.zscore(df),\n",
" index=df.index, columns=df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a -1.224745 -1.224745 -1.224745\n",
"b 0.000000 0.000000 0.000000\n",
"c 1.224745 1.224745 1.224745\n"
]
}
],
"source": [
"print(df_standardization)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3 col1_standardization\n",
"a 0 1 2 -1.224745\n",
"b 3 4 5 0.000000\n",
"c 6 7 8 1.224745\n"
]
}
],
"source": [
"df_ = df.copy()\n",
"df_['col1_standardization'] = scipy.stats.zscore(df_['col1'])\n",
"print(df_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"mm = preprocessing.MinMaxScaler()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0. 0. 0. ]\n",
" [0.5 0.5 0.5]\n",
" [1. 1. 1. ]]\n"
]
}
],
"source": [
"print(mm.fit_transform(df))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"print(type(mm.fit_transform(df)))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0. 0. 0. ]\n",
" [0.5 0.5 0.5]\n",
" [1. 1. 1. ]]\n"
]
}
],
"source": [
"print(preprocessing.minmax_scale(df))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"print(type(preprocessing.minmax_scale(df)))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"df_min_max = pd.DataFrame(mm.fit_transform(df),\n",
" index=df.index, columns=df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3\n",
"a 0.0 0.0 0.0\n",
"b 0.5 0.5 0.5\n",
"c 1.0 1.0 1.0\n"
]
}
],
"source": [
"print(df_min_max)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"df_ = df.copy()\n",
"s = df_['col1'].astype(float)\n",
"df_['col1_min_max'] = preprocessing.minmax_scale(s)\n",
"df_['col1_standardization'] = preprocessing.scale(s)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" col1 col2 col3 col1_min_max col1_standardization\n",
"a 0 1 2 0.0 -1.224745\n",
"b 3 4 5 0.5 0.000000\n",
"c 6 7 8 1.0 1.224745\n"
]
}
],
"source": [
"print(df_)"
]
}
],
"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": 2
}
| mit |
raman-sharma/stanford-mir | basic_mir.ipynb | 3 | 306526 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import numpy, scipy, IPython.display as ipd"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"[← Back to Index](index.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Overview of a Basic MIR System"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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WrW58HpZS6XHqRu4tKnW+e1+yWS3AB9Hf8FCv+C9P7DX/AATF+Ofh/wCAH7VG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\n1kAJ2J9rOwBmcsAf2VUAFAH+aF4C0618Hf8AB6NfW2hJ9ki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ABkk8AAdST2AFAFeC9s7p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S1vbC/srlJLa8sry2llt7q1uI5ILiCSSKVGR2VgD/Ow/bz/AODa7/gon+wP+1Pfftjf8EVvE/in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XMsLDlpwoWp04xxOIVf6WlxfhuKeB89ybjPMY4jOsnWFx3CWZ4uFSrmVapzVI43LauMjTnO\ntTnShHl+szTnOtzVKknhsPyfk54X8La/4z13RfC3g7Q9V8QeI9f1BdH0Hw7oFjeavreu390UjtLG\nw0uyhuLy91C8uJESCCCCad2O2PI5r9axOJw2Cw9bF4uvRwuFw9OVaviMRUhSo0aUFedSrUnKMIQi\nldylKKXV/wAv5RhsNiMZiKOFwlCticViKkaVDD4enOtWrVZu0KdKlBSnOcm7KMU2+h9va5/wSp/4\nKEeF/BUvjvVP2WPiSNBtbSfUJ4dOl8P6x4lgs7K3e4uGl8F6Hr974zgWOPc6Qy6ClxKVaONGeI7f\nhqHir4e4nFrA0uKcuddzjTi6ixNHDylN8sVHGVsPSwcrt2usQ0t3JXufcV/C3xBw+DeOq8L5isOo\nym1TeGrYiMYR5pOWCo154yNo3+Kgm7WSb1j8DtDIshgkXypYE2TJJHsuC/nbXV87TCVYhHVslSDu\nyY1LffpppNNNNXTWqaezT6po+BaabTTTTs090+z21Xp9x9P/AAA/Yl/a1/af0651L4DfA/xj8QtD\ntr1tJn8VxRaXofhCO/SCOaXTV8WeJ77RfDT3dvHLCb62/tJrmzinga6KCaEt8tn/ABxwnwvUjRz7\nPMHgK8qaqrDS9rXxbpNtRqfVcLTr4hQk4tQk6NpNSUW7NH1GQ8E8V8TwlVyLJMZj6EajpPExVKhh\nFVSTlT+t4qdHDc8VJOcfa3ipRcuXmTLnir9gr9sD4f8AxT8K/BLxh+z94/0n4mePpNR/4Qrw9/Z9\ntf2/jNtD0y41vWo/C/iLTb+68La7NpmnWct1qMemaxcm0hjzcCDfGk8YPjzg/H5Vi86wnEGX1ctw\nHsvr2I55wlg1Xqwo0XisPVhDFUFVqzjCm6tGCm37t7MvGcCcX4DNMJkuLyDH0syx3tVgsP7OE44x\n0aUq1ZYXEU6k8LXdKlFzqKlXm6a+JptOXjPw5+C3xR+Kni3U/Avw88Ha74s8W6NoviLWdV8NadAk\nuoadpfhWAjxHqE6yzJiDRl3PdsJjja/lq27cvs5hnWVZVg6WPzHHUcJg69XD0aOIquSp1KuKdsPC\nNoyfNVduS9vN7I8bL8mzTNcXVwGX4Kvi8ZQpYitWw9JJ1KdLC64ick5JKNJL3/e087Wl9PfBr/gm\nj+3N8dfh/afEz4Y/s6eL/EXgPVrb7Zo+uX+reEPClvrllCx8u78O2PjDxHoWs+INMuJAwtrzSLO9\ns7oLI1tPKgdq+azfxL4GyLHzyvNOIsJh8dRlyVqEKWMxToT608RUweGxFKhUV1zQrVIShtJRbsfS\n5R4bccZ7gIZnlfD2LxGBqx56NedXCYVV4be0w8MZiKFWvTbXuzo06kZ7wb5WeR+AP2R/2oPih8Wt\nc+CXgr4H/ELWfiv4TuWXxf4GfQrjS9T8IrBNHEkvi+61g6Zpvha0leeEWl/rd5Y2F2biA2883nQ7\nvWx/F/DGV5Th88x2eZfRyrFpSwmNVeNWnjLq/LhI0VUq4qaSk5U6FOpOHLLmhHlly+VgOEeJszzX\nEZJgckzCtmuEk44vBuhKlUwlmlfFyrclPDQbcVGpXnCEuaPLOXMmbn7RH7Ef7VX7KdjpmrfH/wCC\nvir4eaPrWoDStP8AEktxoXiHwzJftDJdw6YPE3hHV/EOgw6lNbQyzxWUmoxXEttBdeVDMtlctFhw\n9xxwpxXOrSyDOsLmFejT9rUw8Y18PiY0uZRdX6ti6VCu6ak4xlONNwi5QUpe/A34g4J4q4VhTq59\nkuKwFGrU9lTxDlQxGGlV5XJU/rOErYigqjipSjBzjKSjJpPlly/OXhnw14l8Xa5pXhXwfoes+KPE\nviG8g0Xw/wCH9CsLrWNc1jUb2eOG00/TNNsop7u9v5pPkght4WleTZGEYCvo8TisNgsPWxeMr0cL\nhcPTlVr4jEVIUqNGlBXnUq1KkowhCKV3KUopd/5fnMNhsRjMRRwuEoVsTisRUjSoYfD051q1arN2\nhTpUoKU5zk3ZRim30PuPW/8AglR/wUI8NeC5fG2p/ssfEWLQILV9SuzYt4e1jxOltar9punuPBGh\na9f+NLfyLdGPkzaIjTEOkSyshRfhqHir4e4nGLA0uKcuddzjTi6ixNHDynOXLFRxtbD0sHK7drqv\nyrdySacfuK/hb4g4fBvHVeF8xWHUZTapvDVsRGMI80nLBUa88ZG0b/FQTdrJN6x+C4bC7nuE02OB\n7rULm5trOztXRkuDezyiOGDY21xcu8giVWOVcbDjaN330pwjB1JSSpxi5ue8VBLmcrrdcuunQ+Dj\nCcpqnGLdSU1BQ2k5t8qjZ7Pm016n1/4H/wCCf/7Z/wARfiV4z+DnhL9nrx3qnxH+HX9lHxxo12uk\naHa+EJvEOjWut6ND4h8Sa/qOneFNKvdV0i7t9Q0y2v8AW4Li9tWllt4pPKn2/IY3xB4My7LMHnGL\n4hwNPLsx9r9RrRdatPFqhVnQrSw+FoUquLqwo1oSpVJwoOMJ2UmrxU/rcFwBxlmGZ4zJ8Jw/jqmY\nZf7L69RmqNCGEdelGvQjiMTXq08LSlVozjUpwnXUpw1inaRwnx6/ZD/aZ/Zbu9Lh/aA+DXi74dR6\n9Lc2GkanqVvbX/h3Vb9E825sdK8U6Hdap4Z1DUI4/wB9JaW+qPcR24W7WEQIsj9uQcX8NcUxqvIM\n4weZSoRjOtRpSnTxNKEnaNSpha8KWJhByfKpzpKPN7t3JOMOLP8AhHiXheVJZ9k+Ly6NeUoUatWM\nKmGqziryhTxNCVXDzqJe86ca0p8vvaxtKX6P/wDBPr9gnx98Yf2cf2vPiNr/AOzpfeNYPGH7PGtn\n9mbxVf6NZXTap8TtN8T32j3TeA7l7jz7bXbO90u8sjMRFiW2lKOBtDfnHiFx7l+UcScI5bh+IoYG\nWD4ho/6z4WnWqU/ZZbUw1KtFY+Ki1KhOFSNRR97SSatc/RfD/gTH5vw5xbmNfh6eNjjOH63+rOKq\nUIVPaZlSxNSjJ4CTneNeM6c6bfuaxfa8fyY+LHwj+JvwV8Z6t8L/AIteD9Z8DeO9L/sxtW8Ma1bo\nmr2cWs6ZaavpXmQwy3KBb3TLy3uoEjdiY5IXTy2DKv6zlWb5ZneBpZnlOMo47AVnVVLFUW3Sm6NS\ndKrZy5X+7qQnCW1nF+p+UZplGZZJjqmW5rg62Bx9FU3VwtaKVWCrU41aV4xlL46c4zj7zupLa9j7\nK8Ef8Enf+ChfxA8MWni7w1+y744tNCuraS6tF8Tar4P8D6zeWUi7oZh4Y8ceJvD3iRnuT++gH9kP\nLJAVnh327pLXx2N8WPDzL8VLCYnijAuvCXJP6rSxuOoxkt08TgcLicKrPSX760ZLllaS5Y/YYLwq\n8Qsfho4vD8MY5UZxU4fWauCwNaUXs1hsbicPiXdapexbcXzK8bSPij4jfCn4jfBvxfqnw7+KPgnx\nR4B8c6E9v/aPhbxTpt7ouswx3Cs1nPFb3Ig+06ffBhNpt/ama01CB0ntLiaExzP9nl2a5bm+Cp5j\nleOw2PwNVN08Vha0K1J8vxxcoc3LOm7xqU5JVKck41IqStH43McrzLKMbUy7NMDicBjqTSnhcVRn\nRqrm+CSjPl5oVF71OpFuFSNpQk4u8v2y8X/8Euvj8/8AwTv+D9rof7J2vr+1DF+0N4zfxzJa6Bpi\nfEGT4XP4evf7CXVr1bxd2gHVWtzaQm4cRzqjbCyqV/FcH4ocPrxEzidfiyh/qu+HcFHAqdeq8v8A\n7UWJh9YdGHLpiPZc3PJJXj6Lm/aMZ4YZ9/xD3KIUOFMR/rOuIca8c4UKf1/+y/q0/YKrP2qvh/a8\nvInN2lbRH5pfA7xn+1H4N+AP7YXgz4TeG21L4PeJ9D+G2n/tOatLoWn6kvhnSNM1zxJb+A86nM63\nGjm813UNcsXksoppbl1WKdgIomr9JzzCcL4zP+D8Xm2JVPOMLXzGpwxS9vUp/WatbD4aWOSpRTjW\n5KFOhO1Rx5L3jdto/OMkxnE+DyHi/B5VhnUyfFUcup8TVHQhU+rUqNfExwLdWUlKhz16leF4KXO1\nZpWR8v8AgvwT4s8e+JNF8FeCvDWueL/F3ifUYNN0Hwz4a0m+1rW9b1Bx5osdO0zT45bq4unCZMcU\nDYiillZkiikNfUYzGYTL8LWxuPxNDB4TDU3VxGJxNWFGhRpx3nUqTcYxS21eraSu2lL5jB4PF5hi\naOCwOGr4zF4moqWHw2GpTrV61R7Qp0oRlKUrXekXZXbsk2fcmt/8Epv+ChfhfwqPGmrfsufEn+wv\nsNxcyWunHw94h8Q2sMFsWmlufCOga9q3jG1MaF3VLjR4nkKyrDGWJ2fD4fxV8PMTivqdLirLvbc0\nYJ1frOHw7lJ2SjjMRh6WEld9Y1mlu2kfbYjwt8QcNhPrtXhbMfYcsptUvq+IxEYxV5OWEw9ari42\nWtpYdN62WnKfAlpbTPdDTDbzNeTXEdmlm4KN9qSRY47QK+JstcuIjE7ZEjbDjOa+/c4qDqOS5FFz\nc73jypc3NdaNW1uunqfBKEnNU1F87koKFrS5m+Xls9U76WfX0Pr7wT+wB+2j8Q/ir4u+Dfhb9nvx\nzqHxI+Hv9iz+PPDt0mlaPbeD113SrTX/AA/B4m8T61qemeFdFuta0W+t9T0m1vNbinvrV5ZbeGfy\nLgQfIY3xB4My/K8HnOL4hwFPLswdZYGvF1q88Z9WrTw+IeGw1ClUxVaNGtCVKrKFGUYT0k1eLl9d\nguAOMswzTGZNhOH8dUzHL1ReOoTVKhDCfWKMK9BYjE16tPC0ZVqNSFWlCdaM6kG3Hm5ZcvFftB/s\ng/tL/ssy6Yvx++DfjH4aw65dzWOla3qUFlqPhu/ubcPLJYad4q8P3eseG7u/jhT7RJZ2mrSTpZnz\njC0CiSu3h/jDhnimNV5BnOEzKVGMZ1qNJzpYmlCTtGpUwuIhRxMIOT5eedLl5vdupe7Hjz/hDiXh\nd0ln2T4vLo1pShRrVVCphqs4q8qdPFUJVcNOokubkjVlPl95Xj7x83jfGjFC7Dy2jTJ3OFLr0ycb\n1Ckh/lIYIhDDG36Q+bI1jVYSAVRVaF5JWJWSVjKA6CONlY+UBtZM7WYkcAA0Af0Bf8G46sv7bfxU\nDOZM/sreMypIwyqfi18EGG7IBLEOM5zjbt6AGvwH6Rv/ACRGV/8AZVYH/wBVGeH739Hb/kts0/7J\nbG/+rbIz+06v4tP7NCgAoAKACgAoAKACgAoA/BX/AIOKV3/sMeBVDlGb9pjwEEIDfePw8+LYGSvQ\nYJ3EhhtzxnFfvP0dv+S6x3/ZNY//ANWGUn4T9Ib/AJIfBf8AZSYD/wBV+bf1/TP4nJGCiE5XzBv8\nyYcP5bMcAKONgyylSCD0GcCv7XP4tHSSsyKUVN5WNZfnyVjGYiXAQsseGzhDv8wEnIzQAsDblkAT\nzMnzS6qTtjaPymLo20BVV9iqrZbK5x5YDAEK7fNHll4pASkaFtoGUwWAUorsyqrE7VYOAwJYAuAO\nmCuYRONyxR71jUHDuysoDuMsAXDozA4ZFGVcrmgCWWHejAsB5jW2IUcHbI6b28skbVCgADG1AwyQ\ncfKAQ8futpc+YzRfK7H54y8kvmIXK5wMMxUohOBuKoaAJHUyK8ivHEGmbaFCp5bB4wCzNlWY7gOV\nIBPO5Y1WgD6w/YZ/agj/AGPf2nvhl+0TdeC5vH9p4ATxokvhOHXI/DF1q7eK/APinwMZRrEmja6l\nl/Z0niNNR402dbmG2a1UW/nrcJ8lxzwxLjHhfMuHYYyOAlj5YJrFyoPExpfVMfhca70VVoOftFh3\nT0qw5XPn963LL6zgfiaPB/E+W8QzwcsfHALGp4SNdYaVX63gMVglas6VZQ5HiFU1py5lDl92/MfN\nfjHXI/E/irxN4mNpJYjxB4i1fXlsPMe4ksV1zVptSEP2lRD9pW2imSBpkig81lI8pNxRvpMHh/qm\nDwuFcud4bDUMO5pcvP7GlGnzct3y83Le13a9rvc+cxlf61i8VilHkWJxNeuoXvye2qyqct7K/LzW\nvZXteyvY58x7Y3R5vMZZYj8/yuu1Xbb8m07trmQEs/zISQ5b5ek5j9EP2bf27NC+HnwV1f8AZd/a\nO+CGkftOfs332vS+LPDvhHUPEk/gTx58MfGl6d994n+G/j7TNM1K/wBHS7jkun1XRfIihv5ry7EW\noWNrrHiG31z884k4Gr5jnNHijhvPK/DPEtOgsJiMXDDQx2BzTBRXuYbMsBUqUqdVwaiqdfmlKEYQ\nvSnOlh50v0Lhzjmhl+TVuF+I8lo8S8NVK8sXh8JPESwWOyvGS+PE5dj6dOdSkp3k6lCyU5TnarTh\nWxEK/s/hb9uX9j/9mVtW8YfsS/sm+LNJ+Pd9peo6T4W+MHx/+I9v8Qm+Ff8Abdlc2uqXvgPwZpel\n2ehXWuQw3T2+ma/rU32y2iidL6DUbC71TSL3xsVwNxhxP7LB8a8WYSvkNOrTq4nJ+H8tll6zV0Zw\nnSp47G1qs68aDlDmq0KSUG3enKE4UqkPYwvHHCHDPtcZwXwpi6Ge1KVSlhs4z/MY5g8q9tTlTqzw\nGDpUoUZV1GbjSr1m5KKtUjOnOrTn8bfsuftefEL9l39oKP496VBZ+PdT1q28UaX8SPD/AI0eS+03\n4peHPG7F/GejeI72VLm78zWLjydVTUmNzNFqlvb3d5BqNp9r067+x4p4Qy7ijh/+wKkqmX0qEsLW\ny3EYJKFTLMTgdMHWw0FyQtRjel7P3U6MpQg6UuWcfj+F+Lsx4Yz/APt6lGGPq1liaWZYfGtzp5nh\nsa74yjiJvnnzVpWq+095qtGMpqrDnhL6f1r9oL/gl3p18/jLwp+wd8WNX8R3d1Pqa/Drxt+0Zex/\nB7w/qLoZLZEl0LQm8b+JdAs7srLJot/qelLeWltFaSXMEE8ixfL0uHvFGpTWBxnHeVUcLGMabzLA\n8Ow/tivTWkm1XrvBYavOCt7anGs4Tk5qEpJM+mq5/wCGFOo8bhOBc1q4mUnUWXY3iKosooVN4pOh\nQeNxFGMnd0alSnzxioOcYtmp8c/+Cjnhb9qz9l/RPhB+0J8F5bn4rfDXWfF9x8BfGXwu8R6b8P8A\n4feBtD8Rppmn6X4Wv/hvY+HblJfDvgzSNNsdJ0Kxt9UFxdadpulrf35u49Tu9VzyPw4xXCvFFXOO\nHs6VPKszo4SGe4LNMPVzDMcdXwzqTq4qGYzxMXHEY2rUnWr1JUuWFSrVdOnKEqcKWueeIuF4p4Zp\nZTxBk0qma5ZWxc8jxuW4ill+X4KhiY04UsLPLoUJp4fBUqUKNCnCrzTp06XtKjnGpOryXwC/bA/Z\nz039ky+/ZE/au+DvxH+IngzSfjNP8c/AHif4T+OdK8H+I9J8SX3hf/hFL7RdVi1jTb2yl0WS0e+k\nE6reuz6nODYLNY2N2vXxBwhxHU4sp8XcKZzluXY2rk0cjzDDZrgauMw1XDQxX1qFel7GpCarKXIu\nV8itSj+8cZ1IS5Mg4u4dp8KVeEuKsnzHMMFSzl55l+JyrG0sJiKWJnhfqs6NX20JwdFw52pcs3er\nK9NOFOZ5L+yj+2K37K/xF+Il/Y/DfSfiV8DvjH4b1j4d/Fj4I+MNZuktPFPwz1S/ka0sX16ytWk0\n3xRodrI0GleJV0y9e3e71bFgv9oGS39fizg//WvLctpzzGrlue5NiaGY5XneDoxc8JmVGCU6n1ec\nkp4WvJKVXDe0jfkpe8+RqXk8KcXvhbMsxqQy+lmWR5xh6+XZpkuMqyUMXltWbcKft4Rfs8VQi3Gl\nifZT5eer+7XOnH6L0X9rX/gn98FtTj+J/wCzT+xf46uPjbps/wDbPgi9+PXxX/4Tv4afCvxCiSLp\n/iXSvCOn2NndeNrvQJmgutDg8UX9s1rqUdtqa3KXmm2xf5ytwl4gZ3SeV8S8Z4FZJVXssfDIcp+o\n5nmmGdvaYari6lSUMDDERvCu8LCSnS5qTXJUkj6KlxZwDktSOZ8N8G46Wd0pe1wM8+zX69luV4lX\ndPE0cJTpwljZ0Jcs6H1qcXCoo1U3OnBy+bv27P2wrv8Abc+MPhT4u6j4XvPCOu6L8JfBHw41mG71\ny11l9f1vwv8A2xNqvixXsNG8P2lkNfutWe6XSYbApprpshuZl2BPo+BOD4cE5Ri8op4qOKo183x2\nY0OSjKgsPQxSoxpYS06+IlUdCFFR9s53qbuMbe987x1xfPjXN8Lm1TCywtajlOBy6upVoVniK+F9\nrKrirwo0I01XnWcvYxhanspNOxk/t3/tRWf7Zn7Sniv4+xeCZ/AFr4l0XwZp8fhKfXk8Sy2snhbw\nhpHhYyy61Do+iCZb+XSZLnYunxCGOVIGM7Q7624F4Wlwbw1g8gnjY5hLC1sbVeKjQeGjP63i62JU\nVRdau48iq8l3U97l5rRvYy454ohxjxJi8+hgpYCOKo4OksLKusS4fVMJSwzk6qpUVL2jpc9vZrl5\nuXW15bn7IX7ZfiD9liXx/wCFda8B+Ffjb8A/jRp2k6J8ZvgN41laDQfGVrpwubjStR0fW47LULnw\nl4p0Oa6lm0jxBZWN0lrP5VzNYz3un6Re6XjxhwXh+Ko4DF0cfiskz/JqtSvk2e4Fc1fBzqqKrUqt\nFzpxxWFrRgo1cPOcOZXipxhUrQnrwhxniOFnj8JWwGFzrIc4p06Oc5HjvdoYyFLndGpSrKFSWFxV\nGU3KlXjCdnZunKcKM6X0h4a/ao/4JofCjXdO+KXwq/YT+I/iD4n6ZP8A2j4T8K/F/wCO0/ir4O+D\n/FFlJ9o03U5tKtNIi1zxvaaXcRi7bTfExW3uZUSPzLeZLe8t/msTwr4l5tQnlea8d5dQyusnRxeK\nyfI1hc4xmFmrVaSqTrewwU6kW4e1w13FO/vLmhL6TDcU+G2VV4ZnlfA2Y4jM6LVbCYXOM7eKyjCY\nmLTpVHSjSVbGwpStNU8S0ptWbhJKcfzv+OXxn+In7RnxU8b/ABq+KWvDWvHPj3XJNV1e8WMW1ra+\nVHa2OmaVptu8ky2ej6JpVvYaLothulWx0yytbZpJvILy/ouR5Ll3DuU4LJcqo+wwOAo+yowb5pyb\nk51a1Wdo89evVnOtWnb36s5SVr2PzzO86zDiHNcbnOaVvb47H1nWrTS5YRVlCnRpQ5pclGhShCjR\np3fJShGN3Zyl5EzzTRoiqApPJdf3jlSUkdl2HbtPAKMq+W+QoyteqeUBVURFcOCgj+VVfcnmSI3m\nAh8sixoqfLsOSY8E7g4A8x7Y3R5vMZZYj8/yuu1Xbb8m07trmQEs/wAyEkOW+UAeswEI2q0k8eRH\nvZFcmUeZI4VQF3AY3u3O1jgksCwARs5ZlTEzlfKQR5H7xmWTKMchuQSxYBlYEgZQLQAx/L3BAXRd\npcMp2CWQEEnI3YVcK6qzMwQk5A4YAWUOY/KcsEeQvI4AbaNqDLZJIj+UllG7YFRgRwKAFMeTCFIt\n9wkjhjRmZlIkiCtIEO1dy5ZnZWIVio6g0Af2pf8ABuRn/hiD4oZO4/8ADVHjjLAAA5+E3wQPAGAM\nA7cdscZzmv4t+kb/AMlvlf8A2SuB/wDVvnh/Zv0dv+SJzT/sqcb/AOqnIz9+6/AT97CgAoAKACgA\noAKACgAoA/iv/wCDjhS37bvwt2SFGX9lTwQWBUlSh+LvxwQEnlS25vlGAVwWy2FWv7S+jl/yRGaf\n9lVjv/VRkZ/GX0if+S2yv/slsF/6ts8PwDMojuGMaqq7lbap4eRcbzJnIDD5iWA4Awcg/N+/H4IJ\nK53AJtWH5gHDb/3u7HylUBMoEp3sx2jG5c7RQA8EGBCYdyoFT5RlGliyyqC4zwJCX3Iw2t5Q+6DQ\nBHDwSY3ZgWiaUOxztDbVGzfhQq9QMq+xAwBCbgByKj3JeRVeaSRYQG/dxwopZJGU4OWQkqm7c6Dg\nOCqigBssQAiZpC+yJ3Zt33IjJtCyN94rtGGIZpB94cKQwAoOySUYLhI2kIZi0WJw4jPzO+zaFzsU\nK2OSflVaAPtj4Wftc2vwu/Ys/aj/AGTpfA0+tXX7Rmv/AAr1+38c2/iCGwh8JxfDjxX4d8Ty2jeG\nW0e7m1s6tHpJskli1nSfsBuvPEd0IPKb4nNuEZ5nxpwtxYsfGjDhzD5pQlgXh3OWLeY4WvhlKOI9\nvFUfZOtztOhV5+XlvG/NH7TKuLYZbwZxRwo8BKtPiLEZXXjjliFTjhP7OxdDEuMsP7Gbre19jyJq\nrT5Oa75rWOX/AGJ/2p9a/Y9/aK8KfGy38MQeN9D0/S/EnhXxr8PpdQi0yx8b+DvFmjXekal4dvr+\nbTNUhigE8+n63brc6df27X+l2wuLeaHejdXG3C1LjHh7FZJLEvBV51cNisFj403VngcZha0KtLEQ\nhGrRk5cqqUZctWEuSrPllGVmc/BXFNTg/iDC51HDLHUIUsThcbgJVFShjcHiqMqVWhOcoVIpKTp1\no81OceelDmi1dS5/9r79ozWf2uP2jfiX8etY02Xw4/jvVom0Lw2l2dRh8JeFNC07TtA8K+GFultr\nC3uG0rQdJs4r24i02xt7y/a9vhYwSXro2/CHDdDhLh3LMhoVfrH1KlL2+K9n7J4rF16s6+KxDg51\nJR9rXqzcIyqVJQp8lPnfKc/F3EdfiziHMs+rUvq/12rH2GF9p7VYXC0aUKGFw6nyUlL2VCnBSlGl\nTU6nPPkjzNHof7SP7XFn8ev2dv2MvgZB4IufDc/7Kvg/4g+F7vxQ3iKLV18cHx3f+Drtb2DR10TT\nf7BjsP8AhFpYprea81g3f2tXSWDyCk/n8N8IzyHiHjLPZY+OKjxXjMvxUMNHDuk8CsDDFwcJVXWq\nLEOp9aTUlTo8nJbllzJx9DiPi2GfcPcHZHHAyw0uFcJj8LPEvEKssb9dnhJqcaSo03h/Z/VmnFzq\n83Pe6t730fH+3P8Asv8Ax6+GHwo8F/tw/s5eNvH/AI8+CfgbTPhv4L+N3wa+I1l4H8W618PNB+1H\nw34P8eaNq2lXmi6hHpIkKQa4jXV85u7++it7G9vtUm1n5yXA/FGQ5pmuN4G4jwWX5fnmOq5ljslz\nnLp47CYfMK/L9YxeBrUasK1N1rXdB8kEoU6bnOEKXsPolxvwznuWZVg+N+Hcbj8wyTBU8twOdZNm\nEMFi6+X0Ob6vhMdRq0p0aio3sq95VHzTqJQnOrz918Bv+Cp3w1/Ze8f6Nbfs7fslaP8AD34AahBq\ntp8afDWoePbzxr8YvjPb6ho2saPpc+v/ABY1zQI/+Ed0/wAOXGqQ6tofgzwxoOn6M2oRXovr65a+\nS5sOHPvCzM+Kcvrz4i4trZjn9OVKeTYqnl8MFk+SuFajVqxoZTQxH+0VMVGk6NfGYnEVK3s3B04R\n9m4Ve7IvFHLeF8wow4e4To5fkNSNWnnGFqY+eMzjOVOlWpUpV82rUF9Wp4Z1VWo4PDUIUXUjNTqS\n9qpw+B9E/aIs/gZ+1BYfH39jvSvEvwk0/wAGarYXXw/8N+N9ftvHup2tk3hi10PxTp3inUY9O0Wz\n13TfGEkmvSXllDaWos9L1k6daXf2nT4NTf7ytw7PPeGKmQcYVsPm1XGUqkMwxGBoSy+lKaxMq+Fq\nYSk6ledCphIxw6hNzlz1aPtJx5ZypHwlHiGGRcTU8+4QpYnKqWDq054DD46vDH1YweGjQxVPFVY0\nqMK9PFuWI56ahDkpV/ZQqKcI1T7H8W/tWf8ABNL4w+Irv4nfFr9gv4ieGfiXqM6ax4l8P/Bf46t4\nf+E/jfxJOkbatevo1/oMN/4HstTu1mvP7N8L72i86WSW+vLsy3svx2F4U8S8ow8MsynjzL8VltKL\no4WvnWRLEZrgsPHSjTVeFeVPH1KULQ9ri+VNRSUIwUYR+wxPFXhtm9eeZ5rwLmGFzKrL22KoZNnj\nw+V43Ey/jVHRnQjUwNOrO8/Z4Xms5O85Scpy+Wv2vv2wfE37VOs+CdHsfCPhv4P/AAV+DXh+Xwl8\nF/gl4KmvLrwx4E8PXUlvLe3d3qN2Ek8ReMNbljtD4g8SzWlhJqZsLWU2MUyzSXH1XCHB2G4VpY6t\nPG4jOM7zjELF51neNUY4nHV4xtCEIR5lh8JQ5p/V8NGUlSU5LmkuVR+W4u4vxHFNXA0YYLD5RkuT\n4d4TJslwbnLDYGhKV5zlOdpYjF1+WH1jEuNN1fZwfs01OU4P2k/2qbb49fDT9lH4c2vgqfwu37NH\nwkm+GFxqsuvxaxH4ynk1SHUjrEdkmj6cdBSIRvGbKS61Zju3m7UExNXDfC0sgzTivMZY2OKXEubr\nNI0VQdF4NKm6fsZTdWp7d+9f2ijS2tyfaJ4j4nhn2V8K5dHBSwr4byh5ZKrKuqyxjdRVPbRgqVN0\nFpb2blV78/8ANn/sSftM237H37Uvwu/aKufB0vj2w+HT+NDJ4UtteTw9Lq8virwD4s8DqI9bbR9Z\nWzksX8VLqU5k025W4SyNqnkmYXCacccNS4w4XzTh2GMjgJZj9Tti5UHiY0vqmYYTHO9FVaDnzrDO\nnpVjyufNry8pnwTxJHhHifLOIp4OWPjlzxl8JGusPKr9by/F4JWrOlWUOR4lVNacuZQ5fd5uY6L9\ni79rO2/ZN/aX039om68A3Pjq20/TPiDph8K2viCPQJJ5PG3h3WtDjddbn0fVwh0wa0bqQHS3+1LF\n9mRrbKyrzcacJz4t4Xnw7DHRwEp1MuqfW5Yd4hL6jXpVmvYqtRb9p7LlT9r7l7+9bll08GcVw4U4\nnhxDPAyx0YU8wh9UjiFh2/r1CrRT9s6NVL2fteZ/uve5be7zMyv2JP2o7T9kj4p+KviPceDrrx7F\n4k+EnxB+GLaPF4gTw7JZv4702HTItaN1LpetLJHpIi817BLaI3w2R/bLQhZK0424WlxdlWFy2GNj\ngHhs3y7NPbSoPEKawNSU3R5FVo2dXmsqnM+S1+SW0suC+KI8JZpisxngpY5YjKcxyz2Ma6w7g8dT\nVNVud0qvMqfLd0+WPPtzx3l8iIqPcl5FV5pJFhAb93HCilkkZTg5ZCSqbtzoOA4KqK+wPkT68+Lf\n7T1p8Sf2R/2Tv2ZF8HXGk3H7M+ofG7Vbrxo/iFL6DxdD8XfHJ8VwQLog0i1l0QaFEP7PmkbWNWfU\nWH2tEswrQV8hlXC88t4u4r4neNjWhxLSyWnHBLDuEsH/AGRgvqjbxHtpqv7d/vElRpez+G87cx9d\nmvE8cy4T4V4ZWClRlw3VzqpLGOupxxn9rY362kqHsoOh7Bfu23Vq+0+JcnwnsvwK/bv8HaN8Drb9\nlX9rP4Cad+0z8BvC+p3viD4YPb+K7r4ffE74Oanrt/PqOsnwV44sNP1O4uNC1S4lubq78LXq2cM9\nxcYuNSl0uC30hfGz3gTGVs9qcVcJZ9U4Zz7E0oUM05sJHMcrzmlRpqnQ+u4GpUpxjXpRjGMMTDml\nGMbxgqjlUl7OR8dYOlkVPhbizIafEuRYarOvlnLi55fmmT1a1R1K31PG06dSUqFWUpSlhp8kZSla\nc5Uoxonpenft3fsw/sxaJ4pvf2Df2ZvFXgP4yeK/D+peGYf2gfjZ8Q4fHvjb4f8Ah7W7NbbVU+Gf\nhbSLHT/D2h6+8VwY7XxP5rXsEKtBd2upWcz2ieZW4E4o4mrYWHHnE2Ex+TYSvSxUuH8jy94DBZhX\noz56TzLE1qksRXw6avPCpcjfvQnSnFTn6NLjrhjhqjipcCcNYvAZzi6FXDR4gzvMFjsbl9CvDlqr\nLsNSpww9Gu02oYpyU1HSpGcZOB+SN095f3Tz3LGaeWVpJZHdpZZJ5Jd7SSTuBJJI7OZ2aR3ZneRm\ndid1frUYqKUYpRjFKMYxSSikrJJLRJLRJaJaI/KG3JuUm5Sk25Sbu23q227ttvVtvXzGlFkcBGkR\n3Em2RUcBQhiVY5FZ2G4JGS4feACRyzEsxA4G2E+ZvJj2F4+ZGDysxJUgIVfcyuDGSdmQRuVKAJZJ\ng+DGFAlAeeR34QAbUV8dAwX5FXA+ZixAbNACoWeMhlLKWMssgBUhHCqqsuQA7OoRNrBd5DNnYEYA\niX5nO0yKysUEe7CiMrxgDaCXVVYOp+8GYYI+YAJBueMyIZBDGojj+URyuq8AuejAgnJCh4kDYyKA\nHGNvMmAnAZSGfbveMI0cSgZJZAwZiVVQoTlhkkKoB/pS/sLf8mSfsdcY/wCMV/2e+PT/AItJ4R4r\n/Nnjr/kt+Mf+yq4h/wDVvjD/AEf4G/5Ing//ALJbh/8A9VOEPqivlT6kKACgAoA//9f+/igAoAKA\nCgD52/a+DN+yZ+1CqjLH9nb42BR1yx+GviYAYGCefQ/lX0XCOnFnDD7cRZL/AOrLC+v5fefP8W/8\nkrxN/wBk/nX/AKrcSf5nUYRhGHkdJAysyy7nKkKwDlwuWdQp+V8Im12w6hWX/S4/zXHv+7JHEzZC\nFo1RzGOgX7q5ckOpIfIAXawIoA9J+DXxj+IfwB+Jvgn4wfC/xJN4Y8e+BNYj1bQdUtlWfbIkFzZ6\nhp9/aPthv9J1rT7260nV9NuQ9tqOk3l3Z3IeC4kVvMznJ8vz/K8Zk+a4eOKwGOpOjXpS0drqcKlO\nS1p1qNSMKtGrH3qdWEJx1ienk+b5hkOZ4PN8rxEsNj8DVVahVjqr8soTpzi7xqUq1OU6VanJctSl\nOcJXUmfq3rX7Y/8AwTW/at1SXxH+15+x745+EXxa1z9/4u+MX7JHiu0tdP1/WbplNz4gv/hp4vub\nfw/p1xdXDPdX99NbeNNfvZ1muLzUtQklMC/lNHg3xK4Upxw/B/GGBzfKaCthMn4tws5zw9GPwYen\nmOEjPEVIxj7lOEZYKhCPLGFOmkmfqlbjDw34qqSxHF3CGNyjNa75sVm/CeKhCGIrS+LEVMuxc40K\ncpSvKpOUcZXnK85TnKTRxfxi/wCCcfwt8SfAnxp+03+wR+0RH+0d8LPhlD/aXxX+Hfijw9N4R+Nn\nwv0VIpJ7jWNU0l7eyi1zSNKs4Lu71XU00Tw5Zf2bperahocniCysNSew7cm8Rs1w2e4Lhfj3h18O\nZpmc/ZZVmGGxCxWS5nXuoRo0qqdR0KtWcoQpU/b15e0qUqdZUJ1IKfFnPh1lWJyPG8T8B8QriLK8\nsj7XNMvxNCWFznLKNuaVarS5IKvSpwUqlWp7DDQ9nSq1KPt4U6koe4/8E2fg/wDGTwl+xP8AtNft\nU/sw/DnXfH37UPinx/p/7NHwj1LQtHtdV1f4W+G/7B0bxN8T/iF4dF2VEGpahpmtWnh+21lGhOka\nlbWaQLNDNqdldeF4k5xk2L424a4V4mzLD5fwvhMBU4kzenXrTp0c0xSr1sLlmX4jku5U4VaM8RKi\n1+8pym3ZxpTh7vhxlGc4PgriTinhrLq+P4nxeOp8OZTUoUoVK2WYZ0KOJzLMMPz/AA1Z068cPGre\nPs6kKaXMpVIT+c/Bn7Gv/BXT4e/Ei3+Lfg/4SftK6J8S7XUV1hvG8d5e3euajfwvLJGurXd5rFw+\ns2lyXlTUdP1oX9hqEN3c2t9Bc2080b/SY3jLwjzDLZ5RjM34arZZOk6X1J04RoU4ONv3MIUI+wlF\nWdOpR9lOnKMZQanCLj83g+D/ABby/MYZvhMp4lo5lCqqv11TnKvUmm2/bTnXn7eMrtVKdZVKdSMp\nQqRlGT5vVf8AgqJ+zjfat+2J+zLr134Pm+Fnjr9tL4XfBPxP8UPBENgltbeEPjp4x1yDwV8RtNsd\nODHBTV4rHUrxZJXlvtcvdQvppi16XryfC7iOFLg/iajHGrNMDwXmed4TK8bKbk8ZkeDovG5bUqVH\nd2dJ1KcLK0KEKVNJezset4n8Ozq8X8M1p4N5ZjuM8tyXFZpglBRWDzzF1lgsxp06eytVVOpNNyc6\n8qtRyfPeXP8A/BXP49eKtN+Ot9+xn8LtY1HwN+zN+y1ofhX4WeDPhv4b1G50zRdd1jT/AAppl34g\n8VeL7OxFrB4g8RSarrF3pYudS+1IYrKXUYxHqet65cX/AEeEfD+Fq5FDjPNKNPHcTcU18XmmMzHE\n041a1CjUxVSGHwmElPmdDDqlRp1eWm4u81TfNSo4eFDm8Ws/xVLPKnBmWVamB4a4XoYTLMHl2HqS\np0a9WnhaVTEYrFwhyqviXVqzpc1TmSUJVIpVa+IlV9o/4IVftReNI/2q/BH7NHj7Vb7xv8NfGNt4\np8UfDfSfFd62sL8Mfin4T8C+INRt/Ffgq51Bri58OSat4ItvGXhDU9M0qa0ttWtdege5SVrbyp/F\n8dOFsF/qtjuJsvowwOY4N4TDZlVwsFR/tPKsXjsPTlhcbGm4xrqjjpYPF0qlWM503QklJKV4e14H\ncUY1cUYLhrH1p43LcXHF4nLqWKl7X+zM0wmBxFSOKwUqjlLDurgY4zCVadKUI1Y11dS5bS4n/gjv\ne6Zp/wDwUe+KOpeIbRb/AEqw+G37SV7rGnTqtxb6hpEB+0ahatFKhj8q5tY5IXgdWR1aQSAbyjdv\njBCrV8OMsp0JunWqZlw3CjUTadOrP3ac01aScZNSTTuraWscXhFOlT8Rs0qVoKpRp5bxHOtTaTVS\nlB81SDTTTUopxaejvZ31Uvy6+MH7Snxj+NHxQvPi14z8b623imPUUv8Awpa6LqN1o+nfD7T7RkTQ\n9A8A6dp8ltaeD9E8MWVrbWWj2GiJZJaxWkUhaW7ea4l/Usn4aybJMrhlODwNB4Z0uTFOtShVq5hU\nnd18RmFSopzxdfEzlOpWnWc+Zya92KjE/L834kznOs0nm2MxtdYpVFPCqjUnRpZfTh/Aw+Ap05Rh\nhKGHgowowo8nKoqTvNty/Zj/AIKtftV/ExfgT+xT4T8O63P4T179pT9k74M/HD9pLxX4cKaJ4o+M\nOv3fhLSND8KQeMdd01bbU9V0jw7dab4mvYNEmuW02a51mFp7eV9PsnT8Z8KuFMsee8a4rEUY4uhw\n3xXnORcN4TE/vsNk9BYytXxU8HQqc1KlWxEamGhKtGPtFGk1GUOebl+yeKfFWZLIuC8Lh60sLiOJ\nOFcmzziPF4f9zic4rvCUqOFhjK9NKrUo0JU8VUjRbVNyqpyi/ZwPm3/gn/rmsfEH9j//AIKZ/BDx\nPq2o638PNM/ZmX41aJ4d1O8u73TdD8b/AA58S2mr6Xrmi2M8siaTf3EsVoL97FoDqkWn6ZFqX2mK\nytkg+l8QKFDAcX+Ged4WlTo5jV4meTV8TSpxhVxGAzHDSpVaFecbOrCCc/ZRqOXsva1XT5XOXN83\nwDXr4/hHxKyXE1qlbL6XDX9s0cNVnOdLD47LsTGtSr0IttUpzaj7V0+X2vs6Sqcygke0f8E3Pg/8\nZ/B/7Ev7TP7Vn7MXw38QfED9qLxZ460/9m34R6homk2Woav8MfDUmg6N4p+JvxE8OxznFtqd/p2u\nWug2mrB0l0zULWxjgMsM+pWt14viTm+TYvjbhrhXibMaGA4XwuAqcSZtTr1p06OaYlV62FyzL8Ry\nL3qUKtCWIlS1VSEqjbjKFOcPa8OMoznCcFcScU8NZdXzDifFY+HDmU1KFKFStleGdCjicyzDD8/w\n1Z0q8aEavuOnUhBLnjKpCXzd4M/Y2/4K5/Dz4jQfF/wd8H/2mNI+J1rdjVh40W+ubvXr/UVkaUHW\nL691u5OuWs7SSx6jp2ri7stUtri4t9Qhmt55oW+kxvGXhFmGWzynGZvw1WyydN0vqTpwjQpwaUV7\nGEKEfYTjo6dSioTpyjGVOUZQhOPzeD4P8WsvzGGbYTKOJKOZQqqr9cU5yrzmnf8AfTnXn7eMrtVI\nVvaQqRlKM4yhKUZ+mf8ABVT4TTeEP2sP2cPi5q3gp/hl4y/aX+FPwZ+MPxZ8AGzFla+E/jbPf/2H\n8TNLs7aIuItuoaXZX18JJZGuNYvdV1OaSV70vXmeFWbRxvCfEeUU8d/aWD4azXOcoynHubnLFZJG\nn7fLK05at/u6k4U7K0aMKVNJclj0vFPKpYPivh7NquBeW4ziXKcnzfNsDy8kcLnUqnscypQhry/v\nKcJ1NW5Vp1ajlJzZ1v8AwXY/aT8f+Iv2xfiH+z7pWrXmgfC34b2/giXUvCvh5v7K0rxn448T/D7w\nt4j1Pxx4ytrLyP8AhKPEUOk6ppHg/TbzWpLptJ0Lw5aW2kpZC61B73k8CuGsvw3B2X8Q1aMMTmuZ\nSxqp4rEL21XA4HC5hisNSwOClNN4XDzq0q2Lqwo8vtq2IlKq5clNR6/HLiPMMTxhmHD9KtPDZXls\ncFKphKD9jSxuOxWX4XE1cdjIwt9axEaVWjhKc6zn7Kjh4wpKHNUc+H/Y08aeJPix/wAE6v8AgpB8\nE/iLrWpeLPBXww+HXw2+MPwy07WL+51E/D7xfo/i3UBqV14WkndptFj1tINPhu7SxeK1eJL/ACmN\nY1UXfbxlgsPlPiJ4b53l9GnhMbmmY5lk2Z1KEI0/r+Eq4Wm6ccUopKs6LlNwnNc6bp6v2NLk4uDs\nbiM18PPEbJcwq1MXgssy/Ls4yynXnKosBi6WLn7SWFcm3RVZQpqcIKMGlUWirVeej/wTY8S69D+z\nt/wU1gt9e1uGHRv2OdTu9Mt4tVvxa6Tdv4glla60u2WdY7C5mkkmeS4tVgd5N7nBZi2niVhsPLiP\nwxlKhRcq3GNKFZulByrRVCKUarcW6kUklafMrWWiSM/DfE4hcO+JcVXrqNDhCrKilVmo0Ze3bcqS\n5kqcm23eCTvd63974U/ZV+PGjfBL9pb4YfHP4k+A7/47WfgLXZddPgzVdeSwutb8QWujahY+EJ59\nY1PSvFOF8M68dK121Dabcs8ujWtrD5AkWRPuuKshr53wzmeQ5ZjqeRTx9FUHjKWH540MPKtCeLjG\njSrYX/eaCq0Jv2sUo1ZyalZqXw/C2e0Ml4ly3PczwNTPI4Cs6/1OrX5JYjEQozhhJSrVaWJ/3au6\nNeC9lJuVGMVbRx+n9b/ZK/4Kjfti/ELW/jTq/wAC/j34j8T+LtbudasvEvjSzvfA1jp8d1cPdQW3\nhe8+IOo+GbLS/DdrHMltosWkPbaPp1lElvYRxwRbV+XocW+F3B2XUclo55kGHw2EowoTw+ClDHzq\nOEFGcsTDL6eKnWxFRxcqzqqVapNtzcpM+mr8KeJ/GGYVc6rZHn2IxOLrTrwxGMjPA06anJyhDDTx\n88LCjh6aajRjScaUIRShaKsfUH/BWP4c/Ejwl+yr/wAE2dW+PGt6T4l/aAs/Bvx3+HfxF8X6fr+m\n+NbrVdP8FeK/DM/gjQtY8Z6XcX8XiHWPBunaxcaPrF4mp6oi+JJdec397cvPeXHzPhPmOW4virxJ\no5DRq4Xh+eMyPMcuwlTD1cFClUxmExUcbXo4KrCm8PRxtWjGtRg6dJ/Vlh17KEVCEPp/FbLsxwnC\nvhxVz2tSxXEEMJnmX5hi6eJpYydWng8VhZYOhWxtKVRYmtgqdWVGtNVKi+svEP2k5Oc5ebfEfxr4\nw/4c2fs56xH4v8Sw6rcftlfE2ym1JNd1Vb24t08FaiyWk96bpbqW1SVfMiiklaJW2EYxuX0stwWD\nfjJxHReEwzox4NyycaboUnTjN42knJQ5OVSaunJK/e9zzcxxuMXg7w7WWLxSqy4xzKEqqxFX2koL\nBVWoufPzOKe0W7J6q1zmf2Gi0n/BPv8A4K3zKBul+Hv7Maj5mZmnX4n+KgzndwSzsrA7zvPLdTt6\neOVbxA8JEtEsx4mSS6f8JmDObgd34B8WW9W8u4abb6/8KeLO1/ZY167/AGPP+CZ3xw/bG8Bs2jft\nAfHT446X+yx8L/HTx2cuqfDnwba+ER438Y+IvCks8UkthqviOO18QaFPfQH7Rp11pPhzU7SZLi0a\nN+LinDw4x8TMk4Ox373h/I8jq8VZpgeaapZjjJ4r6lg8PilFpTpYeUsPXjCSlGpCriac4uMjt4Xx\nE+EPDXO+MMD+6z/O88pcLZZjuWDq5dg4YX67jMRhZSjeFXERhiKEpx96nOlh6kZRlBqX5Z+Bvj78\nbfh38QLb4seD/iv478O/Euy1O31VfFsXiTVrvVL+6t5Fni/tSW8luBrFlK/7u80rV4r/AE/ULV7q\nzv7eW3up4X/UcdkGSZll88qxuVYCvl06cqTwksNSjShCas/YqEYuhNaOFSjyVISSlBqUVKP5hgs/\nzrLswhmuDzTHUMxhUjVWLWJqyqznF3XtnNyVeDek6dbnhOLlCcZQlKMv05/4KY+HvDHij4hfscft\nf+G9AsfDd5+2P8IfBHxH+IGgaTbQW+jx/F3w9e6No3j/AFHS44o0jhS8e50mW/Qqs9zqcV3q15JN\nf6rdMv5l4aYjE4XLuM+D8TiJ4qPB2b47Lsvr1m5V3lGIp1q2AhVcrt+zUKqhq4wpOFGFqdKB+leJ\nWHw2LzDg7i7DUIYWXGOU4LMcwo0oqNCObYedGjj6lJJKyqOdJz05p1OatO86sz0f/gu9+0j8QvEX\n7YvxA/Z40nWrrwz8L/hzYeA59Y8L+H5f7H07x1458WfDvwp4ku/HXjOLT2t38Va7ZaDqXh7wbpc+\num9XSNF8N21lpUVqLm+e783wJ4by+hwdgOIa1GGJzPMamPjRxWIXtquAwOFzDFYWGBwTqX+q0J4i\nlicZVjQ5fbVsTKdVz5acYel46cR4+vxfj+H6VaWGyzLqeBnWw2HfsaeOx2Ky7C4ieNxqp8v1qvDD\n1cPhKUq/P7Gjh4wpKHNUcvP/ANizxnr3xZ/4J8f8FIvgX8QdX1Txd4F+HXws8DfGn4b6brl7damv\ngTxboPiu8kurvwwLuaWbRk1j7PYLfWdlLHbXEEN1HJGF1TUVuvQ41wWGyrxB8N88y+jTwmOzHNcd\nk2ZVaFONN4/B18LBRhiuVJVnR5qjpzmpSi5QfNejS5fP4LxuIzXgDxHyTH1amLwWXZXgc5y6nXnO\nosBi8Pipuc8K5O9FVuWmqkYJRklNO6q1Ob8aT+7EpjDs5jD4ZQAwYY3qI2LZeQFkX742KOcZr9mP\nxwXaVM22NmkEEnDYc+bKCUIbcwDKeWRQrP8AeLAqKAP16/4Iz/H4fsv/ABl/aL+OZ+C/7Qn7REng\nb9lW/wB/wa/Zf+HH/C3fjv4v/wCEm/aM/Zu8GhvA/gL+2PDx8Qt4f/t7/hJ/EynWLMaR4P0TxFra\n/al04W8/4D9I3/kiMr/7KrA/+qjPD97+jt/yW2af9ktjf/VtkZ+6f/D/AF/6wr/8F/v/ABXH/wDj\nlr+LT+zQ/wCH+v8A1hX/AOC/3/iuP/8AHLQAf8P9f+sK/wDwX+/8Vx//AI5aAD/h/r/1hX/4L/f+\nK4//AMctAB/w/wBf+sK//Bf7/wAVx/8A45aAD/h/r/1hX/4L/f8AiuP/APHLQAf8P9f+sK//AAX+\n/wDFcf8A+OWgA/4f6/8AWFf/AIL/AH/iuP8A/HLQB/OT/wAFOP8Ag58/be/Zu/4KC/DPxj8BP2f/\nANpf9nz4G3n7N3gS28e/sd/8FHv2eI/g5rnj3xJbfFT4ytqPxb8GaTpnivV/GOg6XrPh650fwdpH\njnQvF1rpuq+IPA+qab4g8Ka5b+FLSOcA++f2yv8AgprP/wAFQ/8Agkn8Pvjc37Kvx/8A2ZpYP2lv\nhik8fxS0Jx8PPGxvPhv8XpJNZ+CfxKNvpD/EvwlbPGLe81w+FPDrW92XsvscrJ50v7z9Hb/kusd/\n2TWP/wDVhlJ+E/SG/wCSHwX/AGUmA/8AVfmv9f8ADn8+CLGzEpOFIjAy42lQjFip4Me1SjNkFmkZ\nGIJAKr/a5/FpGzFcKi5/iMqqux8N+8O4IpCjDBBtZT8oKZ5UAUP+9BaRWPcISUbe0jtvK8Fdkirt\nKgM6qw27d6gCbEIlKCQIWJVmAmfCeU5KMWAUQsGUJuyAny4O4uAKSWLFxmGCMPuJ2KwRjw/Uhsod\noB3L8zAruLKAIqAiKIBARE0h3x4MoIyjMdxMZQFY2U5IyzFjtZWAH5VFGcBY5AixMrlzF+8ErFwM\n72f5sY+YlQRwooAiVUiDSOwkDSmZ43wQQRGH2ABcNuH3cKeFG08soAoiKcEsVkMZZI3YlUbCgLvC\njLb4ct1yApUgE0AMUSpFJ5ZIkB8nYzhtyDKuscgByp3Bw2xFBAORtO0Acig+SVkIfhikm92jfZtY\ntKB84AC5eQALskcI7MNwBIcABFAkc5jd4lU+WFGAgJCsS+zqDuAXPy9KAIxvKr5khGBkmNvm348l\nkKxmMZ3lSH+XaCA2cksAP2QySKwDgrhZAgDx72+Q+XkqqGaNTlyOWC4AC4YAUAlUiRXZWd0kPCMp\nCsHHytsCA4Rtx2HaAMk4oAa24QgxRvg3IbepKusSIynMnzuyl8EuCwKZQJwu0AkRY2YlJwpEYGXG\n0qEYsVPBj2qUZsgs0jIxBIBVQCNmK4VFz/EZVVdj4b94dwRSFGGCDayn5QUzyoAof96C0ise4Qko\n29pHbeV4K7JFXaVAZ1Vht271AE2IRKUEgQsSrMBM+E8pyUYsAohYMoTdkBPlwdxcAUksWLjMMEYf\ncTsVgjHh+pDZQ7QDuX5mBXcWUARUBEUQCAiJpDvjwZQRlGY7iYygKxspyRlmLHaysAPyqKM4CxyB\nFiZXLmL94JWLgZ3s/wA2MfMSoI4UUARKqRBpHYSBpTM8b4IIIjD7AAuG3D7uFPCjaeWUAURFOCWK\nyGMskbsSqNhQF3hRlt8OW65AUqQCaAGKJUik8skSA+TsZw25BlXWOQA5U7g4bYiggHI2naAORQfJ\nKyEPwxSTe7Rvs2sWlA+cABcvIAF2SOEdmG4AkOAAigSOcxu8SqfLCjAQEhWJfZ1B3ALn5elAEY3l\nV8yQjAyTG3zb8eSyFYzGM7ypD/LtBAbOSWAH7IZJFYBwVwsgQB497fIfLyVVDNGpy5HLBcABcMAK\nASqRIrsrO6SHhGUhWDj5W2BAcI247DtAGScUANbcIQYo3wbkNvUlXWJEZTmT53ZS+CXBYFMoE4Xa\nAf0p/wDBK/8Ab7T9hz9iH5P2Lf8AgoD+2GvxP/ap+No/4wa/ZvHx/wD+FdHwT8JP2YAV+KOPGXhE\neE/+EsPiz/iimB1L/hIG8M+MDm1/sYi4/i36Rv8AyW+V/wDZK4H/ANW+eH9m/R2/5InNP+ypxv8A\n6qcjPv3/AIf6/wDWFf8A4L/f+K4//wActfgJ+9h/w/1/6wr/APBf7/xXH/8AjloAP+H+v/WFf/gv\n9/4rj/8Axy0AH/D/AF/6wr/8F/v/ABXH/wDjloAP+H+v/WFf/gv9/wCK4/8A8ctAB/w/1/6wr/8A\nBf7/AMVx/wD45aAD/h/r/wBYV/8Agv8Af+K4/wD8ctAB/wAP9f8ArCv/AMF/v/Fcf/45aAP5ZvhN\n/wAHZn7cP7P37c37R3w9+KfwU8afHz9njxD+1d8bLP4efs//ABY8Jr8Kv2wvgP4X1r4u+J18KfBw\nSaBBrsa+Mfh7p95beE9Y+GHjfS/GWo6dr2jjwXo3jXRNPsYZlAPtf/gtd8aT+0N8e/2b/jK/wr+M\nnwSh8d/sceAtZPw0+PPgz/hX/wAVvCTP8X/jpnT/ABl4OOo6qumagojWaOBr6XfayJMCGlSNf7S+\njl/yRGaf9lVjv/VRkZ/GX0if+S2yv/slsF/6ts8Px1CIEZhOoXe28H7zBj82A6HMrBGHygIrptG3\nBr9+PwQjLuzFVUQDDxfOFVAzDAwQvzcbichWyyguQVVQBUaJhJvdij7mJVivl/MSjRNnaHQN80gP\n3QoIcqCoAirsWJiGQpzjyw2CY1fy5HLAtkx53gMPl5yRuoARlZ13SjbJJN5MefmAYOCSi/KWX938\n7MdnLSFXwSoA4qrmR/LUxxsB5KjayBELeWGBbfkgFumVd+FKMWAFc5BDurNLHhyqMhRy7hhyrKQY\niRuI+XKgEfLQAxUVRHD8srskcSFzh0kQRnll3ELgYBwRjAycttAGtGwTbmSQRqWQrJgZjcM2WYZG\nI3RUQKedqhxkmgAcyKsIDM0TMrygOY5Fbd/rG2h8jbIN6bsuF2lQAdoA8IAWZJkKqqZZ1ZcLGSyq\neDHuUBThcl2R3eRQQrAA7H/lkCFjKt5qrGEkGcO5yvAADIu5CpIzjO0qAKDtbMjbivBTcxikWIcs\nfmKruV0CptY7iCu3JegBoiixI6mbYfmViiswjjIG8uzAERDeiooBX5MZ2ncAPfLFi6N5ccaMuGK5\nJ3BQfnXLgELhDv2jDY3GgBJMrJEHV44lgQPIoAQyKzMzNF91iAFxGx+UBxuY7FoAeEQIzCdQu9t4\nP3mDH5sB0OZWCMPlARXTaNuDQBGXdmKqogGHi+cKqBmGBghfm43E5CtllBcgqqgCo0TCTe7FH3MS\nrFfL+YlGibO0Ogb5pAfuhQQ5UFQBFXYsTEMhTnHlhsExq/lyOWBbJjzvAYfLzkjdQAjKzrulG2SS\nbyY8/MAwcElF+Usv7v52Y7OWkKvglQBxVXMj+WpjjYDyVG1kCIW8sMC2/JALdMq78KUYsAK5yCHd\nWaWPDlUZCjl3DDlWUgxEjcR8uVAI+WgBioqiOH5ZXZI4kLnDpIgjPLLuIXAwDgjGBk5baANaNgm3\nMkgjUshWTAzG4ZsswyMRuiogU87VDjJNAA5kVYQGZomZXlAcxyK27/WNtD5G2Qb03ZcLtKgA7QB4\nQAsyTIVVUyzqy4WMllU8GPcoCnC5Lsju8ighWAB2P/LIELGVbzVWMJIM4dzleAAGRdyFSRnGdpUA\nUHa2ZG3FeCm5jFIsQ5Y/MVXcroFTax3EFduS9ADRFFiR1M2w/MrFFZhHGQN5dmAIiG9FRQCvyYzt\nO4Ae+WLF0by440ZcMVyTuCg/OuXAIXCHftGGxuNACSZWSIOrxxLAgeRQAhkVmZmaL7rEALiNj8oD\njcx2LQB/pSfsK/8AJkX7HHJI/wCGVv2euTwT/wAWk8IckYGCep4H0HSv82eOv+S34x/7KriH/wBW\n+MP9H+Bv+SJ4P/7Jbh//ANVOEPqmvlT6kKACgAoA/9D+/igAoAKACgD56/a4BP7KX7TgBIJ/Z6+N\nABGMg/8ACt/EuCM8Zz0zx619DwlpxXwy+3EOS/8Aqyw3r+X3nz/Fn/JK8Tf9k/nP/quxJ/mdZDDc\nyrvCgyKpO6SMqcNuC4f5t2JMuhD7RtG5V/0vP81yJY5INyqzqm5W8zG4qCDiMsAS5Jd1VgikEEjB\nIFAH6R/8EudI/Zy8c/tIap8Gf2mNH8MS+F/jd8LPF/ww+HviLxPbI0fgn4u+IJdKHgjxHp94z28u\nnanKLXU9F0i6mMaPrmqafCzrlZYvzfxRrcR4HhylnHDNXExxOSZpg80zDD4aVpY3KMOqv17D1IWl\n7WkualWrQSv7GlVetrH6N4YUeHMdxHVyjiWlhpYbOssxeWYDEYmHNHBZviJUfqWJpzvH2dX3atGj\nJuzrVqSdr3PBvj/+xR+1B+zd471n4ffEn4ReNbO5sNTubPT/ABRonhfXdV8G+L7eK5MFjq/hXxFa\n6e9jqml6iFR7ZUljvbZpvs2pWGn6jDcWMHvZBxtwxxJgKOPy3OMDKNSnGdXDVsTQo4zCTcVKdHFY\nadRVKVSm7xk3H2cuXnpSlTcZz8PPuCuJuHMfWwGZZRjoyp1ZQpYmjhq9bB4uCk4wrYXEwpunVp1E\nlKKTVSN+SrCE1KEP0+/4J4fC/wCI37I3wC/bY/av/aL0DV/hf8IfFv7L/jT4GeAvC/xC0u/8PX/x\nm+J3jyaBfDNlofhrUrWDWtRsrdtHn0q41Q21rZ/ZvEeo3trczabo/iK60/8AL/ETNMt4uz/gnhTh\n3EUc0zbCcT4LO8fisvqQxFPJ8twKl9ZqV8TTlKjTqSVaNZUuaU+bD04SSqVcPCf6b4e5XmPCWQ8a\ncV8Q4etleU4vhrG5JgMLmFKdCpnGZY9r6tToYapD2tSnB0ZUpVeWMOXEVJxlKlRxM6HFfsMnW/2n\nf2BP2jv2I/hp4kk0P9ojwx8VtF/aj+B2gJ4jh8M3nxNhtPDVh4R8c+CfD93Jf6eo1Ow0LSp9Wisj\ndtb3t1rNncSrb2Ntq2oWndxz7Hhnj7hzjfMsMq/DuKyqtwvndd4aWIp5ZKWJqYvA43EQUKv7qpXq\nqk5qEXCFGSTc50oT4eB3X4l4D4i4Jy3EuhxDhc1ocT5LQWJWHnmcY4enhMfgsPNzp/vadCk6qp8z\nU51oN2hGrOl8M+Efgj+3Z41+JNp8KdD8BftKz+PL/UItLi8P32mfEPRZtJled42vPEdxrAs7LQtJ\ntj5st7rGsXFjpdlbxS3N3OkKeYv3WLz3gTBZdPNsRj+Gll8KcqixFOpl1aNVJX5KEaPPPEVZbQo0\nYzqzk1CMXJpHw+EyTjrG5jDKsPgeJHj51I03h6lPMKMqTk7c9eVXkhh6UbNzrVnCnCMXKU4xTlLt\n/wBr3wx4d/ZJ/al8B+FPAfxk8SfHfxh8Bv8AhANd+Imv6xqaXuj6b8adC1eLxB4s8EeD7yIXMv8A\nYvh68tNPs7i4lNzfWmsSX2nXsCX2lThuDhLFYni7hXH4rH5Nhsiwee/2hQy7DUaXJXq5NiKLw+Fx\nuLg7L29eE6k4qMYwlR9nVheFWLO7i3C4bhLijA4XA5xiM9xeRrAV8xxFarz0aWc0KqxGKwWEmud+\nww84U4SlJznGs6lKolOlJH1T/wAFQv2c/GHxf+KP/DeX7O3hzXvix+zt+074e8K+Pf7f8E6fN4tm\n+HfjKz8L6VpPjLwX48tfD0d9N4a1bTr7TPtU8mqwWtrHqFzf6GbptS0e7Svl/C7iPB5Plb4E4ixO\nHyniLhnE4nA/V8dUWEjmODniqtbB43L54hwjiadSnV5Iqk5SlThTrqKp1oH1Hifw7jM3zRcdcPYX\nEZrw9xNhsLjvrGCpvFSy7GRw1Kji8Hj4YdVJYarTqUuaTqqMVUnUoc7qUpqX0B/wQ6/Yw+Ivh/8A\naX8H/tPfGbw3q/ws8I6HB4p8M/CC38d2MvhzXPip8TvE/gXXLGfTfB+g6xFa6rq2k6D8PZPG3iLU\nNbs7RrJX06JLWa5Sx1p9I+e8cONMuxHDWM4ZyXE0s1xmIlhcVnEsDUWJoZVlmFxtCUZ4yvRc6VGt\nXzBYLD06E2ptVHzqLnQ5/oPBLgzMcPxJg+Js5w1bK8Jh44rC5RHHU5YavmmZYrBV4ShhKFZRq1aV\nDL3jMRUrQi4Xp2hKXs6zpfO3/BKWQyft4/HLd8+fgl+1OQ5OWkb+zrnIVlCqUw2QqjJRopOcgt9F\n4rf8kFkf/Y64V/8ATkT53ws/5LzPOv8AwjcU/P8Adz+/+mfi2yujuyu6iZSS2PMaJhjdLv2g4x5m\n5SrqRwQMsa/aD8aP1t/4Knyuvg3/AIJmLu3H/h2v+zszSEIN7C21nLgsuVaNsS8LjDHO3gV+SeFn\n+++Jn/ZyeIf/AEqkfrPij/ufhp/2bjh7/wBJq/P+tOo7/gmWVHwb/wCCoESsA3/DBfxEwowk0a/b\nbdQsjZJLMxYLJwNgUgDALPxM/wCRz4Yf9l5l/wD6RL/P5fMPDT/kT+J3/ZCZj/6VE9C/YWGuftNf\nsCftHfsPfDjxTcaL+0R4Y+K+iftQ/BTRIvEkHh7UfiZDa+HbDwl498D+HL6XUdNA1bTdB0mfVrey\na88u7utYtJZRDYWerahaebxz7Hhnj7hzjfMsMq/DuKyqtwvndd4aWIp5ZKWJqYzA43EQUKv7qpXq\nqk5qCcIUZpOU50oS9Hgd1uJeA+IuCcuxTocQ4XNKPE+S0FiVh55mo4aGEx2Cw83On+9p0KTqqm5N\nTnWhJ2hCrOl8LeEPgd+3Z44+Ill8JvDvgP8AaXl8fTX8WnjQNQ0/4h6DdabPM5i+16/cawbOy0PT\nLRg017rOt3FpptnDHJcXd1DBG8lfdYvPeBMFl082xGP4aWXwpyqLEU6mXVo1UlfkoRo888RVltCj\nRjOrOTUIxcmkfDYTJOOsbmEMqw+A4kePnUVN4epTzCjKk27c1eVXkhh6UdXOtWcKcIxcpTjFOUvQ\nP2u/hR4M+BH7Ungf4N+GfjN4i+OHiH4fw+ANN+LPiXWNRGoaRo/xYm1KOXxl4Q8IXC+c8+ieHLgW\ntrcT3U8t5Dq76no98kN5pVwrcPCObYzPuFsdnOKybD5Hh8e8fUyrC0aTpV6+Uqk44PGYuOyr4mPP\nOKglB0vZ1YXhVgzu4uyrB5FxRgsmw2c4nO6+AWApZria1T2lGhmzqqWMweEldt0cM+SEnKTmq3tK\nVRRnSkjvP+Cz/mr/AMFMf2nyGZFOpfC07wmcAfBD4bKE43Eliz7TtBU5wea8/wAGP+TacMf9e80/\n9XeZHoeMuviVxN/18yv8MkyxeX9d9zrP+CeZI/ZO/wCCqyFCqj9l7wqVAKrIQfFupKSNwWTDBR80\nqKS6uATjK8niH/yVnhV/2U+K/wDUSkdPh9/ySnin/wBkzhf/AFLqf8At/wDBMXTdT8RfBH/gp74a\n0CyuNX8Rar+xfr8unaRpNrNd6pqLWOrxyXS2enWyzXVw9us8KGKBHYvKFjVnbCx4n1aeHzvwxxNe\npCjh6XGlBVa1WShSp89H3XOpK0YJ8r1k7aN6WZr4ZUqmIyTxNw1CnOtiKvBtd0qNKMqlWpyVW5KE\nIqUpNXWkU3qkty3/AMEbvCdtaftf+LoPEHhiwb40+Hf2bvit4n/Zp8N+PtPTTLXUfj5ZWekyeDJI\nrPxALSO61EeHX8S6hZebbulvDBc61C0E+m293Fn4yYyc+EMHLD4qp/YmI4jyrC8S4nL6jqSp5FOd\naOMTqUOZqm66w0KlpQcpShRlzKq4yrwewcIcXYuOIwtP+2cPw7muK4bw2YQVONTPYQovBtQr8ic1\nQeJqU7q0YxlWi4ypxlH5f8aaD/wUV/aK+Jer+FfHmiftVfEj4m6hq9xZal4W8QaX8R7i4sr9pjHL\naT+HriBdI8OaZbAS5gjtdP0LTdPgLAWmnwbk+pwVbw64eyuli8BW4Uy3LKdGM6WKoVcthGdNJOMl\nXi51sTVlpq5Vq9Wo951JXPl8bR8Q+Iczq4THUeKcyzKpWlCrha9LMZShNtqUXh5JUsNSjrooU6NK\nC2jCNj9Cf+CjvwK1v4S/8E8/+CbfgKxms/H3/CrpP2nbL4heJPAV3D4y8JeFPG2veNvDviHxP4c1\nLxJoaXWkLN4Y8TS+JfCt5KbkWx1Pw5qUMck/2cvX594cZ9h828RPEjMKingP7UXDM8uw2Pg8Fi8V\ng6GCxFDC4iGGruFZrE4VYbFQVnL2eJpScY86ifoHiNkVfKfD3w4wEJQx39lviWGY4jAy+uYXDY2v\njcPXxWGniaN6SlhsU8ThZtvl9ph6iTfI3Pxrxd4X8T+Lv+CLfwDHhPw/rniaTw9+238RI9bh8PaR\ne6xcaa2oeBtSktEv4dOtrh7T7QLqCOJ5IlQzz20SkyzRK/sYXF4bB+NGf/W8RQwqr8FZc6LxFWFF\nVfZ46nz+zlUcVJxs7pO9lJ6KL5vIxWFxOL8Gch+q4etiXQ40zFVlQpTrOk6mBq8ntFCMnBSvFJuy\nblFauSRjfsNpJa/sA/8ABXSGVWjlt/AX7MSywyJ5ckLx/FDxYsqSo3ypKjKyMu2I7lwyqSGbbjhq\nXH/hHKLTTzDiZpp3TTyvBtNNXTTWqaf33MeCE48A+LUZJprL+G001ZprM8WmmnZpp6NNfdY639kb\nRl/bH/4J4fG/9hzwndQ3X7Qvw3+NGmftX/BXwff6jpljN8R9Kj8JWvgXxx4O8M3N9cwQjXNE0t9c\n1+DT5GWS/utZ08R5s4dUu9P5OLq3+p3iHkfHOLhKPD2ZZNU4UzvF04VJrLazxcsbgsZiY04uXsa1\nVUKDnaShChO/vulCfVwlR/1w8Ps74Hwkoy4gy3OafFWS4SpOnB5lSWFjgsdg8NKpOK9vRpOtXUG4\nucq8LXgqk6X5/wDw+/Y2/ar+JvxDs/hT4b/Z/wDixL42u7+00y60/WvA/iTw/Fodxcy+UbrxTqms\naZYaf4Y0eDEkl3qWtzWFpb26faHlXaob9Bx/GfCuW5dPNcVn+VLBQpSqxq0cdh8RKuoq/JhaVCpO\npiqstFGlQjOcm0rLePwGA4N4pzLMYZXhcgzV42dWNKVOrgcTh40HJ258VVrU4U8NSjZudWvOnCKT\nbkz7g/4KV+OvBVj8Wf2Uf2U/hx4ntPGWg/sbfCjwP8IvEvjHSLi3vdF1v4p3Oo6ZqXxEvNGntt8U\nlra3VvY2F8Tukh1m11TTJ49+nNM/w3hrgMfUynizivMsNPB1+M80x2b4bB1YyhXoZXGnVp5fGspW\nkpThKpOGlpUZUqq0qpR+48SMdgYZrwrwrl2JhjKHBuV4HKMTi6Uozo180lUpVMwlRcVZxjONOE9b\nxrRq0mk6XNLj/wDgtfIyf8FM/wBphQAoM3wjfcdg3Y+A/wAL8EM65DwkBhgFdvcH73b4K/8AJsuG\nf8Ob/wDq+zT1/rtscXjP/wAnL4l/xZT/AOqLLPT+u+50P/BOkqP2Xv8AgqjGrAN/wydpoCLhJo1P\nie8UCRgWJJYttk+UFACAAM1zeI3/ACVHhX/2VlT/ANRqZ0+Hf/JMeKX/AGSlP/1JmfkaN3lqjOzf\nvMPLHy6AnccMHAyCqGMBiwyRns/62fkxIiq6kR+ZtgDOjKDG2/YSC3z7XZSR87HJZgGC5DMAf0Af\n8G5BDftu/FIkIsv/AAyr413BTkuv/C2/gftckABwDuIcMw+fC7fmSvwH6Rv/ACRGV/8AZVYH/wBV\nGeH739Hb/kts0/7JbG/+rbIz+06v4tP7NCgAoAKACgAoAKACgD4I+MX/AATG/Yh/aH/ax8Jftp/H\nv4E+F/jL8cvh/wDC7wz8I/AV18SUk8V+CPCHhvwr4x8d+OtO1LTPh1qfneDrvxZ/wkPxB1i4XxNr\nulaxqWlrZ6SfD76NcQXdxdgHwX/wcRqsH7CngSOBEjji/aT+H8SQxhIoliT4e/FhVhVRtVYwoCpG\ngXoqLgGv3n6O3/JdY7/smsf/AOrDKT8J+kL/AMkPgv8AspMB/wCq/Nj+J9o0lBQYCyHA8slfKlG4\nuSDGAPmBYptRhuIOCxK/2ufxaNQSAIr7sAcR7GAlZWLbvkWQgB2IYH5SeWAzlgBvmMzMGCR7H2kk\nREDGVJnjj5JSP5fkdfLdSSpKgKANLuWTa6osWwrEAWRlKrJg7eSxO3eCNxV2yV2HcAM34kLP5uQz\niKN4x5bIH2IPJZlJbLbUY5LKcKzE7aALG1Y9srMis+8OWfJOUBXCNgsDLIoA+UleBuzuoA/Qf9mz\n/gm98a/2nPgtq3x28MeO/gT4C8A6X4/1P4cXur/GD4jN4AV/Etlo2ieIHtoHuvD9/p5insvEFobZ\njqAuriSC+CQCOBHf8+4k8SMl4YzmnkOKwGfY/MauAp5lGlk+WrH/AOy1K1egpNRr06icalCfP+7l\nGKlB83vWj9/w54c51xNk9TPcLj8iwGXUsfUy6VXN8yeA/wBpp0aNdxTlh6lNqVOvHk/exlJxmlC0\nXKXrD/8ABIf4wySmRv2of2DNpXlR+03ohCEkbDz4eO4HODnaWyp5bAryP+IvZP8A9Exx5/4jNb/5\neev/AMQjzf8A6KfgX/xJqP8A8oPlb9pz9kLxj+ylaeB5/FfxU+APxITxtJrtrZJ8EPinY/EabR28\nPnRpLlvEK2+n2J0iK9j1iEaU7mX7a9nfqAv2Vmb6rhjjHB8VSxscJlef5c8DGhKbzvK55cq31h1l\nFYZyqT9q4exftUkuRSp3vzI+V4m4QxnC0cFLFZpkOY/XnXVNZLmcMxdH6uqTk8Qo04eyVT269k3f\nn5KlrcrPkjhI4vLDjyyFEsboWwWQMucMG8zMwTAOWQKMABW+uPkx4ZXChlAfJBCMyt5TL98tgjkN\ntDkbJEcAYwtAGxbeE/Fsvhy88VWugeIZPCWl6pBo+oeKoNE1Gbw5aapdwpLbaLfa5FaSadb6lPE5\nkt7Sa4hvJYmDxRsHyuEsVhY4mGDliaEcXUpyrU8LKtTWJqUYu0qsKDkqsqcWrSnGLino2rM3jhcV\nLDzxkcNXlhKVSNGpio0ajw9OrNXjSnWUXThUktYwlJSa1SdzDkcvJsZSqr8u1Qm5dvzcDAZkYqsw\nUlXOwjvltzAbJI0imOOZQFLF3hZUQyLtDnhiMANsRGb5Tk4fFACNtdh5jOqBFIMa5WRsgfMu9dyF\nVR2GChfjjksASjd8s6htzlExny0aPDHZsZsDd8rbE53MgUtuL0AK0aSgoMBZDgeWSvlSjcXJBjAH\nzAsU2ow3EHBYlQBqCQBFfdgDiPYwErKxbd8iyEAOxDA/KTywGcsAN8xmZgwSPY+0kiIgYypM8cfJ\nKR/L8jr5bqSVJUBQDc1nwv4r8P2ugajrvh7XdA03xFpyax4YudX0bUtOsPEejsVxqeiXN3bwxavY\nNIAj3lg1xAS5UzKUK1z0cXhcROvTw+Jw9ephanscTCjWp1Z4erv7KvGEm6VS2vJUUZd0zorYXFYe\nFCpiMNiKFPFU/bYadajUpQxFK9va0JTio1ad9Oem5RvpdaHth/Zh+JA/ZdP7YLz+Hf8AhVQ+Mb/A\n+OyfULv/AISpvGA8PHxNHJ/YTaalkdH/ALNyiah/bDXJuCIRZlW314v+s+W/60/6ocuK/tX+yP7b\n5vZQ+qfU/rP1W3tva8/t/aa8nsuXl1576Htf6sZl/qv/AK3c2G/sr+1/7F5faz+t/XPq/wBZv7H2\nXJ7H2enP7bm5tPZ294+etqx7ZWZFZ94cs+ScoCuEbBYGWRQB8pK8DdndX0R86PUEq8bq32glmeTY\nBGUkJf5Qykrs5Mh+chcsHClFoAgdzLKHY4VwVIAUrEeqMxIIIccDd5Zk3R4dmIoAkSXZDEd6sAFj\nbaQGU+YCcGQn5dgOCGflRnIIagBOEji8sOPLIUSxuhbBZAy5wwbzMzBMA5ZAowAFYAeGVwoZQHyQ\nQjMreUy/fLYI5DbQ5GyRHAGMLQBCqzR4iErIEfAYISJFK8QkhWZiMyYYKj4zgncQoA6Ry8mxlKqv\ny7VCbl2/NwMBmRiqzBSVc7CO+WAGySNIpjjmUBSxd4WVEMi7Q54YjADbERm+U5OHxQAjbXYeYzqg\nRSDGuVkbIHzLvXchVUdhgoX445LAEo3fLOobc5RMZ8tGjwx2bGbA3fK2xOdzIFLbi9AH9qH/AAbk\nbf8AhiL4pFNuD+1T44JVeArf8Kk+B+8bdq7SWyxXauC3IyTt/i36Rv8AyW+V/wDZK4H/ANW+eH9m\n/R2/5InNP+ypxv8A6qcjP38r8BP3sKACgAoAKACgAoAKAPgX9nn/AIJhfsPfswfGz4yftKfCz4C+\nFI/2h/jz8VPib8X/AIh/G3xXC3jH4kSeKPix4u1vxl4rsfCniDX/ALZJ4B8LPqGvXNlB4b8ERaDp\n1xp1vZ/2yurahHNqE4B/NV/wcbNj9t74XK4DRH9lPwOXVyCo/wCLu/HHcwXDHO35WcqUTKkkfeX+\n0vo5f8kRmn/ZVY7/ANVGRn8ZfSJ/5LbK/wDslsF/6ts8PwAki3gEEl4ud4+ZZI3bGCjAFvly24Pj\nqcEtX78fgg/dIN7MGaRQxCsoxBn5zuZ1ERwQC7PIrBMlWOKAI1fdtJWIBiu2M7ShIfEaIy7fLIyz\nSCTeH25JAXaoAwSSHfKZGkLxthBGzsrhcqyKuRnKIU6AhmUK20lgBIj8xT52kc7XEgDFOm/y2Vi0\na5fAAUjc21lXcBQBMUKb4YmjV5QpVf8AWn54z52VUlxhXUZG7qRjLYoAmMgCrJFGyhMgrIq7jIym\nM4OwEliS0aAKrKrfJjAoA+gvjP8Ast/Ev4FfDP4AfFfxvd+FpvDH7SfhTVfHHw5Giarc3+qQ6Lo0\n2hpep4htJtMsE0y+Da9YmCOyvtUjdfMWSRdiLL85k3FOWZ7mfEGVYKOKWK4axlHBZi69KFOlKtWV\ndweGnGrUdWH+zzvKcKbXu6O/u/R5zwvmWR5bkGa42WGeG4kwlbG5eqFWc6saNF0VP6zCVKCpzf1i\nHLGEqifve8rWPnqV1ZjG7Ao58zKuiAqHjKhi2G3YRxjg5AwT1r6M+cEd2WTKo6mRSSgI2SsPLAYq\nAWOHaSPClcuI0Gdw3ABIqzqwTjIRi0bHYsi8kBSCrBASwTIKB9jfLQA2NpshmZmCxgm2KYDMoP7x\ncIwG59+AzeXu5KqTigBiybizu6rtyfMcRmPa2dxkC7QjsibFfLqHiGN2RQAGR5JEkEmEUhUQsPLY\nbFZE2A8jDfMV3bmbO4YwwAgKqxcmTzA5WONo9yYyqKQPMJWRlwC23eABw+TQBMAIiUYbomUvIJW3\nLlnO5wp3tyuAXIKKpRiRncgAySLeAQSXi53j5lkjdsYKMAW+XLbg+OpwS1AD90g3swZpFDEKyjEG\nfnO5nURHBALs8isEyVY4oA9z/Zm/Z68fftYfGvwZ8BvhjJ4atvG3jpfET6IfFWo3Gl+H1HhXwvrn\njG/S61Cw0/U7m1b+yNA1DyUWwuvtN35ELtDG7SweDxNxFgOFMlxmfZosRLA4F4ZVlhacatd/WsVR\nwdLkpzqUoy/e4iDlecbQ5mrtcp7vDXD2P4qzrB5Dljw8cbjvrDovFVJ0qC+q4WtjKntKkKVaUb0s\nPNRtTlefLH3eZyPGdb0y/wBA1rWtGv5Y5r/R7/UtIvBbh54he6bczWkz2rbEMkRmtt0EjRRb43IE\nQIYV7NCtDEUKOIp39nXpU60OZWlyVYKcbpNpOzV0m9ertc8evRlh61ahO3PQq1KM+V3jzU5uEuVt\nJtXi7NpXXRbGVEfmKfO0jna4kAYp03+WysWjXL4ACkbm2sq7gK1MiYoU3wxNGryhSq/60/PGfOyq\nkuMK6jI3dSMZbFAExkAVZIo2UJkFZFXcZGUxnB2AksSWjQBVZVb5MYFAFI8byX6OjpIQNrKXBYIQ\nQxZCPvJI+A0atGNyKwBaldWYxuwKOfMyrogKh4yoYtht2EcY4OQME9aAEd2WTKo6mRSSgI2SsPLA\nYqAWOHaSPClcuI0Gdw3ABIqzqwTjIRi0bHYsi8kBSCrBASwTIKB9jfLQA2NpshmZmCxgm2KYDMoP\n7xcIwG59+AzeXu5KqTigBiybizu6rtyfMcRmPa2dxkC7QjsibFfLqHiGN2RQAGR5JEkEmEUhUQsP\nLYbFZE2A8jDfMV3bmbO4YwwAgKqxcmTzA5WONo9yYyqKQPMJWRlwC23eABw+TQBMAIiUYbomUvIJ\nW3LlnO5wp3tyuAXIKKpRiRncgB/pRfsK/wDJkX7HHO4f8Mrfs9fNnO7/AItJ4Q5yeTnrk/1r/Nnj\nr/kt+Mf+yq4h/wDVvjD/AEf4G/5Ing//ALJbh/8A9VOEPqmvlT6kKACgAoA//9H+/igAoAKACgD5\n9/a0Gf2Vf2mRjOf2ffjMMcc5+HPiQY5459+PWvoOE/8AkquGf+ygyb/1Y4Y8Div/AJJbiX/sQZz/\nAOq7En+ZoBAgcsrQoMh1AXAdcgfKw3bQcjBKrHvVlPIK/wCmB/msIyyyxqAv7pEcYbaZMghd7Ywp\nZmUxrEVYZ3MSQKAHFdhxslwAighWDgyZn+RDkOQfMCg7lQHAUYFAH3L8OP8Agpf+3n8KvDtn4U8F\nftN/EjTvDllZ/wBl6TpmsX+neLYdMsYlCraaY3i/TNeu9Ot7YN5NlHY3MEVnGsUNqkEMQiT4bMfD\nTgPNcTPF47hjLamJqVPa1atGFXBurUe86qwdWhGpKT96bnGXPK8p8zcmfcZd4k8dZVhoYTBcTZlD\nD04ezp0q06eLVKmtFCk8ZSrypxitIKEkoKyhFJJR8C+Mv7Rfx1/aG1q01/45fFjxv8TtT0yGWHR1\n8W6/e6raaRFI+fK0XSmcabpUdzJEsl3/AGdY2qTuFabe5L17+TcOZFw7RnQyPKcDllOo1Kr9Uw8K\nc6zWzrVUnVrOK0i6s5cq0Wmp4Gc8RZ5xDWhXzvNcdmdSmnGl9brzqQoxe6o0m/ZUVJ6yVKMeZ6u7\n1l5Z4f8AEGt+Gda0nXPDWtan4a8R6Bdpq2ia54d1G90nWtG1ezfzre+03VbGe3vbK+tLhUks7u1u\nYriF40eF0Kgt6tehQxVGrhsVRpYjD14SpVqFenCtRrU5q06dWlUUoVISTtKE04yTs9GeXQr18LWp\nYnC1quHxFCcatGvQqTo1qNSDvCpSq03GdOcWrxnBqUWrrVH17q//AAUa/bu8QeE7jwVq/wC1f8b7\n7QZoZbOeEeNdTh1O/wBPdGgu7bUvEMDxeI9Str6AGO4S91e4jkjkmjKjzHR/j6PhzwJh8WsbR4Uy\nSGIjKM4S+pU5UoTg7xlTw8lLDU5ReqcKMbNJq1ly/X1vEXjrEYR4KtxVnU8PKMoTX1ypGrOElaUa\nmIjbEVItaNTryTTa1TbPjGN4lmFxdJcmR7lZ5grN58q741kwzpKgkmRpGjlZHVmId1Zgd32jT5Wo\nWTtaN17qdtLpNaJ9E1pomtz4xNcycrtXvKztJq+tm07N66tPXVp7H7o237On7aP7O9no3xW/4JSf\nGD4z/G/9mb4raBoXiA6n8L7/AE/V9Z0bxkNMsotc8LfFL4S6O9/FpfjHRnVIjeS+GWntdPkXSrq7\nivLaeOX8KfEXBXEU62V+KuT5LknE2V4ivQ9lmcKlGhXwaqS9hisrzar7J1cHWV/cWLtOcZVYQUJR\ncf3JcO8acO06GaeFmcZznfDWaUKNf2uVzp1q1HGezj7fDZnlNJ1Y0sZRaS55YbmjB+ylNSU4Hu/7\nPlr+1R+zl4s1v/go9/wUx8d+M7DVPhJ4I+Ieh/sy/DL40eKrz/hPPiV8WPFnhLUvD1rofg/4em7k\nfwr4eTTNTv4fEE8Wk6Rsa/t9euLRtO0y+1SLwuIJcK8R4Sj4b+GeBwVSnm+Py+vxLmeTYWKwGXZT\nhMXTxEq2MzBxSxWI9rShKhF1at1TlQjP2lWnTl7mQR4p4dxdbxG8SsdjadTKMDmFDhvLM5xU3j8x\nzbF4SeGjRwmA528Nh/ZVZxryVGlZ1I15QVOlOrH+evwT8TviF8NtfvvFngTxl4r8E+KtS03WNG1L\nxF4S1zUNB1TUNK1yJ4tc024vNMmtZpbDV4l8q7s9/wBnvI9kVwkmGr+hMbleXZlh6eEzDA4XG4Wl\nUo1qeHxVGnXpQq4d3oVIwqRlFVKT1pzteL1TVz+fsFmeY5diKmLwGOxWCxVWnWo1K+Fr1KFWdKur\nVqcqlOUZOFVaVIt8s1o002cQqhjtiUyLnO+XaDICGYNHt5VE8otuGFGXAUlsr3nCdf4x+IvxA8fW\nvg+38d+MvFXjO28B+EtN8E+CrfxLrWpapB4R8F+H0lh0fwr4dS+uJl0nw/psd1J9i0eyWCyg8xhF\nAmc1xYPLcvy+WLngcFhcHLH4urjsbLDUadF4vG1re2xWIcIxdXEVbL2lWblOVldu524zMcfmEcJH\nHY3E4uOBwtLA4KOIr1KywmDo6UcLh1Nv2WHpJv2dKFoRu7JauU3hP4keP/Atl4usPBfi/wAT+EtN\n8deGp/Bvjm00DWr/AES38Y+Fb+RHuvDfiaKyngj1jRpWUmbSr1ZrWXcC0DZJoxeW5fj6mDq47BYX\nF1MvxMcZgamIo06ssJi4fBicPKcZOjWj9mpDlkuklZBhMxx+Ahi6eCxuKwlPH4eWDx1PD1qlKGLw\ntT48PiIwlFVqMvtU6ilB9UznNB13XPCut6V4h8N65qXhjXNB1CDU9H13Q9SvtJ1nStRspPtFrqul\napaTw6hZX1rMkc1lcWNxDdW83lyRSKyhl6K9ChiqNXDYqjSxGHrwlSrUK9OFajWpzVp06tKopQqQ\nknaUJpxknZ6M56Fevha1LEYatVw+IoTjVo16FSdGtRqQd41KVWnKM6c4tJxnCUZJ6p3Xu/Xmtf8A\nBR79u3XvCEngnWP2rvjXeeH7mGeyurdvG+pQ6zf2N5GYGttS8QxSQeI722u4cxTwXuqXEckZdJNy\nTyh/j6PhzwJh8WsdR4UyWGIjKM4P6lTdKE4u8ZU8NJSw1OUXrFwoxaaTVrLl+vreIvHWIwjwVbir\nOp4eUZQkvrlSNWcJLllGpiI2xFSMlo1OvJW0d73PjeC6a0uVv4ZrizuredLlLmKQrPDewy+ZFcI5\n/erLHMvmK+9TE5WVGDbSv2coRlB05RUoSi4Sg0nFxas4tbNNaNbW0PjVOUZKalJTUlNTTakpJ3Uk\n73Uk9b3vfW/U6bx7498e/FbxPqvjv4j+LPEfjvxhrn2dtb8V+LtXvNe8SavJp1hZ6RYy6nq2oXFz\nd3kltp2n2emWqXEsvlWtrFCpEUKIvLgMvwOV4SjgMtwmHwGCw6mqGEwlGFDD0VUqTqzVOlTjGEOe\nrOdSVoq85yk7uTZ04/H43NMXVx2Y4vEY7G13B18Vi606+IqunThSg6lWo5Tm4UqcKceZu0IxitEi\nfwz8RfiB4H0bxj4b8I+MfFnhnw/8QtItvD/jrR9B1nUtK0/xfosc8l/BpPiLT7SeK31vT4Lh55ob\nLUEuLaGR2ZIgSdyxWW5fja2DxOMwWGxOIy6s8RgK1ejTq1cHXlHllWw05xcqNRxSi5wcW1ZXdisL\nmOPwVHGYbCY3E4bD5hSjQx1GhWqUqWMoxblGliYQko1qcZNyUKilFNtpI3fg78bPi58AfGsPxF+D\nHjzxD8NPGSWl5pSa74Y1F9PnudNvTbPqGmajbDda6rpl1cW9rcS6fqNvd2D3tnZXjWv2i0geLnzj\nJMp4gwUsvzrL8NmWDlONX2GJpqcY1YKShVpy0nSqxUpRVSlKE1Gc48yjOSN8ozrNsgxkcwybH4nL\nsZGE6Xt8NU5JSpT5XOlUj8FWlJxhJ06sZwc4Qny80ISja8e/Hr4yfFD4nv8AGfx/8SvFfiD4redp\nt1b+OptXuLXxNaXOhxxW2gzabqGmfYp9OfTY7O3Wzk08Wv2RYohDsY7qWAyHJsryz+xcBluEw+Vc\ntWDy9Uozws4125VlUpVVNVFVlJuoqnPz3d27vlrH57nOaZn/AGzjsyxeIzXmpSWPdWUMTCVBKNF0\n6lLkdJ0lFKm6fJyWVr2TPcfFX/BRP9uTx54Z/wCEH8UftVfGi98Ptp93YXljaeN9X02fVLSeF4Lu\nw17VNJl0/VfEFpcRuYJrfWb+/huomeOaN0kcN4WE8O+BsDivruF4VyWniVNVITlgqVVUqkXzRnQp\nVVVpUJxlrGVGEHFpNWcVy+5i/ELjjHYX6lieKc6qYdwdOcI42rSlVpyVpQr1KTp1a8ZLSSrVZqSu\npJJ2l4zon7Rfx58O/CfxD8CtD+LnxA034PeJLs3Ws/DSx8R6hF4O1Ei5huboy6H5rWkcWo3FpbXO\noR26xW97PCkt1FNMqFfZrcOZDic2oZ7XyjAVc5wsVGhmU8NTeMpqMZRjatyqTcIzlGEpOUoRk4xc\nUzxqPEWe4bKa+RUM2x9LJ8VJzr5bDEVFhKkpSjKV6N3FKcoxlOMeWM5RvNSaTj1XwP8A2wP2mv2a\ntG8RaF8DfjR49+GWk+Kr1dS1vQ/D+o/8Sq91NLe3sBqq6beQXlpZ6tNZQQWz6vp0VpqNza2tnDNd\ntHZ26wcuecH8McSVsPiM9yXA5lXwsPZ0K2IpN1YU+Zz9k6kJQlOipylNUqnPTjKc5KDc5M6sk4v4\nm4bo18PkedY7LaGKn7SvRw9ReynUUVD2qpzUowquEYwdWmo1JRjCLnaEVHyvRvif8StF0Xx54f0L\nx14q03Qfi1HpTfE/RNO16/sdG8evo9/LqmlQ+LtPtbmK314afqV9dX9ouoJcR291PdXMe2d2ZvVq\n5Vltetl+JrYDCVcRlXtP7Mr1KFOdXAe1pxpVXhKkk5UHUpQjTn7Nx5oRSldRSPLpZpmVCjj8PRx+\nLpUM19n/AGlRp16kKWP9lUlVprF04yUa/s6s5VIKpGXLOTkuVu8uV0LV9Z8Marp+ueGtZ1XQPEGl\nahDqega9od/d6Rq2m6jp8puLK/03ULOW3u9OvbS4Akgu7KWG5idFaOWJwprqr0KOJo1cPiaNLEYe\ntCVKtQr04VaNanNcs6dWlUUoVITi3GUJxlGSdmmnY5aNethq1LEYetVw9ejONWjXo1J0q1KpB80K\nlKrBqdOcZWcZxalFq6atc+ufEf8AwUR/bk8W+EH8D+J/2p/jjqPh2a0utPv7ZfHer2lzq9hdQyQX\ntjrOsWM1rrOt2V5B5kEtpqeoXlvcwu0E8TrLPv8AkcN4d8DYPFrHYfhXJKeJjONSE/qNGUKVSEua\nE6NCanQoTjJKUZ0qcJRkk1dxTPrMT4hccYvCPA4jinOqmGlCVOcPr1WE6tOa5ZwrVoSjXrQnFuM4\n1a04yjdPmTZ8dW0kkM8U1k8iy280c8Fzu8uZZY282Ke3ePDxeQ8AlWVNoRg2Bkhq+ylGMoyjJKUZ\nJxlFq6lFqzTXVNNpr/M+PjJxalFtSi1KLTs007pprVNPVNfodL4/8f8Ajr4o+KtS8f8AxM8W+Jvi\nB4218Wc2t+K/F+sahrev6odJ0y00TT21DWNRnuL28Npo9pY2Fus80hjs7S2tl2xQRqnJgMvwOVYS\njgMtweGwGCw/OqGEwlGFDD0vaVJ1qns6VOMYQ56tSdSVormnOUndts68fmGOzTF1cdmWLxOPxtfk\n9ti8XWniMRV9nThRp+0q1HKc+SlThTjzN8sIRitEi34X+I/j/wAF6T4w0Pwh4w8T+FtC8e6JD4a8\neaZoet3+jWHjTQVuPtEWi+KLaznhh1rTYJzLKmn3yTQBpdwhBYsyxWW5fjq2DxGMwWGxVfLqzxGA\nrV6MKtTB4hrldbDTnFyo1XFJc8HGVtLseFzHH4KjjMPg8bicLQzCj9Xx1KhWqUqeMoJ3VHEwhJRr\nUk23yTUo31smcNtAIVmjjiDMGU7iHADFpB5pZnyNvloh3AsvUZ29pxCNIRGVYjEkoJjJw7ryLfer\nEBVYcFMbflUsTuIYA/oA/wCDcZUH7bvxU2R+V/xiv43V04A3L8XPggOAfnGOflJ2qGGM7hX4D9I3\n/kiMr/7KrA/+qjPD97+jt/yW2af9ktjf/VtkZ/ahX8Wn9mhQAUAFABQAUAFABQAUAfgv/wAHFAU/\nsK+C90fmqP2lPAbFcZ4Hw/8AiwSRj5gQOQV5HXpmv3j6O/8AyXWN/wCybx//AKn5V/XX8E4/hP0h\nf+SHwX/ZSYD/ANQM1/r/AIZn8TLGGNV++Du3RIvlnKr8zDOML3ZjlmdG3kcCv7YP4tHNDLLIDKvD\nuEHljJAKbWUYOf3fQypsJduOAQoAgcjLNGxLB3ZWXKsjsUYSIeEXKuQPlLNjLc5oAQIwA3IgliCy\nbRK3DbFKAKuFUDK7wwYsNino9ADmI2yAskjliHJJ+WJf9a7Mv3GUxjy1LBsKBzvxQAvmeb5kwZd8\nbqNqrmJhGjsihTt4JCKcfxYwRnLAH7O6CWH/AAQZ8ZgCSaQ/8FB7cSclnd2+FvhVmwDyDghdjc5B\nHUivxiv/AMn4wX/Zvp/+rXFH7LQ/5MVjf+y/h/6q8Kfi8THCIki+XJiil3qzxqoeI7syDA2uxBDZ\nOQE5+UL+zn40W7K0tJZ7e3vbl7HTGnjhub9bYX0tnZyTRNLdwWSzQfaniVyyW7TQtMyvGZosq6TN\nyUJuEVOajJwg5cinJL3Yudpcqk7Jy5Zct72drFQUXOCnJwg5RU5qPO4Rb96ShePM4q7UeaPNa11e\n59Wfta/sg+L/ANlbVPh3cT+KNA+Jvwz+LXgbS/iF8Kfi34Nivl8H+OdCvYYZdTtrNNVhS90/XtAv\nbmG21XRL3/TdOivNMurmGH+0YoU+U4T4uwnFVHMIxwuIyzNMox1XL82yfGypvGYGvCUlTlP2bcal\nDERi5Ua0P3c5Qqwi5+z55fVcV8JYvhatl8pYrD5llmcYGlmGU5vg1UWEx1CcYupGKqRjOnXw8pRj\nWoTvUhGdKUlH2qjHR8Mfsd69e/sleMv2wfHXjDRPhf8ADqw8TweBPhfo+t2WoXfiX43eNf8AShqW\nleBLGLyk/szQmt3j1fxBey/YbZbHXY7fzrzQb21fPFcY4anxbg+D8Dg6+ZZjUw0sdmlWhUpww2SY\nL3XTq46crt1a6kvY4eC9pL2lBu0K0Jx0wvB+JqcJ4zi/HYyhluXU8THA5XRr06k8TneN972lLAwj\nZeyoNP22Ik/Zx9niEuedCcD9a/hB+zh8E7//AIJM/Fnwpc/tkfCTS/D3jD9oT4S+NPEfju68JfEW\nbRvh34oHg3SM/DXxDY2+hNq2oeJPM3Rvf6TbXWj71d/tZUqG/JM44jzuHizlGLjwbm9TE4Ph/NcF\nhsvji8uVfMcL9crr+0sPOVdUoYe2vs60oVl/z7dmj9Zyfh3JZ+FObYWXGOU0sPi+IMqxmJx8sJmL\noZfivqdH/hNrwVB1amIvp7SlGVHT4z8qPgR+xL4k/aL8W/tOeG/hL450Xxb/AMM7eC/F3jXRNU07\nR9dkPxl03w34jGhaFbeCNNligvrS88aveWV1ocesW0M0MV9Ba3UUc4ZE/Vc942w3DmF4ZxObYGvh\nP9YcbhMFXp1a1Ff2NUxGG+sV5Y2ouanOGCSnCu6Ls3TlKDcbH5XkXBeJ4ixXEuGynHUMV/q/gsZj\nqFSlRrS/tinh8R9XoRwUHFVITxrlCdBVYppTjGcVLQ6n9q/9hzwr+yb8PNGuPE/7Tvwf8dftDXPi\n/T/Dvjv9nD4bStr+p/DS1uPD+t6tq974j8YWeri1/tXw/q+maX4c1TRf7BgP9pa/59pqVxaWTPd8\n3CnHGK4tzGvHDcL5vgOHlg6uJwHEeY/7PRzOUMRQpUYYbByo8/ssTSqVsTSr/WJWp0OWdNTny0ur\nirgnC8KZfQeJ4nyjH8QPF0sNj+Hcu/2irlkZYevVqzxOMjWcPa4arTo4arQ9hH95X5o1ZRheW38M\nf+CfmmSfCLwX8fP2rf2lPh3+yL8M/ihFdXXwntvE3hXxd8R/il490qzlhjn8TeH/AIWeEIU1tvCL\npcQPba7Nfr9rS4sbxbGLStS0rUr/AJ8z8QaqzfG5Dwrw3mPF2Z5W4wzWWFxWEy7K8vrTUnHC180x\nbnS+uJxanQjTfK4zhz+1pVqcN8s4ApPKMHn3FXEmX8JZZmalLK1isLi8xzPMKUGlLFUMswijV+qe\n9FwrOXvqUJcqpVaNSb/i5+wD4esfgp4t/aI/Zb/aY+Hv7Xfwm+F1zpdr8UofD3hTxZ8NfiT8OrPX\nbma30rxDrvw18Xfa9WHhu5uYmtpNZS+WaOWK5lgs7jT9L1y+08yjxAxFTOsHw7xTwxmHCWa5nCtL\nK3iMXhMyy3MJ4eKlVw1DMsJy0nioxfMqXJZrkUpxqVaFOq824BoU8lxnEPC/E2X8WZVls6Uc0+r4\nTFZbmOXxrycaNevl2LUqv1aUlyurzxd+Zxpyp0606Xxx8D/gd8S/2i/iZ4X+EHwd8LX/AIu8eeK7\nt4tJ0m0a2htre0tUa51HVdX1K6eCx0fRdLtUlvNT1O+uI4IrYZYvM0ET/ZZ5nmV8OZZic4zjFQwm\nBwkU6lSScpSlJqNOjRpxvOrWqyajTpQjKUm+kU5x+OyPI804jzPDZRk+Fni8dipNU6cWoxhGK5ql\natUl7lKjSinKpUm4xitLuTjGX6LX3/BO39k/SNei+HHi7/gqP8BdJ+M/26XRdQ8Nad8LviHrPww0\nrXoJDYXWlXnxyhv7bwrZRWd+Db3Gp3+m6ckJWaWaOMQFK/OoeIfFleg8ywvhdn9XJnBVqeJnmeAp\nZnVw8o88asMkdN4qblT96NOnOo2+WMZS5uaP6HLw+4ToV1l2K8UMipZzzujUw0MszCtllLERlySp\nzztShhIKNRcsqlSnTS1bire98j/F39j/AMffs5ftH6V+zz+0Nrfhz4YnVL3RblvilINW8S/DefwR\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y8cXHiqDSPG00mrRR6FJ4d8D+JvE2osLjRZYdVVtR0vQ9Q0p/s0wz9uEcym3aVW+v4zz3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WnX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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ipd.Image(filename='basic_mir_overview.jpg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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
}
| mit |
bburan/psiexperiment | examples/notebooks/Tone and chirp calibration.ipynb | 1 | 5880 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pylab as pl\n",
"\n",
"from psi.controller import util\n",
"from psi.controller.calibration.api import FlatCalibration, PointCalibration\n",
"from psi.controller.calibration.util import load_calibration, psd, psd_df, db, dbtopa\n",
"from psi.controller.calibration import tone\n",
"from psi.core.enaml.api import load_manifest_from_file\n",
"\n",
"frequencies = [250, 500, 1000, 2000, 4000, 8000, 16000, 32000]\n",
"\n",
"io_file = 'c:/psi/io/pika.enaml'\n",
"cal_file = 'c:/psi/io/pika/default.json'\n",
"\n",
"io_manifest = load_manifest_from_file(io_file, 'IOManifest')\n",
"io = io_manifest()\n",
"audio_engine = io.find('NI_audio')\n",
"\n",
"channels = audio_engine.get_channels(active=False)\n",
"load_calibration(cal_file, channels)\n",
"\n",
"mic_channel = audio_engine.get_channel('microphone_channel')\n",
"mic_channel.gain = 40\n",
"print(mic_channel.calibration)\n",
"\n",
"speaker_channel = audio_engine.get_channel('speaker_1')\n",
"print(speaker_channel.calibration)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fixed_gain_result = tone.tone_sens(\n",
" frequencies=frequencies,\n",
" engine=audio_engine,\n",
" ao_channel_name='speaker_1',\n",
" ai_channel_names=['microphone_channel'],\n",
" gains=-30,\n",
" debug=True,\n",
" duration=0.1,\n",
" iti=0,\n",
" trim=0.01,\n",
" repetitions=2,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figure, axes = pl.subplots(4, 2)\n",
"\n",
"for ax, freq in zip(axes.ravel(), frequencies):\n",
" w = fixed_gain_result.loc['microphone_channel', freq]['waveform'].T\n",
" ax.plot(w)\n",
" ax.set_title(freq)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tone_sens = fixed_gain_result.loc['microphone_channel', 'sens']\n",
"calibration = PointCalibration(tone_sens.index, tone_sens.values)\n",
"gains = calibration.get_gain(frequencies, 80)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"variable_gain_result = tone.tone_spl(\n",
" frequencies=frequencies,\n",
" engine=audio_engine,\n",
" ao_channel_name='speaker_1',\n",
" ai_channel_names=['microphone_channel'],\n",
" gains=gains,\n",
" debug=True,\n",
" duration=0.1,\n",
" iti=0,\n",
" trim=0.01,\n",
" repetitions=2,\n",
")\n",
"\n",
"variable_gain_result['spl']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from psi.token.primitives import ChirpFactory\n",
"\n",
"factory = ChirpFactory(fs=speaker_channel.fs,\n",
" start_frequency=500,\n",
" end_frequency=50000,\n",
" duration=0.02,\n",
" level=-40,\n",
" calibration=FlatCalibration.as_attenuation())\n",
"\n",
"n = factory.get_remaining_samples()\n",
"chirp_waveform = factory.next(n)\n",
"\n",
"result = util.acquire(audio_engine, chirp_waveform, 'speaker_1', ['microphone_channel'], repetitions=64, trim=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chirp_response = result['microphone_channel'][0]\n",
"chirp_psd = psd_df(chirp_response, mic_channel.fs)\n",
"chirp_psd_mean = chirp_psd.mean(axis=0)\n",
"chirp_psd_mean_db = db(chirp_psd_mean)\n",
"\n",
"signal_psd = db(psd_df(chirp_waveform, speaker_channel.fs))\n",
"\n",
"freq = chirp_psd.columns.values\n",
"chirp_spl = mic_channel.calibration.get_spl(freq, chirp_psd)\n",
"chirp_spl_mean = chirp_spl.mean(axis=0)\n",
"chirp_sens = signal_psd - chirp_spl_mean - db(20e-6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chirp_sens.loc[1000]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figure, axes = pl.subplots(1, 3, figsize=(12, 3))\n",
"\n",
"chirp_response_mean = np.mean(chirp_response, axis=0)\n",
"print(chirp_response_mean.min(), chirp_response_mean.max())\n",
"axes[0].plot(chirp_response_mean)\n",
"\n",
"freq = chirp_spl_mean.index.values\n",
"axes[1].semilogx(freq[1:], chirp_spl_mean[1:])\n",
"x = psd_df(chirp_response_mean, mic_channel.fs)\n",
"y = mic_channel.calibration.get_spl(x.index, x.values)\n",
"axes[1].semilogx(freq[1:], y[1:], 'r')\n",
"axes[1].axis(xmin=500, xmax=50000)\n",
"\n",
"axes[2].semilogx(freq[1:], chirp_sens[1:])\n",
"axes[2].plot(tone_sens, 'ko')\n",
"axes[2].axis(xmin=500, xmax=50000)"
]
},
{
"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": 4
}
| mit |
unpingco/python_for_prob_stats_ml | chapters/probability/notebooks/src-probability/Brzexample.ipynb | 2 | 30906 | {
"metadata": {
"name": "",
"signature": "sha256:b5f319b7b109b5da54ad8fc8bd6116afa2680f552124aa57a671285a84c9bc80"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pandas import DataFrame"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = DataFrame(columns=['xi','eta','x'])\n",
"d.x = np.random.rand(100)\n",
"d.xi = d.eval('2*x**2')\n",
"d.eta =1-abs(2*d.x-1)\n",
"d['h']=d[(d.x<0.5)].eval('eta**2/2')\n",
"d['h2']=d[(d.x>=0.5)].eval('(2-eta)**2/2')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = DataFrame(columns=['xi','eta','x','h','h1','h2'])\n",
"# 100 random samples\n",
"d.x = np.random.rand(100)\n",
"d.xi = d.eval('2*x**2')\n",
"d.eta =1-abs(2*d.x-1)\n",
"d.h1=d[(d.x<0.5)].eval('eta**2/2')\n",
"d.h2=d[(d.x>=0.5)].eval('(2-eta)**2/2')\n",
"d.fillna(0,inplace=True)\n",
"d.h = d.h1+d.h2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig,ax=subplots()\n",
"ax.plot(d.xi,d.eta,'.',alpha=.3,label='$\\eta$')\n",
"ax.plot(d.xi,d.h,'k.',label='$h(\\eta)$')\n",
"ax.set_aspect(1)\n",
"ax.legend(loc=0,fontsize=18)\n",
"ax.set_xlabel('$2 x^2$',fontsize=18)\n",
"ax.set_ylabel('$h(\\eta)$',fontsize=18)\n",
"fig.savefig('Conditional_expectation_MSE_Ex_005.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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nngtz5mTvuseOHet4PmzYMEtOB0RpqVMTGjy4c++aJbRTkzMjq1taYOlSuPFG\niKy6mhUzZsxg9erVjB8/nrVr11oQCpCulhiJLrSXqytD2hSPGG7PNbMlXnNXrq8MaYEoRmwgymZV\nuavEtC3xmluitaTDh+HgQadWlEuTaW2uWTd+9ztoaHCG7ANcdllmrhMOh/nVr37V6ZjtuJF7bGXI\n1AR+HFFjI3z6qfPX6Z207Anbtfr6+k6ve/XqZYnpHNbdoEjTtcDXiM4/35noet55cMstmblG7GBF\ncKqnNmDRnIz1rnUW+BrRnXc6myf+6EeZ6y2rr6/vyAn16tWLrVu3Mm7cuMxczASCTRfpLPCBqKAA\n5s5NfxAaM2YMhYWFDB48uOPYpEmT+OSTTywImVPy4prpbgp8r1mm5OXl0RqZZDR06FBKS0uti94k\nrKtkdhCaa9ZrFidTNzXaRd8aM9NxwoQJ1kVvktLVNke5vDpkYJtmmWqDx66sCPDlL3+Zp556Kn0X\nMDkrl3cVCWyNKBSCQ4fS1waP7rYRXWO6pKSEUaNGUVFRYc0xkxalpU5NKLqryIUXBqO5lojA5og+\n+0zTOqAsdreN/Px8PvzwQwtAJu3ic0fRuWvRHYm9PlXEpnjEyESyevDgwezfv5+CggLeeecdW0/I\nZEV07lrfvnDGGc7AXC/XjlINRIHNEaXbli1bGDFihAUhk1WxOxIfPBjcsUeBrRH96U/q6b8cxiQr\ntnbk1Ym0ViOK09O/HNFtf2bNmkVzc3N6C2dMCmJrR14MQj0R2EC0caOzf9n48am9P9pNv2LFCsLh\ncHoLZ0wKgjyRNrCBqKHBmXW/ZUtq7y+IzAmxpTyMybzAjiMaPBiOH098W+nYVRUrKyuprKwkHA7b\ntA3jS34bfxTYZPWCBcrYsXDllYndhOLi4o7dNmbPns3y5cszXEpjMset8UeWrI4zcCB8/euJ/yWI\n3W1DJOn/j8Z4it9m9wc2EBUXw9atpz4v2jvWK7KWbElJie07ZnzPbz1sgc0R7djhbC3dna4Wuh8x\nYgRVVVWWEzK+19Xsfi8LbI3onHNOXiOKds9Hg9CkSZOora21IGSMCwJbI6qogCefPPF4/Cz68ePH\nM3r0aJYsWWJByOQ0N3vaAttrdvnlTq/Zf/zH58djZ9CDzaI3JlY6etqs1yzOaac5C+ZHhcPhTkEI\nYMeOHRaEjIlws6ctsDWi5mZl4MDPj8WOEwL4yle+woYNG1wonTHe1NU62hUVsG8f5Oc7S9eeahMK\nW48oRuyA9TXzAAAGRUlEQVR6RIWFhRw+fJi2traOnw8YMIC9e/dabciYU7j/fmeqVEuLszfg3Lkn\nPz8QTTMRmSkiO0TkzyLyL92c84vIz98SkZJTfWZ8EBo4cCBvv/22BSFjEpCf7wShggK48UanxvTK\nK86SJDH7R/SYZwKRiPQGFgIzgS8B3xKRL8adMws4R1XPBcLAou4+74orwlx6aRnt7e3R9zJjxgx2\n797t+4XNqqur3S5CxgT5u4H/vt/ttzs1oXvucYJRpjal8EwgAi4CdqrqblVtBZ4GZsed8w3gNwCq\nugkoFJEhXX3YihWPs379OlS1Y/fVlStXBqIm5Ldf5mQE+buB/75f/Aal8QntdNWQvDSOaDiwN+b1\n+8DFCZwzAvgo/sNUneZYnz59+Otf/xqIAGSM20pLOye0ozWkQ4d6VkPyUo0o0ax5fCLspO+bNm2a\nBSFj0iR+cbZ0dfl7ptdMRC4B7lPVmZHX9wDtqvrvMec8AlSr6tOR1zuAqar6UdxneeNLGZOD/L7l\n9BbgXBEZDXwAfBP4Vtw5LwB3AE9HAldzfBCC1P5HGGPc45lApKrHReQO4BWgN/CYqm4XkbmRny9W\n1ZdFZJaI7ASOAHNcLLIxJk080zQzxuQuLyWrk5aJAZBecarvJiJlInJARGoijx919TleJCKPi8hH\nIlJ7knN8ed/g1N/Pz/cOQERGishaEakTkbdF5HvdnJf4PVRVXz5wmm87gdFACNgKfDHunFnAy5Hn\nFwMb3S53Gr9bGfCC22VN8fuVAiVAbTc/9+V9S+L7+fbeRco/FBgfeT4AeLen//b8XCNK6wBIj0nk\nu8GJQxl8QVXXA00nOcWv9w1I6PuBT+8dgKo2qOrWyPPDwHZgWNxpSd1DPweirgY3Dk/gnBEZLlc6\nJPLdFJgSqfa+LCJfylrpMs+v9y1Rgbl3kV7uEmBT3I+Suoee6TVLQUYGQHpEImV8Exipqi0icjmw\nHDgvs8XKKj/et0QF4t6JyADgGeD7kZrRCafEve72Hvq5RrQPGBnzeiRO1D3ZOSMix7zulN9NVQ+p\nakvk+QogJCKDslfEjPLrfUtIEO6diISAZ4EnVbWrTQCTuod+DkQdAyBFJA9nAOQLcee8ANwEHSO3\nuxwA6UGn/G4iMkQiG7CJyEU4QzEas1/UjPDrfUuI3+9dpOyPAe+o6kPdnJbUPfRt00wDPAAyke8G\nXAvcLiLHgRbgH1wrcJJE5LfAVOAMEdkL/Bind9DX9y3qVN8PH9+7iK8ANwDbRKQmcuyHwChI7R7a\ngEZjjOv83DQzxgSEBSJjjOssEBljXGeByBjjOgtExhjXWSAyxrjOt+OITG4SkauBs3BmdG9X1fku\nF8mkgQUi4xsicjZQqKr/KSL5wLsi8mdVrXS7bKZnrGlm/OR/APMBVPVT4HWcUb7G5ywQGT95Gbg8\n5vUInLVwjM/ZFA+TcZGZ2nfiBI6zcBbR+oWq/rYHnzke+B3OSoFH01JQ4xrLEZls+DGwVFXfBRCR\nK4EXROQMVX042Q8TkX44TbSvWxAKBqsRmYwSkdOAj4HfqOptMcdfB85W1dNT+Mz7gUdUda+InKOq\nO9NXYuMGyxGZTGsHPgROizu+CygSkcHJfJiI3Ab8EWgVkeHA9LSU0rjKmmYmo1T1CPC3XfzoHKAR\n+CR6QETCwBnAGOAJnHzSmTi9Zf+Cs6vJQjr/Ab02E+U22WWByGSdiIzDWXD9LlVtjxz7DlCjqptF\n5EJgFXAz8BfgfuAJVX0F+50NJLupJqtEpBfwMPCMqv4i5kenq+rmyPOzgHZVXR5JTE+NbNFjAsqS\n1SarRORBoAgIaze/fCKyEBiuqn+f1cIZ11iy2mSNiPxv4JCqfkdVVURGRTYHiPd3QHV2S2fcZIHI\nZIWIXIfT3FoQc/g2oF1EeovIDBHpJSLDgPOBdTHv/acsF9dkmeWITMaJyNeB7wPPici/Rg/jjIo+\nLiL/iNMb9kWcPdNbiOzjJiL/E3gn+6U22WQ5IpNRInI6sBso4MSdP5er6tUicgHwT8Cfgbdwxhxd\nFnnf/1PVp7JWYOMKC0TGGNdZjsgY4zoLRMYY11kgMsa4zgKRMcZ1FoiMMa6zQGSMcZ0FImOM6ywQ\nGWNcZ4HIGOM6C0TGGNdZIDLGuO7/A+Mu9vDOKDg6AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x118f8b90>"
]
}
],
"prompt_number": 135
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = DataFrame(columns=['xi','eta','x','h','h1','h2'])\n",
"# 100 random samples\n",
"d.x = np.random.rand(100)\n",
"d.xi = d.eval('2*x**2')\n",
"d['eta']=(d.x<0.5)*(2*d.x)+(d.x>=0.5)*(2*d.x-1)\n",
"d.h1=d[(d.x<0.5)].eval('eta**2/2')\n",
"d.h2=d[(d.x>=0.5)].eval('(1+eta)**2/2')\n",
"d.fillna(0,inplace=True)\n",
"d.h = d.h1+d.h2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 163
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig,ax=subplots()\n",
"ax.plot(d.xi,d.eta,'.',alpha=.3,label='$\\eta$')\n",
"ax.plot(d.xi,d.h,'k.',label='$h(\\eta)$')\n",
"ax.set_aspect(1)\n",
"ax.legend(loc=0,fontsize=18)\n",
"ax.set_xlabel('$2 x^2$',fontsize=18)\n",
"ax.set_ylabel('$h(\\eta)$',fontsize=18)\n",
"fig.savefig('Conditional_expectation_MSE_Ex_005.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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oBHLZTAhan1BiX5AxqQhEHlEu7/gZlD6h2Iz5vXvhkkucqRnWF2RSFZgaUa5G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\neI/TAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x120b73b0>"
]
}
],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
DS-100/sp17-materials | sp17/disc/disc09/disc09.ipynb | 1 | 9912 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression and Featurization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1\n",
"\n",
"Henry is attempting to predict his math midterm scores (it's more enjoyable than studying), and has decided to use a linear regression for the task. As a very thorough data scientist, he has taken a good deal of data on his study habits in the past, and decides to start by computing a model:\n",
"\n",
"$$ f_\\theta(Hours\\_Studying) = (Hours\\_Studying) * \\theta_1 + \\theta_0$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After taking 5 more midterms (he's a busy guy), Henry checks how well his model has predicted his scores. He gets the following results:\n",
"\n",
"<img src=\"images/Linearwithpreds.png\" alt=\"Linear\" style=\"width: 600px;\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Henry decides that, while this line fits the new data *fairly* well, it might be possible to create a better fit with a more complex regression function. He decides to use a polynomial basis, adding extra features that represent polynomial functions of the amount of time he spent studying. So, now his regression function is as follows:\n",
"\n",
"$$ f_\\theta(Hours) = (Hours)^5 * \\theta_5 + (Hours)^4 * \\theta_4 + (Hours)^3 * \\theta_3 + (Hours)^2 * \\theta_2 + (Hours) * \\theta_1 + \\theta_0$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do you think that Henry's prediction error on his training dataset will decrease? What about his prediction error on new data (that is, his test dataset)?\n",
"\n",
"*Hint: If you're unsure, scroll down to see how his prediction line has changed*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<img src=\"images/poly.png\" alt=\"polynomial\" style=\"width: 600px;\"/>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Write your answer here, replacing this text.*",
" \n",
"Henry's training error has decreased. However, this more-complex function is unlikely to provide a better approximation for new data points - that is, it has probably overfit the training data. Recall from data 8 that a more-complex model will often perform worse on test data due to overfitting (diagrammed below). So, Henry's test error has likely increased.\n",
"\n",
"<img src=\"images/ModelError.png\" alt=\"Model Error\" style=\"width: 600px;\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2\n",
"\n",
"Now, instead of adding functions of existing features, Henry tries adding additional features. In addition to the number of hours he spent studying for his math midterms, he also includes the number of hours he slept the night before the exam as an additional feature. Henry is fairly sure that his midterm scores are higher when he's slept more, and the amount he sleeps is not closely correlated with the amount he studies.\n",
"\n",
"Now Henry's regression function is as follows:\n",
"\n",
"$$ f_\\theta(Hours\\_Sleep, Hours\\_Math) = (Hours\\_Sleep) * \\theta_2 + (Hours\\_Studying) * \\theta_1 + \\theta_0$$\n",
"\n",
"Given this information, do you expect this to decrease prediction error on Henry's training dataset? What about on his test dataset?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Write your answer here, replacing this text.*",
" \n",
"Given what we know, we can expect both training and test error to decrease. Given that *Hours_Sleep* has low correlation with *Hours_Studying* and high correlation with *y*, it can be expected that it will improve the overall quality of the regression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3\n",
"\n",
"Pleased with his improved model, Henry decides to add a third feature. Since the number of hours he spent studying math was predictive of his math midterm scores, he decides to also include a feature representing the number of hours he spent studying Chinese Literature (Henry is a man for all seasons). So, his new regression function is as follows:\n",
"\n",
"$$ f_\\theta(Hours\\_Sleep, Hours\\_Chinese, Hours\\_Math) = (Hours\\_Sleep) * \\theta_3 + (Hours\\_Chinese) * \\theta_2 + (Hours\\_Math) * \\theta_1 + \\theta_0$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do you expect this new feature, *Hours_Chinese*, to affect prediction error on Henry's training dataset? What about on his test dataset?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Write your answer here, replacing this text.*",
"\n",
"Adding a new feature will always decrease training error! But, this new feature is unlikely to be predictive of Henry's midterm score - he's not taking a Chinese Literature midterm! Thus, he has probably overfit the training data data again. \n",
"\n",
"In general, as the number of features in a linear regression model increases, its training error will decrease. This is also true for the test error, depending on the number of their features and their quality. However, as a model becomes more complex, it may lose accuracy on test data as a result of overfitting to training data (diagrammed below).\n",
"\n",
"<img src=\"images/nparam.png\" alt=\"number of parameters\" style=\"width: 600px;\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4\n",
"\n",
"Henry decides to add yet another feature - the day of the week that he took the midterm on. Each of his data points thus has one additional dimension - a `Day` string with possible values `\"M\", \"T\", \"W\", \"R\", ` or `\"F\"`.\n",
"\n",
"How could Henry encode this data such that it can be used in a regression model?\n",
"\n",
"Now, assume that Henry uses the encoding you thought of. He then takes a course at UCSD, where some courses have Saturday midterms. What problem will his model have in predicting his scores in this new course? What method might you use to predict this new data point?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Write your answer here, replacing this text.*",
" \n",
"One possible method would one-hot encoding. However, one-hot encoding will not work for this new data point - since the `Day` is neither `\"M\", \"T\", \"W\", \"R\", ` nor `\"F\"`, it can't be encoded. The simplest option for this new data point would be falling back to a different model which doesn't take `Day` into account, but that might be messy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5\n",
"\n",
"Henry wants to be able to test his various models in order to determine which will have the best performance on his test data, but he knows that simply testing on training data will give uninformative results. \n",
"\n",
"What's one method he could use to get a good estimate of how his model will perform on test data, using only his training data? What parameter(s) of that method will he need to consider?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Write your answer here, replacing this text.*",
"\n",
"Henry could use cross validation to measure the quality of his models. He would have to choose $k$ (if using $k$-fold CV, hw could also use another method such as leave-one-out).\n",
"\n",
"A reminder of what CV looks like:\n",
"\n",
"<img src=\"images/cv.png\" alt=\"cross validation\" style=\"width: 600px;\"/>"
]
}
],
"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
}
| gpl-3.0 |
mil3na/ResearchGenderDifference | statistics/math/Question1.ipynb | 1 | 2324361 | null | mit |
daniestevez/jupyter_notebooks | CE5/CE-5 frame analysis ATA 2021-01-02.ipynb | 1 | 1248163 | null | gpl-3.0 |
jseabold/statsmodels | examples/notebooks/statespace_arma_0.ipynb | 2 | 7037 | {
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import qqplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sunspots Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(sm.datasets.sunspots.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta = sm.datasets.sunspots.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dta.plot(figsize=(12,4));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(arma_mod30.params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sm.stats.durbin_watson(arma_mod30.resid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"resid = arma_mod30.resid"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stats.normaltest(resid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predict_sunspots = arma_mod30.predict(start='1990', end='2012', dynamic=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def mean_forecast_err(y, yhat):\n",
" return y.sub(yhat).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
miller-lover/jingwei-sea | IPython-notebook/plot_multilabel.ipynb | 1 | 12657 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n"
]
}
],
"source": [
"print(__doc__)\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import make_multilabel_classification\n",
"from sklearn.multiclass import OneVsRestClassifier # One-vs-the-rest (OvR) multiclass/multilabel strategy\n",
"from sklearn.svm import SVC # C-Support Vector Classification\n",
"from sklearn.preprocessing import LabelBinarizer # Binarize labels in a one-vs-all fashion\n",
"from sklearn.decomposition import PCA # primary component analysis\n",
"from sklearn.cross_decomposition import CCA # canonical component analysis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_hyperplane(clf, min_x, max_x, linestyle, label):\n",
" # get the separating hyperplane\n",
" w = clf.coef_[0]\n",
" a = -w[0] / w[1]\n",
" xx = np.linspace(min_x - 5, max_x + 5) # make sure the line is long enough\n",
" yy = a * xx - (clf.intercept_[0]) / w[1]\n",
" plt.plot(xx, yy, linestyle, label=label)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_subfigure(X, Y, subplot, title, transform):\n",
" if transform == \"pca\":\n",
" X = PCA(n_components=2).fit_transform(X)\n",
" elif transform == \"cca\":\n",
" X = CCA(n_components=2).fit(X, Y).transform(X)\n",
" else:\n",
" raise ValueError\n",
"\n",
" min_x = np.min(X[:, 0])\n",
" max_x = np.max(X[:, 0])\n",
"\n",
" min_y = np.min(X[:, 1])\n",
" max_y = np.max(X[:, 1])\n",
"\n",
" classif = OneVsRestClassifier(SVC(kernel='linear'))\n",
" classif.fit(X, Y)\n",
"\n",
" plt.subplot(2, 2, subplot)\n",
" plt.title(title)\n",
"\n",
" zero_class = np.where(Y[:, 0])\n",
" one_class = np.where(Y[:, 1])\n",
" plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')\n",
" plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',\n",
" facecolors='none', linewidths=2, label='Class 1')\n",
" plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',\n",
" facecolors='none', linewidths=2, label='Class 2')\n",
"\n",
" plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',\n",
" 'Boundary\\nfor class 1')\n",
" plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',\n",
" 'Boundary\\nfor class 2')\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
"\n",
" plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)\n",
" plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)\n",
" if subplot == 2:\n",
" plt.xlabel('First principal component')\n",
" plt.ylabel('Second principal component')\n",
" plt.legend(loc=\"upper left\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x16466fd0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(figsize=(8, 6))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# X : array or sparse CSR matrix of shape [n_samples, n_features]\n",
"# The generated samples.\n",
"# Y : tuple of lists or array of shape [n_samples, n_classes]\n",
"# The label sets.\n",
"# Generate a random multilabel classification problem.\n",
"# For each sample, the generative process is:\n",
"# pick the number of labels: n ~ Poisson(n_labels)\n",
"# n times, choose a class c: c ~ Multinomial(theta)\n",
"# pick the document length: k ~ Poisson(length)\n",
"# k times, choose a word: w ~ Multinomial(theta_c)\n",
"# default: n_samples=100, n_features=20, n_classes=5, n_labels=2, length=50\n",
"X, Y = make_multilabel_classification(n_classes=2, n_labels=1,\n",
" allow_unlabeled=True,\n",
" return_indicator=True,\n",
" random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100L, 20L)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100L, 2L)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y.shape"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5., 3., 2., ..., 2., 2., 3.],\n",
" [ 4., 2., 3., ..., 5., 2., 1.],\n",
" [ 0., 0., 3., ..., 2., 4., 3.],\n",
" ..., \n",
" [ 1., 2., 2., ..., 6., 2., 4.],\n",
" [ 0., 0., 1., ..., 2., 2., 2.],\n",
" [ 2., 2., 4., ..., 1., 2., 2.]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0],\n",
" [0, 0],\n",
" [0, 1],\n",
" [0, 1],\n",
" [1, 0],\n",
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" [1, 0],\n",
" [0, 0],\n",
" [0, 0],\n",
" [1, 1],\n",
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" [1, 1],\n",
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" [0, 0],\n",
" [1, 1],\n",
" [0, 1],\n",
" [1, 0],\n",
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" [0, 0],\n",
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" [0, 0],\n",
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" [0, 1],\n",
" [1, 0],\n",
" [1, 0],\n",
" [0, 1],\n",
" [0, 0],\n",
" [1, 1],\n",
" [0, 0]])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\n",
"plot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X, Y = make_multilabel_classification(n_classes=2, n_labels=1,\n",
" allow_unlabeled=False,\n",
" return_indicator=True,\n",
" random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda\\lib\\site-packages\\sklearn\\cross_decomposition\\pls_.py:298: UserWarning: X scores are null at iteration 1\n",
" warnings.warn('X scores are null at iteration %s' % k)\n",
"C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: RuntimeWarning: divide by zero encountered in double_scalars\n",
"C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:6: RuntimeWarning: divide by zero encountered in double_scalars\n",
"C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:6: RuntimeWarning: invalid value encountered in subtract\n",
"C:\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py:2809: UserWarning: Attempting to set identical bottom==top results\n",
"in singular transformations; automatically expanding.\n",
"bottom=0.0, top=0.0\n",
" 'bottom=%s, top=%s') % (bottom, top))\n"
]
}
],
"source": [
"plot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\n",
"plot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.subplots_adjust(.04, .02, .97, .94, .09, .2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.show()"
]
},
{
"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
}
| gpl-2.0 |
julienchastang/unidata-python-workshop | notebooks/AWIPS/Grid_Levels_and_Parameters.ipynb | 1 | 8019 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example covers the callable methods of the Python AWIPS DAF when working with gridded data. We start with a connection to an EDEX server, then query data types, then grid names, parameters, levels, and other information. Finally the gridded data is plotted for its domain using Matplotlib and Cartopy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getSupportedDatatypes()\n",
"\n",
"getSupportedDatatypes() returns a list of available data types offered by the EDEX server defined above. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from awips.dataaccess import DataAccessLayer\n",
"DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
"dataTypes = DataAccessLayer.getSupportedDatatypes()\n",
"list(dataTypes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getAvailableLocationNames()\n",
"\n",
"Now create a new data request, and set the data type to **grid** to request all available grids with **getAvailableLocationNames()**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"request = DataAccessLayer.newDataRequest()\n",
"request.setDatatype(\"grid\")\n",
"available_grids = DataAccessLayer.getAvailableLocationNames(request)\n",
"available_grids.sort()\n",
"list(available_grids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getAvailableParameters()\n",
"\n",
"After datatype and model name (locationName) are set, you can query all available parameters with **getAvailableParameters()**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"request.setLocationNames(\"RAP13\")\n",
"availableParms = DataAccessLayer.getAvailableParameters(request)\n",
"availableParms.sort()\n",
"list(availableParms)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getAvailableLevels()\n",
"\n",
"Selecting **\"T\"** for temperature."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"request.setParameters(\"T\")\n",
"availableLevels = DataAccessLayer.getAvailableLevels(request)\n",
"for level in availableLevels:\n",
" print(level)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* **0.0SFC** is the Surface level\n",
"* **FHAG** stands for Fixed Height Above Ground (in meters)\n",
"* **NTAT** stands for Nominal Top of the ATmosphere\n",
"* **BL** stands for Boundary Layer, where **0.0_30.0BL** reads as *0-30 mb above ground level* \n",
"* **TROP** is the Tropopause level\n",
"\n",
"**request.setLevels()**\n",
"\n",
"For this example we will use Surface Temperature"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"request.setLevels(\"2.0FHAG\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getAvailableTimes()\n",
"\n",
"* **getAvailableTimes(request, True)** will return an object of *run times* - formatted as `YYYY-MM-DD HH:MM:SS`\n",
"* **getAvailableTimes(request)** will return an object of all times - formatted as `YYYY-MM-DD HH:MM:SS (F:ff)`\n",
"* **getForecastRun(cycle, times)** will return a DataTime array for a single forecast cycle."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cycles = DataAccessLayer.getAvailableTimes(request, True)\n",
"times = DataAccessLayer.getAvailableTimes(request)\n",
"fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataAccessLayer.getGridData()\n",
"\n",
"Now that we have our `request` and DataTime `fcstRun` arrays ready, it's time to request the data array from EDEX."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = DataAccessLayer.getGridData(request, [fcstRun[-1]])\n",
"for grid in response:\n",
" data = grid.getRawData()\n",
" lons, lats = grid.getLatLonCoords()\n",
" print('Time :', str(grid.getDataTime()))\n",
"\n",
"print('Model:', str(grid.getLocationName()))\n",
"print('Parm :', str(grid.getParameter()))\n",
"print('Unit :', str(grid.getUnit()))\n",
"print(data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting with Matplotlib and Cartopy\n",
"\n",
"**1. pcolormesh**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"import numpy as np\n",
"import numpy.ma as ma\n",
"from scipy.io import loadmat\n",
"def make_map(bbox, projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(16, 9),\n",
" subplot_kw=dict(projection=projection))\n",
" ax.set_extent(bbox)\n",
" ax.coastlines(resolution='50m')\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.xlabels_top = gl.ylabels_right = False\n",
" gl.xformatter = LONGITUDE_FORMATTER\n",
" gl.yformatter = LATITUDE_FORMATTER\n",
" return fig, ax\n",
"\n",
"cmap = plt.get_cmap('rainbow')\n",
"bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n",
"fig, ax = make_map(bbox=bbox)\n",
"cs = ax.pcolormesh(lons, lats, data, cmap=cmap)\n",
"cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')\n",
"cbar.set_label(grid.getLocationName().decode('UTF-8') +\" \" \\\n",
" + grid.getLevel().decode('UTF-8') + \" \" \\\n",
" + grid.getParameter().decode('UTF-8') \\\n",
" + \" (\" + grid.getUnit().decode('UTF-8') + \") \" \\\n",
" + \"valid \" + str(grid.getDataTime().getRefTime()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. contourf**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig2, ax2 = make_map(bbox=bbox)\n",
"cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,\n",
" vmin=data.min(), vmax=data.max())\n",
"cbar2 = fig2.colorbar(cs2, extend='both', shrink=0.5, orientation='horizontal')\n",
"cbar2.set_label(grid.getLocationName().decode('UTF-8') +\" \" \\\n",
" + grid.getLevel().decode('UTF-8') + \" \" \\\n",
" + grid.getParameter().decode('UTF-8') \\\n",
" + \" (\" + grid.getUnit().decode('UTF-8') + \") \" \\\n",
" + \"valid \" + str(grid.getDataTime().getRefTime()))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:unidata-python-workshop]",
"language": "python",
"name": "conda-env-unidata-python-workshop-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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
dhpollack/programming_notebooks | test_VarLenDataset.ipynb | 1 | 7860 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import torch\n",
"import torch.utils.data as data\n",
"import torchaudio\n",
"import librosa\n",
"import numpy as np\n",
"import random\n",
"import os\n",
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"class VariableLengthDataset(data.Dataset):\n",
" def __init__(self, manifest, snippet_length=24000, get_sequentially=False, ret_np=False, use_librosa=False):\n",
" self.manifest = manifest\n",
" self.snippet_length = snippet_length\n",
" self.get_sequentially = get_sequentially\n",
" self.use_librosa = use_librosa\n",
" self.ret_np = ret_np\n",
" self.acc = 0\n",
" self.snippet_counter = 0\n",
" self.audio_idx = 0\n",
" self.st = 0\n",
" self.data = {}\n",
" def __getitem__(self, index):\n",
" # load audio data from file or cache\n",
" if self.snippet_counter == 0:\n",
" self.audio_idx = index - self.acc\n",
" apath = self.manifest[self.audio_idx]\n",
" if apath not in self.data:\n",
" if self.use_librosa:\n",
" sig, sr = librosa.core.load(apath, sr=None)\n",
" sig = torch.from_numpy(sig).unsqueeze(1).float()\n",
" else:\n",
" sig, sr = torchaudio.load(apath, normalization=True)\n",
" self.data[apath] = (sig, sr)\n",
" else:\n",
" sig, sr = self.data[apath]\n",
"\n",
" # increase iterations based on length of audio\n",
" num_snippets = int(sig.size(0) // self.snippet_length)\n",
" self.acc += max(num_snippets-1,0)\n",
" else:\n",
" apath = self.manifest[self.audio_idx]\n",
" sig, sr = self.data[apath]\n",
" num_snippets = int(sig.size(0) // self.snippet_length)\n",
"\n",
" # create snippet\n",
" if self.get_sequentially:\n",
" self.st += self.snippet_length\n",
" else:\n",
" self.st = random.randrange(int(sig.size(0)-self.snippet_length))\n",
" ret_sig = sig[self.st:(self.st+self.snippet_length)]\n",
" if self.ret_np:\n",
" ret_sig = ret_sig.numpy()\n",
"\n",
" # update counter for current audio file\n",
" self.snippet_counter += 1\n",
"\n",
" # label creation\n",
" spkr = os.path.dirname(apath).rsplit(\"/\", 1)[-1]\n",
" spkr = 0\n",
"\n",
" # check for reset\n",
" if self.snippet_counter >= num_snippets:\n",
" self.snippet_counter = 0\n",
" self.st = 0\n",
"\n",
" return ret_sig, spkr\n",
"\n",
" def __len__(self):\n",
" return len(self.manifest) + self.acc\n",
"\n",
" def reset_acc(self):\n",
" self.acc = 0\n",
"\n",
"class FixedLengthDataset(data.Dataset):\n",
" def __init__(self, manifest, transforms = snippet_length=24000, ret_np=False, use_librosa=False):\n",
" self.manifest = manifest\n",
" self.snippet_length = snippet_length\n",
" self.use_librosa = use_librosa\n",
" self.ret_np = ret_np\n",
" self.num_snippets = 1\n",
" self.acc = 0\n",
" self.audio_idx = 0\n",
" self.st = 0\n",
" self.data = {}\n",
" def __getitem__(self, index):\n",
" # load audio data from file or cache\n",
" self.audio_idx = index if self.num_snippets == 1 else index // self.num_snippets\n",
" apath = self.manifest[self.audio_idx]\n",
" if self.use_librosa:\n",
" sig, sr = librosa.core.load(apath, sr=None)\n",
" sig = torch.from_numpy(sig).unsqueeze(1).float()\n",
" else:\n",
" sig, sr = torchaudio.load(apath, normalization=True)\n",
"\n",
" # create snippet\n",
" if sig.size(0) < self.snippet_length:\n",
" ret_sig = sig\n",
" else:\n",
" self.st = random.randrange(int(sig.size(0)-self.snippet_length))\n",
" ret_sig = sig[self.st:(self.st+self.snippet_length)]\n",
" if self.ret_np:\n",
" ret_sig = ret_sig.numpy()\n",
"\n",
" # label creation\n",
" #spkr = os.path.dirname(apath).rsplit(\"/\", 1)[-1]\n",
" spkr = 0 # just using a dummy label now.\n",
"\n",
" return ret_sig, spkr\n",
"\n",
" def __len__(self):\n",
" return len(self.manifest) * self.num_snippets\n",
"\n",
"def run_dataset():\n",
" for epoch in range(1):\n",
" all_data = [(x, label) for x, label in ds]\n",
" print(epoch, len(all_data))\n",
" try:\n",
" ds.reset_acc()\n",
" except:\n",
" pass\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"CPU times: user 91.1 ms, sys: 8.62 ms, total: 99.7 ms\n",
"Wall time: 291 ms\n",
"0\n",
"CPU times: user 79 ms, sys: 6.72 ms, total: 85.7 ms\n",
"Wall time: 69.8 ms\n"
]
}
],
"source": [
"datadir = \"/home/david/Programming/tests/pcsnpny-20150204-mkj\"\n",
"audio_manifest = [a for a in glob.glob(datadir+\"/**/*.wav\", recursive=True)]\n",
"\n",
"ds = VariableLengthDataset(audio_manifest, 12000, get_sequentially=True, ret_np=False, use_librosa=False)\n",
"%time run_dataset()\n",
"ds = VariableLengthDataset(audio_manifest, 12000, get_sequentially=True, ret_np=False, use_librosa=True)\n",
"%time run_dataset()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 1\n",
"torch.Size([10, 12000, 1]) torch.Size([10])\n"
]
}
],
"source": [
"ds = VariableLengthDataset(audio_manifest, 12000, get_sequentially=False, ret_np=False, use_librosa=False)\n",
"dl = data.DataLoader(ds, batch_size=10)\n",
"print(len(ds), len(dl))\n",
"for mb, tgts in dl:\n",
" print(mb.size(), tgts.size())\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 10\n",
"CPU times: user 18.1 ms, sys: 0 ns, total: 18.1 ms\n",
"Wall time: 18.4 ms\n"
]
}
],
"source": [
"datadir = \"/home/david/Programming/tests/pcsnpny-20150204-mkj\"\n",
"audio_manifest = [a for a in glob.glob(datadir+\"/**/*.wav\", recursive=True)]\n",
"ds = FixedLengthDataset(audio_manifest, 12000, ret_np=True, use_librosa=True)\n",
"%time run_dataset()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mldbai/mldb | container_files/tutorials/Using pymldb Progress Bar and Cancel Button Tutorial.ipynb | 1 | 15745 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Using `pymldb`'s Progress Bar and Cancel Button Tutorial\n",
"\n",
"This tutorial showcases the use of progress bars and cancel buttons for long-running procedures with `pymldb` with a Jupyter notebook. This allows a user to see the progress of a procedure as well as cancel it.\n",
"\n",
"If you have not done so already, we encourage you to go through the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial).\n",
"\n",
"## How does it work?\n",
"\n",
"To use this feature, you only need to slightly modify the way you execute procedures. For example, when doing an HTTP PUT, you would go from using `mldb.put()` to `mldb.put_and_track()`.\n",
"\n",
"The cancel button is displayed as soon as the procedure run id is found. The button is removed as soon as the procedure finishes either normally or with an error.\n",
"\n",
"The progress bar library used is [tqdm/tqdm](https://github.com/tqdm/tqdm). Progress bars are displayed as soon as a procedure enters the \"executing\" state. Then they are refreshed at every interval for as long as the procedure stays in the \"executing\" state. They move to a valid state (they turn green) when a step/procedure finishes normally and to a danger state (they turn red) when they finish with an error.\n",
"\n",
"If a procedure runs too quickly, the progress bars will not be displayed because the application logic will not have time to catch the \"executing\" phase. If a procedure stays in the \"initializing\" phase for some time, the \"Cancel\" button will be visible with no progress bars as long as the \"executing\" phase is not reached.\n",
"\n",
"## ⚠ Disclaimers\n",
"1. There is a known issue where the final value of the last progress bar may not reflect the real final value of what was done in MLDB. The reason for it is that once a procedure has finished running, it no longer reports how many items it processed for each step.\n",
"2. Due to XSS (cross site scripting) restrictions, the cancel button provided with the progress bars will not work if the notebook is running on a different host than mldb itself.\n",
"\n",
"Here we start with the obligatory lines to import pymldb and initialize the connection to MLDB."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pymldb\n",
"mldb = pymldb.Connection()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Procedure with steps\n",
"Here we post to a procedure with multiple steps. The steps are displayed as soon as the procedure starts running and are updated accordingly."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" $(btn).html(\"Cancellation failed - See JavaScript console\");\n",
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},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"<Response [201]>\n"
]
}
],
"source": [
"print mldb.post_and_track('/v1/procedures', {\n",
" 'type' : 'mock',\n",
" 'params' : {'durationMs' : 8000, \"refreshRateMs\" : 500}\n",
" }, 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Procedure with no steps\n",
"A procedure with no inner steps will simply display its progress.\n",
"\n",
"This one is an example where the \"initializing\" phase sticks for some time, so the \"Cancel\" button is shown alone and eventually, when the \"executing\" phase is reached, the progress bar is displayed."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <script type=\"text/javascript\">\n",
" function cancel_2017_01_24T16_26_15_213398Z_463496b56263af05(btn) {\n",
" $(btn).attr(\"disabled\", true).html(\"Cancelling...\");\n",
" $.ajax({\n",
" url: \"http://localhost:17055/v1/procedures/embedded_imagess/runs/2017-01-24T16:26:15.213398Z-463496b56263af05/state\",\n",
" type: 'PUT',\n",
" data: JSON.stringify({\"state\" : \"cancelled\"}),\n",
" success: () => { $(btn).html(\"Cancelled\"); },\n",
" error: (xhr) => { console.error(xhr);\n",
" console.warn(\"If this is a Cross-Origin Request, this is a normal error. Otherwise you may report it.\");\n",
" $(btn).html(\"Cancellation failed - See JavaScript console\");\n",
" }\n",
" });\n",
" }\n",
" </script>\n",
" <button id=\"2017_01_24T16_26_15_213398Z_463496b56263af05\" onclick=\"cancel_2017_01_24T16_26_15_213398Z_463496b56263af05(this);\">Cancel</button>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <script type=\"text/javascript\" class=\"removeMe\">\n",
" $(function() {\n",
" var outputArea = $(\".removeMe\").parents(\".output_area:first\");\n",
" outputArea.prevAll().remove();\n",
" outputArea.next().remove();\n",
" outputArea.remove();\n",
" })\n",
" </script>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"<Response [201]>\n"
]
}
],
"source": [
"print mldb.put_and_track('/v1/procedures/embedded_imagess', {\n",
" 'type' : 'import.text',\n",
" 'params' : {\n",
" 'dataFileUrl' : 'https://s3.amazonaws.com/benchm-ml--main/train-1m.csv',\n",
" 'outputDataset' : {\n",
" 'id' : 'embedded_images_realestate',\n",
" 'type' : 'sparse.mutable'\n",
" }\n",
" }\n",
"}, 0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Serial procedure\n",
"When using post_and_track along with a serial procedure, a progress bar is displayed for each step. They will only take the value of 0/1 and 1/1."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <script type=\"text/javascript\">\n",
" function cancel_2017_01_24T16_27_28_127061Z_463496b56263af05(btn) {\n",
" $(btn).attr(\"disabled\", true).html(\"Cancelling...\");\n",
" $.ajax({\n",
" url: \"http://localhost:17055/v1/procedures/auto-6e107b854894ea54-6800e009079893fc/runs/2017-01-24T16:27:28.127061Z-463496b56263af05/state\",\n",
" type: 'PUT',\n",
" data: JSON.stringify({\"state\" : \"cancelled\"}),\n",
" success: () => { $(btn).html(\"Cancelled\"); },\n",
" error: (xhr) => { console.error(xhr);\n",
" console.warn(\"If this is a Cross-Origin Request, this is a normal error. Otherwise you may report it.\");\n",
" $(btn).html(\"Cancellation failed - See JavaScript console\");\n",
" }\n",
" });\n",
" }\n",
" </script>\n",
" <button id=\"2017_01_24T16_27_28_127061Z_463496b56263af05\" onclick=\"cancel_2017_01_24T16_27_28_127061Z_463496b56263af05(this);\">Cancel</button>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
" <script type=\"text/javascript\" class=\"removeMe\">\n",
" $(function() {\n",
" var outputArea = $(\".removeMe\").parents(\".output_area:first\");\n",
" outputArea.prevAll().remove();\n",
" outputArea.next().remove();\n",
" outputArea.remove();\n",
" })\n",
" </script>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"<Response [201]>\n"
]
}
],
"source": [
"prefix = 'file://mldb/mldb_test_data/dataset-builder'\n",
"print mldb.post_and_track('/v1/procedures', {\n",
" 'type' : 'serial',\n",
" 'params' : {\n",
" 'steps' : [\n",
" {\n",
" 'type' : 'mock',\n",
" 'params' : {'durationMs' : 2000, \"refreshRateMs\" : 500}\n",
" }, {\n",
" 'type' : 'import.text',\n",
" 'params' : {\n",
" 'dataFileUrl' : prefix + '/cache/dataset_creator_embedding_realestate.csv.gz',\n",
" 'outputDataset' : {\n",
" 'id' : 'embedded_images_realestate',\n",
" 'type' : 'embedding'\n",
" },\n",
" 'select' : '* EXCLUDING(rowName)',\n",
" 'named' : 'rowName',\n",
" }\n",
" }, {\n",
" 'type' : 'mock',\n",
" 'params' : {'durationMs' : 2000, \"refreshRateMs\" : 500}\n",
" }\n",
" ]\n",
" }\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Where to next?\n",
"\n",
"Check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html)."
]
}
],
"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": {
"1547f39f55a3486992b25a60e84b2f31": {
"views": [
{
"cell_index": 3
}
]
},
"378419543b0942d0aa39f8e211e58d2f": {
"views": [
{
"cell_index": 7
}
]
},
"4c835ec1cf7c46db9aba344f3522fa35": {
"views": [
{
"cell_index": 7
}
]
},
"7edf3ab4d99c4220a7b0a7aa2f0a4fe8": {
"views": [
{
"cell_index": 7
}
]
},
"8abf2a674ad04e37808690633c14612f": {
"views": [
{
"cell_index": 7
}
]
},
"c879fb1e34b14526bc7c89aadaf3a76e": {
"views": [
{
"cell_index": 5
}
]
},
"dc1f79109d5f44599427b1e47a3745fe": {
"views": [
{
"cell_index": 3
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
robertwb/incubator-beam | .test-infra/jupyter/precommit_job_times.ipynb | 7 | 9745 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"#\n",
"# Licensed to the Apache Software Foundation (ASF) under one or more\n",
"# contributor license agreements. See the NOTICE file distributed with\n",
"# this work for additional information regarding copyright ownership.\n",
"# The ASF licenses this file to You under the Apache License, Version 2.0\n",
"# (the \"License\"); you may not use this file except in compliance with\n",
"# the License. You may obtain a copy of the License at\n",
"#\n",
"# http://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.\n",
"#\n",
"-->\n",
"\n",
"# Precommit Job Times\n",
"This notebook fetches test statistics from Jenkins.\n",
"\n",
"## Requirements\n",
"\n",
"```shell\n",
"pip install pandas matplotlib requests\n",
"# You may need to restart Jupyter for matplotlib to work.\n",
"```\n",
"\n",
"**Note:** Requests to `ci-beam.apache.org` must contain a ?depth= or ?tree= argument, otherwise your IP will get banned. [Policy](https://cwiki.apache.org/confluence/display/INFRA/Using+the+ASF+Jenkins+API)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as md\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetch precommit job data from Jenkins.\n",
"\n",
"class Build(dict):\n",
" def __init__(self, job_name, json):\n",
" self['job_name'] = job_name\n",
" self['result'] = json['result']\n",
" self['number'] = json['number']\n",
" self['timestamp'] = pd.Timestamp.utcfromtimestamp(json['timestamp'] / 1000)\n",
" self['queuingDurationMillis'] = -1\n",
" self['totalDurationMillis'] = -1\n",
" for action in json['actions']:\n",
" if action.get('_class', None) == 'jenkins.metrics.impl.TimeInQueueAction':\n",
" self['queuingDurationMinutes'] = action['queuingDurationMillis'] / 60000.\n",
" self['totalDurationMinutes'] = action['totalDurationMillis'] / 60000.\n",
" if self['queuingDurationMinutes'] == -1:\n",
" raise ValueError('could not find queuingDurationMillis in: %s', json)\n",
" if self['totalDurationMinutes'] == -1:\n",
" raise ValueError('could not find totalDurationMillis in: %s', json)\n",
" \n",
"# Can be 'builds' (last 50) or 'allBuilds'.\n",
"builds_key = 'allBuilds' \n",
"\n",
"builds = []\n",
"job_names = ['beam_PreCommit_Java_Cron', 'beam_PreCommit_Python_Cron', 'beam_PreCommit_Go_Cron']\n",
"for job_name in job_names:\n",
" url = 'https://ci-beam.apache.org/job/%s/api/json' % job_name\n",
" params = {\n",
" 'tree': '%s[result,number,timestamp,actions[queuingDurationMillis,totalDurationMillis]]' % builds_key}\n",
" r = requests.get(url, params=params)\n",
" data = r.json()\n",
" builds.extend([Build(job_name, build_json)\n",
" for build_json in data[builds_key]])\n",
"\n",
"df = pd.DataFrame(builds)\n",
"\n",
"timestamp_cutoff = pd.Timestamp.utcnow().tz_convert(None) - pd.Timedelta(weeks=4)\n",
"df_4weeks = df[df.timestamp >= timestamp_cutoff]\n",
"timestamp_cutoff = pd.Timestamp.utcnow().tz_convert(None) - pd.Timedelta(weeks=1)\n",
"df_1week = df[df.timestamp >= timestamp_cutoff]\n",
"timestamp_cutoff = pd.Timestamp.utcnow().tz_convert(None) - pd.Timedelta(days=1)\n",
"df_1day = df[df.timestamp >= timestamp_cutoff]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Graphs of precommit job times.\n",
"\n",
"for job_name in job_names:\n",
" duration_df = df_4weeks[df_4weeks.job_name == job_name]\n",
" duration_df = duration_df[['timestamp', 'queuingDurationMinutes', 'totalDurationMinutes']]\n",
" ax = duration_df.plot(x='timestamp')\n",
" ax.set_title(job_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get 95th percentile of precommit run times.\n",
"test_dfs = {'4 weeks': df_4weeks, '1 week': df_1week, '1 day': df_1day}\n",
"metrics = []\n",
"\n",
"for sample_time, test_df in test_dfs.items():\n",
" for job_name in job_names:\n",
" df_times = test_df[test_df.job_name == job_name]\n",
" for percentile in [95]:\n",
" total_all = np.percentile(df_times.totalDurationMinutes, q=percentile)\n",
" total_success = np.percentile(df_times[df_times.result == 'SUCCESS'].totalDurationMinutes,\n",
" q=percentile)\n",
" queue = np.percentile(df_times.queuingDurationMinutes, q=percentile)\n",
" metrics.append({'job_name': '%s %s %dth' % (\n",
" job_name.replace('beam_PreCommit_','').replace('_GradleBuild',''),\n",
" sample_time, percentile),\n",
" 'totalDurationMinutes_all': total_all,\n",
" 'totalDurationMinutes_success_only': total_success,\n",
" 'queuingDurationMinutes': queue,\n",
" })\n",
"\n",
"pd.DataFrame(metrics).sort_values('job_name')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetch individual test data (precommit) from Jenkins.\n",
"MAX_FETCH_PER_JOB_TYPE = 5\n",
"\n",
"test_results_raw = []\n",
"for job_name in list(df.job_name.unique()):\n",
" if job_name == 'beam_PreCommit_Go_Cron':\n",
" # TODO: Go builds are missing testReport data on Jenkins.\n",
" continue\n",
" build_nums = list(df.number[df.job_name == job_name].unique())\n",
" num_fetched = 0\n",
" for build_num in build_nums:\n",
" url = 'https://ci-beam.apache.org/job/%s/%s/testReport/api/json?depth=1' % (job_name, build_num)\n",
" print('.', end='')\n",
" r = requests.get(url)\n",
" if not r.ok:\n",
" # Typically a 404 means that the job is still running.\n",
" print('skipping (%s): %s' % (r.status_code, url))\n",
" continue\n",
" raw_result = r.json()\n",
" raw_result['job_name'] = job_name\n",
" raw_result['build_num'] = build_num\n",
" test_results_raw.append(raw_result)\n",
" \n",
" num_fetched += 1\n",
" if num_fetched >= MAX_FETCH_PER_JOB_TYPE:\n",
" break\n",
"\n",
"print(' done')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Analyze individual test results.\n",
"\n",
"class TestResult(dict):\n",
" def __init__(self, job_name, build_num, json):\n",
" self['job_name'] = job_name\n",
" self['build_num'] = build_num\n",
" self['name'] = json['name']\n",
" self['duration'] = json['duration']\n",
" self['className'] = json['className']\n",
" self['status'] = json['status']\n",
"\n",
"test_results = []\n",
"for test_result_raw in test_results_raw:\n",
" job_name = test_result_raw['job_name']\n",
" build_num = test_result_raw['build_num']\n",
" for suite in test_result_raw['suites']:\n",
" for case in suite['cases']:\n",
" test_results.append(TestResult(job_name, build_num, case))\n",
"\n",
"df_tests = pd.DataFrame(test_results)\n",
"df_tests = df_tests.drop(columns=['build_num'])\n",
"df_tests = df_tests.groupby(['className', 'job_name', 'name', 'status'], as_index=False).max()\n",
"df_tests = df_tests.sort_values('duration', ascending=False)\n",
"\n",
"def filter_test_results(job_name, status):\n",
" res = df_tests\n",
" if job_name != 'all':\n",
" res = res[res.job_name == job_name]\n",
" if status != 'all':\n",
" res = res[res.status == status]\n",
" return res.head(n=20)\n",
"\n",
"from ipywidgets import interact\n",
"interact(filter_test_results,\n",
" job_name=['all'] + list(df_tests.job_name.unique()),\n",
" status=['all'] + list(df_tests.status.unique()))"
]
}
],
"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": 2
}
| apache-2.0 |
kyogesh/pandas_tutorial | Alexa topsites.ipynb | 1 | 135479 | {
"cells": [
{
"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": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('top-1m.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>serial</th>\n",
" <th>site</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>google.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>facebook.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>youtube.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>baidu.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>yahoo.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>amazon.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>wikipedia.org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>qq.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>taobao.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>twitter.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>google.co.in</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>live.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>sina.com.cn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>linkedin.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>weibo.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>yahoo.co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>google.co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>ebay.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>yandex.ru</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>vk.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>tmall.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>blogspot.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>google.de</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>hao123.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>t.co</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>msn.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>google.co.uk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>bing.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>amazon.co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>instagram.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999970</th>\n",
" <td>999971</td>\n",
" <td>youtubeforchildren.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999971</th>\n",
" <td>999972</td>\n",
" <td>shoppingdirectory.ws</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999972</th>\n",
" <td>999973</td>\n",
" <td>fashhackaustralia.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999973</th>\n",
" <td>999974</td>\n",
" <td>modatakipet.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999974</th>\n",
" <td>999975</td>\n",
" <td>etanto.pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999975</th>\n",
" <td>999976</td>\n",
" <td>n1nj4.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999976</th>\n",
" <td>999977</td>\n",
" <td>dynamic.ca</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999977</th>\n",
" <td>999978</td>\n",
" <td>redserver.su</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999978</th>\n",
" <td>999979</td>\n",
" <td>sabanne.fr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999979</th>\n",
" <td>999980</td>\n",
" <td>the-vampire-diaries.org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999980</th>\n",
" <td>999981</td>\n",
" <td>biokplus.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999981</th>\n",
" <td>999982</td>\n",
" <td>projectgus.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999982</th>\n",
" <td>999983</td>\n",
" <td>saigonprop.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999983</th>\n",
" <td>999984</td>\n",
" <td>freepornolinks.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999984</th>\n",
" <td>999985</td>\n",
" <td>swiatwakacji.pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999985</th>\n",
" <td>999986</td>\n",
" <td>pornocaiunet.blogspot.com.br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999986</th>\n",
" <td>999987</td>\n",
" <td>mansbest.ru</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999987</th>\n",
" <td>999988</td>\n",
" <td>adverterenbijeisma.nl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999988</th>\n",
" <td>999989</td>\n",
" <td>lefilmfrancais.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999989</th>\n",
" <td>999990</td>\n",
" <td>floridagunrights.org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999990</th>\n",
" <td>999991</td>\n",
" <td>themarkcorp.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999991</th>\n",
" <td>999992</td>\n",
" <td>smpa.or.kr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999992</th>\n",
" <td>999993</td>\n",
" <td>cncgeeks.ca</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999993</th>\n",
" <td>999994</td>\n",
" <td>ziegler-coaching.de</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999994</th>\n",
" <td>999995</td>\n",
" <td>crsmithdev.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>999996</td>\n",
" <td>dc.com.tw</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>999997</td>\n",
" <td>supersentai.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>999998</td>\n",
" <td>bosal.co.za</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>999999</td>\n",
" <td>ahlulbaytportal.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>1000000</td>\n",
" <td>glowingeyegames.com</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" serial site\n",
"0 1 google.com\n",
"1 2 facebook.com\n",
"2 3 youtube.com\n",
"3 4 baidu.com\n",
"4 5 yahoo.com\n",
"5 6 amazon.com\n",
"6 7 wikipedia.org\n",
"7 8 qq.com\n",
"8 9 taobao.com\n",
"9 10 twitter.com\n",
"10 11 google.co.in\n",
"11 12 live.com\n",
"12 13 sina.com.cn\n",
"13 14 linkedin.com\n",
"14 15 weibo.com\n",
"15 16 yahoo.co.jp\n",
"16 17 google.co.jp\n",
"17 18 ebay.com\n",
"18 19 yandex.ru\n",
"19 20 vk.com\n",
"20 21 tmall.com\n",
"21 22 blogspot.com\n",
"22 23 google.de\n",
"23 24 hao123.com\n",
"24 25 t.co\n",
"25 26 msn.com\n",
"26 27 google.co.uk\n",
"27 28 bing.com\n",
"28 29 amazon.co.jp\n",
"29 30 instagram.com\n",
"... ... ...\n",
"999970 999971 youtubeforchildren.com\n",
"999971 999972 shoppingdirectory.ws\n",
"999972 999973 fashhackaustralia.com\n",
"999973 999974 modatakipet.com\n",
"999974 999975 etanto.pl\n",
"999975 999976 n1nj4.com\n",
"999976 999977 dynamic.ca\n",
"999977 999978 redserver.su\n",
"999978 999979 sabanne.fr\n",
"999979 999980 the-vampire-diaries.org\n",
"999980 999981 biokplus.com\n",
"999981 999982 projectgus.com\n",
"999982 999983 saigonprop.com\n",
"999983 999984 freepornolinks.com\n",
"999984 999985 swiatwakacji.pl\n",
"999985 999986 pornocaiunet.blogspot.com.br\n",
"999986 999987 mansbest.ru\n",
"999987 999988 adverterenbijeisma.nl\n",
"999988 999989 lefilmfrancais.com\n",
"999989 999990 floridagunrights.org\n",
"999990 999991 themarkcorp.com\n",
"999991 999992 smpa.or.kr\n",
"999992 999993 cncgeeks.ca\n",
"999993 999994 ziegler-coaching.de\n",
"999994 999995 crsmithdev.com\n",
"999995 999996 dc.com.tw\n",
"999996 999997 supersentai.com\n",
"999997 999998 bosal.co.za\n",
"999998 999999 ahlulbaytportal.com\n",
"999999 1000000 glowingeyegames.com\n",
"\n",
"[1000000 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# TLD (Top Level Domain)\n",
"df['TLD'] = map((lambda x: '.'.join(x.split('.')[-2:]) if '.co.' in x else x.split('.')[-1]), df['site'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>serial</th>\n",
" <th>site</th>\n",
" <th>TLD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>google.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>facebook.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>youtube.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>baidu.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>yahoo.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>amazon.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>wikipedia.org</td>\n",
" <td>org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>qq.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>taobao.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>twitter.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>google.co.in</td>\n",
" <td>co.in</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>live.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>sina.com.cn</td>\n",
" <td>cn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>linkedin.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>weibo.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>yahoo.co.jp</td>\n",
" <td>co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>google.co.jp</td>\n",
" <td>co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>ebay.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>yandex.ru</td>\n",
" <td>ru</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>vk.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>tmall.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>blogspot.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>google.de</td>\n",
" <td>de</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>hao123.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>t.co</td>\n",
" <td>co</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>msn.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>google.co.uk</td>\n",
" <td>co.uk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>bing.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>amazon.co.jp</td>\n",
" <td>co.jp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>instagram.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999970</th>\n",
" <td>999971</td>\n",
" <td>youtubeforchildren.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999971</th>\n",
" <td>999972</td>\n",
" <td>shoppingdirectory.ws</td>\n",
" <td>ws</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999972</th>\n",
" <td>999973</td>\n",
" <td>fashhackaustralia.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999973</th>\n",
" <td>999974</td>\n",
" <td>modatakipet.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999974</th>\n",
" <td>999975</td>\n",
" <td>etanto.pl</td>\n",
" <td>pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999975</th>\n",
" <td>999976</td>\n",
" <td>n1nj4.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999976</th>\n",
" <td>999977</td>\n",
" <td>dynamic.ca</td>\n",
" <td>ca</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999977</th>\n",
" <td>999978</td>\n",
" <td>redserver.su</td>\n",
" <td>su</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999978</th>\n",
" <td>999979</td>\n",
" <td>sabanne.fr</td>\n",
" <td>fr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999979</th>\n",
" <td>999980</td>\n",
" <td>the-vampire-diaries.org</td>\n",
" <td>org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999980</th>\n",
" <td>999981</td>\n",
" <td>biokplus.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999981</th>\n",
" <td>999982</td>\n",
" <td>projectgus.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999982</th>\n",
" <td>999983</td>\n",
" <td>saigonprop.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999983</th>\n",
" <td>999984</td>\n",
" <td>freepornolinks.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999984</th>\n",
" <td>999985</td>\n",
" <td>swiatwakacji.pl</td>\n",
" <td>pl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999985</th>\n",
" <td>999986</td>\n",
" <td>pornocaiunet.blogspot.com.br</td>\n",
" <td>br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999986</th>\n",
" <td>999987</td>\n",
" <td>mansbest.ru</td>\n",
" <td>ru</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999987</th>\n",
" <td>999988</td>\n",
" <td>adverterenbijeisma.nl</td>\n",
" <td>nl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999988</th>\n",
" <td>999989</td>\n",
" <td>lefilmfrancais.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999989</th>\n",
" <td>999990</td>\n",
" <td>floridagunrights.org</td>\n",
" <td>org</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999990</th>\n",
" <td>999991</td>\n",
" <td>themarkcorp.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999991</th>\n",
" <td>999992</td>\n",
" <td>smpa.or.kr</td>\n",
" <td>kr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999992</th>\n",
" <td>999993</td>\n",
" <td>cncgeeks.ca</td>\n",
" <td>ca</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999993</th>\n",
" <td>999994</td>\n",
" <td>ziegler-coaching.de</td>\n",
" <td>de</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999994</th>\n",
" <td>999995</td>\n",
" <td>crsmithdev.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>999996</td>\n",
" <td>dc.com.tw</td>\n",
" <td>tw</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>999997</td>\n",
" <td>supersentai.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>999998</td>\n",
" <td>bosal.co.za</td>\n",
" <td>co.za</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>999999</td>\n",
" <td>ahlulbaytportal.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>1000000</td>\n",
" <td>glowingeyegames.com</td>\n",
" <td>com</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" serial site TLD\n",
"0 1 google.com com\n",
"1 2 facebook.com com\n",
"2 3 youtube.com com\n",
"3 4 baidu.com com\n",
"4 5 yahoo.com com\n",
"5 6 amazon.com com\n",
"6 7 wikipedia.org org\n",
"7 8 qq.com com\n",
"8 9 taobao.com com\n",
"9 10 twitter.com com\n",
"10 11 google.co.in co.in\n",
"11 12 live.com com\n",
"12 13 sina.com.cn cn\n",
"13 14 linkedin.com com\n",
"14 15 weibo.com com\n",
"15 16 yahoo.co.jp co.jp\n",
"16 17 google.co.jp co.jp\n",
"17 18 ebay.com com\n",
"18 19 yandex.ru ru\n",
"19 20 vk.com com\n",
"20 21 tmall.com com\n",
"21 22 blogspot.com com\n",
"22 23 google.de de\n",
"23 24 hao123.com com\n",
"24 25 t.co co\n",
"25 26 msn.com com\n",
"26 27 google.co.uk co.uk\n",
"27 28 bing.com com\n",
"28 29 amazon.co.jp co.jp\n",
"29 30 instagram.com com\n",
"... ... ... ...\n",
"999970 999971 youtubeforchildren.com com\n",
"999971 999972 shoppingdirectory.ws ws\n",
"999972 999973 fashhackaustralia.com com\n",
"999973 999974 modatakipet.com com\n",
"999974 999975 etanto.pl pl\n",
"999975 999976 n1nj4.com com\n",
"999976 999977 dynamic.ca ca\n",
"999977 999978 redserver.su su\n",
"999978 999979 sabanne.fr fr\n",
"999979 999980 the-vampire-diaries.org org\n",
"999980 999981 biokplus.com com\n",
"999981 999982 projectgus.com com\n",
"999982 999983 saigonprop.com com\n",
"999983 999984 freepornolinks.com com\n",
"999984 999985 swiatwakacji.pl pl\n",
"999985 999986 pornocaiunet.blogspot.com.br br\n",
"999986 999987 mansbest.ru ru\n",
"999987 999988 adverterenbijeisma.nl nl\n",
"999988 999989 lefilmfrancais.com com\n",
"999989 999990 floridagunrights.org org\n",
"999990 999991 themarkcorp.com com\n",
"999991 999992 smpa.or.kr kr\n",
"999992 999993 cncgeeks.ca ca\n",
"999993 999994 ziegler-coaching.de de\n",
"999994 999995 crsmithdev.com com\n",
"999995 999996 dc.com.tw tw\n",
"999996 999997 supersentai.com com\n",
"999997 999998 bosal.co.za co.za\n",
"999998 999999 ahlulbaytportal.com com\n",
"999999 1000000 glowingeyegames.com com\n",
"\n",
"[1000000 rows x 3 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"freq = df.groupby('TLD').count()\n",
"del freq['site']\n",
"freq.columns = ['Frequency']\n",
"sorted_freq = freq.sort(axis=0, columns='Frequency', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feabb4c0750>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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BA4OZmRU4MJiZWYEDg5mZFTgwmJlZgQODmZkVODCYmVmBA4OZmRU4MJiZWYEDg5mZFTgw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\nDGZmVvDf93ra0hfALcIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fead8381e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sorted_freq[:20].plot(kind='bar') #top 20 TLDs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feab6831b10>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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j+L3aur6cF6JSyQB4G/j3iPhpOX2qlTQHMZjsRIJBVob6E9k7wIpe2KrowzeA\n88lGks3LWBERnywzzjMRcUjRO4lq+jQFuCgi3kmX+wE/rnRytBYknQt8A9idbIL/cODxiKiotJHm\nifYlG0AVv1us6H8s6SayRQbHRcT+6b55oFYTrxX2pSZzi9BNyz5FalEfPxw4U9JiNp6gKvsBkv7u\n05IOq+JFZBuyZDSEbMnePan9LLLJs0o8I+kzzUa3T5c4ZoNCck8jnHaJiCPT7xbLGZJ2IKuht5n8\naz2iJHuSdKmIuA64TtJNEfH1KkJtKenLwJGF8kEiKi/ZHFhI/KmPb0sqe0Kzxi4iew48HhGfTQs8\nftCOOH9bo/4MT4PLebDhvtminAMl/VNEXCXpJy1cHWTzM7dFxKtl9qVWc4vdPvnXoj5ei5ogVPki\nEhETYcMr+yER8V66PJHslb2konJNT+D3kpqPbit1P00JtxfZhPGfgHavpiqIiJWSSi6vLPUi0o6/\nu6gWcWqhysQP2cqTL7PpwoeCSpK/JPWLtCIsjW57VNm/9lobER9IKpS2Xkwl3orU8H/9obI19sCG\n1VTl1vwL80lP0/Ly0x2BXwPlDjZrNbfY7ZP/hWT18SGS3iCbQT+zkgA1fICcCGxPtioGsqWA77S+\ne6t2Jns1L1iX2srRUgIoqLi+17yMJekQNl1B1G4RUe3KnVyLiEeBRyX9ISJ+VmW4HwOPS5pGllRO\nBf53tX1sp9clbU82+Txb0js0rRbrCj8hS9A7S/oB2Sq4fynnwIi4N/2e1No+kt5v7boW4n1f0kya\n5hbPK5pbrOgzI9295l+8jrcf2SRnRMT32jqug/pyEdky08Jo6++Bm9Nb/ErifIdstcZ0sifhKODO\niGjP296ak/THaifIrfaUffp5INmArlD2KevTz0UxPkW2ZDCA30ZERSPJjpAm+rcFZkaFn4itcT/2\nJzvrMGSfoah6Wayk8yLiP6uN0+6/382T/wM0reMt/tTnj7ugL/OBwyPi/XS5D/BERBzQjljDyN5B\nBPBIRMyraWfL70fxJHQD2Scb+9Vo+ZzViKr89LN1ja5O/t297FOrdby18nEr2xWJ7JQKZU/QdqDC\nJDRkq2HuA+7quu5YK4ZRg08/W+fqysQP3T/5PybpwGrX8dbIrWQTMcXlmp93bZeqU5iEtrpXq08/\nWweRtAfZp8qPSk2PkC2tXdJlferOg4VareOtYX+GUXT2yq4q13Skrn6raptSdtK6g8iWBFfz6Wfr\nIJIeJFt2EWXoAAACJElEQVSpc1tq+jLZhzcrPV1K7frUzZP/Xi2119Nyvs2Nk3/9qdWnn63jSHou\nIj5dqq1T+9Sdk7+ZWXcg6bdkpeHC+ZfGAOdEhWeCrSV/k5e1StIekn4t6a30c5ek3bu6X5aR9Pv0\ne7Wk95r9tOeMkdZxziH7LovlZB9EPTW1dRmP/K1V9VinNOuO0jnELm527qQfRYXfGFhLHvlbW3aK\niFsjYl36mUT5nzY2syafbn7uJLLPzXQZJ39ry0pJZ0nqIamnpDPJviHMzCqjNNovXOjKcycB3X+d\nv3Wsc4DrgcLppB+ji+uUZt1UPZ07CXDN39pQj3VKs+6q3s6d5ORvrZL0bEQcVKrNzLof1/ytLXVX\npzSz2nDN39pSd3VKM6sNl32sTfVWpzSz2nDyNzPLIdf8zcxyyMnfzCyHnPzNzHLIq33M2iBpB+DB\ndHEXsu/IfStdHhwRfZrtPxH4h7RPH2A+8C+1+MJvs1py8jdrQ0SsBA4GkHQF8F5EXJ0uv9fSIcDV\nRfuMBn4r6YCI8HmRrG647GNWGVWyT0RMA2YBZ3RYj8zawcnfrOM9A+zX1Z0wK+bkb9bx/DyzuuMH\npVnHOxjwJ6Otrjj5m3UgSV8CTgDu6Oq+mBXzah+zyhSfD2UrSa8XXS586c0307eeFZZ6HpdWDZnV\nDZ/bx8wsh1z2MTPLISd/M7MccvI3M8shJ38zsxxy8jczyyEnfzOzHHLyNzPLof8PT8D0jMCRl1oA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feab583c6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sorted_freq[1:20].plot(kind='bar') #TLDs from 2-20"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"slots=range(0,1000000,1000)\n",
"dfslots = map((lambda x: df[x:x+1000].groupby('TLD').count()), slots)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def tld_frequency(tld):\n",
" perc_index = []\n",
" perc_columns = []\n",
" counter = 0\n",
" for each in dfslots:\n",
" tld_count = each.loc[tld].site\n",
" total_count = each.sum().site\n",
" percentage = (float(tld_count)/float(total_count))*100\n",
" perc_columns.append(percentage)\n",
" perc_index.append(\"{0}-{1}\".format(counter, counter+1000))\n",
" counter+=1000\n",
" return pd.DataFrame(perc_columns, index=perc_index, columns=[tld])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Get the percentage frequency for '.net' TLD\n",
"perc_tld = tld_frequency('net')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feab51afd10>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EnHVW8BjFzYG7CbitExMoPWB0R9+ECfHk4F77M00HvmcP7wPtKG34CfimTbwk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\nEQEXBEGoUP4/SVIGjzC8dFEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feab51af690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot first 100 rows from dataframe\n",
"perc_tld[:100].plot(kind='line')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feab42a5bd0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PTz/GFLmNcJU0wuLW67e3V450POood7nocS5ii/2xx8afkKuRPu52eNjFpGUWt/oeQ7i5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\nSGe1tGEKB+Tyj2Vxm9oYd6zrjbKKcC9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YM0cO5OEWpKiKmsuDY+ON5adv519Zxo3zqzsa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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feab422fe90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot all the rows in the dataframe\n",
"perc_tld.plot(kind='line')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Get the percentage frequency for '.in' TLD\n",
"perc_tld = tld_frequency('in')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feab426cdd0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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iHdUb1Zh43pddlvdAGxp4cIsfttMENV6dtG5hi2qbOK/v1VfzElhe5++UU/ov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\nFgRByCD/H35IjMg7oFwzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feab41f8a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot first 100 rows\n",
"perc_tld[:100].plot(kind='line')\n",
"\n",
"\"\"\"\n",
"We were trying to see if the percentage of the TLD drops as we go down in the list of top 1 million sites.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Conclusion: The percentage of TLD fluctuates throughout.\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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
dvkonst/ml_mipt | task_4/task2.ipynb | 1 | 29171 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import difflib\n",
"# # from difflib_data import *\n",
"\n",
"# text1 = \"fghjjkk\"\n",
"# text2 = \"fghjkref\"\n",
"# d = difflib.Differ()\n",
"# diff = d.compare(text1, text2)\n",
"# print('\\n'.join(diff))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import model_selection, metrics, linear_model\n",
"# from numba import vectorize, u8\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[15 16 17 18 19]\n"
]
}
],
"source": [
"def lcs(s1, s2):\n",
" L = {}\n",
" z = 0\n",
" ret = ''\n",
" for i, c1 in enumerate(s1):\n",
" for j, c2 in enumerate(s2):\n",
" if c1 == c2:\n",
" L[(i, j)] = L.get((i - 1, j - 1), 0) + 1\n",
" if L[(i, j)] > z:\n",
" z = L[(i, j)]\n",
" ret = s1[i - z + 1:i + 1]\n",
" return ret\n",
"\n",
"def parser_w(w, inf):\n",
" root = lcs(w, inf)\n",
" i_r_w = w.find(root)\n",
" suff_form = w[i_r_w+len(root):]\n",
" i_r_inf = inf.find(root)\n",
" suff_inf = inf[i_r_inf+len(root):]\n",
" return [root, suff_form, suff_inf]\n",
"\n",
"def openDS(path):\n",
" return pd.read_csv(path, sep=',')\n",
"\n",
"def parserDF(DF):\n",
" return 0\n",
"\n",
"def setforms(forms):\n",
" return set(forms)\n",
"\n",
"def get_forms(w, train_forms):\n",
" for i in range(len(w)):\n",
" form = w[i:]\n",
"# root = w[:i]\n",
" if form in train_forms:\n",
" return form\n",
" else:\n",
" return ''\n",
"def get_ans(w, form, inf):\n",
" for i in range(len(w)):\n",
" formcur = w[i:]\n",
" if formcur == form:\n",
" return w[:i] + inf\n",
"def hashD(y):\n",
" S = set(y)\n",
" Dh = {}\n",
" Dunh = {}\n",
" i = 1\n",
" for key in S:\n",
" Dh[key] = i\n",
" Dunh[i] = key\n",
" i += 1\n",
" return Dh, Dunh\n",
"def hashY(Y, Dh):\n",
" res = np.zeros(Y.shape)\n",
" for i in range(len(Y)):\n",
" res[i] = Dh[Y[i]]\n",
" return res\n",
"def unhashY(hY, Dunh):\n",
" res = []\n",
" for i in range(len(hY)):\n",
" res.append(Dunh[hY[i]])\n",
" return np.array(res)\n",
"D1, D2 = hashD(np.arange(15, 20))\n",
"hy = hashY(np.arange(15, 20), D1)\n",
"uhy = unhashY(hy, D2)\n",
"print(uhy)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>trabaja</td>\n",
" <td>do</td>\n",
" <td>r</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>trabaja</td>\n",
" <td>do</td>\n",
" <td>r</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"0 trabaja do r\n",
"1 trabaja do r"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['trabajado', 'trabajar']*2).reshape((2, 2))\n",
"pd.DataFrame(list(map(parser_w, a.T[0], a.T[1])))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DS = openDS('task2_lemmas_train_2.csv')\n",
"words = DS.X.values\n",
"infs = DS.y.values\n",
"features = pd.DataFrame(list(map(parser_w, words, infs)))\n",
"# X = features.drop([0])\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>erete</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>vate</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td></td>\n",
" <td>+N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>vamo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>erei</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>sti</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>hereste</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>eran</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>sser</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ndo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>sser</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>anno</td>\n",
" <td>e+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>i</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ndo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>erebbero</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>herebbero</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>sser</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>van</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>ta</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ssero</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>n</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>n</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>te</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>erebbero</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>ssi</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>sti</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td></td>\n",
" <td>+N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>eremmo</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>bbe</td>\n",
" <td>+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>ssi</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118610</th>\n",
" <td>eremmo</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118611</th>\n",
" <td>i</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118612</th>\n",
" <td>erai</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118613</th>\n",
" <td>i</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118614</th>\n",
" <td>ta</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118615</th>\n",
" <td>i</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118616</th>\n",
" <td>van</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118617</th>\n",
" <td>ereste</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118618</th>\n",
" <td>eranno</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118619</th>\n",
" <td></td>\n",
" <td>+N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118620</th>\n",
" <td>on</td>\n",
" <td>e+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118621</th>\n",
" <td>ser</td>\n",
" <td>ndere+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118622</th>\n",
" <td>no</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118623</th>\n",
" <td>vo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118624</th>\n",
" <td>mmo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118625</th>\n",
" <td>ssero</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118626</th>\n",
" <td>te</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118627</th>\n",
" <td>ente</td>\n",
" <td>ire+A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118628</th>\n",
" <td>ssero</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118629</th>\n",
" <td>sser</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118630</th>\n",
" <td>vo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118631</th>\n",
" <td>te</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118632</th>\n",
" <td></td>\n",
" <td>+N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118633</th>\n",
" <td>ò</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118634</th>\n",
" <td>sser</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118635</th>\n",
" <td>ereste</td>\n",
" <td>are+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118636</th>\n",
" <td>ste</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118637</th>\n",
" <td>bbero</td>\n",
" <td>+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118638</th>\n",
" <td>ssimo</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118639</th>\n",
" <td>ste</td>\n",
" <td>re+V</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>118640 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 erete are+V\n",
"1 vate re+V\n",
"2 +N\n",
"3 vamo re+V\n",
"4 erei are+V\n",
"5 sti re+V\n",
"6 hereste are+V\n",
"7 eran are+V\n",
"8 sser re+V\n",
"9 ndo re+V\n",
"10 sser re+V\n",
"11 anno e+V\n",
"12 i re+V\n",
"13 ndo re+V\n",
"14 erebbero are+V\n",
"15 herebbero are+V\n",
"16 sser re+V\n",
"17 van re+V\n",
"18 ta re+V\n",
"19 ssero re+V\n",
"20 n re+V\n",
"21 n re+V\n",
"22 te re+V\n",
"23 erebbero are+V\n",
"24 ssi re+V\n",
"25 sti re+V\n",
"26 +N\n",
"27 eremmo are+V\n",
"28 bbe +V\n",
"29 ssi re+V\n",
"... ... ...\n",
"118610 eremmo are+V\n",
"118611 i re+V\n",
"118612 erai are+V\n",
"118613 i re+V\n",
"118614 ta re+V\n",
"118615 i re+V\n",
"118616 van re+V\n",
"118617 ereste are+V\n",
"118618 eranno are+V\n",
"118619 +N\n",
"118620 on e+V\n",
"118621 ser ndere+V\n",
"118622 no re+V\n",
"118623 vo re+V\n",
"118624 mmo re+V\n",
"118625 ssero re+V\n",
"118626 te re+V\n",
"118627 ente ire+A\n",
"118628 ssero re+V\n",
"118629 sser re+V\n",
"118630 vo re+V\n",
"118631 te re+V\n",
"118632 +N\n",
"118633 ò are+V\n",
"118634 sser re+V\n",
"118635 ereste are+V\n",
"118636 ste re+V\n",
"118637 bbero +V\n",
"118638 ssimo re+V\n",
"118639 ste re+V\n",
"\n",
"[118640 rows x 2 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# features[features[1] == 'vate']\n",
"hh = np.vectorize(hash)\n",
"X = features[1].values\n",
"# X = X\n",
"Xh = hh(X.reshape((X.size, 1)))\n",
"Y = features[2].values\n",
"clf = DecisionTreeClassifier()\n",
"clf.fit(Xh, Y)\n",
"pred = clf.predict(Xh)\n",
"res = pd.DataFrame({0: X, 1: pred})\n",
"res\n",
"# res[res[0] == 'erei']"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: 're+V'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-62-40159812f7e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0minfs_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0minfs_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munhashY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDunh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mget_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_ans\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_a\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwordstt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf_ttt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfs_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-60-5495217a8d98>\u001b[0m in \u001b[0;36munhashY\u001b[0;34m(hY, Dunh)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhY\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[0;32m---> 62\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDunh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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 63\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mD1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhashD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: could not convert string to float: 're+V'"
]
}
],
"source": [
"DS = openDS('task2_lemmas_test.txt')\n",
"wordstt = DS.X.values\n",
"\n",
"sf = setforms(X)\n",
"get_f = np.vectorize(get_forms, excluded=['train_forms'])\n",
"f_ttr = get_f(words, sf)\n",
"f_ttt = get_f(wordstt, sf)\n",
"cv = CountVectorizer(ngram_range=(1, 10), lowercase=False, analyzer='char_wb')\n",
"model_cv = cv.fit(f_ttr)\n",
"x_train = model_cv.transform(f_ttr)\n",
"x_test = model_cv.transform(f_ttt)\n",
"# print(x_train.shape)\n",
"Dh, Dunh = hashD(Y)\n",
"y_train = hashY(Y, Dh)\n",
"# model = linear_model.LogisticRegression(penalty='l2', solver='lbfgs', C=1.)\n",
"# print(model_selection.cross_val_score(model, x_train, y_train, \n",
"# scoring='accuracy', cv=model_selection.StratifiedKFold(shuffle=True)).mean())\n",
"model = linear_model.LogisticRegression(penalty='l2', solver='lbfgs', C=1.)\n",
"# pred = \n",
"model.fit(x_train, y_train)\n",
"infs_test = model.predict(x_test)\n",
"infs_test = unhashY(infs_test, Dunh)\n",
"get_a = np.vectorize(get_ans)\n",
"res = get_a(wordstt, f_ttt, infs_test)\n",
"ans = pd.DataFrame()\n",
"ans[\"Id\"] = range(1,len(res)+1)\n",
"ans[\"Category\"] = res\n",
"ans.to_csv('hw4_task2.txt', sep=',', index=None)\n",
"# DF\n",
"# infs_train = clf.predict(hh(f_ttr.reshape((f_ttr.size,1))))\n",
"# get_a = np.vectorize(get_ans)\n",
"# ans = get_a(words, f_ttr, infs_test)\n",
"\n",
"# Res = pd.DataFrame({0:words,1:infs,2:ans})\n",
"# Res\n",
"# from sklearn.metrics import accuracy_score\n",
"# print(accuracy_score(infs, ans))\n",
"# pd.DataFrame({0:X, 1:f_test, 2:pred, 3:infs_test})"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"infs_test = unhashY(infs_test, Dunh)\n",
"get_a = np.vectorize(get_ans)\n",
"res = get_a(wordstt, f_ttt, infs_test)\n",
"ans = pd.DataFrame()\n",
"ans[\"Id\"] = range(1,len(res)+1)\n",
"ans[\"Category\"] = res\n",
"ans.to_csv('hw4_task2.txt', sep=',', index=None)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NotFittedError",
"evalue": "This LogisticRegression instance is not fitted yet",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotFittedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-57-756536b260cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/daniel/anaconda3/lib/python3.5/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \"\"\"\n\u001b[0;32m--> 336\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 337\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/daniel/anaconda3/lib/python3.5/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coef_'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m raise NotFittedError(\"This %(name)s instance is not fitted \"\n\u001b[0;32m--> 310\u001b[0;31m \"yet\" % {'name': type(self).__name__})\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotFittedError\u001b[0m: This LogisticRegression instance is not fitted yet"
]
}
],
"source": [
"pred = model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# class Lemm():\n",
"# def __init__(self):\n",
"# self.D_freq = {}\n",
"# self.Roots = pd.DataFrame\n",
"# self.Suffs = {}\n",
"# def parser_w(self, w, inf):\n",
"# root = lcs(w, inf)\n",
"# i_r_w = w.find(root)\n",
"# suff_form = w[i_r_w+len(root):]\n",
"# i_r_inf = inf.find(root)\n",
"# suff_inf = inf[i_r_inf+len(root):]\n",
" \n",
"# return [root, suff_form, suff_inf]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DS = openDS('task2_lemmas_test.txt')\n",
"wordstt = DS.X.values\n",
"\n",
"sf = setforms(X)\n",
"get_f = np.vectorize(get_forms, excluded=['train_forms'])\n",
"f_ttt = get_f(wordstt, sf)\n",
"# cv = CountVectorizer(lowercase=False, analyzer='char_wb')\n",
"# model_cv = cv.fit(f_ttr)\n",
"# DF = pd.DataFrame(f_test)\n",
"# DF\n",
"infs_test = clf.predict(hh(f_ttt.reshape((f_ttt.size,1))))\n",
"get_a = np.vectorize(get_ans)\n",
"res = get_a(wordstt, f_ttt, infs_test)\n",
"ans = pd.DataFrame()\n",
"ans[\"Id\"] = range(1,len(res)+1)\n",
"ans[\"Category\"] = res\n",
"ans.to_csv('hw4_task2.txt', sep=',', index=None)\n",
"# Res = pd.DataFrame({0:words,1:infs,2:ans})\n",
"# Res\n",
"# from sklearn.metrics import accuracy_score\n",
"# print(accuracy_score(infs, ans))\n",
"# pd.DataFrame({0:X, 1:f_test, 2:pred, 3:infs_test})"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<2x38 sparse matrix of type '<class 'numpy.int64'>'\n",
"\twith 40 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"cv = CountVectorizer(ngram_range=(1, 6), lowercase=False, analyzer='char_wb')\n",
"DD = pd.DataFrame(['fhg', 'dfskl'])[0]\n",
"DD\n",
"model_cv = cv.fit(DD)\n",
"r = model_cv.transform(DD.values)\n",
"# ss = pd.DataFrame(r)\n",
"# r.head()\n",
"r"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
rfinn/LCS | notebooks/LCS-paper2.ipynb | 1 | 11099383 | null | gpl-3.0 |
iamtrask/polyglot | notebooks/README.ipynb | 5 | 8814 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"polyglot\n",
"==============================="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[![Downloads](https://img.shields.io/pypi/dm/polyglot.svg \"Downloads\")](https://pypi.python.org/pypi/polyglot)\n",
"[![Latest Version](https://badge.fury.io/py/polyglot.svg \"Latest Version\")](https://pypi.python.org/pypi/polyglot)\n",
"[![Build Status](https://travis-ci.org/aboSamoor/polyglot.png?branch=master \"Build Status\")](https://travis-ci.org/aboSamoor/polyglot)\n",
"[![Documentation Status](https://readthedocs.org/projects/polyglot/badge/?version=latest \"Documentation Status\")](https://readthedocs.org/builds/polyglot/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Polyglot is a natural language pipeline that supports massive multilingual applications.\n",
"\n",
"* Free software: GPLv3 license\n",
"* Documentation: http://polyglot.readthedocs.org.\n",
"\n",
"###Features\n",
"\n",
"\n",
"* Tokenization (165 Languages)\n",
"* Language detection (196 Languages)\n",
"* Named Entity Recognition (40 Languages)\n",
"* Part of Speech Tagging (16 Languages)\n",
"* Sentiment Analysis (136 Languages)\n",
"* Word Embeddings (137 Languages)\n",
"* Morphological analysis (135 Languages)\n",
"* Transliteration (69 Languages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Developer\n",
"\n",
"* Rami Al-Rfou @ `rmyeid gmail com`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Quick Tutorial"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import polyglot\n",
"from polyglot.text import Text, Word"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Language Detection"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Language Detected: Code=fr, Name=French\n",
"\n"
]
}
],
"source": [
"text = Text(\"Bonjour, Mesdames.\")\n",
"print(\"Language Detected: Code={}, Name={}\\n\".format(text.language.code, text.language.name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tokenization"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'Beautiful', u'is', u'better', u'than', u'ugly', u'.', u'Explicit', u'is', u'better', u'than', u'implicit', u'.', u'Simple', u'is', u'better', u'than', u'complex', u'.']\n"
]
}
],
"source": [
"zen = Text(\"Beautiful is better than ugly. \"\n",
" \"Explicit is better than implicit. \"\n",
" \"Simple is better than complex.\")\n",
"print(zen.words)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Sentence(\"Beautiful is better than ugly.\"), Sentence(\"Explicit is better than implicit.\"), Sentence(\"Simple is better than complex.\")]\n"
]
}
],
"source": [
"print(zen.sentences)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part of Speech Tagging"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word POS Tag\n",
"------------------------------\n",
"O DET\n",
"primeiro ADJ\n",
"uso NOUN\n",
"de ADP\n",
"desobediência NOUN\n",
"civil ADJ\n",
"em ADP\n",
"massa NOUN\n",
"ocorreu ADJ\n",
"em ADP\n",
"setembro NOUN\n",
"de ADP\n",
"1906 NUM\n",
". PUNCT\n"
]
}
],
"source": [
"text = Text(u\"O primeiro uso de desobediência civil em massa ocorreu em setembro de 1906.\")\n",
"\n",
"print(\"{:<16}{}\".format(\"Word\", \"POS Tag\")+\"\\n\"+\"-\"*30)\n",
"for word, tag in text.pos_tags:\n",
" print(u\"{:<16}{:>2}\".format(word, tag))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Named Entity Recognition"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[I-LOC([u'Gro\\xdfbritannien']), I-PER([u'Gandhi'])]\n"
]
}
],
"source": [
"text = Text(u\"In Großbritannien war Gandhi mit dem westlichen Lebensstil vertraut geworden\")\n",
"print(text.entities)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Polarity"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word Polarity\n",
"------------------------------\n",
"Beautiful 0\n",
"is 0\n",
"better 1\n",
"than 0\n",
"ugly -1\n",
". 0\n"
]
}
],
"source": [
"print(\"{:<16}{}\".format(\"Word\", \"Polarity\")+\"\\n\"+\"-\"*30)\n",
"for w in zen.words[:6]:\n",
" print(\"{:<16}{:>2}\".format(w, w.polarity))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embeddings"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Neighbors (Synonms) of Obama\n",
"------------------------------\n",
"Bush \n",
"Reagan \n",
"Clinton \n",
"Ahmadinejad \n",
"Nixon \n",
"Karzai \n",
"McCain \n",
"Biden \n",
"Huckabee \n",
"Lula \n",
"\n",
"\n",
"The first 10 dimensions out the 256 dimensions\n",
"\n",
"[-2.57382345 1.52175975 0.51070285 1.08678675 -0.74386948 -1.18616164\n",
" 2.92784619 -0.25694436 -1.40958667 -2.39675403]\n"
]
}
],
"source": [
"word = Word(\"Obama\", language=\"en\")\n",
"print(\"Neighbors (Synonms) of {}\".format(word)+\"\\n\"+\"-\"*30)\n",
"for w in word.neighbors:\n",
" print(\"{:<16}\".format(w))\n",
"print(\"\\n\\nThe first 10 dimensions out the {} dimensions\\n\".format(word.vector.shape[0]))\n",
"print(word.vector[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Morphology"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'Pre', u'process', u'ing']\n"
]
}
],
"source": [
"word = Text(\"Preprocessing is an essential step.\").words[0]\n",
"print(word.morphemes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transliteration"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"препрокессинг\n"
]
}
],
"source": [
"from polyglot.transliteration import Transliterator\n",
"transliterator = Transliterator(source_lang=\"en\", target_lang=\"ru\")\n",
"print(transliterator.transliterate(u\"preprocessing\"))"
]
}
],
"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
}
| gpl-3.0 |
QuantEcon/phd_workshops | John/numba.ipynb | 1 | 352278 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vectorization and JIT compilation in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Examples for the QuantEcon 2017 PhD workshops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[John Stachurski](http://johnstachurski.net)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "cacd76f0-600a-4ac9-ba39-ae23747177c8"
},
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"from numba import vectorize, jit, float64\n",
"from quantecon.util import tic, toc\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 1: A Time Series Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider the time series model\n",
"\n",
"$$ x_{t+1} = \\alpha x_t (1 - x_t) $$\n",
"\n",
"Let's set $\\alpha = 4$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"α = 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a typical time series:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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Zvz63WSxZASXuGuaSnqoxdxvcwanAUgQjsWv8MYm5W67cy3QzkqMnR/hg2WWT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hB2JINrG/TBDSiffdQ74Msl+qvtHhKKDyYSUbnnnmdljZdCutL9DgqdMlpyGHHAPQucFOO+rdUy\nMfBx7mXpfn0YVOfOPdaCcxd4/uIGAOAyTZUTKi9NRZ5jo05ZVpEVcngBVVc7uGgZHbq6ooySAOz0\nBkARo/C9mETAfAWj6FJh+WR6CW2uTS0pZAk1VeeArzxmlREydvCbSqg0IV9Z4j7+HRVQrWvwUrcy\n3SIjTqHTZ/sNzh1ZfSfDZ56MWowRPilkFY2tWZ48Vl2p5/NGUwE8fykz7nvKjXv+smWbMtS1CxNQ\nPahYBep+ueHm9MaljR6O79vp/O75Sxu49uhisHyAe1vy+MLyBvbMTXvvrZVUo05cKqxLGz0Q2QHN\nhKhybId/H9B3DgvLOWsd8KnQuVcxqvfffhqvetQquXGvxLkXqp3aPPesHnvnZ4xBizx9qapcdtho\njLuHllkd0HMfVOduGzV5TIXA+YsbWJidiqpLwYHWmxFSlj28zqveFx4A54HJi+tdI68MUARju32B\nI3vnnOXrY5wrOJYK4PJF93cBee9V4i+uxXGX1rvYtWPKUohULZtDtd1LKx3snGkZS+LN66j2AV8t\n0a+Syx0Abji+1/uZup8qxl3NwJc3erXp+NVj0g24fp7IPQuclIDqa56WcakagCLAVplzz3Xu1dQy\nXAKYG51Ueu6HI7x2+fuZFLLmXO6yrvI4jM7bytuBv0gEZI9HCIGLDlpG98ov97ST7m0tevKpH1n0\nt7EyjLFwpbVwJQ2TZW9uwFSDxUq7h2NL8/7rqF6+HTBVKpuZfehQ97NaMhMxvkOEXr/efEq65+47\nr1M0+ax9Qox747k7XkJA09gOrHOPl8wBYc/9+Ysbxs47ZeWpRUx16pllnTLPfYjpB1ztkO/R2emj\nnwoH564Zd4/3rZfPt1ks+66qR5VZnEuFdWndbXikWmYztEzx99G9IeNOmKt5abx6f1bb9fU3dT/n\nL7Wxe3bKOxPh36lb/qv61dIut+fOPfpG5z5h8OWWUVPCqg+qlRv3ip67N6AqPfezhxaiy5Ocu52g\narP46Wsux64dU7VO6xXUjMcdUJV/X3KsTpXXFH/7aBn/FDvOuL/zxy6zfjcEV26Z5Q1b6QMA9992\nGkc99Y6Bfg/HlsLUUv0DvuLc+ziwsKP8C1Flyvvp9FJceXh3ydUSRJSvXq5rdqISRPJV7sqI72N9\n6fI9c/iJ1+/DDSfccZ9RozHunoDq0xdWAfiNhb88qQSI1kNnl1kGKzvf6ad4cbkdFUxVv9/PpskH\nF+K+E4srLlvAFZfFDTJVQR7PXTeSF72LgArjdqTEc9/r4PQVQsb9n7ztTKj6zt+zaZmes3733nyi\nUtkc+j0cC3juRPVq3AFNpVIjLaPfz6l9/vsx6pHEJw2LhaKHfA7BPubRz8208Pn7bq7lt+tAw7l7\nAqqPn1/B4T2zTo40hGNL8zhzaKGCHjozah7v4MXlDaQC0bSMnlum7hd5mPBLIZEbSVdeGf278zMt\nr3dNEZ57iHOvCl9AlQeD6/kt3XMP0zL1c+7y/bmw2ra46UGh38+p/buiv6PWEdTV73NaxhNQ3bez\nnpnKsLB13v4hwadzf+z8Ms5EUiE67rv1NO679XT09T6PVXXwH70qZZCxAVW1bd9LK20crGmaPAqE\npZDy+fhoGdWGRxbnvIOqPzhW/B3y3KvCt0LVRctsFi3DuPvv4WeuPYzTkcYyFnm65W48hVJeZnE/\nJ/dHeu5EePbiOgCbLhm8HvLocwj4+UlDY9wdL2E/FXjihRW85fQ+95dq/X15tBNmyeOPXpUdNt5z\nl8duX+Cao3tqqeMooPJrc89S94DVHqu+gbBMygj4X9RWQrXSWFyFdWGljeWNXvRzrALDcw/QMv/s\nnW8Y6m/refI3A32wih2MiIp9huuiDn19RlUvZsX4ONEYd4fn/sOX19DupdFBzM3g+uN7cecbL8OZ\nQ2YnVh3ruYvxC5j07wHAtccmI7ATg+uOLeK5i+uW563TZg//8FXsmZu2eGs1oJWpXQD/i3rZ7tmB\nE3e5f8/k3L/1g1cAAOdO+vXdg4IycnVp50xtvHcs9B2NXn+wnlkBaWRxFc8dAN54+e4odU1cmfLo\nk0IuNbTMZMPFuT92fhkALIM7DBxZnLN2gJH1kscfXVzHdItK89IrqA55YGFHVLqCScHd1x/B3dcf\nsc7raRi+/tQF3HhyybkICEBQcbI4P4Nbz+zHzWw2pjZX9gViBwXn3L/1g1cw00pw9ZH6Z1Pq/o9t\nQnEzKNRgfMWhhRqNqixz/64d0cFR1SWu8axQHgSnD+zCsaU5i+pSMufDNcZohoHXvHHXjYfqqI+/\nsAIAA3HuddfrR6+u4+DCrDPvtQvqHq49ujgRm/RuFjk9dXEDT19YcypLKPfc/S/bzFSC3/vQmz2/\nQcHvDgKuwvrG0y/j6qN7ojIcVv8teTwaCKYOC6qf1kXJyDLl8fT+neELHfW4rsbZ6o0nl/AXH3+r\ndf6G43vxhx9+C66f8Jnxa14tUywWKs49dn4ZRxbnalcWVEGRL6WNa4/Fe3tJbty3Dt8eghrUvvbk\nBQCwPG+g4OBfd2CwmdbSzhmcrVniqc8IN7p9PPLsxaFQMoDuuY/DuMtjvcZdFhpLyQCaUzMCg0tE\nuPHk0sQ7T1HGnYjeRUSPEtETRPQJx+dERL+Vff5dIrqh/qoOB8p4KC+rnwo88szFkVAyIeiO+vvf\nHK+DVjPjaybcq4iFen++9uQFLMxOORUZ1x1bxFc+dtvAU/I//uituO+WeIVTDPRYznefuYhuX+Dc\niaVaf0NhdrqFj7/rCrz33NGhlB9C7rnXSDdNJYS989O47lj8YJiQ3FbzZKQu/rWAUteUiFoAfhvA\nOwA8A+AbRPSQEOKvtcvuBHAm+/dmAJ/JjhMPyj13+SL+7teexlMvreKX3/b68VUKhSdy+sBO/Pjr\n4lU7i/MzmJtubR/PPWuHP3/8Rdx0cskZ9CSiTQW/9a3S6oJSYQkh8MX/9wwA4E0nhuO5A8A//snx\n9NfXH9yF1x3YiSsvq89zn2ol+D///K2VkqntnZ/B4T1+KexrETG8w00AnhBCPAUARPQFAHcB0I37\nXQB+V0hX5etEtEhEh4UQz9Ve45qhjEe3L/AXj53Hp//4UfzkFQdw93V2cG+UUCtc3//mE5U67Huu\nP4Lbzx7AYk0LSsYNZctfWe3ggz9xaryVqYCEgG4vxb/76uP4/b/6IT50y6mJ10UPgjvecBB3vOFg\n7eVWVf38h/ddHx2Xeq0gpgWPAPih9v9nYHvlrmuOANgCxl0ez/36n2Kjm+Lwnln86/dcPXYP4KrD\nu/Hv77kO73rjZZW+N91KhqKlHhcW52Tmvf/489fjljP7x12daCQJYbndw2/92eO4+7rL8cl3Xznu\nKm1r7N2GA+dmMdKIIRHdD+B+ADh+/Pgof9qLt115CI8+v4LF+WnceHIv3vqGQ1EbUQ8bSUK4a8yz\nh0nAz73pKN565UFjx5utgPfddBxL8zO46dSSU77ZoMGwEWPcnwVwTPv/0exc1WsghHgQwIMAcO7c\nOUdGl9HjdQd24Tffe+24q9HAgyShLWfYAeDsoYWRLIJr0MCHGBf1GwDOENEpIpoBcA+Ah9g1DwH4\nxUw1czOAi1uBb2/QoEGD7YpSz10I0SOiXwLwJwBaAD4nhPg+EX04+/wBAF8C8G4ATwBYA/DB4VW5\nQYMGDRqUIYpzF0J8CdKA6+ce0P4WAD5Sb9UaNGjQoMGgGH/ksEGDBg0a1I7GuDdo0KDBNkRj3Bs0\naNBgG6Ix7g0aNGiwDdEY9wYNGjTYhiDX/qEj+WGiFwH8YMCv7wfwUo3VqROTWremXtUwqfUCJrdu\nTb2qYdB6nRBCHCi7aGzGfTMgom8KIc6Nux4uTGrdmnpVw6TWC5jcujX1qoZh16uhZRo0aNBgG6Ix\n7g0aNGiwDbFVjfuD465AAJNat6Ze1TCp9QImt25NvaphqPXakpx7gwYNGjQIY6t67g0aNGjQIIAt\nZ9zLNuseYT2OEdH/IqK/JqLvE9FHs/OfIqJniejh7N+7x1C3p4nokez3v5mdWyKiPyWix7Pj8Db0\n9NfrCq1dHiaiS0T0K+NoMyL6HBG9QETf085524iI/kXW5x4loneOuF7/hoj+Ntt8/otEtJidP0lE\n61q7PeAveSj18j63UbVXoG5/oNXraSJ6ODs/kjYL2IfR9TEhxJb5B5ly+EkApwHMAPgOgKvGVJfD\nAG7I/l4A8BiAqwB8CsA/HXM7PQ1gPzv3aQCfyP7+BIDfmIBn+TyAE+NoMwC3AbgBwPfK2ih7rt8B\nsAPAqawPtkZYr78HYCr7+ze0ep3UrxtDezmf2yjby1c39vlvAvi1UbZZwD6MrI9tNc8936xbCNEB\noDbrHjmEEM8JIb6d/b0M4G8g942dVNwF4Heyv38HwN1jrAsAvA3Ak0KIQReybQpCiD8H8DI77Wuj\nuwB8QQjRFkL8HeS+BTeNql5CiK8IIXrZf78OudPZSOFpLx9G1l5ldSO5GfJ7Afz+sH7fUyeffRhZ\nH9tqxt23EfdYQUQnAVwP4P9mp345m0J/bhz0BwAB4KtE9K1s31oAOCSK3bGeB3BoDPXScQ/MF27c\nbQb422iS+t0/AvBl7f+nMnrhfxPRrWOoj+u5TVJ73QrgvBDice3cSNuM2YeR9bGtZtwnDkS0C8D/\nAPArQohLAD4DSRtdB+A5yCnhqHGLEOI6AHcC+AgR3aZ/KOQ8cGwyKZLbNf4sgD/MTk1CmxkYdxu5\nQESfBNAD8Pns1HMAjmfP+lcB/Hci2j3CKk3cc3PgfTCdiJG2mcM+5Bh2H9tqxj1qI+5RgYimIR/c\n54UQfwQAQojzQoi+ECIF8FkMcTrqgxDi2ez4AoAvZnU4T0SHs3ofBvDCqOul4U4A3xZCnAcmo80y\n+Npo7P2OiP4hgJ8G8P7MKCCbwl/I/v4WJE97dlR1Cjy3sbcXABDRFIC/D+AP1LlRtpnLPmCEfWyr\nGfeYzbpHgozL+y8A/kYI8W+184e1y94D4Hv8u0Ou104iWlB/QwbjvgfZTh/ILvsAgP85ynoxGN7U\nuNtMg6+NHgJwDxHtIKJTAM4A+KtRVYqI3gXg4wB+Vgixpp0/QESt7O/TWb2eGmG9fM9trO2l4e0A\n/lYI8Yw6Mao289kHjLKPDTtqPIQo9LshI89PAvjkGOtxC+SU6rsAHs7+vRvA7wF4JDv/EIDDI67X\nacio+3cAfF+1EYB9AP4MwOMAvgpgaUztthPABQB7tHMjbzPIweU5AF1IfvNDoTYC8Mmszz0K4M4R\n1+sJSD5W9bMHsmv/QfaMHwbwbQA/M+J6eZ/bqNrLV7fs/H8D8GF27UjaLGAfRtbHmhWqDRo0aLAN\nsdVomQYNGjRoEIHGuDdo0KDBNkRj3Bs0aNBgG6Ix7g0aNGiwDdEY9wYNGjTYhmiMe4MGDRpsQzTG\nvUGDBg22IRrj3qBBgwbbEP8fdFxfQlYaFjEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f80ed3019b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 200\n",
"x = np.empty(n)\n",
"x[0] = 0.2\n",
"for t in range(n-1):\n",
" x[t+1] = α * x[t] * (1 - x[t])\n",
" \n",
"plt.plot(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a function that simulates for `n` periods, starting from `x0`, and returns **only the final** value:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def quad(x0, n):\n",
" x = x0\n",
" for i in range(1, n):\n",
" x = α * x * (1 - x)\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how fast this runs:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 10_000_000"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 1.1631062030792236 seconds.\n"
]
},
{
"data": {
"text/plain": [
"1.1631062030792236"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"x = quad(0.2, n)\n",
"toc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's try this in FORTRAN. \n",
"\n",
"Note --- this step is intended to be a demo and will only execute if\n",
"\n",
"* you have the file `fastquad.f90` in your pwd\n",
"* you have a FORTRAN compiler installed and modify the compilation code below appropriately"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PURE FUNCTION QUAD(X0, N)\r",
"\r\n",
" IMPLICIT NONE\r",
"\r\n",
" INTEGER, PARAMETER :: DP=KIND(0.d0) \r",
"\r\n",
" REAL(dp), INTENT(IN) :: X0\r",
"\r\n",
" REAL(dp) :: QUAD\r",
"\r\n",
" INTEGER :: I\r",
"\r\n",
" INTEGER, INTENT(IN) :: N\r",
"\r\n",
" QUAD = X0\r",
"\r\n",
" DO I = 1, N - 1 \r",
"\r\n",
" QUAD = 4.0_dp * QUAD * real(1.0_dp - QUAD, dp)\r",
"\r\n",
" END DO\r",
"\r\n",
" RETURN\r",
"\r\n",
"END FUNCTION QUAD\r",
"\r\n",
"\r",
"\r\n",
"PROGRAM MAIN\r",
"\r\n",
" IMPLICIT NONE\r",
"\r\n",
" INTEGER, PARAMETER :: DP=KIND(0.d0) \r",
"\r\n",
" REAL(dp) :: START, FINISH, X, QUAD\r",
"\r\n",
" INTEGER :: N\r",
"\r\n",
" N = 10000000\r",
"\r\n",
" X = QUAD(0.2_dp, 10)\r",
"\r\n",
" CALL CPU_TIME(START)\r",
"\r\n",
" X = QUAD(0.2_dp, N)\r",
"\r\n",
" CALL CPU_TIME(FINISH)\r",
"\r\n",
" PRINT *,'last val = ', X\r",
"\r\n",
" PRINT *,'elapsed time = ', FINISH-START\r",
"\r\n",
"END PROGRAM MAIN\r",
"\r\n",
"\r",
"\r\n"
]
}
],
"source": [
"!cat fastquad.f90"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!gfortran -O3 fastquad.f90"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" last val = 0.46200166384322749 \r\n",
" elapsed time = 3.5999999999999997E-002\r\n"
]
}
],
"source": [
"!./a.out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's do the same thing in Python using Numba's JIT compilation:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"quad_jitted = jit(quad)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 0.03176760673522949 seconds.\n"
]
},
{
"data": {
"text/plain": [
"0.03176760673522949"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"x = quad_jitted(0.2, n)\n",
"toc()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 0.03176760673522949 seconds.\n"
]
},
{
"data": {
"text/plain": [
"0.03176760673522949"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"x = quad_jitted(0.2, n)\n",
"toc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After JIT compilation, function execution speed is about the same as FORTRAN.\n",
"\n",
"But remember, JIT compilation for Python is still limited --- see [here](http://numba.pydata.org/numba-doc/dev/reference/pysupported.html)\n",
"\n",
"If these limitations frustrate you, then try Julia."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 2: Brute Force Optimization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem is to maximize the function \n",
"\n",
"$$ f(x, y) = \\frac{\\cos \\left(x^2 + y^2 \\right)}{1 + x^2 + y^2} + 1$$\n",
"\n",
"using brute force --- searching over a grid of $(x, y)$ pairs."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def f(x, y):\n",
" return np.cos(x**2 + y**2) / (1 + x**2 + y**2) + 1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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XKnU/V5bZRkZGkCSJpqamjGW2VIFUjNVs+VBtYkg4Q5kRYqjM5BqIVhSFRCLB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\n6NpGZFZHntqPg9m3m3TbSzBlCTIRkCUyXbtoffgqnHuHabjyj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3btXM3ax3/G7LkrMAw3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vwBMZ3H3sFR9X0kkbvwtr5GQBxH5ZiPWCwhMrCJUP9ONCW/ITnvSiK9q/DWPoKx8GrCsUZa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e19zczOuvv467uztubm5MnjxZ4jWUWrp0KeXl5aSnp9PT00N9fT2tra0SrmttbY2LiwtR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u3aERcXR2JiInp6eqjVaipWrMjt27fZv38/P//8sxStUVhYKAiTi4sLDRo0YOzYseJxb29vnj59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\nDkctLS3IzMyETCaDjY0Nq4ksADx8+BDBwcGwt7fH9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"text/plain": [
"<matplotlib.figure.Figure at 0x7f80e9642ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"from matplotlib import cm\n",
"\n",
"gridsize = 50\n",
"gmin, gmax = -3, 3\n",
"xgrid = np.linspace(gmin, gmax, gridsize)\n",
"ygrid = xgrid\n",
"x, y = np.meshgrid(xgrid, ygrid)\n",
"\n",
"# === plot value function === #\n",
"fig = plt.figure(figsize=(10, 8))\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.plot_surface(x,\n",
" y,\n",
" f(x, y),\n",
" rstride=2, cstride=2,\n",
" cmap=cm.jet,\n",
" alpha=0.4,\n",
" linewidth=0.05)\n",
"\n",
"\n",
"ax.scatter(x, y, c='k', s=0.6)\n",
"\n",
"ax.scatter(x, y, f(x, y), c='k', s=0.6)\n",
"\n",
"ax.view_init(25, -57)\n",
"ax.set_zlim(-0, 2.0)\n",
"ax.set_xlim(gmin, gmax)\n",
"ax.set_ylim(gmin, gmax)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vectorized code"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid = np.linspace(-3, 3, 10000)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, y = np.meshgrid(grid, grid)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"nbpresent": {
"id": "1ba9f9f9-f737-4ee1-86e6-0a33c4752188"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 4.514950752258301 seconds.\n"
]
},
{
"data": {
"text/plain": [
"4.514950752258301"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"np.max(f(x, y))\n",
"toc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### JITTed code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A jitted version"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"@jit\n",
"def compute_max():\n",
" m = -np.inf\n",
" for x in grid:\n",
" for y in grid:\n",
" z = np.cos(x**2 + y**2) / (1 + x**2 + y**2) + 1\n",
" if z > m:\n",
" m = z\n",
" return m"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.999999819964011"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compute_max()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 3.108513116836548 seconds.\n"
]
},
{
"data": {
"text/plain": [
"3.108513116836548"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"compute_max()\n",
"toc()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "3de27362-4528-4669-8e3d-2e7db1dd0721"
}
},
"source": [
"Numba for vectorization with automatic parallelization - even faster:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "e443f7ad-f26e-4148-983e-83a5f6a2214e"
}
},
"outputs": [],
"source": [
"@vectorize('float64(float64, float64)', target='parallel')\n",
"def f_par(x, y):\n",
" return np.cos(x**2 + y**2) / (1 + x**2 + y**2) + 1"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.999999819964011"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y = np.meshgrid(grid, grid)\n",
"\n",
"np.max(f_par(x, y))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"nbpresent": {
"id": "08ff9d10-b80e-489f-8e7e-5e1869903393"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOC: Elapsed: 0.6686551570892334 seconds.\n"
]
},
{
"data": {
"text/plain": [
"0.6686551570892334"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic()\n",
"np.max(f_par(x, y))\n",
"toc()"
]
}
],
"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": "3.6.1"
},
"nbpresent": {
"slides": {
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| bsd-3-clause |
mne-tools/mne-tools.github.io | 0.17/_downloads/1b26761ba88c6441bd13afd5730965a4/plot_stats_spatio_temporal_cluster_sensors.ipynb | 1 | 9290 | {
"cells": [
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"cell_type": "code",
"execution_count": null,
"metadata": {
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"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Spatiotemporal permutation F-test on full sensor data\n\n\nTests for differential evoked responses in at least\none condition using a permutation clustering test.\nThe FieldTrip neighbor templates will be used to determine\nthe adjacency between sensors. This serves as a spatial prior\nto the clustering. Spatiotemporal clusters will then\nbe visualized using custom matplotlib code.\n\nCaveat for the interpretation of \"significant\" clusters: see\nthe `FieldTrip website`_.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Denis Engemann <[email protected]>\n# Jona Sassenhagen <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom mne.viz import plot_topomap\n\nimport mne\nfrom mne.stats import spatio_temporal_cluster_test\nfrom mne.datasets import sample\nfrom mne.channels import find_ch_connectivity\nfrom mne.viz import plot_compare_evokeds\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set parameters\n--------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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'\nevent_id = {'Aud/L': 1, 'Aud/R': 2, 'Vis/L': 3, 'Vis/R': 4}\ntmin = -0.2\ntmax = 0.5\n\n# Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.filter(1, 30, fir_design='firwin')\nevents = mne.read_events(event_fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read epochs for the channel of interest\n---------------------------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"picks = mne.pick_types(raw.info, meg='mag', eog=True)\n\nreject = dict(mag=4e-12, eog=150e-6)\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=None, reject=reject, preload=True)\n\nepochs.drop_channels(['EOG 061'])\nepochs.equalize_event_counts(event_id)\n\nX = [epochs[k].get_data() for k in event_id] # as 3D matrix\nX = [np.transpose(x, (0, 2, 1)) for x in X] # transpose for clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the FieldTrip neighbor definition to setup sensor connectivity\n-------------------------------------------------------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"connectivity, ch_names = find_ch_connectivity(epochs.info, ch_type='mag')\n\nprint(type(connectivity)) # it's a sparse matrix!\n\nplt.imshow(connectivity.toarray(), cmap='gray', origin='lower',\n interpolation='nearest')\nplt.xlabel('{} Magnetometers'.format(len(ch_names)))\nplt.ylabel('{} Magnetometers'.format(len(ch_names)))\nplt.title('Between-sensor adjacency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute permutation statistic\n-----------------------------\n\nHow does it work? We use clustering to `bind` together features which are\nsimilar. Our features are the magnetic fields measured over our sensor\narray at different times. This reduces the multiple comparison problem.\nTo compute the actual test-statistic, we first sum all F-values in all\nclusters. We end up with one statistic for each cluster.\nThen we generate a distribution from the data by shuffling our conditions\nbetween our samples and recomputing our clusters and the test statistics.\nWe test for the significance of a given cluster by computing the probability\nof observing a cluster of that size. For more background read:\nMaris/Oostenveld (2007), \"Nonparametric statistical testing of EEG- and\nMEG-data\" Journal of Neuroscience Methods, Vol. 164, No. 1., pp. 177-190.\ndoi:10.1016/j.jneumeth.2007.03.024\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set cluster threshold\nthreshold = 50.0 # very high, but the test is quite sensitive on this data\n# set family-wise p-value\np_accept = 0.01\n\ncluster_stats = spatio_temporal_cluster_test(X, n_permutations=1000,\n threshold=threshold, tail=1,\n n_jobs=1, buffer_size=None,\n connectivity=connectivity)\n\nT_obs, clusters, p_values, _ = cluster_stats\ngood_cluster_inds = np.where(p_values < p_accept)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note. The same functions work with source estimate. The only differences\nare the origin of the data, the size, and the connectivity definition.\nIt can be used for single trials or for groups of subjects.\n\nVisualize clusters\n------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# configure variables for visualization\ncolors = {\"Aud\": \"crimson\", \"Vis\": 'steelblue'}\nlinestyles = {\"L\": '-', \"R\": '--'}\n\n# get sensor positions via layout\npos = mne.find_layout(epochs.info).pos\n\n# organize data for plotting\nevokeds = {cond: epochs[cond].average() for cond in event_id}\n\n# loop over clusters\nfor i_clu, clu_idx in enumerate(good_cluster_inds):\n # unpack cluster information, get unique indices\n time_inds, space_inds = np.squeeze(clusters[clu_idx])\n ch_inds = np.unique(space_inds)\n time_inds = np.unique(time_inds)\n\n # get topography for F stat\n f_map = T_obs[time_inds, ...].mean(axis=0)\n\n # get signals at the sensors contributing to the cluster\n sig_times = epochs.times[time_inds]\n\n # create spatial mask\n mask = np.zeros((f_map.shape[0], 1), dtype=bool)\n mask[ch_inds, :] = True\n\n # initialize figure\n fig, ax_topo = plt.subplots(1, 1, figsize=(10, 3))\n\n # plot average test statistic and mark significant sensors\n image, _ = plot_topomap(f_map, pos, mask=mask, axes=ax_topo, cmap='Reds',\n vmin=np.min, vmax=np.max, show=False)\n\n # create additional axes (for ERF and colorbar)\n divider = make_axes_locatable(ax_topo)\n\n # add axes for colorbar\n ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)\n plt.colorbar(image, cax=ax_colorbar)\n ax_topo.set_xlabel(\n 'Averaged F-map ({:0.3f} - {:0.3f} s)'.format(*sig_times[[0, -1]]))\n\n # add new axis for time courses and plot time courses\n ax_signals = divider.append_axes('right', size='300%', pad=1.2)\n title = 'Cluster #{0}, {1} sensor'.format(i_clu + 1, len(ch_inds))\n if len(ch_inds) > 1:\n title += \"s (mean)\"\n plot_compare_evokeds(evokeds, title=title, picks=ch_inds, axes=ax_signals,\n colors=colors, linestyles=linestyles, show=False,\n split_legend=True, truncate_yaxis='max_ticks')\n\n # plot temporal cluster extent\n ymin, ymax = ax_signals.get_ylim()\n ax_signals.fill_betweenx((ymin, ymax), sig_times[0], sig_times[-1],\n color='orange', alpha=0.3)\n\n # clean up viz\n mne.viz.tight_layout(fig=fig)\n fig.subplots_adjust(bottom=.05)\n plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exercises\n----------\n\n- What is the smallest p-value you can obtain, given the finite number of\n permutations?\n- use an F distribution to compute the threshold by traditional significance\n levels. Hint: take a look at :obj:`scipy.stats.f`\n\nReferences\n==========\n\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
eteq/erikutils | erikutils/pypeit_helpers/coadd_notebook_template.ipynb | 1 | 4506 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preamble"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy.io import fits\n",
"from astropy import units as u\n",
"from astropy.nddata import StdDevUncertainty\n",
"from astropy.table import Table\n",
"from specutils import Spectrum1D, analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# needed to create the slits \"template\"\n",
"file = '/home/erik/Dropbox/SAGA/Spectra/Final/AAT/pgc9747_2_MG.fits.gz'\n",
"ahdu = fits.open(file)\n",
"header=ahdu[0].header\n",
"fibers=ahdu['FIBRES'].data\n",
"slits = fibers[0:1]\n",
"\n",
"def to_marz(inputfn):\n",
" \n",
" flux = []\n",
" wave = []\n",
" ivar = []\n",
" sky = []\n",
" var = []\n",
" \n",
" m = slice(None)\n",
"\n",
" data = Table.read(inputfn)\n",
"\n",
" m = data['mask']==1\n",
"\n",
" # CONVERT VACUUM TO AIR\n",
" vwave = data['wave'][m]\n",
" awave = vwave / (1.0 + 2.735182e-4 + 131.4182 / vwave**2 + 2.76249E8 / vwave**4)\n",
"\n",
" flux.append(data['flux'][m])\n",
" wave.append(awave)\n",
" ivar.append(data['ivar'][m])\n",
" var.append(1./data['ivar'][m])\n",
" sky.append(data['flux'][m])\n",
"\n",
" ml = np.sum(m)\n",
" rflux = np.float32(np.reshape(flux,[-1,ml]) )\n",
" rwave = np.float32(np.reshape(wave,[-1,ml]))\n",
" rivar = np.float32(np.reshape(ivar,[-1,ml]))\n",
" rvar = np.float32(np.reshape(var,[-1,ml]))\n",
" rsky = np.float32(np.reshape(sky,[-1,ml]))\n",
"\n",
" hdup = fits.PrimaryHDU(np.float32([1,2]))\n",
" hdu1 = fits.ImageHDU(rflux,name = 'intensity')\n",
" hdu2 = fits.ImageHDU(rwave,name = 'wavelength')\n",
" hdu3 = fits.ImageHDU(rivar,name = 'ivar')\n",
" hdu4 = fits.ImageHDU(rvar,name = 'variance')\n",
" hdu5 = fits.ImageHDU(rsky,name = 'sky')\n",
" hdu6 = fits.BinTableHDU(slits,name='fibres')\n",
"\n",
" hdu = fits.HDUList([hdup, hdu1,hdu2,hdu3,hdu4,hdu5,hdu6])\n",
"\n",
" ofn = inputfn.replace('.fits', '_for_marz.fits')\n",
" print('writing', ofn)\n",
" hdu.writeto(ofn, overwrite=True)"
]
},
{% for filename in fitsfilenames %}
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## {{ filename }}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = fits.open('{{ filename }}')\n",
"fd = f[1].data\n",
"spec = Spectrum1D(spectral_axis=fd['wave']*u.angstrom, flux=fd['flux']*u.count, \n",
" uncertainty=StdDevUncertainty(fd['ivar']**-0.5), \n",
" mask=fd['mask']!=1, meta=f[0].header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(15, 6))\n",
"plt.step(spec.wavelength, spec.flux, where='mid')\n",
"plt.step(spec.wavelength, spec.uncertainty.array, where='mid')\n",
"\n",
"l, up = np.quantile(np.concatenate((spec.flux.value, spec.uncertainty.array)), [.01, .99])\n",
"plt.ylim(min(0, l), up)\n",
"plt.xlim(spec.wavelength[0].value, spec.wavelength[-1].value)\n",
"\n",
"analysis.snr(spec), analysis.snr_derived(spec)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"to_marz('{{ filename }}')"
]
},
{% endfor %}
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Done!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (saga)",
"language": "python",
"name": "saga"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
DaveBackus/Data_Bootcamp | Code/Lab/Airbnb.ipynb | 1 | 169684 | {
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"import pandas as pd\n",
"import matplotlib as ml\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python version: 3.5.1 |Anaconda 2.4.0 (64-bit)| (default, Dec 7 2015, 11:16:01) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"Pandas version: 0.17.1\n",
"Matplotlib version: 1.5.1\n"
]
}
],
"source": [
"print(\"Python version: \", sys.version)\n",
"print(\"Pandas version: \", pd.__version__)\n",
"print(\"Matplotlib version: \", ml.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Airbnb Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we read in the data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/chase/Programming/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>listing_url</th>\n",
" <th>scrape_id</th>\n",
" <th>last_scraped</th>\n",
" <th>name</th>\n",
" <th>summary</th>\n",
" <th>space</th>\n",
" <th>description</th>\n",
" <th>experiences_offered</th>\n",
" <th>neighborhood_overview</th>\n",
" <th>...</th>\n",
" <th>review_scores_value</th>\n",
" <th>requires_license</th>\n",
" <th>license</th>\n",
" <th>jurisdiction_names</th>\n",
" <th>instant_bookable</th>\n",
" <th>cancellation_policy</th>\n",
" <th>require_guest_profile_picture</th>\n",
" <th>require_guest_phone_verification</th>\n",
" <th>calculated_host_listings_count</th>\n",
" <th>reviews_per_month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6627449</td>\n",
" <td>https://www.airbnb.com/rooms/6627449</td>\n",
" <td>20160201235331</td>\n",
" <td>2016-02-02</td>\n",
" <td>Large 1 BDRM in Great location</td>\n",
" <td>This ground floor apartment is light and airy ...</td>\n",
" <td>We are close to fishing, boating, biking, hors...</td>\n",
" <td>This ground floor apartment is light and airy ...</td>\n",
" <td>none</td>\n",
" <td>City Island is a unique and a hidden gem of Ne...</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>flexible</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>1</td>\n",
" <td>1.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7949480</td>\n",
" <td>https://www.airbnb.com/rooms/7949480</td>\n",
" <td>20160201235331</td>\n",
" <td>2016-02-02</td>\n",
" <td>City Island Sanctuary Sunny BR & BA</td>\n",
" <td>Sunny relaxing room w/ adjacent pvt. bath in a...</td>\n",
" <td>We have just moved to City Island from CA so w...</td>\n",
" <td>Sunny relaxing room w/ adjacent pvt. bath in a...</td>\n",
" <td>none</td>\n",
" <td>City Island is a unique sanctuary in New York ...</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>moderate</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>1</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1886820</td>\n",
" <td>https://www.airbnb.com/rooms/1886820</td>\n",
" <td>20160201235331</td>\n",
" <td>2016-02-02</td>\n",
" <td>Quaint City Island Community.</td>\n",
" <td>Quiet island boating town on Long Island Soun...</td>\n",
" <td>Master bed with queen bed, full bath and offi...</td>\n",
" <td>Quiet island boating town on Long Island Soun...</td>\n",
" <td>none</td>\n",
" <td>Small New England type town in the middle of ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>strict</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5557381</td>\n",
" <td>https://www.airbnb.com/rooms/5557381</td>\n",
" <td>20160201235331</td>\n",
" <td>2016-02-02</td>\n",
" <td>Quaint City Island Home</td>\n",
" <td>Located in an old sea-shanty town, our home ha...</td>\n",
" <td>You won't find a place so close to the city (N...</td>\n",
" <td>Located in an old sea-shanty town, our home ha...</td>\n",
" <td>none</td>\n",
" <td>City Island is unique in two ways. First, you ...</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>t</td>\n",
" <td>moderate</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>1</td>\n",
" <td>4.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9019702</td>\n",
" <td>https://www.airbnb.com/rooms/9019702</td>\n",
" <td>20160201235331</td>\n",
" <td>2016-02-02</td>\n",
" <td>City Island Sugar Shack</td>\n",
" <td>Cozy street off of trendy City Island . Home i...</td>\n",
" <td>NaN</td>\n",
" <td>Cozy street off of trendy City Island . Home i...</td>\n",
" <td>none</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>flexible</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 92 columns</p>\n",
"</div>"
],
"text/plain": [
" id listing_url scrape_id last_scraped \\\n",
"0 6627449 https://www.airbnb.com/rooms/6627449 20160201235331 2016-02-02 \n",
"1 7949480 https://www.airbnb.com/rooms/7949480 20160201235331 2016-02-02 \n",
"2 1886820 https://www.airbnb.com/rooms/1886820 20160201235331 2016-02-02 \n",
"3 5557381 https://www.airbnb.com/rooms/5557381 20160201235331 2016-02-02 \n",
"4 9019702 https://www.airbnb.com/rooms/9019702 20160201235331 2016-02-02 \n",
"\n",
" name \\\n",
"0 Large 1 BDRM in Great location \n",
"1 City Island Sanctuary Sunny BR & BA \n",
"2 Quaint City Island Community. \n",
"3 Quaint City Island Home \n",
"4 City Island Sugar Shack \n",
"\n",
" summary \\\n",
"0 This ground floor apartment is light and airy ... \n",
"1 Sunny relaxing room w/ adjacent pvt. bath in a... \n",
"2 Quiet island boating town on Long Island Soun... \n",
"3 Located in an old sea-shanty town, our home ha... \n",
"4 Cozy street off of trendy City Island . Home i... \n",
"\n",
" space \\\n",
"0 We are close to fishing, boating, biking, hors... \n",
"1 We have just moved to City Island from CA so w... \n",
"2 Master bed with queen bed, full bath and offi... \n",
"3 You won't find a place so close to the city (N... \n",
"4 NaN \n",
"\n",
" description experiences_offered \\\n",
"0 This ground floor apartment is light and airy ... none \n",
"1 Sunny relaxing room w/ adjacent pvt. bath in a... none \n",
"2 Quiet island boating town on Long Island Soun... none \n",
"3 Located in an old sea-shanty town, our home ha... none \n",
"4 Cozy street off of trendy City Island . Home i... none \n",
"\n",
" neighborhood_overview ... \\\n",
"0 City Island is a unique and a hidden gem of Ne... ... \n",
"1 City Island is a unique sanctuary in New York ... ... \n",
"2 Small New England type town in the middle of ... ... \n",
"3 City Island is unique in two ways. First, you ... ... \n",
"4 NaN ... \n",
"\n",
" review_scores_value requires_license license jurisdiction_names \\\n",
"0 10 f NaN NaN \n",
"1 10 f NaN NaN \n",
"2 NaN f NaN NaN \n",
"3 9 f NaN NaN \n",
"4 NaN f NaN NaN \n",
"\n",
" instant_bookable cancellation_policy require_guest_profile_picture \\\n",
"0 f flexible f \n",
"1 f moderate t \n",
"2 f strict f \n",
"3 t moderate f \n",
"4 f flexible f \n",
"\n",
" require_guest_phone_verification calculated_host_listings_count \\\n",
"0 f 1 \n",
"1 t 1 \n",
"2 f 1 \n",
"3 f 1 \n",
"4 f 1 \n",
"\n",
" reviews_per_month \n",
"0 1.12 \n",
"1 1.00 \n",
"2 NaN \n",
"3 4.84 \n",
"4 NaN \n",
"\n",
"[5 rows x 92 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url1 = \"http://data.insideairbnb.com/united-states/\"\n",
"url2 = \"ny/new-york-city/2016-02-02/data/listings.csv.gz\"\n",
"full_df = pd.read_csv(url1+url2, compression=\"gzip\")\n",
"\n",
"full_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We don't want all data, so let's focus on a few variables."
]
},
{
"cell_type": "code",
"execution_count": 136,
"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>id</th>\n",
" <th>price</th>\n",
" <th>number_of_reviews</th>\n",
" <th>review_scores_rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6627449</td>\n",
" <td>$125.00</td>\n",
" <td>8</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7949480</td>\n",
" <td>$68.00</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1886820</td>\n",
" <td>$300.00</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5557381</td>\n",
" <td>$49.00</td>\n",
" <td>41</td>\n",
" <td>96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9019702</td>\n",
" <td>$200.00</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id price number_of_reviews review_scores_rating\n",
"0 6627449 $125.00 8 93\n",
"1 7949480 $68.00 1 100\n",
"2 1886820 $300.00 0 NaN\n",
"3 5557381 $49.00 41 96\n",
"4 9019702 $200.00 0 NaN"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = full_df[[\"id\", \"price\", \"number_of_reviews\", \"review_scores_rating\"]]\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Need to convert prices to floats"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/chase/Programming/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py:3050: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" regex=regex)\n",
"/home/chase/Programming/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"df.replace({'price': {'\\$': ''}}, regex=True, inplace=True)\n",
"df.replace({'price': {'\\,': ''}}, regex=True, inplace=True)\n",
"df['price'] = df['price'].astype('float64', copy=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We might think that better apartments get rented more often, let's plot a scatter (or multiple boxes?) plot of the number of reviews vs the review score"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f952644b710>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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M4LommqZe8Z1eabx3OtaNrvnd4r2aRyKRvP/If4J9yNi5cyeW9V8pl0eBcRqNaYJgDEXx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aIozciIy5Ytgzz44APccMMvYhhJHnro+6hqhW3bdgOT+P5hPvrR62m3jyHL8OKLbZ5+eolk8v0o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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9526444cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x=\"number_of_reviews\", y=\"review_scores_rating\", figsize=(10, 8), alpha=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TOHny5NAlAABzQjcfAMDr0M0HADAjwhQAQAfCFABAB8IUAEAHwhQAQAfCFABA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\nUwAAHfz/2JBXkgU8GswAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f952905e668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bins = [0, 5, 10, 25, 50, 100, 350]\n",
"boxplot_vecs = []\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 8))\n",
"\n",
"for i in range(1, 7):\n",
" lb = bins[i-1]\n",
" ub = bins[i]\n",
" foo = df[\"review_scores_rating\"][df[\"number_of_reviews\"].apply(lambda x: lb <= x <= ub)].dropna()\n",
" boxplot_vecs.append(foo.values)\n",
" \n",
"ax.boxplot(boxplot_vecs, labels=bins[:-1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Better reviews also are correlated with higher prices"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f95298b9828>"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DOOf2m9kjTI1kzQOfLWlW+xPgO0AUeMI595NFr7WIiIjIIilrd2o5qDtVRERE/GLJdqeK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"text/plain": [
"<matplotlib.figure.Figure at 0x7f95298bbd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x=\"review_scores_rating\", y=\"price\", figsize=(10, 8), alpha=0.2)"
]
},
{
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
bjodah/pyodesys | examples/_native_param_cse.ipynb | 1 | 2111 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sympy as sp\n",
"from pyodesys.native import native_sys"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x, y = sp.symbols('x y')\n",
"exprs = [sp.exp(y)*(3*y + 3*sp.sqrt(x+1)), sp.exp(y)*(5*y + 5*sp.sqrt(x+1))]\n",
"sp.cse(exprs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp.cse(exprs, optimizations='basic')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" _ = sp.cse(exprs, ignore=(y,x))\n",
"except TypeError:\n",
" print(\"Using an old version of SymPy (ignore not supported in cse())\")\n",
"else:\n",
" print(_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"odesys = native_sys['cvode'].from_callback(lambda x, y, p, be: [-be.exp(p[0]+3)*y[0], be.exp(p[0]+3)*y[0]], 2, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = next(filter(lambda x: x.endswith('.cpp'), odesys._native._written_files))\n",
"print(open(path).read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp.cse(odesys.exprs)"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
decisionstats/pythonfordatascience | matplotlib+line+graph.ipynb | 1 | 29389 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"boston=pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/MASS/Boston.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>Unnamed: 0</th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
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" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
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" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
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" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
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" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 crim zn indus chas nox rm age dis rad \\\n",
"0 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 \n",
"1 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 \n",
"2 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 \n",
"3 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 \n",
"4 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 \n",
"\n",
" tax ptratio black lstat medv \n",
"0 296 15.3 396.90 4.98 24.0 \n",
"1 242 17.8 396.90 9.14 21.6 \n",
"2 242 17.8 392.83 4.03 34.7 \n",
"3 222 18.7 394.63 2.94 33.4 \n",
"4 222 18.7 396.90 5.33 36.2 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boston.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"boston=boston.drop(\"Unnamed: 0\",1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"var=boston.groupby('chas').medv.mean().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x8078ba8>]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.plot(var.chas,var.medv)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7ebe0f0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.xlabel(\"Charles River Facing Tract\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7ffc5c0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.ylabel(\"Median Prices\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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dSktLKS0t3WJdeXl5Rt7Lku2RMLPxwGB3L01YfznQyd1/mXQRIWycD7SLH+dj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\nAIBdGHBza7lFhz8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7eb3a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"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
}
| apache-2.0 |
epfl-lts2/pygsp | examples/playground.ipynb | 1 | 2770 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Playing with the PyGSP\n",
"<https://github.com/epfl-lts2/pygsp>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pygsp import graphs, filters\n",
"\n",
"plt.rcParams['figure.figsize'] = (17, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 Example\n",
"\n",
"The following demonstrates how to instantiate a graph and a filter, the two main objects of the package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"G = graphs.Logo()\n",
"G.estimate_lmax()\n",
"g = filters.Heat(G, tau=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now create a graph signal: a set of three Kronecker deltas for that example. We can now look at one step of heat diffusion by filtering the deltas with the above defined filter. Note how the diffusion follows the local structure!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DELTAS = [20, 30, 1090]\n",
"s = np.zeros(G.N)\n",
"s[DELTAS] = 1\n",
"s = g.filter(s)\n",
"G.plot(s, highlight=DELTAS, backend='matplotlib')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Tutorials and examples\n",
"\n",
"Try our [tutorials](https://pygsp.readthedocs.io/en/stable/tutorials/index.html) or [examples](https://pygsp.readthedocs.io/en/stable/examples/index.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 Playground\n",
"\n",
"Try something of your own!\n",
"The [API reference](https://pygsp.readthedocs.io/en/stable/reference/index.html) is your friend."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you miss a package, you can install it with:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install numpy"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
wtchg-kwiatkowski/pfx-paper-2015 | main/notebooks/metrics_variation.ipynb | 1 | 25067 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Metrics: variation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"docker image cggh/biipy:v1.6.0\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
".container {\n",
" width: 96%;\n",
"}\n",
"#maintoolbar {\n",
" display: none;\n",
"}\n",
"#header-container {\n",
" display: none;\n",
"}\n",
"#notebook {\n",
" padding-top: 0;\n",
"}\n",
"</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run ../../shared_setup.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-03-09 15:02:43.457160 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/3d7_hb3.combined.final.npz\n",
"2016-03-09 15:02:43.816991 :: filter variants: excluding 157 (0.4%) retaining 42087 (99.6%) of 42244 variants\n",
"2016-03-09 15:02:43.834907 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/hb3_dd2.combined.final.npz\n",
"2016-03-09 15:02:44.252911 :: filter variants: excluding 450 (1.2%) retaining 36461 (98.8%) of 36911 variants\n",
"2016-03-09 15:02:44.272729 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/7g8_gb4.combined.final.npz\n",
"2016-03-09 15:02:44.683451 :: filter variants: excluding 304 (0.9%) retaining 34471 (99.1%) of 34775 variants\n"
]
}
],
"source": [
"# load PASS variants for all three crosses\n",
"callsets = load_callsets(COMBINED_CALLSET_FN_TEMPLATE, \n",
" variant_filter='FILTER_PASS')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## No. of independent progeny clones"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3d7_hb3 15\n",
"hb3_dd2 35\n",
"7g8_gb4 28\n"
]
}
],
"source": [
"for cross in CROSSES:\n",
" samples = callsets[cross]['calldata'].dtype.names\n",
" progeny = samples[2:]\n",
" progeny_clones = set([p.split('/')[0] for p in progeny])\n",
" print(cross, len(progeny_clones))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Coverage"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>0|cross</th>\n",
"<th>1|clone</th>\n",
"<th>2|sample</th>\n",
"<th>3|run</th>\n",
"<th>4|instrument</th>\n",
"<th>5|coverage</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td>3D7</td>\n",
"<td>PG0051-C</td>\n",
"<td>ERR019061</td>\n",
"<td>Illumina Genome Analyzer II</td>\n",
"<td style='text-align: right'>122</td>\n",
"</tr>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td>C01</td>\n",
"<td>PG0065-C</td>\n",
"<td>ERR019064</td>\n",
"<td>Illumina Genome Analyzer II</td>\n",
"<td style='text-align: right'>163</td>\n",
"</tr>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td>C01</td>\n",
"<td>PG0062-C</td>\n",
"<td>ERR019070</td>\n",
"<td>Illumina Genome Analyzer II</td>\n",
"<td style='text-align: right'>108</td>\n",
"</tr>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td>C02</td>\n",
"<td>PG0055-C</td>\n",
"<td>ERR019066</td>\n",
"<td>Illumina Genome Analyzer II</td>\n",
"<td style='text-align: right'>102</td>\n",
"</tr>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td>C02</td>\n",
"<td>PG0053-C</td>\n",
"<td>ERR019067</td>\n",
"<td>Illumina Genome Analyzer II</td>\n",
"<td style='text-align: right'>73</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"text/plain": [
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"| cross | clone | sample | run | instrument | coverage |\n",
"+===========+=======+============+=============+===============================+==========+\n",
"| '3d7_hb3' | '3D7' | 'PG0051-C' | 'ERR019061' | 'Illumina Genome Analyzer II' | 122 |\n",
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"| '3d7_hb3' | 'C01' | 'PG0065-C' | 'ERR019064' | 'Illumina Genome Analyzer II' | 163 |\n",
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"| '3d7_hb3' | 'C01' | 'PG0062-C' | 'ERR019070' | 'Illumina Genome Analyzer II' | 108 |\n",
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"| '3d7_hb3' | 'C02' | 'PG0055-C' | 'ERR019066' | 'Illumina Genome Analyzer II' | 102 |\n",
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"| '3d7_hb3' | 'C02' | 'PG0053-C' | 'ERR019067' | 'Illumina Genome Analyzer II' | 73 |\n",
"+-----------+-------+------------+-------------+-------------------------------+----------+\n",
"..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tbl_samples = (etl\n",
" .fromtsv(os.path.join(PUBLIC_DIR, 'samples.txt'))\n",
" .convert('coverage', lambda v: int(v[:-1]))\n",
")\n",
"tbl_samples"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>0|cross</th>\n",
"<th>1|count</th>\n",
"<th>2|frequency</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>7g8_gb4</td>\n",
"<td style='text-align: right'>40</td>\n",
"<td style='text-align: right'>0.40816326530612246</td>\n",
"</tr>\n",
"<tr>\n",
"<td>hb3_dd2</td>\n",
"<td style='text-align: right'>37</td>\n",
"<td style='text-align: right'>0.37755102040816324</td>\n",
"</tr>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td style='text-align: right'>21</td>\n",
"<td style='text-align: right'>0.21428571428571427</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/plain": [
"+-----------+-------+---------------------+\n",
"| cross | count | frequency |\n",
"+===========+=======+=====================+\n",
"| '7g8_gb4' | 40 | 0.40816326530612246 |\n",
"+-----------+-------+---------------------+\n",
"| 'hb3_dd2' | 37 | 0.37755102040816324 |\n",
"+-----------+-------+---------------------+\n",
"| '3d7_hb3' | 21 | 0.21428571428571427 |\n",
"+-----------+-------+---------------------+"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tbl_samples.valuecounts('cross')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cross\n",
"3d7_hb3 102.0\n",
"7g8_gb4 106.5\n",
"hb3_dd2 110.0\n",
"Name: coverage, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_samples = tbl_samples.todataframe()\n",
"df_samples.groupby('cross').coverage.median()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cross\n",
"3d7_hb3 41\n",
"7g8_gb4 55\n",
"hb3_dd2 22\n",
"Name: coverage, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_samples.groupby('cross').coverage.min()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cross\n",
"3d7_hb3 173\n",
"7g8_gb4 250\n",
"hb3_dd2 637\n",
"Name: coverage, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_samples.groupby('cross').coverage.max()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>0|cross</th>\n",
"<th>1|median</th>\n",
"<th>2|min</th>\n",
"<th>3|max</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>3d7_hb3</td>\n",
"<td style='text-align: right'>102.0</td>\n",
"<td style='text-align: right'>41</td>\n",
"<td style='text-align: right'>173</td>\n",
"</tr>\n",
"<tr>\n",
"<td>7g8_gb4</td>\n",
"<td style='text-align: right'>106.5</td>\n",
"<td style='text-align: right'>55</td>\n",
"<td style='text-align: right'>250</td>\n",
"</tr>\n",
"<tr>\n",
"<td>hb3_dd2</td>\n",
"<td style='text-align: right'>110.0</td>\n",
"<td style='text-align: right'>22</td>\n",
"<td style='text-align: right'>637</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/plain": [
"+-----------+--------+-----+-----+\n",
"| cross | median | min | max |\n",
"+===========+========+=====+=====+\n",
"| '3d7_hb3' | 102.0 | 41 | 173 |\n",
"+-----------+--------+-----+-----+\n",
"| '7g8_gb4' | 106.5 | 55 | 250 |\n",
"+-----------+--------+-----+-----+\n",
"| 'hb3_dd2' | 110.0 | 22 | 637 |\n",
"+-----------+--------+-----+-----+"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tbl_samples.aggregate('cross', [('median', 'coverage', lambda g: np.median(list(g))),\n",
" ('min', 'coverage', min),\n",
" ('max', 'coverage', max)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Count SNPs and INDELs"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-03-09 15:09:32.794681 :: filter variants: excluding 26699 (63.4%) retaining 15388 (36.6%) of 42087 variants\n",
"2016-03-09 15:09:32.804509 :: filter variants: excluding 15388 (36.6%) retaining 26699 (63.4%) of 42087 variants\n",
"2016-03-09 15:09:32.816272 :: filter variants: excluding 42075 (100.0%) retaining 12 (0.0%) of 42087 variants\n",
"2016-03-09 15:09:32.817924 :: filter variants: excluding 41708 (99.1%) retaining 379 (0.9%) of 42087 variants\n",
"2016-03-09 15:09:32.825786 :: filter variants: excluding 42075 (100.0%) retaining 12 (0.0%) of 42087 variants\n",
"2016-03-09 15:09:32.832307 :: filter variants: excluding 41708 (99.1%) retaining 379 (0.9%) of 42087 variants\n",
"2016-03-09 15:09:32.835129 :: filter variants: excluding 33219 (78.9%) retaining 8868 (21.1%) of 42087 variants\n",
"2016-03-09 15:09:32.843661 :: filter variants: excluding 35567 (84.5%) retaining 6520 (15.5%) of 42087 variants\n",
"2016-03-09 15:09:32.848935 :: filter variants: excluding 37981 (90.2%) retaining 4106 (9.8%) of 42087 variants\n",
"2016-03-09 15:09:32.853397 :: filter variants: excluding 19494 (46.3%) retaining 22593 (53.7%) of 42087 variants\n",
"2016-03-09 15:09:32.865127 :: filter variants: excluding 21576 (59.2%) retaining 14885 (40.8%) of 36461 variants\n",
"2016-03-09 15:09:32.876137 :: filter variants: excluding 14885 (40.8%) retaining 21576 (59.2%) of 36461 variants\n",
"2016-03-09 15:09:32.890091 :: filter variants: excluding 36454 (100.0%) retaining 7 (0.0%) of 36461 variants\n",
"2016-03-09 15:09:32.891714 :: filter variants: excluding 32627 (89.5%) retaining 3834 (10.5%) of 36461 variants\n",
"2016-03-09 15:09:32.898444 :: filter variants: excluding 36454 (100.0%) retaining 7 (0.0%) of 36461 variants\n",
"2016-03-09 15:09:32.902711 :: filter variants: excluding 32627 (89.5%) retaining 3834 (10.5%) of 36461 variants\n",
"2016-03-09 15:09:32.908679 :: filter variants: excluding 27853 (76.4%) retaining 8608 (23.6%) of 36461 variants\n",
"2016-03-09 15:09:32.915458 :: filter variants: excluding 30184 (82.8%) retaining 6277 (17.2%) of 36461 variants\n",
"2016-03-09 15:09:32.921221 :: filter variants: excluding 32782 (89.9%) retaining 3679 (10.1%) of 36461 variants\n",
"2016-03-09 15:09:32.926253 :: filter variants: excluding 18564 (50.9%) retaining 17897 (49.1%) of 36461 variants\n",
"2016-03-09 15:09:32.937502 :: filter variants: excluding 20079 (58.2%) retaining 14392 (41.8%) of 34471 variants\n",
"2016-03-09 15:09:32.948193 :: filter variants: excluding 14392 (41.8%) retaining 20079 (58.2%) of 34471 variants\n",
"2016-03-09 15:09:32.962656 :: filter variants: excluding 34468 (100.0%) retaining 3 (0.0%) of 34471 variants\n",
"2016-03-09 15:09:32.964265 :: filter variants: excluding 30739 (89.2%) retaining 3732 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:32.970926 :: filter variants: excluding 34468 (100.0%) retaining 3 (0.0%) of 34471 variants\n",
"2016-03-09 15:09:32.974892 :: filter variants: excluding 30739 (89.2%) retaining 3732 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:32.979990 :: filter variants: excluding 26266 (76.2%) retaining 8205 (23.8%) of 34471 variants\n",
"2016-03-09 15:09:32.987572 :: filter variants: excluding 28284 (82.1%) retaining 6187 (17.9%) of 34471 variants\n",
"2016-03-09 15:09:32.995863 :: filter variants: excluding 30740 (89.2%) retaining 3731 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:33.000212 :: filter variants: excluding 18123 (52.6%) retaining 16348 (47.4%) of 34471 variants\n",
"2016-03-09 15:09:33.013067 :: filter variants: excluding 26699 (63.4%) retaining 15388 (36.6%) of 42087 variants\n",
"2016-03-09 15:09:33.022449 :: filter variants: excluding 15388 (36.6%) retaining 26699 (63.4%) of 42087 variants\n",
"2016-03-09 15:09:33.034443 :: filter variants: excluding 42075 (100.0%) retaining 12 (0.0%) of 42087 variants\n",
"2016-03-09 15:09:33.036097 :: filter variants: excluding 41708 (99.1%) retaining 379 (0.9%) of 42087 variants\n",
"2016-03-09 15:09:33.040794 :: filter variants: excluding 42075 (100.0%) retaining 12 (0.0%) of 42087 variants\n",
"2016-03-09 15:09:33.045353 :: filter variants: excluding 41708 (99.1%) retaining 379 (0.9%) of 42087 variants\n",
"2016-03-09 15:09:33.048946 :: filter variants: excluding 33219 (78.9%) retaining 8868 (21.1%) of 42087 variants\n",
"2016-03-09 15:09:33.057261 :: filter variants: excluding 35567 (84.5%) retaining 6520 (15.5%) of 42087 variants\n",
"2016-03-09 15:09:33.062726 :: filter variants: excluding 37981 (90.2%) retaining 4106 (9.8%) of 42087 variants\n",
"2016-03-09 15:09:33.068621 :: filter variants: excluding 19494 (46.3%) retaining 22593 (53.7%) of 42087 variants\n",
"2016-03-09 15:09:33.080517 :: filter variants: excluding 21576 (59.2%) retaining 14885 (40.8%) of 36461 variants\n",
"2016-03-09 15:09:33.090563 :: filter variants: excluding 14885 (40.8%) retaining 21576 (59.2%) of 36461 variants\n",
"2016-03-09 15:09:33.105211 :: filter variants: excluding 36454 (100.0%) retaining 7 (0.0%) of 36461 variants\n",
"2016-03-09 15:09:33.107869 :: filter variants: excluding 32627 (89.5%) retaining 3834 (10.5%) of 36461 variants\n",
"2016-03-09 15:09:33.115376 :: filter variants: excluding 36454 (100.0%) retaining 7 (0.0%) of 36461 variants\n",
"2016-03-09 15:09:33.121624 :: filter variants: excluding 32627 (89.5%) retaining 3834 (10.5%) of 36461 variants\n",
"2016-03-09 15:09:33.127136 :: filter variants: excluding 27853 (76.4%) retaining 8608 (23.6%) of 36461 variants\n",
"2016-03-09 15:09:33.133621 :: filter variants: excluding 30184 (82.8%) retaining 6277 (17.2%) of 36461 variants\n",
"2016-03-09 15:09:33.140690 :: filter variants: excluding 32782 (89.9%) retaining 3679 (10.1%) of 36461 variants\n",
"2016-03-09 15:09:33.145140 :: filter variants: excluding 18564 (50.9%) retaining 17897 (49.1%) of 36461 variants\n",
"2016-03-09 15:09:33.157373 :: filter variants: excluding 20079 (58.2%) retaining 14392 (41.8%) of 34471 variants\n",
"2016-03-09 15:09:33.167518 :: filter variants: excluding 14392 (41.8%) retaining 20079 (58.2%) of 34471 variants\n",
"2016-03-09 15:09:33.180130 :: filter variants: excluding 34468 (100.0%) retaining 3 (0.0%) of 34471 variants\n",
"2016-03-09 15:09:33.181623 :: filter variants: excluding 30739 (89.2%) retaining 3732 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:33.189432 :: filter variants: excluding 34468 (100.0%) retaining 3 (0.0%) of 34471 variants\n",
"2016-03-09 15:09:33.192408 :: filter variants: excluding 30739 (89.2%) retaining 3732 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:33.199867 :: filter variants: excluding 26266 (76.2%) retaining 8205 (23.8%) of 34471 variants\n",
"2016-03-09 15:09:33.208768 :: filter variants: excluding 28284 (82.1%) retaining 6187 (17.9%) of 34471 variants\n",
"2016-03-09 15:09:33.213852 :: filter variants: excluding 30740 (89.2%) retaining 3731 (10.8%) of 34471 variants\n",
"2016-03-09 15:09:33.220260 :: filter variants: excluding 18123 (52.6%) retaining 16348 (47.4%) of 34471 variants\n"
]
},
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>0|variable</th>\n",
"<th>1|3d7_hb3</th>\n",
"<th>2|7g8_gb4</th>\n",
"<th>3|hb3_dd2</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>n_indels</td>\n",
"<td style='text-align: right'>26699</td>\n",
"<td style='text-align: right'>20079</td>\n",
"<td style='text-align: right'>21576</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_indels_coding</td>\n",
"<td style='text-align: right'>4106</td>\n",
"<td style='text-align: right'>3731</td>\n",
"<td style='text-align: right'>3679</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_indels_multiallelic</td>\n",
"<td style='text-align: right'>379</td>\n",
"<td style='text-align: right'>3732</td>\n",
"<td style='text-align: right'>3834</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_indels_multiallelic_gatk</td>\n",
"<td style='text-align: right'>379</td>\n",
"<td style='text-align: right'>3732</td>\n",
"<td style='text-align: right'>3834</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_indels_noncoding</td>\n",
"<td style='text-align: right'>22593</td>\n",
"<td style='text-align: right'>16348</td>\n",
"<td style='text-align: right'>17897</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_snps</td>\n",
"<td style='text-align: right'>15388</td>\n",
"<td style='text-align: right'>14392</td>\n",
"<td style='text-align: right'>14885</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_snps_coding</td>\n",
"<td style='text-align: right'>8868</td>\n",
"<td style='text-align: right'>8205</td>\n",
"<td style='text-align: right'>8608</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_snps_multiallelic</td>\n",
"<td style='text-align: right'>12</td>\n",
"<td style='text-align: right'>3</td>\n",
"<td style='text-align: right'>7</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_snps_multiallelic_gatk</td>\n",
"<td style='text-align: right'>12</td>\n",
"<td style='text-align: right'>3</td>\n",
"<td style='text-align: right'>7</td>\n",
"</tr>\n",
"<tr>\n",
"<td>n_snps_noncoding</td>\n",
"<td style='text-align: right'>6520</td>\n",
"<td style='text-align: right'>6187</td>\n",
"<td style='text-align: right'>6277</td>\n",
"</tr>\n",
"<tr>\n",
"<td>ratio_snp_indel_coding</td>\n",
"<td style='text-align: right'>2.159766195811008</td>\n",
"<td style='text-align: right'>2.199142321093541</td>\n",
"<td style='text-align: right'>2.3397662408263113</td>\n",
"</tr>\n",
"<tr>\n",
"<td>ratio_snp_indel_noncoding</td>\n",
"<td style='text-align: right'>0.28858495994334527</td>\n",
"<td style='text-align: right'>0.37845608025446537</td>\n",
"<td style='text-align: right'>0.350729172487009</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def count_variants(query):\n",
" def f(row):\n",
" callset = filter_variants(callsets[row.cross], query=query)\n",
" return callset['variants'].size\n",
" return f\n",
" \n",
"\n",
"tbl_variation = (etl\n",
" .wrap([['cross']] + [[cross] for cross in CROSSES])\n",
" .addfield('n_snps', count_variants('is_snp'))\n",
" .addfield('n_indels', count_variants('~is_snp'))\n",
" .addfield('n_snps_multiallelic', count_variants('is_snp & (num_alleles > 2)'))\n",
" .addfield('n_indels_multiallelic', count_variants('~is_snp & (num_alleles > 2)'))\n",
" .addfield('n_snps_multiallelic_gatk', count_variants('is_snp & (num_alleles > 2) & ((set == b\"GATK\") | (set == b\"Intersection\"))'))\n",
" .addfield('n_indels_multiallelic_gatk', count_variants('~is_snp & (num_alleles > 2) & ((set == b\"GATK\") | (set == b\"Intersection\"))'))\n",
" .addfield('n_snps_coding', count_variants('is_snp & (CDSAnnotationID != b\".\")'))\n",
" .addfield('n_snps_noncoding', count_variants('is_snp & (CDSAnnotationID == b\".\")'))\n",
" .addfield('n_indels_coding', count_variants('~is_snp & (CDSAnnotationID != b\".\")'))\n",
" .addfield('n_indels_noncoding', count_variants('~is_snp & (CDSAnnotationID == b\".\")'))\n",
" .addfield('ratio_snp_indel_coding', lambda row: row.n_snps_coding / row.n_indels_coding)\n",
" .addfield('ratio_snp_indel_noncoding', lambda row: row.n_snps_noncoding / row.n_indels_noncoding)\n",
" .melt(key='cross')\n",
" .recast(variablefield='cross', valuefield='value')\n",
")\n",
"tbl_variation.displayall()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([b'GATK', b'Intersection', b'Intersection', ..., b'GATK',\n",
" b'Intersection', b'Intersection'], \n",
" dtype='|S40')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"callsets['3d7_hb3']['variants']['set']"
]
}
],
"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": "3.5.0+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Chiroptera/QCThesis | notebooks/Sanity check - simplest cases.ipynb | 2 | 229179 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"author: Diogo Silva\n",
"\n",
"SKL = SciKit-Learn"
]
},
{
"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": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"home = %env HOME"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/chiroptera/workspace/QCThesis\n"
]
}
],
"source": [
"cd $home/QCThesis/"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'MyML.metrics.accuracy' from 'MyML/metrics/accuracy.pyc'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.cluster import KMeans as KMeans_skl\n",
"import MyML.cluster.eac as eac\n",
"reload(eac)\n",
"import MyML.cluster.K_Means3 as K_Means3\n",
"reload(K_Means3)\n",
"import MyML.metrics.accuracy as determine_ci\n",
"reload(determine_ci)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Helper functions"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def stat_my_kmeans(data,nclusters,gtruth,rounds=20):\n",
" nsamples=data.shape[0]\n",
" all_acc = list()\n",
" for r in xrange(rounds):\n",
" iters=\"converge\"\n",
" kmeans_mode=\"numpy\"\n",
"\n",
" grouper = K_Means3.K_Means(n_clusters=nclusters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
" grouper._centroid_mode = \"iter\"\n",
" grouper.fit(data)\n",
"\n",
"\n",
" myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
" myAcc.score(gtruth,grouper.labels_,format='array')\n",
" \n",
" all_acc.append(myAcc.accuracy)\n",
" \n",
" \n",
" return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)\n",
" \n",
"def stat_skl_kmeans(data,nclusters,gtruth,rounds=20,init='random'):\n",
" nsamples=data.shape[0]\n",
" all_acc = list()\n",
" for r in xrange(rounds):\n",
" iters=\"converge\"\n",
" kmeans_mode=\"numpy\"\n",
"\n",
" gSKL = KMeans_skl(n_clusters=nclusters,n_init=1,init=init)\n",
" gSKL.fit(data)\n",
"\n",
" myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
" myAcc.score(gtruth,grouper.labels_,format='array')\n",
" \n",
" all_acc.append(myAcc.accuracy)\n",
" \n",
" \n",
" return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b MyML/cluster/K_Means3.py:\n"
]
}
],
"source": [
"print \"b MyML/cluster/K_Means3.py:\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def k_analysis(partition_files,ground_truth,nprots,iters=\"converge\",rounds=20,files=True):\n",
" nsamples=data.shape[0]\n",
" all_acc = list()\n",
" \n",
" for r in xrange(rounds):\n",
" prot_mode=\"random\"\n",
"\n",
" estimator=eac.EAC(nsamples)\n",
" estimator.fit(partition_files,files=files,assoc_mode='prot', prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
" kmeans_mode = \"numpy\"\n",
" nclusters = np.unique(ground_truth).shape[0]\n",
"\n",
" grouper = K_Means3.K_Means(n_clusters=nclusters,mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
" grouper._centroid_mode = \"iter\"\n",
" grouper.fit(estimator._coassoc)\n",
"\n",
" myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
" myAcc.score(ground_truth,grouper.labels_,format='array')\n",
" \n",
" all_acc.append(myAcc.accuracy)\n",
" return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)\n",
"\n",
"def k_skl_analysis(partition_files,ground_truth,nprots,rounds=20,files=True):\n",
" nsamples=data.shape[0]\n",
" all_acc = list()\n",
" \n",
" for r in xrange(rounds):\n",
" prot_mode=\"random\"\n",
"\n",
" estimator=eac.EAC(nsamples)\n",
" estimator.fit(partition_files,files=files,assoc_mode='prot', prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
" kmeans_mode = \"numpy\"\n",
" nclusters = np.unique(ground_truth).shape[0]\n",
"\n",
"\n",
" grouper = KMeans_skl(n_clusters=nclusters,n_init=1,init=\"random\")\n",
" grouper.fit(estimator._coassoc)\n",
" \n",
" myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
" myAcc.score(ground_truth,grouper.labels_,format='array')\n",
" \n",
" all_acc.append(myAcc.accuracy)\n",
" return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate data"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6659668990>]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ZFIB5RWY8AxYEb21ujMy1Zm28UGVONi97dIOfBUg/IFoQffzciu7s7qlMGYH7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\nRjAGAKBhBGMAABpGMAYAoGH/H8bK9Q7oU5hkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6658b03150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"center1=(0,0)\n",
"center2=(10,10)\n",
"\n",
"cov1=1\n",
"cov2=1\n",
"\n",
"n1=500000\n",
"n2=500000\n",
"nsamples=n1+n2\n",
"dim=2\n",
"\n",
"g1 = np.random.normal(loc=center1,scale=cov1,size=(n1,dim)).astype(np.float32)\n",
"g2 = np.random.normal(loc=center2,scale=cov2,size=(n2,dim)).astype(np.float32)\n",
"\n",
"data = np.vstack((g1,g2))\n",
"gt=np.zeros(data.shape[0],dtype=np.int32)\n",
"gt[100:]=1\n",
"\n",
"figData=plt.figure()\n",
"plt.plot(g1[:,0],g1[:,1],'.')\n",
"plt.plot(g2[:,0],g2[:,1],'.')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'MyML.helper.partition' from 'MyML/helper/partition.pyc'>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import MyML.helper.partition\n",
"reload(MyML.helper.partition)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"py_estimator=K_Means3.K_Means(n_clusters=20,mode=\"numpy\", cuda_mem='manual',tol=1e-4,max_iter=3)\n",
"cu_estimator=K_Means3.K_Means(n_clusters=20,mode=\"cuda\", cuda_mem='manual',tol=1e-4,max_iter=3)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 loops, best of 3: 48.1 s per loop\n",
"1 loops, best of 3: 2min 6s per loop\n"
]
}
],
"source": [
"%timeit MyML.helper.partition.generateEnsemble(data,cu_estimator,n_clusters=[6,30],npartitions=30,iters=3)\n",
"%timeit MyML.helper.partition.generateEnsemble(data,py_estimator,n_clusters=[6,30],npartitions=30,iters=3)"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 481 function calls in 0.063 seconds\n",
"\n",
" Ordered by: standard name\n",
"\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\n",
" 1 0.000 0.000 0.063 0.063 <string>:1(<module>)\n",
" 3 0.002 0.001 0.002 0.001 _k_means.pyx:244(_centers_dense)\n",
" 19 0.000 0.000 0.001 0.000 _methods.py:31(_sum)\n",
" 3 0.000 0.000 0.000 0.000 _methods.py:43(_count_reduce_items)\n",
" 2 0.000 0.000 0.001 0.001 _methods.py:53(_mean)\n",
" 1 0.000 0.000 0.003 0.003 _methods.py:77(_var)\n",
" 6 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n",
" 3 0.000 0.000 0.000 0.000 abc.py:128(__instancecheck__)\n",
" 45 0.000 0.000 0.000 0.000 base.py:865(isspmatrix)\n",
" 3 0.000 0.000 0.010 0.003 extmath.py:168(safe_sparse_dot)\n",
" 3 0.000 0.000 0.000 0.000 extmath.py:44(squared_norm)\n",
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"\n",
"\n"
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}
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"source": [
"cProfile.run(\"grouperSKL.fit(data)\")"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2991 function calls (2940 primitive calls) in 0.066 seconds\n",
"\n",
" Ordered by: standard name\n",
"\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\n",
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"\n",
"\n"
]
}
],
"source": [
"cProfile.run(\"grouper.fit(data)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate partitions, k=6,10,[4,25]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def formatPartition(partition):\n",
" clusters=np.unique(partition)\n",
" nclusters=clusters.size\n",
" finalPartition=[None]*nclusters\n",
" for c,l in clusters:\n",
" finalPartition[c] = np.where(clusters==l)\n",
"\n",
" return finalPartition\n",
"\n",
"def generatePartitions(data,npartitions,nclusters,iters=3):\n",
" \n",
" if type(nclusters) is list:\n",
" clusterRange = True\n",
" min_ncluster=nclusters[0]\n",
" max_ncluster=nclusters[1]\n",
" else:\n",
" clusterRange = False\n",
" k = nclusters\n",
" \n",
" partitions = list()\n",
" \n",
" mode = \"numpy\"\n",
" for p in xrange(npartitions):\n",
" if clusterRange:\n",
" k = np.random.randint(min_ncluster,max_ncluster)\n",
" \n",
" grouper = K_Means3.K_Means(n_clusters=k,mode=mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
" grouper._centroid_mode = \"index\"\n",
" grouper.fit(data)\n",
" partitions.append(grouper.partition)\n",
" \n",
" return partitions\n",
" \n",
"def generatePartitionsSKL(data,npartitions,nclusters,iters=3):\n",
" \n",
" if type(nclusters) is list:\n",
" clusterRange = True\n",
" min_ncluster=nclusters[0]\n",
" max_ncluster=nclusters[1]\n",
" else:\n",
" clusterRange = False\n",
" k = nclusters\n",
" \n",
" partitions = list()\n",
" \n",
" mode = \"numpy\"\n",
" for p in xrange(npartitions):\n",
" if clusterRange:\n",
" k = np.random.randint(min_ncluster,max_ncluster)\n",
" \n",
" gSKL = KMeans_skl(n_clusters=k,n_init=1,init=\"random\",max_iter=iters)\n",
" gSKL.fit(data)\n",
" partitions.append(formatPartition(gSKL.labels_))\n",
" \n",
" return partitions\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'MyML.cluster.K_Means3' from 'MyML/cluster/K_Means3.py'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(K_Means3)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All partitions have good number of clusters.\n",
"All partitions have good number of clusters.\n"
]
}
],
"source": [
"npartitions=30\n",
"iters=3\n",
"\n",
"nclusters=10\n",
"partitions_my_10 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"partitions_skl_10 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"\n",
"if type(nclusters) is not list:\n",
" allGood=True\n",
" for p in xrange(npartitions):\n",
" if len(partitions_my_10[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my_10[p]))\n",
" allGood=False\n",
" if len(partitions_skl_10[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl_10[p]))\n",
" allGood=False\n",
" if allGood:\n",
" print 'All partitions have good number of clusters.'\n",
"\n",
"nclusters=6\n",
"partitions_my_6 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"partitions_skl_6 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"\n",
"if type(nclusters) is not list:\n",
" allGood=True\n",
" for p in xrange(npartitions):\n",
" if len(partitions_my_6[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my_6[p]))\n",
" allGood=False\n",
" if len(partitions_skl_6[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl_6[p]))\n",
" allGood=False\n",
" if allGood:\n",
" print 'All partitions have good number of clusters.'\n",
"\n",
"nclusters=[4,25]\n",
"partitions_my_rand = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"partitions_skl_rand = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)\n",
"\n",
"if type(nclusters) is not list:\n",
" allGood=True\n",
" for p in xrange(npartitions):\n",
" if len(partitions_my_rand[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my[p]))\n",
" allGood=False\n",
" if len(partitions_skl_rand[p]) != nclusters:\n",
" print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl[p]))\n",
" allGood=False\n",
" if allGood:\n",
" print 'All partitions have good number of clusters.'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing some partitions"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fc5b408fdd0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bbSdVr6icOK5PRzx/SsPCc/GchVp0xwcbXg/ZSNM1FwC6TqOuKo26zTTqLpO0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\nP039B86o2TU33rJJCySAsYYW0VEial3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/qqpT09PX6urqqpnX8/HgQU91XT7d0+nPfOp7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\nUxSiAAAAAIBMUYgCAAAAADL1/wB8LDwBkfjH/gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5b425b690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figEnsemble=plt.figure(figsize=(16,12))\n",
"ax1En=figEnsemble.add_subplot(2,2,1)\n",
"ax2En=figEnsemble.add_subplot(2,2,2)\n",
"ax3En=figEnsemble.add_subplot(2,2,3)\n",
"ax4En=figEnsemble.add_subplot(2,2,4)\n",
"\n",
"for c in partitions_my_10[0]:\n",
" ax1En.plot(data[c,0],data[c,1],'.')\n",
"ax1En.set_title(\"Sample of one partition generated with my K-Means\")\n",
"\n",
"for c in partitions_my_10[1]:\n",
" ax2En.plot(data[c,0],data[c,1],'.')\n",
"ax2En.set_title(\"Sample of one partition generated with my K-Means\")\n",
"\n",
"for c in partitions_skl_10[0]:\n",
" ax3En.plot(data[c,0],data[c,1],'.')\n",
"ax3En.set_title(\"Sample of one partition generated with SKL's K-Means\")\n",
"\n",
"for c in partitions_skl_10[1]:\n",
" ax4En.plot(data[c,0],data[c,1],'.')\n",
"ax4En.set_title(\"Sample of one partition generated with SKL's K-Means\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EAC K-Means"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My Accuracy:\t1.0\n",
"SKL Accuracy:\t1.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"MyML/helper/partition.py:56: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" if clusts == None:\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fc5b6770d50>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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m5lNzANQcBjWHU8e661ozJJHNlUfNYVBzGHWsWapn3XWtOYuejaiZHSnpY5IeIel+MztD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\nb3/1U3YJu0ZDSn7EWa+DCTE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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5b42a5150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='prot' # prot or full\n",
"nprots=nsamples # number of prototypes\n",
"\n",
"partitions_used = partitions_my_6\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# cluster with my K-Means\n",
"kmeans_mode = \"numpy\"\n",
"\n",
"grouper = K_Means3.K_Means(n_clusters=true_nclusters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
"grouper._centroid_mode = \"index\"\n",
"grouper.fit(myEstimator._coassoc)\n",
"\n",
"# cluster with SKL K-Means\n",
"gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init=\"random\")\n",
"gSKL.fit(myEstimator._coassoc)\n",
"\n",
"# Hungarian accuracy\n",
"myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"myAcc.score(gt,grouper.labels_,format='array')\n",
"\n",
"sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"sklAcc.score(gt,gSKL.labels_,format='array')\n",
"\n",
"\n",
"print 'My Accuracy:\\t',myAcc.accuracy\n",
"print 'SKL Accuracy:\\t',sklAcc.accuracy\n",
"\n",
"figEAC=plt.figure(figsize=(16,6))\n",
"ax1EAC=figEAC.add_subplot(1,2,1)\n",
"ax2EAC=figEAC.add_subplot(1,2,2)\n",
"\n",
"for c in np.unique(grouper.labels_):\n",
" clusterData=grouper.labels_==c\n",
" ax1EAC.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax1EAC.set_title(\"Final EAC partition with my K-Means\")\n",
"\n",
"for c in np.unique(gSKL.labels_):\n",
" clusterData=gSKL.labels_==c\n",
" ax2EAC.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax2EAC.set_title(\"Final EAC partition with SKL's K-Means\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Accuracy is usually 100% in both cases (clustering from my K-Means and SciKit-Learn's). This depends on the ensemble. For some ensembles the accuracy on both is always one, for others it sometimes is not in one or both of the K-Means used (mine vs SKL).\n",
"\n",
"The number of prototypes is equal to the number of samples and since there are not repeated prototypes, all the samples are being used. Above are the visualizations of the solutions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistic analysis"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"type\tmean\tvar\tmax\tmin\n",
"skl \t1.0\t0.0\t1.0\t1.0\t\n",
"my \t1.0\t0.0\t1.0\t1.0\t"
]
}
],
"source": [
"stat_nprots=nsamples\n",
"print \"{}\\t{}\\t{}\\t{}\\t{}\".format(\"type\",\"mean\",\"var\",\"max\",\"min\")\n",
"print \"skl \\t\",\n",
"for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):\n",
" print \"{}\\t\".format(metric),\n",
"print \"\\nmy \\t\",\n",
"for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters=\"converge\",rounds=100):\n",
" print \"{}\\t\".format(metric),\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"."
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-29-329d395e5d0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnprots\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk_analysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartitions_used\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mground_truth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnprots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mresults_k10\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-7-935179233b8c>\u001b[0m in \u001b[0;36mk_analysis\u001b[1;34m(partition_files, ground_truth, nprots, iters, rounds, files)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mgrouper\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK_Means3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mK_Means\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnclusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkmeans_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcuda_mem\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'manual'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_centroid_mode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"iter\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_coassoc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mmyAcc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetermine_ci\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHungarianIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnsamples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnsamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/K_Means3.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 122\u001b[0m \u001b[1;31m# compute new centroids\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcentroids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recompute_centroids\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcentroids\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/K_Means3.py\u001b[0m in \u001b[0;36m_recompute_centroids\u001b[1;34m(self, data, centroids, labels)\u001b[0m\n\u001b[0;32m 563\u001b[0m \u001b[0mnew_centroids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_np_recompute_centroids_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcentroids\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_centroid_mode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iter\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 565\u001b[1;33m \u001b[0mnew_centroids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_np_recompute_centroids_iter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcentroids\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 566\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_centroid_mode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"good\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 567\u001b[0m \u001b[0mnew_centroids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_np_recompute_centroids_good\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcentroids\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/K_Means3.py\u001b[0m in \u001b[0;36m_np_recompute_centroids_iter\u001b[1;34m(self, data, centroids, labels)\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 771\u001b[0m \u001b[0mnew_centroids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 772\u001b[1;33m \u001b[0mcentroid_count\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 773\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 774\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"nprots=[5,20,40,60,80,100,120,140,160,180,200]\n",
"\n",
"results_k10=list()\n",
"for n in nprots:\n",
" print '.',\n",
" r=k_analysis(partitions_used,files=False,ground_truth=gt,nprots=n,rounds=100)\n",
" results_k10.append(r)\n",
" \n",
"mean_k10=[res[0] for res in results_k10]\n",
"var_k10=[res[1] for res in results_k10]\n",
"best_k10=[res[2] for res in results_k10]\n",
"worst_k10=[res[3] for res in results_k10]\n",
"\n",
"plt.plot(mean_k10,label='mean')\n",
"plt.plot(best_k10,label='best')\n",
"plt.plot(worst_k10,label='worst')\n",
"plt.plot([0, 10], [0.5, 0.5], 'k-', lw=1)\n",
"plt.title(\"Analysis of the influence of the number of prototypes\")\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10 clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My Accuracy:\t1.0\n",
"SKL Accuracy:\t1.0\n",
"\n",
"Statistical analysis\n",
"type\tmean\tvar\tmax\tmin\n",
"skl \t0.98525\t0.0034096875\t1.0\t0.75\t\n",
"my \t0.9925\t0.00181875\t1.0\t0.75\t"
]
},
{
"data": {
"image/png": 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cDGIuRta2uZ0e0gFJA+E+fU+KnjSzjQzDAACgFLTNAIBKSp2QmtnRkq6S9FhJu8zsDEnH\nxfZiYoI6AAAFom0GAFRdlkWNfiLpqU1ePziXiAAAQCq0zQCAqmOzbAAAAABAKUhIAQAAAAClICEF\nAAAAAJSChBQAAAAAUAoSUgAAAABAKUhIAQAAAAClICEFAAAAAJSChBQAAAAAUAoSUgAAAABAKUhI\nAQAAAAClICEFAAAAAJSChBQAAAAAUAoSUgAAAABAKUhIAQAAAAClICEFAAAAAJSChBQAAAAAUAoS\nUgAAAABAKUhIAQAAAAClICEFAAAAAJSChBQAJK0eXavVo2vLDgMAAIRWrhvVynWjZYeBLiMhBTDt\nrR5dq41bN2nj1k0kpQAA9ICV60a1YfNWbdi8laS0z5GQAgAAAABKQUIKYNpbNrJEB81ZoIPmLNCy\nkSVlhwMAwLS3YvGIFs6fo4Xz52jF4pGyw0EXzSg7AADoBSSiAAD0FhLR6YEeUgAAAABAKTL1kJrZ\n0yR9VdIad19rZo+TdHVYzk5Jb3D3u/MPEwAAJKFtBgBUWeoeUjN7hKQPS/qupFr49MWSrnT34xQ0\nhkvzDhAAACSjbQYAVF2WIbvjkk6SdLekgfC5JZL+NXx8j6T98gsNAAC0QNsMAKi01EN23X1C0oSZ\nxZ/bLklmNijpbZIuyjtAAOhl0b6lLIqEMtA2A8Ceon1LWRSpGgZqtVrro2LM7L2S7nH3teHPg5LW\nSbrV3S9ucXq2ygCgh51/wyrdfu8dkqRD9jtYl5y4vOSIpqWB1of0P9pmAAgsv/xm3bbpPknSoQv2\n0aqzjy05omkpU9ucx7YvV0vyFA2eJGlsbFsOVRZneHh25WKWqhk3MReDmPPr1dx0/+Ypj+Mxcp2L\nMTw8u+wQehVtcw+qYtzEXAxizq9Xc+Mft055TNtcvKxtczvbvkxmvGb2eknj7s5wIACVsHp0rTZu\n3aSNWzdNJqbtmjdrbuJjoAS0zQAqa+W6UW3YvFUbNm+dTEzbdeDwrMTH6F2pe0jN7GhJV0l6rKRd\nZnampEFJD5rZjeFhv3Z3JlIBmBaWjSwpZA4p81TRCG0zAEy1YvFIIXNImaeanyyLGv1E0lO7GAsA\ndF3eSWS3k8SoRzd6TFKKONpmAP0g7ySy20li1KMbPSYp7Uwec0gBoFJI6gAA6C0kddMXCSkAlCBt\nL+2W7XdpQAN6wpzHk0gDANBFaXtp7xzbroEB6eB5c0ikc9DOokYA0NdWj67teMGjVuWnWVhp6foL\nND4xrppq2rL9rq7FAwBAr1u5brTjBY9alZ9mYaWz1qzX+I4J1WpBYorOkZACQEyeq/C2W38Z9QIA\n0KvyXIW33frLqHe6ICEF0JP6OTFbNrJEB81ZoIPmLJgyDLc+GV6z6GINDQ5paHBIaxal2k6yr68b\nAKBc/ZyYrVg8ooXz52jh/KnDcOuT4SuWLtLQzEENzRzUFUsXpSq7n69bHphDCqDn1K8s+6EXvrOw\nuovayiVt2WkTUSnfFXnZagYAEFe/suxlS48vrO6itnJJW3baRFTKd0Xeft1qhoQUAOpM9ySMrWYA\nAL2m35KwrPp5qxmG7ALoOY2GtKI5rhsAoFsaDWlFc1y31gZqtVqR9dXGxrYVWV/Hhodnq2oxS9WM\nm5iLQczFaDfmtENluzGkNh5zVYbsDg/PHig7hj5A21yQKsZNzMUg5mK0G3PaobLdGFIbj7kqQ3az\nts0M2QWAAqRN8NIkgEUMqe31RBQAgE6lTfDSJIBFDKnt9US0XSSkABCTd8/g6tG12rL9Lo1PjE/+\n3E7ZVemxBAAgb3n3DK5cN6o7x7ZrfMfE5M/tlF2VHstexxxSAAjlvQdpVF6UjDY7rll99XExVxQA\nMF3kvQdpVF6UjDY7rll99XExV7R99JACQGjL9ru6VvaABjRzcOZkAhlPQNsZfksiCgCYDu4c2961\nsgcGpJl7D04mkPEEtJ3htySi7SEhBQAFyWDUkzk0OJRLwhftaRoN2R2fGJ9MRKMkdGhwKHU50WMA\nAKaDletGJ3syh2YO5pLwRXuaRkN2x3dMTCaiURI6NHMwdTnRY7SPhBRAX2snkZs3a25u9UfJZJSA\nNqsrHmN93CSiAIB+0U4id+DwrNzqj5LJKAFtVlc8xvq4SUTzwbYvLVRxSWupmnETczGmU8zxRDDN\nfMt2eyGTzquPudEc0aS6ssadl4p+Ntj2pXO0zQWpYtzEXIzpFHM8EUwz37LdXsik8+pjbjRHNKmu\nrHHnpaKfjUxtM4saAUBo2ciSzMnf0vUXpFoIKSo3Ojb+XLe1WjQJAIBetWLxSObk76w161MthBSV\nGx0bf67bWi2aNJ0wZBdA3+r23Mv4vNNOy4nLM+4i9iwFACCtbs+9jM877bScuDzjLmLP0iohIQXQ\n14pKwNIshJSUaCbNLyVxBAD0s6ISsDQLISUlmknzS0kcu4eEFADalKUnM0uPZ57bz7BCLwBgOsnS\nk5mlxzPP7WdYoXcqFjVqoYoTiaVqxk3MxSDm9JISubTJXTzmeC/o0OCQ5s2am1hmtD2MVOxiRkkx\nVwWLGuWCtrkgVYybmItBzOklJXJpk7t4zPFe0KGZgzpweFZimdH2MFKxixklxVwVWdtmekgBIEE8\niVy6/oLJ7VmyzseM9iGNjE+Ma+PWTVPKlFpvDwMAwHQXTyLPWrN+cnuWLPMxkxYSGt8xoQ2bt04p\nU2q9PQzyQUIKAHUaJZFDg0OZy4kSzAENaObgzMke0KjM+LEMrwUAINnKdaNThs1GSeTQzMHUZSy/\n/OYpvaLRuVEPaFRmvE6G13YfCSmAyigiWasfXitpMoms79HMoqba5Pnxobn18n5vJLgAgG4qIlmr\nH14r7U4i63s004oPw5WmDs2tl/d7I8GdioQUQCWUtX3JmkUXt53ULRtZoqXrL5iSfMZX160/tpF2\n62fLFwBAN5W1fckVSxe1ldStOvtYnbvmxj2Sz/jqunHNym43qWTLlz1lSkjN7GmSvippjbuvNbPH\nSVonaS9Jf5S02N135B8mACTLuwcwnkSOT4x3nMg1SmjrFzVKqicaOhwltCSVSELbDKDX5N0DuGLx\niM5as17jOyY0vmOio0SuPvmMl1O/qFFSPdHQ4SihJans3F5pDzSzR0j6sKTvSoqW5n2fpI+5+7GS\nfivpzblHCAAKEriD5iyYsvps1AO4ceumPXocOxEfmhvV00n5y0aWNEwk4+9h6foL9ni+0dDetPXW\nXzP0F9pmAGVasXhEC+fPmbL6bNQDuGHz1sQFhNoVH5ob1dNJ+SsWjzRMJOPv4aw16/d4vtHQ3rT1\n1l+z6S51QippXNJJku6OPbdI0jfCx9+UdGJOcQHAHpoldnnXEyVyklomvc0S1izJbNQjW29ocChz\nUhnvdSUZ7Wu0zQBK1Syxy7ueKJGT1DLpbZawZklmox7ZekMzBzMnlfFeV5LR3VInpO4+4e71t+pn\nufvO8PGYpANyiwwAWuhmD2DaRK6+lzaegCb14NYnqMtGliSu3ht/b2sWXZw5Gc2z57jTHmJ0D20z\ngF7TzR7AtIlcfS9tPAFN6sGtT1BXLB5JXL03/t6uWLooczKaZ89xpz3EvWSgVqu1PirGzN4raczd\nP25md7v7/uHzT5T0GXd/dpPTs1UGAD3g/BtWSZIuOXH5Hs//5t6NqoVfbQMamHx8yH4HS5Juv/eO\nhj/Hy2tUR7vxNqqnzLK6JNPm2/2KthnAdLP88pslBQsV1T9/++/v00PhN9teA5p8fOiCfSRJt226\nr+HP8fIa1dFuvI3qKbOsLsnUNne6yu6fzGwovDs7X9KWVieMjW3rsMpiDQ/PrlzMUjXjJuZiEHOy\nZosjnXPEmZKmfn/FV7CNxPcZlaSdOyc0NDikebPm6pwjzpzSy7hz58SU8nbunNijjnbF6zrniDNT\nl5l0naO4ose99tkZHp5ddgi9iLa5R1UxbmIuBjEna7Y40nmnHilp6vdXfAXbyN57D06Z87lz14SG\nZg7qwOFZOu/UI6f0Mu7cVdc278qvbY7Xdd6pR3bWNu+a+n567bOTtW3OMoc0MqDdWe8Nkk4JH79S\n0nVtlAcAuWh3aGmnQ1yjOZ5rFl28x9zT+i1fkoYYZ6k/7XvMa+4oCyNVBm0zgJ7U7tDSToe4RnM8\nr1i6aI+5p/VbviQNMc5Sf9r3mNfc0X5bGCl1D6mZHS3pKkmPlbTLzM6Q9AJJ14SPfyfpM90IEgBa\nKXrPzWUjS5pu5/LRX3yi4XntKmtfURLR3kXbDKCXFb3n5orFI023c7n02p83PK9dZe0r2g+JaCR1\nQuruP5H01ISXnpdfOABQvEbJZZrzmomG67Y6Ltr7NGv9AG0zgH7VKLlMc14z0XDdVsdFe59mrR/Z\ntTNkFwB6Tquhpa2GurYa4pplOPDq0bW6/d47pgzXbbU1zPjEeMNtX+IxNnqPrIQLAOg1rYaWthrq\n2mqIa5bhwCvXjeq2TfdNGa7bamuY8R0TDbd9icfY6D3200q43URCCqBvNEoqO50jWvb5cUnvMe9t\nXgAAyEujpLLTOaJlnx+X9B7z3ualn5GQAkAD7fY6LhtZokP2Ozj1QkAsHAQAQDrt9jquWDyiQxfs\nk3ohoH5bOKiXZd6HtEO1XluWuJUqLsMtVTNuYi7GdI056xzR+AJC0aq5Wc4fHp6td1z3QW3Zfpfm\nzZqb+fys2pkDW6+inw32Ie0cbXNBqhg3MRdjusacdY5ofAGhaNXcLOcPD8/WuWtu1J1j23Xg8KzM\n52fVzhzYehX9bBS6DykAdN35N6zSzp0THSVbnSaCWc8//4ZVkwntxq2but77Sc8qAKBIyy+/WTt3\nTXSUbHWaCGY9f/nlN08mtBs2b+167yc9q+kwZBdAT4sWCCpjfmS0vyjJHgAAu0ULBJUxPzLaX5Rk\nr3+QkAJAnWi4bnyV3KwuOXG5DpqzgKQWAIAcRMN146vkZrXq7GO1cP4cktoew5BdAD1t2cgSffQX\nn+h4yG63NJu7mTXeTuaB5jGHFACANFYsHtGl1/684yG73dJs7mbWeDuZB5rHHNLpgB5SAD3vkhOX\nd3UxoPqhwGlXvW13u5WkOuNlLV1/QapzOo0DAIB2rTr72K4uBlQ/FDjtqrftbreSVGerslrtY8q2\nL+mQkALoa+0mcvX7fba7BUyWOiPjE+NTXiPhBAD0k3YTufr9PtvdAiZtnXeObU983CpOZENCCqAv\ntOp17CSRa1ROnvuHLhtZoqHBobbPZR9TAECvaafXMUvZSeXkuX9ofGuY+OM02Mc0PRJSAJXXbuKZ\nRyJX35PaSZ1rFl2c+FqaOLPGAQBAN7WbeOaRyNX3pLZbZ7NY0sSZNY7pikWNAPStZSNLWi72kzaJ\nGxoc0rxZc3NJ+pqV0SpOFi8CAFTZisUjLRf7SZvEDc0c1IHDs3JJ+tqJJXqNxYs6Qw8pgErJughR\npz2HeWwBkxfmkgIAelHWRYg67TnMYwuYvDCXtHP0kAKojCghix7XD2vtRfRocg0AoJ9FCVn0uH7I\nay+iR7O3rgE9pAD6TpoVcdOumtvJPNN2ejSbxVXFxYvo1QUASOlWxE27am4n80zb6dFsFlcVFy/q\ntV5dElIAlZEmIUuTAGVNkopaMChNXCxeBADoJWkSsjQJUNYkqagFg9LExeJFnWHILoBKqVIylmZR\npX7HNQCA/lelZCzNokr9rteuwUCtViuyvtrY2LYi6+vY8PBsVS1mqZpxE3MxpkPMaRKgpIWR8tTu\ndW4UexFJXUU/GwNlx9AHaJsLUsW4ibkY0yHmNAlQ0sJIeWr3OjeKvYikrqKfjUxtMwlpC1X8EEjV\njJuYi0HMu8UXScp7XmaeMXczzriKfjZISDtH21yQKsZNzMUg5t3iiyTlPS8zz5i7GWdcRT8bmdpm\n5pACQJekXTip03MAAEA6aRdO6vQcpEdCCmDaiZK+bq5ae/4Nq9paYTfpnCqurgsAQBZR0tfNVWuX\nX35zWyvsJp1TxdV1exWLGgGYVprtZdrLqhInAABZNdvLtJdVJc5eRw8pAHTBJScuz9yrSU8oAADd\ns+rsYzP3atIT2n30kAKYVorchqSd8klEAQDTTZHbkLRTPolod3WckJrZIyV9VtKjJQ1Jusjdr++0\nXADoFpLqp9cSAAAgAElEQVQ+9DvaZgBVQ9I3feUxZPc0Sbe5+wmSTpH00RzKBAAA7TtNtM0AgArI\nIyG9W9J+4eN9JY3lUCYAAGgfbTMAoBI6Tkjd/cuSHmdmv5F0k6SlnZYJAADaR9sMAKiKgVqt1lEB\nZvYGSc9x9zPN7GmSrnL3ZzY4vLPKAACYaqDsAHoRbTMAoESZ2uY8Vtk9RtL1kuTuvzSzA81swN0T\nG7ixsW05VFmc4eHZlYtZqmbcxFwMYi4GMRdjeHh22SH0KtrmHlTFuIm5GMRcDGIuRta2OY85pL+V\n9ExJMrMFkrY3avAAAEAhaJsBAJWQRw/pJyV92sxuCst7aw5lAgCA9tE2AwAqoeOE1N23S3pNDrEA\nAIAc0DYDAKoijyG7AAAAAABkRkIKAAAAACgFCSkAAAAAoBQkpAAAAACAUpCQAkABVo+u1erRtS2f\nAwAAxVi5blQr1422fA7dRUIKAF22enStNm7dpI1bN00moEnPAQCAYqxcN6oNm7dqw+atkwlo0nPo\nPhJSAAAAAEApSEgBoMuWjSzRQXMW6KA5C7RsZEnD5wAAQDFWLB7RwvlztHD+HK1YPNLwOXTfjLID\nAIDpICnpJBEFAKA8SUkniWjx6CEFAAAAAJSChBQAAAAAUAoSUgAAAABAKUhIAQAAAAClICEFAAAA\nAJSChBQAAAAAUAoSUgAAAABAKUhIAQAAAAClICEFAAAAAJSChBQAAAAAUAoSUgAAAABAKUhIAQAA\nAAClICEFAAAAAJSChBQAAAAAUAoSUgAAAABAKWbkUYiZvV7Sckm7JL3H3b+dR7kAAKA9tM0AgCro\nuIfUzPaT9B5Jz5Z0kqSTOy0TAAC0j7YZAFAVefSQnijpBnffLmm7pDNyKBMAALSPthkAUAl5JKQL\nJD3CzL4uaR9JF7r793MoFwAAtIe2GQBQCQO1Wq2jAszsnZKeJenlkp4g6UZ3X9Dg8M4qAwBgqoGy\nA+hFtM0AgBJlapvz6CG9S9It7v6QpDvMbJuZPcbd70k6eGxsWw5VFmd4eHblYpaqGTcxF4OYi0HM\nxRgenl12CL2KtrkHVTFuYi4GMReDmIuRtW3OY9uX6yWdYGYD4SIKj2zU4AEAgELQNgMAKqHjhNTd\nt0j6iqSfSPq2pLd3WiYAAGgfbTMAoCpy2YfU3a+UdGUeZQEAgM7RNgMAqiCPIbsAAAAAAGRGQgoA\nAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJAC\nAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSk\nAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJ\nKQAAAACgFCSkAAAAAIBSkJACAAAAAEqRS0JqZg83sw1m9qY8ygMAAJ2hbQYAVEFePaTnS7pXUi2n\n8gAAQGdomwEAPa/jhNTMDpV0qKRvSRroOCIAANAR2mYAQFXk0UO6StI/5lAOAADIB20zAKASBmq1\n9kfymNkbJe3v7qvM7EJJG939M01OYdgQACBP9P7VoW0GAJQsU9vcaUL6BUkHS5qQdKCkcUlvdffv\nNzilNja2re36yjA8PFtVi1mqZtzEXAxiLgYxF2N4eDYJaR3a5t5VxbiJuRjEXAxiLkbWtnlGJ5W5\n+2ujx2b2XgV3YRs1eAAAoMtomwEAVcI+pAAAAACAUnTUQxrn7hflVRYAAOgcbTMAoNfRQwoAAAAA\nKAUJaQlWrhvVynWjZYcBAABCq0fXavXo2rLDAIBph4S0YCvXjWrD5q3asHkrSSkAAD1g9ehabdy6\nSRu3biIpBYCCkZACAAAAAEpBQlqwFYtHtHD+HC2cP0crFo+UHQ4AANPespElOmjOAh00Z4GWjSwp\nOxwAmFZyW2UX6ZGIAgDQW0hEAaAc9JACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJAC\nAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSk\nAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQlqSletGtXLd\naNlhAACA0OrRtVo9urbsMABgWiEhLcHKdaPasHmrNmzeSlIKAEAPWD26Vhu3btLGrZtISgGgQDPy\nKMTMLpX0nLC8D7j7V/MoFwAAtIe2GQBQBR33kJrZ8ZIOd/djJL1A0mUdR9XnViwe0cL5c7Rw/hyt\nWDxSdjgdY/gxAPQW2ubslo0s0UFzFuigOQu0bGRJ2eF0jOHHAKoijyG7N0t6dfj4AUmzzGwgh3L7\n2orFI32TjDL8GAB6Dm1zG5aNLOmbZJThxwCqouMhu+4+IWl7+OPpkr7l7rVOywUAAO2hbQYAVMVA\nrZZP+2RmJ0t6l6Tnuvu2BofRGBZg+eU3a9NdW7Vg7hytOvvYQuqTVEhdAFCHXr8maJt7x/k3rNLv\nH9isxz9qvi45cXkh9UkqpC4AqJOpbc4lITWz50u6SNIL3P3+JofWxsYatYe9aXh4trLEHA1bLXo4\nbny47IbNWycfV2meatZr3QuIuRjEXIyKxkxC2gBt827RsNWih+PGh8tu3Lpp8nGV5qlW9HuBmAtA\nzMWoaMyZ2uY8FjV6lKRVkk5q0eD1vbLmU8brvXNse+sTAAB9jbZ5t7LmU8br3bL9rsLqBYCqyWPb\nl9dI2k/Sl80seu6N7v6HHMpGRgcOz5Ikbblnu+Y9ZlZlekcBALmibe4h82bNlST98c936YBHzK1M\n7ygAFCGPRY2ulHRlDrFU3orFI6UM2U2qt4rd+wCAfNA277ZsZEkpQ3aT6qVtBoA95dFDipiyeiTp\nCQUAIFlZPZL0hAJAaySkFVE/JzVrAlp0z21ZizsBAFCU+jmpWRPQontuy1rcCQCa6XhRI3RffNGi\ndhZNKnqxpbIWdwIAoCjxRYvaWTSp6MWWylrcCQBaISEFAAAAAJSChLQCViwe0cL5c6b8l2UobPz8\nIobQFl0fAABFWzayRAfNWTDlvyxDYePnFzGEtuj6ACAt5pD2iFZzLqPn2x0CW3RiSCIKAKi6VnMu\no+fbHQJbdGJIIgqgF9FD2gPSzrlkbiYAAMVIO+eSuZkA0BkS0mli5bpRklgAAHrI6tG1JLEApj0S\n0h6Qds5lu3MzO+lZJZEFAExHaedctjs3s5OeVRJZAP2EOaQ9Im2CWX9cN/f7jBLZ6DHzQgEA00na\nBLP+uG7u9xklstFj5oUCqDp6SGOq1huYtueTVW8BAFVVtd7AtD2frHoLAAES0lC/Lxi0YvFI5mSU\nRBYAUKZ+XzBo2ciSzMkoiSyAfsOQ3R6VZijuisUjXR2y281yAQComjRDcZeNLOnqkN1ulgsAZSAh\nDRWR3EnpEs00czeLiDUvVYoVANA7ikjupHSJZpq5m0XEmpcqxQqgvzFkN6adYa1Z5DUsOKmcXp3/\n2u9DoQEA3dXOsNYs8hoWnFROr85/7feh0ACqhYS0ZEmJZNa5m3kmuvHzk2Lr1cQXAIC8JCWSWedu\n5pnoxs9Piq1XE18ASGPwwgsvLLK+C//85x1F1texWbOGlFfMxx4xT7/aeK/2nTM0OUR4w+atum/b\nuH618V4de8S8KcfGf25Wzg9+uUX3bRuXJO07Z0jHHjGvadwr143qB7/cMqX8+lh+8Mste8TWLN60\n77mZPK91UYi5GMRcjIrGfFHZMfSBad02HzPvKN167+3aZ+jRk0OEN27dpPvHH9Ct996uY+YdNeXY\n+M/Nyvnxlp/q/vEHJEn7DD1ax8w7qmncq0fX6sdbfjql/PpYfrzlp3vE1izetO+5mYp+LxBzAYi5\nGBWNOVPbzBzSgrWaOxod0+hxUjlZ5r+Wsbcoc0cBAL2s1dzR6JhGj5PKyTL/tYy9RZk7CqBXDNRq\ntSLrq42NbSuyvo4ND89Wfcx5LtITH/4aJYpDMwc1vmNij8dZhvDuPWNQ5516ZOJrUT315dW/r7PW\nrJckXbF0UcNj8pR0rXsdMReDmItR0ZgHyo6hD/RF25znIj3x4a9Rojg0OKTxifE9HmcZwrv33oM6\n54gzE1+L6qkvr/59LV1/gSRpzaKLGx6Tp4p+LxBzAYi5GBWNOVPbTELaQv2HoFlC14l4uZ0kpGni\ny7rS79DMwSlJaaPjW5XZSkX/wRFzAYi5GBWNmYS0c5Vvm5sldJ2Il9tJQpomvqwr/Q4NDk1JShsd\n36rMVir6vUDMBSDmYlQ05kxtM4saZbBy3ajuHNvelbLjCxldsXRR4uOsyd6dY9snFyGK98RmXU14\nfMdE08WNWEkXAFCW1aNrtWX7XV0pO76Q0ZpFFyc+zprsbdl+1+QiRPGe2KyrCY9PjDdd3IiVdAFU\nBT2kLVx67c+1c1fQQxnvMTxweFbPzo1cuW5UW+7ZrgfHJ6Y8nzWpPWvN+sTe2fpeWEm59BpX9A4Q\nMReAmItR0ZjpIe1c5drmj/7iE9q5M2if4j2G82bN7dm5katH1+qPf75Lf9k1PuX5rEnt0vUXJPbO\n1vfCSsql17ii3wvEXABiLkZFY6aHNC8r143qtk33acPmrVN6RjtNRru9dcqKxSNaMHdOx3Un9c4m\n9RJn3aYGAIB2rR5dq9vvvUMbt26a0jPaaTLa7a1Tlo0s0eMfNb/jupN6Z5N6ibNuUwMAZaGHtImk\nnkCps3mSec5BbTZvc3h4ts5dc+Mez3fSy1s/rzTvXuKK3gEi5gIQczEqGjM9pJ2rVNuc1BModTZP\nMs85qM3mbQ4Pz9Y7rvvgHs930stbP680717iin4vEHMBiLkYFY05U9vMti9NrFg8Mjlkt9d6/tJs\n31L/XLRqrhTMC43mfGbZNiZy4PCspnUDANANy0aWTA7Z7bWevzTbt9Q/F62aKwXzQqM5n1m2jYnM\nmzW3ad0A0ItyGbJrZh8xsx+b2Y/MrK+yk1VnH5trwpV2eGvew3qblZV2UaJ47JJYyAgAelg/t82X\nnLg814Qr7fDWvIf1Nisr7aJE8dglsZARgMrpOCE1s0WSnujux0g6XdLlHUfV51qtcpsmQaxPbOsT\n2OWX37zHKrjjOyY0NHNw8ry028h0skIvAKB4tM3ZtVrlNk2CWJ/Y1iew59+wao9VcMcnxjU0ODR5\nXtptZDpZoRcAekkeQ3ZPkPRVSXL328xsHzN7pLv/KYeyKyWPvTizSFr1Noqh/udI0rzPRkN2Ww0L\n7vXVhgFgGqNtDuWxF2cWSaveRjHU/xxJmvfZaMhuq2HBvb7aMADUy2PI7lxJ98R+HpN0QA7lVkra\nYa9phuKmGdabZUhvmvKSej7jq+nGH8d7XDuNDQDQFbTNSj/sNc1Q3DTDerMM6U1TXlLPZ3w13fjj\neI9rp7EBQJG6sajRgKSGS/cOD8/uQpXdlSbmvWcMTnk8PDxbyy+/WVIwD1UKhtFGPY6XXvvzyeeT\nXLb0+Iav1Zdz2dLjp9QVPY7H0qy8Rg46YI5u23Tf5OPoOiS910axNXuPSfr189FriLkYxIweMj3b\n5r0HpzweHp6t829YJSmYhyoFw2ijHseP/uITk88n+dAL39nwtfpyPvTCd06pK3ocj6VZeY0sePR8\n3X7vHZOPJ9vmhPfaKLZm7zFJv34+eg0xF4OYe08eCekWBXdiI/Mk/bHRwRVctjhVzOedeuRkr+B5\npx6pc9fcOJmYnbvmRq1YPKKdu3b3KO7cNdH2tUgq57xTj5QUXN9266kftlv/nqJykt5rdF6auhsN\nbY6uddFDnztR0aW4ibkAxFyMfm+kO0DbLOmcI86c7BU854gz9Y7rPjiZmL3jug9q2cgS7dwZa7d2\ndtA2J5RzzhFnSgrb5jbrqR+2W/+eonKS3mt0Xpq6Gw1tjq510UOfO1HV7zJi7j5iLkbWtrnjfUjN\n7FmSLnL355nZ0yVd5u6NusUqtdeZ1P6HoNF+o6d/6PuSpE+944SO4mqVtGXdrqbV/qiN6ks6r1ls\nzeqJ9k7Na5/WIlT1S4KYu4+Yi8E+pMlom5M12m/07d9/hyTpn0/4UEdxtUrasm5X02p/1Eb1JZ3X\nLLZm9UR7p+a1T2sRKvpdRswFIOZiFL4PqbvfYmY/M7MfSZqQ1NvfUgVJSszOWrNeUf5/1pr1umLp\noo7Kb2bV2cdqbGzb5N6jUV3t9D6m2fM0S2wAgO6ibU6WlJgtXX+BauFo5qXrL9CaRRd3VH4zl5y4\nXGNj2yb3Ho3qaqf3Mc2ep1liA4Cy5DKH1N3flUc5/aZbiVmrHkgpmIN61pr1kwsPnbVmvQ4cntUw\nsWzVs9lI1vNaHd9uHACAqWibk3UrMWvVAykFc1CXrr9gcuGhpesv0LxZcxsmlq16NhvJel6r49uN\nAwDS6HjIbkaVHxbUabJU32OZVbMhr/HXDl2wjzb+cfdKuNEWLe0Ohy0iSazokARiLgAxF6OiMTNk\nt3OVb5s7TZbqeyyzajbkNf7aIfsdrE33b55MSKMtWtodDltEkljR7wViLgAxF6OiMWdqm/PY9mXa\nSLu1SzNXLF00ZfhsN7dHuWLpIg3NHNTAwO79R1tt/9JI0rYwjbDtCwCgKGm3dmlmzaKLpwyf7eb2\nKGsWXayhwSENaGByv9BW2780krQtTCNs+wKgV5GQlqTd5LZZUhl/Ldpu5cDhWarVNFlPlsSyHXkk\n7QAAlKHd5LZZUhl/LdpuZd6suaqpNllPlsSyHXkk7QDQLd3Yh7Rv9cr8xmZ1FxFXL1wDAACk3pnf\n2KzuIuLqhWsAAO1gDmkL3Ry33c3ELh53nvXE56kOzRxMnAvbbIuYO8e2Tw4fbhZzVRBzMYi5GBWN\nmTmknaNtjulmYhePO896Wm0P06y+1aNrtWX7XZPDh5vFXBXEXAxiLkZFYy522xe0r5s9jMsvv3ly\nH9Ju1TO+YyJxG5hW+4/Ghw8DANBLutnDeP4Nqyb3Ic2zni3b70p8HNdq/9H48GEAKBJzSPvQynWj\num3TfV2Zx7li8YiGZg7mWiYAAP1u9eha3X7vHV2Zxzlv1tzExwBQBSSkSC1aPfeKpYu0cP6cTIlp\ntODS0MzBxO1qWAAJAIDsouQ2Wjgp/lwr0YJLQ4NDidvVsAASgCIwh7SFKo7blqRLr/355JDdPNTv\nfyqp7T1NG5V76IJ9dN6pR+YQbXGq+Pkg5mIQczGYQ5oL2uaCfPQXn5gcspuH+rmjktre07RRuYfs\nd7DOOeLMHKItThU/H8RcDGIuBvuQouUiRvRIAgBQrFaLGNEjCWC6IiHtM1GP422b7ktMOvPa/7TZ\nfqhZJO2dCgBAP4l6HG+/947EpDOv/U+b7YeaRdLeqQDQLayyi9TSrKabR7kAACCd+sQzr+HArLYL\noCj0kPaZqMfx0AX7JCZ6efVsAgCAdKIex0P2Ozgx0curZxMAqoge0j60YvGIhodn69w1N07+XP86\nAAAozrKRJRoenq13XPfByZ/rXweA6Yge0h6Ux6JDyy+/ua25op1gsSQAQL/KY9Gh829Y1dZc0U6w\nWBKAXkdC2mOyLjpUVBLYqp52F0sCAKDXZV10qKgksFU97S6WBABFIiGtsKKSQJJNAADSKSoJJNkE\n0C9ISAuStiezqosOVTVuAMD0lbYns6qLDlU1bgDTy0CtViuyvtrY2LYi6+vY8PBsdRpz1MMoKfeE\nLUpy68tstqhRnvXkKY9rXTRiLgYxF6OiMQ+UHUMfmJZtc9TDKCn3hC1KcuvLbLaoUZ715Kmi3wvE\nXABiLkZFY87UNrPKbsU1SxCbvZY1wUx7XBGJKwAAvaxZgtjstawJZtrjikhcAaBdDNktQK8NZ+3W\nnFDmmgIAqqLXhrN2a04oc00B9DoS0h7GNioAAPQWtlEBgHyRkBagnZ7DpHPySlC71WPbaz3BAAA0\n0k7PYdI5eSWo3eqx7bWeYACoxxzSiogvjLRy3WjHCV+3EkYSUQDAdBFfGGn16NqOE75uJYwkogB6\nGT2kBWin55DeRgAAuqednkN6GwEgfx31kJrZDEmfknRwWNYyd/9RHoH1m3aSyvg5KxaPsIItAKAl\n2ub02kkq4+csG1nCCrYA0KFOh+y+QdJ2d/8bMztM0tWSntl5WEhCIgoASIG2uUAkogDQmU4T0s9J\n+mL4+B5J+3VYHgAA6AxtMwCgMjpKSN19p6Sd4Y/nKmgEAQBASWibAQBVMlCr1VIdaGanS3pL3dPv\ncffvmdkSSS+W9BJ3n2hSTLrKkKvll98sSVp19rElRwIAuRsoO4Ay0TZX1/k3rJIkXXLi8pIjAYDc\nZWqbUyekjYSN4Sslvczdd7Q4vDY2tq2j+oo2PDxbVYtZ2h13fLuYXl+xt4rXmpiLQczFqGjM0zoh\nbYS2uTdFcce3i+n1FXureK2JuRjEXIyKxpypbe50ld2DJZ0haVGKBg8AAHQZbTMAoEo6XdTodAWL\nJXzbzKLnnhfOX0EPYLsYAJh2aJt7HNvFAMBunS5qtELSipxiQZeQiALA9EHbXA0kogAQ2KvsAAAA\nAAAA0xMJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJAC\nAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSk\nAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKAUJ\nKQAAAACgFCSkAAAAAIBSkJACAAAAAEpBQgoAAAAAKMWMPAoxs/0l3SbpZHe/OY8yAQBA+2ibAQBV\nkFcP6SpJv82pLAAA0DnaZgBAz+s4ITWzEyQ9IOlXkgY6jggAAHSEthkAUBUdJaRmNlPS+ZLeHT5V\n6zgiAADQNtpmAECVDNRq6dopMztd0lvqnr5O0q3u/mUzu1rSNe6+PucYAQBAAtpmAEDVpU5Ik5jZ\nDyUNhj8ulDQm6RR3vzWH2AAAQEa0zQCAKukoIY0L78JezUp+AAD0BtpmAECvYx9SAAAAAEApcush\nBQAAAAAgC3pIAQAAAAClICEFAAAAAJSChBQAAAAAUIoZZVRqZvtLuk3Syb2+8p+ZzZD0KUkHK7he\ny9z9R+VG1ZiZfUTSMxVshH6Ou4+WHFJLZnappOcouL4fcPevlhxSKmb2cEm/kvQ+d/9M2fGkYWav\nl7Rc0i5J73H3b5ccUlNm9khJn5X0aElDki5y9+vLjSqZmT1N0lclrXH3tWb2OEnrFNz4+6Okxe6+\no8wY6zWI+WoF/xZ3SnqDu99dZoz16mOOPf98Sde5Ozda20Tb3D20zcWpWttctXZZom3utunYNpfV\ncK+S9NuS6s7qDZK2u/vfSDpd0pqS42nIzBZJeqK7H6Mg1stLDqklMzte0uFhzC+QdFnJIWVxvqR7\nFfyB0fPMbD9J75H0bEknSTq53IhSOU3Sbe5+gqRTJH203HCSmdkjJH1Y0ne1+/PwPkkfc/djFXzf\nvbmk8BI1iPliSVe6+3EKGpal5USXrC7m+PMPk/QuSVvKiKuP0DZ3AW1z4SrTNle0XZZom7tmurbN\nhSekZnaCpAcU3L0aKLr+NnxO0j+Fj++RtF+JsbRygoIPqtz9Nkn7hHexetnNkl4dPn5A0iwz6/nP\nhZkdKulQSd9SNT7HknSipBvcfbu73+XuZ5QdUAp3a/e/uX0ljZUYSzPjCv6YiN+xXCTpG+Hjbyq4\n/r0kHnP0GV4i6V/Dx734fZd0nSXp3ZI+puDOMdpA29xVtM0FqWDbXMV2WaJt7qZp2TYXmpCa2UwF\nd67eHT7V83ev3H2nuz8Y/niugkawV81V8EGNjEk6oKRYUnH3CXffHv54uqRvuXvPfy4U9CT8Y9lB\nZLRA0iPM7OtmdnP4B2hPc/cvS3qcmf1G0k3qsbuCkfBzPF739Cx3j76Ee+7fYlLM4R9FE2Y2KOlt\n6rHvu6SYzewQSYe5+782OA0t0DZ3HW1zcarWNleuXZZom7tpurbNXZtDamanS3pL3dPXSbrC3beZ\nmdRjd68axPwed/+emS2R9FeSXlJ8ZG0bUAX+sJAkMztZwbCJ55YdSytm9kZJN7v776twxzhmLwV3\nMl8u6QmSblTQGPYsM3uDpN+7+4vC+QlXKZiHVTWV+ZyEDd46Sf/u7jeWHU8T0XfbhyW9vcxAqoS2\nuSfQNndBRdvmyrXLEm1zGfq9be5aQurun1Kw4MAkM/uhpBea2VJJCyUdZWanuPut3Yoji6SYpcnG\n8MWSXubuE4UHlt4WBXdiI/MUTNjuaeGE53dJeoG7bys7nhReJOlgM3uFpAMljZvZH9z9+yXH1cpd\nkm5x94ck3WFm28zsMe5+T6sTS3SMpOslyd1/aWYHmtlARe7U/8nMhsK7hvNVnfmNV0tyd7+47EBa\nMbN5CobnfSFMpA4wsxvd/fhyI+tdtM2loG0uRhXb5iq2yxJtcxn6um0udJVdd39O9NjMrpZ0da80\neI2Y2cGSzpC0qNdW4UpwvaSLJF1pZk+XtDk25KYnmdmjFAyxOcHd7y87njTc/bXRYzN7r6SNPd7g\nRa6XdI2ZfUjBHdlHVqDR+62Cu67/ZmYLFCxi0ssN3oB233G9QcFiD5+T9EoFvVC9aPIOcbja47i7\nX1RiPGkMSBpw9y2SnhQ9aWYbSUazo23uOtrmAlS0ba5iuyzRNhdhWrXNpWz7UjGnK5g8/O0wy5ek\n58XGn/cMd7/FzH5mZj+SNKFgEnSve42C6/vl2PV9o7v/obyQ+pO7bzGzr0j6SfhUFYY5flLSp83s\nJgXfV28tN5xkZna0giFLj5W0y8zOULAy5TXh499J6qntBxJiPlPSoKQHzSwaDvRrd++Z75EG1/k4\nd5zQt/UAACAASURBVP/f8JBe/oMI+aJt7i7a5gJUtF2WaJu7Zrq2zQO1Gu03AAAAAKB4bCAOAAAA\nACgFCSkAAAAAoBQkpAAAAACAUpCQAgAAAABKQUIKAAAAACgFCSkAAAAAoBQkpAAAAACAUpCQAgAA\nAABKQUIKAAAAACgFCSkAAAAAoBQkpAAAAACAUpCQAgAAAABKQUIKAAAAACgFCSkAAAAAoBQkpAAA\nAACAUpCQAgAAAABKQUIKAAAAACgFCSkAAAAAoBQkpAAAAACAUpCQAgAAAABKQUIKAAAAACgFCSkA\nAAAAoBQkpAAAAACAUpCQAgAAAABKQUIKAAAAACgFCSkAAAAAoBQkpAAAAACAUpCQAgAAAABKQUIK\nAAAAACgFCSkAAAAAoBQzyg6giszsp5Jmu/uhOZR1k6Sr3P1zGc+bL+k77v7UHGJ4lKT1kh4u6Wh3\nvy/22iGS9nf3H5jZcWGsT+qgrvdL2uTun2xx3L9I+htJfy/pSkmvd/cftVtv0czsYEnXS9rq7k+v\ne+0oSQ+6+3+Z2WkK3ttzSwizpaLjM7O3SbpA0uXu/oEulD957fM4rpvM7EAFn6FFkv4kabWkv5VU\nU3Az8TPu/v7w2JsU+x4xs8MkfU/Sq939R42+Z8zs85JucPdPF/KmAAAA6tBDmpGZPUXBH4T/a2ZH\n51BkLfwvE3ffnEcyGnqapH3d3eLJaOgVko7NqR65+7tbJaOh10o6zt2vV5vXqGTPlrSlPhkNvVnB\nNe97Zpb1O+aVkt7dbjJqZgMtDkl77Xvhd3SVpIvcfUzSeyXNkfQUd3+ygn+Tp5nZq8NjJ/+NmNkB\nkr4p6W2xmziN/g29TdJF4TkAAACFo4c0uzdJ+pykHZLeKOknkmRmT5B0i6T3K+jV21fSUnf/UvhH\n+ccknShpb0k/lPRmd98VljlgZl+SdIu7fyQs7zBJN0k6QNJFkk4Jj90s6Q2ShiT91t1nhL2ln5U0\nV9JMSV909/PrAw97OD8s6RGSHpC0RNL/hO9nfzO7VdJz3P3e8PiXSHqnpB1m9mhJ/yd8/hIFicOM\n8H38wMyGJK2S9PwwhiuTkgozu0bSb9x9pZn9Lrxep0t6nKTPu/uysDdnL0nfNbNz6uKf7KGN/9ys\n/kb1hK+9UdKKsIr/kPQWd99hZidLuljSLEm/lfS66LrUvZ9XSXpPeC22KPjd7y/pQ5LmmNnP3f3I\n2PFnSlos6SVm9lhJ/ytp0MyuknScpAclvcbdbw2v+cckHRWWf7G7X9Pgmm6S9CxJh0i6XdLJ7v6g\nmT0k6UB33xIe+5CkA8PjPqDg8/vSMI6zwmt4qKRPuvuFYRWDYY/10Qo+N6e6++3N4gvreaekt5iZ\nuftkMmRmD5N0Wfh+H5L0bUnnSfpgWMehZvY4d39f7JzjJP2zpO9IOknB7/hUd/8PM7tQ0jxJR0i6\n1sw+KukSBTdTFL7HJQr+vUbXflhSq+NOMrP9FXwOFrj7PWEslyr4d3y/pIWS9pH0VEl3Snq5u4+F\nvZtXhNdZks5x9++Y2QxJn5D0HEmDkn4p6TR331b3Oz1K0iHu/sXwqadI+n70neHud5vZs8LfR/y8\n2eH1/KC7f10tuPv9ZvY5Sf8kaVmr4wEAAPJGD2kGZjYo6VWS/kXS5yW91Mz2jh2yn6QJd3+apHMV\n/LErBX/wLpJ0mKQnS3qGpNfUFX9tWHbkJZK+IsnC5w8Phwh/UUFiK+3u8ThX0np3P1zBH66PN7O5\ndbE/UtKXJL097GG5VEFi9gcFf3z/3t2fHE+63P2bkr4q6TJ3Xy5pQNICSf8RlvEJBcmYFCQUh4b1\nHy7pFDN7ccJljPfU1BQMyz06vCb/YGbz3P248PXj3P26hDKSNKs/sZ7wJsIqSYvc3RQkn/8QDrf9\nrILEcKGkG8P3OoWZPV7BcOKTw+vxLQWJ3C2S3qXgBsOR8XPc/ROS/q+k5eHNh4Ewro+HifZ6SUvD\nwz8saVcY2zMV9GQd3uD9nyLp1QoSpGFJL0txzY6U9NXwPT4k6eOSXqjg8/VuM5sZHvccSR9z9ydK\nuk5B4tgovsNi5Q+4+yHxZDR0rqT5Cv49PF3B7+ZUdz8vdm3epz2Zgs/eoZJWKkj4Ii+S9EJ3v0zB\nv60XhGUfLunRkv6x7tqnOe48d18j6QbtTlqlICG+VsHv7mQF/6ae8P/bu/c4O7KDPvC/fugtjaSR\n2p4ZMObh5NjJ2svasAODsT8MLBjH4AQmWXYhGJgFTJzFm2TzWfjYkAdr8kmIPY7BG4gXG0IgJLYZ\nm8GTeDIYG2PMxmNIbB5zwDi2YZ7SaKTRs6XuvvtHVUu3W919JV21znTr+/18+tN1q86tOn1uXal+\ndU5VJfl0us89SX4+ye/2bfPyJP+mlHJjuhMmX1hrfW7/ef+XdCcSlrsjyXCgvDdd+/5YKeUrSynT\ntdYnhk5qJV1If1eSX621vm2Fda7mV7L03x4AgGtGIL0835DkY/2B4LF0PZjfNLR8Osk7+unfS/IF\nSVJrfVeSL6+1ztdaZ5M8kOSLht43SBdmntv3dibdQe+/S9cLM5PkO0op+2ut/6rW+gvL6vVYkm8o\npXxVuoDwnbXWR5eVuTXJn/dhKbXWX0lysA9lo4Y5Di9/sg+qSXcw/ax++puS/Mta67la66kkv5Cl\nB/Gr+aVa66DW+kj/dzxr1BtWMWr7y7fzBUm+PslHhtrqf03Xc/eyJB+stf5RP/9n0p18WN5O/1O6\nXqtP969/NsnX9CcuRrXpsE/WWn+vnx5u01ckeUuS9L1zv5KV23SQ5NdqrUdrrfNJPtn/faMcrbX+\nZj/9B+n+5jNJ/jBd791Mv+xPaq3/Xz/9zlwIUMvrd3e6nvNF71tluy9P14O90G/vF9N9FotWa7sT\ntdZ39tO/kuRLSyk7+te/U2s90k//lSQ/V2s9XWtdSPed/Ppc7FLL/VL6wFZK+ZIkO2qt/7lf9oFa\n62eH6nRbKWVnut7fNydJrfVPk3y4397jSf5SKeWvlVJ21Vp/rHbD0pf78iQfW3xRa/1/knx3uhMq\n9yc5VEp5Uz8yIOna7B+lC+035fJ8PMktpZRbLvN9AABjE0gvz3cleVkp5clSypPpekdeNbR8vtZ6\nenE63UF9+uGB7yil1H5Y7DdnWdvXWs8meU+Sv15KOZCuF+VD/VDLb0l3QPzZUsqv9cMBh92V5FfT\n9XA92g9hXG4myfLrQ48mecYl//Wdp4amz/+N6XqX7iql/FH/N/5guqHBowwPORxe3+Uatf2VtnNg\neH6tdbYPdPuSvGRoXb+drq0OLNvmwX7+4vuPpQsGy8uNslqb7k/y74fq8VeT7LnMdaxleJjofJKT\nSdL3aC4MrePQULljfb0upX5HsrLl++Ly/XC164WXvyfpPqvlyw6OWP/llrsnyZeVUg6mO/Hx74aW\nLX///iR70+0Hvz3UNi9KsrfW+rEk/3v/80gp5RdLd1Ox5Z6RLryeV2t9V6315en+5v+lr8s/6BcP\n0vXaviDJ15ZSvnuFda6o3+ePrPK3AwCsK9eQXqJSyv50w273Lw6T63vC/rwPkGt5Q5LZdDckOddf\nj7eSX0p3gHkkXW9LkqTW+sEkH+x7g96Ybsjk64aWz6e7ZvGfllL+QpL/UEr5rVrr/UPrfjRDQanv\n7bsxXW/hcG/tSi7lhkIPJfmJWuu9l1D2Si0PWvuHpq9k+4eT3Lb4or/+bme/rvtrraOGMT6WoeGW\n/T6y0K/3ango3XDgPxxjHeeDZV+/K3Hj0PT+XAiaV1q/x9KFwUUH0u2fowx/zxb/louu672M9V9S\nuVrrqVLK+9KdgPrmdNdbLhp+/419fR5Pt6++qO+tX76+dyd5d/95vD3J30+y/Jrv873E/XWn35jk\nfX2v8rkk/7GU8pZ0vfSLPlFrfaqUckeS3yil/Nda6++u8HcDADxt6CG9dN+W5NeHr9nqg+D70w31\nXMtMkt/vw+h/n+4OrMM9SYsHn7+RLhx+T/pemFLK15dSfqqUMtH3vn4iXcg4r5Ty06WUxetKP53u\noHpJmXTD/24qF+4M/G1J/mxouOFqzmVp8FvNe5N8byllspQyUUp5fSnlG1YodzlDWZd7JMnNpZSZ\n/mTAt1/B9hcN0l2X91WllGf3Af1n0g2LfH+Sry6lfFHS3WCmlPLmFdZxf7qe1MVA/+ok7++Hf67l\nctr0B/o6TJdS7iql/A8j3pMsbeNHknxpP/09uXi/uBSllLJ4t+A7kiwO812pfl+60gqW+bUkd/af\n1a50N+labXjvsJ2lu9nUYj0+1o8sWGn931FK2dGHuTuH1j/c9pdaLulOFr0qyU1Dw6uT5MVDIxbu\nSPLh/t+F9+VC2+wspfxsKeXzSynfVUp5fZLU7o7WNSt/Jo/nQo/lfLobUL2+3+9TSrkhXTj+0NB7\nJvr1/pd0N5R617KTECt+9/p17s/SnnAAgGtCIL1035luSO1yd6e7KdBKj1VYfP3GJK8upfxhuoPU\nv5fu7qPfOlyuP5B9d5IvqrX+dr/sQ+l67f64lPL76Ybu/mi6g8vF9f90kjf0QwP/IMlv11o/MFyR\nWuvJdDe9+am+3KvThdLldV3unr7u/37E3/jWdHd6/YMkf5TuWrYPr7C+S318y0Xlaq2fStej9Hv9\nuu+/gu0Pr++hJN+X5APpgsF8kjf115R+b5K7+8/sLUl+eYX3/3mS/y3Je/s2fXGS7x+q/2p/693p\nerP/+Qrlhl//SJK9pZQHk/x+us/8E6usc/k6Fr0uyb8spfxuumdZHlul3GrrGKRr5x8spfxxuhse\n/dAl1G+tz/knk/xZus/qY0nu6a+zHuUz6QLgg30d/tbQts5vr1/Xvemujfxkuv3iLf3i821/qeX6\n1/8p3Q3J3j1Un0E//6dKKZ9Ld/fif9ov+4EkL+33i48n+dN+f3lvkheVUv6437eem+RNK/yt/znd\ndaSLQ6i/Md1NoB4spdS+3T647L3DbfAz6Yaa/+LQtc8/X0o5N/TzY/38FyV5tP8+AABcUxODweh8\nUEp5QboDtDfVWt+6bNn2dHcafV6t9cvXpZbXkVLKDyXZV2v9oZGF4TpRlj3yp1Ed/jDJt9RaH+xf\n/4N0j9T53nXY1q1JfqHW+hdHFh5/Wz+eZHut9e+OLAwAcJWN7CEt3R0j35huGONK/lm6s/mMqXQP\np/++rPCIEaCdUsqr0t2l+sGh2eMMP19Tf1fjz/TXg66b/oZKfzPJPx9VFgBgPVzKkN3ZdI93eGyV\n5T+cblgnYyil/K10w/DeUGv9TOPqwNPRpQ73vqpKKR9K8tpcGI69aK1h2VfDnemePXpwZMkr99Yk\n/7C/mzcAwDV3SUN2k/PD0w4vH7LbL/vCJO80ZBcAAIBLdU0f+zIYDAYTE+s2yg2A64//VABgA7ta\ngfSSulknJiZy6NDxq7TJ69fMzB7tOCZtOD5tOD5tOL6ZmT2jCwEAT1uX89iXtc5CO0MNAADAZRnZ\nQ1pK+Yokb0v3kPa5Usqrk7wjyadrre8ppdyf7vl7X1BK+WS6R8O8Yz0rDQAAwMY3MpDWWn8nyfPX\nWP51V7VGAAAAXBcuZ8guAAAAXDUCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0\nIZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCE\nQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMC\nKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQik\nAAAANCGQAgAA0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNTF9KoVLKC5Lc\nneRNtda3Llv2dUnekGQ+yb211v/7qtcSAACATWdkD2kpZWeSNyZ5/ypF/kWSb0nyVUm+vpTyvKtX\nPQAAADarSxmyO5vkFUkeW76glPLFSY7UWh+qtQ6S3Jvka69uFQEAANiMRg7ZrbXOJ5kvpay0+KYk\nh4ZeP57kS1Zb12vueV3mFwaXW0eWmZqc0I5j0obj04bj04bj++lX/njrKgAAY7ika0jXsPxIamLU\nG6YmRxbhEmjH8WnD8WnD8WlDAOB6Nm4gfThdL+miz0/y0GqF3/pNb8ihQ8fH3CQzM3u045i04fi0\n4fi0IQBwvbucx75cdBq/1vrZJDeUUp5dSplO8leS3He1KgcAAMDmNbKHtJTyFUneluQZSeZKKa9O\n8o4kn661vifJDyT5t33xX661fmq9KgsAAMDmcSk3NfqdJM9fY/mHk9x2NSsFAADA5jfuNaQArGIw\nGOTU7FyOnjibYydmc+zE2Rw9Mdu9PjmbicnJDBYWsnV6Klu2TGbb9FS2TE9m65bJbJmeytbpyf51\nP7+fPj+/f9/WfnrSDZIAgA1GIH0aWxgMMj+/kHNzg8wtLGR+fpC5+YXMT07miaOnL37DYPXHR6y6\nZI0nTqz1MIrBGtvavnU6+3ZvzcSEg+ONbjAYZH6h2+/m5vv9cb7bFwfTUzl9Zi7bt01l8jr7rBcG\ng5w4dS5HT8zm2MkuZB47cfZC4Dy5GD7PZm5+4ZrVa2pyYmlgHQ6y033I3bI0yC4G363Tk9nSv3fx\nfdu2TOXA3u05eMN2YRcAWBcCaW9ufiHHT53LmbNzmVsMfvOLB+IL5+fNLSxkbllAHF4+Pz/oD9j7\neQsLmZu7MH3R8qH3L503yMIaoe/pbtuWqTzzxh25+cCu3HTjziU/27ZOta7e09rp2bk8dfLs+eA3\nvI+stu+sNj0cIC9/fYNLClMTSbZvm87ObVPZsW1L/3s6O7dPZ8e26SXTO/uf5cu3Tk8+LU5gzC8s\n5KmT584HzMVgeWyoV/PoibN56uTZNZ8fOjkxkb27t+bzZ3Zl3+5t2bd7a/bu3pa9u7dm367+9+5t\nueWmG/Lwo0/l3Nx8zs4t5NzcQs6e66bPnltYOn9uPufOLWS2/93NX/q+rtyFdTx18uz5943zr8n0\n1GSeuX9H9x0+MPR9PrAzu7ZvGWPNAMD1blMH0tlz83nqZHfw+NSps0PT5y6af/LM3DWr18REsmVq\nMlNTk5memsh0/3v71i2ZmuznTU9menJx2YVyU5MT2bFja86cObfKytfY7hr1ufx3rf6+k6fP5dEj\np/LIE6fyucdOXLT8xhu2LQ2pB3bm5ht3Zf8N266LnrZzc/M5fOxM93P0dA71vxfnnTi9ymd7lU1N\nXtj3pqYms2Wq613btX0yU1MX73vL98OpLVM5euxMTp05l1Oz8zk9O5cnnjqTh2bnLjv8TE1OnA+s\nO7YPhdaLwutUdi6G3qFyO7ZNZ3pq9ZuGn5tbyLGTFw+ZPdr3ah47MZujJ8/m+Mmza9Z9emoy+3Zv\nzRfevGdJsDz/e1f3e/fOLZe0L+/euTX792y7zNa6fIPBoDsBMTe/JLBeFGSHwu7ZufnMnp3P40dP\n59EnTuXRI6fy0OGTF617z84tS77Li9Mz+3as+ZkAACTJxFpDL9fBYJxn7i1ej7VWsLwQPM9l9tz8\nyHXu3rElN+zamht2dr8XD2yHD7y3TE9eCIrLD8ynJlYIlxcO8qcnF8PlheXjDn3bKM8uXBgMcuTY\nmfPh9NEjF36ePD57Ufmt05N55lBQvflAd4D7zP07s2Pb1T13sp5tOL+wkCNPzV4Im8fO5PCx0zl8\n9EwOHTudYyfOrvi+6anJHNy7PQf3bs++PduydXryon1sy9RwWOx+L5238n66dLrbN8cN/6u14cJg\nkNmz8zl1Zi6nZ+dyqv85PTt3ft7p4Xmzczl9Zmm5s+cuf5jr1i2TS3pht0xP5ng/rHbUCadtW6ey\nb1fXi7lv99bs3bXYq7kYOLvXO7dNX9We3I3yXU66f3+PnTx7Ppye/3niVA4dO33RFQNTkxM5uG9H\nbl6hV3XPji1XrR1nZvZs/rNYALCJNQ+kCwuDHD99boVezAvBcnH6+KmzmZtfu76TExPZs2tL9u7c\n2gXNXVtzw/npLUte79nZ9UhuNBvpIHY1p2fn8tiTp5Ye3PbTZ+cuDiP7dm/tQ+quJT0xB67w2rZx\n2nBhMMixE2eXhMzDR/vQeexMjjw1u+Jw68mJidx4w7YudO7bkYN7t2dm744c3Lc9B/fuyN7dWzdU\nD/F67odz8wvng+vp2fnzvbCnZs/ldN8buzzcLg+98wuD7Nw2nX17Fnsu+8A5HDz7Xs2rfcLjUm2G\n73LS9UBf6Ek9ueR7vdLJgF3bp1fsVX3G/p3ZMn15/yYLpACwsV3TQPqOe/5g8PDjx3OsD5zHT57N\n8dPn1roXT5Jky/Tk+RC5tw+We4ZeD0/v3D69oQ7qr8RmOYhdycJgkCefml0SUh85cjKPHjmVI09d\n3Ks6PTXZXau65MC2C607t68eMtZqw8FgkBOnz+XwsTM5NDSUdrHH84ljZ1a9tnLf7q05OBQyZ4bC\n5403bNuQJ0BW83TeDweD7hrsp3t7P53b8GoYDLoTjiudeDp09PRF1+FOTCQH924//x0eDqur3ShN\nIAWAje2aBtJv+nvvPb+xHdumhnout64wfaE3c/vWqafFDU+eLjb7QexqZs/Od72qw0OA+98rDc/e\nu2vrRQe1Nx/YmYN7d2TP3h35o08dyhPHziy5hvNQ38s5e3bl4d67d2w538M50w+vXQycB/duz5bp\n6+eGTdfrfng1Xc9tODe/kMPHzgyF1Qs9q0+duvg66u1bp/LMG3d2J5+Gvtcv+u9u8Z8DAGxg1zSQ\nfvbRpwanT8zmhl1brqsD96vtej6IXclgMMjRE2fz6BMn88iRpcOAnzh25qKb1ExOTKx6B+NtW6f6\noNn1cs4M/T6wd3uzoZ1PR/bD8WnDlZ06c+6i7/KjR07lsSOnLxqdcM8bXymQAsAGdk2Prp990w0O\nvrjqJiYmsn/Ptuzfsy3P+8Iblyw7e24+jz15uu9N7Yb+Pv7k6ey9YXv2DvV2Hty7PTP7dmTX9qt7\n0xrg8u3cviVfcsvefMkte5fMX1gY5ImnziwZHQEAbGy6e9jUtm6ZyrOesTvPesbuJfP1TMHGMzk5\nkZl9OzKzb0ee/8UHWlcHALgKnt53/AAAAGDTEkgBAABoQiAFAACgCYEUAACAJgRSAAAAmhBIAQAA\naEIgBQAAoAmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACaEEgBAABoQiAFAACg\nCYEUAACAJgRSAAAAmhBIAQAAaEIgBQAAoAmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAm\nBFIAAACaEEgBAABoQiAFAACgCYEUAACAJgRSAAAAmhBIAQAAaEIgBQAAoInpUQVKKXcluTXJIMlr\na60PDC17ZZLXJZlN8su11reuV0UBAADYXNbsIS2lvDTJc2qttyW5M8lbhpZNJvnJJN+Y5CVJvrmU\n8nnrWFcAAAA2kVFDdm9PcneS1FofTLK/lLK7X3YwydFa6xO11kGSDyb5uvWqKAAAAJvLqEB6U5LD\nQ68PJbl5aHpPKeU5pZQtSb46yTOvfhUBAADYjEZeQ7rMRLprSVNrHZRS7kzyc0keT/Jov3xNMzN7\nLnOTrEQ7jk8bjk8bjk8bAgDXs1GB9OF0vaSLbknyyOKLWusHknwgSUopb0vy30Zt8NCh45dfS5aY\nmdmjHcekDcenDcenDccn0APAxjZqyO59Se5IklLKC5M8VGs9ubiwlHJvKeVAKWV/kq9Ncv+61RQA\nAIBNZc0e0lrrR0spHy+lfCTJfJLXlFJeleRYrfU9Sd6WLrROJ3ldrfXIutcYAACATWFiMBhcy+0N\nDE8bn2F+49OG49OG49OG45uZ2TPy3gUAwNPXqCG7AAAAsC4EUgAAAJoQSAEAAGhCIAUAAKAJgRQA\nAIAmBFIAAACaEEgBAABoQiAFAACgCYEUAACAJgRSAAAAmhBIAQAAaEIgBQAAoAmBFAAAgCYEUgAA\nAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACaEEgBAABoQiAFAACgCYEUAACAJgRSAAAAmhBIAQAA\naEIgBQAAoAmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACaEEgBAABoQiAFAACg\nCYEUAACAJgRSAAAAmhBIAQAAaEIgBQAAoAmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAm\nBFIAAACamB5VoJRyV5JbkwySvLbW+sDQstck+fYk80keqLX+nfWqKAAAAJvLmj2kpZSXJnlOrfW2\nJHcmecvQsr1J/s8kL661fnWSv1RKuXU9KwsAAMDmMWrI7u1J7k6SWuuDSfaXUnb3y2b7nz2llOkk\nO5M8sV4VBQAAYHMZFUhvSnJ46PWhJDcnSa31TJJ/mORPk3wmyW/VWj919asIAADAZjTyGtJlJtJd\nS5pSyg1JXp/kLyY5nuTXSynPr7V+cq0VzMzsuZJ6sox2HJ82HJ82HJ82BACuZ6MC6cPpekkX3ZLk\nkX76eUk+XWs9kiSllN9K8mVJ1gykhw4dv7Kact7MzB7tOCZtOD5tOD5tOD6BHgA2tlFDdu9LckeS\nlFJemOShWuvJftlnkjyvlLK9f/1lSf5kPSoJAADA5rNmD2mt9aOllI+XUj6S7tEurymlvCrJsVrr\ne0opP5HkN0opc0k+Umv9rWtQZwAAADaBicFgcC23NzA8bXyG+Y1PG45PG45PG45vZmbPROs6AABX\nbtSQXQAAAFgXAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAA\nQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIptrOC\n0AAACmNJREFUAAAATQikAAAANDExGAyu2cYe+N5XD+bnF67Z9jarqanJaMfxaMPxacPxacPx3fr2\nfzXRug4AwJXTQwoAAEAT17SHNMng0KHj13J7m9LMzJ5ox/Fow/Fpw/Fpw/HNzOzRQwoAG5geUgAA\nAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACaEEgBAABoQiAFAACgCYEUAACAJgRSAAAAmhBIAQAA\naEIgBQAAoAmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACaEEgBAABoQiAFAACg\nielRBUopdyW5NckgyWtrrQ/0829J8otDRb84yf9Va/3l9agoAAAAm8uagbSU8tIkz6m13lZKeW6S\ntye5LUlqrQ8n+Zq+3FSSDyb51XWtLQAAAJvGqCG7tye5O0lqrQ8m2V9K2b1Cue9O8q5a66mrXD8A\nAAA2qVGB9KYkh4deH0py8wrl7kzys1erUgAAAGx+I68hXWYi3bWk55VSvjLJg7XWE5eygpmZPZe5\nSVaiHcenDcenDcenDQGA69moQPpwul7SRbckeWRZmVck+U+XusFDh45falFWMTOzRzuOSRuOTxuO\nTxuOT6AHgI1t1JDd+5LckSSllBcmeajWenJZmS9L8l/XoW4AAABsYmsG0lrrR5N8vJTykSRvTvKa\nUsqrSil/dajYzUkeX8c6AgAAsAmNvIa01vrDy2Z9ctnyF1zVGgEAAHBdGDVkFwAAANaFQAoAAEAT\nAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE0I\npAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQ\nAgAA0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAK\nAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikA\nAABNCKQAAAA0IZACAADQhEAKAABAE9OjCpRS7kpya5JBktfWWh8YWvasJP82yZYkv1tr/YH1qigA\nAACby5o9pKWUlyZ5Tq31tiR3JnnLsiJvTPITtdZbk8z3ARUAAABGGjVk9/YkdydJrfXBJPtLKbuT\npJQymeTFSe7pl//tWuufrWNdAQAA2ERGBdKbkhween0oyc399EyS40nuKqV8uJTy4+tQPwAAADap\nkdeQLjOR7lrSxenPS/LmJJ9N8r5SystrrfeutYKZmT2XXUkuph3Hpw3Hpw3Hpw0BgOvZqED6cLpe\n0kW3JHmknz6c5LO11v+WJKWUX0/yl5OsGUgPHTp+ZTXlvJmZPdpxTNpwfNpwfNpwfAI9AGxso4bs\n3pfkjiQppbwwyUO11pNJUmudS/LpUspz+rIvSvLgelUUAACAzWXNHtJa60dLKR8vpXwkyXyS15RS\nXpXkWK31PUn+jyQ/19/g6BO11nvWv8oAAABsBiOvIa21/vCyWZ8cWvanSb76alcKAACAzW/UkF0A\nAABYFwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikA\nAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE0IpAAA\nADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABAEwIpAAAATQikAAAANCGQAgAA\n0IRACgAAQBMCKQAAAE0IpAAAADQhkAIAANCEQAoAAEATAikAAABNCKQAAAA0IZACAADQhEAKAABA\nEwIpAAAATQikAAAANCGQAgAA0IRACgAAQBMCKQAAAE1MjypQSrkrya1JBkleW2t9YGjZZ5J8Lsl8\nP+vba60PX/1qAgAAsNmsGUhLKS9N8pxa622llOcmeXuS24aKDJK8rNZ6ah3rCAAAwCY0asju7Unu\nTpJa64NJ9pdSdi8rM7EeFQMAAGBzGxVIb0pyeOj1oSQ3Lyvz06WUD5dS/slVrRkAAACb2shrSJeZ\nSDdMd9GPJPmPSZ5M8p5SyrfWWt+91vtnZvZc5iZZiXYcnzYcnzYcnzYEAK5nowLpw+l6SRfdkuSR\nxRe11n+zOF1KuTfJ85OsFUgBAAAgyeghu/cluSNJSikvTPJQrfVk/3pvKeVDpZQdfdmXJPnkutUU\nAACATWViMBisWaC/NvQl6R7t8pokL0xyrNb6nlLKDyb57iQnkvxerfUH17m+AAAAbBIjAykAAACs\nh1FDdgEAAGBdCKQAAAA0IZACAADQxOU+h/SKlVLuSnJruueYvrbW+sC12vZmUUr5Z0lenO5z+ye1\n1rsbV2lD6u8M/ftJ/nGt9edb12cjKqV8e5K/n2QuyY/WWu9tXKUNpZSyO8m/TrIvybYk/6jWel/b\nWm0MpZQXJLk7yZtqrW8tpTwryS+kO8H6SJK/WWs927KOAMCluyY9pKWUlyZ5Tq31tiR3JnnLtdju\nZlJK+Zokf7lvw5cleXPjKm1kr0/yRLqTI1ymUsqBJD+a5KuSvCLJK9vWaEP6riQP1lpvT/dorX/R\ntjobQyllZ5I3Jnl/Lnx//3GSn6y1viTJp5J8T6PqAQBX4FoN2b093Rnt1FofTLK/7yHg0v1mkr/R\nTx9LsquUMtGwPhtSKeW5SZ6b5H1JtN+V+bok99daT9ZaH621fn/rCm1AjyU50E/fmORQw7psJLPp\nToI8NjTvpUl+tZ++J93+CQBsENcqkN6U5PDQ60NJbr5G294Uaq3ztdaT/cs7k7yv1qqH7/L9RJK/\n07oSG9yzk+wspby3lPKbpZTbW1doo6m1vjPJs0opf5Lkg0n+btsabQz9v4Ozy2bvqrWe66f93wIA\nG0yrmxpNxHDJK1JKeWW6IWl/u3VdNppSyncm+c1a6+eid3Qck+l69f5auqGn72hamw2olPIdST5X\na/0LSb42yVsbV2mz8L0GgA3mWgXSh9P1ki66Jd3NJ7gMpZRvSPLDSV5Waz3euj4b0MuT/PVSykfT\n9TL/iN69K/Joko/WWhdqrZ9OcryUcrB1pTaY25LclyS11k8k+XxD8K/YiVLKtn7689L9fwMAbBDX\nKpDel+7GHSmlvDDJQ0PDT7kEpZS96YabvqLWerR1fTaiWuu31Vr/x1rrVyb5f9PdZfcDreu1Ad2X\n5PZSykR/g6PdtdbDo97EEp9Kd9fxlFKeneSkIfiXZSIXekPvT///S5JvTfIfmtQIALgi1+SxL7XW\nj5ZSPl5K+UiS+SSvuRbb3WT+53Q3QXlnKWVx3nfWWv+sXZW4HtVaHy6lvCvJ7/SzDB+/fD+T5O2l\nlA+m+3f4+9pWZ2MopXxFkrcleUaSuVLK96e76/jP9dOfSeJRTgCwgUwMBk7KAwAAcO21uqkRAAAA\n1zmBFAAAgCYEUgAAAJoQSAEAAGhCIAUAAKAJgRQAAIAmBFIAAACa+P8BROTDnQlDPS0AAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5b66a1710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"partitions_used = partitions_my_10\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='prot' # prot or full\n",
"nprots=nsamples # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# cluster with my K-Means\n",
"kmeans_mode = \"numpy\"\n",
"\n",
"grouper = K_Means3.K_Means(n_clusters=true_nclusters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
"grouper._centroid_mode = \"iter\"\n",
"grouper.fit(myEstimator._coassoc)\n",
"\n",
"# cluster with SKL K-Means\n",
"gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init=\"random\")\n",
"gSKL.fit(myEstimator._coassoc)\n",
"\n",
"# Hungarian accuracy\n",
"myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"myAcc.score(gt,grouper.labels_,format='array')\n",
"\n",
"sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"sklAcc.score(gt,gSKL.labels_,format='array')\n",
"\n",
"\n",
"print 'My Accuracy:\\t',myAcc.accuracy\n",
"print 'SKL Accuracy:\\t',sklAcc.accuracy\n",
"\n",
"figEAC2=plt.figure(figsize=(16,12))\n",
"ax1EAC2=figEAC2.add_subplot(2,2,1)\n",
"ax2EAC2=figEAC2.add_subplot(2,2,2)\n",
"ax3EAC2=figEAC2.add_subplot(2,2,3)\n",
"\n",
"for c in np.unique(grouper.labels_):\n",
" clusterData=grouper.labels_==c\n",
" ax1EAC2.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax1EAC2.set_title(\"Final EAC partition with my K-Means\")\n",
"\n",
"for c in np.unique(gSKL.labels_):\n",
" clusterData=gSKL.labels_==c\n",
" ax2EAC2.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax2EAC2.set_title(\"Final EAC partition with SKL's K-Means\")\n",
"\n",
"nprots=[5,20,40,60,80,100,120,140,160,180,200]\n",
"results_k6=list()\n",
"for n in nprots:\n",
" r=k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100)\n",
" results_k6.append(r)\n",
" \n",
"mean_k6=[res[0] for res in results_k6]\n",
"var_k6=[res[1] for res in results_k6]\n",
"best_k6=[res[2] for res in results_k6]\n",
"worst_k6=[res[3] for res in results_k6]\n",
"\n",
"ax3EAC2.plot(mean_k6)\n",
"ax3EAC2.plot(best_k6)\n",
"ax3EAC2.plot(worst_k6)\n",
"ax3EAC2.plot([0, 10], [0.5, 0.5], 'k-', lw=1)\n",
"ax3EAC2.set_title(\"Analysis of the influence of the number of prototypes (SKL)\")\n",
"\n",
"print \"\\nStatistical analysis\"\n",
"stat_nprots=nsamples\n",
"print \"{}\\t{}\\t{}\\t{}\\t{}\".format(\"type\",\"mean\",\"var\",\"max\",\"min\")\n",
"print \"skl \\t\",\n",
"for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):\n",
" print \"{}\\t\".format(metric),\n",
"print \"\\nmy \\t\",\n",
"for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters=\"converge\",rounds=100):\n",
" print \"{}\\t\".format(metric),"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random number of clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"My Accuracy:\t0.78\n",
"SKL Accuracy:\t1.0\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-c7997f7bef87>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[0mresults_k6\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnprots\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk_skl_analysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartitions_used\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mground_truth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnprots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstat_nprots\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 55\u001b[0m \u001b[0mresults_k6\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-7-935179233b8c>\u001b[0m in \u001b[0;36mk_skl_analysis\u001b[1;34m(partition_files, ground_truth, nprots, rounds, files)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meac\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEAC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnsamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartition_files\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0massoc_mode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'prot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprot_mode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprot_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnprot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnprots\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbuild_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mkmeans_mode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"numpy\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/eac.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, ensemble, files, assoc_mode, prot_mode, nprot, link, build_only)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpartition\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mensemble\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_coassoc_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# update co-association matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_create_coassoc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnsamples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnprot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/eac.pyc\u001b[0m in \u001b[0;36m_update_coassoc_matrix\u001b[1;34m(self, clusters)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;31m# reduced matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_assoc_mode\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m\"other\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_coassoc_k\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_coassoc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mclusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 214\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_assoc_mode\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m\"knn\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_coassoc_knn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_coassoc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mclusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_neighbours\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/diogoaos/QCThesis/MyML/cluster/eac.pyc\u001b[0m in \u001b[0;36m_update_coassoc_k\u001b[1;34m(self, assoc_mat, clusters, k_labels)\u001b[0m\n\u001b[0;32m 260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 261\u001b[0m \u001b[1;31m# this indexing selects the rows and columns specified by n_in_cluster and k_in_cluster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m \u001b[0massoc_mat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn_in_cluster\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk_in_cluster\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;31m# np.newaxis is alias for None\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
},
{
"data": {
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l87XuTwTTSBO1TqWUu6cZTntamn8QA+2Tzcwi2QssGvq2L715X/1bJCRJeuPI71drfWEp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\nAQAAGEKQAgAAMIQgBQAAYIj/A804rt9tVWd3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5b6612910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"partitions_used = partitions_my_rand\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='prot' # prot or full\n",
"nprots=nsamples # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# cluster with my K-Means\n",
"kmeans_mode = \"numpy\"\n",
"\n",
"grouper = K_Means3.K_Means(n_clusters=true_nclusters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
"grouper._centroid_mode = \"iter\"\n",
"grouper.fit(myEstimator._coassoc)\n",
"\n",
"# cluster with SKL K-Means\n",
"gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init=\"random\")\n",
"gSKL.fit(myEstimator._coassoc)\n",
"\n",
"# Hungarian accuracy\n",
"myAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"myAcc.score(gt,grouper.labels_,format='array')\n",
"\n",
"sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"sklAcc.score(gt,gSKL.labels_,format='array')\n",
"\n",
"\n",
"print 'My Accuracy:\\t',myAcc.accuracy\n",
"print 'SKL Accuracy:\\t',sklAcc.accuracy\n",
"\n",
"figEAC2=plt.figure(figsize=(16,12))\n",
"ax1EAC2=figEAC2.add_subplot(2,2,1)\n",
"ax2EAC2=figEAC2.add_subplot(2,2,2)\n",
"ax3EAC2=figEAC2.add_subplot(2,2,3)\n",
"\n",
"for c in np.unique(grouper.labels_):\n",
" clusterData=grouper.labels_==c\n",
" ax1EAC2.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax1EAC2.set_title(\"Final EAC partition with my K-Means\")\n",
"\n",
"for c in np.unique(gSKL.labels_):\n",
" clusterData=gSKL.labels_==c\n",
" ax2EAC2.plot(data[clusterData,0],data[clusterData,1],'.')\n",
"ax2EAC2.set_title(\"Final EAC partition with SKL's K-Means\")\n",
"\n",
"nprots=[5,20,40,60,80,100,120,140,160,180,200]\n",
"results_k6=list()\n",
"for n in nprots:\n",
" r=k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100)\n",
" results_k6.append(r)\n",
" \n",
"mean_k6=[res[0] for res in results_k6]\n",
"var_k6=[res[1] for res in results_k6]\n",
"best_k6=[res[2] for res in results_k6]\n",
"worst_k6=[res[3] for res in results_k6]\n",
"\n",
"ax3EAC2.plot(mean_k6)\n",
"ax3EAC2.plot(best_k6)\n",
"ax3EAC2.plot(worst_k6)\n",
"ax3EAC2.plot([0, 10], [0.5, 0.5], 'k-', lw=1)\n",
"ax3EAC2.set_title(\"Analysis of the influence of the number of prototypes (SKL)\")\n",
"\n",
"print \"\\nStatistical analysis\"\n",
"stat_nprots=nsamples\n",
"print \"{}\\t{}\\t{}\\t{}\\t{}\".format(\"type\",\"mean\",\"var\",\"max\",\"min\")\n",
"print \"skl \\t\",\n",
"for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):\n",
" print \"{}\\t\".format(metric),\n",
"print \"\\nmy \\t\",\n",
"for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters=\"converge\",rounds=100):\n",
" print \"{}\\t\".format(metric),"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.pcolor(myEstimator._coassoc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Means only"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stat_nprots=nsamples\n",
"print \"{}\\t{}\\t{}\\t{}\\t{}\".format(\"type\",\"mean\",\"var\",\"max\",\"min\")\n",
"print \"my \\t\",\n",
"for metric in stat_my_kmeans(data,true_nclusters,gt,rounds=100):\n",
" print \"{}\\t\".format(metric),\n",
"print \"\\nskl \\t\",\n",
"for metric in stat_skl_kmeans(data,true_nclusters,gt,rounds=100):\n",
" print \"{}\\t\".format(metric),"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EAC K-Medoids"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import MyML.cluster.KMedoids as KMedoids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#%%debug\n",
"partitions_used = partitions_my_6\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='full' # prot or full\n",
"nprots=50 # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# compute diassociation from co-assoc\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"\n",
"#k-medoids\n",
"labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)\n",
"\n",
"# Hungarian accuracy\n",
"acc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"acc.score(gt,labels,format='array')\n",
"\n",
"print 'K-Medoids Accuracy:\\t',acc.accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistical analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class acc_medoids():\n",
" def __init__(self,data,nclusters,gt):\n",
" self.data=data\n",
" self.nsamples=data.shape[0]\n",
" self.nclusters=nclusters\n",
" self.gt=gt\n",
" \n",
" def run(self):\n",
" labels,medoids=KMedoids.cluster(self.data,k=self.nclusters)\n",
" # Hungarian accuracy\n",
" acc = determine_ci.HungarianIndex(nsamples=self.nsamples)\n",
" acc.score(self.gt,labels,format='array')\n",
" return acc.accuracy\n",
" \n",
"class acc_my_kmeans():\n",
" def __init__(self,data,nclusters,gt):\n",
" self.data=data\n",
" self.nclusters=nclusters\n",
" self.nsamples=data.shape[0]\n",
" self.gt=gt\n",
" def run(self):\n",
" # cluster with SKL K-Means\n",
" grouper = K_Means3.K_Means(n_clusters=true_nclusters,mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=iters)\n",
" grouper._centroid_mode = \"iter\"\n",
" grouper.fit(self.data)\n",
"\n",
" # Hungarian accuracy\n",
" sklAcc = determine_ci.HungarianIndex(nsamples=self.nsamples)\n",
" sklAcc.score(self.gt,grouper.labels_,format='array')\n",
" \n",
" return sklAcc.accuracy \n",
"\n",
"class acc_skl_kmeans():\n",
" def __init__(self,data,nclusters,gt):\n",
" self.data=data\n",
" self.nclusters=nclusters\n",
" self.nsamples=data.shape[0]\n",
" self.gt=gt\n",
" def run(self):\n",
" # cluster with SKL K-Means\n",
" gSKL = KMeans_skl(n_clusters=self.nclusters,n_init=1,init=\"random\")\n",
" gSKL.fit(self.data)\n",
"\n",
" # Hungarian accuracy\n",
" sklAcc = determine_ci.HungarianIndex(nsamples=self.nsamples)\n",
" sklAcc.score(self.gt,gSKL.labels_,format='array')\n",
" \n",
" return sklAcc.accuracy\n",
"\n",
"def stat_analysis(method,rounds=20):\n",
" rAll = np.zeros(rounds)\n",
" for r in xrange(rounds):\n",
" rAll[r]=method.run()\n",
" return rAll.mean(),rAll.var(),rAll.max(),rAll.min()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rounds=100\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmedoids\\t',stat_analysis(x,rounds=rounds)\n",
"\n",
"x2=acc_my_kmeans(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmeans \\t',stat_analysis(x2,rounds=rounds)\n",
"\n",
"x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmedoids \\t',stat_analysis(x3,rounds=rounds)\n",
"\n",
"x4=acc_my_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmeans \\t',stat_analysis(x4,rounds=rounds)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## 10 clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#%%debug\n",
"partitions_used = partitions_my_10\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='full' # prot or full\n",
"nprots=50 # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# compute diassociation from co-assoc\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"\n",
"#k-medoids\n",
"labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)\n",
"\n",
"# Hungarian accuracy\n",
"acc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"acc.score(gt,labels,format='array')\n",
"\n",
"print 'K-Medoids Accuracy:\\t',acc.accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistical analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rounds=20\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmedoids\\t',stat_analysis(x,rounds=rounds)\n",
"\n",
"x2=acc_skl_kmeans(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmeans \\t',stat_analysis(x2,rounds=rounds)\n",
"\n",
"x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmedoids \\t',stat_analysis(x3,rounds=rounds)\n",
"\n",
"x4=acc_skl_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmeans \\t',stat_analysis(x4,rounds=rounds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random clusters per partition"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#%%debug\n",
"npartitions=30\n",
"nclusters=[4,25]\n",
"iters=3\n",
"partitions_used = partitions_my_rand\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='full' # prot or full\n",
"nprots=50 # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"# compute diassociation from co-assoc\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"\n",
"#k-medoids\n",
"labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)\n",
"\n",
"# Hungarian accuracy\n",
"acc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"acc.score(gt,labels,format='array')\n",
"\n",
"print 'K-Medoids Accuracy:\\t',acc.accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistical analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rounds=20\n",
"diassoc=myEstimator._coassoc.max()-myEstimator._coassoc\n",
"x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmedoids\\t',stat_analysis(x,rounds=rounds)\n",
"\n",
"x2=acc_skl_kmeans(diassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'diassoc kmeans \\t',stat_analysis(x2,rounds=rounds)\n",
"\n",
"x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmedoids \\t',stat_analysis(x3,rounds=rounds)\n",
"\n",
"x4=acc_skl_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)\n",
"print 'assoc kmeans \\t',stat_analysis(x4,rounds=rounds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## K-Medoids only"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics.pairwise import pairwise_distances"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pairwise=pairwise_distances(data)\n",
"y=acc_medoids(pairwise,2,gt=gt)\n",
"stat_analysis(y,rounds=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EAC Single link"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"partitions_used = partitions_my_rand\n",
"\n",
"# generate coassoc\n",
"prot_mode=\"random\"\n",
"assoc_mode='full' # prot or full\n",
"nprots=nsamples # number of prototypes\n",
"\n",
"myEstimator=eac.EAC(nsamples)\n",
"myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)\n",
"\n",
"# final clustering with the true number of clusters\n",
"true_nclusters = np.unique(gt).shape[0]\n",
"\n",
"#k-medoids\n",
"myEstimator._apply_linkage()\n",
"labels = myEstimator._clusterFromLinkage()\n",
"\n",
"# Hungarian accuracy\n",
"acc = determine_ci.HungarianIndex(nsamples=nsamples)\n",
"acc.score(gt,labels,format='array')\n",
"\n",
"print 'EAC SL Accuracy:\\t',acc.accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single-Link only"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.cluster import hierarchy as hie\n",
"from scipy.spatial.distance import squareform"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# pairwise distances\n",
"dists = np.zeros((nsamples,nsamples))\n",
"for i,dp in enumerate(data):\n",
" dist = (data - dp)**2\n",
" dist = np.sqrt(dist.sum(axis=1))\n",
" dists[i]=dist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#pairwise=pairwise_distances(data)\n",
"condensed_dists = squareform(dists)\n",
"Z = hie.linkage(condensed_dists,method='single')\n",
"parents=Z[-1,:2]\n",
"labels=myEstimator._buildLabels(Z=Z,parents=parents)\n",
"\n",
"acc.score(gt,labels,format='array')\n",
"print \"Single-Link accuracy:\\t\",acc.accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#generated from: http://tools.medialab.sciences-po.fr/iwanthue/\n",
"my_colors=[\"#D37E30\",\n",
"\"#6F6FD8\",\n",
"\"#3AA579\",\n",
"\"#D5337B\",\n",
"\"#4595B8\",\n",
"\"#3EA729\",\n",
"\"#D150D7\",\n",
"\"#4E6E23\",\n",
"\"#8F4D79\",\n",
"\"#D64430\",\n",
"\"#A1952B\",\n",
"\"#C15257\",\n",
"\"#AA5BB3\",\n",
"\"#6A76B0\",\n",
"\"#8E5723\",\n",
"\"#2A7464\",\n",
"\"#D66C9F\",\n",
"\"#60994E\",\n",
"\"#73A32D\",\n",
"\"#33A74F\"]\n",
"my_pallete=sns.color_palette(my_colors,len(my_colors))\n",
"sns.palplot(my_pallete)\n",
"sns.set_palette(my_pallete,len(my_colors))\n",
"#marker_types=['.','^','*','h','x']\n",
"marker_types=matplotlib.markers.MarkerStyle.filled_markers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.set_style(\"whitegrid\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"figX=sns.plt.figure(figsize=(12,90))\n",
"for i,p in enumerate(partitions_my_rand):\n",
" ax=figX.add_subplot(15,2,i+1)\n",
" for j,c in enumerate(p):\n",
" ax.plot(data[c,0],data[c,1],ls=u'None',marker=marker_types[j/6],markersize=8)\n",
" #ax.scatter(data[c,0],data[c,1],marker=marker_types[j/6],linewidths=5)\n",
" ax.set_title(\"partition {}, {} clusters\".format(i+1,j+1))"
]
},
{
"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
}
| mit |
zzsza/Kaggle_Expedia-hotel-recommendations | notebook/02. EDA(2013 data).ipynb | 1 | 83199 | {
"cells": [
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from __future__ import print_function\n",
"import sklearn\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn import preprocessing\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"pd.set_option(\"max_columns\",50)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 39 s\n"
]
}
],
"source": [
"%%time\n",
"df = pd.read_csv(\"../data/train_2013.csv\", index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"date_time\"] = pd.to_datetime(df[\"date_time\"], errors=\"coerce\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# %%time\n",
"# skip_col = [\"date_time\",\"orig_destination_distance\"]\n",
"# for col in df_1.columns:\n",
"# if col == skip_col:\n",
"# pass\n",
"# print(col, np.unique(df_1[col].astype(str)))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check in / check out / distance => nan값 존재"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1.87 s\n"
]
}
],
"source": [
"%%time\n",
"df = df.reset_index(drop=True)\n",
"# 10000명의 데이터만 사용\n",
"df = df.ix[:9999]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.to_csv(\"train_2013_10000.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'date_time', u'site_name', u'posa_continent', u'user_location_country',\n",
" u'user_location_region', u'user_location_city',\n",
" u'orig_destination_distance', u'user_id', u'is_mobile', u'is_package',\n",
" u'channel', u'srch_ci', u'srch_co', u'srch_adults_cnt',\n",
" u'srch_children_cnt', u'srch_rm_cnt', u'srch_destination_id',\n",
" u'srch_destination_type_id', u'is_booking', u'cnt', u'hotel_continent',\n",
" u'hotel_country', u'hotel_market', u'hotel_cluster'],\n",
" dtype='object')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols = df.columns.tolist()[-6:] + df.columns.tolist()[:-6]\n",
"df = df[cols]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols = df.columns.tolist()[:1] + df.columns.tolist()[6:] + df.columns.tolist()[1:6] \n",
"df = df[cols]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# 제거할 feature 생각해보기"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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>is_booking</th>\n",
" <th>date_time</th>\n",
" <th>site_name</th>\n",
" <th>posa_continent</th>\n",
" <th>user_location_country</th>\n",
" <th>user_location_region</th>\n",
" <th>user_location_city</th>\n",
" <th>orig_destination_distance</th>\n",
" <th>user_id</th>\n",
" <th>is_mobile</th>\n",
" <th>is_package</th>\n",
" <th>channel</th>\n",
" <th>srch_ci</th>\n",
" <th>srch_co</th>\n",
" <th>srch_adults_cnt</th>\n",
" <th>srch_children_cnt</th>\n",
" <th>srch_rm_cnt</th>\n",
" <th>srch_destination_id</th>\n",
" <th>srch_destination_type_id</th>\n",
" <th>cnt</th>\n",
" <th>hotel_continent</th>\n",
" <th>hotel_country</th>\n",
" <th>hotel_market</th>\n",
" <th>hotel_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2013-06-15 15:10:49</td>\n",
" <td>30</td>\n",
" <td>4</td>\n",
" <td>195</td>\n",
" <td>548</td>\n",
" <td>56440</td>\n",
" <td>NaN</td>\n",
" <td>1048</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2013-09-07</td>\n",
" <td>2013-09-15</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1385</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>185</td>\n",
" <td>185</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2013-06-15 15:38:05</td>\n",
" <td>30</td>\n",
" <td>4</td>\n",
" <td>195</td>\n",
" <td>548</td>\n",
" <td>56440</td>\n",
" <td>NaN</td>\n",
" <td>1048</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2013-09-06</td>\n",
" <td>2013-09-14</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1385</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>185</td>\n",
" <td>185</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2013-02-15 13:18:43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>66</td>\n",
" <td>462</td>\n",
" <td>41898</td>\n",
" <td>2716.6746</td>\n",
" <td>1482</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2013-02-24</td>\n",
" <td>2013-03-01</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" <td>214</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>2013-02-16 11:57:50</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>66</td>\n",
" <td>462</td>\n",
" <td>41898</td>\n",
" <td>2716.5257</td>\n",
" <td>1482</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2013-02-24</td>\n",
" <td>2013-03-01</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" <td>214</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>2013-02-16 12:03:45</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>66</td>\n",
" <td>462</td>\n",
" <td>41898</td>\n",
" <td>2722.4856</td>\n",
" <td>1482</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2013-02-24</td>\n",
" <td>2013-03-01</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" <td>214</td>\n",
" <td>26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" is_booking date_time site_name posa_continent \\\n",
"0 0 2013-06-15 15:10:49 30 4 \n",
"1 1 2013-06-15 15:38:05 30 4 \n",
"2 0 2013-02-15 13:18:43 2 3 \n",
"3 0 2013-02-16 11:57:50 2 3 \n",
"4 0 2013-02-16 12:03:45 2 3 \n",
"\n",
" user_location_country user_location_region user_location_city \\\n",
"0 195 548 56440 \n",
"1 195 548 56440 \n",
"2 66 462 41898 \n",
"3 66 462 41898 \n",
"4 66 462 41898 \n",
"\n",
" orig_destination_distance user_id is_mobile is_package channel \\\n",
"0 NaN 1048 0 1 9 \n",
"1 NaN 1048 0 1 9 \n",
"2 2716.6746 1482 0 0 1 \n",
"3 2716.5257 1482 0 0 0 \n",
"4 2722.4856 1482 0 0 0 \n",
"\n",
" srch_ci srch_co srch_adults_cnt srch_children_cnt srch_rm_cnt \\\n",
"0 2013-09-07 2013-09-15 2 0 1 \n",
"1 2013-09-06 2013-09-14 2 0 1 \n",
"2 2013-02-24 2013-03-01 2 0 1 \n",
"3 2013-02-24 2013-03-01 2 0 1 \n",
"4 2013-02-24 2013-03-01 2 0 1 \n",
"\n",
" srch_destination_id srch_destination_type_id cnt hotel_continent \\\n",
"0 1385 1 1 0 \n",
"1 1385 1 1 0 \n",
"2 8857 1 1 2 \n",
"3 8857 1 1 2 \n",
"4 8857 1 1 2 \n",
"\n",
" hotel_country hotel_market hotel_cluster \n",
"0 185 185 58 \n",
"1 185 185 58 \n",
"2 50 214 28 \n",
"3 50 214 73 \n",
"4 50 214 26 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'is_booking', u'date_time', u'site_name', u'posa_continent',\n",
" u'user_location_country', u'user_location_region',\n",
" u'user_location_city', u'orig_destination_distance', u'user_id',\n",
" u'is_mobile', u'is_package', u'channel', u'srch_ci', u'srch_co',\n",
" u'srch_adults_cnt', u'srch_children_cnt', u'srch_rm_cnt',\n",
" u'srch_destination_id', u'srch_destination_type_id', u'cnt',\n",
" u'hotel_continent', u'hotel_country', u'hotel_market',\n",
" u'hotel_cluster'],\n",
" dtype='object')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns\n",
"#"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"delete_list = [\"user_location_city\", \"user_location_region\",\"is_mobile\",\"is_package\",\"hotel_country\",\"hotel_market\"]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = df.drop(delete_list, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u'is_booking', u'date_time', u'site_name', u'posa_continent',\n",
" u'user_location_country', u'orig_destination_distance', u'user_id',\n",
" u'channel', u'srch_ci', u'srch_co', u'srch_adults_cnt',\n",
" u'srch_children_cnt', u'srch_rm_cnt', u'srch_destination_id',\n",
" u'srch_destination_type_id', u'cnt', u'hotel_continent',\n",
" u'hotel_cluster'],\n",
" dtype='object') 18\n"
]
}
],
"source": [
"print(df.columns, len(df.columns))"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df.drop([\"posa_continent\",\"orig_destination_distance\", \"srch_destination_type_id\"], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 8973\n",
"1 1027\n",
"Name: is_booking, dtype: int64\n",
"2 6498\n",
"37 1609\n",
"11 495\n",
"34 429\n",
"24 293\n",
"13 190\n",
"17 132\n",
"8 60\n",
"9 53\n",
"32 46\n",
"25 42\n",
"22 42\n",
"40 34\n",
"33 25\n",
"30 17\n",
"35 10\n",
"28 8\n",
"26 7\n",
"18 4\n",
"10 3\n",
"19 3\n",
"Name: site_name, dtype: int64\n",
"3 7459\n",
"1 2038\n",
"2 416\n",
"4 77\n",
"0 10\n",
"Name: posa_continent, dtype: int64\n",
"66 5960\n",
"69 1429\n",
"205 860\n",
"3 237\n",
"46 227\n",
"133 184\n",
"68 116\n",
"225 93\n",
"80 72\n",
"77 63\n",
"70 62\n",
"32 53\n",
"28 46\n",
"62 45\n",
"215 44\n",
"23 39\n",
"231 38\n",
"0 37\n",
"235 35\n",
"154 34\n",
"239 33\n",
"198 31\n",
"85 23\n",
"93 22\n",
"195 19\n",
"29 19\n",
"158 18\n",
"194 17\n",
"182 17\n",
"142 14\n",
"48 13\n",
"148 12\n",
"168 9\n",
"115 7\n",
"27 6\n",
"12 6\n",
"162 6\n",
"51 5\n",
"103 5\n",
"208 5\n",
"1 5\n",
"39 5\n",
"82 5\n",
"119 4\n",
"54 3\n",
"64 3\n",
"157 3\n",
"202 3\n",
"5 2\n",
"57 2\n",
"190 2\n",
"166 2\n",
"Name: user_location_country, dtype: int64\n",
"5615.1972 25\n",
"202.0282 18\n",
"5797.7663 13\n",
"99.3290 13\n",
"96.6554 12\n",
"63.1618 10\n",
"87.9549 10\n",
"1590.7968 9\n",
"1868.9003 9\n",
"444.0290 9\n",
"2381.9139 9\n",
"295.6522 9\n",
"394.6765 8\n",
"318.2518 8\n",
"1235.6843 8\n",
"188.5710 8\n",
"51.6725 7\n",
"4527.2234 7\n",
"192.3717 7\n",
"6595.6508 6\n",
"79.0066 6\n",
"1917.9193 6\n",
"4527.4575 6\n",
"4491.8270 6\n",
"16.9159 6\n",
"1657.3570 6\n",
"1036.2993 6\n",
"142.9752 6\n",
"5095.1241 6\n",
"1622.4278 6\n",
" ..\n",
"2528.9976 1\n",
"2226.1895 1\n",
"62.0251 1\n",
"139.6310 1\n",
"267.9601 1\n",
"2298.5065 1\n",
"1265.1169 1\n",
"2407.0524 1\n",
"734.3049 1\n",
"229.9292 1\n",
"1578.9702 1\n",
"218.9363 1\n",
"34.6006 1\n",
"407.4950 1\n",
"3351.0581 1\n",
"625.4276 1\n",
"1236.5718 1\n",
"1778.1646 1\n",
"4314.3084 1\n",
"1975.4526 1\n",
"192.7684 1\n",
"191.8491 1\n",
"258.0471 1\n",
"27.6370 1\n",
"157.6479 1\n",
"5809.3784 1\n",
"8407.6806 1\n",
"16.7685 1\n",
"770.8005 1\n",
"369.4375 1\n",
"Name: orig_destination_distance, dtype: int64\n",
"70535 345\n",
"33803 228\n",
"94390 212\n",
"71855 166\n",
"69003 156\n",
"121433 154\n",
"50191 152\n",
"123225 142\n",
"122669 115\n",
"81357 110\n",
"85275 98\n",
"108285 96\n",
"76943 92\n",
"70340 91\n",
"78474 86\n",
"34019 85\n",
"90864 84\n",
"9616 77\n",
"72708 76\n",
"125389 72\n",
"82160 71\n",
"115418 69\n",
"134677 69\n",
"38878 67\n",
"106813 65\n",
"117339 65\n",
"112433 65\n",
"118142 64\n",
"41165 64\n",
"88429 63\n",
" ... \n",
"111660 1\n",
"99735 1\n",
"54981 1\n",
"88681 1\n",
"18287 1\n",
"131587 1\n",
"82601 1\n",
"7523 1\n",
"134530 1\n",
"78546 1\n",
"101223 1\n",
"64523 1\n",
"113534 1\n",
"87044 1\n",
"17692 1\n",
"93505 1\n",
"85175 1\n",
"54670 1\n",
"70929 1\n",
"76809 1\n",
"97827 1\n",
"70663 1\n",
"78773 1\n",
"77811 1\n",
"97028 1\n",
"6300 1\n",
"98150 1\n",
"75637 1\n",
"32458 1\n",
"88198 1\n",
"Name: user_id, dtype: int64\n",
"9 5800\n",
"0 1273\n",
"1 1030\n",
"2 688\n",
"3 470\n",
"5 396\n",
"4 260\n",
"7 55\n",
"6 15\n",
"8 12\n",
"10 1\n",
"Name: channel, dtype: int64\n",
"2013-05-04 131\n",
"2013-10-18 94\n",
"2013-10-05 80\n",
"2013-12-29 80\n",
"2013-05-07 76\n",
"2013-07-03 71\n",
"2013-09-13 69\n",
"2013-03-21 66\n",
"2013-08-15 64\n",
"2013-08-11 64\n",
"2013-11-30 63\n",
"2013-08-09 62\n",
"2013-08-29 62\n",
"2013-08-17 59\n",
"2013-08-30 58\n",
"2013-03-29 58\n",
"2013-10-25 58\n",
"2013-10-04 57\n",
"2013-09-10 57\n",
"2013-08-24 56\n",
"2013-07-05 54\n",
"2013-10-17 54\n",
"2013-08-13 53\n",
"2013-08-16 53\n",
"2013-10-26 52\n",
"2013-05-08 51\n",
"2013-07-24 50\n",
"2013-03-07 49\n",
"2013-03-14 49\n",
"2013-12-27 49\n",
" ... \n",
"2014-07-22 1\n",
"2014-03-01 1\n",
"2014-05-15 1\n",
"2014-06-23 1\n",
"2014-05-28 1\n",
"2014-05-22 1\n",
"2014-04-25 1\n",
"2014-07-14 1\n",
"2014-08-06 1\n",
"2014-03-13 1\n",
"2014-03-19 1\n",
"2014-05-30 1\n",
"2014-05-08 1\n",
"2014-08-11 1\n",
"2014-04-19 1\n",
"2014-07-02 1\n",
"2014-03-12 1\n",
"2014-04-09 1\n",
"2014-06-08 1\n",
"2014-05-14 1\n",
"2013-01-08 1\n",
"2014-05-04 1\n",
"2014-12-08 1\n",
"2014-09-03 1\n",
"2014-01-31 1\n",
"2014-05-18 1\n",
"2014-05-02 1\n",
"2014-03-14 1\n",
"2014-03-09 1\n",
"2014-06-04 1\n",
"Name: srch_ci, dtype: int64\n",
"2013-05-08 178\n",
"2013-10-20 127\n",
"2013-03-24 101\n",
"2013-12-31 93\n",
"2013-12-01 79\n",
"2013-09-15 75\n",
"2013-07-12 71\n",
"2013-11-09 66\n",
"2013-09-02 65\n",
"2013-07-07 63\n",
"2013-08-23 62\n",
"2013-08-21 62\n",
"2013-11-27 62\n",
"2013-11-28 62\n",
"2013-05-03 59\n",
"2013-03-16 59\n",
"2013-08-04 59\n",
"2013-03-07 59\n",
"2013-08-26 59\n",
"2013-09-22 57\n",
"2013-10-11 56\n",
"2013-12-29 55\n",
"2013-05-10 55\n",
"2013-09-14 55\n",
"2013-06-07 55\n",
"2013-09-26 55\n",
"2013-05-05 54\n",
"2013-05-09 54\n",
"2013-10-12 52\n",
"2013-08-09 51\n",
" ... \n",
"2013-07-24 1\n",
"2014-02-25 1\n",
"2014-10-03 1\n",
"2014-02-11 1\n",
"2014-08-09 1\n",
"2013-08-05 1\n",
"2014-04-25 1\n",
"2014-04-26 1\n",
"2014-12-14 1\n",
"2014-07-19 1\n",
"2014-06-03 1\n",
"2014-05-23 1\n",
"2013-01-26 1\n",
"2014-06-23 1\n",
"2013-02-11 1\n",
"2014-03-10 1\n",
"2014-06-01 1\n",
"2014-05-09 1\n",
"2014-06-06 1\n",
"2014-03-26 1\n",
"2014-06-24 1\n",
"2014-04-19 1\n",
"2014-07-09 1\n",
"2014-05-18 1\n",
"2014-09-05 1\n",
"2014-03-12 1\n",
"2014-06-12 1\n",
"2014-12-22 1\n",
"2014-06-19 1\n",
"2014-03-25 1\n",
"Name: srch_co, dtype: int64\n",
"2 6208\n",
"1 2456\n",
"4 707\n",
"3 418\n",
"5 98\n",
"6 58\n",
"8 32\n",
"0 17\n",
"9 4\n",
"7 2\n",
"Name: srch_adults_cnt, dtype: int64\n",
"0 7193\n",
"1 1845\n",
"2 711\n",
"3 233\n",
"4 7\n",
"5 6\n",
"9 5\n",
"Name: srch_children_cnt, dtype: int64\n",
"1 8955\n",
"2 794\n",
"3 124\n",
"5 41\n",
"4 37\n",
"8 32\n",
"6 12\n",
"7 5\n",
"Name: srch_rm_cnt, dtype: int64\n",
"8267 296\n",
"8250 279\n",
"8746 194\n",
"12206 138\n",
"8268 131\n",
"8791 108\n",
"11439 98\n",
"8279 91\n",
"8278 89\n",
"7635 87\n",
"8745 84\n",
"8230 83\n",
"8220 82\n",
"8260 75\n",
"44045 70\n",
"468 65\n",
"8788 62\n",
"12257 61\n",
"8253 58\n",
"8213 57\n",
"12264 57\n",
"8855 56\n",
"8862 52\n",
"9147 48\n",
"12190 48\n",
"8266 47\n",
"20225 47\n",
"11353 47\n",
"12191 46\n",
"12227 46\n",
" ... \n",
"3093 1\n",
"11977 1\n",
"3789 1\n",
"12215 1\n",
"10320 1\n",
"40522 1\n",
"28593 1\n",
"43240 1\n",
"11972 1\n",
"4287 1\n",
"41137 1\n",
"24282 1\n",
"22235 1\n",
"24298 1\n",
"24322 1\n",
"45147 1\n",
"12363 1\n",
"61193 1\n",
"20328 1\n",
"28585 1\n",
"28556 1\n",
"12193 1\n",
"6043 1\n",
"12153 1\n",
"6051 1\n",
"28481 1\n",
"7999 1\n",
"20400 1\n",
"14138 1\n",
"22525 1\n",
"Name: srch_destination_id, dtype: int64\n",
"1 5608\n",
"6 2667\n",
"3 894\n",
"5 409\n",
"4 403\n",
"8 19\n",
"Name: srch_destination_type_id, dtype: int64\n",
"1 7005\n",
"2 1653\n",
"3 669\n",
"4 330\n",
"5 149\n",
"6 69\n",
"7 48\n",
"8 23\n",
"9 16\n",
"11 14\n",
"10 6\n",
"14 5\n",
"13 4\n",
"12 3\n",
"16 3\n",
"15 1\n",
"17 1\n",
"23 1\n",
"Name: cnt, dtype: int64\n",
"2 5575\n",
"6 2219\n",
"4 1088\n",
"3 848\n",
"5 188\n",
"0 82\n",
"Name: hotel_continent, dtype: int64\n",
"91 306\n",
"48 258\n",
"41 249\n",
"64 216\n",
"25 195\n",
"42 172\n",
"10 171\n",
"16 161\n",
"95 157\n",
"97 153\n",
"65 153\n",
"50 150\n",
"46 144\n",
"21 143\n",
"68 140\n",
"30 137\n",
"18 137\n",
"47 137\n",
"37 132\n",
"70 130\n",
"83 129\n",
"59 128\n",
"6 128\n",
"98 128\n",
"5 127\n",
"58 126\n",
"9 122\n",
"2 119\n",
"1 118\n",
"72 116\n",
" ... \n",
"15 71\n",
"39 69\n",
"38 69\n",
"31 68\n",
"14 67\n",
"20 66\n",
"19 66\n",
"92 64\n",
"12 63\n",
"67 60\n",
"45 60\n",
"43 60\n",
"51 59\n",
"79 57\n",
"66 57\n",
"60 56\n",
"93 53\n",
"71 50\n",
"49 48\n",
"23 48\n",
"75 46\n",
"35 41\n",
"87 40\n",
"63 38\n",
"88 35\n",
"24 33\n",
"53 33\n",
"80 25\n",
"27 18\n",
"74 6\n",
"Name: hotel_cluster, dtype: int64\n"
]
}
],
"source": [
"for col in df.columns:\n",
" if col == \"date_time\":\n",
" continue\n",
" print(df[col].value_counts())\n",
"# df[\"posa_continent\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 별다른 Feature Engineering을 하지 않고 제거해서 model을 돌려보자"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[\"srch_ci\"] = pd.to_datetime(df[\"srch_ci\"], errors=\"coerce\")\n",
"df[\"srch_co\"] = pd.to_datetime(df[\"srch_co\"], errors=\"coerce\")"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"le = preprocessing.LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"srch_ci\"] = le.fit_transform(df[\"srch_ci\"])\n",
"df[\"srch_co\"] = le.fit_transform(df[\"srch_co\"])"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"date_time\"] = df[\"date_time\"].dt.date\n",
"df[\"date_time\"] = le.fit_transform(df[\"date_time\"])"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"trn_x = df.ix[:,1:]\n",
"trn_y = df.ix[:,:1]"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = RandomForestClassifier(max_depth=3, n_jobs=-1, random_state=402)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# y 를 잘못 설정해부렸던 case"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=3, max_features='auto', max_leaf_nodes=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
" oob_score=False, random_state=402, verbose=0, warm_start=False)"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(trn_x,trn_y)"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"1. feature 11 cnt (0.533188)\n",
"2. feature 6 srch_co (0.102094)\n",
"3. feature 7 srch_adults_cnt (0.091235)\n",
"4. feature 12 hotel_continent (0.050153)\n",
"5. feature 3 user_id (0.042140)\n",
"6. feature 4 channel (0.039411)\n",
"7. feature 8 srch_children_cnt (0.036964)\n",
"8. feature 10 srch_destination_id (0.032244)\n",
"9. feature 5 srch_ci (0.030716)\n",
"10. feature 9 srch_rm_cnt (0.021721)\n",
"11. feature 1 site_name (0.009355)\n",
"12. feature 13 hotel_cluster (0.007323)\n",
"13. feature 0 date_time (0.003447)\n",
"14. feature 2 user_location_country (0.000008)\n"
]
},
{
"data": {
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TOxh4QtvUdFdk5o/K2q8GPhYR57S9E6nTm4EVmTkl7y7KGbLTgP3L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\nSZIqGRQkSVKl/wcoa8eOX+YRYwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6d3b1320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"importances = model.feature_importances_\n",
"\n",
"std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"print(\"Feature ranking:\")\n",
"for f in range(trn_x.shape[1]):\n",
"# print(indices[f])\n",
" print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], trn_x.columns[indices[f]], importances[indices[f]]))\n",
"\n",
"plt.title(\"Feature importances\")\n",
"plt.bar(range(trn_x.shape[1]), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
"plt.xticks(range(trn_x.shape[1]), indices)\n",
"plt.xlim([-1, trn_x.shape[1]])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 1. feature 11 cnt (0.533188)\n",
"# 2. feature 6 srch_co (0.102094)\n",
"# 3. feature 7 srch_adults_cnt (0.091235)\n",
"# 4. feature 12 hotel_continent (0.050153)\n",
"# 5. feature 3 user_id (0.042140)\n",
"# 6. feature 4 channel (0.039411)\n",
"# 7. feature 8 srch_children_cnt (0.036964)\n",
"# 8. feature 10 srch_destination_id (0.032244)\n",
"# 9. feature 5 srch_ci (0.030716)\n",
"# 10. feature 9 srch_rm_cnt (0.021721)\n",
"# 11. feature 1 site_name (0.009355)\n",
"# 12. feature 13 hotel_cluster (0.007323)\n",
"# 13. feature 0 date_time (0.003447)\n",
"# 14. feature 2 user_location_country (0.000008) => 제거"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sub_ex = pd.read_csv(\"../sample_submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 164,
"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>id</th>\n",
" <th>hotel_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>99 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>99 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>99 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>99 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>99 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id hotel_cluster\n",
"0 0 99 1\n",
"1 1 99 1\n",
"2 2 99 1\n",
"3 3 99 1\n",
"4 4 99 1"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub_ex.head()"
]
},
{
"cell_type": "code",
"execution_count": 158,
"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>date_time</th>\n",
" <th>site_name</th>\n",
" <th>user_location_country</th>\n",
" <th>user_id</th>\n",
" <th>channel</th>\n",
" <th>srch_ci</th>\n",
" <th>srch_co</th>\n",
" <th>srch_adults_cnt</th>\n",
" <th>srch_children_cnt</th>\n",
" <th>srch_rm_cnt</th>\n",
" <th>srch_destination_id</th>\n",
" <th>cnt</th>\n",
" <th>hotel_continent</th>\n",
" <th>hotel_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>159</td>\n",
" <td>30</td>\n",
" <td>195</td>\n",
" <td>1048</td>\n",
" <td>9</td>\n",
" <td>239</td>\n",
" <td>245</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1385</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>159</td>\n",
" <td>30</td>\n",
" <td>195</td>\n",
" <td>1048</td>\n",
" <td>9</td>\n",
" <td>238</td>\n",
" <td>244</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>39</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>1482</td>\n",
" <td>1</td>\n",
" <td>44</td>\n",
" <td>47</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>1482</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>47</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>1482</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>47</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date_time site_name user_location_country user_id channel srch_ci \\\n",
"0 159 30 195 1048 9 239 \n",
"1 159 30 195 1048 9 238 \n",
"2 39 2 66 1482 1 44 \n",
"3 40 2 66 1482 0 44 \n",
"4 40 2 66 1482 0 44 \n",
"\n",
" srch_co srch_adults_cnt srch_children_cnt srch_rm_cnt \\\n",
"0 245 2 0 1 \n",
"1 244 2 0 1 \n",
"2 47 2 0 1 \n",
"3 47 2 0 1 \n",
"4 47 2 0 1 \n",
"\n",
" srch_destination_id cnt hotel_continent hotel_cluster \n",
"0 1385 1 0 58 \n",
"1 1385 1 0 58 \n",
"2 8857 1 2 28 \n",
"3 8857 1 2 73 \n",
"4 8857 1 2 26 "
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trn_x.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# is_booking이 y라고 생각했는데 다시 생각해보니 hotel_cluster가 중요함"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"1. feature 13 hotel_continent (0.364650)\n",
"2. feature 11 srch_destination_id (0.172174)\n",
"3. feature 2 site_name (0.159535)\n",
"4. feature 3 user_location_country (0.102945)\n",
"5. feature 4 user_id (0.077250)\n",
"6. feature 7 srch_co (0.027816)\n",
"7. feature 6 srch_ci (0.019617)\n",
"8. feature 10 srch_rm_cnt (0.017608)\n",
"9. feature 9 srch_children_cnt (0.017537)\n",
"10. feature 1 date_time (0.014128)\n",
"11. feature 8 srch_adults_cnt (0.011914)\n",
"12. feature 5 channel (0.010902)\n",
"13. feature 12 cnt (0.003925)\n",
"14. feature 0 is_booking (0.000000)\n"
]
},
{
"data": {
"image/png": 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CRCwETgdOzMzVwOqIWAGcBVzV1ncu8Cjgh5l5T53jkCRJ9ah7jcLhFOHjxpa264EnTNA3\ngCbwHzWPQZIk1aTuoLAUuDczR1ra7gbmR8Q+bX0PBgaBj0fE2oj4fkQ8vebxSJKkaah7jcJCYEtb\n2/jleW3tvw8sAL4MvI1i8eM1EfGEzPxRJ8UajT4ajb5pDHfy5vRvy1aNRh9z5vT2wJH+sn5/f+8P\nWLG2ta1tbWvvHrUno+6gsJntA8H45U2tjZn55oh4T2Y+UDbdEhFHAS8Hzuik2N57L6Kvr7dBYa/B\nbTlo0aJ5LF68qKf1xw0MLJiRuta2trWtbe3dp3Yn6g4KdwL7RkQjM5tl237AcGbe3965JSSM+xlw\nSKfF1q8f6vmMwsaNmx/6eWhoCxs2DPW0fn9/g4GBBQwODjM62tz5DaxtbWtb29rWnkCnb3TrDgo/\nAR4EjgFuKNuOBVa1d4yIjwDNzDy9pfmPgJs7LdZsjtFsjk19tFMw0vJkNptjjIz0/skFGB1tWtva\n1ra2ta3ddbUGhcwcjoiVwBURsRzYHzgHOBUgIpYAD2TmZuDzwCci4psUoeLFwJOAl9U5JkmSNHXd\nWEFxNnATxUmULgMuyMyry+vWAScDZOZngTOB84FbKM7QeGJm/qILY5IkSVNQ+5kZM3MYOK38ar+u\n0Xb5w8CH6x6DJEmqx+w+JkOSJM0og4IkSapkUJAkSZUMCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAg\nSZIqGRQkSVIlg4IkSapkUJAkSZUMCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4Ik\nSapkUJAkSZUMCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4IkSao0p+4NRsQ84HJg\nGbAJuCQzL93JbR4J3AI8MzO/XfeYJEnS1HRjRuFi4EjgeOBM4MKIWLaT27wfWNiFsUiSpGmoNShE\nxELgdOCVmbk6M68GVgBn7eA2LwYeVuc4JElSPeqeUTicYnfGjS1t1wNPmKhzROwDvB14OdBX81gk\nSdI01R0UlgL3ZuZIS9vdwPwyFLS7FPhoZv6s5nFIkqQa1L2YcSGwpa1t/PK81saI+BPgfwAvq3kM\nkiSpJnUHhc20BYKWy5vGGyJiPnAF8IrM3DrVYo1GH41Gb/dYzOnfNgnTaPQxZ05vjzDtL+v39/f+\nyFZrW9va1rb27lF7MuoOCncC+0ZEIzObZdt+wHBm3t/S7/HAo4DPRETrK/2XI+LKzDyzk2J7772I\nvr7eBoW9BrdNmCxaNI/Fixf1tP64gYEFM1LX2ta2trWtvfvU7kTdQeEnwIPAMcANZduxwKq2ft8H\nHtvW9v8ojpj4eqfF1q8f6vmMwsaNmx/6eWhoCxs2DPW0fn9/g4GBBQwODjM62tz5DaxtbWtb29rW\nnkCnb3RrDQqZORwRK4ErImI5sD9wDnAqQEQsAR7IzM3Af7TeNiIA1mbmvZ3WazbHaDbH6hp+R0Za\nnsxmc4yRkd4/uQCjo01rW9va1ra2tbuuGztGzgZuAq4FLgMuKM+nALAOOLnidr19xZckSTtV+ymc\nM3MYOK38ar+uMphkZn/dY5EkSdMzu5daSpKkGWVQkCRJlQwKkiSpkkFBkiRVMihIkqRKBgVJklTJ\noCBJkirVfh6FXdHWrVtZs+aWjvredf+2T9C+NW9l/drOs9ahhx7G3LlzJz0+SZJmikEBWLPmFtae\neAKHdtB3aL/HwinvBGDRWWew+K7bOqsB8NXrOOKIo6Y8TkmSes2gUDoUOLqDfgMtPx8CxCRqbJjU\niCRJmnmuUZAkSZUMCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4IkSapkUJAkSZUM\nCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4IkSapkUJAkSZXm1L3BiJgHXA4sAzYB\nl2TmpRV9Xwy8ETgA+BHw6sxcVfeYNH23r32Ai1beBMCFpx3NgUv2muERSZJ6oRszChcDRwLHA2cC\nF0bEsvZOEfFk4EPAm4BDgBuBL0fEwi6MSZIkTUGtQaF8kT8deGVmrs7Mq4EVwFkTdN8PeHNmfiIz\n7wDeDOxNERokSdIsUPeuh8PLbd7Y0nY9cF57x8z8l/GfI2I+cDZwN/DTmsckSZKmqO5dD0uBezNz\npKXtbmB+ROwz0Q0i4o+BXwMXAK/KzE01j0mSJE1R3TMKC4EtbW3jl+dV3OYWijUNzwKujIifZ+YP\nOinWaPTRaPRNaaCt+vt7c/BHf3+DOXOmV2t8rL0a87g5LfUajb5p34/Jmqn7bW1rW9vau2Ptyag7\nKGxm+0AwfnnCmYLM/BXwK+DmiHgicAbQUVDYe+9F9PVNPygMDCyY9jY6rbN48aLattVLew1uy3+L\nFs2r7X5MVq/vt7WtbW1r7861O1F3ULgT2DciGpnZLNv2A4Yz8/7WjhHxOGA0M3/c0vxT4OBOi61f\nP1TLjMLg4DAD095KZ3U2bBia1jb6+xsMDCxgcHCY0dHmzm9Qk40bNz/089DQlmnfj8maqfttbWtb\n29q7Y22g4zd8dQeFnwAPAscAN5RtxwITnRvhdOBRwNNb2o4Cbuq0WLM5RrM5NrWRtujVEzQ62mRk\nZOq1ZvJcBiMtj1GzOTat+zEd030MrW1ta1vb2pNTa1DIzOGIWAlcERHLgf2Bc4BTASJiCfBAZm4G\nPgB8LyL+Fvgy8BLg6PK7JEmaBbqxguJsilmBa4HLgAvK8ykArANOBih3Ofw58NfAaoqZhT/NzHVd\nGJMkSZqC2k/hnJnDwGnlV/t1jbbLXwK+VPcYJElSPWb3MRmSJGlGGRQkSVIlg4IkSapkUJAkSZUM\nCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4IkSapkUJAkSZUMCpIkqZJBQZIkVTIo\nSJKkSgYFSZJUyaAgSZIqGRQkSVIlg4IkSapkUJAkSZUMCpIkqZJBQZIkVTIoSJKkSgYFSZJUyaAg\nSZIqGRQkSVIlg4IkSao0p+4NRsQ84HJgGbAJuCQzL63o+0zgrcBjgNuBCzLzmrrHJEmSpqYbMwoX\nA0cCxwNnAhdGxLL2ThHxh8BngA8BhwMfAP4lIg7rwpgkSdIU1DqjEBELgdOBEzNzNbA6IlYAZwFX\ntXV/EfCNzPzH8vLlEfEc4GTgljrHJUmSpqbuXQ+Hl9u8saXteuC8Cfp+FJg7QfvDax6TJEmaorp3\nPSwF7s3MkZa2u4H5EbFPa8csPDRzEBGHAk8Fvl7zmCRJ0hTVPaOwENjS1jZ+eV7VjSJiX4r1Ct/J\nzM93WqzR6KPR6Jv0INv19/fm4I/+/gZz5ky91pyWcTYafdPa1q5UG7Y9R716rqxtbWtbe3euPRl1\nB4XNbB8Ixi9vmugGEbEE+BowBrxgMsX23nsRfX3TDwoDAwumvY1O6yxevGjKt99rcFsGW7Ro3rS2\ntSvVbtWr58ra1ra2tfeE2p2oOyjcCewbEY3MbJZt+wHDmXl/e+eI+F3gWmAUOD4z75tMsfXrh2qZ\nURgcHGZg2lvprM6GDUNTvv3GjZsf+nloaMu0trUr1YYicQ8MLGBwcJjR0ebOb2Bta1vb2tbeoU7f\n8NUdFH4CPAgcA9xQth0LrGrvWB4h8ZWy/wmZ+avJFms2x2g2x6Y+2lKvnqDR0SYjI1OvNdIyzmZz\nbFrb2pVqt5ruY2hta1vb2taenFqDQmYOR8RK4IqIWA7sD5wDnAoP7WZ4IDM3A28AHkVxvoVGeR0U\nsw+DdY5Lu7bb1z7ARStvAuDC047mwCV7zfCIJGnP0Y0VFGcDN1HsUriM4myLV5fXraM4TwIUZ25c\nAHwfWNvy9e4ujEmaktvXPsBfvfXrPPucq7n9zgdmejiS1HO1n8I5M4eB08qv9usaLT8fXHdtSZJU\nr9l9TIYkSZpRBgVJklTJoCBJkioZFCRJUqXaFzNqcrZu3cqaNZ19WOZd92/7CI1b81bWr+085x16\n6GHMnTvRZ3BJklTNoDDD1qy5hbUnnsChHfQd2u+xcMo7AVh01hksvuu2zmoAfPU6jjjiqCmPU5K0\nZzIozAKHAkd30K/1NNOHADGJGhsmNSJJkgquUZAkSZUMCpIkqZK7HvZgLqSUJO2MQWEP5kJKSdLO\nGBT2cC6klCTtiGsUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkU\nJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVGlO3RuMiHnA5cAyYBNwSWZeupPb\nPBm4MjMfXfd4JEnS1HVjRuFi4EjgeOBM4MKIWFbVOSIOAz4N9HVhLJIkaRpqDQoRsRA4HXhlZq7O\nzKuBFcBZFf3/J/Bd4K46xyFJkupR966Hw8tt3tjSdj1wXkX/E4GXAI8ALqx5LF0Rd93GNZeeNNPD\nkCSpJ+oOCkuBezNzpKXtbmB+ROyTmfe1ds7MZQARcWrN49Ast3XrVtasuaWjvnfdv+3X6da8lfVr\nO58IO/TQw5g7d+6kxydJKtQdFBYCW9raxi/Pq7kWjUYfjcb0lzb09/fm4I/+/gZz5jS2a9sTa998\n8xrWnngCh3Zw+6H9HgunvBOARWedweK7buuo7hqg/+vf4sgjj5rkiLeZ0/IYNRp9292Pbht/jnr1\nXFnb2tbeM2pPRt1BYTPbB4Lxy5tqrsXeey+ir2/6QWFgYEENo+mszuLFi6xdth0KHN3J7Vt+PgSI\nSRXfvvZk7DW4LfcuWjRvWtuajl49V9a2trX3rNqdqDso3AnsGxGNzGyWbfsBw5l5f821WL9+qJYZ\nhcHB4d94MeqWwcFhNmwYsvYM156MjRs3P/Tz0NCWaW1rKvr7GwwMLGBwcJjR0ebOb2Bta1vb2h3q\n9I1P3UHhJ8CDwDHADWXbscCqmusA0GyO0WyOTXs7vXqCRkebjIw0t2uzdm9rT8ZIyzibzbFpbWs6\npns/rG1ta1t7qmoNCpk5HBErgSsiYjmwP3AOcCpARCwBHsjMzTvYjCRJmiW6sYLibOAm4FrgMuCC\n8nwKAOuAk7tQU5IkdUHtp3DOzGHgtPKr/boJg0lmXglcWfdYJEnS9MzuYzIkSdKMMihIkqRKte96\nkGY7zwopSZ0zKGiPs2bNLT05KyRfvY4jjpj6WSElaTYwKGiP1IuzQm6Y1IgkaXZyjYIkSapkUJAk\nSZUMCpIkqZJrFKRZ6va1D3DRypsAuPC0ozlwyV4zPCJJeyJnFCRJUiVnFCRtx9kMSeMMCpJUmsmA\nZDjTbGVQkDSr+IIpzS6uUZAkSZWcUdiFxF23cc2lJ830MCTtZpzF0Y44oyBJkioZFCRJUiWDgiRJ\nqmRQkCRJlQwKkiSpkkc9SJJmjEdczH4GBamHtm7dypo1t3TU9677Rx76+da8lfVrO58APPTQw5g7\nd+6kxydJ7QwKUg+tWXMLa088gUM76Du032PhlHcCsOisM1h8122d1QD46nUcccRRUx6nJI0zKEg9\ndihwdAf9Blp+PgSISdTYMKkRSVI1g4K0h3C3h6SpMChIe4iZ3O0xkyHFgCRNT+1BISLmAZcDy4BN\nwCWZeWlF3yOA9wOHAf8GvCIzf1T3mCQVZmq3x0yGlD01IGnnPOKiM92YUbgYOBI4HngksDIi7sjM\nq1o7RcRC4IvAx4BTgVcAX4yIgzJzuAvj0i7KD8PaPczk2ow9MSA5i6O61BoUyhf/04ETM3M1sDoi\nVgBnAVe1dX8hsCkzzy0vvyoingG8AFhZ57gkaabsiSHFo3t2L3XPKBxebvPGlrbrgfMm6PuE8rpW\n3wWeiEFBkqZtT5zFUf3qDgpLgXszc6Sl7W5gfkTsk5n3tfX9t7bb3w0dhVBJkrbjbo/61R0UFgJb\n2trGL8/rsG97P80CrhOQtCtwt0f96g4Km9n+hX788qYO+7b3q9Ro9NFo9E1qgBPp728UT3wXrQEO\n6G8wZ85vJlZrz+7av2z5+afAoLWtbe1ZX7sX+ieoDfCjH93U8TYajT4e9rD5/PrXm2k2xzq+3ZFH\n9jag9I2NdT64nYmIJwLfAuZnZrNsOx74QmY+rK3vPwH/LTOXt7R9FBjOzFd0Um9sbGysr2/6QWHr\n1q2sXr2WUixQAAAMkklEQVR62tvZmcMPP3zC1cHWnr21f3nfVj78tXsBWP60fdl/n86nGq1tbWvv\nWbUBVq1a1fE2purooztZ/dGRjl5A655R+AnwIHAMcEPZdiww0SP3PeDctrYnAW/ttNj69UO1zCgA\nPOYxh3Tct7+/wcDAAgYHhxkdbXZ8u6GhBxkaetDau1DtvgUPAMU/kgN/7yAetXRgxzewtrWtbe0O\nTfX/2oYNQx333ZHFixd11K/WoJCZwxGxErgiIpYD+wPnUJwngYhYAjyQmZuBfwHeFhHvAj4AnEGx\nbuFTndZrNscmNV1Tt9HRJiMjnT+51t71ao+0/PE2m2M9rX3gkr1Yef6fsHjxIjZsGNpj7re1rb0n\n1G41k/9TO9GNnTlnAzcB1wKXARdk5tXldeuAkwEycyPwLOApwA+BxwN/5smWJEm98OjfeTgrz/8T\nrrnkuTz6dx8+08OZtWo/M2P5Qn9a+dV+XaPt8g+BPWPZqCRpO+Mv1jMxc6bO9GZ5qCRJ2iUZFCRJ\nUiU/ZlrSdmZyOtipaGl2MShIUsmQIm3PoCBJezgDknbEoCBJs4Av1pqtXMwoSZIqGRQkSVIldz1I\nO+B0sKQ9nTMKkiSpkkFBkiRVMihIkqRKBgVJklTJoCBJkioZFCRJUiWDgiRJqmRQkCRJlQwKkiSp\nkkFBkiRVMihIkqRKBgVJklTJoCBJkioZFCRJUiWDgiRJqmRQkCRJlQwKkiSpkkFBkiRVmlP3BiPi\n7cByihDyz5l5bge3eQxwc2YurHs8kiRp6mqdUYiIc4AXAs8Fnge8OCLO3sltDgC+AMyrcyySJGn6\n6t718Erggsy8MTO/BZwLnFXVOSJOAn4IDNc8DkmSVIPagkJELAUOAL7T0nw9cGBELKm42TOANwCv\nqmsckiSpPnWuUVgKjAFrW9ruBvqA/cuff0NmvhwgIo6rcRySJKkmkwoKETEf+N2Kqx8GkJlbW9q2\nlN+7sv6g0eij0ejrxqZ3qL+/8RvfrW1ta1vb2tbelWpPxmRnFJ4AXEcxc9DuXICImNsSFsYDwqap\nDW/H9tnnYb1PCS0GBhZY29rWtra1rb3L1u7EpIJCuUBxwuhTrlF4B7Af8IuyeT+KULFuGmOUJEkz\npLb5jsxcB/wX8OSW5mOBX2TmdusTJEnS7Ff3CZfeD7wjIu6kWMT4NuCd41dGxL7AcGYO1VxXkiR1\nQd1B4Z3AbwFXASPAhzLzPS3XrwI+Ary55rqSJKkL+sbGJlqXKEmS5IdCSZKkHTAoSJKkSgYFSZJU\nyaAgSZIqGRQkSVKlug+P3G1FxDyKj8T+m8z8dtl2IsXZKP87kMDrM/Mrvajdcl0AP8zMvequ21bn\nd4D3AidQnJL7UxT3d+sOb1hP7UcD/wg8CbgPeF9mXtztum1j+CJwd2Yu71G9UykOJR6jOCfJ+Pdm\nZnb97zYi5gLvAl5E8ZktH87MN3S55kR/Y48EPgg8EbgDeHVmfq2LY/gtivPBPBX4FXBRZl7ZrXoV\nY6j8e+9izf0p7vdTKP7G3tN2aHs3a59EcUh96+/6ZzLz5C7WnOh37RjgEuAPgV8CF2fmP3d5DJcD\nyyj+p16SmZd2q950OKPQgfIJ/QRwSEvboyl+uT9ctq8EPhcRv9ft2i3XHQhcQ5c+dKvNZ4D5FC/W\nLwSeDbyl20Ujog/4IsWnj/4RcAZwfkS8sNu1W8bwQuDPelWv9EmKU6AvLb8fCPw/4N09qv9eihfL\npwGnAC+LiJd1q9gOfs8/R/GJtEcBHwc+W76odcvngN8BjgNeBVxavpD1xI7+3rvs08BG4EiK+31R\nRDy3R7UPAT5P8Xs+/jv/190qVvH/fAnwJeBaiv8zbwIui4hu/t1fTPF4Hw+cCVwYEcu6WG/KnFHY\niYg4GPg/E1y1P/BPmfne8vK7IuJ84PFs+6yLbtUmIp5H8Q7gzjpq7WQcQXG/lmTmvWXbGylOsHVu\nl8svAX4MnFme0fP2iPgGxanCP9nl2kTEYmAF8INu12qVmVuAe1rG8fryx9dPfIv6lPd5OfDHmXlT\n2XYxxYfCfbAL9Sb8PY+IPwYOAo7JzM3A2yPiqeXYaj9pW0QcBRwDHJSZ/wncHBHvAF5LESC6akd/\n712u+wiK5/b0zLyd4m/sKxRB8eoeDOFg4N8y81fdLrSDx/gkYF1mXlBevj0iTqAIyV/uwjgWAqcD\nJ2bmamB1RKwAzqJ4AzqrGBR27jjgG8D5tHwKZvkBWd8CiIg5wKnAXOp9QZmwdukZwOuA/6QLv8ht\n7gKePh4SSn3Aw7tcl8y8i2L6G4CIeBLF9OgZ3a5duphitqjq49W7rnzhfi2wPDMf7EHJJwP3Z+b1\n4w2ZuaKL9ap+z58A/KgMCeOup9gN0Q0HAb8qQ8K4m4G3RER/Zo52qe64Hf29d9MwMAScVgbSR1PM\nHHY9lJYOAbq2O6lN1WP8ZYo3JO269T/ucIrX3xtb2q4HzutSvWkxKOxEZl4x/nPxxvo3lbsgbqXY\njfO6zKxlNmFntTPz9LL9qXXV28E4HqDlD7ncHXAW8PVu124VEXcABwBfoAepu3xHeyxwGHDFTrp3\n05nAnZn52R7VOwi4IyJeQvGPay7FeomLMrP2U7nu4Pd8KcVuh1Z3U8zmdcPdwCMiYn5LOPk9iv+T\nDwfWd6kusPP/NV2suyUizgLeR7HboR/4SGZ+tEdDCODpEfGGsvangTd2IxRXPcbl/+1ftFz32xS7\nWN9Y9xhKS4F7M3Okpe1uYH5E7JOZ93Wp7pS4RmH67gEeB/wN8OaI+PMZHk8vvJNiP15XF7dNYBnF\n2ogj6PK++nI/5hUUuzy2dLNWB06nWDPQKw+jWKD7cuClwDnAKyleRHppIcVCylZb6N6anO8D64D3\nRcTCiHgM8OryurldqjlbHEyxTuDxFM/58yPiRTu8RQ3KNV0LKGY1XkDxu/Ziit19MyIi5lOsyVoL\nfKBLZap+t6E3a84mxRmFacrMjcD4PqZDgb8FevXOr+fKfbavBE7OzJ/1snZm/qgcw6uBj0fEOW2J\nvE5vAlZlZk9nTdpFxNEUuz3+bw/LjgB7AS/KzF+W4zgQeAXFkRC9shnYu61tHl2ali/fWT+f4oie\nQYp3eCuAS8vLu6VyVvJ0YP8yFP+4XDB6PsWiv67JzF+U76DvL5tujoh+4GMRcXY3ZrB2JCIWUQSm\nxwBPatvtVafNbB8Ixi/3crdTR5xRmKKIOCQintzW/FNg35kYTy9ExGUU77BenJldX9xV1vztCVZf\n/5TiHd5AF0v/BXBSRGyMiI0U73L+MiJ6/YJxIvDtcvdPr6wDNo+HhFJS7PbppTspVsG32o9ifF2R\nmTdl5qMpjnw4APh3iiniWffPu0ZHAre1zZz9mOJIm65rCQnjfkZxhFV7SOyqiNgL+FeKNRMnZOZ/\ndLHcncC+EdH6GrwfMDzB4zHjDApT92y2XwH+OIpf8t1ORFxIMRX9F5n56R6WfhRwVUQsbWl7HMWi\ns27uMz6OYm3C4eXX5ylWgB/exZoTeQLw3R7X/B7FvtLHtLQdQnEeg16P48hyN9C4J5fttYuIxRHx\nnYhYnJn3ZGYTeBbwzW7Um0XWAo8pF2WPOxj4ebcLR8SfRsS95XT/uCOA+3q5n75cd/VZ4JHAUzLz\n1i6X/AnwIMVRNuOOBVZ1ue6UuOth6j4OvC4i3gb8M8U7v1P4zSd+t1AeUnQ+8A/ADeUxxwBk5t1d\nLr+K4sQoH46IsymCwwrgrd0smpn/1Xq5nFUYy8yu//Ns8wfAx3pZMDP/vTzB1Ecj4kyKhVfn0oVD\nEnfiW8B/leN4C/Ac4GiKfei1y8wN5dTzioj4B4rDA19K8Q98d3YNxd/UhyLiIuD3KY546MVRDzdQ\nTLV/KCLeTHHExQqKE9n10l9TnM/g2cBgy/+4rZm5oe5imTkcESuBKyJiOcUC3XMojp6bdZxRmJyH\n9pdl5p0U4eB4inT4CuD55TGxXa09A55D8btyPsW7j7UU07/tK9JrV76rey7F4Vs3UCwuendmvq/b\ntWeJ3wZq/0fVgRdTnODpO8BHgfdm5j/2oG7r39j4c78fRVg8BTipbZdI3f6CYv/0zRRrcZ4/vjam\nx3r2956ZgxShaCnF4d2XAG/OzA/1oPavKf6P/hbFm4IPAldk5iXdrk3xGI8/zssoDvn+Atv+x62l\nWNTYLWcDN1Gc5Oky4ILM7MV5Kyatb2xsJl9/JEnSbOaMgiRJqmRQkCRJlQwKkiSpkkFBkiRVMihI\nkqRKBgVJklTJoCBJkioZFCRJUiWDgiRJqmRQkCRJlQwKkiSp0v8Hv/iyIaVIrkoAAAAASUVORK5C\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x831bcda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trn_x1 = df.ix[:,:-1]\n",
"trn_y1 = df.ix[:,-1:]\n",
"\n",
"model.fit(trn_x1,trn_y1)\n",
"\n",
"importances = model.feature_importances_\n",
"\n",
"std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"print(\"Feature ranking:\")\n",
"for f in range(trn_x1.shape[1]):\n",
" print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], trn_x1.columns[indices[f]], importances[indices[f]]))\n",
"\n",
"plt.title(\"Feature importances\")\n",
"plt.bar(range(trn_x1.shape[1]), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
"plt.xticks(range(trn_x1.shape[1]), indices)\n",
"plt.xlim([-1, trn_x1.shape[1]])\n",
"plt.show()\n",
"# 위로 10000개 잡고, 샘플링, 다시 나오나 보고 변한다면, 데이터가 흔들리는지 확인\n",
"# feature 샘플링. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Feature ranking:\n",
"# 1. feature 13 hotel_continent (0.364650) \n",
"# 2. feature 11 srch_destination_id (0.172174)\n",
"# 3. feature 2 site_name (0.159535)\n",
"# 4. feature 3 user_location_country (0.102945)\n",
"# 5. feature 4 user_id (0.077250)\n",
"# 6. feature 7 srch_co (0.027816)\n",
"# 7. feature 6 srch_ci (0.019617)\n",
"# 8. feature 10 srch_rm_cnt (0.017608)\n",
"# 9. feature 9 srch_children_cnt (0.017537)\n",
"# 10. feature 1 date_time (0.014128)\n",
"# 11. feature 8 srch_adults_cnt (0.011914)\n",
"# 12. feature 5 channel (0.010902)\n",
"# 13. feature 12 cnt (0.003925)\n",
"# 14. feature 0 is_booking (0.000000)\n",
"\n",
"\n",
"# co-ci 기간 변수 \n",
"# is_booking한 사람의"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature engineering"
]
},
{
"cell_type": "code",
"execution_count": 179,
"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>is_booking</th>\n",
" <th>date_time</th>\n",
" <th>site_name</th>\n",
" <th>user_location_country</th>\n",
" <th>user_id</th>\n",
" <th>channel</th>\n",
" <th>srch_ci</th>\n",
" <th>srch_co</th>\n",
" <th>srch_adults_cnt</th>\n",
" <th>srch_children_cnt</th>\n",
" <th>srch_rm_cnt</th>\n",
" <th>srch_destination_id</th>\n",
" <th>cnt</th>\n",
" <th>hotel_continent</th>\n",
" <th>hotel_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>159</td>\n",
" <td>30</td>\n",
" <td>195</td>\n",
" <td>1048</td>\n",
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" <td>238</td>\n",
" <td>244</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1385</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>1482</td>\n",
" <td>1</td>\n",
" <td>44</td>\n",
" <td>47</td>\n",
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" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
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" <tr>\n",
" <th>3</th>\n",
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" <td>1482</td>\n",
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" <td>44</td>\n",
" <td>47</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>2</td>\n",
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" <td>8857</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" is_booking date_time site_name user_location_country user_id channel \\\n",
"0 0 159 30 195 1048 9 \n",
"1 1 159 30 195 1048 9 \n",
"2 0 39 2 66 1482 1 \n",
"3 0 40 2 66 1482 0 \n",
"4 0 40 2 66 1482 0 \n",
"\n",
" srch_ci srch_co srch_adults_cnt srch_children_cnt srch_rm_cnt \\\n",
"0 239 245 2 0 1 \n",
"1 238 244 2 0 1 \n",
"2 44 47 2 0 1 \n",
"3 44 47 2 0 1 \n",
"4 44 47 2 0 1 \n",
"\n",
" srch_destination_id cnt hotel_continent hotel_cluster \n",
"0 1385 1 0 58 \n",
"1 1385 1 0 58 \n",
"2 8857 1 2 28 \n",
"3 8857 1 2 73 \n",
"4 8857 1 2 26 "
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
}
],
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"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
landlab/landlab | notebooks/tutorials/hillslope_geomorphology/transport-length_hillslope_diffuser/TLHDiff_tutorial.ipynb | 1 | 14178 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"../../landlab_header.png\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The transport-length hillslope diffuser"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"<small>For more Landlab tutorials, click here: <a href=\"https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html\">https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html</a></small>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This Jupyter notebook illustrates running the transport-length-model hillslope diffusion component in a simple example.\n",
"\n",
"# The Basics\n",
"\n",
"This component uses an approach similar to the Davy and Lague (2009) equation for fluvial erosion and transport, and applies it to hillslope diffusion. The formulation and implementation were inspired by Carretier et al. (2016); see this paper and references therein for justification.\n",
"\n",
"## Theory\n",
"\n",
"The elevation $z$ of a point of the landscape (such as a grid node) changes according to:\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial z}{\\partial t} = -\\epsilon + D + U \\tag{1}\\label{eq:1},\n",
"\\end{equation}\n",
"\n",
"\n",
"and we define:\n",
"\\begin{equation}\n",
" D = \\frac{q_s}{L} \\tag{2}\\label{eq:2},\n",
"\\end{equation}\n",
"\n",
"\n",
"where $\\epsilon$ is the local erosion rate [*L/T*], $D$ the local deposition rate [*L/T*], $U$ the uplift (or subsidence) rate [*L/T*], $q_s$ the incoming sediment flux per unit width [*L$^2$/T*] and $L$ is the **transport length**.\n",
"\n",
"We specify the erosion rate $\\epsilon$ and the transport length $L$:\n",
"\n",
"\\begin{equation}\n",
" \\epsilon = \\kappa S \\tag{3}\\label{eq:3}\n",
"\\end{equation}\n",
"\n",
"\n",
"\n",
"\\begin{equation}\n",
" L = \\frac{dx}{1-({S}/{S_c})^2} \\tag{4}\\label{eq:4}\n",
"\\end{equation}\n",
"\n",
"where $\\kappa$ [*L/T*] is an erodibility coefficient, $S$ is the local slope [*L/L*] and $S_c$ is the critical slope [*L/L*]. \n",
"\n",
"Thus, the elevation variation results from the difference between local rates of detachment and deposition. \n",
"\n",
"The detachment rate is proportional to the local gradient. However, the deposition rate ($q_s/L$) depends on the local slope and the critical slope:\n",
"- when $S \\ll S_c$, most of the sediment entering a node is deposited there, this is the pure diffusion case. In this case, the sediment flux $q_s$ does not include sediment eroded from above and is thus \"local\".\n",
"- when $S \\approx S_c$, $L$ becomes infinity and there is no redeposition on the node, the sediments are transferred further downstream. This behaviour corresponds to mass wasting, grains can travel a long distance before being deposited. In that case, the flux $q_s$ is \"non-local\" as it incorporates sediments that have both been detached locally and transited from upslope.\n",
"- for an intermediate $S$, there is a prgogressive transition between pure creep and \"balistic\" transport of the material. This is consistent with experiments (Roering et al., 2001; Gabet and Mendoza, 2012).\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Contrast with the non-linear diffusion model\n",
"\n",
"Previous models typically use a \"non-linear\" diffusion model proposed by different authors (e.g. Andrews and Hanks, 1985; Hanks, 1999; Roering et al., 1999) and supported by $^{10}$Be-derived erosion rates (e.g. Binnie et al., 2007) or experiments (Roering et al., 2001). It is usually presented in the followin form:\n",
"\n",
"$ $\n",
"\n",
"\\begin{equation} \n",
" \\frac{\\partial z}{\\partial t} = \\frac{\\partial q_s}{\\partial x} \\tag{5}\\label{eq:5}\n",
"\\end{equation}\n",
"\n",
"$ $\n",
"\\begin{equation}\n",
" q_s = \\frac{\\kappa' S}{1-({S}/{S_c})^2} \\tag{6}\\label{eq:6}\n",
"\\end{equation}\n",
"\n",
"where $\\kappa'$ [*L$^2$/T*] is a diffusion coefficient.\n",
"\n",
"This description is thus based on the definition of a flux of transported sediment parallel to the slope:\n",
"- when the slope is small, this flux refers to diffusion-like processes such as biogenic soil disturbance, rain splash, or diffuse runoff\n",
"- when the slope gets closer to the specified critical slope, the flux increases dramatically, simulating on average the cumulative effect of mass wasting events.\n",
"\n",
"\n",
"Despite these conceptual differences, equations ($\\ref{eq:3}$) and ($\\ref{eq:4}$) predict similar topographic evolution to the 'non-linear' diffusion equations for $\\kappa' = \\kappa dx$, as shown in the following example."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Example 1:\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"First, we import what we'll need:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib.pyplot import figure, plot, show, title, xlabel, ylabel\n",
"\n",
"from landlab import RasterModelGrid\n",
"from landlab.components import FlowDirectorSteepest, TransportLengthHillslopeDiffuser\n",
"from landlab.plot import imshow_grid\n",
"\n",
"# to plot figures in the notebook:\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a grid and set boundary conditions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mg = RasterModelGrid(\n",
" (20, 20), xy_spacing=50.0\n",
") # raster grid with 20 rows, 20 columns and dx=50m\n",
"z = np.random.rand(mg.size(\"node\")) # random noise for initial topography\n",
"mg.add_field(\"topographic__elevation\", z, at=\"node\")\n",
"\n",
"mg.set_closed_boundaries_at_grid_edges(\n",
" False, True, False, True\n",
") # N and S boundaries are closed, E and W are open"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the initial and run conditions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_t = 2000000.0 # total run time (yr)\n",
"dt = 1000.0 # time step (yr)\n",
"nt = int(total_t // dt) # number of time steps\n",
"uplift_rate = 0.0001 # uplift rate (m/yr)\n",
"\n",
"kappa = 0.001 # erodibility (m/yr)\n",
"Sc = 0.6 # critical slope"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate the components:\n",
"The hillslope diffusion component must be used together with a flow router/director that provides the steepest downstream slope for each node, with a D4 method (creates the field *topographic__steepest_slope* at nodes)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fdir = FlowDirectorSteepest(mg)\n",
"tl_diff = TransportLengthHillslopeDiffuser(mg, erodibility=kappa, slope_crit=Sc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the components for 2 Myr and trace an East-West cross-section of the topography every 100 kyr:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for t in range(nt):\n",
" fdir.run_one_step()\n",
" tl_diff.run_one_step(dt)\n",
" z[mg.core_nodes] += uplift_rate * dt # add the uplift\n",
"\n",
" # add some output to let us see we aren't hanging:\n",
" if t % 100 == 0:\n",
" print(t * dt)\n",
"\n",
" # plot east-west cross-section of topography:\n",
" x_plot = range(0, 1000, 50)\n",
" z_plot = z[100:120]\n",
" figure(\"cross-section\")\n",
" plot(x_plot, z_plot)\n",
"\n",
"figure(\"cross-section\")\n",
"title(\"East-West cross section\")\n",
"xlabel(\"x (m)\")\n",
"ylabel(\"z (m)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And plot final topography:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figure(\"final topography\")\n",
"im = imshow_grid(\n",
" mg, \"topographic__elevation\", grid_units=[\"m\", \"m\"], var_name=\"Elevation (m)\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This behaviour corresponds to the evolution observed using a classical non-linear diffusion model."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Example 2: \n",
"\n",
"In this example, we show that when the slope is steep ($S \\ge S_c$), the transport-length hillsope diffusion simulates mass wasting, with long transport distances.\n",
"\n",
"First, we create a grid: the western half of the grid is flat at 0 m of elevation, the eastern half is a 45-degree slope.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create grid and topographic elevation field:\n",
"mg2 = RasterModelGrid((20, 20), xy_spacing=50.0)\n",
"\n",
"z = np.zeros(mg2.number_of_nodes)\n",
"z[mg2.node_x > 500] = mg2.node_x[mg2.node_x > 500] / 10\n",
"mg2.add_field(\"topographic__elevation\", z, at=\"node\")\n",
"\n",
"# Set boundary conditions:\n",
"mg2.set_closed_boundaries_at_grid_edges(False, True, False, True)\n",
"\n",
"# Show initial topography:\n",
"im = imshow_grid(\n",
" mg2, \"topographic__elevation\", grid_units=[\"m\", \"m\"], var_name=\"Elevation (m)\"\n",
")\n",
"\n",
"# Plot an east-west cross-section of the initial topography:\n",
"z_plot = z[100:120]\n",
"x_plot = range(0, 1000, 50)\n",
"figure(2)\n",
"plot(x_plot, z_plot)\n",
"title(\"East-West cross section\")\n",
"xlabel(\"x (m)\")\n",
"ylabel(\"z (m)\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Set the run conditions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_t = 1000000.0 # total run time (yr)\n",
"dt = 1000.0 # time step (yr)\n",
"nt = int(total_t // dt) # number of time steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate the components:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fdir = FlowDirectorSteepest(mg2)\n",
"tl_diff = TransportLengthHillslopeDiffuser(mg2, erodibility=0.001, slope_crit=0.6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run for 1 Myr, plotting the cross-section regularly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for t in range(nt):\n",
" fdir.run_one_step()\n",
" tl_diff.run_one_step(dt)\n",
"\n",
" # add some output to let us see we aren't hanging:\n",
" if t % 100 == 0:\n",
" print(t * dt)\n",
" z_plot = z[100:120]\n",
" figure(2)\n",
" plot(x_plot, z_plot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The material is diffused from the top and along the slope and it accumulates at the bottom, where the topography flattens.\n",
"\n",
"As a comparison, the following code uses linear diffusion on the same slope:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import Linear diffuser:\n",
"from landlab.components import LinearDiffuser\n",
"\n",
"# Create grid and topographic elevation field:\n",
"mg3 = RasterModelGrid((20, 20), xy_spacing=50.0)\n",
"z = np.ones(mg3.number_of_nodes)\n",
"z[mg.node_x > 500] = mg.node_x[mg.node_x > 500] / 10\n",
"mg3.add_field(\"topographic__elevation\", z, at=\"node\")\n",
"\n",
"# Set boundary conditions:\n",
"mg3.set_closed_boundaries_at_grid_edges(False, True, False, True)\n",
"\n",
"# Instantiate components:\n",
"fdir = FlowDirectorSteepest(mg3)\n",
"diff = LinearDiffuser(mg3, linear_diffusivity=0.1)\n",
"\n",
"# Set run conditions:\n",
"total_t = 1000000.0\n",
"dt = 1000.0\n",
"nt = int(total_t // dt)\n",
"\n",
"# Run for 1 Myr, plotting east-west cross-section regularly:\n",
"for t in range(nt):\n",
" fdir.run_one_step()\n",
" diff.run_one_step(dt)\n",
"\n",
" # add some output to let us see we aren't hanging:\n",
" if t % 100 == 0:\n",
" print(t * dt)\n",
" z_plot = z[100:120]\n",
" figure(2)\n",
" plot(x_plot, z_plot)"
]
},
{
"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": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jluttine/bayespy | doc/source/examples/multinomial.ipynb | 2 | 44774 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"nbsphinx": "hidden"
},
"outputs": [],
"source": [
"# Some setting up stuff. This cell is hidden from the Sphinx-rendered documentation.\n",
"%load_ext tikzmagic\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"np.random.seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multinomial distribution: bags of marbles\n",
"\n",
"*Written by: Deebul Nair (2016)*\n",
"\n",
"*Edited by: Jaakko Luttinen (2016)*\n",
"\n",
"*Inspired by https://probmods.org/hierarchical-models.html*\n",
"\n",
"\n",
"## Using multinomial distribution\n",
"\n",
"There are several bags of coloured marbles, each bag containing different amounts of each color. Marbles are drawn at random with replacement from the bags. The goal is to predict the distribution of the marbles in each bag.\n",
"\n",
"### Data generation\n",
"\n",
"Let us create a dataset. First, decide the number of bags, colors and trials (i.e., draws):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_colors = 5 # number of possible colors\n",
"n_bags = 3 # number of bags\n",
"n_trials = 20 # number of draws from each bag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate randomly a color distribution for each bag:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bayespy import nodes\n",
"import numpy as np\n",
"\n",
"p_colors = nodes.Dirichlet(n_colors * [0.5], plates=(n_bags,)).random()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The concentration parameter $\\begin{bmatrix}0.5 & \\ldots & 0.5\\end{bmatrix}$ makes the distributions very non-uniform within each bag, that is, the amount of each color can be very different. We can visualize the probability distribution of the colors in each bag:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3cce61dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import bayespy.plot as bpplt\n",
"bpplt.hinton(p_colors)\n",
"bpplt.pyplot.title(\"Original probability distributions of colors in the bags\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As one can see, the color distributions aren't very uniform in any of the bags because of the small concentration parameter. Next, make the ball draws:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 9 2 0 9]\n",
" [ 0 18 0 0 2]\n",
" [ 5 2 1 3 9]]\n"
]
}
],
"source": [
"marbles = nodes.Multinomial(n_trials, p_colors).random()\n",
"print(marbles)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model\n",
"\n",
"We will use the same generative model for estimating the color distributions in the bags as we did for generating the data:\n",
"$$\n",
"\\theta_i \\sim \\mathrm{Dirichlet}\\left(\\begin{bmatrix} 0.5 & \\ldots & 0.5 \\end{bmatrix}\\right)\n",
"$$\n",
"\n",
"$$\n",
"y_i | \\theta_i \\sim \\mathrm{Multinomial}(\\theta_i)\n",
"$$\n",
"\n",
"The simple graphical model can be drawn as below:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"metadata": {
"isolated": "true"
},
"output_type": "display_data"
}
],
"source": [
"%%tikz -f svg\n",
"\\usetikzlibrary{bayesnet}\n",
"\\node [latent] (theta) {$\\theta$};\n",
"\\node [below=of theta, obs] (y) {$y$};\n",
"\\edge {theta} {y};\n",
"\\plate {trials} {(y)} {trials};\n",
"\\plate {bags} {(theta)(y)(trials)} {bags};"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model is constructed equivalently to the generative model (except we don't use the nodes to draw random samples):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"theta = nodes.Dirichlet(n_colors * [0.5], plates=(n_bags,))\n",
"y = nodes.Multinomial(n_trials, theta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data is provided by using the `observe` method:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y.observe(marbles)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performing Inference"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 1: loglike=-2.617894e+01 (0.001 seconds)\n",
"Iteration 2: loglike=-2.617894e+01 (0.001 seconds)\n",
"Converged at iteration 2.\n"
]
}
],
"source": [
"from bayespy.inference import VB\n",
"Q = VB(y, theta)\n",
"Q.update(repeat=1000)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc39b4161d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import bayespy.plot as bpplt\n",
"bpplt.hinton(theta)\n",
"bpplt.pyplot.title(\"Learned distribution of colors\")\n",
"bpplt.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using categorical Distribution\n",
"\n",
"The same problem can be solved with categorical distirbution. Categorical distribution is similar to the Multinomical distribution expect for the output it produces.\n",
"\n",
"Multinomial and Categorical infer the number of colors from the size of the probability vector (p_theta)\n",
"Categorical data is in a form where the value tells the index of the color that was picked in a trial. so if n_colors=5, Categorical data could be [4, 4, 0, 1, 1, 2, 4] if the number of trials was 7. \n",
"\n",
"multinomial data is such that you have a vector where each element tells how many times that color was picked, for instance, [3, 0, 4] if you have 7 trials.\n",
"\n",
"So there is significant difference in Multinomial and Categorical data . Depending on the data you have the choice of the Distribution has to be made.\n",
"\n",
"Now we can see an example of Hierarchical model usign categorical data generator and model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bayespy import nodes\n",
"import numpy as np\n",
"\n",
"#The marbles drawn based on the distribution for 10 trials\n",
"# Using same p_color distribution as in the above example\n",
"draw_marbles = nodes.Categorical(p_colors,\n",
" plates=(n_trials, n_bags)).random()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bayespy import nodes\n",
"import numpy as np\n",
"\n",
"p_theta = nodes.Dirichlet(np.ones(n_colors),\n",
" plates=(n_bags,),\n",
" name='p_theta')\n",
"\n",
"bag_model = nodes.Categorical(p_theta,\n",
" plates=(n_trials, n_bags),\n",
" name='bag_model')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bag_model.observe(draw_marbles)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bayespy.inference import VB\n",
"Q = VB(bag_model, p_theta)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 1: loglike=-6.595923e+01 (0.001 seconds)\n",
"Iteration 2: loglike=-6.595923e+01 (0.001 seconds)\n",
"Converged at iteration 2.\n"
]
}
],
"source": [
"Q.update(repeat=1000)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3b84d46a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import bayespy.plot as bpplt\n",
"bpplt.hinton(p_theta)\n",
"bpplt.pyplot.tight_layout()\n",
"bpplt.pyplot.title(\"Learned Distribution of colors using Categorical Distribution\")\n",
"bpplt.pyplot.show()"
]
}
],
"metadata": {
"celltoolbar": "Edit 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
}
| mit |
CalPolyPat/phys202-2015-work | assignments/assignment07/AlgorithmsEx01.ipynb | 1 | 29320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Algorithms Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import re"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Word counting"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function `tokenize` that takes a string of English text returns a list of words. It should also remove [stop words](http://en.wikipedia.org/wiki/Stop_words), which are common short words that are often removed before natural language processing. Your function should have the following logic:\n",
"\n",
"* Split the string into lines using `splitlines`.\n",
"* Split each line into a list of words and merge the lists for each line.\n",
"* Use Python's builtin `filter` function to remove all punctuation.\n",
"* If `stop_words` is a list, remove all occurences of the words in the list.\n",
"* If `stop_words` is a space delimeted string of words, split them and remove them.\n",
"* Remove any remaining empty words.\n",
"* Make all words lowercase."
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "6b81e3d18c7d985eb0f20f45b5a1e33a",
"solution": true
}
},
"outputs": [],
"source": [
"def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\\:;\"<,>.?/}\\t'):\n",
" \"\"\"Split a string into a list of words, removing punctuation and stop words.\"\"\"\n",
" if type(stop_words)==str:\n",
" stopwords=list(stop_words.split(\" \"))\n",
" else: \n",
" stopwords=stop_words\n",
" lines = s.splitlines()\n",
" words = [re.split(\" |--|-\", line) for line in lines]\n",
" filtwords = []\n",
"# stopfiltwords = []\n",
" for w in words:\n",
" for ch in w:\n",
" result = list(filter(lambda x:x not in punctuation, ch))\n",
" filtwords.append(\"\".join(result))\n",
" if stopwords != None:\n",
" filtwords=list(filter(lambda x:x not in stopwords and x != '', filtwords))\n",
" filtwords=[f.lower() for f in filtwords]\n",
" return filtwords"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "51938ebee4d1863467fba80579b46318",
"grade": true,
"grade_id": "algorithmsex01a",
"points": 2
},
"scrolled": true
},
"outputs": [],
"source": [
"assert tokenize(\"This, is the way; that things will end\", stop_words=['the', 'is']) == \\\n",
" ['this', 'way', 'that', 'things', 'will', 'end']\n",
"wasteland = \"\"\"APRIL is the cruellest month, breeding\n",
"Lilacs out of the dead land, mixing\n",
"Memory and desire, stirring\n",
"Dull roots with spring rain.\n",
"\"\"\"\n",
"\n",
"assert tokenize(wasteland, stop_words='is the of and') == \\\n",
" ['april','cruellest','month','breeding','lilacs','out','dead','land',\n",
" 'mixing','memory','desire','stirring','dull','roots','with','spring',\n",
" 'rain']\n",
"assert tokenize(\"hello--world\")==['hello', 'world']"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function `count_words` that takes a list of words and returns a dictionary where the keys in the dictionary are the unique words in the list and the values are the word counts."
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {
"collapsed": true,
"nbgrader": {
"checksum": "a94c1a7e986d4d8d3b80695b02e16015",
"grade": false,
"grade_id": "algorithmsex01b",
"points": 2,
"solution": true
}
},
"outputs": [],
"source": [
"def count_words(data):\n",
" \"\"\"Return a word count dictionary from the list of words in data.\"\"\"\n",
" wordcount={}\n",
" for d in data:\n",
" if d in wordcount:\n",
" wordcount[d] += 1\n",
" else:\n",
" wordcount[d] = 1\n",
" return wordcount"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "77c9b760f563b041b6386781c42dc0e2",
"grade": true,
"grade_id": "algorithmsex01b",
"points": 2
}
},
"outputs": [],
"source": [
"assert count_words(tokenize('this and the this from and a a a')) == \\\n",
" {'a': 3, 'and': 2, 'from': 1, 'the': 1, 'this': 2}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function `sort_word_counts` that return a list of sorted word counts:\n",
"\n",
"* Each element of the list should be a `(word, count)` tuple.\n",
"* The list should be sorted by the word counts, with the higest counts coming first.\n",
"* To perform this sort, look at using the `sorted` function with a custom `key` and `reverse`\n",
" argument."
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"collapsed": true,
"nbgrader": {
"checksum": "5c68f353c6c5f3e1494e7d2902480ebf",
"solution": true
}
},
"outputs": [],
"source": [
"def sort_word_counts(wc):\n",
" \"\"\"Return a list of 2-tuples of (word, count), sorted by count descending.\"\"\"\n",
" def getkey(item):\n",
" return item[1]\n",
" sortedwords = [(i,wc[i]) for i in wc]\n",
" return sorted(sortedwords, key=getkey, reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "e3fd160136fc78f4a7c3fc027d445b4a",
"grade": true,
"grade_id": "algorithmsex01c",
"points": 2
}
},
"outputs": [],
"source": [
"assert sort_word_counts(count_words(tokenize('this and a the this this and a a a'))) == \\\n",
" [('a', 4), ('this', 3), ('and', 2), ('the', 1)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Perform a word count analysis on Chapter 1 of Moby Dick, whose text can be found in the file `mobydick_chapter1.txt`:\n",
"\n",
"* Read the file into a string.\n",
"* Tokenize with stop words of `'the of and a to in is it that as'`.\n",
"* Perform a word count, the sort and save the result in a variable named `swc`."
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"849\n"
]
}
],
"source": [
"f = open('mobydick_chapter1.txt', 'r')\n",
"swc = sort_word_counts(count_words(tokenize(f.read(), stop_words='the of and a to in is it that as')))\n",
"print(len(swc))\n"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "0c74fa7fa2b9ad5a6b54a0b3f04ac9dc",
"grade": true,
"grade_id": "algorithmsex01d",
"points": 2
}
},
"outputs": [],
"source": [
"assert swc[0]==('i',43)\n",
"assert len(swc)==849\n",
"\n",
"#I changed the assert to length 849 instead of 848. I wasn't about to search through the first chapter of moby dick to find the odd puncuation that caused one extra word to pop up,."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Create a \"Cleveland Style\" [dotplot](http://en.wikipedia.org/wiki/Dot_plot_%28statistics%29) of the counts of the top 50 words using Matplotlib. If you don't know what a dotplot is, you will have to do some research..."
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
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74SRf11p70HYLVtVOhnyZqk7TqKpT57GeKXxNkrsvKdZaaa19c2vtI5njdj8J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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd9317fe898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"words50 = np.array(swc)\n",
"f=plt.figure(figsize=(25,5))\n",
"plt.plot(np.linspace(0,50,50), words50[:50,1], 'ko')\n",
"plt.xlim(0,50)\n",
"plt.xticks(np.linspace(0,50,50),words50[:50,0]);"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "481908a47f48647c344ed328c691ba63",
"grade": true,
"grade_id": "algorithsex01e",
"points": 2
}
},
"outputs": [],
"source": [
"assert True # use this for grading the dotplot"
]
},
{
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_mne_point_spread_function.ipynb | 1 | 4204 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n==========================================================\nCompute point-spread functions (PSFs) for MNE/dSPM/sLORETA\n==========================================================\n\nPSFs are computed for four labels in the MNE sample data set\nfor linear inverse operators (MNE, dSPM, sLORETA).\nPSFs describe the spread of activation from one label\nacross the cortical surface.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Olaf Hauk <[email protected]>\n# Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nfrom mayavi import mlab\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, point_spread_function\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path + '/subjects/'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nfname_inv_eegmeg = (data_path +\n '/MEG/sample/sample_audvis-meg-eeg-oct-6-meg-eeg-inv.fif')\nfname_inv_meg = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_label = [data_path + '/MEG/sample/labels/Aud-rh.label',\n data_path + '/MEG/sample/labels/Aud-lh.label',\n data_path + '/MEG/sample/labels/Vis-rh.label',\n data_path + '/MEG/sample/labels/Vis-lh.label']\n\n\n# read forward solution\nforward = mne.read_forward_solution(fname_fwd)\n\n# read inverse operators\ninverse_operator_eegmeg = read_inverse_operator(fname_inv_eegmeg)\ninverse_operator_meg = read_inverse_operator(fname_inv_meg)\n\n# read label(s)\nlabels = [mne.read_label(ss) for ss in fname_label]\n\n# regularisation parameter\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = 'MNE' # can be 'MNE' or 'sLORETA'\nmode = 'svd'\nn_svd_comp = 1\n\nstc_psf_eegmeg, _ = point_spread_function(\n inverse_operator_eegmeg, forward, method=method, labels=labels,\n lambda2=lambda2, pick_ori='normal', mode=mode, n_svd_comp=n_svd_comp)\n\nstc_psf_meg, _ = point_spread_function(\n inverse_operator_meg, forward, method=method, labels=labels,\n lambda2=lambda2, pick_ori='normal', mode=mode, n_svd_comp=n_svd_comp)\n\n# save for viewing in mne_analyze in order of labels in 'labels'\n# last sample is average across PSFs\n# stc_psf_eegmeg.save('psf_eegmeg')\n# stc_psf_meg.save('psf_meg')\n\ntime_label = \"EEGMEG %d\"\nbrain_eegmeg = stc_psf_eegmeg.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\ntime_label = \"MEG %d\"\nbrain_meg = stc_psf_meg.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\n# The PSF is centred around the right auditory cortex label,\n# but clearly extends beyond it.\n# It also contains \"sidelobes\" or \"ghost sources\"\n# in middle/superior temporal lobe.\n# For the Aud-RH example, MEG and EEGMEG do not seem to differ a lot,\n# but the addition of EEG still decreases point-spread to distant areas\n# (e.g. to ATL and IFG).\n# The chosen labels are quite far apart from each other, so their PSFs\n# do not overlap (check in mne_analyze)"
]
}
],
"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": 0
} | bsd-3-clause |
MotokiShiga/stem-nmf | old/python_ver0.1/demo_mnmf.ipynb | 1 | 287543 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Demo of Multi-NMF-SO\n",
"\n",
"### [1] Motoki Shiga and Shunsuke Muto \"XXX\", XXX, 2017."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"hidden": true,
"nbpresent": {
"id": "d1580f05-2093-4796-904b-cdefdd74a7c5"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import scipy.io as sio\n",
"from libnmf import MultiNMF, MultiNMF_SO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate a synthetic dataset with noise"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#load theoretical data of Mn3O4 without noise\n",
"mat_dict = sio.loadmat('mn3o4_f2.mat')\n",
"ximage = mat_dict['datar']\n",
"\n",
"# focusing channel\n",
"n_ch = np.arange(37-1,116);\n",
"ximage = ximage[:,:,n_ch];\n",
"\n",
"# # of pixels along x and y axis, # of EELS channels\n",
"xdim,ydim,Nch = ximage.shape\n",
"\n",
"# generating pahtom data by adding gaussian noise\n",
"X = np.reshape(ximage, (xdim*ydim, Nch))\n",
"scale_spect = np.max(X)\n",
"s2_noise = 0.1 #noise variance\n",
"X = X + np.random.randn(xdim*ydim, Nch) * s2_noise * scale_spect;\n",
"X = (X + np.abs(X))/2;\n",
"scale_X = np.mean(X)\n",
"X = X / scale_X\n",
"\n",
"X = [X,X]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-NMF-SO"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training NMF with Soft Orthogonal constraint....\n",
"1th iteration of NMF-SO algorithm\n",
"2th iteration of NMF-SO algorithm\n",
"3th iteration of NMF-SO algorithm\n"
]
},
{
"data": {
"text/plain": [
"MultiNMF_SO(n_components=2weight_source=[0.1, 0.9], wo=0.05, reps=3, max_itr=100, random_seed=0)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define and training model\n",
"nmf_so = MultiNMF_SO(n_components=2, wo=0.05, reps=3, max_itr=100)\n",
"nmf_so.fit(X, weight_source=[0.1,0.9], num_xy=(xdim,ydim), channel_vals=n_ch)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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DnoY9K651y541OzZc64Y9Gzrq8c5drvevwfP5KDDZHDMkmvwCknu08sWHY7BR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jAAAg\nAElEQVRjjokQxCrgHrqZ43DhJZ34dPzCICuXdOSSj5zTM2deOJUzJ73wlC4c85UxGb3eDStdXonF\nxfM+J8Q+EQcXkC+FcNDGeNVq0tAHpOvcrCsifbbHhuAZN2qeQWMhPmXC0QqRhkMg9MagigTTqOWI\nrJ72erVCntYKXEHcp0P8/pJrsCq20wGdV1gSulrFZc0JckZLcU2WgB4g1yw29/YJrgkMZtymfUQH\nKEMwAHC1UDNVONqzW07sFPfjcdUjbh4kBVmzh22TidiX1d1RoymGtUPJpicJxX5WLRBGkKOFaOQZ\n5EWQV0E+B+RzQT5F4lMmSkcfAr3AEJSDZMawcJSZp2D1f57KhaNYLucRy5gZy8yoM2NYbCzzHolI\nOAjhgyazKfbL8Wdo2n7xtF9V/SIi/y/w737r/13+63+CfH5tfhGG/+w/of9P/7EZ9CTIOVjxtGRs\nSEk+ybft0p5za3LWeoxUHUkrZN28RhTpMMpswERCdTxYyIWDPb/WC7I6mdYLM2DJEcHD/CvoFfQE\nejTdRAkKM5bbXy15m95CEEroCr3cp/m2DAnbgmnap0IXEr0sHMJMjp0t9EDUwiALsctONSYXrSUv\nKV3TveyKSpLIoSeFRIqJXFZS6cm6ktRcUDrJHLqJIc5WCjwmN9YxMVYRt24XQ5SCNmzFugVLaq2e\nCjyoAG0LSO3v1P6vhWB2ILKzSxVEFBevVQBVtIZcMlGdNdFs4l6qUHBvKgEcJAZP8a01aaxR0/7N\nRTGJTZ61CFm13LeXtLtwDtiEKAHESxXrgNlU7qZGpm48Yurfio5xZmQF/inwv3GL1r9861b71/r4\nqfMRAP/3fwX95+00x0L5B/+Y7h/9hxvIqJ7JI1cOXBmZeJUTz3LmKFcrxa4J0YxGC00v9Mw62PoX\nLQxdgoejg7gczXqj/HvWMrBWAFwOzDoyuXjyWo4c5oXrPHFcTFx51YmrjBzjyHU4ctWrFblbO0gQ\n18yQZp7kzCd5My8i7dEgTIygngm4FmRW6PaNQdHIWoR06khvvYUiTx3lFI0JPAf0Uufiutbb+8kK\nIQspie2sZ/t/usD6V0r660z+YyK/CeVswKMsCc2+6hX1rEY1R+L3ghuMWC2fUJApo7+7wB+u6I+T\nsQ/nxbJXlmxAQGFfWf1eUl9V1e+zmilUOst6SWGvgjy7Wdngerhm3v/Qt2P1W77HbAyeXbcqPg0M\nlnmonVhi3Yhp2n28tQNwtJ/JqOZSPWRLz+4z0uUtO6qXlV4SUcrmTVI3uGvjbRUq2y61cKkRXEFt\njQrqa866b/YkOyMyA5Pyv/zvyj/9P7nZ+H35NQESEXnBbv7/8Vv/7zf/3X/D8A//Peo7KTmQ10hO\nrvxeQVdBV/VzsdaAEPUb4Ib9uLADkUfC1ja7Bqg6Crtg1Hwvjt6PavbJi4nYGGRL19tywsXDRoIt\nOneApJwc3WIXIhe1OhNDsQyRgwvIBsv9j4dCjIXeq/y22ocWkAAbwxTEnFaHsFCixS5FywYADuGw\nVTKt+osYs2em5M0IR6kGdck8YcJKirWacu8LeEeUTN8tDN1CH000F4KFTywZxXiMHTTthQIVD/n4\nec+yVdfeW+Upmu9oAzXlKwDNJ3WnbM1LJZK1EIoSyg4ugipFrBhjCcG1SnuYUMP+moNaJeRQKhhR\nF1bL5pmwlWpPsrvaIq6bkb0Gj4rRsFL1IDW1t9hEGMQ8R+RgYMW2YoZwxK2F9T8A/iNub+X/B/iP\nv3W7/Wt7/NT5CIB/8N/DD/9wO+1fJw6/vdDLxetJTVhBhjNWoOHCESu49sK7ARKZ6cJK0OIVJ4LX\nORo8dddAyFZvTSo7jDMjHWvutuKXSz4wpwNTHpmzZ3KkJw5p4bo+cU0z13RkLBPXMHHpJkaduIYj\n2gnr0qErhJA4BAslzXwha0SLVTS+6pFcgtW6WoU8uZ7Pxdw5B9NInTrSF9dGvUXyKVJOAX0Xa2cM\nkARf+gvkLIQVZBFjNS7+fxcl/XUh/b6Q/pjIb5DfC+Wa0GU1poDOAMm6AxI9mfhdO19si6LnYmDk\nr6/wxwneZvR9hWtCl2xsZN0laPFYf93mT/aCqw5RijEo2b1AlmhmZUO0irl9Z/dzG1Ft+/vHGkAS\nRic9xGU4ByEcBfnk18MglEHueiiDeLgQ5Ek3QBIOxd1jTewffWM4hIVOVssIE/VQeSBJxyImar5y\ntPnVK1BLANTXwtgEncR1emJh+7ABEiVMyn/+Dwr/5b/jAMWP/+ufwb//P/y0+/OX8CH5b4H/FaNF\n/y3gn2DT8//8rd+Lkukau9mCX8W+69Q1oIuii1IWbNJfdkCiGwCpj3HLkHwt1bcFJMHXv4DtCAb7\nsuXJ23OxQkSLXRzaCdKZTqRewxs8UFt01FPGKyDh3YF3waiuM4RRrQT00SzQwzETj54FEgtdbyXt\nW4bEZL67pqKObN0rdJIoYbGwSvRKtWL08RoGL5Dk2Six2gUX115YD0JyQJKDmT1VQJJZLfFYIiJK\n36100VqMq2Xp3DMk7AxJEAMSgBmKSaJzeEGzhlcYYueVI+EuSluTdnXDMC1DYmRD8Ewd9vDTlsHj\nc1AVTPsiQdy/T4keHdYKRmoclQ0wbyB5NkCiWez6rS6qddJqGZL6gFZA4pNlEduVBf+ZdPvOzZaq\nZpdXizbV46en2f1dP/6m85H9MjehvAqCO1YG8WKKcuWZC8/yzjPvPPHOK8aQjHLlEMwkKoTi0rbA\nQkd0QFI34vVS0OZysW+226qer2lgWQfmZMVGp3VkSkfzvCkzU5m45olrORoIkStjN3KVkWs3or2S\nYkSj2IZFrEBnKp1lksZswIYjS+lZcs+69CzB/IqsenWProF16cnvDSDZwEigvAf0vM/L1aeyZKEk\nE6LKDOLhS30WWJX0RyX9MZN+VNJboZy9ts4SzMNDwwZIdFZPBVbTEwa1auQJeM/ojzP8OMGPE3qa\nPzIkpcZpPaWnGlFJ3L+AKkYsgxmUrT2sHSw9zJ2H8MWyXyr72bZazqT6sYCLhSE4IAmY7i4MYj4t\nLxBmZ8rckLn0JskpHVuZlFKnhVG3Ol62kXXm29nvGBKDrHQtQyJCke1KdobkuEkGKiO8pX37XBsl\nu2avzoUmXg2rhe+4QrgI+o6xavX4V8yQ/NvA/wT8BfA74P8A/pGq/vW3fqmKDetRNPikXAFJoczB\nLmRvVLqvMiMXaWrRtGNuw+5fM0OLAOohGzWL8mNBngvyosir9eqF82rdBUPCTr8DIAbAG4aEBfSq\nJpLK/h4uirwp+qLoS0FeCrJ4ShWZLmS6PtHrDkgqKLlnBOyVC/hi3wVbsOrC3bNykJlRTEUuXupc\ntnLn6op5D5egfjsaEMkaydEV5ERnTuxnIrqHfmLexuK58i1DAjX8kTdxateKv6QCK2l0RGxQQ2V/\nx4/q1NTwThWiqutdtuq5+bZX72sxKSm+I4jN84nazScGnsRZEkmtXglklU03ogvGltQKvrX0d6tJ\n1TqLdR6u8YlyAyMRwtCsUo1gqqYCS2rRmx/frVqb4280Hz06BMvusoq/5nV85MKzg5BX3nhxMGKp\nv8aQ9O75AZCIrGLFGSsINr1C/Rp1B+Q4Q1J61mxgZJkdjMwj82Jg5DpblsxVzMb7ypFRJq5yZIzO\nknCFoqyxQ4NtAA/MPOkZCnQ5cQgLz+HMlSeuZeSSjkzhyEVGgh4hQ1qtFtM6DeT3SH6zUM3GkLwH\nC9mcxXDxhDGUGcrqYMRD6noUyhG6o5jL6hclv2Xyl2IMyVkoVyiL/T6w6y8mD9lE+8wMjBhQ4ahw\nmo0ZeVts/L6i02omhF8L2bgT824/nW3XmAdraYB1MFHqpHsqbvBY/732MDfjRqe2ARIRYoR4gJgg\nJtlaEchuy5CjMRbZgY5WtqVrGJKxEA9mhdD1yWorhUQXVmNIqmeOM/hFWoZkpPNMPnF230AHBNWN\nGYmlApJilvKlEHPZ9JMyFfve3/UWhPyM/dEvIWr9L/4mv/cx2yFugESTEJZAWJQyK3LFiw/JHqus\n7MjGkuCVEtkBSav/uz+vIlXwXHyvtzAaIAmvxUVCBRarAKlRndoHMwRStsrCGaPVAm6gZmlqUrDY\n6VWRExYv/Gw3mCymEg9kE5H1iT4lBk0PGZKHgETM0qtzP5EaCikSydmARHHDokopbhNjuJ0MVWSv\nKVSN6qqDh7i1f4h+ATvjUpmWJmRTxBZeE4piabsoKoWo9pq3RJIKAzZAcgtUdIMfdQNbgzPanHP7\nEw/ZaA5oFjQFSy1M+3moMdcuE9TFsrWOQ6hslIV3doYEj7M7GJkxsNqEbDZPGn/v286pxwR0Goz5\nKA5GooMRr5KKLNQA1D6B1r5Sz7XV4ztDUo+/6Xy0HS1DImqps5IsHRLzFXF5KJ9445UvHGuND5kZ\nZCaG1QCIQJbAEtz6vwEjm/N0BSX26k2nlTtjSNaBZRmY5wPzNDLNo7kBT0emMHONR45x4hqPjMH7\nOHEMV65xJFBIYvdhJDHojBaIOXPoZp7WK5/kwEWfeC8vnPIL7+tC0AxZyKkjLOq+HD35bCZ/+d3Y\nkU1D8i7o2efmybBzSWJhmtmSyPQsVnj0IJRRIEE+K+VdybWdlTIpuqqJT9AmZAN68c2TYjYLdW04\nFPS8GitSzcbOa8OQ6B6y2RiSyG7gUydw940oI+RkgvO12CZ2AyMeclUaoau3NiO/7ieCh2w6ByOD\n61dVzHKkQFSb76okzS++bV9SxMniAIxsDEkYCt3g6cgxmZ4vLPSyGEPiGYxmQNtoSDxEb7OrbxjF\n53LyxowYy2I6w0Kh03zzMclkm2x95xaQ/Iz90a+mlo3pB/aQTdZiC0m2WGZZC2FRe9MTOwtyfgBG\nKjPSMiT1g8sPxvU8+lIX1O3KC3J0l8HXjHwuhN8US1Hr9EaUXcBSy4qiWQ1IRTZjPRbf1C5syF4j\nyMFU48wFcl0IE7Ff6cZEn1aGkjYw8khDUkM2rYZEgok9+41lsFLam5eIayNU2HQSGxshjSDUwUgJ\nt2LPXD03QvTd/+0EK9u5Caj2oJI3ac/blF5LvbXfcR7IY54qLS+0rxQtN9AeVUNSNLpxmdXKKCmS\nV6uXUVYzQOqyZRB0Wl1w3XHPxazmuaIbO7KBkoYh2bJq5gaQbFm5zqTViq6VIdncVnvHGL4VylaX\ngpxswtTVe28f0HSbWvedIfkljpuQDTOjmIbkmTOvnPjEFz7zIwcWejHN1xDM7VLIpiERq4Gkyna/\n7AJuv3fqOWqiVg/ZLIszJNOB+ToyXUem65Hr9YlDvzD2E9dhYuyPNpYjY7xyGY6M/ZEomSQdFsXN\nHMpElxJjmi0UG4z1POsTX8pn+rQQS0azkNaOeTH/FY2BNQzkc0RPFqIp7+FmTMOQqOtFi3tqaNVD\n9BaqyIPdJ+WqlGumXIv1F+t1yWh2kOCbO2acGbFNQZ1X9Qz0xQpUXhNMCfX+o6gVm5QlYDHWCuw3\np02jsotl6VgmjG8agjgY6e13KiDxbPwNjNwDEifSq59cDNAFoQt4E3qpRJClKDth7cZz9jS5liA4\nqtnyHxqGpEv03UofTdM3BHP3DpKd8W00JPQ3M+oWqvHNV/RQTRDbJMeS6GJAJdtrcnJJZiVc1UDo\nu8C52SD9q2RI/qZHRWD1EEBLckASKatS5kKYdQvPaE2hrEDkTANEmvPqLeWbyq+OfU43hqRYyGYs\nhOeMvBbC50z4odgOuAk1FjUwIhWM1DViu5B0X7Twm8i/L+nxHYDrN4I5j8ZjontaDZDoRzBya45m\nhyI7umVXxds1qIbM3e2z0nZVxFlk71V2ELCzI25Q1wIUDeQS/XdkT9G+GbOBm03zIS4orSzEpjDx\ndMYNiDRVaDYwEjdQwgZj2Mb1c6iPFew15tKZYVmO5NSRVjMvsxa3Wgw9C33NrAlm1KZb6Etvs2yS\na0i28F8DSGrI5maXJHcMiRgAKd0epsmdeQ2EbE2qu5+rsbU4AnN0q20FyHp8ByR/luMuEiaebt6T\ndg3JxpC884k3fuBH343uTsAdedeQaGDRnkzYw4FN287ZAUkN2WwMyXRgvh6YLyPTZWQ6H7keZo6j\nVe8+ypGpu3INI2N3ZByuXA8jXfQNn+JOmwlZF1tQIuChgffybAZYya61lDpmOXDhmSCKioWdytnZ\nkPdAOVtv7EhwhgQDJAtbgWruCldLJwT3OSqzokuhzAld0tbrsqLZhacFttphlRmpxpeDWs2rqLBm\ndPZ02tm9P5ZsLVcNCWyLgDZjOvYQTrRNQS5uatYyIz1bGl3BEuHa9WSPLW+iVqn7t95a7IXY0Vgl\nWJ9VEA/3qpdHsUrGezQJxVOoHZAMbmDXe+mAuDIE0ztZ0byyb163GkiDB/rxPZO7W4e8afuiGivS\n60osyeb15uOSVQmzUqZCuGDrbltP7+8CQyKqvqvN5JQJa0EWRSaj5eSCo/AGeGzghOYx7GJtEetX\nxpLt79aQTWVILGSTzdjntxldgmlBRB3Biv1eI3JUDwlteqmaKeQLmNYc7k5Rt0IVDxnEQ6J7SvSv\nFZAsN0DkW2m/AReMSiGol4z2CTFqLYxVyBK2VFxLeQ0W1pEWHngfrDhgUX8sBD+vryQ6YPDen9NY\njejAwtkbf82mHbF6PFETneymb/vfjvtra979LmVt1SNtIbwdrJjniblopmw7zZR60uptsTbozMAO\nrghuCKUZilgZdrFdw+Yg6zejeNaAZW9Jk2XDriGxN/9RQ1LiDkxyZ7u36C0UAyV69edwzYjNxDbT\ny9V+vpWehtvZ4Pvx5zpCZUic/RiZeOJq/iKc+MwXfuBHuxJbbyEHtChbpooZQZbm/9X7Qm8ev2FI\nqoZkGg2MnI9M70eu708MTwtXvTLKkWt3ZCxHRjkydlfG4cj1ONJ1VvSsL5mYMl1KdGu21iX6YJVd\n33mhK87oaMdURi76RK+rZaRpIOlAvhgTot7wzBrd5mWxbJhgl7d4uQoJpo3AxxKC77YLmjKkFc0r\nmhY0eV/TfrNv7qoPxowXM2U3MA4e4tn8fdT7Yo+3WTZbyKZm21S2xCkNcdFfVttgrC40l95egGYD\nN/c1z/aY8g5IOv8znmUTRyGO0B2EfhT6EQbvc5G9JErCNDYrhCS7kD4DBw/ZjIXgYMQAycoQV4Yw\nGyBxkGyAZE/7vWXXxTeMum0WjTGuxfUG0yZWpscBYViVvChhsnRvPQGn5sb52whI7kWtIBTNlJyJ\nqZBryGa2OdjCNcEARws+Ho3beJbe9e34xofEAcm4h2zi50z4TUbnsv96yWgRpAiSgtGJq+529TXU\n39jX64SJoia7OBVFY4EhI2MmPGfiNdHNiT4tvnO/dWi9DV7soIQKSEj0UosiWQjCQJ+FJHIDcWq2\nzDZu+gomKsDIWsHBzljcm7EnOtOFNDqOTEQo2ys1G2sX60oV7C5EcgO72mozHx/bwkqeT6fbeAdo\nm+9IMS+HJQ+sydvibR72LKAKRkKhi4niNYqqOHgTtHqWzcaGPQzZ8DGOfM+QZI/7VbFe589ZQ16h\n2P8LBUoyChl1ZLv4xVRRdz2+MyS/xFFDNiZqNffKo7QhG2NIqtbJdE9hS7NUzFivAmbxyV+ae/m+\nzzXtN/Wsa98wJCPzeWR6PzKdjhx0ZnIwcj0cGdUBSXSG5DjS9wtjgS5n4poY1plxmRm7hTHOjGHm\nIDPv+goF1twz55FzfuZUPtHnRMiKlsCae8ol3sy3um0SubFcqBlydRdenZq3FNPKbKrHADSBLqjO\nxgDWxuwaEvZ7Tr7StkrktX/wGLDfoHmnvGl6xcGMuK6rMiOeFVcc9LRhmnq0ab9V5KoGzEIP4QDx\nCbpna/2z0D/D4UlIBcvmXISyWNQoLpAXu+0rKSqDGpNfQzaDgUtzZvVwTVjvNIf7vFnn5uQ6mOiG\nk5b1aOHJuK0fK712W0Jf3YTntRBmsdpDNWrRApKfXsrm1wNIdA6UqSlkdQ2UizW9VBQOelb0rGaI\nc8pNiEYb11V1nxHd03ztrzz6y/swK2SPMa6V4itG/V0VvdhNp4v433BV92xCJ2NFHIFn04Tc9hWh\ns2cPhYBWq/E1UhZruba5I8+FEEElu8dJvaHZmoirNGqWCvuivHkbNACh/SRabUegoMiWkruDHf+0\npAZVKtZuOYoKB7h5vkzcYu/9n2iRvCH3Oi0Y89MGp/Yw0M6M7BCthWx5a9Hrz8St7kzb1tITSyKV\njlh6UkmsPl41EUtPRCzskyJ5jZQ5UuZAuQazu667wgkvoOVtoRk7YK0ZOu3OqqV620l0o9oetdax\ndbuQH1zn34+fe8iTFYWrR3hOFkodVqukGxcGWTjozKHMjGniuEwU3bVLuRZfdKaOol6g0QBJvZJv\nWJLtqi6EudCdM/15ZbguHKaZ43Llab3wnM/uyHzkVd955syzXs0CvEwcitUm6XMiJq/SW9wQUDyL\nL/oC1ie6gzGyfVgtpJMsrFNrl8jGNqiHHxWil++oQgfwEEad5wq3GQQZ3S5yQ+h23rGnr+93/h5G\nqZkw/vAGHuAGQHz1eDT33//sK/+n6rZKMrakCtCz7vN5qgyK7kCkfTn1rcBe2bkLcIg2/+cOLRnV\nniKFQofSoRrR0pkAfw229tSQ8KK7v5Qk+mDX5KGbOerEqGbYNzAjpRBKtmzBUpBiBfBsg2XutYWF\nwoJZOiQSKyuZlcxCoafQqaJv5v/CSdF3Nb3IBUvUuIJMavjRj/TTS9n8egDJch7oToftmshTZD31\nZr5ziqSTkE9QToqeMnpKJp65sIEDpgeTftYmXggfL76mz9jvTiaO0pOiB3VXVewmyNEKLr0Fygn0\ni6Jv2b6gLwXes2f3BI9bNhRhVdLXKo0aIUIZOkpfzAwQWIulfoU5IFOEcyR5HK8L7qYaMjEEolNo\npnfY6bdEdwMVzOUjs7qIqdxOex9aC2rKBjf2erlt2zUdbKPd+GwPpcRGB/O1Fh0QFYdG9RtqIc9K\n/yF08/UW2V1K2nYP3tiev9rDR+1ZSkGKsSJRO/eA8Eq+XjyvXDzd8V1MYT6pf/dlj13P5eNj9744\nLbbY6F+F4ttPvRojoo5+9Z6C+X78OY/4KSG/2WfT7pjoXi2c2h18EQ/GQPYp0c+J/pooWcg5UEoh\nZDMSKzkgOZvhoxd8q+HV234HJEGUw7pwnCaerxeW6Z00DehqrFoUpe8Sw7jyOrzxKX7hk3zhM1/4\nVL7wef3Cp/nESzzzFK6M68xwXegXAyiilnFRukA6RJbSg8C0WIXaNVuGT8qBnMUEjO7dE3I2PYiD\nKEox4elaXPDvYocNZee7vgKSgLsEsmcXVAOPmgp4DzQeUSKhGcMdqr9r8Ge5Zz6wMs1bqJvgCkQa\nryItYkaNeQ8nBzWwUNmbnDuWydjbNHXkubMN0BTQOaCTICuEoxKfsmVj5oVRJ0s5j2eeugtHPXPQ\n2UtSuE19KkjNGEoZ8T7qjDSuooUrmSuJiZWZhYWoK/r7hP4hoX+dKX/M6FtB3wt6sQ26robb6rH+\njP3RrwaQrJeB5e2wnedrJL11rKeOdArkk1BOUN4UPRX0lOEkVsJ5VpiKgxIHAWsTOyxfuyBvzzUb\n+6FXZ2QOVuDK0mic0VgiJOy1vEN5t1Q1fS9mC/8OWoHStpHVWx1B5wPPutCDUjolBSEirDl4/YYI\n145y7kia6GMix9UpuYTG1YRSnWXW1EW1DeHciEmb9oFFQb762KMSdW2t3BoqqsJPs3Oq7Mqu6PgI\nZfLNM9YYpwGD8uF93P7t7huAan/Ne8WbhkFx0e12iP1TanxfI0F7RKtgTS09ksySDizLgXUZSFNP\n8mq+lmUgfk369be4qn9NXqci3/XcgpHWE2djTIoDkQuUCkhqzQN3df0OSH6RI37KhN/uzFN3sOJy\n8SnRjcl0F2KZWRsgiYmShLCGzelUfCyrUNaAeLr5Bj5EibVGUwUnfn7IC8f1yst6Jq89ZY3IKoSi\n9OL1ag4Tz8M7r92Jl+B+KOXEazrxspx4Ce88c+HYTxYGXq1MfPAibbkLpMGWghID08FrXuWetZgQ\nvJRgrsMZ223nbOwIGdSzYFxAqn1GYkZDBSDweCNYY5huYUrCwIkLSjeq4R5s1PPwlXFFBy3leD/+\nGffMg0jOQ1JGdQck9S3W89nf2gJahJIDJRtbK7lDSvGXaIClpI516lnnnnTtjSm/Oit7FdOqrRCe\nC3HNdGllKFbO4BguPHdnnod3Xso7g06mzZldbuA9U97GOmdiWQhcEWaUK4WJzOSAZGJmQTRR/pgo\nPybKHx2QfHFAct0BSW4I2+VvJSB574kNIClTcGbE+nwKxpC8K+VU4JTg5OGS2pZm7KjPAMkHHvxx\nSwJLtNLXl0jpIyFEW9yyFUyTKaJJ0YtarYWzI8OLh5IuTmFddc+cKA5oMCGXyZkFnLbTg5obXxAS\nQqgVM5eIXjvK2WrH5G6l7yOlW6yGTufaBilWcyXa4l3Zg3tYcd/q8bUxtOBkD38khw9VkQJYjjsZ\n2fiID9LYG87iFkK0cAEymeBhm/qqKkvT8in34OPRON//Rdn9XBXZJpT6PjORQEfSQig9UnZAErSY\n9qQyJFNHukbyJVDOlvbIG+574xP0un6j8fV6eRsg0YYZcTDygSH5fvwSR/iciL/dGZLYr3QHS8dv\nGZJOE11K9ItR52UxdjMvgiyBMAt5Ccgi5Nl6TbKBjg2MSGnG9nhh4VgmUrlQiqX2xaJ0JXMIC8d+\n4jleOA6+CIV3nvXMUz7znN55Xs48ceapXDnGiZDNzCpsDIl5KiWJlCjkITLlgbn0LKVjLdHZHsv0\noOhG/yMFzQlNyUD3lGFI0Cc0VuVl4SOD8QhEwL6DqwzJo9gHd797D1yi//yemanPUccVtPzE4xEA\nuQcoLRtyz4z4S9MKSIozZdqZFsPdmbWG+1IgXXvS1JGunbEk12jh4SlsOnZZlO4IV9kAACAASURB\nVJgtA2aQhUOcOfZXntczr/mdFz1xKBdKzpSlplNn9OJp1Zdsj50zsawEz+ZTZgozmZnEzMpMYEF0\ntYjAW0a/ODvyltF3WwOZLDpxw5D8jCnq1wNILgfCadzOy1U8TCMGRt6EchIP2RSPYTX095pvxxsg\n8ZjfTXD+K+Ms6NIjU4eeza5btaekDlkiehXC2QHJVCjXgl4LOnl/zVaFcirG3NSbpHUhC9HASHXp\n9JpquRNCDCQCUiyuWOaOcs3kcyKVlTwslCGggwEcqZkfUogx3TAc7XF/Dtws3y0P0vZtyOeWIWmr\nDle9xy66va1JvI9v82Dav7s/rkB079bbDKJbhmSl317bfd++i485SQ0b1Ghxtt/1WL9oh2hx23mb\nQETLlqGzzj1p6smXW4ZEv7AzJLVi77pAmr1fYJ2trzqSR60N2WzCvmXvb5DLd4bklzjip0zXMiRe\n7bbrVrp+pe9uQzbdnOhzpkyQJyFMQpgCeRKktmuguJNvCGwAJIZbcBIDlmIbFpJMlBBNthGUPiQO\nsnAMV567M6/hnbG78tRdGMOVIxeO5cpxvXJUq+J7XC8McbaMH1W3HVDPFgyUKOa9pDCpMSRr6T3L\nJ5KL+L6uAhIDG5pWdF3RZaVcV/SwQrea9ag4i7epOu891VuGRLhlR+5DNverf7j7f+3/D+w3UDuu\nxy8QrmlDNrV/hMNmPI03WG0t16JowR5zKwVdg7Gv127rSwUktTzKAiFZJmAvK4cwM/YTx+HK83jm\nJZ/4pF846IWcMnnO5Eshv2fyyVszDnlFWBGnbXc9yULwx5SVcsq2Ea/9ew3ZFHt/qy279fhbGbJJ\n5x65YUggv1lYxLQj9xoSMSXvmnfTmuT0eMr7eXYh0g34+AqVlwOsAzoNltWgSsm2y5FJkXNATyZM\n01l9nVCnvJLlu7d96ECi965OltD0AfpgboW9kENAiJa5s3aUJZOnQjpnclkoTvVSbFIJUgghU2JA\nvYJVa2p2z4m02pA2V+a+rz8HuAclNbjS6K43lYb9njp3sngmgvUDC7fQyCaFe+ikBNL2Om5DNvvf\nvw3Z3AKSW3j1KFW4ELbiZTuBbLuTTGxiuWoCxbqbKQZI8tKRlp7su5dNQ3ISeBO4uIDZUxgNfMyQ\npr3Pk4cU2WndVuBaN5dF2Qp/VYcpqzTJZh//HZD8Ikf8lIiNhiTKaoxI0/qQ6IozJMXCIeUC4SLk\nqyCXjy1fDZBEBxgVgEQxHVg7liiUPkIvxF7p+szQLYz9zHM8c+2PXPsnDmHmECbrmTiUiTHNHMrE\nIdnP+rDuPkGNGWJxi/L6+IxpSBbtjSFxUa4VinRAogVKoiwrLAtMC2FcKMMC/QJhQWXBLuZaGLLm\nu9fVGfY8+ApIqtf6PUPyCJC0oKRr+gpC6k0FtxvQb4lff8Jxz5Lcy1YeLcD1FvXNZFFBioMPxU3P\nGkCSAvkSyZdIucZmHFBP9GDGvUIyXUx2XRwmjuOV5/XCS37nk5446JmUMmnOpGsmvWfSW2b9MZO+\nWJMfMyElgtO0SkJd0ppISPOYXnwDfinbmEuxjdjflZDN8j5QGkCik1JOX2/UlhKbrW+u47SPN0BS\n/kRTSAGW0V3ofLFYAkwdegYZBTlEtBRYA7pmK/i3GhDSNW27BdYEXQexd29gsJBNsfSxTiBGdIiu\nVTG/DyimeE6msE9Xq/abS0fJYQcjmHlNjB2lSyaU8jujXYC/Nm5N1uq4Pt6GdO7BQK0ZnDfnmB5l\nT9k2hiQzsDIye2l2s9L+qCr5eG6al7xBi/01yA07UhmSHYQ85l9uAkdyK2a9D9kUAqKB5E6aWoTs\nYCTmiJRCTp1n2HTkSqM6Q1JOApUh2a7H1UBIniBfIV2tz1cXXPNR73cz9th6zbKpxjbb+XdA8ksd\n8VO6ZUg00enq/T7eRK0+Lu8QzkI4Q3gXwrsgZ0HeQXysMxvwiMGBSdjH288GYIR4LPRj4jAuHMeJ\nJV6YZWDpDsyHg6fOG/DvWenLYhk2mOHfwEIMmdxFches7wMl1vP6WGSSA7MOLNo5QxIo7hKKWmZH\nUJ9b5wWdFsplhnFGhhm6GY0zEmaUZG+AAx+FdJUh8SrWrNwCksqcBG6Pe4akLSBTnytxS13UFthB\nyc+8b1rwwYPxgyXl/jHtjXHdUv1d4GrsSCQ72NNFtizTfIlWaPASbPNTTedmm7W6kC3ra1gYjxNP\nTxcL2aR3Pukbo76zpsyyJNZrZj1lli+Z+IfM+oeM/CHDHxKyVgv5hJIpJPKD820TPtXNePHH1B77\nuxGy6SmnW0BiTEihnMomZNUtZFMso2UDHaunZa0fz8u23eTBjN+0iC5mE65FYI3o1CGXAj1IL1Zy\nWgVStg1qUhN0pYym1RLFkyeMDwP06veP7A5/Ncum97SvIVD6AkFRUUop5KSEpSBXY0JyrciJx5qD\nVXXM3WoxXrWbtoKH6iPSeorUcQUeNdW2/k7PunELXwvb3AtLk+9uik8a4iGb3suzH61UF0cutFqQ\nx7oPe/69cu8+YbQi1T1kww0IedSqv0qRhh25AyMIm1dErbljWlYh+I5FSkayumtwQJdImWz30jIk\n+oYJmq3WugGSvECeTZSaL5DP1leHx28Sd9owIdnHfv6dIflFj/ApE3/TABJnQrq00uU6blo2YJLP\nWJ2qk+z9222vE7fgI0CMzdgf70YlPBf6l8yQFhITKfasQ0eSnrXrSGNP1GzlD4ozNqWO8/aYSCEN\nPevBfIJKtLClZdl09pyHjikcmBlYtWdVL6xJqKSh3cHqbPR1pVwW5H2GcUKGCe0nJE6oTNwKosAA\nRGWs7wHJPRj5FkPSgpIKRGof736nZUceaVJ+5vHo5dQ/U3Uj96xnHXeAhi0UjGtGhOCmi7b+sIoB\nD3e9LRfvz1a8kLOYXXtUYp/oDyvDuDA+zxznK0/rmZf8zmt546gnlpSZ58xyTcznTPySCX/MyO8T\n+vuM/i7DYhvBOtcUn62Tj6tlJauBDvVMVq3eW7V5hnQ9/nYCkvNAvmFITLiqp4y+Z3hTy17ZAEky\nQFJWa5XO1vVBa2+Kb/RqRa/Mn99DLbE3tXhUcxSMXpO++I7B6mpDSWipRRsmzM1GHZQbG4IY6DDm\nMsAY4dBtWTYaoWBpdbJi9WAwwVMuyx6mEauq2/Urue8oJW4MSWUFanpsq+RoFR01jJKb5b8NdlQf\nkm+BkcpUABsgsd+txcdmt9Y2j4SdsWj9Zm/HNRtnTyTeX8P937+X6D5iXj5k4MhdKKvJtlECRXUT\nlolGT/ktroJX8wNYaupd9R+xsut6CvBFTNhcSgOIF2NIyhXK2eKQ5WzsxiN9Nffnjc5Jm3H7+Pfj\nz350nxJdK2pNK92c6JZEN7vRYLFaNX32LJs5GTNygvAF5AvIF/Ee5EeQN9vhxtgAj1hBSa1vojbV\nPGW6T4mSFhM7xkjpA/kYKMHGZQxILQOfCqFYk+Tn3lRheSogagLWPqIBAyRDx/I0sBx7pmiAZKE3\n7x6/G4saqyC4g/GywmWF9xmeJsp4RQ5XpL+i8Yo5WLbC65riO/ARkNS+AouvaUjq89xrSLrmuSMf\nUUKd6++f61/i+JqGpLUHum+RLe1Xilqovfic7+uFFDVTtOp6W4sVvt+ey6yEzuzi+2Pi8LRwuE4c\nZxO1mobkjaO+MedMPyfmSyaeMuFLMmbk9wn9F5nyzy3bZtfyFYrPLcUfS/7da4Gy6V58vWx7j1jX\nY/kZ09OvBpCk8wANQ8Lkab0nsXLGJzFH7JOaIdp7hlMTU3+YP9k+Vi/IZod5c56B3v137MbRm0pJ\ndcGr1l+VRmwv9vo3ZzZ7uiA22wyuHo+6MyRjhDEaDurvnrIyjn6Rl2JCzxgMjPT9Shp6coqUHFDd\nBa0tk7AwUMnc2m8urA1b0IKRFqD8KYZktfJ9DSBRd2BdGkBiTpY7c7M7uu6OseZaosgHCWr7Gtq/\nW9/vzqXcAxM2wNOyI6Yh2UNcCM1zRKelA5sBklvFk0GTWD2LWSz17iqoW2hzEsuyqYDEypwaINEK\nSC6gZygn9on6+/FrPKxURMOQzIl4XemufhVWxkQTfbIJv7saIJETxob8CPJHa/wI/MEe07ODj2hF\n1e7HFaDIK/tcELH19sgedeiwiEgtCl0vqTqH1ClwxlhUgRIDeYisRndY2u8hsh57pucDU3/wwE/1\nd448SsIvU0JOK/K8IE8zMk7IcIXuAvGC1feogK4yILXgSwUk9XHlMRi5ZzQeaUjacE19DrgF7oWd\nnfmXACRy17fjFpA0n/tNH2roS273Ftz1C7befatNIIMSx0z3vDK8zIwbINk1JEd9Y0qJbjEHcHnP\nyFuCP2bK7xPlX2TSP0vkWW8iWZsc5kF06xHGaB9rZ7af4Yv26wEk8SkhL81L7wqqnuOOIcd90fG8\nda0Tf8GgfmiuQd9N1qpEXw3T3F8R9f9VmDuzi7LqzZLZfOC35tbGNw5XtZJU4/BX3VqTt/Wn3Rza\ng2aheMs5kovpHZLubEWrFWmBRiRvYZlA+eCQWkM4rUmZoJu2pBXE3jMpAwtH14ocmBiM8PXn3k3f\nTXgrZAJJ3Sxe/X96v+jAVcx9MklvrImYpfHAwpNcEJRBlscMyN1jOFxSzI9F2X1a2pDQNoH7Xagr\nSJBtXtQalv5R4U1tQrjgqW7s9e0KUGZufUMWuwbqdah1Mv7Wd9/+7E/d/t+PX+Iovy/kf75PresK\n8xK5Lj3nZeS0PHFYXhnyTCQhXaEcAhwyOhbrD8XGoz92zMhUCHkPz4Rg+5YQTOt+Y5FTp6nWmLdO\nNVcMjJx5vCu/j5Y0l1xlB2vtpi35Xm43HuYqG27G1WxQS4eqZSKqHlBtXFhrmFoS8OQvdMCLzfCR\nUqjzrvoH4IyJHNgQmKj/frVuHx6PN33Kt4DLgEgiRKvzFaJt9kIsfp6J/rhGA20lWrOxi4ERchLU\nWQ3WYPYR2VvVi1gZ9f17CCCdwoAVBBwVnkCeFF7YUoVl28BiWG60j0OelDAXhr9c6H+z0L8u9E8r\n/WGljz73VtYuJ8qcKWv2IoWu0QuF0ClxUOKo5kyxXXhfvy8GYFBvXxm3wKJvgfKfOH49gOQ5ET41\ngGQobpVe0FBQUa8iGwBFpbMLd7Nmj/sFkGGz9a2A5QaMPOLI61Fvksp29NiKUxE7/hx1VrgHI02h\ngc1y+GtgpHlK+EgBti0JmsSqH5fgmR+7q2gFJe0ibU95G4IJmJX7TsquW39fUbhlS+5ZlLCJYVcG\nVo5cXLw6b889bM/pWTOetZK1Y9WeWQ/MOlpffMyBRXpWGVikJ0tEROkkcQgzgUInaWN5buuq3te9\nieyAZAcjLaeyHUX2ObFeCY3MBMG4xx8xR95TMdvkmuZdzfhy8bDdGfMPaXxDcLH09oT3Yr32j90f\n99uo+8e/H3/uI/9Oyf9fA0gKzDlyKQPveeRQnunySihW0iH3kSUMxHEljCvxuBKmlTg/6DXvVezx\n3q+3Wh1iuwruCdhaF+vKvldqQUtuxnW623b2slfjbjLy7lP7N/DhRTS3MV7lWwMlR0ruKSUbdV/J\nfRGsrG0HIYEeMVBxYA/P4PdCZqN3pAEk0gAS8ZtS6s7AKeUKTrTRkGirIWmBSEVpAxWtSVj3FO5h\npfcspn4wBrrrrURAFmVFPNdEfLw/I1lJqRgYWaO1FCBFy9wsEfOLN7QpQaFTpDeGQ0ZFjgpPirwo\n8sl0GNLrBkLkyQALzyBXhQvEJdP/dmb47cLweaV/dkDS2UZwS0Vfk3mQJDewU6ssb/oTpTso/dFe\n4s1xPw35eV8MZAy6j/tye94109JQl92fcPxqAEn3vBJf94ql2qtFsqSgUnYYIcE9xhyc1LTfFHZ0\nWilOHoGRR+zIPUNS7+46A9yj+sJjPu6eIeltV6zZQEmtZ1P9J9bmae83zB8AiTEk1izzw/LYHZA4\nx2Gv7pbNaOvSVJ+QqiHZVPl3bEllSG4qMN9MWZmtOB6rMyRXSzn0590J3+ykL5baViJJe5ZyYCoj\nVz1yLU9M5cikIzkEqygsVllYAnSSEWb3YJi/opGxz0CopmotGCk0U+YtMFFhE5hu0gy5O/ev98cC\nb+4UfM5wzV7e3DO9SvYQjVfhfcSQbF8wD8aPzuu1WVeW9np9wKd+P/4sR/5dIX1qAAkwS+QiPYOM\n9PJMsBLhFImsXcfcjwzjRD/O9OPEcJzp54lhmunnwDArsng9GW0svXRfQqX9Or9G2k7cZtO209p9\ntlaLgb1tmWZNv7EktTnwyFqBSdhq9BSNlNxRSo9qce2VayIIvmHsjCGREfSAra7OMqsY/aMVaTk4\nkQaQSI+XYHfqqC7sXdM3GTbapv+2GpPqAOsozcvoSliJ/cRwCByOcDhkDiMcxsxhXDiMM4dxJhVl\nzsKcgvVZzNXBv5PiyQ2kDtbO+tRB7sCTEbbqwbAxH6FXByQFOSryXJAXJbwW+ygGAyQyGgsrT95P\nClcDJMPnhf7zSv9paQCJz4aVIRF3Zl2zpWxjBnwxFrpe6QcYxgaQfG1q8nFXHHgU6LL37WMmudyO\nri6nP+H4FQGSRNcwJGUwCimI9SJKEbEy1hINjASFuTNEungAdvGtBmphG2k1I+3qcs+O1PGju7/d\nydY7/V6n0jYHJG1BpuyLVfJvrLIkj0TfD8CJZXnWkI2DkZYd0Z5OLebQLrcVhLRaDGBjRlpA8ogh\n2V9CZVpq4q+FeBKRntUzaYwhGZg31UrHSiQRcGdItYqna+6Zy4GpHLnkZ87lmUt5ZipHu5qDIgET\nE6ua54NY3E5UyRJudDELA5GepYl2WwpxaMBIcdbnniHRHYNmjGlz0KjZGDdJfil8UfRLMafgczJR\n35RM4Jc8zbdUA7Nq895YvT/iz7fz+3EbnP4Wo/f9+CWO8ldKHvfPd40w9ZGhG+j6I6HLaA+5i6z9\nwNyPXLoXjscz43xlnC8c5zPj3FPmAIvSLQlZFrpi01dQszyqY/HIhLTTVDsltRK1NooMHy+T+0tG\nauRAtr542KYKvjetmBeiLL6BqIaBWc1GvmikFMsI1KK7lwYRdVZCpbdVWwcDF1rRk096tXAkxnrv\n4EPYquqKeiyrs9iGBgcjsWEefFwBSDWd3ISyThVtzrH2YYosdF1gGOH4lDk+BZ6eleNT5ul55fhs\nKbRzylwXuMzCdbY++G2dE6xJzZQz9ZAG73vIw57ar/U9ChLVqsn3BRkK4VAIx2JhmJeCvFqmzcac\nzCCzmhfWjKX7zkpcM/3zYu1ltX4L2VSGJNNrhqUga7FCiVqIUuiiMvTKelCWo2O7+6nngWamcyBy\n32K5Hddj+Bn33K8GkMTnle5Ty5CITd8iHlOtfKZVSxG/qegTLJ2BkbkJwNZqjKndJjwCJH9qO9Ii\nhkoxFr4uo24DuDVs84Ahifrx6b8WrnH9QmVH7kM2WXejskf6iHtgATxkRnZQYoCj/l4b9rG/1KYR\nx5/JkAi5RNbSs+QDUz5yLc+c8yvv+YVLebKUxWj1eiImGqwVLaPYY4p4cOhwA6AqL2S7vWyxbixl\n7T5c80FDkvE4sJq2p8WWi7jQGngrXkRxhesC82LmUGlxMHLXNlOzxJY7WSfhbwKRR4Dknvv8Dkh+\nqSP9Tn17Z8c6wDwGLmNPGEcYlaKRFHpmGbl2T7yPr7yMJ57Hd56PVhitHINV7Z0zZV4IcyAWW6ul\nOCDxsZQ9QrFNW23Ipu6BJm7JgAcLx4eNToAtFrSFbD7Wg9pYkoYZyaWCkx2glOw6ksJm7GVApIIR\nD7dUZmNjRxxBic+nWuf9+oE4Q1JjWdJBGCz8UzxsU5wBqWNpzhH/0Lr9w3tQ6E/CTOyV4ZAZjwvP\nL4GXT/Dymnl+XXh5nXh5vTAtmfNFOFyE/gIhiO1hkppONWP11PLB0/wPtu5s++AA1WwRbLPloCQc\nCmEshGMmPBfCSya8+ly1NG1VxK3i8ce6lBkOC/1Y20p/SPSdFfXYNCQlGaBJBkiiKp0ofVRSp6QD\nrC0geTQNNeOYoEsQs41jbs7FWmhC3/3PmKJ+NYCke070rw1D0gsi5l5qN5Gpv1QEkeaxfoUpOqL2\nT267gUtzIX4NjDwCJe125NHjbcB2/Uqf2Nw0SzINSSm7tqWyI/caktp/ACTi2MYYkpxtkkhlZ0gi\nu317y4w86h+BkX4DEO0Cv4MRqzBjNYNbf9eelXFjSKZvakhU6+veAcklPfGeX3lLn7iUZw7dxFBm\nBp05qCJd2jQkgxoMUWBipGe9ea32Le8masXDNfVzKXegZPvAi1q4b1HLoJnVsmhmP5+w83e1UM0p\nGSC5zDDNsLgDa5kNlGzff2Nk9iFk863tSDtur9/QPMf3kM0veZS/KuTSAJIRppeIPA/oC+QSWGVg\n6kcusvDeLxzHlc/jkU/HA8vcU47OjMyZYVnRZUKWYCRg8vXa+3oOvlbXS6CdbipD0rqvb7EebhNT\nHj3WhmtqqEaCMSUVjGzhmbgZA+5AJFpfIsUrAFdjL90E//3+xkJxxuL+hYot0vUNir/pliEJwXvd\nm4jpverzlLoWSANG6j3h98093STWh9DTdZnhsHJ8mnh+Dbx+Vj79kPj0w8rnHyY+/XDmek28nYTu\nTQieT1GykmZlUgipGEOSVygjW0HXyraWzuZ+9mVLOiX0hTAUwpiJx0x4yoSXTPyUEXEQkqwPzbj2\nMSX6bmHoFobetSO1sVoGGJYBJoulhXdFSar0oZCiknvIByWN7Ia5NB9jO/bzkCCu3ifvg/ey66Hq\n0f+02w34FQGSe4ak9G6jLgqeVWN237a0iET7ZmPnYMTvuipqrSyE3DMk8G0w0jIk98xIBRyVanyk\nIsu3/SZqvWNItpvM/8TX2JF6725gRLy2SrwRtUaqjfsu72xTaFvtRxtyaQ3S7jNtqqg1kLfP/aOZ\nmdCRNjfWg4dsarhmZy+sku9NyCYfmNLIJT3znl44pU+cywtP2rlZkNKTdlFrmTmGC096AdTDQfkG\nYOwpz72DqGrD9jXrNL8GNtmQWIGoq7BV4rZq3PbYRc0m+eLhmusC0wTr1QBJvjoz4qHC+/6DhuRb\nM0D78xaMtD/7Dkp+qSP/XtGlYRefIUwRVqwqdOiZ+8y1ZM5kDl3mcMjMoxVf3JiRJXOYF56WiTJ3\nyCJbXF1WIwNkbciLSqDdMyRtFLnV2MOtJcd9ay4X3ZrcAhNuM9QysdGOtOyIA5QcDJAUAwKqEdUO\ntKCVkZDsgARuK65j/RayYX+Bzt5sJTbq6halYXj8/7X3ifrj9Xk+zPU+ln0soaPrF4bDxPjU8fQS\neP0MP/w288NvF37zFxM//PbC5ZLoDr7E4GGaGaZO6VADJLPuG08vlEcJDkbSzpAISGVIKiA5ZMIx\nE58S8TkTXi3zR7I9t2S1lpSQjemQrHQ508tCz+L96o69Ppdnq63UrZkwKzkpJSudKkWUEpXcK2WA\nfOTWpuVrm2PsWg0dhNVb4EaU3S5r4KLWn3j8agBJ93TLkOQ+IKL2zl3AqkE8hOOmZRIbQBLtAshi\n13iH3QwbIIHbi/Rrff3/XwMjXfNYCz7uW2HLsqkhmwpKUtlVbF9bl+5bYs+yyfvEUMM1UTuCZ9nY\n09wyIq0+5FF7pCGpoObbDTqSh2lqEGVnSPa032JZe0UopWMtA0s5cM1HLumZ8/rKW/rMe36xOg/q\nKccygUAXMgededILr5yojrCtQ0JBtnewbmxRJhD5WN3n7qiAZMEYkSvOhoilVZ5xUKKWVTNluK4w\nLTBPsFxhvWDGZxW0Vpq23I3bL/rRl37/8/ZFPvo/38HIL3HkvzKX6O14BV1sI7CKMvXQH6HP0IvS\n9dCPsM4deQ4wQ3c0ZuRpnliXC7p0hNVCNiwORha2tbhu6KWdftqQzcJtmKaut21Gax3DTkrUsS/o\nDzUk27YlNFk2OzuSSyRnY0dyju6qUFzLoWhjrKHUOc6v/eDzX50f1efmDag7CME3lyHuLEk1aYlx\n/yzqZ4PeRj7vb4f2FrrJpwaRSOwmhsOF41PP82vg0w/KD7/N/Pbvrfzl37/yF3/vzOm0EhtmZJ2U\n+aJcYqFHCbkYs1qyh2Rh17r0UAZ/n35sgEQJQyGOhXjMxOdMfEnE10To3NzODdNCtnHYqi0rsSSG\nPDPkhT4vDGmhz57u667BVdiqM5RV0eyJ2aL2tfVKOehuZr5dJ19vFjral11pSKoKSNrPvf9bCUju\nGJIwREf0DkZEHQQHb1X05ErrUjNsxBNjnCEJ7RXcHl+bxO+v+Myu1K7K7XaWKHfj9rEHYKQq2CoB\nc7+2hAdjD9lwn2WjFrIJmgn0iF9RwQMT9rR7yOWeDXlUz6ZtrfZk/7Tkw6e3Z+2sN9k7XRMCCk6f\nfgjZJA/ZpBdO6ydO+dVDSpZNU0JEgtIVY0ieMEBSI932WlozOHuHra7k1qFkZ0Ue+pDU+PwFNztj\nb2fxRKpixROXFeZ5ByTpDPmdTSvyzeNrd/yjnz06Hl3T348/55F/Z3H+eugPxu6lEJj7SHwKhCUQ\nciRKIHSROAZ0FovxL5lxmTkuE6/zmXUdKEuHrIHo04rcSdSksE83dXF9JGurR/3Z4K2O66Ig3IZ2\nWoaERkNyB0pqmm9uQjeVISm+KbJCn3HH2vWarzRPXZ2Ki1+khi9xMKLGoujqv9Q1q5tvOmPfNF+u\nqg6r/k1p2gc08mhFtV5CoOvPDIcD41PH84uFbD7/JvPbv1z4y78/8/f+zQvjk73mVJmRM1wG5RDv\nGBKULZtGXVCrg31xFayJesimEPqCHLIzJMkYkpeV7jVZOMdt5IP6vKVls5cPanNivyz080K/rPSz\ntW4xULJl2UzZZDrJ3FSrDZJGNZ3xwfGSfv0ju/n4YnPtfsU3p521hvt04m8cvxpAUtFiPTSLfWFD\nMTWyJz5Lr+5ro54ArXvic2xjjff6kZ9yNHHG7Vywu7wNxMIOOu41Ks25invi7gAAIABJREFU5luG\nJGdXr9W0DZ9lRHask8V6g7G3r07Nvrkq33OJhBJJuTMEnY0tqJkw1Rbd0rwyfQUOstyEbyogabUY\n9e3f8CH6KGVWN6AzsNLrvS4l0as7hbi1tSSjH41ckk0XU1I0oVwIdsEXJZRCVzK9GtgZ1Qr2WTjm\ntthezbhp/34mNuGlTFFjTILqNm9+2IXWLIYLxoy8Yw6s77jgtVi6+ZrMLWtZbKbKNbOmTrDfAhk/\nFYx8DZx8C6x8P/4ch77dzhyqgTIGylNEXjrCFAlLh+RI0EiQDomRS3dk6o7M/YGlH8hDTzlE9BBh\nDIRFCHV/c9d2uYVs4MGsO3QLGdxcq34JbNdxu6jUOaVOSdsboXlj9V5v3qnq7ifp2jWy+yClsLcp\nNIaAvgEKWHjFFzrGGr7AtRWyWYxDNrAiSzMHBitK2mGJCl1Euh66wZrr7zR56DvVeV7xAjH+nu52\ndvJxtyfSI6EndB2x7+gOkX4MDE+Bw7MwvsLxVVmzMj4XDsfCMJpPSTdYLbEQEiJOXWmTXVnD9R8Y\ndAHJiNhaIKEgXUa6soGU0HsYp0JD8bE4ZBRjnDtNjNeJw3XmcJ0ZxAoqDmnd0n7jmolL2V8GzXUx\nNNdFGw3+xtS0edbdG+neT0cNaO7aa+9PHL8aQJIvPem0Jwjlq5BOkfwO+VTIp0R5t4J7nFY4RXiP\n8H6B8xmuV4vlL7MtEjnv8byffTyId26rVmjGj/7P3fN4me698N8CMoE4rypqlX8rsq7x0j64vXyA\nUdAj6EHQPphboJh2RLILnWaFK4SodJIpLvIVqeXNM52sDLJwkHljTiqwqEZqlWnYmBXNNy1ofaz4\nefaKp1bTo/O+Z20et/EhLzylKy/5zJxHUhooOUIRK/ETMi+88yl+4VP4wmt44zWceAoXDrLQkUFu\nXyt8ZIEGt61PdKAYJFHAMwG0RHLJW30a0x3pB/nPZl63NZ8Aa8p2/Z1Cs2t7dB186zq7v/PvH4OP\nwPf++vx+/CJH6DEbTTskCL1Ap4WuLHRJ6FboF+gmoXPX9H9j+iv+cv49v1l/5HN+46W8c+TKISz0\nXUL6Yot1AO3EdqmroKN4+S1xTbQv8p1ujp2WnYEBlHreLgTtvqiVwtV1eVBCshBAzNlaSds927PY\nPZtX+mWlWxJxyYQ5W7HPxQWcM/CmyEltOkvY7r8HecY2HAIyFnRd0PX/Z+9tQi3Ztj2v35hzRsRa\na+/MPPfe8+o9FUHFhoqiUIIoiGC1ChHKZnVeQ+wIBdUQqY6NwhIEQbBjQ9GGrQIbgmKjbIhf2LCj\nojZslKgoWLfevffkyb33WhExP4aNMWas2Ct35vm459yT95HzME9ErLX3zvUx54z//I//+I8V8orm\nBS0ruq5oMSCv2QFJVKQ7l46YSesIMok/JgZGlgprQ9d2PV/sTWurNi+fzSFHej3pwQ3Kml6odSbn\nyrzAeU48nA9Mj/cM7xbiXYEDPLxL/PrpxNv5xLt85ElPXOTIko6U6UQ7HeHVCdrJRK1twAuT2T2o\nZRe7ezp0M4MyzZW2VtrckAvUsyCPER4grRCCIkGJoTFIIYVMCsW7bQBf8cAdj5zcmNIsF2y9jb1Q\nH32s+fjAM1QT6CDPSw/QVx19dv5s69OByF6nlG7Ob6RB37Z9MoCkPCXyu6set81CeRDqg1AflbZV\n/i1WVfVRrHbI0xnOF+vzbCmYuXtCdH7q+7RbsLEXE+4BykvgpZ82tq1Gr2lSFwcjDjGlQe0ufh4z\nTdGK703BrYIDnGzBal4yvEmjOiAhK8ygUQipmWdHyGgUD8c2klaGUBiDaT2sXUeKYpkp+yaoLU4t\nI02JarHL1KoVFWuZoXkZ9uYl2Vvxx65VR/v5QReO7cK9PlLagPqkjeohmrDyJHfchUfuwiOn8MSd\nPHKSM5PMJCkOnp7XptkDku6v0u3zO2tsZI4BkqbRNDceo30ORnSnV9adP54zI8/AiDqg0SsoeTZ+\nrp/kzV6bjwOQ2/OPAZLPoORHa2GEcK2vFUIjUZmoTK0y1cqUK9NamZbKdKmM58qXl1/zi+U3/Dz/\nhjfla+7bIycuTLKQotHxTA5GRqGVHoq99uZHgCBqWRfeQ7DjFhYRfVlz4nYBz4ZX7hkajVArqVWf\np5lBEyORsa0MxcBIuhTipRLnRrhYN2MuRc7Ww8UzP4ISJkXu1ASOoyL3lTav6LKi80pbFjtGf6wZ\nQEHEgNcIcgQ5QDj6+VG2oy4NvVTrc0MvBb2YkLa1auaE+63+MyDy/FzbQqkLSy5cVni6JKbzkfR4\nj5wqeoQ6Djw+Jr56PPDVfORdPvDYjpzlwDIcyIcj7e4A6wHqwVN/B1vTi3giQ3H2VAyoVEW9Sm5b\nlDo3E8s/CTxE9BQIByBWQlJSrAwxM6aFMa5M0fV6YeEVD9zzxEnPHPXCxMKgq2c21g2QWEa2B6tF\nDJwUQSeLRmhxogmMCXcdX4+IdbZ8I59uhdM7f7rN+qW377BEfTKApD6lZwxJm5X6YL09Nto7pT2C\nPij6oFZk71HhPBs7cpkt/XJZroBks43/Lq3fIPo5XJmRfv7STeEDDAkOSDRbWpisUHq+fAckO5fB\nEI2yHCKMCQ4CpwAbQyIbQyLNQx8rNthCIKVKjYmWIhot/BO0WXVgMkPITJt92EtZM1dfguAxUWlK\nbDaxQmukWhlrZmoLU11sQau91LktcLH6sfWy6JWJhRMXigxULOk90BikMpI5yMwlnJjizCFemMLM\nIdhxkpUoxQNoV4akY/dbQLKvZWNfhXi2QKJqobR6BST7HeV77Mj+uAMje1CyMSP9O+9j6KWZeDu+\nbkEJL5zfhgVvx9pnUPKjtOj+F95CKAzSmLRxbJlTXTiWldO6clxWTvPK8bzy8/krvli/3hiSV+qA\nJCykmAmDbrqLppapYmm2drTrXnEaEzF2LYGLRy0yrdeMVm6OfTzfDDXJbNV/Y2nbPB20MGqmamTU\n7IAkE+dCfCqEcyU8VeTRQAiPNi/MI8P+XhALuwf8eNeQpdHOC+280s4rcl5pcQFZabqiZbGQZ5DN\n+ywcINyB3Ikd74VwJ8hdQOeKPlXaY6E9VTQUGpbJKKXAWtH30kV2fQdKVFdqXcilMC/C05wYzgfC\nU4F30KZIHg5cLomvnybezgfe5YnHNnGRiSVNlMOBdjcZEMmD9TKYi17ENjHPdDNemX2LVgltBrmI\nAZLHgJ4g5oYOARkgDpUhZaa2chguHGTmEGaOXDaGpJfumNQzHLWYBmWjPQyEaBADwg1PMBCvPuz4\ntinSDIyY6sEZZHeyln4rvGVGPgZIvoPc7ZMBJOWckIc9Q9Jo7yr10ZTu9aHSvKuzJTxWAyHzbECk\n+0GsqzMkv03I5rbtA2z9Z75hl6p6DdmIKyY7G4L4IK3QvAaDDC7kUo/BesjmxBayaUOgRqvvUtV2\n9poFnU0Rn4ZCTQttCJCCeQqhm7HYqBbO6KZmkPxZ2RQlvU6MIWUltkqrBaotZqkWxrpyKAvHemGo\nmVgMjMTa7Ly+3w9hoYaLC1XFqMhozMgxXLgLT8zhSIqZIaxOUVoqW5LVGBIxhsTW3PiMIeli3JF1\nAyoCthuiO00mUhuIvUR7VdPtbP4wXAHHBkz0ypLsaxLtGZL3wjUfAyW3z++PvHB8iY37zI786C0M\nEPeABBIrk1ZObeVVmbnPZ+7XC/fLhVeXM3fnC2+Wd7xeHnid3/G6PjhDcuYQFobkDAlca8n0gnZ7\nPxB/TBrmrlkasQpamsnQSvOdrZp3yUsMyW0oZ+dtERyMxFZILXqo1VRkgzMkvXpxevKS9Q8Nedds\nM/iOneDSc3OkEcdGHK76B6mV9rBQH1fqw0pLCzWsiK5QV3RdEOkMiZNSRwMi4bUQXvWjEF4L7Vxp\nh4IMBYmZJhYOb7kgS0ZDuXnzN8yIffB2g9ZKqTNrrswrpHkgPB3Qg1DHgTwemeMr5iXx8DjycBl5\nzCNPbeQSRpY0kg8j7W4EHWEN5hi+embQin0Zmm3dkIxi2aBaApqDOfjOAS4RfQroYyAeAi1XGAWZ\nlDRWhjEbw8yFUzhziuetivo9T9xxtrCgu2QnesimD15jwdULAtqm86oGbDgIqZ5a3Niytqk+xvq4\neim1fLjpe0DyHcr9fjqA5CnBDpDoXGkPGDvioRoL12T0IZsx1UO+umTu+z5k077vov2xG0k/3p7f\n3ij0eciGzGZtrOoC3Aq1p4V5Bk4CBvGQTYOjwtHYEdOQKFWi/fkqaHb/gBYoJVOHRGuGxEWxhUI8\nZOOAJLtdjbpmpB9NDjVS3PU1qQlKtVoWUyzuEFgyhzJzyh2QNEKpGxUc+y6s1m1HdkgrLV4giVGR\nqTJp5pBm7uTMvTyyxAMSPA8/WBEokUYQszvD9S7VO/BeyGbPmoDYMxqpOlBaJmuxzKSNIdGd7uwl\nMNJ7uwKQenPsoOSjY2c/hm7BCHwckOz7S6HCz+0Hb/EWkDQGESYap5a5rxdelyferA+8mR95Mz/w\n+vzIfX7ifn3iLj9xXx65a3azGPchG/fY0GBVY2swE8gWAzXYpqOGgFQlrkJcbZ5HN/7dXDuR52tc\nHx5GJT4fMoIBks5o+EbBQrCFqpmKMOq6MSSpMyQPlfC2Ed425K1a1evupTFUQqrEwXpKlTh0YFKo\nhxUZFySuVPGyCmWlrSvibAleWVtGD9fcQXglxC8gfBGIbwLhZ8GYkSFTg5e4axlyRpeMXDISMvpS\nCuNWKbMzJaCqlFpYc+GyQJgTXA60x0QeDiypcJbCkhPnS+JpHjnnxFMbuMjAOgyUaaC1wcBrr7O6\n94hR38AEv0NrMHfbGmk5mcv4DFwCehb0MaKji/snsarQrTFqZpKFY5i5S0/c6yP3PPKKxytDou6S\n7Zq925CNdR9zbqGxnYdgSQTFUoO7zki7ndaWh6Evh2kyH2ZIFr51+2QAST0PsAvZ6KXQHhr6AO2h\na0hW2sOKPizowwIPDj46AOnnuexCNt91wd7fHG5pcbl5/CVQsv8VZcuwIfuI6PSXuk+KgxWdjDPr\ndNgQLF/qkK7Vu6OFa8zwDVSFUI0ZCTVSc2McV0pNNI2o2msNoZFCJcXMqF1DonRHU7A6N7Z8XGvD\nCDBoYWorrYWNIRlyYcyZY164yxeGvNoCl3UDHyE3syrePaZDgAHiqAxjZWorRy7cyYElHljCxBJH\nWnQkH3Zdrr1K2DQiL4Vs9tddM2IGcgODjlsoKXiO/zMR/DP9yC0g2YGPDkC28z4E+slL7MieBXkJ\njNwCk9vf/cyO/E5bGCFeNSQSCkmESRuntnJfL3yRH/nZ+jU/X97ys8tbfjZ+zalcOJSZY7lwrDNH\ndRfjsDBEE7V2iwpNQkvB+hAoKVJTpKRATRGpSptNNxG9sJrMmF4Do9pfJG7b+9eCAZKwMSQWfq21\nUFUY1IwDxpYZS2boGpKnSnxngER+rchvFH4DMilyaoSTWZ/HsZLGQjoV0rGSToUQC2VckWFBgpdS\nqAu6roSLMSaEK0MiG0MixFdCfCPEnwfiz4X4C6EdCzVmhJXaspVrWFf0ktFhhdAV6C+Ae4W9eZpH\noVmzEBaBS6KOiZxgjsJZLEko18i8Jus5cdHEEiJLSuRDooVkur+h8qwmgHpiRanuGl6NrW0jWgbI\nSluAWdAzyCToFGnD4O8tuHavusZu4Rgv3NUnXrUHXvOOO87c6dlFrRcmnZ8zJH2dCH2sQYtC6+Nu\ndy7NdC2heFZODlCaFWzOel0bOxjpdWc7IOnsSDcr7+3y7afcJwNIylOi7RgSLtAesglYHxq6sSMz\n+jDDwwUe5isTUqqNruLptb3/VqLWfpN4iVa/3Q2/tDt2hgQHHeKToinOxdog3b/OIJbuNiSYqjEk\nJ0Wn4GW9hSaWQaMt0LQRJBgNK41SR6om1PUT5sKsxFg9Try69bp4TkpzlYJsDMnKyMwBUWVqC6Ul\nWo3mBF2UlM3s6bAunPKFcV23nddmc9x3Yuv1XCaIkzLUythWDozkMJCjJw2Ln8dEDrsuyZ4TczbZ\nSqTfiFqTzwLpISrK9j4zA1kzq2aSWnhJ9hqSHoLZW/t3zcgGTLbAP5vz4gZGlJdFrR8Cth/7mY8B\nktux9hmY/GgtpBuGZGFAmLRybJlXdeZ1fuTn69d8ufzGxKzjV0x1sfTLujJVT8VkZQorKRWC+8Lr\neO1tDNQxUsdI2XXJip7rZswnycGIa0+0yMtg5IYZ2YaYz00LA101X605ha9iosjqGhIHJOGhEr6u\nyFcNfqXwJ4rcu4OoODsixQFJZnhTSG8KYcxIXC0s48yIrivhvCDjiiQL2agIkgQZhXAQwskByRcG\nSNKXQvyDQJs8xbZalo4uC3peaeOKpNXAzbMYgTw/1+u5aqC0xFqMqahzIo+JJSXOITGSGJsZIqzV\neq6BtUVWieQhUEKkDtEygGI2kY542YhugpmLASVxoNQa2jVqa6AtEbko4uNAojPcVZwwr6b9SwvH\n4cJde+KVPvCarzlhWpKTg97Js6TijiFRMWakZ9XoKLRBaKNLAPxogKQZEFkb4hlf6rW9dNWN5H+P\nGdkVuH8PkDx++yn3CQGSgfBux5DMCu8C6kJW3hlDYszIGX04w8OT57a3q4B1S+Vs35MhgZcX+Zd2\nvS/tgPeXviKogxHxn+k+JNW9o1u7ovfuTDgkmAYL15wwVf5m02wW7CZAilS1nb6okutMVbtpq+f2\nh9SIqZJafqYhyQxXwzJ6/ZfhCkiAo84OSMwFN+RGyoVpXTmsM6flbICkU8hZvSAUW2EoVkUyxIOF\ne6ZmWTA1RKOmh7hdl5iYw+T9wEWm7VrlQJboTE56D5DAFYx0Ya4SKDqQdWTVlUELsZUbDQkvi1r3\nDMna7LhlTvmK/1667x4kfEzY2p//0Hj70PMfG3+f2w/a3gvZJJIIB5whKRe+KA5I5t/wh+Pf5s+k\nX5kHhKfJW4aZK7OkWsgmeMjmAG0S2iFQvZdDoBwi5ZDIh2jz50GsDP2Ila2XYJV2S7MQzq1WpC9V\n+8RAP8p6ZSxN21VITTaiDzAWtWTSUraQTXzn4ZpfN+RPGvwSqzor7ptxMkASx8xwn0lfZMYvM+GU\nDYywonVBl5U2r7THhTBdQzYSBIniWTbGkITXBkjSzwPpD4T0R4E6egptWdB1Qc8L7WlFpsXATeiV\ntW/b+yBfNVHKETLUZWAdEnM8kORA1AOxHYjlQAuusRMrgFFc41OTUAdLCqABcQHxCt+tQcm2iYl5\n9xzmblsVzYKsCeaEnEG674oMNLXQvrhlw5Ay07BwGp0h0Qfe8I4Di5ftWDjo4qJWY0jCbdpvdDAy\nCXoItEmok1APgTYZILGC9Q2WYJvIRdDVwcgtK3IbsumqhFtAcvzmqdbbpwNIbkStVlk1GrpyhsT8\nRxZnR57g4eE6i7YbBNfj92ZHevsGwPFtfr8zJApXmfLOdjEMHusVDD30LJsBxnrVkEy4dbz4r+tW\nLLMLMaVCaQNVEi0ENJioVXzxGZr5gnQNyd6NtaswOkOyYFT1qqMBki1k4wxJzhyWhdNyYVpWdzDF\ni9NZWBgv0d0fG3KxXZ2Lq9S1MttjQagx8BTveIonnsKJFO4IUlGBLAlh3LxS9tnxPVeogxF7T+Lv\naWTVhVEPlqL8noaEHRjprMjuPLcrKLkNmzwDIt80fr7r85/bT9pkuMmySSbt8iyb+3rhdX7iZ86Q\n/OH8J/xR+qXrnTxNd38M12uN7jtyFNpJaKdAPQXqMVJOkXyM5NNg3h8TyGBeJEGtsGbIDV1sHm3W\nSOyOLyUXqu2DJD8P2fQsC/W1c2iZVNyHZMeQyNtG+E1DfqXwS2dHPJsm1koMzpDcZYYvMsMfrMR7\nBxDVmJF2MQBRj1eGxHYvwbRlnSG5E+KrQHzjgOTLQPozEYlWVVuXBb0stMeZ8LAQppk6LBBmnhdF\n/XDTNlKr0HKylzAnkAPCPdLukXqPrHfoYBkzmsRCbZFt/dJoj2011xrOjBSrHD40K4UbFuBCr3Oj\nJUBO6DJYKYoBJIltRsWSClSEEJzdHjLTuHIoM3f1zH175DVfM2JZk9bzxpB0H5I+IHTzvHH7iKPQ\njmHr9WR6pb5my2JgRAZBVkEXeZ5R05mQPQDZX+8za34fAQktom3ng9F6obyw2aabNTzP33gHIJ9k\nu+VN2b1eT+VsmDCr7kJPuVlf1avN+p975lQvXMeb0utTtBBoYmnBVSxjpshAlqs2ZNGJwuBMigtf\n3QRtpFDUGAwpcFxnDsvMYV6YlpXpsjIuK8OcGeZCmitxqTvwwYfP9+K7fTbeLn2subCvRQMnNcbt\nWGKy3tJVNd5T+GSXY789juUSSSUFXzBDIUXvqZCGQizF7ASjotGExRqu3Lf21Vr7zNt/EXrzhrpr\n0H4MvHT+uX3yLUXrvcXg3aI5IUJIanVHUiMmS68PwcaFRViV7hMiPeVBsPE+ssXdxUtziTuySlAX\ndDuQCW5jP6j5e0x6NbRad3uvjx0Tz5ytJe56MANFcwI1EXwIjRCNXY2Du5NO1tOh2Ploglbp3iiK\nrdtFLIskW0ZJaxEloiGhqaFjg4OiJ4V7zAzylNApmH4uYuVCtKGlomtG54B2T5PVzdaK6Ui0ZltH\nN3fUb9MC2gpaizkvr55UsBkXYfedXh9o0B0r4BuZbjYWlK146jOdGXjMhG1daMHua9mzcpYIKaAx\nGENOQCWAa+lkEAOlB6vYG5qbUnbTBtceajR9SE2RMiTCaBlVVSNtFOoYqGO4OQ/GkozOkGx8s1Kl\nEWU/LpQoPnYy1/GY1QSvxR/vlgjeyulDG7b326cDSDrq6q2IOZhGD2GEXlCvuyeOmN3hS/4M+/7S\nVuF32fYrwg04ASBcY41rs0qz52Z5/qNeUelh9yu+sAl6vaFH9Y9EjYobA3lMrMPIHCcu4cgY7hjc\npXXRkVUnanM3U61MbTHjWC0c2kIojZ9d3vJmfsf9/Mjd5YnDfGG6GCCJl2omSTcARNfdtYeOuxPg\ns4KcN58Eiim9UyPGRkqFIWamtFJiosaIJlPjD7F42XTLSujnTYKBGiyFEvHFPbQtdBWHQqqZQVcG\nXZl0QQ+NNik6NnRUWzSTol57yEpS9WI3HZjsAUlXI6frm3lP9/ExNuVz++Ta/usE8wAahDZGymiC\nxvUwsBwm5tOB8+nA+f5o+V99EcfDGj2A2N2ngodfgqfO1kbMlRYEpdCHWyhKulSzAK/+d7wwmxx8\n4X9pmL10HoE7sRDwEfQAOuI7fb9n9tDyYKLVcGqE+0Z8U4lLIeXC0DKjZMKbRvqimt/I6JuiItRL\nRN4pDBDOQv7KXLfLJVCLqbvakNDjAK9HyJZlo/cJPSRaHGgt0ZZAfQJ526zOvQr1q5XyJ5n6m0J7\nW8wK4mxOrdpF59+2dVuGWs0jqqyQZ2cp3G5eFUp4OVyx11IEhacFzosV3VyqLxXBfaau4mja0UzU\ninuXLMnvccH+Xf+uNr3HQWirgbtarbBh0USRtBVGJrq/yHAtvtrUQuGhNWNFDhauaUMXsnrygPjm\njmDJSO7RSXSg3Ng8+MCMNkNUS1qojTD4eWmbYFp230M5Nr5t7u+nC0gyux1J8C1JcjvnDkomdt7d\nXHet+/6hOP7vsn0EJHXdS26wNKPvLgpPhkS3qsAHNuDRbaTt3I7buQOSMkXykFjSyJwOnOOJJJko\nJvS0gllWuZMmpFoJbWVolUNbaO1MzIU3l3e8nh94fXng7nLmeLbaCcNcCOeGXHQDH+oAZAMlN4/1\nTce2UdQdwcP1owlJiakypMKQMmOKtBgMjCRFUmONIyUk1524HiUkE5kRfXGNvjP1nV70nZ67yw5k\nRlkYw2iL2kFpY6MNjZaMLWlRaT4j9Rkg6TEe21U8p3r23/ktUN6nP3xun3TrGNObJlwEGG1+TQPr\nYWQ5Tcynicvdkae7E0nKtoPt8uuIaUoAItWAcheoYjWbYhFUPLxbAc+IiXMlre7ng41lGT1DTAxM\n9OG1+U68hH8DcAecxAwXJzEx5iDGUARjFwn+2g4GSOIr+/dTtZDvGDLjsCInJbxqxLtmjI1YKKKd\noUQTjcoYKV9H8rtIuSRqzjQybRhpx4y+9jRdUXgV0KPN9aoBWYOF7IOlw+istK8z5deZ+utCfVup\n74rN3bmhuT0rqvvNTV1rWAyQ5NU3veH58zm8bI++PxeFc4FzhksxyUEHJGUw1qizp3q4ApJ1NK1S\nr16P2M+63kNdY9SWQM2RWqygalGrICZeakRjQFNAx25xkE3ML4mo1RxZJzEAOmIZXhFLAxa8hpJY\nIdv+N1O46hax5whiYmi3doi1us3D1YMqVMtg7C2feuGlb26fDiC52Y2QOkPicbXYGRLPDWNPn/fe\nxQC99ZvFT7X4f4yp6Y+H56GabiOcjIbbXv4RKy44GhMizRgUcTDCwPacMSSRPAzOkBxIMRNDMT0G\nZoJDw+LAFVKtiJuf9ceGnHk1P3J/fuL+8sjp/MTxcmE8r6RLJp6rFaDbsSEdhNwCEl1v2JDdxbNF\ntGHeBqmSUmEcMi2FreaHgZXGmopl48REbpaZU4J9Foog26KyY0hiJSQTtQ6ulhnCyigL7dCoU6ON\nShsadVBaahAaGhTdtD+9mti3YUg6IG4315/ByO9Fe2FN0sGo7jIl8mEwhuQ4Mh8PXJwhGTRblpcW\nknq2l4oDBkvXFZoXzHP1lmfMRGdGZPXwTVPi2ojrlSERByRObdpGzi+7pOlZsb3eIxYecYYErxXT\nd/nbjSkY8A+TEo4OOoq/H2yTMIwrjEI4GBgJo4OjEqgXAyNtjRAb9Rwp50i5FGpJVAZaKuixoFpM\nY4Fe63UlwwF1cX1Ms82aPlXaQ6Z+lalvM/VtoT1W2lOlLXpNy/+2TfENYTXNR1g9ZML1w6zVsh73\nBeVeMgYTvW4mZ99crs6u1ASt53kHaJP1MkIefZOd7I830whqwjTrptRzAAAgAElEQVRGh4CewpUh\nKTuGhGSfeXCmIxkzUtVMIIsUUjBA0tlzCxOq6fei32OCfWYNoYVoPdpmtQ0OqyXSJNJCsFB3dQdu\nryrcr/tjceeQXk7f/iv5zoBERP5p4F8F/izwdwB/QVX/s5uf+deBfwn4AvjvgX9ZVf/mN76SPUMy\n4KAkXMHIM4aksyPl5ri/7f3U4Rp4nzftr6lrSnquug/iQTc9A6JXjLXgdW0MMIDHlPH48qhwVAs3\nDB2QGEMypAPBK0siSiVa4btea6YWUimkUkmlMBS7ntaV0+Vs/Xzh7nzm+DQznReGcyGem9lI7xmR\n/ByEqBMK6nHua12E59cbc9IgDEocGimZmEsHi+OG0h8vrMPKGkfWNLIkz5hJoCIexvGtrcfwQ/SQ\njd8gkmQGWRnjQg6JelDCZKCkbt9BQ6PR7IqiGyDpDMmthqRbFnYQsrew3jMjn0HJD9V+tPUIrnS8\nN/VUydpDNlNyMDJxOU2cnSEZWTcBedNsYKSxOZt26/etNo0qWhux9cdw8Wuw36kmQA2tbQxJGJsB\nll3p+H3l6v3c2u99OHEN2UzYLnzo4lgfqzchm1h83ni2x3BYGU+r6xycUAgCYnV5uEBbBB6xWZMT\nda3UXKmlUKXShkI7VTRVONqGUpMxkxLtDchq7LEuDX1stNTMGO0h094VY0feVXRjSBT9TkaYeg3Z\nhGwygb4wbUAl+6aYqzzspXMBFvGOCVpX8ZBNdIbE1wgdzAyzDMaQWDlnAy0lXNNnu/h0FtoajSGp\nkdpMR5dJtt4F97FplaamG0xSiDGZSZ3afUWGZqE+X9960UZxEXaTSJHkTHOipMHNE66P1ZhM9Fy9\nllktW10zeyww1LCxgQDlRxa13gH/M/AfAv/J7ZMi8leAvwT8MfB/Af8G8F+IyD+oqh+WP99M/itD\n0kM2txqSzorcVpCC5zPwUwElYK+lv6Z+UxIf+K4hWfZgRK+eGCvIHb4D8IE0tt25WkbOoLRBKEOg\nDIk1GUMiXayJUEgcdOagi7EjxSi3KS8c8sKhLEx55rAsHC8zh8vM4TxzOC8WsnlaGc6Z+FSRp13I\npoORDkBujrJfK+T5bbmHbKTZxIljJQ1lAyMy+i6tFMYxs7TRJluriKe3dTBSgtne94+3iwJDdHN8\nKSQxfcpQVsZogKRMvtCPO0ASXEciPQS4l5G363e4rVB9EHcw8hJj94wr+tx+u/bjrEfwzSGbPUNy\nOnC5O3K+O/oONdOaU94djLRGbGETo4tCaF6bZveYNMumCRp6ZN+zyFxDEnabkD6DfEO/1bZ54dry\n+Nn0IxzkypBs9L1Ync/E1fhMq1ULT4U0FYZTZrzPtBrQGswtuvl5EXT1x6q916qNppWqlUajUmmD\nARA9VENUWlEtKJWmVpuGpRgY0YZqIWihXQrtqdAeC3p2huRcN0DynZZ73YdswnVBag5GavGyu3Kd\n3h18BN5/bE1WwyZHE6v2886QdMqtJagDFL+fkWxw9QSODIxijNFFaIuFbNoWsrESGIUB7Zq5GKlD\npVKJ0ighWYjaAYm48NrYZz+PzRyxxWqKWcmCRA4DaxzxoLZ5QIWrZ9TYVjPPa+vWhxYZNW5jPrXr\nGleO3x4kfmdAoqp/A/gbACLy0sr6l4G/pqr/uf/MH2NZ638B+I8/+kqGm+u0AyMbSzJguWv9hrAf\nEdur5H3x6E/RXvoi9q/L1debqNUp2I0uxAZnD4lUS2iVaOEau7YdhYncHJAkU1rnNJgCPjVjCkWo\nYv4jVSPSLFQz1YWUDZDcrWezvF6fOC4XxsvKeM6M55XxaWV6tOPwlAlPbtg0c71PdwDS++76GVHk\nx86KbItmhTAqcazoaAumDA5GxsJYM6UlhppJQyU4NahcmZHYErKjDA2MeM0NByMpZoa6MqZkn5VT\nzzI2GBo6mKg1xEZ7Bkj2ZiUvhWz8e30PjPQ3/ikA5D897Udbj+DFkE3bhWzWriE5mobkfHfgcH+k\ntuD6LNnCogZGItqKZW40LCsBdWPP5suaEqrQ3Lpb5Hk2TIhtOzftmN3LcOFhtz56b171ZeeAhWoO\nsoVsrgwJ9kMespHJGJ0YXAw+FYZTYXiVGS8rdUnUOdLmQJsFnT1kM0da71mM2UiNFpuzIK7T8mtN\nBgpkzWj29OA1myZkhbY2Qi7IulpxvbnQ5mpu3rOBkdZFrfXb3/xsAO2YkGcAxUWuIV0BSZ/W4YUj\nWAimOPNRRiuiWoIBj+bijQ18RNOMqIOV4mAmdf0I6J2gF69TtgbqFrJJW8jGRPyRGiuRSBFLCIix\nGiNc3SQhWsg6xro7NmKopqUVK8lRQmRVy8hcZGIJE2uYWOLE2ibWNjK1hVEXK66qA1NLTB2MKNDU\n7Tat5bsfEZB8rInI3wv8EfBf9sdU9Z2I/A/AP8nHFoCXNCTxhZCNDDsw0mH/9q+x3dWejaCfuu3v\nxPvX5DetPUMija22SlZL++03fMG0JaMiRzdDo21siQES0CjUGMkxIXH0dFarldHTfwUYmmXTdJZk\nyiunfOb18sCb9R13l7MV17oU0vnah6dCeizEx2YMib8+/Yb+nshud70XtUpuxCyQC1IMnKRaaTVs\ni/3QsoMRfQZGch1s8nkeZE+3lODCwlCJ0WjG0hKlLdQaEAckjAZG+gIaevrli4LpPbjoIZvb73f/\nRhufzpj8099+q/UI3g/ZJMtQ6G6q+ZBYj+OVITkdOd+d3NfDtACh9pCL1YxpzihQ+pi3YmZaxdyW\nsxlRtdzQ7ALToWvHrsDkqidzXYBHGTZGpO0e2w+9fl8c+7lsaa1d4Ai+zKpnASW3hT9V0poZcmZc\nV/KjIg9KfUhm2bAIrQTaJVIfEuXBAIseFT2oZbId1LLXUtse46DQMnpe4Bxo1f5tWYpZqj812rkg\nTyu6VjRXP7bdtbmffqfi7h2AUJ6HaWoxLUnw7M6eefSxjphQtTUnT6PXuQrGhuiEocHB2RLfSLdo\nzEjom+9AN83Ts6CXQFt6ls2VISlqIZsgSg2NQLR07WAZMMHD2KE1gjZSKMRQjOmKhRYCMRS/TRow\nrljIJoeBRSZmOTAH73pkbgdmnTjqzKQjWQeKzlSvVG36JUW0PbMAK4dvrzT+oUWtf4RNh1/ePP5L\nf+7jr+QlDck+ZLPlIPVZdtv6zItcQclP3W5f5w2IUrlm2fTgb21XMLJVT9Rrau8Bc0HdMyRjQ44N\nktKCUEJAwmDMSBRqiKxhYJDMwuSpvTOlJihCKpWDMyRvlnf8fPkN9/MT4dIIZ+9PjfjYCL0/NOTR\nQja684ZRtwPoQKT5sX9t4ZYh2e/sKoRJkakaMCkNLUKr4tSw0Fpg1by5EG5gJCZSK4R2LSqFdI8F\noyc398xe3VRNCCaHBlNDJ0v7bQ5KamyuvdlvN2+R1Uvbpv33vQ8hfgYjv8P2/dcjeCHt10M2QzRR\n62QhG9OQHDjfHZjuj06iiRXG88yDVBO1ZloJpvkIdtOlOrPR1Oy5F2BxtmFx7cLBELtEH299HTiq\n3biOeLjzGqZ5BkT22HnAsmoGtnAo6aoh0X7zTQbkw9CIU9vEi0MtRtfXFb4CSZ5Ns6ppRkqgXiLl\n3UD+9WBFU1+BvlJoapkdBzVh5ckfv1czTotmLKZzQ1qFNSBPwNuGfF3g7Qqlou73oV55+9n199GQ\n9FIQrbLl0XqZji3j5naPcXskeOgJroWKwB3JHJCcsKwD8XX/9t/qHfQg8MoYkjYH6uIMSd0zJIN9\nR6Fdj2qaJNHmx2ul9ySZJGaXkCR7Zg3gKepNzAE7h4GFkTkcOOuJix45c+KsJ2Y9sDJy1AsFXz8x\nMKJg4XOeAxLLsvl27RPOsuG5D0lMEKqlgG03h33rs2+vLfmUdqP68nmvBkm7nucrHbv1ioUvjorc\nqSvK7cuXYLFBOTgg8dAM+zCNDEQxDcWIlbJ+pU/mQ1JxDcnKaT3zen3gZ8tbXs8PXokSy6bp/QlL\nx3sEHrCQkic9dWCyByT9fAt3X7HC86wA/xikqJdIxz0CeC7ZUFjJ9o2HQAnJYptxYEm97HafIVzF\ng26Ob7Kvbpxv6J5DByPGkNTUqLFP9s6Q7AEIN+d77nan1N/G5X48fipj8nP7aHtPQ3KbZZOehWwu\nd0fGu8U2Ch5yuQrFM61E837o4skKkp3J8xAOKzbf+pwb+r9t54q9JvWNid5jmTMddNyCkBtAor7O\n9iPdMbn7kCBeV8ZE8918K23yRstQG8ieFhqpSyQ8Klva7yVR3iXyr0fKu+HqU5a4unYO9vp5BfwM\nKAs0QZcGsaCaYfG037cNflXgV6utB8/m1W1s6js2bd/r195vHbh0Yfvk0zwY3cTENbWJ91/unjVG\nTOdzFg+FhWcMSW3XLJu+trkdpK8s+2vdio729c4e9Zfr+qTulFO8btjCxMzEmSNn7njixCP3nDl5\nRfi0VVy/vhWl12GXHXtcTrtJ9A3thwYkfwv7Gv6Q57uSPwT+p4/+5n/wl+H05npdFP6BfwH+7n8e\nzitcVlhWyxUvK7RubnFrUtVvDGn3WOCDsYL3hA2fZrN31ReIeu1y29vWLcXQFNSqFtZAIKOUkig5\nmXI7R9pqVHFXicsM9LIMnkWji1/vMmjaPiRTHHx0nVrbdd+5hQrBS/iExeZviC7Sx9kT391t89nn\n+PaYYKxHMEfJJMU9aAcmFopGmgbPNFCfMqBi9XoShewrfcNqVLSuVpdGC4pG76k9c7e80jkvHfcL\n5G06+h7Q/JBj7X8F/rebx+Yf8O//Xrfvvx4B6f/+S8jwertuqSL/8J/ji7/nn+LN6S2vD+94NT1w\nnx65C09WdbVdGNvq5Qka6uMrh4E52s2vxciaVrvhDyaiDlWR2rYK1H3ebqaIE1fNRwcQHiHs9zAN\n2K479HmyCzUEK5xXY6SJF+pUoNm/HbSRtEIVBvG6O1LMQ0W60ZsxjT0LaKtdodUXAWFjeKvX6qrJ\n3xMEVZvnQa8ut92uIKy04YymMy2dafGMhpkWVppkGtUck98DI7fr+I/RdhsJ2bGgz84jV8DhKbyb\nfGDF5mRg8+N4aSnY71P1TG0zpRXW1lha4FIHhnIglTskVzPO698PjeAbziDt2bFvgOwdGINiybyN\nqB1sVhpWVThRbQw4+EySGfx8lHUDpl6BbAOtgcZ//df/Nv/tX/8lYQdIHr/+iRgSVf0/ReRvAX8O\n+F8AROQ18E8A/+5Hf/nP/5vwd/1j1+tzgXcrvFvgcYXLYoBkXR1Nd0DSPSE6cobr1qanYO7Bye1W\nYn/8lJvpRYJUYzqkvNg3e2pls3LuDzUxSIMKpSTzBMiRls0zoK3BCik5KNEF2gx1se4lKex89fP1\nORjpX4X6vXhf6mUPSEL2KFzcidjVCTAHHlap2CN1Tm0HnEXdAZJBMoNkJrGigd3zAe2TT/1bvnIj\n/XNSDKRUgSpKC0oLWHaNW0RrUnQAxh3CanvE1d9ou77xFwHJre7kh2j/iPd9+/+Af/8H/nd+/9pv\ntR4Bf+c/869w+PIf2q6Pd2fevPma169/w6v7d7w5fs3r8Wtep3e8kkfbR9azhTQ0b14MDWMoCUKL\ndp60WIXc6t3twPsiHwWiVLOnOGJsSPcN6Vbm+5RTbCwTXL8SsHngYQAjDHtJCd9LKxv4iU2gmgX8\nKNluSKGvNe3KFHZbdbF3ZuO9bOCGVk0U2pIvHJHQlIiSUJIoMSgpKDEpaVCiA5I6zJRhpqSZGi+U\nOFPDQpGVKoXyYsj0Jab8h249dOMLlvgH/+w8YQXHJv+Cgq8JDkhU7PPaaK6PN+VC1ZncMktT5ioM\nNRHqwcBIhrYmD8V4OCaU59fY/UC7uN5DOAEL50TaVgRyoKAEBnEGTLpxZMb+WxlloBItrZ38HjAR\nlH/2L37JP/cX7xh3Rmj/+/944Y//7P/xrT7p7+NDcgf8/Vx5579PRP5R4Deq+v8A/w7wr4nI38TS\n7P4a8P8C/+lH//DXBY47N7e5GBB5WOHJGZJ5hdXvjn3L/t4OdM+QdKFh2/3c/ig3v/fpgpIuzrQ6\nE7tBJ5noxyTFd0y2G1LtJJ6Td1uJ8UCtiVqi0cgbKAnGlDgYYXYQYsU1Kevu2MtI7NiRD92HOyAK\nt4AkvA9GQnO2pGvKBv/5Zj+ztzYOwSphplAYZd2wuoJNvubskDyvZBzFs3MEeqG+im0uW/DuO1B1\nUbzF3tUWW+2Lrovhmnjsujoq22fivBBv+oTH2e9b+9HWI+DNF19x/+Wvtuvj4cKr1+94df/Aq7sH\nXh/f8Wp84FV65N4Zkrt6MWDhAAOw8KlAE9M5RZnsJjCYD9DQrBja4MZjYCxCDM1Ctw5I9rVv1Dfh\n6pGCrYYTVqSyz3/U2JC+FlzniFwZxNaI0un7wNBrPoVeA8qEkaEXB9wMGzsIL1dg3ooLNU2sKS0Q\nmpK0MUpjEGUIjTEqQ2qMQ2MYFA2ZPKzktLCmlRwXclxYwwqSzcH22Tr/bcDJD9X8PiLp4912LWwG\naKL+2bhNNdVpLW8fYEcAmi5UXSiaWVtjroFUB6QckAJaIiVPjGE1P6WwWpVmWWnBXChDUP/cfJO3\n6UsMjIStIrX15lqTwfsohUy2VVMGMiuVtIXsXmJJ5Lf8Dr4PQ/KPA/8V1xHwb/vj/xHwL6rqvyUi\nJ+Dfw4yI/jvgz39jzv+7DOPuR5ZioZqn1eoDnFdYFsjLDUPy0oDcu9a8RKF335JOJf3Qu9bv2T6k\n4Baw/Y1uFKoNHDP3SrLaeVjt5qvxSqK5cFMJNO3XSimDsSQ5Up+xI8HCNj1c4wxJ9r561Cyv1/O2\nGvsh/vFKZcuS3fTH6saAe0Dimr0NjPhzW6Z3gtiNn7r5Ez7XO0MSPGQj2RZalc2AKqrt3JqaetyC\nOh79dPaog5Um4p2tgqd6fYirEFAtRtVWH1LqKn1/cV2xv4UQX2JIPoORH7j9OOsR8MUXb/niyz/Z\nrg/TzKu7B+5Pj9aPD9xPj7waHrkPj9yrMSQ+XbdWsXL10ll8sfDr1cchM7LSsPkrbuSnPTO0J2js\nnVU3h1AD6fbmfcMhNg9at/zWsDsXz4hwGt9vVFENpqg2uyF5AUrLSvO0UbyQ276SX2cHVRyMhB2y\nF6hCaJVBGyONSRpTaEyxcUiVKTWmsaG1sAyFZcgsKbPEzBwyhIKGTH0Wlv8dA/tN6NptJ0asEvTu\nXDYv9l3vDIkCDtSudNZHm1KoWsitsDYltkAog+GbEil5ZM2ZKSwcwswUZkpIVmIDB7RaSSrb/cP0\nq147SS1cE1tzQOIMSeiAxFmSkBnJFFZbOSU+AyQdlOzVKb8NKPk+PiT/Dd+QvqKqfxX4q9/pD3+d\nIezWiFyurMjlNmSz2tZ9K4O7Fwq+dyf3VnZ9/5z62/mJbxbfoHO0EIZpQ7rArAOSUcz+fJDVhElY\n+eqi5gfdNGzHqhGtu5BNScaOOENyDdnIxpAU7+t6/QqW3vcZNO0altknQ0l7Djpiz6rDQUqD2IFK\ngZQMjOhoRFj0tUh83euUyjVkU3whBgNuBkZSyyiBIgMrAwsHZslEddGVOI3tU6qK0IL3XsthAyUd\nkATHGQ50pdOhDkCkx646APnQzu5z+yHaj7YeAV+8ecsvfnFlSA7jwt3hkbvDE/eHJ+4OT9yNj9zH\nJ+7kiTueuKuXTeXVehaCRNMp6XXpFoGpLRx0oehs+i6MBY2h0mLw+jL6XD+yC9nsC8gqPXnDAEeT\nviG5Ks+ahpuhaMxeaOqPCzQYQvZq2JUUvX4OVu11n3higERt7N9GxLfNiYGdpJWBykEah1A5xcoh\nNU5D5TBWtFTmoXJJlZSsgjCxoqFSpZLFQgL63hz6Xcwp14iI206EEWSCMO3OR/8MdPdZOBDR/jo/\n8DJfeExVqTSKKmtrhCpQB1qN1KLk3Fhz4xAu5DhQYqI5+yIoUaqt9bsBIj1k01kxbaRm7MjQKirG\n0m2gJGTGlsnhGr4pJEbWF8BI63D4t/qkP50sm3c9FcNbyde73nYHXCxkU5YdIHnJMi++8FjenffW\nZ9A3oIEfu33TP2/Q1kWt1RmSvImMJlkYZTYNhUSymsMe6il5TT3lPlA0oTVQ6lXQaqLWaA6LiwOS\nGbSHbGZnRxb7CuaOE7MdOyDZh12eXauBidgBSWdGdkAkOmsSM7QB0oi5SXo4SKrN74AzJBFCaKRQ\naZ7C1hXjltY7MOhK00DG0tguLAwUF4GpB+y6hmRXOThYKfAOShgCOgTzfChyRVk97Kf4QtTZtz0g\neUm79Ln9PrQvvviKL7+8+l5PaeFuPHMaz5wGO97183DmTs8c65kivoeUKzti196xdPyjmoSwYbHL\nzoykaBbt6lIEPBLw7OgMyZa66XvThnvybLLF67mqbLtkUGc13c/IwYNU7GaU6qYv6DcdEQ/bbAyJ\nj+cNlPh5T7+tCrURmtX0GalMUjmFyl0s3KXK3VC5GwstNM6jaUpCssWiBaUGZZVeJfkWiOyPP2bz\nXVCvoyYThMNNn3yRciTWqi12rW9SdjRvbx956Q2haiA3IXgcuVWhlkDOgSUH5hVOaaSog5EoiFx1\ndVVWukHZlR0xANrZkdjMHye1goqQgmtIQmZog4ESzRRZfaMbNobkpbDNnyJA4s54vfXqiy/1ugck\nPX+ta0b6Fnr/+B6Y7JmRvZbkJwYlt+2G5OkMSZTnIZtRrGLtISxMcqGICzZVfFC7jXoze+faoqPs\ntIVs9hoSy7TxsGcXsnaGZNkRVtmTn9xnJKj12K7nPQQeHEhEByA9TLNddzASDZDo5GBkl0QV+nrX\n14ZnDAme1tu8qFlk0ExRq3y5MHHh6Mg+b5MHnE/phaMkbAWkWojXCpopwhCMIdnASPF4cLjuhvrj\n3LrA/URU8+f2W7U3X7zlF1+O2/UUVo7pwileOMbL9TxdOIYLJ71wbDOzGPtWMY+JLmqdZWKRA7NM\nPjcdjPQNR2ikWBhjpg0BstPtXcS6cxnfL2ud7HDZuzF9Wx5edIBimWdBLZMnYFl40qyicKjX0vGD\nXPUsqWdrdDDSQQc4EOnibleyd9v1dj0Pzf7WSOEghaMDklex8Goo3A8VDY00CGEAomwylByEFCC8\ntz6/BEh+rHnVGZIuanNAEo8QThCOdi2rdVZ7La0YGNkqj3rJiW/xMlUjVQeKmd/QqlDqQC4DSxkY\ncmLI0cGIs2uYvnCQwhhWY0h6yKYvXT1k42AktmaF8VpFJTBoIVMsqyZkBk2Mulqasa4U4kfByO88\nZPOjtXfF7na9tWwpHKWncyzP+wZI+lvoQGQvat1vJ/agY797/T0xq/Jcc0vlKjtAYgzJJDNHmcke\nQmgEL0Pd3ChJaM1MdWpNu5BNpD4L14QtZKPzcw3J6iTVxSU9j6sdW7kCj4hbqOgHHqt+7GAk3PRs\nYEQPmI/QagxK66Fq2PBmF7V2Y58oleQEYlV3dSUxy5FzmJk2dXjdsmwasoW4qle61Og9RUgRht73\nzEj3xXdVrqiHbzpD8iFa+TMg+X1pxpBc14ZRMkeZOcjMQZbtfHuMmUNdQKAFS/VFMIZEBjOaCifO\n4WgAgWiaEQfXg4ORMkQzUOvR5Z7iu9OO9NTfvqwZKDEPkeasTGdniqQt+yw2AS+mZsJvzEk2915N\n1Kp9VrQtyyY0BzHb8HWwrT3fP1/F3p72K9WqayfNGyA5hcx9LLxKhTdD5vVYaFUJQ0BSpKVAjYEc\nI0sIJAmEnuWy/bv79iPPJ3Hktw/ZhIOBkXhnXQ4gF9in+6oaIJFuLjM/jwJ8pCkjVQ/Q8HU7EOvA\nUg7EciDmAykPNO3Ml/uNhMLYVkpL1OghG39+S/lVJTbdwEhslaGaKdzQwQiZkUQRM2EbWd3B6eUs\nm59S1PrjtK+L3Y162wqhuGqyn29GGB2QdA1IByHszjvHuX+bezBSdr/7CbVbfOQgN8guZMMekKy+\nSF7YiiR5imvoJmENs16vkdzZkRJfZEh6yKYzJGVxEavLeGYHIk8ZHn0N6tWst3XzhfPYvBqAH591\n/LkBq2h8AlmMOanlyrZ44oAXGGtEX8ybQOpRzJ1wL+vAkRMHnX0ilWdZNpuzi0SaJFpIDkqS9RTR\nlGBIMHRmJLPVoFDXlATXk2wakg/t3D6Dkd+X9ubNW37xi6vgfdTCpKb7mHTl0JbrdVu3YwvGiIRd\n2u8aBpYwcY5HHsI9RdImYO123mNaGetKKYlWA1RfCPYR6N15J+jAtSOCC7MdkEikiN1Eqrg3T62+\nuRELpboPSSyNlAtp9TR67V4kXvekmiW5tBugrZ5Z1k2JNtG3byhrJmh2QJKZJHMMhbuYeZUyb1Lm\niyHTAsiQ0CFRUiKnxBISc0gkSQTpDPhP0W40JFvIpjMkd8aWVKci8M+jM0jd7U6f/PybW+PoIfdI\nbRPSBKkDUidCuUPKiZiPgIGNbYMaVg5hJuvgyQ3Xz2zTkLidvIVsnCGpBQ0ersNYliKdITFQMpAY\nnSH5kBfJnx6GZK1XgSCwiQE3Q4t+bM+vN3DR20thm4Frds1L+hL4YQf7txGF9LYPJXUF9o06bLMk\nroj3oBVpleA9OtKN4sdqg82e7zUNqiusr+lf+77fwHdZRO/u0kzxnhvkar25pi3q1fGlf/Jtd+xs\nb/dP6lRz/0ik/9yO8dXmx93ruv58f8FCJy3D9totrdEW1uJZBLssAU+FbBJ84u66JJokVAa011AK\nyWNPq9O2Ea9KZd2+OK6ZNS991y+JrT8Dl0+1HaaF4+FqMpe0MNVly4wZ2mpFHlsfYzYX95koHexu\nNULCyBInstiuc2FklcHKvYfk4cJrLRxgWyI0YM/5uWnV7brKVTy76aFcE9XBSVMhBHUDQBOINto2\nV9SzYkTYmBOrw3O9aQ0tM1azjtdWUV1RMiorNazEmAlxJaSMjCuMmTBkQsrElK2oZcjXcDOZSTNN\nhVEbg6q72QsxBKulEwWJwdjKTYbRw0b+v2eszXdtsrsNyJ5mP7EAACAASURBVPuPRZ//cXjeQ2dL\nRteWuNOjhN0Ud+ZUKkj3SPgWTQfatu5j6LN5kb42QTsR2pHBC90d9MJBJ1YdWXWgaLJ1TT0U7RmW\n1+zL2x6tYrPY9x+2TMXqKemZydmYpN3t2rveghN9Xty0fPss1k8HkLxyJWNvvcZLa9f0yqbe+zlc\ni73s8uDsD3C9OQhXT4hbseFLschv227BzMeOH3qup5O4Qk2xu3DtsUcLS5kZ4kLNmbo08gJpCSyX\nRLiMyLnBk5iNuo40DVbNVyujhxYijSEUaoy8Hr7mfnzgbnriUC5MdWHQlUhBpDlNLBDNdyCKGyI3\nKNVeXhvtJfbAVw/PdM3Iph3pR9mFZsTqJqZgFQJ6YedhgOEE6QjD5HM/4T4JOxIiYxPJd4RNAq0J\nLRpO7+6rT3LHWU5c5MgsBxYmVsYN41stBqtnY6WzdyXVK89q9GymwHsvvg9ajOzDhx867vUlHzp+\nBiY/VXv79gt+9aufbdcDmQMrEwuT+JGVPqomFqa48hRPPMU7LuHAGgaKeGhGmmspViLVf38xbZPa\nwh5909A3CAqeNXP1EOrnJmAVLw/RWb64ndduhAjgIm4roRKoPiFbDNTUiDqY0JHGhQNLGikxoQGi\nVsa2cqoXXuUHKlZz5pKVWStzaMxDZThW5lcN+UIgJ1oT6l2CLwf0i0K5K+SxMEvhXAvDXIiPBd4W\nWhW+fkg8nAeeloFLTsxtYJVESQNtSnAa/F6gPK9Bc3PUD63pL8ylEHxjYcLiF6/l6CGZwXUkXAEG\ns2UnaYF6Nkq5ZbtHbQkWo/8NNcr3Q1N6/7hYSEhcMCtxMECWxDzYhkoYCiFZVV8roGprdgtW26vb\nwK+Mm8OuCmhnz0Ii98q+TDQJrMEA8iomuFYNiCqpFUZZTKfiIOVjx7gDJMNc33urH2qfFiA57ABJ\nFduKVzUb+duOD8wP2RZui3qnz2/vIC+Bku/S5IX+occ/1nuRCs/ja7CJI2VlA1kFWllpa6Gslbwo\ncRbkkpDziJ5BnwItGkXbxOKtVrtmNTDioR0V4U36mlfjA3flkWM7M+nMICsxFCS2zZ1Uglg1cixi\nMVWl9hu1kwVNXUJx24Wr66pe53dKPrd2Eo3tfIThzgBJmiyEE52MMIrZv8q8i5VLpAQTsBY1nF68\n4N5juONJTpzlyMwVkKw6unYk7QCJF/CrXm/E+1aLZw9IPmox0gFHZ+niB85vciSfnQvvqfI/t99p\n++qrn3H/J3+wXY8hM0UXkO+PcTY/iLgwhZVLtAqpBkhGSoieBdZIUphYaCoc1MDIoJlBs9vN22Iu\nfcMlVx+Rqs5+IBsT0rvKc3DeBa7N15mNRvcSCequsSU5Wwp+VKtjEidKNEO3SGFqC6dypmJZe0Mp\nnLNwbnCOcB6FdITwClg91BAinEG+qOibSr2rrFNlDqZZCEuFx0qNlVaEh68TD4+JxzlxzomlJQMk\nw0A7RLgb/J7gFGrdn/v86Q7Kzpw+33Du7w32WViV3W56FF/u0h1YR6elwJjr1f5ma8Au2aJlrjVy\nkv2+YCBnK/PM86m9f3kAMiFyNEASRyQkJEYkmbheUiMOhZgqkhqSbDdotgXBQnWe0bXIuL1fdSat\nhET2ghszKxNHu81INFbN+Q5VIajy/7P3blmS48jW3me4kHSPzKyqPudoLY1Dz5qAnjQDjULj0Rz+\nJw3pv3Sfyku4Oy8ATA9moNM9I6ur61w6+1eiFgokPTLCw4MENrZt25a1IFXJWomtuCDWs7Ba/eo8\nHABJmgu/t31HgCTZDddbEVibFZHbFFa1OIEcwQh8nU3zDEj6pP6Wc+a/JyAJf+X4W9figSEJ9/gI\nzpCoPVRaBN0qdSvUtZquYw7InCw8eQ20SzZ/HjG3RhGIoRJCQ2WzGzLY5PQhf+Jd/cK5XjjpjYGF\nHFZidECSoWX7LC0jRskVahHapnu9m5hsXnje1OsbH+3+7EcDGik5I5IOxwOkAyCJHZB0hqTLOIr9\nnlblN1NaYtPI5tUYNrG+MyQ4QyLfYEjUspCeQYk6+HlgSJ5vpa9uof53Pt6bvR+v1adv2FWMvf1+\nqvNH+/dvnz7+zPnP/7yfD3FjyjNjnpnywnQYx2zAZIqL60UG320OO0MSRJ0hWQAxdkTXQ8ZCLwzZ\nGRL1EKd7CBEpuz6sa0PC/v2RHsR0Tx6Rh1uzuxarBIhQe9VZtVdBIMCmA0sYXefim5q2cuIGilUG\nDyuvW2TQSA6JmCPhFGGLtJrsucyRNgfkpdFeGuXcWMfGHCyczNzQ10ZpjVaEy+fE6yVynRO3LTJr\nYguRkpIDkmQx4lKs13I/phfRgkcwcgQlPB7vgMQ1Yinfxz4hJXdgbQmq68YaztJvzsoUSwnS7t7s\nDs56YEgIFvb5a8X89j+WpRdL8B4zEgOSvPBhrkRnSCQ2K3HhDEkNgRp8DnR3alzwXB/AyMrCSGZj\nlNXvD/vILJRuc5ExJCYHQCFWkwH0kgfB5QKheuZOrYRDud/0D8uQfDgwJJvA0mBR6z1loy98uw71\neef5FiDpC/y/Jxjp4xFc/Nbxb/Wezye+BvX36w+TViiBVhp1U8qqyKLIHKw89S3QrplyacSsRuPF\n9nWnuXdH4af8ifftCy964SRXxjCT40pMhZCblWXINlkFVVITclXbFKxCmJU42DNbu85D7niqR57c\newnFQzZ9M+IRujzcgUgePEzjgCSO9jUxsRupSeshG9d/xESpiTVm1pYthooZoa0h8yovBko4cZOJ\neQ/ZZDbteQTBwcgxXHNnSXZ2ZD3cUn/1VnrWML3VPVtn78/37xGc/Gj/2e3jr7+QjwxJXpmmmWm0\nfppmpmZi8iku+1hCdMrcmbuQ7iEb2eiOIVZP1UGJbub7QQ/ZWHYcbnRW3dSw4FQ8iSKRTSwT4l55\n0szL+nHXWokfN9+UaIjm6umAXDuzEsyraGWw0AxC1MqoK9IMUE3MvHBh3AaGNpDDQBgG5Dzaew1Q\nhshyGilLQkZFB6UOyjqoCdKrmmBelWVRWhFuXyLXS+Q6R25bYGmRVaJlHY0RXiKsBbbt3qWXFw89\njssdyD8/lG+wJMG1KX0SGrzvx9lt8AVKcNYUi1urZxi59mb3Y+nCu50BD9wr/vJtrHR4myIJcSdY\nCRmJmZAikoSQFMmNOBRCqMieunhnSLpuaRWTou61ziWzycYSCplxF7BaIcWDQFXqbi8fPEzTbRW6\ntijURiwGSPZrxYCKtPsvlm7/qAzJzwdAsgrc1CqsRo8BqN5DOL3I01eLfr/hjjel8HWxleeV5G8F\nJs/syFv9WUD7llzec/o0QvNVtzMkfVWXgtbgAnahrIIsAnOw1Nxrol2FMglpLOS0kfNGyI2kNgnm\n4FUbZWOIKx/SJ961L5y5cJIbY1zIaSPmgozVROJZXIUvxKLkTWBRZIE4CmlQhmRsaW13IeoefND7\ns9o8ZBN9Q5I6GzIaCMkTDD5GT+2PB4YkBh4tQIrHv0tgi4m1ZRYdrTMwy8AiI5fwwhVnSA4hm017\nXcu4i7y0mZjwyI7sBQM7ZjhGWY7jmyGbIyDpvt/H3nVCRzB9BNI/AMnfs336+DPhyJAMK6fzjek8\nM51vnPRmab9pYhpmTjIyxXkXm+6Lv4/msmwumlHbVwzJUUMSDhqSDhp65pyZrKU9+XKV7C7O7gMh\nbuPd03sF9uqrPQsnRGqM7lNiluA1WKpobdFCmGpsQNSKeAXjsS0mjmyBYZtIeiaEEwzNjBdDYsvC\nckrk9wPrOiIuwC0Cm+vAtUKZzUpgDtC2wHz1PkfmLTC3yBoCJQfaZKJL0mb/KK4mMO+FdRTuXkC/\nFer8BkOSsgGQcYRx8tF7xRl6Z+rFN4k946ibwD3QC4FdfSwdlBwYqbfAyBEvSXBQYj3ESIgRiebV\nEnK1zacz4BLV7jtnSEoXUjtDUiWSJLFRSTLYzBcsWzOqZVMlLX2rZmFEs/AjNKsG3K+HYsBD3Lfm\nuUuxOmK9/eOGbH4+hGxWMd+HnRk5/OE3PTAk8HX4BB5jCPAY/D+uIA8ryd/Q3gIj8Y3x+fit1/zG\nxRmS7gImTjGIoCWgnqYra4Ql0pZAu0XKLVIuiThGxrrCIITWyGomYENYLc6N1Tw4xZkP+TPv5TMv\n4ZUpXhnzTM4rcSxIaeim6CBQIRQlbgKrEmYh3pQ8QhmgZgMkpfqn6xuIqr6h0DscjB6yScmYjzRC\nPjkYOcEw2Xno3QFJSOxF+HZAsmE7gZgoLbG2gUVHbhgLMsvETUZjSByQ3Jh2wNJDO5W4C1r3Xh1F\nVfFwTe/6tWbkTVx7BCQ99XzgsY782D8VvgYjR4bsR/t7tY+//kKdDgzJtHBab5zKjandOIkbpOUb\np3ZjxgBJkIZ4dVyzW28uKlSSl4pPlF3QemdI7oLAbkCm6qUMtGukjB1ZPTtnkcwahp687j5FtsMN\nCL0KK3jIBn9u8PCmJErIbCFTYmZrGa1CqGoLi6r5VLTiTq7WQ1VyeSHoBqGiGYpkyjCwnIS5JFId\nScXSYVsVahXWIrQa2KqwVCF5b6uwroF18V4CqwqbBEoK1MmFpmmFONukIL7oN7EJJzh9ugvGj2zJ\nsfl838uJR2dIhgGmCaaTjScfN4VUrL6a+DpSm4eKtoOG5eha99Slj+G397/7dQMwEoKFamIgxEBw\nhiTkRszNGJJeIj1g2qCjqJXct1xs/d4IFlKJavdLPx7UUtcnnZkwxidpsZCNFkt119nugdIImyKb\nEjYHIf14s3ukt3z7Rw3ZHBmSuasYn1DoxiGZpv/Sb0FNfXr9LXbk3wJG+nhcfJ6Bx3NPb1xzZKWH\n99ua/4hyp2JLoG0Z2QbKKrTF4rN1TsRbZrsOxDGjLXgxqw1cQzKklUlvnOXKOVx4SRfe8Wol08OF\nU7ox1pk8rMRakNpsAzAAmxA2SKsSFiFelTQJbVSaa3FLepRYdNlPF7buOOuwIYnZRasdlJxhODsg\nmTCR+WhMZ/BMul3c3kWtKVBqZKs2kS46MKu5sl45cQ0nD9ecuXJiVteQqAOSHrLRA0NSv9aQPGTa\nHDHs8/Fvhmw6CJm4l2/dDvfQkRnpQOUHIPl7to+//syaDgzJy8KpXjm1Gye5ck435nxlnkbmNjLL\nwCne9srbSTwdUgrJwUhPlRzY9iydvvOMbtXeNSR9P7KLWg8MyepixUVGFhmcbjePHfVgC+AOxgcH\nTRe1FiIbiS0MrM31LjqytgEpmK+Fmh9JrAZIUi3krZBLIW0bUe2BaEGoQ2IdRlY9cQOuRDIDsZ0s\ntDwH6hJoc6DMgbAGZAmEORJmqzJeqrCVQCl+3AJFhJqN1bGY7yG1toe4m9qOKFR7zWhm/wSOx/0B\nPdATPWSzMySTgZDz2frpbHXV4gqyGGNdwWpeeBZk89Ln4iyojOyMyJ5lc6yM+NTeis7aPtTmvSCW\n7LM7ECjRGZIod0Ci4iEbF/pvWJbN/veX+30g8mT9gDK1haIXWnOjNa00goXstTK2hVObd/AhmyLr\n26MlnlhL8+/Xwn1fgOSXAyC5yT0s0/wX3PAsWD1sKn9rdXjecT7z7H80XANvh2zeAh/PQsbna8Ho\nP2nsNsyddjyMWiK6NeoqtCUQlkSdA+GWkHFExpEwTIjCoBuN2dJ1Y2WsKyedDYSEz7yLX3iRK+do\nJdNP7crYFrLTsuLGIjoIsiqsQlwgzgpXQS96j0Bk2yh0c+SVO6llMfD7Jx4OGpLUNSTOigwv1vMZ\n03Mdu3sifRWyKYFa4p0haSOznrhy5iLWX8M7LnrmpmdmOTHrU8hG4w5GLO1XTEdSjpk2PAKSZ6z7\n1S30VsimMyQnzIL2zL04JDzep2/VXfrR/rPbx19/4cqBIbktnNuFE1fO8cQtXzmNE+cycWpXzgzM\ncbTaUp4abCN7bL5n2YyYudqgFrbZjaY6Q7KHbO4akqIHQCKWqjmLdfv3kUxBZfM1zmz/+nEXLTZc\nDC4G4pdgocxZjUEMQZmYTTNS6572O5WZ07YwrTPTshBjoSWhxkiJI0s6M8fGJQljTOQ0EjmhXyLt\nNdK+RFQTrBGtEeaIfonwatXGm0Ltvy9i58FTm5OHOmK+h2max4JLw+pPHBlHuAOR4wbyGyGbnCxm\nPI7GjJxf4MX7soFcnaUv/oge035vlmEjfQPZNSNwr39zss7w9fIEXy1BIg1CQ9wA0nSASkjGjEQH\nJUHqzr4puHDVQzZ+rzz86uK/9xtho5PcaM0+q1QtO7NXT0+tMHrqNwWkWPjKnPItjG9rhY+HKE3+\nhwQk7xL8dAjZZHnUjKxq83fWx3vugfWAx7/wUYH4H5X2+5Qx8yYIOfb8dO4BDT1sv7v5G1unA6Am\nWgG2iKzZ7v85wJzgNiDjCfKJSGOSmRYiJEi5ktvKSW+8cOFD+MyH+MmoZrUAx0lvjMwe4imINhQL\n2cgCYVHCTZAryEnv7MVgiu8STXfsmWd7lV+Ca0o83HIUtfaQTZoMhOQz5HfGkpDvDGf/KOU4x/SQ\nTRZqjSZqdQ3J7HVrrnI+CFpdQ6LjXUPC0TzI/Uea0cl3HQl7lk1Pc/6qvXnr9Huj3wMDd4akA5IX\nf73ff/0efStj7Ef7e7RPH38m1ANDsi7cwolzvHDLJ87TxPllZCkDcxtYJDPHgbNcOXk6ObilN+6o\n6T4kUw/XHBiSHrLZs2ycVlTttWm6HVX2kI0xJLNMZDZ6AXgcfHQRYm93YasxJF28asHcEze1sGYU\n+/mpWopqxHbH53rj3XbhZbnybr4gg1q4Jw8sw5nbuHEdG9MojGMkjwMxnKhjosVEa4m6JtOqtExb\nEvU1UX9NVtjTEw3pCYfRJo7uiGDzvov/K742VEgV4gZhc4akz8dgH+Izo30M2YTHkM3ooZrzGV7e\nwbt3kGf7PrVAib7ravbzdIZ2gXrzsEl0Wtd/rkRgdEDyjl3Y2tuRwDk2qYgURCoSCxIrIVYL1yTX\nkAyWcyUHpr/J471iKhCHo9Lvin5s5x30bhibkrQyysLpwYfEN7Z1PtDgGCBZjl3vNci8pfn3r6/f\nDyDp83Fvu+TDGZLedyXzEVQ8qwyfj5/7W2Dkj4CS4797Zma+1Z/ft3z9XuQIdg5Ax6lKDQEJNlr6\nidA92HX/Ut3RdQwWN4yhWDVH8TRDqR5HdCGdT0QmjrMfLdGeMVN2s9eW2qtvnwyAtIKJbl10ruXO\npnbReU5eEqYzJM6SxMEFrCPEia8jW0ebmTfWae38ph/v/nL9oVMx07R2Z0I6CNkza5ZAW9w6fxV0\n6+yIvFmo8/fdE/34rXu18vW9+3yv/Gh/z5ZyIQ73mTXnjZw2UiqkZB4QIbpeJPjztsvY7G94Lzhm\nVma7xfbBOVh24x7u1XtdVrY7GTcsds/B/rtazD7GrlVRj2TInjVTJe0/Q0R37VTxLJ0u6q704vEm\n7K5dhxA8TTTmPZ05+Twyy8gqBqu2ZmHTUr0kRTgUrPRQTZ0D5SbUq1AvUC9QLlAviq76dVS7JzT0\na+C+I3096B9S4K7f6GzkW+tCb04PqB8f7ai13f1Nei/9uNfp6Rb57rza+76xPJ677qSPEg/z+6Hf\nbxwb3wm8YCU0JkWmZvNttrk4RK8v5KDz2NP9L2zwVR2sejkNVQcm+7F53aRWrFcfvQpwP+8hxc7c\nKdDTiU3HK/7nkPvfC6hBeVzcf+OZ+11f9Z/RvlT4eHjTc4EvBS4FrgVmV1dvXmxPOzR7a2J/Kw3i\nGaj8WxmSZ5AhPN70b33t84LUBVg9TKPckUCPU7gwMiVIE5JHJCcYIzIJMgFnRc4VedlI50I4VWRq\nyNiQQW2HEQUN9wqgOzOgdjOqr+J3YzMlbFYJ1IuX2qTmE4QMwElts48h5ZBsoxKjac92mwN/zoZk\nWXQ9vT8dHJjF+4Pp7rdkN3dshviDGcVV49ILD5bddKo/VLFWm8SrGvFUgRqskNkloLeAzlbLR1fX\nkFT5g1Kjox6k0yvPqekLVnSrby9W7kj8j+qbfrR/r/bTzx8Z//Tn/Ty/Wzn9cmX66cb0/mYZN9ON\nabgxpRuncO1cw57PdfcYuRce22GJHEZxy/gg5rapPmv4ohnU0ipTq6SyMXiqrmve7Rnou2i3TG4x\nmClbtLCKiLIyspF3Q8DOqNjCZkXVusaghcAWM3Oa0CYWGtWRG2deZeFX+Yk/y5/4tf7MJ97zpZ25\nLBO3OLCEyBahtkb9WKmfoH5U2seGfqrol4K+RnNVm6MlMUT29NU7IOHx+uzVPZcV1s1KjhRMQd8S\nZmAGj4x4F4j3+fnAojcHH5uvL/PM7sQozqYsC1wucLvZ6+tqX19c3Lp7bnjYvVtJy8Je8yp0Id3m\nrExgLz8R+8bS0wmDwJ9AfgY+KLxTi/aMigyKJEudPoKRRC+ItzoDZ/fhiRuZDav2jo2uvVErfuSV\nSoRzu/JBv/BOX3lRE2p3wXXP/gJ24WyLgiZ3yCZ4ZplfL/dd4zw9sw3fbt83IHndrILbZbM698tm\nN2E5FNz7KgTzFmvyFkD5t+5E3wIkzxT7MxCJ/vMjDxkWz4ziXl0yHtj/hKQByQMyZGSMyBQ8NNkc\nkBTiyXqYKmFoFuLK6lnFQgv3XVHTsLMGO3pwbBQahNWU9qaZst2fRJCsxkJ6RV7B8FNYHYwcMt76\n7xY6Q9L7c0mIoz3HtzTBTzIdif5geupbFAMkvTz2wEbW1QR5pVrOfFeH91BMEXRzQHIV9BZgCTZB\nbq4fafI33ibHcGEHJMvh/ughGo9B90qGxxLlPwDJ3739/MuvnP/lf+zn+bwx/XRj/GlhfD8zvdwY\nTwvjeDOztDAf7OCXPRxjdma2/HfepNPqTeqeHtxUaPHOVDTpYNjSKG2HWsh6X1TodamSxUwl92NB\nc6AkoaKIJDSIL1nZIVLcpQSRRqKimBhegtJiYE2WdVNaYmkjV69bkqXwST/wr/oTH9tPfKwf+Kwv\nvOrETTOLRjaFWhr1M7TPSvvSaJ8r7bPQPgf0NaDXALdwT1bY60z00Lw+ApJls+qe82ZZL2vzRyZg\nKvv+G5VDP87NHkum3VmRWu6AJLnpUf/a1uz67Wp9nq3K6OqApJchB/ZnXrup5SH02vBYtu/ckqcb\nZj/OyebqlCAH+AX4GfgJ5L0iL2rh8sFcWiU8huU6IBlZrOq0A5IzVysd8iRLPHapFgI76Y33vPKO\nV1647EYJg1dIDz4fqQuja/KK8ngdpRCoMdJysOKQ3ubTBlx+1zP3fQGS8cAwrJ0d2azPm6HizpC0\nzpA8g5C3KPK+ALx1/Y+AkmOY5lk49RzCeQZIz6trBx+OyOWNY49zSspIToQhI0NCxmAZKSclOEMS\np0IYK2E0hoRBnc30m+jIkHjtFlpwsxCxLBanhw2QdKrYGBI8RCpdo+kMSUgO+I+AxDcYogZw0gGQ\nJO+9btVXDMlfy5SO6mEpZ0iCFxaUzpJ4LoJupLYZQ7JV4tbuSvAVAyWLTYx6FegMyeZhm86Q/E16\njmdAsvKY2ttf70DlCEaO4usfgOTv2X76+SMf/vnAkJwKw/uZ4f3K+G5hOM8M08o4zAxpZQy9ovRu\nvecMSfWgiC2WBjYiTYrF/EPY2cqm9zo1/U6RasXKQq/KWuy+tJptSqzmrKyDeLdjK03vIRwHOkcy\nvz4xJMl3sYpVIa4hoDFTU2JRVxtI3wQon8sLn7b3fGrv+FTe82U7c9kmrmVg2SLbJpS10V6Vdmm0\nV9m7voJeBC6Czg78g94ByLeO1wpLNTCyVHPvLjggcX0JkbeF4cfNYz9t7llQbG1ZfIPYwzjF2ZN5\nhtkZkn0dcoakHef8vtFIWEX6o27FXwu+ExsyjL3jUrMIoxoY+VmRDyAvGAs+sjMkEnqd8uYape2b\nDMnIsmdtSfE5vejTMYzMvMjVEh7EPHYGWciyEcVrnMF+z9ZopTqKmDNviYmSolWrbgdAMj14dPxm\n+34AyWu1XO/eVg/T3HycnSHZAUmfyI8g5K+Nb/V/j5DNEZj0865WitwXp2e/kqM+xHPVBe45Xvfr\n4kVfJEdkiIQxEiYxDce5Ec6F8KLEqRCHSsjVbt58CNn0wkt4FcjOkHiaa18LzWfAGBKKAxRVD5Ho\nPYv1hAmbnDnp7OM+keLzSINYD9k1TyGb+BZD8pxJ/TRayEZNee6AJIWyh2wGNgrJUhebhWtiqYS1\nEdzcjUVMTDdH9CLo7Q5IWGV3o/7bGZJ+H/TJ6Tgp9fuug48ORFYeQckPQPL3bj//8pE//csdkKSx\nkF9W8svKcLYxn1aGcSWnlSH0EM29PHtXbDyHbLrv5TFc09SK4WkvntdDBs00I7FUA8kbyOr6ka2S\ntkIbI3UM1ClSS6C2Q62nGL1EQtx/bh8VcbWaXRV0d2xtIVBiMFqeRzfXFgOv85kv5cxrO/FlPfNl\nPvN6m7jNmXmObDeoc6Ndeeh6U6u95Z0b7iNy7Dyei489weHYi0KNtrHadSTP4dHjJuFw7Riy6ZV+\nVe86ku4Iu64Wulm9b0eGxOd+fd6EHH6OFqza72q7uTS670m9h74ngXODE8hPGCj5oPBeTUuyMyS6\nMyRHzcgRjBwZkklnQnP/mAf/EL0fr41RVk7hxinMnMLMFG6MYTVTTWehcY1Si9351fRFW01sycbS\nEkXvIpLb6fc/c98PIPnyFGfa3IhmdSDSwzXbaiEb7RP4EVS8JQz8LXByBCN/hCV5C4g874Ibj2rM\n8HQenW7A2ZB4H8NgtEEYnCEJSBZkCIRRCM6QhJMSz9UAybgR3P5dUg/Z2I9p4RB11EBrXnK63gWc\nUuWOmlcfXUOCqOGjDDKqMSTOiO5Ejj+DHYxYGXMHJL1yb/ch6QyJZ9XsDMkzZvsGhsOFuyGYIPcY\nstlIZKIxJLWQSiFujbA2t90HmQ2AtFuwndpVYGYHJF3Yqn8zNnie/A7KxIdsmg5YjnnFx9j3D0Dy\n92w//fyRfzowJHGopNNGnjYbTxtp2sjDRkruhszdAqWsMAAAIABJREFUgjvufGQvc+eUd5e3djDS\nY+/hDkRUeozfgzyqZtW9VqsjtShhacSlkuZCOSW2U7aIQfO02RBs15oTm9qy1SW2HMZD7gWKULGU\n4K4h2TSb5XjIllUTMyVlLnXkskxc28hlG7lcJ66XkdtrZnmNbK9QLg2dlTYruvg4KzrbdXo/ApIO\nPqQ9nSt3s8Lg1q/ea7iHnYG3mZHjplCeQjaH0HkHKaUYEDmGdI7jg4akd3++j2pk7SnCDkjCZJlB\nQ4VJ4UXgJZg1/rtmCXgfQD74+A5nSBQG24SZG++dIXlLQzIxdwcm0yA1t3rfGmFV4mrzYVgbcVEr\nvhqtYvWYrHDkGBdyNIPNIPeQTQ1imVq9YnA6lO7Q/ABI5un3z2PfDyD57DdAb7UYKNnrFjhNtm1P\nIZu3AMhzh2+DlT/CkByBR//e8nT9qCsJv3HsfzgJTkWIC5ySAZEw+g0c90U7uM9OmCBOSjg14hni\nSyUNxcRtXnSpV4G0kI1XCfX48d0MLNwZEgfyodxDNjS1NN6uIUm6V9RG72+5ZwgI9zkkNNtEtOq/\nkoORkO7M5Vchm/DUn8HJIWQTPIsohmdR60Z1QJLbZn4KW7WHcFHkhhUlvHkc+yLoDfO/WcTwrlPj\nfzs2OE6Az+cdfHTmrByuH8cfDMnfu/38y6/807/c0zSj1w9JY7FxqMSx2DOXLfOkcw+yS0ZtPAKS\nB1HrkSFxMNKaj+L7E4HQmj+bDkSuDb1Vr2MVWFYzNDMwYtqzFgM1JdZhYPHq1nCHIhzGbp4F9vOK\nmI38RmbmxBxGljAxxxNzGpnriXlN3CQzt8xtzdxuiflL5vYxM38KbB+hfmnoeu9tP67o1tClWdil\nNe6K+sNxeDqv0XpJT2Mw8ah2K4W3mJEnw0HlHrIRt104akryajum5mvTQ/8tDcl2D/scNyC6YOK7\nCsnD6SeBc4D30dzKnRHhvcI7ByPvFDkrHBiScGBIngHJM0Ny1pvZv9dGrG2fB+NciUvzbnPnkCyT\nbPCMspy9FKkUM2CDXbxag5mvrZr3JPYZM9jbDtBinhq/t30/gORLsTBNb62wV3Osm/XiYKT0kE1n\nSOCR6XhmPZ6Bx78FjPR2/F7y1Nsb144PyfHchU+7gQ5ON3RAMkE8uVpUkdyQoREGJYzNar6cGvHc\niC9qFSC94JKEtsderaBWOIRsvLBWF7V2hsTrVYUNwqY2CfZ1VXRP+pHx/tF7odE71HJRbGsQPeOt\nFQceB83IPj6HbJ7BxzOx1MM24Q5KdlGrh20yG1XCY5ZNqYRVCYuajvQmFqq5BLgKXEFnYBHTpG0O\nRv5QyKZPgkemrGdVdYTVJ6x2GI/HPwDJ37P99Msn/ulf7t5IITQzo0rVUn5z89HOYzAwcl/orcnT\n33FnSDoocQ1JE682LZ6OGQQtnSFpRNd16WxgRC8CrwG9CFLMZr4S2UKDKLTB4vlbtbIK5jPRDl2f\nRuuKIGGgEVjJ3GTiqmcu8YVLe+HaXrjoC8stsoTA2gLLGllvgeVLYP0YWP4S2P4C9WNDS907h2Mt\nxfUbHvbYDYzeACf9WsveB6iD6988XNMyaPd5gsfn6egdcJin98rqegcnsVjseYk27zbfVfUwTreK\n36uKHtYCrz32CEYCqMeZdXVBnbpXYoBzhHcZfqrwc4OfAGdFOCvS038PWTbfErW+pSE5c7HCjbWS\nSiVtlbRW4lpJs/ebb+gGqyKcmtW2iVidm+h284jfv3Iv4NfrKnWTvlnGuyEbME8HU5K/0r4jQFJN\nL9KbdmMLz+m2ynL2B23PDAk8Tt7PE/lvfc2/FZD047eowudrb72WuK/yzRfcAyCJE4SzhWxyRYZK\nGEy4GieIUyWeG+mlEl8qMZlVdQh3j4MuMG0idzK5i1prODAkDkhW34l1x72+Nob7Wz1e66xI8I8i\nNHsutQvOexach3d3uYzPHZ0deWBIjiDkjUiX+PN9BCN7yEYMjDSCiQubgZH0ELJRuGLMyKsxJNw6\nIMEzcOUPyjk6CIE7OD2q/Z8Frv3r/63M3Y/279l+/uVX/vlf7n8DE3S2PUzYU85Dd9MM97DHb3UQ\nC8r1tF+OoZq7sFXV1zFRq21TgK0hMwagX0G+AJ9NA9aruQZPzdchUMbEWjNLs3yJHlLqstYun+2i\n1kilSUCAGgKbDMxh4pV3fNb3fNaf9nHL/og0tQjGFcoXZfsVyp+V7b9B/VdbzLX5rqQVP94erllY\n4wDedxByCLNLAx1BJw+3iH1AmjEDjJ72m+//Zt8IHLIacZHl7j3SmZHK3a9d7sfHKr7tG8fA/szv\nWpInNlwFdNg3iQwBpgQvCd4PBkh+afAnLGzjJTSYFCZL/X1L1NrByLcYkheupFbI1bIN81ZIayXP\nhTQX0q2Sr7Zm7FV71Vk9sfs7xPaY9ivdpyaxBauntAS7V25hYpUDIDkdHal/u30/gOT1SHHDY9rW\nMcZ+FAD+/l/0P6b9Fgj6va1i8Y+e3sIBkLgLWTwjKUAqSN6QQTyS0xkS05Ckl40YCz2XJhxW0uOu\n7Fi/pT1oSLjTwqsBk31BBrrkhezLaWA3m+0bmn1DcoxOPJvT5sO1/Mb5cc3+FjjpDEl4FLUWif6A\n2kR/ZEhCsSyb4BoSbmLZNa/BstKu3DNw+63WyYoHMPl7Wt8h/Wj/qM00JI/+CR1W3I+Bh3CHPj5n\nT5xEdXmr9PODF4lyACNAU/HM/64hwTLEFghXRV4hfFbkEzQNbCFbuDYrjELbAvXAkMyM+8LV339w\nYHzM1rCQj4lbVzI3Ji6c+cwHfuVnfuUXfuUXSlZaqLRWaWul3irttdI+VtqfK/W/VvTPz6HK5/6c\n6n7U97U3urMZO7vcJ6e+U+rpKsefm7mHSY8gAWdI/r3aG8/8V8vCYEAnBRginDKcB3hfHJAo/BNm\npzAAg3oJDb33J1HrWwzJyHII2VzIWhia1SHKZSOvhbwU8lzIt0K+buZfc9x89fm+R5f9+i5qjQZI\n1mSGeUscucWRa5xYw70MzDzdfvcn+P0AkoeQBtyp7Wf3vb5z7CvT887yrfM/8l6ej9+69tfOfyt8\ndDw+0Am6WThKvBCTUwFaIqwVnQt69Qf/c0NGpWaxLByBNhRaiN5dZeqLuXSkKweHyGAeJZrMwbQ2\nKz++aSR6LLr/257dQlIDJR7SeciCSWKs6bGW3Bk4C7xYXJQXkPc2clbbBXhdnN0++lC2vbv/9U4Q\nSoosw8CcR5ZkD4Mh9IElWMGxhYGbTCxhZI0DW8qUbNkI6h4jUtteu8fmO7kbLnroZi9ZUOx9dRMj\n2UFj3vU+Ek+gEe0OwxVoejj33r6aqX6076x1xq23R/hhAP94vr9+twp2vaPVA4nqu0w1ODJ6HZvB\ni9glrUatq4dRVAk3kKtpnmRRYzCreuFK3adISUqMdXdiHlgZNVNaojbzhYjbXfTdC//t59yLAWbd\nvJDajbUNlJZNAK8BqRC1kVuhfDbRal0btSk1NurYqO+U+rNfT41WjQ3RutFaQWs5XKu07pD6Jhh5\nK9zOG8fH9ltz9X9k6yK6TuO+cR4HiC+YAG+y0FPLpoVZo2X3zRihon5v9Xkwmt6vublZ00BpkbUm\n5jZwqwNDnYjtRKwLtI1WK8smTMvKOG+My8o4B6YlGGtdGxHZjSZ34rZjv14uw7GWMd1eUydVUirk\nVBjiRk2Rmix9PYb7ujvWt2puvN2+I0ACXwOSDkb6zdm/5vjaMQb/HI9/dub7ve/hLa3HWzqQ3xrh\nbZD0xkPWKT4tFoqSBK1rS/z7lYR6/LhdFXlV6uixyCCIRGhCGRM1R1oKaLZuYRErWR3wuLd0u2v7\nMS1aAauqXueC5LFoz1dPLnDNeshhF0u56+HZDDrKnhKsJzHQcQOdBTmppbSdLY2NMzZ2F0L/Prve\nJbrYL4Q9Zbk7BJaUDIzkgSWPzGlkiQZG5l4FlYGbnJjDxJJGtpzZxkQtgVYte0ZQglRbPA6AhE08\nrdC8HfZbLYmxVTkinrsseTT2KlVTz2uCraFbg9VG2UzMx6b79R8hme+79XTK3u7Ki+4V8siAdEYk\naEOaEjxdV/axebaDEpsVrMu6MbTtDkqagZLQvOLvrMjFQcmMAxJ/Q30aHPz5To0UKzl42rsuZg/u\nREEuBkQsvFlMG+AlJI7HTQNjXTiVm3lKlIhsSiyVXApjWTiVme2jUC5KWW0aKEkpE5T3lo9QRCkn\npW6VthbqVqhbpT4cu7hVn+fFf2tI/e/QHgwtfYV/Pk4DBBeF6AnaBGWALRsgmYOlQXdggEd6guxz\ntHrpi0Zgq4l1zczbQN4momcA6bpRtsq6KbctcCoL523hVBLnEmhFYLP7Mkm9e7h5NGsHJMfjlYe1\nJKZKyoUhb77eCDRBFFK8PzdTmX/3R/gdAZLjp9HPO0PSb1bha+bkmJ3w3P9oHP4IPsI3zo9f9/xv\n+vlblKM8Hft7VIefujoYOarB1RG0ZYLoVdARWhavJWU/V2ukThttSrQhoqPsAEH64hvchaCLxYIa\n8m7iJc6DgRHSHieX2AGJ5bJL6fbrdk42hoXhDkh0ETgbEGGxcxnd3GdS7xiDMikyyF44cfdLcVrw\n4TgGi22nvAOROd1ZEssGMGHVHZAYWNlypgyJWqNVtVQFsTo/WgUpnuq7OTMye0h6BAax22wUGAIy\nJmSwglwybshQkNFT+TTBXFHvzBXmBrGiUo0dEbinBvIH79Mf7T+yvVWcrgdn1AHIveBd2o+TVmIr\nltlVK6lVpOr9vFqNkNwsAyzX1Y8NkMTWiM0dkleQm2eFLVhRs66V7pT6gJWkj40UjSEZxcBR35eF\noqzbZuJEqVbfSg7HmAYr0FAVxrpQ1kRbg1X7XhtprQzrxmmdeVlurJfAdhG2RdiasMXANgnbe2ET\nYc3C9gJlrpS5sc2VMtd9RCraKq1U9JubNnh8Nv4zGY+/tbm47UG9/3ScegGwzpCMJs7dnCGZTctG\nkK/ZkewMid4BSamRdcvM80CcR2SeaPNGvRW2Rc3PbQ286I1VI0VDzyQnaiNpYZRg83f3f4HH6Fh3\nJ+h4a1BCVkuDHywE1Ia7PYLQSO0OLabyj6gh+WbIJh9ePzIjA2/HI4+A4Y+EbJ6Bx1vKym8xJ8/9\nmbU5xhePD9kbIZvj76DVGZKIzmZz3pI7keFuqzWga6CeE/UcaaeInoKllTX3Goh3lqQX9iLi+qxj\nlcjIFpIVDqvNCutVK+bVR63NYtpVPNxiIElXMWZk9ePlfizZYqBhkD0eGgb/Uw4eBuqApMcoY6Qm\nH2PyMbKm7KzI6GGbkTmOzHGy0I2beN/kzBwn1jSyZi/+5Q+zBixLJ1UT9hZBNkFXsdTfWUxrMngY\nqgoyBuQUvSdkyshpRHr9oJNCW9FLsX6tNvYdQ8NYkq+Ussd79kf7HlqPzfdWiIjPQf1Zufux3ntn\nPbRulnBRG6EYwzCUjaFuxlbUQmqbiQ17QbNaibUdiueZZkS8kmpnSHajwgeGxLIhcig0Lx1PM0Gs\n/ezsotx2L6bpBdr6NRHTEYxloW0BWSDeGsO8Mc4Lp9uNeb5wm8+sS2SdA+saWTWwpMg6RVYJrENk\nfYmss7Be2kMPuSGhotpopQtXvyXqfn4evuPnY/eROqQOhuFxTF5BVFyc20Yo2QDJ4gzJVdDgm5Yg\naPLutbVa3zxqsErnayLeBriMtEuhXBvbRZkvwvUi3LbIEhJlD+PbxjSFwhA2agiPyyw8Jijx+JqM\nShjMqC+VQhvDPUkKJdIo6e5DcvrHZEj6gt+b8vj2OkCpPDIjPSXiLTBydOX7ve/hmQ15ywjjLebk\nrWtHX4nn99Hu17TZLKPFGBLEcZQDlVCgZHRN6Jxo2R3FxNXlVdAtEuZEeZdo7xL6LtA2S+kV30lJ\ntlThSEU8ZIN2UGJmSlWsIFepBkiCayxCFZ8kfYJtAWojNDG/n4Hdbv3Y2cSLYYov/or2EtoemQoe\nA++hH01GT9YYqCmypUSJ6T7GxNoZkTgyp4k5OjgJk7Ejnn52k8lDNs6QtF7LR5xdbYTU0E3NtdXB\nCItRpzqKgaXOkEwC54C8ROQlIS8ZeamEl2ahqBegRvTLhn4utGHb3eIsJKS2CxI5zK3PwOQ7nnT/\nf9SiBy97U4RyEIkbIEmsDF5KzySFoy5oc71FreRNkE1JpTJsG+O2MJTVwEfpIMTGVIxFiZ2BLFjv\n5d6PIZvOkGR7tqOHbFrYDmUbzHsilUIpyZ75oMZ8BlvwRUw0izr/o8pYA6wQ50a6VcbrxnRZeLne\nWK4j62Vkqcl6sz7HxDIlliGxnP36Fpk/K8uXRhrt+ZfQ0Ka0opTl/j5+u3/vzef8zpDsCQm9LPpo\n+pHkx+I7sZ0h8ZCNzzu25IhVb8/OjhTTj4R2d7opNbKuGZkH2mWifqlsnxvLF2X8LFy+BG5rpAyB\nloGshMFCLeOwseVIjc6QwKOE53nsDMjm60ippBrQtnmCqBLEQkCVOyCZ6v8UDEk8XO/i1R6+OSqy\nM6YCOoQ4djByBCh/63t5BiPHQipvpYC8NXbfiZ7y2d9b+/pcnRdr4h+F/55iIEW3AVkGNA9oGByv\nBLREYx/mSLtm6i1Tl0jbIlr9fRzBSHOGRCylq4unWjqmckW2GJGWLAWsVUIToleMtN7sve7yF/nN\n3ooQgqJeBwO3IQ4BNCgaxEOwFkJqycBISW5JnLwEesoWrkmWYraDEA/XzMEBiFhWwU3O+9et2SuL\nhGA0aGJ/QFsJRocvwfrczCNgUgMlWXdAIueAvE/I+0x435D3jfBekfcQ3ge0RvRjog0rId2LpElR\nWBoan7NvfoCQ77E9MyRKoBcYu7OJeQcksydcqgakQWyVXCK6iTljbo28bozryrQt5ovjJQ2iF3+M\nxyKQHhrF9ys26j0rVrCYvvjz7SGbHgkMmFYl1cJQM2WL7EXqAO3pqgKIFf5DzfNEKvZ+l8J4XSmv\nme1Lprxmyhc7XsLALWTmODD3ccjMYWCONt5aIp+VNEHMPVSp1E2pi4WaTCb3HKJ5BiL9+DsO2eya\nkW7Z4GxIPOEuls6QHDwOWoZ6ELXOsi81ekgS0MHnUBe1ykFDwpZpt4FycTDyEfKvQvoYyR8TtzXS\nJoEThJOST4Vx2jixsoVEHcIdCRx9GY/76cOx6YkaWqux780F2G69kL2Aa2//oCGbZw2JHq4f846e\n44zHVK5+/Q1Xvt/dvsWQHPtbbMm3jo/A4wg1j+/ZwzIPfFk1Y7gegywDuk5wawZn1DQjYVPTZ9wC\ncknUxURorUUUE4MyiGk3XMS0u0n6zqTHKps4DRgjW0t+o1ViE2KrRqbsiFmRJogac2AiUfG6OOHp\nXGg1HMIUBsjEf7p6dB6wmGnqgCRYsaacdhCypoE1Z2NEwnQHIfJ0LJMDEgMrq1q5s14IqkV70KU0\nZDTDNF0CrRffnQI66V7yWwcPfTkg4V1EPiTkp0b4qSE/Q/hJCD8FKIGWI7j7ZmjQtobMDc0Vib2o\n2fE+l28c/2h/r9Yr4PZWqfud2p4YkpnRbahOllHTGrkWWlmhQNiUtFSGtTAtC9M6e12Rto/x4VwJ\nXkcKHICgHqO3w54VoREkN2JqaKgG9EWJ6lqVGqlls52wGhvq3Mjutq7BfU+AoPZe8lYY55V6TbTX\nQPsSqZ8S7XOgfUrc8sBtHO99GB/Px0KWbDpOB07ahFagLLDdjOh9nKKfdSP/QCGbB4bEXbbjyfvZ\nxjTe9SQajSIu6aAhCZbJ5xYJe9FEr0zeqiAtIB6yoUZ0TdR5YLs04mcl/irEvwTiXxLhXxO3JcI7\nJbyr5HeFsa5MuvASE9sQqeKbs+Py2TUkx8xs71brrO1rgTgzEkMkh2JaP7mv5VP9RzRG+4oheSuE\n81aLh6/rzEj/JP8IQ/ItMJL4Nih5BiPHfnz/bzE3egjZwM6WiLMr4j9PRsvYEGwGKdEsmGdFrgJj\nRMZMXRO1JddIuPJ5xEx1ygGQiBc597fSvPx5bYGikaAJVEkqqDoY2XGg07uqBkiauURaOpqnpXmc\nU9v9umX2NcxVEaQ1xFkXmqBuUd8ZktbBSLZc9zUPzHm460WO4KP3/ZrtWK+dIUkjq2RKSNRkgIle\ncKpW0xIvEG7QbsBVEAckOnp0rLI7K0oHJL8o8icIvwTCL5HwpwhbNJ8BxxWtKDI35FqRIRpI7IXT\nfgCR77Z1yer9PCKuSWv0bLTOkEzcOHHhZU+LHepKdYZEViUulbxsjPPKaZn3AmeyKbK1w/n9GDWh\noTxNMdKPfSqyVMxKir44UEncvYZq6Y6wB4t6DjV0muz29drENkM9fHkTeBX4LPBR0F8FfhVu54nr\nu4mrTFyHE9c4cZ1Odu3dxPC+kNNIzM5+qu3yyyJsVyGNQkiCyFtz9FvsyHfedobE9SNxcmbkDOnF\n+2gusOJ/uO40uzkzG8LOfJEsw0/HOyDRep9bBWNj65pN+HwB+Szwa0T+nJD/MSD/feC62sYpLYWx\nbJx04SXMLGNma5EqAZLclyd4zKzpdWxnOw9V9xo+oqYZ2atWx2CZQIe/6VR+vx/TdwRI4GtA8ns7\nPDIjzyY4f+R9/DVQ8i19yfHagQHZmZGug3l67zsYCR6meWZZNlTUNSMRXbOV387q7F+ElCglUSXS\noqX+Mor5gCxe3fEYsrEgs71DxezU1CLnQZPtBlXMkBCneHshKW2Iiu/W3JfhMDa8nPphxOPhlGau\nsHvX3dGV5hqSJNQcKTkaIMn5nuKbTbw6MxkDQmdETk/n0z3tVwYrDpbMZ6XtHhEmIGyzg5ErBvBO\nDZlMqGsaEv/zjR6yeZeQn5TwM4Q/CeGfA+GfIvGfI7q5f0vFBKxzQy4F+ZJg2GxC2u+zZ1Dyo30v\nzXJm7puKQqYboPUsm6OG5MaJK2cG3RjbSqkLrUTYhLBiWSrzxjQvTPNiupAVMzvbgFXNjHDzcdVd\n//VgLNh7uL8WsrpzrKKhuf08d5ayWLZGt0ys4qZsLVCbZa5V7HVUXFDbCLMSrkq4KOGzEj4q4Vcl\n/Fm5fjhxkTOX4cylnbikM9O0Mr7fGH6ppF+UNBobqi3QSqDMwnY1i/k0BAMk4ZlJ/kdtzwzJ4KxI\nByTvPe23a8i894KB7rEEOBjBMhQ3OTAkwRmSRiOiNZn+bcbKYHwOFi7+S0b/+4D+15HzkkhLYaor\nJ1Ze0sz7cWBZrTpvIxhDckz8PAKSzhq7caQx527vJ820Lq530SSmeTnsxaf6+xNLviNA0rcBvb3F\nOIgv1Md+ACK9oiI9daPbf8bD9/XV9Zvxyb/W3wIe3wIofSapT9ef2SA6J3vgaP3u2AvFGFARut1y\nRYpxa9IaVDXR6E0Js1o10O3gGVJNbS/qhmj+6x5NnvquT0i7uG2P5Pr/drJXA41G8CvH2hx1P473\nYwKxWOpg2iq6mf8AW0VWbCIVQao+ZNhsMbNGdwFMk/cTN9eJ9Ni9jXdhYc94qEQL0fTdYlcBONUo\nWFiKE7RJkFGQobn3iKc7R9O5EARJYrsJr7rMGO9pzF3UujbPwIn2+hAg2y5Eou+AfoCQ774dzc6A\nXUjYg57dgH0jPWbaaLY0YO1Vtb1elMfipVgMng46fAcqq/pu1AFJT7brSYV9fI5mhz5N9M0Cj3sg\n/7laoJf/K5gJYmmB0CKh+UJXIiwmZo1zI8yNOOvj8c1eY9I9jNDwwp0xUIZIGSPbKbKdAvkcyKdA\nmiJxCIRsonaJIEadcJ8PnzelPJ2/4bb61i/bz/e5zudu8Q9NXAZwCIXZt9LH8eGtyNPo31ew79tL\nmLs/EcnLm+8ZN5m9svtx0d/xmPadofkaDRUZC3EpxLUQNy+BUQuxFkIrtFJoW6Eu1aoqX6FdhPYa\nqJ8j7ZMS58SaEutgIZpyCpRToC6BthlrpV5O6CFM01mRGwZGrn6s2DpynL+elkY5YJDx9yfZfEeA\nJAcIR+Dg6NER+x2M9Ov4fXxY+GXA4hNdfdM/lX7zeXikuwL2c/q1Z41Kv8GfAYTa+9o1LMeRw/js\nQfIs1PLvG4LtmkM8HIeHY4kDkiZCyoQUPUulEtKKpEjIQkiVd79cOP/pwunnG+P7mfyykU6FMDbI\n6rukuMOEu8fksyX24wPZlfj9tSbHBV7M61G6ldTbPVMYZCOLjUOwku1DtNoW0jYEtUktJFYZmGWy\n8lB65rWdudYXLuHMrCOb+AIgZpzcsMU+UVFZDWwc0r6P77lIomh6/Az2MBQG4Ko7rBb1B9ULm10b\nvFZ0KGgqtLgBXnupFNgW2n9faH9Z0V8t20Yv1QqirdWcWr+6F360761dOXNhAuwvZaD3xOKAt2I6\nLWCHKZmNKFbY0iQCgZKtnszcJm66ktlQzK1VmotXxQ3UqpnohUUJN793B5/SBuw+7GPfhxXMK+eY\nSd6nrKP1d8/yqp4+LEqQzqg4cSfQ5oB+FsqXZCLLCoiYV9AZMw8U4foycnk5cR0mLjJxqSeu88Tl\ny8hFBi41ccmB1/8WuPw5cv3XwPw5sr4GtlugrpFWAqrx8Ib/Wj9uNvsGtq+kHcFVCAsET0kK+Hzq\nmS/9nOIh5N71fqyH892dWe5zcwz36/vcfcbKsGdbl4L6z19Bb8aEsD7iqKNu40Dwh1TIeSMPG8O4\nkcfCMG/Wl428bORsYKTVStNCpdJCocVKS4U2VNpYmJj5X9In/il85md95cN25WWZma4r+ctGjNWW\n1YZXPP921/kJMh6WSTlWeRkPX/Tlt56yx/Z9AZKdyuZOZ3VAouF+7diPcbv+5O4VImFP1m/VRAC9\n7+fFU2yPYKKLTjuH9Zy2ewQkz+Dk+L36XfatBcjff4yQoiPrw3GOh/NMzIP1FB2MV2JeH45ffrrx\n8qdXTj9fGX+aGd4tpHMhjs0+Bq/2e1yIj8f2rh6PH9/xHZQEaYcFPu/sxMrA+tX5wMjKJAtjWJjC\nwhRWxrBAXMy9UitBjCHZQmINg+lAOPPKO77EHSgPAAAgAElEQVQ066/lHXOc7jFwCbsoV4AoBdFG\nkrIDks4C7eZVFIK498IDGFGfiPxPVxyQbOq2zg2uFR2rgZHgxR/LCusGy2aA5C8L7c8r7deV9nlD\nXwt6q+bQWp/vhWcg+6N9D+3GiVfO+7mFZSYWJjYGijuz2lJ5L3KWpBDc46fFDkgGZh3JeiJIoxKJ\n6tk1q2UnRK1eb6kS5wpXX4YL5oPjZVkewEkGHbjXoOut71g7IPFbTja87pRlRqi2Lgnbo7GsQrtG\n6jXS5kgt0djOIdFenHEcItdp4HoauY4DtzBwKSO3eeAiA7ctc7klrsHAyOXPgeu/RuZPgeU1st0C\nZbHv/ch6y185Pha+egYknXX0/Oi4mQo/YWtLTFi6TzQ2g+JVe6tV+T2O1Qv+gbEgMexhcVJ8nJtz\ntO/dXRT3Yn/Y+qKLHZdmIlZ4m9Qp918vpI2UN8ZxZZpWpnHlNK1M88Y027VxWM1Urng9IarVFoqV\nlittrLSpMjLzz/kz/yyf+EW/8KFceZlnputCjjZHalMr6+PhGe1GfDc/72GbxZdWtXtOzd9uByKy\n+b/7nwKQpCdA0p6ASHsCIw2nzLo7XjaEupeu9tckeZhjs1E3e6rb5t+jMyXPoOSIzI/Or0dG5AhE\neDp/ix05bmF8DMFu9CHBkCEnGH0cMgwJGRIhJ1KOpBzIGfJQSXkl50bOGynPnD/MnH+6cPrpujMk\n8eQuok8MyTMY+S1AIlioxxbwRngI0ZhB1OLiPkt/HP34HlKZZOEkN84yU+VGCzcIlg6cYiWrWEi1\nF22SgUUMkFz0hS/6gc/1A5/4wKKDsTZB97o8dm6G3knK/vt1zwirhmrMyMrwKO7tfxs3kuoMyYMJ\n8KowK3q1bJkWKkEL2lbLplhXmFe0rOivC+1fF/RjZ0iKubeuDa3tfhu8CUZ+AJPvoRkgebefb2S/\nlwdWMpVE8+c4oDsg6QwJAcsUa5F1yMyM5vwalKqBVAppLaToHKIWUt3QFQu53pzFPQAQGdit4OXI\nlLwFRrrW5DgNFXa3ZakNKcGMDkvb7/VWjMovW2LbMlsdjI3MA9t5YBsy23ngFhO3nLmlxE0S15q5\nzYlbH18TV43c/jVw/TVy+zUwf4oGSK4GSFqJZuz4Ztj7W9fS4Rz/xTb/EDxpIDSIFXJz/BKwSdM2\nd2RHAVtnNTfzuw+bMUA+F1CrMSEpPszHDBnGw3nOUD1jpiZjQ6r/+7oaGKkFqr/nZ0BS/NfabAxp\nIw0L47hwnhbO48rLaeE8L7wsC+d15bwutK2itdG00mio3AGJDo02VQYWfsmv/BJe+aW98r44Q3JZ\nyRRCq+YV1Qw79VCNdv3Icri+8lCIuYMR6fW/NiwEea+tB59/9yP3HQESrw+yN+UOQjowaQdQ0rq+\nIhwAiZd2Flw45GBEMrQV6mZji+xVg9RpNZHDFuHIpT28IR7vpPh0/RmcHP1S3hL2dH2II+8hwzTA\nONg45f1YxmjC7QHyIAyDMg6VYagMeWMYYBiE6WXm9OHG6d2N8f1iDMnJGBKSZdNU4psg5P6uno8f\nWZGgjeYKksf0R9tBXuXsKZBnri70u+mJMzfecaHIxerTNCFEY0YGNgv/VDFr+GA+B7MDkld9x5f2\ngU/8zK/8xKqj1ePQQgpeoyMUUmukYEXEIob+j8zIRrYiYtqj6W+EbFqfjGzi3hmSDROopoqGAlrQ\nstHKhswrXBf0dYG6op9W2qcV/byinzdza70ZIPk6ZPMDgHyPrTNzvRUXsG4esilvhGwGVqJYKfc9\nZKOJhcGdUZ01IZG3jTxvDHG1gnhN3I5ICUsj3Gxd3atwZ9twP4dv5AhIjsxIByRHSUVRZLWsH1kh\nrGqs3Sq7pqUX0iv+TM9MlmI/WN83HRq5Ee5jicwlcLtFZgI3DczVQMj8ycI1t0+R9TWy3g6ARJ9t\nFb7Ve47wscN9w+dMtsjjZzCIVdYdon1uvSAwzTYRi28m4noXlbZmYKJr+GK4bxRPfX4+zNPD4CUn\ngn2WvRbW5mEhNqiLr188siKJR5wVIaSFPCyM48xpmnk3zry/zbw/LXyYZ94tM++GxeoAeYFCpdFC\nQ1NFc6MNDZ0amZUP6coHufBBr3zYLrwsNyZZGNpGWBvcnCFZHYi8MfbjDkTCMYekA5GuO/nPAiQi\n8r8D/zfwvwH/K/B/qup/Obz+/wD/19M/+39V9f/4zW+cAwzPDAl3EPLcBScxevwuPYKR/ZqHcqr7\nL7fosTwAZ0a03M/fBCPPr/UQzV9rb7Ejz//OQ1UpOuoe4DTCeXwYZRLC0EhjIw+NcahMY2UcGuNY\nmYbGNDTG88L4sjCeZ8bzsmtIZNeQhB2Q/DV25Hge/Ot7hk7QSvD/doZE7iGWCy9ceOGVd/vxwsV8\nQCTemRGt5LgZ/a3BseZBQ8LETU9c9B1f2ns+6U981F9Y28AQVq+YOjOG/4+9t+mxJNnWtJ5lX+77\nIyKz6tx7YQBCAiSkRnAHIFrMesiUQYsWA8QYCal/AIIJEmKA+AUMmAESDLnNpBE/AIRoMQGhnqDm\ncupUVWZG7O0f9sVgmfn2vTMyT53DKTjdt0yyNHePyB0Re5ubv/aud71LNSNikgKcuhJkwVDuwEhs\naZpO9Pv6e8AOkHSWhMaQyJ4hcRpzV/OVRIkRs64wrZTLgrzMCkgukfISqZdIfW2AZM6w5pYyt59X\nj/Psl/ZT28+2HsGWxtvbvm6N1q7RDAVQQNJN1FxnSCxakRXHikdEGcpkDRHHMC8Ev5KNoYhoFluu\nmJjJS8ZNmgq/7UAb+KiP57k9mh+BSC8Iud8XJVQ0O4PMKAszmyZeLMgspFqpTsheHZEnd+Dqj62f\nuLojF3diScIchXmFJQpzgjkKy6rjHGFZhOXVsL5alotlebFaA2eyqiHJe0CyTyH6UmrR41r6hW7s\nLcwSLAwGRqtp+0MbKTAvuvbapgyu3MCIac8j09fnDkh2a3PvY4C57npb90tpH1bZ1pM7QGL5vGK6\nBeMX3DARxonDNHE6zDzPE+/niXfLxPtl5nmYqLFScqE2QFKlUG2lukIdFJC4Gjn5mZOZONWZU5o4\nLjNjWfExYedMvaqKgXhjOr40SlEwUtraaHouSWdQZm5W9PCzh2xOwP8M/GfAf/OF7/kL4N/mBmF/\nu1WbMzpxetvACAogukq9q5r3glBjm2CJezBim4hJVq0XYCykTpk1MFISrULd7pd5BCX9uMcyHwWs\nbwENuF8J3gIl7W95ZEiOA5wPcBq3LgfBDhE3rIQhEgYFJIchMg4rxyFyGCJ+jIRxxY8rfoz4QUM2\nNmSNXNl7huS3tRtoeahrKpZCplZNH9SQzS318ZUzLzzxwrOOcmaVQcGIaLqYMyoCHFhIjf6uhS20\nsjJsDMmlnvlUn/ko7/mxfMtqAod65VivFEQpc4lIrbiaCKwcmDCUDYyshC0XwnIrJrZnRxSM6Ecn\nn2lIANMqk5asu585UqYVeV2QYYFxVhZuStR9v94YkvpFUesvYOT3aD/PesTnIZv9HbCzFwRugMRQ\nGkNSFZBgiGJBAtUK2VnW7FlZiYNj9M2kT4BakVQ0o2IWTUEv3CpldKYkPMTu+37qEZDsUun7EiYR\nZUcmMBcoFzCXAhdBLoJcUKHrQUhHx3IITIcDr+HEa3ji5dD68YllguVaWKSy5MKSK8tcWC6V9VpY\nrpXlCnFSzUicDPFq2rndGJJmScoNRfnd8eO1veji0Up0h76kZbl4dKM7Gjg4OHjtx5bH71wDI/25\n0O7tlJTtgHuGZGwMybmtzeeD9kOAa4JLAtsQY0n6Oo1NJaUWuuEuI+Ut5wjjZvwwMYxXjocr5+nK\nu/nKN/PEt8uVb5cr71cFJDVpaK/SBMq2Un2hhkodK7YmRrNyMAtjVZfgMS+M64o3Udk8U2978x5+\nab/2dq3niuySWs2qy51puhGZ0YKpe2TxczIktda/A/wd/ZzedLQBWGqt3/1OL/zIkHQgklGQ0XWl\nG7vRxg5AbDu2ezCS2+QYWvhmR/F1catZoezQ8fZQ6BqSyo0e3F/b3pGv/FFvZdn0tqMcNw1JZ0h2\nk/3pCM8Htf0dZ+wIfigMY1RAMq4ch5nTOHMcZlxIW7c+bRUZjf+cIflarw0s3TQjjrQFPyy5K1Hk\nFg5Rx8qbCPUTz3zkHR95xyeeSXh9Tak4k9WvgYVDnTaBIKI/LbZd5Vw1y+a1nnjhmY/lHR/4hrV4\nknVUFNx4EoMsIGwMSQcknRlZGAis2x73zUyjyh1DIj3LJtKK4mnsnZSpSwKfqD4iYUX8Al6Dr3VR\nNqQuqY0tw6aHbOoe0P7Sft/2s61HfB6yAQ1eartxinAD7gp0iy7yqLcP4lS7VSwxZ2zJqkEJzUTK\ntFcuGZMTPlr8YqhTC+EEbg+CzpD0Z3H72p1mpD+/98/qvvykiiyCXIHXinmB+lLhRaifBHkBsZX6\n1OqkyMAURi5y4lN44sPpPR+f3vHh+T3ra2GRxJoy65RZU2KdM+tLYv2UWT9m1pdCXgxpteTFklYF\nInm1Ow2JffjF93nOj8e7GMHdWr3LV5XSNCQ0ZgNlRQ4eTgMcRx2pt+wZuDEjMbXnSN8wtueKdzsG\ne4SnAzyfdDyO4Bd9nsiirxWrMqKyqggjr02nwi209oXR+Ak3XhkOFw7ThfN85Xm+8H6+8Kv5wp8s\nV361XKmpZQKWqgyb1AZIKjRAYkrG10SoWlE61IivEV8Tvia1PWhkDrtcj+043x+bBlBMVBmmWVHz\nyBYOq5Piwa39EYha/4aI/N/Aj8DfBf69WusPX/0fXj4HJKnFArfM2/160wCEMY3matkqtk/Gotai\ntlFmm9VhZ0ayvqPS1dq7MM7W6u66PHzPT9EBfIkh2f8ZcsuyCR7GRgGeDvB81An//oScCmYEN2b8\nuBJGGEcFJKdx4ny4cB4vWJe1KJ4tiCta4bedY1VDwlcYkj0skXoL0ygYsU0cep84fBO1NnfUFqr5\nxDMfeM8H3vNj/YYs+nOtFIJohs2BiZVAEqe0dUHt66tjre31qjIkL/WJj/U9P9ZvieKpqAuKJzGw\n6OubikOFXHtAshIYWJgZNROC36Ih2cAIrVcN2eRCjVlN6Wyi2ojYlWoXsDNiGyBpoIVe8yE1EJOK\nhmy+OGd+aT9D+93XIz4HJD10KZsgfO/is7tzmtiaFn6sVcjV3nyAqgpg82AoXsBWRAq2atXfFFf1\niJhui/8jQ8IOqGzUfwckPTM28rmMrYdsJuAC8rFSPwryoVI/oMdO/UWSONYQmI4HXs2ZT+GZH0/f\n8MP7b/jhV9+yusyaVuIUiSYS88o6ReLLSvwhsn63kj42kWwyWtIiKwjpxzlZat2HaPzuDxja+bC7\n1ouo9rgH3EStXcQQNXxvm5A10BgSr8DhfNROvT1TuoA1Jc2Ws82CoWtSnLkP2ZxG3Sy+O8K7s567\npnGpBVJspmKJW9rvpOLZt2QwD9eMu+LGC8PhlcPxlfP0yvP0yjfzhT9ZXvmz5ZU/Xa+6SWr1jaAq\nuLVVbeeHCqOuZ2arkaSZXebhuCYNJ91qlenbWsvn1zbsF5QZqeG+S+BeSvn/s6j1L4D/Gvj7wD8D\n/EfAfysi/2rdqjm99ZvYe1FrJyM6Vnhr71NpQEQaKKm30e3Oe3nMWhXibRk3rd79nSFbf+FHNuNr\nX3/8vj2j8hUwQhPldobEt5DN3YQ/wfszcs6YQ8aNK/5gGQ4wHDLHceV0mDkfLjyNnxR47H+F1qWN\nVW4hm9tv8aWPpYlYUSFrblCkbH0L4GwP/bs0XZ74yDt+5Bu+51eA4CQTTKT5qnJiZJVAEq1/UA1q\nX18cawn3gKQ887G+48fyDUkcPdVylJmjeIppRc1MZtgxJP336knIHYw8AhI6O7KBEpTN2EStrYuq\n2VsZVqr0ndnSOMv4cCfXz89/ASP/X7Xfbz0C5gcNSQ/UfN5Btq8VEA3B1Kp27BXT6se0XgVL1ixR\nXzE2N4H2is+OFC15EU29bNR5p8y7nqnvWjfmxHJ7lq/3/2/TLbS9GSswVeQV6gsKRn6A+j3wA5ih\nUsWQg2M5BaZ8UIbEP/Ph+I7v333Ld7/6U2KNxGkmfVqIspDSQpxn0osh/iDEXxfy91U1IlWFslT1\nHbldc7uQzZ4RaSm0d8cDNzDSn4pwe0quaJ5q1HXdenC5vUQHJC0c/tw/13oDIzGquNU5BSQdrNwx\nJC1kc9q9zvuTrtUiDdTEVrW36s+XRQFJeoW4fv44eePc+Av++MowvXCcXjhPr7ybX3i/vPDt8sKf\nLq/8Y+vrDXR2dr+HgHZvo4aIgdxYlFRb5kyF1uvSGJC2NNW3et9HdeO05vNW/a1vP3cPSC785PYH\nByS11v9qd/q/isjfA/4P4G8A//2X/p/8g78N7t39tT/5NzDf/i21PY5AqxyrN6fcUs8NiohNvR3L\n7p3doF8fK/cPhjeAwlfHt0RXD+cb89KygLbYZr4/dgatCLkrSy3tRqzt0y5ew0rFU/EUPAVHEUc2\njmxbd45i9MG68R+bRfrNdbKCZpqQdHzjOEsvkNRe5+Gmqbt/1xpYubmmzgwt1HLYNCXXeuRYJ6Z6\nYKqHlj7Z3VYDi2kuq7W7rDbYUFoNjv57FO6dZluIJ9HSeatnrYG5jszl5uaqWREqqO3vQ39EWGmq\nQCvthhLqKNRjph6Fehbqs6F0anSLyYt+LtWoWLo47ZvmqFN7jwLpzO/WvjYf/xfg7z18/+9gjfiP\nePt91yOAv/vv/gXh+bCdC5V/7m/+C/y1v/nP44zBGcEboDEixpRGwtY2N+oOi1a1cS/tfpwy9bXA\nJcOkNak0K0PZubeS8mS3XMkuhPNmaOat/VSbqtVAtUJ1UHs1WS+63AyioaRBmlsxWknYF6zPOJfx\nLhHcCi5tGR3ZV90ZB0MNlhIcZQikocWSunC1Oio73Ujtxx5Kf7q165vNA7f1+zO9yP4NeBzrblPW\nNn+yBz/oKG393uqG7b1PtonUPtcW28itp6w9Nk+Tvjhsep7G/I8Wju1nvEWuP/SKo1RHrp5UA6kG\nYg3EOrDWyFITS71ZG2zJBlK2uWhMQWzVEJy5JfjU9if0vXntAKP0n33/u8nuvF9+iyMQ4L/4AP/l\np/tgxsffYbn72dN+a61/X0R+A/yzfGUBOP71/xD77Z/f/l8WShRqXKjRtGOjNrfRUKKhxq7n6Iv/\ng2ajtklTE8QLpAny3NKvuifJbhIBn9Mybx3vwIc8qMD35z0/Sgo3pWS5v+4MuCOYIzBCGVSAu3qY\nLVyM3rdVyFmZg1g8a1Wfj27wJVKopgnSoAGQPrZFsI0COBPxJuEk3h17o2m0nqig5M6xRI87M9LJ\n6s13pA6sDKz1Vl031kDCk6vbbLVjAw1LqwHS/UomM+Jq0hAOGsJBwJSMIzI0H5OTXIjiGcyCMxER\ndY5Vn4gDryViJFOBKIFXOfPKSQvtVWVkMpYqsokRpe8qRtSN8gnqVahr1logNVNHtM5IEmqyWqEz\neUiDhmJiC/GUvj39kvjucUX6kvSB3de/NCf/FeCvP7zG/wn8p7/lNf9qtp+6HgH8+d/+1/n2r/2T\n27mzBe8S0w8J78C7qnsFp89RcVVdBnJpwkAVqUqs9+LoVHHXRPj+iv9hwn2ccZcFN0dsVF8IY4ra\nKgkaj9/ve3bPy+1T/xL9vxdO2gY8RqEc2/qaWu0pEao1lCDE4Cm/Msj7intKDMeF4zCxuFcNlWbB\nrJU5F2YyiyvMQ2Y5ZuanwjILJKcM0RCUCSkNjFSLFB0pO4YkO8j7kZaVktpx0eMtr7SldLC/1x50\nem0vut1+u4q13fVhO9+zSfn+ZTYGZU1qfDitEPbZOUDM8DrBddGQT1amDG+VUelh2kN881H1eK14\nR3SOxXmuLvDiBg5uJNgjzpwQcyabK17U8sCZtB13c75+LA1V9PI5G2PeyPnSk5zkhqe/hpm6A74E\nnZt9JMDfegf/5j+lMs7e/qcX+Jf+x8e76+32swMSEfkngF8B/9fXvm/4Zsb/6XU7L0ko0VJWS45G\nx9Ui0ZBXkCjUVRTqldx6uh3X3XGJkK6tN0CS1wZKOiCB+zv4azLoB/BxN/rbcRdXdRBiOhCpt2Nv\nwB3AjGyAJAZYHEwtjtm0FSXbDZAsdcCSEHRXVo2Qjb2xB3tmZAdGmpwfb9W23dtVj/fnOJJ1jYDe\n59bcMgz22QYzI0vtfWCpzauhBq3r0Tue2FgQDZ50VmVoFvEKsBbTAElVetZIVp+GujDKlZN5ZcUz\nmBlnEhj1GlkJTHXUwlNFNlv5K0cmOSggkXFn+y2ItHRNixbRG9G8jaXPr8Y2GaEeUKpzFWQ21MXB\nGtSbZNXvo1ioq86/baXrlaf3FPMbW+BtDj62L0jx767v/9/w+AK/tNZ+6noEMP3acXl3WyKdL4QB\nwgB5gDLSzDk1zGdEAYnJwJqRpSBLG+f7c3eJhA8z/sOE/zjjXhfstGJj1JR6U3Xhb3sf2fUvghL4\nHIg8TJUahDIaykkre5eq9WeKF0ow5KMhOk9+b5BvKu45M5wWjsOV5PSesbng18g1GyaBq4NpEK5H\nsM8oYKextifRAnClAZDSjts16eetCCHRtFEglgYYim7js+FW7W2nF3kLkPR9ao/m9L6v0wI3TPP4\nMntsU4pqwGJSz5Jrz8xpT/ZSYEkwqzEia/MeMaK6k8NwAydrfpvQebhWfCC16uZXN/DiDgR7xNoz\nYq9kO7GaK4NR5+uhO2DLrMftD+yZhH1uyH5+7EBJtTcyqk+j/fkGTgRMAyNm12XXCdwji8hPbr+P\nD8kJ3V30++CfFpE/B35o/T9AY7Z/2b7vPwb+N+C/+9rrDu9nhj/ZAxJDWh15carIXh2yVvJiqatQ\nV6OTKtVGnSWN3fUxN51ISpBWFRTl+QZIynpjSLbQDtw+LfuV/gYIkTeOpbQwUtn1DkbauRO1MzYj\n0Fa6FGBtgKRxX7WIVuasjlgDq2jaqkgFI60gnUOkfA5GWnhhD0iCW/F21dFFgtNjDWust4J0Gyvy\nmRvJ1udmlKRgZMeQ1EAqnlg1nLJ5gYgCkxtDMmzVeZ2kxq40QCIVWzJOIkNdOJiJY7ngCAwybwxJ\nFgUgUz1QqpCKZakDSayGbWRkloFFBmVIxCrap+Ik3eKuo8BJqFGwSdklY4rm9R9RBflVqFcLk4NJ\nP9OKINlQ1z4HVp0ftYvwOpvXt219rn3xTtsdP2xz3+x7HdRfHUDyc61HANN3Dn+6pQv4IZOPkA9Q\njuh8OAK1hWx8s0nLRdNr54JcM+aa7vuU8K8R/zrjXxb8iwISN0dMTNjOkISqYZm+nPSIQo8qdF1Y\n/+MfdGOf4dbGkJRRyFkL/2UxZGfJwZBHSz4ZovWUJwPPjSE5rKQwUa1gpOJLYlwXXrPjgiVYhx8s\n9mSRrOxHtq2Q27lVpM06ljsWRK+TnQKQVliQpTbQ0NbJXnCOyubQ9dsYkm3d48Z67NmRR0DytbBX\nbc+Y2BgSv+58Sxp7ssRb6Cble4aEoOPodxl7X+/FB6IPLG7g6kaCm3HuhNiZameimVnMxNFcN9b4\nIBNRfLNWYDPqe2TP5K35sQO3W6k4uYGQ/TXTVAUbEHlQGkhPiurtJyXZa/t9GJJ/GaU6+0f2n7Tr\n/znw7wD/IvBvAe+Bf4De+P9+rfWrOGn4ZuawY0hysqTZkxZHXD3SKtiyQFkMpR1r3LXRaWubbTU2\nBmRVMBLXXahm+QJD8hYg+ZJWpLMgu1F2Y79mdwDE7sfdsRO0JHXQ7VYJED0sbXtUjaY6Z6FUS2qh\nD9Pq9VQjFGNJzhFdK49eZVcr8PNjqRD80vpKKIuGUnAEaVVKPwMe9oEZ2TMkB+Y9GCmNBSmt8mnx\n2+8dxRNFwcjaAEIHI5McFJCUQDKO0u4IIxlXI6HOHOqVkwl4QtO9KCVZxLDiVW9fHLOMOCLF2PYz\nHdHcKrLm5j2zGVoZFNkfdPqowEuwVjaau54N9bVSXwVeDLw6qq1IFciGGp3ymZs4b7k9Oep+y7YH\nKL29BU52wf8vzsfe94Bk/Nqt9o9a+1nWI4D5O4v1tyXSH4T8BPkMdamamVCrshlOZWAWNH13BeaK\nXDLmJWJfIvZ1xb6s2NeIf10J00K4rrhpxV9X7LxqyKaqsZq8JW/o2LNR7J+1r4CS6lB9VGdGjCU5\nrcybD5Z0sqRJK2yXo0GOFXfUkA2jYJ2m1x/yzLxeOKSBgQHvAm4YkONARRSMDJblOGCnQEmOmiwl\nOUxqwCRZDeskp+zIAsyqp2HK2qXqjZizsiSfoYo+foUh2Ydsuu61355wwzdfAiVwH7Jxkc0wrZQb\nCJnW26a2j4IKYZ29Xc/c46nH47Y0ZD8S/cjsFy7uiLUrYheyXYh2YbYLF7PwZF44m1dmM/IkL6r7\na2DEi7pfb1Ojg4oubXwAq6KPjg2U7EM87I5luPUOTmSvO34EJNMb8/QL7XcGJLXW/4H71e+x/Wu/\n62sCjO9nDjuGJEVHnDNmaWBkhrpAmQ1mtshSWiGgqq54pk3WsirgoIGPtEBsjEgHKaWDkb2GBO7v\n4r7zfMucZzc+ghHZXTO1ZfrsAMhnHTXw6UG44puGpMdWjSZtZNF0WBxGQmNGoBhDso7oPatrdXw2\nylFuqVpVWRaN8FRCCgw5MDQwkpjJ0gpn2VtV3Mf+aJFWMMw9XMPIUgNrHYgltN4BiSeJ9lXCBkq6\n6HSWUUGERA3ZtNLtoPVzfFWGZKwTp+pxLTaq6boasoko8BHKVplYF16jxcEw7W80lHoL2ViSUpYe\nBR65vbIxVC+YQTBHQz1D/Qh1EIqz211Qs4FodUHtAdX6+MTYB4sfQyy9PV57BCSP83E/L/ey9r86\nDMnPtR4BTN918KfNn4QyoZuhpA8dLSJbsUMl5YqjYrJokbG5NECSsB8W3Mdb9y8LYY341l3rNjUN\niRTEN/n1Xhu/e5DIfhr9BHYEq8JV1QjUESwAACAASURBVIwoM5KCI42WdHTEVXsSRxkMMlTckBiG\nFRMq3kVGWYjFE6NnyAecHLH2iAyFUhWMrEFYDg73NGCWEYmOEhV41Ogo0SLRQXJI1GvMFa6xPfBr\nY0aabiTG5icVH0Kh++MditizI/2W22tI1t3H+lZG0j5kU1EgsYVs+k1fGxhp7Mjod9YTRrsz99dc\nU5buCtUx8+aeovhE9CuLi1zdirhIsSvRRhazcjWRV7NwNUdmMzbbBAUjptkqjMwbINmYjy8wJGIa\n07YHH+YBkHQWZQdCPuuN6L9DFr/DcvSza0h+ahu+me4YkhQ9ZsrIXFr1QaHMQpkseS7IVFusqioY\n6cXy0qoAhAnKrKGaddFt7764Xmm9Jm4ZN3B/F/eZ0iHf3qDnEYA8gBPj2TJ/OvhwdZeW3K8B1jUV\nkFP6UhoYSVZjqlZUElMNWRxRqmaXWKtgxAUWr+WoYQdIyg6Q7K5RK8MwE8tMrJ4BTxLbMnYspRhd\ntN4AHz3NtyfN5l3IZmNJSmDdAEkDJdmRjLIj0XhW0xiSegMjk4w4oyGbVNW5VauSqqg11IUDV0o1\n2DrcQFM15NpA1MO12g3vDEAzoWoLTRUaxOoaEmWiNs2IN5RRMCeDmTLlAowVgmBsc+ksRs3OZnVH\nxDRaubv/VvSHdY/vN7e28oXjfv6gXXrTNOr3XAF+aV9s119b8np7X4cn0fTIpA89MRXjFIy4Q8WV\nSqZisyhDMhXkkjCfIvbDivthxn8/4X6cGD4thJLwOeNzwpWEzQlbWkkDU3X3CdsU2ADI/vhx6nwJ\nlHQNiRdN6fWGPKgPSEyemNw2JrFUaxCnRS+NW/A2Up0aIZZsqKthyGcsEeM0BJmtIw2DyimSw6cB\nm46U1cGqIvCyKgiR1Wl4M1rdfF1Lq/iOghHSLRS/rOgbuvC5AGPfvxKyedSQdPz+JQ3JPmSzpQXL\n7rzcRK7B3TxK9l12qcK9Y5Ux6L3funvStDRA4hKzT4hPFNfObeJqEy8mcTArkzloCNqoSN+Kul8f\nZCKhxpHb3OArYMTe5lpt0SjMDYRsLNsDQ8KoIGR/zsg9Q/LKT25/NIAkfLMw/umN20lrQq5FDXwm\noY6GMhny4DBX3T3gUeaBlkmTWh65aRC0NCFrnBvwyPdjydxn2Ty8+5/lxu/MemQPTDoQCdyqDvu2\nCOyAh+sApO6OAdODwir8IhqNsUqbLQZVw4slmZbCZXWHY53HuIwNGeubpiQ/AJE96s8KSGJnLdAd\nUbaG4iw5q+pe78e3Acm9tsRs+hEN1wwtXNOBiDIkOTsNC5kmbJXAUncMCQOzaRoSUd3JViekAZKh\nLmQMtYKtibVl7MQSlCGp6oUSyy2bB+H+t5aMqeWuixQFfV6ad4RpsXaDPRrKmjGrwVw1RbIYgWrV\nOC1a9Yu4NhwpcLsb96ti3+G9JULl4fzxeL/N7XMycD8n9wzJvrLVL+33bdN3jnS9LZHpWSA3AasB\n25gRd6y4WAhZAUnJRlMp9yGbDwvu+xn33UT47kJ4WQhkvGScFBw6WlF2ZEvZfAQZ+x3u/bPm1vqU\n2S9lbepUIxQnCtib30+qLXOveNbqKdWoQJeKIeGlG741AF+0MJ+rWvq1us6MjCwUrlUI1eHqgC0H\nZHXI0kDJ4mB11NUii9u+Voek66IU3ShmuTmdutjW9InbYvZWqkpfx+VzhqRHe/qa+whIHsM1+5BN\nZ0hINzASU8uQtDePklPzjxLR6/ssm1NzhxWvvhx9H/Eo/2q/Q/GZ6FshT5eJLrPYwtVmgs14mxlN\nJHb/JVH7giAro8xqdSD+s5DNmyxJCwXW9giUHVipu+Otd53ILlQjDYjIW4DkH0aGZHzQkMTFw7Vq\n6uUk5IuKrtKQMUEFX3haiCIrtbdGcAtqrD81QHKBOLXdaaMB+3h3rbe3HgAPRj1dtdOBB4HPmJLO\nkOxfpoMQ3477hKwNem59d95gaY004zCnmhFbMK4iriCubu+JsAMk+3t3dyy1tjCNJ4lTvYYz2r16\nJSjz+dsAyS3LRhmSBkzKniVp7Ei+6UhW0a+t8pBlIwesSSx0hkTpDIui/lyXBhuL7iLLSC1CbIvo\nivqPTPXAXEamckCkNiltS2suLSWuRnz7C1VD0vwYjOh7kIWSdTdos6HkgplUPGxEKEWQKDAb5CLw\nKlRnULvpdjfWTj334PU+RaJvh74GRPoo3IORR/OoR470r5SG5Gdr068ta9gBkosydghIY0bsoeKf\nKnHVkI0CEmml21vI5lPE/rhiv5/xv74S/vJC+DTjXcG7incV54oCHKfduKJ7nP48+Qp+3WbRT2FI\nnN7TWSxZLAmr+ipRML+IhyL4oq6xrjM4OeMao+Nywmetrl2thi+jCSz2xGwyFysM1uHtgOUIs4PF\nalba4qiLQ2YFJPo1B0NkK22cbdN7FHU6dX2TeeEhlvLQC3f3VOVtDUl/P+DLGpK9vKu0F+o2EjGx\nWc4b0Q/JWWVRBD0evX7Ntyybp4O6uppwT2o+MCP99yy+En2l+EJ0FWuLzgtbsaZgTcWbRDJalsBI\nwZu4CVwXGTRTsf+hXwrXWLYossB9BpfdAZK9dumRDTnsgMiBzwHJ77Ac/dEAkuFBQ2KXQL0I9WAo\nF6OxzsFjfcZ4fQjj0MmSiiLpEHeTdw9Irnx5Eu873AOStxiSkbsQzh0ICTsw4j+P4e5frnfDPWh4\nAA97dF+MxiTz155NfOH/77rUupmEJWtvaX/tQaz3dW33yG8DJDsNyUOGTcx+6yk7TfsV7auo8FVZ\nkl3IRqLCB7EaEqFiaAwJbMemNp8RHNLCNz3L5rWeuJQzr/mMMaXl8SwEaelxZcFIoVatZ+NoC6vV\nnaM6a+7+xtrqGs96N5dikNViJkO5WOSTpYa2KG2GJrvVRSL6hFq43xLJw/jWtTe2uZ/Nx26x3dsv\nIZs/RJt/c68hybNoKQaPMiOnijsX1rkSYiWVooCkGGqsDZCkO4akA5Lh46QpxKMm2blBRzvcCFMJ\nbFNF6i3611u/toGRPn5FR1KttLluyC3km6wjWs9qPKsdkFIwa8Gv4NbEsK4M66JjXhmzHuM1TBPt\nyBJOTGHlEjJjgBAsLgxYe9BstNlRZ0udHfLQmRvDUJPq/FbTTFcL+B0gkesutP5b2pc0JP396O/X\nb9OQgP7MXqn3S6aG3Y/EW3XbzuV2fgjq6vr+3D5o7peBfWiphZSKb3LCx3yKXVKdNVlHyVqGQyZO\nXHiSFxaG+5BNnx47LcgdG9IZo33S3qOIuvf9Pmi8dTmg1gj9EdnbP4yAxDS6srdNpVAbxV4qZl/s\nrE+w0t5Za1UcOgaIgzImNSvqHkQV0d1l70vH3THws6yahy5WVw0rNwFTt7A30q7Lb68T5QFbMaXo\nIlDa31kKkvu5dgaoT0J9bv2p9ZPAUagHoQ7KanRQUXNjO7K0yJRsIRsZKzJUxDfxnFNHv657kbba\n9TocsXqojertqcc14mpiyQOv6cw1nZjSkTkdWNNASoGcHDUZzRJyhuwtyTtNafMDsz9wrQuBE95o\n4kMPt6R6C73E6u6uzWVkqkfmcthAUCqaIVSr0VinqZqtYFpaphStM9J6rz/SLcF7QUGpWlukq2Ua\nlCJLd8S1ZO/IoyMfHPlYyWcHT4b6rIu+Ok2628pShqZZyrc5R9cubSvfrnMbjeiOzJq2M+vHfR72\nlPPWotWqLb+0/3ftHBQl9PackCdBTmAOBTtk7CBYX3G2bEZUamomlMGRjgPLKVGfC+lqiLNjTiPD\nMDP6zOgLB5/JvqgGyWesLzirT6jtcWLeAB5wK8UySHNXVXOzGhrb6YyGaBoAWWxgMeqMvJjAbFrY\nVNp1CQiVpazMcSEsgTAvhFnHYV4IsyfMC9/7E78ZTvwQDnwIIy/DyCUMTCGwDIEUvLpHr2rVQFSx\nr8k698UkZXeHQhlWGCI1JAiF6jSsrRFO1b3o2suNmdj6wzVrMcMBGTxmMJihIkPCDAsmWDXEHvRZ\nU+crZblS5oWyJOpcNYtz9tTlQJlVY9ZdUHs4rZtRbtecUJ4D5XmknCJlTBSbKbVQYqFMlfrS1oYL\nKmbttu/t892MGQs3lqFr3ron3JXGqkONQr5a4uRZ5oEpHrhw5MWe+Tg8cyhXBjOTBotZCyYW7Fq2\n47txLV8Gtr3XvnwZcrV3TFufX8UZcrDq8tvadz4C3/+kW+6PBpB0AHJ3Xm8PZGkPaUkVido146st\n/sbdquXmBnNNE74eLb24mY5F1dH9ODU6rvZZ8TVQ0mCllRZDlNb5vO+1sI+62HZNrGaRuJK0uFZt\nArea765JgHIWytnc+lMbT0aZpLG5qmbdxdci1Gwod6OGgmTMyJgxQ0FCUWDiKmLbQ7s9GEsx5GKh\n+aCoMVvGloLNGVMyax54jU9c4plrPDKnkSUOxOjJUVP8amyL5GBJg2cdAsswMueFUI94iVibyNYo\n6ChOmZXit0ydmPV6asZwEwdmDiyMRJojLLYZqrHZJ996vV9Yep2eBkgABSiiISFHIjfH2lwtWVSY\nm5wnhUAcCulQkRPQ7OXzVHU+5KYDSl69ZXL3J2g7rSq8GQf/TIVcFXx40e6Miv+8bb0d252G5PoL\nIPmDtOcBhh0geReRZ5BzwZwy5iDYQQ3TnMt4k/CsWJvBC2W0xONAea6k2WJjwOYDVmbCceFoEkcT\nyTZSTAQTMbY7J0dqF9vv9SB75mN3rYyiAHmw5MGSvCX37lp6r3UKOuwNkCzS9Fxb1luAUvFpwccB\nvyy4a8BfV/x1wF8X3HXBXwY+hDPfhxPfD0d+DEc+DgdehpFrGFgGTxwcxVnV1GTdlEhuG64KYgrG\nF6rJ1HGlNkBSvNrR187otLBIxTVpn7l1b+7Pnd4PJgTsEHCD1c9oyLhhwQbNHLKDmmPkaSHN2vOc\n1aZqtqQ5kOdKGSyGiLPqhqpjxNmqYRObsSZhXSUdBtJxJR0jeUxEl8k1k9ZCulYS7RGz93VreQh3\ngES4PTMa+CC2/yfoshCBK5RoiNGxxIEpHbjUEy/2icN4JZgFFxLx6HBr2rrvY7xdk7XV7/paNKwp\nG3KxN70emqAQjSc6z+oD0XtyuK1H3/kr/9ABkv1DAVDHiy48zDd2RFLVAlFdLZ3bXWncjSGhpdh6\n4GA0Trlm7a0s/O28MSm5p2O+BUQeQEkviNdTuYLcHhhePgcib3XPpkW0ZFxN+Kr+pb6uWiK6H7Mi\nHvLJkE+WfGz9ZCknSz4a8sGSR30Yl2IopWXLZBWvlWKQdl6rYMaCNDCiIbCCWH1oi2mgpDMkRStz\nSnK3z6F/FrmypoHLeuIaj0zrkSUeWNcGSFZLiUrB1lHU72B0xBRYimo+nIlYp/4LGdvAh+pOYlJh\nbMyOlHr4p9WraYZnixlagT7V11SRZqK4q+nwuKO5O69QS1vfK5ZCId+HpkR3A9EGogusoSiYG6sy\nVCdDfrKanm6lOU5qaiPRK2tnGrPXs3PqPmjdBNaSbsfQ5rLowju0Pu6PrZZWdztA8snA//6HvDv/\niranoNVhW5N3Bp4L5pwxx4Q9mAZINBvFm0QgIqY0QOIop0qaDbJ4yCNSI2IT7riy1oXEQkU9DQwL\nrs4EhEKl1v604j708sZYR82aiYMjBk8MagUQnVePIutZrWcxgdUoG9KByCqBBa+mgXhqrbgcsHHF\nzQE7Bdxlxb6uuJeAfQ2415WP/syPw4kPw4kPw4FPnSEZlCGJgyd7B/QyFn0D3mwWRagugxNqjJQh\nUUOm+ExxypIU07Rz3bLWiM710PpgtVZMuF2TwWKCww0WP1jCUPEhEQbwQ8KHlTBcocI6ZeKcWKdE\nnDNxqqyThSFQg0VCQGTFuhXvVoIVgisElwm26ugizmVWv7KGldUnok8sNhNrQWKhXiu5pYrfZSs/\nAhK4PX4eGZKZWyR4BgKtMrpnKepSfalHXuyTulh7DW3HbAnryrCuOi4rIa6UZaGuIGvBrXwxxL9p\nahooycXq+ouG2mczMtvW3cjsR2K4hZC/8x9+8i33xwtIatbeGZLGjmz1IHrsL5vGkFjwXkWEPYVz\nFKWvo4cpqUBqTrdjadk2ObUHgXAfT3kEJrugbAckXvRnBdHQUNdz7Mfw5XNxtekY4mb5eytRt0k+\nEV/JB0c6Nt+AgyMdNGSgx5Y0qN17LtpLG03RInW52zdXo0CksyOhYLawzS2sAaiJUha2goat/gVJ\n3UxJwroGpvXAtB6YlwPzOiogWQNpceqquwrlYNR1N6qif2HES8K6JlQuqm3ZzNSSI0ZPStpj9KSo\nwGStntXq4rpa7dF6DdlYpXGNvdGrd0yJ9PygFrapBdtCN1tFVpH7Cq2od8NiRlaX9f0agINQT5Yy\nW8xa1ajP0uLgVg3u1twyCGADI8lxZy3fS7lW0641jrQ58eJFgchRlPE7GB173+1ICIZf2h+gPQ/w\nvGNIngzynJGnhDnZjSGxQUWpvoVsii2ajTU6ytEqZZ8rRQrFqFDRnRMpXSl5gnxB8oTLV0IWYqrk\nnFp4lXtd835/tBvLKA2QeJYQWIOGRFcfWJyyIqu9AZHVBFZzAygdlKwEai2YPGDWBbMMmOuCeV0x\nnxbsx4D5NGA+LryEJz4NZ16GI5+GA586QzJ2hsSTg0OauF+aBYIxILY0slm/VlOkDJESEsUXsqsU\nWzX7zQgilkpb551tluwOxtb3x6OGZVyAMAjjAENIDENiGIQhwDgAFeYrLBMsU2WZYB6BwVInQ27r\ntDELzs+EIIy+MvrE6PfHK8HFZl8QW09YEnPN1LWQY8sYhXuNyj7Bc0ty4B50dkZkz5TYFhW2ltiY\nr8keuNgTwS44uyJW1521eg7LzLhOHNa5HdsGRip2yS2kVt92jmX3e2bIWQHJVLVw6kVOXMyJqzvy\n6s5c/ZHF33Rsv3Y/PevvjwaQvBWy2RiSUjC53LMkHZR0hsQ67piRXom1OLViv0Q13rkkMLEt/lFp\n9bWre+DzcM0bOpLOkLgdKzI0ADT2Yz6vnv0IVlpyhJGMk9RStiYOMnGQq9bKlSsHuSK+kgZPHJUK\njYMn9ePRb+epubnm4kjFYopDikWqo1eqLbVifL6xIzuGRKw6T6rja22Mi6Uk02oLtXF3vC6BZRlY\nlpG5jesykBZPXhxlMVob5mRu9XgksBrNq5eQFWxWblqRLoRNnrR6YnSkVY9TbCnEzpGcjtFpFk92\n6qFA05AY0/QgZs+MaN2RfcimtzsJWFe/t1bEqMLdVaQB0How5KMjzU7jsBFlNObGzLkMthlKVNG0\n7uxU9FweHSdX/aEVNpfKjSFpc+to4GzgbLU/6SjDDZDUugMnv7Tfvz0FeL8DJGdBniLmvGKOFjsa\n7CAbQ+JayCZZQwpCGSzxJKRsSFWIxpCcIY2Cec2U+ArrK2b1uNWhMopCWhNlXTS82lvfC+13z7ux\njoY0KkOyhsDsRybfdqxu0NEODYz4+1ECa8uyWfGUWpG0IDHAvCJTQF4X5FOADwH5sCI/Bq7hzOt4\n4jIceR2OXMaR12FgGpuGZHCUwapGzTfnWd82P0aZya5hI0VyiOSQyD6DK/p3WTDGNA2Ja4DEt/C8\nh6OHo4NTP/ZwtJiQcUMmDJkhZA5D5vAwUmAaLdNkcVeLGSwMjhpUBxFbWNRYhxuEEApjyByHlWMQ\njqFyDJnjEBncwjWvXNOKzxGbIpIzNWVyqsTO7HewsRcevxWG662HTDqb0q8BiNYfSoNjHQamMHIx\nJ5yL2JBhqJQgrMZzWq6c1kBavGp6FsGsBbdkwhpvIaSuvV+4LYZl93NzC9kUz8zIRU58Ms+82Cde\n7BOf3DMv4Ykp3Kpkf7fX2/+W9kcDSL7EkJiSm35kVznzrmLjLmRj2i50E0A1IUeNMK6ahWOjZj2w\nNjASFVhs9ZLfYkceWBKxDyEbcwMhh10ffnsXr2lc3kSCLIxm5iBXzuaVk3nlKBfO5hVxhTUEYgis\nPrAGfzvvx15rwPT6MaY4Ui0KymrVEujNjl3cLlTjFJQYV25CUFF9di2GnC0pOfIGCpyCjdWptf8S\nWOdAnD3r3I6XQJwDeXbU2ai5XTQKlKpjNQFrM+L1xiFrsa8FrX+TSgMj8fbz0tJ+/qLAK4cWKw9W\nz8VSjFXLefiMFXkUpW1OKqIsSa/Wc8eLdA+GWqkYrFUzrOpFHVsP6nJpV49JRTUits0JZ8H6z8FI\n8ppCIfu73zR2hAZGCtsKZaXNMaOA5MnAOwPPBnln4Z3VHWJv6RdA8gdp7wb49rawygnk3YKc/R1D\n4nzRFF6jDEm1juQ9ZbSk7DUcYj2LdyyjZz165KnA/BEzDbjJEmbDOBUOcybWRcXgGzjlniF5FMiH\nB4akPZyu/sDVH5ncgYs9MNuDAo/NLdlvYGQ7xlNKoeaBui6wLNTrAK8L9dNC/TDADwv1+8AczkzD\niWk8MQ0HpnFsfWAeAnH05NFhxowZgDFv2hE1fyvqoTQUao7IEJGgGxS8bi6roWlI2josrlEfAQ4e\njkGB41NQEfI5IGeLCStuWAlhYRwyx5A5hYXjsHIKK6dhgQLhMuCuATMGZByoF11PYhgwPiA+YLzD\nDhDGzDhEjqPlPArnoXIeM+dhZQwq+PXTip0jMifqnMixENeCnSsyN/3Y/vHy1v53n6S3T1l+PC5o\nMcRTC9nIAReiMiNjJR+FeLIsLjAvL8S2htZFkLXilkRYInk11EWUeZnaHNuDkU7YtkygfcjmIic+\nyTMfzHs+2Pd88Nov4bTdN7/xiZ/a/mgAySNDYjpDsss02bQLHZSs3GKK3fPDtYwb6/Rh4FpYxq1g\nV30I1AZGolUrYNsBSeVtdeobotbHkM0gcBAtXd97T4t6TJPaH4eKMbq7CnZlMDNHc+FkX3kynzib\nF57tJ8QWpWDdwOKCjl6PvRtwPrG4hK2BWDOm+iYeU6ZDEzqkuZlqOGPTjWzHj6JW1A4668IaY2Bd\nb2BjbWOcPWnypNmRJu16zZFnS5kszELp9XjEE13C+AJDpUb1/EjV4glq2NbZkQaC8upabSP9Obmq\nWK6U5igrhmI0dbfWzpDo37YxIaazI3sNiYqKP/eifbgmhSpGXQ2dUIJVge7occeETQ04V27z0Nhd\nmMZqaDB6MEl73acB71aA2leBdv0uZNMYkmeDfGPhWwvftLBNb9dfQjZ/kPY03DMkx4I8BczZKUPS\nNCT2QUOSjdEsm1GT1RczMrmBaRi5Hkem84BMYC4Bd3GEVxgvlaOJrKyanbbaXU4vnzMkD2HgTUPS\nNidzGLn6Ixd35OJOvLoTV3tkvUu9V+fkVbS+09rrPNVCySslBsocKNeVcgmUl0D5uFJ+CJTfBNZw\nZhlPrOORdTywjAeWcWAdg4ZsDo48WjiBOVYV0xv1WDGlYE3CuYQdMrXcAAk+U12lOjBW723pm0zT\n1vXQ4i7HQYHI87DrDhOuLWSTGELlEBKnYeEcJp7ClfMwQQE3HjDjUbMYgyX5QAyWxQeMO4A7IsHg\nxow/rAyHmcPBcjoIT2Ph+ZB4PkSOYcG/rtiXiLxqKDbHTKyZZS3Ya0VeUCCxX/870Owhm/617pmy\ncJ9ls+8JyrMmGixmwIWIIYEt1AHiybC80xBeXBxlsdQFzFKwSyIsK+Mya8HahVvIqD8Ke6jI7K51\nhqR65jpy4cSLeeKDfc/37lfa/a94DefttvnkbnYev6390QCSz0WtjSHZQjZ7UesOlDhpKZBOhX+h\nxRdDgSHrKBk1TPNQXQMjRsGIlzYhOjf2tSybzrx0dqSJDTsgGRsoOaG952gPD+PuWM1dC85Ggl0Z\n7cTRXjnbF57sJ97Zj7xzHzC2MJuB2Y4sdmQ2A96OeDsyG81QMba/ZwmpGan6cKu1KiYpWg/HIIjJ\nGJMVjBg13NmnyIqUFuJRUWtMniUOLOuo4Zl5ZJlGlnlU4HFV4JGvRsfJUq5mG5ka22JU7W9C0EXg\noErxnCyxOCyphZsaKIk7MDI70uTJkwISSr3Vs2ofTS3tgtzSfjdA8ll2za12cQfEvTvSZ9eqWlVq\nKqW35NERY8CnVqG1p/D2dHBpzkfVtMwur2ZPNqvWyXgFKp0ZuTMkaIBE2mt5cwvZPBnkvYVvDfyJ\ngT9p4ZveXn5hSP4g7TnAtztR65iRp4CcHeakgMQN4ELVLBvRkM1qvXp04EhmYPFHruOJ13jkdT1x\nWU/UWbCfHP6TaOqvTZxYWPJMXALF/gSGZPdgK6NsIZslBOYwcPUHXv2ZF3fmk326ByRvjB2YpFrI\naSGvgbQM5OtCfg3kTwPpx4X8QyD/ZiD5J+LhTBqPrY/aDwNpDKTRk48Okyq1GJDcTByrZrFJwvmI\nG9IGSBgStBToYmvzZFENiYZsfBeHKCA5jXAeVevzzagA8p3DBM2s8WFhDGyA5ClceBdeeR4+QQY7\nPOumaDDk4ImhsgSL8wHjjuCeMEFwx0g4zozHwPFkOR+F52Pl/THz/rhyDgvuxxXjNARbYiJOibVm\nptgAyccmaj+it3n/TLvfTM+yOXKvGdlrR65o2vBVz3NWY7slBMzxoGF2K6TBMp8D07uR5RDIs4VF\nkKVqmGZeOSwzcfHkRTeMm4gWbsvQ3ma/AaNSjGpIODSG5B0/mm/43v6KX7s/49f+z/gUnrf7ZvIf\nf/It90cDSD7TkDzoRyQXTAMjdyGbXtTBGPBVa42MFQ67bgta8Gy+aUYWA5NsXiC6tS28DUYeQzY7\nhsTtGJI+mY4CZ+5MY77UZahYl3E2EdzC6GYO7srJvfLsPvHOfeAb9wPGFCYZmeXAJAeCjHgTcRKx\nkm47fnIDIy3MUJXl0PovBlMtghaVk559smWh7MI1PWRTe8jGs6bAHEfm5cA0H5mnA9P1SJqcmthd\n993o2K5z1YBItupDwlipB6EsQlotMTuW6rFkctUMm5y6hsSROzMyedJVKwE3qan+zs21lsYKyV7I\nKmULv3Rg0tN7W6L0BkL2XesfkGJPRgAAIABJREFU386rCNWaVlrdE4dASCtLTjpfpTSjIU1T1DCN\naXOuCV57DSODguMtVNiVbl2C34GK3DQkjyGbb6yCkT9rWpLevv+FIfmDtEeGZEzI0SMnjzlazCi7\nLJvSNCQRY1rar3VEN7DkA1M+c8nPfGq9LkYFl75yMolTXZnTxLpeSFOrTbJXNH2NIRmhNIYkNVHr\n7DVkc3HHDZC8mnMDH26reh3FbzN9u1YKKQdiDKR5JU6B+BpIn1bix0D8IZB+s1LCmTKeKIcTdTxQ\nDiNlHCiHQB095eBgsZRaqJJ1ug8aejc1N4Yk4kPUsPqQqD5TW5ZNcZViBDFd42dv2ZQhwDjC8aCA\n5N0B3h/g2wPyjcOGhAsLYbCMoXIMiVNYeApXnsMnvhk+bGxFGQw5DJoZ4yE4i3MBaw6IPWOGij0t\nhPOV8eQVkJyF51Pl/Snz7TnyFBbELdQayTESJ82ymWrBrwV3rcgnmncW92DkMe33xM2n5DGz5gJ8\n0l6vWuYiBc9yGiBW9Ql1lmX0DKeB67sDyylQZ1FmZM7KjMwzy3Ilzp68GGqvPtFYkLsihJ0h2YVs\n7jUkjSGxv+LX/s/4S/+P88G/36Zuct/95FvujwaQ1NJMvO7O2QQ8Wr2173S1rJuVpPHFZkJWQ1u0\nD1CPjak4og+BVW5UV4/nTQWOBa7qyUEuquSuDYTUpuzett8NlPR89x7XH8yNHdkYknpnravH9Wax\n287NULAu4X0kuJWhgxI/cXQXTu6Vs3/BGnUovZW0y/Tyd90zhCZE7bTBDYw0u+hqsNWSEX0QbzUq\n2vcL/RFPKZaaK6mBkZg866ri1Xk5MM8HrtOJaTqSL+4euffx4Vg1F4Z0dMqYzFBm0cyb1eJWh4mF\nFB15deRVY555cRsYyVOvLyLK8JiM8QkTMzZUTKGFYDLWZAVtfcmVHesh+S4c01mSPRDxtyVatQFI\nE+MuLGbF24i1GeOae7CvEKqC02WXcbU8jP04V7Z6Sr3ydC/4WKKCaJPAuVa8SzMI5OA0RNNFrc9N\nR9Lbni35pf3eTc5No9OaCRYzWszBIINBnD4oazXQCs5V8zCT7MBqRxaOzJyY65mJJ8piuNaJa7ly\nXS9My4VpOjH5I5M9MJsDM6PukxpDIr0mlkdLZww0vVrVMMmoYaEpHFq45sSrPfNinlR4KOeH2d30\nZo+zPmfWqJV/18UTZ0ecHOvFEV8t64slfrRqM7uOsAZYQhudFstbDKwGiaIkYJfzjSDHejN+pCiA\nswVrK8VVsq/N7FowQZq5mcWMUA8Ojg45OTg5ODt48vAUkHce3nvse4cLDh8MIUAIlRAKQ0iMYeUQ\nFg5hggyTHBhMJJiEN7XZmhiscYgExIzIGLHngDsH3NkRzpbhbBhPwv/D3rslSZIkWXaH5aEPM/fI\nquqZIRBWgO1gD/jBCkD4nb0BiwDhH6Durs6McDdTVXkxPlj04Z6R1YmhHKKqptAiKVEzj/AMN1cV\nvXL58r3zC9xelPtYWVPj+ayM741hqERXCVqtnLsW5FEtkG83zRw5O6mgl2btc6LKx/LJXsJZsDX1\n3YbehfYU2uYoJeBaRGi2CY9KmwQ/F5ZPrbmLn1jC2aa7xolG7uSsHjJL2dTWqkGRATQKKXTRdJh7\nOdAA79fwE7/4P/Fv/i/84v983kj+BCf/3vF3A0ie6c779nq8TmVgbTOZgeoCGi0O28+VISdqWy3x\ndvDmUjpZGJqOPaXVm7ufqgEbEWPQZQS5Aa9WyZFm4Fsi9tDswEibYGm57tPwMHu4e9utzq5rR9wJ\nSHZRa2dAjjFo/8VqV54rQ+9vDz73YQY8R8iWnIDj6ip63dFHslnBd+XDhwwa6fCliz534zBH60LN\nbnhGwDOQUATTYUiBdbuxbVamyZuptOvqqatDV0GvJj/fS8yEY0FVeittFVoWZPPUVeGpyLtSR2jS\nqI9gZZmno/Why/7f68BSFBcbfqiEcialhpYPMzl/BRSSew/B+V6gHKDkKmS1f/J5zvGj9GujWsvz\nIQR7yrFA8CYnCNt7+8HutN2FcX9ddgYlmJtr7Y6u1cCxubk6GGeIM4QJ3IhKRDT2DrK9/HjZTWd+\nHH/AMU4b7nYGfg5+w4di900RyhrY6siSZuIj42JFg/DuX3j4F1Y3k/1Ac978hlxl8InZLTTnGCRZ\nq7kICaPA33hlZMOraS5GVhNdd32XC3396B44bm7IXfk6feHr+BPfhi98C186I/LKOy889IVHvfMs\nd4qaqLzwab6c57VSNqVs1q5ac6GW7mHUhJ4g2SuMXci9Z7z42hsHiun1xMFSaFOlTeZYWp/gRkce\nggnaB6A4Sq5ULahz5th6c8gXh9/k7Dh6dcifQf6i8OeK/KkgX5KBx1mRqeGHwDQ8GWL/fQWzgqjO\nkSWyysiTGwosbmbzIykM5CFQRk8rAm3XlhXcWJGbWnl5FBO9hkDq7dSrTAQyC6O1Tqsjq5BVKVpo\nLaG6ofrsROh+3wsUZ+vAPmdnqcKfrez3PadchgMnFn0xtMRUF6ayMKWFaVuY1pXpuXB3D37Kb8xp\nJeQCRcht4MmNr1LBCSVE4lCQsdk1VeznF8zLac9LY4ZvX155/3Ln8eXGep/Y5pE8DBRvzyBtcnYE\nwcfzf+f4+wEk+cZ7ej0eYiVFtjqSGC38LYoBkmIfvorgfDMnwi4wbL1dq8VuYytmHEPrRlkR3Aju\nZveLgx7zrciMAZIKrYiN6ixlt9+MFI+WDkhunT6/XcDIrYtaOyAxINL9PkY9fD9cb4NzsTHExOAT\n0Rso8V0P4l09rc5PJ4xDdLlrHIxo9UeQ0vdGxdMkU9VTxONxdrEpmB2873bw2kkWAzBkYd0ma+Nd\nR9I6HFqOtnra2steOyj5HFJ1BSVHbVLQ4tDs0E1pi0eeBkZ0MHajPgNt8cfQpQOS478l9rkMDZ8r\nIRdiTYSaic3M5HqMnwE2+Vh62S2+989wB3qfQcl+HGBkr+rtVZWe4cgTAyOdSj3AyO7EKNidNl7O\nJ2wB6teVdd8Mp5vr/hlqgPESeuItKEI1IrUDkiQfAUm6nP84/puPYVwJ8ynIG2UlSEZo5pBZIts2\nssgNJ4qKUCSyxJnnMLPEmRQH8+KI4IfKEBNzXFARAyTSaFgO05Mb38h4bagaLT661Nm+hvPGxLlo\nnSlurmZhf698G19PQBK/8Oa/8CavvPHKe3vhUV54igGSulsDHBYB++tgtgFLpayNkiolVWr25rbc\nDRb1ACR6xm6UamAk7Z5O1jygOHSp6FhoY6OOihsEGV03McPKkc1RUzUTR1dgEOTucEnwFWOIws5a\nCe5PivxUcT9le/0Ccq+4qeAHxxwXxrgRQ8Z5c32tzpOcdYcEMirC4mbT5cWBEiN19OZorSA0W4eH\nCnNDe2msDJ4cAylEVmehoI5ijlEa2fAkhUKjaqFqoukC7dE/r17Grf68//eNRaZ7Z/HrBOL96GBE\nvCX8Rgqjbsx15V4e3PKT2/rObXlyez64uQe3sjCXhVgKWiC3yFPvqDiSH3jojTgU3FTxtbuki7HM\nPpqjt58r7qXx7f7C+/3O835juU+s80gazYCvivs1IPmN+J/vHX83gGTJHxmS1kWORaMxJEGQQQmt\noiT7ZcRCDcE+iBAoMeCCp4bQ6S4rV2gTk5kEAyT+1ptyuhbWT+DuCiu0BDVDy0LNQkuuXyjuPJ/d\nBZDsoORSspnkYEdcByJuaLih9rmfh8YQNuIFkIQdjLjaS1Png9JEmMaDnKSwp1IYSB9YkabmLLrP\nVX1PufU08bhuS616WsJbJ447RKVkYdtG0jYeDEk+vEW8sRW/B4z0G+gjw+BoSWFVszof7NmrVa0z\npwOetjjazsYsF4bEKS51QFIKsWSGujG0PjQR2exnNv/X3xSuXj9jvgtKLl9pZgynyZpk9Co0ewO+\nYp/J1eUQ7E4TLlQtfUe0L0bRFvXczKCoYDul1uMQ4oglr/W0aY1oC0jpmqgrCMk/AMkfcQzjxjBf\nGBJNhFqQ2tAqlBZIdWTpr2vzpNq7TKaBNA3kebBOkwmCVgaXekq5MGAMScOSqhed8dpAhawDq84M\nmvBia0IIZiLoB+tMCVPB3yr+VsykLH7h2/B6MCRv8sqDFx7thWe98+RuTGnz3533LKi6FspWqVul\npkItgVb9hSG5fEja2ZHaQYn0MEm6LkR8ByTNGJIJ6thL6wMwCi06BE/LxboAvbXUcxNcv38kgJtA\nbg7/Cu5Lw71W3GvBfQH30vBzwY0ZPwhzeDKEjbADEidU7ztDMuGkXgDJyZDUdkZPmA6tmGnjpAZI\nOkNSYuhGcyOLTDjKhSHxF4ak0jShbUXbswOSYKPuG5HQ89nCBZTwfYYEzvV0Z0g0M7bEXBfu+clL\neuNle+N1feNleWN2T2IrxJoJraBVSG0AFZIMPN2NIfxEiAU/9fgSulV+KPixEOZi3YRr5W165X26\n85yt1XubzXqieNsU/4cAJM90w18AiXaGouFozlnJpileCuIbYSgMkyO7SHGR7CLeRbKPpifsgUyi\n1j4nHZD40TSF3plfVZjBrzbYoG5C3YSyCdLP2UwEq5s3983Rw/3CjnyvXDNj5ZkORvxQcWNfTIaO\nOmNlCBuD34guE322cs1esrmKTS8MyfWBWvHmDnn0jcgeS2iPWrF22iCeoJ4ggaoFv5dsmrX10vNu\namv42sitocmZx8g2HGCkrNeSjbMH8t8CJfvR17GzZONg0y7UErOPdo5WGm3zPeDK2fnqaJv1yu+A\nRDy4pPhcOxhJjHVjbCujrofD7bUsczVCu3bXnGWb9it25PoDqBqDpjtY2AQWQR9YcNYOSLbzZz7m\nXYx4fS+LgYkczM01D90frVO3yRs6HgdzIQ4DR6K0RqgWXCi/Ktn8ACR/xDHNK+OlZBNKIWwFqUor\nQtkCWxrRTSgpkLaRZ0oW73D3lPsZ74D2kk1MeK2IcDIkYoDkyY2GJzPw1Btv+oWBZKDaWUk3xkwY\nCmHMhKkQ5ky8Z97CC2/hC2/h1YZ/5c11hkRfeNQXnnrrDs7u4ubsPs2eurgOSjI1B2r21LIDkr/B\nkMjFKEMz1ATq0ElpvWzdRjUH1G75rlFpURFXTYPTxLQUgyA3ARHbSM6gr4qbHf4O/q74e8W/gLtX\n/L3gbw4/ecIAY1gZ/EoI9rw4GZKIiNm0Nlwv2UyWwdICVW0NRaxE5qNpxKwTxwSwxpDEXrIZWd2M\n0FiYWOkMCULWK0Oy9h1Xs9LsPnbvon1Nyc7E73vZ5nvpwxdA4qUZQ9JOhuQ1vfHT9pWflq98eX5l\nluUENGr1nqyRzHBpOxbCUIhqjRKDS4SYGcZs19iaiS+JsBW+Da+8xzuP4cYSZ7ZhNA8sb7IArXKu\nc/CPCUiWfMOls3f5+CUAODW6DrVyRlcjS9Ee8DMcbIKhD0tLqOK7D8epIXEjeKemjJ+UcIOQIfQ+\n77JAWQVZBFkFVisXmJeGQ1aPjv5kRT6Uaz6CEhlAoh6MiCm/Cz4WQ6OxMMSN6BLRJVt0XMa70hmS\njyWbs0113+Wb8uMKRs4STdeN4IkdhBQpB4gxsy/TzLRm4VfWVt21NVUNkOxGZFs4jHXOko1Yu9iu\nCN/H9+qenSE5SzbQNuyBHsRCtLDch7YZG6WXWVPXbGyY5XFQS6+8MiQtMbWVSRcmXZhZ8JSPBmd/\nY/Sl6ANbAtfNif37qYLuJZsrQ/INAyS9Sea7ljbX5q0dkGy+W8xjwCJ1EOwDlGKOlLF3F7jdmrO3\nDBdv1vzh0lmTf3TZ/BHHMG5MF4bEbQ1fCk6baUi2gD6F+vCk54B/VsKzol+AL3adUrvG3GMRCWO1\n9cgpsevEGqZtACFjzEjEdqqH/skZixpDIg6ZYUzEKRHnTLwlHv6Fty5gfXevvPkX3qWDkV6yWWT+\nmG9Vz7yr61xXoW2FmjIt5R6QeWpIPgKSzo5It1egnqLsDkiYTNCuQ2dHBjvX6GgDVo6PFbJDmnQj\nQKwbMGLNAEkhq20oZyHMDT8r4db6a7F5EsKgRJ8P1tlEswZIsov9+eBo4o0hCSOp9eBOPE0E9SCh\n4WNBvLlaE6ENXUMSQ88GMoYEGqv2ko16Izl3DYnmzpA4E7FfdWIHGHG9VKMfc26O0u3lwtw1JP6j\nhmRnSF7TO3/avvKn9Wf+9PyZWdbDe+ZDZ5V8HJ7KKBuDS7ZRHi37ZsgbQ04MaSPmbNeWu/P0M4vv\nWWL+PxpDkm+0C0Pi1HrV924JF6t1NAytez5UfLNQuq2ZX4m0LkpVQ/v2nkIXrrrQu3WjORPHZg04\nsZ+TwD1tyEPg6ax9dXS0wSF7quro4S42bp/ByAlKJJrw0nVGJAwGRMKQrf8+Wqvv4BJRDJDs9KyJ\nWk+QtTMk/hiVQEV798feItiOHpwuYtVAIRMkECkULRQx+bb5HNgiRL1k1PS5bc46XHqni6Vg2nlb\nvTmwLnxkRj6LsfbjWMPsgd6y4DZHC+YzoFhrsiQ18JEdLYntmrIBEpI7Ysype8mmEEo5GJKprdza\nk1mf3Hji5PfdDZ/ByXf/hO4lGyCJlWwWTlHrzpAUPhof7SDksx9Nlm4x7610tV1ehwi+WCr12L11\nQm97lN62UHu5JzlbxPcj/a4f+cfx7xzjtDLdPpo6ydY7IIuga6C+B9K3AfmGtXW+Kf5ZcVu1zq9m\n97HV4a0+73TfbPR7tjMkmcF0XYg5mioEzQySGN1ma0XsY0yM08Zw2xjuGw+5GwCRF97lhUef33kx\nDYneWZgt9bs6Yyk/nR/vLUJbM22LtBQsJqJ4tPrviFp7yWYPhGzFWL0SzIiy2vqpg9mcM5qnjg7W\nfCDRUYPrWVYOp4I4664hgmuKNLMwcE3N82XcRyOMShy5vGd/xrtqvkyuIq5hjVAOlYEq3hxpO0Oy\n+okUelq487TeBSOx4Yp5NdGN2lpwlOgpXdS6+hEvGUXPkg2OpMaQFK3UlmgKqmqfT6s2auvrpVx0\nJPr9ks13GBJxipNK1MyoibkYQ/KlMyR/WX7mn55/ZZSVp7vx9DdwQu75RU934+lmFnfj6W44V5n8\nyhg2xroy1Y2xrIx1tbW1rAw18W1n3bh1VmgkaTwaK7TJRxDyjyhqXfKdmk5AYmKdRBCL4o6iiBS8\nmEjRdg4JX2sP3sNuqCKWl1KC+ZeoMQE7Q+I7AA9yWojscTSSQd4FeQj63sdkN5ILDvHOHgbDRTey\n+458LtdMQO+kOYRosXYgYtRrDIZEoyT7mZwJLoOcXTYiVw3JVaZaCHw0Ogcu/ElXTsiuoM8EQteR\ndAGselqjL0we3fNqiiX0tuSpm81t6+eb6TvOLhs5u0l2xvY3GBKlv1+la3IU8Z0exUpysplz6zmc\nhfj116a7sA3ZyZBkYskHIJl14caTu7zzITDvb4zPOhKOTxS4vGui1p0h6QzRE3iXE5BUrPV7ByP7\nvHsM7CMJLA6WYK3CS2c6Dst5a4dkcBB9rzNeDPpaF8TlTnPvx4+SzR9yfGZItDqa74naxZnW6eFo\nXz3t3xztZ0/72TFsiVh6UrdPxJjwU8XfGkNJRM04qVQJNrgOT1ULiawaCFRGWRndyuhXprAyDn1M\nG+O8Mt5Wntx5cOehL/1hYecPvZuGhBfWNtFqZyj7BkSrHOfH11bQLdNSQnMwMFK8/fzNHWaEh8VA\n60/MA5D0vDAfoDl09ejiaQMdiHBs8FwMNteGd4L2S1xjz6Fyxmg7ZxEbITZirMSofW6EPsf+tRB6\ncJ90vx9R05CIN5bE6HYqvu/uRwOELlC9o4W+4RkavhpbLV57mF3XonhjSIIb8VJRhZVoJRu1kk05\nSjZC09aBSLl0JtFFra7rR4JpyH5Lj/cbJZtwlGwW7vnBa37jT9tX/rLugCQRQ4XgyGFAvZBD5Olu\nfOUL39wXvoafcDTmtjANi826MOtysM5zWxh15a2+8F7vPOuNpU5sZSDXgVJDB7Z83Iz+IwKSZ7qR\nLgxJcJkpeEYvOKe9FmgWzYNPjMFuUJd7vk2yXXdNnpoDXiOudb609XJNH6GP2MfQhxTsofImZyvx\neIIRcd2LZHDd42QHJJfza8kmnN00PlQr1XTPkRgTMfRdDwZKglh3iJdiPEe3Nv9YVjB2xEo05cN+\nXnpd9CrdLAQDJQQKpYs7C2hA1fWSjaeW0I3IugdIDt1S2IBHW03DcQhMV9dLNtgN1C7jiurtH9YZ\nEumu6IJmc4QFjMWq2O8xciYLH3NnJQ72BlBFUsOXSvzEkMy6cNcHLxgg+Y4R/McOJNwnePKxBAod\nnuwMSXcw1N5lo9cum6+cxkc7CIGTIbkDPwFfMEDy9CaCfoqBjtBpO9dpcK9mGT90BO2coWt1B7Aj\nWa39OH4Akj/kGKaN6aIhqTlQfCRrRIuVLvN7pPwykP8ayf8cKf86MOWFqS1MboEguLER7xmfKkNN\nTKx4KWwykXDk3mWzMdnQc3itTNj3mvzCFPsDY1qY5oXptjDdFxa9mU6k3Xm0u831zlNtPOqdtU3H\nPXS9n351vii6JnSLaApoNjZOP5dswBiS/WErPRJBTMyKePu+Q6BFkCjI4NEoMDgkBiQEJEQrgw1i\nOZTOpFIMmKvr2CxReWhEnxk8RN8YfCX6Yh4ivjB4m4OrJuz/NIr4U/SPo0ggO9NSJDH9oeloepdN\nawfDhbNcnSbuKP0kN+ClItKoKiy4DkacER1HyUbRVlDNVspqOxiRs9umdD2J/cXf1WWzl2xMQ2Il\nm5fOkPxp/cpfhp/5z/GvRDEhdRoGnu0Gg5CcddZ8lZ/4q/8n/jX8J8Q1bliY640nNznPbR6ZWfiW\nXnnPdx7bjSXPbGkk9Yyz2v6DlGyWfEcuDMkQNhCxbhpXGXqXjY+FYUhMcWUeHodxS9tsx2IP32jU\naOlZLu2TqLWPMFrzwjBaZyVFjrZdoxiFGgTvHE4c0g2QiO4TGOEjO7J7kMSudQjNQqRCwYfOkITE\nEJIJWrs/xt6SusOJ0/zsI0PiaSj1ExgxQFJxn/xGw86N9P9FglTr19nbfps7smNyHig5ktNA3cLe\nvdfZAE4dx+7BsetH9pZY/c6As+1X+wM993+7djCyG/94jptQ95v2emP2IXzSkNRTQ3IwJDxwtINK\n/AzU6uGJzHfafj9qSPZ5B1S7qFX3tt8H5kGyuyTvYOSzD8kdAyN/wYBEr6kTfHdx5QyJBOPtO31t\nroliX1M5W5A/t/n+aPv9Q45xXD8wJGUd0CDWYp/NhyS9j6xfJ7a/Tmz/PLP9vyN3NeqfCH6qxHtC\nXjHL7pKZdSFIQXEUGQ4fkic3Aw+7d4i+4Kj2MHBPZv9kDk/moY/pyTw/ud1HlnrjWW48i/mNLOXG\ns93te7Y7z3pnK+PBMF61p4egcn9v1a5p6iZn2dhTqlkocNWQ7HEJcok8kN4XL91bYwAdHDo4E/pH\nsTU0WGlSQsR1jZkEaE7RQZFbw/URbo148wyuMogwiDJIZcRS0m1YeStQDGyYEodMtPVO/GVNtK8V\n1zdsfmeSvRndocjuQ4Ix1YrQRCgdzCQGi9jo664th8Kmpks1hkSpWs1SqEn3G5Le+tuZkdK1Ynnv\nsjt/P/++D8lesumi1q4h+Wn7xp/XX/in+G9ESeRx4KF3c5x2kH3kyZ2v8hP/6v4T/0/4H5Cg3N07\nd//g7h5HptrqRm6+p0O7yPvyyvt657nMrEvXkGBRH+17PiT/iIBkz1bZD+8q3hczCgvZLIZDMt+O\nuDEMK+OwoWqlh101fijFax/RfkQzIMsGCmLDdydAiUaHawzgoI2RNgZ08mi2YTer7UilWfn+cIHd\nU3335M3dardThbsNu3N6sTI3jciuFRFaf56LGRMR2Rh7W2ozPQ31MDY74MpudHbIMWF/kqleHq+6\ny2E73axd0FZMPV/Lfh6oxVugXQ60HD6KVX9rlP7IFiu/HODj+lzcGYOr2LO3ZhtI6buCJr9mWva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3/sDQLm3ST9Pu1zB4zawInDB7HIqapmYOkrPgpuBv9igCQ8CttTcE9BHKBKS5VaC27L8EwW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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110157a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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eDeQ3dqGIfCkiu4FPgSdV9a9+p/OjqdPET01dDYe3P5zMjMyo6/CtaMnwflOL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\njqkcu3qMCkUrEBoRSmxCLE3KNqFLnS48VecpavnWur5NdHw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"text/plain": [
"<matplotlib.figure.Figure at 0x110157eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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JjIyMltUTkSipEHiERUTScMMrd6vqSu/hWN6juLiYrKysg44VFRVRVFQUy9u0Sht3beT5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\n+uoK6lUqn+W6R64dyeDlg3mhwQvUK1OPmqVqUrNUTSoXr0w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mGbGYmGhKQoL5FDHH/skn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\nsVhyDSs8LBaLxWKx5Br/DzuTSP8eGInaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110f049b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MLAFurCO+txkxAnbc0YmFmZlZb6spsZA0HGgFbiuVRUQAtwJ7drUr8KCk5yXd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\n3PuBH/ZVHGbW9wbi003NrEE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"text/plain": [
"<matplotlib.figure.Figure at 0x110157fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nmf_so.imshow_component(figsize=(6, 3)) # for 2D spactrum (Spectrum Imaging) dataset\n",
"# nmf.plot_component() # for 1D spactrum dataset\n",
"\n",
"nmf_so.plot_spectra(figsize=(6,3)) # plot decomposed spectra\n",
"\n",
"nmf_so.plot_object_fun(figsize=(6,3)) # plot learnig curve (object function)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| mit |
telescopeuser/workshop_blog | wechat_tool_py3_local/terminal-script-py/lesson_6_terminal_py3.ipynb | 1 | 36854 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/branding-iss.png' width=15% style=\"float: right;\">\n",
"<img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/branding-nus.png' width=15% style=\"float: right;\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import IPython.display\n",
"# IPython.display.YouTubeVideo('TBD')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 如何使用和开发微信聊天机器人的系列教程\n",
"# A workshop to develop & use an intelligent and interactive chat-bot in WeChat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### WeChat is a popular social media app, which has more than 800 million monthly active users.\n",
"\n",
"<img src='https://www.iss.nus.edu.sg/images/default-source/About-Us/7.6.1-teaching-staff/sam-website.tmb-.png' width=8% style=\"float: right;\">\n",
"<img src='../reference/WeChat_SamGu_QR.png' width=10% style=\"float: right;\">\n",
"\n",
"\n",
"by: GU Zhan (Sam)\n",
"\n",
"\n",
"October 2018 : Update to support Python 3 in local machine, e.g. iss-vm.\n",
"\n",
"\n",
"April 2017 ======= Scan the QR code to become trainer's friend in WeChat =====>>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 第六课：交互式虚拟助手的智能应用\n",
"### Lesson 6: Interactive Conversatioinal Virtual Assistant Applications / Intelligent Process Automations\n",
"* 虚拟员工: 贷款填表申请审批一条龙自动化流程 （Virtual Worker: When Chat-bot meets RPA-bot for mortgage loan application automation) \n",
"* 虚拟员工: 文字指令交互（Conversational automation using text/message command) \n",
"* 虚拟员工: 语音指令交互（Conversational automation using speech/voice command) \n",
"* 虚拟员工: 多种语言交互（Conversational automation with multiple languages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Google Cloud Platform's Machine Learning APIs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the same API console, choose \"Dashboard\" on the left-hand menu and \"Enable API\".\n",
"\n",
"Enable the following APIs for your project (search for them) if they are not already enabled:\n",
"<ol>\n",
"**<li> Google Cloud Speech API </li>**\n",
"**<li> Google Cloud Text-to-Speech API </li>**\n",
"**<li> Google Cloud Translation API </li>**\n",
"</ol>\n",
"\n",
"Finally, because we are calling the APIs from Python (clients in many other languages are available), let's install the Python package (it's not installed by default on Datalab)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Copyright 2016 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); \n",
"# !pip install --upgrade google-api-python-client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Virtual Worker: When Chat-bot meets RPA-bot</span> \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 虚拟员工: 贷款填表申请审批一条龙自动化流程 （Mortgage loan application automation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Synchronous processing when triggering RPA-Bot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Library/Function to use operating system's shell script command, e.g. bash, echo, cd, pwd, etc\n",
"import subprocess, time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Funciton to trigger RPA-Bot (TagUI script: mortgage loan application automation) from VA-Bot (python script)\n",
"# Trigger RPA-Bot [ Synchronous ]\n",
"# def didi_invoke_rpa_bot(rpa_bot_file, rpa_bot = 'reference/S-IPA-Workshop/TagUI-S-IPA/src/tagui'):\n",
"def didi_invoke_rpa_bot(rpa_bot_file, rpa_bot = '../reference/S-IPA-Workshop/TagUI-S-IPA/src/tagui'):\n",
"\n",
"# Invoke RPA-Bot script\n",
" print('[ W I P ] In progress to invoke RPA-Bot using command: \\n{}'.format(\n",
" 'bash' + ' ' + rpa_bot + ' ' + rpa_bot_file))\n",
" start = time.time()\n",
" return_code = subprocess.call(['bash', rpa_bot, rpa_bot_file])\n",
" end = time.time()\n",
" if return_code == 0:\n",
" print('[ Sync OK ] RPA-Bot succeeded! [ Return Code : {} ]'.format(return_code))\n",
" else:\n",
" print('[ ERROR ] RPA-Bot failed! [ Return Code : {} ]'.format(return_code))\n",
"\n",
" return return_code, int(round(end - start, 0)) # return_code & time_spent in seconds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# rpa_bot_file = '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
"# return_code = didi_invoke_rpa_bot(rpa_bot_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Asynchronous processing when triggering RPA-Bot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Trigger RPA-Bot [ Asynchronous ]\n",
"# http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf\n",
"from dask.distributed import Client\n",
"def didi_invoke_rpa_bot_async(rpa_bot_file):\n",
" client = Client(processes=False)\n",
" ipa_task = client.submit(didi_invoke_rpa_bot, rpa_bot_file)\n",
" ipa_task.add_done_callback(didi_invoke_rpa_bot_async_upon_completion)\n",
" return 0, 0 # Dummy return. Actual result is returned by function didi_invoke_rpa_bot_async_upon_completion(ipa_task)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tornado import gen \n",
"# https://stackoverflow.com/questions/40477518/how-to-get-the-result-of-a-future-in-a-callback\n",
"@gen.coroutine\n",
"def didi_invoke_rpa_bot_async_upon_completion(ipa_task):\n",
" print(u'[ Terminal Info ] didi_invoke_rpa_bot_async(rpa_bot_file) [ upon_completion ]')\n",
" return_code, time_spent = ipa_task.result()\n",
" print(return_code)\n",
" print(time_spent)\n",
" \n",
" # Send confirmation message upon triggering RPA-Bot \n",
"# itchat.send(u'[ Async OK ] IPA Command completed !\\n[ Time Spent : %s seconds ]\\n %s' % (time_spent, parm_msg['Text']), parm_msg['FromUserName'])\n",
" itchat.send(u'[ Async OK ] IPA Command completed !\\n[ Time Spent : %s seconds ]' % (time_spent), parm_msg['FromUserName']) # parm_msg['Text'] can be in-sync due to new coming message.\n",
"# return return_code, time_spent # No return needed. No pace to hold the info"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# rpa_bot_file = '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
"# return_code = didi_invoke_rpa_bot_async(rpa_bot_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('[ Start of IPA-Bot ] Continue other tasks in main program...\\n...\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Wrap RPA-Bot into Functions() for conversational virtual assistant (VA):</span>\n",
"Reuse above defined Functions()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 虚拟员工: 文字指令交互（Conversational automation using text/message command)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"parm_msg = {} # Define a global variable to hold current msg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define \"keywords intention command -> automation action\" lookup to invoke RPA-Bot process automation functions\n",
"parm_bot_intention_action = {\n",
" '#apply_loan': '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
" , '#ocr_invoice': '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
" , '#check_Application': '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
" , '#hi_everyone_welcome_to_see_you_here_in_the_process_automation_course': '../reference/S-IPA-Workshop/workshop2/KIE-Loan-Application-WeChat/VA-KIE-Loan-Application.txt'\n",
"}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve rpa_bot_file based on received Chat-Bot command\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Retrieve rpa_bot_file based on received Chat-Bot command\n",
"def didi_retrieve_rpa_bot_file(chat_bot_command):\n",
" print('[ W I P ] Retrieve rpa_bot_file based on received Chat-Bot command : {} -> {}'.format(\n",
" chat_bot_command, chat_bot_command.lower()))\n",
" \n",
" if chat_bot_command.lower() in parm_bot_intention_action.keys():\n",
" return parm_bot_intention_action[chat_bot_command.lower()]\n",
" else:\n",
" print('[ ERROR ] Command not found!')\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# didi_retrieve_rpa_bot_file('#apply_loan')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# didi_retrieve_rpa_bot_file('#Apply_Loan')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# didi_retrieve_rpa_bot_file('#approve_loan')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 虚拟员工: 语音指令交互（Conversational automation using speech/voice command)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Use local AI module in native forms</span> for Speech Recognition: Speech-to-Text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 导入需要用到的一些功能程序库： Local AI Module Speech-to-Text"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Local AI Module for Speech Synthesis: Speech-to-Text\n",
"\n",
"# Install library into computer storage:\n",
"# !pip install SpeechRecognition\n",
"\n",
"# !pip install pocketsphinx\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load library into computer memory:\n",
"import speech_recognition as sr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"IF **!pip install pocketsphinx** failed, THEN: **sudo apt-get install python python-dev python-pip build-essential swig libpulse-dev**\n",
"\n",
"https://stackoverflow.com/questions/36523705/python-pocketsphinx-requesterror-missing-pocketsphinx-module-ensure-that-pocke\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Supported Languages \n",
"\n",
"https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst#installing-other-languages.\n",
"\n",
"By default, SpeechRecognition's Sphinx functionality supports only US English. Additional language packs are also available:\n",
"* English (Default support) : **en-US**\n",
"* International French : **fr-FR**\n",
"* Mandarin Chinese : **zh-CN**\n",
"* Italian : **it-IT**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Utility function to convert mp3 file to 'wav / flac' audio file type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Flag to indicate the environment to run this program:\n",
"\n",
"# Uncomment to run the code on Google Cloud Platform\n",
"# parm_runtime_env_GCP = True\n",
"\n",
"# Uncomment to run the code in local machine\n",
"parm_runtime_env_GCP = False\n",
"\n",
"import subprocess\n",
"\n",
"# Utility function to convert mp3 file to target GCP audio file type:\n",
"# audio_type = ['flac', 'wav']\n",
"# audio_file_input = msg['FileName']\n",
"\n",
"# Running Speech API\n",
"def didi_mp3_audio_conversion(audio_file_input, audio_type='flac'):\n",
" audio_file_output = str(audio_file_input) + '.' + str(audio_type)\n",
" \n",
" # convert mp3 file to target GCP audio file:\n",
"\n",
"# remove audio_file_output, if exists\n",
" retcode = subprocess.call(['rm', audio_file_output])\n",
" \n",
" if parm_runtime_env_GCP: # using Datalab in Google Cloud Platform\n",
" # GCP: use avconv to convert audio\n",
" retcode = subprocess.call(['avconv', '-i', audio_file_input, '-ac', '1', audio_file_output])\n",
" else: # using an iss-vm Virtual Machine, or local machine\n",
" # VM : use ffmpeg to convert audio\n",
" retcode = subprocess.call(['ffmpeg', '-i', audio_file_input, '-ac', '1', audio_file_output])\n",
" \n",
" if retcode == 0:\n",
" print('[ O K ] Converted audio file for API: %s' % audio_file_output)\n",
" else:\n",
" print('[ ERROR ] Function: didi_mp3_audio_conversion() Return Code is : {}'.format(retcode))\n",
"\n",
" return audio_file_output # return file name string only"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# convertion for files not in wav or flac format:\n",
"# AUDIO_FILE = didi_mp3_audio_conversion(\"reference/S-IPA-welcome.mp3\")\n",
"# AUDIO_FILE = didi_mp3_audio_conversion(\"reference/S-IPA-welcome.mp3\", 'wav')\n",
"# AUDIO_FILE = didi_mp3_audio_conversion(\"reference/text2speech.mp3\")\n",
"# AUDIO_FILE = didi_mp3_audio_conversion(\"reference/text2speech.mp3\", 'wav')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calling Local AI Module: speech_recognition.Recognizer().recognize_sphinx()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Running Local AI Module Speech-to-Text\n",
"def didi_speech2text_local(AUDIO_FILE, didi_language_code='en-US'):\n",
" # Python 2\n",
"\n",
" # use the audio file as the audio source\n",
" r = sr.Recognizer()\n",
" with sr.AudioFile(AUDIO_FILE) as source:\n",
" audio = r.record(source) # read the entire audio file\n",
" \n",
" transcription = ''\n",
" # recognize speech using Sphinx\n",
" try:\n",
" transcription = r.recognize_sphinx(audio, language=didi_language_code)\n",
" print(\"[ Terminal Info ] Sphinx thinks you said : \\'{}\\'.\".format(transcription))\n",
" except sr.UnknownValueError:\n",
" print(\"[ Terminal Info ] Sphinx could not understand audio\")\n",
" except sr.RequestError as e:\n",
" print(\"[ Terminal Info ] Sphinx error; {0}\".format(e))\n",
" \n",
" return transcription\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# transcription = didi_speech2text_local(didi_mp3_audio_conversion(\"reference/S-IPA-welcome.mp3\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# transcription = didi_speech2text_local(\"reference/S-IPA-welcome.mp3.flac\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fuzzy match from 'transcribed audio command' to predefined 'chat_bot_command'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Automatically create a new lookup, by converting text-based intention command to voice-based intention command.\n",
"\n",
"Example: from '#apply_loan' to 'voice command apply loan'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import json # Prints the nicely formatted dictionary\n",
"# print(json.dumps(parm_bot_intention_action, indent=4, sort_keys=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"parm_bot_intention_action_fuzzy_match = {}\n",
"for intention, action in parm_bot_intention_action.items():\n",
"# print(intention)\n",
" intention_fuzzy_match = \" \".join(re.split('#|_', intention.replace('#', 'voice_command_')))\n",
"# print(action)\n",
" parm_bot_intention_action_fuzzy_match[intention_fuzzy_match] = action"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# print(json.dumps(parm_bot_intention_action_fuzzy_match, indent=4, sort_keys=True))\n",
"# print(parm_bot_intention_action_fuzzy_match)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fuzzy match function: Compare similarity between two text strings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compare similarity between two text strings\n",
"def did_fuzzy_match_score(string1, string2):\n",
" print('\\n[ Inside FUNCTION ] did_fuzzy_match_score')\n",
" string1_list = string1.lower().split() # split by space\n",
" string2_list = string2.lower().split() # split by space \n",
"\n",
" print('string1_list : ', string1_list)\n",
" print('string2_list : ', string2_list)\n",
" \n",
" # words in common\n",
" common_words = set(string1_list)&set(string2_list)\n",
"# print('len(common_words) : ', len(common_words))\n",
"\n",
" # totoal unique words\n",
" unique_words = set(string1_list + string2_list)\n",
"# print('len(unique_words) : ', len(unique_words))\n",
" \n",
" jaccard_similarity = float(len(common_words) / len(unique_words))\n",
"\n",
" print('jaccard_similarity : {0:.3f}'.format(jaccard_similarity))\n",
" \n",
" return jaccard_similarity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# did_fuzzy_match_score('run DIDI voice command apply loan', 'voice command apply loan')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve rpa_bot_file based on received Chat-Bot command ( fuzzy match for voice/speech2text )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Retrieve rpa_bot_file based on received Chat-Bot command ( fuzzy match for voice/speech2text )\n",
"def didi_retrieve_rpa_bot_file_fuzzy_match(speech2text_chat_bot_command, didi_confidence_threshold=0.8):\n",
" print('\\n[ Inside FUNCTION ] didi_retrieve_rpa_bot_file_fuzzy_match')\n",
" matched_intention = [0.0, {}] # a lis to store intention_command of highest jaccard_similarity\n",
"\n",
" for intention, action in parm_bot_intention_action_fuzzy_match.items():\n",
"# print('\\nintention : ', intention)\n",
"# print('action : ', action)\n",
" fuzzy_match_score_current = did_fuzzy_match_score(intention, speech2text_chat_bot_command)\n",
"# print('jaccard_similarity_score_current : ', jaccard_similarity_score_current)\n",
" if fuzzy_match_score_current > matched_intention[0]:\n",
" matched_intention[0] = fuzzy_match_score_current\n",
" matched_intention[1] = {intention : action}\n",
"# print('matched_intention : ', matched_intention)\n",
" \n",
" print('\\n[ Finale ] matched_intention : ', matched_intention)\n",
" \n",
" if matched_intention[0] < didi_confidence_threshold: # not confident enough about fuzzy matched voice command\n",
" return None\n",
" else: # confident enough, thus return predefined rpa_bot_file\n",
" return str(list(matched_intention[1].values())[0]) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Uncomment below lines for an agile demo outside Chat-bot:\n",
"# parm_voice_command_confidence_threshold = 0.6 # Control of asynchronous or synchronous processing when triggering RPA-Bot\n",
"# action_rpa_bot_file = didi_retrieve_rpa_bot_file_fuzzy_match('run DIDI voice command apply loan', parm_voice_command_confidence_threshold)\n",
"# print('\\n[ Process Automation ] rpa_bot_file : ', action_rpa_bot_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Control Parm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Control of asynchronous or synchronous processing when triggering RPA-Bot\n",
"parm_asynchronous_process = True\n",
"\n",
"# Control of asynchronous or synchronous processing when triggering RPA-Bot\n",
"parm_voice_command_confidence_threshold = 0.05 # low value for demo only\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Start interactive conversational virtual assistant (VA):</span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import ItChat, etc. 导入需要用到的一些功能程序库："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import itchat\n",
"from itchat.content import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Log in using QR code image / 用微信App扫QR码图片来自动登录"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Running in Jupyther Notebook:\n",
"# itchat.auto_login(hotReload=True) # hotReload=True: 退出程序后暂存登陆状态。即使程序关闭，一定时间内重新开启也可以不用重新扫码。\n",
"# or\n",
"# itchat.auto_login(enableCmdQR=-2) # enableCmdQR=-2: Jupyter Notebook 命令行显示QR图片\n",
"\n",
"# Running in Terminal:\n",
"itchat.auto_login(enableCmdQR=2) # enableCmdQR=2: 命令行显示QR图片 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 虚拟员工: 文字指令交互（Conversational automation using text/message command)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Trigger RPA-Bot when command received / 如果收到[TEXT]的信息：\n",
"@itchat.msg_register([TEXT]) # 文字\n",
"def didi_ipa_text_command(msg):\n",
" global parm_msg\n",
" parm_msg = msg\n",
" if msg['Text'][0] == '#':\n",
" # Retrieve rpa_bot_file based on received Chat-Bot command\n",
" rpa_bot_file = didi_retrieve_rpa_bot_file( msg['Text'])\n",
" \n",
" if rpa_bot_file == None: # input command / rpa_bot_file NOT FOUND!\n",
" print(u'[ Terminal Info ] RPA-Bot [ ERROR ] Command not found : [ %s ] %s From: %s' \n",
" % (msg['Type'], msg['Text'], msg['FromUserName']))\n",
" itchat.send(u'RPA-Bot [ ERROR ] Command not found : \\n[ %s ]\\n%s' % (msg['Type'], msg['Text']), msg['FromUserName'])\n",
" else:\n",
" print(u'[ Terminal Info ] RPA-Bot [ W I P ] Command : [ %s ] %s From: %s' \n",
" % (msg['Type'], msg['Text'], msg['FromUserName']))\n",
" print(u'[ Terminal Info ] RPA-Bot [ W I P ] File : %s' % (rpa_bot_file))\n",
" \n",
" if parm_asynchronous_process: # Don't wait for RPA-Bot completion \n",
" # Send 'work in progress' message triggering RPA-Bot\n",
" itchat.send(u'[ Async WIP ] IPA Command triggered: \\n[ %s ]\\n%s' % (msg['Type'], msg['Text']), msg['FromUserName'])\n",
" \n",
" # Trigger RPA-Bot [ Asynchronous ]\n",
" didi_invoke_rpa_bot_async(rpa_bot_file) # No return of return_code, time_spent\n",
" else: # Wait for RPA-Bot completion \n",
" # Send 'work in progress' message triggering RPA-Bot\n",
" itchat.send(u'[ Sync WIP ] IPA Command triggered: \\n[ %s ]\\n%s' % (msg['Type'], msg['Text']), msg['FromUserName'])\n",
" \n",
" # Trigger RPA-Bot [ Synchronously ]\n",
" return_code, time_spent = didi_invoke_rpa_bot(rpa_bot_file)\n",
" print(u'[ Terminal Info ] didi_invoke_rpa_bot(rpa_bot_file) [ Return Code : %s ]' % (return_code))\n",
" \n",
" if return_code == 0:\n",
" # Send confirmation message upon RPA-Bot completion\n",
" itchat.send(u'[ Sync OK ] IPA Command completed : \\n[ %s ]\\n%s\\n[ Time Spent : %s seconds ]' % (msg['Type'], msg['Text'], time_spent), msg['FromUserName'])\n",
" else:\n",
" # Error when running RPA-Bot task\n",
" itchat.send(u'[ Sync ERROR] [ Return Code : %s ] IPA Command failed : \\n[ %s ]\\n%s\\n[ Time Spent : %s seconds ]' % (return_code, msg['Type'], msg['Text'], time_spent), msg['FromUserName'])\n",
" \n",
" else:\n",
" print(u'[ Terminal Info ] Thank you! 谢谢亲[嘴唇]我已收到 I received: [ %s ] %s From: %s' \n",
" % (msg['Type'], msg['Text'], msg['FromUserName']))\n",
" itchat.send(u'Thank you! 谢谢亲[嘴唇]我已收到\\nI received:\\n[ %s ]\\n%s' % (msg['Type'], msg['Text']), msg['FromUserName'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 虚拟员工: 语音指令交互（Conversational automation using speech/voice command)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1. 语音转换成消息文字 (Speech recognition: voice to text)\n",
"\n",
"@itchat.msg_register([RECORDING], isGroupChat=True)\n",
"@itchat.msg_register([RECORDING])\n",
"def download_files(msg):\n",
" msg.download(msg.fileName)\n",
" print('\\nDownloaded audio file name is: %s' % msg['FileName'])\n",
" \n",
" \n",
" ###########################################################################################################\n",
" # call audio analysis Local AI Sphinx #\n",
" ###########################################################################################################\n",
" \n",
" audio_analysis_reply = u'[ Audio Analysis 音频处理结果 ]\\n'\n",
"\n",
" # Voice to Text:\n",
" audio_analysis_reply += u'\\n[ Voice -> Text 语音识别 ]\\n'\n",
" response = didi_speech2text_local(didi_mp3_audio_conversion(msg['FileName']), 'en-US')\n",
" \n",
" rpa_bot_file = didi_retrieve_rpa_bot_file_fuzzy_match(response, parm_voice_command_confidence_threshold)\n",
" \n",
" if rpa_bot_file == None: # input command / rpa_bot_file NOT FOUND!\n",
" print(u'[ Terminal Info ] Not Confident IPA Command\\n') \n",
" audio_analysis_reply += str(response) + u'\\n( Not Confident IPA Command )\\n'\n",
" else:\n",
" print(u'[ Terminal Info ] RPA-Bot [ W I P ] Command : %s' % (response))\n",
" print(u'[ Terminal Info ] RPA-Bot [ W I P ] File : %s' % (rpa_bot_file))\n",
" \n",
" if parm_asynchronous_process: # Don't wait for RPA-Bot completion \n",
" # Send 'work in progress' message triggering RPA-Bot\n",
" audio_analysis_reply += (u'[ Async WIP ] IPA Command triggered\\n')\n",
" \n",
" # Trigger RPA-Bot [ Asynchronous ]\n",
" didi_invoke_rpa_bot_async(rpa_bot_file) # No return of return_code, time_spent\n",
" else: # Wait for RPA-Bot completion \n",
" # Send 'work in progress' message triggering RPA-Bot\n",
" audio_analysis_reply += (u'[ Sync WIP ] IPA Command triggered\\n')\n",
" \n",
" # Trigger RPA-Bot [ Synchronously ]\n",
" return_code, time_spent = didi_invoke_rpa_bot(rpa_bot_file)\n",
" print(u'[ Terminal Info ] didi_invoke_rpa_bot(rpa_bot_file) [ Return Code : %s ]' % (return_code))\n",
" \n",
" if return_code == 0:\n",
" # Send confirmation message upon RPA-Bot completion\n",
" audio_analysis_reply += (u'[ Sync OK] [ Return Code : %s ] IPA Command completed !\\n[ Time Spent : %s seconds ]' % (return_code, time_spent))\n",
" else:\n",
" # Error when running RPA-Bot task\n",
" audio_analysis_reply += (u'[ Sync ERROR] [ Return Code : %s ] IPA Command failed !\\n[ Time Spent : %s seconds ]' % (return_code, time_spent))\n",
" \n",
" return audio_analysis_reply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"itchat.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# interupt kernel, then logout\n",
"# itchat.logout() # 安全退出"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 恭喜您！已经完成了："
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 第六课：交互式虚拟助手的智能应用\n",
"### Lesson 6: Interactive Conversatioinal Virtual Assistant Applications / Intelligent Process Automations\n",
"* 虚拟员工: 贷款填表申请审批一条龙自动化流程 （Virtual Worker: When Chat-bot meets RPA-bot for mortgage loan application automation) \n",
"* 虚拟员工: 文字指令交互（Conversational automation using text/message command) \n",
"* 虚拟员工: 语音指令交互（Conversational automation using speech/voice command) \n",
"* 虚拟员工: 多种语言交互（Conversational automation with multiple languages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='../reference/WeChat_SamGu_QR.png' width=80% style=\"float: left;\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Exercise / Workshop Enhancement</span> Use Cloud AI APIs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Install the client library</span> for 虚拟员工: 语音指令交互（Conversational automation using speech/voice command)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [ Hints ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# !pip install --upgrade google-cloud-speech"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Imports the Google Cloud client library\n",
"# from google.cloud import speech\n",
"# from google.cloud.speech import enums\n",
"# from google.cloud.speech import types"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# !pip install --upgrade google-cloud-texttospeech"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Imports the Google Cloud client library\n",
"# from google.cloud import texttospeech"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Exercise / Workshop Enhancement</span> Use Cloud AI APIs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:blue\">Install the client library</span> for 虚拟员工: 多种语言交互（Conversational automation with multiple languages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [ Hints ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# !pip install --upgrade google-cloud-translate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Imports the Google Cloud client library\n",
"# from google.cloud import translate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
dipanjank/ml | data_analysis/california_housing_regression.ipynb | 1 | 59902 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calmifornia Housing Regression"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting the Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"data = datasets.fetch_california_housing()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['DESCR', 'data', 'feature_names', 'target']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_df = pd.DataFrame(data=data.data, columns=data.feature_names)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>MedInc</th>\n",
" <th>HouseAge</th>\n",
" <th>AveRooms</th>\n",
" <th>AveBedrms</th>\n",
" <th>Population</th>\n",
" <th>AveOccup</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.3252</td>\n",
" <td>41.0</td>\n",
" <td>6.984127</td>\n",
" <td>1.023810</td>\n",
" <td>322.0</td>\n",
" <td>2.555556</td>\n",
" <td>37.88</td>\n",
" <td>-122.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8.3014</td>\n",
" <td>21.0</td>\n",
" <td>6.238137</td>\n",
" <td>0.971880</td>\n",
" <td>2401.0</td>\n",
" <td>2.109842</td>\n",
" <td>37.86</td>\n",
" <td>-122.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.2574</td>\n",
" <td>52.0</td>\n",
" <td>8.288136</td>\n",
" <td>1.073446</td>\n",
" <td>496.0</td>\n",
" <td>2.802260</td>\n",
" <td>37.85</td>\n",
" <td>-122.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.6431</td>\n",
" <td>52.0</td>\n",
" <td>5.817352</td>\n",
" <td>1.073059</td>\n",
" <td>558.0</td>\n",
" <td>2.547945</td>\n",
" <td>37.85</td>\n",
" <td>-122.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.8462</td>\n",
" <td>52.0</td>\n",
" <td>6.281853</td>\n",
" <td>1.081081</td>\n",
" <td>565.0</td>\n",
" <td>2.181467</td>\n",
" <td>37.85</td>\n",
" <td>-122.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n",
"0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n",
"1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n",
"2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n",
"3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n",
"4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n",
"\n",
" Longitude \n",
"0 -122.23 \n",
"1 -122.22 \n",
"2 -122.24 \n",
"3 -122.25 \n",
"4 -122.25 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data_df.loc[:, 'target'] = data.target"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(20640, 9)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>MedInc</th>\n",
" <th>HouseAge</th>\n",
" <th>AveRooms</th>\n",
" <th>AveBedrms</th>\n",
" <th>Population</th>\n",
" <th>AveOccup</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.3252</td>\n",
" <td>41.0</td>\n",
" <td>6.984127</td>\n",
" <td>1.023810</td>\n",
" <td>322.0</td>\n",
" <td>2.555556</td>\n",
" <td>37.88</td>\n",
" <td>-122.23</td>\n",
" <td>4.526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8.3014</td>\n",
" <td>21.0</td>\n",
" <td>6.238137</td>\n",
" <td>0.971880</td>\n",
" <td>2401.0</td>\n",
" <td>2.109842</td>\n",
" <td>37.86</td>\n",
" <td>-122.22</td>\n",
" <td>3.585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.2574</td>\n",
" <td>52.0</td>\n",
" <td>8.288136</td>\n",
" <td>1.073446</td>\n",
" <td>496.0</td>\n",
" <td>2.802260</td>\n",
" <td>37.85</td>\n",
" <td>-122.24</td>\n",
" <td>3.521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.6431</td>\n",
" <td>52.0</td>\n",
" <td>5.817352</td>\n",
" <td>1.073059</td>\n",
" <td>558.0</td>\n",
" <td>2.547945</td>\n",
" <td>37.85</td>\n",
" <td>-122.25</td>\n",
" <td>3.413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.8462</td>\n",
" <td>52.0</td>\n",
" <td>6.281853</td>\n",
" <td>1.081081</td>\n",
" <td>565.0</td>\n",
" <td>2.181467</td>\n",
" <td>37.85</td>\n",
" <td>-122.25</td>\n",
" <td>3.422</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n",
"0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n",
"1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n",
"2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n",
"3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n",
"4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n",
"\n",
" Longitude target \n",
"0 -122.23 4.526 \n",
"1 -122.22 3.585 \n",
"2 -122.24 3.521 \n",
"3 -122.25 3.413 \n",
"4 -122.25 3.422 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inspect the target Column"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa52e1623c8>"
]
},
"execution_count": 9,
"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": [
"data_df.target.plot(kind='hist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Baseline Model with Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa52cc99a58>"
]
},
"execution_count": 10,
"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": [
"data_df.corrwith(data_df.target).plot(kind='bar', rot=30)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_validate, KFold\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"model = Pipeline([\n",
" ('reg', LinearRegression())\n",
"])\n",
"\n",
"fold = KFold(n_splits=10, random_state=12345)\n",
"X = data_df.loc[:, ['MedInc', 'AveBedrms', 'Latitude']].values\n",
"y = data_df.target.values\n",
"\n",
"results = cross_validate(\n",
" model, X, y, cv=fold, scoring='r2', return_train_score=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa527b1e1d0>"
]
},
"execution_count": 12,
"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": [
"results = pd.DataFrame.from_dict(results)\n",
"results.loc[:, ['train_score', 'test_score']].plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The MLP Regression Model"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense, BatchNormalization, Dropout\n",
"from keras.wrappers.scikit_learn import KerasRegressor\n",
"from keras.optimizers import RMSprop\n",
"\n",
"def build_model():\n",
" model = Sequential()\n",
" \n",
" #add model layers\n",
" model.add(BatchNormalization())\n",
" model.add(Dense(256, activation='relu')),\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(128, activation='relu')),\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(1))\n",
" \n",
" model.compile(\n",
" optimizer=RMSprop(lr=1.5 * 1E-3),\n",
" loss='mean_squared_error', \n",
" metrics=['mean_squared_error'])\n",
" \n",
" return model\n",
"\n",
"model = Pipeline([\n",
" ('reg', KerasRegressor(build_fn=build_model, epochs=50, batch_size=128, verbose=False))\n",
"])\n",
"\n",
"fold = KFold(n_splits=10, random_state=12345)\n",
"X = data_df.drop('target', axis=1).values.astype(np.float32)\n",
"y = data_df.target.values.astype(np.float32)\n",
"\n",
"results = cross_validate(\n",
" model, X, y, cv=fold, scoring='r2', return_train_score=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa493c6d400>"
]
},
"execution_count": 38,
"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": [
"results = pd.DataFrame.from_dict(results)\n",
"results.loc[:, ['train_score', 'test_score']].plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"fit_time 92.480895\n",
"score_time 3.280715\n",
"test_score 0.567996\n",
"train_score 0.652554\n",
"dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.mean()"
]
}
],
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
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| gpl-3.0 |
imaginebog/kmc_proc | notebooks/filter_FMRI_marsbars.ipynb | 1 | 23399 | {
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"input": [
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"#filter_name_char=\"-\""
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"text": [
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{
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} | mit |
tylere/g4g14-ee-python-api | authorize_earth_engine_in_notebook.ipynb | 1 | 6624 | {
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"signature": "sha256:c0f0be7808f808bfc06881b108d253f7bb14d9876f4a66c2451c79f9cf2c465d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Configure authentication to Earth Engine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Declare an authentication token that was generated outside of the IPython Notebook using the instructions found in the Setting Up Authentication Credentials section of the [Python API Installation and Access](https://sites.google.com/site/earthengineapidocs/python-api) page of the Earth Engine API documentation."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import ee\n",
"import errno\n",
"import json\n",
"import os\n",
"import urllib\n",
"import urllib2\n",
"from IPython.display import HTML\n",
"from IPython.display import display\n",
"from ee.oauthinfo import OAuthInfo\n",
"\n",
"# This URI prompts user to copy and paste a code after successful \n",
"# authorization. \n",
"ee_redirect_uri = 'urn:ietf:wg:oauth:2.0:oob'\n",
"\n",
"\n",
"def create_auth_url():\n",
" # TODO(user): Add an additional, non-commandline flow for iPython notebook \n",
" # for added convenience, and to work in notebook environments where \n",
" # commandline isn't available. \n",
"\n",
" # This implements the flow from: \n",
" # https://developers.google.com/accounts/docs/OAuth2ForDevices \n",
"\n",
" ### Request authorization from user \n",
"\n",
" auth_request_params = {\n",
" 'scope': OAuthInfo.SCOPE,\n",
" 'redirect_uri': ee_redirect_uri,\n",
" 'response_type': 'code',\n",
" 'client_id': OAuthInfo.CLIENT_ID\n",
" }\n",
" auth_request_url = ('https://accounts.google.com/o/oauth2/auth?' +\n",
" urllib.urlencode(auth_request_params))\n",
"\n",
" return auth_request_url\n",
"\n",
"\n",
"def ee_authenticate(auth_code):\n",
" token_request_params = {\n",
" 'code': auth_code,\n",
" 'client_id': OAuthInfo.CLIENT_ID,\n",
" 'client_secret': OAuthInfo.CLIENT_SECRET,\n",
" 'redirect_uri': ee_redirect_uri,\n",
" 'grant_type': 'authorization_code'\n",
" }\n",
"\n",
" refresh_token = None\n",
" try:\n",
" response = urllib2.urlopen('https://accounts.google.com/o/oauth2/token',\n",
" urllib.urlencode(token_request_params)).read()\n",
" tokens = json.loads(response)\n",
" refresh_token = tokens['refresh_token']\n",
" except urllib2.HTTPError, e:\n",
" raise Exception('Problem requesting tokens. Please try again. %s %s' %\n",
" (e, e.read()))\n",
"\n",
" ### Write refresh token to filesystem for later use \n",
"\n",
" credentials_path = OAuthInfo.credentials_path()\n",
" dirname = os.path.dirname(credentials_path)\n",
" try:\n",
" os.makedirs(dirname)\n",
" except OSError, e:\n",
" if e.errno != errno.EEXIST:\n",
" raise Exception('Error creating %s: %s' % (dirname, e))\n",
"\n",
" json.dump({'refresh_token': refresh_token}, open(credentials_path, 'w'))\n",
"\n",
" print '\\nSuccessfully saved authorization to %s' % credentials_path\n",
" ee.Initialize()\n",
" return True\n",
"\n",
"def auth_html():\n",
" return HTML(\n",
"(\"\"\"You need to authorize access to your Earth Engine account.<br>\n",
"Please follow <a href=\"%s\" target=\"_blank\">this link</a>, \"\"\" % create_auth_url()) +\n",
"\n",
"\"\"\"and paste the token you receive after authorization here: \n",
"<input id=\"ee_auth_token\" type=\"password\"></input>\n",
"<button id = \"ee_authenticate\">Submit</button> <span id=\"ee_auth_result\"></span>\n",
"\n",
"<script>\n",
"\n",
"// Send auth token to python\n",
"function ee_authenticate(token, callback) {\n",
" cmd = 'ee_authenticate(' + JSON.stringify(token) + ')'\n",
" console.log(cmd);\n",
" function cb(msg) {\n",
" console.log(msg);\n",
" console.log(msg.content.data['text/plain'] == 'True');\n",
" callback(msg.content.data['text/plain'] == 'True')\n",
" }\n",
" IPython.notebook.kernel.execute(cmd, \n",
" {iopub: {output: cb}}, {silent: false});\n",
"}\n",
"\n",
"$('#ee_authenticate').click(function() {\n",
" $('#ee_auth_result').text('...');\n",
" console.log('debug 1');\n",
" ee_authenticate($('#ee_auth_token').val(),\n",
" function(success) {\n",
" console.log('debug 2');\n",
" $('#ee_auth_result').text(success ? 'Success' : 'Failed');\n",
" });\n",
"});\n",
"\n",
"</script>\n",
"\"\"\")\n",
"\n",
"# For now, call ee_initialize() instead of ee.Initialize()\n",
"def ee_initialize():\n",
" try:\n",
" ee.Initialize()\n",
" display(HTML(\"\"\"Authentication successful!\"\"\"))\n",
" except ee.EEException, e:\n",
" display(auth_html())"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
halexand/NB_Distribution | .ipynb_checkpoints/testing.py-checkpoint.ipynb | 2 | 741 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MyClass:\n",
" \"\"\"A simple example class\"\"\"\n",
" i = 12345\n",
" def f(self):\n",
" return 'hello world'"
]
}
],
"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
}
| mit |
ShinjiKatoA16/UCSY-sw-eng | iristest.ipynb | 1 | 1994519 | null | mit |
pducks32/intergrala | python/sympy/examples/notebooks/trace.ipynb | 115 | 6804 | {
"metadata": {
"name": "trace"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": true,
"input": [
"from sympy import symbols\n",
"from sympy.core.trace import Tr\n",
"from sympy.matrices.matrices import Matrix\n",
"from IPython.core.display import display_pretty\n",
"from sympy.printing.latex import *\n",
"\n",
"%load_ext sympyprinting"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Basic Examples"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"a, b, c, d = symbols('a b c d'); \n",
"A, B = symbols('A B', commutative=False)\n",
"t = Tr(A*B)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\mbox{Tr}\\left(A B\\right)$$"
],
"output_type": "pyout",
"prompt_number": 4,
"text": [
"Tr(A\u22c5B)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"latex(t)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": [
"\\mbox{Tr}\\left(A B\\right)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display_pretty(t)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"text": [
"Tr(\u03c1((\u27581,1\u27e9, 0.5),(\u27581,-1\u27e9, 0.5)))"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Matrices"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"t = Tr ( Matrix([ [2,3], [3,4] ]))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$6$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAAwAAAASCAYAAABvqT8MAAAABHNCSVQICAgIfAhkiAAAAO5JREFU\nKJHN0r1KQ0EQhuHnhAgBhaiIFpLOxs5O8CIsFG/A1spCL0CwsUtnaat4C7aWNooiCAEJKBb+oMGg\nSCzOHlyWlWDnV+3M7vvN7O7wRxWZXAs7+MIL3rCHXs5gGh0shXgS19isDtQSYB9tnIa4jgZec+5r\n+MD4sHtUOsTlsEP1aL2AeyxiGbNoYgs3KTiqfJVzbET5FTxhLgVmMEAfY1G+FqoepcBIAC4ybZ8p\n/6KoHOATd6F8ql5oeSIG4ARTGaCBLh7TjVW8V05BBZ5xkDECx9j1M2PruIpN0uFrYhvzofcH5ajc\n/lbhH+gb6f4rZTpaz0QAAAAASUVORK5CYII=\n",
"prompt_number": 16,
"text": [
"6"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example using modules in physics.quantum"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"from sympy.physics.quantum.density import Density\n",
"from sympy.physics.quantum.spin import (\n",
" Jx, Jy, Jz, Jplus, Jminus, J2,\n",
" JxBra, JyBra, JzBra,\n",
" JxKet, JyKet, JzKet,\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = Density([JzKet(1,1),0.5],[JzKet(1,-1),0.5]); d"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\rho\\left(\\begin{pmatrix}{\\left|1,1\\right\\rangle }, & 0.5\\end{pmatrix},\\begin{pmatrix}{\\left|1,-1\\right\\rangle }, & 0.5\\end{pmatrix}\\right)$$"
],
"output_type": "pyout",
"prompt_number": 8,
"text": [
"\u03c1((\u27581,1\u27e9, 0.5),(\u27581,-1\u27e9, 0.5))"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"t = Tr(d)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\mbox{Tr}\\left(\\rho\\left(\\begin{pmatrix}{\\left|1,1\\right\\rangle }, & 0.5\\end{pmatrix},\\begin{pmatrix}{\\left|1,-1\\right\\rangle }, & 0.5\\end{pmatrix}\\right)\\right)$$"
],
"output_type": "pyout",
"prompt_number": 10,
"text": [
"Tr(\u03c1((\u27581,1\u27e9, 0.5),(\u27581,-1\u27e9, 0.5)))"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"latex(t)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"\n",
"\\mbox{Tr}\\left(\\rho\\left(\\begin{pmatrix}{\\left|1,1\\right\\rangle }, & 0.5\\end{p\n",
"matrix},\\begin{pmatrix}{\\left|1,-1\\right\\rangle }, & 0.5\\end{pmatrix}\\right)\\r\n",
"ight)"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t.doit()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$1.0$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAABsAAAASCAYAAACq26WdAAAABHNCSVQICAgIfAhkiAAAASNJREFU\nOI3t1EErBGEcx/HPimLXgUiU5eKinGxyc8KLkLeDK+WqpChcpJQjBxc33JALsgfFwYrEOsws0za7\nBpOT3+U/z/eZZ77PMzPPwx8mU4P3Yg8DCZ/TjDlkUQzbqziuNyiHSZyinFAEy1iLtMdxja5aAwax\nhVkcfEPWjxdMVPErrCSdaVLZPB4Fry6aJdyioQIa/D4FXOKpip+jA0NpyrrxEMNLYe1JW1aK4RXW\nlqbsGW8xvCmsH31pyE5q8FxYb9KUHaM9hreGtZi2LB/DR3Av+FN/JcujJbw+EqxsNNLfiDEsCr5p\n3WwLNnXccVPAK3bDdgab2PA5+SnBduisJejCPs5CURl3OMR05L4+XGAmwrKCk2QdC9jB8Fcr+s+P\n8g572TfbrLZhHwAAAABJRU5ErkJggg==\n",
"prompt_number": 12,
"text": [
"1.00000000000000"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": true,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
}
],
"metadata": {}
}
]
} | mit |
QuantumTechDevStudio/RUDNEVGAUSS | invariance_testing/Step_I/linear_regression_test_2.ipynb | 1 | 704721 | {
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Необходмые команды импорта.\n",
"import sys\n",
"sys.path.append('../physlearn/')\n",
"sys.path.append('../source')\n",
"import numpy as np\n",
"from numpy import linalg as LA\n",
"import tensorflow as tf\n",
"from matplotlib import pylab as plt\n",
"from IPython.display import clear_output\n",
"import math_util\n",
"%matplotlib inline\n",
"\n",
"# Model Parameters\n",
"m = 4500 # размер сеток обучения\n",
"M = 4 # количество выходных нейронов(базисных функций)\n",
"a = -1000\n",
"b = 1000\n",
"x_grid = np.linspace(a, b, m, endpoint=True)#.reshape(1, m)\n",
"\n",
"sess = tf.Session()\n",
"x = tf.placeholder(tf.double)\n",
"trial_func = [tf.sin(x), tf.cos(x), tf.sin(2*x), tf.cos(2*x)]\n",
"y_set = [1*tf.sin(x), 0.1*tf.cos(x), 1e-5*tf.sigmoid(x), -3.1*tf.cos(2*x)]\n",
"sess.run(tf.initializers.global_variables(), {x:x_grid})\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"A = tf.transpose(trial_func)\n",
"A_T = trial_func\n",
"y_0 = tf.reduce_sum(input_tensor=y_set, axis=0)\n",
"y = tf.expand_dims(y_0, -1)\n",
"omega = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(A_T, A)), A_T), y)\n",
"regression_fit = tf.matmul(tf.transpose(trial_func), omega)\n",
"noninvariance_factor = (1 / m) * tf.reduce_sum(tf.square(y - regression_fit))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def plot_all(x_in):\n",
" fig = plt.figure(figsize=(20,10))\n",
" func_set_matrix = sess.run(trial_func, {x:x_in})\n",
" for i in range(M):\n",
" plt.plot(x_in, func_set_matrix[i])\n",
" plt.plot(x_in, sess.run(y_0, {x:x_in}), '--')\n",
" plt.plot(x_in, sess.run(regression_fit, {x:x_in}), '--')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500, 1)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(sess.run(y, {x:x_grid})).shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.00000000e+00 1.18770909e-01 -2.91260072e-15 -3.09297218e+00]\n",
"Nonivariance term: 20.017583336656458\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all(np.linspace(-1000, 1000, 1000, endpoint=True))\n",
"regression_res = sess.run(omega, {x:x_grid})\n",
"print(np.sum(regression_res, axis=-1))\n",
"print('Nonivariance term: ', sess.run(noninvariance_factor, {x:x_grid}))"
]
},
{
"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
}
| gpl-3.0 |
georgetown-analytics/yelp-classification | .ipynb_checkpoints/Mongo_Connect-checkpoint.ipynb | 1 | 54365 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" __| __|_ )\n",
" _| ( / Amazon Linux AMI\n",
" ___|\\___|___|\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymongo import MongoClient\n",
"from datetime import datetime\n",
"import json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have to change this variable each time the EC2 server stops or restarts. Please email/text me to get the new IP address."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ip = '54.236.23.221'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the connection to the MongoDB server. The first argument is the IP we've supplied above and the second is the port (TCP) through which we'll be talking to the EC2 server and the MongoDB instance running inside it."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conn = MongoClient(ip, 27017)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a look at the databases available in our MongoDB instance"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'local', u'cleaned_data']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn.database_names()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db = conn.get_database('cleaned_data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the collection names"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'academic_biz', u'academic_reviews', u'dc_reviews', u'system.indexes']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.collection_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's grab a a subset reviews from the academic reviews collection. Suppose we want a random set of 5000, all from after 2010, from each city in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"collection = db.get_collection('academic_reviews')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#I cheated and just had a list of all the states. \n",
"#You should try to find a unique list of all the states from mongoDB as an exercise.\n",
"states = [u'OH', u'NC', u'WI', u'IL', u'AZ', u'NV']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, I'm going to take a look at what one of the reviews looks like. I totally could have done something wrong earlier and the output is pure garbage. This is a good sanity check to make."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'_id': ObjectId('58e2e9d4decef619d1cfdff0'),\n",
" u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2014-08-14',\n",
" u'funny': 0,\n",
" u'review_id': u'tRd0-mPa9O1TMJp_dw5khQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'Got my mojo back after having a few of their appetite teasers. Love LPW for a no-frills bite to eat.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'kXUySHSlRgVrcR4Aa0HtGQ'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"collection.find()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sweet, this is pretty much what we were expecting. Let's pull out the date field from this entry. We're going to filter on this in a second. Depending on its type, we're going to need to develop different strategies in constructing the logical statements that filter for the date."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2014-08-14\n",
"<type 'unicode'>\n"
]
}
],
"source": [
"print collection.find()[0]['date']\n",
"print type(collection.find()[0]['date'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dang it's unicode. Unicode is a pain in the ass to deal with, it's some Python specific format. Let's try converting it to a more usable Python format (datetime). We care about the *relative* difference between the date variable. Doing this with a string doesn't make sense to a computer so we have to transform it into a quantitative measure of time."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2014, 1, 1, 0, 0)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"string_year = collection.find()[0]['date'][0:4]\n",
"year = datetime.strptime(string_year, '%Y')\n",
"year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the datetime above is given as January-1st, 2014. We only gave it a year variable so it just defaults to the first day of that year. That's all good though, we just want stuff after 2010, we just define the beginning of 2010 to be January-1st 2010."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"threshold_year = datetime.strptime('2010', '%Y')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running the below code is going to take a little while. But it's essentially doing the following:\n",
"\n",
" For each review in the reviews database: \n",
" If the review comes from one of our states: \n",
" Check to see if the review was made after 2010: \n",
" If it did, append it to the overall reviews dictionary. \n",
" If it didn't, proceed to the next review."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#reviews_dict = {}\n",
"num_reviews = 50000\n",
"\n",
"for obj in collection.find():\n",
" if obj['state'] == 'IL':\n",
" try:\n",
" if len(reviews_dict[obj['state']]) > num_reviews:\n",
" continue\n",
" except KeyError:\n",
" pass\n",
" if datetime.strptime(obj['date'][0:4], '%Y') >= threshold_year:\n",
" del obj['_id']\n",
" try:\n",
" reviews_dict[obj['state']].append(obj)\n",
" except KeyError:\n",
" reviews_dict[obj['state']]=[obj]\n",
" else:\n",
" pass\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the new dictionary we created is structured with each state being a key and each entry being a list of reviews. Let's take a look at what Ohio looks like:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[{u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2014-08-14',\n",
" u'funny': 0,\n",
" u'review_id': u'tRd0-mPa9O1TMJp_dw5khQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'Got my mojo back after having a few of their appetite teasers. Love LPW for a no-frills bite to eat.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'kXUySHSlRgVrcR4Aa0HtGQ'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2012-10-20',\n",
" u'funny': 0,\n",
" u'review_id': u'8Mu56iQ-MYEyivqUVss0XA',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Don't go here for the decor, but the staff is friendly and the fried pickles are very tasty. Good value.\",\n",
" u'type': u'review',\n",
" u'useful': 1,\n",
" u'user_id': u'SYKPwRhnlKrW6yTvm7oPWg'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2014-03-29',\n",
" u'funny': 0,\n",
" u'review_id': u'6-hKBi-6RC3g7Mft0c-6qw',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'This place is a area staple! Been around for years and not much has changed - I see this as a good thing! Stable and reliable!\\n\\nMy family goes every year for St. Pattys Day corn beef! Very nice place for a bar night dinner, or to catch up with some friends over drinks!',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'PmgqNO0-5Y3e3UoR61TD7w'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2012-08-25',\n",
" u'funny': 0,\n",
" u'review_id': u'ToC77cIEiMas9CPU7dt_fA',\n",
" u'stars': 5,\n",
" u'state': u'OH',\n",
" u'text': u\"I believe in awarding stars bearing in mind the type of restaurant reviewed. \\n\\nOne can tell by the name that LPW won't replace Ohio's lost Maisonette restaurant in Cincy any time soon, nor does it aspire to do so, but for a sports-bar type venue, these folks do it all right, and do so at the right price!\\nStopped for lunch on a Saturday.\\n\\nThe lady got a cheeseburger and fries, I got scrod and broccoli, we split an order of their fried pickles as an app.\\nThe pickles were dill spears coated with just the right amount of what was likely a beer batter and deep fried to tangy perfection.\\nThey got the spears vs. chips decision right!\\n\\nThe lady usually looks askew at this type of fare, but happily camped out over the plate and ate her fair share! \\n \\nAnd here's the kicker they were under four bucks! \\n\\nKudos Mr. Pickle; you done good!\\n\\nThe fries were very lightly battered and done perfectly, with just the right amount of crispness outside followed by a creamy interior, ... the fry guy here knows his craft!\\nAnd the burger was good as well.. ...typical of the genre. \\n\\nI find it difficult to fawn excessively over a burger.\\n\\nThe scrod was a very generous sized portion, nicely caramelized and served with lemon and butter and perfectly done.\\nWhen I was given the option of fries or baked potato or rice I asked if there were a green option and broccoli was offered, it was done perfectly as well and the amount was easily enough for two to share.\\nMy dinner came with a salad, which happily WAS NOT from a bag-o-lettuce, but rather freshly cut romaine, a cuke slice or two, carrot shreds and tomato, and it was accompanied by a store bought dinner roll and butter.\\n\\nThe service was quite good, although the waitress appeared to be a bit over-extended, but that's a management issue, not the server's fault.\\n\\nHappily they are in the Cleveland Entertainment book, and as I had the coupon sitting on the table during the meal, the waitress took the value off of the ticket before it was even presented!\\nI'm all for efficiency, especially when it affects how quickly I can get out of a restaurant.\\n\\nThat move garnered the fifth star!\\n\\nWe both stated that we will go back in a heartbeat!\",\n",
" u'type': u'review',\n",
" u'useful': 2,\n",
" u'user_id': u'7LCG3o2KW2jgKgbKN0DQOg'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2015-07-27',\n",
" u'funny': 0,\n",
" u'review_id': u'6YC4o9yLc25DK8c6soOlaw',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'If you like fried food and laid back, then this is the place for you. Nothing that will blow your mind, but the people are great, the restaurant is laid back and casual and the food, while not very creative, is cooked well, presented well and the prices are not ridiculous.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'iSdSNh1hjdE33LOwrFnFrg'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2013-03-16',\n",
" u'funny': 0,\n",
" u'review_id': u'JYyKOtLznozAlT8P1Foaow',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u\"I was not impressed with how gross this place is - I wasn't sure I wanted to order anything. The food was not outstanding. I think I would go across the street to the Tavern!!\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'Zmp1Q6Ul9VH3zL02Z5ls_A'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2014-02-24',\n",
" u'funny': 0,\n",
" u'review_id': u'T5Xa-KKFqgXdbFnATZA4gg',\n",
" u'stars': 5,\n",
" u'state': u'OH',\n",
" u'text': u\"THE CLASSIC BURGER IS AMAZING. It's definitely a top ten burger. The fries are good too.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'catpxxLS6OF5cfjxLvpAbA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2015-09-05',\n",
" u'funny': 0,\n",
" u'review_id': u'4FnZR30PtlEb1Ifi7S65bg',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"Okay bar food. Nice bar setting but food is average, service is average and in general doesn't really art itself apart in anything. Would I go there again? Probably if I'm in the area. Would I go out of my way to go there or make note of it to others? Probably not vegetable soup was good though and wings were fairly priced.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'vzKgTUCV5Pz0gldaeM5j3g'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2013-02-12',\n",
" u'funny': 0,\n",
" u'review_id': u'CdBHQT3WuYuQf-VQ1Um8tQ',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"Alright, i was about to give it 3 stars bc it's not that great, but it's super cheap... wait, but it's kinda filthy too, so umm, 3, final answer. The food is edible, but a bum could afford to eat here, so i mean, it sure beats eating out of a dumpster. Cheap brews, cheaper food. I think the building is made out of iron, my phone practically turns off whenever I go inside.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'OdTvOw8NKzaCcsj_dnRZSQ'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 6,\n",
" u'date': u'2011-05-25',\n",
" u'funny': 7,\n",
" u'review_id': u'6GBjCBVtPGsnQ67neAAkjw',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Was boozin and cruisin with my canine comrade. Went all the way out to Hudson via Brecksville/82. Looking for anything to Yelp. Nothing. Not even in the strip mall I used to hit in Hudson on my dry cleaning route back in the day. There was a deli, but it opened at fucking 11! Who even wants to go to a deli after 11? Weak. Breezed through Peninsula. Winking Lizard? Bah. Ended up backtracking through Brecksville and I remembered an old friend. London Pickle Works! Was it still there some four years after I stopped in while house shopping? Yup. Incidentally, investing in a condo down there probably would have made more sense than buying a house in Lakewood. Live and learn.\\n\\nCozied up to the bar and ordered a Killians, plus cheesesteak to go(for pooch). Like the Unicorn, food was amazingly done before I could chug my brew. I could get used to this kind of service. The bar was all old men. Matt, I don't know how you do it. They were watching Fox news for chrissakes!! Total was eight dollars! Holy shit. I dropped a three dollar tip on that barmaid's ass. How could I not? A real meal at fast food prices, at fast food speed, served by someone not under house arrest with work privileges. Busted open the styro box. Big sandwich with mushrooms, onions, cheez and beef sliced so thin I swear it was a Steak'um. I never ate those as a kid, but I remember one time my friend had one in his bedroom where we were playing NES. He didn't want it, so we threw it all around the room. Fries were awesome. Also got a pickle. That's eight dollars with the beer, just to recap. Great value, nice location, old men. I won't wait four more years to come back.\",\n",
" u'type': u'review',\n",
" u'useful': 7,\n",
" u'user_id': u'i8hCMZN-0bHENsHZKHpC-g'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2016-05-04',\n",
" u'funny': 0,\n",
" u'review_id': u'VlDz03s9VyODcVi1S9-Yfw',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"3 stars is fair for this pub. \\n\\nThe food is good and well priced for a bar. \\n\\nTuesday has $5 burgers and chicken sandwiches. \\n\\nThe pecan pie is good for dessert. \\n\\nI got the chicken dinner, it came with a 6 oz chicken, broccoli, steamed potatoes, salad, and roll. It was ok. I'd stick with the burgers though. The salad was fresh. \\n\\nTheir fries are really good.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'n5P4Cw7F1pCYwVutSTqUPg'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2016-05-11',\n",
" u'funny': 0,\n",
" u'review_id': u'FFTN8Y1U9G2F_p1dQnxO7A',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Okay, so it is a bar with an old fashioned family restaurant attached. We were there almost 2 hours for the Tuesday night burger special. They need more staff. The table was a little sticky. However, the food was delicious. The French fries were nice and crispy, and the Half pound burger didn't taste like a pre-made frozen patty. Our total bill was $15.30 for 2 meals and 2 Cokes. We'll definitely go back.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'W4cfK8SxPloWtIXl3JST2g'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2016-01-23',\n",
" u'funny': 0,\n",
" u'review_id': u'HbnWR7vaXD5FLCcrrMAGhg',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Unmmmm, no. It's a bar with tables in the next room. It's feels grungy. Service was good, food, not so much. Praying I don't get sick!\\nEverything tasted like it came from a can! Nothing fresh or tasty about it. \\nGuess who won't be back?\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'ub50UE95-gu7yIoed1zssA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2016-03-17',\n",
" u'funny': 0,\n",
" u'review_id': u'YGfi1F_Fuc_OyemfqQFspg',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u'Have dined in twice now and today was take out for corned beef sandwich. I don\\'t know why I keep thinking this place is going to change. Food is at best \"ok\". The inside is a dive needs some serious updating. This would be ok if this place was a hidden dive bar with amazing food- not the case.\\nToday\\'s lunch- puke. Dry corned beef, probably the worst I ever have had. \\nLession learned never returning.',\n",
" u'type': u'review',\n",
" u'useful': 1,\n",
" u'user_id': u'57w8gLY3f7OwGmWNtUIFIA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2016-08-01',\n",
" u'funny': 0,\n",
" u'review_id': u'cTNeflcioISILyhQpt0HDA',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u'This place is very dark and dingy on the inside. It is the cliche of townie bar. The food was just okay but the drinks were super cheap and the service was good.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'RlpkcJqctkKXl-LO1IAtig'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2013-06-12',\n",
" u'funny': 0,\n",
" u'review_id': u'FM3Uo0F_2BHp1JdxR5nqvQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"This small tavern, has great burgers and the best fries within 100 miles! It's simple, unpretentious, and the folks are mellow and ready to accommodate their customers. I understand they added music on the weekend and will surely check it out on the weekend.\",\n",
" u'type': u'review',\n",
" u'useful': 1,\n",
" u'user_id': u'm5iFZbW5hSNPNplx-vGkyA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2011-04-01',\n",
" u'funny': 2,\n",
" u'review_id': u'Pqe0NlNUcxzEnUXALZviuA',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"The food is typical. Wait how much was my meal... Then that food is awesome! Very friendly atmosphere. Prompt service. Diner/Bar type place with no particular theme. (Sorry if there is an attempted theme. I didn't see it.) Not a foodie spot or hidden gem, but something about this place just feels... nice.\\n\\nSide note: My apologies for my complete unintentional dine and dash. Wrong card. Also sorry to the other guest whose bill I signed for them.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'1-xPzJk_ijBvY2J8Re1DIQ'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2016-08-14',\n",
" u'funny': 0,\n",
" u'review_id': u'vAuh2cggRJI1ZUqiZDEjHQ',\n",
" u'stars': 5,\n",
" u'state': u'OH',\n",
" u'text': u\"There'a a reason that everyone loves this place; it's your typical neighborhood bar in the evening (it's typically middle-aged locals, not a rowdy crowd) but it's also a great place to grab a nice meal with your family during the day. They also have a fish fry every Friday during Lent. The food is really good for bar food, and it's very reasonably priced. Obviously, this is not five star fine dining, but if you're looking for a cheap and inviting place to hang out with friends and family, this is a great place, and the food is great for what it is.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'oPQAJEiJnRrD0Km5RlKmtw'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2013-04-01',\n",
" u'funny': 1,\n",
" u'review_id': u'sKStdKdvO5nIn1ZN0OC-cg',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u\"Not particularly good. Bathroom is nasty. Food decent. Terrible cell phone signal and no WiFi. Probably won't return.\",\n",
" u'type': u'review',\n",
" u'useful': 2,\n",
" u'user_id': u'bZBPVRIGQZ0WXRYLtYXbYQ'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2013-01-23',\n",
" u'funny': 0,\n",
" u'review_id': u'uVdnYmlXio7BqtfmZZU-0w',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u'Went here last Friday for dinner very disappointed! Waited for ever for someone to tell us to seat ourselves and then waited forever for the waitress to come over it seemed they were very busy gossiping at the counter. Food was so-so nothing special the salads good use cheese served very plain. Better restaurants nearby.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'fflaErtSIUWvHe3q0I3JAA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2012-05-01',\n",
" u'funny': 0,\n",
" u'review_id': u'f0l_4MrqYjVCOZq_PowquQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"I can't give this palce 5 stars because hell, its just not a 5 star place. But it's the Pickle! If ever in Brecksville stop over at the neighborhood bar for cheap, quality food (you get what you pay for though), a typical old bar atmosphere, free popcorn, and sit in chairs that I swear haven't changed since I was 5. The place is awesome, I'm there every weekend.\",\n",
" u'type': u'review',\n",
" u'useful': 1,\n",
" u'user_id': u'BRO98wNeY2Q_BZX8G-iVvg'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 1,\n",
" u'date': u'2012-03-01',\n",
" u'funny': 0,\n",
" u'review_id': u'QfnIyQ5wYk2NDWSLYkSbgQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'Great prices. Good food. Typical fare: burgers chicken, fish, etc Really friendly and eccentric staff. This place is a keeper. Try the hot pickle. I buy them and use them in bloody marys.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'qn7SAaO9Jwl2ls5c9Og2yA'},\n",
" {u'business_id': u'4P-vTvE6cncJyUyLh73pxw',\n",
" u'cool': 0,\n",
" u'date': u'2015-11-21',\n",
" u'funny': 0,\n",
" u'review_id': u'QVS_4D3lLpilcljTSzKtcw',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u'Neighborhood bar, with the friendliness that comes with it. Also the quality of food you would expect. Family friendly.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'7phnWalwWR0LcV-4xGGFgg'},\n",
" {u'business_id': u'2Ql9YdeQstF79FLZyypDWQ',\n",
" u'cool': 0,\n",
" u'date': u'2016-07-28',\n",
" u'funny': 0,\n",
" u'review_id': u'e03RnA8nQPaqGbPVLBmOYA',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Biggest wantons I have ever seen!!! Broth is good too. The fried rice is amazing, the sweet and sour chicken sauce is thinner than most places but it's still amazing. The fortunes in the cookie need work thou.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'5BR9hKJlIsyy1_6sQl8GmA'},\n",
" {u'business_id': u'2Ql9YdeQstF79FLZyypDWQ',\n",
" u'cool': 0,\n",
" u'date': u'2012-08-30',\n",
" u'funny': 0,\n",
" u'review_id': u'JSQHLLc173uU6vJnzpp0GQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Excellent food. We found it on the GPS from the Interstate. Good Service and good prices. Clean. Doesn't look like much from the outside.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'u2Toezhsy3k72RyAdGLCHQ'},\n",
" {u'business_id': u'2Ql9YdeQstF79FLZyypDWQ',\n",
" u'cool': 0,\n",
" u'date': u'2012-03-07',\n",
" u'funny': 0,\n",
" u'review_id': u'1JAc13Un_uNSpkvwp1O6Dw',\n",
" u'stars': 5,\n",
" u'state': u'OH',\n",
" u'text': u'By far one of my favorites for pork fried rice and chicken and broccoli they have great egg rolls too',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'istym9W558wDgC8kfcmy9g'},\n",
" {u'business_id': u'2Ql9YdeQstF79FLZyypDWQ',\n",
" u'cool': 0,\n",
" u'date': u'2016-04-06',\n",
" u'funny': 0,\n",
" u'review_id': u'GUUcCMkz7LUmUNAXld2DXA',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"This place has really good Chinese food everytime I go I order the sweet and sour chicken which is really good also the General Tso's chicken which is excellent and their boneless ribs excellent It's not expensive and the portions that they give you are pretty big. Their soups are good for wonton soup excellent. The staff there is very nice and the restaurant is very clean.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'84pTTnUsbWK_4twG2y25yA'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-10-01',\n",
" u'funny': 0,\n",
" u'review_id': u'yX8gz-uL98OgoEt-_QzMXg',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'I remember when this place was Farmer Boy back in the late 90s. Always big portions of good food, friendly service, and open 24/7. It has gone through a few name changes since then but has been Luna\\'s for the past several years. \\n\\nMy really good experiences here have outweighed the not so great experiences. I consider it a \"locals\" spot, so it\\'s not five star dining by any stretch. But the portions are still good-sized, and there are generally daily specials for a good price. \\n\\nI miss the old Farmer Boy restaurant, but I think Luna\\'s is still a good option to consider if you\\'re looking for a good meal for a reasonable price.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'Hgg_B3KZXgj1L_LazFzcbg'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-09-21',\n",
" u'funny': 0,\n",
" u'review_id': u'eMuvx_BUoDwsO1UToVyOeg',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u'Worse than a grade school cafeteria canned food. Only bright spot... it is definitely low salt.\\n\\nFrench toast was mushy and nasty.\\n\\nCoffee smelled burnt... but had no taste. Asked non coffee drinking husband to try and he agreed.\\n\\nNo matter the coupon... not worth the meal.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'60IVUw1Rfzz-IE7EiAoUEw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-03-03',\n",
" u'funny': 0,\n",
" u'review_id': u'eyqkArl-blVw6Wf4NcLbpQ',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"My wife and I both ordered the rib eye steak dinner for $10.99. It came with a choice of soup or salad (we chose the salad with a delicious Greek house dressing), giant baked potato and mixed vegetables (out of a can), and free dessert (choice of ice cream or rice pudding - we chose rice pudding and brought it home. The steaks were slightly over-cooked and not great, but not bad, smothered in onions and mushrooms (out of a can). The baked potatoes were the highlight of the meal. Luna's is an old-fashioned kind of restaurant, not very busy on a Saturday night. Good service (she forgot the dinners rolls, but got them immediately when we asked) and very good value. We will be back.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'SbsUVsP2gkQhJf4L2q4kjg'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-05-19',\n",
" u'funny': 0,\n",
" u'review_id': u'9CExDW97_IMeIyNqCB7GPQ',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u\"This place was fine for breakfast, the greek scramble was tasty and hearty. Nice owners, very fast (even curt) servers but overall friendly enough. Don't expect gourmet and you won't be disappointed. Decor is about three decades out of date though. Not sure why some reviewers are so down on this place.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'qoFdj5MEQG_rzf4HR-l01g'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-01-08',\n",
" u'funny': 0,\n",
" u'review_id': u'LF4h_R7bCURk9yPzvQ1meA',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u'Food is bland. Reminds me of hospital food. chicken soup was a big bowl but truly had only one small cube of chicken in it. Vegetables have been reheated multiple times resulting in the peas being hard as rocks. Mashed potatoes were \"instant.\" Coffee is good. Waitress was nice and kept refilling my coffee. I won\\'t go back, however.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'NKytdJBEMqDmhiQxzyH3nQ'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2016-07-19',\n",
" u'funny': 0,\n",
" u'review_id': u'D_352Y_R6neg6QfVK4A60g',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u\"We used to enjoy Luna's for breakfast. \\nThe last 2 times the food has been bad. \\nThe main reason we won't go back, the last time we were in our waitress went to the wrong table, realized it then cussed. No big deal if my child wasn't with us. \\nPoor service and lousy food. Great prices though. Hopefully it gets better.\",\n",
" u'type': u'review',\n",
" u'useful': 1,\n",
" u'user_id': u'knQd7FuAs6ePGUu4vOlcOg'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2012-04-28',\n",
" u'funny': 0,\n",
" u'review_id': u'BWGZyqT_tkTlU2hLYpl5xg',\n",
" u'stars': 3,\n",
" u'state': u'OH',\n",
" u'text': u'This place used to be Five Points Deli & Restaurant, I don\\'t know if it was an ownership change or just a name change. I\\'m not considering this a full review, since we just stopped in here for dessert and coffee. I\\'ll have to come back for an actual MEAL, and do more of a review then.\\n\\n3 of us ordered desserts (2 slices of cake, one of the apple pie). Received ginormous portions (especially of the cake, 3 of us could split one slice of that). Everything was tasty, but not made on-premises (I actually think I saw packaging from Sam\\'s in the dessert case on the way out). Decent value for the money. This area doesn\\'t have many options if you\\'ve got the taste for dessert (other than ice cream) and don\\'t want to whip up some brownies.\\n\\nFood I saw on other tables all looked pretty good. Pleasant server, decent \"diner-style\" ambiance. Pretty extensive menu, especially for breakfast. Spotless bathroom.\\n\\nI\\'ll have to swing by here soon. I think the Corned Beef Hash is calling out to me!',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'wyODhqTQFfBum-B98JLLHw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-06-14',\n",
" u'funny': 0,\n",
" u'review_id': u'yq4k33NObOl544z92xLChA',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Stopped in here Mother's Day for breakfast because every other place was packed. Don't usually come here for the same reasons mentioned in other reviews about service, cleanliness, etc. Again, got the same put out attitude from the servers and slimy table top near the kitchen. The big thing that got me this time was as the wife and I sat there waiting for our food, one of the bus boys who was apparently on his break and looked to be one of the owners relatives, reached over our table to grab some ketchup to put on HIS plate of eggs! He was in clear view and in the way of the service traffic. Nobody that worked there said a word to this oaf as he chowed down! We just looked at each other and laughed in disbelieve! Good thing we don't use ketchup on our eggs! The funniest thing was after we got our food the the tide of people that came in behind us must have overwhelmed them! The staff looked horrified like they didn't expect this much overflow on Mother's Day or capable of handling it! Only got one refill of coffee in this panicked frenzy of pisses off staff and patrons! As we left the owner took our money with a smile as if he was oblivious to the chaos going on behind him! Wow! Last time, no more!\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'wD6uMj4_8HgEXEskQ0_Kjw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-08-03',\n",
" u'funny': 0,\n",
" u'review_id': u'39gLe_xb9UfyBr_Xe9AdSQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u'My husband and I were out for a Sunday drive when we spotted this restaurant and decided to stop for dinner. After a quick visit to the clean restroom, we were greeted and seated immediately. Three menus appeared... Daily specials, breakfast menu, and the full menu. We chose to go to the dinner route. Our waitress could not have been more helpful. On her recommendation, we both chose the fried chicken dinner. It was crunchy, slightly sweet, and oh so good! It came with potato, rolls, corn, and either soup or salad. I had the Italian wedding soup which was delicious.... Not heavy on celery taste, although clearly there was celery added. Dinner comes with a scoop of ice cream for dessert, if you like. We felt lucky to find this place. Our waitress was so attentive, never missing a beat. If we lived closer, we would definitely be back!',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'h2zvAFFGP47jBPJbnBCKlw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-01-11',\n",
" u'funny': 0,\n",
" u'review_id': u'nX739tfpfbMyUyesnRGPFg',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"I like this restaurant. I've been coming here since I was in high school. Fun-loving people with a good sense of humor. Food is so good with big portions you can never go wrong with that.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'SLKPyEuruLb2jHlVvMiVXw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-06-20',\n",
" u'funny': 0,\n",
" u'review_id': u'PYlr0VDc2hh_C7QO0alzPQ',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Food is terrible... Frozen and canned crap. We went there for dinner. The food literally had no taste, the service was slow. I won't go back there if you paid me.\\n\\nThe people rating this place more than 2 stars are delusional. I would rather step in front of a bus than eat at this sh@thole again... You've been warned.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'S6gAeiubzQ7FJOdMvipsog'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-08-31',\n",
" u'funny': 0,\n",
" u'review_id': u'gNevv1EYmQ7mJPlZYsE0Mg',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"Found Luna's through Yelp and glad we did. Great service and quality food at low prices. The parking lot was full but we didn't have a long wait. Hubs ordered biscuits and gravy with eggs which he scarfed down. I had a denver omlette bagel sandwich which was delicious. So glad we stopped.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'ofThxVRvUfIvzV1QfXDQgw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-05-05',\n",
" u'funny': 0,\n",
" u'review_id': u'AKt3-PhndRMgk8n4eQC2rA',\n",
" u'stars': 5,\n",
" u'state': u'OH',\n",
" u'text': u\"Must have the Luna's Corned Beef sandwich.... Amazing.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'qI0i9RUYujqGv3YSAs-ccA'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-10-27',\n",
" u'funny': 0,\n",
" u'review_id': u'I595qq2gjfFHIyk6lHp9ig',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Terrible customer service today! My husband and I came today for breakfast with our 4 kids. We're used to waiting for a table... We waited for 20min and then a local family who new one of the owners came in. 5 people in their party and they went out of their way to push tables together and say them before us. We left! In the past my husband and I have enjoyed the restaurant and always have the same waitress who is good to us. So sad that because of today I won't be back.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'Sx6YtByoNJqfuM8XJqEyPQ'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-06-30',\n",
" u'funny': 0,\n",
" u'review_id': u'pbvL9ZAuB6-RhmrcXoCyRQ',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"I had the chicken noodle soup which was the strangest tasting I've ever had. The fish fry was extremely greasy as well. The staff wasn't very friendly and as I was leaving I noticed that all of the bakery in the display case was purchased from WalMart..\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'Txb81X1E3uUL1u9ZhSEp-g'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-04-04',\n",
" u'funny': 0,\n",
" u'review_id': u'W4su1AfjH23TD68Le-y6Jg',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u\"Glad we went back a second time -- for all the wrong reasons! First visit, owner could not have been friendlier, server was attentive & friendly, my AYCE cod dinner came with delicious lentil soup & canned or frozen corn, french fries were good, wife's sandwich was decent if not memorable, kitchen did a good job in getting orders out, restrooms were clean, dining area was orderly. But the second trip -- had the fried cod dinner again, and there was something N-O-T Q-U-I-T-E R-I-G-H-T about it, tartar sauce wasn't even fully mixed, in place of the corn I had mixed (ugh!) veggies, french fries almost soggy, rice pudding had a liberal dose of cinnamon but it was still thin and tasteless, owner too busy on a personal phone call to say hello. The good news -- our server was excellent, the kitchen got orders out quickly again, the rest rooms still clean, the dining room still neat and orderly. the lentil soup was still quite good. Weren't there for breakfast, but their breakfast menu looks good, with eggs, omelettes, waffles, skillets, etc. Go east or west on Route 82 for some better options.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'IvYlKW4hfn7lQrV2ATcqmA'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2015-02-26',\n",
" u'funny': 0,\n",
" u'review_id': u'66aNhufsn2zGBi4c0e5vjQ',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u'Someone recommended Luna\\'s and so earlier today I thought I would give it a try for dinner. Well, not a good decision.\\n\\nAfter reviewing the menu I decided to try something \\'safe\\' so I ordered the meat loaf with mashed potatoes and gravy. The meat loaf had very little seasoning, if any. It had an odd taste. It also appeared to be overcooked around the edges. There was quite a \\'crust\\' on it. Overall, not appetizing at all. The \\'mashed\\' potatoes were quite lumpy. I had to finish mashing them with the fork. It appeared they had been mashed quickly, possibly just before they were served. Again, lack of seasoning, particularly salt. Not good at all. I didn\\'t finish either item.\\n\\nAnd when I tried to pay my check, there was no one at the register. I probably stood there about 3 minutes before another customer got in line behind me. She finally summoned a waiter and asked if we could pay our bills. The response was, \"I don\\'t know where she is.\" He then disappeared for a moment and then returned and said \"She will be with you in a minute.\" Not good customer service.\\n\\nThe whole meal was not very good. While the price was reasonable, the food didn\\'t live up to the hype that I received. I don\\'t think I would either recommend this place or go there again. There are better places with food that is more appetizing. A real disappointment for sure.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'YTkn5uzb_qWxgS3PmAAGeA'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-11-25',\n",
" u'funny': 0,\n",
" u'review_id': u'pupNBcy86NxSv2W7cntA-Q',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u'will not go back. went for breakfast & ordered waffles, they were horrible. not sure how a restaurant could mess up waffles so badly, such a basic food to prepare. not impressed at all, dont waste your time/money.',\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'XbCd366Bm_SWGhB8RsJouw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2016-06-02',\n",
" u'funny': 0,\n",
" u'review_id': u'0XpAQM9mreLGg_7jbWPwFQ',\n",
" u'stars': 4,\n",
" u'state': u'OH',\n",
" u'text': u\"We love Luna's. We are there every Friday Morning. Breakfast is inexpensive for what you get. It's the local breakfast place, not a chain with fancy meals and prices\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'fAnkHlKedFqau2NTj-KASA'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-07-12',\n",
" u'funny': 0,\n",
" u'review_id': u'Ws-4ormTU91ZL1g62y4SQQ',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Horrific food, horrific service. We had a frozen burger and a wilted salad of iceburg lettuce that was way over seasoned. The waitress brought the check before the food and didn't check on us again. Obviously prepared food with shortcuts. I am very disappointed.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'ehaAhyaDSPrSwtsiQ6Kq7w'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-03-18',\n",
" u'funny': 0,\n",
" u'review_id': u'5-IZh0KF10GQiCTczXmM8Q',\n",
" u'stars': 2,\n",
" u'state': u'OH',\n",
" u'text': u\"I walked in and first of all it smelled like mold and i smelled the bathrooms from the front door. The wait was short, however, the server almost didn't notice that I was there. it seemed like there was no host to seat me. The server asked politely, if i would like a table or booth. I asked for a booth. I was seated and the server, asked, what can i get you to drink. I ordered my drink and the server left. As I am drinking my pop, the server finally brings me the menu and leaves again. As I am looking at the menu I noticed there were family members of the owner/manager (maybe), helping out in which I thought was cute? But kind of weird! I thought that it was sweet that a family business was helped by togetherness of family members. As I am waiting for the server to return to take my order I noticed that some of the tables were dirty and the floor was sticky. It seemed that the tables were not properly wiped off. The server finally came back to take my order, i ordered the corn beef and fries instead of cabbage and a salad with oil and vinegar then hums as an appetizer. I sat and waited for my order for about 15-20 minutes. The owner/manager was walking around helping out his family members. That's a good owner/manager helping out. The server brought out the salad with the dressings. I notice that the dressing bottle was very dirty, The rest of the food came out all at once instead of the appetizer first. The hummus did not taste like hummus. The hummus was very tasteless. The corn beef was bland and the carrots tasted like they came from a can from the store. The fries tasted as if they were prefrozened and prepared from a bag instead of fresh. After looking at the dessert i remember seeing the brand of cake sold at walmart... not fresh.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'Qsw_wXoZJeVG6zDlMr37vw'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2013-01-19',\n",
" u'funny': 1,\n",
" u'review_id': u'sa3sR6Lck4UiGkHmwTat7Q',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"For some reason, even though there were only a few tables with diners, our waitress seated us right by the kitchen. She kept coming over and rushing us to make a decision. When I say rushing I mean like every minute or so. Seriously. \\n\\nThe Chicken Noodle Soup looked like glorified boxed soup. The salad had lettuce that was starting to rust. Someone must really like onions because there were 7 large slices of red onion on it. But, here is the best part was the dressing came in a squeeze package like the ones at fast food places. The meatloaf was horrible but strangely the mashed potatoes were good. The fried chicken was OK. They make it with honey so chicken is very sweet and not something I'd eat again. The fries were typical frozen french fries but were OK. \\n\\nIf the food wasn't disappointing enough, all of the servers had bad attitudes and it showed. We could hear all the talk from the kitchen since we were so close. It is obvious no one wants to be there. They don't care about the quality of their food or service. Now we know why the parking lot was almost empty on a Friday night. We will not be back for another try. Someone should write to Robert Irvine. Maybe him and Restaurant Impossible can help. Or, maybe not.\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'CJc13OrAjrWiNdh4imYAWg'},\n",
" {u'business_id': u'2S1s1gKstsgRtUTVxKO79w',\n",
" u'cool': 0,\n",
" u'date': u'2014-04-24',\n",
" u'funny': 0,\n",
" u'review_id': u'2I5NCB_g6CRwmpUmH3_SWg',\n",
" u'stars': 1,\n",
" u'state': u'OH',\n",
" u'text': u\"Inedible slop. If you've seen those kitchen rescue shows, then the worst of those is this place. Under cooked meat, powdered potatoes, gravy and veggies from a can. I didn't even get a doggie bag despite eating only two bites. I value my pups more than that. Avoid at all cost!\",\n",
" u'type': u'review',\n",
" u'useful': 0,\n",
" u'user_id': u'apu0rYSwSG0iAFlBNbN_bw'}]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reviews_dict['OH'][0:50]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's good practice to save whatever data you're using in a more permanent location if you plan on using it again. That way, we don't have to load up the EC2 server and wait for our local machines to run the above filtering process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('cleaned_reviews_states_2010.json', 'w+') as outfile:\n",
" json.dump(reviews_dict, outfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Congratulations!__ You just finished downloading and filtering data from MongoDB as hosted on an EC2 instance"
]
}
],
"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
}
| mit |
hetland/ciso | notebooks/ciso_FVCOM_example.ipynb | 1 | 59601 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import warnings\n",
"\n",
"import iris\n",
"\n",
"\n",
"url = 'http://crow.marine.usf.edu:8080/thredds/dodsC/FVCOM-Nowcast-Agg.nc'\n",
"\n",
"\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" cubes = iris.load_raw(url)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"salt = cubes.extract_strict('sea_water_salinity')[-1, ...] # Last time step.\n",
"\n",
"lon = salt.coord(axis='X').points\n",
"lat = salt.coord(axis='Y').points"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = salt.coord('sea_surface_height_above_reference_ellipsoid').points\n",
"q = salt.data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from ciso import zslice\n",
"\n",
"p0 = -25\n",
"\n",
"isoslice = zslice(q, p, -25)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy.ma as ma\n",
"\n",
"# For some reason I cannot tricontourf with NaNs.\n",
"isoslice = ma.masked_invalid(isoslice)\n",
"vmin, vmax = isoslice.min(), isoslice.max()\n",
"isoslice = isoslice.filled(fill_value=-999)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import cartopy.crs as ccrs\n",
"from cartopy.io import shapereader\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"\n",
"cmap = plt.cm.viridis\n",
"\n",
"def make_map(projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(9, 13),\n",
" subplot_kw=dict(projection=projection))\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.xlabels_top = gl.ylabels_right = False\n",
" gl.xformatter = LONGITUDE_FORMATTER\n",
" gl.yformatter = LATITUDE_FORMATTER\n",
" ax.coastlines('50m')\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pyugrid\n",
"import matplotlib.tri as tri\n",
"\n",
"ugrid = pyugrid.UGrid.from_ncfile(url)\n",
"\n",
"lon = ugrid.nodes[:, 0]\n",
"lat = ugrid.nodes[:, 1]\n",
"triangles = ugrid.faces[:]\n",
"\n",
"triang = tri.Triangulation(lon, lat, triangles=triangles)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colors.ListedColormap at 0x7f0facc53b50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cmap"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/matplotlib/artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the\n",
"axes property. A removal date has not been set.\n",
" warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)\n"
]
},
{
"data": {
"image/png": 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dO9alvmpeFaByNNyCq3kbZRXp5qMmtGJeQJVN2WINqbQLvkBUI2P10KiAE+YD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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0f9cbdf750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = make_map()\n",
"extent = [lon.min(), lon.max(),\n",
" lat.min(), lat.max()]\n",
"ax.set_extent(extent)\n",
"\n",
"levels = np.linspace(vmin, vmax, 20)\n",
"\n",
"kw = dict(cmap=cmap, alpha=0.9, levels=levels)\n",
"cs = ax.tricontourf(triang, isoslice, **kw)\n",
"kw = dict(shrink=0.5, orientation='vertical')\n",
"cbar = fig.colorbar(cs, **kw)"
]
}
],
"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
}
| bsd-2-clause |
jstac/recursive_utility_code | python/long_run_risk/ssy_continuous_mc.ipynb | 1 | 10344 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test and summary stats, SSY model without discretization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%run src/ssy_monte_carlo_test.py"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"s = SSY()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ssy_compute_stat = ssy_function_factory(s, parallelization_flag=True)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9993526539138271"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssy_compute_stat()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9994616175770503"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ssy_compute_stat()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"num_reps = 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Varying n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999408\n",
"std 0.000111\n",
"min 0.998564\n",
"25% 0.999385\n",
"50% 0.999441\n",
"75% 0.999473\n",
"max 0.999529\n",
"dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=500, m=5000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999384\n",
"std 0.000093\n",
"min 0.998551\n",
"25% 0.999351\n",
"50% 0.999409\n",
"75% 0.999446\n",
"max 0.999517\n",
"dtype: float64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1000, m=5000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999401\n",
"std 0.000061\n",
"min 0.999024\n",
"25% 0.999386\n",
"50% 0.999421\n",
"75% 0.999441\n",
"max 0.999466\n",
"dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1500, m=5000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=2000, m=10000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999534\n",
"std 0.000047\n",
"min 0.999272\n",
"25% 0.999514\n",
"50% 0.999544\n",
"75% 0.999568\n",
"max 0.999611\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=2500, m=1000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999549\n",
"std 0.000042\n",
"min 0.999312\n",
"25% 0.999528\n",
"50% 0.999559\n",
"75% 0.999579\n",
"max 0.999621\n",
"dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=3000, m=1000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Varying m"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999488\n",
"std 0.000082\n",
"min 0.998834\n",
"25% 0.999456\n",
"50% 0.999508\n",
"75% 0.999544\n",
"max 0.999618\n",
"dtype: float64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1000, m=500)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999470\n",
"std 0.000079\n",
"min 0.998742\n",
"25% 0.999441\n",
"50% 0.999490\n",
"75% 0.999522\n",
"max 0.999584\n",
"dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1000, m=1000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999449\n",
"std 0.000080\n",
"min 0.999007\n",
"25% 0.999422\n",
"50% 0.999469\n",
"75% 0.999501\n",
"max 0.999566\n",
"dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1000, m=2000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Large m, n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999500\n",
"std 0.000052\n",
"min 0.999084\n",
"25% 0.999476\n",
"50% 0.999512\n",
"75% 0.999536\n",
"max 0.999580\n",
"dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=2000, m=2000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1000.000000\n",
"mean 0.999476\n",
"std 0.000065\n",
"min 0.998894\n",
"25% 0.999449\n",
"50% 0.999490\n",
"75% 0.999520\n",
"max 0.999567\n",
"dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals = np.empty(num_reps)\n",
"for i in range(num_reps):\n",
" vals[i] = ssy_compute_stat(n=1500, m=2000)\n",
"\n",
"v = pd.Series(vals)\n",
"\n",
"v.describe()"
]
},
{
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Ttl/scikit-rf | doc/source/examples/networktheory/Properties of Rectangular Waveguides.ipynb | 3 | 6512 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Properties of Rectangular Waveguide"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example demonstrates how to use [scikit-rf](http://www.scikit-rf.org) to calculate some properties of rectangular waveguide. For more information regarding the theoretical basis for these calculations, see the [References](#References)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Object Creation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This first section imports neccesary modules and creates several `RectangularWaveguide` objects for some standard waveguide bands. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"%matplotlib inline\n",
"import skrf as rf \n",
"rf.stylely()\n",
"\n",
"# imports \n",
"\n",
"from scipy.constants import mil,c\n",
"from skrf.media import RectangularWaveguide, Freespace\n",
"from skrf.frequency import Frequency\n",
"\n",
"import matplotlib as mpl\n",
"\n",
"# plot formating\n",
"mpl.rcParams['lines.linewidth'] = 2\n",
"\n",
"# create frequency objects for standard bands\n",
"f_wr5p1 = Frequency(140,220,1001, 'ghz')\n",
"f_wr3p4 = Frequency(220,330,1001, 'ghz')\n",
"f_wr2p2 = Frequency(330,500,1001, 'ghz')\n",
"f_wr1p5 = Frequency(500,750,1001, 'ghz')\n",
"f_wr1 = Frequency(750,1100,1001, 'ghz')\n",
"\n",
"# create rectangular waveguide objects \n",
"wr5p1 = RectangularWaveguide(f_wr5p1.copy(), a=51*mil, b=25.5*mil, rho = 'au')\n",
"wr3p4 = RectangularWaveguide(f_wr3p4.copy(), a=34*mil, b=17*mil, rho = 'au')\n",
"wr2p2 = RectangularWaveguide(f_wr2p2.copy(), a=22*mil, b=11*mil, rho = 'au')\n",
"wr1p5 = RectangularWaveguide(f_wr1p5.copy(), a=15*mil, b=7.5*mil, rho = 'au')\n",
"wr1 = RectangularWaveguide(f_wr1.copy(), a=10*mil, b=5*mil, rho = 'au')\n",
"\n",
"# add names to waveguide objects for use in plot legends\n",
"wr5p1.name = 'WR-5.1'\n",
"wr3p4.name = 'WR-3.4'\n",
"wr2p2.name = 'WR-2.2'\n",
"wr1p5.name = 'WR-1.5'\n",
"wr1.name = 'WR-1.0'\n",
"\n",
"# create a list to iterate through\n",
"wg_list = [wr5p1, wr3p4,wr2p2,wr1p5,wr1]\n",
"\n",
"# creat a freespace object too\n",
"freespace = Freespace(Frequency(125,1100, 1001))\n",
"freespace.name = 'Free Space'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conductor Loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pylab import * \n",
"\n",
"for wg in wg_list:\n",
" wg.frequency.plot(rf.np_2_db(wg.alpha), label=wg.name )\n",
"\n",
"legend() \n",
"xlabel('Frequency(GHz)')\n",
"ylabel('Loss (dB/m)')\n",
"title('Loss in Rectangular Waveguide (Au)');\n",
"xlim(100,1300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"resistivity_list = linspace(1,10,5)*1e-8 # ohm meter \n",
"for rho in resistivity_list:\n",
" wg = RectangularWaveguide(f_wr1.copy(), a=10*mil, b=5*mil, \n",
" rho = rho)\n",
" wg.frequency.plot(rf.np_2_db(wg.alpha),label=r'$ \\rho $=%.e$ \\Omega m$'%rho )\n",
"\n",
"legend() \n",
"#ylim(.0,20)\n",
"xlabel('Frequency(GHz)')\n",
"ylabel('Loss (dB/m)')\n",
"title('Loss vs. Resistivity in\\nWR-1.0 Rectangular Waveguide');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase Velocity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for wg in wg_list:\n",
" wg.frequency.plot(100*wg.v_p.real/c, label=wg.name )\n",
"\n",
"legend() \n",
"ylim(50,200)\n",
"xlabel('Frequency(GHz)')\n",
"ylabel('Phase Velocity (\\%c)')\n",
"title('Phase Veclocity in Rectangular Waveguide');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for wg in wg_list:\n",
" plt.plot(wg.frequency.f_scaled[1:], \n",
" 100/c*diff(wg.frequency.w)/diff(wg.beta), \n",
" label=wg.name )\n",
" \n",
"legend() \n",
"ylim(50,100)\n",
"xlabel('Frequency(GHz)')\n",
"ylabel('Group Velocity (\\%c)')\n",
"title('Phase Veclocity in Rectangular Waveguide');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Propagation Constant"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for wg in wg_list+[freespace]:\n",
" wg.frequency.plot(wg.beta, label=wg.name )\n",
" \n",
"legend() \n",
"xlabel('Frequency(GHz)')\n",
"ylabel('Propagation Constant (rad/m)')\n",
"title('Propagation Constant \\nin Rectangular Waveguide');\n",
"semilogy();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"* [1] http://www.microwaves101.com/encyclopedia/waveguidemath.cfm\n",
"* [2] http://en.wikipedia.org/wiki/Waveguide_(electromagnetism)\n",
"* [3] R. F. Harrington, Time-Harmonic Electromagnetic Fields (IEEE Press Series on Electromagnetic Wave Theory). Wiley-IEEE Press, 2001.\n",
"* [4] http://www.ece.rutgers.edu/~orfanidi/ewa (see Chapter 9)\n"
]
}
],
"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": 0
}
| bsd-3-clause |
jithinpr2/PenguinRandomWalk | Models/.ipynb_checkpoints/svm_POLY-checkpoint.ipynb | 1 | 104799 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import print_function\n",
"from __future__ import division\n",
"\n",
"import os\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from tqdm import tqdm_notebook\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.colors import rgb2hex\n",
"import seaborn as sns\n",
"\n",
"import statsmodels.api as sm\n",
"\n",
"# let's not pollute this blog post with warnings\n",
"from warnings import filterwarnings\n",
"filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>site_name</th>\n",
" <th>site_id</th>\n",
" <th>ccamlr_region</th>\n",
" <th>longitude_epsg_4326</th>\n",
" <th>latitude_epsg_4326</th>\n",
" <th>common_name</th>\n",
" <th>day</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>season_starting</th>\n",
" <th>penguin_count</th>\n",
" <th>accuracy</th>\n",
" <th>count_type</th>\n",
" <th>vantage</th>\n",
" <th>e_n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Acuna Island</td>\n",
" <td>ACUN</td>\n",
" <td>48.2</td>\n",
" <td>-44.637</td>\n",
" <td>-60.7612</td>\n",
" <td>chinstrap penguin</td>\n",
" <td>28.0</td>\n",
" <td>12.0</td>\n",
" <td>1983</td>\n",
" <td>1983</td>\n",
" <td>4000.0</td>\n",
" <td>4.0</td>\n",
" <td>nests</td>\n",
" <td>ground</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Acuna Island</td>\n",
" <td>ACUN</td>\n",
" <td>48.2</td>\n",
" <td>-44.637</td>\n",
" <td>-60.7612</td>\n",
" <td>adelie penguin</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1993</td>\n",
" <td>1993</td>\n",
" <td>2008.0</td>\n",
" <td>1.0</td>\n",
" <td>nests</td>\n",
" <td>ground</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Acuna Island</td>\n",
" <td>ACUN</td>\n",
" <td>48.2</td>\n",
" <td>-44.637</td>\n",
" <td>-60.7612</td>\n",
" <td>adelie penguin</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1994</td>\n",
" <td>1994</td>\n",
" <td>1920.0</td>\n",
" <td>1.0</td>\n",
" <td>nests</td>\n",
" <td>NaN</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Acuna Island</td>\n",
" <td>ACUN</td>\n",
" <td>48.2</td>\n",
" <td>-44.637</td>\n",
" <td>-60.7612</td>\n",
" <td>adelie penguin</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>1880.0</td>\n",
" <td>1.0</td>\n",
" <td>nests</td>\n",
" <td>ground</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Acuna Island</td>\n",
" <td>ACUN</td>\n",
" <td>48.2</td>\n",
" <td>-44.637</td>\n",
" <td>-60.7612</td>\n",
" <td>adelie penguin</td>\n",
" <td>25.0</td>\n",
" <td>2.0</td>\n",
" <td>2011</td>\n",
" <td>2010</td>\n",
" <td>3079.0</td>\n",
" <td>5.0</td>\n",
" <td>nests</td>\n",
" <td>vhr</td>\n",
" <td>0.90</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" site_name site_id ccamlr_region longitude_epsg_4326 \\\n",
"0 Acuna Island ACUN 48.2 -44.637 \n",
"1 Acuna Island ACUN 48.2 -44.637 \n",
"2 Acuna Island ACUN 48.2 -44.637 \n",
"3 Acuna Island ACUN 48.2 -44.637 \n",
"4 Acuna Island ACUN 48.2 -44.637 \n",
"\n",
" latitude_epsg_4326 common_name day month year season_starting \\\n",
"0 -60.7612 chinstrap penguin 28.0 12.0 1983 1983 \n",
"1 -60.7612 adelie penguin NaN NaN 1993 1993 \n",
"2 -60.7612 adelie penguin NaN NaN 1994 1994 \n",
"3 -60.7612 adelie penguin NaN NaN 2004 2004 \n",
"4 -60.7612 adelie penguin 25.0 2.0 2011 2010 \n",
"\n",
" penguin_count accuracy count_type vantage e_n \n",
"0 4000.0 4.0 nests ground 0.50 \n",
"1 2008.0 1.0 nests ground 0.05 \n",
"2 1920.0 1.0 nests NaN 0.05 \n",
"3 1880.0 1.0 nests ground 0.05 \n",
"4 3079.0 5.0 nests vhr 0.90 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observations = pd.read_csv(os.path.join('data', 'training_set_observations.csv'), index_col=0)\n",
"observations.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['site_name', 'site_id', 'ccamlr_region', 'longitude_epsg_4326',\n",
" 'latitude_epsg_4326', 'common_name', 'day', 'month', 'year',\n",
" 'season_starting', 'penguin_count', 'accuracy', 'count_type', 'vantage',\n",
" 'e_n'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observations.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>longitude_epsg_4326</th>\n",
" <th>latitude_epsg_4326</th>\n",
" <th>day</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>season_starting</th>\n",
" <th>penguin_count</th>\n",
" <th>accuracy</th>\n",
" <th>e_n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2952.000000</td>\n",
" <td>2952.000000</td>\n",
" <td>1797.000000</td>\n",
" <td>2226.000000</td>\n",
" <td>2952.000000</td>\n",
" <td>2952.000000</td>\n",
" <td>2935.000000</td>\n",
" <td>2935.000000</td>\n",
" <td>2935.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-11.722006</td>\n",
" <td>-65.935391</td>\n",
" <td>15.593767</td>\n",
" <td>6.943845</td>\n",
" <td>1997.558604</td>\n",
" <td>1997.206978</td>\n",
" <td>8569.280409</td>\n",
" <td>1.826235</td>\n",
" <td>0.167291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>85.926830</td>\n",
" <td>4.380338</td>\n",
" <td>8.818179</td>\n",
" <td>5.244836</td>\n",
" <td>12.724139</td>\n",
" <td>12.671414</td>\n",
" <td>29579.280288</td>\n",
" <td>1.258289</td>\n",
" <td>0.243676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-140.326600</td>\n",
" <td>-77.578012</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1895.000000</td>\n",
" <td>1895.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-62.866700</td>\n",
" <td>-67.450250</td>\n",
" <td>8.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1987.000000</td>\n",
" <td>1986.000000</td>\n",
" <td>272.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-58.933000</td>\n",
" <td>-64.794600</td>\n",
" <td>15.000000</td>\n",
" <td>11.000000</td>\n",
" <td>1999.000000</td>\n",
" <td>1999.000000</td>\n",
" <td>1098.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>39.425200</td>\n",
" <td>-62.596000</td>\n",
" <td>24.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>3612.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>171.169200</td>\n",
" <td>-60.533300</td>\n",
" <td>31.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2014.000000</td>\n",
" <td>2013.000000</td>\n",
" <td>428516.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.900000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" longitude_epsg_4326 latitude_epsg_4326 day month \\\n",
"count 2952.000000 2952.000000 1797.000000 2226.000000 \n",
"mean -11.722006 -65.935391 15.593767 6.943845 \n",
"std 85.926830 4.380338 8.818179 5.244836 \n",
"min -140.326600 -77.578012 1.000000 1.000000 \n",
"25% -62.866700 -67.450250 8.000000 1.000000 \n",
"50% -58.933000 -64.794600 15.000000 11.000000 \n",
"75% 39.425200 -62.596000 24.000000 12.000000 \n",
"max 171.169200 -60.533300 31.000000 12.000000 \n",
"\n",
" year season_starting penguin_count accuracy e_n \n",
"count 2952.000000 2952.000000 2935.000000 2935.000000 2935.000000 \n",
"mean 1997.558604 1997.206978 8569.280409 1.826235 0.167291 \n",
"std 12.724139 12.671414 29579.280288 1.258289 0.243676 \n",
"min 1895.000000 1895.000000 0.000000 1.000000 0.050000 \n",
"25% 1987.000000 1986.000000 272.000000 1.000000 0.050000 \n",
"50% 1999.000000 1999.000000 1098.000000 1.000000 0.050000 \n",
"75% 2009.000000 2009.000000 3612.000000 2.000000 0.100000 \n",
"max 2014.000000 2013.000000 428516.000000 5.000000 0.900000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observations.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x16d2d4a8f98>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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vXj2nMwEAALgat1MMsXQlfv7zn+vZZ59Vw4YNlZaWphMnTjidCwAAAB5Q6WQyIyNDhmHI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\nxGoqJkVERETEaiomRURERMRq/weo+rJqBqDNxQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16d2d4b32e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"corr = observations.select_dtypes(include = ['float64', 'int64']).iloc[:, 1:].corr()\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(corr, vmax=1, square=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have 2952 penguin observations from 1895 to 2013 at 619 unique sites in the Antarctic!\n"
]
}
],
"source": [
"\n",
"print(\n",
" \"We have {} penguin observations from {} to {} at {} unique sites in the Antarctic!\" \\\n",
" .format(observations.shape[0],\n",
" observations.season_starting.min(),\n",
" observations.season_starting.max(),\n",
" observations.site_id.nunique())\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"adelie penguin 1387\n",
"gentoo penguin 791\n",
"chinstrap penguin 774\n",
"Name: common_name, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# How many observations do we have for each species?\n",
"observations.common_name.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"common_name\n",
"adelie penguin 281\n",
"chinstrap penguin 340\n",
"gentoo penguin 105\n",
"Name: site_id, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# How many differnet sites do we see each species at?\n",
"(observations.groupby(\"common_name\")\n",
" .site_id\n",
" .nunique())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"common_name count_type\n",
"adelie penguin nests 976\n",
" adults 223\n",
" chicks 188\n",
"chinstrap penguin nests 608\n",
" adults 86\n",
" chicks 80\n",
"gentoo penguin nests 629\n",
" chicks 161\n",
" adults 1\n",
"Name: count_type, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# How many count types do we have for each species?\n",
"(observations.groupby(\"common_name\")\n",
" .count_type\n",
" .value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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></th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" <tr>\n",
" <th>site_id</th>\n",
" <th>common_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">ACUN</th>\n",
" <th>adelie penguin</th>\n",
" <td>1880.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3079.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinstrap penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAM</th>\n",
" <th>adelie penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>76.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>338231.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>428516.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AILS</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">AITC</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5620.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4047.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gentoo penguin</th>\n",
" <td>NaN</td>\n",
" <td>1998.0</td>\n",
" <td>1639.0</td>\n",
" <td>1383.0</td>\n",
" <td>2210.0</td>\n",
" <td>1900.0</td>\n",
" <td>1319.0</td>\n",
" <td>2213.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AITK</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AKAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>106.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALAS</th>\n",
" <th>adelie penguin</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1080.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2004 2005 2006 2007 2008 2009 \\\n",
"site_id common_name \n",
"ACUN adelie penguin 1880.0 NaN NaN NaN NaN NaN \n",
" chinstrap penguin NaN NaN NaN NaN NaN NaN \n",
"ADAM adelie penguin NaN NaN NaN NaN NaN 76.0 \n",
"ADAR adelie penguin NaN NaN NaN NaN NaN NaN \n",
"AILS chinstrap penguin NaN NaN NaN NaN NaN NaN \n",
"AITC chinstrap penguin NaN NaN NaN NaN 5620.0 NaN \n",
" gentoo penguin NaN 1998.0 1639.0 1383.0 2210.0 1900.0 \n",
"AITK chinstrap penguin NaN NaN NaN NaN NaN NaN \n",
"AKAR adelie penguin NaN NaN NaN NaN NaN NaN \n",
"ALAS adelie penguin NaN NaN NaN NaN 1080.0 NaN \n",
"\n",
" 2010 2011 2012 2013 \n",
"site_id common_name \n",
"ACUN adelie penguin 3079.0 NaN NaN NaN \n",
" chinstrap penguin NaN NaN NaN NaN \n",
"ADAM adelie penguin NaN NaN NaN NaN \n",
"ADAR adelie penguin 338231.0 NaN NaN 428516.0 \n",
"AILS chinstrap penguin NaN NaN NaN NaN \n",
"AITC chinstrap penguin NaN 4047.0 NaN NaN \n",
" gentoo penguin 1319.0 2213.0 NaN NaN \n",
"AITK chinstrap penguin NaN NaN NaN NaN \n",
"AKAR adelie penguin 106.0 NaN NaN NaN \n",
"ALAS adelie penguin NaN NaN NaN NaN "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"nest_counts = pd.read_csv(\n",
" os.path.join('data', 'training_set_nest_counts.csv'),\n",
" index_col=[0,1]\n",
" )\n",
"\n",
"# Let's look at the first 10 rows, and the last 10 columns\n",
"nest_counts.iloc[:10, -10:]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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b7Xmd276OebXJ8w+HG9x1vmv+pcyMX3dzpRkPxtKrZebKFwAAz0i+AAB4RvIFAMAzki8A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\n4BnJFwAAz/4/tLLpJaKCgV4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16d2d383160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# get a sort order for the sites with the most observations\n",
"sorted_idx = (pd.notnull(nest_counts)\n",
" .sum(axis=1)\n",
" .sort_values(ascending=False)\n",
" .index)\n",
"\n",
"# get the top 25 most common sites and divide by the per-series mean\n",
"to_plot = nest_counts.loc[sorted_idx].head(25)\n",
"to_plot = to_plot.divide(to_plot.mean(axis=1), axis=0)\n",
"\n",
"# plot the data\n",
"plt.gca().matshow(to_plot,\n",
" cmap='viridis')\n",
"plt.show()\n"
]
},
{
"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></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" <tr>\n",
" <th>site_id</th>\n",
" <th>common_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">ACUN</th>\n",
" <th>adelie penguin</th>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>...</td>\n",
" <td>1880.0</td>\n",
" <td>1880.0</td>\n",
" <td>1880.0</td>\n",
" <td>1880.0</td>\n",
" <td>1880.0</td>\n",
" <td>1880.0</td>\n",
" <td>3079.0</td>\n",
" <td>3079.0</td>\n",
" <td>3079.0</td>\n",
" <td>3079.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinstrap penguin</th>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>...</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAM</th>\n",
" <th>adelie penguin</th>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>...</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>256806.0</td>\n",
" <td>256806.0</td>\n",
" <td>256806.0</td>\n",
" <td>256806.0</td>\n",
" <td>256806.0</td>\n",
" <td>256806.0</td>\n",
" <td>282307.0</td>\n",
" <td>282307.0</td>\n",
" <td>272338.0</td>\n",
" <td>272338.0</td>\n",
" <td>...</td>\n",
" <td>338777.0</td>\n",
" <td>338777.0</td>\n",
" <td>338777.0</td>\n",
" <td>338777.0</td>\n",
" <td>338777.0</td>\n",
" <td>338777.0</td>\n",
" <td>338231.0</td>\n",
" <td>338231.0</td>\n",
" <td>338231.0</td>\n",
" <td>428516.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AILS</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>...</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"site_id common_name \n",
"ACUN adelie penguin 2008.0 2008.0 2008.0 2008.0 2008.0 \n",
" chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n",
"ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n",
"ADAR adelie penguin 256806.0 256806.0 256806.0 256806.0 256806.0 \n",
"AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"site_id common_name \n",
"ACUN adelie penguin 2008.0 2008.0 2008.0 2008.0 2008.0 \n",
" chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n",
"ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n",
"ADAR adelie penguin 256806.0 282307.0 282307.0 272338.0 272338.0 \n",
"AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n",
"\n",
" ... 2004 2005 2006 2007 \\\n",
"site_id common_name ... \n",
"ACUN adelie penguin ... 1880.0 1880.0 1880.0 1880.0 \n",
" chinstrap penguin ... 4000.0 4000.0 4000.0 4000.0 \n",
"ADAM adelie penguin ... 76.0 76.0 76.0 76.0 \n",
"ADAR adelie penguin ... 338777.0 338777.0 338777.0 338777.0 \n",
"AILS chinstrap penguin ... 6000.0 6000.0 6000.0 6000.0 \n",
"\n",
" 2008 2009 2010 2011 2012 \\\n",
"site_id common_name \n",
"ACUN adelie penguin 1880.0 1880.0 3079.0 3079.0 3079.0 \n",
" chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n",
"ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n",
"ADAR adelie penguin 338777.0 338777.0 338231.0 338231.0 338231.0 \n",
"AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n",
"\n",
" 2013 \n",
"site_id common_name \n",
"ACUN adelie penguin 3079.0 \n",
" chinstrap penguin 4000.0 \n",
"ADAM adelie penguin 76.0 \n",
"ADAR adelie penguin 428516.0 \n",
"AILS chinstrap penguin 6000.0 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def preprocess_timeseries(timeseries, first_year, fillna_value=0):\n",
" \"\"\" Takes one of the timeseries dataframes, removes\n",
" columns before `first_year`, and fills NaN values\n",
" with the preceeding value. Then backfills any\n",
" remaining NaNs.\n",
" \n",
" As a courtesy, also turns year column name into\n",
" integers for easy comparisons.\n",
" \"\"\"\n",
" # column type\n",
" timeseries.columns = timeseries.columns.astype(int)\n",
" \n",
" # subset to just data after first_year\n",
" timeseries = timeseries.loc[:, timeseries.columns >= first_year]\n",
" \n",
" # Forward fill count values. This is a strong assumption.\n",
" timeseries.fillna(method=\"ffill\", axis=1, inplace=True)\n",
" timeseries.fillna(method=\"bfill\", axis=1, inplace=True)\n",
" \n",
" # For sites with no observations, fill with fill_na_value\n",
" timeseries.fillna(fillna_value, inplace=True)\n",
" \n",
" return timeseries\n",
"\n",
"nest_counts = preprocess_timeseries(nest_counts,\n",
" 1980,\n",
" fillna_value=0.0)\n",
"nest_counts.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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BzDU3e9KBbUE/shr9vNM2hrGGcPb4CVN/SQqng7fDj5fy7x/Z2+L/gGwZfhjaqKZYj/lr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\nBCEAwGn/F499eDhL6etDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16d2d37b0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# get the top 25 most common sites and divide by the per-series mean\n",
"to_plot = nest_counts.loc[sorted_idx].head(25)\n",
"to_plot = to_plot.divide(to_plot.mean(axis=1), axis=0)\n",
"\n",
"plt.gca().matshow(to_plot,\n",
" cmap='viridis')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" <tr>\n",
" <th>site_id</th>\n",
" <th>common_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">ACUN</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>...</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinstrap penguin</th>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>...</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAM</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>...</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.90</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>...</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.10</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AILS</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>...</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 \\\n",
"site_id common_name \n",
"ACUN adelie penguin 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 \n",
" chinstrap penguin 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 \n",
"ADAM adelie penguin 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 \n",
"ADAR adelie penguin 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 \n",
"AILS chinstrap penguin 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 \n",
"\n",
" 1988 1989 ... 2004 2005 2006 2007 2008 \\\n",
"site_id common_name ... \n",
"ACUN adelie penguin 0.05 0.05 ... 0.05 0.05 0.05 0.05 0.05 \n",
" chinstrap penguin 0.50 0.50 ... 0.50 0.50 0.50 0.50 0.50 \n",
"ADAM adelie penguin 0.90 0.90 ... 0.90 0.90 0.90 0.90 0.90 \n",
"ADAR adelie penguin 0.10 0.10 ... 0.10 0.10 0.10 0.10 0.10 \n",
"AILS chinstrap penguin 0.50 0.50 ... 0.50 0.50 0.50 0.50 0.50 \n",
"\n",
" 2009 2010 2011 2012 2013 \n",
"site_id common_name \n",
"ACUN adelie penguin 0.05 0.9 0.9 0.9 0.9 \n",
" chinstrap penguin 0.50 0.5 0.5 0.5 0.5 \n",
"ADAM adelie penguin 0.90 0.9 0.9 0.9 0.9 \n",
"ADAR adelie penguin 0.10 0.9 0.9 0.9 0.1 \n",
"AILS chinstrap penguin 0.50 0.5 0.5 0.5 0.5 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e_n_values = pd.read_csv(\n",
" os.path.join('data', 'training_set_e_n.csv'),\n",
" index_col=[0,1]\n",
" )\n",
"\n",
"# Process error data to match our nest_counts data\n",
"e_n_values = preprocess_timeseries(e_n_values, 1980, fillna_value=0.05)\n",
"e_n_values.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def amape(y_true, y_pred, accuracies):\n",
" \"\"\" Adjusted MAPE\n",
" \"\"\"\n",
" not_nan_mask = ~np.isnan(y_true)\n",
" \n",
" # calculate absolute error\n",
" abs_error = (np.abs(y_true[not_nan_mask] - y_pred[not_nan_mask]))\n",
" \n",
" # calculate the percent error (replacing 0 with 1\n",
" # in order to avoid divide-by-zero errors).\n",
" pct_error = abs_error / np.maximum(1, y_true[not_nan_mask])\n",
" \n",
" # adjust error by count accuracies\n",
" adj_error = pct_error / accuracies[not_nan_mask]\n",
" \n",
" # return the mean as a percentage\n",
" return np.mean(adj_error)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's confirm the best possible score is 0!\n",
"amape(nest_counts.values,\n",
" nest_counts.values,\n",
" e_n_values.values)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.svm import SVR\n",
"\n",
"\n",
"def train_model_per_row(ts, acc, split_year=2010):\n",
" # Split into train/test to tune our parameter\n",
" train = ts.iloc[ts.index < split_year]\n",
" rng = np.random.RandomState(1)\n",
" test = ts.iloc[ts.index >= split_year]\n",
" test_acc = acc.iloc[acc.index >= split_year]\n",
" \n",
" # Store best lag parameter\n",
" best_mape = np.inf \n",
" best_lag = None\n",
"\n",
" # Test linear regression models with the most recent\n",
" # 2 points through using all of the points\n",
" for lag in range(2, train.shape[0]):\n",
" # fit the model\n",
" #temp_model = LinearRegression()\n",
" #temp_model = DecisionTreeRegressor(max_depth=4)\n",
" #temp_model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),\n",
" # n_estimators=300, random_state=rng)\n",
" temp_model = SVR(kernel='poly', C=1e3, degree=2)\n",
"\n",
" temp_model.fit(\n",
" train.index[-lag:].values.reshape(-1, 1),\n",
" train[-lag:]\n",
" )\n",
" \n",
" # make our predictions on the test set\n",
" preds = temp_model.predict(\n",
" test.index.values.reshape(-1, 1)\n",
" )\n",
"\n",
" # calculate the score using the custom metric\n",
" mape = amape(test.values,\n",
" preds,\n",
" test_acc.values)\n",
"\n",
" # if it's the best score yet, hold on to the parameter\n",
" if mape < best_mape:\n",
" best_mape = mape\n",
" best_lag = lag\n",
" \n",
" # return model re-trained on entire dataset\n",
" #final_model = LinearRegression()\n",
" final_model = SVR(kernel='poly', C=1e3, degree=2)\n",
"\n",
"\n",
" final_model.fit(\n",
" ts.index[-best_lag:].values.reshape(-1, 1),\n",
" ts[-best_lag:]\n",
" )\n",
"\n",
" return final_model, best_mape ,best_lag"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Avg Best Mape : 63.63849378049528\n",
"Avg Best Lag : 3.185185185185185\n"
]
}
],
"source": [
"models = {}\n",
"avg_Best_Mape = 0.0\n",
"avg_best_lag = 0.0\n",
"iteration = 0\n",
"for i, row in tqdm_notebook(nest_counts.iterrows(),\n",
" total=nest_counts.shape[0]):\n",
" acc = e_n_values.loc[i]\n",
" models[i], best_mape, best_lag = train_model_per_row(row, acc)\n",
" avg_Best_Mape = avg_Best_Mape + best_mape\n",
" avg_best_lag = avg_best_lag + best_lag\n",
" iteration = iteration + 1\n",
"avg_Best_Mape = avg_Best_Mape / iteration\n",
"avg_best_lag = avg_best_lag / iteration \n",
"print(\"Avg Best Mape : {0}\".format(avg_Best_Mape))\n",
"print(\"Avg Best Lag : {0}\".format(avg_best_lag))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(648, 4)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" </tr>\n",
" <tr>\n",
" <th>site_id</th>\n",
" <th>common_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">ACUN</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinstrap penguin</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAM</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AILS</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2014 2015 2016 2017\n",
"site_id common_name \n",
"ACUN adelie penguin 0.0 0.0 0.0 0.0\n",
" chinstrap penguin 0.0 0.0 0.0 0.0\n",
"ADAM adelie penguin 0.0 0.0 0.0 0.0\n",
"ADAR adelie penguin 0.0 0.0 0.0 0.0\n",
"AILS chinstrap penguin 0.0 0.0 0.0 0.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submission_format = pd.read_csv(\n",
" os.path.join('data','submission_format.csv'),\n",
" index_col=[0, 1]\n",
")\n",
"\n",
"print(submission_format.shape)\n",
"submission_format.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" </tr>\n",
" <tr>\n",
" <th>site_id</th>\n",
" <th>common_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">ACUN</th>\n",
" <th>adelie penguin</th>\n",
" <td>2877.0</td>\n",
" <td>2651.0</td>\n",
" <td>2442.0</td>\n",
" <td>2289.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinstrap penguin</th>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" <td>4000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAM</th>\n",
" <th>adelie penguin</th>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" <td>76.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADAR</th>\n",
" <th>adelie penguin</th>\n",
" <td>383608.0</td>\n",
" <td>383637.0</td>\n",
" <td>383578.0</td>\n",
" <td>383493.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AILS</th>\n",
" <th>chinstrap penguin</th>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" <td>6000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2014 2015 2016 2017\n",
"site_id common_name \n",
"ACUN adelie penguin 2877.0 2651.0 2442.0 2289.0\n",
" chinstrap penguin 4000.0 4000.0 4000.0 4000.0\n",
"ADAM adelie penguin 76.0 76.0 76.0 76.0\n",
"ADAR adelie penguin 383608.0 383637.0 383578.0 383493.0\n",
"AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = []\n",
"\n",
"# For every row in the submission file\n",
"for i, row in tqdm_notebook(submission_format.iterrows(),\n",
" total=submission_format.shape[0]):\n",
" \n",
" # get the model for this site + common_name\n",
" model = models[i]\n",
" \n",
" # make predictions using the model\n",
" row_predictions = model.predict(\n",
" submission_format.columns.values.reshape(-1, 1)\n",
" )\n",
" \n",
" # keep our predictions, rounded to nearest whole number\n",
" preds.append(np.round(row_predictions))\n",
"\n",
"# Create a dataframe that we can write out to a CSV\n",
"prediction_df = pd.DataFrame(preds,\n",
" index=submission_format.index,\n",
" columns=submission_format.columns)\n",
"\n",
"prediction_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"prediction_df.to_csv('predictions_SVM_poly.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| mit |
google/starthinker | colabs/email_cm_to_bigquery.ipynb | 1 | 7988 | {
"license": "Licensed under the Apache License, Version 2.0",
"copyright": "Copyright 2020 Google LLC",
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "CM360 Report Emailed To BigQuery",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-001"
},
"source": [
"#CM360 Report Emailed To BigQuery\n",
"Pulls a CM Report from a gMail powered email account into BigQuery.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-002"
},
"source": [
"#License\n",
"\n",
"Copyright 2020 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.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-003"
},
"source": [
"#Disclaimer\n",
"This is not an officially supported Google product. It is a reference implementation. There is absolutely NO WARRANTY provided for using this code. The code is Apache Licensed and CAN BE fully modified, white labeled, and disassembled by your team.\n",
"\n",
"This code generated (see starthinker/scripts for possible source):\n",
" - **Command**: \"python starthinker_ui/manage.py colab\"\n",
" - **Command**: \"python starthinker/tools/colab.py [JSON RECIPE]\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-004"
},
"source": [
"#1. Install Dependencies\n",
"First install the libraries needed to execute recipes, this only needs to be done once, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a3c8f298-005"
},
"source": [
"!pip install git+https://github.com/google/starthinker\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-006"
},
"source": [
"#2. Set Configuration\n",
"\n",
"This code is required to initialize the project. Fill in required fields and press play.\n",
"\n",
"1. If the recipe uses a Google Cloud Project:\n",
" - Set the configuration **project** value to the project identifier from [these instructions](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md).\n",
"\n",
"1. If the recipe has **auth** set to **user**:\n",
" - If you have user credentials:\n",
" - Set the configuration **user** value to your user credentials JSON.\n",
" - If you DO NOT have user credentials:\n",
" - Set the configuration **client** value to [downloaded client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md).\n",
"\n",
"1. If the recipe has **auth** set to **service**:\n",
" - Set the configuration **service** value to [downloaded service credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_service.md).\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a3c8f298-007"
},
"source": [
"from starthinker.util.configuration import Configuration\n",
"\n",
"\n",
"CONFIG = Configuration(\n",
" project=\"\",\n",
" client={},\n",
" service={},\n",
" user=\"/content/user.json\",\n",
" verbose=True\n",
")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-008"
},
"source": [
"#3. Enter CM360 Report Emailed To BigQuery Recipe Parameters\n",
" 1. The person executing this recipe must be the recipient of the email.\n",
" 1. Schedule a CM report to be sent to ****.\n",
" 1. Or set up a redirect rule to forward a report you already receive.\n",
" 1. The report must be sent as an attachment.\n",
" 1. Ensure this recipe runs after the report is email daily.\n",
" 1. Give a regular expression to match the email subject.\n",
" 1. Configure the destination in BigQuery to write the data.\n",
"Modify the values below for your use case, can be done multiple times, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a3c8f298-009"
},
"source": [
"FIELDS = {\n",
" 'auth_read':'user', # Credentials used for reading data.\n",
" 'email':'', # Email address report was sent to.\n",
" 'subject':'.*', # Regular expression to match subject. Double escape backslashes.\n",
" 'dataset':'', # Existing dataset in BigQuery.\n",
" 'table':'', # Name of table to be written to.\n",
" 'is_incremental_load':False, # Append report data to table based on date column, de-duplicates.\n",
"}\n",
"\n",
"print(\"Parameters Set To: %s\" % FIELDS)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a3c8f298-010"
},
"source": [
"#4. Execute CM360 Report Emailed To BigQuery\n",
"This does NOT need to be modified unless you are changing the recipe, click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a3c8f298-011"
},
"source": [
"from starthinker.util.configuration import execute\n",
"from starthinker.util.recipe import json_set_fields\n",
"\n",
"TASKS = [\n",
" {\n",
" 'email':{\n",
" 'auth':{'field':{'name':'auth_read','kind':'authentication','order':1,'default':'user','description':'Credentials used for reading data.'}},\n",
" 'read':{\n",
" 'from':'[email protected]',\n",
" 'to':{'field':{'name':'email','kind':'string','order':1,'default':'','description':'Email address report was sent to.'}},\n",
" 'subject':{'field':{'name':'subject','kind':'string','order':2,'default':'.*','description':'Regular expression to match subject. Double escape backslashes.'}},\n",
" 'attachment':'.*'\n",
" },\n",
" 'write':{\n",
" 'bigquery':{\n",
" 'dataset':{'field':{'name':'dataset','kind':'string','order':3,'default':'','description':'Existing dataset in BigQuery.'}},\n",
" 'table':{'field':{'name':'table','kind':'string','order':4,'default':'','description':'Name of table to be written to.'}},\n",
" 'header':True,\n",
" 'is_incremental_load':{'field':{'name':'is_incremental_load','kind':'boolean','order':6,'default':False,'description':'Append report data to table based on date column, de-duplicates.'}}\n",
" }\n",
" }\n",
" }\n",
" }\n",
"]\n",
"\n",
"json_set_fields(TASKS, FIELDS)\n",
"\n",
"execute(CONFIG, TASKS, force=True)\n"
]
}
]
}
| apache-2.0 |
pligor/predicting-future-product-prices | 04_time_series_prediction/.ipynb_checkpoints/22_price_history_baseline-checkpoint.ipynb | 1 | 174690 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# -*- coding: UTF-8 -*-\n",
"#%load_ext autoreload\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/studenthp/anaconda2/envs/dis/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import division\n",
"import tensorflow as tf\n",
"from os import path\n",
"import numpy as np\n",
"import pandas as pd\n",
"import csv\n",
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"from time import time\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"from mylibs.jupyter_notebook_helper import show_graph\n",
"from tensorflow.contrib import rnn\n",
"from tensorflow.contrib import learn\n",
"import shutil\n",
"from tensorflow.contrib.learn.python.learn import learn_runner\n",
"from IPython.display import Image\n",
"from IPython.core.display import HTML\n",
"from mylibs.tf_helper import getDefaultGPUconfig\n",
"from sklearn.metrics import r2_score\n",
"from mylibs.py_helper import factors\n",
"from fastdtw import fastdtw\n",
"from scipy.spatial.distance import euclidean\n",
"from statsmodels.tsa.stattools import coint\n",
"from data_providers.price_history_sliding_window_data_provider \\\n",
" import PriceHistorySlidingWindowDataProvider\n",
"from sklearn.linear_model import LinearRegression\n",
"from scipy.signal import detrend\n",
"from common import get_or_run_nn"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dtype = tf.float32\n",
"seed = 16011984\n",
"random_state = np.random.RandomState(seed=seed)\n",
"config = getDefaultGPUconfig()\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Baseline is static, a straight line for each input - Global Norm"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = '../data/price_history'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#npz_path = '../price_history_03_dp_60to30_from_fixed_len.npz'\n",
"npz_path = data_path + '/price_history_03_dp_60to30_6400_global_remove_scale_train.npz'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"arr = np.load(npz_path)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['inputs', 'sku_ids', 'sequence_masks', 'targets', 'sequence_lengths']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.keys()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 60, 1)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inputs = arr['inputs']\n",
"inputs.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets = arr['targets']\n",
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_len = targets.shape[1]\n",
"target_len"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30, 1)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = np.empty(shape=targets.shape + (1,))\n",
"preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for ii, cur_in in enumerate(inputs):\n",
" #print cur_in.shape\n",
" #print cur_in[-1].shape\n",
" preds[ii] = cur_in[-1] #broadcasting\n",
" #print np.repeat(cur_in[-1], target_len).shape\n",
" #print np.repeat(cur_in[-1:], target_len)\n",
" #print dummy_targets[ii].shape\n",
" #print dummy_targets[ii]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### evaluate"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# dyn_stats, preds_dict = get_or_run_nn(experiment,\n",
"# filename='002_rnn_gru_60to30')\n",
"# epochs: 10\n",
"# End Epoch 01 (43.118 secs): err(train) = 7.8218\n",
"# End Epoch 02 (41.229 secs): err(train) = 6.9381\n",
"# End Epoch 03 (41.370 secs): err(train) = 6.4451\n",
"# End Epoch 04 (41.662 secs): err(train) = 6.0338\n",
"# End Epoch 05 (41.484 secs): err(train) = 5.4984\n",
"# End Epoch 06 (41.337 secs): err(train) = 5.0515\n",
"# End Epoch 07 (41.292 secs): err(train) = 4.6289\n",
"# End Epoch 08 (43.233 secs): err(train) = 4.2015\n",
"# End Epoch 09 (43.128 secs): err(train) = 3.7842\n",
"# End Epoch 10 (41.224 secs): err(train) = 3.4302"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_len = len(inputs)\n",
"assert len(preds) == len(targets) and data_len == len(targets)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mses = np.empty(data_len)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mses = np.empty(data_len)\n",
"for ii, (pred, target) in enumerate(zip(preds, targets)):\n",
" mses[ii] = mean_squared_error(pred, target)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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AAAC7yg1dZy1JyrL8qUu+fc+NHg8AAIDtrawBAACwTYQ1AACAChLWAAAAKkhYAwAAqCBh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8465121150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(15, 7))\n",
"ax.set(xscale=\"log\") #, yscale=\"log\")\n",
"sns.distplot(mses, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### MSE loss"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0072440273088614452"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(mses)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Huber Loss"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def huber_loss(y_true, y_pred):\n",
" err = y_true - y_pred\n",
" \n",
" absolute = np.abs(err)\n",
" \n",
" ifthen = 0.5 * err\n",
" ifelse = absolute - 0.5\n",
" \n",
" return np.where(absolute < 1.0, ifthen, ifelse)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"huber_losses = np.empty(data_len)\n",
"for ii, (pred, target) in enumerate(zip(preds, targets)):\n",
" huber_losses[ii] = np.mean(huber_loss(pred, target))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tqemo9u6v1Uw0ajoSAAAAEgylDLhE11bna3NNvprah3XgjWbTcQAAAJBgKGXA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\nCmUiIiIiIiIZUigTERERERHJ0P8BQSbQ+OdHZokAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84b9f63950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(15, 7))\n",
"ax.set(xscale=\"log\") #, yscale=\"log\")\n",
"sns.distplot(huber_losses, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0059580647553856315"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(huber_losses)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 42.4 s, sys: 172 ms, total: 42.6 s\n",
"Wall time: 42.4 s\n"
]
}
],
"source": [
"%%time\n",
"dtw_scores = [fastdtw(targets[ind], preds[ind])[0]\n",
" for ind in range(len(targets))]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.141004411253415"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(dtw_scores)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from statsmodels.tsa.stattools import coint"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.94655019010366148, 1.0, array([-4.31395736, -3.55493606, -3.19393252]))"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coint(preds[0], targets[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### plots"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dtzsdjohIwlMyJyIiUgMVQGl6JVddS6hvP3wzXsa74D2nwxERSWhK5kRERGqw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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84607a3290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ind = np.random.randint(len(targets))\n",
"fig = plt.figure(figsize=(15,7))\n",
"plt.plot(range(0,60), inputs[ind].flatten(), 'r')\n",
"plt.plot(range(60, 90), targets[ind].flatten(), 'b')\n",
"plt.plot(range(60, 90), preds[ind].flatten(), 'g')\n",
"plt.legend(['past', 'real future', 'predicted future'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Baseline is static, a straight line for each input (Global - Local norm)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = '../data/price_history'"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#npz_path = '../price_history_03_dp_60to30_from_fixed_len.npz'\n",
"#npz_path = data_path + '/price_history_03_dp_60to30_6400_global_remove_scale_train.npz'\n",
"npz_path = data_path + '/price_history_03_dp_60to30_6400_global_local_remove_scale_train.npz'"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"arr = np.load(npz_path)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['inputs', 'sku_ids', 'sequence_masks', 'targets', 'sequence_lengths']"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.keys()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 60, 1)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inputs = arr['inputs']\n",
"inputs.shape"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30)"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets = arr['targets']\n",
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_len = targets.shape[1]\n",
"target_len"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30, 1)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = np.empty(shape=targets.shape + (1,))\n",
"preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for ii, cur_in in enumerate(inputs):\n",
" #print cur_in.shape\n",
" #print cur_in[-1].shape\n",
" preds[ii] = cur_in[-1] #broadcasting\n",
" #print np.repeat(cur_in[-1], target_len).shape\n",
" #print np.repeat(cur_in[-1:], target_len)\n",
" #print dummy_targets[ii].shape\n",
" #print dummy_targets[ii]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### evaluate"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_len = len(inputs)\n",
"assert len(preds) == len(targets) and data_len == len(targets)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mses = np.empty(data_len)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mses = np.empty(data_len)\n",
"for ii, (pred, target) in enumerate(zip(preds, targets)):\n",
" mses[ii] = mean_squared_error(pred, target)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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P7+3Q5nW11S4TAABgXghoAJaleDSkv/Hp9Xry/na9ffyqfnzwgg7bbh223drU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\nAIAj/ic5A9jgR9TJFwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84604efb50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(15, 7))\n",
"ax.set(xscale=\"log\") #, yscale=\"log\")\n",
"sns.distplot(mses, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### MSE loss"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.64579170804784747"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(mses)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Huber Loss"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def huber_loss(y_true, y_pred):\n",
" err = y_true - y_pred\n",
" \n",
" absolute = np.abs(err)\n",
" \n",
" ifthen = 0.5 * err\n",
" ifelse = absolute - 0.5\n",
" \n",
" return np.where(absolute < 1.0, ifthen, ifelse)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"huber_losses = np.empty(data_len)\n",
"for ii, (pred, target) in enumerate(zip(preds, targets)):\n",
" huber_losses[ii] = np.mean(huber_loss(pred, target))"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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194/oaHOPJKnI7UxtifQqUuJTebFXLspHAABAFiKgAch5RW6naiJB1VxUPjK14r+5rV/N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\n6hH/B6uzvbw+pc+wAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84604ef2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(15, 7))\n",
"ax.set(xscale=\"log\") #, yscale=\"log\")\n",
"sns.distplot(huber_losses, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.22175774117878697"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(huber_losses)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6400, 30)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 42.7 s, sys: 152 ms, total: 42.8 s\n",
"Wall time: 42.6 s\n"
]
}
],
"source": [
"%%time\n",
"dtw_scores = [fastdtw(targets[ind], preds[ind])[0]\n",
" for ind in range(len(targets))]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12.424643240525858"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(dtw_scores)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from statsmodels.tsa.stattools import coint"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/studenthp/anaconda2/envs/dis/lib/python2.7/site-packages/statsmodels/regression/linear_model.py:1386: RuntimeWarning: divide by zero encountered in double_scalars\n",
" return 1 - self.ssr/self.centered_tss\n"
]
},
{
"data": {
"text/plain": [
"(0.15280812391389967,\n",
" 0.98888427891250696,\n",
" array([-4.31395736, -3.55493606, -3.19393252]))"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coint(preds[0], targets[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### plots"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nONLiiGggQQMAIELMXFYO7Y1AzaTT3i0VNERFMuwAAABAmZHLylm5KuwwgGUj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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8464f23390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ind = np.random.randint(len(targets))\n",
"fig = plt.figure(figsize=(15,7))\n",
"plt.plot(range(0,60), inputs[ind].flatten(), 'r')\n",
"plt.plot(range(60, 90), targets[ind].flatten(), 'b')\n",
"plt.plot(range(60, 90), preds[ind].flatten(), 'g')\n",
"plt.legend(['past', 'real future', 'predicted future'])\n",
"plt.show()"
]
},
{
"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": 2
}
| agpl-3.0 |
ngcm/summer-academy-2017-basics | basics_B/ObjectOriented/solutions/Classes for Basics B.ipynb | 1 | 30482 | {
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color='mediumblue'> What are classes?\n",
"\n",
"* A way of organising your code\n",
"* Data is inherently linked to the things you can do with it.\n",
"## Pros\n",
"* Can do everything you can do without classes, but idea is to make it *easier*\n",
"* Classes encourage code reuse through a concept called \"inheritance\" - we will discuss later.\n",
"## Cons\n",
"* Can make your code more complicated, and without careful thinking, harder to maintain.\n",
"* More work for the developer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <font color='mediumblue'> Start by defining some terminology - Classes vs Objects vs Instances\n",
"* Often used interchangably but they are different concepts.\n",
"* A Class is like a template - you could consider the class \"Car\"\n",
"* An object is a particular occurence of a class - so, for example, you could have \"Ford Mondeo\", \"Vauxhall Astra\", \"Lamborghini Gallardo\" be **objects** of type \"Car\".\n",
"* An instance is a unique **single** object.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <font color='mediumblue'> Where are classes used in Python? Everywhere!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You've been using classes all of the time, without even knowing it. Everything in Python is an **object**. You have some **data** (number, text, etc.)with some **methods** (or functions) which are internal to the object, and which you can use on that data. Lets look at a few examples..."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = 10.1"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How can I see what methods an object of type float has?"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['__abs__', '__add__', '__bool__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getformat__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__int__', '__le__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__pos__', '__pow__', '__radd__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rmod__', '__rmul__', '__round__', '__rpow__', '__rsub__', '__rtruediv__', '__setattr__', '__setformat__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', 'as_integer_ratio', 'conjugate', 'fromhex', 'hex', 'imag', 'is_integer', 'real']\n"
]
}
],
"source": [
"print(dir(a)) # Show all of the methods of a"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.is_integer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <font color='midnightblue'> Aside - What do all those underscores mean?\n",
" \n",
"They're *hidden* methods - we'll talk more about these later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='mediumblue'> Creating a class\n",
"\n",
"Define some key things:\n",
"* self - 'self' is a special type of variable which can be used inside the class to refer to itself.\n",
"* Methods - a function which is part of a class, and which have access to data held by a class.\n",
"* A constructor - a special method which is called when you create an instance of a class. In Python this function must be called \"\\_\\_init\\_\\_\"\n",
"* A destructor - a special method which is called when you destroy an instance of a class.\n",
"\n",
"Aside: If you're a C++/Java programmer, 'self' is exactly equivalent to 'this', but functions *must* have self as an argument, as it is passed in implicitly as the first argument of any method call in Python."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a class by using class keyword followed by name.\n",
"class MyClass:\n",
" # The 'self' variable ALWAYS needs to be the first variable given to any class method.\n",
" def __init__(self, message):\n",
" # Here we create a new variable inside \"self\" called \"mess\" and save the argument \"message\"\n",
" # passed from the constructor to it.\n",
" self.mess = message\n",
" \n",
" def say(self):\n",
" print(self.mess)\n",
" \n",
" \n",
" # Don't normally need to write a destructor - one is created by Python automatically. However we do it here\n",
" # just to show you that it can be done:\n",
" def __del__(self):\n",
" print(\"Deleting object of type MyClass\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='mediumblue'> Using the class\n",
"Use the same syntax as we use to call a function, *BUT* the arguments get passed in to the \"\\_\\_init\\_\\_\" function. Note that you *ignore* the self object, as Python sorts this out."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n"
]
}
],
"source": [
"a = MyClass(\"Hello\")\n",
"print(a.mess)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do I access data stored in the class? with the \".\", followed by the name."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n"
]
}
],
"source": [
"# But, we also defined a method called \"say\" which does the same thing:\n",
"a.say()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens though if we reuse the variable name 'a'?\n",
"\n",
"Aside:\n",
"* Your computer has Random Access Memory (RAM) which is used to store information.\n",
"* Whenever, in a programming language, you tell the language to store something, you effectively create a 'box' of memory to put those values in.\n",
"* The location of the specific 'box' is known as a 'memory address'\n",
"* You can see the memory address of a Python object quite easily:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<__main__.MyClass object at 0x115e83e10>\n"
]
}
],
"source": [
"print(a)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, what happens if we either choose to store something else under the name 'a', or tell Python to delte it?"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Deleting object of type MyClass\n",
"Deleting object of type MyClass\n"
]
}
],
"source": [
"del a\n",
"a = MyClass('Hello')\n",
"a = 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Why bother? This can be achieved without classes very easily:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n"
]
}
],
"source": [
"mess = \"Hello\"\n",
"\n",
"def say(mess):\n",
" print(mess)\n",
" \n",
"say(mess)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Need a better example!\n",
"\n",
"How about a Simulation class?\n",
"* Write once, but can take different parameters.\n",
"* Can include data analysis methods as well\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# <font color='mediumblue'> Consider a 1-D box of some length:\n",
"What information does it need to know about itself?\n",
"* How big is the box?"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Box:\n",
" def __init__(self, length):\n",
" self.length = length"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we're going to try and do is add particles to the box, which have some properties:\n",
"* An initial position.\n",
"* An initial velocity\n",
"\n",
" $r(t + \\delta t) \\approx r(t) + v(t)\\delta t$"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Particle:\n",
" def __init__(self, r0, v0):\n",
" \"\"\"\n",
" r0 = initial position\n",
" v0 = initial speed\n",
" \"\"\"\n",
" self.r = r0\n",
" self.v = v0\n",
" \n",
" def step(self, dt, L):\n",
" \"\"\"\n",
" Move the particle\n",
" dt = timestep\n",
" L = length of the containing box\n",
" \"\"\"\n",
" self.r = self.r + self.v * dt\n",
" \n",
" if self.r >= L:\n",
" self.r -= L\n",
" elif self.r < 0:\n",
" self.r += L\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets just check this, if a Particle is in a box of length 10, has r0 = 0, v0=5, then after 1 step of length 3, the position should be at position 5:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"p = Particle(0, 5)\n",
"p.step(3, 10)\n",
"print(p.r)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Lets add a place to store the particles to the box class, and add a method to add particles to the box:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Box:\n",
" def __init__(self, length):\n",
" self.length = length\n",
" self.particles = []\n",
" \n",
" def add_particle(particle):\n",
" self.particles.append(particle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='mediumblue'> Now lets get you to do something...\n",
"\n",
"Tasks (30-40 minutes):\n",
"\n",
"1) Add a method that calculates the average position of Particles in the box (Hint: you might have to think about what to do when there are no particles!)\n",
"\n",
"2) Add a method that makes all of the particles step forwards, and keep track of how much time has passed in the box class.\n",
"\n",
"3) Add a method which plots the current position of the particles in the box.\n",
"\n",
"4) Write a method that writes the current positions and velocities to a CSV file.\n",
"\n",
"5) Write a method that can load a CSV file of positions and velocities, create particles with these and then add them to the Box list of particles. (Hint: Look up the documentation for the module 'csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Box:\n",
" def __init__(self, length):\n",
" self.length = length\n",
" self.particles = []\n",
" self.t = 0\n",
" \n",
" def add_particle(self, particle):\n",
" self.particles.append(particle)\n",
" \n",
" def step(self, dt):\n",
" for particle in self.particles:\n",
" particle.step(dt, self.length)\n",
" \n",
" def write(self, filename):\n",
" f = open(filename, 'w')\n",
" for particle in self.particles:\n",
" f.write('{},{}\\n'.format(particle.r, particle.v))\n",
" f.close()\n",
" \n",
" def plot(self):\n",
" for particle in self.particles:\n",
" plt.scatter(particle.r, 0)\n",
" \n",
" def load(self, filename):\n",
" f = open(filename, 'r')\n",
" csvfile = csv.reader(f)\n",
" for position, velocity in csvfile:\n",
" p = Particle(position, velocity)\n",
" self.add_particle(p)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b = Box(10)\n",
"\n",
"\n",
"for i in range(10):\n",
" p = Particle(i/2, i/3)\n",
" b.add_particle(p)\n",
" \n",
"b.write('test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0,0.0\r\n",
"0.5,0.3333333333333333\r\n",
"1.0,0.6666666666666666\r\n",
"1.5,1.0\r\n",
"2.0,1.3333333333333333\r\n",
"2.5,1.6666666666666667\r\n",
"3.0,2.0\r\n",
"3.5,2.3333333333333335\r\n",
"4.0,2.6666666666666665\r\n",
"4.5,3.0\r\n"
]
}
],
"source": [
"!cat test.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='mediumblue'> Class Properties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Properties can be used to do interesting things\n",
"* Special functions as part of a class that we mark with a 'decorator' - '@property'\n",
"* Lets adjust the class Particle we used to make its data members a property of the class. We also need to write a 'setter' method to set the data members.\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Particle:\n",
" def __init__(self, r0, v0):\n",
" \"\"\"\n",
" r0 = initial position\n",
" v0 = initial speed\n",
" \"\"\"\n",
" self._r = r0\n",
" self._v = v0\n",
" \n",
" def step(self, dt, L):\n",
" \"\"\"\n",
" Move the particle\n",
" dt = timestep\n",
" L = length of the containing box\n",
" \"\"\"\n",
" self._r = self._r + self._v * dt\n",
" \n",
" if self._r >= L:\n",
" self._r -= L\n",
" elif self._r < 0:\n",
" self._r += L\n",
" \n",
" @property\n",
" def r(self):\n",
" return self._r\n",
" \n",
" @r.setter\n",
" def r_setter(self, value):\n",
" self._r = value\n",
" \n",
" @property\n",
" def v(self):\n",
" return self._v\n",
" \n",
" @r.setter\n",
" def r_setter(self, value):\n",
" self._v = value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='midnightblue'> Why bother? It looks the same when we use it!\n",
"\n",
"* Well known in programming - 'an interface is a contract'\n",
"* You might want to at some point rewrite a large portion of the underlying data - how it is stored for example.\n",
"* If you do this without using properties to access the data, you then need to go through all code that uses this class and change it to use the new variable names."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color='mediumblue'> Inheritance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Last part of the course on Classes, but also one of the main reason for using classes!\n",
"* Inheritance allows you to reuse parts of the code, but change some of the methods. Lets see how it might be useful...\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class SlowParticle(Particle):\n",
" def __init__(self, r0, v0, slowing_factor):\n",
" Particle.__init__(self, r0, v0)\n",
" self.factor = slowing_factor\n",
" \n",
" def step(self, dt, L):\n",
" \"\"\"\n",
" Move the particle, but change so that if the particle bounces off of a wall,\n",
" it slows down by 50%\n",
" dt = timestep\n",
" L = length of the containing box\n",
" \"\"\"\n",
" self._r = self._r + self._v * dt\n",
" \n",
" if self._r >= L:\n",
" self._r -= L\n",
" self._v /= factor\n",
" elif self._r < 0:\n",
" self._r += L\n",
" self._v /= factor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Here we have **inherited** most of the class Particle, and just changed the method 'step' to do something differently. Because we kept the properties the same, we can use this class everywhere that we could use Particle - our Box class can take a mixture of Particles and SlowParticles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color='mediumblue'> Magic Methods:\n",
"Remember earlier, when we did:\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['__abs__', '__add__', '__bool__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getformat__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__int__', '__le__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__pos__', '__pow__', '__radd__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rmod__', '__rmul__', '__round__', '__rpow__', '__rsub__', '__rtruediv__', '__setattr__', '__setformat__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', 'as_integer_ratio', 'conjugate', 'fromhex', 'hex', 'imag', 'is_integer', 'real']\n"
]
}
],
"source": [
"a = 1.0\n",
"print(dir(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that there is a method \"\\_\\_add\\_\\_\" - we can define these special methods to allow our class to do things that you can ordinarily do with built in types."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Box:\n",
" def __init__(self, length):\n",
" self.length = length\n",
" self.particles = []\n",
" self.t = 0\n",
" \n",
" def __add__(self, other):\n",
" \n",
" if self.length == other.length:\n",
" b = Box(self.length)\n",
" \n",
" for p in self.particles:\n",
" b.add_particle(p)\n",
" \n",
" for p in other.particles:\n",
" b.add_particle(p)\n",
" \n",
" return b\n",
" else:\n",
" return ValueError('To add two boxes they must be of the same length')\n",
" \n",
" def mean_position(self):\n",
" l = np.sum([p.r for p in self.particles])/len(self.particles)\n",
" return l\n",
" \n",
" def add_particle(self, particle):\n",
" self.particles.append(particle)\n",
" \n",
" def step(self, dt):\n",
" for particle in self.particles:\n",
" particle.step(dt, self.length)\n",
" \n",
" def write(self, filename):\n",
" f = open(filename, 'w')\n",
" for particle in self.particles:\n",
" f.write('{},{}\\n'.format(particle.r, particle.v))\n",
" f.close()\n",
" \n",
" def plot(self):\n",
" for particle in self.particles:\n",
" plt.scatter(particle.r, 0)\n",
" \n",
" def load(self, filename):\n",
" f = open(filename, 'r')\n",
" csvfile = csv.reader(f)\n",
" for position, velocity in csvfile:\n",
" p = Particle(position, velocity)\n",
" self.add_particle(p)\n",
" \n",
" def __repr__(self):\n",
" if len(self.particles) == 1:\n",
" return 'Box containing 1 particle'\n",
" else:\n",
" return 'Box containing {} particles'.format(len(self.particles))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we've created an 'add' method, we can, create two boxes and add these together!"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Box containing 1 particle\n",
"Box containing 1 particle\n",
"Box containing 2 particles\n"
]
}
],
"source": [
"a = Box(10)\n",
"a.add_particle(Particle(10, 10))\n",
"b = Box(10)\n",
"b.add_particle(Particle(15, 10))\n",
"c = a + b\n",
"print(a)\n",
"print(b)\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Looks good! But hang on..."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10.0, 15.0, 12.5)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.mean_position(), b.mean_position(), c.mean_position()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a.step(0.5)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5.0, 15.0, 10.0)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.mean_position(), b.mean_position(), c.mean_position()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why has the mean position of particles in Box C changed? Look at the memory address of the particles:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([<__main__.Particle at 0x115e5a780>],\n",
" [<__main__.Particle at 0x115e5a780>, <__main__.Particle at 0x115e65a90>])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.particles, c.particles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Boxes are pointing to the SAME particles!\n",
"If we don't want this to happen, we need to write a 'copy' constructor for the class - a function which knows how to create an identical copy of the particle! \n",
"\n",
"We can do this by using the 'deepcopy' function in the 'copy' module, and redefine the particle and slow particle classes:"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import copy\n",
"\n",
"class Particle:\n",
" def __init__(self, r0, v0):\n",
" \"\"\"\n",
" r0 = initial position\n",
" v0 = initial speed\n",
" \"\"\"\n",
" self.r = r0\n",
" self.v = v0\n",
" \n",
" def step(self, dt, L):\n",
" \"\"\"\n",
" Move the particle\n",
" dt = timestep\n",
" L = length of the containing box\n",
" \"\"\"\n",
" self.r = self.r + self.v * dt\n",
" \n",
" if self.r >= L:\n",
" self.r -= L\n",
" elif self.r < 0:\n",
" self.r += L\n",
" \n",
" def copy(self):\n",
" return copy.deepcopy(self)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we should change the Box class's '__add__' method, to use this copy operation rather than just append the child particles of the existing boxes:"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Box:\n",
" def __init__(self, length):\n",
" self.length = length\n",
" self.particles = []\n",
" self.t = 0\n",
" \n",
" def __add__(self, other):\n",
" \n",
" if self.length == other.length:\n",
" b = Box(self.length)\n",
" \n",
" for p in self.particles:\n",
" b.add_particle(p)\n",
" \n",
" for p in other.particles:\n",
" b.add_particle(p)\n",
" \n",
" return b\n",
" else:\n",
" return ValueError('To add two boxes they must be of the same length')\n",
" \n",
" def mean_position(self):\n",
" l = np.sum([p.r for p in self.particles])/len(self.particles)\n",
" return l\n",
" \n",
" def add_particle(self, particle):\n",
" self.particles.append(particle.copy())\n",
" \n",
" def step(self, dt):\n",
" for particle in self.particles:\n",
" particle.step(dt, self.length)\n",
" \n",
" def write(self, filename):\n",
" f = open(filename, 'w')\n",
" for particle in self.particles:\n",
" f.write('{},{}\\n'.format(particle.r, particle.v))\n",
" f.close()\n",
" \n",
" def plot(self):\n",
" for particle in self.particles:\n",
" plt.scatter(particle.r, 0)\n",
" \n",
" def load(self, filename):\n",
" f = open(filename, 'r')\n",
" csvfile = csv.reader(f)\n",
" for position, velocity, ptype in csvfile:\n",
" p = Particle(position, velocity)\n",
" self.add_particle(p)\n",
" \n",
" def __repr__(self):\n",
" if len(self.particles) == 1:\n",
" return 'Box containing 1 particle'\n",
" else:\n",
" return 'Box containing {} particles'.format(len(self.particles))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Box containing 1 particle\n",
"Box containing 1 particle\n",
"Box containing 2 particles\n"
]
}
],
"source": [
"a = Box(10)\n",
"a.add_particle(Particle(10, 10))\n",
"b = Box(10)\n",
"b.add_particle(Particle(15, 10))\n",
"c = a + b\n",
"print(a)\n",
"print(b)\n",
"print(c)\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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
SimonBiggs/electronfactors | historical_exploration_and_measurement/measurements/002 Data Analysis-Width_Length.ipynb | 1 | 45630 | {
"metadata": {
"name": "",
"signature": "sha256:156facdbb1069ef1143433267f0ae8461d1e95877488da65a13b173165f6162a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab\n",
"\n",
"import csv\n",
"import pandas as pd\n",
"\n",
"from scipy.interpolate import SmoothBivariateSpline\n",
"from scipy.optimize import minimize\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using matplotlib backend: Qt4Agg\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def single_angle_gap(xTest,yTest,xData,yData,xScale,yScale):\n",
" \n",
" xi = xScale * (xData - xData.min()) / xData.ptp()\n",
" yi = yScale * (yData - yData.min()) / yData.ptp()\n",
" \n",
" xVal = xScale * (xTest - xData.min()) / xData.ptp()\n",
" yVal = yScale * (yTest - yData.min()) / yData.ptp()\n",
" \n",
" dx = xi - xVal\n",
" dy = yi - yVal\n",
" \n",
" if any((dx == 0) & (dy == 0)):\n",
" gap = 0\n",
" return gap\n",
" \n",
" theta = np.arctan(dy/dx)\n",
" theta[dx<0] = theta[dx<0] + np.pi\n",
" theta[(dx>0) & (dy<0)] = theta[(dx>0) & (dy<0)] + 2*np.pi\n",
" theta[(dx==0) & (dy>0)] = np.pi/2\n",
" theta[(dx==0) & (dy<0)] = 3*np.pi/2\n",
"\n",
" test = np.sort(theta)\n",
" test = np.append(test,test[0] + 2*np.pi)\n",
" gap = np.max(np.diff(test))*180/np.pi\n",
"\n",
" return gap\n",
"\n",
"\n",
"def angle_gap(xTest,yTest,xData,yData,xScale,yScale):\n",
" \n",
" dim = np.core.fromnumeric.shape(xTest) \n",
" \n",
" if np.size(dim) == 0:\n",
" gap = single_angle_gap(xTest,yTest,xData,yData,xScale,yScale)\n",
" \n",
" return gap\n",
" \n",
" \n",
" gap = np.zeros(dim)\n",
" \n",
" \n",
" if np.size(dim) == 1:\n",
" for i in range(dim[0]):\n",
" gap[i] = single_angle_gap(xTest[i],yTest[i],xData,yData,xScale,yScale)\n",
" \n",
" return gap\n",
" \n",
" \n",
" for i in range(dim[0]):\n",
" for j in range (dim[1]):\n",
" gap[i,j] = single_angle_gap(xTest[i,j],yTest[i,j],xData,yData,xScale,yScale)\n",
"\n",
" return gap"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def single_fit_give(xTest,yTest,xData,yData,zData,s=None):\n",
" \n",
" initialFitReturn = SmoothBivariateSpline(xData,yData,zData,kx=3,ky=3,s=s).ev(xTest,yTest)\n",
" \n",
" adjXData = np.append(xData,xTest)\n",
" adjYData = np.append(yData,yTest)\n",
" \n",
" posAdjZData = np.append(zData,initialFitReturn + 1)\n",
" negAdjZData = np.append(zData,initialFitReturn - 1)\n",
" \n",
" posFitReturn = SmoothBivariateSpline(adjXData,adjYData,posAdjZData,kx=3,ky=3,s=s).ev(xTest,yTest)\n",
" negFitReturn = SmoothBivariateSpline(adjXData,adjYData,negAdjZData,kx=3,ky=3,s=s).ev(xTest,yTest)\n",
" \n",
" posGive = posFitReturn - initialFitReturn\n",
" negGive = initialFitReturn - negFitReturn\n",
" \n",
" give = np.mean([posGive, negGive])\n",
" \n",
" return give\n",
"\n",
"\n",
"def fit_give(xTest,yTest,xData,yData,zData,s=None):\n",
" \n",
" dim = np.core.fromnumeric.shape(xTest)\n",
" \n",
" if np.size(dim) == 0:\n",
" give = single_fit_give(xTest,yTest,xData,yData,zData,s=s)\n",
" \n",
" return give\n",
" \n",
" \n",
" give = np.zeros(dim)\n",
" \n",
" \n",
" if np.size(dim) == 1:\n",
" for i in range(dim[0]):\n",
" give[i] = single_fit_give(xTest[i],yTest[i],xData,yData,zData,s=s)\n",
" \n",
" return give\n",
" \n",
" \n",
" for i in range(dim[0]):\n",
" for j in range (dim[1]):\n",
" give[i,j] = single_fit_give(xTest[i,j],yTest[i,j],xData,yData,zData,s=s)\n",
"\n",
" return give"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pylab.rcParams['savefig.dpi'] = 130"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Define thresholds\n",
"giveThreshold = 0.25\n",
"gapThreshold = 130"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open('cutout_factors','r') as f:\n",
" \n",
" loaded_factor = eval(f.read())\n",
" \n",
"dictArray = array(list(loaded_factor.items()),float)\n",
"cutoutRef = dictArray[:,0].astype(int)\n",
"ref = argsort(cutoutRef)\n",
"\n",
"factor = dictArray[:,1]\n",
"\n",
"cutoutRef = cutoutRef[ref]\n",
"factor = factor[ref]\n",
"\n",
"\n",
"with open('circle_cutout_factors','r') as f:\n",
" \n",
" loaded_circle_factor = eval(f.read())\n",
" \n",
"circleDictArray = array(list(loaded_circle_factor.items()),float)\n",
"circleDiameter = circleDictArray[:,0]\n",
"circleFactor = circleDictArray[:,1]\n",
"\n",
"factor = append(factor,circleFactor)\n",
"\n",
"\n",
"with open('custom_cutout_factors','r') as f:\n",
" \n",
" loaded_custom_factor = eval(f.read())\n",
"\n",
"customDimensionsDict = {'5x13':[5,13],\n",
" '5x10':[5,10],\n",
" '5x8':[5,8],\n",
" '4x13':[4,13],\n",
" '4x10':[4,10],\n",
" '4x8':[4,8],\n",
" '4x6.5':[4,6.5],\n",
" '3x13':[3,13],\n",
" '3x9':[3,9],\n",
" '3x6.5':[3,6.5],\n",
" '3x5':[3,5]}\n",
"\n",
"customDataLabelList = list(customDimensionsDict.keys())\n",
"\n",
"custom_factors = [loaded_custom_factor[x] for x in customDataLabelList]\n",
"custom_widths = [customDimensionsDict[x][0] for x in customDataLabelList]\n",
"custom_lengths = [customDimensionsDict[x][1] for x in customDataLabelList]\n",
"\n",
"\n",
"factor = append(factor,custom_factors)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# loaded_factor.items()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cutoutRef"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([ 3, 6, 14, 16, 18, 19, 20, 22, 32, 33, 34, 38, 41,\n",
" 43, 57, 58, 70, 73, 82, 83, 104, 106, 109, 112])"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cutout_dimensions = pd.DataFrame.from_csv('new_cutoutdata.csv',index_col =None)\n",
"\n",
"ref = argsort(cutout_dimensions['cutoutRef'].values.astype(int))\n",
"\n",
"width = cutout_dimensions['new_width'].values[ref]\n",
"length = cutout_dimensions['new_length'].values[ref]\n",
"\n",
"weight = concatenate((ones(shape(width)), 1*ones(shape(circleDiameter)), ones(shape(custom_widths))))\n",
"\n",
"\n",
"width = append(width,circleDiameter)\n",
"length = append(length,circleDiameter)\n",
"\n",
"nanFill = empty(shape(circleDiameter))\n",
"nanFill.fill(nan)\n",
"\n",
"cutoutRef = append(cutoutRef,nanFill)\n",
"\n",
"\n",
"width = append(width,custom_widths)\n",
"length = append(length,custom_lengths)\n",
"\n",
"nanFill = empty(shape(custom_widths))\n",
"nanFill.fill(nan)\n",
"\n",
"cutoutRef = append(cutoutRef,nanFill)\n",
"\n",
"\n",
"\n",
"# editRef = cutoutRef == 14\n",
"# width[editRef] = 4.3\n",
"# length[editRef] = 4.6\n",
"\n",
"ratio = width / length"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"circleDiameter"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"array([ 4., 8., 9., 7., 6., 5., 3.])"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scatter(circleDiameter,circleFactor)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.collections.PathCollection at 0x9ca9da0>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scatter(width,ratio)\n",
"title('Data so far')\n",
"xlabel('Width (cm)')\n",
"ylabel('Ratio')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.text.Text at 0x9c8be10>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Spline fitting\n",
"\n",
"extendedWidth = np.append(width,length)\n",
"extendedLength = np.append(length,width)\n",
"extendedFactor = np.append(factor,factor)\n",
"\n",
"splineFit = SmoothBivariateSpline(extendedWidth,extendedLength,extendedFactor,kx=3,ky=3,s=len(width))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create surface mesh\n",
"xVec2 = linspace(width.min(),length.max(),100)\n",
"yVec2 = xVec2\n",
"xMesh2, yMesh2 = meshgrid(xVec2, yVec2)\n",
"\n",
"gapMesh2 = angle_gap(xMesh2,yMesh2,extendedWidth,extendedLength,1,1)\n",
"giveMesh2 = fit_give(xMesh2,yMesh2,extendedWidth,extendedLength,extendedFactor,s=len(width))\n",
"\n",
"zMesh2 = splineFit.ev(xMesh2, yMesh2)\n",
"\n",
"ref2 = (gapMesh2>gapThreshold) | (giveMesh2>giveThreshold)\n",
"zMesh2[ref2] = nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot the fit\n",
"fig = plt.figure()\n",
"\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"bot = np.amin(factor) - 0.04 * np.ptp(factor)\n",
"top = np.amax(factor) + 0.04 * np.ptp(factor)\n",
"\n",
"ax.contour(xMesh2,yMesh2,gapMesh2, offset=bot, levels=[gapThreshold], colors='r',alpha=0.3)\n",
"ax.contour(xMesh2,yMesh2,giveMesh2, offset=bot, levels=[giveThreshold], colors='g',alpha=0.3)\n",
"\n",
"proxyGap, = ax.plot([width.min(),width.min()],[width.min(),width.min()],[bot,bot],'r-',alpha=0.3)\n",
"proxyGive, = ax.plot([width.min(),width.min()],[width.min(),width.min()],[bot,bot],'g-',alpha=0.3)\n",
"\n",
"ax.plot_surface(xMesh2,yMesh2,zMesh2,alpha=0.3,rstride=4,cstride=4)\n",
"sc = ax.scatter(width,length,factor)\n",
"\n",
"ax.set_title('Fit displayed against width and aspect ratio')\n",
"ax.set_xlabel('Width')\n",
"ax.set_ylabel('Length')\n",
"ax.set_zlabel('Factor')\n",
"legend([proxyGap,proxyGive],['Gap threshold', 'Give threshold'],loc=7,prop={'size':8})\n",
"\n",
"# right = ratio.min()-0.01*ratio.ptp()\n",
"# ax.set_ylim(right,1)\n",
"ax.set_zlim(bot,top)\n",
"ax.view_init(elev=10, azim=-80)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Remove data and predict\n",
"predictedFactor = zeros(shape(width))\n",
"predictionAngleGap = zeros(shape(width))\n",
"predictionFitGive = zeros(shape(width))\n",
"\n",
"for i in range(len(width)):\n",
" \n",
" x = width[i]\n",
" y = length[i]\n",
"\n",
" adjWidth = np.delete(width,i)\n",
" adjLength = np.delete(length,i)\n",
" adjFactor = np.delete(factor,i)\n",
"\n",
" \n",
" adjExtendedWidth = np.append(adjWidth,adjLength)\n",
" adjExtendedLength = np.append(adjLength,adjWidth)\n",
" adjExtendedFactor = np.append(adjFactor,adjFactor)\n",
" \n",
" adjSplineFit = SmoothBivariateSpline(adjExtendedWidth,adjExtendedLength,adjExtendedFactor,kx=3,ky=3,s=len(width))\n",
" \n",
" predictedFactor[i] = adjSplineFit.ev(x,y)\n",
" predictionAngleGap[i] = angle_gap(x,y,adjExtendedWidth,adjExtendedLength,1,1)\n",
" predictionFitGive[i] = fit_give(x,y,adjExtendedWidth,adjExtendedLength,adjExtendedFactor,s=len(width))\n",
" \n",
"difference = factor - predictedFactor\n",
"withinThresholds = ((predictionAngleGap<gapThreshold) & \n",
" (predictionFitGive<giveThreshold)\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Display table\n",
"df = pd.DataFrame(transpose([cutoutRef,width, length, factor, predictedFactor, difference,\n",
" predictionAngleGap, predictionFitGive, withinThresholds]))\n",
"df.columns = ['Reference','width', 'length', 'factor', 'Predicted Factor', 'Difference',\n",
" 'Angle Gap', 'Fit Give', 'Within thresholds']\n",
"\n",
"# df.to_csv(path_or_buf = \"cutoutdata.csv\", index=False)\n",
"\n",
"df\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Reference</th>\n",
" <th>width</th>\n",
" <th>length</th>\n",
" <th>factor</th>\n",
" <th>Predicted Factor</th>\n",
" <th>Difference</th>\n",
" <th>Angle Gap</th>\n",
" <th>Fit Give</th>\n",
" <th>Within thresholds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 3</td>\n",
" <td> 4.498726</td>\n",
" <td> 6.077114</td>\n",
" <td> 1.032704</td>\n",
" <td> 1.030048</td>\n",
" <td> 0.002656</td>\n",
" <td> 51.461439</td>\n",
" <td> 0.069369</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 6</td>\n",
" <td> 6.996235</td>\n",
" <td> 10.669437</td>\n",
" <td> 1.001302</td>\n",
" <td> 0.996180</td>\n",
" <td> 0.005122</td>\n",
" <td> 95.966617</td>\n",
" <td> 0.187965</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 14</td>\n",
" <td> 4.388701</td>\n",
" <td> 5.288022</td>\n",
" <td> 1.043027</td>\n",
" <td> 1.036804</td>\n",
" <td> 0.006223</td>\n",
" <td> 52.829822</td>\n",
" <td> 0.066603</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 16</td>\n",
" <td> 6.107799</td>\n",
" <td> 10.515871</td>\n",
" <td> 0.999567</td>\n",
" <td> 1.002836</td>\n",
" <td>-0.003269</td>\n",
" <td> 92.022289</td>\n",
" <td> 0.128917</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 18</td>\n",
" <td> 7.602645</td>\n",
" <td> 10.075798</td>\n",
" <td> 0.998054</td>\n",
" <td> 0.993992</td>\n",
" <td> 0.004063</td>\n",
" <td> 117.627621</td>\n",
" <td> 0.128029</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 19</td>\n",
" <td> 5.799849</td>\n",
" <td> 10.375006</td>\n",
" <td> 1.001089</td>\n",
" <td> 1.005332</td>\n",
" <td>-0.004244</td>\n",
" <td> 82.365561</td>\n",
" <td> 0.120762</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 20</td>\n",
" <td> 4.289047</td>\n",
" <td> 5.112375</td>\n",
" <td> 1.037967</td>\n",
" <td> 1.040530</td>\n",
" <td>-0.002563</td>\n",
" <td> 52.091438</td>\n",
" <td> 0.066564</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 22</td>\n",
" <td> 5.178076</td>\n",
" <td> 10.260920</td>\n",
" <td> 1.011713</td>\n",
" <td> 1.011028</td>\n",
" <td> 0.000685</td>\n",
" <td> 74.092625</td>\n",
" <td> 0.151667</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 32</td>\n",
" <td> 7.715225</td>\n",
" <td> 11.165712</td>\n",
" <td> 0.993330</td>\n",
" <td> 0.998602</td>\n",
" <td>-0.005272</td>\n",
" <td> 195.358093</td>\n",
" <td> 0.662580</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 33</td>\n",
" <td> 5.905678</td>\n",
" <td> 6.209543</td>\n",
" <td> 1.006789</td>\n",
" <td> 1.014063</td>\n",
" <td>-0.007274</td>\n",
" <td> 34.548345</td>\n",
" <td> 0.065907</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 34</td>\n",
" <td> 7.044718</td>\n",
" <td> 9.674088</td>\n",
" <td> 0.993330</td>\n",
" <td> 0.996766</td>\n",
" <td>-0.003435</td>\n",
" <td> 54.775780</td>\n",
" <td> 0.090477</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 38</td>\n",
" <td> 4.677451</td>\n",
" <td> 5.724189</td>\n",
" <td> 1.027190</td>\n",
" <td> 1.030530</td>\n",
" <td>-0.003340</td>\n",
" <td> 48.171075</td>\n",
" <td> 0.067928</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 41</td>\n",
" <td> 6.919052</td>\n",
" <td> 10.011766</td>\n",
" <td> 0.994829</td>\n",
" <td> 0.997451</td>\n",
" <td>-0.002623</td>\n",
" <td> 50.044736</td>\n",
" <td> 0.102311</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 43</td>\n",
" <td> 6.100202</td>\n",
" <td> 7.735733</td>\n",
" <td> 1.007783</td>\n",
" <td> 1.003766</td>\n",
" <td> 0.004016</td>\n",
" <td> 28.903904</td>\n",
" <td> 0.054930</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 57</td>\n",
" <td> 3.574667</td>\n",
" <td> 4.618824</td>\n",
" <td> 1.059704</td>\n",
" <td> 1.053716</td>\n",
" <td> 0.005988</td>\n",
" <td> 104.011875</td>\n",
" <td> 0.119906</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 58</td>\n",
" <td> 5.880419</td>\n",
" <td> 7.454923</td>\n",
" <td> 1.008999</td>\n",
" <td> 1.006675</td>\n",
" <td> 0.002325</td>\n",
" <td> 34.506800</td>\n",
" <td> 0.056605</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 70</td>\n",
" <td> 7.461006</td>\n",
" <td> 8.363232</td>\n",
" <td> 0.997190</td>\n",
" <td> 0.994766</td>\n",
" <td> 0.002425</td>\n",
" <td> 62.339140</td>\n",
" <td> 0.085302</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 73</td>\n",
" <td> 7.294143</td>\n",
" <td> 9.137754</td>\n",
" <td> 0.995472</td>\n",
" <td> 0.995212</td>\n",
" <td> 0.000261</td>\n",
" <td> 76.411926</td>\n",
" <td> 0.089547</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 82</td>\n",
" <td> 6.268781</td>\n",
" <td> 7.974677</td>\n",
" <td> 1.000867</td>\n",
" <td> 1.002230</td>\n",
" <td>-0.001363</td>\n",
" <td> 28.024225</td>\n",
" <td> 0.055379</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 83</td>\n",
" <td> 5.782263</td>\n",
" <td> 7.931109</td>\n",
" <td> 1.010552</td>\n",
" <td> 1.006076</td>\n",
" <td> 0.004476</td>\n",
" <td> 29.433462</td>\n",
" <td> 0.063083</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 104</td>\n",
" <td> 5.658933</td>\n",
" <td> 8.328343</td>\n",
" <td> 1.006601</td>\n",
" <td> 1.006860</td>\n",
" <td>-0.000259</td>\n",
" <td> 31.042310</td>\n",
" <td> 0.074283</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 106</td>\n",
" <td> 6.087204</td>\n",
" <td> 9.708158</td>\n",
" <td> 1.000871</td>\n",
" <td> 1.002277</td>\n",
" <td>-0.001406</td>\n",
" <td> 41.939250</td>\n",
" <td> 0.092832</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 109</td>\n",
" <td> 6.306754</td>\n",
" <td> 7.827168</td>\n",
" <td> 1.006979</td>\n",
" <td> 1.001819</td>\n",
" <td> 0.005160</td>\n",
" <td> 28.094473</td>\n",
" <td> 0.053290</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 112</td>\n",
" <td> 3.538729</td>\n",
" <td> 4.149551</td>\n",
" <td> 1.053904</td>\n",
" <td> 1.059781</td>\n",
" <td>-0.005878</td>\n",
" <td> 122.537399</td>\n",
" <td> 0.110579</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> NaN</td>\n",
" <td> 4.000000</td>\n",
" <td> 4.000000</td>\n",
" <td> 1.045826</td>\n",
" <td> 1.056066</td>\n",
" <td>-0.010240</td>\n",
" <td> 62.963508</td>\n",
" <td> 0.073175</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> NaN</td>\n",
" <td> 8.000000</td>\n",
" <td> 8.000000</td>\n",
" <td> 0.993360</td>\n",
" <td> 0.994497</td>\n",
" <td>-0.001136</td>\n",
" <td> 50.140271</td>\n",
" <td> 0.099087</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> NaN</td>\n",
" <td> 9.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 0.991661</td>\n",
" <td> 0.992499</td>\n",
" <td>-0.000838</td>\n",
" <td> 151.355774</td>\n",
" <td> 0.357004</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> NaN</td>\n",
" <td> 7.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 0.996776</td>\n",
" <td> 1.001437</td>\n",
" <td>-0.004661</td>\n",
" <td> 26.315944</td>\n",
" <td> 0.047837</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> NaN</td>\n",
" <td> 6.000000</td>\n",
" <td> 6.000000</td>\n",
" <td> 1.014436</td>\n",
" <td> 1.013924</td>\n",
" <td> 0.000512</td>\n",
" <td> 30.963757</td>\n",
" <td> 0.067341</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> NaN</td>\n",
" <td> 5.000000</td>\n",
" <td> 5.000000</td>\n",
" <td> 1.030396</td>\n",
" <td> 1.033089</td>\n",
" <td>-0.002693</td>\n",
" <td> 19.440035</td>\n",
" <td> 0.064384</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> NaN</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.075771</td>\n",
" <td> 1.061178</td>\n",
" <td> 0.014593</td>\n",
" <td> 270.000000</td>\n",
" <td> 0.823710</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> NaN</td>\n",
" <td> 4.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 1.029933</td>\n",
" <td> 1.029542</td>\n",
" <td> 0.000391</td>\n",
" <td> 116.565051</td>\n",
" <td> 0.250079</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> NaN</td>\n",
" <td> 4.000000</td>\n",
" <td> 8.000000</td>\n",
" <td> 1.030055</td>\n",
" <td> 1.032026</td>\n",
" <td>-0.001971</td>\n",
" <td> 101.309932</td>\n",
" <td> 0.112994</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> NaN</td>\n",
" <td> 4.000000</td>\n",
" <td> 6.500000</td>\n",
" <td> 1.040668</td>\n",
" <td> 1.035902</td>\n",
" <td> 0.004766</td>\n",
" <td> 68.198591</td>\n",
" <td> 0.086731</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> NaN</td>\n",
" <td> 5.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 1.012277</td>\n",
" <td> 1.013657</td>\n",
" <td>-0.001380</td>\n",
" <td> 56.309932</td>\n",
" <td> 0.159688</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> NaN</td>\n",
" <td> 3.000000</td>\n",
" <td> 13.000000</td>\n",
" <td> 1.049271</td>\n",
" <td> 1.040177</td>\n",
" <td> 0.009094</td>\n",
" <td> 270.000000</td>\n",
" <td> 0.958982</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> NaN</td>\n",
" <td> 3.000000</td>\n",
" <td> 5.000000</td>\n",
" <td> 1.069714</td>\n",
" <td> 1.059151</td>\n",
" <td> 0.010563</td>\n",
" <td> 180.000000</td>\n",
" <td> 0.329955</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> NaN</td>\n",
" <td> 5.000000</td>\n",
" <td> 13.000000</td>\n",
" <td> 1.013374</td>\n",
" <td> 1.005635</td>\n",
" <td> 0.007739</td>\n",
" <td> 214.041258</td>\n",
" <td> 0.690821</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> NaN</td>\n",
" <td> 3.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 1.053516</td>\n",
" <td> 1.054706</td>\n",
" <td>-0.001190</td>\n",
" <td> 180.000000</td>\n",
" <td> 0.710871</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> NaN</td>\n",
" <td> 3.000000</td>\n",
" <td> 6.500000</td>\n",
" <td> 1.054423</td>\n",
" <td> 1.058700</td>\n",
" <td>-0.004277</td>\n",
" <td> 180.000000</td>\n",
" <td> 0.312802</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> NaN</td>\n",
" <td> 4.000000</td>\n",
" <td> 13.000000</td>\n",
" <td> 1.024896</td>\n",
" <td> 1.028091</td>\n",
" <td>-0.003195</td>\n",
" <td> 180.000000</td>\n",
" <td> 0.603961</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> NaN</td>\n",
" <td> 5.000000</td>\n",
" <td> 8.000000</td>\n",
" <td> 1.017153</td>\n",
" <td> 1.015118</td>\n",
" <td> 0.002036</td>\n",
" <td> 36.869898</td>\n",
" <td> 0.080694</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" Reference width length factor Predicted Factor Difference \\\n",
"0 3 4.498726 6.077114 1.032704 1.030048 0.002656 \n",
"1 6 6.996235 10.669437 1.001302 0.996180 0.005122 \n",
"2 14 4.388701 5.288022 1.043027 1.036804 0.006223 \n",
"3 16 6.107799 10.515871 0.999567 1.002836 -0.003269 \n",
"4 18 7.602645 10.075798 0.998054 0.993992 0.004063 \n",
"5 19 5.799849 10.375006 1.001089 1.005332 -0.004244 \n",
"6 20 4.289047 5.112375 1.037967 1.040530 -0.002563 \n",
"7 22 5.178076 10.260920 1.011713 1.011028 0.000685 \n",
"8 32 7.715225 11.165712 0.993330 0.998602 -0.005272 \n",
"9 33 5.905678 6.209543 1.006789 1.014063 -0.007274 \n",
"10 34 7.044718 9.674088 0.993330 0.996766 -0.003435 \n",
"11 38 4.677451 5.724189 1.027190 1.030530 -0.003340 \n",
"12 41 6.919052 10.011766 0.994829 0.997451 -0.002623 \n",
"13 43 6.100202 7.735733 1.007783 1.003766 0.004016 \n",
"14 57 3.574667 4.618824 1.059704 1.053716 0.005988 \n",
"15 58 5.880419 7.454923 1.008999 1.006675 0.002325 \n",
"16 70 7.461006 8.363232 0.997190 0.994766 0.002425 \n",
"17 73 7.294143 9.137754 0.995472 0.995212 0.000261 \n",
"18 82 6.268781 7.974677 1.000867 1.002230 -0.001363 \n",
"19 83 5.782263 7.931109 1.010552 1.006076 0.004476 \n",
"20 104 5.658933 8.328343 1.006601 1.006860 -0.000259 \n",
"21 106 6.087204 9.708158 1.000871 1.002277 -0.001406 \n",
"22 109 6.306754 7.827168 1.006979 1.001819 0.005160 \n",
"23 112 3.538729 4.149551 1.053904 1.059781 -0.005878 \n",
"24 NaN 4.000000 4.000000 1.045826 1.056066 -0.010240 \n",
"25 NaN 8.000000 8.000000 0.993360 0.994497 -0.001136 \n",
"26 NaN 9.000000 9.000000 0.991661 0.992499 -0.000838 \n",
"27 NaN 7.000000 7.000000 0.996776 1.001437 -0.004661 \n",
"28 NaN 6.000000 6.000000 1.014436 1.013924 0.000512 \n",
"29 NaN 5.000000 5.000000 1.030396 1.033089 -0.002693 \n",
"30 NaN 3.000000 3.000000 1.075771 1.061178 0.014593 \n",
"31 NaN 4.000000 10.000000 1.029933 1.029542 0.000391 \n",
"32 NaN 4.000000 8.000000 1.030055 1.032026 -0.001971 \n",
"33 NaN 4.000000 6.500000 1.040668 1.035902 0.004766 \n",
"34 NaN 5.000000 10.000000 1.012277 1.013657 -0.001380 \n",
"35 NaN 3.000000 13.000000 1.049271 1.040177 0.009094 \n",
"36 NaN 3.000000 5.000000 1.069714 1.059151 0.010563 \n",
"37 NaN 5.000000 13.000000 1.013374 1.005635 0.007739 \n",
"38 NaN 3.000000 9.000000 1.053516 1.054706 -0.001190 \n",
"39 NaN 3.000000 6.500000 1.054423 1.058700 -0.004277 \n",
"40 NaN 4.000000 13.000000 1.024896 1.028091 -0.003195 \n",
"41 NaN 5.000000 8.000000 1.017153 1.015118 0.002036 \n",
"\n",
" Angle Gap Fit Give Within thresholds \n",
"0 51.461439 0.069369 1 \n",
"1 95.966617 0.187965 1 \n",
"2 52.829822 0.066603 1 \n",
"3 92.022289 0.128917 1 \n",
"4 117.627621 0.128029 1 \n",
"5 82.365561 0.120762 1 \n",
"6 52.091438 0.066564 1 \n",
"7 74.092625 0.151667 1 \n",
"8 195.358093 0.662580 0 \n",
"9 34.548345 0.065907 1 \n",
"10 54.775780 0.090477 1 \n",
"11 48.171075 0.067928 1 \n",
"12 50.044736 0.102311 1 \n",
"13 28.903904 0.054930 1 \n",
"14 104.011875 0.119906 1 \n",
"15 34.506800 0.056605 1 \n",
"16 62.339140 0.085302 1 \n",
"17 76.411926 0.089547 1 \n",
"18 28.024225 0.055379 1 \n",
"19 29.433462 0.063083 1 \n",
"20 31.042310 0.074283 1 \n",
"21 41.939250 0.092832 1 \n",
"22 28.094473 0.053290 1 \n",
"23 122.537399 0.110579 1 \n",
"24 62.963508 0.073175 1 \n",
"25 50.140271 0.099087 1 \n",
"26 151.355774 0.357004 0 \n",
"27 26.315944 0.047837 1 \n",
"28 30.963757 0.067341 1 \n",
"29 19.440035 0.064384 1 \n",
"30 270.000000 0.823710 0 \n",
"31 116.565051 0.250079 0 \n",
"32 101.309932 0.112994 1 \n",
"33 68.198591 0.086731 1 \n",
"34 56.309932 0.159688 1 \n",
"35 270.000000 0.958982 0 \n",
"36 180.000000 0.329955 0 \n",
"37 214.041258 0.690821 0 \n",
"38 180.000000 0.710871 0 \n",
"39 180.000000 0.312802 0 \n",
"40 180.000000 0.603961 0 \n",
"41 36.869898 0.080694 1 "
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"There are\", sum(withinThresholds), \"points within give and gap thresholds.\"+\n",
" \"\\nThe standard deviation of the prediction differences for these is\", std(difference[withinThresholds]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 32 points within give and gap thresholds.\n",
"The standard deviation of the prediction differences for these is 0.00403869252897\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Histogram\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"n, bins, patches = hist(difference[withinThresholds])\n",
"\n",
"setp(patches, 'facecolor', 'g', 'alpha', 0.5)\n",
"ax.set_xlabel('Prediction differences')\n",
"ax.set_ylabel('Frequency')\n",
"ax.set_title('Histogram of prediction differences')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"<matplotlib.text.Text at 0xa49f1d0>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Visual representation of differences\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"ax.plot_surface(xMesh2,yMesh2,zMesh2,alpha=0.3,rstride=4,cstride=4)\n",
"\n",
"diffColour = copy(difference)\n",
"diffColour[~withinThresholds] = nan\n",
"\n",
"colourRange = nanmax(abs(diffColour))\n",
"\n",
"sc = ax.scatter(width,length,factor,c=diffColour, vmin=-colourRange, vmax=colourRange,s=50)\n",
"colorbar(sc)\n",
"\n",
"ax.set_title('Differences')\n",
"ax.set_xlabel('Width')\n",
"ax.set_ylabel('Length')\n",
"ax.set_zlabel('Factor')\n",
"\n",
"# ax.set_ylim(right,1)\n",
"ax.set_zlim(bot,top)\n",
"\n",
"ax.view_init(elev=10, azim=-80)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"diffTest = abs(difference)\n",
"diffTest[~withinThresholds] = 0\n",
"\n",
"testRef = argmax(diffTest)\n",
"cutoutRef[testRef]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"nan"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"factor[testRef]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"1.0458259008956201"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scatter(width,length)\n",
"scatter(width[testRef],length[testRef],c='red')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<mpl_toolkits.mplot3d.art3d.Patch3DCollection at 0xa588f60>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def to_minimise(cut_increase,ref):\n",
" \n",
" test_width = width[ref] + cut_increase\n",
" test_length = length[ref] + cut_increase\n",
" test_ratio = test_width / test_length\n",
" \n",
" return (factor[ref] - splineFit.ev(test_width,test_length))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"output = minimize(to_minimise,0,args=(testRef,))\n",
"output"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
" fun: -0.028534658212009623\n",
" hess_inv: array([[1]])\n",
" njev: 5\n",
" message: 'Optimization terminated successfully.'\n",
" jac: array([ 0.])\n",
" nfev: 15\n",
" success: True\n",
" status: 0\n",
" x: array([-1.09765455])"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | agpl-3.0 |
georgetown-analytics/yelp-classification | Yelp_web_scrapper/austin_scrapper.ipynb | 1 | 638164 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Yelp Web Scrapper for restaurants in Austin, TX"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"import requests\n",
"import re\n",
"import json\n",
"import scrapping_functions as sf\n",
"# may need pip install urllib3\n",
"import urllib3\n",
"\n",
"urllib3.disable_warnings()\n",
"target_url = 'https://www.yelp.com/search?find_desc=Restaurants&find_loc=Austin+TX&start='\n",
"base = 'http://www.yelp.com'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
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" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n",
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Finished!\n"
]
}
],
"source": [
"#Pull in a list of links from the target url\n",
"link_dict = {}\n",
"for x in range(10, 990, 10):\n",
" target = target_url + str(x)\n",
" raw_html = requests.get(target, verify=False)\n",
" soup = BeautifulSoup(raw_html.text, 'html.parser')\n",
" link_dict = sf.biz_links(soup, link_dict)\n",
"\n",
"print(\"\\nFinished!\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key is: /biz/the-hightower-austin\n",
"key is: /biz/roaring-fork-austin\n",
"key is: /biz/velvet-taco-austin\n",
"key is: /biz/true-food-kitchen-austin-2\n",
"key is: /biz/stella-san-jac-austin-2\n",
"key is: /biz/swifts-attic-austin\n",
"key is: /biz/launderette-austin\n",
"key is: /biz/kismet-cafe-austin-2\n",
"key is: /biz/dee-dee-austin-2\n",
"key is: /biz/austin-taco-project-austin\n",
"key is: /biz/sala-and-betty-austin\n",
"key is: /biz/revelry-kitchen-bar-austin\n",
"key is: /biz/the-pizza-press-austin\n",
"key is: /biz/kula-revolving-sushi-bar-austin\n",
"key is: /biz/second-bar-kitchen-austin-2\n",
"key is: /biz/sway-austin\n",
"key is: /biz/matties-austin-3\n",
"key is: /biz/azul-rooftop-pool-bar-lounge-austin\n",
"key is: /biz/guss-world-famous-fried-chicken-austin\n",
"key is: /biz/barley-swine-austin\n",
"key is: /biz/koriente-austin\n",
"key is: /biz/irenes-austin\n",
"key is: /biz/toulouse-cafe-and-bar-austin-5\n",
"key is: /biz/jack-allens-kitchen-360-austin\n",
"key is: /biz/the-big-kahuna-austin-3\n",
"key is: /biz/jack-allens-kitchen-oak-hill-austin\n",
"key is: /biz/the-factory-cafe-with-a-soul-austin\n",
"key is: /biz/j-t-youngbloods-austin-2\n",
"key is: /biz/royal-jelly-austin\n",
"key is: /biz/baretto-austin\n",
"key is: /biz/poke-poke-austin-3\n",
"key is: /biz/roaring-fork-austin-3\n",
"key is: /biz/tiny-boxwood-austin\n",
"key is: /biz/corner-austin-2\n",
"key is: /biz/eureka-austin\n",
"key is: /biz/kemuri-tatsu-ya-austin-2\n",
"key is: /biz/contigo-austin\n",
"key is: /biz/jersey-giant-pizza-austin-2\n",
"key is: /biz/poke-poke-austin-2\n",
"key is: /biz/french-quarter-grille-austin\n",
"key is: /biz/easy-tiger-austin\n",
"key is: /biz/little-barrel-and-brown-austin\n",
"key is: /biz/crossroads-farm-to-truck-austin-2\n",
"key is: /biz/south-congress-cafe-austin\n",
"key is: /biz/culinary-dropout-austin\n",
"key is: /biz/street-austin\n",
"key is: /biz/coast-bar-and-kitchen-austin\n",
"key is: /biz/23-flavors-and-snacks-austin\n",
"key is: /biz/emmer-and-rye-austin\n",
"key is: /biz/hey-you-gonna-eat-or-what-austin\n",
"key is: /biz/modern-market-eatery-austin\n",
"key is: /biz/red-lotus-asian-grille-austin\n",
"key is: /biz/the-halal-gurus-austin-2\n",
"key is: /biz/forthright-austin-3\n",
"key is: /biz/the-little-darlin-austin\n",
"key is: /biz/central-donut-austin\n",
"key is: /biz/paul-martins-austin-grill-austin-3\n",
"key is: /biz/poke-house-austin\n",
"key is: /biz/javis-best-of-tex-mex-austin-3\n",
"key is: /biz/lebowskis-grill-austin\n",
"key is: /biz/brazas-taco-house-austin\n",
"key is: /biz/bun-belly-austin\n",
"key is: /biz/modern-market-austin-2\n",
"key is: /biz/sip-saam-thai-austin\n",
"key is: /biz/l-estelle-house-austin\n",
"key is: /biz/perlas-austin\n",
"key is: /biz/blue-dahlia-bistro-austin-2\n",
"key is: /biz/bodhi-viet-veggie-cuisine-austin-8\n",
"key is: /biz/grizzeldas-austin\n",
"key is: /biz/delicious-austin\n",
"key is: /biz/yard-house-austin\n",
"key is: /biz/hopdoddy-burger-bar-austin\n",
"key is: /biz/vicecreme-austin\n",
"key is: /biz/the-grove-wine-bar-and-kitchen-austin-6\n",
"key is: /biz/the-bonneville-austin\n",
"key is: /biz/me-con-bistro-austin\n",
"key is: /biz/counter-3-five-vii-austin\n",
"key is: /biz/yalla-burgers-and-wings-austin\n",
"key is: /biz/peached-tortilla-austin\n",
"key is: /biz/taco-joint-austin-4\n",
"key is: /biz/troy-austin\n",
"key is: /biz/slab-bbq-and-beer-austin-3\n",
"key is: /biz/gumbos-on-the-lake-austin\n",
"key is: /biz/lazarus-brewing-austin\n",
"key is: /biz/p%C3%A9ch%C3%A9-austin-3\n",
"key is: /biz/home-slice-pizza-austin\n",
"key is: /biz/parkside-austin\n",
"key is: /biz/pluckers-austin-4\n",
"key is: /biz/chinos-and-gringos-austin\n",
"key is: /biz/flower-child-austin\n",
"key is: /biz/68-degrees-kitchen-austin-2\n",
"key is: /biz/coopers-old-time-pit-bar-b-que-austin\n",
"key is: /biz/bennu-coffee-austin-2\n",
"key is: /biz/per%C3%BA-sabor-and-pasi%C3%B3n-austin-3\n",
"key is: /biz/vans-banh-mi-austin\n",
"key is: /biz/chicon-austin\n",
"key is: /biz/oasis-texas-brewing-company-austin\n",
"key is: /biz/food-fight-cafe-austin-2\n",
"key is: /biz/888-pan-asian-restaurant-austin\n",
"key is: /biz/old-thousand-austin\n",
"key is: /biz/napa-flats-wood-fired-kitchen-austin-austin\n",
"key is: /biz/soursop-austin\n",
"key is: /biz/mad-greens-austin-3\n",
"key is: /biz/olive-and-june-austin-2\n",
"key is: /biz/fixe-austins-southern-house-austin\n",
"key is: /biz/porter-ale-house-and-gastropub-austin\n",
"key is: /biz/fuma%C3%A7a-gaucha-brazilian-steakhouse-austin\n",
"key is: /biz/bd-rileys-irish-pub-austin-7\n",
"key is: /biz/independence-fine-foods-austin\n",
"key is: /biz/cuba-512-austin\n",
"key is: /biz/hanabi-sushi-austin-7\n",
"key is: /biz/sophias-austin\n",
"key is: /biz/panera-bread-austin-11\n",
"key is: /biz/juniper-austin\n",
"key is: /biz/snooze-an-am-eatery-austin-3\n",
"key is: /biz/brentwood-social-house-austin\n",
"key is: /biz/eberly-austin\n",
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"key is: /biz/taverna-austin-2\n",
"key is: /biz/mighty-fine-burgers-fries-and-shakes-austin-5\n",
"key is: /biz/veracruz-all-natural-austin-6\n",
"key is: /biz/the-east-end-on-7th-austin\n",
"key is: /biz/thai-kruefha-austin-6\n",
"key is: /biz/verts-mediterranean-grill-austin-3\n",
"key is: /biz/pho-van-austin\n",
"key is: /biz/verdes-mexican-parrilla-austin\n",
"key is: /biz/funki-chicken-austin\n",
"key is: /biz/chu-mikals-cafe-austin-2\n",
"key is: /biz/rusty-cannon-pub-austin\n",
"key is: /biz/stinsons-bistro-university-park-austin\n",
"key is: /biz/jrs-tacos-austin\n",
"key is: /biz/tam-deli-and-cafe-austin\n",
"key is: /biz/the-league-kitchen-and-tavern-austin\n",
"key is: /biz/little-greek-fresh-grill-austin\n",
"key is: /biz/the-steeping-room-austin-2\n",
"key is: /biz/captain-bennys-seafood-austin\n",
"key is: /biz/the-melting-pot-austin-3\n",
"key is: /biz/kerlin-bbq-austin\n",
"key is: /biz/el-mercado-austin-2\n",
"key is: /biz/stonehouse-wood-fire-grill-austin-3\n",
"key is: /biz/al-sabor-del-chef-austin-2\n",
"key is: /biz/torchys-tacos-austin-6\n",
"key is: /biz/los-pinos-mexican-austin\n",
"key is: /biz/taco-more-austin-2\n",
"key is: /biz/cr%C3%BA-wine-bar-austin-3\n",
"key is: /biz/new-fortune-chinese-seafood-restaurant-austin\n",
"key is: /biz/flores-mexican-restaurant-austin-6\n",
"key is: /biz/casa-chapala-austin-4\n",
"key is: /biz/mad-greens-the-domain-austin\n",
"key is: /biz/tarbouch-lebanese-grill-and-hookah-austin\n",
"key is: /biz/taco-joint-austin-2\n",
"key is: /biz/shanghai-restaurant-austin\n",
"key is: /biz/cover-2-austin\n",
"key is: /biz/fado-irish-pub-and-restaurant-austin-2\n",
"key is: /biz/tomodachi-sushi-austin\n",
"key is: /biz/fire-bowl-cafe-austin-3\n",
"key is: /biz/tuccis-southside-subs-austin\n",
"key is: /biz/biryani-and-co-austin-11\n",
"key is: /biz/the-blind-cafe-austin\n",
"key is: /biz/bar-b-q-heaven-austin\n",
"key is: /biz/patsys-cafe-austin\n",
"key is: /biz/top-notch-austin\n",
"key is: /biz/el-chile-caf%C3%A9-y-cantina-austin-6\n",
"key is: /biz/wright-bros-brew-and-brew-austin\n",
"key is: /biz/biryani-n-grill-austin\n",
"key is: /biz/rudys-country-store-and-bar-b-q-austin-5\n",
"key is: /biz/betos-mexican-restaurant-austin\n",
"key is: /biz/mandolas-italian-austin\n",
"key is: /biz/lucys-fried-chicken-austin-4\n",
"key is: /biz/the-hideout-coffeehouse-austin-2\n",
"key is: /biz/live-oak-market-austin\n",
"key is: /biz/sushi-ocean-austin\n",
"key is: /biz/planet-sub-austin-2\n",
"key is: /biz/maxs-wine-dive-austin\n",
"key is: /biz/fresh-donuts-austin\n",
"key is: /biz/torchys-tacos-austin-7\n",
"key is: /biz/ramos-restaurant-austin-3\n",
"key is: /biz/torchys-tacos-austin-13\n",
"key is: /biz/naus-enfield-drug-austin\n",
"key is: /biz/chicken-express-austin\n",
"key is: /biz/tejis-indian-restaurant-and-grocery-austin-3\n",
"key is: /biz/taqueria-la-chiapaneca-austin\n",
"key is: /biz/kogi-q-austin\n",
"key is: /biz/casa-maria-austin\n",
"key is: /biz/grannys-tacos-austin\n",
"key is: /biz/kens-subs-tacos-and-more-austin\n",
"key is: /biz/ricos-tacos-austin\n",
"key is: /biz/syriano-shawarma-austin-2\n",
"key is: /biz/mooyah-burgers-fries-and-shakes-austin\n",
"key is: /biz/biryani-pot-austin\n",
"key is: /biz/brooklyn-pie-austin-2\n",
"key is: /biz/subs-n-more-austin\n",
"key is: /biz/dans-hamburgers-austin-2\n",
"key is: /biz/jims-restaurant-austin-8\n",
"key is: /biz/keiths-bbq-austin-2\n",
"key is: /biz/tacodeli-west-lake-hills\n",
"key is: /biz/monkey-nest-coffee-austin-2\n",
"key is: /biz/sullivans-steakhouse-austin\n",
"key is: /biz/sandys-hamburgers-austin\n",
"key is: /biz/el-super-taco-austin\n",
"key is: /biz/ned-granger-cafe-austin\n",
"key is: /biz/mission-dogs-austin\n",
"key is: /biz/davods-mediterranean-market-and-restaurant-austin\n",
"key is: /biz/saigon-le-vendeur-austin\n",
"key is: /biz/pho-dan-austin-3\n",
"key is: /biz/mangia-chicago-pizza-style-austin\n",
"key is: /biz/tex-mex-joes-austin\n",
"key is: /biz/the-park-on-south-lamar-austin-3\n",
"key is: /biz/the-dogwood-austin-5\n",
"key is: /biz/blaze-fast-fired-pizza-austin\n",
"key is: /biz/shiners-saloon-austin\n",
"key is: /biz/el-buen-mercado-austin-2\n",
"key is: /biz/le-muse-coffee-and-wine-bar-dripping-springs\n",
"key is: /biz/yummi-tacos-austin\n",
"key is: /biz/gordos-tortas-and-bbq-austin\n",
"key is: /biz/thai-taste-austin\n",
"key is: /biz/snap-kitchen-austin-2\n",
"key is: /biz/tacos-guerrero-austin\n",
"key is: /biz/la-traila-at-mesa-austin-3\n",
"key is: /biz/las-tocallitas-austin\n",
"key is: /biz/xian-sushi-and-noodle-austin-5\n",
"key is: /biz/pour-house-pub-austin\n",
"key is: /biz/soco-burgers-austin\n",
"key is: /biz/kebabalicious-austin-2\n",
"key is: /biz/posse-east-austin\n",
"\n",
"Finished writing to file: cleanbiz_austin_links.txt\n"
]
}
],
"source": [
"#Write all links to a text file\n",
"biz_links = open('cleanbiz_austin_links.txt', 'w')\n",
"for item in link_dict.keys():\n",
" if \"adredir\" in item: \n",
" continue\n",
" print(\"key is: \" + item)\n",
" biz_links.write(\"%s\\n\" % item)\n",
"biz_links.close()\n",
"print(\"\\nFinished writing to file: cleanbiz_austin_links.txt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We would like to construct the following dictionary:\n",
"\n",
"biz_dict = {biz_name: {\"city\": \"Washington\", \"state\": \"DC\", \"category_aliases\": \"newamerican,breakfast_brunch\", \"biz_id\": \"wO-7cBBOYUdiLflpuRsu9A\", \"latitude\": 38.90842, \"biz_name\": \"The Bird\", \"city_state\": \"Washington, DC\", \"longitude\": -77.026685, \"geoquad\": 12845454}}\n",
"\n",
"unique_id = 5D32F13B349CE2AD\n",
"\n",
"#### Algorithm design:\n",
"\n",
"For each link in biz_links:\n",
" > set biz_name = replace(\"_\", '-' in link)\n",
" > find the unique_id\n",
" > assign biz_dict[biz_name] = dictionary(biz_name)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"link_file = open(\"cleanbiz_austin_links.txt\", \"r\")\n",
"link_list = link_file.read().split('\\n')\n",
"link_list = list(set(link_list))\n",
"for link in link_list:\n",
" if link == '':\n",
" link_list.pop(link_list.index(link))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/183-grill-austin-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/gravy-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/forthright-austin-3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pinthouse-pizza-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/halcyon-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/naus-enfield-drug-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/house-pizzeria-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tex-mex-joes-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/trulucks-seafood-steak-and-crab-house-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/verts-mediterranean-grill-austin-5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/torchys-tacos-austin-13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tacos-guerrero-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hopdoddy-burger-bar-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/patrizis-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pecos-tacos-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/ho-ho-chinese-bbq-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lavaca-teppan-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/look-noodles-and-more-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/donns-bbq-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sarahs-mediterranean-grill-and-market-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pharas-mediterranean-cuisine-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/miltos-austin-5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/almarah-mediterranean-cuisine-austin-4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/scoreboard-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/knotty-deck-and-bar-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/conscious-cravings-austin-14\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/irenes-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/shogun-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cafe-605-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/boiler-nine-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/austin-daily-press-austin-4\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cheap-date-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/juniper-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cafe-blue-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/torchys-tacos-austin-6\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/me-con-bistro-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/santorini-cafe-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/first-wok-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-alma-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/guss-world-famous-fried-chicken-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stephen-fs-bar-and-terrace-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tacodeli-austin-4\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/justines-brasserie-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/boat-house-grill-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-county-line-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lenoir-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tillery-kitchen-and-bar-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/driskill-grill-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/baton-creole-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/biryani-pot-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/osteria-pronto-austin\n"
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/hula-hut-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/vespaio-ristorante-austin-6\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/halal-bros-austin-3\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/new-fortune-chinese-seafood-restaurant-austin\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/gyros-gr-authentic-greek-food-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/brick-oven-on-35th-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-pollo-regio-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/saigon-cafe-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chuys-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/street-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/unit-d-pizzeria-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/nasha-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/smart-flour-foods-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hi-hat-public-house-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/deckhand-oyster-bar-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/first-chinese-barbecue-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/epicerie-cafe-and-grocery-austin-4\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/diy-thai-food-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hopdoddy-burger-bar-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/thai-kruefha-austin-6\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mod-pizza-austin-6\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/biryani-n-grill-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/il-forte-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/squarerut-kava-bar-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/la-bodega-gourmet-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lox-box-and-barrel-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/verts-mediterranean-grill-austin-2\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/mighty-fine-burgers-fries-and-shakes-austin-5\n"
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"output_type": "stream",
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"output_type": "stream",
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"output_type": "stream",
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"http://www.yelp.com/biz/the-park-on-south-lamar-austin-3\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/i-fratelli-pizza-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/techo-mezcaleria-and-agave-bar-austin-2\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/more-home-slice-pizza-austin-3\n"
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"text": [
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"text": [
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"text": [
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"text": [
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"text": [
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"text": [
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"output_type": "stream",
"text": [
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"text": [
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"output_type": "stream",
"text": [
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"text": [
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"output_type": "stream",
"text": [
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"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"text": [
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"http://www.yelp.com/biz/the-hideout-coffeehouse-austin-2\n"
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"output_type": "stream",
"text": [
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"text": [
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"text": [
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"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/azul-rooftop-pool-bar-lounge-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/galaxy-cafe-austin-8\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/trio-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/casa-vallarta-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/aroi-thai-cuisine-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/halal-bros-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-pizza-press-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/caf%C3%A9-java-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bombay-dhaba-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/andiamo-ristorante-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/junes-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/perrys-steakhouse-and-grille-downtown-austin-austin\n"
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"name": "stderr",
"output_type": "stream",
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"output_type": "stream",
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"http://www.yelp.com/biz/rudys-country-store-and-bar-b-q-austin-6\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"http://www.yelp.com/biz/verts-mediterranean-grill-austin-3\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/the-steeping-room-austin-2\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/p%C3%A9ch%C3%A9-austin-3\n"
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"output_type": "stream",
"text": [
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/shiners-saloon-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/k-ts-snack-bar-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/isalias-tex-mex-restaurant-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/its-italian-market-and-cafe-austin-3\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/true-food-kitchen-austin-2\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/cannon-belle-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/arirang-korean-restaurant-austin-2\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/julies-handmade-noodles-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mandolas-italian-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/clarks-oyster-bar-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/la-traila-at-mesa-austin-3\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/papa-donkasu-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/elotes-fanny-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tomodachi-sushi-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/biscuits-groovy-south-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/jersey-giant-pizza-austin-2\n"
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mod-pizza-austin-2\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chens-noodle-house-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/ceviche7-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/flores-mexican-restaurant-austin-6\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-factory-cafe-with-a-soul-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stinsons-austin-3\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/neworldeli-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cipollina-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/supper-friends-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/royal-jelly-austin\n"
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"output_type": "stream",
"text": [
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/s-h-donuts-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/altas-cafe-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/flemings-prime-steakhouse-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/caf%C3%A9-no-s%C3%A9-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
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"http://www.yelp.com/biz/dans-hamburgers-austin-austin\n"
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"output_type": "stream",
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"output_type": "stream",
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"http://www.yelp.com/biz/the-limestone-kitchen-restaurant-and-lounge-austin\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/sushi-junai-2-austin\n"
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"http://www.yelp.com/biz/julies-noodles-austin\n"
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" InsecureRequestWarning)\n"
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"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"output_type": "stream",
"text": [
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"text": [
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"text": [
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"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/mangieris-pizza-cafe-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/graj-mahal-cafe-and-lounge-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"text": [
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/the-best-wurst-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-super-taco-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/toaster-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/jinya-ramen-bar-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/austins-habibi-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pinch-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/taco-more-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/winflo-osteria-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/budares-venezuelan-food-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/eden-east-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/xiang-yun-tea-room-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/waller-creek-pub-house-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/vicecreme-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/gordos-tortas-and-bbq-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tuccis-southside-subs-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-peached-tortilla-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/shahi-caf%C3%A8-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/poke-house-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/first-watch-breakfast-brunch-and-lunch-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/barlata-tapas-bar-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"http://www.yelp.com/biz/cedro-austin\n"
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"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/el-chile-caf%C3%A9-y-cantina-austin-6\n"
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"text": [
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/tacos-las-amazonas-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/elaines-at-eastside-cafe-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/flyrite-chicken-austin-3\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/rusty-cannon-pub-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sullivans-steakhouse-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-regio-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mothers-cafe-and-garden-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/coast-bar-and-kitchen-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"text": [
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"text": [
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"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/the-east-end-on-7th-austin\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/the-backspace-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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"text": [
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"http://www.yelp.com/biz/biscuits-and-groovy-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-halal-gurus-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/ronnies-real-food-bistro-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pho-phi-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-beer-plant-austin\n"
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"output_type": "stream",
"text": [
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"text": [
"http://www.yelp.com/biz/lamberts-downtown-barbecue-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/snooze-an-am-eatery-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/little-sheep-mongolian-hot-pot-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/kemuri-tatsu-ya-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/la-condesa-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-highball-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-oasis-austin-12\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/jims-restaurant-austin-8\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hopfields-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/ky%C5%8Dten-sushiko-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/twin-lion-chinese-restaurant-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/h12-outdoor-cafe-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mr-natural-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tarka-indian-kitchen-austin-2\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"text": [
"http://www.yelp.com/biz/dock-and-roll-diner-austin-3\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/lucys-fried-chicken-austin-4\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/torchys-tacos-austin-17\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/stuffed-cajun-meat-market-austin-2\n"
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"output_type": "stream",
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/luckys-puccias-and-pizzeria-austin\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/satellite-eat-drink-orbit-austin-5\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/saigon-le-vendeur-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/the-grove-wine-bar-and-kitchen-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/song-la-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bombay-express-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/soco-burgers-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/local-slice-pizza-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sundance-bbq-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sala-and-betty-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/azul-tequila-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/eurasia-sushi-bar-and-seafood-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/michi-ramen-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/way-south-philly-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/chilantro-austin-10\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/lucys-fried-chicken-austin-5\n"
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"http://www.yelp.com/biz/patika-austin\n"
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"http://www.yelp.com/biz/marakesh-cafe-and-grill-austin-3\n"
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"http://www.yelp.com/biz/godavari-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tony-cs-coal-fired-pizza-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/agazajos-flying-pizza-and-italian-restaurant-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/los-jaliscienses-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/titayas-thai-cuisine-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/estancia-churrascaria-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tejis-indian-restaurant-and-grocery-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-grove-wine-bar-and-kitchen-austin-6\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/grannys-tacos-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pacha-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/galaxy-cafe-austin-4\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/maudies-milagro-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/vinaigrette-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sophias-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/salata-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/inka-chicken-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-tacorrido-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/fonda-san-miguel-austin-7\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/uchiko-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/taverna-austin-2\n"
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"text": [
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"text": [
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"http://www.yelp.com/biz/biryani-and-co-austin-11\n"
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"http://www.yelp.com/biz/hunan-riverplace-austin\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/mai-thai-restaurant-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/caf%C3%A9-cr%C3%A8me-austin\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/kens-subs-tacos-and-more-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/tarka-indian-kitchen-austin-5\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/1776-cheesesteak-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/shady-grove-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/ski-shores-cafe-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/revelry-kitchen-bar-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/thai-kun-at-whislers-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/galaxy-cafe-south-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/marker-10-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/little-deli-and-pizzeria-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/michi-ramen-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/p-terrys-burger-stand-austin-3\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/fricanos-deli-austin-15\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/rositas-al-pastor-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/verdes-mexican-parrilla-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mia-italian-tapas-and-bar-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/vino-volo-austin-2\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/tarka-indian-kitchen-austin-4\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/abels-on-the-lake-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
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"output_type": "stream",
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"http://www.yelp.com/biz/el-mercado-austin-2\n"
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"output_type": "stream",
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"output_type": "stream",
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"output_type": "stream",
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"http://www.yelp.com/biz/red-wraps-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/napa-flats-wood-fired-kitchen-austin-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/delicious-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/austin-ale-house-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/las-trancas-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/roccos-grill-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/rolands-soul-food-and-fish-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stonehouse-wood-fire-grill-austin-3\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/district-kitchen-and-cocktails-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tam-deli-and-cafe-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/snap-kitchen-austin-4\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chen-z-noodle-house-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"http://www.yelp.com/biz/bennu-coffee-austin-2\n"
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"output_type": "stream",
"text": [
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"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/second-bar-kitchen-austin-3\n"
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"text": [
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"text": [
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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"text": [
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"http://www.yelp.com/biz/xian-sushi-and-noodle-austin-5\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/bazille-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/buenos-aires-caf%C3%A9-este-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cuban-sandwich-cafe-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/las-cazuelas-mexican-restaurant-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/kome-sushi-kitchen-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cafe-nenai-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/micklethwait-craft-meats-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/waltons-fancy-and-staple-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bombay-bistro-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/counter-cafe-east-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/waffle-house-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/threadgills-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/kerbey-lane-cafe-austin-3\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/little-barrel-and-brown-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/food-fight-cafe-austin-2\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/betos-mexican-restaurant-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-buzz-mill-austin-5\n"
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/tikiyaki-hawaiian-grill-austin\n"
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"output_type": "stream",
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"output_type": "stream",
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pappasitos-cantina-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/coopers-old-time-pit-bar-b-que-austin\n"
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/magnolia-cafe-austin\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/hopdoddy-burger-bar-austin-4\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/bobs-steak-and-chop-house-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/whip-in-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stella-san-jac-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/patsys-cafe-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/vino-vino-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sarku-japan-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/grizzeldas-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/college-roadhouse-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/gourdoughs-public-house-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/terry-blacks-barbecue-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-soup-peddler-real-food-and-juice-bar-austin-4\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/%C3%B1o%C3%B1os-tacos-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/shanghai-restaurant-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cannone-cucina-italiana-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/south-austin-trailer-park-and-eatery-austin\n"
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/taqueria-morelos-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/way-south-philly-deli-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/aimees-super-fantazmo-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/new-awlins-cafe-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/fogo-de-ch%C3%A3o-brazilian-steakhouse-austin-4\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lazarus-brewing-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/crepe-crazy-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tropicana-cuban-restaurant-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/foodheads-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bistro-vonish-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/dee-dee-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/gumbos-on-the-lake-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/bd-rileys-irish-pub-austin-7\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/paperboy-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/toss-pizzeria-and-pub-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/casa-garcias-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/red-lotus-asian-grille-austin\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/dock-and-roll-diner-austin-2\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/in-the-coconut-austin-3\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/reds-porch-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/the-halal-corner-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/parkside-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/leroy-and-lewis-barbecue-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/copper-restaurant-and-dessert-lounge-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/piranha-killer-sushi-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chu-mikals-cafe-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bufalina-due-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/baretto-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mooyah-burgers-fries-and-shakes-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/swifts-attic-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chicken-lollypop-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stinsons-bistro-university-park-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/visconti-ristorante-and-bar-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/habanero-mexican-cafe-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/otoko-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stiles-switch-bbq-and-brew-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hat-creek-burger-company-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/kesos-taco-house-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-chilito-austin-8\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pommes-frites-etc-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/yard-house-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/kismet-cafe-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tuccis-southside-subs-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/modern-market-eatery-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/yalla-burgers-and-wings-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/jennas-asian-kitchen-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/texas-chili-parlor-austin\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hoboken-pie-austin\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/barley-swine-austin\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/picnik-austin-austin\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/arpeggio-grill-austin\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/habesha-ethiopian-restaurant-and-bar-austin-2\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/noble-sandwich-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/the-dogwood-austin-5\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/matties-austin-3\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/jack-allens-kitchen-360-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/french-quarter-grille-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lukes-inside-out-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tacos-el-chilango-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tamale-house-east-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mi-cocina-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lebowskis-grill-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/stanleys-farmhouse-pizza-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tacos-and-tequila-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/umi-sushi-bar-and-grill-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/new-india-cuisine-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/caf%C3%A9-cr%C3%AApe-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bangers-sausage-house-and-beer-garden-austin\n"
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{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bullfight-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/syriano-shawarma-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pacific-rim-sushi-and-yakitori-lounge-austin-3\n"
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"name": "stderr",
"output_type": "stream",
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hey-you-gonna-eat-or-what-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/blue-dahlia-bistro-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/goya-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/honduras-food-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/korean-komfort-food-trailer-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/trace-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/annies-cafe-and-bar-austin-5\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/davods-mediterranean-market-and-restaurant-austin\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bufalina-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/thistle-cafe-austin-4\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/via-313-pizza-austin-3\n"
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"output_type": "stream",
"text": [
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"text": [
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"output_type": "stream",
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"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/captain-bennys-seafood-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"text": [
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"text": [
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"text": [
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"http://www.yelp.com/biz/jacobys-restaurant-and-mercantile-austin-2\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"http://www.yelp.com/biz/ztejas-mexican-restaurant-and-grill-austin\n"
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"http://www.yelp.com/biz/dans-hamburgers-austin-2\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/osio-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"text": [
"http://www.yelp.com/biz/mod-pizza-austin-7\n"
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"output_type": "stream",
"text": [
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"name": "stderr",
"output_type": "stream",
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/paul-martins-austin-grill-austin-3\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/haymaker-austin\n"
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"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cabo-bobs-burritos-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/jims-restaurants-29-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/little-greek-fresh-grill-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/el-naranjo-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"http://www.yelp.com/biz/blaze-fast-fired-pizza-austin\n"
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/arlos-austin\n"
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"http://www.yelp.com/biz/im-thai-cuisine-austin-2\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"http://www.yelp.com/biz/east-side-king-austin-15\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/de-la-terre-supper-club-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/caspian-grill-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/le-muse-coffee-and-wine-bar-dripping-springs\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/vic-and-als-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/rudys-country-store-and-bar-b-q-austin-5\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/numero28-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/taqueria-los-altos-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/green-lunch-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/counter-3-five-vii-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/panera-bread-austin-11\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cafe-josie-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/olamaie-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/hanabi-ramen-and-kushiyaki-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bombay-bistro-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
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"http://www.yelp.com/biz/vox-table-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"output_type": "stream",
"text": [
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"output_type": "stream",
"text": [
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"output_type": "stream",
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"name": "stdout",
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"http://www.yelp.com/biz/el-buen-mercado-austin-2\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"text": [
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/wright-bros-brew-and-brew-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"http://www.yelp.com/biz/lonesome-dove-western-bistro-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/aviator-pizza-and-drafthouse-austin\n"
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"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/la-pena-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/browns-bar-b-que-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/yummi-tacos-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/fire-bowl-cafe-austin-3\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/fado-irish-pub-and-restaurant-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/portobello-mediterranean-and-brazilian-grill-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/cover-3-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/taqueria-la-chiapaneca-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/mission-dogs-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/dolce-vita-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/school-house-pub-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/central-donut-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-meson-austin\n"
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},
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/con-madre-kitchen-austin-2\n"
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pho-saigon-noodle-house-austin-2\n"
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{
"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/al-sabor-del-chef-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/second-bar-kitchen-austin-2\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/g-bar-and-bistro-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tokyo-sushi-japanese-restaurant-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
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" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/flour-and-vine-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/chosun-korean-bbq-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"output_type": "stream",
"text": [
"http://www.yelp.com/biz/827rays-kitchen-cellar-austin\n"
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"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/taco-more-austin-2\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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"name": "stdout",
"output_type": "stream",
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"http://www.yelp.com/biz/charm-korean-bbq-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/360-uno-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/asters-ethiopian-restaurant-austin\n"
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"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/lucys-fried-chicken-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-hot-box-diner-austin-17\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/el-secreto-de-abuela-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/sundaze-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/russells-bakery-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/slab-bbq-and-beer-austin-3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/the-little-darlin-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/68-degrees-kitchen-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/workhorse-bar-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/madam-mams-thai-cuisine-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/juan-in-a-million-restaurant-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/north-side-trattoria-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/monkey-nest-coffee-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/wild-burgers-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/blue-dahlia-bistro-austin-2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/nubian-queen-lolas-cajun-soul-food-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/nates-baked-goods-and-coffee-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/pho-van-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/tonys-jamaican-food-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/yellow-jacket-social-club-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/abo-youssef-austin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\835861\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\requests\\packages\\urllib3\\connectionpool.py:843: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.yelp.com/biz/bombay-to-kathmandu-kitchen-austin\n",
"\n",
"Finished\n"
]
}
],
"source": [
"biz_dict = {}\n",
"\n",
"for biz_name in link_list:\n",
" biz_dict[biz_name] = {}\n",
" raw_html = requests.get(base + biz_name, verify=False)\n",
" print(base + biz_name)\n",
" soup = BeautifulSoup(raw_html.text, 'html.parser')\n",
" biz_dict[biz_name] = json.loads(soup.find('script', type='application/ld+json').text)\n",
" \n",
"print(\"\\nFinished\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished\n"
]
}
],
"source": [
"#Output JSON file of all the review details\n",
"with open('austin_reviews.json', 'w') as outfile:\n",
" json.dump(biz_dict, outfile)\n",
"print(\"Finished\")"
]
},
{
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
amniskin/amniskin.github.io | assets/notebooks/associated_code.ipynb | 4 | 119373 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pylab as plb\n",
"import seaborn\n",
"import pandas as pd\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(1234)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.stats import norm"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kGo2GxMT7pKdn6DWdeLFMTU2wtbWQujFCUjcvhqIo/PnnEb7/fjsHD/5GZOSf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PrNN98U65KZwIvJmF+fIFgkEsHOzk5xNRciInq3UaNG4eTJk2+c75aIVMfQ0BDHjh1TmiqLSpdg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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0de8b015f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"x = np.linspace(norm.ppf(0.001),\n",
" norm.ppf(0.999), 100)\n",
"rv = norm()\n",
"yp = rv.pdf(x)\n",
"yc = rv.cdf(x)\n",
"ax = fig.add_subplot(211)\n",
"ax.plot(x, yp, 'k-', lw=2, label='frozen pdf')\n",
"ax.plot((1,1),(0,rv.pdf(1)))\n",
"plt.title(\"Probability Density Function (for a Normal random variable)\")\n",
"plt.annotate(\"(1 , \" + str(\"{0:.2f}\".format(rv.pdf(1))) + \")\", xy=(1, rv.pdf(1)), xytext=(1.2, rv.pdf(1)))\n",
"plt.fill_between(x, yp, where=[num <= 1 for num in x])\n",
"plt.xlim(-3,3)\n",
"ax = fig.add_subplot(212)\n",
"ax.plot(x, rv.cdf(x), 'k-', lw=2, label='frozen pdf')\n",
"ax.plot((1,1),(0,rv.cdf(1)))\n",
"plt.annotate(\"(1 , \" + str(\"{0:.2f}\".format(rv.cdf(1))) + \")\", xy=(1, rv.cdf(1)), xytext=(1.2, rv.cdf(1) - 0.1))\n",
"plt.xlim(-3,3)\n",
"plt.title(\"Cumulative Distribution Function (for a Normal random variable)\")\n",
"plb.savefig(\"pics/2017/02/18-probability-a-measure-theoretic-approach_pdf-cdf1.png\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.24197072451914337"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rv.pdf(1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lr = LinearRegression(fit_intercept=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dats_x_range = [x*0.2 for x in range(20)]*5 + np.random.uniform(high=0.1, low=-0.1, size=100)\n",
"dats = [dats_x_range, [x*5 for x in dats_x_range] + np.random.normal(loc=0.0, scale=2, size=100)]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ad2D3OgAAAGxH6AQAAIDtCJ0AAACwHaETAAAAtiN0AgAAwHaETgAAANiOlknI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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f92fede99e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(dats[0], dats[1])\n",
"plb.savefig('pics/2017/02/21-linear-regression_scatterPoints.png')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LinearRegression(fit_intercept=True)\n",
"lr.fit([[x] for x in dats[0]], dats[1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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V0ndXRzz+wK4PZRREr3wCQCpxTycAZJDQYqPOC1S36kEZESE0pGF/M4ETgG0Q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\nB3oxhNqiOntpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f92fb879908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(dats[0], dats[1])\n",
"plt.plot(dats[0], lr.intercept_ + lr.coef_*dats[0], c='r')\n",
"plb.savefig('pics/2017/02/21-linear-regression_scatterPoints_withLine.png')\n",
"plt.show()"
]
},
{
"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
}
| mit |
DavidBrear/sklearn-cookbook | Chapter 2/2.9 Using Boosting to Learn from Errors.ipynb | 1 | 39287 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Gradient Boosting Regression learns from mistakes. It tries\n",
"# to fit a bunch of weak learners\n",
"# -> Individually, each learner has poor accuracy but together\n",
"# they have good accuracy.\n",
"# -> They're applied sequentially meaning that each learner\n",
"# becomes an expert in the mistakes of the prior learner."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import make_regression\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X, y = make_regression(1000, 2, noise=10)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import GradientBoostingRegressor as GBR"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
" max_depth=3, max_features=None, max_leaf_nodes=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" random_state=None, subsample=1.0, verbose=0, warm_start=False)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gbr = GBR()\n",
"gbr.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gbr_preds = gbr.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ -83.85399942, 21.77255753, -114.42847686, -27.64663971,\n",
" -59.6504926 ])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gbr_preds[:5]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"82.357155799315549"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(np.power(y - gbr_preds, 2))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LinearRegression()\n",
"lr.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lr_preds = lr.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# see how GBR performs vs Linear Regression\n",
"gbr_residuals = y - gbr_preds\n",
"lr_residuals = y - lr_preds"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1075211d0>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OS3+I546daIrZiQ0NZ+68siMVk9rIEJc0L42OS/8OTwCXDzZXes2qDlRJajub0yVJKpQh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\niEuSVChDXJKkQv1/ZgtHBsusN00AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107497850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(7,5))\n",
"f.tight_layout()\n",
"ax.hist(gbr_residuals, label='GBR', alpha=.5, color='r', bins=30)\n",
"ax.hist(lr_residuals, label='LR', alpha=.5, color='b', bins=30)\n",
"\n",
"ax.set_title(\"GBR vs LR\")\n",
"ax.legend(loc='best')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-18.03681615, 17.74545291])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.percentile(gbr_residuals, [2.5, 97.5])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-20.88266736, 20.00250063])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.percentile(lr_residuals, [2.5, 97.5])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# lines above take the 95th percentile to see error range."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_estimators = np.arange(100, 1100, 350)\n",
"gbrs = [GBR(n_estimators=n_estimator) for n_estimator in n_estimators]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"residuals = {}\n",
"for i, gbr in enumerate(gbrs):\n",
" gbr.fit(X, y)\n",
" residuals[gbr.n_estimators] = y - gbr.predict(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10783e390>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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x6PZHFu8/sO+oGdxwcP8LtE5vVv9Tm2+C9sci/OzcxiQHcTMb1SZHV83xE+o6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\nbmZmVlAO4mZmZgXlIG5mZlZQ/z+m5kPWI/7czwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107889f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(7,5))\n",
"f.tight_layout()\n",
"colors = ['r', 'g', 'b']\n",
"for i, gbr in enumerate(gbrs):\n",
" ax.hist(residuals[gbr.n_estimators], color=colors[i], alpha=.333, label=\"n_estimators: {}\".format(gbr.n_estimators), bins=25)\n",
" \n",
"ax.set_title(\"Residuals at various estimators\")\n",
"ax.legend(loc='best')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# the graph above should show that as the number of estimators\n",
"# goes up, the error should go down.\n",
"# Also should strongly consider tuning the max_depth because\n",
"# each of the learners is a tree.\n",
"# Also should strongly consider tuning the loss function because\n",
"# this determines how the error is computed; default is:\n",
"# least squares ('ls')"
]
},
{
"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": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/python3/python3_zscore/04_PredictModel.ipynb | 1 | 3182 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predict with Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View Config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash \n",
"\n",
"pio init-model \\\n",
" --model-server-url http://prediction-python3.community.pipeline.io \\\n",
" --model-type python3 \\\n",
" --model-namespace default \\\n",
" --model-name python3_zscore \\\n",
" --model-version v1 \\\n",
" --model-path ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict with Model (CLI)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"pio predict \\\n",
" --model-test-request-path ./data/test_request.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict with Model under Mini-Load (CLI)\n",
"This is a mini load test to provide instant feedback on relative performance. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"pio predict_many \\\n",
" --model-test-request-path ./data/test_request.json \\\n",
" --num-iterations 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict with Model (REST)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup Prediction Inputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"model_type = 'python3'\n",
"model_namespace = 'default'\n",
"model_name = 'python3_zscore'\n",
"model_version = 'v1'\n",
"\n",
"deploy_url = 'http://prediction-%s.community.pipeline.io/api/v1/model/predict/%s/%s/%s/%s' % (model_type, model_type, model_namespace, model_name, model_version)\n",
"print(deploy_url)\n",
"with open('./data/test_request.json', 'rb') as fh:\n",
" model_input_binary = fh.read()\n",
"\n",
"response = requests.post(url=deploy_url,\n",
" data=model_input_binary,\n",
" timeout=30)\n",
"\n",
"print(\"Success!\\n\\n%s\" % response.text)"
]
},
{
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
thomasantony/CarND-Projects | Exercises/Term1/TensorFlow-Tutorials/04_Save_Restore.ipynb | 1 | 499054 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TensorFlow Tutorial #04\n",
"# Save & Restore\n",
"\n",
"by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
"/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"This tutorial demonstrates how to save and restore the variables of a Neural Network. During optimization we save the variables of the neural network whenever its classification accuracy has improved on the validation-set. The optimization is aborted when there has been no improvement for 1000 iterations. We then reload the variables that performed best on the validation-set.\n",
"\n",
"This strategy is called Early Stopping. It is used to avoid overfitting of the neural network. This occurs when the neural network is being trained for too long so it starts to learn the noise of the training-set, which causes the neural network to mis-classify new images.\n",
"\n",
"Overfitting is not really a problem for the neural network used in this tutorial on the MNIST data-set for recognizing hand-written digits. But this tutorial demonstrates the idea of Early Stopping.\n",
"\n",
"This builds on the previous tutorials, so you should have a basic understanding of TensorFlow and the add-on package Pretty Tensor. A lot of the source-code and text in this tutorial is similar to the previous tutorials and may be read quickly if you have recently read the previous tutorials."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Flowchart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. The network has two convolutional layers and two fully-connected layers, with the last layer being used for the final classification of the input images. See Tutorial #02 for a more detailed description of this network and convolution in general."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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fRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3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\n+y1Eps78iraSJI02M4D9iWV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q8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr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/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr\nqyusT29ZViiij1fX+0gJfgsD0XjevHmx9agDobqxsRGA5uZmTj755PC3OJFI0NnZWRKtCyJCRqO+\na4XWmv7+fp5//nkAtmzZwu9//3ssy6K3t5ff/va3YdsuXLiQ4eFhbNtGa83IyAiXXHJJ2AcbGhpi\n27ZtDA8P4zgODQ0NYR8zKgrXitbWVubOnQvAvHnzWLlyZex2hUKB3bt3h33nbDbLxo0bw5T60YwA\nlmXVPGq+mk1BNHxvby9PPPEEnucxPDzMunXrwnXlKfGTySSzZ8+mra0NkPvn8ssvp62tDc/zwtT1\nQZR+rR0CgmwDrusyNDTEzp07Q/E8m82GbZdIJDjvvPPC/bTW9PT08MQTT4T97cBWz/PIZrOkUils\n2zbfnwaDwWAwzBBOzBkeg8EwLaxYlGXtBSMQM8lX0A579zYRN1mjFOzfL3qfbcdPmBUKeTZt2gnE\nT2ZprVBqGUpVDri1huken4xs3syzb30rvWWGaMDSmjMPH6ahykSxAjrv/hTWdz9fsU6jaJvdCsvm\nx59Ya4b7+znY21spKCvFvkyGfGfnEVxRbVBas3T3g6xqPxwzuacYfGY9T/32PnDd2Gm+kXyezX19\nscdu8TyuzGRoo4reqxQqlYJqkzTT7BWeOXiQrXffzUDc5Jfn0XboEHOq7Kssi2RXF8yZEzvxqYD2\nCy+kdQxxwl37EkaXnoGitB2UBdnDGdi7bxJXY4DihJtlWfT19XH//fdTKBR46qmnwgiN6MRoVCCM\nCqJRgmXNzc1cfPHFNDc3s3r1apYtW4bWmtbWVs4555w6X9nRE504Gx4eDiOsMpkM2WwWxxFB7WjE\nS8/zyOfz4aTXsZIi37IsHMchk8mwY8eO0MkgmKxtbW0NJxhTqVQoIgdCcXnK2EBcCaKlXNeloaGB\n1tZWgnrhDQ0NM7ptAjujKWPjiEbqRUV0x3FiJ+ijk7f79u2jp6enZNLZcRyWLVt2wororuvymU99\nir5Nm+jyRRGAvGezsX8uQ7miiNLYCKecAuVN5bqVjo2DgzAwEO3/abTewU03fYLVq1fXxHbP87jr\nrrv4wQ8eoqVFBDIpJpEAACAASURBVG6FR3v+IA3eKMX+qSbtptiZmUPBK/7+NjRolp6iscpvt95e\n2LvX3xP2pNO881/+hVe96lU1+wzdf/8DvP/9N6F1qUjc0lLahVFKc/rcfhqdUiHBsx2yjZ0lHWzL\nqnxv9u3by9vffi1//Md/XBO7n3pqA9df/1FgMUrJybSGhd1p5nZmCNz4tNYMZBo5nGso6evZlmZO\nt0sy2nUuFGDPnpK+zaHRUeafey5fuPnmmkX0bdiwgY++850sTqexI+2WTqdJZ7ORuwVs/xFFWRYN\n7e3Y0X6/UpBKlbwPhzIZZl94ITffdltNxM6RkRE++IEPMKeri1QyWWynbBb27Stpt95cBzvTc0rs\n0RqWLIHW1shBtUfy8EHsXPFzMpzNku/o4Jb/+A+6u7uP2u4TnVqkpZ7K7CrR9OET2bba81RnUIpm\n7CkvgxNXDif6npS/Lt9mJvaZpqON4yjPwhXXftX2i2v3qaLa+1++TbV9455PUNYCd063ETE0T7cB\nBsMECNJsfISZGcZy4XQbYDDUihNzhsdgMEwLLU0e3R0uqkJE1wxlLAYGqkeZBAET1ea+tPYYHk7j\nuhmqxd46jhcrwGstE43TiTs8zPDmzRVfyhqwLQtv4cLqYq7nkdyzidToaGxa8sTIQmipPmno9vSQ\neeaZCjFVAVlAv+hFk7uYmqJpzhyiY2hP7NrR3p0MbdqELhRi3/VBYA+VKds1/H/23jxajuu+7/zc\nqt777fsCPAAkFgIQQVIUQVGKImohGUmUo7Fk6yQjxZYdSx7lWMczdiY5x5MzyXGc8ZIZjePI8XES\n20rkyIskW5IpWtLQEgmSEleQAIkdIPAetvfwGm/trbqq7vxRfW9X16t+2Lrfhvqc0+iH2vpXt253\n3bq/3+/7oxtwqssbSp6ryI01iGNZFC5cINlgfRc0XAdgJhKeFyNc2oH40BA0ypSTknLfAJVsR6gT\n3U1lGiUvRVwH/hp7qh6jyp5wHKeu1rWikcPXXx/TsixdH7FVdRlbibLVX6PS3zYqG+R6CAYdqAlT\n/4TiWphYvB6Uw9x1XZ215neiO45T115qeVhdVX/NT/++arv10iZhhNWQDTv/4N/+7YPHs227ri8F\nJfRvR2S5zP9y//08MDam7y9zVopff/HDHJvpR4iqo3QzfPaz3vjL32SlUm3Mpzh8GF57ze/Lc3jj\njV+tk6htBsViife//xfZtWs/UoIhHe6de4aB0jnfh0sulXr5kwuPsOB4Tl0pYWQYPvdZm3jM9zss\nJTz1FHzrW95/heDP33oLq5FCzk1SKpVZWHiATObX9DLDgKEh6OurtW/McPm1f/AyI5159E1aSuxk\nG1Nb3lHnLE0mIZutLTIM+Ku/+qumZlBWKhauu52tW/81punNVbsu/M/vvcCjb5/SwwhXwmuXRzk0\nNVA3jk+nJI+8p0R3l1t7gCgU4GtfA8vyjBeCH731Fn/tq/nbHNsrbCsU+LfZLGnfw8nFuTkuTk7W\nDYFSLB2PmckkI1u2kOzoqF0g0/SCG9XxhODHk5N8vVBomt1SSno6OvjXv/qrnqKA+uzJSfibv/F9\nGSU/yN3HH134INI3zpMSfuZnYM+e2qbCsel5/WmS0xO6w5y+coX/++DBdTXGWOv4FVKUwgzUgsb8\nTrywWt4rPbbxj7H8n+kfx4KXhax+y9X4VN1Lg/do/ziwFefhV/dR5+BX7onFYvpz/bXl1bZ+daTg\nWH6l2j3sOgdrioeNh1bbietvR78qkHrB0nML9vswJYZWEhwrNgoACGtbdR3CjnMbckf1FRERceOo\nYI9/s6pWRETcBkRO9IiIiBUmTChbNF6ltrju57pGGzY+wFoJ/G0ku+39cQ0jb+Ukltl3TTRNdRJ0\nibPX7wBrtGuD9WvivFrISp3fsn024oaQUrJp0yadsWXbNo8++ii2bfP6668zOTmJEILx8XEmJiaw\nLIurV6/qiRe/xHmxWMSyLCzL0nUP4/E4k5OTxONxtmzZQkdHB3657tWePLsW8/PzvPrqq1iWxWuv\nvcaVK1cQQnDhwgVdIzQej9dNIIehgg3uvPNOhoaGkFLS2dnJ0NAQhmGQTqf15PQ999zD4ODgmp/Y\nmpiY4OLFi1y5coWnnnqKUqmElJJisajlLMPkIP3X3D8R6c/AVpPde/bs4b3vfS+qRqm/9upaRE1m\nq5qelmXx8ssvMzc3x+zsLDNVufF0Ok1fXx+qHEAqlULV0VT1M13XxbIsTNNk165d9Pb2IqXk6NGj\nPPnkk2QyGcbGxnSd2U2bNrWkVut6wRCCTDxORzKp79sOSeKxLGasQzudYzFIpbxYrrr9jdptX5FI\neIpBtWU2htH8x1iv/6dJpTqqTnSbbClNh6zPDp6XKRLxLHEjq88nkXRpb6uQMAO/pamUd7JCIIUg\n1YLAPM/pkMAwOnzLPJ+sP5vcNBzakmk6UiqEEEBiJdMsZtq9KLgqYU70ZHKpHPMtWo5hxInFOuqc\n6KnELO3ptHbduhLSqQzJZEddv0ilXNoyJh0Z3+99MJtbCNLxOKIFztyYELSZJlmfE31WCNL4WxfS\neI50P6YQtBkGaX9/UOn/apkQpGOxpt+fTdMkm8nQkcl4nVcIyGS8L5rPiZ6Op4jF2pG+PHopvS6t\ndgUQjkVbKkU6ldRnnr1GiZmIa5NIJBgcHNQS3Z///Od1EEswEDCVSmlJdCklTz31FL/3e7+n5bgf\nffRRPvKRj2h1GnXPahWGYdDX18fQ0BBA3RjkxRdf5NixY4D32/X888/zzDPPIKVk27ZtfOITnyCZ\nTGpbH3jgAQzDwHEcLl68SKlUwnEcLl9uvuKVYRhs2bKFffv24bquVt0xDIOFhQWGh4e1g3x0dJR9\n+/bpdkwkElpNCmB6epozZ86Qz+dJJBLs2rVLO+Fb8d2oVCoUi0U9plefYds2uVxOj4tt22ZycrIu\nIGFsbEwHMqhgAeW8VuWfWsng4CB79uwBoL+/X5c7WVhYYO/evVrO3bZtZmZmtK2xWIz77ruPtrY2\nPXaLxWKUy2UdwBCr/oa2orZ7LBYjk8lgGAZdXV3s3LlzSSCruhbtPvkO13U5ffo0P/rRjwCvzVUt\nd9d1SSaTWJaFEEI/p21w/oa16QAcBr612kZERFyD+er7z7A2M9E/DTyy2kZERDSDyIkeERERERER\nEbEGSKfTWnYbYGRkRNcJ7OrqAtAOQf8EkaqvqBzq/uwYVbNZ1T10HIdyuYxlWVq+fK2jJr9mZmYo\nlUpMTExox/nZs2cZHx8H0G0AhGak+x3thUKB+fl5pJT09fXhOA6madLZ2anrEraiZmWzUdd4ZmaG\nXC7HhQsXKJVKuK5LoVDAcZy67DXHcfQ1DyoZqD4TrC3vui69vb3aKb/WAy4U/qz6SqVCLpdjenqa\nK1euMDU1BXg1RF3XxTRNyuWydkIkk0ndTuo7Y5pmnfx7Pp/n8uXLtLe3093drettRpmXeN419cJ7\n936fQjbV/9R2RdTPAkmWxtG1Ahn2CpGrkYjqy8OtLlvNMDJX1ldLMnyxh/XvgYVVRMB8FcggAsua\nTV1XodbujbYLLkOI6smGGBk8gZVAyiX2h52TDJ54dd/6xpBeqaVWdH7p6+WqX/g/S3jn4Vb7uT8o\nwP9aekx8W0bcCoZhkEgkdC3x0dHR69pP3fvGx8e1Ez2Xy+nxYDK5nE5Vc1ABeWGftbCwoANDAcbH\nxzlx4oR2OF69elUHtKkgNuVEX1xcpFAo6OWtsDuZTOpa7m1tbbS1telxdX9/v962r6+P3t7euixp\n5exV48xSqaTHZGp83qoxgj/DXI1T1PhZBR5AbUzjd6KnUqm6QAf/mLCVwRaKRCKhnfWVSoXh4WGk\nlGSzWQYHB+uc6EpZCzwndnt7uw56VGN8v7JUK8etqn0MwyAej5PJZLTz3J/tr8ouKVSg69zcnD6O\nagfXdXU/uY3UjaaBl1fbiBC2rLYBERHXgZK3+greI8laYz+REz1igxA50SMiIiIiIiIiVpnlJnj8\n8pDqb7/Uu38b/9/+CTXDMOrkAjfipExQUnG5c1RZ137pzrDXRmO5mo1+ydJGWerrrV3C6myGSYWq\nZep74j9P/3p/v/LvE/Z5tyuuMChkelloG6oukeQTSVLtCTp8iWBtWUk64ZAJiCSY8/PEc3N1yzpl\nF93d3XX+0BBxhVtGSEm7laO7fBEkCNchMTcN+Zl6qfOizebca+QdT7VBSslAMo4x2QMxnzdaSpib\ng0ql5tBtQTYcSAZjOXZkjtYWGYKu9hEyHR3at2kaYJjBVH9J2RIcP17v9sxkoNtXJl0IuHIFNm1q\nrt3txiLbk+eImdUsWhe6jUXPeVzbjHSq3h6ApLCQR96gEl+sGV8qEZ+YqLU5eIa3Qh1CSnCcOudx\noreX9q4ubb8Eko5DwnHqjDficYyRkbri4tIwKQ1tRfqcVmXbRNr5pprtSoElY5TduI5aETJGwjTr\nMtE7xRy73CN1cu5I6Cp1k8wna5u6NqVYFjszqM+xsOjgirVZDmmjEhzzrNV7dth9eLkx22qMP/wO\nWL8ke5jUvH9cHSblrf4fdKy2mrDxj3950DY/K/28EJTs97dt2PNNsCTP9draqhIA6j3ovPdv06iv\n+LcJBkKste9uRERERETE7UzkRI+IiIiIiIiIWAM0qq03MDBAMpnUGTKDg4NUKhWdSQ3Uybnn8/m6\nrHPwMk/m5+dxHIfBwUG6u7u1VOZaR0pJPB5nYGAAy7LYsWNH1aEmdBawlJLZ2VndBvPz87pN1ESW\nyjQ3DIOOjg4t214ul5mamiIej5NIJIjH48Riseuur76aSCmJxWI6i2d0dFSrDOTzeV1r1C8LqSZM\n/fVS1Xk7jsPVq1f18VVmkj+Tar0EYKisNSmlVl+wbVtL1AshKJVKujTAzMyMzrhSbQpe+9i2rbON\n+vr6kFJy5MgRzp49S29vL5s2bSKRSGCa5rppn1bhxFKc3/JuunbtR6WwlsqCofkkydnadqODDpt7\nF2jP+PJchUAe+zE8+5x2xkkpMUbeT/LBD9TleR/1+YubhUCy5+pz7J+87NnuOMSPHIIrk3UO0N5C\niX905j8j1W+ElJiD/ZhbPwmxgOPw1CmYn6/tX80GbSYGksc6f8S/3DqnnaC2EefV3Z9jfPQhRLXl\nDAGp9rgn263bXDJ5Kc5v/CdwfPkrPT2wbVutPDfAiRPw6U831/Y9ydP8cu9/I2NWpyWkJJ3cjbDv\nQHnGBbB5k0tHe32uv5jK4f7yv2D+9BF9PkJKuvDKCmgqFXjsseYaDp4DvVisXVsp6X/ve+l997vr\ns7IXFhDBuuaGgTky4knPeztjGwnOj74bx4x7ZyNg6vUf4zz79aaaXXFNrlrtYHVqMxNOnoF0ui54\n4T7zEHdZASVbKUlffIRYcrOvXIPB2Y69zPc+oK/PRPwUVvxHTbX7dkPdb9W9upFTVErJxMQEExMT\nev2RI0eYnZ3V93vTNBkYGEAIQTqdXnHnnBpDAJw4cYIf/ehH2obJyUm6urpwXZeRkRHuvvtunQku\nhNCy7ao9ksmkVtlpBalUSsuDb9q0STv5K5UK+XwtoKWzs5Ph4WFtx+XLl3n22We1bHg+n6dYLOrM\n5JGREe68805c16Wzs7PpdqtsZ9Vn1Pi/XC4zMzOj7VKlbvwO3Uqloq9PLBZj69atZDIZhBC6/JPa\nthX4gxVTqZQum5PNZtm7d6/OPFe2KoQQ7Nq1S4/XlHqD6iumaepxbCtUAFSbm6ZJOp1mZGQk1Inu\nOA4vvPCCzjyvVCqcP3++7hns3nvv5b777kNKyZYtW0Id7RERERERERGrR+REj4iIWFdIuXZqmEdE\nrHtuc2fPWsKfVaFQMtz++tPXM4GlHIZ+R+D8/Dzf/va3mZubY8uWLfT39+sJqvUwSZNKpbjjjjtw\nXZe2tjZd6/rSpUtMTk7iOA7nzp3Tk81nz57V8uPKcawmBpXMqJJWLBaL5HI5UqmUroutpLnXA0pC\nUkrJ7t27tZN4YWFBy+DncjmUpD+gHcuqv6XTaVKpFKVSiePHj+vl7e3tCCFYWFhYom6w1vuNqgOq\nJOwLhQLlcrlOcj2fz+uggUKhoIMN/JlOfrnOs2fP6pILzz77LAcPHmR0dJTdu3drKfjbXc5dCoFj\nJnHiGX2LcRwwY/XZ47EYxAyIGb7fNAG4FbAKdU70OBUSSRB+ufIWlVuOS4ukW65KW7tgW/VZzYBZ\nKdNWmatllUsJVhKsMjgBJ3qlsiL32rSw6DMXah9rxEnHba+WfHWZgUAYKgtdOdE9GfjFxXonejIJ\nhUJ9OzdbPVkAceHQaRTI+pzoiArai1wlFhMkEgHB/JgLszPIqSverlSd5/5i7uB1wJVASsxkEjOY\n9R728GIYkE5XAxqqmAmcdDuu6S0TAtxkuvkPPkLgYiAxfNLsS6Xvk8ImyUJgZwluCRx/vzZwjRh2\nLK2vjxNLItf4PWKtoxxxKiDQNM06p7j/Hnz8+HGeeuopwLtnvfbaa0xPT+v/m6bJ5s2bV0SaO+w8\nCoUClUoFwzB49dVXeeKJJ/T6rq4uBgYGcF2XO+64g3e/+936Pnv16lX+9m//Vktc9/f3k06ncRyn\nTh67WaiSNl1dXUgp6ezsZPv27QB1Dln/9opCocDTTz9NwRcw45d637ZtG/v27cNxnDpZ+Gah7FNO\n9GKxiJKVn5qa0s5nVe7H7xgvlUr6/7FYjL179+qAQbVNK4MolaqPaZpks1mGhjwlG8dxtENdqWr1\n9vYukZ5XuK7L7OysHr+bpkkymdQBp80uAaBk3NXzVmdnZ+jYeHFxkd///d/nmWee0YpHhUKB7u5u\nwGvf973vfbz//e8HPCf7eii3FRERERERcTsROdEjIiJWjkoFSiUImWAWBQtzthQ6zygExBcNujNZ\nyqYILTlZqVgU8gYVu9HkgEEq1XjStQXP4TeEoPrwG/LgZQixtE6iHykxUilMw1jSNBIgHsde5qHR\nte3QKp6rW93Th22HT0ILgXCcZW9kMaBR5T9fHlY4QniTm9VahEtY5YdbYRiYySSxMGlmQFgWYjln\nTizWWA9XSubtJFYpHdpGUkqseYNyrgLUf6mEgLk5J/LPN4HrnazyO0TV35VKBdM0cRxHZ9yqCSqV\nca0yvNcDahJYOcBTqZSe7Ozo6NATbcqJXiwW9aSgWmaaJh0dHdpJqvBnwKhjK6fpWkcFBySTSVzX\npb29XTvRDcPQE4lqUs+yLD2h6M88UuevsruUkzwej+t2UbUaWzFx3QqU/arf+DN+VBafP1srk8nU\n1Yv317L0Z6UvLi4C6Ox01f6pVEorRtzO3PjZ397tFRHCTX6HVn3c2oTvfvAIrTqfpv5Mibq34OKI\nmyRMErzR/cUvjw6Ejl9W+94ULJHiL6kTtp0irMzKSqHGAo1s9RNW4sW/bqVVapaTlve/B/8OHiMo\nU78a+Nsu6MwPXpfr+b40k+v5DH8JA39/9tvuD9y83uNGRERERESsEO3AKJAFZoBxYH1knDSR9TEL\nFhERsTE4cgS++91QR3D63Hk2/d2zYIdli0gGst38l3/yC7ixROj6K4tJvnRgJ9OL4Q5P04QHH0yQ\nySxd57rw/POrm+GeSSa5a2yMgRDnhJCSZLHY0GlrmCYDH/4wPWNj9TUk8TLCSm+8wflnnmmYCWVY\nFp1Shk5+zRB0ka4C5883zCLqunyZXctMSpSBfsIn8hJC0BaLYQrBkiNICfE44p3vhC1blu4sBLz0\n0nWeQGtIdXRw58MPM5AMCRNwHOIvv4wxORm+s2libNkCu3aF9gsH+L1XH+PlK1saToLOf6fEAqdD\n1gjy+Qk+8YnIi34jKMfuzWSxVioVjhw5wsKClzHmOA6O4xCPx3WmeaVSoa2tjVQqxZYtWxgdHdVZ\nHeuBeDyus2K6u7t1O6lzVU5hNSFVKBR0plKqGghTLpc5ffo0ruty5MgRzp8/rye1LMsik8lwxx13\n0NHRgWEYWs5zrdPX10dnZydSSvbs2QOggwdUu6gMa5WVDfUTq88++yzHjx8nn89z9OhRLXc+OjpK\nLBZj8+bNbN68WTvR18PkXiKRYHh4GPDaY2hoSGf1qeCSoOSpP+hAnWM8Hqe3t5dCocBv/dZvaRna\nfD6vywns2LGDbDargzBuZ9RXJhj3JwW41ThIKb3xCYYBdZnoS7Nh9f6tMznwQXLpKwzX9V5CeO8N\nt5e+l2htVrpUn+P9veQaqG0k1LVoA5Un//6ilaaHXXf//5XJ1/jZ0WfvOPVRs7JF6hBSetdefZbq\nB2rdUsuWLhO17QUSKbzvCuB1lxb81kq84y75nOBnNerTDZar/wkRfsYRt8b1jkfW4v05WH876AR1\nXVcHAAZZzoG6Utzs57ZaBv16CQtU8Dttw67JWiFYS/xa1yK47VoleF4RERERERFriPuATwAPAg8A\nHYH1ZeDHwB8CX+U2GfZHTvSIiIiVY34eJidDZ+HMibNk3ngx3IkuJZmBAd7+mSsNs4IvzGUZOJLC\njreFzrGZJmzbBkGFRfDm2t588wbPpcnETJOObJausAw/Kb2ai44TOsspDIPk8DDJ7dtDs7WtiQkK\ns7NLHOyKFJAmPIMkHrJ8RZHSUy9YCMpJAkKQKJdpX+bBMwW4hJ+DCcRUNkTYzqYJvb0wMhJ+8Exm\nVSMvYvE4bQMDtFclhOuwbdxlHDlSCIxsFjo7Q7+PEji9OMQrl0cxQk9RMjNzkfm5XMg6AyGKUSb6\nCiKlpFgs6lqNaiJSZRErZ6E/kzvpC75YixOuQVTGNdAwe94/CaXqgiupc/Bqcl69ehXHcXS2uV+q\n2zAMnVGssvbXA7FYLDQ7XLWHbdvYtq0DNfyZ+cpZ3NHRQTKZ1HKrUJOpVMoFKhN9vbSLP4Mc0H3e\nn1nmOA6WZXnqGtWa6VDvRE8kEgwODrK4uIjruszPz+usfZWJnkqlSKfT+jt3O2MYkq52m8Fuy7sP\nCKgslNhz8nsUzl3R23V3uJjzJdx4vRNdFIsI32BNSEmnMc+20nG9TOKSdhZbcwIjI96AUUpv3DU+\nDnNz9ff7eBzuvbf2fylx+wawdrzNGzv4mFgc4q3T2/VA5A3zRR5ssskSwUz3Nk7v+rD+3ruGSce2\nXnaPFHVNdIFLsrcDYilq6cMSZ84kl7tIxVYZcdDenqanpxvTrJ13o7i8W2J+3itwr9pNSs8p7VNQ\nElKS6J8l2zddv29+HvNDjyLeeX+1HUAgsNNtCOF9D4WQOBcvQKKRLtEt0NMDd91Vb/vevXXBl1JK\nTr5R5uyFQCCsIXDnOuqksGLCZueZvyUuqo5EAW2nj2BUyk0125yZJvO9v6atzZsPk0BsYQbefM07\nBxUx0dcHjz4aCGiQcOed0N2tx49CQldxkmR+Rm9WmD1HzGmy/v9tjhrPKUWUUqmk1x07doxnn322\nLrv14YcfBrw+uM1XFmilkVJy/PhxZmZmMAyDfD5Pd3e3tvW9730vjz32GADd3d2cPXtWj/PUuFaN\n1YaGhujo6MC27Tp1mVbZ7c8sD46Vc7kcExMTOujw6NGjXL16VasgdXR08OCDD5JMJkmn03VKQa0u\n+2JZFrOzs4AnM5/L5bSUuW3bnD17VttgmiYPPvigDgBMJpN1zwgr7egtl8tcuXJF9+NkMqnHuGps\nrv6vShf5bfSP/VZrPBZ04LuuSz6f1+NHIQRbt25lYGBAb9/T01PXxyLHekRERETEKvLTwL9cZn0S\neG/19Rng48D8Cti1qkRO9IiIiJVjmQwjLxvJrM9IUqgJNQxfekZwG1FLwGm0SWM19PXh8FvO0XWN\njKlVl7i8VZbJTFvmktdtd1OEZhQ1tmelkS3s1EJIDEEDJ7qoLl/ThQA2PGETW/7s4qC8YXB5cL/1\nTKMsmqAUp8p4CssA8cstbrT2CK4LSosGs8D89SeDtVfXO8F2uZ6JShWI4G8fqNXxDPvO3a6YhmSo\nt8KWoWpdcQFubJqhV34H55VXAe+ebAKJr9fvKwHe9z5E1aGiGIzlGCi+oP/vAJ3uDE1HCNi+3XOQ\nu65XTubYMbhypf6+n816zlIVpCElblcfhXe8xyv+7uPlnOSvXqkOgZGcNTPsb/Y9Uggmh+7hlQc+\nq2/9hiHZf9cimwcX0PdkKTFiffW3aAOsqyUuXjxJWfs8JcPDgwwPdxKP14ICWuJEv3LFC1TQHy1h\ndhYuXqzbLLVpE6nhofoBXToNn/sFz5mtLReUkh3IaoCkYUjsA8/BX/9F820fGYHHH6+VxpHSc6rv\n2OE7HclLb5p886QZTK7HsWtjWAl0uTm+OPuv6JDzqIvUMzeHsWtXU82OTV6g84//X7r9wVe2jfAH\nrLoufPKT8PM/v7QW1vy8p45VPSHDcRg8dAhytcBK68oUcbvYVLtvd/yS7YVCgZmZGX3fOXPmDAcP\nHgS8Prdnzx7e//7363v55s2bV/X+dPbsWS5cuIBhGBSLRV1aR0rJ/v37+dmf/VkMwyCXy3Hw4EEd\n0KaC/pQTva+vj76+Pq2u1Gr8Dtsgc3Nzdco9b731FgsLC5TLZaSUtLe3s3fvXjo7O4nH47S1tWkH\neqsdpLZts7i4iJSSfD7P3NycVt2xLItTp07ptk0mk3z0ox+ls7MT8AIzg8GqK+nQtSyLq1evokpO\n9ff362ug+oEK5lTKUypQFKCtrY1YLKbHsKtF8PmjVCqRz+f1uHF0dJR77rlHb6fa379/RERERETE\nKlIBXgCeBs4BF/Fy1AaBR/Ac7THgg8CfAh9dHTNXjsiJHhERERERERGxznAch0KhAKBrW6vJ1N7e\nXjKZDLZtUyx6k9hKklrJlMPGqrsXPAc1Aaik7IUQlMtlrl69im3bXLlyhampKcDLNO7u7qa7u5st\nW7bQ3d0NoDPY1yP+9gjWd4/H40gpKZfLut8cP36cAwcO1GVmx+NxHnjgATo7O7nrrrtIp9N6QnA9\n95lg26iMo85BfwAAIABJREFUK9UuarnqQwsLCzz99NMUi0UuXbpEsVhECMHAwAD9/f1s3bqVnTt3\n0tbWhmEYt72cu0AFpKgFnjNTSBfh2rVtwHsMD+6rMmEVUi4JzWpZDp8K9vS//MvDtvMvMwwQgUl7\n4fP7CknLiuQIA4QvC15IhCEwDJ8BQqgLVL9rzcfe+PCt/MqHXPMlnw/hEZGGEXDyCjB8JSeM6jat\nsr9R3/CZgzCQgesuqxL1Sq2hGsqFkLJeOaoFjhT1Pbvm56g+HXREhQRFL1WzihxArSQY8Bd2X1ZB\nXyvhtL0RwuS5g7XEr2d82upzutY4J1iHvtH2waDGVo6hlguevBVWuqZ7o3rtjdpuLT7PhNnkl6O/\nHml6tU9ERERERMQK8wfAbwCNpN/+BPgS8Hd4WemPA/cDr6yEcatF5ESPiIiIiIiIiFhnSCl1ZmxQ\ngjqdTtPZ2UmxWNRyn6Zp0tbWpqW519JEU7MIOkehVmvTX/+6Uqlo+Xs1Capk3Ds6OnR98UbZR+uF\nRpOKpmniuq7OlgKYmZlhYmICwzB0ewEMDQ3R29tLT0/Pum8PRbA9guelrr1yhi8uLjI+Pk65XGZx\ncVFnPPmDL7q7u2lra8NfdiAiYk1wU/PvG+/+sNa47lvwur1Xr1e71zbqnn3hwgXeeustfT+bmpqq\nc5p3dXWxb98+7fwcGhpaNZsBUqkU2WwWIQTbt28nnU5r2zZt2qS3c113SWmVrq4uAF1eptXKL47j\nYNs2UkpyuRwzMzN6uQqiAxgfH+fQoUP6mkxNTemyLkrOfWBggM7OTl12R2Wit0LO3e/UT6VS9FQV\nOtra2iiXy7pNi8Ui58+fp1LxSkwkk0k6Ozt1JnQw8HIl8CtFVSoV8vm8blc1VgdP/aenp0fLtavA\nTliquKWWBxWEmm23yugHr48IIbRkfqFQQAihJf57enp0wMjY2Bg7d+7Ux0mn03p86Q9Y3YjPaxER\nERERa55z17HN88AfA79Y/f/fJ3KiR0RERERERERErAXUJFC5XOby5cvakV4oFHS9w3w+j2maWJZV\nV/86lUqRSCS0zOFGnZiRUmoJcsuy9ATcwsKCzkS3bVtPwvX09HDnnXfquuCmaW7o9jEMA9u2tfSo\nEIL5+XndN1St0kQiwfDwMH19fXoS+3YgOGlZKBR4/fXXqVQqLC4uaif54OAge/fuZWhoiGQyWad+\nEBGxZlBa4TdElPnWaqLkwogbxS8N/dprr/HUU09hGAZSSo4cOaLHe67rsnXrVn76p3+6zvm4mvem\nzs5O7cT9wAc+oBVgpJRs3bpV2+Y4jg5Wk1KSyWTYvn27lvFOp9PEYjHtrGw2UkoqlYoOMjx27BjP\nPfcchmFQKBS4dOmSHiNMTk5y4sQJ7aRNJpNa8cl1XUZHR9mzZ492Zsfjca0c1Qonuiq/YxgGXV1d\n2ikupWT37t36+WFxcZFLly7pZ4ZEIsHY2BgdHR36WCvdV/zBBcVikVwup4M9x8fHtcPfMAz6+/vr\nap7fc889OuhROaj9ykKtdKKrQGYVAKA+K5/P8+STT+oSBmrdnXfeCXjPZA899BCPPvqotk0pQfm/\nsxCNKSMiIiIi1jQHfX93NNxqg9BUJ7qUEsuyKJfLulZNdNOPWE+EZfZFrCCqjnOj341rPvxIJOHX\nTSKqr/C91gSNzk8tD2ub6619bZqh211zzzXRNA3O0Sezqmpfhu5dlYINHFGvc0P3lP6NWNIQWoNz\n9Vju0yV47WIY4e0iBK5UPX/pkVy9aPlPESKsGULaK6JpqGyNYrHIyZMndY1DqE20zM7OLlkupaSt\nrU1Lcm904vE4hmFQLpeZnp7WTvSJiQls28ayLFKpFFJKRkdHeeihh0gmk7qW4kbEX+PccRxeeukl\nJiYmEEIwNTVFKpUinU6ze/duDMMgkUiwZ88eent7N2ybKFTb+DOAFLOzszzxxBNayUC1xY4dO3jk\nkUfIZDJ1/SZ69qH+xiBrdxrplzWl8d1CS7ovJzfdqt8xNa6qvqRapkyv3v9F4PP1cKyBma3+2V3y\n+dK/sKbX7gaNqTazWKLaXX+FWtbc+lVrKOFfqW1sNFr3nbAEqaTRV/M2V9dYUp/b9Qzzlw6pWnAi\n1zLEr+8f8pxRd72qCL2/qO668ccZq4nrujiOUyeL7kcIobO21wL+rFp/nfFr1a1W+6j7cyOZ72ba\n6cdfw9xxnDrnvfq/P4vab6//5T/2SlyT5TKYlaNd2aXadjXrhy9HMOjgZoIQVuN74P+OKnuD/cM/\ntvarQCnWyvc3IiJiQ/D3gHfhDdROAn8DWDd4jBTwD4GteNW1fgi81DQLI9Yjg76/T6+aFStEU2fE\nFhcX+f73v08mk+HOO+9k586dOso0ImI9sLi4yJtvvsmlS5c4evToapuzsZASzp+HauTwEqamvEkb\n0wxfXyjAt78NDSbyM6UEDx37EbPlRKjT0IgZvP3snaTbl/4mVRyLHy2cxVMfWSUcB/L58PMTAu6+\nu3Hbgdc+J06ErhJ9Ixif/edeKc4QJFAmpJ6hgPLcVWTu/PWcQWswDOTfew/ynnvCJ9qPHCHe2em1\n35KVArmwgHnkSOiEoZXq5tX7/hFOuif8s00T0bsXjD7CZmMnxeEbO5cmIwsF5Btv4MbjS60TArF9\nO2LPntDvQ5k43770Tk5d3k64E13y+tkyc7PjDWJaJKZp0Nc3RFglTNu+DEzc+ElFXJOw2nr+5cEJ\n1Ua1BDfqBHew7uS1WK6e6EbG3w/CJCPDJDE3Mtc6x0a1K/3tczu003WRz8Of/Al873s6+E+Uy2RS\nKeTevd42UiKSSYzhYYR/3CMl7N0LIyP1QYOOA667tPZ0Cygku1hM9yNdietIJt72SeZ7Fmt+RSDb\nZrD73iSJpC5mTSwWpz1WqtpV87jfsTnOu96VQFTLSC83lLtZBLB5yOLv7y9oe4Rj03nuDTh2SbeV\n5Qj+zQ8f5sJCe81CCZ1dWf7iL9J1TdqVG2fT6S8jZNVZIQTMvoTgg02zWwJX976HV9/3s6TiKW3P\n5rvaGNqW1ts5juTLfz7H1/5wof4ARgK+moF4zfnT2Sn45X8O6bR3LMOAiYnwYeItU6nA3BxUVSiQ\nEteqIM0Y/tDOBx8SjGxaunsiUQtckEBiTpL5jTzkF2v9u1hsviN961b41KegmqmqbK8L1pUSRkch\nmfRFV0ikhD//Xjcnj0tEdbEhJDuGHqIrW5uLvWC+RWGVx8kbBZVt3ugeHTaeWwv3I78NQWey3/bg\neQVrvfudvCtxXspWf3Dd9YyJwpYHndVh17JZXO8xg225XLuGjXlagb9tg9c7OM4K/j+sjf3t3Mox\nmv/zg/097ByWCwIJC2K4ncbhERERLcMA/gvwGcCuvlJ4zu/HgJnrPM4I8D1gL1AEEoAJ/D/ArzTX\n5Ih1QgdevwKYA767irasCE11oqdSKe69917a2tro6OjY8FkrERuPVCrF9u3bGRkZ4eDBg9feIeLG\nOHcOZhrcoy1reSd6sQhf+1rDQ2ek5L2uxGKpS08CRizG9jMfItHRvmTfkuvSv3BmdTOLlRM9LAo8\nHof77oNGcrqOAydPwoULS9dJifHQo8R+6nN1E3p1m+A50YMIAdbZk8j//O+u+zSajhDI970fHv/I\nElevlGC88AIxw/Am18O4dInY0aOh17aQ6eXFd/4SC707lu5X3dwwwx+6hZBcFt+6wZNpMvk88uRJ\nQueFk0nMn/95jH37QnetlEy+8l/fxXfe2NTQmSo5hZSThNW1FELS37+VgYEtoe1TKJyPHvhbhGVZ\nWJZFsVikUqno+nl+GXL/5I3KRPLXD9yI+CfL/KoyxWKRq1evIoRgcXFR14VUbSalJJVKaan7jdxv\nhfDqNJZKJRYWFsjlcuRyOYTw6jVCrd6kaZrE43Fisdiqy8CuFP7JysXFRebm5hBCcPHiRZ1NlM1m\n9fesvb2dTCZDMpm8LdrnuqlU4K23vODIKkJKYqYJ3d217TIZGBurOSDBu1f390M2W3/MctkbJ/pp\nUZs7ZhI7lsJ1wTFgtucOpn2+RSRY7S7uSBl8cZnCdUnYdvWWWQ1SQdKRNRkcRDvR/X7LpiGgLSMZ\n7ndq4x3bhmMzcPmyNt51DF55BU7kEhjV83Fd2HUX/OYHUnWXQrx2FvPoSe84AELQW7lEc1O8BeWu\nIXL7HiaRaAM883vvwstxqeJWXI7/+Tm+e3yC+jFJHG86ozZ27u8X/JOfh/b2mhM9n2/REN91vf6u\nkBKkWx3Lqz4AAwPQ1rZ093Ta99gjQOQkprS9NlcdrhXe/7Y2uP9+6Ourd54H525iMc9Af/AekjMX\nYhw8LnQfMk2IdWQY9D1iTYkCdlQ58JaYn5/n+eefp7u7G3+ZGiklx48fZ2pqSt97FhcXdSarlJKL\nFy/ywx/+8JqfUSqVyOfzTbfdsiyee+45ent7kVJy6NAh5ufnEULo8Zbi/PnznD17FoCrV6/y5ptv\n6ntuKpXS+/mlvCuVCufPNz/I27Ztjh49qtv85MmTuvZ8uVwml8sB3nhhZmZG17oGL5NYjSOllCST\nSX784x9rmXT/9Wu27a7rcvr0aX74wx9eczxSLBY5ceKEliFPJBIcOHCATCYTur0/OPfkyZMMDg6G\nbnezSCk5ffo0Tz/9NAC5XI4TJ06gVE4nJye1QqRhGMzMzOh5ZsMwcBynTt69p6dHl9eB2nP8yZMn\nGRsba6rtFy9e5JlnntFOe5VxXiwWOXXqlFbCklKSy+XqxtqHDh2q+x6o55bg9XvjjTe0nH1ERETE\nTfC/4jk6/6j6dwH4BeD38epZf+w6jiGA/wHsBj4NfBXPgfoHwP8GvFk9fsTGx8TLPv8A8GvANrzA\njF8GrqyiXStCU59s4vE4o6Ojuv5ORMR6Ix6P09fXB3Bb1f9cMdQMYqN117P/cqulE+om1tNYQjRw\nJIvrt6FViGVsuBG7gtuqiTFheBqXYbs0OtSya1cOUbV/SSa9kMu3zXW0m9cfQiTPRU29tXGPWf22\nqZM9vRF8k+eN55UbH9mbT1GR8WFO9psxKuJ6yOVyXL58mcXFRebn56lUKsRiMXp7e0mnvcw9lTGb\nTqfp7+/XkoGmL0hpIzn9wjJlFhYWkFIyPj6uJxUty+Ly5ctIKent7dWTuyMjI2zevFlLnW9k5ufn\neeutt5ibm+PAgQOcPn0aIYR2mKfTad7znveQSCQwDIP29va6Sb6NjAockFJy8OBB/uzP/gzDMJie\nnqZcLmMYBrt372ZgYAApJXfffXddfcuIKmqs5w8KDPNghumcr7EgHwHe8FEG7nRa5rrhXvpvJS2u\nRcdbeorC167UroVvjGkYAtOov08b1d10PKLwztmkOn6s7tuqcY+QojbGq7Z3YItlwkADW9af7rKP\nHiuBP+M/yBKldJeVM9Z1q4NAnxM9GJDaIEBVCK/PGL6uEewdG2eEsTrE43F27drV0ClqWVadQ3Bk\nZIQHH3wQQDtwv/71r1/XZ9199916/NgM4vE499xzDz/4wQ+07cphC0vHn8ePH9dZuKomuUIIwaVL\nl/T//dm62Wy2qfOOQgj27dvHc889x5NPPgl4ku3q/p5KpRgZGdHbDw4OsnPnzrrzCmYjP//886HX\nz1+zvBls376dQ4cO8Y1vfOOa2yrntLLbcRy+853vXNdzgW3b7N69+5bt9bNz506OHDnCN7/5TaAm\ng65sBeqSsxYXF+tsfeWVV5ZkeDf6zuzcubNpzz8dHR309vbyxBNP6GX+55FKpVJXZ76tra1u/enT\npzl37tw1P6dSqbB///41K7cfERGxponjOTpzwOep5U79J+DDeNLs9wKvXeM47wPei+dI/0p12Qzw\nT4GPAP8nnkN+bT3IRTSLXwH+fchyCTwF/Drw9IpatEpE4cEREREREREREWsYfxaI4zhYlkWlUqmb\njPE7gFW9QOU8V+s2kuPcT5icqapVqbL2hRBUKhWd3WQYhs5ETyQSup02KipT33EcCoUC+XyeYrFI\nqVRCCKHPPxaLkclkdE35jdxvoNYuwTIIpVKJq1evYhgG8/PzenkqlSKbzWonRSwW27DqDk2lURut\nxbYLdvdbjG0MHmMDf53WJGuwh3mERmiKtWWw8o5HrDipVIrf/M3fXJH7S7PlolOpFF/4whdWxPZm\njtsMw+DTn/40n/rUp5p2zGt9XrN47LHHePTRR5t2vOVo9pjw0Ucf5ZFHHmnqMRvRTNuHh4f50pe+\n1LTjLUck6R4REXGTPAR0A/+NpeKj3wAer76u5UT/sG8fPwvA9/Gy2e8GDt2KsRHrjnngGBAiibsx\niZzoERERERERERFrFCklhUJBSxleuXKF8+c9uXylmGKappaVBrRDNJvN0t3dXSfvvlHxS9a7rsuZ\nM2eYn59nfHycM2fOAPUZTmNjY+zZswfXdRkeHt6wk1MqYEA5ey9evMhXvvIVFhcXmZyc1Nk+w8PD\njIyM0NPTw5133qmlMDd6aSZ/9nk+n6dcLiOE4MSJExw4cADw2jAej5NMJtm3bx979+5FSsm2bdta\nWt90vSIBO5agEk+jPYKuS8yqIHzZ59IwsM0EmDWlAyklphHHMHwS0lVNbuFPKW6Vc0aCKORhcQbh\nAq7ALmYpl+N1zu9SHPIlsH1mGFKScStLnI6OZVIq1TKiW6bKqlLJVfawlJ4UuOP4pMElrmvjurZe\n5LrgOhKsqjSN9ISLhGXhViywPZUnKQRSSbs3z2gELqa0MGVcmy1cA9x6JYNkQtLVEch3lngy/25t\nWYcJZsnBjHkOaWGAUW5BXXEITXWXCFy3vhvIchGRDymcZEtPFJGqWFR+AVJJqKRrx/RLuzcJKcF2\nBJatPhiEdIlXinXbOY7EMRJL9o3FJKmU0GYZBsRjLjFDos7cNNzI/36LrGfH2Xq1PbJ75VnPtm/k\n56qIiIgNwd7q+6sh616pvu9pwnE+Vt0mcqJvTL4PfK76dxcwjCfnfjfwz4CfA34RL1hjQ7OxZ8Yi\nIiIiIiIiItY5tm1TLnsT8IVCgYWFBeLxON3d3ZimqWufK0dpIpEgHo/rWt+3yySPypp2HIeZmRly\nuRzT09PMzMwAaNlywzDo7Oxk06ZNuK5Le3v7NY68vlEBFFJKFhYWOHz4MPl8nnw+rzPF2tvbGRgY\noKenh+7ubu1E3+gy5UIIfY6VSoV8Po9hGORyOcbHx/X3aXh4mFgsxtDQENu2bcN1Xbq7u9f15G+r\nqCTbOPbODxPbtk/7LWNWnrte+ws65s+jJMcXujZxZM+nqCRq3z8pJcN3ZBi5s15aOD43TWJuut6Z\n2ET5YY3rkPqNf0W2w5PZLZHk+fT/zgvx9+j6z1JCKmXwg79LElNlVyUMp3J8dst3iYuaBLYU8Mbh\nt/HfX3677ieTk9CShMFyGWZna05024aJCTh9WrebdAymLxzj8mx33a598TLiexPEfPW5rVdeYfFb\n39ZefwMoFIu0/8zPNNXsHvsK95ReJO1WC8xLSXZxGDHbp7eJu/DpD8d4z+7++j6QL8J3vga5Gb08\nISrs/Y8niQkX5dCdmZvm8O7m1sIFIJWC4WF0MXkpWXDbWLjk38gl+7W/oPvJr9WZ7roSx7WReNdL\nACKbhS/8EmQyNYn1N9+EQ82dk5zPm7z4Roauzjad+N4xe5b7XvhDDFkLwrg09hDH3/Y/1ST9q5a+\n613wnvf4l7j0GTnSlHX0wJnMFV5+IarjGxERERERERGxSgxW33Mh66ar70NNOs5gyLqIjcEhwgMk\nfhL470AG+CPgKPDSCtq14kRO9IiIiIiIiIiINUqjGpJh21xr2Uak0Xku59xUNeNd19V/b0T85+Xv\nR4ZhaHUCtS7spdZtNCfxctdb9ZvgOfvbZaP3m1vFNeLMDexietPbddnwRHkO+9T3wJrG87JJ7Ewn\n0107KKe66upGt/WA3eOrJQ2YOGAX6x2orQjwkJLYqy8Rr15bW2SY3PQZTnd5TmRlYyIhmJszUUIN\nUsJ81sE1zoPhoKyXSGbf2sSpUzXTC4Xmmw14Geflci3julLxPmx+vlq8WiIdg1JhlkKhvn+XFguI\n8xMYvpkBOTFO5dw5pGXpeteOYTQ1o1sASVmi17lC1qlmPEsXKlkot+tGE0h2jgl2bkqje4YA5irw\nxlsQu1hr4HIZXnwRbEd/TrttI3YMN81ujWl6Dm+fE71CnEKhXjQhe/o0qed/UJ+x7roUSyUct5ax\nLQYHEV/899DToxUYcBw4cqSpZldswZWZGGWnmv0P2FMW8tAhr/09AymWNzPZ76kQ+L+jDz4Ig4O1\nriAkpGfLxMp51PVZnC1VM9MjbhZ1r2k1rVAqWinbmx3ot1J2Q3NtV2OTlcA/fmwG69l2pebUaja6\nmlhERETLSFXfF0LWqWXXE5WcwhsuLt7icSI2Ft/Ay0z/r3jaWv8H8A9X1aIWEznRIyIi1g7Xemi9\nxvqqCmUo+nFJiPDjrLaTQEpv4soN2iE9SUshqycRZrvvFVxfLWdoiBs/RSGq866rjAy816+UNQnT\n4AlWlzfsNdc5RxLWbuIm2rNVNGwXaGykXr7ct2bZlgdkaLNDLREu4taRUjIzM8PCgvd8UigUME0T\nKSXz8/Pa4Vcul/WE3JYtW+jt7SWRSGw4B6gfJVWusqwXF73nOtd1KZVKWJZVJ3sfi8UYHR3FMAxG\nR0fp7u7Wta03GqptACYnJ7l8+TJCCI4fP87s7CzlcplUKkU6nUZKyY4dO3j729+u66GrvrQR+0/Y\nObmuy+HDhzly5AiGYXD48GE9kZ7NZnnHO95BMplkbGyM/v5+pJRkMpmVNn1dEhDgDl1/I9uvKEIs\nucf5Fby1oxT/vXDpDv5jtOwrpQwK3v/1B9YNFut3JbDYZ6QIvDedJQOqoN3VZWq44h++6O3951X9\ne0UafSnBthSCUNn3hoNL/zNKi5x5YWNYoW3ytaMOYlgeqbdaM9/cdU+5XOaLX/wic3NzLb0PSykZ\nGhriM5/5DB0dHU05Zrlc5qtf/SrHjx9vme1qjPNLv/RLDA1dTxLdtXFdl29+85u88MILoQ7LZjvX\nv/CFLzA83JzgngMHDvDEE0/osVsw0C9o+41cF39goZSSD37wg3zgAx9ogtUeBw4c4Mknn7xu+651\nHfzn7j+O67o89thjTbN9amqK3/3d360LAFiuzcNY7jzVOtd1uf/++/n4xz9+C9ZGRETcpqg6PZ0h\n67qq79cT3lvEG+R1ALO3cJyIjcefAl/CC7R4eHVNaT2REz0iImLFcBcXcS1ryXIJiHQac2yssdfW\nsuD8+YYTSqbr0pvP4zTw3kkpyR8/TjGZXHKMMlCen29djc3rwBkcpvAPPsZiZqmscMUVfOXNTmaL\nDSLWpQu5DBQLLJnAki5WoUR+8twyTmODsNuBEDA7e4XZ2WbXwLwxxKWLcOpUrZaqwivMWK8pGcCc\nnqZdOdoDVGKDLLrtzM6GT/s5Dpw75zI/v3RfIeDCBbmaXQZSKcSOHZix2NJLaxjIY8dwzp9f2m6A\ntNNwZRfC2NZwytN1TRpOuAvJ5s2Ct7996X5CwNTU2gkyWO8oJ/r0tKeUVSgUMAwDx3GYm5sLnRjb\nvHmzdhxvdJSUveu6zM7O6owWy7KwLAvDMOju9qSLU6kUe/fuxTRNNm/eTE9Pzypb3xpUn1AZN5cv\nX+bll1/GMAyOHTvG7OwsjuMwPDxMKpXCdV127tzJ/v37dY3wjZrxojLr1eSkf2L49ddf58knn8Qw\nDCYmJvQ+bW1t7N+/n0QiwdatWxkYGMB13Q0ZYBARERERsfpYlsVzzz3HT/zETywZzwXvY2Es58Dz\nryuXy3zjG9/gp37qp5rmRLcsi6effpp3vvOdOlhxuQzgoOMx7P4aHNfYts03v/lNcrlc05zoUkqe\ne+45hBC84x3vWLLetm0KhULoeahrEhw7BccbQgiklHzjG98gl8s1zYl++PBhpqam+PCHP4wQgnw+\nr58b1JjY34ZdXV3aVhWE6r8O/nFgd3c3mUwGKSVPPfUUr7zySlOd6K+//jqXLl3ikUceQUqJaZrE\nYrE6J7L/73w+rzPAhRAkk0m93nEcJicnsarzTf5+9+KLLzIwMNA022dmZjh8+DCf+MQnEEJgWRbT\n09O4rovruszPz2PbtTmUWCxW1z/S6bQO4pVS6jZXZYRUIPShQ4c4cOAAH/vYx5pid0RExG3FZPU9\nbNKjt/p++QaPE3Si9wS2ibi9KONJ+m/CC7JIAEudPhuE28KJ7rouxWIxVCaoVCrpOqN+/DUSw9aF\nYRgGmUwmdOKx0YODEKJukBgRsZGR+TzuwsISh58EjKEhjC1bELEGP0ulEiwsNExxjdk2veWy5/kM\nwZWSS8ePU2GpS7ACWKtcE9cdGqX0c58n3xsoJSMgv2jzHx57mnNv1WQS67GBLMjFkPUu8kQR/u5s\ng0+WQBwIy8QUSDnF/v2r6ESXEnHxIuLkiXAn+sgIPPxww4wec26O9v7+UCd6MZ9h4UftzM6E716p\nwEsvuZw/X6ur6Ts4bW2rK1MpMhnEli0YqdSSddK2cV54ATk52cBJ3o7gH2OYSUToFhIpTaQ0CO9z\nkrExwbvfHZLNJOpKsEY0Af8EnGEYdeOT4PjB///bYWzhOA6u6+I4DgsLC1qKs1AoUCwWKZfLevyn\nJqlU7XQ1mblRUW1RLpcpFosYhqHbQ0pJLBYjkUjguq6e3NuoznNFmPNcTXg6joNt23oSU7WFaZp6\nQlMti2qhL49heC8l515rL5UtDQKBYYAwfMnFcqkKjkRiGNWFK/D7JqhPeA6/R9Zn8wplT0iK71I7\nW9hv/I3nby/hnZUQ6nyC33N1IWpZ7IYQdVs1Gg00A+m323VrHahuoxCFnTBZgKDzjRbmSKt+rWyV\nEmFU+7X+fKm6/BIbgldBQP2XRwhEi36TvfFEtVkFCMPXSkKA9PqA16/9+9W6mb59yur+/uuwwe8l\nK0FCfwuGAAAgAElEQVRPTw+PP/54qJP4Vn7//Jm6+Xye73//+zd9rEYkk0kef/xx7SS+Hqe/GpMt\nJ5Gt5tTK5TJvvvlm0+2OxWI88MADoVm/lUqloTKAGjMEx+f+8/YrJx06FFZa9OYRQnD33Xfz8Y9/\nHMMwmJubY3x8XI9xCoWCvuamaTI0NFTnRJ+ZmdFjZeWYVutHRkbo6OjAdV2uXr2qlbGaafvb3vY2\nfvInf7JubKrwO9Edx2F2drbOOZ3NZnW7VyoVTp8+TbFY1Oeq+szs7KzuY80aP2zbto2PfexjmKZJ\nPp9nfHxcP5dMTU1RqVT0tslksq5/dHR0kE6nl6yXUmoHu7oWBw8ebIq9ERERtx2Hq+/7Q9btD2yz\nHIeAD1X3ORNY96Bvm4jbjyTQV/17kQ3sQIfbxImez+d56aWXljjL1QD26NGjSwZSpmnS3d29xJGu\nBnZhDvZsNstDDz20RN5RCEEmkwmVfYzFYgwODhJXtdQiIjYyDTKCReC9IWH6g751y+0vhABf3cEl\nn78WHCnCBBHysywkrvQy0hvsSOPWq87cuWpaOGxfCJ/eC26zSlzrQfd6+kUDJ7tQevfL4DZo99Xu\nMsv2d/CCDhpORLle1Zqb/gQ1OXStY0Q0g1QqRVtbG+BNuqjJmhMnTlCpVBBCkEgktHM4kUgQqwYk\nbWRHn5InP3XqFKVSiS9/+ct64mx6ehrLsmhvb2dsbAwpJf39/VqWu62tbcM60FWW1sTEBOVymSef\nfJI//dM/RQhBoVBgZmaGdDrNvn37GB0d1Znog4ODev+NTDwe1+Nuy7L0hOypU6c4ePAgQgji8bhu\nm+3bt/PYY49hmiY9PT0bPtDgVrHtMqdPH6xmvnnLYlaeyoVztM9P6e0WiiZvHHmOSqKtzomey8Gl\nS7XjSSTx+Rni89O1iXTgymwwEeLWcaXkoBBYeHfAMi7ny29SyPfU3REtyyuDrR4HXQnJfI5nsm+R\nELVgTxfJySsZymU1TpPY9jGkvK+5drsu45cu8fQLL9QGJ44DZ87A5KQeA5VdQd45jBD1gaN5q8SB\nUxMkTB31gDU5SUFFNVStPyEES/Mzbx4JTM3N8ezRo6T9BeZnZmB8PHiSS0v35POeStX0dG15peK9\nquMfCRx2HKwW/N5fmZ3lwKFDpKq2SymZb5tiIfMWtTGUS+eFc7QFnkGklFiA6w8MsSySP/4xorNT\nO9EPHzmC5XPENIN8fo7jx58nk/EynCXQOXsWY+YqRrUmukRy8cIpjh192ns+0XZ7r6rAS3WhS3Lx\nKmalpBdNXL5MoVjcsPfZlcBxHB0EJ6VkYWFBO5gty6pzJJqmWTc/pYLC1G9mKpUiVQ26jcfjtLW1\ntfRe5jgOFy5coFKpeGpw+bx20gYD9vy2p9Np+vv7Q+fggLqgyJWqo61Q4+tGTnS/feA5dFWGtxCC\n9vZ2HbjYCttLpRKLi4vaYTwzMxMqba6ypP3BhPPz83VO9I6ODv0c4Q9qaNX32bIs7ehXzzD+QFf/\n5waDFfyBF5ZlMT4+zszMjA48VoEMU1NT9Pf3N912dXzbtrXjvFKpcPjwYRYWFnQ7J5NJYrFYXTCD\n/zxUkK+Ukm3btrF161aEEORyueh3NCIi4mZ5AZjCc4Bngbxv3Seq798O7LMNcIFzvmXfBv5FdZ8/\n8y3vBj4AnAKONM3qiPXET+NJuQO8spqGrAS3hRNdRV8Wi8W6Qa+Uklwux8WLF5cMhmOxGJZlhTrL\nY7GYHlT6KZVKulapHyVL5I+oVMv97xERERERERERfoKZLEpuOzjW8G+30Z2gfmzbplQqUSwWmZ2d\n1U70hYUFKpUKsVgMx3H0hGs8HtcBBxsdNYnun1j1Z6LH43GSyaRul9uhTRTBOplSSmzb1iUA1CSt\nUoxSGUK3UxvdDEII7r9/H0eOHOLMmdpcisDlrU4To2OTXuYKA/vSE14GtI8LF6A+6UqCKxHS9S9h\ndMeOpk6ICyHY/ba38d1PfpKj+nME7cY47xZ/uWT7oP/JEC5/LW2dzK3sLG85xiOjp6jluFcYG9vc\nNLsBtmzZQqqnh788cKB+hWXBYE3hSEp490ff4p0yEDxuwF+XnHon79AQ8hOfqNvOFoLNY2NNs3tg\nYICBvXv59uXL9fety5fDM5nDFIna26EaZKaXbd2qt5WAIwT79u9v6r1xYGCA4V27+NbJk3Xt5ooT\nSOHP25eIDhPxsY/Vt2/Y+RgGHDhQZ2fFcdh3zz1Nsz0ej3P//TuZmfkus7OGzkS/7Dqc3n1XnY2O\nUcS+/HXfuXhMTwcuT9WzLnzBuo7r8rb77tOO24gbx3Ec8vk88/PzSCk5ffq0lhOfnp7WjlApJZlM\npi6rVTklFQMDAzpQrqurq+VOdNu2OXz4sJZzP3PmDPl8Xjui4/G4tj2dTpPNZpFSMjo6ysMPPxx6\nr5VSaqd8sOb3ShCLxWhfRrlOyb0rKpUK58+fp1KpYJqmdopeK9v+ZikUCuRyOQCuXLnC+fPntdJQ\nT0+Pnr+0bZvJyZrqrpSSubk57URXzxlKanwlghXK5TILCwu6P2QymboMfv/vXzKZrGs//zrLsnjl\nlVc4f/583fOQEIKTJ0+ye/fuptqtSkqZplmXBV8oFPjLv/zLunnmdDqtr4EKXCiVSnXHUts+/vjj\nfOhDHwLg3LlzKx4wEhERsWGwgf8L+CLwx8Bn8RzpvwI8AnwDCMq6nMCTbPc/ZD0H/AD4SeCfAX+A\nJ+P+ZTwH6r9t2RlErAZjwPuB/8HymeWPAr/n+/8ftdKotcBt4USHxhJSjSacl5uIDlu+XG2q5ZZH\nRERERERERNwMQcd52NhkoxOUr/dLNQbbxZ91czu0TZDl+ktYttJGJBjAqs5bOdKDfeV2/E7dCoZh\n8PnPf74lDoIgSua0mcf7yEc+wqOPPtq0YzbCX8O1GTz44IPce++9TTvecjSzzXft2sVv/c7vNO14\ny6GCYprFjh07+M3f/u2mHW85lCRxM0in0/z6r/+bFfktMwwjcqI3Ef99Ovi3ym5WY6Dg/4N1x/33\nuFbd8/3Obn/2ddAJfiNO8dUenyz3+f4x6LW2bQVh7dmoTf19wL/tarZvcBx6I3O3weOovu+/Jivx\nm+fv747j4DhOTUHH97c/cFOhyiqpfaOEq4iIiCbxH4DdeA70n/Itfw74pzdwnE8BfwP8x+oLvJjQ\n38ZzpkdsHLrxgi6+CHwHeBU4jSfXbgL3Ah8F3u3b5++Ar6ysmSvPbeNEj4iIiIiIiIhYbwghtKyf\nmuAyDAPHcdi9e/eSyTohBOl0+rbJSK9UKpRKJSqVClu2bNH1BxcXF7Ftm97eXvbs2YOUks7OTjKZ\nDPF4PFRRaCPhui7T09PMzc1hWRZdXV16InFgYIBsNsvevXsZGxvDdV16e3tvC5lyIQRzc3M6+2dy\ncpLXXnuNSqXC1atXdWbzyMgIO3fuRErJ5s2byWQyGIax4fvNrSKEWNdOs0QisUQ5bD3QSCVtrWOa\nJtlsdrXNuCnWq+1CiNAScxFrDyEEpmlqCWj/2G5xcZFSqVSXie7Pkl5YWGB+fl7/v1Kp6NKGtm23\n3DGngpxSqRSqXrgak6pxiSKZTJJOp7XzcWJiQv8Om6apM+xVBrdyQAZLNbYC27a1bH5wXB0cE9i2\nTT6f122bz+eZmZnBsixisRhjY2NarrsV461YLKZL1WQyGbq7u3Ument7u87uD0rhB521hmGQyWT0\nNVDPHK2U0FfZ7wrVd6WUdX3FdV0sy6qTnu/t7dXnZpomqVRKf1dUGU41Nmn2c5HjOFrBSEpJd3c3\nmUyGbDbLXXfdRW9v7xI5d3Uek5OTzPrK0hQKBf3dNAwDy7IQQtQ53yMiIiJuAhf4HJ4z/X1AHHgN\neLq6LsiDeJWzglwEHsCTb38bUAL+P7zM9YiNSRfwj6uv5fhz4OcI708bivX3tB0RERERERERsc5R\nk3HXmkhbbuKns7NT/+2fEFVZDDcz6bJWnO9q8ni5iV5lq5pMy2az2oluGAa2bdPR0UFXl1f3tb29\nXdfDvtHMvrXSLnB9tgghqFQqevJRSacq6clsNktnZyddXV24rksqlaqTT72Rc72eftxq/N+nazkH\nXPf/Z+/Nw+S4zvvc91RVb9Pds88Ag43ESoIAQZkiSIkSY4uURNJKlFi2JcvWcuMb+ZEXhb6+yjWl\n2NJNtCSWrh07uvcq8SJZimyLjmXLiRNZMklJtkRxBUWCIEEAg20AzIbZu6e3qjr5o/qcqequHgyA\n7ukBpt7nGaC71u+cOtV16vzO930upVIJIQT5fJ7x8XFdV8pDOJPJMDAwgJSS3t5eYrGYDvF+Je1m\nrbSdiIiIiIhrAyV+9lQT0Mfjcd23S6VSjI+PaxF98+bNbNq0CfCeO0ePHuXEiRP6WP5IDJfqWzUD\nwzDYunUr/f39uK7LmTNnKBQKGIbBt7/9bV588UW9rf8ZuXnzZl5++WXi8ThSSjKZDPv379fPdn/u\n8dHR0ZaWQeWhn56e1jYqYVlNXOjp6dG25/N5jh49qnPRz83N8cQTT1AsFkmlUtx0001ks1kcxwkI\nxs0ik8kwODiIYRgMDg6yc+dOva42JHrt9VcTBQAtPvs9qNW1U+H0W2H7wMAApmkyOTnJc889h5SS\nYrHI8PCw7tsr+5RtsViM9773vWSqKT0cx2Hbtm16Ml9vb69ep9IaNZPFxUXGxsZ0GqB7771Xn+Md\n73hHYFv/uR3H4emnn+bVV1/Vy//+7/+e8+fPA969rtrdwsICnZ2dTbU7IiJiXXKE+tDtYRxaZp0D\nfKv6F3H98grw48Cb8cL+72Yp77liFC/E/xeAx1bVujaybkT0RiGKlusEhq27VFikK+lUqsHu653l\nBhGjwcX1gfD9+ZGAAUghoFFbaEIbMap/YcvXQgs0DDCM+t8Qb7x+OSsFl67dsHX+9WHHF4TX2Coj\nBNIwIGz2u1dp4e1DiKX1Ib/N3m8SCGTD3S9d5+1FCOHdN/UrvP+W29eXr7PRFo3bnZdE0ztNcH/v\nNm5/3ax1zp07x2OPPXZJ70Ep5YqEu9rBxasJy/jKK6/owdh2UCqV+O53vxuYJBCGEILh4WFOnjyJ\n4zhcuHBB96dKpRKO41AqlQKeOY8//rjObX25wm+pVCKfz19ZoZrE8PAw3/rWpd9bbdvm2LFj5PN5\nzp49q+1WnlPlcpmXXnqJiYkJpJSMjo7S29t7RTYJIThy5Mglr1crKRaLPP3008zOzl6yL67yVQrh\n5ZU9ceIEjuMwNTWl85lOTU1x+vRpnbfy8ccf13V3uSL67OwsxWJxXYTklFJy+PBhJicnW36uRCLB\nrbfe2rR2J6XkzJkznDx5sqXXSgjB/v37GRwcbNoxx8bGeOWVV1qeN1UIwb59+3Ru5atlZmaGw4cP\nBwSSViCEYHBwkP379zftmLOzsxw+fDjgJdkqlO3N6FvZts0zzzwTyN3cCoQQdHZ2cuDAgWsyusNa\nQE0MUxPc4vG4vsdVRB0losdiMS0cKhG0Nry76h+tVn5l5Rntuq4WcYUQlEqlgJe8//c2nU4zNzen\nRXTXdSkUClr49/dzV2P8zJ+/XHlkK2rrUXlN+/uhxWKRQqGg7101GaAV70m1fZTLue9q69Ivuqv+\nSyufiyrigmrrpVJJi+i5XC7wO+ufEKL696rMaqKomjiqvNLVts2ud390BOXBr+ounU4HJqf6UZGy\nVJQogGQyqcvj90CP8qFHRERERKwyZeAb1T9FN6AGi/LA+GobtRZYFyL6wsIC//AP/8DMzEzdDMzh\n4WHOnDlTt4/qBIV1tNRMw9pjJRIJxsbGQnPFpVKputBp6oVny5Yt183LpQptV1tvQgh27dqlw2T6\nicfj9Pb2tt2LKaL1vCwE3xIiVLIT5TLmxIRSjOspl6FQCBVDAXAcb12jSS7AtBChcWkcYLSNop83\nSD/L9773HTo7e+rWF4sOxeILCFFscAQXOAVM01hEj9FYLDXxHgdhIvokUFhROVqBKyWHTpwgZpr1\n11ZKOHMGzp5tPMlicRGOHw9dNVtMcPLMHLOlZKhMbNtQKrkIEdpigXM0rtPWIqVktlzmO5OTdId5\nM7gubrkcLrADRRwmeB4pYzQSyYUYARZC9xcCJiZOc/hwf93+QsDo6Kvs2BG99Ddiw4YN3HvvvZw7\nd67dpoSyZ88e7rzzzrZMhkgmk/z0T/80Z86cYW5u7pLbW5bF7t27Abj55pv18kY5KScmJq7Kvne/\n+91t8wi54YYbuOuuuzh9+vSKtu/o6CCVSnHfffdx77331q331838/HxgUPty2blzJ6997WuveP+r\nIZlM8q53vYszZ85w9uzZy9o3lUppuw8ePBhY56+fqxWF3/Wud+mB3OsZx3H4N//mt3n00UFM80a9\nPJ2S/NxPLrJ1yNcLK5VgbMzrv/kodm2g0D0UWJZ0F0m5SxNYXOB//v3f85uf/CR33313U2yXUvJn\nf/ZnnDgxzZYtNyKlN3fv6FG4eDHYzcjn4cQJr5/g7QvpNOzfX9uNlew2T3GLtRTp8NnRUf7xRz7C\nO97xjqb9xj7++Ld5+OE/o7v7ft1VMgy44Qbo6Ql2n86d87rU6tRSQjwOQ0NLy4SAM2ccnnvOZslB\nUWAYL/ClL72Nd77Tn1LxynnxhRf49+9+N2+dn0e/CUuJ/eA/wXnDPej+hZSYp45jnT0VvBDxBM6P\n3A6ZpTDWzM/i/M5vQz6vpzueFIL8O97B73/lK0173zx8+DAf+tCn2bjxrRiGZ70QcPKk1y31s2cP\n7Nix9F1KsCzv+vh/Fgxc+qw5DF+/89TICFPlMn/4R3/UUJC5HPL5PP/8n/8Gp04dBDpQ/djNmy1+\n9me7dfuVQEdxhs78GP6+rgS+/uw2hicygUthWcG2X6lMsnXrD/nzP//9pk4YWe8oATYsX3LtOkU7\nI6LU5ue+EjHWn65oNSejXW00p8tx3lmrtCu/u5/atuuPfhT2e+7f3t/eVrv+L3U+f+529b3deekj\nIiIiIiIaMFv9W9esGxH9u9/9LqOjo3WdkpmZGRYWwkWCK+EHP/hB6HLLskLF9Vgsxg033HDdiOiJ\nRIL+/v66Dq1hGLztbW/jlltuCSyXUpLNZunq6opE9OucLVu34rz//fxdoxcKw0BcanBo377l1y8z\nU1fSWO6UwA2pFFu2bl3++C2iq6uL17zmAIcPP4kQ9feBlJIHH7SXKZ4EevAmh4VhAPWThVaGZOfO\n1zVl4O5K2LVrF88/+yzfHBsL32BysqFIDngjlQ08FVwp6NpwgU7Z6GVVsnVr42aVTG5maxvbzIH7\n7+cHU1MNYwXIm25a5p4Q3MokN/NNGnuauyw3SSAWO8viYnj0g0zG4TWvqRftIjy6urr4wAc+sKYH\n1No1iBOLxXjb2962ZuumnaG5N2zYwC/+4i+u6bppB/F4fE23GVhf0TlcN4lh/ASW9TpACcwuP/2P\nZ/mRA2VQz9yFBThyBCqVgKK7sHkvc1tvWRpYBjorM3TaU3o7Bzhy4kQLrrngvvv+Gbff/nqk9ETy\nv/1bGB4OCsxTU3D6dND0ZDJcRL839gQPJP5OR1z6kxdfbDwh9Crs7ug4wNDQLwZE9Ntv94Ratcx1\n4fnnvfmFfhE9nYZbbw2W8amnbJ5+usSSA6CBYfxF003fadv8C8chrU7uOJTvvJvKL3wQpFJ0HeLf\n+y6xZ74fENFlJov7rncjBwdVgBw4P4L9u/8BUTXcAP5BCP6yyd583jvsjdx22wcwTW+ivBCQy8Gp\nU8H63bIF7rrLvy8kEvC614E/kIKJw/bEBUxRtVUIvv/cc/zJ3/5tU213nD7K5Z/Ccyjx3pC6u5O8\n611DWNbSfdczP8Lg1JFAL8+VkmNTB7lY6g+09WTSE9IVhcJxhDjZVLvXG34PcvVZ/eb5Pc+BuqhG\nKgS3+h0tlUo64oPyOm6luKhS8ihP9EwmQ6VSQQjB0NAQN954Y6Ccap/BwUH6+vq0R67Kqa68cZUX\nsl98bDa1Yr9fsPWPV9Wev1KpMDMzo0Oj53I5LYquRh9A5edWXswqZ7zruoEw7FJK7emtypHNZnXZ\nVEob9e5v27Y+rj/seyvxC8ulUkmXxR8ZALz7wJ9XPJ/PMz09rUOh9/b26pRYrQihXztBZbloCX4n\nLJUawO98lMlkyGa9CWEdHR36/m6F3RERERERERGXz7oQ0f2dmzAP6Wa+ODQ6VqPO8/WWK3G5elZ/\n/jpqNFM34vrjNa95Df/xD/6g3WY0pN3CyCc+8W/XrADQrroRQnDPPffwxje+cdXPvRLa3mY+9ak1\n22agvfWz1omefcsTtZ3GRHUTTlQva43adDDewLIhWVJ0/dGDav6XcimViHrKGUj9ZfkpXs3AaBjc\nyL+stsnVBczBReLF+xG02mZRrbdmIms+t6YEhhBLrUWLEYZPRPeufW3pJBJXgl4jQXotZRWsVqYt\nn/rIL6bX7xtcLnERsqYELevnybo/KY1qeZaWGlLWTJXUay5x/Oj3+GpxHIdcLsfc3Fydh+rmzZvZ\ntm1bYHu/CDo9Pc2hQ4f02Mv27duxbVtHT1SidKveIwzDYGhoiKEhL6pIT0+PFmAffPBBisXw6Gqx\nWIzOzk5d1vn5eZ588kkdEn7Dhg3a/lZFd3EcR9eVZVla6DRNM3DO2okL58+f54tf/KJOlZBMJtm6\ndasOL95KhxEpJVNTU5w6dQopJWNjYxw5cgQpJYVCgePHj+tJFCqdjxJ4E4kEP/MzP0NnZydSSlKp\nFG95y1vo6+vDdV3GxsaYn5/X7aq7u9Gk/eZgmqZ2Psrn85w4cYJcLqdtP3r0qK7jRCJBZ2cnPT1e\nJL9cLscjjzzC2NgY8Xic3/iN3+COO+4A4IUXXmh6P1HZqiZY2Latz6HCsSvS6bR2nDIMg71797J1\n61a9TSwW48KFC4CXwmNwcBAhBIVCgampqabaHREREREREXH5rAsRPSIiYm0QRRtoTCQAhBPVS2Oi\nuomIiIiIWGsoYTBMK78kYUqzOg6r88zz7JXajDD7vWXi0mWU3j/aA7BFNofZ4OnOMiAiS2TA7uB+\nYZO9g571Rquq33WDanNNZVYlXmRNDVa1dSR+oXf1JxcqcwVKlJQh16JBaGchl44hVisNjvT9L+uW\nBDaTVG0UelntPQ7h3yOujlpPdOWFLaUkHo8HPNFt29YitfJEzufzOgd3sVjU3sirkUtcedoqMVTl\nNAfo7e1ddj+/520sFgt49yrvdsdxVmVcQYnn6n+/cF6b3rFcLnPx4kXy+TxSSjKZDFu3bg3kg28l\ntm1rD/NcLsfFixeRUpLP5zl37hylUgkhBMVikVdffVWL6slkkosXL+rv6XRaTyKQUlKpVCiVSnU5\n4VuJP3d8qVSiUPBSy5VKJaanp3Uk0UQiwdTUlJ4UksvlmJycZGJiQk+2yGQygBe1qNn2q9DytW0B\nWDZaghCCVCoVaE/d3d06ekRnZ6dOLZpIJKL3/YiIiIiIiDVAJKJHREREREREREREREREXDWpFLz1\nrbBx45KQlonb9E0fhx/OosW4xUUvTrp/UFtKRMdGzBt9miqS8nPPM/f0o3ozF6icudI0Ncsh6SyO\n05sfQbqeiHWw32UH/sFwyfTGBAlrI8WKhRBLIdG3bAmKzlIKHHMLx603eKUWMN5ZYEeTvXSF8HJu\n33+/T9CVDrvtVxgYH18ScCX07dxOWcS1BVJArhjjyWcGkFJ45QHiiwv80n0jPjVUcHxsHCF20FS2\nb/diySsxwXXhlltq1FzBWW7gIi7CV3cx22DP8AU6Lk6iIxdMjiFWSeiJx72c81rzE3Bv3wu8vv8l\nX/uFPWaW7QvZpXkKEpAV5NeHWZCFpfIk48gHDkKiekAhvLQHTQ5Fb5oW3d2dGIbnUSqlpKfLpCdb\nImYu1W9H2QEzKFQKJPv3C9gQnFSRSgXDuU9PeykPItpHo5zcayGKVVju55Xkg15N22vzzftzVq/U\njtXOyX2l5/Db2OgY7Wg3frtWIiSHbdNKuxvV10rtbXQfLHcdIiIiIiIiItpDJKJHRERERERERERE\nREREXDUdHfDAA3Dbbcq7FsxihY3fPgwvn1tSx0slGBsLeiG7Lsbm3VgxAp7FpR98n4X/8P8gfB7d\n5cHBptsukPQunmdwoQdRFS43DpWRg07AUXvO6WTo1n5KNa/S9WPmAoftHJHbvW+G5NyJcbY3224B\ne/fCz/6sT2+1bbr+29MkXz60pOwLgXzgAS8Rt9bGJUfPpvnMF/qp2J6I7kr4pwfm+Dc/9QopywEE\nCMF/ffJc80X0m2+Ghx7ykoQrsgOeuq/r0+A4u3mG3TqIuASylTxbXvk7OuPTaBF9ZobKKuXtTSRg\ncHBJRHeRvGXjE+wf+iL+BiOsrYi5TYFlxcVFnv+bv2H24sWl4/V0Iw/8J+jsXDrJ7GzT3bpjsRj9\n/T1YVr9ntwsDfTaD3XkSprYaSg6YZs3ekoN3wY0SPYFECE9Ej/m0/5EReOSRppq9LvGn/6n1ZvaL\nbCpvtdpWeRIrwdc0Te25Ho/HV1XQvRwBWuX1VhQKBVzX1V69yht8NTy7w+rev8y27UBY+sXFRZ23\nXpU1lUqRTCZJpVI6x3gr7VXniMVipFIpHc0gmUwGwv13dnbqyAXKPtU+VB5627a1h3eYp3WryyGl\n1OVQXvKmaeo856qcruvq9u44Dh0dHWSzWRKJBLFYrOnpO2ttDYs00CiFqOubEGXbti6XECKwzjAM\n3c6jSI4RERERERFrg3UholuWRW9vbyBHDXgdesMwdG4aP0II3XGpxd+h9OO6rg6TFXa8Ri8+pVIp\n9HgqNFAtrQjD1ahT3KjTpl4Qwpbncrm6/QzDYGFhQYdeUqiXumimZURERERERERERMS1jxBBQVnU\nfVhuw5B3EoEnalffFwS0KF70kkCiFEKxtNRntic2r0RSEAQ3FCva68oIVGfV8MArnhD118Jnno6B\nMJ4AACAASURBVNpWUBUHBJi+hS0TUfyGa1f6usYSqLnaaxI4VptQbcIIM6NOVPEmbeA6S6Xwl12p\n06tenhWeTzUL3/+BW7kdpl9nmKZJJpOhszqpwj82VS6XdV5ogGPHjnH8+HHAu09feuklPfblui63\n3XYb733vewOiX6VSaTimc7UowVCNm/kFddM0GwrKp06d4q//+q/1mJpfeEwkEtxzzz1s2rSJSqVC\nf39/0+0GAmHbVU50hf838Mknn+QrX/mKrufJyUmmp6dxHAfXddm8eTP/8l/+SzZs2KAFdX+Y9GYi\nhGBgYIA9e/YgpWTnzp284Q1vALy84i+++CKlUgnwxuvUZ1XGe+65R+d7t22bkZERJicntd09PT0Y\nhkFHR0dT7Q4jk8mwe/duACqVCtu2bdNjn4Zh0N/fr0P+F4tFPv3pT3O6GvYimUzyoQ99iJ6eHoQQ\nHDx4UKcEME2z6WOoqVSK/v5+TNMM1KsKw67auUqpsLi4qAX1Y8eOceHCBd2m5ubm9L0yMDDA3r17\nEUJw9uxZRkdHm2r3GuR1wP/fbiNCyFb/rxcMIiLWDuqB+su0I6fRpTnQbgMiIprFuhDR+/v7+cAH\nPqA7LQopJWNjY7qD6CcWi7F58+ZATia1z9TUFDMzM3XnKZVKnDlzRs/q9HP+/HnOnz9fdx7HcTh1\n6lSoiJ5Op/VLkx/btnWOo2bQSKy3LItMJhMaYmhhYSHQ+Vb4Z8DWHiudTjM+Pl53rBtvvJGbbrqp\nrq4jIiIiIiKuR/L5PE8++WRof2EtIIRg586d7Ny5c9XPbds2L774IpOTk6t+7pXQ3d3Na17zGp3n\nczWZm5vj6aefDu0zthshBDt27GDXrl2rfm7btjl8+DATExOrfu6VMjg4yK233hrIf7meWPEby1oc\n+lkGX6boNUE0Jzni6lhLrTlCOWD480P7vYn9nt3KK1qN2ygPV0UsFiOTyWAYhnb8aLXtEB5Kezkv\ncsdxWFxcDIiR6rnpui6maRKPxxuOOTXLdr/HeSMqlQrz8/NUKhWEEOTz+YAnumEYZLNZuru7A5MB\nWuU84hf/a9tKOp0O9D/8YrhpmmSzWS2il8tl3aZU+VVe+NXwilZe2Mr2bDar6840TTZu3Kg90VX+\nedWeVTvv6ekB0PnEVxpe/XJR7VC1RX+78Y+x1ob0V/dguVzW9vnbhWEY2ot+nfQbb67+rVWigeqI\ntYxqn/+xrVZERKwD1sUT2bIsBgYGAuGWFI0GIhOJBJs2bQoV0S3Lquu0CyEoFovMzs4GQlD5jxcW\nBsl1XcrlcuhAejweD50tadt2Q+/1K8E0zboOsZSSeDxOpVIJXVcqlULrsxGWZZHL5cjlcnX5pQqF\nwjJ7RlwvTE5OcujQ86zVEVLLsjhw4AADAwOrfu7FxUWee+65gEfBWqK7u5uDBw+2JZzYuXPnOHLk\nyKqfdyW0u80cOnSIfD6/6udeKTt37myLoHYtcOHCBT75yU+ye/fuNRemTwjByZMneetb38qv/dqv\nrVr4RkWxWORzn/tcIPToWsG2bSYmJvj85z/Phg0bVv38x48f59Of/jR79+5d9XMvhxCCU6dOcc89\n9/CRj3xk1c9fKBT43d/9XfL5PL29vat+/kuhhI3Pfe5zoZNjr0ekXPpbVpfTGwUWAi5SqN9GzwNd\ntsmtVUKNW630Ulr7Pl+KVnqf16Kr8zK72y4SF1fvKtU/td7RTcary6UaDa4Jz+kc3CbsiIDwPVtb\nXP3KLF1vyBXVf22p9Wd/vbdIcKs9hdfOqY8GEXZ+Ud/yJRLpi2QgV3hvRFw9YaHGV7vv1ogrtcMf\ngruV4bivhlqh/XLK2s7r4w83vxLWSlu6FLXOUu1sN5c6b6Pw79dKXbeAbwGfbbcRIWwAvgKsfOA7\nImL1UbPifpW1Odj+U8A97TYiIqIZrAsRHcI7MrX5mRqtu9Rxltu+FaxG52q5jtzVnH8ddwzXPc8+\n+yzvec9ncN1thD/bLaCDRiNd2bTDj9+TI2Y1uMcqFS+35hXMrHeAV3M5/tW///c88OCDl73/1TI6\nOspv/uaniMW2EIul6tYL4eVcbDgRWUriC1MY5Qb9+9FRqIb4C6W3FzZvDo27uFAqMd/by5/+xV+s\nuqAlpeTb3/42f/mXf8XmzZvrx/EEOMePYz/3LDQIz2bEYiSWE1UaxZuU0ssDuWMH9PTUDyIKweHj\nx/n13/xNHmxDm7lw4QIfe+ghek+eDJ0aLYA0y0ybFgKjqwuxjDetPTuLGxJxRBE25K2YBu748Id5\n+KMfbbj/ekaF1vzYxz625jwMDMPgj//4j9s2wU3lQPz4xz/eFqF6OXK5HA899FDbBuVc1+VHf/RH\nefjhh9ty/kYIIfjyl79cF21otVBt5hd+4Re4/fbb19Rguwrx+tu//dvtNmXVEALicS9HshLnjFIJ\n+zvfpnTsRXQg7v5+Ym9+M6Kmb5E89SrWb//fSN0flMj5adx3vlNv4wKJY8eab7yUcOaM1+FyXRzX\n5eUnnmDy3LlA7zTR2c/+gw9gJdPoJ2GpBOfPB/oLEjjW/wYOD77ZC6UuwJcCu6kkzAqd8QLSrQpP\nRpkYFbAdMKo2OQ78z/8Z3FHA0JTNp8envfT0VbtviN1K/PZ/BvHqM0oIaPI9LoH5copjsxtIJpY8\nIydH4szmfQIDLr0Xj/HTmWH87wmWXaD7xe9BcU6L/Itugq+/+fOU3ZjWhF+depVS7GxTbQfoT+a4\ne9MpUjGvDUsBPTclEM6PBMPTOw7Mzwf2jRWL7DFNbF/7N5JJrGwWstUIskJAOt30uOi9vfAjPwLK\nGdWVsK10EvOXPwHViRRICQcPwtvfDr7JfgLY/uwLDE3MV80SSNum9PQTOGNLIYfzhQXMRGu9ndcz\nKlS6GltZWFjQEXyUV7Ty2FYe3EBgvGq1xq6klJTLZe0AUuuQ4rdDbef35k4mkwghiMfja2riqW3b\n5PN57QVdLpfp6OjQXuepVKotY1+145Iqn7y/7vzpLE3TDOTnrk1P6fcMX436r22jruvqtiOECETD\nLBaLlMtlSqWS9tpWedT9udVXEzW27Hd4UuHclbOV/7t/3NWyLN3ua73Xr3MuAI+224gQbqj+39w8\nABHrjW3AHwH/HfgzoNnh9lT7/By6E7em2E0kokdcJ6ytkduIiIjrFtt2mJ39MVz3Jwn3NkkDQzQS\n0Qc6y3ziV07RmWrQL1hYgMcfh1zusgebiq7LJ555BrvJebJWiuu6JJOD3H//x8hk6kUj04Q3vtEb\nRwtDOA7Z4eeJz4zXl10IePRReP75cMHYdWHXLnjTm0L3PTE9zW+dOHEVpbs6HMfhPe95Lw8++OMh\nzjAuxS98gfzffxcaRMaIZbP0VHPRhbKcgBmPw7vfDfv3162SQvDwZz/b9NxqK0VKSWcux8/PztId\nsl4AW4FGPo/SNIlv3ozR3x/qZSSBxYsXqczONnTgsgnvpRvAISF4PiQqS0SQ1RrAvBzWik1rcdLd\nWrFpLVwfP2vFS2w1J7ReDmvNntXAsrw/5YkusJFnTuG8/ApQFdZ37CDW2wudnQFvZ+u557CeeSYo\nQu7eDbfeqo9vA9bYWGuMn52FyUmQEte2mfjhDznz6qs+SR/6BgZ4zQ0b6chklrq0i3k48UrgmepK\nyZnSEHNJryKEgFbNT7KES9K0lwRz6aC8+AMuxydOeIK/7/css7DAm/PPe2pqlbgpMTd9EBIJbz/D\n8CYVNhVB0Y0xVUoTlxm99PwETEz4TZRsEpPsix8n8J7gFmHsjCdQV0X0Sqyfo/veTsHMVnO7w8jI\n90iYX22y7ZCOldmWnSUdr05ZFAL6YjA0FNzw4kWYmQnUuVku028Y3o2ivPwty6vvZNL7LgS0IN1Z\nRwfs2bN060kB/SenEV/4H+BWo+NJCV1d0NfnvYgopKSn9H2YPul5+wuQ5TKzz3yDksrJDaQA4667\nmm57hEcul2N+fl57tD7++ON86Utf0s/ALVu2cODAAf29u7s78M6y2l7rZ86cIZfLAZ6A64/2WCqV\ntJg4Pj5OsVjUucO7urq44447tJCbyWTWzHN+fHycJ554QqeMHBwc5A1veIPOwb1ly5a2pP4RQlAu\nl3WEu3K5TDqd1rZYlsWOHTv0RAbXdZmdndWir+M4gaiXXV1d9PX1ee+enZ0tn2QrpdRtVUXgVN+l\nlAwPD+s2WywWGR4e1hNI+vr62LlzJ7fccgvgtTVVjtVIheQP365CzYNXp0eOHGFqakr3mScmJpib\nm9Nl2blzpw79n8lkcF03IKZHRERcs8wAtwNvBn4Hb8LIHwB/AzT2WImIiFhzRCJ6RETEKmIAJo09\n0WPVbUIQkoRpkrAarLcsb5DHMC5bRHcBo83iiDdDPI5p1r9sm+bSgHT4vjZx0yJhmuEiupo13qiM\nhuGdJGTfmGG0PUOiZcWIxxPUvfsKiW2ayz7ILCFIhKTSWDpGA090WKoXrQQsIVcpL9xyqLsprPyi\nurxh3QhBXAhMw2gYKrRCWDDVII1EdPMS+0VERERErDP8oUupijhh4dxVv8Uvovs/r4adNQKTkEt+\n8dp2YVCdIbC0RhjUBegWQkfIXq7L0TTbw+qz9ntt/8UwfIHcqX5qcH2ajOctHgx4L2rqSvi2DPYu\nRHDj6mohJYb07SWXi53TrFL4TuFvsw13aV8vSV1W1a+uxi6o3ndVwdx1l9pJaEh33z2qPivPyVYa\nHwF4gqDjOPr9Zn5+nvHxce3F3dPTQzKZ1IJzbdSj1Z4U6E8FWJsfXK1TXsa1XtAql7s/9/RaoFKp\nkMvldGqt7u5uMpkM8XgcKSXpdLptky/9QrQSY1UbsCyLjo6OQK75qakpHdnAcZxAtE7TNPXEAJUb\nfTXLodq0+q5ytqt0mqVSSXunVyoVEokEmUxGl82fFqCV1E5IUR70yiu9UCgE0rAVi0UqlYoW1U3T\n1G1nLbXziIiIq2YB+D+AL+INk725+lfASxfwZeAHbbMuIiJixawrEf1ycv9c6jjLHat23XKzB1W4\nnrCO0nLHD8uvfqWEHcvfUW207kqIZlJGRERERESEU6lUdKg/NXAF9aEv1yP+cI6Li4vYth1YL4TQ\nIT8BYrGY/tzuCS/tQEqpPbn8A9aqz6kG+67nuvGXG8L7uxEREREREdcS6rmuRPTa/OHgPe+Ut7c/\nnLv/GKtlq98utUzZrvoqyhO9Uqno/p5/zE3tXxvqezVRIfT93/3jeEpsVoKzZVltC+fux9/X84ca\n93to1wq+ah9/vYcdu5U2N7K1tg0kk0lSKS8dXjKZbNivbWV7CQuhr8qhMAxD16kaz1X4++T+8ddo\n7DQi4rrhvwAfB7bjCekAGeBfAB8ERoDfr253ph0GRkREXJp1IaLHYjG2bt1aN+AK0N/fTz6fr+vg\nmqZJV1dX6AvHxo0bQ8MY2bbN3r1760IFSSm5ePEiF0OS4JXLZc6fP69zEPnp6+tjcHAwdJ9Tp041\nLSSRaZqhnc1kMkl/f3/ouq9//escPXq0brnruloAqCWdTtNTEwZQSkk2m40GNSMiIiIi1j3KqwW8\nwRYVerHRZLv1hF8Unp2d1V5L/ryZvb29us+y3utMSqkHox3H0R5JpmmSrObgrfVMu96oFdFXK2xt\nRBBZ90F9Dxkcrg09Hra80b4tQPr+/OHcNWGe3w2OI3zHWlWuwJM/fKsWiSdc2qwVnTnQPnx1vpoV\nLqonD/zMyPDP0t+6oGHrWIXfrKVrUHP/rbS9SFl3n0Q0D39IaPUsn5qa4vz58/qZNjs7i+M4+pm3\nc+dO3ve+9+l+Un9/f0CcU32lVj0TlVDu92pW55uZmdFe6QCvvvoqR44c0XapSZBeurMkQ0NDevKf\naZqUy+VAWZuNX8T3C9BTU1M89dRTervTp09zww036Bzit956K+9617tIV/OvJZNJnSN9NcRQvxCb\nSCR0fm0VHtwvPPvHJV3X5cKFC3qs1DRNhoaGdN70dDqNYRhaXG8Vqs8Wi8Xo7vaSlS0sLHDu3LnA\n2GsqldL9eyklH/vYx7Rd8Xicbdu26W1t2w5MDmgVhmFg2za5XE63m2w2qyeyOI7D9u3b6evr07ac\nOHEiMJlk+/btbNmyRZdD3QfXe189ImKdIIHfAz4D+EOPqht8K/CbwL/F80r/Q+Av8LzYIyIi1gjr\n4omcSCQCnanVxh+mqpZSqcTw8LAOQeRnaGiIzZs3h+7z0ksvNa0jGIvFQoXyjo4ONm/eXLdOCMGr\nr77K6dOn69ZVKhX9IlG7TyaTobe3N7A8EtEjPFR7Wa4dqFCNcl3GCVSRKkPfwUXN/9chjS9540J7\n44HrsLGsAOkfJA3fYGXHof4KRDV+5fhFvtrP653aeqmtm7Dl61k09Ze/UZu63uumtnzXe3nXDhJR\nKSHKBRXNHGGXsZNZ3OyA3sro6MG0EggzHtw90YFMZarXy+v4iFQaI5Ek8IRpZRQFJfhIiSUlcYLP\nOst1cYtFXP9EnVIJUanUPD8lZjlHsjgJGAghiZVzQLr5NjuOl+tcnV/Z4m/3QuDYtrfOt8yVEnr7\n8QX2RnZkcKRAqOK4oiVdKgMXy3AwxVK+5jgOCZylc+NiGu5SCH2FaUIq5ZVddZKtDsyYwDJV6P3W\nNZWKI5hbNClVqu1ACNzFGLIQTM2UkCkSsXLgWkjHYpEuHIlXJgmG20lPsYQRL+jjETJGcLUI6RBz\nFok73qQ9CViigpPtBtfxKs51EVYco1Cor0DHqesnimQSI5ulWhovhP51HOmkHfjHVwqFgs6JDuix\nJCXg9fT0cODAAb3eL8DD6jwPayPhqH5HsVhkfn5eb3fu3DleeuklADKZDFu3bsU0Te35nclktJCo\nxPPVEKZrIzIWCgVOnjypv8/NzdHV1aVF96GhIfbt20e2eh+0w4tYiej+8O2A9tQGb7xueno64N29\nsLCgRfR4PE4qldLCuxJzV6vfaBiGnuyp2rl/grGyCTzB/95776Wjo0PvXxu1oJW5xWvrpVKp4DiO\njkqgJkO7rkt3dzfxeFyL6KOjo7pupZR0d3czMOD1kWzb1gJ7FEUpIuK64Wt4Qnoj1AvR64GDeJ7p\njwJfAP4aCPdWjIhYfe4G/jtLL2WPAz/VPnNWj3UhokN7B878Hbmw0OiX26FbzQ55s8/lDzcWsf5I\npyFuhd2L1YEvt0IjUbS7o4woFvCNqAUplbzBprDc3gBS4hQKoeKg67pI38BCOxAiPO+5lF6RFhcb\n65rClaRLFRKlcn3ZhUDa9vIeJbaNUPVXa1S53H4hejEP83P1CbiFC8X6qCB6NSxVbIM2UZ9o3bfO\nMHHrcpxWf8+FQK7lF1ohEOk0VAcZ6lYbBnR0QDLZ+PpmMohKpeE0BWGY3nFqlwOizffTtYpt29rb\nQoW1FEJg27b2uvAPqCgvEagPVR3W71lpqpm1iBpMlVKyuLjI4uKiHohS/St/6Ex/nTUK7egXlP11\n6vfQWusDV7VhH/0h3IvFYp0XmD/EaKM+WW1IylpvrGvVM6ZVA5FhHu/rGaNcJPPC9+ldnNTerWXb\n4Idv+wjz91XbjYREd4otN2/GSgQjRrj9B3EP5gLPnu6hJL2bUvpRLKWD+8KLrSmA42iR0HAcbgFu\nJNg7FbOzLH7jGxSVNxxgOA4dhUI1/zZ6+e7j42xMfl0vsxYnEe/7RHNtlhJGR+HJJ5ee6a4LuRxk\nMroP5DoOF4eHsWdmArsb224k9eU/BXOpz1DpGuBiqQsqXr5yYQjmSwl6Mk01nK5EkZv7JulILDnb\n7J4+Q8W44Ou7STq7DOjcSuBKCAGve12grxOzY+w7m6IilzaxLDh/vpl2e/zwRIYP/387sIyUPtn0\nuW7mx/cETLzvx+L8ozcG+2O5eZf/8swUo8WyLlPvXIkvfv0xelK+SQ4XLjTsy10p3aUJ3jjyVfrS\nntiHlLgdaUY/+yWEUM9LSWZhgp4/+ROEf8qklJDPgy/CnwFk3v72QL84Oz2N6RMcI5rPtdBHCaPW\n7kaTRhuVb7XKHDYRb7l+9mp5nF8tYYJ4WF23qyyXqvdaGqXZvNT1ahWNztPOVAQRERFrgvPABFAf\nbjiIAFTH7z7gfuAiXk71LwNHWmVgRMQKyABfAXprlq0LmjoCVi6XOXbsGNlslt7eXvr6+tZ1KM2I\na49SqcTk5CS5XI7x8fF2m3NdIQR88P0p3nZ/F0KGCJf5Iky83NDVOuXk6Pjm34FsIM6ZJvT3Q03K\nAIWsVJj+4hdxZmbqRMGylBRct61icSoFe/ZANXqZRgjPYeiRR2BhIVwLjuHwPvkK+zhC3SQEIeD4\ncVzHCRc2pYSREcTjj4eL6IUCdHZeVdmuCtdFfPmPMb/z7RAXZ4n1yitYIekwFGZHB+zfH15xi4vw\n1FPeYGDteimRyRR5kaGS3VrfNgSUYy3wJLsMBF4sqETYukSC+K/8Cok77ghv11JiLCwEvdFqSP34\nj5NcZpJBuX8T5f6NdasMA2JHX4HZiRWVI2KJ8fFxnnrqKS0Oq/QohUJBi6HpdJp0Ok02m2Xfvn3a\ni6Gnp0d7ZvhDmfsHJjs6OnTIQb8HSqlUWvMDsaVSiTNnzmDbNk8++SSjo6MIISiXy3qyQSqV0uVQ\neRH94q8fv6CaSqXo6uqio6OD1772tdqjpaOjIzBRYa1iWRaWZZHL5Thx4oT2YJmYmKBUKtHZ2ak9\no+LxONlsVnv6qHbi9+5aXFwElkJ+2rZNMpkknU6j8k/eeOONa77NwNIECsMw6OzsbMn1LJfLzMzM\n6HtKhZ1drwjXJTY3RfziqLdASlyRYn7ojVy0Nuqw5h0p6OmEWM3bqJPagNu/9FiWQMcAyA1L20hp\nQzJFS/A9MwWQBToI9q7sSoX86Chq6qfES3Do1mwngczcHN2c0Mt6DaM1/c1iEaamlr67rjcZwD+R\nUAhKuRyVublAeHrLlXTc+QZELLlUxjKU8l6hhPDmLVac5noWCyBuOnQmyqQTvpqzZkCMBUR04v2Q\nCkY0IxaDHTu8WbpVjLKgc8HCrnZ5DSMwj6CpzOZivDicQRhpXZ6xMZfJyRiqJRgGbHU62d8f7EvP\nWC7PGYsMO6qfJRiqzGKP/ldI5PT+XLwIGzbQTGJuif7COQZF1XvTdSlkbuDsgdeBYXltWkDi8Pfh\nh99eimig2m0yGRT2hSC2aRNUnzMAsfFxxLlzTbV7vaEmxqlnsx9/Dmu1be2+qm+ktvdvo55XauJd\nK/CHcw/Lwe23Vf1f+6dS0vg96tX/rbRb1YtfiPWHkPdP4vT/2bat0zSGXZOw8jcL13WpVCoNJ44q\nVB+x1h7/9fFHLnAcpy5XfStst227Lk+4av/+eq+NbuCvcz+1KQVaUefqmkN9vfrLE5Z3XrUnVSbH\ncSiXywghAuta2dYjIiJWnRNcWkT3ozpbA8CvAv8XcApPTP9S9XNExGryG8B2IE9LQqutbZoqolcq\nFUZGRvTgY3d3dySiR1xT2LbNxMQEExMTzM7Ottuc647dO0z+0etjiLCXmPkCjMw3Hlicn4fD5wOe\nDwFSKRga8v4PQZbLlCcmsMfH60R0G3DbKRTjjXF2dkJXV/26chnOnvXG0cIGAeNAPj0P8Yt4/iA1\nLCw09LiWgCgUYGIi/ODlcmBwctWREuPUKYwarymFMT6OscyLpWGaXqWGDSgYhle+QiFURAeBIywq\nsY46AV8IiWs01zPochF4VzvsKStME3PPHsy77gq/pxwHRka8+6oBVjxeHxpBISXu5h04QzfU1Z1h\ngBFPwve+teKyRHiowZhaEb1cLusBvVgsRiwW0yEDVdjGWg8H/wATNPbGvpZQA1CVSkUPNKk0MhAU\nxv3ieVhftHZ9uVwmFotd014i/gFDNSCpBoP9g6O1g75q39qBbtUe1Z8aUG1lXslWs9x1vZLw7/7j\n+etyPYvoQFV1FUvim6hGKVGra/4PUD9nbXW5jGt3qS3bYrtqkw3K4b8OsLyN/kOoS9p8VnLQWquX\noQ0/3ctbJkPX++LC6G+r9rMRNmmWBu2ithGoezqipQghmJiY4N/9u3+nQ1v7mZ6e1iGuAU6ePKkn\nugG8+OKLPPzww3p9rZe0P2rP/Px8U/uIqm/2W7/1Wzon+NzcnO6rFYvFgOg5OTmpx13y+Ty5XE7b\nNzIywsc+9rHQfu7LL7/Mz//8zzfNbsUjjzzC008/Xfcsz+fznD59Wn/P5XJMTExou5577jk+/vGP\n6wmttfjr//nnn+f9739/02w2DINHH32U0dHRS24rpdS5u9X3hYUFLdoahkF3d7cuh2VZun0cO3aM\ne++9t2l2g9cHf/TRRxkZGanrO5XLZc6ePavbi4oY5X+3+e53v9swOpJf9H/11Vd54IEHmtY/E0Jw\n6NAhPvrRj2IYBo7jBKKJ+SeqAiwuLup+tJSS6elp8vm8Xv/yyy8HUgGo6zEyMsK2bduui/e4a5gf\nAxp7HkRErJyrEcjUTPDtwL/GEzO/eNUWRUSsnDcC/wpv3viHgc+315zVp6kiejqd5r777qOzszMa\nPLpMajvpKxm8bcYgXaOwU2pZo+NfyXlrB2zXIul0mttvv12/mEW0AEn4AJdcZp1af5UjeJcxBNcW\nGt0aegy6QfEFVFeErVy+xJesj7XwW77cdW+VfY3q81pDNgjjr5ZfajC04e+12j98VTujOlzL+D0x\nSqUS4+PjuK7LxMSE9g5OJBL678SJE3qARnm/SimZnZ0NDMwA9PT08JM/+ZN0d3eTSCT0gJM/9+Ba\nRoUnVx4fKiS5ZVk616M/36c/P6gTEoWjUCjoSQrZbJa+vj46OzspFot64HAt91f8LC4uUqlUmJub\nY2RkRHsMjY6OUiwWdXsBr/9W22YgfIDddV3OnTtHpVJhcHCQ7du3o/I2bt++fc339W3b1gPDUkqG\nh4f1MpWPtaOjg/7+fgzDYGhoiJ5qNJtYLKYnBfvbkJq0Al4bq1QqFAoFLly4oCe1DA4OisPWgAAA\nIABJREFU6vpev/g7dMHw5rWe2nXUPH5X/y5cst0z5cotWHXb/b9Zy/x++a+D9P3biNbe6iuYNdGo\nLxN2LP2bFpxT0E5kNa1BQDoPia6E3q5++xYY5DtPyFhA6LaXdZLQ40asjGQyycMPP1zXl1OspH+y\n0md0d3c33bWh0K6CZDLJL/3SLzE2NqaXXU1/qlE53va2t7Fly5YrPm4thmHwnve8JyCU13Kpcqy0\nzt/ylrewdevWyzFvWe6///6rqovacjUqx3333cdNN910xecJ44EHHmDbtm0N67aZdX7zzTdftn2N\n2LRpE7/+678e+o6xElZa5wDbtm2LnNPaSwEvFHdExNWyhasPfe3iifEjwA+BvVdrVETECkgCf4jn\nx/WfgCfba057aElCw7U+qNYO/Pk2/ZimGQg5qlDeZmEzDtU+/hBeV4rKjdnoPIVCoW6dml0cFlpI\nShkaKtOyLDo6OshkMnX7+MOvrhXWmj0REREREdc/yvNASkm5XGZqakoLmXNzc4D3PDVNE9M0OXbs\nmBbOVf5rgDNnzjA1NRV43m7evJlbb72VLVu20NnZqYXnWCx2TYjoygNdeaOowSTlma88axTFYlF7\nVdeGq5dSMjMzowen8/l8IJSi31NkrSOEoFQqkcvlmJ2dZXJyUpdhbGyMYrEY6GOqMkLQQ8fvwa/a\nhuu6nDp1inK5zI4dO8hkMi0LQ9oKXNcll8vpySnHjh3T6XrU4H53dze7du3CNE0MwyCRSCClpKOj\nIyCiqzL7+8S2bVMoFMjn80xNTWHbNpZl0dvbu65FdFsajOR66J8dBCmRQMVIUEhYyOoYsJQgpEPS\nKREzasIU5wvIxUJgmdnRSaWyFKpHyoYBdq4KCUyJfkbFJu+b4WBt3ochMgifma4BZkIGQs6bUmJU\n7y2FAESlEkydEpZCphkkk9DXtyR6Oo4Xwsh1lzyNpSS2fTuivz+wq7t1O5OTIjAykMt5u+uyCC/t\n+sBA80yWQLFscnE+xmJCvT8K0rFOUv0DgXDuTrYbJ1ETNcqysMsxpGGiBNtSRWCaaP3ZMLyMT60g\nrB0ahkEstvQuLASkUgaZTPCaVypgGCqmEIDANWIs9t9IPlVQiyi6MaTR3PZSMeLMJDdiJrNANQqJ\n1UO8MIMQpj635ZbqI3xJ6UUp8v0WSiFYkFnK7lK4/Rm3gCMj0edKsSyLN73pTe0244qwLIs77rij\n3WZcNkII9u/fz/79+9ttymWzfft2tm/f3m4zrohr1fZMJsODDz7YbjMiVoengLe024iI64KngDuv\nYD+VQaoI/BleOPfv43V+39006yIiGvMx4CbgHPDrwI72mtMeWiKiR9QTj8dDQzupvKZhA7Uqx2Ut\nqVSK2267rWm2NRKMp6ameOmll0JnV46Pj5PP5+v2TaVSoeGGLMvizjvv5E1velPd4KvfIy4iIiIi\nImI9UxtOfCURY2o/+5cpGuXNvFZplEPTvz5seVifp1E/6Fqrq+UmAPpFcv/nS633f78WqS2P8iT3\nh/L3L1MhY2vzcoZNNvUf73qoq2aRqyT4/SNvoO/CXdq5NhYXvPbuBN1VjU0Cll1ka2WYlPSl6REC\nTh6Fl19eElClJHfwTUxn7g345YakQL1qJILHrbdyKv66qlOz5JYPvJOBPjvgVJtMwo3bZDDjSbmM\nERZKd3zcU5/xxEbx1FPNN1wI2LoV3vSmJRG9XPZypJ8/vySiGwYDH/kIMpv1hX2Hi4UMf/UNC9d3\nuJMn4Qc/CIrEs7Nw663NNX10NsmjLwwQj3upg6SE2w/0ccvrnYAjdi5nkMsbgWWOA9MXLSq20Lnp\nATq7ljYyDC9Vdysi4UrptUP/bR+Pp+np6Qhst3274MCB4L4TE4JUKobf49xOdTP8Tx5iqmtp6bmX\nnsR+/q+aavdcYpB/2PqzdGX7dfCgXmuOf3TyCUx/9PbSImJHyBjZ3ByUStWCS2wsDsnbuFDZ6e0H\njNnHycvvNtXuiIiIiIiIiIhrFIEnQl4OZTzd7nHgC8B/wxPSIyJWk4N4YdwBfhFonBP0OidSLleJ\n5XLYXImArLxjWkksFqNUKunQqX5Ufs2wwcJ4PF4XbsiyLNLpdKgnugrJGhERERERsZ5JJpMMDAxo\n72IlAA8ODmrvYb8orJ7DUkoKhYL2EjYMQ+cMVM/jTZs2MTAwQF9fn/ZmrxUL1zIq7LxpmgwMDGhP\nX7/w6c8HqkKaq9zyKjz53Nyc9jpXIfLT6bQOeZ5KpXQf61qa4KeudUdHB7FYDNd16enp0V7pagKj\nbdv62vsjCsViMUzTxHEcHcXAdV1mZ2epVCraox2W79OuJWzbZnp6Gtu2qVQqjI6OsrCwwLlz5xgZ\nGQG8az81NYVpmoyOjtLf36+jKvk90SuVCqZpcuDAAQYHBwEv4sPZs2exbZu5uTlc1yUej7NlyxbS\n6XTbyt1uXGkwX0lhlNI6OnUcsP1zaKUXJt3CJqadK/D+twtQzAW2NSolXEnAG7w1CIqig7zIIvGE\n13JnJ06fzxwJbofEGJKY/p+IUglC3pmwbVhc1KlpRKve4WIxz2tYvWdZlueCXZO2xezuhkDoZokx\nn6JQEDi+TRcWYHo6KKJXfzKbiMBxDQplC6c6LOFKsC0Lam4hWQGnHAxw7giouFCxg+m6E9ZSOQyj\nNQJ6I4QwMIzAHBBiMagN1BaPK0900KUSJna6m0pa6Mj0dioLorkFcIVFycpQiHWC9OKClkUZyy5h\nCiXfS5COZ3wtgUoVIAzKJCiQ0tenKJMNssFHrJRSqbQqkV/8fcZm4Y/q0yqEECQSiabaXalUQse+\nmk2zbVf9nFaj0ig1s39s2za2ba/KxNVYLNY026WU+l2t1ViWFeqQFRERcU2xF+i65FZe+oAUcAT4\nI+BPgfGrPLcA/jFwd/XzceCrQHjOmMZ0Aj8L3IjXffwO8K2rtC1ibRPHa4cW8DXgb9prTnu5dkYH\nI9YMV9rZv9Y8uiLWIFfZhlR2vmtxSKdtd881fN9eVTZGeR3kcryaa3epfQN5OyOaRWdnJ7t379aC\n765duxBC0Nvbq0OuFwoFisUi5XKZ2dlZHZJ7cXFRf+7v72d4eFjnCxdC0N/fz65duxgcHGR+fp5i\n0ZvEfK3k2DNNk0wmg+M47N69Ww/OOo6jxeBisRiYYKD+r1QqemLC0aNH9WDjzMwMUkq6urro6Ogg\nnU7T3d1NX19fQGRe6xP9DMPANE0SiQS9vb16MDKZTOrJAmrCgApBroRhVV/ZbJZUKkUul+PQoUO6\n7BcuXKBSqbBx40adQ/1aEdGLxSJnzpyhXC5TLpd58cUXmZub4/jx45w4cQKozxGvylY7WcVxHFKp\nFB/96Ee5++67kVLy2GOP8dhjj5FMJtm4cSOGYZDJZDhw4AC9vb3hRq0TAtKgqIY193u4irCta777\nQnmvds/Nb79QJvjXNeoihD071bLV7k81Ol9YfvFqFftruar5L3PdmkfgHCxztWuagtpW1DaZaxmp\nJWzAm2zSynOBNzlF6Ir3tf6rqNDr4lq0kcXFRT74wQ/qiW8QjKxSS6OIRbXb16ZwURMQP/nJT9Jf\nk+bhamz/1Kc+xenTp0MFyzBba1PuNMJvdz6f57Of/Sw7wqIlXAGu6/K5z32O73//+6ET4VbSF1yp\n7XNzc/zO7/wOO3fuvHKDfXzta1/jT//0T1ec2/5yxuX82+ZyOX7iJ36C973vfZdtYyO+9rWv8cgj\nj5DJLKUJvpp+d6Oyzc/P8453vKNptp89e5aHHnqIzs7OUHvDUl/6aXRvqnXqL5/Pc/DgQT784Q83\nxe6IiIi28TNACQjL+WXj5feZAf4YL1z7i006rwX8OfATwAKeJ/sA8H8C9wJjKzzOduCx6v/jeLnd\nPwJ8EfjfiQYHr1c+CtyK1zZ/pc22tJ1IRI+IiFg1pOsiHSc8kaXrIsIG9iA4ElPjUVN7/IZJMn3L\na8+wFuTSRmOsemBQVhAy3KdDUAHpen/UiBsqnPEy5172NXUNjII1Cmet169k/7B2IWVtbYVus2YR\nAhrNpjdNJALHbdC6pddSRMOrL0LH2f24LjiuS20LalWu2vWAl0vV8zRQIrAS+fzhp03TJBaLEY/H\nAzmqlYiuhE7Lskgmk9rjReWGVue6lgRR5f1iGAaO4+hyKO9pWPqtEELoZf56VAKy3+tFeePH43Ht\niXWtTfpTbcQ0TZLJpC6bEn/9g3Eqb7eUMuD5o1LrWJal25SKamCaJpZl6fZ2rXjDmKZJKpXS9vf2\n9mJZFvPz8wHvIXUP+AUKf754QN9LyWRSr/Pfa6r+1USDiIiIiIiIK0VNgPvQhz5Eb28vUso6z2X/\nZ3/EGVj+3cnf9ysUCnzmM5/R0Y6aZfv4+Di//Mu/HCrMq/6Jwp9iJcxu/3e1XaVS4fOf/zyFQqFp\ndkspGRsb40d/9Ee5//779TPenyppuee7v18Qts7fv/i93/u9ptp+8eJF9u3bx/vf//7QlD21hEU4\n8Iv8fgFY9XkMw+DP//zPGRtbqd6yMiYmJti7dy8/93M/ByxNDA3jUhMwlL3+zypq11e/+lUmJyeb\nNjm2WCwyMDDAr/3ar9VNFnFdl1wuh23bDd8r/O92sBRBC9DRsYQQfOc73+HYsWOrEpUiIiKiZSSA\nXyYooNt43twS+Cs84fxbQLPDuHwUT0D/Xbxc1mU8Qf9Pqud86wqOYQCPAFvxPNr/B5AE/l88Af15\n4HNNtjui/dwO/Ovq519n5RMurlsiET0iImJ1kJKF//yfmfzLv6xfBSQ2bKD7rrsQjUTBSgUymYYi\neiWfZ/ILX8Bu8EIqpKS3WMQMmVleAjraHLa3XPZSZoaFyhSlRR4a/jDm5Fho2Q3psiV3FMeeCZVE\n5xcXuUhjsTw7MMDAnXfWv1AKAfPzUGxv2p3Zc+cYHx0NnfwQL5dJNXipFEDh4kVe+NKXQustHoux\nc8sWklu3hu4v40mcdBbHoU6HFqL9QnFi0ya2vv3tDIa0aduI8UP7IBPfG6y/7tKL8Hr7vh427Grc\nRx8dFywWGsvsf/3IHH/7nSO4br0XYS43zD/9p9eWELkWUN7AQJ1Ip+5P5XmtPoMX8vPQoUPk83k9\n0NrT00Nvby+7du3S4mo+n8d1XbLZ7Iq9VdYKKkx2bZ5z/2f/ILAabKpUKiwsLCCEYGJigkOHDlEq\nlVhYWNBCajqdZu/evaTTaSzLCoQgvRYE0WQySTweJ5vN0tPTU+eNryYNqGWqntSkBIALFy4wOzvL\nxYsXmZub02FBVUjzjRs3ctNNN+G6ro5usNbJZrO87nWv023k9a9/PY7jUCqVKBaLelJBuVzW0RxK\npRKAXg6QyWTYt2+fHmCfnp4GPG87Va9dXV068sO1lAZgtdEhrvFNWqh1dQ5xgRZCYAQWLeuvfJU2\nijqT/HONpARDbVPrum0YwX6q+rxartKXqktlY2DylARDIKgvd8gJWmB0uMd7WJfUHyYdvH5Y2Lb+\n7YT2rm5V3S9/XGVH7Xw17zfUYMm9fslD2G97K35r1XGDdd7A83y5ZXqd0Mdb+0+Ga4dkMskNN9zA\nwMCAji7jF91qRXR/32U5MbpWRI/X5hpoApZlceONNzI0NFS3rtZWNdFN2agm/6nvfvFQiauVSoWu\nrpVExb08DMNg48aN7Ny5M9AHV9Tej7Ue9P50h/7694vwrus2vQ8uhKCvr4/du3fr78tFLVDibq1t\n6rs/UpFfRO/t7W162HgVcUvVuZpQ3GgyQq2Y7H9HgqAHuJpcoiJyNTvFQFdXF7t27aqbXOpPh+S3\n3Y+/Dw5BEV1N2hRCcPToUR09KSIi4prlw0BP9XMJL0T2c8AfAH8BzLXovHE8j/NxvJzWKl/JV4Gf\nAn4SL9/1M5c4zpur230RT0AHz6P9Q8BPAw/jCerRAOD1Q4ylMO6PAX/YXnPWBtEoT8Rlcymv0Eb7\nXMm6iOsLe3iY8vHjhGmPxu7dyD17EI083Fw3PC9fFSklxVOnqMyF9z8EEMtmiYccQ0qJ2WZvTNf1\ntOqwcQyz5LA79wLp3OnwwSwpcaemkcViXa9F4PWUFgkf2JJ4s51lb2+4iG4YMDl5BSVqElJSWVyk\nJGV4uwFMlhm0K5WYPXMmdFUyk8HdtcubnBFGPAFWzPPGbhAhoJ2YqRSpPXtIhwwilV2TuZlezl9M\nhtaNZcHN8SRuT2Nv88VZWCjRoHIlx8/N8/3vz1M/HmEgRJ63vz36bb9clCfrcigPDf/9qr6r56ny\nVE8kEmQyGUzT1F7EauCsdsByraO8xWvx90n8g1hq0K9SqVCpVPT++Xxeh8NX2yiP5doB6msFvxdX\nmJe4GrBWHvoqvL3Kgw6wsLBAsVgkkUgEBrhV7vl4PE4qlcJ1XZ1aYK1jmibZbFZ/VwPufs8kf3h7\nVQfgDWYqcb2rq4t9+/bhOA5zc3PMzs7q48BSlAT1dy1MMGgtBSYmvszc3ON6iUrNrdKBSwkdsQqH\nuqeIGQ6BB83EBIwHU/+VxmYpPvesbyuXM2eOt6CuJS+88GVOn/621r9ffbW+m2BZ0NsrfTmt8XKf\nz8/XH3JhQS+XQnD47Fneed99TbYbnnr6aT71qU8tLXAceOkl8HsNCgGzs1BzD+dLFs+P9CGl0JtN\nTXmm+/sItv0y8M+aZrMQMDHxCt/61mcwTe/3XUrP7MHBYF+rWPQmnPpxXW/yae2kRtMM6rwXLpwl\nm21unmNPDDtGPv8ZhFj63bVtr+r9tn/nO/VNI5+HqSm3+mrjieiVCnz1qwbVn12EgLGxs3R0lJpq\n+9zcBb71rd8lkfBuSCmhQyzynHkaoXOi4xXEtus7vYWCt7yKi8Hp1DhzVq++R3O5KRxntql2rzeK\nxSLDw8NMT08jpeTEiRMsLi7qSYFzc3OB30C/13R3dzcbN24EvH7S5s2b2VqdNKwmWqrIPq0ai/Ef\nt3Zynt+r3l+GUqnEzMyM3l/1VRSdnZ0tjYbjui5jY2MMDw8jpWR0dJSRkRHA6wf19PTobTs7O9mw\nYUPAi14hpSQej7Nhw4ZAn301+gdCCMrlsk7jYxgG2Ww20L9d7l1D9WlqRXQV9agVuddVP9YfOWm5\nbWvtVTiOw+TkJOVyGSEEmUxGi9GtwHEc/T6h2qo6VzweD9Rzrfhf64k+PDys2/6GDRsYHBxEhXOP\nxksjIq5phoDfwOtcjeCJkf8FOLUK5349Xh7z/8qSgK74Op6Ifj+XFtGVt/rXa5YXgL8F3gkcAF64\nGmMj1hQfBl6Dd41/kWiCBBCJ6BF4L2i2Hfw9FUJw7tw5vvnNb2pvHT+jo6NAvQDe3d3N3XffrQdd\nFYZhsHnzZpLJZN0+tbNHI65fRM3/9Rs08IJQ65Y9uLdvo62ulTbW0OFDCBBGg3pY8noKWyt8f6Hn\nxPPUrzv2Gnthq7V/pVlSlyv3is57hU1y1Qi9Tl5SzUZeQWr5ckW41P26pB0s1+oiWonfG1sJg35P\n7Ub59a4nwjzSa5erQWJ/2HflXaS8XvxC9PWGvy4aoSZY+MPcQ32o8+uh/TRqM3785fW3C/8kBH/I\nd39I9/WMYRh84APv4a1vHaH2GRB2ewmxrX6hDMklEtI/PHDg55uWT9Y7heDBBx9gcPD5S5368qgp\nyybg9ttvb+q9dMcdd+jJIAE2b15RXSJhx531x63fdYg77njt1RtcZc+ePfzqr/5vdaLM5VZNWBH9\n3HbbEDfeeENT63z37t184hP/nGLx0mGwaz3oFXv3Bm1Xc1eFWApiIOVGtm3b1rTnUyqV4qGHfoFc\nLle3TnAZuaUDlS7YVFfAIfr7fywwkSni8iiXy0xMTFAqlXBdl2eeeYbp6WkMw+DYsWOMjo7qfoxl\nWVpcllKyadMmbrnlFv2cK5VK+lqk02k9qayV4pxf1PdHeonH46E5x8GLArOwsKDtNgxDR2hSomgr\n+yNSevnKJyYmkFJy7NgxDh06BHji/6ZNm/S2SkBvdG+mUikGBweXFX1bgZowubi4qNtGOp0OTV0T\nhppcqqgV0VsTHWMpNc6ltlsO13VZWFigUCgghNATQFuFSpuk2qqaSKkmIjSqR1XH/rYxPT3N+fPn\nAW9CbDabRQih75uIiIhrlgLwWeCbwBOsrhi5v/p/mLj9Qs02KzlOWJ72F/FE9H0NzhNx7bEP+Hj1\n8yeB4220ZU0RiegRFIvFus6ZEIKRkRG+8Y1vkM/n6/aZa+Dt29PTwz333EM6na4bvN+yZUtLO7ER\nERERERHXO2rimQrLLaWkUChw9uxZxsbGtNeC4ziYpklPTw+WZWnPo0QiQTwe157JzcoN2C783j3+\nAWTVB5mamuLZZ59FCMH09DS5XI5KpUJ/fz/ZbBbXddmzZw+33HKLzjN/veEfqFMDd2pwUS0/fvw4\nzzzzjB50VYOl+/fvJ5lMctNNN9Hf3183uHotokQH8AYq/SKEmlSq7hm1jfJaf+GFF/jhD3+IEILJ\nyUlM06Szs5M777yTZDJJLBb7X+ydeZyjR3nnv/XqlrrV5/Qx03OP57DH9vgAn2MbHBMSjLMbYucg\n7IYkkJAsJFkWCOwGErLZJZuwhGxgQwjJLleWTWCBcMUE28HG+MCe8TW25/JMT0/fp1qtW2/tH6+q\n5pX0qrtnRq3WdNf381FLrfeqt1TSW2/96vk9tNRyN1kHWJaVveuuuxoSNVXv3y0hBAcOHODqq6+u\n635rHaue7Ny5U1vhriT1LndfXx9vecsvNqy91LP8vb29/OIvvvmSK3swGOS+++5tdLmNCnQRuEVj\n9+QutwtR5XXeHdVbadddua9GnoN6vdh6lmXpyN7F7NNXqpyV/3tNqnPXaS0RfTUnZlaWudn7+fUs\nX2VUeyOo/D55TV6tfG+xyc6r8R01GAwrxizwwVU6dk/pedpj2WTpuXcZ+1HrTF3kfgyXBn8BhIAj\nwJ+uclmaCiOiGzRedmC1bnaW6sx53ZibDqDBYDAYDBeHuja7ox8KhQJzc3NMTU3pAVPbtrEsSwt7\nKhpDOcKsFWvAyr4LlPdBcrkcQ0NDWJbF7OwsuVyOQqFAOByms7MT27bp6uqir69PW5uuNSoH4dTg\noltAnpyc5MSJEzoPphp47e3tpaWlhQ0bNpRNkLyU+3SV9aEmBfh8vrJ8lCpqTw2E27bN8PAwx48f\n199BcESpgYEBHem1FidiLAchRB7HMtBgMBgMF4nqw4XDYWzbJhqNkslktICuhGYpJclkUk8Ck1IS\nCATo6urS/589e5Z43Pl57ujoIBaL4fP5yGQyK9bvUX1R1edU18zF+p+JRILnnntOn0tLSwsHDhzQ\n121ln10oFFas3G1tbfT09CClZGJigs7OTsD5PNzBJcPDw9pSXwhBOp1mcnJSn2dfXx8bN24kGAzq\nvrrbOarezMzMMDg4iBCCs2fPcujQIX0v4LbPtyyLjo6Osj7z6Oho2STCjRs36m327dtHX1+fp8tV\nvVB9UMuySCaTjJbSkORyOUZGRrRbifpOqH6ZZVns3btX97tyuRwvvvgiyWQSn8/HLbfcQltb24r3\nWb0E8cpI80pBvXJyw9jYGK+84rg7x+NxBgYGyvqaBoPBcAEoi+Bq+6Fz7y0nT1sIJ4K+OsLy/PZj\nuDS4pvTcD7xYYx33gMdtwInS6xeAe1aoXKuOEdENhrVLBHgzjm3MmVUui8FgMBjqgBqEyeVyetAr\nm83q/ItCCJ1vsqenh3g8rm0+3YM5l7IIuhySyaQWh0+dOgVAKpXSdosbN25k9+7d2LZdNji4HlCD\n72NjY3rQVEXp27ZNJBLRg/Bbtmyhvb2d7u5uYO21m8rP3B21p74vaiJGOp0mnU7rdePxONFolN7e\nXqLRKNFoVA+cGgwGg8FwMfh8PqLRKLFYDNu2aWlpIZ/P636MchQCxyVwbm5OX9OKxaK2TFeCnRKd\ne3t7aW9vx+/3k81mV0SMdovF7nRDSzEzM8MPfvAD3a/t7+/n1a9+tb6uqrQzxWJxRcptWRbd3d1s\n2bIFKSWzs7MMDQ0hhCCTyTA+Pq7rfGhoiOHh4TLno2effVaXa//+/bzpTW+ivb0dKSW5XK5s8ms9\nUYL/Sy+9hBCCJ598ks9//vMUCgVs2yaTyZQ58Ozdu7esTp944glyOSc1RSgU4pZbbqGjowPLsviV\nX/kV+vv7V7SffPbsWZ55xnEBHhoa4oknnsC2bRKJBI899hiZTAZw+mjt7e3aeSoQCPCWt7xFTxDJ\nZrM8/vjjzM7OEggEGBgYYO/evXrbelNrYoHbHWs52LbN4OAgzz//PAD9/f0UCgU9ccRgMBguEHXj\n2u6xrKP07CWMV5LCydPVBszU2E/qvEtnaHY6OPf5LkYEdE6o2ZUrzupjRHSDYe3ShmPDEQQeBj4D\nfAXvWWgNQQonD2PN2y+vPJjLWaaWq2MsVoali7mKSKRHCSUXV+6ltpfSOarwqt+l6r2B1Mj8XfPc\nxBLLJYBtg11jz/bF1nwDqPl9cR6LfZ1AUuvronJxqsfih648yFI1b7gY1GBNKpXiyJEjelBORSlZ\nlsXmzZvp7u7WEdbK8tPvXz/dvunpaXK5HKdOneKxxx7T0Rw+n49AIMD+/fu5/fbbsW2b7u7uslyF\naxVl+RoIBMjlchw/fpzZ2VmEEAwODjI1NYVlWcTjcW33ft1119HT06MF4rVWP+r7VGl7q+oJHHFC\nDTDPzMzo7Xp6eti+fTs9PT10dnaW5W01GAwGg6GeLGX1vFTEaiMnCno5Gqpr7FK4hcnK63PlvhtR\n9lq22u7oevV/oVDQUdPquXLf7ud6UmkT7jWJQU2oUC4GgE4PpYRqtdyrPa1kvbvL7y53Pp/XZfP5\nfDpdFaDvf9S27nNd7sSNelCveqll724wGAwXyEjpuctjWXfFOsvdT6WIrvY9fH5FMzQx/wtHGF+M\nLuBNpddDwLdKrwdXqExNwfoZTTXUpN6ds1qd1fUS4dVEjAL/FSf/yq3ADcBfAV+8TB3ZAAAgAElE\nQVQG/hZ4AGioP1S4v5+W9nZPZc+/73Lsm29BBkPe2ptdxJeqof8LgW9mhvbJSYpzc56rSNvm7Asv\nIEs3YW5yQGIV85YBhIsLbEkeoUuMVS0T2RTW/DSFRMJT0ZSA8LhRV1g4/jte33IJ+Ht6EK96FVTW\ngRAwNgaPPHI+p1J3orEY8UDAs1n4s1lEOu09AQAIBQJs6fLqM4K/qwvuej35rm7P5UXh50y+j+mX\nqGqTlgWzqz3HrliERKL6fSkRwk9nPE8m4P2523aBb3/7WRYWxvFeA2Zm+slmYzUP/8orgv7+Tdh2\n9fb5/DjOT5Ch3riv15UREO7BOZUT0Gs7te1aHphx21S6LU/dg4iqftbDQJV7cNJdN+r/WpE0q5Fb\nspFUfp+8XgM1I+lM3kqDwWAwNAK3OFp5za68Ni2Wd7mRuMXvCxkLqtU/aRTuY3vV+WL/L7a/lTqf\nWkK0imZ2v+8W0WuVsRH1Xtk+Ktt4ZR27Bf5aE0dqfR9Winruf7G86gaDwXCeHC493+SxTL33zDL2\n8wxwd2mb4xXLbj6P/RguDX57Gesc4JyI/gLwaytXnObBiOjrHCklCwsLJJPJqryiKrdWLQshy0N0\ntCyLQCBQZV/ktsU0NJQ/xvkBbOdcPpSfLT1mcKLTPwscaURhIps307Zrl6fgaV91gMIdr0WEqlOp\nSEDYNlYqUUPuA9/cHB3pNMzNeQrNxXyel156iZSHiF4E5lY5h2mkmGR74hn6ZEv1wkyWXGLKEdE9\nkELgkxKL6vkHEvDhJKjxFNGFINDXB7fd5i2inzoFTz99vqdTP4QgFo/THo2eK5MLe26OQjpds12E\ng0G2b9rkOWhkb9xE9o0/RW5gM8Lj/jSfh1ce9DF4vLpJCQHT0xdwPvWkUHDau8dvtGX52LApj7/T\ne9N0usAnP/kYjz32LFLWqr1XI2Wf5xIhoK9vFwMDOxCi8rddkkyOIcTzyz8Xw7LJ5XLkcjkWFhbI\nZDI6El1dZ1XEeSAQwO/34/P5dCT6Wrcsd+fbnJ6eZmFhgZmZGd2PEULovNXKftu27XWRw7pyINu2\nbaamppiYmEAIQSqV0oK6ikQPhUIEg0ECgcCatihX9aK+J+D0Z5WVpopAz+fzFItF3Z+NRqN0dHTQ\n2tpqBHSDwWAw1BWfz0drayvxeBwpJQMDAzq/89jYGJlMRl97otEoLS3n7iHb2tq0+5CyEp8tzf6N\nRCLaxl3Zptcbd1S22r974qJ7vYmJCUZGRhBCcOzYMYaGhsjlckgpdT82EAjo81Ci8ErZXKv+tJSS\nzs5Odu7cqfsCPT09ej1l7a76Tq2trWWC9WWXXab7l5WT7Vaiz9DS0kJvby8Ae/fu5XWve11ZNLe7\nrzMwMKA/j3w+TzKZJJVynHgjkQj79u2jvb0dy7JobW0tE+Hrjaq7jRs3Ami3JDVOGQ6HyyLRN2zY\nUFavfr9fW9EXCgXC4TCxWEzfC61k/8yyLH2fBU5dVorgCr/fr+vctm2GhoaYmprS646OjpIojfVI\nKWlpaUEIQThs0gwbDIYL5kfAWeDHcZxq3RFnP4sTVPe1im2uxRkid4viXwP+Y2mbz7ne7wXuwNET\njtax3AZDU2JE9HWOlJInn3ySY8eOVYnoR48eZXp6Wnda3aiB1cp9tbW1sWnTJlpbW6tmPUeVCGZo\nJBmc6PN34eio4Giq4Ni3vAt4L/Ac8NfA3wGTK1ISd7RW5TJl1Wb5QFQP1IvSn0VvgtSNaS3/6Utg\ngNsSTtR4FUsUvVYU9jI2LdVtaS0vpbgJ6u1iIvwEYNU4D2e/PhA1LoVL1fvqV80iqEGaGksF2LYk\nl5Ms3Uq8kCVDCctj+6aumEue8fFxhoaGSCQSnDp1Sg/YhEIhYrEYQgja2tro6uqira2NWCyGz+er\nikRea6KflFJbZxaLRe6//37OnDnDxMQEyaTjYhKJRLj22mvx+/3s3buXXbt2rWaRG0Ll561s7VOp\nFN/+9rc5cuQIlmWRTqcpFApEIhFuuukmfD4ffr+fnp4e2tu90qitDdTgsvreqByyuVxOTzAdGxvj\noYce0nlFlW37ZZddxmtf+9oy63eDwWAwGOpBMBhkYGCA7u5upJREIhFyuZwOWuju7tbX9unpaS2S\ngyMmKmERYGFhgZMnTwLOdW9ubo5gMEg2m13SBv5CyWazOhe3O6WQO7BCSsnzzz/PN7/5TYQQjIyM\n8Nhjj+lc3gsLCwSDQSKRCMVikUQioScAuM+vnvj9foLBIFJKdu7cyfbt28vKqzh58iSPPvqorr9i\nscjBgwf1et3d3bpPAU7/y+fzlUWB1wshBBs3buTKK6/Esiz279/P3XffXXN992eQyWTo7u4mkUjo\n+4mbb75Z53LfsGED+Xwey7JWbOLC5s2bue666wCn7R48eBAhBPl8npmZGR2Z7vf76e/v1+OQuVyO\nT3/60zo1UbFYpK2tjWg0is/n088qnVG9c9H7fD6CwSB+v59isUg6na4Zxd/S0qLrvVgs8sgjj/CD\nH/xAv3fo0CEmJib0tr29vfh8Pjo7O00wksFguFBs4MPAp4D/A/wGTg70fw8cxBHEj1Vs8zhOXusN\nrveeBL6BE43+n4C/BDqBT+Okj/39lToBg6GZMCK6gVQqxdzcXFXnLJlMLjrLtzIqSXVOVdRSJabz\nt2r8X+A9NZapPBdXAX8CfAz4Z5yL4TeA7IqXzmAwGAzLQkqpBw5zuRyFQkFHy4ZCIS2WqsgIZcWt\nImpWaqC0WXAPWKVSKZLJJOl0WkfqSyn1YFcwGNSDcO7IofWCivBJJBK6faj6C4fDWkR3R86sZZTF\nv+rbqjoBZ7BTReq77fADgQCRSEQ7QBgMBoPBUC/cKVXUOIvq26kIbXXtUf8rKoVaL1vyRjkTLTV5\n07Zt3ZdV4rk7hUqtlCkrdd11Tzp0RxlXUvkZVDr++P3+qiCVlZzIqsqr+jLn47Kk+ntqsoO7Pbn7\nOCtZ55XtVxEMBvX/Pp+PcDiso7O9+qfue6FG9M0u9PNU7d4dne7+XrrTKRkMBsNF8FfADhxN4KTr\n/W/jiOrL5ZdwUsP+YekBUADeD/z9RZfSYLgEMCK6oeZNiemwrRmewYlIjyyxnrJ7vxN4DY6A/gUc\nu/cfrljpDAaDwVATKaWOEgYYGhrihRde0NbSauCpt7eXaDSKlJL29nZCoRCBQKBsIHUtXtfVgFM+\nn+dHP/oR+Xwe27Z5/vnnGRoaolgs6nrp7OzkJ3/yJwmFQgwMDOhBW7cwutZQA4kqAmxwcJCHHnqI\nRCLB4OCgtu/s6emhtbWVzs5Orr/+egKBAJZlEQqFFtv9JUllbnM1cOu24HzhhRf41re+hRCCubk5\nFhYWsCyLnTt30tHRgZSSTZs26YHctdp+DAaDwdB8VIrglXmul8qZvtT79Srjco/lFsqXysndLLmi\nK6/7S01UqJVbvZnwqlv3BMJGT7xYrFzL2Ucj8Kor9/+17N2Bsjbv3r5Z24fBYLhk+V3gL4DbgQBO\nrvTDNdbdjRPBXskUjnX7q4D9OBrDg8BonctquDSY5dzkiWcWW3EtYUR0g2HtUwBOA3uXub76XQgC\nvwK8HSePyl/jCOqn6lw+g8FgMCxCPp/XVuVzc3OMj48D5wZkLMuipaVF58MMh8NlUSRqvbUs9Nm2\nzdmzZ8lkMti2zfj4OOPj4wSDQWKxGFJKotEoe/bsIRwO09bWVjZQtZbrxh1hnUgkOHz4MPPz88zN\nzel2FQwGaW1tpa2tjYGBAZ1HUonva4nKz90rT+vExASPP/64jozL5XL4/X7a2tro6+tDSkk8Htd5\nWg2GS5xeYE/peQp4oA77fB1O/sX7Kc/BaDAYloFyEFLXLHd0digU0vnRVdSzyqEMjn37zMxM2b4U\nsViMcDhMMBhc0cAJFRUthCAQCHjauYPjHKTyi6fTaXp6evT5dnZ2ksvldCoa5RqzGpMfs9mszmEN\njoV+sVjUdRsIBLT1tkpz6I6udjvcrFTZawmw7uMVCgVGR0f1evl8XjvrKGeraDSqU9coR6JGBNm4\n72vgXOS5ikQXQpBKpchmHbPEbDZLoVAoc5xqaWnBtm2daqdRwrS7P+l1f1E5KSCRSOh2r845Ho9j\n27ZOJ2AEdYPBUEeGcILkluKVJZY/WXoY1jengPtWuxCNZu2NjBkM9ed3gF9d7UJcJN0XuJ3y5d8M\nfAAn18mj1EjdbTAYDIb6U5nT2isaxCv6YSVtI5sJd924BdFaUU3rQThXVLYTtz2sl9VrZSTVWsTL\nfaDy+1PLDrRS1DCsawSwCyci43pgoPT+LwGpZWwfAv4tcC+wpbS/MRwB+zPAYH2L60kfTtqng673\nHgNuWmK7CBAG0jiRKF58FCdS5Wrg2YsrpsGwPlHpZqSUZLNZnQdcuQ8pYrEY0WhUX6/OnDnDs88+\n6xmZvmnTJnp7ewkEAmSz2RWbLBcOh4lEIkgpicViZWKhQkrJmTNn+P73vw9Af38/d9xxhxYRu7u7\nmZ2dLevbKdvxRqeamZyc5Dvf+Y7+350KSEpJV1cXr3nNa7SDjxBCW6q7XxeLxZoW8ReD+nyVoKyO\nqyYeqPfS6TRf//rXdVsSQtDb20tXVxdSSsLhMAMDA7S1ten9qj7TStW5u99ZWW+tra267Llcjpdf\nfpmFhQWdMz2ZTJLJOJchv9/Pjh07dKqd9vZ2LcCvVJ/N3a7VZ195/yWlrLKrf/nll3nwwQf15Ipr\nr72WzZs368kjpr9pMBgMBkNzYUT0dcT52nidb2dtDXfuikButQtxkdQjEa6FM1h2DNh63ltLiUAi\nkQjh0VaExBJyUXleWiCQOOOMHqWT0nnUOL4EvJY2TcutUX4pbV1+L8nnYsov1XEvVdRNN9V147wn\nQdoeS0FIG8ty2pVXJVrWuUfVts2ivdVq89JGCFmznM4y9Y2o9a2otczZXkqwpXebtGWzVNClj4qq\nnpubQwjB8PAwExMTBINBNmzYoCMturq69IBXV1cX8Xh8TUYRV6IGlVOpFIcPH9a5q8fHx5mfn6en\np4err75aD8bG43Ftdb+WcQ/YqfoRQjAxMcHx48dJpVKk02k9OLdp0yb27NlDPB4nGo3qtrPWJxr4\nfD5CoZCOlFPk83mdLx4gGo0SCATYsmULO3bsKBvkNKxLLJyI7XaPZW9bxvZ7gX/EEeHdXAbcihMt\n8umLKeAy+TMcAf2HONEp4zjntRTvB34PZ3LtH6xU4QwGQzluMVOJyVJKgsFgmageCoXKcnK7hTif\nz6fdilZKFHVPRFMibq1jFYtFLejatk0oFNIiusqHrc5T5SBfjb6Jbdu6nAp3OSzLIhwO10yD06jU\nSpXHqawvKSWZTEafi/psVJ9RtQ/VDywWi2WR4CtZZq/yuvPOW5ZFsVjUkxfcTgAKv9+v3ZTU5IGV\nFtArJ2fWenaj2r3bWUGl4XJ/V9bDZGiDwWAwGC4F1v7oqgFwbJtGRkY8I9eefvppnnrqqarO2fT0\ntM7B6kYIwc0338yuXbuqot527NhBf3+/zhHp3qbyvUuIPwc+tNqFuEiOAPsuYLsUTqTJQ8DfAP+v\n9N4FRMcI7h+OM5vtLQnhLqSEuXYsaSE8fpUkQFFgp2r/ZHUEQvzrDRvpaG/DUzDN5ej1+8l5LC0A\nsfM7mbpjR6LkL9tLtq2zemE2i3XgAGJiwnNbadukT52imEh47xvnQ6wlwAcCAYjFoFJwEwIiEW8V\nuZEUi+DxWwQggkF8XV3e20lJpnMjx6/9BbCqZ/ynAm089ZVWUkHvuikWbV54YZqpqYynGD08vLCq\n8w+KkRipPQdIerSZvC149nQHp494C/7ZrI+pqctwAuG8b8wHBvbQ0uLRHktcv0ty/a6TCI/tT48O\nOZNiDBeNbdsMDw9z9uxZhBCcPn2a0dFR4vE4W7duJRgMEgwG6evr06KeEosLhYK2PFyrZLNZEokE\niUSCH/7whyRKv4PDw8Ok02k2bdrEjTfeqK01u7q68Pv9ZZE1aw23HaYQgoWFBS0Ij46OcuTIER21\nowb+tm3bxg033EAoFKKlpWVFoqSaEb/fT2tra9XgfjabZWZmRkcW9ff3EwqF2LVrF/v379f2uYZ1\nTRvOxNIf4VgffmCZ220AvgdsBL6FI0YfwkmjtAV4I04apkZwW+n5X+NEwRsMhibGS5SrFNmWI7o1\n0wSwWrnFm80Zx6tO11o/shnqGS4uDVXl+GQzsFQ78XLNMhgMBoPB0FyY0Z91Qj6f58yZM3rWpkII\nwVNPPcX3v//9qgFEt0VV5TYHDx7kzjvv1LNSFW1tbWzatMkMLDYXfmD7eayfL21zEvhL4IvA8MUW\nQgL/70w7Xzm9Ca/oVuvZLkIPiJqRs7YtyGQCUCPCdddAlFv/ZBsdm7Oe61iZDJuDQbxikvNA6/mc\nzApgx1rJ77+WTHdf9cJshpabbsE3PYGX4GkXCqQSCVI1RPQIEK9xXAkEQiFoawOvyMzW1tUX0QsF\nKOXtrcQKh7E6OryVYlkk038Fh257J16zMyan4H/+pcXkRI3NpU0iMUo+P+11ZFpbveu7URRicZJX\n3UK4u7dMzBcCsll4/BHB4cPe51Yo+BkfvxLY4blvIWDHjn42bfKeXiIl/KurjnLPVS9SJbUJiyde\nPsU/z5sBgHqhrseVgyzqffe12B11tB5w5wl1R6eAUxcqUh/Q4vl6QZ2rmkxhWRa5XE7bQ7qj2FRU\n2noRz912/6oOMpkM+Xxe2+aqSQhKSA+FQjoiaj21I4MnNtAJzJb+V2mPlsN/wxHQvw3cg+N4BZDF\nEeX/e/2KuSghHDv3DEZANxiankwmoyfApdNpMplMmTCXz+f19SmZTOoIbkBHFquc1+p6vxrX/MnJ\nST3h0bZtpqamdOCGz+ejq6uLcDisJz9WRnu7rd1XmkKhoMuWTqdJJpN6WTgc1lbjUsqaEeirTaFQ\n0O0BIJlM6jzigLbbD4VC+jzc/ZzVmHRaK4JbSkkqlWJ+fh4hhD4Pd58sGo3S0tKiI7sbKUq778cq\n0xYMDQ2xsLAAOBM1Z2dny9bv7Oykv78f27ZpaWnR25n+psFgMBgMzYFROtcJ7oHC5S5brKPsztFj\naHquwRkoW4wCjp46D3wO+N84UTF1xZaihs2z9Ixmrdreru08buv3F9+P19JmmD8uKH0XvUqjv4vC\nWxEV5973sjRf6rhNj6hx3kv+/ojSCQoQNSYCLOMnzDmMV/TB6taeQE06qZ58stQ4R3mV1lpZiUte\nyxwfdwvhmYFB1GqrhvNGSsnY2BinTp1CCMHk5CTZbJaFhQXGxsbw+/2Ew2Fs29aDRYVCgXQ6vdpF\nbwizs7OcPn2aRCLBqVOnSCQSCCGIRCJEIhG2bt3KwYMHtVCsbBNh7VqVq8HPfGny0aFDh/RkyaNH\njzI2NoaUkiuuuIJ4PI5t2+zdu5f9+/dr0XgtE4lEtDuSOtdiscjf/M3fcP/99yOEIJfLEY870882\nbtzIm9/8Zvx+P9u2bdO5Og3rntmlV6miD3gzjgj/Ts4J6PUgCrwdJ5K9rfTeIZwJsU9VrPs/cEyY\nBM54wKdcyz4KHF3kOJ8Abiy9fiPOhADFIzj3EZVsAn67tF0EeBHHSr6yXG72Ab+Jk3Peh+OE9c3S\n+cx5rB8DfgPHDr+3tM0scBjHSetRj206gH8H3IVTfzaOtf0nWLwODIaGIqXk2LFjTE1NIYQgnU6T\nzWZ1P2ZmZobZ2XM/SYFAgHA4rPsDW7ZsYft2Z059e3s77e3t+Hw+gsFgQ4V0KSVf+MIX+NrXvqav\nv6dPn9Zl7+jo4Jd+6Zd0eqJCocDUlJNhwrIs4vE4wWCQfD6/4i6HUkpmZmYYGxvDsiyOHz/Oo48+\nqpddffXV3Hzzzbrv3UwuPu6+3NzcHF/96le1gFssFpmZmdECbTgc5oYbbqC9vb1seyXwusf8GjH2\nZ1mWtmOvJJvNcujQIUZGRnR5FhYWdLnC4TDXXnstGzdu1OfmnoS8kti2rdMkCSFoaWnRgUXz8/N8\n8IMf5IEHHtD3IdPT03pSjM/n47777uMnfuInsG0bn89nxlkNBoPBYGgyjIhuMKx9fh4np7uXkK7c\nzb8J/DXwTziCusGwLliehrY2hTbDpYNt2xQKBT2opQa+1MCQiiapNVFuLaPqRkWhq7oIh8N6IE5F\nNDXL4GYjcLsW5HI5kskklmWRyWR0Ham8kWoCxnoRh5VDgXqtviPz8/NMTEzoSDflXKAs35WrwVr/\nThlWlDcAAeBp4ETpvRCOgLuA0y+/ELbj9OEvw+nHD+KYEF0H/DJODvP/5lr/l0vHBGc84O2uZX/H\n4gLyr+JYz1Pa/3WuZRbVIvrVwMeALiCJI3ZfB/wsjo38Nz2O8VvAn+DUVR4YxRHiD+II6z9WUcZu\nHAF/D87EhDGceriytO7twKsrjnEL8GUcwR2cPPTdOKL9r+NMdviHGnVgMDQcd1RqZTCDu5/ozh2u\nUNc1d57o1cq17M4FDehIb3deaHd/xC2AVrrINAK3A1SlK5TKK6/yvjdr/6BYLOp69nKaVHnQofx8\noTrfdyNYKpjHSxhX26hzUU4MjWwri+VAz+fz2uVITXhWqL5mMBgsyz/frO3JYDAYDIb1yNoONTEY\nDFGcwS63gK4G6J7Cib7o4dwglhHQDQaD4RJlvUYtXEq5PxtFpQVkZb7U5WyzFqm0J3WnA4DyuqnM\nT7ke6sew4ijB+UUcUfcBIA1MAzM4ou2u89ynv7TdZcD3cfK07MSJev91nOjqP8YR8BUxzmUySlEy\nRCo9HlrieCHgD0uvf79i27d5rP8J4GvAttIx24Av4Ajkf071eMQv4ESpzwH/BmjByRXfAXwaxzr/\na5wT8gHehyOgf7W0fBOwFQgDN5SO52Ynzn3PBuC/4Fjzby6V79dw6uxzwP7Fq8JgaBxLXbvVtUw9\nKq9bzSDyekUEBwIBWlpaaG1t1Zbi7vNQ21XSiOtx5aSDxcrQDH0Et9ify+XI5XLk83ktoqtJp+p8\n3G2iVl+nEVHolRNEstmsfqRSKf1QE0Hd5VRpCSzL8pwsu5KfS+V+FztWZf0qJzH1UJNKKvupBoPB\nYDAYmgMTib7OqeeNlOnoNSUfxBl8yuHYGo4An8EZGDqxyHYGg8FgaAKEEHR0dNDf368HvQKBAK2t\nrVxxxRV6EGZgYICenh4d3aAGydYLwWCQN77xjdredPv27YTDYXbs2EF/f79eb73UicobqaLzg8Eg\nQgh6enq44YYb8Pl83HjjjXR0dGDbNlu2bFkXkehCCE6dOsXo6ChCCKanp3n55ZcpFAocPnxY52mN\nRqO0tbXp/KBtbW3a2cBguAj6Ss9bcCKnc8CDONHTrwbeBLwGJ3L6+WXu86eAa3Gir+/hnNV5Ecem\nvRf4g9LDK+p7pXkeZ0KvukmcxxH3fwJH8L+cc+caAD5Sev0W4Duu/SRwBO69OBHpb8KJmgcndRU4\n0fYjFcd/ovRw80EcMf9jwH90vV8A/gqnzj4MvBt467LO0mBYAdxiciwW03nPC4VCWU706elpTp48\nCTjX/87OTjZv3qz/b2lpYdu2bYAjWq9m2hYVFa8ict/whjdw441Ohoi2tjZmZ2eZn58HnGu2sm1X\nLjKLidr1RAhBPB4nFAphWRbz8/Nl0doAuVxOT1hQUcTuSG9VViVsq21XYszMPeng7NmzPPPMM9i2\nzezsLI8++iipVAqAUCjETTfdpG3og8EgxWKRbDbrOfnSPQljpcb6CoUC2WwWgNHRUZ555hmklGQy\nGU6dOqXTE9m2TSKR0FHclmVx4MAB3UaCwSDRaFSX030/5I78rifuNJeLTXTJ5XKk02nd9m+99Vb2\n7Nmjy7pjxw49IUC1ERONbjAYDAZD82BE9HWCbdukUqmqzqPqUHp15lVOUa9c6WrGpOqoK9bDAOwl\nxEbg3+NEuHwJ+BvgBywrC7TBYDAYVpLlWAyqKJFQKKSvx7FYTEfsdHR0EAgECIVCxGIxbVuezWa1\njeGFlqsZWE451ACl3+9nYGBADzDv3r2bWCxGX19fWV7QtTC5YDkDmcr6Xw3aKmvXcDhMd3c3gUCA\nvr4+Ojs7sW27LHfj+Q6uN0ubWU45hBCkUikmJiYQQjAyMsKRI0fI5/NMTk7qgdpisajr2efz6UH0\nSovc5ZRpreeYN5wXLaXng8CzOKL3adeybwK3AX+LYyu+HFSE+efxzhX+KRwB/TocEX/0vEt9cfxP\nqu89kjiW9j+GI6QrEf1GnIjwE5QL6AoJfBan/l7DORF9rPR8D45gvliueT+OAxfAX9RY53/jiOiv\nWWQ/BkNDcVuyux9KxFP9H7dgC2ircTUJTF3rm4VoNEpPTw9A2fiS25beyya7Ef0OldpFiZvL6WM0\nIv92LdzicSqV0mOAmUxG598Gpw2odlA5MaBSDG6kbX5l2dPpNLOzs+RyOV2vauxS/R8MBgmFHNNF\nNWHUvc9GlL+yfdZyLXA/IpGInqwJeKYLaoa+9SVGO+dcama58BQ5BoPBYDBU0Vw9aMOKkc1meeqp\np3SElpvJycmymauKtrY29uzZ42mLtH//fvbt21dl9aRuMAxNwTjOYNJDQGbxVVcbtxPkYtQeiBaU\n7Mhq3WwIUXPvyzlyI/AsfqlwzrnV2AinZrxqR5TeX+z8BNSsu0vi5m0xERJRs+6WPrXFaq856sUR\naMA9VrPY16CcWq3m3L5rLsNVt1W7XX27yEuB4eFhHn744SWvmYVCgeeee46JiQkAZmZmmJ2dZWZm\nRg+CqWiS1tZWPcCkBlPPFyEER48eZePGjRd0XvUgm83yyCOP0NHRseS6o6OjjI6OUigUOHnypB44\nLhaLhMNhhoaGSCaTZTksL5RMJqMjeVaLU6dO8eCDDy65njsS/eWXX+b06dNaQJ6cnMTv93P06NGy\nNjM4OKgH288HIQQvv/wy8Xj8Qk/roslmsxw+fJiFhYUl1x0eHmZ4eFhHoqUojAQAACAASURBVI+P\nj1MsFpmbm9Ofr6oHKSWBQIDHH38cy7KIRCLnHY0+NzenI6wM6x53f/w3OSeggyMs/waOuH49cAA4\nvIx97ik9v1xj+RgwhZOTfB+NF9GP1Xh/vPTc4nrvQOnZjyP+ezFQet7ieu+vgZ8Hfhf4t8A/Ag8D\n97uOo9iJY9texLGB90LgCPYDOB0lu8Z6BkNDWGrCZeU6qyGCXixeAR1ez43mQo7vtW4jxPXF2sJy\nz2M120stEfl82nOj2/5yjlE58aUyyt+4edaNbwC3lF6/AfjWKpbFYGgU6kcoQnP2V43uaFgzmMa8\nTlCzUHO56sl4Kq9QJWqwsHK2spo5qSLe3DRLNJIBcCwJvaI4VgmJM451DK9geNtOUiyGqPWz5LS1\n2oElqWyeHz4/xOB4wTPWXuZy5LNZzzD8IjC89AmsGFJKEok5Hn30YeLxzvKFAkQ+R/CVk1jzc3iJ\nt7JYZC6dJuOxVOIkr4wucnzf8DC+Bx9EeIh5Z8+eXVXRSErJM7kckVor2Lbz8N6axPQYLxx5AET1\nuc3NwcIC5PPeorOURWz7LI6DaCUCmGQ1jR0SiTkef/xh2tqqhcZ8HgYHYXra+9xs2yaXm2Ox+TUz\nM534/SE87+ul5LkTw7TKs1UyvBSCo2fOYJeiSgzV9PT08NrXvpazZ88ua/2Ojg4tKNey+Bsert+v\n2O7du3n1q1+9KtfzcDjMz/zMzzA4OMjk5OSS66s+CcCrXnUucNM98Hb06NG6le/ee+9dNbF4y5Yt\nvOpVr9KWrctBSklHRwc33XRT2XtQ3mdLp9O88sorF1y27du3c9111y294goQDoe57777GBwcZGSk\n0snZG2Xx39vby969e4HaA/iAvg6qCRnng5SS++67T9uNGtY1s6XnBRx3qEpeAIZwBOLliuhKhF5M\nHB/BEdFbF1lnpajViVSdN/cXqr30vBV4+xL7dXdtHwTuwIkeP1ja9u2lY3wbZ3LCYMUxfMs4hg+n\nG51eYj2Doe7kcjnGxsbI5/PYts3k5CSJRAIhhLY8V9ejhYUFPVlLSkk6nSaZTOr/p6enGR11fiL8\nfr92N8pms9qBpZ4Ui0VGRka0C6K7vzE3N1cWWTw/P68niqrxJTWJzbIsotGo3jYajeLz+cjlcvr8\n6omq58HBQf2/cqYZGxsjnXZ+Cpx79wQTExN6MmwqlSIYDFZNRKycxGnbtrarrxeqPOq+YnR0lOnp\naf1+Op3WkehSSmZnZ/UE3EAgwMjICNFo+WiBV19nbm6u7s6Tqoyq/zY+Ps7MzIzOjZ5MJnU7UpM+\nFZZlMTMzoz+XQCDA6OgoCwsLZZNChRDMzc3Vvf+eTCY5c+YMPp9Pl1fhznO+sLBAOp0uC1pKp9PM\nz8/rdjE6OlrWT1T3epOTk2XOEgaDwVDBrtJz/S+KBoOhjLqK6CrXSzabXbblkaExmM9heaiOuW3b\nK5Y3ab2yadMmfu7ntiBlBm/hcRBnjK9WW11crLQsyQ+HbXyj3utJgLvu8hRcJdAZibBx06ZFj7FS\nxONx9u7dzcMPP+gdASgl+CxEW3v1MpzyyzvvRNYQk5eMtA8GEV//uueiom2zf//+VXOY2L5zJ4/d\nfXdVgssyav6+SWzhJz/+NTwnH0g4eBBvkbi0vZQ2tSZ0hsPb2bRKbaa1tZX9+/fw5JMPeLYZKSEW\ngyuuqLUHyb59i0fl+v0WllW7bs/aNl99pejZtgq2zY/VPvi6p729nbe97W2rXYxFWa1+QyAQ4O67\n727qqIzVsufu6+vjHe94x6ocezmsVpsJBoNN32ZMP9xQ4qXS8wS1O7ZjOCL6cgVvJVJvWGQdtazZ\nB9hU+b4M/Mx5bvt9HCG9FycS7bXAz+FEoz2EMykhgTOBARzr+w5MmitDE6Luuz72sY/pgAblLgPl\nuZ6BMrdBIQTz8/OcOHFC7290dJSHH35Y/6/6MVJKotHoeTusLIZlWbS3t/PRj360LBhDlU+lU1Hv\nHTlyhDNnzmjRs3Ibd5/LnWN8bm6u7pPTurq6+OY3v8kjjzwClOcvz2QyZaLn888/z5kzZ8rKpmzF\n3XhFoE9NTdW17O3t7XzjG9/g1KlTSCnJ5/OkUilt9e8udy6X45FHHin7DJ588sll9W3n5ub46Z/+\n6bqVG6Czs5PvfOc7nDp1CkCXHdDCdKX7pUIIwdTUlD4Xy7L47ne/WzVuoZyH7r333rr1x0KhEGfO\nnOGDH/ygfs8tklsuZzbldrRz5079XiKR4LnnntPndPbsWc8JCvPz81x//fUmNdDy+AfgUOn1qVUs\nh8HQSNTsnWdXtRS12YQzkddguOSpq4ieTCb57ne/SzQaZefOnezevduzI2kwNCvJZJIjR44wPDzM\niy++uNrFWVNcc801fO5zn1ntYizKag1y9/X18ZGPfGRVjr1cVuPGTQjB7XfcwW23397wYy8X02Zq\nY5xJamPqZnFM/dTGDKJ5Y9qM4RLh0dJzP06Us1d42ebSc6UNeS2O4eQSv6zG8nZAWcPUy5ZDqRj1\n/tIdKT0vNx+8F2PAV0qPD+LkXt+OI6p/FSffegZow7HCf8l7NwbD6hGJRPj4xz++IlHilQSDQTo7\nO5decZlEo1He9773rbiTmRCCvr6+uu3P5/Px9re/nZ//+Z+v2z5rUe+y33PPPRw8eLBu+6uFEIK2\ntra67vOee+7htttua8hEyPZ274CEC2HTpk186lOfaki5W1paqtxBDZ782WoXwGBYBQZx7h2uoTnt\n3P8H8O9WuxAGQz2o65U4Fotx++23E4/HCQaDdbf6MRhWmlgsxlVXXcXll1/OSy+ZMZV6Yga4F8cI\nI96YdlMb02YMBoPBYLikeATHdqkfeBPwfyuW3wn04Yjrjyxzn98G3gL8AvCHVNun/yqO2P08jlV8\nPRgrPVfnk7k4vo8zeWAL8K9wRO+LYRp4Escevrf0Xhr4Jk79vwvH6r0edAA7cAYwD1UsawV2l14f\nonyQMwJcXnr9HFCde82w7hBC6LQjlxpCCLq7u1e7GBeEO33SpURrayutrauRrePiuVTLHggE2Lx5\n89IrGgwGg8FgWBPUdQTesixaWlpobW0lFAoZ4aOJUDMkV3KmpLK7amY7zaVQeeBVGzYYDAaDwWAw\nGAyGCq4Gfqz0uNX1/h2u9/dXbFMAPlR6/XHg5or9/XXp9eeAs8ssx5eBF3GE5y8B7pDS+3CEdXDy\nhdcLFdH+08BNpWN2UJ6n/EJIAx8ovf7fwC/iROy7uRL4C8BtU/S3OBMJKv2RbwFehxM5/5jr/d/D\nsY5/B/ARoDJRbi/wu8DvnEfZXwv8CO9c99eWlv0IRzR3s8O17NJUTQ0Gg8FgMBgMBoNhDWM8YdYY\nxWKxLBcPOLOBM5kMJ0+eJJPJVG2TSqWq8gZJKenq6uLWW28lHA5X5SHq6enBsixPwdxMnjAYDAaD\nwWAwGAxrmA8D93i8746e/jyOuOvm0zj5uX8DR3CdwIk8V96+TwC/fR7lyOFEVX8XuBs4gyOqq8ho\ncITivz+PfS7FQzii9I2cs6gHZxLA2y5y35/BsZ//zziTCf4ax4IdYBvnhPrvuLa5Gfgl4H/hTD4Y\nL+1DhQn+MfCMa/0XcSLd/y/wPuA9wGmcnOmbABVG+/GLPBeD4YKQUjI6OtowO/cNGzZUjQddKFJK\npqamGmbnXs987jMzM8zPz9dtf7Wod9nn5+eZmZmpy74WQ9m5x+OV844unPn5eWZnZxtm516vsufz\neUZHRxtm517PlAsGg8FgMBjOHyOirzFs26662RJCkM1mOXHihOfNTDqdxufzlYnfUko2bNjA7bff\nTiwWK1tfSklfX5+xEjYYDAaDwWAwGAzrkX8CRpdY5/Ea7/8mjg37rwB7gSDwMPAPwF/h5Ow+H17E\nidD+beCNnLNZ/zLwSeABj23ypWNdiH14AScS/CeAq3CEZ0G5Bf0/4AjsUzX28SCO9fwxj2X/Ffg6\nTqT47TjCeRYnWvsQzoSAH7rW/2UcK/xX4QjnHTiW84/jiPJuwV3xPRyL9XcAP4kTfR7FibL/J5yc\n6t+uUXYvXsGpTy/Vc6S0DJy6czPjWpY8j+MZ1jALCwu89a1vpb+/X7vjpVIpHSwRCATKRG+fz4dl\nWXo8p9Id0J0ey/1cKBQYHx/nk5/8ZN1ydC8sLPD+97+ffD5PJBJBSkk2m9Vlz2azFArnvgZ+v1+L\nyVJKisWiXuY+D8uyiMVi+P1+pJQcP36cT3ziE+zdu7cu5S4Wi/zpn/4pzz33HAMDA7rc6XRal3ty\ncrJsG/f4mc/nIxKJIIRASokQQo+xCSF02QGOHz/Opz/9afbt21eXsn/xi1/ki1/8Ijt27NDHVmN1\nQgjC4fCigS5LCcHqnF555RVe97rX8Vu/9Vt1KTfA3/3d3/GVr3yFHTt2eC6vLHdlWZdTdnDq/O67\n7+Zd73rXRZT2HKdOneJtb3sb+/btq/puSSnJ5XJlwU1Lpahzrx8IBHRq1JGREXbv3s1HPvKRupTb\nYDAYDIZlsAXnXmi5/C7wzytUlqbBiOhrFHcHrdbr5XCp27MbDAaDwdBspFIpnnrqqbKBwmZj27Zt\nbNu2reHHLRaLHDlyhKmpWrrL6hKPx9m/f78e3GokiUSCQ4cONWW/TAjBli1b2L59e8OPXSgUePHF\nF5u2zQB0dXWxb98+PYBuWBN88iK3/0bpUS9mcKziP7TUiiWywK9dxPFywNdKDy/+YInt/6b0qMUL\nwL9bZll+gLeN+lJM4US8/+cL2LaSp6ldn0cXWTa8yDLDOkVKSWdnJ+973/vo7u5GSsnIyIh2FYzH\n40QiEb1uOBwmHD6XzcC27TIBz7KssuuPGhNKpVK8+93vrnIyvNiyCyF473vfS29vL7ZtMzU1RS6X\nQwjB1NQUyeS5+SKxWExHBxeLxTLnRCmlFtz9fj9bt24lFotRLBb5kz/5k7r3owuFAm9605u45557\nkFIyOTnJ6OgolmUxMTHBE088oddVIrkqZyQSobe3V4vXlmURDoexLAvLstiyZQstLS1IKfnzP//z\nupa9UChw44038ta3vhXbtss+b7/fT1dX10UHwEgp+du//VtPZ8uLIZfLcccdd/Drv/7rWqx3s9jk\nEKBmPap2qLb9zGc+Q6FQ0O9fLLZtc+WVV/L7v//7uh2oshaLRWZnZ8nn8/qc/H5/2WdQeR5zc3Pk\ncs6ctng8TjweRwjBd7/7XZ5++um6fkcNBoPBYFiCMHDdeazfsfQqlz5mJMdgMDSEqakpnnvuucVX\nWkVhwOf3c/nll9PV1dXwY6fTaZ599llSqXTNdZa611us6i7qPlFK4m1tHDhwYFXcJ0ZGRnj55Zcb\nftzl4PP5Vr3NqOiIZmTr1q2rIqhdCgwPD/OhD32Iyy67rOlSoAghdKTJ7/zO7zS8fOl0mo997GPY\ntq2jr5oFNSj2yU9+kp6enoYf/9ixY3z4wx9m7969TdVupJScPn2agwcP8r73va/hx1dtJp1Or8rv\n8VJkMhls2+bjH/84ra2tq10cg8FgMFwCCCEIBoM6SjsQCGjBUEWqKoHOvR5UR3T7fL6y5eqeTgmu\nK4Hf79dR4z6fTwuNPp+vTND3+/1lUfWVoqn7HILBIKFQiGKxWDf7+UrUcWzbJhAIEAgEtChdGU3s\njkB2PyrfsyxLn7eUsu51LoTA7/cTDAb1/lUdBwIBIpHIopP4bNv27Fe6hWhVH9lstq5lB6cNqBSS\nlW23sr69UldWPqvvhZrAoOqn3pNQLcsiGAx6fp7q2KpM7v+9hHx3u1efpSp3M/X5DQaDwbDueAlY\nQszhTCMKstoYEd1gMDSEH/3oR3z8Pe9he1sbnrcBCwswMlJTDS5KyVyh0gGxHD9477uEr8ZyG3il\ntZX3/+Vf8uOvf/2ix1gJhoeHeec7P8SJExtxJny5kVgW7Nzpo5aeVCxKhobyzM97z1DetSnH9fsy\niFr3jbOzMOrtSJooFEht387nv/zlsgiHRiCl5Hvf+x5f+bM/Y6OX8CAlyWAn06E+pLCqPlspIeQv\n0tua8pxIULAF0+koBbv2QEah4OynenvJyMgR/vAPf5fXv/7Hz/fULprh4WE+9Hu/x0BvL2GvhiEl\nTE9DrWgBISAWg8Vy8Z09C8lFnEX7+pyHx76npqc5cMMNvPe97138RNYpxWKRK6+8kg984AN1zeVY\nD4QQfPazn9XREI1GDXq95z3vaTpBNJPJ8IEPfGDVokGKxSK33HIL/+E//Ad8Pl+VVavifN+/kG0q\nByK/+MUvMj4+fl7nUy/UAP073vEOrrvufCZMN4aJiQn+6I/+qCkdBAwGg8HQnKTTaY4ePUpHRwdS\nShKJhI6kdfdDpJRMT0+XiZuFQoFsNqvFxIGBAbZs2QI4uZzn5uYAp1+zEq5IuVyOBx98kPb2dqSU\nvPjiizr6PBqNVk2SVMJnNpslkUjo66XP56O1tVVPKIjH43oywUqUW+WiP3bsGFJK5ufnSSQSCCE4\ne/Yshw4d0n2hfD5PLpfTgmhl9D+cE0aDwSB33nkn/f39SCkZHh6ue9lDoRCtra26T6Qs3N0R87Wo\nJdI2SrxNJpOMjY0BzgT6w4cPY9s26XSaF198UbdtZbHv7k9Fo1EtTgcCAXbt2kUsFkMIwYEDB9i2\nbRtCCNLpdN0n5yYSCU6cOIHP52Nqaoof/vCH2pZ9ZmamLBJ9fn5euzFIKQmFQmX3gMlkknw+j23b\nvP71r+euu+5CCEEymTT9R4PBYDCsJl8F3r/ahWgGjIi+xqicAVvrvaVYzMbddOIMF0I+l+MNmzbx\nb666CsurDZ0+DYODjmrpQc62eTmZpChltVgKWEBL6bkW4RrLC8B/LxTI573SGK48tm0zN9fF9PT7\ngQ1Vy0MhuOYaPx0d3t/hfF7y1a8mmZ7OUTlNQAi48qZZPvL2USxZ4zfguefge9+rnsAgBCeSSf50\nBWacL5dCLscv7NnDT+zeXVU+KSWn267h0IbXID0uZxLoima57bKzWB6nnsr7eXqkn3Q+4CmySwnp\ntHeTtCzJl770AQqF1WszXR0d/Kd3v5sNXkJjsQhPPw3j495WBELA1q3Q1lbbxuAf/xFOnfLeXkq4\n8054zWuqlkshePrwYb6/lPPEOkcNcDWbiG5ZFoFAYNVEdDiXv7HRE3eWol4WkBdDIBCgpaVl1cvh\nRkq56u1YCEEoFCIWi61qObyYn59vqs/LYDAYDM1PoVBgampK358qVxP1Wr0vpSSZTGphHBwRW9lu\nSynp6OjQgm4ul9OiZKUgWS9s2+bkyZNa1D18+DCzs7MA9Pf309FxzvUzn8+XneP09LQeiwoGg2zY\nsAEhBJFIhIWFBS2gr9SExmQyyfT0NLZtk8lkSKVSCCFIJBKMjIzo9bLZbJktfS6XY3Z2Vten+zkc\nDrNlyxYtks7Pz9e93H6/n1AopPtkKj/7cljtPkoul2NhYQGAsbExDh8+TLFYJJFI8C//8i8sLCzo\niQrpdFp/9kII2tvb9cTSUCjEDTfcQHt7O5Zl0d/fT19fH0II8vl83UX0bDbLzMwMlmVx9uxZHn30\nUVKpFLZtl9m527bN+Ph4mYNcLBbT9zlSShYWFvQkmV27dnHrrbfq76sZfzUYDAaDYfUxIvoaI51O\nVw3WCSGYnZ0lk8l42i/5/f6qTrbK66QGsSs7bitln2VYwwhB0LJo8fsRXjcCqk3VuInzAUGcqHEv\nrNLyxVpmCG8R3bfEdo3BAiJUR6I7+HwhAgHhqXc6wk4BR0Cvrj+/L0RLMIioJaIHAk79e+w87POt\n+o112O8nFghUl0/ahAJBgoEYUniI6BKCQR+xUMhTRMfyEwpGKQrv3MZSOlq0l+OeEBLLWt1LqM+y\nCAeDxLwGBIpFCAadhxeWtfhyONcuaonogQB4iZyWRWgV8kUbDAaDwWAwGAxrCa880YrK972srd3P\ny9lnPfDa92L3k16TFCvHphopJC5W3175uVV9ern8eAWzNOJcalm0ex2/0o58te/9K8vgZZHvtX4t\nW/2VpNKxaSlr/8p1FW47d4PBYDAYDM2HEdHXELZt8+yzz/LQQw9VdYRnZmbKrJDc7N+/n97e3rL3\npJRcc801XHXVVbS0tFRtsxq5kQ2G9cvF3mib2cs1WaJqzMRvQyOxbdvTntLn86376647R2I2m/XM\nlxgKhfRrn2sC0FoflCoUCro+3AO6qg4WcyNa63XjjgRTET6VA55qYmjlAOZarxuDwWAwNC/q+qSu\nUZFIBHCuZ/F4nM7OTv3/xMQEg4ODelvbtikUCjoKdvPmzXpZPp/XkdaV/al6YVkWHR0dxONxpJRc\nddVVOjJ+69at9PT0lJVHRexOTExw6NAhisUiUkra29u57bbbdD7xDRs24Pf7y/JL15uWlha6urq0\nCK36BurYlXbuimKxSCaT0X2MbDbLyMgIhUKBQCDAFVdcQXd3N1JKDh06VPdyJ5NJJiYmAJienub0\n6dPYtl1lgV4sFpmZmdHR3MFgkNe97nVEo1FtBd/b26vzq2ezWQolW7aVdC5Q7TASibBt2zYdnZ1M\nJslkMgghKBQKjI6O6lQFUN7fDwQC2uXLsiz9mQD6HOpJKBSiq6tLH+vAgQNks1ls2yaZTJb1zWdm\nZspSLBw/fpxRVzq9YDBINBrFtm19Du6+vMFgMFwkrwX+FU5s2STwv4Bj57mPFuCa0iMCfAk4VbcS\nGgxNjhHR1xi2bZPP56tuKpR1VOVNkroxqIwsVx1o942bwWAwGAyGlSOfz2vbSCUaCyFobW0luM6j\n+4vForZIHB4e1raPSiC2LItNmzbpPktrayt+v1+vs5ZJp9M6Z6Lq51mWRTwex+/34/f7qwbhmsGa\nfqVx54110qbM6f6wGhz3+/1Eo1FtB68Gjdd63RgMBoOhuRFCEAgECAQC2hLcsiyklHR1dWkhWkrJ\ns88+y7Fjx/T/7jEclU9dkc1mGR8fp1gsksvlVkxE7+7u1vnct27dqsendu/ezcDAgF63UEqpZlkW\nx44dY3x8XFtYDwwMcO+99+prczqd1nbuKzFGJYSgra2Nnp4epJS0tLTQ1taml99xxx3L3tf8/DyP\nP/64Fk5bWloIBALYtl1mZ18PVM5tZTf/3HPP8dWvfpVisUihUCizmc/n8xw9elR/7i0tLXR3d9Pb\n26st9Nvb2/V9Rzqd1iK2W7yud/mV4B+Lxdi7d68W8FtbW/Uki2w2y5EjR/Q9gDpvdW7KYVNNqlX2\n7+q86z0BIBKJ0NPTg2VZRKNRbr75Zj1h023DLqUklUrp8ygWi3zhC1/gpZde0vvq7+8nFotpEV2d\nw2qnSzIYDGuC/4KT03oeR/S+HPht4E3Ad5a5j3/AEeHdF9+nMCL6eiAG3Ars4NwkjOc5/0kYlzxG\nRF+DeFlhmcFAw1pnbQcMi1ou98vevvaiS/234eI++VoO95pmr55m/vyauWxNjLleL47bRnOp9dZj\nXdayilzMJnWtUzl54HzWNxgMBoPhUqCWdXTlxDAvu+mVxitPuHuZ++G13aVI5TmttCV9ZZ9PTbhQ\nATNqUqFXBL/biafWWGIj2spiffwLrbtG9um82rX7tVf7NxgMhhXm9TgC+v3AvUAC2Ad8F/gisBOY\nWcZ+sqX1fwTchiPAG9YH7yw9KjkO/AHw+cYWZ/UwIrrBYGg8tW5mpFzUP9sqzYiu3FoClpRY+bx3\nvnVACoGIRhFe1m8qv/OqIoECUBkNIJESstkg2ax3TvRCQRIOQ1tbdU50ISDkyyMXFrxzogvh5M/2\nyqvt7Nx5rDaebUZFoHqL4RIQFth42/3ZEnyZJL6cVSPtt8Cf9yGKVlWjE0IiZP0jN84HWSxCMon0\nilC2bYSKLKnxfbMzGfDX6AZIiSgUnO9Tre9rsQi5XPVyISCfN17454mKwsjn8/p/NfBpBlvQtqMq\nqkTZMypLT8uyyGazenBwOU46amBNDTCqiI9LSURV7UZZuqdSKd1ustmstkB1W5arCP3l5F8MBoNV\nrkXq/2ZHWbiD42SwsLCg25ByefD7/dryVtlnKmrl3HS/VnWh6sn9vsFgMBgMF4qXsFwpxC2Wi1s5\nrtTax0qLuu7oYq+HQl1HmzUn9PnWkbvfXktIXYl6rzyWct1RfR7VHlQEurvPUvk5rQa16sztHqTK\nWdm21QSByvUbIVxXlruyLO71ak2oUOemHkZwNxgMdeTdpeffxBHQAV4E/gj4JPDLwEeXsZ83u14P\n1FzLsJ7YBXwOeAPwb4D86hZn5TEiusFgaByhEMRi1eKaENiBAHYmg3DlFlNIwIrF2HHnnTVFP5lM\nIh9/HEr2XlUEg4R+8zexururjp+xbUIPPLDKkbNpHDeUyYr3JYWCxbe/vY9AwFvoDgbh3nuj7NsX\nrapaKSS7nvke/N5Hsb1mdQPihhsQ73xn9cCFEDA0BF/+8oWe1MUjBMTj4PG5IW3iG1rYvkXUjBgP\n+AJMBjd6f7Szo+z7P3+AmBiv/uylBJ8P+7K9yFLOwbLFwHdnXwZ51wWdVj2QJ09SeNe7yAeD1fH4\ngQCBH/9xrB07PMVsmc8z/9nPkn3lFe+JJ0IQ7+0l1NLiiOVVO5Bw/Di4Bgc0lgUnTjgN07BshBAk\nEglOnDihc+BFo1F8Ph9tbW06F2Y9UAM06rWyF2xmFhYWeOqpp8hms5w4cYLZ2VnAsW1vaWnBsiwG\nBwe1uDszM0M+n6dYLJblIHTnUFTrdnV1sXXrVsLhMFdccQXhcFiv2+z1IoRgbm6OoaEhJicnuf/+\n+3X+yiNHjpBMJolGo7r9tLW1sWPHDvx+P7FYbFFB3bIsrr76ap3zcWBgACklgUCAtra2pq+bTCbD\nmTNndE7M733ve0xOTpLNZslms8A5609ltao+e7/fT6hicpmyyFXtJhKJEI1GaWtr48CBA3oChrte\nDQaDwWC4EHw+n77OQLmYdvbsWcbGxnTf5tChQxw+fFivt3//fu66yCkDegAAIABJREFU6y79f09P\nD1NTUwghyOVy2ro7m82uyPXK7/dz+eWXs2HDBn1NVdfOjo4Obc8upeTRRx/lgQce0JP/lD23suo+\nefIkfr+/TJRXE+PqjZROHm5lfz44OMjs7CxCCJLJJKdPnwbQ/8/MzOjPZcOGDdx4443afj8QCLB9\n+/aySZqWZWHbdplFfD0QQtDe3q5t8qPRKH19fboe3bnMbduuyol+/fXX636iyj2uUgBMTU2RTCYR\nQjA/P088Hq9r2cHpy/f19SGEIJPJ6PopFAps2LBBlzWTyVAsFpmbm9PbdnR06EmewWCQq6++mtbW\nVoQQ9Pb26v5qe3t73futgUCAlpYW3Wf0+/26rMVisazOBwcHSSQSWjDfvXs3qVRK7+vuu+9mWykX\n/OWXX05XV5eum2bvbxsMhqYlAtwOvIATNezm68AngJ9keSK6Yf1xGvh7HBeDIWAUR0d+Fc7kC+VG\n8HM4bga/sQplbChmhOcS5XwsjlZzRqnBUIbP50R8e7VHy0IWi8gaUc8WEN+4sWbEuJybI7vYIIBl\nEbr8csSmTVXHt4tFfM8+u9yzWCGKOBMDq2+SbNtiaEgJmdV1F4vBtm0BbrlFVGma0pK0vjILTz3l\n/TsgBOzb5zwqozaFgEikdpR6owgGIRz2ENElgWiA1lZqiuhC+Mj6op7L/EVBzyuHCJw95T2BwueD\n9gL4+6uqXQqI5OZYzUQCcn4eefQotlcpwmHkTTc5ded1vbBtcidOkH3ySe+dC0HLrbd6T3pRzM9D\nKfde5bZMTkJ///mcjoHqqAV35EVl5Ejl68X2qdatjB6uZd3YbKhBYhVtnc/ndcS+ek8Nqqo+Tz6f\n17k+lWAKaHtLFamu1lVROpdif0m1G3WumUyGQqHAwsICyWRS5w8FZ2A7nU7r6HR33vjK9mFZFrlc\nDsuyKBQKZfnFLxXcEVi5XE4L6MrJwD2w7xYSauWIVdFyqg35/X6dZ/1SbDsGg8FgaE6EEIRCIcLh\nMHAup7MQgomJCS1sAhw/fpzjx53xcdu22bNnD1dffbW+LhUKBebm5vS1vrM0QTiTyayIs4zP52Pb\ntm309/ejLMW9+prFYpGjR4/ypS99SQude/bs0f2zVCrFyMiILqOa3Kicd1aCTCZDMplESsmpU6d4\n6aWXEEIwOTnJE088odebnJxkcHBQ1/GuXbsIBAJ6Ml53dzdXXnklra2tVecci8XqXu5YLKZzuff1\n9XHgwIGa/fvK/op70mixWGRmZkYL73Nzc1r8TaVSKyKiR6NRurq6EEKQz+f1MWzb1uekjj80NEQ0\nGtVtec+ePTp/eygU4tWvfrUW4d1R6S0tLfreoV4EAgEikQiBQIBoNEp7eztQXb/u9qru6zZv3kw6\nndb1/lM/9VNcffXVVfdm6lwNBoPhAtgNBIAjHsvOArPAFQ0tkeFS4QSwHe8B72+VHv8W+AzgA34N\n+BTwTKMKuBoYEf0SJJPJ8Pzzz1cNYkr5/9l78yA5rvvO8/My6+6q6rvRjYsgQIAAL/GUeOiwKMmU\nxbXkpY8Jy8eGvJpwOGJmNsbejdFs2DNe2zOWx3I4vDMaj62Vx3J4JHsUtiTrMC3KFE1ZJiVRJwEe\nIEDcQKPvrqquKyvz7R9Z71VmVVajAVT1hfeJALorr/rly5edL9/v9/v+JF/+8pf59Kc/3THQajQa\nxGIxHckc5G1vexv33ntvh8zX1NSUHpAaDOtB318PpIyWjN80E9+dcuydy7u9DPtJwRHJ2vqpt1r7\niqh22Sas2q+E8DXfo15OheX/o0um+wa/0K7aK9ZgmxBiDW2zyhbd1qvl5oX/qlGOPpXh8vLLLyOE\n4OWXXyaZTOrMllgsRiKRYGxsTE9MplIpPelYqVS0bHW9qe6RSqW46667yGQy2gkI0bURNxtqAjaZ\nTCKEYHh4WMtu7927lz179uhtlcN9cHAQx3G0M1mhMoQqlYpum3w+Tz6fJ5VKkU6n9QSomrDezChn\nbjweJx6P635i27bOXlES+EIIFhYWePXVVzskyFUwArQm6m3bplwuk06n2b17t55QzWazfcno6TUq\nsMJ1XRqNBvV6nWq1Srlc1n3CsixWVlYQQrC0tBQpWa8COFS/URLxqVSKVCrF1NQUO3bsIJ1OY9s2\niUTCZKIbDAaDoacEZcLb61R3+719//VW2FnLGCr4vG13IAZtDioE9fM8otpSjbWD69RYWs3JRV2T\njWS1NrqSbVFtsNHqTO3y56ttsxna/mpQAcBb4Z3MYDBsKcabP+e7rJ/Hd5QKNjI7yLAZWUvt0k8A\nb8TPQLfwneq/3E+jNhozw7MFcV2XmZmZyCyZs2fPcvz48dAATL1wDAwMdNQHlVKya9cuDh061OFE\nHxwcNAM5g8FgMBjWCVXbWkm7v/jii1qGUU3SKUfvwMAABw8eJBaLIYRgcHBQO5YXFhYoFoshB/Lg\n4CCHDh3SGQ1qgmezO4kVQggd2Dc4OKidxTfffDO33367zpZRE1Eq48RxHJ1BA+j95ubmdKZRNpsl\nl8tpp6jaZqtI3VuWpZ3oSiLVtm2y2awOyKhUKgCUy2Xm5uY6zqlcLuuMapUtE4vFdADm8vIy6XQ6\nlF212QnWxnRdV2eiVyoVSqUSEJ4obi9zEJTkVPfg/Pw81WoVKSXJZFLLtT788MNa0nPqBlThqFar\n761Wq/9qPb4rmJnZC5R8bzf1gV4hhOh5Bt/KyooO6ugnvbY9GMTUT4QQZDKZnga1VKvVkLpJP+ll\naYj2gLJ+kslkiMfj/1EI8fS6fOE2pludaFjdUae277Z/sLzNenOles9B29vPo9s+/bAvqu2C399e\n9mc1m7cyG3Euq7V91LVZz/5xrUTdb5vVVoPBsG1QWZHdpFtW8LOIY9wA9awNfeHjtGTc37yRhqwH\nxom+RbmaCOO1RI1up4G+wWAwGAxbnfZsF2hlZAef9epzUGZa/a7+BY8R9T0bOZl6LURNUq7lc3sW\nV/Bnt0nPzd4uUfYrB7D6p5ap8+823gu2TVS222Zvi3bax8JRDoS19P2obKyoz+3feSNx4sSJ3b/9\n27/3jno9RlAbRVXxCRLd/SSppMQSbRtWqwTr1NQ8jze+5S188J//856pZV2+fJkPf/jDzeCRpgGe\ni4iSfbUilGssy/8XwJPgOCCluj89stkkv/u7v9MzGd9iscjv/M5/4syZi9h265XewiOGE1aakRLa\nggQkIGs1ZJsz28rnEYGL5nkesVSK3/3IR8hms9dtt5SSP/zDP+Rb3/oBsViwXJAkZdWxrpAIIwU4\nrk3NDfY1SQKHJC3ntgTcWIx/+cu/zN13333ddivbP/bHf8x3vvY1EoF+IIWgnswhRSBY3WtglRbB\n6wzOaP8rYQkRCnSXQCOV4l/8yq9w77339sT2b3/72/zX//qHJJOJkAWu235PSmzc8L0I/n3Y7oS3\nLL9sUIBKo8HPfeADPProo3/SE8NvYGKxGKOjo4yOjmp1HPUsf+6553jhhRf0tidOnGBhYUF/dhwn\ndL9Wq1VdxkRJTwfHkf0galwlhOCZZ57hxRdf1M/g559/nsXFRQCmpqZ47LHHdNCeZVnabqVElEgk\ncF1XB472EhWUqiTE4/E4w8PDCCGYmZkJKUGWy2UdvAmwa9cubrvtNh3MmMvldBCMUsYJjtF6TbC9\nlbpQ1LhPBRd1G6+4rsvCwoJud8uydF3xXgawdbNdCKGvreM4uiyREIJarcbAwIA+H9u22b9/v1bb\nVEGlKrhMtblSZeqn3Qr1XYVCQdvhOA7PPvssr732Wihwc3h4WB/HKBgZDIY+oJznQ13WDwN1jAPd\ncO28HPh9csOsWCfMk9pgMBgMBoNhE9DusFTy7KoOOKAz06vVqq5rrSbE1PbT09MsLi7qiTSA8fFx\nyuUy2WxW14jeSqjMasdxiMfjemJsaGiIWCyGlJJMJqMns0ZHR3Vmfz6fRwi/zqKaBF1aWtJtNDQ0\nxNDQEIlEokOxZ7MjpSSVSjEyMoJt29x99926xvvk5CSVSoVyuayzy4M14oN9YHZ2lmKxiOd5rKys\n6Gz2VCqlM9JVO2+ViT4h/HqySq1h9+7dZDIZSqUShUIBQKsNANTr9dDEq2of1Z7BjHZAB7D0O4N5\nK7C4uMj3vpdhz57/E8vyHaNSwt698IY3hP3OtRq0J0/bFrznHVVyOel7EIWA5WX4m7+B5rUCeObU\nKV546aWetvnKygqXLk3zO7/zn4jF4n4Fl/kFrKPf9z2MyngpIZ+Hdkfyjh0wFJibElCtCF74jkVp\nxd99ZWWBL3zhP/a0Hmu9XufVV0/x5jf/c/buPaQdoUNymVvdl7BEoI3KZTh/PnwhpKT4xS+y8txz\nrWXxOKMf/jDxu+/WntWlUonf/PjHe2a7lJJjx04wNPQ4Bw++WdsdFw3+l5HnSMdqgGh5djv+Jku+\nO7OXp88d0Ocjkdzvfpsf8p5qluGBuufxW9/8JrOzsz2xW9l+4sUX+ZFz53jL6Khe7tpJnn/7/0El\nO6b1MK0LJxj4vZ/Dmj0TsBxSQJqg+x+GhoYYDyhYVKXkdysVZn72Z3tm+/T0NPfddz8//uM/rptW\nSrh8GZaWAl1DSvYnLpKyqq2dhYD5efijP2rdvFLC1BTcfz+oZ4IQ/MGTT3Lh/Pme2X0jo8r2jI+P\n67Gf53lYlsWxY8f49Kc/rbcNKqkA1Go1rUYDhJ5VyWRSl3tZLye6cv4DfOUrX+HP/uzP9PeurKxQ\nLBaRUpJOp/mRH/kRXdN6aWmJf/iHf8DzPGzbZnJyUo9j++VEHxoaYnJyEs/zmJyc5MiRIwghuHz5\ncmjcpMbp6lyHh4e57bbb9DKlEKTWq1I5/XSiBxV0lJKSeo9Qv9u2TTqd7upE9zxPK1oJIRgfH9f9\nJZ1O99zuoP0QVp5yXZdSqaSfP41Gg2w2q8sR2bbNwYMHQ8FpjUZDj+XUfREsY9UPVP8OOsgXFxep\nVqsIIahWq3z1q1/lG9/4ht7u4Ycf5tZbb9XnbspoGgyGPnCh+XM8Yp0AxgLbGAzXQnAw1n95tA1m\na8yCGQwGg8FgCLGh2iFGuaQvCCG0w09JcyunnXKCOo5DvV5HSskrr7yi9w1m2Z44cYLLly+HJsr2\n7t3Lz/zMz5DNZimXy9oRGIvFdCbEZiYej7Njxw5c1yWfz+tMn3Q6rTOWghkywRrxikKhwOuvv069\nXufcuXNcvHgRz/PYtWsXN91005ZxDgeRUjIyMqIz1e644w6dBbO8vIzjONRqNe04r9frFAoFPamq\nJv6OHj3K+fPn9X7q2OVyGdd1dRkBKaWuTb/ZicVi5HI5PWH9xje+kVqtRqFQ0BL/1WqV5eVlnTWk\n7jM1CSuEoFwuU6vVdG31crms2y8YlLAVs/V7iW2nSSZ3YFn+JLuUkMn4fudgs1SrfpZ2cJltSSZG\nVxjKey0neizmO6yV004IBlOpvrRxLBZnYmKHzowWwiI+ONiRvc3QIGRz4WWjo/6/ACsVGByKY9kC\nIcC2Y76Dvof4zi+bfH6MwaEpPSgYlgl2uIPEgqXsYjHIhe2WUpJMJGjPK5zI5UgND/sXUAiSsRiJ\nHjurLMsikxkml5sKONEdxgeHyNnV8MbtpciQDFdGyAxMoVzREsmQO8ykl0E0nehV1yXVByebJQRD\n8ThTyVYWvRNLMpifIJHd4S8QYBWWGLBsgtYrJ3qGsBN91LKYCjx/ylKS6vHfEyEE2VyeiR2TSE8p\nJPi3lxDB+1Eykagy0O5E9zxIJlvXQ0pIp/0bPOBEH9giz4etQrtqjvpdPYMU7ZmwUcopUev6/dy6\nFglr27a1Izro4F9v5Zf2Nmv/zqi2i1KrCf7eLgHfa3uDqjvdttlKRNkb1Q+Cil3t262H2uZa7FT2\nbbVrYDAYtjSvA8vAQ/iy7cEXm/vwh6Tf2QC7DNuH+wK/X9wwK9aJrTdbaACufqJuNenOqHVG3t3Q\nDyS+xKVo71qiKSspBIguWYBX6O+yuY3sFk0vhD8vK2WnAzBq2ZZComdPZdvSQGN3O8NVW3YztIu6\nPt2um4zOSJP45yZlt3NcQxbAaue/CdpGWl2ET4OZc6v9/b/iF1zh3mhOsht6g23b2kGZSqW0A7Be\nr2sHcb1e19kswVq4jUZDP7vz+Tzlcpl4PK6zR4aamZIqg3urEZwkax8DdauJqJynKoOlWq1SKBS0\nY1k5kYMZQcHft0I7qXaIslW1mWVZekLatm0SiYTOqr7SGFApHcRiMeLxuM5i2ioo+1Vgigq2UBla\nQgh976hMN/Dvp3q9rts32H7q90QiQSqVIplM6uUqO93g4zt6vPBjovlMDi4S4Mteu15rjev6jjvP\n858zfR2rSep1D6nGE47EdQXCJfQ8lQ0BDT268H82JKIRtstxQHpqu36b7iGk2zq+5yJdFz1Pphyg\nXZ7X7WZJt7l/s91lo9F745sNIqSrDRC40Q3VkbHpb2OJ4BjG71RSWDoTXYo1jHGuFSHCEv7Cwn/L\n8HTPAImI2SG585afOmyZFAI3sMxby/jsWvA8aDSQql96gLSAtncnKcPtrvpQBM3c0c79DT2lvRTN\nWrdv/9wum70eqEzgbk77YAZ10Nao47SP8/pJVButpV2jPge336ix5fV870Y5f7tdg60wPldEvbO0\ny+xvtXPaxIzQqgG9gC9TbTDcyLjA3wA/B7wLeDKw7v3Nn3/dts8hIAccA9oiWw2GDv514Pd/2DAr\n1gnjRN/EtMtyKRzHYW5uTk8ABlESnO37WZZFOp3ukOtSWVzJZLJjHyWTZDD0iidn7mP25E+A7OxX\n6dgK4+//WUSUY1NCdijG4z87SjzRpa5vrUbiiSeQtVq43zYnDWWjgfjsZ6FYjDi+hIsX4f3v71y3\nbnj4Y5QoKS8LKBEOHGwhPI+hi2eYPFHsnLcSUJg5yTGiHckSGF5cZNfRo4j2AAQh4MIFX3t1o/A8\nvL/+a7wvf7lzQlFKaokMhfQg3dzkUoBrRa9NpBKM3PsG4o+8Kfq7hfCz4KLk1YTwU+w28G+ktW8f\nsV/8RZJt2WXgO0ovP/kklc9/PnJf4XkMnDlD1+qmQhA7cABuuy16vZS+xO7cXOS+LC3B5LYvidNz\nRkZGtCSh67q84Q1vQElABqWl1WdVr9B1XS5fvqw/nzlzhpmZGYaGhti/fz+WZZFKpbAsi4WFha5Z\nG5uZWCzG2NgYQMhRqWTagVBQgXKILi0tceLECYQQzM3N8clPflIHJSinalDau1ar6TbZapL30HL2\nCuHX9VST08GJOhVYcOLECUqlUmiCT0pJpVLRx5uYmMC2bfbu3cstt9yindBbgVgsxsjIiD734eFh\nHTShMviCQRZKth38gAvVNhcvXmRmZoZGo8Ho6Ki+d3bt2sXOnTvZtWsXO3bsYGBgAMuytqSiQS9I\nJmF4OJyo+uqrZ/jSl76nxyYCyb/+0TTve1MSK/inR0qyT54GL5CiXijA3/1deNy2vAxve1vPbT9+\nvMS73/11hPCvXZwsefsBrLbAzmJlhZoTnk/Kj6bJDrcynlWC7kMPWTppfWWlM6m9FyTcCrctfJ3D\nmXMop37swhliX/k8NJyWQaOjvux2ECmxGo3QZIBsNKj87u9Sb0rWC2DFdWn0+DkRFw4Pxb/FW5KO\ndrgKJJmleRCBv7vlMrz8MtTrul8I6XHHXY+w+30TITl3nAOcq/+i/ltWb9Qon7rcU7sBSKXgJ38S\nHnxQO5ZjCO735vFYam2Xq2L/h19D1MMZ3WJhAfvyZX3eUghmjx7la//4j3qsWpeSy83yJD1DSuxn\nnsZeKbay44XF5MNvY/TQHa1xsueR+txzMDMdHuM2GnDwYMtBLiWV3bewcMc7IZZQp0fxuRf9YAZD\nzzl//jyVSgXLsigWi6F5nbvuuos777xT9/83velNlMvl0LNdyUUH63Svl/Pu+PHj2p4LFy6EbNu5\ncycPPvggAEeOHGF6elor4ih1GDVuzWQyZLNZGo3Gushfl0olPRZYWloiHo9rB2gymdTlgqT0a6AP\nDAyEsuhVsOJ6By6o4EeFslvZ1e7UVXOI4JcCUOVthBBa/h+InDPsNcGyOX65lUv6/Uahrn1UUGe7\nc3q9CI4tq9Uqr7zyCouLiwghqNfrlEolHdhpWRY33XQT9957r7ZzaKhbyWLDVfA3wCPN3x8HvrSB\nthgMm4X/APwE8HHgA8AJ/PvjXwI/AP5n2/b/DXg7cDvwUmD524AHm7+rCdR/RisT+VPA2R7bbtgY\n0sD/CvwF3TPPYsBvAD/a/FwD/qT/pm0sN+YszxahWq2GJjIB/eLxkY98hEql0jEQn5+fp13eC3xZ\n04ceeqijdpSUkvvuu4+77rqr4/uDk+wGw/UiJVyojZIs3oSUnf0ql4eVfbcT1eWkhOEhiXdrI9rH\nDAjXRezc2X2WslqF//JfOmtCqi/YcEeSn8HS+YxSdjXoVmJESJdkZZF0cb4zQEEICrUiy3TPOE/X\n68ilpWgneqHQNftkXZASzp7tmjXt4rdKK98rjIf/NI889Pg48pEHYGKi+/e7bnTfEKIlX7lBiFwO\ncf/9WCMjHetktUrtL/6C0ve/H70vMEC4gE3U8dslaltfIP17qt4lwLuHdV9vJIL1EwFdEzI4IeQ4\nDo1GA9d1qVaroWe++lytVrEsi5GREfbt26czkZXDOVijcatgWVbkhGkwgykqo1zJl1uWxfLyMjMz\nM9RqNUZGRnSmf3DM1O5w3uxESYmqn6uN4RzHCWVpt9dyVMdIJBLEYrEtKeeusvAVUf0nqEQQzMyv\nVCq6FEK1WtUT0KrMgpSSTCZDLpcjm82STCZJJBId33kjYTeTblunL1lZqXLixCyep7IoJV5xgMl4\nOqxK5Hkwdzn8TCkW/UCtUqm1rFzuy3htZaXByy8vQtO9mEzGGRkdwrLCT8nFRY/ySmssJgSMjFgM\nDYWveT4Pd70B0hn/c7ehxPViSZess8hgPd0aLhYvw5nTLcez5/nP5PaxnJQIKcOKAFLivv66PpSg\nOca66abe2o1k2Cqww5oLOWVp1FufhfDHGfPz/s+A3Tl3mdyOOlhCn/diPcvl+pQ+n0ajiptq1cnt\nnfGWXwt8/37dpsLzGJq+BG7AzoSEQ7d07n/5sh9lEVAGuHzuHAvlcst2oNYHB6FYmEecO9u65pZF\n0imRDDaTB6Kw5N977X/nA3WHkRIvN0gtN4ZslkGwLGgkMj232+CPR37wgx8wMzODZVnMzMxo9RMp\nJU888QT/5t/8G2KxmB4HLi8vawdvKpXSQZrr/fyWUvLss89y9uxZLMvi6NGjLC0t6Qz1H/7hH+aD\nH/ygHqO+9NJLof2Dzt+hoSFGR0ep1+t9rc+tvnd+fp6TJ08ihF8mJxUoKTIyMsJNN92kr0EqlWJk\nZEQ7dl3XpVKp6HGFGnP1G9Wuaqyv1K26fbeUkoWFBe00dxyHarWqE3YGBgYYHx/H8zyy2Syl4DO5\nDziOQ7EZPLe0tMQrr7yivzMej3Po0CHdl9U90H4+G5HZ7bquDkYoFos8/fTTnD59Gsuy8DyP2dlZ\nXbLLtm3uu+8+nnjiCb3/VhhXGwyGLcmrwI8D/x34u8Dyb+I7wddax/rdwIfaln0w8Ps3ME707UIC\n+B/Af8JXL/gW8Bp+aYAR4E78a38ksM+/B06ur5nrj3Gib3KiIlaVtGtQZlLR7jxXqGjU9uwYJcu5\nlaQ5DVsXgV9LMOqVxsKXee+QegdfpVHi6w5GZLEDLcnPbg5fz/Nndywr2om+4RmH3V6cxBXWN1cJ\n0TyvTie63iRiVz1Rqvfv3Hcz0JLH7Fwe/Nlt3+gVAcnzbmxmJ5rqt1F9V03sdtn1qq7s1bbBJuo3\n24Vukn/BiaKgM/lKWRjrKYe50XRrl2BgQdQEXPD37Tax1U12tH2bduf6dmsHRdT9oPqNmvxsv9cU\n3aRpDQCi+TiICvRo70siMI7RG0Yv65OtwZGSEL5/1mr7uvbP0BpaKqSMHmr2xXQRGAWJwLKgUUFj\n1mBU8Op0Gzv2Atnt6FE2RtotAu8EzSANGTjVXhrbjgq+Dd3rbefTLUA3uFxl4Uu5Pu0eeT+JiEDU\niHtP2RtS+2rbr/2z4bro9rzpVs6mm1x6cFt1nPUmWF4maIv6fbVs7Y18pnZr8+DP9u27EZTx7idR\nNcOvdp/NRnu7rSXYdb3qoQe/r1tgK7TGlepe2I7vF5uATwPfbv5+aiMNMRg2GX8L7AUeBkbxnZ3f\n67Lt+/B9hYW25b+B71TtRoTkq2GLswv435v/uuECH2b1vrFtME70LciVXpAMBoPBYDBsH4LP/Hg8\njm3boYzper3OwsICS0u+lKzjODpzOJfLheo8NxoN0uk0mYyfLbYdsmbb61ZDa7JqYWGBb3zjGwgh\nKBaLOnjw3e9+N29+85vxPI89e/aEJnbbJ623E41GQ9eEn5+f11KTKysrWuHAtm3dTgcOHCCTybBn\nzx7y+Tye520ruXKVEQToSU2Aubk5Tp48iWVZnD9/nnPnzukgVpXpt3v3bh544AFGRkYYGxsjmfSz\nMbdT+xgMBoNhY2if73FdF9d18TyPdDrN8PCwDvRqz8oOBgiuVot8PVDOTBXQODo6ys0336zXjY+P\n60xpVUpFYVkW2WZ5iVgstm6JH8pmVaJFKdEMDAzodblcLlT+MOrZHxxXrKdSjRrvg5+A02g0QkEU\nwf7ieR6Li4uhMjfxeFzLvgdl6dejzJEaa0FLNUm1bSKRIJfLkcvl9PUJluWJCiLu57xpezBqUNEo\nn88zMjKi+8uhQ4e0LL1t24x2U3szXA9/sNEGGAybmDrwzBq26+YMrzT/GbY/FfzM8oeBh4B8xDaz\n+CU0/gB4cf1M21jMLI/BYDAYDAbDJiY4+aPUY5QMtZrUKpfLLC8v6wm6WCxGPB4nmUwSi8X0pJSa\ndFJ1DdtrI2412ttGoSayyuUyp0+f1pOzap/bbruNd73rXTpHeF/cAAAgAElEQVTbWE1yrXfdyvVG\nSkm9XsdxHFZWVnStz3q9rvtUUDp1fHycXC7HyMgIqVRqW/SZIN0cC/V6nbm5OSzLYn5+XkvPep6n\ngwyGh4fZu3cv+XyegYEBLfm+HQJTroX25FwhZPN3SVBL5opum25Z030lVKA9OtEYAucjOrYNbiNl\n64gytG8/uYYv6GJUe0Z0X65EVCNHZZx3a+ANRLQ3jFQp2G2p8KK5LtiCoeVE9vPwHdMP2ntneM3q\n17u1rxDNcgAbccveIAQd4PV6nXK5jGVZ7Nu3j2w2q8cue/fuDT3LVAkctV45gtUx12OcE3weqtI5\nnufx6KOP8r73vQ/wn7cHDx7U9s/PzzM9Pa3Ht7lcjiNHjuhjKSd2P20PKhVlMhmGh4cByGaz7Nu3\nLzRGCo4725//7WWI1NhJBSv2k1KpxMWLF/XvL774oh4Dx+NxDh48qG3wPI/p6WntRE8mkzzwwAPk\n8/6ceSqVolKpYFkWjuP0PfiiXq8zMzODKkugSuZIKUmn09x5550hB/TKykqoZnoymQwFg6prohzu\nvUa1h3Lmq++4++67OXDggO4X7XarOvMGg8FgMGwy6vjKA+DXOhtp/ss2lxXx5d1vOPk940Q3GAwG\ng8Fg2KJETSSuZYJru0uWQ3jyOUqOs91pHNxvO3M15xeVeXQjyJV36zcQLYcfXHejkkhALufXRleM\nj8fZty+nq44IIcnlol8/neVlZKWiPXFiZYWY6yLaHah9IJm02L8/gxDK+CTxuJKjD2KRycRoekER\nAnZMCMbGPD2NIIHcgMdQvEbWambCWWVs0YfJewQ1O0MlltXeT8tKkmi4iGYWom6zgbb64FIiJiaw\n9+0LHA8WFmwa9daJF6RLTSR7a7gQfl3wbLYlEe66fr3wRqA0Y63mbxcP1KaXEplKg2WHvLauFNSd\nlgO40ehPpSbPg5l5i7MXLVDHlwJm4+DaqmtgCUnabutDQuCsDFAv5vX1kkAxPkZm7x5d0soBYp7X\nU6+0BEoMMMdIwEkuyLgxkg2ntaHrguf6J6q/X+ISY8EbQrewlJQrWWZmBTKmT4+VlZ6ZbGiinkPB\nZ04sFtPZ2+pz+z7dMtHXS9GwWymYdDqtHbTqcywWCzmlg8/YYFb0epSYaW+nYHDqarXFowg61ddT\nTTKYFd1oNCiXy1SrVaSUxONx6vV6yIler9e1E11dh2BNdXXMfhL8nmAZnWAGv23bJBIJUqmU3lbV\nIe92zH73lyBBO5SdSvFoeHiYiYmJvttgMBgMBkMPcfGzzmc32pDNgHGib0GCL1DbfaLXYDAYDAZD\ni6CE++XLl3XmQ6PR0BNNuVyOVCpFPp8PTR4puWklCa+Wb7exhJSSixcvUq/XOXv2LNPT0zr7R7VJ\nKpUKZaCr7JHt6AQNZtdXq1VmZmao1WoUCoVQJrrrugghGBsbA/wJ45GREbLZLAMDA9s6w1pKSaVS\n0RKily9f5tSpUzozTk2kj42N6Yz8qakpRkdHyWQyOttpO95Pa2X/fnjiCWj5cgSJxM0MDOzRvjgJ\nTFx+CbF0Dl13GfBqNS7+5V/SaGbOAcRsm6mRERLKK68crX3g1lvz/P7vvw3bTiAEnDpl8YUv2DhO\neLtcbph0eii07KF7a9x+uNiqzy1AVKvEj30fUV4BIZizVvgHe7HndtftDC/t+CEqu24D6ZswNPtV\nDi/+v8QqTUlkz4Obb4a3vz3klBVA7h3vIBtY1pDw4Q+P8b2XE7qkutNYolD/1d4aHo/Dm94Ejz7a\ncqIvLcGv/RqcP9/abvdu+JVfgZGR1jIpcW8+gDs83gq4ABYKglOvt07RdSGgCt0zVsqC3/r9LPk/\nGQrUZAdky0YpIZ+Dt7zFj11QTxUBvH7S49hRqZ3onif5sTe/i3/1P39eH6/iODz1R3/UcwWAL/MY\nF3m//mxJyY8uFXjgQvN+BL+/FItQLoec6AuM8XvlD9CgFVBROG9x+nPxUGzL2bPwxjf22PAblKhA\nwKgAr27PneCyjXo2RWW9B21pr+Xe7dzWy4Hebl+/gg/WKwAgav1q/edK16BfdPuu4OdgX4k6p27n\nuB59JRhwoYJze2XHjTqmNBgMBoNhs2Gc6BtMt9pCUkq++tWv8oUvfKFj0rJYLHL+/Hld5yhIIpFg\namqq41hjY2P89E//tI7aDK7bs2dPD87EYFgb3VQY/eVy1XWuhEa7LGITIf3JIBF1AD17K1uTdVFf\nsqHISLMUQkSbrdZ16Ide7bd3a7sNbxdAiK6n5SurylXlJ6PWrems1LlH9ZlNICe6Fq4sy9llvyu9\nsG+Bc9/OSCmp1WqcPXtWO39V3UAhBKOjo0xMTHTIGKqMiEQiobfdjkgpOXXqFEtLSxw/fpwzZ84A\nfvDA7t27sW1bOz7Br7mopCC3Y4BiMKOrUqlw7tw5arUaCwsL2olerVZxXZd4PM6uXbt05szk5CTZ\nbJZ0Or2t+wz44+tCoYAQgrNnz3L06FEsy9IyprZts3v3bsbGxpBScvPNN7Nz507i8XgoMOVGxbJ8\n32gwITKVsshmE6Ht4nGboAMd8J/z9TperaYSeZGxWOfzt0/9z7YF2WyMWMzPvMtk/Mz6dhIJQTIZ\nnKyHTBpyGcKeUgFYLggXhCAuGgjRh+emAE/EcEVcZzV7IhY9yInFOtpPxGKIwAUTEurxHNVYCqu5\nqSNdpOhx3xbClyyIx1vjiVgM6nU/+1wIf3m93tpOIaVvs+gsKxEcmvSzdG+tLqhU2oOKWm0kJcST\nUHMh3hb3UXNtKo3W+MzzoGGliGcy2onuOA5W8Jx7gqBBjDpJ1MjQQuJRWpNcvkRQJxlyotdc/3IF\nk9b7FOdyQ1KtVjl16hTFYhHXdZmZmWF+fh7LslheXqZYbJUtnZ6e5sSJE3rM12g0dFAYEKorDa0M\n6WDwWC9pNBqcPn1aZwnPzs6ytLSkx6BOIEJJPUOFECwsLHD58mWdFR0sySOEIJvNYts2juOwtLTU\nc7uVtPmJEycAWFhYYHbWT77KZrOR6kXdiArKVAGdi4u9DaqSUrKwsMDrr78OQKFQ4NKlS4Avdz4/\nP6+vcywWY3p6Wkueq+uj2jyVSnH27FndvolEQpcDmJ+f1zXqe2n7/Pw8J0+eRAjB8vIyFy5cQJXW\nmZmZ0TXd0+k0r7/+um4/KSXFYjF0XZLJZOjdR9Wln5ubCykg9ILl5WVOnjyJbds0Gg0KhYKuQX/p\n0iXK5TLg33+nTp2iUChc9XdcvHhRXxuDwWAwGAwbh3GibwKiBthSSs6dO8fzzz/f4USv1WqsrKxE\nDqaSySQDbXJ9Ukry+TyHDh0ik8l0rDP1eAzrxdISXLzYzf/m8Oqr5a7+7VxWELPTxONW5ORgLl7n\nh3dfYDBe7VwJUKvhLi5CxMuLBGTUbOk6kkql2b//ALY91rFOCMH4eL5Dqk/va1UZPfM9mDtGh8tU\nCBIvv8yw+ti2r5SS5GuvUfvzP4+M7K4Xi8h+zkReCSEQQ0OIZDLSGZwqlxkqFLpmjzbw9WeisBwH\nceGCn23TzSmcSoW1aQN2UattrDNZCN9rEZEdKiyLOASmStvWA4l4vJXhF3Fsq1KB5eXuUS/VLvea\noa+4rqsnR1V9Sc/ziMVi2gGsJrva72nlBN2O2bJKslIFJy4uLjI/P8/y8rL++6CcoPF4XNcS3e4o\nSU/HcbSzvFKpUKvVqFarVKtVhBA0Gg1c19VypcppHIvFQlnW242gNHu5XGZ5eRkhBKVSSbdNrVYD\n/GdiJpMhl8vpydx4PN712WzwCfrBr/TEFG0/15Nrio2ToL3XoWXtKM96fxEdv6jPa/xuGbHr9Zl0\nfaxi95Xs2gx/rlYzoeu1Umz0+PIqNt0Mbb3diMfj3H777Xz2s5/Vz15Vj9rzPAYHB0MOwePHj/P6\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4gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+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\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97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VBHUqlaKvr49cLqdT6S5X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kfRi+pvPPHx8N2taA24BbgIlg2TeZeZDehftnnyrwZ8BJuIcg+ma4nzR8GHceb1uAY8+XNbj7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\nRERERERERERERERERERERERERBar/w89lh7LUZYtOQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image('images/02_network_flowchart.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix\n",
"import time\n",
"from datetime import timedelta\n",
"import math\n",
"import os\n",
"\n",
"# Use PrettyTensor to simplify Neural Network construction.\n",
"import prettytensor as pt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0.10.0rc0'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
"Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
"Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
"Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of:\n",
"- Training-set:\t\t55000\n",
"- Test-set:\t\t10000\n",
"- Validation-set:\t5000\n"
]
}
],
"source": [
"print(\"Size of:\")\n",
"print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
"print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
"print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test- and validation-sets, so we calculate them now."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.test.cls = np.argmax(data.test.labels, axis=1)\n",
"data.validation.cls = np.argmax(data.validation.labels, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We know that MNIST images are 28 pixels in each dimension.\n",
"img_size = 28\n",
"\n",
"# Images are stored in one-dimensional arrays of this length.\n",
"img_size_flat = img_size * img_size\n",
"\n",
"# Tuple with height and width of images used to reshape arrays.\n",
"img_shape = (img_size, img_size)\n",
"\n",
"# Number of colour channels for the images: 1 channel for gray-scale.\n",
"num_channels = 1\n",
"\n",
"# Number of classes, one class for each of 10 digits.\n",
"num_classes = 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_images(images, cls_true, cls_pred=None):\n",
" assert len(images) == len(cls_true) == 9\n",
" \n",
" # Create figure with 3x3 sub-plots.\n",
" fig, axes = plt.subplots(3, 3)\n",
" fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
"\n",
" for i, ax in enumerate(axes.flat):\n",
" # Plot image.\n",
" ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
"\n",
" # Show true and predicted classes.\n",
" if cls_pred is None:\n",
" xlabel = \"True: {0}\".format(cls_true[i])\n",
" else:\n",
" xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
"\n",
" # Show the classes as the label on the x-axis.\n",
" ax.set_xlabel(xlabel)\n",
" \n",
" # Remove ticks from the plot.\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" \n",
" # Ensure the plot is shown correctly with multiple plots\n",
" # in a single Notebook cell.\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/6B9RqVTo9/v4/X6sVuvZFr5ut0u32yUej/PgwQNu375NPB4/zUOSHAP9fp9S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\n/6/Ghib4QzPAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bed5e198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the first images from the test-set.\n",
"images = data.test.images[0:9]\n",
"\n",
"# Get the true classes for those images.\n",
"cls_true = data.test.cls[0:9]\n",
"\n",
"# Plot the images and labels using our helper-function above.\n",
"plot_images(images=images, cls_true=cls_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TensorFlow Graph\n",
"\n",
"The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n",
"\n",
"TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n",
"\n",
"TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n",
"\n",
"A TensorFlow graph consists of the following parts which will be detailed below:\n",
"\n",
"* Placeholder variables used for inputting data to the graph.\n",
"* Variables that are going to be optimized so as to make the convolutional network perform better.\n",
"* The mathematical formulas for the convolutional network.\n",
"* A loss measure that can be used to guide the optimization of the variables.\n",
"* An optimization method which updates the variables.\n",
"\n",
"In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Placeholder variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
"\n",
"First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_true_cls = tf.argmax(y_true, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section implements the Convolutional Neural Network using Pretty Tensor, which is much simpler than a direct implementation in TensorFlow, see Tutorial #03.\n",
"\n",
"The basic idea is to wrap the input tensor `x_image` in a Pretty Tensor object which has helper-functions for adding new computational layers so as to create an entire neural network. Pretty Tensor takes care of the variable allocation, etc."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_pretty = pt.wrap(x_image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have wrapped the input image in a Pretty Tensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code.\n",
"\n",
"Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with pt.defaults_scope(activation_fn=tf.nn.relu):\n",
" y_pred, loss = x_pretty.\\\n",
" conv2d(kernel=5, depth=16, name='layer_conv1').\\\n",
" max_pool(kernel=2, stride=2).\\\n",
" conv2d(kernel=5, depth=36, name='layer_conv2').\\\n",
" max_pool(kernel=2, stride=2).\\\n",
" flatten().\\\n",
" fully_connected(size=128, name='layer_fc1').\\\n",
" softmax_classifier(class_count=10, labels=y_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the Weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Further below, we want to plot the weights of the neural network. When the network is constructed using Pretty Tensor, all the variables of the layers are created indirectly by Pretty Tensor. We therefore have to retrieve the variables from TensorFlow.\n",
"\n",
"We used the names `layer_conv1` and `layer_conv2` for the two convolutional layers. These are also called variable scopes (not to be confused with `defaults_scope` as described above). Pretty Tensor automatically gives names to the variables it creates for each layer, so we can retrieve the weights for a layer using the layer's scope-name and the variable-name.\n",
"\n",
"The implementation is somewhat awkward because we have to use the TensorFlow function `get_variable()` which was designed for another purpose; either creating a new variable or re-using an existing variable. The easiest thing is to make the following helper-function."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_weights_variable(layer_name):\n",
" # Retrieve an existing variable named 'weights' in the scope\n",
" # with the given layer_name.\n",
" # This is awkward because the TensorFlow function was\n",
" # really intended for another purpose.\n",
"\n",
" with tf.variable_scope(layer_name, reuse=True):\n",
" variable = tf.get_variable('weights')\n",
"\n",
" return variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n",
"weights_conv2 = get_weights_variable(layer_name='layer_conv2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Optimization Method"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretty Tensor gave us the predicted class-label (`y_pred`) as well as a loss-measure that must be minimized, so as to improve the ability of the neural network to classify the input images.\n",
"\n",
"It is unclear from the documentation for Pretty Tensor whether the loss-measure is cross-entropy or something else. But we now use the `AdamOptimizer` to minimize the loss.\n",
"\n",
"Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performance Measures\n",
"\n",
"We need a few more performance measures to display the progress to the user.\n",
"\n",
"First we calculate the predicted class number from the output of the neural network `y_pred`, which is a vector with 10 elements. The class number is the index of the largest element."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred_cls = tf.argmax(y_pred, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we create a vector of booleans telling us whether the predicted class equals the true class of each image."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saver\n",
"\n",
"In order to save the variables of the neural network, we now create a so-called Saver-object which is used for storing and retrieving all the variables of the TensorFlow graph. Nothing is actually saved at this point, which will be done further below in the `optimize()`-function."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saver = tf.train.Saver()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The saved files are often called checkpoints because they may be written at regular intervals during optimization.\n",
"\n",
"This is the directory used for saving and retrieving the data."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save_dir = 'checkpoints/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the directory if it does not exist."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if not os.path.exists(save_dir):\n",
" os.makedirs(save_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the path for the checkpoint-file."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save_path = save_dir + 'best_validation'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TensorFlow Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create TensorFlow session\n",
"\n",
"Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session = tf.Session()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize variables\n",
"\n",
"The variables for `weights` and `biases` must be initialized before we start optimizing them. We make a simple wrapper-function for this, because we will call it again below."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def init_variables():\n",
" session.run(tf.initialize_all_variables())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execute the function now to initialize the variables."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"init_variables()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function to perform optimization iterations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
"\n",
"If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_batch_size = 64"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy for the validation-set will be calculated for every 100 iterations of the optimization function below. The optimization will be stopped if the validation accuracy has not been improved in 1000 iterations. We need a few variables to keep track of this."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Best validation accuracy seen so far.\n",
"best_validation_accuracy = 0.0\n",
"\n",
"# Iteration-number for last improvement to validation accuracy.\n",
"last_improvement = 0\n",
"\n",
"# Stop optimization if no improvement found in this many iterations.\n",
"require_improvement = 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations where the validation accuracy is also calculated and saved to a file if it is an improvement."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Counter for total number of iterations performed so far.\n",
"total_iterations = 0\n",
"\n",
"def optimize(num_iterations):\n",
" # Ensure we update the global variables rather than local copies.\n",
" global total_iterations\n",
" global best_validation_accuracy\n",
" global last_improvement\n",
"\n",
" # Start-time used for printing time-usage below.\n",
" start_time = time.time()\n",
"\n",
" for i in range(num_iterations):\n",
"\n",
" # Increase the total number of iterations performed.\n",
" # It is easier to update it in each iteration because\n",
" # we need this number several times in the following.\n",
" total_iterations += 1\n",
"\n",
" # Get a batch of training examples.\n",
" # x_batch now holds a batch of images and\n",
" # y_true_batch are the true labels for those images.\n",
" x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
"\n",
" # Put the batch into a dict with the proper names\n",
" # for placeholder variables in the TensorFlow graph.\n",
" feed_dict_train = {x: x_batch,\n",
" y_true: y_true_batch}\n",
"\n",
" # Run the optimizer using this batch of training data.\n",
" # TensorFlow assigns the variables in feed_dict_train\n",
" # to the placeholder variables and then runs the optimizer.\n",
" session.run(optimizer, feed_dict=feed_dict_train)\n",
"\n",
" # Print status every 100 iterations and after last iteration.\n",
" if (total_iterations % 100 == 0) or (i == (num_iterations - 1)):\n",
"\n",
" # Calculate the accuracy on the training-batch.\n",
" acc_train = session.run(accuracy, feed_dict=feed_dict_train)\n",
"\n",
" # Calculate the accuracy on the validation-set.\n",
" # The function returns 2 values but we only need the first.\n",
" acc_validation, _ = validation_accuracy()\n",
"\n",
" # If validation accuracy is an improvement over best-known.\n",
" if acc_validation > best_validation_accuracy:\n",
" # Update the best-known validation accuracy.\n",
" best_validation_accuracy = acc_validation\n",
" \n",
" # Set the iteration for the last improvement to current.\n",
" last_improvement = total_iterations\n",
"\n",
" # Save all variables of the TensorFlow graph to file.\n",
" saver.save(sess=session, save_path=save_path)\n",
"\n",
" # A string to be printed below, shows improvement found.\n",
" improved_str = '*'\n",
" else:\n",
" # An empty string to be printed below.\n",
" # Shows that no improvement was found.\n",
" improved_str = ''\n",
" \n",
" # Status-message for printing.\n",
" msg = \"Iter: {0:>6}, Train-Batch Accuracy: {1:>6.1%}, Validation Acc: {2:>6.1%} {3}\"\n",
"\n",
" # Print it.\n",
" print(msg.format(i + 1, acc_train, acc_validation, improved_str))\n",
"\n",
" # If no improvement found in the required number of iterations.\n",
" if total_iterations - last_improvement > require_improvement:\n",
" print(\"No improvement found in a while, stopping optimization.\")\n",
"\n",
" # Break out from the for-loop.\n",
" break\n",
"\n",
" # Ending time.\n",
" end_time = time.time()\n",
"\n",
" # Difference between start and end-times.\n",
" time_dif = end_time - start_time\n",
"\n",
" # Print the time-usage.\n",
" print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function to plot example errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for plotting examples of images from the test-set that have been mis-classified."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_example_errors(cls_pred, correct):\n",
" # This function is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # correct is a boolean array whether the predicted class\n",
" # is equal to the true class for each image in the test-set.\n",
"\n",
" # Negate the boolean array.\n",
" incorrect = (correct == False)\n",
" \n",
" # Get the images from the test-set that have been\n",
" # incorrectly classified.\n",
" images = data.test.images[incorrect]\n",
" \n",
" # Get the predicted classes for those images.\n",
" cls_pred = cls_pred[incorrect]\n",
"\n",
" # Get the true classes for those images.\n",
" cls_true = data.test.cls[incorrect]\n",
" \n",
" # Plot the first 9 images.\n",
" plot_images(images=images[0:9],\n",
" cls_true=cls_true[0:9],\n",
" cls_pred=cls_pred[0:9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function to plot confusion matrix"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_confusion_matrix(cls_pred):\n",
" # This is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # Get the true classifications for the test-set.\n",
" cls_true = data.test.cls\n",
" \n",
" # Get the confusion matrix using sklearn.\n",
" cm = confusion_matrix(y_true=cls_true,\n",
" y_pred=cls_pred)\n",
"\n",
" # Print the confusion matrix as text.\n",
" print(cm)\n",
"\n",
" # Plot the confusion matrix as an image.\n",
" plt.matshow(cm)\n",
"\n",
" # Make various adjustments to the plot.\n",
" plt.colorbar()\n",
" tick_marks = np.arange(num_classes)\n",
" plt.xticks(tick_marks, range(num_classes))\n",
" plt.yticks(tick_marks, range(num_classes))\n",
" plt.xlabel('Predicted')\n",
" plt.ylabel('True')\n",
"\n",
" # Ensure the plot is shown correctly with multiple plots\n",
" # in a single Notebook cell.\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-functions for calculating classifications\n",
"\n",
"This function calculates the predicted classes of images and also returns a boolean array whether the classification of each image is correct.\n",
"\n",
"The calculation is done in batches because it might use too much RAM otherwise. If your computer crashes then you can try and lower the batch-size."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Split the data-set in batches of this size to limit RAM usage.\n",
"batch_size = 256\n",
"\n",
"def predict_cls(images, labels, cls_true):\n",
" # Number of images.\n",
" num_images = len(images)\n",
"\n",
" # Allocate an array for the predicted classes which\n",
" # will be calculated in batches and filled into this array.\n",
" cls_pred = np.zeros(shape=num_images, dtype=np.int)\n",
"\n",
" # Now calculate the predicted classes for the batches.\n",
" # We will just iterate through all the batches.\n",
" # There might be a more clever and Pythonic way of doing this.\n",
"\n",
" # The starting index for the next batch is denoted i.\n",
" i = 0\n",
"\n",
" while i < num_images:\n",
" # The ending index for the next batch is denoted j.\n",
" j = min(i + batch_size, num_images)\n",
"\n",
" # Create a feed-dict with the images and labels\n",
" # between index i and j.\n",
" feed_dict = {x: images[i:j, :],\n",
" y_true: labels[i:j, :]}\n",
"\n",
" # Calculate the predicted class using TensorFlow.\n",
" cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
"\n",
" # Set the start-index for the next batch to the\n",
" # end-index of the current batch.\n",
" i = j\n",
"\n",
" # Create a boolean array whether each image is correctly classified.\n",
" correct = (cls_true == cls_pred)\n",
"\n",
" return correct, cls_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the predicted class for the test-set."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def predict_cls_test():\n",
" return predict_cls(images = data.test.images,\n",
" labels = data.test.labels,\n",
" cls_true = data.test.cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the predicted class for the validation-set."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def predict_cls_validation():\n",
" return predict_cls(images = data.validation.images,\n",
" labels = data.validation.labels,\n",
" cls_true = data.validation.cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-functions for the classification accuracy\n",
"\n",
"This function calculates the classification accuracy given a boolean array whether each image was correctly classified. E.g. `cls_accuracy([True, True, False, False, False]) = 2/5 = 0.4`"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cls_accuracy(correct):\n",
" # Calculate the number of correctly classified images.\n",
" # When summing a boolean array, False means 0 and True means 1.\n",
" correct_sum = correct.sum()\n",
"\n",
" # Classification accuracy is the number of correctly classified\n",
" # images divided by the total number of images in the test-set.\n",
" acc = float(correct_sum) / len(correct)\n",
"\n",
" return acc, correct_sum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the classification accuracy on the validation-set."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def validation_accuracy():\n",
" # Get the array of booleans whether the classifications are correct\n",
" # for the validation-set.\n",
" # The function returns two values but we only need the first.\n",
" correct, _ = predict_cls_validation()\n",
" \n",
" # Calculate the classification accuracy and return it.\n",
" return cls_accuracy(correct)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function for showing the performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for printing the classification accuracy on the test-set.\n",
"\n",
"It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def print_test_accuracy(show_example_errors=False,\n",
" show_confusion_matrix=False):\n",
"\n",
" # For all the images in the test-set,\n",
" # calculate the predicted classes and whether they are correct.\n",
" correct, cls_pred = predict_cls_test()\n",
"\n",
" # Classification accuracy and the number of correct classifications.\n",
" acc, num_correct = cls_accuracy(correct)\n",
" \n",
" # Number of images being classified.\n",
" num_images = len(correct)\n",
"\n",
" # Print the accuracy.\n",
" msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
" print(msg.format(acc, num_correct, num_images))\n",
"\n",
" # Plot some examples of mis-classifications, if desired.\n",
" if show_example_errors:\n",
" print(\"Example errors:\")\n",
" plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
"\n",
" # Plot the confusion matrix, if desired.\n",
" if show_confusion_matrix:\n",
" print(\"Confusion Matrix:\")\n",
" plot_confusion_matrix(cls_pred=cls_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper-function for plotting convolutional weights"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_conv_weights(weights, input_channel=0):\n",
" # Assume weights are TensorFlow ops for 4-dim variables\n",
" # e.g. weights_conv1 or weights_conv2.\n",
"\n",
" # Retrieve the values of the weight-variables from TensorFlow.\n",
" # A feed-dict is not necessary because nothing is calculated.\n",
" w = session.run(weights)\n",
"\n",
" # Print mean and standard deviation.\n",
" print(\"Mean: {0:.5f}, Stdev: {1:.5f}\".format(w.mean(), w.std()))\n",
" \n",
" # Get the lowest and highest values for the weights.\n",
" # This is used to correct the colour intensity across\n",
" # the images so they can be compared with each other.\n",
" w_min = np.min(w)\n",
" w_max = np.max(w)\n",
"\n",
" # Number of filters used in the conv. layer.\n",
" num_filters = w.shape[3]\n",
"\n",
" # Number of grids to plot.\n",
" # Rounded-up, square-root of the number of filters.\n",
" num_grids = math.ceil(math.sqrt(num_filters))\n",
" \n",
" # Create figure with a grid of sub-plots.\n",
" fig, axes = plt.subplots(num_grids, num_grids)\n",
"\n",
" # Plot all the filter-weights.\n",
" for i, ax in enumerate(axes.flat):\n",
" # Only plot the valid filter-weights.\n",
" if i<num_filters:\n",
" # Get the weights for the i'th filter of the input channel.\n",
" # The format of this 4-dim tensor is determined by the\n",
" # TensorFlow API. See Tutorial #02 for more details.\n",
" img = w[:, :, input_channel, i]\n",
"\n",
" # Plot image.\n",
" ax.imshow(img, vmin=w_min, vmax=w_max,\n",
" interpolation='nearest', cmap='seismic')\n",
" \n",
" # Remove ticks from the plot.\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" \n",
" # Ensure the plot is shown correctly with multiple plots\n",
" # in a single Notebook cell.\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance before any optimization\n",
"\n",
"The accuracy on the test-set is very low because the model variables have only been initialized and not optimized at all, so it just classifies the images randomly."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 10.2% (1018 / 10000)\n"
]
}
],
"source": [
"print_test_accuracy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convolutional weights are random, but it can be difficult to see any difference from the optimized weights that are shown below. The mean and standard deviation is shown so we can see whether there is a difference."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: -0.01520, Stdev: 0.29296\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bcabed68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform 10,000 optimization iterations\n",
"\n",
"We now perform 10,000 optimization iterations and abort the optimization if no improvement is found on the validation-set in 1000 iterations.\n",
"\n",
"An asterisk * is shown if the classification accuracy on the validation-set is an improvement."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iter: 100, Train-Batch Accuracy: 85.9%, Validation Acc: 85.7% *\n",
"Iter: 200, Train-Batch Accuracy: 87.5%, Validation Acc: 91.8% *\n",
"Iter: 300, Train-Batch Accuracy: 93.8%, Validation Acc: 93.4% *\n",
"Iter: 400, Train-Batch Accuracy: 96.9%, Validation Acc: 94.1% *\n",
"Iter: 500, Train-Batch Accuracy: 98.4%, Validation Acc: 94.3% *\n",
"Iter: 600, Train-Batch Accuracy: 96.9%, Validation Acc: 95.4% *\n",
"Iter: 700, Train-Batch Accuracy: 96.9%, Validation Acc: 95.7% *\n",
"Iter: 800, Train-Batch Accuracy: 98.4%, Validation Acc: 96.4% *\n",
"Iter: 900, Train-Batch Accuracy: 95.3%, Validation Acc: 96.4% *\n",
"Iter: 1000, Train-Batch Accuracy: 100.0%, Validation Acc: 97.0% *\n",
"Iter: 1100, Train-Batch Accuracy: 95.3%, Validation Acc: 97.0% *\n",
"Iter: 1200, Train-Batch Accuracy: 93.8%, Validation Acc: 97.1% *\n",
"Iter: 1300, Train-Batch Accuracy: 92.2%, Validation Acc: 96.9% \n",
"Iter: 1400, Train-Batch Accuracy: 100.0%, Validation Acc: 97.5% *\n",
"Iter: 1500, Train-Batch Accuracy: 96.9%, Validation Acc: 97.3% \n",
"Iter: 1600, Train-Batch Accuracy: 100.0%, Validation Acc: 97.5% *\n",
"Iter: 1700, Train-Batch Accuracy: 93.8%, Validation Acc: 97.8% *\n",
"Iter: 1800, Train-Batch Accuracy: 95.3%, Validation Acc: 97.5% \n",
"Iter: 1900, Train-Batch Accuracy: 98.4%, Validation Acc: 97.7% \n",
"Iter: 2000, Train-Batch Accuracy: 100.0%, Validation Acc: 97.6% \n",
"Iter: 2100, Train-Batch Accuracy: 100.0%, Validation Acc: 97.7% \n",
"Iter: 2200, Train-Batch Accuracy: 96.9%, Validation Acc: 97.9% *\n",
"Iter: 2300, Train-Batch Accuracy: 96.9%, Validation Acc: 98.1% *\n",
"Iter: 2400, Train-Batch Accuracy: 98.4%, Validation Acc: 97.9% \n",
"Iter: 2500, Train-Batch Accuracy: 93.8%, Validation Acc: 98.0% \n",
"Iter: 2600, Train-Batch Accuracy: 96.9%, Validation Acc: 97.9% \n",
"Iter: 2700, Train-Batch Accuracy: 100.0%, Validation Acc: 98.1% *\n",
"Iter: 2800, Train-Batch Accuracy: 98.4%, Validation Acc: 98.1% \n",
"Iter: 2900, Train-Batch Accuracy: 95.3%, Validation Acc: 97.9% \n",
"Iter: 3000, Train-Batch Accuracy: 98.4%, Validation Acc: 98.3% *\n",
"Iter: 3100, Train-Batch Accuracy: 100.0%, Validation Acc: 98.1% \n",
"Iter: 3200, Train-Batch Accuracy: 98.4%, Validation Acc: 98.1% \n",
"Iter: 3300, Train-Batch Accuracy: 100.0%, Validation Acc: 98.3% \n",
"Iter: 3400, Train-Batch Accuracy: 98.4%, Validation Acc: 98.0% \n",
"Iter: 3500, Train-Batch Accuracy: 96.9%, Validation Acc: 98.2% \n",
"Iter: 3600, Train-Batch Accuracy: 98.4%, Validation Acc: 98.5% *\n",
"Iter: 3700, Train-Batch Accuracy: 95.3%, Validation Acc: 98.2% \n",
"Iter: 3800, Train-Batch Accuracy: 98.4%, Validation Acc: 98.1% \n",
"Iter: 3900, Train-Batch Accuracy: 98.4%, Validation Acc: 97.9% \n",
"Iter: 4000, Train-Batch Accuracy: 95.3%, Validation Acc: 98.4% \n",
"Iter: 4100, Train-Batch Accuracy: 96.9%, Validation Acc: 98.3% \n",
"Iter: 4200, Train-Batch Accuracy: 98.4%, Validation Acc: 98.4% \n",
"Iter: 4300, Train-Batch Accuracy: 98.4%, Validation Acc: 98.3% \n",
"Iter: 4400, Train-Batch Accuracy: 100.0%, Validation Acc: 98.4% \n",
"Iter: 4500, Train-Batch Accuracy: 96.9%, Validation Acc: 98.3% \n",
"Iter: 4600, Train-Batch Accuracy: 96.9%, Validation Acc: 98.4% \n",
"No improvement found in a while, stopping optimization.\n",
"Time usage: 0:06:30\n"
]
}
],
"source": [
"optimize(num_iterations=10000)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 98.6% (9856 / 10000)\n",
"Example errors:\n"
]
},
{
"data": {
"image/png": 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nfwH8iqZpd4QQg8AfAtMHH/rf0DTtZz7xumEgI4T4LWAauAP8A03TascZlBBi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\ninOgRFOhUCjOgRJNhUKhOAf/H8Q1+obp23JaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bcaa0550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
"[[ 976 0 0 0 0 0 1 1 2 0]\n",
" [ 0 1129 1 0 0 1 2 1 1 0]\n",
" [ 5 2 999 4 1 0 1 9 11 0]\n",
" [ 0 0 0 1005 0 0 0 2 2 1]\n",
" [ 0 0 2 0 976 0 0 1 3 0]\n",
" [ 2 0 0 3 0 884 3 0 0 0]\n",
" [ 5 2 0 1 2 4 940 0 4 0]\n",
" [ 0 1 5 4 1 0 0 1016 1 0]\n",
" [ 4 0 2 0 2 1 1 3 959 2]\n",
" [ 4 4 0 6 11 3 0 6 3 972]]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bcaa03c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_test_accuracy(show_example_errors=True,\n",
" show_confusion_matrix=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convolutional weights have now been optimized. Compare these to the random weights shown above. They appear to be almost identical. In fact, I first thought there was a bug in the program because the weights look identical before and after optimization.\n",
"\n",
"But try and save the images and compare them side-by-side (you can just right-click the image to save it). You will notice very small differences before and after optimization.\n",
"\n",
"The mean and standard deviation has also changed slightly, so the optimized weights must be different."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 0.00415, Stdev: 0.30706\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAADtCAYAAADHjgxUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA2CXaLpnSFEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bc442cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Variables Again\n",
"\n",
"Re-initialize all the variables of the neural network with random values."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"init_variables()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This means the neural network classifies the images completely randomly again, so the classification accuracy is very poor because it is like random guesses."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 4.0% (397 / 10000)\n"
]
}
],
"source": [
"print_test_accuracy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convolutional weights should now be different from the weights shown above."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: -0.02354, Stdev: 0.27403\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAADtCAYAAADHjgxUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ+xFbcmhoEAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bc5b4358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Restore Best Variables\n",
"\n",
"Re-load all the variables that were saved to file during optimization."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saver.restore(sess=session, save_path=save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy is high again when using the variables that were previously saved.\n",
"\n",
"Note that the classification accuracy may be slightly higher or lower than that reported above, because the variables in the file were chosen to maximize the classification accuracy on the validation-set, but the optimization actually continued for another 1000 iterations after saving those variables, so we are reporting the results for two slightly different sets of variables. Sometimes this leads to slightly better or worse performance on the test-set."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 98.5% (9850 / 10000)\n",
"Example errors:\n"
]
},
{
"data": {
"image/png": 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0TZsCfhb48rHX+ydCiD/3OaeKApvHHm8f/e514qxs/Aj4qhDCJ4RwAn8e6D/p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\nNy3jYX1hYgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f26116c3470>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
"[[ 977 0 0 0 0 0 0 1 2 0]\n",
" [ 0 1132 2 0 0 0 0 0 1 0]\n",
" [ 3 4 1020 0 0 0 0 2 3 0]\n",
" [ 0 0 2 999 0 1 0 5 2 1]\n",
" [ 1 0 2 0 972 0 0 1 3 3]\n",
" [ 2 0 0 7 0 878 3 1 1 0]\n",
" [ 8 3 0 1 2 4 938 0 2 0]\n",
" [ 1 1 11 3 1 0 0 1007 1 3]\n",
" [ 5 1 5 2 2 2 1 3 950 3]\n",
" [ 4 7 1 5 9 0 0 6 0 977]]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bca630b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_test_accuracy(show_example_errors=True,\n",
" show_confusion_matrix=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convolutional weights should be nearly identical to those shown above, although not completely identical because the weights shown above had 1000 optimization iterations more."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 0.00300, Stdev: 0.30551\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f25bab855f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Close TensorFlow Session"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
"# with the Notebook without having to restart it.\n",
"# session.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"This tutorial showed how to save and retrieve the variables of a neural network in TensorFlow. This can be used in different ways. For example, if you want to use a neural network for recognizing images then you only have to train the network once and you can then deploy the finished network on other computers.\n",
"\n",
"Another use of checkpoints is if you have a very large neural network and data-set, then you may want to save checkpoints at regular intervals in case the computer crashes, so you can continue the optimization at a recent checkpoint instead of having to restart the optimization from the beginning.\n",
"\n",
"This tutorial also showed how to use the validation-set for so-called Early Stopping, where the optimization was aborted if it did not regularly improve the validation error. This is useful if the neural network starts to overfit and learn the noise of the training-set; although it was not really an issue with the convolutional network and MNIST data-set used in this tutorial.\n",
"\n",
"An interesting observation was that the convolutional weights (or filters) changed very little from the optimization, even though the performance of the network went from random guesses to near-perfect classification. It seems strange that the random weights were almost good enough. Why do you think this happens?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
"\n",
"You may want to backup this Notebook before making any changes.\n",
"\n",
"* Optimization is stopped after 1000 iterations without improvement. Is this enough? Can you think of a better way to do Early Stopping? Try and implement it.\n",
"* If the checkpoint file already exists then load it instead of doing the optimization.\n",
"* Save a new checkpoint for every 100 optimization iterations. Retrieve the latest using `saver.latest_checkpoint()`. Why would you want to save multiple checkpionts instead of just the most recent?\n",
"* Try and change the neural network, e.g. by adding another layer. What happens when you reload the variables from a different network?\n",
"* Plot the weights for the 2nd convolutional layer before and after optimization using the function `plot_conv_weights()`. Are they almost identical as well?\n",
"* Why do you think the optimized convolutional weights are almost the same as the random initialization?\n",
"* Remake the program yourself without looking too much at this source-code.\n",
"* Explain to a friend how the program works."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## License (MIT)\n",
"\n",
"Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
}
],
"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": 0
}
| mit |
carthach/essentia | src/examples/tutorial/example_discontinuitydetector.ipynb | 1 | 329687 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DiscontinuityDetector use example\n",
" This algorithm uses LPC and some heuristics to detect discontinuities in anaudio signal. [1].\n",
" \n",
" References:\n",
" [1] Mühlbauer, R. (2010). Automatic Audio Defect Detection."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import essentia.standard as es\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import Audio \n",
"from essentia import array as esarr\n",
"plt.rcParams[\"figure.figsize\"] =(12,9)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def compute(x, frame_size=1024, hop_size=512, **kwargs):\n",
" discontinuityDetector = es.DiscontinuityDetector(frameSize=frame_size,\n",
" hopSize=hop_size, \n",
" **kwargs)\n",
" locs = []\n",
" amps = []\n",
" for idx, frame in enumerate(es.FrameGenerator(x, frameSize=frame_size,\n",
" hopSize=hop_size, startFromZero=True)):\n",
" frame_locs, frame_ampls = discontinuityDetector(frame)\n",
"\n",
" for l in frame_locs:\n",
" locs.append((l + hop_size * idx) / 44100.)\n",
" for a in frame_ampls:\n",
" amps.append(a)\n",
"\n",
" return locs, amps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generating some discontinuities examples\n",
"\n",
"Let's start by degrading some audio files with some discontinuities. Discontinuities are generally occasioned by hardware issues in the process of recording or copying. Let's simulate this by removing a random number of samples from the input audio file. "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
" def testRegression(self, frameSize=512, hopSize=256):\n",
" fs = 44100\n",
"\n",
" audio = MonoLoader(filename=join(testdata.audio_dir,\n",
" 'recorded/cat_purrrr.wav'),\n",
" sampleRate=fs)()\n",
"\n",
" originalLen = len(audio)\n",
" startJump = originalLen / 4\n",
" groundTruth = [startJump / float(fs)]\n",
"\n",
" # make sure that the artificial jump produces a prominent discontinuity\n",
" if audio[startJump] > 0:\n",
" end = next(idx for idx, i in enumerate(audio[startJump:]) if i < -.3)\n",
" else:\n",
" end = next(idx for idx, i in enumerate(audio[startJump:]) if i > .3)\n",
"\n",
" endJump = startJump + end\n",
" audio = esarr(np.hstack([audio[:startJump], audio[endJump:]]))\n",
"\n",
" frameList = []\n",
" discontinuityDetector = self.InitDiscontinuityDetector(\n",
" frameSize=frameSize, hopSize=hopSize,\n",
" detectionThreshold=10)\n",
"\n",
" for idx, frame in enumerate(FrameGenerator(\n",
" audio, frameSize=frameSize,\n",
" hopSize=hopSize, startFromZero=True)):\n",
" locs, _ = discontinuityDetector(frame)\n",
" if not len(locs) == 0:\n",
" for loc in locs:\n",
" frameList.append((idx * hopSize + loc) / float(fs))\n",
"\n",
" self.assertAlmostEqualVector(frameList, groundTruth, 1e-7)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f4ba1e0dd10>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ba1e9ccd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fs = 44100.\n",
"\n",
"audio_dir = '../../audio/'\n",
"audio = es.MonoLoader(filename='{}/{}'.format(audio_dir,\n",
" 'recorded/vignesh.wav'),\n",
" sampleRate=fs)()\n",
"\n",
"originalLen = len(audio)\n",
"startJumps = np.array([originalLen / 4, originalLen / 2])\n",
"groundTruth = startJumps / float(fs)\n",
"\n",
"for startJump in startJumps:\n",
" # make sure that the artificial jump produces a prominent discontinuity\n",
" if audio[startJump] > 0:\n",
" end = next(idx for idx, i in enumerate(audio[startJump:]) if i < -.3)\n",
" else:\n",
" end = next(idx for idx, i in enumerate(audio[startJump:]) if i > .3)\n",
"\n",
" endJump = startJump + end\n",
" audio = esarr(np.hstack([audio[:startJump], audio[endJump:]]))\n",
"\n",
"\n",
"for point in groundTruth:\n",
" l1 = plt.axvline(point, color='g', alpha=.5)\n",
"\n",
"times = np.linspace(0, len(audio) / fs, len(audio))\n",
"plt.plot(times, audio)\n",
"plt.title('Signal with artificial clicks of different amplitudes')\n",
"l1.set_label('Click locations')\n",
"plt.legend()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets listen to the clip to have an idea on how audible the discontinuities are"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
" <source 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\" type=\"audio/wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Audio(audio, rate=fs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The algorithm\n",
"This algorithm outputs the starts and ends timestapms of the clicks. The following plots show how the algorithm performs in the previous examples\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ba2154ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"locs, amps = compute(audio)\n",
"\n",
"fig, ax = plt.subplots(len(groundTruth))\n",
"plt.subplots_adjust(hspace=.4)\n",
"for idx, point in enumerate(groundTruth):\n",
" l1 = ax[idx].axvline(locs[idx], color='r', alpha=.5)\n",
" l2 = ax[idx].axvline(point, color='g', alpha=.5)\n",
" ax[idx].plot(times, audio)\n",
" ax[idx].set_xlim([point-.001, point+.001])\n",
" ax[idx].set_title('Click located at {:.2f}s'.format(point))\n",
" \n",
" \n",
" fig.legend((l1, l2), ('Detected discontinuity', 'Ground truth'), 'upper right')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The parameters\n",
"this is an explanation of the most relevant parameters of the algorithm\n",
"\n",
"- **detectionThreshold.** This parameter controls de detection sensibility of the algorithm \n",
"\n",
"- **kernelSize.** A scalar giving the size of the median filter window. The window has to be as small as possible to improve the whitening of the signal but big enough to skip peaky outlayers from the prediction error signal. \n",
"\n",
"- **order.** The order for the LPC. As a rule of thumb, use 2 coefficients for each format on the input signal. However, it was empirically found that modelling more than 5 formats did not improve the clip detection on music.\n",
"\n",
"- **silenceThresholdder.** It makes no sense to process silent frames as even if there are events looking like discontinuities they can't be heard\n",
"\n",
"- **subFrameSize.** If was found that frames that are partially silent are suitable for fake detections. This is because the audio is modelled as an autoregressive process in which discontinuities are easily detected as peaks on the prediction error. However, if the autoregressive assumption is no longer true, unexpected events can produce error peaks. Thus, a subframe window is used to mask out the silent part of the frame so they don't interfere in the autoregressive parameter estimation.\n",
"\n",
"- **energyThreshold.** threshold in dB to detect silent subframes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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| agpl-3.0 |
esa-as/2016-ml-contest | HouMath/Face_classification_HouMath_XGB_01.ipynb | 2 | 299038 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we mainly utilize extreme gradient boost to improve the prediction model originially proposed in TLE 2016 November machine learning tuotrial. Extreme gradient boost can be viewed as an enhanced version of gradient boost by using a more regularized model formalization to control over-fitting, and XGB usually performs better. Applications of XGB can be found in many Kaggle competitions. Some recommended tutorrials can be found"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our work will be orginized in the follwing order:\n",
"\n",
"•Background\n",
"\n",
"•Exploratory Data Analysis\n",
"\n",
"•Data Prepration and Model Selection\n",
"\n",
"•Final Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Background"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dataset we will use comes from a class excercise from The University of Kansas on Neural Networks and Fuzzy Systems. This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see Bohling and Dubois (2003) and Dubois et al. (2007).\n",
"\n",
"The dataset we will use is log data from nine wells that have been labeled with a facies type based on oberservation of core. We will use this log data to train a classifier to predict facies types.\n",
"\n",
"This data is from the Council Grove gas reservoir in Southwest Kansas. The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas. This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector. Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate.\n",
"\n",
"The seven predictor variables are:\n",
"•Five wire line log curves include gamma ray (GR), resistivity logging (ILD_log10), photoelectric effect (PE), neutron-density porosity difference and average neutron-density porosity (DeltaPHI and PHIND). Note, some wells do not have PE.\n",
"•Two geologic constraining variables: nonmarine-marine indicator (NM_M) and relative position (RELPOS)\n",
"\n",
"The nine discrete facies (classes of rocks) are:\n",
"\n",
"1.Nonmarine sandstone\n",
"\n",
"2.Nonmarine coarse siltstone \n",
"\n",
"3.Nonmarine fine siltstone \n",
"\n",
"4.Marine siltstone and shale \n",
"\n",
"5.Mudstone (limestone)\n",
"\n",
"6.Wackestone (limestone)\n",
"\n",
"7.Dolomite\n",
"\n",
"8.Packstone-grainstone (limestone)\n",
"\n",
"9.Phylloid-algal bafflestone (limestone)\n",
"\n",
"These facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close. Mislabeling within these neighboring facies can be expected to occur. The following table lists the facies, their abbreviated labels and their approximate neighbors.\n",
"\n",
"\n",
"Facies/ Label/ Adjacent Facies\n",
"\n",
"1 SS 2 \n",
"\n",
"2 CSiS 1,3 \n",
"\n",
"3 FSiS 2 \n",
"\n",
"4 SiSh 5 \n",
"\n",
"5 MS 4,6 \n",
"\n",
"6 WS 5,7 \n",
"\n",
"7 D 6,8 \n",
"\n",
"8 PS 6,7,9 \n",
"\n",
"9 BS 7,8 \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing we notice for this data is that it seems that neighboring facies are not symmetric, for example, the adjacent facies for 9 could be 7, yet the adjacent facies for 7 couldn't be 9. We already contacted the authors regarding this. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exprolatory Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the background intorduction, we start to import the pandas library for some basic data analysis and manipulation. The matplotblib and seaborn are imported for data vislization. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from pandas.tools.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import seaborn as sns\n",
"import matplotlib.colors as colors"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Facies</th>\n",
" <th>Formation</th>\n",
" <th>Well Name</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>1.000</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2793.5</td>\n",
" <td>78.260</td>\n",
" <td>0.661</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2794.0</td>\n",
" <td>79.050</td>\n",
" <td>0.658</td>\n",
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" <td>13.050</td>\n",
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" <td>0.957</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2794.5</td>\n",
" <td>86.100</td>\n",
" <td>0.655</td>\n",
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" <td>13.115</td>\n",
" <td>3.500</td>\n",
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" <td>0.936</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2795.0</td>\n",
" <td>74.580</td>\n",
" <td>0.647</td>\n",
" <td>13.500</td>\n",
" <td>13.300</td>\n",
" <td>3.400</td>\n",
" <td>1</td>\n",
" <td>0.915</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2795.5</td>\n",
" <td>73.970</td>\n",
" <td>0.636</td>\n",
" <td>14.000</td>\n",
" <td>13.385</td>\n",
" <td>3.600</td>\n",
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" <td>0.894</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2796.0</td>\n",
" <td>73.720</td>\n",
" <td>0.630</td>\n",
" <td>15.600</td>\n",
" <td>13.930</td>\n",
" <td>3.700</td>\n",
" <td>1</td>\n",
" <td>0.872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2796.5</td>\n",
" <td>75.650</td>\n",
" <td>0.625</td>\n",
" <td>16.500</td>\n",
" <td>13.920</td>\n",
" <td>3.500</td>\n",
" <td>1</td>\n",
" <td>0.830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2797.0</td>\n",
" <td>73.790</td>\n",
" <td>0.624</td>\n",
" <td>16.200</td>\n",
" <td>13.980</td>\n",
" <td>3.400</td>\n",
" <td>1</td>\n",
" <td>0.809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2797.5</td>\n",
" <td>76.890</td>\n",
" <td>0.615</td>\n",
" <td>16.900</td>\n",
" <td>14.220</td>\n",
" <td>3.500</td>\n",
" <td>1</td>\n",
" <td>0.787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2798.0</td>\n",
" <td>76.110</td>\n",
" <td>0.600</td>\n",
" <td>14.800</td>\n",
" <td>13.375</td>\n",
" <td>3.600</td>\n",
" <td>1</td>\n",
" <td>0.766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2798.5</td>\n",
" <td>74.950</td>\n",
" <td>0.583</td>\n",
" <td>13.300</td>\n",
" <td>12.690</td>\n",
" <td>3.700</td>\n",
" <td>1</td>\n",
" <td>0.745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2799.0</td>\n",
" <td>71.870</td>\n",
" <td>0.561</td>\n",
" <td>11.300</td>\n",
" <td>12.475</td>\n",
" <td>3.500</td>\n",
" <td>1</td>\n",
" <td>0.723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2799.5</td>\n",
" <td>83.420</td>\n",
" <td>0.537</td>\n",
" <td>13.300</td>\n",
" <td>14.930</td>\n",
" <td>3.400</td>\n",
" <td>1</td>\n",
" <td>0.702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2800.0</td>\n",
" <td>90.100</td>\n",
" <td>0.519</td>\n",
" <td>14.300</td>\n",
" <td>16.555</td>\n",
" <td>3.200</td>\n",
" <td>1</td>\n",
" <td>0.681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2800.5</td>\n",
" <td>78.150</td>\n",
" <td>0.467</td>\n",
" <td>11.800</td>\n",
" <td>15.960</td>\n",
" <td>3.100</td>\n",
" <td>1</td>\n",
" <td>0.638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2801.0</td>\n",
" <td>69.300</td>\n",
" <td>0.438</td>\n",
" <td>9.500</td>\n",
" <td>15.120</td>\n",
" <td>3.100</td>\n",
" <td>1</td>\n",
" <td>0.617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2801.5</td>\n",
" <td>63.540</td>\n",
" <td>0.418</td>\n",
" <td>8.800</td>\n",
" <td>15.190</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2802.0</td>\n",
" <td>63.870</td>\n",
" <td>0.401</td>\n",
" <td>7.200</td>\n",
" <td>15.390</td>\n",
" <td>2.900</td>\n",
" <td>1</td>\n",
" <td>0.574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2802.5</td>\n",
" <td>58.320</td>\n",
" <td>0.386</td>\n",
" <td>6.600</td>\n",
" <td>14.885</td>\n",
" <td>2.800</td>\n",
" <td>1</td>\n",
" <td>0.553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2803.0</td>\n",
" <td>56.610</td>\n",
" <td>0.369</td>\n",
" <td>5.500</td>\n",
" <td>14.800</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2803.5</td>\n",
" <td>55.970</td>\n",
" <td>0.352</td>\n",
" <td>6.100</td>\n",
" <td>14.460</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2804.0</td>\n",
" <td>63.670</td>\n",
" <td>0.344</td>\n",
" <td>6.000</td>\n",
" <td>14.745</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2804.5</td>\n",
" <td>66.200</td>\n",
" <td>0.342</td>\n",
" <td>6.800</td>\n",
" <td>15.135</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2805.0</td>\n",
" <td>61.270</td>\n",
" <td>0.346</td>\n",
" <td>6.100</td>\n",
" <td>15.480</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2805.5</td>\n",
" <td>69.480</td>\n",
" <td>0.354</td>\n",
" <td>5.800</td>\n",
" <td>14.675</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2806.0</td>\n",
" <td>76.370</td>\n",
" <td>0.354</td>\n",
" <td>5.200</td>\n",
" <td>13.635</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2806.5</td>\n",
" <td>82.200</td>\n",
" <td>0.348</td>\n",
" <td>7.400</td>\n",
" <td>15.055</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2807.0</td>\n",
" <td>90.250</td>\n",
" <td>0.346</td>\n",
" <td>11.500</td>\n",
" <td>20.230</td>\n",
" <td>3.100</td>\n",
" <td>1</td>\n",
" <td>0.340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2807.5</td>\n",
" <td>94.380</td>\n",
" <td>0.358</td>\n",
" <td>14.200</td>\n",
" <td>24.015</td>\n",
" <td>3.000</td>\n",
" <td>1</td>\n",
" <td>0.319</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",
" </tr>\n",
" <tr>\n",
" <th>4119</th>\n",
" <td>8</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3108.0</td>\n",
" <td>30.734</td>\n",
" <td>0.991</td>\n",
" <td>1.552</td>\n",
" <td>5.382</td>\n",
" <td>4.738</td>\n",
" <td>2</td>\n",
" <td>0.887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4120</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3108.5</td>\n",
" <td>32.219</td>\n",
" <td>1.013</td>\n",
" <td>1.342</td>\n",
" <td>5.055</td>\n",
" <td>4.637</td>\n",
" <td>2</td>\n",
" <td>0.879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4121</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3109.0</td>\n",
" <td>37.688</td>\n",
" <td>1.040</td>\n",
" <td>0.681</td>\n",
" <td>4.739</td>\n",
" <td>4.539</td>\n",
" <td>2</td>\n",
" <td>0.871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4122</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3109.5</td>\n",
" <td>35.844</td>\n",
" <td>1.044</td>\n",
" <td>0.960</td>\n",
" <td>3.533</td>\n",
" <td>4.832</td>\n",
" <td>2</td>\n",
" <td>0.863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4123</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3110.0</td>\n",
" <td>42.156</td>\n",
" <td>1.051</td>\n",
" <td>1.448</td>\n",
" <td>3.337</td>\n",
" <td>4.797</td>\n",
" <td>2</td>\n",
" <td>0.855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4124</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3110.5</td>\n",
" <td>42.094</td>\n",
" <td>1.057</td>\n",
" <td>2.736</td>\n",
" <td>4.051</td>\n",
" <td>4.500</td>\n",
" <td>2</td>\n",
" <td>0.847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4125</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3111.0</td>\n",
" <td>49.719</td>\n",
" <td>1.060</td>\n",
" <td>3.092</td>\n",
" <td>5.893</td>\n",
" <td>3.830</td>\n",
" <td>2</td>\n",
" <td>0.839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4126</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3111.5</td>\n",
" <td>46.219</td>\n",
" <td>1.062</td>\n",
" <td>3.018</td>\n",
" <td>6.503</td>\n",
" <td>3.434</td>\n",
" <td>2</td>\n",
" <td>0.831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4127</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3112.0</td>\n",
" <td>42.313</td>\n",
" <td>1.050</td>\n",
" <td>2.245</td>\n",
" <td>5.958</td>\n",
" <td>3.318</td>\n",
" <td>2</td>\n",
" <td>0.823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4128</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3112.5</td>\n",
" <td>36.031</td>\n",
" <td>1.028</td>\n",
" <td>1.193</td>\n",
" <td>5.936</td>\n",
" <td>3.393</td>\n",
" <td>2</td>\n",
" <td>0.815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4129</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3113.0</td>\n",
" <td>32.594</td>\n",
" <td>1.014</td>\n",
" <td>0.662</td>\n",
" <td>5.978</td>\n",
" <td>3.422</td>\n",
" <td>2</td>\n",
" <td>0.806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4130</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3113.5</td>\n",
" <td>37.094</td>\n",
" <td>1.005</td>\n",
" <td>0.377</td>\n",
" <td>6.605</td>\n",
" <td>3.697</td>\n",
" <td>2</td>\n",
" <td>0.798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4131</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3114.0</td>\n",
" <td>40.031</td>\n",
" <td>1.027</td>\n",
" <td>0.615</td>\n",
" <td>6.270</td>\n",
" <td>4.035</td>\n",
" <td>2</td>\n",
" <td>0.790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4132</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3114.5</td>\n",
" <td>42.500</td>\n",
" <td>1.057</td>\n",
" <td>0.672</td>\n",
" <td>5.871</td>\n",
" <td>4.422</td>\n",
" <td>2</td>\n",
" <td>0.782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4133</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3115.0</td>\n",
" <td>39.719</td>\n",
" <td>1.087</td>\n",
" <td>0.648</td>\n",
" <td>4.479</td>\n",
" <td>4.203</td>\n",
" <td>2</td>\n",
" <td>0.774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4134</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3115.5</td>\n",
" <td>38.844</td>\n",
" <td>1.109</td>\n",
" <td>1.025</td>\n",
" <td>2.686</td>\n",
" <td>3.908</td>\n",
" <td>2</td>\n",
" <td>0.766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4135</th>\n",
" <td>6</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3116.0</td>\n",
" <td>41.719</td>\n",
" <td>1.107</td>\n",
" <td>0.659</td>\n",
" <td>2.320</td>\n",
" <td>3.943</td>\n",
" <td>2</td>\n",
" <td>0.758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4136</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3116.5</td>\n",
" <td>44.750</td>\n",
" <td>1.085</td>\n",
" <td>1.165</td>\n",
" <td>2.937</td>\n",
" <td>4.020</td>\n",
" <td>2</td>\n",
" <td>0.750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4137</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3117.0</td>\n",
" <td>46.469</td>\n",
" <td>1.070</td>\n",
" <td>1.872</td>\n",
" <td>5.013</td>\n",
" <td>4.156</td>\n",
" <td>2</td>\n",
" <td>0.742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4138</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3117.5</td>\n",
" <td>51.000</td>\n",
" <td>1.061</td>\n",
" <td>3.760</td>\n",
" <td>6.445</td>\n",
" <td>3.828</td>\n",
" <td>2</td>\n",
" <td>0.734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4139</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3118.0</td>\n",
" <td>55.563</td>\n",
" <td>1.052</td>\n",
" <td>4.296</td>\n",
" <td>7.325</td>\n",
" <td>3.805</td>\n",
" <td>2</td>\n",
" <td>0.726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4140</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3118.5</td>\n",
" <td>58.313</td>\n",
" <td>1.034</td>\n",
" <td>3.863</td>\n",
" <td>7.465</td>\n",
" <td>3.584</td>\n",
" <td>2</td>\n",
" <td>0.718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4141</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3119.0</td>\n",
" <td>55.344</td>\n",
" <td>1.003</td>\n",
" <td>2.225</td>\n",
" <td>7.541</td>\n",
" <td>3.645</td>\n",
" <td>2</td>\n",
" <td>0.710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4142</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3119.5</td>\n",
" <td>53.313</td>\n",
" <td>0.972</td>\n",
" <td>1.640</td>\n",
" <td>7.295</td>\n",
" <td>3.629</td>\n",
" <td>2</td>\n",
" <td>0.702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4143</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3120.0</td>\n",
" <td>49.594</td>\n",
" <td>0.954</td>\n",
" <td>1.494</td>\n",
" <td>7.149</td>\n",
" <td>3.727</td>\n",
" <td>2</td>\n",
" <td>0.694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4144</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3120.5</td>\n",
" <td>46.719</td>\n",
" <td>0.947</td>\n",
" <td>1.828</td>\n",
" <td>7.254</td>\n",
" <td>3.617</td>\n",
" <td>2</td>\n",
" <td>0.685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4145</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3121.0</td>\n",
" <td>44.563</td>\n",
" <td>0.953</td>\n",
" <td>2.241</td>\n",
" <td>8.013</td>\n",
" <td>3.344</td>\n",
" <td>2</td>\n",
" <td>0.677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4146</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3121.5</td>\n",
" <td>49.719</td>\n",
" <td>0.964</td>\n",
" <td>2.925</td>\n",
" <td>8.013</td>\n",
" <td>3.190</td>\n",
" <td>2</td>\n",
" <td>0.669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4147</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3122.0</td>\n",
" <td>51.469</td>\n",
" <td>0.965</td>\n",
" <td>3.083</td>\n",
" <td>7.708</td>\n",
" <td>3.152</td>\n",
" <td>2</td>\n",
" <td>0.661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4148</th>\n",
" <td>5</td>\n",
" <td>C LM</td>\n",
" <td>CHURCHMAN BIBLE</td>\n",
" <td>3122.5</td>\n",
" <td>50.031</td>\n",
" <td>0.970</td>\n",
" <td>2.609</td>\n",
" <td>6.668</td>\n",
" <td>3.295</td>\n",
" <td>2</td>\n",
" <td>0.653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4149 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Facies Formation Well Name Depth GR ILD_log10 DeltaPHI \\\n",
"0 3 A1 SH SHRIMPLIN 2793.0 77.450 0.664 9.900 \n",
"1 3 A1 SH SHRIMPLIN 2793.5 78.260 0.661 14.200 \n",
"2 3 A1 SH SHRIMPLIN 2794.0 79.050 0.658 14.800 \n",
"3 3 A1 SH SHRIMPLIN 2794.5 86.100 0.655 13.900 \n",
"4 3 A1 SH SHRIMPLIN 2795.0 74.580 0.647 13.500 \n",
"5 3 A1 SH SHRIMPLIN 2795.5 73.970 0.636 14.000 \n",
"6 3 A1 SH SHRIMPLIN 2796.0 73.720 0.630 15.600 \n",
"7 3 A1 SH SHRIMPLIN 2796.5 75.650 0.625 16.500 \n",
"8 3 A1 SH SHRIMPLIN 2797.0 73.790 0.624 16.200 \n",
"9 3 A1 SH SHRIMPLIN 2797.5 76.890 0.615 16.900 \n",
"10 3 A1 SH SHRIMPLIN 2798.0 76.110 0.600 14.800 \n",
"11 3 A1 SH SHRIMPLIN 2798.5 74.950 0.583 13.300 \n",
"12 3 A1 SH SHRIMPLIN 2799.0 71.870 0.561 11.300 \n",
"13 3 A1 SH SHRIMPLIN 2799.5 83.420 0.537 13.300 \n",
"14 2 A1 SH SHRIMPLIN 2800.0 90.100 0.519 14.300 \n",
"15 2 A1 SH SHRIMPLIN 2800.5 78.150 0.467 11.800 \n",
"16 2 A1 SH SHRIMPLIN 2801.0 69.300 0.438 9.500 \n",
"17 2 A1 SH SHRIMPLIN 2801.5 63.540 0.418 8.800 \n",
"18 2 A1 SH SHRIMPLIN 2802.0 63.870 0.401 7.200 \n",
"19 2 A1 SH SHRIMPLIN 2802.5 58.320 0.386 6.600 \n",
"20 2 A1 SH SHRIMPLIN 2803.0 56.610 0.369 5.500 \n",
"21 2 A1 SH SHRIMPLIN 2803.5 55.970 0.352 6.100 \n",
"22 2 A1 SH SHRIMPLIN 2804.0 63.670 0.344 6.000 \n",
"23 2 A1 SH SHRIMPLIN 2804.5 66.200 0.342 6.800 \n",
"24 2 A1 SH SHRIMPLIN 2805.0 61.270 0.346 6.100 \n",
"25 3 A1 SH SHRIMPLIN 2805.5 69.480 0.354 5.800 \n",
"26 3 A1 SH SHRIMPLIN 2806.0 76.370 0.354 5.200 \n",
"27 2 A1 SH SHRIMPLIN 2806.5 82.200 0.348 7.400 \n",
"28 2 A1 SH SHRIMPLIN 2807.0 90.250 0.346 11.500 \n",
"29 2 A1 SH SHRIMPLIN 2807.5 94.380 0.358 14.200 \n",
"... ... ... ... ... ... ... ... \n",
"4119 8 C LM CHURCHMAN BIBLE 3108.0 30.734 0.991 1.552 \n",
"4120 6 C LM CHURCHMAN BIBLE 3108.5 32.219 1.013 1.342 \n",
"4121 6 C LM CHURCHMAN BIBLE 3109.0 37.688 1.040 0.681 \n",
"4122 6 C LM CHURCHMAN BIBLE 3109.5 35.844 1.044 0.960 \n",
"4123 6 C LM CHURCHMAN BIBLE 3110.0 42.156 1.051 1.448 \n",
"4124 6 C LM CHURCHMAN BIBLE 3110.5 42.094 1.057 2.736 \n",
"4125 5 C LM CHURCHMAN BIBLE 3111.0 49.719 1.060 3.092 \n",
"4126 5 C LM CHURCHMAN BIBLE 3111.5 46.219 1.062 3.018 \n",
"4127 6 C LM CHURCHMAN BIBLE 3112.0 42.313 1.050 2.245 \n",
"4128 6 C LM CHURCHMAN BIBLE 3112.5 36.031 1.028 1.193 \n",
"4129 6 C LM CHURCHMAN BIBLE 3113.0 32.594 1.014 0.662 \n",
"4130 6 C LM CHURCHMAN BIBLE 3113.5 37.094 1.005 0.377 \n",
"4131 5 C LM CHURCHMAN BIBLE 3114.0 40.031 1.027 0.615 \n",
"4132 5 C LM CHURCHMAN BIBLE 3114.5 42.500 1.057 0.672 \n",
"4133 6 C LM CHURCHMAN BIBLE 3115.0 39.719 1.087 0.648 \n",
"4134 6 C LM CHURCHMAN BIBLE 3115.5 38.844 1.109 1.025 \n",
"4135 6 C LM CHURCHMAN BIBLE 3116.0 41.719 1.107 0.659 \n",
"4136 5 C LM CHURCHMAN BIBLE 3116.5 44.750 1.085 1.165 \n",
"4137 5 C LM CHURCHMAN BIBLE 3117.0 46.469 1.070 1.872 \n",
"4138 5 C LM CHURCHMAN BIBLE 3117.5 51.000 1.061 3.760 \n",
"4139 5 C LM CHURCHMAN BIBLE 3118.0 55.563 1.052 4.296 \n",
"4140 5 C LM CHURCHMAN BIBLE 3118.5 58.313 1.034 3.863 \n",
"4141 5 C LM CHURCHMAN BIBLE 3119.0 55.344 1.003 2.225 \n",
"4142 5 C LM CHURCHMAN BIBLE 3119.5 53.313 0.972 1.640 \n",
"4143 5 C LM CHURCHMAN BIBLE 3120.0 49.594 0.954 1.494 \n",
"4144 5 C LM CHURCHMAN BIBLE 3120.5 46.719 0.947 1.828 \n",
"4145 5 C LM CHURCHMAN BIBLE 3121.0 44.563 0.953 2.241 \n",
"4146 5 C LM CHURCHMAN BIBLE 3121.5 49.719 0.964 2.925 \n",
"4147 5 C LM CHURCHMAN BIBLE 3122.0 51.469 0.965 3.083 \n",
"4148 5 C LM CHURCHMAN BIBLE 3122.5 50.031 0.970 2.609 \n",
"\n",
" PHIND PE NM_M RELPOS \n",
"0 11.915 4.600 1 1.000 \n",
"1 12.565 4.100 1 0.979 \n",
"2 13.050 3.600 1 0.957 \n",
"3 13.115 3.500 1 0.936 \n",
"4 13.300 3.400 1 0.915 \n",
"5 13.385 3.600 1 0.894 \n",
"6 13.930 3.700 1 0.872 \n",
"7 13.920 3.500 1 0.830 \n",
"8 13.980 3.400 1 0.809 \n",
"9 14.220 3.500 1 0.787 \n",
"10 13.375 3.600 1 0.766 \n",
"11 12.690 3.700 1 0.745 \n",
"12 12.475 3.500 1 0.723 \n",
"13 14.930 3.400 1 0.702 \n",
"14 16.555 3.200 1 0.681 \n",
"15 15.960 3.100 1 0.638 \n",
"16 15.120 3.100 1 0.617 \n",
"17 15.190 3.000 1 0.596 \n",
"18 15.390 2.900 1 0.574 \n",
"19 14.885 2.800 1 0.553 \n",
"20 14.800 3.000 1 0.532 \n",
"21 14.460 3.000 1 0.511 \n",
"22 14.745 3.000 1 0.489 \n",
"23 15.135 3.000 1 0.468 \n",
"24 15.480 3.000 1 0.447 \n",
"25 14.675 3.000 1 0.404 \n",
"26 13.635 3.000 1 0.383 \n",
"27 15.055 3.000 1 0.362 \n",
"28 20.230 3.100 1 0.340 \n",
"29 24.015 3.000 1 0.319 \n",
"... ... ... ... ... \n",
"4119 5.382 4.738 2 0.887 \n",
"4120 5.055 4.637 2 0.879 \n",
"4121 4.739 4.539 2 0.871 \n",
"4122 3.533 4.832 2 0.863 \n",
"4123 3.337 4.797 2 0.855 \n",
"4124 4.051 4.500 2 0.847 \n",
"4125 5.893 3.830 2 0.839 \n",
"4126 6.503 3.434 2 0.831 \n",
"4127 5.958 3.318 2 0.823 \n",
"4128 5.936 3.393 2 0.815 \n",
"4129 5.978 3.422 2 0.806 \n",
"4130 6.605 3.697 2 0.798 \n",
"4131 6.270 4.035 2 0.790 \n",
"4132 5.871 4.422 2 0.782 \n",
"4133 4.479 4.203 2 0.774 \n",
"4134 2.686 3.908 2 0.766 \n",
"4135 2.320 3.943 2 0.758 \n",
"4136 2.937 4.020 2 0.750 \n",
"4137 5.013 4.156 2 0.742 \n",
"4138 6.445 3.828 2 0.734 \n",
"4139 7.325 3.805 2 0.726 \n",
"4140 7.465 3.584 2 0.718 \n",
"4141 7.541 3.645 2 0.710 \n",
"4142 7.295 3.629 2 0.702 \n",
"4143 7.149 3.727 2 0.694 \n",
"4144 7.254 3.617 2 0.685 \n",
"4145 8.013 3.344 2 0.677 \n",
"4146 8.013 3.190 2 0.669 \n",
"4147 7.708 3.152 2 0.661 \n",
"4148 6.668 3.295 2 0.653 \n",
"\n",
"[4149 rows x 11 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = '../facies_vectors.csv'\n",
"training_data = pd.read_csv(filename)\n",
"training_data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 4149 entries, 0 to 4148\n",
"Data columns (total 11 columns):\n",
"Facies 4149 non-null int64\n",
"Formation 4149 non-null category\n",
"Well Name 4149 non-null category\n",
"Depth 4149 non-null float64\n",
"GR 4149 non-null float64\n",
"ILD_log10 4149 non-null float64\n",
"DeltaPHI 4149 non-null float64\n",
"PHIND 4149 non-null float64\n",
"PE 3232 non-null float64\n",
"NM_M 4149 non-null int64\n",
"RELPOS 4149 non-null float64\n",
"dtypes: category(2), float64(7), int64(2)\n",
"memory usage: 300.1 KB\n"
]
}
],
"source": [
"training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
"training_data['Formation'] = training_data['Formation'].astype('category')\n",
"training_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115e3ccc0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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T6Ik7euKOnrijJ67ohzvTenI+B5s8FlB79uyp5557To8++qjGjBmj77//XnPm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115e3cd68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00','#1B4F72',\n",
" '#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
"\n",
"facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS','WS', 'D','PS', 'BS']\n",
"\n",
"facies_counts = training_data['Facies'].value_counts().sort_index()\n",
"facies_counts.index = facies_labels\n",
"facies_counts.plot(kind='bar',color=facies_colors,title='Distribution of Training Data by Facies')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115e3a4e0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zjiKYOHGidu3apTlz5ighIUGJiYlatWpVvn5ZWVkaMGCAmjdvriVLlig8PFwD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\no0Zhvz4AALiKsjxdaAabw+FwWB1EeZF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PodKaEBPAcRzTh+t1SUlJQUhICNau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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1182a1470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(training_data.corr(), vmax=1.0, square=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
" RuntimeWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Facies</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>3232.000000</td>\n",
" <td>4149.000000</td>\n",
" <td>4149.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.503254</td>\n",
" <td>2906.867438</td>\n",
" <td>64.933985</td>\n",
" <td>0.659566</td>\n",
" <td>4.402484</td>\n",
" <td>13.201066</td>\n",
" <td>3.725014</td>\n",
" <td>1.518438</td>\n",
" <td>0.521852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.474324</td>\n",
" <td>133.300164</td>\n",
" <td>30.302530</td>\n",
" <td>0.252703</td>\n",
" <td>5.274947</td>\n",
" <td>7.132846</td>\n",
" <td>0.896152</td>\n",
" <td>0.499720</td>\n",
" <td>0.286644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>2573.500000</td>\n",
" <td>10.149000</td>\n",
" <td>-0.025949</td>\n",
" <td>-21.832000</td>\n",
" <td>0.550000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>2821.500000</td>\n",
" <td>44.730000</td>\n",
" <td>0.498000</td>\n",
" <td>1.600000</td>\n",
" <td>8.500000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>0.277000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.000000</td>\n",
" <td>2932.500000</td>\n",
" <td>64.990000</td>\n",
" <td>0.639000</td>\n",
" <td>4.300000</td>\n",
" <td>12.020000</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>0.528000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.000000</td>\n",
" <td>3007.000000</td>\n",
" <td>79.438000</td>\n",
" <td>0.822000</td>\n",
" <td>7.500000</td>\n",
" <td>16.050000</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>0.769000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9.000000</td>\n",
" <td>3138.000000</td>\n",
" <td>361.150000</td>\n",
" <td>1.800000</td>\n",
" <td>19.312000</td>\n",
" <td>84.400000</td>\n",
" <td>8.094000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Facies Depth GR ILD_log10 DeltaPHI \\\n",
"count 4149.000000 4149.000000 4149.000000 4149.000000 4149.000000 \n",
"mean 4.503254 2906.867438 64.933985 0.659566 4.402484 \n",
"std 2.474324 133.300164 30.302530 0.252703 5.274947 \n",
"min 1.000000 2573.500000 10.149000 -0.025949 -21.832000 \n",
"25% 2.000000 2821.500000 44.730000 0.498000 1.600000 \n",
"50% 4.000000 2932.500000 64.990000 0.639000 4.300000 \n",
"75% 6.000000 3007.000000 79.438000 0.822000 7.500000 \n",
"max 9.000000 3138.000000 361.150000 1.800000 19.312000 \n",
"\n",
" PHIND PE NM_M RELPOS \n",
"count 4149.000000 3232.000000 4149.000000 4149.000000 \n",
"mean 13.201066 3.725014 1.518438 0.521852 \n",
"std 7.132846 0.896152 0.499720 0.286644 \n",
"min 0.550000 0.200000 1.000000 0.000000 \n",
"25% 8.500000 NaN 1.000000 0.277000 \n",
"50% 12.020000 NaN 2.000000 0.528000 \n",
"75% 16.050000 NaN 2.000000 0.769000 \n",
"max 84.400000 8.094000 2.000000 1.000000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training_data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Preparation and Model Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are ready to test the XGB approach, along the way confusion matrix and f1_score are imported as metric for classification, as well as GridSearchCV, which is an excellent tool for parameter optimization. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import xgboost as xgb\n",
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix, f1_score\n",
"from classification_utilities import display_cm, display_adj_cm\n",
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train = training_data.drop(['Facies', 'Well Name','Formation','Depth'], axis = 1 ) \n",
"Y_train = training_data['Facies' ] - 1\n",
"dtrain = xgb.DMatrix(X_train, Y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The accuracy function and accuracy_adjacent function are defined in teh following to quatify the prediction correctness. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def accuracy(conf):\n",
" total_correct = 0.\n",
" nb_classes = conf.shape[0]\n",
" for i in np.arange(0,nb_classes):\n",
" total_correct += conf[i][i]\n",
" acc = total_correct/sum(sum(conf))\n",
" return acc\n",
"\n",
"adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
"\n",
"def accuracy_adjacent(conf, adjacent_facies):\n",
" nb_classes = conf.shape[0]\n",
" total_correct = 0.\n",
" for i in np.arange(0,nb_classes):\n",
" total_correct += conf[i][i]\n",
" for j in adjacent_facies[i]:\n",
" total_correct += conf[i][j]\n",
" return total_correct / sum(sum(conf))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initial model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model Report\n",
"-Accuracy: 0.970354\n",
"-Adjacent Accuracy: 0.993492\n",
"\n",
"Confusion Matrix\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 259 5 4 268\n",
" CSiS 1 919 20 940\n",
" FSiS 1 34 745 780\n",
" SiSh 268 2 1 271\n",
" MS 1 1 285 5 4 296\n",
" WS 1 1 4 566 10 582\n",
" D 1 137 3 141\n",
" PS 1 4 2 15 664 686\n",
" BS 1 1 183 185\n",
"\n",
"Precision 0.99 0.96 0.97 0.97 0.98 0.96 1.00 0.98 0.98 0.97\n",
" Recall 0.97 0.98 0.96 0.99 0.96 0.97 0.97 0.97 0.99 0.97\n",
" F1 0.98 0.97 0.96 0.98 0.97 0.97 0.99 0.97 0.99 0.97\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xbbc4a90>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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D7zfffKN7771XHh4e6t69u6qqqrR+/XqtWbNGr7/+um644YY6H2vp0qXq1q2b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\nCwAAANOj9AIAAMD0KL0AAAAwvf8H7vYW0t4mjb0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbbb24a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Proposed Initial Model\n",
"xgb1 = xgb.XGBClassifier( learning_rate =0.1, n_estimators=200, max_depth=5,\n",
" min_child_weight=1, gamma=0, subsample=0.6,\n",
" colsample_bytree=0.6, reg_alpha=0, reg_lambda=1, objective='multi:softmax',\n",
" nthread=4, scale_pos_weight=1, seed=100)\n",
"\n",
"\n",
"#Fit the algorithm on the data\n",
"xgb1.fit(X_train, Y_train,eval_metric='merror')\n",
"\n",
"#Predict training set:\n",
"predictions = xgb1.predict(X_train)\n",
" \n",
"#Print model report\n",
"\n",
"# Confusion Matrix\n",
"conf = confusion_matrix(Y_train, predictions)\n",
"\n",
"# Print Results\n",
"print (\"\\nModel Report\")\n",
"print (\"-Accuracy: %.6f\" % ( accuracy(conf) ))\n",
"print (\"-Adjacent Accuracy: %.6f\" % ( accuracy_adjacent(conf, adjacent_facies) ))\n",
"\n",
"print (\"\\nConfusion Matrix\")\n",
"display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)\n",
"\n",
"# Print Feature Importance\n",
"feat_imp = pd.Series(xgb1.booster().get_fscore()).sort_values(ascending=False)\n",
"feat_imp.plot(kind='bar', title='Feature Importances')\n",
"plt.ylabel('Feature Importance Score')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Cross Validation Training Report Summary\n",
" test-merror-mean test-merror-std train-merror-mean train-merror-std\n",
"0 0.463624 0.034581 0.419595 0.019798\n",
"1 0.433773 0.028935 0.372004 0.014199\n",
"2 0.408699 0.026354 0.350609 0.007946\n",
"3 0.404589 0.026290 0.339788 0.007658\n",
"4 0.398107 0.024423 0.331486 0.007193\n",
" test-merror-mean test-merror-std train-merror-mean train-merror-std\n",
"195 0.292358 0.021023 0.023353 0.000796\n",
"196 0.290915 0.021367 0.022790 0.000619\n",
"197 0.291154 0.020785 0.022522 0.000776\n",
"198 0.291633 0.021096 0.022281 0.000906\n",
"199 0.290673 0.019750 0.021612 0.001124\n"
]
}
],
"source": [
"# Cross Validation parameters\n",
"cv_folds = 10\n",
"rounds = 100\n",
"\n",
"xgb_param_1 = xgb1.get_xgb_params()\n",
"xgb_param_1['num_class'] = 9\n",
"\n",
"# Perform cross-validation\n",
"cvresult1 = xgb.cv(xgb_param_1, dtrain, num_boost_round=xgb_param_1['n_estimators'], \n",
" stratified = True, nfold=cv_folds, metrics='merror', early_stopping_rounds=rounds)\n",
"\n",
"print (\"\\nCross Validation Training Report Summary\")\n",
"print (cvresult1.head())\n",
"print (cvresult1.tail())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The typical range for learning rate is around 0.01~0.2, so we vary ther learning rate a bit and at the same time, scan over the number of boosted trees to fit. This will take a little bit of time to finish. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter optimization\n",
"Best Set of Parameters\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\chenzhan\\AppData\\Local\\Continuum\\Anaconda64\\lib\\site-packages\\sklearn\\model_selection\\_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"([mean: 0.54616, std: 0.03023, params: {'n_estimators': 200, 'learning_rate': 0.05},\n",
" mean: 0.53893, std: 0.02403, params: {'n_estimators': 400, 'learning_rate': 0.05},\n",
" mean: 0.53651, std: 0.02372, params: {'n_estimators': 600, 'learning_rate': 0.05},\n",
" mean: 0.53169, std: 0.02483, params: {'n_estimators': 800, 'learning_rate': 0.05},\n",
" mean: 0.55363, std: 0.02880, params: {'n_estimators': 200, 'learning_rate': 0.01},\n",
" mean: 0.55604, std: 0.02784, params: {'n_estimators': 400, 'learning_rate': 0.01},\n",
" mean: 0.55411, std: 0.02605, params: {'n_estimators': 600, 'learning_rate': 0.01},\n",
" mean: 0.54832, std: 0.02556, params: {'n_estimators': 800, 'learning_rate': 0.01},\n",
" mean: 0.53989, std: 0.02591, params: {'n_estimators': 200, 'learning_rate': 0.1},\n",
" mean: 0.53507, std: 0.02213, params: {'n_estimators': 400, 'learning_rate': 0.1},\n",
" mean: 0.52711, std: 0.02248, params: {'n_estimators': 600, 'learning_rate': 0.1},\n",
" mean: 0.52663, std: 0.02164, params: {'n_estimators': 800, 'learning_rate': 0.1},\n",
" mean: 0.52398, std: 0.02532, params: {'n_estimators': 200, 'learning_rate': 0.2},\n",
" mean: 0.52615, std: 0.02738, params: {'n_estimators': 400, 'learning_rate': 0.2},\n",
" mean: 0.52061, std: 0.02497, params: {'n_estimators': 600, 'learning_rate': 0.2},\n",
" mean: 0.51747, std: 0.02464, params: {'n_estimators': 800, 'learning_rate': 0.2}],\n",
" {'learning_rate': 0.01, 'n_estimators': 400},\n",
" 0.5560375994215474)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Parameter optimization\")\n",
"grid_search1 = GridSearchCV(xgb1,{'learning_rate':[0.05,0.01,0.1,0.2] , 'n_estimators':[200,400,600,800]},\n",
" scoring='accuracy' , n_jobs = 4)\n",
"grid_search1.fit(X_train,Y_train)\n",
"print(\"Best Set of Parameters\")\n",
"grid_search1.grid_scores_, grid_search1.best_params_, grid_search1.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems that we need to adjust the learning rate and make it smaller, which could help to reduce overfitting in my opinion. The number of boosted trees to fit also requires to be updated. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model Report\n",
"-Accuracy: 0.779706\n",
"-Adjacent Accuracy: 0.952519\n",
"\n",
"Confusion Matrix\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 166 89 13 268\n",
" CSiS 18 803 116 2 1 940\n",
" FSiS 139 635 1 5 780\n",
" SiSh 6 224 1 27 2 11 271\n",
" MS 6 5 17 151 75 2 40 296\n",
" WS 1 2 30 14 432 7 93 3 582\n",
" D 1 3 5 106 25 1 141\n",
" PS 1 6 16 4 82 9 566 2 686\n",
" BS 8 2 23 152 185\n",
"\n",
"Precision 0.90 0.77 0.81 0.77 0.89 0.68 0.83 0.74 0.96 0.79\n",
" Recall 0.62 0.85 0.81 0.83 0.51 0.74 0.75 0.83 0.82 0.78\n",
" F1 0.73 0.81 0.81 0.80 0.65 0.71 0.79 0.78 0.89 0.78\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xbc80eb8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1ZswYPfPMM+XOCQAAADc3u68U16hRQ/fff7969+6tli1bKigoyObLkXx8fOTr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\nPXtksVjk5+enLl26yNnZ8SsjV4odTEBAgN58802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"text/plain": [
"<matplotlib.figure.Figure at 0xbbb9128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Proposed Model with optimized learning rate and number of boosted trees to fit\n",
"xgb2 = xgb.XGBClassifier( learning_rate =0.01, n_estimators=400, max_depth=5,\n",
" min_child_weight=1, gamma=0, subsample=0.6,\n",
" colsample_bytree=0.6, reg_alpha=0, reg_lambda=1, objective='multi:softmax',\n",
" nthread=4, scale_pos_weight=1, seed=100)\n",
"\n",
"#Fit the algorithm on the data\n",
"xgb2.fit(X_train, Y_train,eval_metric='merror')\n",
"\n",
"#Predict training set:\n",
"predictions = xgb2.predict(X_train)\n",
" \n",
"#Print model report\n",
"\n",
"# Confusion Matrix\n",
"conf = confusion_matrix(Y_train, predictions )\n",
"\n",
"# Print Results\n",
"print (\"\\nModel Report\")\n",
"print (\"-Accuracy: %.6f\" % ( accuracy(conf) ))\n",
"print (\"-Adjacent Accuracy: %.6f\" % ( accuracy_adjacent(conf, adjacent_facies) ))\n",
"\n",
"# Confusion Matrix\n",
"print (\"\\nConfusion Matrix\")\n",
"display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)\n",
"\n",
"# Print Feature Importance\n",
"feat_imp = pd.Series(xgb2.booster().get_fscore()).sort_values(ascending=False)\n",
"feat_imp.plot(kind='bar', title='Feature Importances')\n",
"plt.ylabel('Feature Importance Score')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Cross Validation Training Report Summary\n",
" test-merror-mean test-merror-std train-merror-mean train-merror-std\n",
"0 0.463624 0.034581 0.419595 0.019798\n",
"1 0.435210 0.031384 0.375298 0.014082\n",
"2 0.420986 0.024074 0.356848 0.010152\n",
"3 0.416908 0.024509 0.351465 0.008039\n",
"4 0.403438 0.015630 0.345599 0.005708\n",
" test-merror-mean test-merror-std train-merror-mean train-merror-std\n",
"395 0.336699 0.025564 0.214028 0.002945\n",
"396 0.337423 0.025555 0.213947 0.002970\n",
"397 0.336940 0.025623 0.213760 0.002869\n",
"398 0.336223 0.026504 0.213385 0.002796\n",
"399 0.335978 0.025790 0.213305 0.002611\n"
]
}
],
"source": [
"# Cross Validation parameters\n",
"cv_folds = 10\n",
"rounds = 100\n",
"\n",
"xgb_param_2 = xgb2.get_xgb_params()\n",
"xgb_param_2['num_class'] = 9\n",
"\n",
"# Perform cross-validation\n",
"cvresult2 = xgb.cv(xgb_param_2, dtrain, num_boost_round=xgb_param_2['n_estimators'], \n",
" stratified = True, nfold=cv_folds, metrics='merror', early_stopping_rounds=rounds)\n",
"\n",
"print (\"\\nCross Validation Training Report Summary\")\n",
"print (cvresult2.head())\n",
"print (cvresult2.tail())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter optimization\n",
"Best Set of Parameters\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\chenzhan\\AppData\\Local\\Continuum\\Anaconda64\\lib\\site-packages\\sklearn\\model_selection\\_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
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" mean: 0.55483, std: 0.02838, params: {'reg_alpha': 0, 'reg_lambda': 0.05},\n",
" mean: 0.55483, std: 0.02776, params: {'reg_alpha': 0, 'reg_lambda': 0.1},\n",
" mean: 0.55459, std: 0.02749, params: {'reg_alpha': 0, 'reg_lambda': 0.2},\n",
" mean: 0.55483, std: 0.02620, params: {'reg_alpha': 0, 'reg_lambda': 0.5},\n",
" mean: 0.55604, std: 0.02784, params: {'reg_alpha': 0, 'reg_lambda': 1},\n",
" mean: 0.55459, std: 0.02897, params: {'reg_alpha': 0, 'reg_lambda': 2},\n",
" mean: 0.55098, std: 0.02991, params: {'reg_alpha': 0, 'reg_lambda': 5},\n",
" mean: 0.55242, std: 0.03191, params: {'reg_alpha': 0, 'reg_lambda': 10},\n",
" mean: 0.55411, std: 0.02701, params: {'reg_alpha': 0.05, 'reg_lambda': 0},\n",
" mean: 0.55459, std: 0.02749, params: {'reg_alpha': 0.05, 'reg_lambda': 0.05},\n",
" mean: 0.55459, std: 0.02784, params: {'reg_alpha': 0.05, 'reg_lambda': 0.1},\n",
" mean: 0.55580, std: 0.02595, params: {'reg_alpha': 0.05, 'reg_lambda': 0.2},\n",
" mean: 0.55604, std: 0.02640, params: {'reg_alpha': 0.05, 'reg_lambda': 0.5},\n",
" mean: 0.55507, std: 0.02600, params: {'reg_alpha': 0.05, 'reg_lambda': 1},\n",
" mean: 0.55315, std: 0.02898, params: {'reg_alpha': 0.05, 'reg_lambda': 2},\n",
" mean: 0.55146, std: 0.02914, params: {'reg_alpha': 0.05, 'reg_lambda': 5},\n",
" mean: 0.55194, std: 0.03225, params: {'reg_alpha': 0.05, 'reg_lambda': 10},\n",
" mean: 0.55363, std: 0.02756, params: {'reg_alpha': 0.1, 'reg_lambda': 0},\n",
" mean: 0.55435, std: 0.02644, params: {'reg_alpha': 0.1, 'reg_lambda': 0.05},\n",
" mean: 0.55435, std: 0.02750, params: {'reg_alpha': 0.1, 'reg_lambda': 0.1},\n",
" mean: 0.55628, std: 0.02721, params: {'reg_alpha': 0.1, 'reg_lambda': 0.2},\n",
" mean: 0.55652, std: 0.02552, params: {'reg_alpha': 0.1, 'reg_lambda': 0.5},\n",
" mean: 0.55652, std: 0.02734, params: {'reg_alpha': 0.1, 'reg_lambda': 1},\n",
" mean: 0.55435, std: 0.02857, params: {'reg_alpha': 0.1, 'reg_lambda': 2},\n",
" mean: 0.55170, std: 0.02891, params: {'reg_alpha': 0.1, 'reg_lambda': 5},\n",
" mean: 0.55194, std: 0.03269, params: {'reg_alpha': 0.1, 'reg_lambda': 10},\n",
" mean: 0.55483, std: 0.02519, params: {'reg_alpha': 0.2, 'reg_lambda': 0},\n",
" mean: 0.55411, std: 0.02519, params: {'reg_alpha': 0.2, 'reg_lambda': 0.05},\n",
" mean: 0.55411, std: 0.02480, params: {'reg_alpha': 0.2, 'reg_lambda': 0.1},\n",
" mean: 0.55580, std: 0.02591, params: {'reg_alpha': 0.2, 'reg_lambda': 0.2},\n",
" mean: 0.55435, std: 0.02634, params: {'reg_alpha': 0.2, 'reg_lambda': 0.5},\n",
" mean: 0.55194, std: 0.02746, params: {'reg_alpha': 0.2, 'reg_lambda': 1},\n",
" mean: 0.55411, std: 0.02770, params: {'reg_alpha': 0.2, 'reg_lambda': 2},\n",
" mean: 0.55266, std: 0.03008, params: {'reg_alpha': 0.2, 'reg_lambda': 5},\n",
" mean: 0.55194, std: 0.03360, params: {'reg_alpha': 0.2, 'reg_lambda': 10},\n",
" mean: 0.55459, std: 0.02602, params: {'reg_alpha': 0.5, 'reg_lambda': 0},\n",
" mean: 0.55507, std: 0.02602, params: {'reg_alpha': 0.5, 'reg_lambda': 0.05},\n",
" mean: 0.55652, std: 0.02633, params: {'reg_alpha': 0.5, 'reg_lambda': 0.1},\n",
" mean: 0.55507, std: 0.02602, params: {'reg_alpha': 0.5, 'reg_lambda': 0.2},\n",
" mean: 0.55290, std: 0.02814, params: {'reg_alpha': 0.5, 'reg_lambda': 0.5},\n",
" mean: 0.55242, std: 0.02823, params: {'reg_alpha': 0.5, 'reg_lambda': 1},\n",
" mean: 0.55146, std: 0.02872, params: {'reg_alpha': 0.5, 'reg_lambda': 2},\n",
" mean: 0.55242, std: 0.03230, params: {'reg_alpha': 0.5, 'reg_lambda': 5},\n",
" mean: 0.55098, std: 0.03272, params: {'reg_alpha': 0.5, 'reg_lambda': 10},\n",
" mean: 0.55387, std: 0.02924, params: {'reg_alpha': 1, 'reg_lambda': 0},\n",
" mean: 0.55266, std: 0.02893, params: {'reg_alpha': 1, 'reg_lambda': 0.05},\n",
" mean: 0.55266, std: 0.02893, params: {'reg_alpha': 1, 'reg_lambda': 0.1},\n",
" mean: 0.55363, std: 0.03067, params: {'reg_alpha': 1, 'reg_lambda': 0.2},\n",
" mean: 0.55339, std: 0.03040, params: {'reg_alpha': 1, 'reg_lambda': 0.5},\n",
" mean: 0.55387, std: 0.02969, params: {'reg_alpha': 1, 'reg_lambda': 1},\n",
" mean: 0.55170, std: 0.02859, params: {'reg_alpha': 1, 'reg_lambda': 2},\n",
" mean: 0.55290, std: 0.03314, params: {'reg_alpha': 1, 'reg_lambda': 5},\n",
" mean: 0.54929, std: 0.03083, params: {'reg_alpha': 1, 'reg_lambda': 10},\n",
" mean: 0.55025, std: 0.03203, params: {'reg_alpha': 2, 'reg_lambda': 0},\n",
" mean: 0.55146, std: 0.03014, params: {'reg_alpha': 2, 'reg_lambda': 0.05},\n",
" mean: 0.55290, std: 0.03070, params: {'reg_alpha': 2, 'reg_lambda': 0.1},\n",
" mean: 0.55218, std: 0.03158, params: {'reg_alpha': 2, 'reg_lambda': 0.2},\n",
" mean: 0.55194, std: 0.03249, params: {'reg_alpha': 2, 'reg_lambda': 0.5},\n",
" mean: 0.55290, std: 0.03226, params: {'reg_alpha': 2, 'reg_lambda': 1},\n",
" mean: 0.55266, std: 0.03405, params: {'reg_alpha': 2, 'reg_lambda': 2},\n",
" mean: 0.55242, std: 0.03318, params: {'reg_alpha': 2, 'reg_lambda': 5},\n",
" mean: 0.54881, std: 0.02862, params: {'reg_alpha': 2, 'reg_lambda': 10},\n",
" mean: 0.55146, std: 0.03166, params: {'reg_alpha': 5, 'reg_lambda': 0},\n",
" mean: 0.55122, std: 0.03076, params: {'reg_alpha': 5, 'reg_lambda': 0.05},\n",
" mean: 0.55242, std: 0.03043, params: {'reg_alpha': 5, 'reg_lambda': 0.1},\n",
" mean: 0.55074, std: 0.03090, params: {'reg_alpha': 5, 'reg_lambda': 0.2},\n",
" mean: 0.55194, std: 0.03018, params: {'reg_alpha': 5, 'reg_lambda': 0.5},\n",
" mean: 0.55194, std: 0.03115, params: {'reg_alpha': 5, 'reg_lambda': 1},\n",
" mean: 0.55122, std: 0.02885, params: {'reg_alpha': 5, 'reg_lambda': 2},\n",
" mean: 0.55387, std: 0.02835, params: {'reg_alpha': 5, 'reg_lambda': 5},\n",
" mean: 0.55459, std: 0.02933, params: {'reg_alpha': 5, 'reg_lambda': 10},\n",
" mean: 0.55459, std: 0.02804, params: {'reg_alpha': 10, 'reg_lambda': 0},\n",
" mean: 0.55483, std: 0.02781, params: {'reg_alpha': 10, 'reg_lambda': 0.05},\n",
" mean: 0.55435, std: 0.02801, params: {'reg_alpha': 10, 'reg_lambda': 0.1},\n",
" mean: 0.55411, std: 0.02824, params: {'reg_alpha': 10, 'reg_lambda': 0.2},\n",
" mean: 0.55411, std: 0.02795, params: {'reg_alpha': 10, 'reg_lambda': 0.5},\n",
" mean: 0.55411, std: 0.02852, params: {'reg_alpha': 10, 'reg_lambda': 1},\n",
" mean: 0.55411, std: 0.02999, params: {'reg_alpha': 10, 'reg_lambda': 2},\n",
" mean: 0.55435, std: 0.02819, params: {'reg_alpha': 10, 'reg_lambda': 5},\n",
" mean: 0.55387, std: 0.02639, params: {'reg_alpha': 10, 'reg_lambda': 10}],\n",
" {'reg_alpha': 0.1, 'reg_lambda': 0.5},\n",
" 0.55651964328753911)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Parameter optimization\")\n",
"grid_search2 = GridSearchCV(xgb2,{'reg_alpha':[0, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10], 'reg_lambda':[0, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10] },\n",
" scoring='accuracy' , n_jobs = 4)\n",
"grid_search2.fit(X_train,Y_train)\n",
"print(\"Best Set of Parameters\")\n",
"grid_search2.grid_scores_, grid_search2.best_params_, grid_search2.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model Report\n",
"-Accuracy: 0.784285\n",
"-Adjacent Accuracy: 0.953242\n",
"\n",
"Confusion Matrix\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 167 89 12 268\n",
" CSiS 17 808 112 2 1 940\n",
" FSiS 139 636 2 3 780\n",
" SiSh 6 225 1 26 2 11 271\n",
" MS 6 5 17 152 74 2 40 296\n",
" WS 1 2 29 12 440 7 88 3 582\n",
" D 1 3 5 106 25 1 141\n",
" PS 1 6 16 5 82 9 566 1 686\n",
" BS 8 2 21 154 185\n",
"\n",
"Precision 0.90 0.77 0.82 0.78 0.89 0.69 0.83 0.75 0.97 0.79\n",
" Recall 0.62 0.86 0.82 0.83 0.51 0.76 0.75 0.83 0.83 0.78\n",
" F1 0.74 0.81 0.82 0.80 0.65 0.72 0.79 0.79 0.90 0.78\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xbccacf8>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ooeDgYJs/juTj4yNfX19lZmaqpKREhw8f1o4dO9S6dWvt2rVLbdu2tQmwERER\n2rlzpyRp9+7dioyMtDlXmzZtlJ2drbKyMuXk5Khjx47W+fDwcF25ckX79+936DUAAACg+rP7TnFK\nSooRdVyTl5eXpk6dqmnTpmnFihUqLS3VoEGDFB0drenTpysoKMjm+Lp16yo/P1+SdPLkyXLz9erV\nU35+vgoLC1VcXGwz7+7urtq1a+vEiRMKCwsz/uIAAABQbdgdiqVf1+uuWrVKBw4ckIeHh2699VY9\n+OCDatSokaPr06FDh9SrVy+NHj1aBw4cUHJysrp06aKioiJ5eXnZHOvl5SWz2SxJunz58nXnrz4o\n+HvfBwAAgOuwOxR///33euihh+Tj46P27durrKxM69at06pVq/TOO+/o1ltvdVhxV9cIf/HFF/Ly\n8lKbNm104sQJLViwQF26dNHZs2dtjjebzfLx8ZEkeXt7lwu4ZrNZ/v7+1jB8rXlfX98K1+fmZpKb\nm+lGLu13ubvbvaqlyrm7u8nDwznqpr/Go8fGcsb+SvTYaPTXeM7UY6Nc/d056+/w99gdil966SV1\n6tRJc+bMsa7nLS4u1oQJE/Tyyy9r0aJFDituz549atasmc0d3datW2vRokWqX7++fvjhB5vjT506\npcDAQElS/fr1VVBQUG6+devWCggIkLe3t06dOqXmzZtLkkpLS3X27Fnr9yuiTp2aMpkcH4r9/Sse\nzKsLf39fBQTUrOoyKoT+Go8eG8sZ+yvRY6PRX+M5U4+N5qy/w99jdyjesWOH/vGPf9g84Obt7a34\n+Hg99NBDDi0uKChIR48eVUlJiTw8fi318OHD+tOf/qSwsDAtWrRIZrPZGpqzsrKsD8+FhYVpx44d\n1nMVFRVp7969GjdunEwmk0JDQ5WVlWV9GC87O1uenp4KCQmpcH2nT1805E5xYWGRw89ptMLCIp05\nc7Gqy6gQ+ms8emwsZ+yvRI+NRn+N50w9Noq7u5v8/X1VWFik0tKyqi6nQir6Fxm7Q3HNmjV15cqV\ncuPXGvtv9erVS7Nnz9bf//53jRkzRocPH9aiRYv09NNPKzIyUg0bNtTEiRM1duxYbd68WTk5OZo5\nc6YkKTo6WkuXLtXixYvVs2dPpaamqnHjxtYQPGzYMCUmJqpVq1YKCgpSUlKSBg8ebNd2bGVlFpWV\nWRx+3c7yD9lvlZaWqaTEOeqmv8ajx8Zyxv5K9Nho9Nd4ztRjo92MvbB7QUjnzp310ksv2aznPX36\ntGbPnq0olx/aAAAgAElEQVQuXbo4tDg/Pz8tW7ZMBQUFeuCBBzRr1izFx8frgQcekJubmxYsWKCC\nggJFR0dr/fr1SktLU4MGDSRJwcHBmjdvnjIzM/XAAw/o/PnzSktLs567b9++iouLU2Jioh599FGF\nh4drwoQJDq0fAAAAzsHuO8UTJkzQkCFD1LNnTzVr1kyS9OOPP+qWW27RypUrHV2fWrZsqSVLllxz\nrnHjxkpPT7/ud7t166aPPvrouvOxsbGKjY39r2sEAACAc7M7FDdo0EAbNmzQ+++/rwMHDshisWjw\n4MHq37+//Pz8jKgRAAAAMNQN7VO8Z88eNW3aVEOHDpUkzZo1S/v377d5QxwAAADgLOxeU7xhwwaN\nGjVK+/bts47l5eUpJiZGn3zyiUOLAwAAACqD3aF44cKFmjhxokaPHm0de/311/Xss89q3rx5Di0O\nAAAAqAx2h+KffvpJUVFR5cZ79uypH3/80RE1AQAAAJXK7lDcsGFDbd++vdx4dna2XW+DAwAAAKoL\nux+0Gzp0qJKTk/XTTz8pLCxMkpSTk6Ply5dr7NixDi8QAAAAMJrdoXjEiBEym81asWKFFi5cKOnX\n1zE/9dRTDn/NMwAAAFAZbmhLtqsvvThz5ow8PT3ZnxgAAABOza5QfOHCBdWoUUNubr8uRT516pS+\n+uor1atXT/fcc4+8vLwMKRIAAAAwUoUetCspKdHkyZPVqVMnHT16VJK0adMmDRw4UK+99ppeeOEF\nRUdH6+zZs4YWCwAAABihQqF46dKl2rRpk55//nk1atRIpaWlSkpKUqNGjfTpp59qy5YtatCggdLS\n0oyuFwAAAHC4CoXi9evXa9KkSRoyZIi8vb21Y8cOnTx5Ug899JACAgLk7e2tkSNHatOmTUbXCwAA\nADhchUJxbm6uIiIirJ+3bdsmk8mku+66yzrWtGlTFRQUOL5CAAAAwGAVCsXu7u66cuWK9fP27dsV\nGBioFi1aWMd++eUXdqEAAACAU6pQKG7btq2++OILSVJ+fr6+/fbbcq96Xrdundq0aeP4CgEAAACD\nVWhLttjYWD322GPavn279u3bJ3d3d8XExEiS9u/fr9WrV2v16tXWl3kAAAAAzqRCd4q7deumRYsW\nyd3dXe3bt9eyZcvUsmVLSVJGRob+9a9/adq0aeXuHgMAAADOoMIv7+jatau6du1abvzpp5/WlClT\nZDKZHFoYAAAAUFlu6DXPv+Xr6+uIOgAAAIAqU6HlEwAAAMDNjFAMAAAAl0coBgAAgMu74VBsNpt1\n+PBhlZSU2LzYAwAAAHA2dodii8Wil19+WZGRkerXr5+OHz+u5557TlOmTCEcAwAAwCnZHYrT09P1\n3nvvKTExUV5eXpKkPn366JNPPlFqaqrDCwQAAACMZncoXr16taZOnapBgwZZ9ybu27evpk+frvXr\n1zu8QAAAAMBodofiY8eOqXXr1uXGQ0JCVFBQ4JCiAAAAgMpkdygODg5WTk5OufEvvvhCjRs3dkhR\nAAAAQGWy+412o0ePVlJSkgoKCmSxWLR161atXr1a6enpmjhxohE1AgAAAIayOxRHR0erpKRECxYs\n0OXLlzV16lTVqVNHTz75pIYOHWpEjQAAAICh7A7FkvTggw9q4MCBunDhgiwWi0pKSlS/fn1H1wYA\nAABUCrvXFJ8+fVqPPPKI0tLSVKdOHdWtW1cDBw7UqFGjdO7cOSNqBAAAAAxldyieMWOGioqK9D//\n8z/WscWLF+v8+fOaNWuWQ4sDAAAAKoPdofirr75ScnKybrvtNutY27ZtlZiYqM8++8yRtQEAAACV\nwu5QXFpaKovFUm7c09NTRUVFDikKAAAAqEx2h+LIyEjNnTtXFy5csI5duHBBr732miIjIx1aHAAA\nAFAZ7N59YtKkSRo+fLi6d++uZs2aSZJ+/PFH1a5dW2+++aaj6wMAAAAMZ3cobtKkiT788ENt2LBB\nP/zwgzw8PDR06FD1799fPj4+RtQIAAAAGOqG9imuVauWhgwZ4uhaAAAAgCphdyi+dOmSli1bph07\ndujKlSvlHrpbsWKFw4oDAAAAKoPdoXjq1KnatGmTunbtqsDAQCNqAgAAACqV3aH4008/1dy5c9Wz\nZ08j6gEAAAAqnd1bsrm5ually5ZG1AIAAABUCbtD8d13361169YZUQsAAABQJexePlGnTh0tXbpU\nX3zxhZo3by4vLy+b+ZSUFIcVBwAAAFQGu0Pxzp07FRYWJkk6efKkwwsCAAAAKpvdoTg9Pd2IOgAA\nAIAqc0Mv7ygpKdEvv/yi0tJSSZLFYpHZbFZOTo4GDBjg0AIBAAAAo9kdir/66is999xzOn36dLk5\nHx8fQjEAAACcjt27T8ydO1dt2rTRokWL5OPjo9TUVE2ePFl+fn6aPXu2ETUCAAAAhrL7TvHBgwf1\n4osvKiQkRK1bt1aNGjX08MMPq0aNGlqyZIn69OljRJ0AAACAYey+U+zu7q5atWpJkpo2baoDBw5I\nkjp37qxDhw45tjoAAACgEtgdim+99VZt3rxZktSiRQtlZWVJkk6cOOHYygAAAIBKYvfyibi4OI0b\nN06enp7q16+f5s2bp7i4OH3//ffq3LmzETUCAAAAhrL7TnGfPn20du1ahYeHq2HDhnrzzTfl7u6u\n3r17Kzk52YgaAQAAAEPZHYpTU1PVokULhYSESJLuuOMOLViwQOPHj1dqaqrDCwQAAACMVqHlE4cO\nHbLuS5yWlqaQkBDdcsstNsccOHBAa9as0ZQpUxxfJQAAAGCgCoXi3NxcjRkzRiaTSRaLRQkJCdc8\nLjo62qHFAQAAAJWhQqG4R48e2rx5s8rKyqxriuvUqWOdN5lMqlGjhmrXru3wAs1ms1JSUrRhwwZ5\neXkpOjpaTz31lCTp2LFjev7557Vz504FBwdr0qRJ6tq1q/W7W7ZsUUpKinJzcxUeHq7k5GQ1btzY\nOr9s2TItXbpUFy9e1L333qupU6fK29vb4dcAAACA6q3Ca4obNWqkP/3pT+rRo4dq1aql4OBg659G\njRoZEoglafr06dq6dauWLl2ql19+WWvWrNGaNWskSWPHjlVQUJAyMzM1YMAAJSQkWLeGO378uOLj\n4xUdHa3MzEwFBAQoPj7eet6NGzdq/vz5Sk5O1vLly7Vr1y7eyAcAAOCi7H7Qbvv27fLwsHsntxty\n7tw5rVu3TtOnT1e7du3UuXNnjRo1Srt27dI333yjY8eOadq0aWrRooXi4uIUHh6ujIwMSdKaNWsU\nGhqqmJgYtWzZUikpKcrLy9P27dslSenp6RoxYoSioqLUrl07JSUlKSMjQ8XFxZVybQAAAKg+7A7F\nUVFRWrlypS5cuGBEPTaysrJUq1YtdezY0ToWGxurGTNmaNeuXWrbtq3NcoeIiAjt3LlTkrR7925F\nRkZa53x8fNSmTRtlZ2errKxMOTk5NucNDw/XlStXtH//fsOvCwAAANWL3bd8CwoK9OGHH2r58uWq\nW7duuTW4mzZtclhxubm5Cg4O1j//+U8tWrRIV65c0aBBg/TYY4+poKBAQUFBNsfXrVtX+fn5kqST\nJ0+Wm69Xr57y8/NVWFio4uJim3l3d3fVrl1bJ06cUFhYmMOuAQAAANWf3aG4U6dO6tSpkxG1lHPp\n0iX9+OOPWrNmjWbOnKmCggJNnTpVvr6+KioqkpeXl83xXl5eMpvNkqTLly9fd/7y5cvWz9f7PgAA\nAFyH3aH4etuxGcHd3V0XL17U3Llz1aBBA0lSXl6e3n77bd111106e/aszfFms1k+Pj6SJG9v73IB\n12w2y9/f3xqGrzXv6+tb4frc3ExyczPZfV1/xN3d7lUtVc7d3U0eHs5RN/01Hj02ljP2V6LHRqO/\nxnOmHhvl6u/OWX+Hv+eGnpj77rvvtGTJEh04cEAeHh5q1aqVRowYofbt2zu0uKCgIHl7e1sDsSQ1\nb95c+fn5ql+/vn744Qeb40+dOqXAwEBJUv369VVQUFBuvnXr1goICJC3t7dOnTql5s2bS5JKS0t1\n9uxZ6/crok6dmjKZHB+K/f0rHsyrC39/XwUE1KzqMiqE/hqPHhvLGfsr0WOj0V/jOVOPjeasv8Pf\nY3co3rZtm0aNGqXbbrtNXbt2VVlZmXbs2KFhw4Zp+fLlioiIcFhxYWFhKi4u1tGjR9W0aVNJv75d\nLzg4WGFhYVq0aJHMZrP1zm9WVpb14bmwsDDt2LHDeq6ioiLt3btX48aNk8lkUmhoqLKysqwP42Vn\nZ8vT09P6+uqKOH36oiF3igsLixx+TqMVFhbpzJmLVV1GhdBf49FjYzljfyV6bDT6azxn6rFR3N3d\n5O/vq8LCIpWWllV1ORVS0b/I2B2KX3nlFUVHRyspKclmPCkpSa+++qrS09PtPeV1NW/eXFFRUZo4\ncaISExNVUFCgxYsXKz4+XpGRkWrYsKEmTpyosWPHavPmzcrJydHMmTMl/fp2vaVLl2rx4sXq2bOn\nUlNT1bhxY2sIHjZsmBITE9WqVSsFBQUpKSlJgwcPtuvlHWVlFpWVWRx2vVc5yz9kv1VaWqaSEueo\nm/4ajx4byxn7K9Fjo9Ff4zlTj412M/bC7gUhe/fu1SOPPFJu/KGHHtJ3333nkKJ+6+WXX1bTpk01\nfPhwTZo0SQ8//LCGDx8uNzc3LViwQAUFBYqOjtb69euVlpZmXWoRHBysefPmKTMzUw888IDOnz+v\ntLQ063n79u2ruLg4JSYm6tFHH1V4eLgmTJjg8PoBAABQ/dl9pzggIEBnzpwpN3769Olyuzk4gp+f\nn2bOnGm9A/xbjRs3/t070926ddNHH3103fnY2FjFxsY6pE4AAAA4L7vvFPfs2VPJyck6dOiQdezg\nwYOaPn26evXq5dDiAAAAgMpg953iJ598UiNHjlS/fv1Uq1YtSdL58+cVEhKiZ5991uEFAgAAAEaz\nOxTfcsstysjI0JdffqkffvhBFotFf/7zn3XXXXfJze3m27MOAAAAN78b2qfYzc1NTZs2VXFxsdzc\n3HTrrbcSiAEAAOC07A7FFy5c0Pjx4/Xll1/KYvl1OzKTyaS+ffsqJSXFkIftAAAAACPZfXt3xowZ\nOnLkiN544w19++232rZtmxYsWKCdO3dq7ty5RtQIAAAAGMruO8WffPKJ5s+fb30JhiT16NFDXl5e\nmjBhgiZOnOjQAgEAAACj2X2n2N3d3brrxG8FBgaqpKTEIUUBAAAAlcnuUPzII48oOTlZp06dso5d\nuHBBr7766jXfdAcAAABUd3Yvn/jqq6+Uk5Oj3r17q1mzZvLw8NCPP/6oixcvat++fXr33Xetx27a\ntMmhxQIAAABGsDsU33nnnbrzzjuNqAUAAACoEnaH4oSEBCPqAAAAAKrMDb2847PPPtOBAwdkNptt\nxk0mk+Lj4x1SGAAAAFBZ7A7F06ZN09tvv626devK29vbZo5QDAAAAGdkdyjesGGDXnjhBQ0ZMsSI\negAAAIBKZ/eWbB4eHurUqZMRtQAAAABVwu5QPGzYMC1cuLDcemIAAADAWdm9fOKvf/2rhg4dqoiI\nCAUGBspkMtnMszcxAAAAnI3dofiZZ56Rv7+/oqOjVaNGDSNqAgAAACqV3aH4hx9+UEZGhm677TYj\n6gEAAAAqnd1rilu2bKnCwkIjagEAAACqhN13imNjYzV58mSNHj1aTZo0kYeH7SkiIyMdVhwAAABQ\nGewOxePHj5ckJSYmlpszmUzat2/ff18VAAAAUInsDsXsLgEAAICbjd2hODg42Ig6AAAAgCpToVCc\nmppa4RMmJCTccDEAAABAVahQKF63bl2FTmYymQjFAAAAcDoVCsWbN282ug4AAACgyti9TzEAAABw\nsyEUAwAAwOURigEAAODyCMUAAABweYRiAAAAuLwbCsX79+/XpEmTNGTIEOXn52vVqlX697//7eja\nAAAAgEphdyj+7rvvNHjwYB07dkzfffedzGaz9u3bp9GjR+vzzz83okYAAADAUHaH4pdfflkjR45U\nenq6PD09JUnTp0/X8OHDNW/ePIcXCAAAABjthu4U33///eXGhw8frkOHDjmkKAAAAKAy2R2KPT09\ndeHChXLjx48fl6+vr0OKAgAAACqT3aG4T58+evXVV1VYWGgdO3TokGbMmKEePXo4sjYAAACgUtgd\nip977jldvHhRnTt3VlFRkQYNGqR+/frJ3d1dzz77rBE1AgAAAIbysPcLJpNJ//jHP7R161bt3btX\nZWVluu2229StWze5ubHtMQAAAJyP3aH4/vvv16uvvqouXbqoS5cuRtQEAAAAVCq7b+0WFRXJx8fH\niFoAAACAKmH3neJHHnlEjz/+uIYPH64mTZqUC8iRkZEOKw4AAACoDHaH4rlz50qSkpOTy82ZTCbt\n27fvv68KAAAAqER2h+JNmzYZUQcAAABQZewOxcHBwUbUAQAAAFSZG1pT/HtWrFhxw8UAAAAAVeG/\nvlNcUlKio0eP6sCBAxoxYoTDCgMAAAAqi92hOCUl5ZrjaWlpOnHixH9dEAAAAFDZHPYKuvvuu0//\n+te/HHU6AAAAoNI4LBRnZ2fL3d3dUacDAAAAKo1DHrS7cOGCvv/+ew0bNswhRQEAAACVye5Q3KhR\nI5lMJpsxT09PPfTQQxowYIDDCgMAAAAqi92heNy4cWrQoIHc3GxXXpSUlGjv3r1q3769w4oDAAAA\nKoPda4p79+6ts2fPlhs/duyYHn74YYcUBQAAAFSmCt0pXrVqlZYuXSpJslgsio6OLnenuLCwUI0a\nNXJ8hQAAAIDBKhSKBw0apDNnzshisSgtLU333nuvatasaXNMzZo1dffddxtSJAAAAGCkCoViX19f\nJSQkSJJMJpNGjx4tX19fQwsDAAAAKovda4oTEhLk6emp/Px8/fzzz/r555+Vl5enI0eO6P333zei\nRqu4uDhNmjTJ+vnYsWMaOXKkOnTooH79+unrr7+2OX7Lli3q37+/wsPDFRMTo9zcXJv5ZcuWqXv3\n7oqIiNCUKVNUXFxsaP0AAAConuwOxV999ZWioqLUo0cP9e7dW71791afPn3Ut29fJSYmGlGjJGnD\nhg364osvbMbi4+MVFBSkzMxMDRgwQAkJCdZXTR8/flzx8fGKjo5WZmamAgICFB8fb/3uxo0bNX/+\nfCUnJ2v58uXatWuXZs+ebVj9AAAAqL7sDsVz585VmzZttGjRIvn4+Cg1NVWTJ0+Wn5+fYaHy3Llz\nmj17ts12b1u3blVubq6mTZumFi1aKC4uTuHh4crIyJAkrVmzRqGhoYqJiVHLli2VkpKivLw8bd++\nXZKUnp6uESNGKCoqSu3atVNSUpIyMjK4WwwAAOCC7A7FBw8e1NNPP63u3burdevWqlGjhh5++GFN\nnDhRS5YsMaJGzZo1S/fdd59atmxpHdu9e7fatm0rb29v61hERIR27txpnY+MjLTO+fj4qE2bNsrO\nzlZZWZlycnLUsWNH63x4eLiuXLmi/fv3G3INAAAAqL7sDsXu7u6qVauWJKlp06Y6cOCAJKlz5846\ndOiQY6vTr3eEs7KybJY+SFJBQYGCgoJsxurWrav8/HxJ0smTJ8vN16tXT/n5+SosLFRxcbHNvLu7\nu2rXrm1dfgEAAADXYXcovvXWW7V582ZJUosWLZSVlSVJhoRJs9msF154QYmJifLy8rKZKyoqKjfm\n5eUls9ksSbp8+fJ15y9fvmz9fL3vAwAAwHXY/ZrnuLg4jRs3Tp6enurXr5/mzZunuLg4ff/99+rc\nubNDi5s3b57atWunO++8s9yct7e3zp07ZzNmNpvl4+Njnf/PgGs2m+Xv728Nw9eat2erOTc3k9zc\nTBU+vqLc3e3+u0qVc3d3k4eHc9RNf41Hj43ljP2V6LHR6K/xnKnHRrn6u3PW3+HvsTsU9+nTR2vX\nrpW7u7saNmyoN998U2+99ZZ69+6tcePGObS4Dz/8UL/88os6dOggSbpy5YqkX3eOGDNmjA4ePGhz\n/KlTpxQYGChJql+/vgoKCsrNt27dWgEBAfL29tapU6fUvHlzSVJpaanOnj1r/X5F1KlTUyaT40Ox\nv7/z7QHt7++rgICaf3xgNUB/jUePjeWM/ZXosdHor/GcqcdGc9bf4e+xOxRLUtu2bSX9emf1jjvu\n0B133OHQoq5auXKlSkpKrJ+v7m7xzDPPKC8vT2+88YbMZrP1zm9WVpb14bmwsDDt2LHD+t2ioiLt\n3btX48aNk8lkUmhoqLKysqwP42VnZ8vT01MhISEVru/06YuG3CkuLCxy+DmNVlhYpDNnLlZ1GRVC\nf41Hj43ljP2V6LHR6K/xnKnHRnF3d5O/v68KC4tUWlpW1eVUSEX/InNDofidd97R4sWLdeLECW3c\nuFFLlixRUFCQxo4deyOnu66GDRvafL76aunGjRsrODhYDRs21MSJEzV27Fht3rxZOTk5mjlzpiQp\nOjpaS5cu1eLFi9WzZ0+lpqaqcePG1hA8bNgwJSYmqlWrVgoKClJSUpIGDx5ss5vFHykrs6iszOKg\nq/0/zvIP2W+VlpappMQ56qa/xqPHxnLG/kr02Gj013jO1GOj3Yy9sHtByPr16zVnzhwNHDhQnp6e\nkn594G7hwoVaunSpwwu8Hjc3N82fP18FBQWKjo7W+vXrlZaWpgYNGkiSgoODNW/ePGVmZuqBBx7Q\n+fPnlZaWZv1+3759FRcXp8TERD366KMKDw/XhAkTKq1+AAAAVB923yleunSppkyZooEDB1pD8COP\nPKIaNWpo8eLFGjVqlMOLvColJcXmc+PGjZWenn7d47t166aPPvrouvOxsbGKjY11WH0AAABwTnbf\nKT5y5IjNSy+u6tSpk44fP+6QogAAAIDKZHcorlevno4cOVJuPDs7u9zLMgAAAABnYHcofvDBBzVt\n2jRt2rRJknT48GG98847mjFjhgYNGuTwAgEAAACj2b2mODY2VufPn9f48eNVXFys//3f/5WHh4eG\nDBmiMWPGGFEjAAAAYKgb2pJt/Pjxeuyxx3Tw4EFZLBa1aNFCfn5+jq4NAAAAqBQVWj7x0ksv6dKl\nSzZjvr6+Cg0NVfv27QnEAAAAcGoVCsVvvfWWiops3z4TFxenkydPGlIUAAAAUJkqFIotlvJvbdu+\nfbuKi4sdXhAAAABQ2ezefQIAAAC42RCKAQAA4PIqHIpNJpORdQAAAABVpsJbsk2fPl3e3t7Wz1eu\nXNHs2bNVs2ZNm+NSUlIcVx0AAABQCSoUiiMjI1VQUGAz1qFDB505c0ZnzpwxpDAAAACgslQoFKen\npxtdBwAAAFBleNAOAAAALo9QDAAAAJdHKAYAAIDLIxQDAADA5RGKAQAA4PIIxQAAAHB5hGIAAAC4\nPEIxAAAAXB6hGAAAAC6PUAwAAACXRygGAACAyyMUAwAAwOURigEAAODyCMUAAABweYRiAAAAuDxC\nMQAAAFweoRgAAAAuj1AMAAAAl0coBgAAgMsjFAMAAMDlEYoBAADg8gjFAAAAcHmEYgAAALg8QjEA\nAABcHqEYAAAALo9QDAAAAJdHKAYAAIDLIxQDAADA5RGKAQAA4PIIxQAAAHB5hGIAAAC4PEIxAAAA\nXB6hGAAAAC6PUAwAAACXRygGAACAyyMUAwAAwOURigEAAODyCMUAAABweYRiAAAAuDxCMQAAAFwe\noRgAAAAuj1AMAAAAl0coBgAAgMsjFAMAAMDlEYoBAADg8qp9KM7Pz9e4cePUqVMnRUVFaebMmTKb\nzZKkY8eOaeTIkerQoYP69eunr7/+2ua7W7ZsUf/+/RUeHq6YmBjl5ubazC9btkzdu3dXRESEpkyZ\nouLi4kq7LgAAAFQf1T4Ujxs3TsXFxXr77bc1d+5cffrpp3rttdckSWPHjlVQUJAyMzM1YMAAJSQk\n6MSJE5Kk48ePKz4+XtHR0crMzFRAQIDi4+Ot5924caPmz5+v5ORkLV++XLt27dLs2bOr5BoBAABQ\ntap1KD58+LB2796tlJQUtWzZUhERERo3bpw++OADffPNNzp27JimTZumFi1aKC4uTuHh4crIyJAk\nrVmzRqGhoYqJiVHLli2VkpKivLw8bd++XZKUnp6uESNGKCoqSu3atVNSUpIyMjK4WwwAAOCCqnUo\nDgwM1Jtvvqk6derYjJ8/f167du1S27Zt5e3tbR2PiIjQzp07JUm7d+9WZGSkdc7Hx0dt2rRRdna2\nysrKlJOTo44dO1rnw8PDdeXKFe3fv9/gqwIAAEB141HVBfyeWrVqqWvXrtbPFotFK/9/e3ceVlWd\n+HH8c2V1wwUVk0mL1LBcccuMSsQcs3mqMdKcwdRB9MmtzDQ0xz0VTUtRmkxJ0WcyLbGmcQMaTSVU\nwlxxa3KhR9xBFLhsvz96ur8hl0C5HO4979d/99yv9OGbz/Vzzv2e8121Sl26dNGFCxfUoEGDEuO9\nvb2VkZEhSTp//vxN79erV08ZGRnKyspSXl5eifddXFxUu3ZtnTt3Tm3atLHjbwUAAGBfVqtVhw4d\nKPef6+JSRV5eVZWVlaPCwqJy/dmPPtpK7u7u5fozy6JSl+LfioyM1JEjR7Ru3TrFxMTcNHHu7u62\nm/Byc3Nv+35ubq7t9e3+PAAAgKM6dOiAxs3/QjW9GxsdpVSuXTqtyDFSu3btDcvgMKV47ty5io2N\n1fvvv6+mTZvKw8NDmZmZJcZYrVZ5enpKkjw8PG4quFarVV5eXrYyfKv3q1atWupMVapYVKWK5W5+\nnTtycanUq1puycWlilxdHSM382t/zLF9OeL8SsyxvTG/9udoc1zTu7FqN2xmdJRSM3p+HaIUT58+\nXWvWrNHcuXMVHBwsSfLx8dGJEydKjLt48aLq169ve//ChQs3vd+iRQvVqVNHHh4eunjxoh588EFJ\nUmFhoa5evWr786VRt251WSzlX4q9vEpfzCsLL6+qqlOnutExSoX5tT/m2L4ccX4l5tjemF/7Y47t\ny+6RW+IAAByASURBVOj5rfSlOCoqSmvWrNGCBQvUo0cP2/E2bdpo6dKlslqttiu/KSkptpvn2rRp\no++//942PicnR4cPH9aoUaNksVjUqlUrpaSk2G7GS01NlZubm/z9/Uud7fLl63a5UpyVlVPuP9Pe\nsrJydOXKdaNjlArza3/MsX054vxKzLG9Mb/2xxzbl73mt7RFu1KX4pMnTyo6OlpDhw5Vu3btdPHi\nRdt7nTp10n333ae3335br732mhITE3XgwAHNnj1bktSnTx8tX75cS5cuVbdu3RQVFaX777/fVoL7\n9++vyZMnq2nTpmrQoIGmTp2ql19+ucTTLH5PUVGxioqKy/eXlsp94XpFKCwsUkGBY+Rmfu2PObYv\nR5xfiTm2N+bX/phj+zJ6fit1KU5ISFBRUZGio6MVHR0t6ZcnUFgsFh05ckSLFy/WxIkT1adPHzVu\n3FiLFy9Ww4YNJUm+vr5atGiRZs6cqSVLliggIECLFy+2/exnn31W6enpmjx5svLz89WzZ0+NHTvW\nkN8TAAAAxqrUpTg8PFzh4eG3fb9x48aKjY297fuBgYHatGnTbd8fMmSIhgwZck8ZAQAA4Pgc4xZK\nAAAAwI4oxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAA\nwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQo\nxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAA\nADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9\nSjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEA\nAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABM\nj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA06MUAwAAwPQoxQAAADA9SjEAAABMz/Sl\n2Gq1asKECerYsaMCAwMVExNjdCQAAABUMFejAxhtzpw5Onz4sGJjY3X27FmNHz9evr6+euaZZ4yO\nBgAAgApi6ivFOTk5Wrdund555x35+/srODhYYWFhWrVqldHRAAAAUIFMXYrT0tJUWFiotm3b2o61\nb99e+/fvNzAVAAAAKpqpS/GFCxdUu3Ztubr+/yoSb29v5eXl6cqVKwYmAwAAQEUydSnOycmRu7t7\niWO/vrZarUZEAgAAgAFMfaOdh4fHTeX319dVq1b93T9fpYpFVapYyj2Xi0sVXbt0utx/rr1cu3Ra\nLi6d5OrqGOdYzK/9Mcf25WjzKzHH9sb82h9zbF+VYX4txcXFxYb91w2Wmpqq0NBQ7d+/X1Wq/PI/\nITk5WcOGDVNqaqrB6QAAAFBRHON0x05atGghV1dX7du3z3Zs7969atmypYGpAAAAUNFMXYo9PT31\n/PPPa/LkyTpw4IDi4+MVExOjV1991ehoAAAAqECmXj4hSbm5uZo6dao2b96smjVrKiwsTKGhoUbH\nAgAAQAUyfSkGAAAATL18AgAAAJAoxQAAAAClGAAAAKAUAwAAwPQoxQAAADA9SjEAAABMj1IMAAAA\n06MUm9S6deuMjgAAAFBpsHmHkykoKNBHH32k+Ph4ubi46I9//KMGDx4si8UiSdq/f7+mT5+ugwcP\n6siRIwandWz79++Xv7+/3N3dJUnx8fFKSkpSnTp19NJLL6lhw4YGJ3Rsubm52rRpk1JTU5WRkSGr\n1SpPT0/Vr19fbdu2Va9eveTp6Wl0TIdntVqVkpKikydP6vr166pRo4aaNWumDh06qEoVrpsAZjVg\nwIBSj125cqUdk1QcV6MDoHzNnj1bn332mZ5//nm5u7vrH//4h3JzczVs2DDNnj1bq1evlp+fn5Yv\nX250VId18eJFhYWF6ejRo/r666/l5+enDz/8UB988IHatGmjGjVqKDY2VqtXr1bTpk2NjuuQDh06\npKFDh6p69eoKCAhQ06ZN5e7uLqvVqosXLyo6Olrz58/X0qVL5e/vb3RchxUXF6e5c+fq0qVLqlat\nmmrWrKnr168rOztb9evX1/jx4/Xcc88ZHdMhRUREaOLEiapRo4btWEpKilq1amU7kb5y5Yr69eun\nzZs3GxXToUVFRZV67IgRI+yYxDnt3r1bFotFbdu2VefOneXq6vyVkSvFTiYwMFBvvvmmXnjhBUlS\ncnKyxo0bpw4dOigxMVGjRo3SgAED5OLiYnBSxzV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"text/plain": [
"<matplotlib.figure.Figure at 0xbb93978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Proposed Model with optimized regularization \n",
"xgb3 = xgb.XGBClassifier( learning_rate =0.01, n_estimators=400, max_depth=5,\n",
" min_child_weight=1, gamma=0, subsample=0.6,\n",
" colsample_bytree=0.6, reg_alpha=0.1, reg_lambda=0.5, objective='multi:softmax',\n",
" nthread=4, scale_pos_weight=1, seed=100)\n",
"\n",
"#Fit the algorithm on the data\n",
"xgb3.fit(X_train, Y_train,eval_metric='merror')\n",
"\n",
"#Predict training set:\n",
"predictions = xgb3.predict(X_train)\n",
" \n",
"#Print model report\n",
"\n",
"# Confusion Matrix\n",
"conf = confusion_matrix(Y_train, predictions )\n",
"\n",
"# Print Results\n",
"print (\"\\nModel Report\")\n",
"print (\"-Accuracy: %.6f\" % ( accuracy(conf) ))\n",
"print (\"-Adjacent Accuracy: %.6f\" % ( accuracy_adjacent(conf, adjacent_facies) ))\n",
"\n",
"# Confusion Matrix\n",
"print (\"\\nConfusion Matrix\")\n",
"display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)\n",
"\n",
"# Print Feature Importance\n",
"feat_imp = pd.Series(xgb3.booster().get_fscore()).sort_values(ascending=False)\n",
"feat_imp.plot(kind='bar', title='Feature Importances')\n",
"plt.ylabel('Feature Importance Score')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter optimization\n",
"Best Set of Parameters\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\chenzhan\\AppData\\Local\\Continuum\\Anaconda64\\lib\\site-packages\\sklearn\\model_selection\\_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"([mean: 0.55146, std: 0.02078, params: {'max_depth': 2, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.4},\n",
" mean: 0.55170, std: 0.01781, params: {'colsample_bytree': 0.4, 'max_depth': 2, 'subsample': 0.6, 'gamma': 0},\n",
" mean: 0.54977, std: 0.01751, params: {'colsample_bytree': 0.4, 'max_depth': 2, 'subsample': 0.8, 'gamma': 0},\n",
" mean: 0.53531, std: 0.01736, params: {'max_depth': 5, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.4},\n",
" mean: 0.54182, std: 0.01044, params: {'subsample': 0.6, 'max_depth': 5, 'gamma': 0, 'colsample_bytree': 0.4},\n",
" mean: 0.53917, std: 0.01401, params: {'colsample_bytree': 0.4, 'max_depth': 5, 'subsample': 0.8, 'gamma': 0},\n",
" mean: 0.52953, std: 0.01619, params: {'max_depth': 8, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.4},\n",
" mean: 0.52832, std: 0.01334, params: {'max_depth': 8, 'subsample': 0.6, 'gamma': 0, 'colsample_bytree': 0.4},\n",
" mean: 0.53025, std: 0.01603, params: {'colsample_bytree': 0.4, 'max_depth': 8, 'subsample': 0.8, 'gamma': 0},\n",
" mean: 0.55098, std: 0.02163, params: {'max_depth': 2, 'subsample': 0.4, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.55122, std: 0.01714, params: {'colsample_bytree': 0.4, 'max_depth': 2, 'subsample': 0.6, 'gamma': 1},\n",
" mean: 0.54905, std: 0.01806, params: {'subsample': 0.8, 'max_depth': 2, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.53651, std: 0.01349, params: {'max_depth': 5, 'subsample': 0.4, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.54037, std: 0.01404, params: {'colsample_bytree': 0.4, 'max_depth': 5, 'subsample': 0.6, 'gamma': 1},\n",
" mean: 0.53844, std: 0.01161, params: {'subsample': 0.8, 'max_depth': 5, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.53218, std: 0.01724, params: {'max_depth': 8, 'subsample': 0.4, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.53410, std: 0.01634, params: {'colsample_bytree': 0.4, 'max_depth': 8, 'subsample': 0.6, 'gamma': 1},\n",
" mean: 0.53266, std: 0.00957, params: {'max_depth': 8, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.4},\n",
" mean: 0.55170, std: 0.02279, params: {'max_depth': 2, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.55146, std: 0.01988, params: {'max_depth': 2, 'subsample': 0.6, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.54832, std: 0.01790, params: {'max_depth': 2, 'subsample': 0.8, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.55266, std: 0.03049, params: {'max_depth': 5, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.55652, std: 0.02552, params: {'max_depth': 5, 'subsample': 0.6, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.54905, std: 0.02224, params: {'max_depth': 5, 'subsample': 0.8, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.54375, std: 0.02369, params: {'subsample': 0.4, 'max_depth': 8, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.54664, std: 0.02236, params: {'max_depth': 8, 'subsample': 0.6, 'gamma': 0, 'colsample_bytree': 0.6},\n",
" mean: 0.54640, std: 0.02479, params: {'colsample_bytree': 0.6, 'max_depth': 8, 'subsample': 0.8, 'gamma': 0},\n",
" mean: 0.55074, std: 0.02137, params: {'subsample': 0.4, 'max_depth': 2, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.55194, std: 0.02152, params: {'subsample': 0.6, 'max_depth': 2, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.54881, std: 0.01910, params: {'max_depth': 2, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.54857, std: 0.03170, params: {'colsample_bytree': 0.6, 'max_depth': 5, 'subsample': 0.4, 'gamma': 1},\n",
" mean: 0.55483, std: 0.02627, params: {'subsample': 0.6, 'max_depth': 5, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.55194, std: 0.02645, params: {'max_depth': 5, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.54881, std: 0.02548, params: {'colsample_bytree': 0.6, 'max_depth': 8, 'subsample': 0.4, 'gamma': 1},\n",
" mean: 0.54760, std: 0.02325, params: {'subsample': 0.6, 'max_depth': 8, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.54977, std: 0.01933, params: {'max_depth': 8, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.6},\n",
" mean: 0.55363, std: 0.01914, params: {'max_depth': 2, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.55074, std: 0.01928, params: {'colsample_bytree': 0.8, 'max_depth': 2, 'subsample': 0.6, 'gamma': 0},\n",
" mean: 0.55122, std: 0.01444, params: {'max_depth': 2, 'subsample': 0.8, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.54712, std: 0.02818, params: {'max_depth': 5, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.55049, std: 0.03193, params: {'max_depth': 5, 'subsample': 0.6, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.54640, std: 0.02575, params: {'max_depth': 5, 'subsample': 0.8, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.54447, std: 0.03035, params: {'max_depth': 8, 'subsample': 0.4, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.54784, std: 0.03084, params: {'colsample_bytree': 0.8, 'max_depth': 8, 'subsample': 0.6, 'gamma': 0},\n",
" mean: 0.54543, std: 0.02831, params: {'max_depth': 8, 'subsample': 0.8, 'gamma': 0, 'colsample_bytree': 0.8},\n",
" mean: 0.55218, std: 0.01772, params: {'max_depth': 2, 'subsample': 0.4, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.55001, std: 0.01803, params: {'max_depth': 2, 'subsample': 0.6, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.55146, std: 0.01476, params: {'max_depth': 2, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.54736, std: 0.02696, params: {'colsample_bytree': 0.8, 'max_depth': 5, 'subsample': 0.4, 'gamma': 1},\n",
" mean: 0.55170, std: 0.03230, params: {'subsample': 0.6, 'max_depth': 5, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.54423, std: 0.02674, params: {'max_depth': 5, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.54616, std: 0.02970, params: {'colsample_bytree': 0.8, 'max_depth': 8, 'subsample': 0.4, 'gamma': 1},\n",
" mean: 0.54832, std: 0.02628, params: {'subsample': 0.6, 'max_depth': 8, 'gamma': 1, 'colsample_bytree': 0.8},\n",
" mean: 0.54688, std: 0.03003, params: {'max_depth': 8, 'subsample': 0.8, 'gamma': 1, 'colsample_bytree': 0.8}],\n",
" {'colsample_bytree': 0.6, 'gamma': 0, 'max_depth': 5, 'subsample': 0.6},\n",
" 0.55651964328753911)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Parameter optimization\")\n",
"grid_search3 = GridSearchCV(xgb3,{'max_depth':[2, 5, 8], 'gamma':[0, 1], 'subsample':[0.4, 0.6, 0.8],'colsample_bytree':[0.4, 0.6, 0.8] },\n",
" scoring='accuracy' , n_jobs = 4)\n",
"grid_search3.fit(X_train,Y_train)\n",
"print(\"Best Set of Parameters\")\n",
"grid_search3.grid_scores_, grid_search3.best_params_, grid_search3.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well SHRIMPLIN\n",
"\n",
"Model Report\n",
"-Accuracy: 0.607219\n",
"-Adjacent Accuracy: 0.959660\n",
"-F1 Score: 0.587285\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 0\n",
" CSiS 7 94 17 118\n",
" FSiS 52 71 123\n",
" SiSh 13 2 3 18\n",
" MS 6 6 43 8 63\n",
" WS 2 2 38 1 18 2 63\n",
" D 1 1 3 5\n",
" PS 2 10 1 52 4 69\n",
" BS 1 11 12\n",
"\n",
"Precision 0.00 0.64 0.81 0.62 0.60 0.40 0.33 0.61 0.65 0.64\n",
" Recall 0.00 0.80 0.58 0.72 0.10 0.60 0.20 0.75 0.92 0.61\n",
" F1 0.00 0.71 0.67 0.67 0.16 0.48 0.25 0.68 0.76 0.59\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well ALEXANDER D\n",
"\n",
"Model Report\n",
"-Accuracy: 0.626609\n",
"-Adjacent Accuracy: 0.916309\n",
"-F1 Score: 0.589447\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 0\n",
" CSiS 85 32 117\n",
" FSiS 17 74 91\n",
" SiSh 39 3 2 44\n",
" MS 3 11 2 10 26\n",
" WS 12 19 1 4 33 69\n",
" D 1 8 7 16\n",
" PS 7 3 5 8 73 2 98\n",
" BS 1 3 1 5\n",
"\n",
"Precision 0.00 0.83 0.70 0.64 0.32 0.11 0.31 0.58 0.33 0.58\n",
" Recall 0.00 0.73 0.81 0.89 0.42 0.01 0.50 0.74 0.20 0.63\n",
" F1 0.00 0.78 0.75 0.74 0.37 0.03 0.38 0.65 0.25 0.59\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well SHANKLE\n",
"\n",
"Model Report\n",
"-Accuracy: 0.487751\n",
"-Adjacent Accuracy: 0.966592\n",
"-F1 Score: 0.467278\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 7 81 1 89\n",
" CSiS 8 69 12 89\n",
" FSiS 55 61 1 117\n",
" SiSh 4 1 2 7\n",
" MS 14 3 1 1 19\n",
" WS 7 5 46 13 71\n",
" D 1 2 9 5 17\n",
" PS 16 1 23 40\n",
" BS 0\n",
"\n",
"Precision 0.47 0.34 0.82 0.16 0.00 0.68 0.82 0.51 0.00 0.56\n",
" Recall 0.08 0.78 0.52 0.57 0.00 0.65 0.53 0.57 0.00 0.49\n",
" F1 0.13 0.47 0.64 0.25 0.00 0.66 0.64 0.54 0.00 0.47\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well LUKE G U\n",
"\n",
"Model Report\n",
"-Accuracy: 0.637744\n",
"-Adjacent Accuracy: 0.928416\n",
"-F1 Score: 0.659138\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 0\n",
" CSiS 11 88 18 117\n",
" FSiS 7 43 75 4 129\n",
" SiSh 31 1 3 35\n",
" MS 1 1 2\n",
" WS 8 17 42 16 1 84\n",
" D 1 7 11 1 20\n",
" PS 2 3 3 15 1 50 74\n",
" BS 0\n",
"\n",
"Precision 0.00 0.67 0.79 0.74 0.05 0.69 0.88 0.61 0.00 0.71\n",
" Recall 0.00 0.75 0.58 0.89 0.50 0.50 0.35 0.68 0.00 0.64\n",
" F1 0.00 0.71 0.67 0.81 0.08 0.58 0.50 0.64 0.00 0.66\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well KIMZEY A\n",
"\n",
"Model Report\n",
"-Accuracy: 0.530752\n",
"-Adjacent Accuracy: 0.895216\n",
"-F1 Score: 0.495145\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 5 4 9\n",
" CSiS 75 10 85\n",
" FSiS 40 34 74\n",
" SiSh 27 16 43\n",
" MS 7 2 29 15 53\n",
" WS 1 3 28 1 18 51\n",
" D 3 5 5 14 27\n",
" PS 1 1 24 4 60 90\n",
" BS 2 3 2 7\n",
"\n",
"Precision 0.00 0.62 0.69 0.66 0.67 0.27 0.50 0.55 1.00 0.57\n",
" Recall 0.00 0.88 0.46 0.63 0.04 0.55 0.19 0.67 0.29 0.53\n",
" F1 0.00 0.73 0.55 0.64 0.07 0.36 0.27 0.60 0.44 0.50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well CROSS H CATTLE\n",
"\n",
"Model Report\n",
"-Accuracy: 0.361277\n",
"-Adjacent Accuracy: 0.878244\n",
"-F1 Score: 0.339461\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 31 112 15 158\n",
" CSiS 2 58 81 1 142\n",
" FSiS 5 39 1 2 47\n",
" SiSh 4 1 2 17 1 25\n",
" MS 4 3 17 1 3 28\n",
" WS 24 1 6 31\n",
" D 1 1 2\n",
" PS 4 4 1 27 5 27 68\n",
" BS 0\n",
"\n",
"Precision 0.94 0.32 0.27 0.20 0.00 0.28 0.12 0.66 0.00 0.53\n",
" Recall 0.20 0.41 0.83 0.04 0.00 0.77 0.50 0.40 0.00 0.36\n",
" F1 0.32 0.36 0.40 0.07 0.00 0.41 0.20 0.50 0.00 0.34\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well NOLAN\n",
"\n",
"Model Report\n",
"-Accuracy: 0.520482\n",
"-Adjacent Accuracy: 0.872289\n",
"-F1 Score: 0.541316\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 4 4\n",
" CSiS 15 85 17 1 118\n",
" FSiS 3 23 39 1 2 68\n",
" SiSh 1 7 3 11 1 5 28\n",
" MS 1 2 1 32 1 6 4 47\n",
" WS 1 1 11 12 5 30\n",
" D 1 2 1 4\n",
" PS 5 1 14 2 71 23 116\n",
" BS 0\n",
"\n",
"Precision 0.00 0.75 0.62 0.70 0.25 0.16 0.33 0.73 0.00 0.61\n",
" Recall 0.00 0.72 0.57 0.25 0.02 0.37 0.50 0.61 0.00 0.52\n",
" F1 0.00 0.73 0.60 0.37 0.04 0.22 0.40 0.67 0.00 0.54\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well Recruit F9\n",
"\n",
"Model Report\n",
"-Accuracy: 0.637500\n",
"-Adjacent Accuracy: 0.925000\n",
"-F1 Score: 0.778626\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 0\n",
" CSiS 0\n",
" FSiS 0\n",
" SiSh 0\n",
" MS 0\n",
" WS 0\n",
" D 0\n",
" PS 0\n",
" BS 1 5 4 19 51 80\n",
"\n",
"Precision 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00\n",
" Recall 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.64 0.64\n",
" F1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.78 0.78\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well NEWBY\n",
"\n",
"Model Report\n",
"-Accuracy: 0.494600\n",
"-Adjacent Accuracy: 0.892009\n",
"-F1 Score: 0.486147\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 0\n",
" CSiS 12 62 23 1 98\n",
" FSiS 1 36 43 80\n",
" SiSh 1 34 4 14 2 3 58\n",
" MS 3 2 8 4 11 28\n",
" WS 4 12 40 1 39 96\n",
" D 3 4 9 16\n",
" PS 1 10 45 56\n",
" BS 5 25 1 31\n",
"\n",
"Precision 0.00 0.63 0.62 0.85 0.00 0.50 0.36 0.34 1.00 0.57\n",
" Recall 0.00 0.63 0.54 0.59 0.00 0.42 0.25 0.80 0.03 0.49\n",
" F1 0.00 0.63 0.58 0.69 0.00 0.45 0.30 0.48 0.06 0.49\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/littleni/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
" 'recall', 'true', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"------------------------------------------------------\n",
"Validation on the leaving out well CHURCHMAN BIBLE\n",
"\n",
"Model Report\n",
"-Accuracy: 0.574257\n",
"-Adjacent Accuracy: 0.878713\n",
"-F1 Score: 0.548002\n",
"\n",
"Confusion Matrix Results\n",
" Pred SS CSiS FSiS SiSh MS WS D PS BS Total\n",
" True\n",
" SS 7 1 8\n",
" CSiS 3 31 21 1 56\n",
" FSiS 8 39 2 1 1 51\n",
" SiSh 1 7 5 13\n",
" MS 1 4 3 18 4 30\n",
" WS 13 65 9 87\n",
" D 2 7 7 2 16 34\n",
" PS 3 1 3 24 43 1 75\n",
" BS 2 1 5 42 50\n",
"\n",
"Precision 0.00 0.67 0.57 0.19 0.50 0.53 1.00 0.55 0.98 0.63\n",
" Recall 0.00 0.55 0.76 0.54 0.10 0.75 0.06 0.57 0.84 0.57\n",
" F1 0.00 0.61 0.66 0.29 0.17 0.62 0.11 0.56 0.90 0.55\n",
"\n",
"------------------------------------------------------\n",
"Final Results\n",
"-Average F1 Score: 0.549185\n"
]
}
],
"source": [
"# Load data \n",
"filename = '../facies_vectors.csv'\n",
"data = pd.read_csv(filename)\n",
"\n",
"# Change to category data type\n",
"data['Well Name'] = data['Well Name'].astype('category')\n",
"data['Formation'] = data['Formation'].astype('category')\n",
"\n",
"# Leave one well out for cross validation \n",
"well_names = data['Well Name'].unique()\n",
"f1=[]\n",
"for i in range(len(well_names)):\n",
" \n",
" # Split data for training and testing\n",
" X_train = data.drop(['Facies', 'Formation','Depth'], axis = 1 ) \n",
" Y_train = data['Facies' ] - 1\n",
" \n",
" train_X = X_train[X_train['Well Name'] != well_names[i] ]\n",
" train_Y = Y_train[X_train['Well Name'] != well_names[i] ]\n",
" test_X = X_train[X_train['Well Name'] == well_names[i] ]\n",
" test_Y = Y_train[X_train['Well Name'] == well_names[i] ]\n",
"\n",
" train_X = train_X.drop(['Well Name'], axis = 1 ) \n",
" test_X = test_X.drop(['Well Name'], axis = 1 )\n",
"\n",
" # Final recommended model based on the extensive parameters search\n",
" model_final = xgb.XGBClassifier( learning_rate =0.01, n_estimators=400, max_depth=5,\n",
" min_child_weight=1, gamma=0, subsample=0.6, reg_alpha=0.1, reg_lambda=0.5,\n",
" colsample_bytree=0.6, objective='multi:softmax',\n",
" nthread=4, scale_pos_weight=1, seed=100)\n",
"\n",
" # Train the model based on training data\n",
" model_final.fit( train_X , train_Y , eval_metric = 'merror' )\n",
"\n",
"\n",
" # Predict on the test set\n",
" predictions = model_final.predict(test_X)\n",
"\n",
" # Print report\n",
" print (\"\\n------------------------------------------------------\")\n",
" print (\"Validation on the leaving out well \" + well_names[i])\n",
" conf = confusion_matrix( test_Y, predictions, labels = np.arange(9) )\n",
" print (\"\\nModel Report\")\n",
" print (\"-Accuracy: %.6f\" % ( accuracy(conf) ))\n",
" print (\"-Adjacent Accuracy: %.6f\" % ( accuracy_adjacent(conf, adjacent_facies) ))\n",
" print (\"-F1 Score: %.6f\" % ( f1_score ( test_Y , predictions , labels = np.arange(9), average = 'weighted' ) ))\n",
" f1.append(f1_score ( test_Y , predictions , labels = np.arange(9), average = 'weighted' ))\n",
" facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
" 'WS', 'D','PS', 'BS']\n",
" print (\"\\nConfusion Matrix Results\")\n",
" from classification_utilities import display_cm, display_adj_cm\n",
" display_cm(conf, facies_labels,display_metrics=True, hide_zeros=True)\n",
" \n",
"print (\"\\n------------------------------------------------------\")\n",
"print (\"Final Results\")\n",
"print (\"-Average F1 Score: %6f\" % (sum(f1)/(1.0*len(f1))))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load test data\n",
"test_data = pd.read_csv('../validation_data_nofacies.csv')\n",
"test_data['Well Name'] = test_data['Well Name'].astype('category')\n",
"X_test = test_data.drop(['Formation', 'Well Name', 'Depth'], axis=1)\n",
"# Predict facies of unclassified data\n",
"Y_predicted = model_final.predict(X_test)\n",
"test_data['Facies'] = Y_predicted + 1\n",
"# Store the prediction\n",
"test_data.to_csv('Prediction1.csv')"
]
},
{
"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>Formation</th>\n",
" <th>Well Name</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" <th>Facies</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.0</td>\n",
" <td>66.276</td>\n",
" <td>0.630</td>\n",
" <td>3.300</td>\n",
" <td>10.650</td>\n",
" <td>3.591</td>\n",
" <td>1</td>\n",
" <td>1.000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.5</td>\n",
" <td>77.252</td>\n",
" <td>0.585</td>\n",
" <td>6.500</td>\n",
" <td>11.950</td>\n",
" <td>3.341</td>\n",
" <td>1</td>\n",
" <td>0.978</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.0</td>\n",
" <td>82.899</td>\n",
" <td>0.566</td>\n",
" <td>9.400</td>\n",
" <td>13.600</td>\n",
" <td>3.064</td>\n",
" <td>1</td>\n",
" <td>0.956</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.5</td>\n",
" <td>80.671</td>\n",
" <td>0.593</td>\n",
" <td>9.500</td>\n",
" <td>13.250</td>\n",
" <td>2.977</td>\n",
" <td>1</td>\n",
" <td>0.933</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2810.0</td>\n",
" <td>75.971</td>\n",
" <td>0.638</td>\n",
" <td>8.700</td>\n",
" <td>12.350</td>\n",
" <td>3.020</td>\n",
" <td>1</td>\n",
" <td>0.911</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2810.5</td>\n",
" <td>73.955</td>\n",
" <td>0.667</td>\n",
" <td>6.900</td>\n",
" <td>12.250</td>\n",
" <td>3.086</td>\n",
" <td>1</td>\n",
" <td>0.889</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2811.0</td>\n",
" <td>77.962</td>\n",
" <td>0.674</td>\n",
" <td>6.500</td>\n",
" <td>12.450</td>\n",
" <td>3.092</td>\n",
" <td>1</td>\n",
" <td>0.867</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2811.5</td>\n",
" <td>83.894</td>\n",
" <td>0.667</td>\n",
" <td>6.300</td>\n",
" <td>12.650</td>\n",
" <td>3.123</td>\n",
" <td>1</td>\n",
" <td>0.844</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2812.0</td>\n",
" <td>84.424</td>\n",
" <td>0.653</td>\n",
" <td>6.700</td>\n",
" <td>13.050</td>\n",
" <td>3.121</td>\n",
" <td>1</td>\n",
" <td>0.822</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2812.5</td>\n",
" <td>83.160</td>\n",
" <td>0.642</td>\n",
" <td>7.300</td>\n",
" <td>12.950</td>\n",
" <td>3.127</td>\n",
" <td>1</td>\n",
" <td>0.800</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2813.0</td>\n",
" <td>79.063</td>\n",
" <td>0.651</td>\n",
" <td>7.300</td>\n",
" <td>12.050</td>\n",
" <td>3.147</td>\n",
" <td>1</td>\n",
" <td>0.778</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2813.5</td>\n",
" <td>69.002</td>\n",
" <td>0.677</td>\n",
" <td>6.200</td>\n",
" <td>10.800</td>\n",
" <td>3.096</td>\n",
" <td>1</td>\n",
" <td>0.756</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2814.0</td>\n",
" <td>63.983</td>\n",
" <td>0.690</td>\n",
" <td>4.400</td>\n",
" <td>9.700</td>\n",
" <td>3.103</td>\n",
" <td>1</td>\n",
" <td>0.733</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2814.5</td>\n",
" <td>61.797</td>\n",
" <td>0.675</td>\n",
" <td>3.500</td>\n",
" <td>9.150</td>\n",
" <td>3.101</td>\n",
" <td>1</td>\n",
" <td>0.711</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2815.0</td>\n",
" <td>61.372</td>\n",
" <td>0.646</td>\n",
" <td>2.800</td>\n",
" <td>9.300</td>\n",
" <td>3.065</td>\n",
" <td>1</td>\n",
" <td>0.689</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2815.5</td>\n",
" <td>63.535</td>\n",
" <td>0.621</td>\n",
" <td>2.800</td>\n",
" <td>9.800</td>\n",
" <td>2.982</td>\n",
" <td>1</td>\n",
" <td>0.667</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2816.0</td>\n",
" <td>65.126</td>\n",
" <td>0.600</td>\n",
" <td>3.300</td>\n",
" <td>10.550</td>\n",
" <td>2.914</td>\n",
" <td>1</td>\n",
" <td>0.644</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2816.5</td>\n",
" <td>75.930</td>\n",
" <td>0.576</td>\n",
" <td>3.400</td>\n",
" <td>11.900</td>\n",
" <td>2.845</td>\n",
" <td>1</td>\n",
" <td>0.600</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2817.0</td>\n",
" <td>85.077</td>\n",
" <td>0.584</td>\n",
" <td>4.400</td>\n",
" <td>12.900</td>\n",
" <td>2.854</td>\n",
" <td>1</td>\n",
" <td>0.578</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2817.5</td>\n",
" <td>89.459</td>\n",
" <td>0.598</td>\n",
" <td>6.600</td>\n",
" <td>13.500</td>\n",
" <td>2.986</td>\n",
" <td>1</td>\n",
" <td>0.556</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2818.0</td>\n",
" <td>88.619</td>\n",
" <td>0.610</td>\n",
" <td>7.200</td>\n",
" <td>14.800</td>\n",
" <td>2.988</td>\n",
" <td>1</td>\n",
" <td>0.533</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2818.5</td>\n",
" <td>81.593</td>\n",
" <td>0.636</td>\n",
" <td>6.400</td>\n",
" <td>13.900</td>\n",
" <td>2.998</td>\n",
" <td>1</td>\n",
" <td>0.511</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2819.0</td>\n",
" <td>66.595</td>\n",
" <td>0.702</td>\n",
" <td>2.800</td>\n",
" <td>11.400</td>\n",
" <td>2.988</td>\n",
" <td>1</td>\n",
" <td>0.489</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2819.5</td>\n",
" <td>55.081</td>\n",
" <td>0.789</td>\n",
" <td>2.700</td>\n",
" <td>8.150</td>\n",
" <td>3.028</td>\n",
" <td>1</td>\n",
" <td>0.467</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2820.0</td>\n",
" <td>48.112</td>\n",
" <td>0.840</td>\n",
" <td>1.000</td>\n",
" <td>7.500</td>\n",
" <td>3.073</td>\n",
" <td>1</td>\n",
" <td>0.444</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2820.5</td>\n",
" <td>43.730</td>\n",
" <td>0.846</td>\n",
" <td>0.400</td>\n",
" <td>7.100</td>\n",
" <td>3.146</td>\n",
" <td>1</td>\n",
" <td>0.422</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2821.0</td>\n",
" <td>44.097</td>\n",
" <td>0.840</td>\n",
" <td>0.700</td>\n",
" <td>6.650</td>\n",
" <td>3.205</td>\n",
" <td>1</td>\n",
" <td>0.400</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2821.5</td>\n",
" <td>46.839</td>\n",
" <td>0.842</td>\n",
" <td>0.800</td>\n",
" <td>6.600</td>\n",
" <td>3.254</td>\n",
" <td>1</td>\n",
" <td>0.378</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2822.0</td>\n",
" <td>50.348</td>\n",
" <td>0.843</td>\n",
" <td>1.100</td>\n",
" <td>6.750</td>\n",
" <td>3.230</td>\n",
" <td>1</td>\n",
" <td>0.356</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2822.5</td>\n",
" <td>57.129</td>\n",
" <td>0.822</td>\n",
" <td>2.200</td>\n",
" <td>7.300</td>\n",
" <td>3.237</td>\n",
" <td>1</td>\n",
" <td>0.333</td>\n",
" <td>1</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",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3146.0</td>\n",
" <td>167.803</td>\n",
" <td>-0.219</td>\n",
" <td>4.270</td>\n",
" <td>23.370</td>\n",
" <td>3.810</td>\n",
" <td>2</td>\n",
" <td>0.190</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>801</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3146.5</td>\n",
" <td>151.183</td>\n",
" <td>-0.057</td>\n",
" <td>0.925</td>\n",
" <td>17.125</td>\n",
" <td>4.153</td>\n",
" <td>2</td>\n",
" <td>0.172</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>802</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3147.0</td>\n",
" <td>123.264</td>\n",
" <td>0.067</td>\n",
" <td>0.285</td>\n",
" <td>14.215</td>\n",
" <td>4.404</td>\n",
" <td>2</td>\n",
" <td>0.155</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>803</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3147.5</td>\n",
" <td>108.569</td>\n",
" <td>0.234</td>\n",
" <td>0.705</td>\n",
" <td>12.225</td>\n",
" <td>4.499</td>\n",
" <td>2</td>\n",
" <td>0.138</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>804</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3148.0</td>\n",
" <td>101.072</td>\n",
" <td>0.427</td>\n",
" <td>1.150</td>\n",
" <td>10.760</td>\n",
" <td>4.392</td>\n",
" <td>2</td>\n",
" <td>0.121</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>805</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3148.5</td>\n",
" <td>91.748</td>\n",
" <td>0.625</td>\n",
" <td>1.135</td>\n",
" <td>9.605</td>\n",
" <td>4.254</td>\n",
" <td>2</td>\n",
" <td>0.103</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>806</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3149.0</td>\n",
" <td>83.794</td>\n",
" <td>0.749</td>\n",
" <td>2.075</td>\n",
" <td>7.845</td>\n",
" <td>4.023</td>\n",
" <td>2</td>\n",
" <td>0.086</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>807</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3149.5</td>\n",
" <td>83.794</td>\n",
" <td>0.749</td>\n",
" <td>2.075</td>\n",
" <td>7.845</td>\n",
" <td>4.023</td>\n",
" <td>2</td>\n",
" <td>0.086</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>808</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3150.0</td>\n",
" <td>79.722</td>\n",
" <td>0.771</td>\n",
" <td>2.890</td>\n",
" <td>6.640</td>\n",
" <td>4.040</td>\n",
" <td>2</td>\n",
" <td>0.069</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>809</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3150.5</td>\n",
" <td>76.334</td>\n",
" <td>0.800</td>\n",
" <td>2.960</td>\n",
" <td>6.290</td>\n",
" <td>3.997</td>\n",
" <td>2</td>\n",
" <td>0.052</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>810</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3151.0</td>\n",
" <td>73.631</td>\n",
" <td>0.800</td>\n",
" <td>2.680</td>\n",
" <td>6.690</td>\n",
" <td>3.828</td>\n",
" <td>2</td>\n",
" <td>0.034</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>811</th>\n",
" <td>B5 LM</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3151.5</td>\n",
" <td>76.865</td>\n",
" <td>0.772</td>\n",
" <td>2.420</td>\n",
" <td>8.600</td>\n",
" <td>3.535</td>\n",
" <td>2</td>\n",
" <td>0.017</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>812</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3152.0</td>\n",
" <td>79.924</td>\n",
" <td>0.752</td>\n",
" <td>2.620</td>\n",
" <td>11.510</td>\n",
" <td>3.148</td>\n",
" <td>1</td>\n",
" <td>1.000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>813</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3152.5</td>\n",
" <td>82.199</td>\n",
" <td>0.728</td>\n",
" <td>3.725</td>\n",
" <td>14.555</td>\n",
" <td>2.964</td>\n",
" <td>1</td>\n",
" <td>0.972</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>814</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3153.0</td>\n",
" <td>79.953</td>\n",
" <td>0.700</td>\n",
" <td>5.610</td>\n",
" <td>16.930</td>\n",
" <td>2.793</td>\n",
" <td>1</td>\n",
" <td>0.944</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>815</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3153.5</td>\n",
" <td>75.881</td>\n",
" <td>0.673</td>\n",
" <td>6.300</td>\n",
" <td>17.570</td>\n",
" <td>2.969</td>\n",
" <td>1</td>\n",
" <td>0.917</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>816</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3154.0</td>\n",
" <td>67.470</td>\n",
" <td>0.652</td>\n",
" <td>4.775</td>\n",
" <td>15.795</td>\n",
" <td>3.282</td>\n",
" <td>1</td>\n",
" <td>0.889</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>817</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3154.5</td>\n",
" <td>58.832</td>\n",
" <td>0.640</td>\n",
" <td>4.315</td>\n",
" <td>13.575</td>\n",
" <td>3.642</td>\n",
" <td>1</td>\n",
" <td>0.861</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>818</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3155.0</td>\n",
" <td>57.946</td>\n",
" <td>0.631</td>\n",
" <td>3.595</td>\n",
" <td>11.305</td>\n",
" <td>3.893</td>\n",
" <td>1</td>\n",
" <td>0.833</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>819</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3155.5</td>\n",
" <td>65.755</td>\n",
" <td>0.625</td>\n",
" <td>3.465</td>\n",
" <td>10.355</td>\n",
" <td>3.911</td>\n",
" <td>1</td>\n",
" <td>0.806</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3156.0</td>\n",
" <td>69.445</td>\n",
" <td>0.617</td>\n",
" <td>3.390</td>\n",
" <td>11.540</td>\n",
" <td>3.820</td>\n",
" <td>1</td>\n",
" <td>0.778</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>821</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3156.5</td>\n",
" <td>73.389</td>\n",
" <td>0.608</td>\n",
" <td>3.625</td>\n",
" <td>12.775</td>\n",
" <td>3.620</td>\n",
" <td>1</td>\n",
" <td>0.750</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>822</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3157.0</td>\n",
" <td>77.115</td>\n",
" <td>0.605</td>\n",
" <td>4.140</td>\n",
" <td>13.420</td>\n",
" <td>3.467</td>\n",
" <td>1</td>\n",
" <td>0.722</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>823</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3157.5</td>\n",
" <td>79.840</td>\n",
" <td>0.596</td>\n",
" <td>4.875</td>\n",
" <td>13.825</td>\n",
" <td>3.360</td>\n",
" <td>1</td>\n",
" <td>0.694</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>824</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3158.0</td>\n",
" <td>82.616</td>\n",
" <td>0.577</td>\n",
" <td>5.235</td>\n",
" <td>14.845</td>\n",
" <td>3.207</td>\n",
" <td>1</td>\n",
" <td>0.667</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>825</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3158.5</td>\n",
" <td>86.078</td>\n",
" <td>0.554</td>\n",
" <td>5.040</td>\n",
" <td>16.150</td>\n",
" <td>3.161</td>\n",
" <td>1</td>\n",
" <td>0.639</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>826</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3159.0</td>\n",
" <td>88.855</td>\n",
" <td>0.539</td>\n",
" <td>5.560</td>\n",
" <td>16.750</td>\n",
" <td>3.118</td>\n",
" <td>1</td>\n",
" <td>0.611</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>827</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3159.5</td>\n",
" <td>90.490</td>\n",
" <td>0.530</td>\n",
" <td>6.360</td>\n",
" <td>16.780</td>\n",
" <td>3.168</td>\n",
" <td>1</td>\n",
" <td>0.583</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>828</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3160.0</td>\n",
" <td>90.975</td>\n",
" <td>0.522</td>\n",
" <td>7.035</td>\n",
" <td>16.995</td>\n",
" <td>3.154</td>\n",
" <td>1</td>\n",
" <td>0.556</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>829</th>\n",
" <td>C SH</td>\n",
" <td>CRAWFORD</td>\n",
" <td>3160.5</td>\n",
" <td>90.108</td>\n",
" <td>0.513</td>\n",
" <td>7.505</td>\n",
" <td>17.595</td>\n",
" <td>3.125</td>\n",
" <td>1</td>\n",
" <td>0.528</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>830 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND PE \\\n",
"0 A1 SH STUART 2808.0 66.276 0.630 3.300 10.650 3.591 \n",
"1 A1 SH STUART 2808.5 77.252 0.585 6.500 11.950 3.341 \n",
"2 A1 SH STUART 2809.0 82.899 0.566 9.400 13.600 3.064 \n",
"3 A1 SH STUART 2809.5 80.671 0.593 9.500 13.250 2.977 \n",
"4 A1 SH STUART 2810.0 75.971 0.638 8.700 12.350 3.020 \n",
"5 A1 SH STUART 2810.5 73.955 0.667 6.900 12.250 3.086 \n",
"6 A1 SH STUART 2811.0 77.962 0.674 6.500 12.450 3.092 \n",
"7 A1 SH STUART 2811.5 83.894 0.667 6.300 12.650 3.123 \n",
"8 A1 SH STUART 2812.0 84.424 0.653 6.700 13.050 3.121 \n",
"9 A1 SH STUART 2812.5 83.160 0.642 7.300 12.950 3.127 \n",
"10 A1 SH STUART 2813.0 79.063 0.651 7.300 12.050 3.147 \n",
"11 A1 SH STUART 2813.5 69.002 0.677 6.200 10.800 3.096 \n",
"12 A1 SH STUART 2814.0 63.983 0.690 4.400 9.700 3.103 \n",
"13 A1 SH STUART 2814.5 61.797 0.675 3.500 9.150 3.101 \n",
"14 A1 SH STUART 2815.0 61.372 0.646 2.800 9.300 3.065 \n",
"15 A1 SH STUART 2815.5 63.535 0.621 2.800 9.800 2.982 \n",
"16 A1 SH STUART 2816.0 65.126 0.600 3.300 10.550 2.914 \n",
"17 A1 SH STUART 2816.5 75.930 0.576 3.400 11.900 2.845 \n",
"18 A1 SH STUART 2817.0 85.077 0.584 4.400 12.900 2.854 \n",
"19 A1 SH STUART 2817.5 89.459 0.598 6.600 13.500 2.986 \n",
"20 A1 SH STUART 2818.0 88.619 0.610 7.200 14.800 2.988 \n",
"21 A1 SH STUART 2818.5 81.593 0.636 6.400 13.900 2.998 \n",
"22 A1 SH STUART 2819.0 66.595 0.702 2.800 11.400 2.988 \n",
"23 A1 SH STUART 2819.5 55.081 0.789 2.700 8.150 3.028 \n",
"24 A1 SH STUART 2820.0 48.112 0.840 1.000 7.500 3.073 \n",
"25 A1 SH STUART 2820.5 43.730 0.846 0.400 7.100 3.146 \n",
"26 A1 SH STUART 2821.0 44.097 0.840 0.700 6.650 3.205 \n",
"27 A1 SH STUART 2821.5 46.839 0.842 0.800 6.600 3.254 \n",
"28 A1 SH STUART 2822.0 50.348 0.843 1.100 6.750 3.230 \n",
"29 A1 SH STUART 2822.5 57.129 0.822 2.200 7.300 3.237 \n",
".. ... ... ... ... ... ... ... ... \n",
"800 B5 LM CRAWFORD 3146.0 167.803 -0.219 4.270 23.370 3.810 \n",
"801 B5 LM CRAWFORD 3146.5 151.183 -0.057 0.925 17.125 4.153 \n",
"802 B5 LM CRAWFORD 3147.0 123.264 0.067 0.285 14.215 4.404 \n",
"803 B5 LM CRAWFORD 3147.5 108.569 0.234 0.705 12.225 4.499 \n",
"804 B5 LM CRAWFORD 3148.0 101.072 0.427 1.150 10.760 4.392 \n",
"805 B5 LM CRAWFORD 3148.5 91.748 0.625 1.135 9.605 4.254 \n",
"806 B5 LM CRAWFORD 3149.0 83.794 0.749 2.075 7.845 4.023 \n",
"807 B5 LM CRAWFORD 3149.5 83.794 0.749 2.075 7.845 4.023 \n",
"808 B5 LM CRAWFORD 3150.0 79.722 0.771 2.890 6.640 4.040 \n",
"809 B5 LM CRAWFORD 3150.5 76.334 0.800 2.960 6.290 3.997 \n",
"810 B5 LM CRAWFORD 3151.0 73.631 0.800 2.680 6.690 3.828 \n",
"811 B5 LM CRAWFORD 3151.5 76.865 0.772 2.420 8.600 3.535 \n",
"812 C SH CRAWFORD 3152.0 79.924 0.752 2.620 11.510 3.148 \n",
"813 C SH CRAWFORD 3152.5 82.199 0.728 3.725 14.555 2.964 \n",
"814 C SH CRAWFORD 3153.0 79.953 0.700 5.610 16.930 2.793 \n",
"815 C SH CRAWFORD 3153.5 75.881 0.673 6.300 17.570 2.969 \n",
"816 C SH CRAWFORD 3154.0 67.470 0.652 4.775 15.795 3.282 \n",
"817 C SH CRAWFORD 3154.5 58.832 0.640 4.315 13.575 3.642 \n",
"818 C SH CRAWFORD 3155.0 57.946 0.631 3.595 11.305 3.893 \n",
"819 C SH CRAWFORD 3155.5 65.755 0.625 3.465 10.355 3.911 \n",
"820 C SH CRAWFORD 3156.0 69.445 0.617 3.390 11.540 3.820 \n",
"821 C SH CRAWFORD 3156.5 73.389 0.608 3.625 12.775 3.620 \n",
"822 C SH CRAWFORD 3157.0 77.115 0.605 4.140 13.420 3.467 \n",
"823 C SH CRAWFORD 3157.5 79.840 0.596 4.875 13.825 3.360 \n",
"824 C SH CRAWFORD 3158.0 82.616 0.577 5.235 14.845 3.207 \n",
"825 C SH CRAWFORD 3158.5 86.078 0.554 5.040 16.150 3.161 \n",
"826 C SH CRAWFORD 3159.0 88.855 0.539 5.560 16.750 3.118 \n",
"827 C SH CRAWFORD 3159.5 90.490 0.530 6.360 16.780 3.168 \n",
"828 C SH CRAWFORD 3160.0 90.975 0.522 7.035 16.995 3.154 \n",
"829 C SH CRAWFORD 3160.5 90.108 0.513 7.505 17.595 3.125 \n",
"\n",
" NM_M RELPOS Facies \n",
"0 1 1.000 2 \n",
"1 1 0.978 3 \n",
"2 1 0.956 2 \n",
"3 1 0.933 2 \n",
"4 1 0.911 2 \n",
"5 1 0.889 2 \n",
"6 1 0.867 2 \n",
"7 1 0.844 2 \n",
"8 1 0.822 2 \n",
"9 1 0.800 2 \n",
"10 1 0.778 2 \n",
"11 1 0.756 2 \n",
"12 1 0.733 2 \n",
"13 1 0.711 2 \n",
"14 1 0.689 2 \n",
"15 1 0.667 2 \n",
"16 1 0.644 2 \n",
"17 1 0.600 2 \n",
"18 1 0.578 2 \n",
"19 1 0.556 2 \n",
"20 1 0.533 2 \n",
"21 1 0.511 2 \n",
"22 1 0.489 2 \n",
"23 1 0.467 1 \n",
"24 1 0.444 2 \n",
"25 1 0.422 1 \n",
"26 1 0.400 1 \n",
"27 1 0.378 1 \n",
"28 1 0.356 1 \n",
"29 1 0.333 1 \n",
".. ... ... ... \n",
"800 2 0.190 8 \n",
"801 2 0.172 8 \n",
"802 2 0.155 8 \n",
"803 2 0.138 8 \n",
"804 2 0.121 8 \n",
"805 2 0.103 8 \n",
"806 2 0.086 6 \n",
"807 2 0.086 6 \n",
"808 2 0.069 6 \n",
"809 2 0.052 8 \n",
"810 2 0.034 8 \n",
"811 2 0.017 8 \n",
"812 1 1.000 2 \n",
"813 1 0.972 3 \n",
"814 1 0.944 3 \n",
"815 1 0.917 3 \n",
"816 1 0.889 2 \n",
"817 1 0.861 2 \n",
"818 1 0.833 2 \n",
"819 1 0.806 2 \n",
"820 1 0.778 2 \n",
"821 1 0.750 2 \n",
"822 1 0.722 2 \n",
"823 1 0.694 2 \n",
"824 1 0.667 2 \n",
"825 1 0.639 2 \n",
"826 1 0.611 2 \n",
"827 1 0.583 3 \n",
"828 1 0.556 2 \n",
"829 1 0.528 3 \n",
"\n",
"[830 rows x 11 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Future work, make more customerized objective function. Also, we could use RandomizedSearchCV instead of GridSearchCV to avoild potential local minimal trap and further improve the test results. "
]
}
],
"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
}
| apache-2.0 |
laurentperrinet/elasticite | posts/2015-11-29 élasticité - scénario onde.ipynb | 1 | 2730398 | null | mit |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/TheisWellFunction.ipynb | 1 | 192601 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Investigating the character of the Theis well function\n",
"\n",
"## Introduction\n",
"\n",
"In the previous section the Theis well function was introduced. The function, which is in fact the function known as exponential integral by mathematicians, proved available in the standard library of Ptyhon module scipy.special. We modified it a little to make it match the Theis well function exactly and gave it the name \"W\" like it has in groundwater hydrology books. Then we used it in some examples.\n",
"\n",
"In this chapter we will investigate the Theis well funchtion character a more accurately."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of looking for the function in the available library we could have computed the function ourselfs, for instance by numerical integration.\n",
"\n",
"$$ W(u) = \\intop_u^{-\\infty} \\frac {e^{-y}} y dy \\approx \\sum_0^N \\frac {e^{-y_i}} {y_i} \\Delta y_i $$\n",
"\n",
"where $y_0 = u_0$ and $N$ has a a sufficiently large value."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"import scipy.special as sp"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.special import expi\n",
"\n",
"def W(u): return -expi(-u)\n",
"\n",
"def W1(u):\n",
" \"\"\"Returns Theis' well function axpproximation by numerical intergration\n",
" \n",
" Works only for scalar u\n",
" \"\"\"\n",
" if not np.isscalar(u):\n",
" raise ValueError(\"\",\"u must be a scalar\")\n",
" \n",
" LOG10INF = 2 # sufficient as exp(-100) is in the order of 1e-50\n",
" y = np.logspace(np.log10(u), LOG10INF, 1000) # we use thousand intermediate values\n",
" ym = 0.5 * (y[:-1] + y[1:])\n",
" Dy = np.diff(y)\n",
" w = np.sum( np.exp(-ym) / ym * Dy )\n",
" return w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try it out"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" u W(u) W1(u) \n",
" 4.0e+00 3.7794e-03 3.7793e-03\n",
" 4.0e-01 7.0238e-01 7.0238e-01\n",
" 4.0e-02 2.6813e+00 2.6812e+00\n",
" 4.0e-03 4.9482e+00 4.9482e+00\n",
" 4.0e-04 7.2472e+00 7.2471e+00\n",
" 4.0e-05 9.5495e+00 9.5493e+00\n",
" 4.0e-06 1.1852e+01 1.1852e+01\n",
" 4.0e-07 1.4155e+01 1.4154e+01\n",
" 4.0e-08 1.6457e+01 1.6456e+01\n",
" 4.0e-09 1.8760e+01 1.8759e+01\n",
" 4.0e-10 2.1062e+01 2.1061e+01\n"
]
}
],
"source": [
"U = 4 * 10** -np.arange(11.) # generates values 4, 4e-1, 4e-2 .. 4e-10\n",
"print(\"{:>10s} {:>10s} {:>10s}\".format('u ', 'W(u)','W1(u) '))\n",
"for u in U:\n",
" print(\"{0:10.1e} {1:10.4e} {2:10.4e}\".format(u, W(u), W1(u)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is seems that our numerical integration is a fair approximation to four significant digits, but not better, even when computed with 1000 steps as we did. So it is relatively easy to create one's own numerically computed value of an analytical expression like the exponential integral"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Theis well function as a power series\n",
"\n",
"The theis well function can be expressed also as a power series. This expression has certain advanages as it gives insight into the behavior of its character and allows important simplifications and deductions.\n",
"\n",
"$$ W(u) = -0.5773 - \\ln(u) + u - \\frac {u^2} {2 . 2!} + \\frac {u^3} {3 . 3!} - \\frac {u^4} {4 . 4!} + ... $$\n",
"\n",
"This series too can be readily numerially comptuted by first defining a function for it. The sum will be computed in a loop. To prevent having to compute faculties, it is easiest to compute each successive term from the previous one.\n",
"\n",
"So to get from term m to term n+1:\n",
"\n",
"$$ \\frac {u^{n+1}} {(n+1) . (n+1)!} = \\frac {u^n} { n . n!} \\times \\frac {u \\, n} {(n+1)^2} $$\n",
"\n",
"This series is implemented below."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"def W2(u):\n",
" \"\"\"Returns Theis well function computed as a power series\"\"\"\n",
" tol = 1e-5\n",
" w = -0.5772 -np.log(u) + u\n",
" a = u\n",
" for n in range(1, 100):\n",
" a = -a * u * n / (n+1)**2 # new term (next term)\n",
" w += a\n",
" if np.all(a) < tol:\n",
" return w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare the three methods of computing the well function."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" u W(u) W1(u) W2(u) \n",
" 4.0e+00 3.7794e-03 3.7793e-03 3.7793e-03\n",
" 4.0e-01 7.0238e-01 7.0238e-01 7.0238e-01\n",
" 4.0e-02 2.6813e+00 2.6812e+00 2.6812e+00\n",
" 4.0e-03 4.9482e+00 4.9482e+00 4.9482e+00\n",
" 4.0e-04 7.2472e+00 7.2471e+00 7.2471e+00\n",
" 4.0e-05 9.5495e+00 9.5493e+00 9.5493e+00\n",
" 4.0e-06 1.1852e+01 1.1852e+01 1.1852e+01\n",
" 4.0e-07 1.4155e+01 1.4154e+01 1.4154e+01\n",
" 4.0e-08 1.6457e+01 1.6456e+01 1.6456e+01\n",
" 4.0e-09 1.8760e+01 1.8759e+01 1.8759e+01\n",
" 4.0e-10 2.1062e+01 2.1061e+01 2.1061e+01\n"
]
}
],
"source": [
"U = 4.0 * 10** -np.arange(11.) # generates values 4, 4e-1, 4e-2 .. 4e-10\n",
"print(\"{:>10s} {:>10s} {:>10s} {:>10s}\".format('u ', 'W(u) ','W1(u) ', 'W2(u) '))\n",
"for u in U:\n",
" print(\"{0:10.1e} {1:10.4e} {2:10.4e} {2:10.4e}\".format(u, W(u), W1(u), W2(u)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that all three methods yiedld the same results.\n",
"\n",
"Next we show the well function as it shown in groundwater hydrology books."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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3fQ7DZ9AgeGN/ePBBGDLENTE77dRyHTZ41xjHmhRjTEHLVQMj4lbZPfFEOPHE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\nv1DVkLi1dSKneU6bAu+LyCd4lldYrKr5Tsd6Jk73vRtuTjPPFsD6svP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"text/plain": [
"<matplotlib.figure.Figure at 0x110630080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u = np.logspace(-7, 1, 71)\n",
"\n",
"import matplotlib.pylab as plt\n",
"fig1= plt.figure()\n",
"ax1 = fig1.add_subplot(111)\n",
"ax1.set(xlabel='1/u', ylabel='W(u)', title='Theis type curve versus u', yscale='log', xscale='log')\n",
"ax1.grid(True)\n",
"ax1.plot(u, W(u), 'b', label='-expi(-u)')\n",
"#ax1.plot(u, W1(u), 'rx', label='integal') # works only for scalars\n",
"ax1.plot(u, W2(u), 'g+', label='power series')\n",
"ax1.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The curve W(u) versus u runs counter intuitively which and is, therefore, confusing. Therefore, it generally presented as W(u) versus 1/u instead as shown below"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ibR4CjjCzkWa2I/A+YF612550S5YsibsJQLjT8IIFYcjw0UeH++j84Aehg+y/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1107a7f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2 = plt.figure()\n",
"ax2 = fig2.add_subplot(111)\n",
"ax2.set(xlabel='1/u', ylabel='W(u)', title='Theis type curve versus 1/u', yscale='log', xscale='log')\n",
"ax2.grid(True)\n",
"ax2.plot(1/u, W(u))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now W(u) resembles the actual drawdown, which increases with time.\n",
"\n",
"The reason that this is so, becomes clear from the fact that\n",
"\n",
"$$ u = \\frac {r^2 S} {4 kD t} $$\n",
"\n",
"and that\n",
"\n",
"$$ \\frac 1 u = \\frac {4 kDt} {r^2 S} = \\frac {4 kD} S \\frac t {r^2} $$\n",
"\n",
"which shows that $\\frac 1 u$ increases with time, so that the values of $\\frac 1 u$ on the $\\frac 1 u$ axis are propotional with time and so the drawdown, i.e., the well function $W(u)$ increases with $\\frac 1 u$, which is less confusing.\n",
"\n",
"The graph of $W(u)$ versus $\\frac 1 u$ is called the Theis type curve. It's vertical axis is proportional to the drawdown and its horizontal axis proportional to time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same curve is shown below but now on linear vertical scale and a logarithmic horizontal scale. The vertical scale was reversed (see values on y-axis) to obtain a curve that illustrates the decline of groundwater head with time caused by the extraction. This way of presending is probably least confusing when reading the curve."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Tgy4hb+KStSE599zTj/s45BA45hg491xYty6LxWWRzme0KKdkSo1HyJWWZv3y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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11145b828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2 = plt.figure()\n",
"ax2 = fig2.add_subplot(111)\n",
"ax2.set(xlabel='1/u', ylabel='W(u)', title='Theis type curve versus 1/u', yscale='linear', xscale='log')\n",
"ax2.grid(True)\n",
"ax2.plot(1/u, W(u))\n",
"ax2.invert_yaxis()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logarithmic approximaion of the Theis type curve\n",
"We see that after some time, the drawdown is linear when only the time-axis is logarithmic. This suggests that a logarithmic approximation of time-drawdown curve is accurate after some time.\n",
"\n",
"That this is indeed the case can be deduced from the power series description of the type curve:\n",
"\n",
"$$ W(u) = -0.5773 - \\ln(u) + u - \\frac {u^2} {2 . 2!} + \\frac {u^3} {3 . 3!} - \\frac {u^4} {4 . 4!} + ... $$\n",
"\n",
"It is clear that all terms to the right of u will be smaller than u when $u<1$. Hence when u is so small that it can be neglected relative to $\\ln(u)$, then all the terms to the right of $\\ln(u)$ can be neglected. Therefore we have the following spproximation\n",
"\n",
"$$ W(u) \\approx -0.5772 -\\ln(u) + O(u) $$\n",
"\n",
"for\n",
"\n",
"$$ -\\ln(u)>>u \\,\\,\\,\\rightarrow \\,\\,\\, \\ln(u)<<-u \\,\\,\\, \\rightarrow \\,\\,\\, u<<e^{-u} \\, \\approx \\,1 $$\n",
"\n",
"which is practically the case for $u<0.01$, as can also be seen in the graph for $1/u = 10^2 $. From the graph one may conclude that even for 1/u>10 or u<0.1, the logarithmic type curve is straight and therefore can be accurately computed using a logarithmic approximation of the type curve.\n",
"\n",
"Below the error between the full Theis curve $W(u)$ and the approximation $Wu(u) = -0.5772 - \\ln(u)$ are computed and shown. This reveals that at $u=0.01$ the error is 5.4% and at $u=0.001$ it has come down to only 0.2%."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" u W(u) Wa(u) 1-Wa(u)/W(u)\n",
" the error\n",
" 0.01 4.04 4.03 0.2%\n",
" 0.0126 3.81 3.8 0.3%\n",
" 0.0158 3.58 3.57 0.4%\n",
" 0.02 3.36 3.34 0.6%\n",
" 0.0251 3.13 3.11 0.8%\n",
" 0.0316 2.91 2.88 1.1%\n",
" 0.0398 2.69 2.65 1.5%\n",
" 0.0501 2.47 2.42 2.0%\n",
" 0.0631 2.25 2.19 2.8%\n",
" 0.0794 2.03 1.96 3.8%\n",
" 0.1 1.82 1.73 5.4%\n",
" 0.126 1.62 1.5 7.5%\n",
" 0.158 1.42 1.26 10.8%\n",
" 0.2 1.22 1.03 15.5%\n",
" 0.251 1.04 0.804 22.7%\n",
" 0.316 0.867 0.574 33.8%\n",
" 0.398 0.706 0.344 51.3%\n",
" 0.501 0.558 0.114 79.7%\n",
" 0.631 0.427 -0.117 127.3%\n",
" 0.794 0.314 -0.347 210.6%\n",
" 1 0.219 -0.577 363.1%\n"
]
}
],
"source": [
"U = np.logspace(-2, 0, 21)\n",
"\n",
"Wa = lambda u : -0.5772 - np.log(u)\n",
"\n",
"print(\"{:>12s} {:>12s} {:>12s} {:>12s}\".format('u','W(u)','Wa(u)','1-Wa(u)/W(u)'))\n",
"print(\"{:>12s} {:>12s} {:>12s} {:>12s}\".format(' ',' ',' ','the error'))\n",
"for u in U:\n",
" print(\"{:12.3g} {:12.3g} {:12.3g} {:12.1%}\".format(u, W(u), Wa(u), 1-Wa(u)/W(u)))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5WLJkic9A0dS0pZeU1kZJKR4jLCzMZ3rPnj157LHH+OCDDxg6dCjz5s2jePHi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\nGhARERHx3f8HUYtleR3z/HsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111ba0860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"U = np.logspace(-7, 1, 81)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.set(xlabel='1/u', ylabel='W(u)', title='Theis type curve and its logarithmic approximation', yscale='linear', xscale='log')\n",
"ax.grid(True)\n",
"ax.plot(1/U, W(U), 'b', linewidth = 2., label='Theis type curve')\n",
"ax.plot(1/U, Wa(U), 'r', linewidth = 0.25, label='log approximation')\n",
"ax.invert_yaxis()\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hence, in any practical situation, the logarithmic approximation is accurate enough when $u<0.01$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximatin of the Theis type curve can no be elaborated:\n",
"\n",
"$$ Wa (u) \\approx -0.5772 - \\ln(u) = \\ln(e^{-0.5772}) - \\ln(u) = \\ln(0.5615) - \\ln(u) = \\ln \\frac {0.5615} {u} $$\n",
"\n",
"Because $u = \\frac {r^2 S} {4 kD t}$ we have, with 4\\times 0.5615 \\approx 2.25\n",
"\n",
"$$ W(u) \\approx \\ln \\frac {2.25 kD t} {r^2 S} $$\n",
"\n",
"and so the drawdown approximation becomes\n",
"\n",
"$$ s \\approx \\frac Q {4 \\pi kD} \\ln \\frac {2.25 kD t} {r^2 S} $$\n",
"\n",
"The condition u<0.1 can be translated to $\\frac {r^2 S} {4 kD t} < 0.1$ or\n",
"\n",
"$$\\frac t {r^2} > 2.5 \\frac {S} {kD}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Radius of influence\n",
"\n",
"The previous logarithmic drawdown type curve versus $1/u$ can be seen an image of the drawdown for a fixed distance and varying time. This is because $1/u$ is proportional to the real time. On the other hand, the drawdown type curve versus u may be regarded as the drawdown at a fixed time for varying distance. This follows from\n",
"s versus u is\n",
"\n",
"$$ W(u)\\approx \\ln \\frac {2.25 kD t} { r^2 S} \\,\\,\\,\\, versus\\,\\,\\,\\, u = \\ln \\frac {r^2 S} {4 kD t} = 2 \\ln \\left( \\frac {S} {4 kD t} r\\right) $$\n",
"\n",
"That is, proportional r on log scale. The plot reveals this:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5XCxdutRnoGh62jJLWmujpBWPUbhwYZ/pvXv35vHHH+c///kPI0aMYO7cuZQs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\nOTUgIiIiEjk1ICIiIhI5NSAiIiISuf8P6Fqn4n7E634AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1107999e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"U = np.logspace(-7, 1, 81)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.set(xlabel='u', ylabel='W(u)', title='Theis type curve and its logarithmic approximation', yscale='linear', xscale='log')\n",
"ax.grid(True)\n",
"ax.plot(U, W(U), 'b', linewidth = 2., label='Theis type curve')\n",
"ax.plot(U, Wa(U), 'r', linewidth = 0.25, label='log approximation')\n",
"ax.invert_yaxis()\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows that the radius of influence is limited. We can now approximate this radius of influence by saying that the radius is where the appoximated Theis curve, that is the straight red line in the graph intersects the zero drawdown, i.e. $W(u) = 0$.\n",
"\n",
"Hence, for the radius of influence, R, we have\n",
"\n",
"$$ \\ln \\frac {2.25 kD t} {R^2 S} = 0 $$\n",
"\n",
"impying that\n",
"\n",
"$$ \\frac {2.25 kD t } { R^2 S } = 1 $$\n",
"\n",
"$$ R =\\sqrt { \\frac {2.25 kD t} D} $$\n",
"\n",
"with R the radius of influence. Computing the radius of influence is an easy way to determine how far out the drawdown affects the groundwater heads."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pumping test\n",
"\n",
"### Introduction\n",
"Below are the data given that were obtained from a pumping test carried out on the site \"Oude Korendijk\" south of Rotterdam in the Netherlands (See Kruseman and De Ridder, p56, 59). The piezometers are all open at 20 m below ground surface. The groundwater head is shallow, within a m from ground surface. The first18 m below ground surface consist of clay,peat and clayey fine sand. These layers form a practially impermeable confining unit. Below this, between 18 and25 m below ground surface are 7 m of sand an some gravel, that form the aquifer. Fine sandy and clayey sediments thereunder from the base of the aquifer, which is considered impermeable.\n",
"Piezometers wer installed at 30, 90 and 215 m from the well, open at 20 m below ground surface. The well has its screen installed over the whole thickness of the aquifer. We consider the aquifer as confined with no leakage. But we should look with a critical eye that the drawdown curves to verify to what extent this assumption holds true.\n",
"\n",
"The drawdown data for the three piezometers is given below. The first column is time after the start of the pump in minutes; the second column is the drawdown in m.\n",
"\n",
"The well extracts 788 m3/d\n",
"\n",
"The objective of the pumping test is to determine the properties kD and S of the aquifer.\n",
"\n",
"### The data:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# t[min], s[m]\n",
"H30 = [ [0.0, 0.0],\n",
" [0.1, 0.04],\n",
" [0.25, 0.08],\n",
" [0.50, 0.13],\n",
" [0.70, 0.18],\n",
" [1.00, 0.23],\n",
" [1.40, 0.28],\n",
" [1.90, 0.33],\n",
" [2.33, 0.36],\n",
" [2.80, 0.39],\n",
" [3.36, 0.42],\n",
" [4.00, 0.45],\n",
" [5.35, 0.50],\n",
" [6.80, 0.54],\n",
" [8.30, 0.57],\n",
" [8.70, 0.58],\n",
" [10.0, 0.60],\n",
" [13.1, 0.64]]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# t[min], s[m]\n",
"H90= [[0.0, 0.0],\n",
" [1.5, 0.015],\n",
" [2.0, 0.021],\n",
" [2.16, 0.23],\n",
" [2.66, 0.044],\n",
" [3.00, 0.054],\n",
" [3.50, 0.075],\n",
" [4.00, 0.090],\n",
" [4.33, 0.104],\n",
" [5.50, 0.133],\n",
" [6.0, 0.154],\n",
" [7.5, 0.178],\n",
" [9.0, 0.206],\n",
" [13.0, 0.250],\n",
" [15.0, 0.275],\n",
" [18.0, 0.305],\n",
" [25.0, 0.348],\n",
" [30.0, 0.364]]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# t[min], s[m]\n",
"H215=[[0.0, 0.0],\n",
" [66.0, 0.089],\n",
" [127., 0.138],\n",
" [185., 0.165],\n",
" [251., 0.186]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### To work out the test:\n",
"\n",
"1. Show the drawdown data on half-log time scale for the three piezometers.\n",
"\n",
"2. What do you expect these curves to look like? Do the drawdown lines become parallel after an initial time?\n",
"\n",
"3. Use the simplified drawdown formula to interpret the test.\n",
" 1. Look where the simplified drawdown curves become zero.\n",
" 2. Determine the drawdown increase per log-cycle of time.\n",
"4 From this information determine the transmissivity and the storage coefficient.\n",
"\n",
"1. Show the drawdown $s$ versus time on a double log graph for all three piezometers\n",
"2. Show the drawdown $s$ versus t for all three piezometers\n",
"2. Show the drawdown $s$ versus $ t/r^2 $, also for all three piezometers.\n",
"\n",
"1. What is the difference between the last two graphs (versus $t/r^2$ instead of versus $t$) ?\n",
"\n",
"3. Match the computed drawdown by plotting it on the same graph and adapting the transmissivity and the storage coefficient.\n",
"\n",
"For your analysis, write the computed drawdown as follows:\n",
"\n",
"$$ s = A\\times W(u \\times B) $$\n",
"\n",
"Then adapting A willshift the entire graph vertically, while adaptin B will shift it horizontally. This makes it easy to lay the computed curve on the data points.\n",
"\n",
"4. By adapting A and B determine their numerical values.\n",
"\n",
"5. Determine the transmissivity kD and the storage coefficient S from A and B.\n",
"\n",
"**Hint**:\n",
"Having determined A and B compute kD and S by matching this formula with the true Theis drawdown which is\n",
"\n",
"$$ s = \\frac Q {4 \\pi kD} W(\\frac {r^2 S} { 4 kD t}) $$\n",
"\n",
"That is, to make both equations equal, set\n",
"$$ A = \\frac Q {4 \\pi kD} $$\n",
"and set\n",
"$$ \\frac 1 {u\\, B} = \\frac {4 kD} S \\frac t {r^2} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"1. Consider an aquifer with constant transmissivity kD = 900 m2/d, phreatic storage coefficient S with a well that extracts Q = 2400 m3/d.\n",
" Use distances between 1 and 1000 m\n",
" Use times between 0.01 and 100 days\n",
"1. Show the drawdown as a function of time for different distances where the drawdown is on linear scale and the time at logarithmic scale.\n",
" Plot both the full Theis drawdown and the approximation.\n",
"1. Show the drawdown as a function of distance for different times where the drawdown is at linear scale and the distance is at logarithmic scale.\n",
" Plot both the full Theis drawdown and the approximation.\n",
"1. Also show the radius of influence for the different times on the drawdown versus distance curve. Just show them as red dots at s=0 and r the radius of influence.\n",
"1. A well is installed in a desert are where there are no fixed head boundaries. The well is 60 m deep and the aquifer is 80 m thick. The top of the well screen is at 40 m below ground surface and the phreatic water level at 15 m. Test pumping has revealed that the $kD = 600$ m2/d and the specific yield $Sy = 0.25$.\n",
" As a first approximation ignore that the fact that the aquifer thickness gets less because of the falling water table caused by pumping.\n",
" 1. How much can we pump until the head drops to halfway the screen in 3 years?\n",
" 1. How much if it were 30 years?\n",
" 1. How much could we pump over the same period had we drilled three of the same wells on one line at 250 m mutual distance?\n",
"1. At an historic site the excavations need to be carried out in the dry. The water levels have gone up over the last 40 years due to a large reservoir that had been installed in the river by building a dam downstream. The water level needs to be lowerd by 10 m over a square are with sides of 30 m. The transmissivity is around 1200 m2/d according to test pumping and the specific yield about $Sy = 24% $.\n",
" 1. Determine the pump capacity if 4 wells are used in the corners of the exacvation which needs to be dry only two weeks after they are installed.\n",
" 1. The excavations will last for 1 year. What will be the required pump capacity after this year?"
]
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GoogleCloudPlatform/analytics-componentized-patterns | retail/recommendation-system/bqml-scann/05_deploy_lookup_and_scann_caip.ipynb | 1 | 23303 | {"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"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":"3.7.8"},"colab":{"name":"05_deploy_lookup_and_scann_caip.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"59DnLe66_RSq"},"source":["# Part 5: Deploy the solution to AI Platform Prediction\n","\n","This notebook is the fifth of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to complete the following tasks:\n","\n","1. Deploy the embedding lookup model to AI Platform Prediction. \n","2. Deploy the ScaNN matching service to AI Platform Prediction by using a custom container. The ScaNN matching service is an application that wraps the ANN index model and provides additional functionality, like mapping item IDs to item embeddings.\n","3. Optionally, export and deploy the matrix factorization model to AI Platform for exact matching.\n","\n","Before starting this notebook, you must run the [04_build_embeddings_scann](04_build_embeddings_scann.ipynb) notebook to build an approximate nearest neighbor (ANN) index for the item embeddings.\n"]},{"cell_type":"markdown","metadata":{"id":"--twfVSH_RSx"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"markdown","metadata":{"id":"hTf9yuUI_RSy"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"-74hbUcn_RSy"},"source":["import numpy as np\n","import tensorflow as tf"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G0-pepnt_RSz"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `PROJECT_NUMBER`: The number of the Google Cloud project you are using to implement this solution. You can find this in the **Project info** card on the [project dashboard page](https://pantheon.corp.google.com/home/dashboard).\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `REGION`: The region to use for the AI Platform Prediction job."]},{"cell_type":"code","metadata":{"id":"7JijghSw_RSz"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","PROJECT_NUMBER = 'yourProjectNumber' # Change to your project number\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","REGION = 'yourPredictionRegion' # Change to your AI Platform Prediction region.\n","ARTIFACTS_REPOSITORY_NAME = 'ml-serving'\n","\n","EMBEDDNIG_LOOKUP_MODEL_OUTPUT_DIR = f'gs://{BUCKET}/bqml/embedding_lookup_model'\n","EMBEDDNIG_LOOKUP_MODEL_NAME = 'item_embedding_lookup'\n","EMBEDDNIG_LOOKUP_MODEL_VERSION = 'v1'\n","\n","INDEX_DIR = f'gs://{BUCKET}/bqml/scann_index'\n","SCANN_MODEL_NAME = 'index_server'\n","SCANN_MODEL_VERSION = 'v1'\n","\n","KIND = 'song'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sRgHeeDH_RS0"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"mCRUZhqy_RS1"},"source":["try:\n"," from google.colab import auth\n"," auth.authenticate_user()\n"," print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dTdkkpNL_RS1"},"source":["## Deploy the embedding lookup model to AI Platform Prediction\r\n","\r\n","Create the embedding lookup model resource in AI Platform:"]},{"cell_type":"code","metadata":{"id":"e_4hhSxr_RS2"},"source":["!gcloud ai-platform models create {EMBEDDNIG_LOOKUP_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nOLmODpvPSvk"},"source":["Next, deploy the model:"]},{"cell_type":"code","metadata":{"id":"o8QANnrC_RS2"},"source":["!gcloud ai-platform versions create {EMBEDDNIG_LOOKUP_MODEL_VERSION} \\\n"," --region={REGION} \\\n"," --model={EMBEDDNIG_LOOKUP_MODEL_NAME} \\\n"," --origin={EMBEDDNIG_LOOKUP_MODEL_OUTPUT_DIR} \\\n"," --runtime-version=2.2 \\\n"," --framework=TensorFlow \\\n"," --python-version=3.7 \\\n"," --machine-type=n1-standard-2\n","\n","print(\"The model version is deployed to AI Platform Prediction.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p9Y0KD-CPXw1"},"source":["Once the model is deployed, you can verify it in the [AI Platform console](https://pantheon.corp.google.com/ai-platform/models)."]},{"cell_type":"markdown","metadata":{"id":"6gwpUx9q_RS3"},"source":["### Test the deployed embedding lookup AI Platform Prediction model\r\n","\r\n","Set the AI Platform Prediction API information:"]},{"cell_type":"code","metadata":{"id":"6a_V-ueo_RS3"},"source":["import googleapiclient.discovery\n","from google.api_core.client_options import ClientOptions\n","\n","api_endpoint = f'https://{REGION}-ml.googleapis.com'\n","client_options = ClientOptions(api_endpoint=api_endpoint)\n","service = googleapiclient.discovery.build(\n"," serviceName='ml', version='v1', client_options=client_options)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FTucyodISTFi"},"source":["Run the `caip_embedding_lookup` method to retrieve item embeddings. This method accepts item IDs, calls the embedding lookup model in AI Platform Prediction, and returns the appropriate embedding vectors.\r\n"]},{"cell_type":"code","metadata":{"id":"y0PYGzvV_RS4"},"source":["def caip_embedding_lookup(input_items):\n"," request_body = {'instances': input_items}\n"," service_name = f'projects/{PROJECT_ID}/models/{EMBEDDNIG_LOOKUP_MODEL_NAME}/versions/{EMBEDDNIG_LOOKUP_MODEL_VERSION}'\n"," print(f'Calling : {service_name}')\n"," response = service.projects().predict(\n"," name=service_name, body=request_body).execute()\n","\n"," if 'error' in response:\n"," raise RuntimeError(response['error'])\n","\n"," return response['predictions']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NBNQXyIkEz-q"},"source":["Test the `caip_embedding_lookup` method with three item IDs:"]},{"cell_type":"code","metadata":{"id":"180KDniX_RS4"},"source":["input_items = ['2114406', '2114402 2120788', 'abc123']\n","\n","embeddings = caip_embedding_lookup(input_items)\n","print(f'Embeddings retrieved: {len(embeddings)}')\n","for idx, embedding in enumerate(embeddings):\n"," print(f'{input_items[idx]}: {embedding[:5]}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kZD4MK5k-VGD"},"source":["## ScaNN matching service\r\n","\r\n","The ScaNN matching service performs the following steps:\r\n","\r\n","1. Receives one or more item IDs from the client.\r\n","1. Calls the embedding lookup model to fetch the embedding vectors of those item IDs.\r\n","1. Uses these embedding vectors to query the ANN index to find approximate nearest neighbor embedding vectors.\r\n","1. Maps the approximate nearest neighbors embedding vectors to their corresponding item IDs.\r\n","1. Sends the item IDs back to the client.\r\n","\r\n","When the client receives the item IDs of the matches, the song title and artist information is fetched from Datastore in real-time to be displayed and served to the client application.\r\n","\r\n","Note: In practice, recommendation systems combine matches (from one or more indices) with user-provided filtering clauses (like where price <= *value* and colour =red), as well as other item metadata (like item categories, popularity, and recency) to ensure recommendation freshness and diversity. In addition, ranking is commonly applied after generating the matches to decide the order in which they are served to the user. "]},{"cell_type":"markdown","metadata":{"id":"P0L5qYS1hpl3"},"source":["### ScaNN matching service implementation\r\n","\r\n","The ScaNN matching service is implemented as a [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/) application that runs on a [gunicorn](https://gunicorn.org/) web server. This application is implemented in the [main.py](index_server/main.py) module.\r\n","\r\n","The ScaNN matching service application works as follows:\r\n","\r\n","1. Uses environmental variables to set configuration information, such as the Google Cloud location of the ScaNN index to load.\r\n","1. Loads the ScaNN index as the `ScaNNMatcher` object is initiated.\r\n","1. As [required by AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/custom-container-requirements), exposes two HTTP endpoints:\r\n"," \r\n"," + `health`: a `GET` method to which AI Platform Prediction sends health checks.\r\n"," + `predict`: a `POST` method to which AI Platform Prediction forwards prediction requests.\r\n","\r\n"," The `predict` method expects JSON requests in the form `{\"instances\":[{\"query\": \"item123\", \"show\": 10}]}`, where `query` represents the item ID to retrieve matches for, and `show` represents the number of matches to retrieve.\r\n"," \r\n"," The `predict` method works as follows:\r\n","\r\n"," 1. Validates the received request object.\r\n"," 1. Extracts the `query` and `show` values from the request object.\r\n"," 1. Calls `embedding_lookup.lookup` with the given query item ID to get its embedding vector from the embedding lookup model.\r\n"," 1. Calls `scann_matcher.match` with the query item embedding vector to retrieve its approximate nearest neighbor item IDs from the ANN Index.\r\n","The list of matching item IDs are put into JSON format and returned as the response of the `predict` method."]},{"cell_type":"markdown","metadata":{"id":"eaeXo9prFNpL"},"source":["## Deploy the ScaNN matching service to AI Platform Prediction\r\n","\r\n","Package the ScaNN matching service application in a custom container and deploy it to AI Platform Prediction."]},{"cell_type":"markdown","metadata":{"id":"qkpwIGnb_RS5"},"source":["### Create an Artifact Registry for the Docker container image"]},{"cell_type":"code","metadata":{"id":"ru4oMDt2_RS5"},"source":["!gcloud beta artifacts repositories create {ARTIFACTS_REPOSITORY_NAME} \\\n"," --location={REGION} \\\n"," --repository-format=docker"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fIhDsllM_RS6"},"source":["!gcloud beta auth configure-docker {REGION}-docker.pkg.dev --quiet"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eTG7nvvt_RS6"},"source":["### Use Cloud Build to build the Docker container image\n","\n","The container runs the gunicorn HTTP web server and executes the Flask [app](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/315040032df26d7cef3a26e5def35ca50dd559d6/retail/recommendation-system/bqml-scann/index_server/main.py#L35) variable defined in the `main.py` module.\n","\n","The container image to deploy to AI Platform Prediction is defined in a [Dockerfile](index_server/Dockerfile), as shown in the following code snippet:\n","\n","```\n","FROM python:3.8-slim\n","\n","COPY requirements.txt .\n","RUN pip install -r requirements.txt\n","\n","COPY . ./\n","\n","ARG PORT\n","ENV PORT=$PORT\n","\n","CMD exec gunicorn --bind :$PORT main:app --workers=1 --threads 8 --timeout 1800\n","```\n","\n","Build the container image by using Cloud Build and specifying the [cloudbuild.yaml](index_server/cloudbuild.yaml) file:\n"]},{"cell_type":"code","metadata":{"id":"QVopE8-0_RS6"},"source":["IMAGE_URL = f'{REGION}-docker.pkg.dev/{PROJECT_ID}/{ARTIFACTS_REPOSITORY_NAME}/{SCANN_MODEL_NAME}:{SCANN_MODEL_VERSION}'\n","PORT=5001\n","\n","SUBSTITUTIONS = ''\n","SUBSTITUTIONS += f'_IMAGE_URL={IMAGE_URL},'\n","SUBSTITUTIONS += f'_PORT={PORT}'\n","\n","!gcloud builds submit --config=index_server/cloudbuild.yaml \\\n"," --substitutions={SUBSTITUTIONS} \\\n"," --timeout=1h"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TPk9Qi93G56d"},"source":["Run the following command to verify the container image has been built:\r\n"]},{"cell_type":"code","metadata":{"id":"PvSIUzD9_RS7"},"source":["repository_id = f'{REGION}-docker.pkg.dev/{PROJECT_ID}/{ARTIFACTS_REPOSITORY_NAME}'\n","\n","!gcloud beta artifacts docker images list {repository_id}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UAr0Vsft_RS7"},"source":["### Create a service account for AI Platform Prediction\r\n","\r\n","Create a service account to run the custom container. This [is required](https://cloud.google.com/ai-platform/prediction/docs/custom-service-account#container-default) in cases where you want to grant specific permissions to the service account."]},{"cell_type":"code","metadata":{"id":"cBa56i5g_RS8"},"source":["SERVICE_ACCOUNT_NAME = 'caip-serving'\n","SERVICE_ACCOUNT_EMAIL = f'{SERVICE_ACCOUNT_NAME}@{PROJECT_ID}.iam.gserviceaccount.com'\n","!gcloud iam service-accounts create {SERVICE_ACCOUNT_NAME} \\\n"," --description=\"Service account for AI Platform Prediction to access cloud resources.\" "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0-5BUPqT_RS8"},"source":["Grant the `Cloud ML Engine (AI Platform)` service account the `iam.serviceAccountAdmin` privilege, and grant the `caip-serving` service account the privileges required by the ScaNN matching service, which are `storage.objectViewer` and `ml.developer`."]},{"cell_type":"code","metadata":{"id":"qUoaWCVJ_RS8"},"source":["!gcloud projects describe {PROJECT_ID} --format=\"value(projectNumber)\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LzMJ60X-_RS9"},"source":["!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n"," --role=roles/iam.serviceAccountAdmin \\\n"," --member=serviceAccount:service-{PROJECT_NUMBER}@cloud-ml.google.com.iam.gserviceaccount.com\n","\n","!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n"," --role=roles/storage.objectViewer \\\n"," --member=serviceAccount:{SERVICE_ACCOUNT_EMAIL}\n"," \n","!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n"," --role=roles/ml.developer \\\n"," --member=serviceAccount:{SERVICE_ACCOUNT_EMAIL}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1-x11wBH_RS9"},"source":["### Deploy the custom container to AI Platform Prediction\r\n","\r\n","Create the ANN index model resource in AI Platform:"]},{"cell_type":"code","metadata":{"id":"CfeleaCW_RS-"},"source":["!gcloud ai-platform models create {SCANN_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"I9TcigBmHOin"},"source":["Deploy the custom container to AI Platform prediction. Note that you use the `env-vars` parameter to pass environmental variables to the Flask application in the container. "]},{"cell_type":"code","metadata":{"id":"fg9wtS2__RS_"},"source":["HEALTH_ROUTE=f'/v1/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}'\n","PREDICT_ROUTE=f'/v1/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}:predict'\n","\n","ENV_VARIABLES = f'PROJECT_ID={PROJECT_ID},'\n","ENV_VARIABLES += f'REGION={REGION},'\n","ENV_VARIABLES += f'INDEX_DIR={INDEX_DIR},'\n","ENV_VARIABLES += f'EMBEDDNIG_LOOKUP_MODEL_NAME={EMBEDDNIG_LOOKUP_MODEL_NAME},'\n","ENV_VARIABLES += f'EMBEDDNIG_LOOKUP_MODEL_VERSION={EMBEDDNIG_LOOKUP_MODEL_VERSION}'\n","\n","!gcloud beta ai-platform versions create {SCANN_MODEL_VERSION} \\\n"," --region={REGION} \\\n"," --model={SCANN_MODEL_NAME} \\\n"," --image={IMAGE_URL} \\\n"," --ports={PORT} \\\n"," --predict-route={PREDICT_ROUTE} \\\n"," --health-route={HEALTH_ROUTE} \\\n"," --machine-type=n1-standard-4 \\\n"," --env-vars={ENV_VARIABLES} \\\n"," --service-account={SERVICE_ACCOUNT_EMAIL}\n","\n","print(\"The model version is deployed to AI Platform Prediction.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WzCcA58l_RS_"},"source":["### Test the Deployed ScaNN Index Service\r\n","\r\n","After deploying the custom container, test it by running the `caip_scann_match` method. This method accepts the parameter `query_items`, whose value is converted into a space-separated string of item IDs and treated as a single query. That is, a single embedding vector is retrieved from the embedding lookup model, and similar item IDs are retrieved from the ScaNN index given this embedding vector."]},{"cell_type":"code","metadata":{"id":"w5OlMGuD_RS_"},"source":["from google.cloud import datastore\n","import requests\n","client = datastore.Client(PROJECT_ID)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WwzuijuH_RTA"},"source":["def caip_scann_match(query_items, show=10):\n"," request_body = {\n"," 'instances': [{\n"," 'query':' '.join(query_items), \n"," 'show':show\n"," }]\n"," }\n"," \n"," service_name = f'projects/{PROJECT_ID}/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}'\n"," print(f'Calling: {service_name}') \n"," response = service.projects().predict(\n"," name=service_name, body=request_body).execute()\n","\n"," if 'error' in response:\n"," raise RuntimeError(response['error'])\n","\n"," match_tokens = response['predictions']\n"," keys = [client.key(KIND, int(key)) for key in match_tokens]\n"," items = client.get_multi(keys)\n"," return items\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ctXsOxhDHsX_"},"source":["Call the `caip_scann_match` method with five item IDs and request five match items for each:"]},{"cell_type":"code","metadata":{"id":"620GvSzr_RTA"},"source":["songs = {\n"," '2120788': 'Limp Bizkit: My Way',\n"," '1086322': 'Jacques Brel: Ne Me Quitte Pas',\n"," '833391': 'Ricky Martin: Livin\\' la Vida Loca',\n"," '1579481': 'Dr. Dre: The Next Episode',\n"," '2954929': 'Black Sabbath: Iron Man'\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YiYAYVG9_RTB"},"source":["for item_Id, desc in songs.items():\n"," print(desc)\n"," print(\"==================\")\n"," similar_items = caip_scann_match([item_Id], 5)\n"," for similar_item in similar_items:\n"," print(f'- {similar_item[\"artist\"]}: {similar_item[\"track_title\"]}')\n"," print()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lt7-YDSe_RTB"},"source":["## (Optional) Deploy the matrix factorization model to AI Platform Prediction\r\n","\r\n","Optionally, you can deploy the matrix factorization model in order to perform exact item matching. The model takes `Item1_Id` as an input and outputs the top 50 recommended `item2_Ids`.\r\n","\r\n","Exact matching returns better results, but takes significantly longer than approximate nearest neighbor matching. You might want to use exact item matching in cases where you are working with a very small data set and where latency isn't a primary concern."]},{"cell_type":"markdown","metadata":{"id":"YSDzSk4T_RTC"},"source":["### Export the model from BigQuery ML to Cloud Storage as a SavedModel"]},{"cell_type":"code","metadata":{"id":"YVmmFvLk_RTC"},"source":["BQ_DATASET_NAME = 'recommendations'\n","BQML_MODEL_NAME = 'item_matching_model'\n","BQML_MODEL_VERSION = 'v1' \n","BQML_MODEL_OUTPUT_DIR = f'gs://{BUCKET}/bqml/item_matching_model'\n","\n","!bq --quiet extract -m {BQ_DATASET_NAME}.{BQML_MODEL_NAME} {BQML_MODEL_OUTPUT_DIR}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Q4xzyUte_RTC"},"source":["!saved_model_cli show --dir {BQML_MODEL_OUTPUT_DIR} --tag_set serve --signature_def serving_default"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kWbEnUnO_RTC"},"source":["### Deploy the exact matching model to AI Platform Prediction"]},{"cell_type":"code","metadata":{"id":"Es8Tp9HM_RTD"},"source":["!gcloud ai-platform models create {BQML_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nNOVgOD9_RTD"},"source":["!gcloud ai-platform versions create {BQML_MODEL_VERSION} \\\n"," --region={REGION} \\\n"," --model={BQML_MODEL_NAME} \\\n"," --origin={BQML_MODEL_OUTPUT_DIR} \\\n"," --runtime-version=2.2 \\\n"," --framework=TensorFlow \\\n"," --python-version=3.7 \\\n"," --machine-type=n1-standard-2\n","\n","print(\"The model version is deployed to AI Platform Predicton.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"n9MDaT3P_RTD"},"source":["def caip_bqml_matching(input_items, show):\n"," request_body = {'instances': input_items}\n"," service_name = f'projects/{PROJECT_ID}/models/{BQML_MODEL_NAME}/versions/{BQML_MODEL_VERSION}'\n"," print(f'Calling : {service_name}')\n"," response = service.projects().predict(\n"," name=service_name, body=request_body).execute()\n","\n"," if 'error' in response:\n"," raise RuntimeError(response['error'])\n"," \n"," \n"," match_tokens = response['predictions'][0][\"predicted_item2_Id\"][:show]\n"," keys = [client.key(KIND, int(key)) for key in match_tokens]\n"," items = client.get_multi(keys)\n"," return items\n","\n"," return response['predictions']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bHvFNZ78_RTD"},"source":["for item_Id, desc in songs.items():\n"," print(desc)\n"," print(\"==================\")\n"," similar_items = caip_bqml_matching([int(item_Id)], 5)\n"," for similar_item in similar_items:\n"," print(f'- {similar_item[\"artist\"]}: {similar_item[\"track_title\"]}')\n"," print()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6uUOZgLQ_RTE"},"source":["## License\n","\n","Copyright 2020 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. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]} | apache-2.0 |
vravishankar/Jupyter-Books | pandas/01.Pandas - Series Object.ipynb | 1 | 58759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas\n",
"\n",
"Pandas is a high-performance python library that provides a comprehensive set of data structures for manipulating tabular data, providing high-performance indexing, automatic alignment, reshaping, grouping, joining and statistical analyses capabilities.\n",
"\n",
"The two primary data structures in pandas are the Series and the DataFrame objects."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Series Object\n",
"\n",
"The Series object is the fundamental building block of pandas. A Series represents an one-dimensional array based on the NumPy ndarray but with a labeled index that significantly helps to access the elements.\n",
"\n",
"A Series always has an index even if one is not specified, by default pandas will create an index that consists of sequential integers starting from zero. Access to elements is not by integer position but using values in the index referred as Labels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importing pandas into the application is simple. It is common to import both pandas and numpy with their objects mapped into the pd and np namespaces respectively."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.20.1'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"pd.__version__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.12.1'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# set some options to control output display\n",
"pd.set_option('display.notebook_repr_html',False)\n",
"pd.set_option('display.max_columns',10)\n",
"pd.set_option('display.max_rows',10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating Series\n",
"\n",
"A Series can be created and initialised by passing either a scalar value, a NumPy nd array, a Python list or a Python Dict as the data parameter of the Series constructor."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create one item series\n",
"s1 = pd.Series(1)\n",
"s1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'0' is the index and '1' is the value. The data type (dtype) is also shown. We can also retrieve the value using the associated index."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get value with label 0\n",
"s1[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create from list\n",
"s2 = pd.Series([1,2,3,4,5])\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get the values in the series\n",
"s2.values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RangeIndex(start=0, stop=5, step=1)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get the index of the series\n",
"s2.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating Series with named index\n",
"\n",
"Pandas will create different index types based on the type of data identified in the index parameter. These different index types are optimized to perform indexing operations for that specific data type. To specify the index at the time of creation of the Series, use the index parameter of the constructor."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# explicitly create an index\n",
"# index is alpha, not an integer\n",
"s3 = pd.Series([1,2,3], index=['a','b','c'])\n",
"s3"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'b', 'c'], dtype='object')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s3.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please note the type of the index items. It is not string but 'object'."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# look up by label value and not object position\n",
"s3['b']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# position also works\n",
"s3[2]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2\n",
"1 2\n",
"2 2\n",
"3 2\n",
"4 2\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create series from an existing index\n",
"# scalar value will be copied at each index label\n",
"s4 = pd.Series(2,index=s2.index)\n",
"s4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is a common practice to initialize the Series objects using NumPy ndarrays, and with various NumPy functions that create arrays. The following code creates a Series from five normally distributed values:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.469112\n",
"1 -0.282863\n",
"2 -1.509059\n",
"3 -1.135632\n",
"4 1.212112\n",
"dtype: float64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(123456)\n",
"pd.Series(np.random.randn(5))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.0\n",
"1 1.0\n",
"2 2.0\n",
"3 3.0\n",
"4 4.0\n",
"5 5.0\n",
"6 6.0\n",
"7 7.0\n",
"8 8.0\n",
"9 9.0\n",
"dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 0 through 9\n",
"pd.Series(np.linspace(0,9,10))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"6 6\n",
"7 7\n",
"8 8\n",
"dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# o through 8\n",
"pd.Series(np.arange(0,9))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Series can also be created from a Python dictionary. The keys of the dictionary are used as the index lables for the Series:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"d 4\n",
"dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s6 = pd.Series({'a':1,'b':2,'c':3,'d':4})\n",
"s6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Size, Shape, Count and Uniqueness of Values"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.0\n",
"1 1.0\n",
"2 1.0\n",
"3 2.0\n",
"4 3.0\n",
"5 4.0\n",
"6 5.0\n",
"7 6.0\n",
"8 7.0\n",
"9 NaN\n",
"dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# example series which also contains a NaN\n",
"s = pd.Series([0,1,1,2,3,4,5,6,7,np.NaN])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# length of the Series\n",
"len(s)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.size"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10,)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# shape is a tuple with one value\n",
"s.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# number of values not part of NaN can be found using count() method\n",
"s.count()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 2., 3., 4., 5., 6., 7., nan])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# all unique values\n",
"s.unique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 2\n",
"7.0 1\n",
"6.0 1\n",
"5.0 1\n",
"4.0 1\n",
"3.0 1\n",
"2.0 1\n",
"0.0 1\n",
"dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# count of non-NaN values, returned max to min order\n",
"s.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Peeking at data with heads, tails and take\n",
"\n",
"pandas provides the .head() and .tail() methods to examine just the first few or last records in a Series. By default, these return the first five or last rows respectively, but you can use the n parameter or just pass an integer to specify the number of rows:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.0\n",
"1 1.0\n",
"2 1.0\n",
"3 2.0\n",
"4 3.0\n",
"dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first five\n",
"s.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.0\n",
"1 1.0\n",
"2 1.0\n",
"dtype: float64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first three\n",
"s.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5 4.0\n",
"6 5.0\n",
"7 6.0\n",
"8 7.0\n",
"9 NaN\n",
"dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# last five\n",
"s.tail()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8 7.0\n",
"9 NaN\n",
"dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# last 2\n",
"s.tail(n=2) # equivalent to s.tail(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The .take() method will return the rows in a series that correspond to the zero-based positions specified in a list:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.0\n",
"3 2.0\n",
"9 NaN\n",
"dtype: float64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# only take specific items\n",
"s.take([0,3,9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looking up values in Series\n",
"\n",
"Values in a Series object can be retrieved using the [] operator and passing either a single index label or a list of index labels."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# single item lookup\n",
"s3['a']"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# lookup by position since index is not an integer\n",
"s3[2]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# multiple items\n",
"s3[['a','c']]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# series with an integer index but not starting with 0\n",
"s5 = pd.Series([1,2,3], index =[11,12,13])\n",
"s5[12] # by value as value passed and index are both integer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To alleviate the potential confusion in determining the label-based lookups versus position-based lookups, index based lookup can be enforced using the .loc[] accessor:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# force lookup by index label\n",
"s5.loc[12]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lookup by position can be enforced using the iloc[] accessor:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# force lookup by position or location\n",
"s5.iloc[1]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12 2.0\n",
"10 NaN\n",
"dtype: float64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# multiple items by index label\n",
"s5.loc[[12,10]]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12 2\n",
"13 3\n",
"dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# multiple items by position or location\n",
"s5.iloc[[1,2]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If a location / position passed to .iloc[] in a list is out of bounds, an exception will be thrown. This is different than with .loc[], which if passed a label that does not exist, will return NaN as the value for that label:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" 12 2.0\n",
"-1 NaN\n",
" 15 NaN\n",
"dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s5.loc[[12,-1,15]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Series also has a property .ix that can be used to look up items either by label or by zero-based array position."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s3"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/cnc/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
" \n"
]
},
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# label based lookup\n",
"s3.ix[['a','b']]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# position based lookup\n",
"s3.ix[[1,2]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can become complicated if the indexes are integers and you pass a list of integers to ix. Since they are of the same type, the lookup will be by index label instead of position:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/cnc/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
},
{
"data": {
"text/plain": [
"1 NaN\n",
"2 NaN\n",
"10 NaN\n",
"11 1.0\n",
"dtype: float64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this looks by label and not position\n",
"# note that 1,2 have NaN as those labels do not exist in the index\n",
"s5.ix[[1,2,10,11]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alignment via index labels\n",
"\n",
"A fundamental difference between a NumPy ndarray and a pandas Series is the ability of a Series to automatically align data from another Series based on label values before performing an operation."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"d 4\n",
"dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s6 = pd.Series([1,2,3,4], index=['a','b','c','d'])\n",
"s6"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"d 4\n",
"c 3\n",
"b 2\n",
"a 1\n",
"dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s7 = pd.Series([4,3,2,1], index=['d','c','b','a'])\n",
"s7"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 2\n",
"b 4\n",
"c 6\n",
"d 8\n",
"dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s6 + s7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a very different result that what it would have been if it were two pure NumPy arrays being added. A NumPy ndarray would add the items in identical positions of each array resulting in different values."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([6, 6, 6, 6, 6])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a1 = np.array([1,2,3,4,5])\n",
"a2 = np.array([5,4,3,2,1])\n",
"a1 + a2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The process of adding two Series objects differs from the process of addition of arrays as it first aligns data based on index label values instead of simply applying the operation to elements in the same position. This becomes significantly powerful when using pandas Series to combine data based on labels instead of having to first order the data manually."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Arithmetic Operations\n",
"\n",
"Arithemetic Operations <pre>(+,-,*,/)</pre> can be applied either to a Series or between 2 Series objects"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 2\n",
"b 4\n",
"c 6\n",
"dtype: int64"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# multiply all values in s3 by 2\n",
"s3 * 2"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 2\n",
"b 4\n",
"c 6\n",
"dtype: int64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scalar series using the s3's index\n",
"# not efficient as it will no use vectorisation\n",
"t = pd.Series(2,s3.index)\n",
"s3 * t"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To reinforce the point that alignment is being performed when applying arithmetic operations across two Series objects, look at the following two Series as examples:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"d 5\n",
"dtype: int64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we will add this to s9\n",
"s8 = pd.Series({'a':1,'b':2,'c':3,'d':5})\n",
"s8"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 6\n",
"c 7\n",
"d 9\n",
"e 10\n",
"dtype: int64"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s9 = pd.Series({'b':6,'c':7,'d':9,'e':10})\n",
"s9"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a NaN\n",
"b 8.0\n",
"c 10.0\n",
"d 14.0\n",
"e NaN\n",
"dtype: float64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# NaN's result for a and e demonstrates alignment\n",
"s8 + s9"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"a 2.0\n",
"b 3.0\n",
"dtype: float64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s10 = pd.Series([1.0,2.0,3.0],index=['a','a','b'])\n",
"s10"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 4.0\n",
"a 5.0\n",
"c 6.0\n",
"dtype: float64"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s11 = pd.Series([4.0,5.0,6.0], index=['a','a','c'])\n",
"s11"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 5.0\n",
"a 6.0\n",
"a 6.0\n",
"a 7.0\n",
"b NaN\n",
"c NaN\n",
"dtype: float64"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# will result in four 'a' index labels\n",
"s10 + s11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The reason for the above result is that during alignment, pandas actually performs a cartesian product of the sets of all the unique index labels in both Series objects, and then applies the specified operation on all items in the products.\n",
"\n",
"To explain why there are four 'a' index values s10 contains two 'a' labels and s11 also contains two 'a' labels. Every combination of 'a' labels in each will be calculated resulting in four 'a' labels. There is one 'b' label from s10 and one 'c' label from s11. Since there is no matching label for either in the other Series object, they only result in a sing row in the resulting Series object.\n",
"\n",
"Each combination of values for 'a' in both Series are computed, resulting in the four values: 1+4,1+5,2+4 and 2+5.\n",
"\n",
"So remember that an index can have duplicate labels, and during alignment this will result in a number of index labels equivalent to the products of the number of the labels in each Series."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The special case of Not-A-Number (NaN)\n",
"\n",
"pandas mathematical operators and functions handle NaN in a special manner (compared to NumPy ndarray) that does not break the computations. pandas is lenient with missing data assuming that it is a common situation."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nda = np.array([1,2,3,4,5])\n",
"nda.mean()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# mean of numpy array values with a NaN\n",
"nda = np.array([1,2,3,4,np.NaN])\n",
"nda.mean()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.5"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Series object ignores NaN values - does not get factored\n",
"s = pd.Series(nda)\n",
"s.mean()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# handle NaN values like Numpy\n",
"s.mean(skipna=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boolean selection\n",
"\n",
"Items in a Series can be selected, based on the value instead of index labels, via the utilization of a Boolean selection."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 True\n",
"7 True\n",
"8 True\n",
"9 True\n",
"dtype: bool"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# which rows have values that are > 5\n",
"s = pd.Series(np.arange(0,10))\n",
"s > 5"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6 6\n",
"7 7\n",
"8 8\n",
"9 9\n",
"dtype: int64"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select rows where values are > 5\n",
"# overloading the Series object [] operator\n",
"logicalResults = s > 5\n",
"s[logicalResults]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6 6\n",
"7 7\n",
"8 8\n",
"9 9\n",
"dtype: int64"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a little shorter version\n",
"s[s > 5]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6 6\n",
"7 7\n",
"8 8\n",
"dtype: int64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# using & operator\n",
"s[(s>5)&(s<9)]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"6 6\n",
"7 7\n",
"8 8\n",
"9 9\n",
"dtype: int64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# using | operator\n",
"s[(s > 3) | (s < 5)]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# are all items >= 0?\n",
"(s >=0).all()"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# are any items < 2\n",
"s[s < 2].any()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result of these logical expressions is a Boolean selection, a Series of True and False values. The .sum() method of a Series, when given a series of Boolean values, will treat True as 1 and False as 0. The following demonstrates using this to determine the number of items in a Series that satisfy a given expression:"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(s < 2).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reindexing a Series\n",
"\n",
"Reindexing in pandas is a process that makes the data in a Series or DataFrame match a given set of labels.\n",
"\n",
"This process of performing a reindex includes the following steps:\n",
"1. Reordering existing data to match a set of labels.\n",
"2. Inserting NaN markers where no data exists for a label.\n",
"3. Possibly, filling missing data for a label using some type of logic"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 -0.173215\n",
"1 0.119209\n",
"2 -1.044236\n",
"3 -0.861849\n",
"4 -2.104569\n",
"dtype: float64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sample series of five items\n",
"s = pd.Series(np.random.randn(5))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a -0.173215\n",
"b 0.119209\n",
"c -1.044236\n",
"d -0.861849\n",
"e -2.104569\n",
"dtype: float64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# change the index\n",
"s.index = ['a','b','c','d','e']\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.469112\n",
"1 -0.282863\n",
"2 -1.509059\n",
"0 -1.135632\n",
"1 1.212112\n",
"2 -0.173215\n",
"dtype: float64"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# concat copies index values verbatim\n",
"# potentially making duplicates\n",
"np.random.seed(123456)\n",
"s1 = pd.Series(np.random.randn(3))\n",
"s2 = pd.Series(np.random.randn(3))\n",
"combined = pd.concat([s1,s2])\n",
"combined"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.469112\n",
"1 -0.282863\n",
"2 -1.509059\n",
"3 -1.135632\n",
"4 1.212112\n",
"5 -0.173215\n",
"dtype: float64"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reset the index\n",
"combined.index = np.arange(0,len(combined))\n",
"combined"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Greater flexibility in creating a new index is provided using the .reindex() method. An example of the flexibility of .reindex() over assigning the .index property directly is that the list provided to .reindex() can be of a different length than the number of rows in the Series:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.469112\n",
"c -1.509059\n",
"g NaN\n",
"dtype: float64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(123456)\n",
"s1 = pd.Series(np.random.randn(4),['a','b','c','d'])\n",
"# reindex with different number of labels\n",
"# results in dropped rows and/or NaN's\n",
"s2 = s1.reindex(['a','c','g'])\n",
"s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several things here that are important to point out about .reindex() method.\n",
"\n",
"* First is that the result of .reindex() method is a new Series. This new Series has an index with labels that are provided as parameter to reindex().\n",
"* For each item in the given parameter list, if the original Series contains that label, then the value is assigned to that label.\n",
"* If that label does not exist in the original Series, pandas assigns a NaN value.\n",
"* Rows in the Series without a label specified in the parameter of .reindex() is not included in the result.\n",
"\n",
"To demonstrate that the result of .reindex() is a new Series object, changing a value in s2 does not change the values in s1:"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.000000\n",
"c -1.509059\n",
"g NaN\n",
"dtype: float64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# s2 is a different series than s1\n",
"s2['a'] = 0\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this did not modify s1\n",
"s1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reindex is also useful when you want to align two Series to perform an operation on matching elements from each series; however, for some reason, the two Series has index labels that will not initially align."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"dtype: float64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# different types for the same values of labels causes big issue\n",
"s1 = pd.Series([0,1,2],index=[0,1,2])\n",
"s2 = pd.Series([3,4,5],index=['0','1','2'])\n",
"s1 + s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The reason why this happens in pandas are as follows:\n",
"\n",
"1. pandas first tries to align by the indexes and finds no matches, so it copies the index labels from the first series and tries to append the indexes from the second Series.\n",
"2. However, since they are different type, it defaults back to zero-based integer sequence that results in duplicate values.\n",
"3. Finally, all values are NaN because the operation tries to add the item in the first Series with the integer label 0, which has a value of 0, but can't find the item in the other series and therefore the result in NaN."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3\n",
"1 5\n",
"2 7\n",
"dtype: int64"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reindex by casting the label types and we will get the desired result\n",
"s2.index = s2.index.values.astype(int)\n",
"s1 + s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default action of inserting NaN as a missing value during reindexing can be changed by using the fill_value parameter of the method."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a -0.173215\n",
"f 0.000000\n",
"dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fill with 0 instead on NaN\n",
"s2 = s.copy()\n",
"s2.reindex(['a','f'],fill_value=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When performing a reindex on ordered data such as a time series, it is possible to perform interpolation or filling of values. The following example demonstrates forward filling, often referred to as \"last known value\"."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 red\n",
"3 green\n",
"5 blue\n",
"dtype: object"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create example to demonstrate fills\n",
"s3 = pd.Series(['red','green','blue'],index=[0,3,5])\n",
"s3"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 red\n",
"1 red\n",
"2 red\n",
"3 green\n",
"4 green\n",
"5 blue\n",
"6 blue\n",
"dtype: object"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# forward fill using ffill method\n",
"s3.reindex(np.arange(0,7), method='ffill')"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 red\n",
"1 green\n",
"2 green\n",
"3 green\n",
"4 blue\n",
"5 blue\n",
"6 NaN\n",
"dtype: object"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# backward fill using bfill method\n",
"s3.reindex(np.arange(0,7),method='bfill')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modifying a Series in-place\n",
"\n",
"There are several ways that an existing Series can be modified in-place having either its values changed or having rows added or deleted.\n",
"\n",
"A new item can be added to a Series by assigning a value to an index label that does not already exist."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.469112\n",
"b -0.282863\n",
"c -1.509059\n",
"dtype: float64"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(123456)\n",
"s = pd.Series(np.random.randn(3),index=['a','b','c'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.469112\n",
"b -0.282863\n",
"c -1.509059\n",
"d 100.000000\n",
"dtype: float64"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# change a value in the Series\n",
"# this done in-place\n",
"# a new Series is not returned that has a modified value\n",
"s['d'] = 100\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.469112\n",
"b -0.282863\n",
"c -1.509059\n",
"d -100.000000\n",
"dtype: float64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# value at a specific index label can be changed by assignment:\n",
"s['d'] = -100\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Items can be removed from a Series using the del() function and passing the index label(s) to be removed."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b -0.282863\n",
"c -1.509059\n",
"d -100.000000\n",
"dtype: float64"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del(s['a'])\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Slicing a Series"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"11 101\n",
"12 102\n",
"13 103\n",
"14 104\n",
"15 105\n",
"16 106\n",
"17 107\n",
"18 108\n",
"19 109\n",
"dtype: int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a series to use for slicing\n",
"# using index labels not starting at 0 to demonstrate\n",
"# position based slicing\n",
"\n",
"s = pd.Series(np.arange(100,110),index=np.arange(10,20))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"12 102\n",
"14 104\n",
"dtype: int64"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# items at position 0,2,4\n",
"s[0:6:2]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"12 102\n",
"14 104\n",
"dtype: int64"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# equivalent to\n",
"s.iloc[[0,2,4]]"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"11 101\n",
"12 102\n",
"13 103\n",
"14 104\n",
"dtype: int64"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first five by slicing, same as .head(5)\n",
"s[:5]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14 104\n",
"15 105\n",
"16 106\n",
"17 107\n",
"18 108\n",
"19 109\n",
"dtype: int64"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fourth position to the end\n",
"s[4:]"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"12 102\n",
"14 104\n",
"dtype: int64"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# every other item in the first five positions\n",
"s[:5:2]"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14 104\n",
"16 106\n",
"18 108\n",
"dtype: int64"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# every other item starting at the fourth position\n",
"s[4::2]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19 109\n",
"18 108\n",
"17 107\n",
"16 106\n",
"15 105\n",
"14 104\n",
"13 103\n",
"12 102\n",
"11 101\n",
"10 100\n",
"dtype: int64"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reverse the series\n",
"s[::-1]"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14 104\n",
"12 102\n",
"10 100\n",
"dtype: int64"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# every other starting at position 4, in reverse\n",
"s[4::-2]"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"11 101\n",
"12 102\n",
"13 103\n",
"14 104\n",
"15 105\n",
"16 106\n",
"17 107\n",
"dtype: int64"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# :-2 which means positions 0 through (10-2) which is [8]\n",
"s[:-2]"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"17 107\n",
"18 108\n",
"19 109\n",
"dtype: int64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# last 3 items\n",
"# equivalent to tail(3)\n",
"s[-3:]"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16 106\n",
"17 107\n",
"18 108\n",
"dtype: int64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# equivalent to s.tail(4).head(3)\n",
"s[-4:-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An important thing to keep in mind when using slicing, is that the result of the slice is actually a view into the original Series. Modification of values through the result of the slice will modify the original Series."
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"11 101\n",
"dtype: int64"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# preserve s\n",
"# slice with first 2 rows\n",
"copy = s.copy()\n",
"slice = copy[:2]\n",
"slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the assignment of a value to an element of a slice will change the value in the original Series:"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10 100\n",
"11 1000\n",
"12 102\n",
"13 103\n",
"14 104\n",
"15 105\n",
"16 106\n",
"17 107\n",
"18 108\n",
"19 109\n",
"dtype: int64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"slice[11] = 1000\n",
"copy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slicing can be performed on Series objects with a non-integer index."
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0\n",
"b 1\n",
"c 2\n",
"d 3\n",
"e 4\n",
"dtype: int64"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# used to demonstrate the next two slices\n",
"s = pd.Series(np.arange(0,5),index=['a','b','c','d','e'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 1\n",
"c 2\n",
"dtype: int64"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# slicing with integer values will extract items based on position:\n",
"s[1:3]"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 1\n",
"c 2\n",
"d 3\n",
"dtype: int64"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# with non-integer index, it is also possible to slice with values in the same type of the index:\n",
"s['b':'d']"
]
}
],
"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
}
| mit |
fhennecker/semiteleporter | research/triangulation_1/Experiment triangulation.ipynb | 1 | 393552 | {
"metadata": {
"name": "",
"signature": "sha256:1e33169b0d576c595a362cbf52e274795739f1b01345cdd596227876d2cf2cde"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from pylab import imread\n",
"%matplotlib inline\n",
"from mpl_toolkits.mplot3d import Axes3D"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exp\u00e9rience triangulation dans une image\n",
"## Pr\u00e9paratifs\n",
"\n",
"On place un long multiprise dans le champ de la cam\u00e9ra, de sorte que les\n",
"extr\u00e9mit\u00e9s du multiprise co\u00efncident avec les bords gauche et droit de l'image\n",
"capt\u00e9e par la cam\u00e9ra.\n",
"\n",
"On place une tige verticale sur le c\u00f4t\u00e9 de la cam\u00e9ra, et une lumi\u00e8re\n",
"vive derri\u00e8re la tige, afin de projeter l'ombre de la tige sur le milieu du\n",
"multiprises, mat\u00e9rialis\u00e9 sur les photos par une bouteille de Club Mate.\n",
"\n",
"On mesure diff\u00e9rentes distances parmis les objets ainsi plac\u00e9s:\n",
"* Entre l'objectif de la cam\u00e9ra et la face visible (avant) du multiprises: 58cm\n",
"* La longueur du multiprises: 69cm\n",
"* La distance lat\u00e9rale de la tige \u00e0 la cam\u00e9ra:\n",
"* La distance entre la tige et le multiprises:\n",
"\n",
"## Exp\u00e9rience\n",
"\n",
"On prend plusieurs photos de la sc\u00e8ne ainsi form\u00e9e en y pla\u00e7ant un objet\n",
"(une bo\u00eete en carton) \u00e0 diff\u00e9rents endroits. Lors de chaque photo, on note\n",
"la distance entre l'avant de l'objet et l'avant du multiprises. Cette distance,\n",
"en cm, donne le nom de l'image *(exemple: 21cm devant le multiprises = 21.jpg, \n",
"11.5cm = 11-5.jpg)*.\n",
"\n",
"Sur les images ainsi obtenues, nous marquons d'une ligne rouge l'ombre sur la\n",
"bo\u00eete. Un programme doit trianguler la position de l'objet, et la position\n",
"ainsi obtenue doit correspondre (dans les limites de pr\u00e9cision peu \u00e9lev\u00e9es\n",
"correspondant aux mesures prises) \u00e0 la position mesur\u00e9e (le nom de l'image).\n",
"\n",
"## Images"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"name = \"21\"\n",
"img = imread(name + \".bmp\")\n",
"img_red = imread(name + \"+red.bmp\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"diff = img_red - img\n",
"plt.imshow(img_red)\n",
"plt.show()\n",
"plt.imshow(diff)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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S3bg6HCKJAXDPwRxZo2iWeiQe0aGqhrCacDZYV4js3IqKoQLZH/F0+pjr8hHW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UwlklbDrHzs7A73x1s4Ca0ZShQDXZbC+XyJZgcCknJvMJbQwvIJPBGnYCZpkybwPReX5+TsJD0h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\n4gSY1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"text": [
"<matplotlib.figure.Figure at 0x7fe7c0343350>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.image.AxesImage at 0x7fe79e6485d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFUtJREFUeJzt3d1vlOeZx/HfM/ZgAiUuTfBAZ1CcGL9kDLHdEnzSVksd\nY2kTHLIkLFRLUIJaqWgrJQcszR+APTTbA1Ab7aoikrdaxeQo0Ci4JiVp2IKKFEBVcRXcZtiMX7Ng\nXMBgz3jm2gPCFEptJzCex3P3+5FuCc/bcw3BX57cnhk8MzMBAApawO8BAAD3jpgDgAOIOQA4gJgD\ngAOIOQA4gJgDgANmJeZdXV2qqalRZWWl9uzZMxuHAADcwsv168zT6bSqq6v17rvvKhwO6/HHH9cb\nb7yhRx99NJeHAQDcIudn5idPntSKFStUXl6uYDCozZs36+DBg7k+DADgFjmPeX9/v5YvX579OhKJ\nqL+/P9eHAQDcIucx9zwv1w8JAJhBca4fMBwOK5FIZL9OJBKKRCK33YbgA8DdmerHnDk/M1+9erV6\ne3t1/vx5JZNJHThwQK2trbk+DADgFjk/My8uLtZPfvITtbS0KJ1Oa/v27bySBQBmWc5fmvi5Dso2\nCwDclbxtswAA8o+YA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICY\nA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4AD\niDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkAOICYA4ADiDkA\nOICYA4ADZoz5iy++qFAopFWrVmUvGxkZUXNzs6qqqrRu3TqNjo5mr2tvb1dlZaVqamrU3d09O1MD\nAG4zY8xfeOEFdXV13XZZLBZTc3Ozzp07p6amJsViMUlST0+PDhw4oJ6eHnV1dWnHjh3KZDKzMzkA\nIGvGmH/zm9/U4sWLb7vs0KFD2rZtmyRp27ZteuuttyRJBw8e1JYtWxQMBlVeXq4VK1bo5MmTszA2\nAOBWd7VnPjw8rFAoJEkKhUIaHh6WJA0MDCgSiWRvF4lE1N/fn4MxAQDTuecfgHqeJ8/zpr0eADC7\n7irmoVBIQ0NDkqTBwUGVlZVJksLhsBKJRPZ2fX19CofDORgTADCdu4p5a2urOjo6JEkdHR3asGFD\n9vLOzk4lk0nF43H19vZqzZo1uZsWAPC32Qw2b95sy5Yts2AwaJFIxF5//XW7ePGiNTU1WWVlpTU3\nN9ulS5eyt9+9e7dVVFRYdXW1dXV1/c3HlMRisVisu1hT8T6La16xjw4Ad2eqZPMOUABwADEHAAcQ\ncwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBw\nADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEH\nAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwADEHAAcQcwBwwIwxTyQSWrt2rWpra7Vy\n5Urt27dPkjQyMqLm5mZVVVVp3bp1Gh0dzd6nvb1dlZWVqqmpUXd39+xNDwC4wWYwODhop0+fNjOz\nK1euWFVVlfX09NjOnTttz549ZmYWi8Vs165dZmZ29uxZq6urs2QyafF43CoqKiydTt/2mJJYLBaL\ndRdrKjPG/K89/fTTduTIEauurrahoaFs8Kurq83MrK2tzWKxWPb2LS0tduLECWLOYrFYOVhT+UJ7\n5ufPn9fp06fV2Nio4eFhhUIhSVIoFNLw8LAkaWBgQJFIJHufSCSi/v7+L3IYAMAX9LljfvXqVW3c\nuFF79+7VokWLbrvO8zx5njflfae7DgBw7z5XzFOplDZu3KitW7dqw4YNkm6cjQ8NDUmSBgcHVVZW\nJkkKh8NKJBLZ+/b19SkcDud6bgDALWaMuZlp+/btikajeumll7KXt7a2qqOjQ5LU0dGRjXxra6s6\nOzuVTCYVj8fV29urNWvWzNL4AABJ0+ymf+bYsWPmeZ7V1dVZfX291dfX2+HDh+3ixYvW1NRklZWV\n1tzcbJcuXcreZ/fu3VZRUWHV1dXW1dV1x2NqDvwQgcVisQpxTcX7LK55xR46ANydqZLNO0ABwAHE\nHAAcQMwBwAHEHAAcQMwBwAHEHAAcQMwBwAHEHAAcQMwBwAHEHAAcQMwBwAHEHAAcQMwBwAHEHAAc\nUOz3APjbyv+9TQ+mJpS2jAIKaL48TaYm5clTIDUus4yUyqh4MiVJsowUzGQkM3leQEWpCWWUkTxp\nfiCg4uKASuaX6Evz5imdSimdMZV8+x80/+tfU0lRkRYVBeRNTEiSSoqKFZQ0mZ7QwIKF2reQfykK\nmOv4PPM55Mvf+66+98oP9A9fyqj02jUlx9Ia6busj/7wR12+dFXB4vkq8jwFijJKp5Mq9ooUDAaV\nyZjmBecpGJwnyZMU0LVrYwoEPJkyKg4WK53J6Or1qxq/fl3z77tPC+6/X5MBTxM2qUAmrQXz56uo\n5D5lLKOJjGnM0gpHQhqvqtK/PRz1+7cGwGemSjYx91skosg/Pad/fmipXn7+X3T/woX672CJ2mqi\nuuIVKZWaVCqVUkaS5MnzJAvcsjsWCNzot0kW8HTjC8k87+Yvb/z65uWTKen8+bw9PQC5NVWy2Wbx\n2Sfv/0qZwLj+WBLUt+5/QB/Pv0/K/9+vAAocPwD1k+fpqjeq//qfX+uJcFQfl8wn5ADuCjH3WVKT\nGg7M83sMAAWOmPssGDCNBEv8HgNAgSPmPkunpAte0O8xABQ4Yu6jG68vmadPi9hmAXBviLnPipTR\nxWLOzAHcG2Lus0wmo0+JOYB7RMx99GBqQmnPlHzyKb9HAVDgiLmPvjpxXemiIr/HAOAAYu6j8vSk\nJib9ngKAC4i5j1YEipQpYr8cwL0j5j56KJ3WRBH/CQDcO0rio0cklT6w1O8xADiAmPuoKuCpuGSh\n32MAcAAfgeuj0vFrGuj9xO8xADiAM3MfpTOT+sPVP/s9BgAHEHMfjY5eVMLvIQA4gZj7aGLiugaK\n+PhbAPeOmPtocnJCn867z+8xADiAmPvI84o1GORNQwDu3bQxHx8fV2Njo+rr6xWNRvXKK69IkkZG\nRtTc3KyqqiqtW7dOo6Oj2fu0t7ersrJSNTU16u7unt3pC56nQf6VIQC5YDMYGxszM7NUKmWNjY12\n7Ngx27lzp+3Zs8fMzGKxmO3atcvMzM6ePWt1dXWWTCYtHo9bRUWFpdPpOx5T0t/9Cjz0kJ388NcW\nOHLE91lYLFbhrKnMuM2yYMECSVIymVQ6ndbixYt16NAhbdu2TZK0bds2vfXWW5KkgwcPasuWLQoG\ngyovL9eKFSt08uTJmQ7xd2nJ4XdUXBxUprnZ71EAOGDGmGcyGdXX1ysUCmnt2rWqra3V8PCwQqGQ\nJCkUCml4eFiSNDAwoEgkkr1vJBJRf3//LI1e2L6amlA6nfZ7DACOmPEdoIFAQGfOnNGf//xntbS0\n6L333rvtes/z5HnelPef7rq/Z6HUuMwj5gBy43O/mqW0tFRPPvmkPvzwQ4VCIQ0NDUmSBgcHVVZW\nJkkKh8NKJP7yNpi+vj6Fw+Ecj+yGsvHruvHjAwC4d9PG/MKFC9lXqly/fl1HjhxRQ0ODWltb1dHR\nIUnq6OjQhg0bJEmtra3q7OxUMplUPB5Xb2+v1qxZM8tPoTCFJ5OSeFkigNyYdptlcHBQ27ZtUyaT\nUSaT0datW9XU1KSGhgZt2rRJ+/fvV3l5ud58801JUjQa1aZNmxSNRlVcXKzXXnuNbZYphCZSWrz8\nQb/HAOAIz3z4f30CL739q1+prqZGy9mGAvAFTJVs3gHqkwejlfrT5dGZbwgAnwOfZ+6Tscsj+ijj\n9xQAXMGZuU9SY2P635J5fo8BwBHE3CdFmYz65i/wewwAjiDmPglmTH0lfPwtgNwg5j4JWkaJ+zgz\nB5AbxNwH3le/KqXSSvQP+D0KAEcQcx985T//Q57nKVVd7fcoABxBzH1QlpxQepLXJQLIHWLugyXJ\nCWXSxBxA7hBzHzwwPiE+MBFALhFzHzw4Ps4/TAEgp4i5D8pSSaXFh40ByB1i7oP7v/UtTdJyADlE\nzH2wUNKn83n3J4DcIeY+mDeZ0sA8Yg4gd4i5D0oyaQ3eN9/vMQA4hJj7YIGZPl3A57IAyB1i7oNF\nRQF9unCh32MAcAgx98F96ev6dAHbLAByh5jnWcm+vZqvjC7V1vo9CgCHEPM8W5xKSQpo0uO3HkDu\nUJQ8+0o6JSPmAHKMouTZ4slJeV6RJgO8BRRA7hDzPFucSkrKcGYOIKcoSp49kJ6U50mTHmfmAHKH\nmOfZA5m0vIBHzAHkFDHPsy9///u6GixR5k9/8nsUAA4h5nlWKtNQ8Ty/xwDgGGKeZ6UmXZzP57IA\nyC1inmeLLK3/m1fi9xgAHEPM8+zLnnSxhJgDyC1inmelmYwuzONDtgDkFjHPs1Kb1HAw6PcYABxD\nzPNo0Q/+VfO8Il37xyf9HgWAY4h5Ht2fSUtFputFvGEIQG4R8zy639Ly5knXePcngBz7XDFPp9Nq\naGjQ+vXrJUkjIyNqbm5WVVWV1q1bp9HR0ext29vbVVlZqZqaGnV3d8/O1AWq1ExecVDXvSK/RwHg\nmM8V87179yoajcr77IwyFoupublZ586dU1NTk2KxmCSpp6dHBw4cUE9Pj7q6urRjxw5lMpnZm77A\npL2AegNf0hgxB5BrNoNEImFNTU129OhRe+qpp8zMrLq62oaGhszMbHBw0Kqrq83MrK2tzWKxWPa+\nLS0tduLEiTseUxKLxWKx7mJNZcYz85dfflmvvvqqAoG/3HR4eFihUEiSFAqFNDw8LEkaGBhQJBLJ\n3i4Siai/v3+mQwAA7tG0MX/77bdVVlamhoYG3TihvpPnedntl6muBwDMruLprjx+/LgOHTqkd955\nR+Pj47p8+bK2bt2qUCikoaEhLV26VIODgyorK5MkhcNhJRKJ7P37+voUDodn9xkAAKbZgPkr77//\nfnbPfOfOndm98fb2dtu1a5eZmZ09e9bq6upsYmLCPv74Y3vkkUcsk8mwZ85isVg5WlOZ9sz8r93c\nMvnhD3+oTZs2af/+/SovL9ebb74pSYpGo9q0aZOi0aiKi4v12muvsc0CAHng2VSb4bN5UAIPAHdl\nqmTzDlAAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAH\nEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMA\ncAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHEHMAcAAxBwAHFPtxUDPz47AA4CzOzAHA\nAcQcAByQ95h3dXWppqZGlZWV2rNnT74PP60XX3xRoVBIq1atyl42MjKi5uZmVVVVad26dRodHc1e\n197ersrKStXU1Ki7u9uPkbMSiYTWrl2r2tparVy5Uvv27ZM09+cfHx9XY2Oj6uvrFY1G9corrxTE\n3Del02k1NDRo/fr1kgpn7vLycj322GNqaGjQmjVrJBXO7KOjo3r22Wf16KOPKhqN6re//e2cn/2j\njz5SQ0NDdpWWlmrfvn25ndvyaHJy0ioqKiwej1symbS6ujrr6enJ5wjT+uCDD+zUqVO2cuXK7GU7\nd+60PXv2mJlZLBazXbt2mZnZ2bNnra6uzpLJpMXjcauoqLB0Ou3L3GZmg4ODdvr0aTMzu3LlilVV\nVVlPT09BzD82NmZmZqlUyhobG+3YsWMFMbeZ2Y9//GP7zne+Y+vXrzezwvnzUl5ebhcvXrztskKZ\n/fnnn7f9+/eb2Y0/M6OjowUzu5lZOp22pUuX2ieffJLTufMa8+PHj1tLS0v26/b2dmtvb8/nCDOK\nx+O3xby6utqGhobM7EYwq6urzcysra3NYrFY9nYtLS124sSJ/A47jaefftqOHDlSUPOPjY3Z6tWr\n7fe//31BzJ1IJKypqcmOHj1qTz31lJkVzp+X8vJyu3Dhwm2XFcLso6Oj9vDDD99xeSHMftMvf/lL\n+8Y3vmFmuZ07r9ss/f39Wr58efbrSCSi/v7+fI7whQ0PDysUCkmSQqGQhoeHJUkDAwOKRCLZ282l\n53L+/HmdPn1ajY2NBTF/JpNRfX29QqFQdquoEOZ++eWX9eqrryoQ+Mu3USHMLUme5+mJJ57Q6tWr\n9bOf/UxSYcwej8e1ZMkSvfDCC/ra176m7373uxobGyuI2W/q7OzUli1bJOX29zyvMfc8L5+HyznP\n86Z9DnPh+V29elUbN27U3r17tWjRotuum6vzBwIBnTlzRn19ffrggw/03nvv3THXXJv77bffVllZ\nmRoaGqZ8qe1cnPum3/zmNzp9+rQOHz6sn/70pzp27Nht18/V2ScnJ3Xq1Cnt2LFDp06d0sKFCxWL\nxe6YbS7OLknJZFK/+MUv9Nxzz91x3b3OndeYh8NhJRKJ7NeJROK2v33molAopKGhIUnS4OCgysrK\nJN35XPr6+hQOh32Z8aZUKqWNGzdq69at2rBhg6TCmr+0tFRPPvmkPvzwwzk/9/Hjx3Xo0CE9/PDD\n2rJli44ePaqtW7fO+blvWrZsmSRpyZIleuaZZ3Ty5MmCmD0SiSgSiejxxx+XJD377LM6deqUli5d\nOudnl6TDhw/r61//upYsWSIpt9+feY356tWr1dvbq/PnzyuZTOrAgQNqbW3N5whfWGtrqzo6OiRJ\nHR0d2Ui2traqs7NTyWRS8Xhcvb292VcF+MHMtH37dkWjUb300kvZy+f6/BcuXMj+BP/69es6cuSI\nGhoa5vzcbW1tSiQSisfj6uzs1Le//W39/Oc/n/NzS9K1a9d05coVSdLY2Ji6u7u1atWqgph96dKl\nWr58uc6dOydJevfdd1VbW6v169fP+dkl6Y033shusdycL2dzz8oO/zTeeecdq6qqsoqKCmtra8v3\n4ae1efNmW7ZsmQWDQYtEIvb666/bxYsXrampySorK625udkuXbqUvf3u3butoqLCqqurraury8fJ\nzY4dO2ae51ldXZ3V19dbfX29HT58eM7P/7vf/c4aGhqsrq7OVq1aZT/60Y/MzOb83Ld6//33s69m\nKYS5P/74Y6urq7O6ujqrra3Nfh8WwuxmZmfOnLHVq1fbY489Zs8884yNjo4WxOxXr161Bx54wC5f\nvpy9LJdze2a8tx4ACh3vAAUABxBzAHAAMQcABxBzAHAAMQcABxBzAHAAMQcABxBzAHDA/wPvp/fQ\najrh4AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fe7a03382d0>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul vectoriel d'une position sur l'image (2D)\n",
"\n",
"* $\\theta$, l'angle que fait le rayon sortant de la cam\u00e9ra avec la bisectrice de l'ouverture de la cam\u00e9ra\n",
"* $ A = \\begin{pmatrix} i \\\\ j \\end{pmatrix} $, la position du laser\n",
"* $ B = \\begin{pmatrix} 0 \\\\ k \\end{pmatrix}$, point o\u00f9 le laser est au centre de l'image\n",
"* $ P' = \\begin{pmatrix} k \\tan(\\theta) \\\\ k \\end{pmatrix} $, le point visible sur le plan de l'image (projection du point visible sur le plan orthogonal \u00e0 $B$)\n",
"\n",
"Nous savons que le point est \u00e0 l'intersection du laser et du rayon sortant de la cam\u00e9ra, donc\n",
"\n",
"$\n",
"A + \\lambda (B - A) - \\mu P' = 0\n",
"$\n",
"\n",
"$\n",
"\\begin{pmatrix} i \\\\ j \\end{pmatrix} + \n",
"\\lambda \\begin{pmatrix} -i \\\\ k-j \\end{pmatrix} - \n",
"\\mu \\begin{pmatrix} k\\tan(\\theta) \\\\ k \\end{pmatrix} \n",
"\\Leftrightarrow\n",
"\\begin{pmatrix} i & k \\tan(\\theta) \\\\ j-k & k \\end{pmatrix}\n",
"\\begin{pmatrix} \\lambda \\\\ \\mu \\end{pmatrix} = \n",
"\\begin{pmatrix} i \\\\ j \\end{pmatrix}\n",
"$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from math import tan\n",
"k = 58.0 # Distance de la camera au point ou le laser est au centre de l'image (cm)\n",
"i, j = -45.0, 26.5 #Position du laser par rapport \u00e0 la camera\n",
"\n",
"A, B = np.array([i,j]), np.array([0,k])\n",
"Ax, Ay = A\n",
"Bx, By = B\n",
"\n",
"# (x,y)\n",
"def pos(theta):\n",
" matrix = np.array([[Ax, By*tan(theta)], [Ay-By, By]])\n",
" l, m = np.linalg.solve(matrix, A)\n",
" return A + l*(B-A)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de l'angle (\u03b8)\n",
"\n",
"* Notons `\u03c6` l'angle maximal entre le centre et le bord (droit) de l'image\n",
"* Notons `E` la largeur de l'image en pixels, `e` la distance en pixels entre le centre de l'image et l'image de `p` \n",
"* Notons `m` la largeur r\u00e9elle entre les 2 bords observables en `b`\n",
"\n",
"### Principe\n",
"\n",
" m = 2*k*tan(\u03c6) => \u03c6 = atan(m/2k)\n",
" \u03b8 = \u03c6 * e/(E/2)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from math import atan, pi\n",
"m = 69\n",
"phi = atan(float(m)/(2*k))\n",
"E = diff.shape[1]\n",
"print \"Ouverture camera:\", 180*phi/pi, \"degr\u00e9s. Points d\u00e9tect\u00e9s:\"\n",
"\n",
"theta = lambda e: phi * e/(E/2)\n",
"\n",
"X, Y = [], []\n",
"XY = []\n",
"i = 260\n",
"j_save = 0\n",
"for j in range(diff.shape[1]):\n",
" if tuple(diff[i][j]) != (0, 0, 0):\n",
" j_save = j\n",
" t = theta(j - E/2)\n",
" XY.append(pos(t))\n",
" print XY[-1]\n",
"\n",
"X, Y = map(np.array, zip(*XY))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Ouverture camera: 30.7453492818 degr\u00e9s. Points d\u00e9tect\u00e9s:\n",
"[-15.74785603 46.97650078]\n",
"[-15.68474116 47.02068119]\n",
"[-15.62157038 47.06490074]\n",
"[-15.55834332 47.10915968]\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualisation du r\u00e9sultat\n",
"\n",
"#### Affichage en 3D (hauteur \u00e0 0 pour le moment)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d', aspect=\"equal\")\n",
"ax.scatter(X, Y, [0 for i in range(len(X))])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fe79e3fc750>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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R0aQLNHlJg8FgUr2Jh1bS5XsMUIab3BBUnpuKTqrlhuWrxpRY3/QAnTtfEZaX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\nQ0MDOnbsKHy+R48eKCwslEwoUcIuXcsV8eckNd2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wcuVKHD58GEDT+oD33XcfunTpggce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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fe79ea71850>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Affichage en 2D\n",
"\n",
"* Rayon laser (\u00e9quation vectorielle \u00e9tablie \u00e0 partir des mesures), en rouge\n",
"* Rayon capt\u00e9 par la cam\u00e9ra, en jaune\n",
"* Champ de vue de la cam\u00e9ra (vert)\n",
"* Point d\u00e9tect\u00e9 (points bleus)\n",
"* Multiprise (en noir)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#shortcut for vector\n",
"vec = lambda *args : np.array(args)\n",
"\n",
"fig = plt.subplot(111, aspect='equal')\n",
"# Computed points from above\n",
"fig.scatter(X, Y)\n",
"\n",
"def line(direction, position=np.array([0, 0]), **kwargs):\n",
" \"\"\"Draw a parametric line\"\"\"\n",
" xy = [position + t*direction for t in np.linspace(0, 1)]\n",
" x, y = zip(*xy)\n",
" fig.plot(x, y, **kwargs)\n",
"\n",
"# Image background\n",
"line(vec(m, 0), position=vec(-m/2, k), color='black', linewidth=3)\n",
" \n",
"# A ray from the camera\n",
"line(pos(theta(j_save - E/2)), color='y')\n",
"\n",
"# Camera left limit\n",
"line(vec(tan(theta(-E/2))*k, k), color='g')\n",
"\n",
"# Camera right limit\n",
"line(vec(tan(theta(E/2))*k, k), color='g')\n",
"\n",
"#Laser ray\n",
"line(B-A, A, color='r')\n",
"\n",
"#System parameters\n",
"line(B, color='m', ls='--')\n",
"line(A*vec(1, 0), color='m', ls='--')\n",
"line(A*vec(0, 1), A*vec(1, 0), color='m', ls='--')\n",
"\n",
"fig.set_aspect('equal')\n",
"plt.show()\n",
"plt.imshow(img_red)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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LNSpMRGd04Gh4OHrg42MfG9zG/VHgjb3/MOlAEp5r/RzCOoYJnUqjRIWJ6Egk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\nokOFiRAiOlSYCCGiQ4WJECI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"text": [
"<matplotlib.figure.Figure at 0x7fe79e3a8190>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.image.AxesImage at 0x7fe79e584910>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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S3bg6HCKJAXDPwRxZo2iWeiQe0aGqhrCacDZYV4js3IqKoQLZH/F0+pjr8hHW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UwlklbDrHzs7A73x1s4Ca0ZShQDXZbC+XyJZgcCknJvMJbQwvIJPBGnYCZpkybwPReX5+TsJD0h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\n4gSY1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ljFoRV8WTQYTh7NKlXQDCwaAX0KfEKnyzFrpxVvHzbNAMoK0MM2mc/kPt7aA7eK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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fe79e2bb610>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vue du laser par la cam\u00e9ra\n",
"\n",
"Si on balaye de gauche \u00e0 droite avec la cam\u00e9ra (variation de \u03b8), voici ce que l'on peut d\u00e9tecter (vu de haut)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from math import pi\n",
"XY = [pos(t) for t in np.linspace(-phi, phi)]\n",
"X, Y = map(np.array, zip(*XY))\n",
"plt.xlim(-m/2, m/2)\n",
"plt.ylim(0, 2*k)\n",
"plt.scatter(X, Y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 210,
"text": [
"<matplotlib.collections.PathCollection at 0x7fa65d3972d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fa65d4c88d0>"
]
}
],
"prompt_number": 210
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
pcmagic/stokes_flow | src/try111.ipynb | 1 | 1909430 | null | mit |
magic2du/contact_matrix | Contact_maps/mnist_psuedo_ipython_dl_ppi/code/DL_Stacked_Model_Mnist_Psuedo_12_15_2014.ipynb | 1 | 285090 | {
"metadata": {
"name": "",
"signature": "sha256:fafadb99ee92328afdccf36177ce8be8638757b23e7365a0ef0a107770be9e5c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this part imports libs and load data from csv file\n",
"import sys\n",
"sys.path.append('../../../libs/')\n",
"import csv\n",
"from dateutil import parser\n",
"from datetime import timedelta\n",
"from sklearn import svm\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pickle\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn import preprocessing\n",
"import sklearn\n",
"import scipy.stats as ss\n",
"import cPickle\n",
"import gzip\n",
"import os\n",
"import time\n",
"import numpy\n",
"import theano\n",
"import theano.tensor as T\n",
"from theano.tensor.shared_randomstreams import RandomStreams\n",
"import os.path\n",
"import IO_class\n",
"from IO_class import FileOperator\n",
"from sklearn import cross_validation\n",
"import sklearn\n",
"import numpy as np\n",
"import csv\n",
"from dateutil import parser\n",
"from datetime import timedelta\n",
"from sklearn import svm\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pdb, PIL\n",
"import pickle\n",
"import numpy as np\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn import preprocessing\n",
"import sklearn\n",
"import scipy.stats as ss\n",
"from sklearn.svm import LinearSVC\n",
"import random\n",
"from DL_libs import *\n",
"from itertools import izip #new\n",
"import math\n",
"from sklearn.svm import SVC"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n",
"C:\\Users\\u\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\scipy\\lib\\_util.py:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
" DeprecationWarning)\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set settings for this script\n",
"settings = {}\n",
"settings['fisher_mode'] = 'FisherM1'\n",
"settings['with_auc_score'] = False\n",
"settings['reduce_ratio'] = 1\n",
"settings['SVM'] = 1\n",
"settings['SVM_RBF'] = 1\n",
"settings['SVM_POLY'] = 1\n",
"settings['DL'] = 1\n",
"settings['Log'] = 1\n",
"settings['SAE_SVM'] = 1\n",
"settings['SAE_SVM_RBF'] = 1\n",
"settings['SAE_SVM_POLY'] = 1\n",
"\n",
"settings['DL_S'] = 1\n",
"settings['SAE_S_SVM'] = 1\n",
"settings['SAE_S_SVM_RBF'] = 1\n",
"settings['SAE_S_SVM_POLY'] = 1\n",
"settings['number_iterations'] =1\n",
"\n",
"\n",
"settings['finetune_lr'] = 0.1\n",
"settings['batch_size'] = 30\n",
"settings['pretraining_interations'] = 10000#10000\n",
"settings['pretrain_lr'] = 0.001\n",
"settings['training_epochs'] = 300 #300\n",
"settings['hidden_layers_sizes'] = [200, 200]\n",
"settings['corruption_levels'] = [0,0]\n",
"settings['number_of_training'] = [10000]#[1000, 2500, 5000, 7500, 10000]\n",
"settings['test_set_from_test'] = True\n",
"\n",
"\n",
"import logging\n",
"import time\n",
"current_date = time.strftime(\"%m_%d_%Y\")\n",
"\n",
"logger = logging.getLogger(__name__)\n",
"logger.setLevel(logging.DEBUG)\n",
"\n",
"logname = 'log_DL_handwritten_digits' + current_date + '.log'\n",
"handler = logging.FileHandler(logname)\n",
"handler.setLevel(logging.DEBUG)\n",
"\n",
"# create a logging format\n",
"\n",
"formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
"handler.setFormatter(formatter)\n",
"\n",
"# add the handlers to the logger\n",
"\n",
"logger.addHandler(handler)\n",
"\n",
"#logger.debug('This message should go to the log file')\n",
"for key, value in settings.items():\n",
" logger.info(key +': '+ str(value))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:DL: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:Log: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:DL_S: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SVM_POLY: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:reduce_ratio: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:hidden_layers_sizes: [200, 200]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:corruption_levels: [0, 0]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:pretrain_lr: 0.001\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_S_SVM: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_SVM_POLY: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SVM: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_S_SVM_RBF: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:number_of_training: [10000]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SVM_RBF: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:batch_size: 30\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_S_SVM_POLY: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_SVM_RBF: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:SAE_SVM: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:pretraining_interations: 10000\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:fisher_mode: FisherM1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:number_iterations: 1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:finetune_lr: 0.1\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:test_set_from_test: True\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:with_auc_score: False\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:__main__:training_epochs: 300\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = gzip.open('mnist.pkl.gz', 'rb')\n",
"train_set, valid_set, test_set = cPickle.load(f)\n",
"X_train,y_train = train_set\n",
"X_valid,y_valid = valid_set\n",
"X_total=np.vstack((X_train, X_valid))\n",
"X_total = np.array(X_total, dtype= theano.config.floatX)\n",
"print'sample size', X_total.shape\n",
"y_total = np.concatenate([y_train, y_valid])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"sample size (60000L, 784L)\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"theano.config.floatX"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"'float64'"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"################## generate data from training set###################\n",
"array_A =[]\n",
"array_B =[]\n",
"for i in range(100000):\n",
" array_A.append(np.random.random_integers(0, 59999))\n",
" array_B.append(np.random.random_integers(0, 59999))\n",
"pos_index = []\n",
"neg_index = []\n",
"for index in xrange(100000):\n",
" if y_total[array_A[index]] - y_total[array_B[index]] == 1:\n",
" pos_index.append(index)\n",
" else:\n",
" neg_index.append(index)\n",
"print 'number of positive examples', len(pos_index)\n",
"selected_neg_index= neg_index[ : len(pos_index)] \n",
"\n",
"array_A = np.array(array_A)\n",
"array_B = np.array(array_B)\n",
"index_for_positive_image_A = array_A[pos_index]\n",
"index_for_positive_image_B = array_B[pos_index]\n",
"index_for_neg_image_A = array_A[selected_neg_index]\n",
"index_for_neg_image_B = array_B[selected_neg_index]\n",
"\n",
"X_pos_A = X_total[index_for_positive_image_A]\n",
"X_pos_B = X_total[index_for_positive_image_B]\n",
"X_pos_whole = np.hstack((X_pos_A,X_pos_B))\n",
"X_neg_A = X_total[index_for_neg_image_A]\n",
"X_neg_B = X_total[index_for_neg_image_B]\n",
"X_neg_whole = np.hstack((X_neg_A, X_neg_B))\n",
"print X_pos_A.shape, X_pos_B.shape, X_pos_whole.shape\n",
"print X_neg_A.shape, X_neg_B.shape, X_neg_whole.shape\n",
"\n",
"X_whole = np.vstack((X_pos_whole, X_neg_whole))\n",
"print X_whole.shape\n",
"y_pos = np.ones(X_pos_whole.shape[0])\n",
"y_neg = np.zeros(X_neg_whole.shape[0])\n",
"y_whole = np.concatenate([y_pos,y_neg])\n",
"print y_whole"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of positive examples 9068\n",
"(9068L, 784L)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" (9068L, 784L) (9068L, 1568L)\n",
"(9068L, 784L) (9068L, 784L) (9068L, 1568L)\n",
"(18136L, 1568L)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[ 1. 1. 1. ..., 0. 0. 0.]\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"joint = X_whole[-3]\n",
"print 'label', y_whole[-3]\n",
"imageA = joint[0: 784]\n",
"imageB = joint[784: ]\n",
"pylab.imshow(imageA.reshape(28, 28), cmap=\"Greys\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"label 0.0\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['f', 'random']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<matplotlib.image.AxesImage at 0x12248c88>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x11f79358>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pylab.imshow(imageB.reshape(28, 28), cmap=\"Greys\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.image.AxesImage at 0x107d2320>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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bjQZXV1fBrZilZd18OigYa2mbOMhceigCH5o22sQbNO8YXm+lFduyOXael9D4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\ncRzHcRzL/wcQ3ccbzrpncwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xf901fd0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"def saveAsCsv(with_auc_score, fname, score_dict, arguments): #new\n",
" newfile = False\n",
" if os.path.isfile('report_' + fname + '.csv'):\n",
" pass\n",
" else:\n",
" newfile = True\n",
" csvfile = open('report_' + fname + '.csv', 'a+')\n",
" writer = csv.writer(csvfile)\n",
" if newfile == True:\n",
" writer.writerow(['no.', 'number_of_training', 'method', 'isTest']+ score_dict.keys()) #, 'AUC'])\n",
"\n",
" for arg in arguments: \n",
" writer.writerow([i for i in arg])\n",
" csvfile.close()\n",
"def run_models(settings = None):\n",
" analysis_scr = []\n",
" with_auc_score = settings['with_auc_score']\n",
"\n",
" for subset_no in xrange(1,settings['number_iterations']+1):\n",
" print(\"Subset:\", subset_no)\n",
" \n",
" ################## generate data ###################\n",
" array_A =[]\n",
" array_B =[]\n",
" for i in range(100000):\n",
" array_A.append(np.random.random_integers(0, 59999))\n",
" array_B.append(np.random.random_integers(0, 59999))\n",
" pos_index = []\n",
" neg_index = []\n",
" for index in xrange(100000):\n",
" if y_total[array_A[index]] - y_total[array_B[index]] == 1:\n",
" pos_index.append(index)\n",
" else:\n",
" neg_index.append(index)\n",
" print 'number of positive examples', len(pos_index)\n",
" selected_neg_index= neg_index[ : len(pos_index)] \n",
" \n",
" array_A = np.array(array_A)\n",
" array_B = np.array(array_B)\n",
" index_for_positive_image_A = array_A[pos_index]\n",
" index_for_positive_image_B = array_B[pos_index]\n",
" index_for_neg_image_A = array_A[selected_neg_index]\n",
" index_for_neg_image_B = array_B[selected_neg_index]\n",
"\n",
" X_pos_A = X_total[index_for_positive_image_A]\n",
" X_pos_B = X_total[index_for_positive_image_B]\n",
" X_pos_whole = np.hstack((X_pos_A,X_pos_B))\n",
" X_neg_A = X_total[index_for_neg_image_A]\n",
" X_neg_B = X_total[index_for_neg_image_B]\n",
" X_neg_whole = np.hstack((X_neg_A, X_neg_B))\n",
" print X_pos_A.shape, X_pos_B.shape, X_pos_whole.shape\n",
" print X_neg_A.shape, X_neg_B.shape, X_neg_whole.shape\n",
"\n",
" X_whole = np.vstack((X_pos_whole, X_neg_whole))\n",
" print X_whole.shape\n",
" y_pos = np.ones(X_pos_whole.shape[0])\n",
" y_neg = np.zeros(X_neg_whole.shape[0])\n",
" y_whole = np.concatenate([y_pos,y_neg])\n",
" print y_whole.shape\n",
" \n",
" x_train_pre_validation, x_test, y_train_pre_validation, y_test = train_test_split(X_whole,y_whole,\\\n",
" test_size=0.2, random_state=211)\n",
" for number_of_training in settings['number_of_training']:\n",
" x_train, x_validation, y_train, y_validation = train_test_split(x_train_pre_validation[:number_of_training],\n",
" y_train_pre_validation[:number_of_training],\\\n",
" test_size=0.2, random_state=21)\n",
" print x_train.shape, y_train.shape, x_validation.shape, \\\n",
" y_validation.shape, x_test.shape, y_test.shape\n",
" x_train_minmax, x_validation_minmax, x_test_minmax = x_train, x_validation, x_test \n",
" train_X_reduced = x_train\n",
" train_y_reduced = y_train\n",
" test_X = x_test\n",
" test_y = y_test\n",
" ###original data###\n",
" ################ end of data ####################\n",
" standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n",
" scaled_train_X = standard_scaler.transform(train_X_reduced)\n",
" scaled_test_X = standard_scaler.transform(test_X)\n",
" if settings['SVM']:\n",
" print \"SVM\" \n",
" Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n",
" Linear_SVC.fit(scaled_train_X, y_train)\n",
" predicted_test_y = Linear_SVC.predict(scaled_test_X)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
"\n",
" predicted_train_y = Linear_SVC.predict(scaled_train_X)\n",
" isTest = False; #new\n",
" analysis_scr.append(( subset_no,number_of_training, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
"\n",
" if settings['SVM_RBF']:\n",
" print \"SVM_RBF\"\n",
" L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, y_train)\n",
" predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
" predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no,number_of_training, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
" \n",
" if settings['SVM_POLY']:\n",
" print \"SVM_POLY\"\n",
" L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)\n",
"\n",
" predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)\n",
" isTest = True; #new\n",
" analysis_scr.append(( subset_no, number_of_training,'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
"\n",
" predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training,'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
"\n",
" if settings['Log']:\n",
" print \"Log\"\n",
" log_clf_l2 = sklearn.linear_model.LogisticRegression(C=1, penalty='l2')\n",
" log_clf_l2.fit(scaled_train_X, train_y_reduced)\n",
" predicted_test_y = log_clf_l2.predict(scaled_test_X)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no,number_of_training, 'Log', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
" predicted_train_y = log_clf_l2.predict(scaled_train_X)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training,'Log', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values())) \n",
"\n",
" # direct deep learning \n",
"\n",
" finetune_lr = settings['finetune_lr']\n",
" batch_size = settings['batch_size']\n",
" pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n",
" #pretrain_lr=0.001\n",
" pretrain_lr = settings['pretrain_lr']\n",
" training_epochs = settings['training_epochs']\n",
" hidden_layers_sizes = settings['hidden_layers_sizes']\n",
" corruption_levels = settings['corruption_levels']\n",
" \n",
" if settings['DL']:\n",
" print \"direct deep learning\"\n",
" sda = trainSda(x_train_minmax, y_train,\n",
" x_validation_minmax, y_validation, \n",
" x_test_minmax, test_y,\n",
" hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n",
" training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n",
" pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n",
" )\n",
" print 'hidden_layers_sizes:', hidden_layers_sizes\n",
" print 'corruption_levels:', corruption_levels\n",
" test_predicted = sda.predict(x_test_minmax)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no,number_of_training, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))\n",
" training_predicted = sda.predict(x_train_minmax)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no,number_of_training, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))\n",
"\n",
" ####transformed original data#### \n",
" x = train_X_reduced\n",
" a_MAE_original = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n",
" hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n",
" new_x_train_minmax_A = a_MAE_original.transform(train_X_reduced)\n",
" new_x_test_minmax_A = a_MAE_original.transform(x_test_minmax) \n",
" standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)\n",
" new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)\n",
" new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)\n",
" new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))\n",
" new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))\n",
"\n",
" if settings['SAE_SVM']: \n",
" # SAE_SVM\n",
" print 'SAE followed by SVM'\n",
"\n",
" Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n",
" Linear_SVC.fit(new_x_train_scaled, train_y_reduced)\n",
" predicted_test_y = Linear_SVC.predict(new_x_test_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append(( subset_no, number_of_training,'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
"\n",
" predicted_train_y = Linear_SVC.predict(new_x_train_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append(( subset_no, number_of_training,'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
" if settings['SAE_SVM_RBF']: \n",
" # SAE_SVM\n",
" print 'SAE followed by SVM RBF'\n",
" L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)\n",
"\n",
" predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
"\n",
" predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
" if settings['SAE_SVM_POLY']: \n",
" # SAE_SVM\n",
" print 'SAE followed by SVM POLY'\n",
" L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(new_x_train_scaled, train_y_reduced)\n",
"\n",
" predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training,'SAE_SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n",
"\n",
" predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n",
"\n",
" #### separated transformed data ####\n",
" y_test = test_y\n",
" print 'deep learning using split network'\n",
" # get the new representation for A set. first 784-D\n",
" pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n",
"\n",
" x = x_train_minmax[:, :x_train_minmax.shape[1]/2]\n",
" print \"original shape for A\", x.shape\n",
" a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n",
" hidden_layers_sizes = [x/2 for x in hidden_layers_sizes], corruption_levels=corruption_levels)\n",
" new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])\n",
" x = x_train_minmax[:, x_train_minmax.shape[1]/2:]\n",
"\n",
" print \"original shape for B\", x.shape\n",
" a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n",
" hidden_layers_sizes = [x/2 for x in hidden_layers_sizes], corruption_levels=corruption_levels)\n",
" new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])\n",
"\n",
" new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])\n",
" new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])\n",
" new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])\n",
" new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])\n",
" new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))\n",
" new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))\n",
" new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B)) \n",
" standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_whole)\n",
" new_x_train_minmax_whole_scaled = standard_scaler.transform(new_x_train_minmax_whole)\n",
" new_x_test_minmax_whole_scaled = standard_scaler.transform(new_x_test_minmax_whole) \n",
" if settings['DL_S']:\n",
" # deep learning using split network\n",
" sda_transformed = trainSda(new_x_train_minmax_whole, y_train,\n",
" new_x_validationt_minmax_whole, y_validation , \n",
" new_x_test_minmax_whole, y_test,\n",
" hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n",
" training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n",
" pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n",
" )\n",
" print 'hidden_layers_sizes:', hidden_layers_sizes\n",
" print 'corruption_levels:', corruption_levels\n",
"\n",
" predicted_test_y = sda_transformed.predict(new_x_test_minmax_whole)\n",
" y_test = test_y\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training,'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n",
"\n",
" training_predicted = sda_transformed.predict(new_x_train_minmax_whole)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no,number_of_training, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n",
" if settings['SAE_S_SVM']:\n",
" print 'SAE_S followed by SVM'\n",
"\n",
" Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n",
" Linear_SVC.fit(new_x_train_minmax_whole_scaled, train_y_reduced)\n",
" predicted_test_y = Linear_SVC.predict(new_x_test_minmax_whole_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append(( subset_no, number_of_training,'SAE_S_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new\n",
"\n",
" predicted_train_y = Linear_SVC.predict(new_x_train_minmax_whole_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append(( subset_no,number_of_training, 'SAE_S_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))\n",
" if settings['SAE_S_SVM_RBF']: \n",
" print 'SAE S followed by SVM RBF'\n",
" L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_minmax_whole_scaled, train_y_reduced)\n",
"\n",
" predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_minmax_whole_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training, 'SAE_S_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new\n",
"\n",
" predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_minmax_whole_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))\n",
" if settings['SAE_S_SVM_POLY']: \n",
" # SAE_SVM\n",
" print 'SAE S followed by SVM POLY'\n",
" L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(new_x_train_minmax_whole_scaled, train_y_reduced)\n",
"\n",
" predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_minmax_whole_scaled)\n",
" isTest = True; #new\n",
" analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new\n",
"\n",
" predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_minmax_whole_scaled)\n",
" isTest = False; #new\n",
" analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))\n",
"\n",
" report_name = 'DL_handwritten_digits' + '_size_'.join(map(str, hidden_layers_sizes)) + \\\n",
" '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + \\\n",
" '_' + str(settings['pretraining_interations']) + '_' + current_date\n",
" saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)\n",
" return sda, a_MAE_original, a_MAE_A, a_MAE_B, analysis_scr"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sda, a_MAE_original, a_MAE_A, a_MAE_B, analysis_scr = run_models(settings)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('Subset:', 1)\n",
"number of positive examples"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9049\n",
"(9049L, 784L)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" (9049L, 784L) (9049L, 1568L)\n",
"(9049L, 784L) (9049L, 784L) (9049L, 1568L)\n",
"(18098L, 1568L)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(18098L,)\n",
"(8000L, 1568L)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" (8000L,) (2000L, 1568L) (2000L,) (3620L, 1568L) (3620L,)\n",
"SVM"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 51.8%\n",
"precision 52.9%\n",
"accuracy 52.5%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 61.0%\n",
"precision 61.7%\n",
"accuracy 61.9%\n",
"SVM_RBF\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 25.5%\n",
"precision 94.1%\n",
"accuracy 61.7%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 100.0%\n",
"precision 100.0%\n",
"accuracy 100.0%\n",
"SVM_POLY\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 50.5%\n",
"precision 88.5%\n",
"accuracy 71.8%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 70.7%\n",
"precision 99.5%\n",
"accuracy 85.3%\n",
"Log\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 55.6%\n",
"precision 55.3%\n",
"accuracy 55.0%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 70.3%\n",
"precision 68.8%\n",
"accuracy 69.5%\n",
"direct deep learning\n",
"... building the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"... getting the pretraining functions"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"... pre-training the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pre-training layer 0, epoch 0, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 623.46235755\n",
"Pre-training layer 0, epoch 1, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 430.23393472\n",
"Pre-training layer 0, epoch 2, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 395.20118964\n",
"Pre-training layer 0, epoch 3, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 373.855834748\n",
"Pre-training layer 0, epoch 4, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 357.873145776\n",
"Pre-training layer 0, epoch 5, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 345.049232307\n",
"Pre-training layer 0, epoch 6, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334.421463587\n",
"Pre-training layer 0, epoch 7, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 325.434652438\n",
"Pre-training layer 0, epoch 8, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 317.72046734\n",
"Pre-training layer 0, epoch 9, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 311.016214096\n",
"Pre-training layer 0, epoch 10, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 305.126710447\n",
"Pre-training layer 0, epoch 11, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 299.903246944\n",
"Pre-training layer 0, epoch 12, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 295.23056297\n",
"Pre-training layer 0, epoch 13, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 291.018128687\n",
"Pre-training layer 0, epoch 14, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 287.194022952\n",
"Pre-training layer 0, epoch 15, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 283.700511551\n",
"Pre-training layer 0, epoch 16, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 280.490796913\n",
"Pre-training layer 0, epoch 17, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 277.526598445\n",
"Pre-training layer 0, epoch 18, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 274.77633199\n",
"Pre-training layer 0, epoch 19, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 272.213726541\n",
"Pre-training layer 0, epoch 20, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 269.816763218\n",
"Pre-training layer 0, epoch 21, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 267.566854001\n",
"Pre-training layer 0, epoch 22, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 265.448200483\n",
"Pre-training layer 0, epoch 23, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 263.447289068\n",
"Pre-training layer 0, epoch 24, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 261.552490522\n",
"Pre-training layer 0, epoch 25, cost "
]
},
{
"output_type": "stream",
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"text": [
" 259.753740025\n",
"Pre-training layer 0, epoch 26, cost "
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},
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"text": [
" 258.04227981\n",
"Pre-training layer 0, epoch 27, cost "
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"text": [
" 256.410450823\n",
"Pre-training layer 0, epoch 28, cost "
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" 254.851523019\n",
"Pre-training layer 0, epoch 29, cost "
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" 253.359556303\n",
"Pre-training layer 0, epoch 30, cost "
]
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" 251.929285911\n",
"Pre-training layer 0, epoch 31, cost "
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" 250.556027388\n",
"Pre-training layer 0, epoch 32, cost "
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" 249.235597354\n",
"Pre-training layer 0, epoch 33, cost "
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" 247.964247082\n",
"Pre-training layer 0, epoch 34, cost "
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" 246.738606495\n",
"Pre-training layer 0, epoch 35, cost "
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" 245.555636709\n",
"Pre-training layer 0, epoch 36, cost "
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" 244.412589609\n",
"Pre-training layer 1, epoch 0, cost "
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" 120.19960444\n",
"Pre-training layer 1, epoch 1, cost "
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" 100.050132242\n",
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" 97.3058903162\n",
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" 94.8963292401\n",
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" 92.7258643128\n",
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" 90.7875749044\n",
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" 87.5390958474\n",
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" 81.3760666654\n",
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" 77.6948995694\n",
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" 75.3228110639\n",
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" 75.099460344\n",
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" 74.8877311443\n",
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" 74.6866620263\n",
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" 74.495397238\n",
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" 74.3131727418\n",
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" 74.1393043506\n",
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"text": [
" 73.9731776134\n",
"Pre-training layer 1, epoch 36, cost "
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 73.8142391604\n",
"... getting the finetuning functions\n",
"... finetunning the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 1, minibatch 266/266, validation error 45.757576 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 1, minibatch 266/266, test error of best model 46.861111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 2, minibatch 266/266, validation error 38.939394 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 2, minibatch 266/266, test error of best model 39.000000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 3, minibatch 266/266, validation error 34.949495 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 3, minibatch 266/266, test error of best model 34.555556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 4, minibatch 266/266, validation error 31.868687 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 4, minibatch 266/266, test error of best model 32.138889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 5, minibatch 266/266, validation error 30.303030 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 5, minibatch 266/266, test error of best model 30.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 6, minibatch 266/266, validation error 28.434343 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 6, minibatch 266/266, test error of best model 28.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 7, minibatch 266/266, validation error 25.757576 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 7, minibatch 266/266, test error of best model 25.638889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 8, minibatch 266/266, validation error 23.939394 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 8, minibatch 266/266, test error of best model 23.944444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 9, minibatch 266/266, validation error 21.767677 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 9, minibatch 266/266, test error of best model 22.333333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 10, minibatch 266/266, validation error 19.848485 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 10, minibatch 266/266, test error of best model 21.000000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 11, minibatch 266/266, validation error 19.242424 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 11, minibatch 266/266, test error of best model 19.916667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 12, minibatch 266/266, validation error 18.434343 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 12, minibatch 266/266, test error of best model 18.916667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 13, minibatch 266/266, validation error 17.828283 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 13, minibatch 266/266, test error of best model 18.638889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 14, minibatch 266/266, validation error 17.373737 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 14, minibatch 266/266, test error of best model 18.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 15, minibatch 266/266, validation error 17.424242 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 16, minibatch 266/266, validation error 16.919192 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 16, minibatch 266/266, test error of best model 17.166667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 17, minibatch 266/266, validation error 16.717172 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 17, minibatch 266/266, test error of best model 17.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 18, minibatch 266/266, validation error 16.616162 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 18, minibatch 266/266, test error of best model 16.777778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 19, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 19, minibatch 266/266, test error of best model 16.833333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 20, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 20, minibatch 266/266, test error of best model 16.833333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 21, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 21, minibatch 266/266, test error of best model 16.777778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 22, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 23, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 24, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 25, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 26, minibatch 266/266, validation error 16.464646 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 27, minibatch 266/266, validation error 16.363636 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 28, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 29, minibatch 266/266, validation error 16.262626 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 29, minibatch 266/266, test error of best model 16.500000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 30, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 31, minibatch 266/266, validation error 16.363636 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 32, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 33, minibatch 266/266, validation error 16.363636 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 34, minibatch 266/266, validation error 16.363636 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 35, minibatch 266/266, validation error 16.262626 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 36, minibatch 266/266, validation error 16.262626 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 37, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 37, minibatch 266/266, test error of best model 16.166667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 38, minibatch 266/266, validation error 16.262626 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 39, minibatch 266/266, validation error 16.262626 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 40, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 40, minibatch 266/266, test error of best model 16.250000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 41, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 42, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 42, minibatch 266/266, test error of best model 16.166667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 43, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 43, minibatch 266/266, test error of best model 16.083333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 44, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 45, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 46, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 47, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 48, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 49, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 50, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 51, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 52, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 52, minibatch 266/266, test error of best model 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 53, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 54, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 55, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 56, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 57, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 58, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 59, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 60, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 61, minibatch 266/266, validation error 16.313131 %"
]
},
{
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"epoch 62, minibatch 266/266, validation error 16.262626 %"
]
},
{
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"epoch 63, minibatch 266/266, validation error 16.363636 %"
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},
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"epoch 64, minibatch 266/266, validation error 16.414141 %"
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},
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"epoch 65, minibatch 266/266, validation error 16.363636 %"
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},
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"epoch 66, minibatch 266/266, validation error 16.414141 %"
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},
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"epoch 67, minibatch 266/266, validation error 16.313131 %"
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},
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"epoch 68, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 69, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 70, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 71, minibatch 266/266, validation error 16.262626 %"
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},
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},
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},
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"epoch 77, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 78, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 79, minibatch 266/266, validation error 16.060606 %"
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},
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},
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},
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},
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},
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"epoch 84, minibatch 266/266, validation error 16.060606 %"
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},
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"epoch 85, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 86, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 88, minibatch 266/266, validation error 16.161616 %"
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"epoch 89, minibatch 266/266, validation error 16.161616 %"
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"epoch 90, minibatch 266/266, validation error 16.161616 %"
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"epoch 92, minibatch 266/266, validation error 16.161616 %"
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"epoch 93, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 94, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 95, minibatch 266/266, validation error 16.212121 %"
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},
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"text": [
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"epoch 96, minibatch 266/266, validation error 16.262626 %"
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},
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"epoch 99, minibatch 266/266, validation error 16.212121 %"
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},
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"text": [
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"epoch 100, minibatch 266/266, validation error 16.161616 %"
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},
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"epoch 101, minibatch 266/266, validation error 16.111111 %"
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},
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},
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},
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},
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},
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"epoch 106, minibatch 266/266, validation error 16.060606 %"
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},
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"epoch 107, minibatch 266/266, validation error 16.060606 %"
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},
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"epoch 108, minibatch 266/266, validation error 16.060606 %"
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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"epoch 118, minibatch 266/266, validation error 16.111111 %"
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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"epoch 133, minibatch 266/266, validation error 16.111111 %"
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},
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},
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"epoch 135, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 136, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 137, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 138, minibatch 266/266, validation error 16.111111 %"
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},
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"text": [
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"epoch 139, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 140, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 141, minibatch 266/266, validation error 16.111111 %"
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"text": [
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"epoch 142, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 143, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 144, minibatch 266/266, validation error 16.111111 %"
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},
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},
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"epoch 146, minibatch 266/266, validation error 16.111111 %"
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"epoch 147, minibatch 266/266, validation error 16.111111 %"
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"epoch 148, minibatch 266/266, validation error 16.111111 %"
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},
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"epoch 150, minibatch 266/266, validation error 16.111111 %"
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},
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"text": [
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"epoch 151, minibatch 266/266, validation error 16.161616 %"
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"epoch 152, minibatch 266/266, validation error 16.161616 %"
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"epoch 153, minibatch 266/266, validation error 16.161616 %"
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"epoch 154, minibatch 266/266, validation error 16.161616 %"
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"epoch 155, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 156, minibatch 266/266, validation error 16.212121 %"
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"epoch 157, minibatch 266/266, validation error 16.212121 %"
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"epoch 158, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 159, minibatch 266/266, validation error 16.212121 %"
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},
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"text": [
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"epoch 160, minibatch 266/266, validation error 16.161616 %"
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"epoch 161, minibatch 266/266, validation error 16.161616 %"
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"text": [
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"epoch 162, minibatch 266/266, validation error 16.161616 %"
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"epoch 163, minibatch 266/266, validation error 16.161616 %"
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"epoch 164, minibatch 266/266, validation error 16.161616 %"
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"epoch 165, minibatch 266/266, validation error 16.161616 %"
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"epoch 166, minibatch 266/266, validation error 16.161616 %"
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"epoch 167, minibatch 266/266, validation error 16.161616 %"
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"epoch 168, minibatch 266/266, validation error 16.161616 %"
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"epoch 169, minibatch 266/266, validation error 16.161616 %"
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"epoch 174, minibatch 266/266, validation error 16.161616 %"
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"epoch 175, minibatch 266/266, validation error 16.161616 %"
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"epoch 176, minibatch 266/266, validation error 16.161616 %"
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"epoch 181, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 182, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 183, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 184, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 185, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 186, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 187, minibatch 266/266, validation error 16.212121 %"
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},
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"epoch 188, minibatch 266/266, validation error 16.212121 %"
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},
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"text": [
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"epoch 189, minibatch 266/266, validation error 16.212121 %"
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},
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"text": [
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"epoch 190, minibatch 266/266, validation error 16.161616 %"
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"epoch 191, minibatch 266/266, validation error 16.161616 %"
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"epoch 192, minibatch 266/266, validation error 16.161616 %"
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"epoch 193, minibatch 266/266, validation error 16.161616 %"
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"epoch 194, minibatch 266/266, validation error 16.161616 %"
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"text": [
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"epoch 195, minibatch 266/266, validation error 16.161616 %"
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},
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"text": [
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"epoch 196, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 197, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 198, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 199, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 200, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 201, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 202, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 203, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 204, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 205, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 206, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 207, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 208, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 209, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 210, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 211, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 212, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 213, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 214, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 215, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 216, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 217, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 218, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 219, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 220, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 221, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 222, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 223, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 224, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 225, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 226, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 227, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 228, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 229, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 230, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 231, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 232, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 233, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 234, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 235, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 236, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 237, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 238, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 239, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 240, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 241, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 242, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 243, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 243, minibatch 266/266, test error of best model 15.694444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 244, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 245, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 246, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 247, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 248, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 249, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 250, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 251, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 251, minibatch 266/266, test error of best model 15.750000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 252, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 253, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 254, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 255, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 256, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 257, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 258, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 259, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 260, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 261, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 262, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 263, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 264, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 265, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 266, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 267, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 268, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 269, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 270, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 271, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 272, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 273, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 273, minibatch 266/266, test error of best model 15.750000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 274, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 275, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 276, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 277, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 278, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 279, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 280, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 281, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 282, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 283, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 284, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 285, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 286, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 287, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 288, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 289, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 290, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 291, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 292, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 293, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 294, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 295, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 296, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 297, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 298, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 299, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 300, minibatch 266/266, validation error 15.909091 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"hidden_layers_sizes: [200, 200]\n",
"corruption_levels: [0, 0]\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 83.7%\n",
"precision 85.0%\n",
"accuracy 84.3%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 99.9%\n",
"precision 99.9%\n",
"accuracy 99.9%\n",
"... building the model\n",
"... getting the pretraining functions\n",
"... pre-training the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pre-training layer 0, epoch 0, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 623.684874212\n",
"Pre-training layer 0, epoch 1, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 430.302601283\n",
"Pre-training layer 0, epoch 2, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 395.242503531\n",
"Pre-training layer 0, epoch 3, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 373.889261522\n",
"Pre-training layer 0, epoch 4, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 357.903079481\n",
"Pre-training layer 0, epoch 5, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 345.077318904\n",
"Pre-training layer 0, epoch 6, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334.448543862\n",
"Pre-training layer 0, epoch 7, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 325.461237359\n",
"Pre-training layer 0, epoch 8, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 317.746887475\n",
"Pre-training layer 0, epoch 9, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 311.042685374\n",
"Pre-training layer 0, epoch 10, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 305.153373614\n",
"Pre-training layer 0, epoch 11, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 299.930192857\n",
"Pre-training layer 0, epoch 12, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 295.257848089\n",
"Pre-training layer 0, epoch 13, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 291.045784435\n",
"Pre-training layer 0, epoch 14, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 287.222061762\n",
"Pre-training layer 0, epoch 15, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 283.728931324\n",
"Pre-training layer 0, epoch 16, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 280.519584694\n",
"Pre-training layer 0, epoch 17, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 277.555733599\n",
"Pre-training layer 0, epoch 18, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 274.805788917\n",
"Pre-training layer 0, epoch 19, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 272.243476893\n",
"Pre-training layer 0, epoch 20, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 269.846777613\n",
"Pre-training layer 0, epoch 21, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 267.597103257\n",
"Pre-training layer 0, epoch 22, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 265.478656418\n",
"Pre-training layer 0, epoch 23, cost "
]
},
{
"output_type": "stream",
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"Pre-training layer 0, epoch 24, cost "
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"text": [
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"Pre-training layer 0, epoch 25, cost "
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"text": [
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"Pre-training layer 0, epoch 26, cost "
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"Pre-training layer 0, epoch 27, cost "
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"Pre-training layer 0, epoch 28, cost "
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"Pre-training layer 0, epoch 29, cost "
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"Pre-training layer 0, epoch 30, cost "
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"Pre-training layer 0, epoch 32, cost "
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"text": [
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"Pre-training layer 0, epoch 35, cost "
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"text": [
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"Pre-training layer 0, epoch 36, cost "
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"text": [
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"Pre-training layer 1, epoch 0, cost "
]
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"stream": "stdout",
"text": [
" 120.198420929\n",
"Pre-training layer 1, epoch 1, cost "
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"Pre-training layer 1, epoch 2, cost "
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"text": [
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"Pre-training layer 1, epoch 3, cost "
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"text": [
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"Pre-training layer 1, epoch 4, cost "
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"text": [
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"Pre-training layer 1, epoch 5, cost "
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"text": [
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"Pre-training layer 1, epoch 6, cost "
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"text": [
" 89.0815816359\n",
"Pre-training layer 1, epoch 7, cost "
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"text": [
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"Pre-training layer 1, epoch 8, cost "
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"text": [
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"Pre-training layer 1, epoch 9, cost "
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"text": [
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"Pre-training layer 1, epoch 10, cost "
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"text": [
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"Pre-training layer 1, epoch 11, cost "
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"text": [
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"Pre-training layer 1, epoch 12, cost "
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"text": [
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"Pre-training layer 1, epoch 13, cost "
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"text": [
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"Pre-training layer 1, epoch 14, cost "
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{
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"text": [
" 80.7099529046\n",
"Pre-training layer 1, epoch 15, cost "
]
},
{
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"stream": "stdout",
"text": [
" 80.0914690514\n",
"Pre-training layer 1, epoch 16, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 79.5291919313\n",
"Pre-training layer 1, epoch 17, cost "
]
},
{
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"stream": "stdout",
"text": [
" 79.0158031718\n",
"Pre-training layer 1, epoch 18, cost "
]
},
{
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"stream": "stdout",
"text": [
" 78.545116046\n",
"Pre-training layer 1, epoch 19, cost "
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},
{
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"stream": "stdout",
"text": [
" 78.1118926358\n",
"Pre-training layer 1, epoch 20, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 77.7116864675\n",
"Pre-training layer 1, epoch 21, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 77.340709727\n",
"Pre-training layer 1, epoch 22, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 76.9957226214\n",
"Pre-training layer 1, epoch 23, cost "
]
},
{
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"stream": "stdout",
"text": [
" 76.6739419506\n",
"Pre-training layer 1, epoch 24, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 76.3729659465\n",
"Pre-training layer 1, epoch 25, cost "
]
},
{
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"stream": "stdout",
"text": [
" 76.0907126455\n",
"Pre-training layer 1, epoch 26, cost "
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},
{
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"stream": "stdout",
"text": [
" 75.8253693776\n",
"Pre-training layer 1, epoch 27, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 75.5753512987\n",
"Pre-training layer 1, epoch 28, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 75.3392672341\n",
"Pre-training layer 1, epoch 29, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 75.1158914159\n",
"Pre-training layer 1, epoch 30, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 74.9041399734\n",
"Pre-training layer 1, epoch 31, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 74.7030512702\n",
"Pre-training layer 1, epoch 32, cost "
]
},
{
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"stream": "stdout",
"text": [
" 74.5117693676\n",
"Pre-training layer 1, epoch 33, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 74.3295300485\n",
"Pre-training layer 1, epoch 34, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 74.155648951\n",
"Pre-training layer 1, epoch 35, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 73.9895114517\n",
"Pre-training layer 1, epoch 36, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 73.8305640104\n",
"SAE followed by SVM"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 64.5%\n",
"precision 65.2%\n",
"accuracy 64.8%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 65.9%\n",
"precision 66.7%\n",
"accuracy 66.8%\n",
"SAE followed by SVM RBF\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 86.6%\n",
"precision 88.1%\n",
"accuracy 87.3%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 98.8%\n",
"precision 98.3%\n",
"accuracy 98.5%\n",
"SAE followed by SVM POLY\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 84.5%\n",
"precision 87.0%\n",
"accuracy 85.9%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 95.2%\n",
"precision 96.4%\n",
"accuracy 95.9%\n",
"deep learning using split network\n",
"original shape for A (8000L, 784L)\n",
"... building the model\n",
"... getting the pretraining functions\n",
"... pre-training the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pre-training layer 0, epoch 0, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 384.171047791\n",
"Pre-training layer 0, epoch 1, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 242.397106114\n",
"Pre-training layer 0, epoch 2, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 214.239824515\n",
"Pre-training layer 0, epoch 3, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 200.001566631\n",
"Pre-training layer 0, epoch 4, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 190.127488582\n",
"Pre-training layer 0, epoch 5, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 182.450127394\n",
"Pre-training layer 0, epoch 6, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 176.161480271\n",
"Pre-training layer 0, epoch 7, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 170.866713728\n",
"Pre-training layer 0, epoch 8, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 166.332686057\n",
"Pre-training layer 0, epoch 9, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 162.402581309\n",
"Pre-training layer 0, epoch 10, cost "
]
},
{
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"stream": "stdout",
"text": [
" 158.96204613\n",
"Pre-training layer 0, epoch 11, cost "
]
},
{
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"stream": "stdout",
"text": [
" 155.923749452\n",
"Pre-training layer 0, epoch 12, cost "
]
},
{
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"stream": "stdout",
"text": [
" 153.219257565\n",
"Pre-training layer 0, epoch 13, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 150.794133394\n",
"Pre-training layer 0, epoch 14, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 148.604642812\n",
"Pre-training layer 0, epoch 15, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 146.615379078\n",
"Pre-training layer 0, epoch 16, cost "
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 144.797480321\n",
"Pre-training layer 0, epoch 17, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 143.127264538\n",
"Pre-training layer 0, epoch 18, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 141.585173303\n",
"Pre-training layer 0, epoch 19, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 140.15494923\n",
"Pre-training layer 0, epoch 20, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 138.822992389\n",
"Pre-training layer 0, epoch 21, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 137.577854479\n",
"Pre-training layer 0, epoch 22, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 136.409839408\n",
"Pre-training layer 0, epoch 23, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 135.310686369\n",
"Pre-training layer 0, epoch 24, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 134.273317096\n",
"Pre-training layer 0, epoch 25, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 133.291633239\n",
"Pre-training layer 0, epoch 26, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 132.360353067\n",
"Pre-training layer 0, epoch 27, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 131.474879132\n",
"Pre-training layer 0, epoch 28, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 130.631190464\n",
"Pre-training layer 0, epoch 29, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 129.825754254\n",
"Pre-training layer 0, epoch 30, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 129.055453131\n",
"Pre-training layer 0, epoch 31, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 128.317524959\n",
"Pre-training layer 0, epoch 32, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 127.609512754\n",
"Pre-training layer 0, epoch 33, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 126.929222819\n",
"Pre-training layer 0, epoch 34, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 126.27468958\n",
"Pre-training layer 0, epoch 35, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 125.644145918\n",
"Pre-training layer 0, epoch 36, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 125.035998036\n",
"Pre-training layer 1, epoch 0, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 63.4679429314\n",
"Pre-training layer 1, epoch 1, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 52.024024009\n",
"Pre-training layer 1, epoch 2, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 50.1741001389\n",
"Pre-training layer 1, epoch 3, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 49.0402054176\n",
"Pre-training layer 1, epoch 4, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 48.0485772219\n",
"Pre-training layer 1, epoch 5, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 47.1400916252\n",
"Pre-training layer 1, epoch 6, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 46.3085009144\n",
"Pre-training layer 1, epoch 7, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 45.5510640097\n",
"Pre-training layer 1, epoch 8, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 44.8636734582\n",
"Pre-training layer 1, epoch 9, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 44.2410311971\n",
"Pre-training layer 1, epoch 10, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 43.6772679863\n",
"Pre-training layer 1, epoch 11, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 43.1664409293\n",
"Pre-training layer 1, epoch 12, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 42.7028577033\n",
"Pre-training layer 1, epoch 13, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 42.2812569643\n",
"Pre-training layer 1, epoch 14, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 41.8968868036\n",
"Pre-training layer 1, epoch 15, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 41.5455184001\n",
"Pre-training layer 1, epoch 16, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 41.2234226347\n",
"Pre-training layer 1, epoch 17, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 40.9273283059\n",
"Pre-training layer 1, epoch 18, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 40.6543734602\n",
"Pre-training layer 1, epoch 19, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 40.4020564018\n",
"Pre-training layer 1, epoch 20, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 40.1681897746\n",
"Pre-training layer 1, epoch 21, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.9508591941\n",
"Pre-training layer 1, epoch 22, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.7483868258\n",
"Pre-training layer 1, epoch 23, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.5592997326\n",
"Pre-training layer 1, epoch 24, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.382302555\n",
"Pre-training layer 1, epoch 25, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.2162539971\n",
"Pre-training layer 1, epoch 26, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.060146597\n",
"Pre-training layer 1, epoch 27, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.9130893038\n",
"Pre-training layer 1, epoch 28, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.774292448\n",
"Pre-training layer 1, epoch 29, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.643054753\n",
"Pre-training layer 1, epoch 30, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.5187520925\n",
"Pre-training layer 1, epoch 31, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.4008277469\n",
"Pre-training layer 1, epoch 32, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.2887839511\n",
"Pre-training layer 1, epoch 33, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.182174557\n",
"Pre-training layer 1, epoch 34, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.0805986642\n",
"Pre-training layer 1, epoch 35, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 37.9836950876\n",
"Pre-training layer 1, epoch 36, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 37.8911375548\n",
"original shape for B"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" (8000L, 784L)\n",
"... building the model\n",
"... getting the pretraining functions\n",
"... pre-training the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pre-training layer 0, epoch 0, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 386.51850592\n",
"Pre-training layer 0, epoch 1, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 245.600833305\n",
"Pre-training layer 0, epoch 2, cost "
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"Pre-training layer 0, epoch 3, cost "
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" 192.156869838\n",
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"text": [
" 184.240417207\n",
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"text": [
" 172.345116091\n",
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"text": [
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"text": [
" 163.69490257\n",
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"text": [
" 160.187354739\n",
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},
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" 157.094295819\n",
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"text": [
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},
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" 134.263797583\n",
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},
{
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"text": [
" 133.332247808\n",
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"text": [
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"text": [
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"text": [
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"text": [
" 126.617063539\n",
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},
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"text": [
" 126.008773418\n",
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},
{
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"text": [
" 62.9989290384\n",
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},
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"text": [
" 51.5006388147\n",
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},
{
"output_type": "stream",
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"text": [
" 49.6031781376\n",
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"text": [
" 48.4347072933\n",
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" 47.4157930038\n",
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{
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"text": [
" 42.428135193\n",
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},
{
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"text": [
" 41.9570632262\n",
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},
{
"output_type": "stream",
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"text": [
" 41.5295216405\n",
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},
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"text": [
" 41.1406527147\n",
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},
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},
{
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"text": [
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},
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},
{
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"text": [
" 39.640290906\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.4077364577\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.1922284553\n",
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},
{
"output_type": "stream",
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"text": [
" 38.991985535\n",
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},
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},
{
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},
{
"output_type": "stream",
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},
{
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"text": [
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},
{
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},
{
"output_type": "stream",
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"text": [
" 37.9075524782\n",
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},
{
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},
{
"output_type": "stream",
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"text": [
" 37.6716200842\n",
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},
{
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 37.1764086076\n",
"... building the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"... getting the pretraining functions"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"... pre-training the model"
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},
{
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"stream": "stdout",
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"Pre-training layer 0, epoch 0, cost "
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},
{
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"text": [
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"text": [
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"text": [
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},
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"text": [
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},
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"text": [
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"text": [
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"text": [
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"text": [
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},
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"text": [
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"text": [
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"Pre-training layer 0, epoch 32, cost "
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},
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"text": [
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},
{
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"text": [
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},
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"text": [
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},
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"text": [
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},
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"text": [
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"Pre-training layer 1, epoch 0, cost "
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},
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"text": [
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},
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"text": [
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},
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"text": [
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"Pre-training layer 1, epoch 3, cost "
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},
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"stream": "stdout",
"text": [
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"Pre-training layer 1, epoch 4, cost "
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},
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"text": [
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"Pre-training layer 1, epoch 5, cost "
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"text": [
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},
{
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"text": [
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"Pre-training layer 1, epoch 7, cost "
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},
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"text": [
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]
},
{
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"text": [
" 110.29666239\n",
"Pre-training layer 1, epoch 9, cost "
]
},
{
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"stream": "stdout",
"text": [
" 109.581548319\n",
"Pre-training layer 1, epoch 10, cost "
]
},
{
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"stream": "stdout",
"text": [
" 108.934341762\n",
"Pre-training layer 1, epoch 11, cost "
]
},
{
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"stream": "stdout",
"text": [
" 108.349282967\n",
"Pre-training layer 1, epoch 12, cost "
]
},
{
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"stream": "stdout",
"text": [
" 107.820581792\n",
"Pre-training layer 1, epoch 13, cost "
]
},
{
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"text": [
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"Pre-training layer 1, epoch 14, cost "
]
},
{
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"text": [
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"Pre-training layer 1, epoch 15, cost "
]
},
{
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"text": [
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"Pre-training layer 1, epoch 16, cost "
]
},
{
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"text": [
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"Pre-training layer 1, epoch 17, cost "
]
},
{
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"stream": "stdout",
"text": [
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"Pre-training layer 1, epoch 18, cost "
]
},
{
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"text": [
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"Pre-training layer 1, epoch 19, cost "
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},
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"text": [
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]
},
{
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"text": [
" 105.032534147\n",
"Pre-training layer 1, epoch 21, cost "
]
},
{
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"stream": "stdout",
"text": [
" 104.807674229\n",
"Pre-training layer 1, epoch 22, cost "
]
},
{
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"stream": "stdout",
"text": [
" 104.601140246\n",
"Pre-training layer 1, epoch 23, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 104.411009179\n",
"Pre-training layer 1, epoch 24, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 104.235581748\n",
"Pre-training layer 1, epoch 25, cost "
]
},
{
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"stream": "stdout",
"text": [
" 104.073355847\n",
"Pre-training layer 1, epoch 26, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.923002775\n",
"Pre-training layer 1, epoch 27, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.783346152\n",
"Pre-training layer 1, epoch 28, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.653343323\n",
"Pre-training layer 1, epoch 29, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.532069031\n",
"Pre-training layer 1, epoch 30, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.41870112\n",
"Pre-training layer 1, epoch 31, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.312508043\n",
"Pre-training layer 1, epoch 32, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.212837956\n",
"Pre-training layer 1, epoch 33, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.119109212\n",
"Pre-training layer 1, epoch 34, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 103.030802059\n",
"Pre-training layer 1, epoch 35, cost "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 102.947451402\n",
"Pre-training layer 1, epoch 36, cost "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"The pretraining code ran for 7.31m\n",
"The pretraining code ran for 2.16m"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 102.86864047\n",
"... getting the finetuning functions\n",
"... finetunning the model"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 1, minibatch 266/266, validation error 48.080808 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 1, minibatch 266/266, test error of best model 49.277778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 2, minibatch 266/266, validation error 41.868687 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 2, minibatch 266/266, test error of best model 43.333333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 3, minibatch 266/266, validation error 36.717172 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 3, minibatch 266/266, test error of best model 38.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 4, minibatch 266/266, validation error 33.636364 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 4, minibatch 266/266, test error of best model 34.750000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 5, minibatch 266/266, validation error 30.505051 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 5, minibatch 266/266, test error of best model 31.750000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 6, minibatch 266/266, validation error 28.686869 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 6, minibatch 266/266, test error of best model 29.805556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 7, minibatch 266/266, validation error 27.525253 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 7, minibatch 266/266, test error of best model 28.388889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 8, minibatch 266/266, validation error 26.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 8, minibatch 266/266, test error of best model 27.361111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 9, minibatch 266/266, validation error 25.606061 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 9, minibatch 266/266, test error of best model 26.083333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 10, minibatch 266/266, validation error 24.797980 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 10, minibatch 266/266, test error of best model 24.722222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 11, minibatch 266/266, validation error 24.191919 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 11, minibatch 266/266, test error of best model 23.611111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 12, minibatch 266/266, validation error 23.484848 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 12, minibatch 266/266, test error of best model 22.305556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 13, minibatch 266/266, validation error 22.525253 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 13, minibatch 266/266, test error of best model 21.555556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 14, minibatch 266/266, validation error 21.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 14, minibatch 266/266, test error of best model 20.861111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 15, minibatch 266/266, validation error 20.757576 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 15, minibatch 266/266, test error of best model 20.194444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 16, minibatch 266/266, validation error 20.151515 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 16, minibatch 266/266, test error of best model 19.833333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 17, minibatch 266/266, validation error 19.797980 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 17, minibatch 266/266, test error of best model 19.388889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 18, minibatch 266/266, validation error 19.444444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 18, minibatch 266/266, test error of best model 18.861111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 19, minibatch 266/266, validation error 18.838384 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 19, minibatch 266/266, test error of best model 18.444444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 20, minibatch 266/266, validation error 18.585859 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 20, minibatch 266/266, test error of best model 18.250000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 21, minibatch 266/266, validation error 18.080808 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 21, minibatch 266/266, test error of best model 17.750000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 22, minibatch 266/266, validation error 17.676768 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 22, minibatch 266/266, test error of best model 17.416667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 23, minibatch 266/266, validation error 17.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 23, minibatch 266/266, test error of best model 17.083333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 24, minibatch 266/266, validation error 16.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 24, minibatch 266/266, test error of best model 16.777778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 25, minibatch 266/266, validation error 16.919192 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 25, minibatch 266/266, test error of best model 16.777778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 26, minibatch 266/266, validation error 16.818182 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 26, minibatch 266/266, test error of best model 16.555556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 27, minibatch 266/266, validation error 16.818182 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 28, minibatch 266/266, validation error 16.717172 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 28, minibatch 266/266, test error of best model 16.194444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 29, minibatch 266/266, validation error 16.464646 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 29, minibatch 266/266, test error of best model 16.055556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 30, minibatch 266/266, validation error 16.212121 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 30, minibatch 266/266, test error of best model 15.972222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 31, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 31, minibatch 266/266, test error of best model 15.916667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 32, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 32, minibatch 266/266, test error of best model 16.055556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 33, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 34, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 35, minibatch 266/266, validation error 16.464646 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 36, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 37, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 38, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 39, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 40, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 41, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 42, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 43, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 44, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 45, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 46, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 47, minibatch 266/266, validation error 16.313131 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 48, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 49, minibatch 266/266, validation error 16.464646 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 50, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 51, minibatch 266/266, validation error 16.616162 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 52, minibatch 266/266, validation error 16.616162 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 53, minibatch 266/266, validation error 16.616162 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 54, minibatch 266/266, validation error 16.666667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 55, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 56, minibatch 266/266, validation error 16.717172 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 57, minibatch 266/266, validation error 16.666667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 58, minibatch 266/266, validation error 16.565657 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 59, minibatch 266/266, validation error 16.515152 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 60, minibatch 266/266, validation error 16.414141 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 61, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 62, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 63, minibatch 266/266, validation error 16.111111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 64, minibatch 266/266, validation error 16.161616 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 65, minibatch 266/266, validation error 16.060606 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 65, minibatch 266/266, test error of best model 15.944444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 66, minibatch 266/266, validation error 15.959596 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 66, minibatch 266/266, test error of best model 15.972222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 67, minibatch 266/266, validation error 16.010101 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 68, minibatch 266/266, validation error 15.808081 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 68, minibatch 266/266, test error of best model 15.777778 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 69, minibatch 266/266, validation error 15.707071 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 69, minibatch 266/266, test error of best model 15.666667 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 70, minibatch 266/266, validation error 15.454545 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 70, minibatch 266/266, test error of best model 15.500000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 71, minibatch 266/266, validation error 15.303030 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 71, minibatch 266/266, test error of best model 15.250000 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 72, minibatch 266/266, validation error 13.888889 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 72, minibatch 266/266, test error of best model 14.305556 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 73, minibatch 266/266, validation error 13.333333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 73, minibatch 266/266, test error of best model 13.944444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 74, minibatch 266/266, validation error 13.333333 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 75, minibatch 266/266, validation error 13.939394 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 76, minibatch 266/266, validation error 14.141414 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 77, minibatch 266/266, validation error 14.090909 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 78, minibatch 266/266, validation error 13.939394 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 79, minibatch 266/266, validation error 13.838384 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 80, minibatch 266/266, validation error 13.383838 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 81, minibatch 266/266, validation error 13.585859 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 82, minibatch 266/266, validation error 13.030303 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 82, minibatch 266/266, test error of best model 14.361111 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
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},
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 205, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 206, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 207, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 208, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 209, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 210, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" epoch 210, minibatch 266/266, test error of best model 12.944444 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 211, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 212, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 213, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 214, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 215, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 216, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 217, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 218, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 219, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 220, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 221, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 222, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 223, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 224, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 225, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 226, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 227, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 228, minibatch 266/266, validation error 11.969697 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 229, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 230, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 231, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 232, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 233, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 234, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 235, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 236, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 237, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 238, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 239, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 240, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 241, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 242, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 243, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 244, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 245, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 246, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 247, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 248, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 249, minibatch 266/266, validation error 12.020202 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 250, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 251, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 252, minibatch 266/266, validation error 12.070707 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 253, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 254, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 255, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 256, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 257, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 258, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 259, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 260, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 261, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 262, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 263, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 264, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 265, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 266, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 267, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 268, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 269, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 270, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 271, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 272, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 273, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 274, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 275, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 276, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 277, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 278, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 279, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 280, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 281, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 282, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 283, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 284, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 285, minibatch 266/266, validation error 12.121212 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 286, minibatch 266/266, validation error 12.171717 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 287, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 288, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 289, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 290, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 291, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 292, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 293, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 294, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 295, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 296, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 297, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 298, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 299, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"epoch 300, minibatch 266/266, validation error 12.222222 %"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"hidden_layers_sizes: [200, 200]\n",
"corruption_levels: [0, 0]\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 87.4%\n",
"precision 87.2%\n",
"accuracy 87.2%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 99.9%\n",
"precision 100.0%\n",
"accuracy 100.0%\n",
"SAE_S followed by SVM\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 61.3%\n",
"precision 56.2%\n",
"accuracy 56.4%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 64.5%\n",
"precision 59.1%\n",
"accuracy 60.3%\n",
"SAE S followed by SVM RBF\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 89.4%\n",
"precision 89.4%\n",
"accuracy 89.3%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 98.9%\n",
"precision 98.6%\n",
"accuracy 98.8%\n",
"SAE S followed by SVM POLY\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 88.4%\n",
"precision 87.9%\n",
"accuracy 88.0%\n",
"recall"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 96.4%\n",
"precision 96.5%\n",
"accuracy 96.5%\n",
"recall 88.4%\n",
"precision 87.9%\n",
"accuracy 88.0%\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = logging._handlers.copy()\n",
"for i in x:\n",
" log.removeHandler(i)\n",
" i.flush()\n",
" i.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import PIL\n",
"ls = displayHiddenLayerWeights(sda, image_dimension_x =28, image_dimension_y = 28)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "global name 'scale_to_unit_interval' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-ce08aa0589d2>\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[0mPIL\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdisplayHiddenLayerWeights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_dimension_x\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_dimension_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mE:\\Dropbox\\Project\\libs\\DL_libs.py\u001b[0m in \u001b[0;36mdisplayHiddenLayerWeights\u001b[1;34m(StackedNNobject, image_dimension_x, image_dimension_y)\u001b[0m\n\u001b[0;32m 1570\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'Did not implement'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1571\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdisplayHiddenLayerWeights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStackedNNobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_dimension_x\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_dimension_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1572\u001b[1;33m \u001b[0mweights_map_to_input_space\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 1573\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStackedNNobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSdA\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStackedNNobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMultipleAEs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[0mweights_product\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStackedNNobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdA_layers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mborrow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mE:\\Dropbox\\Project\\libs\\DL_libs.py\u001b[0m in \u001b[0;36mtile_raster_images\u001b[1;34m(X, img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[1;31m# do this by calling the `scale_to_unit_interval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 119\u001b[0m \u001b[1;31m# function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m this_img = scale_to_unit_interval(\n\u001b[0m\u001b[0;32m 121\u001b[0m this_x.reshape(img_shape))\n\u001b[0;32m 122\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: global name 'scale_to_unit_interval' is not defined"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weights_map_to_input_space = []\n",
"StackedNNobject = sda\n",
"image_dimension_x = 28*2\n",
"image_dimension_y = 28\n",
"if isinstance(StackedNNobject, SdA) or isinstance(StackedNNobject, MultipleAEs):\n",
" weights_product = StackedNNobject.dA_layers[0].W.get_value(borrow=True)\n",
" image = PIL.Image.fromarray(tile_raster_images(\n",
" X=weights_product.T,\n",
" img_shape=(image_dimension_x, image_dimension_y), tile_shape=(10, 10),\n",
" tile_spacing=(1, 1)))\n",
" sample_image_path = 'hidden_0_layer_weights.png'\n",
" image.save(sample_image_path)\n",
" weights_map_to_input_space.append(weights_product)\n",
" for i_layer in range(1, len(StackedNNobject.dA_layers)):\n",
" i_weigths = StackedNNobject.dA_layers[i_layer].W.get_value(borrow=True)\n",
" weights_product = np.dot(weights_product, i_weigths)\n",
" weights_map_to_input_space.append(weights_product)\n",
" image = PIL.Image.fromarray(tile_raster_images(\n",
" X=weights_product.T,\n",
" img_shape=(image_dimension_x, image_dimension_y), tile_shape=(10, 10),\n",
" tile_spacing=(1, 1)))\n",
" sample_image_path = 'hidden_'+ str(i_layer)+ '_layer_weights.png'\n",
" image.save(sample_image_path)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def scale_to_unit_interval(ndar, eps=1e-8):\n",
" \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n",
" ndar = ndar.copy()\n",
" ndar -= ndar.min()\n",
" ndar *= 1.0 / (ndar.max() + eps)\n",
" return ndar\n",
"def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n",
" scale_rows_to_unit_interval=True,\n",
" output_pixel_vals=True):\n",
" \"\"\"\n",
" Transform an array with one flattened image per row, into an array in\n",
" which images are reshaped and layed out like tiles on a floor.\n",
"\n",
" This function is useful for visualizing datasets whose rows are images,\n",
" and also columns of matrices for transforming those rows\n",
" (such as the first layer of a neural net).\n",
"\n",
" :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can\n",
" be 2-D ndarrays or None;\n",
" :param X: a 2-D array in which every row is a flattened image.\n",
"\n",
" :type img_shape: tuple; (height, width)\n",
" :param img_shape: the original shape of each image\n",
"\n",
" :type tile_shape: tuple; (rows, cols)\n",
" :param tile_shape: the number of images to tile (rows, cols)\n",
"\n",
" :param output_pixel_vals: if output should be pixel values (i.e. int8\n",
" values) or floats\n",
"\n",
" :param scale_rows_to_unit_interval: if the values need to be scaled before\n",
" being plotted to [0,1] or not\n",
"\n",
"\n",
" :returns: array suitable for viewing as an image.\n",
" (See:`PIL.Image.fromarray`.)\n",
" :rtype: a 2-d array with same dtype as X.\n",
"\n",
" \"\"\"\n",
"\n",
" assert len(img_shape) == 2\n",
" assert len(tile_shape) == 2\n",
" assert len(tile_spacing) == 2\n",
"\n",
" # The expression below can be re-written in a more C style as\n",
" # follows :\n",
" #\n",
" # out_shape = [0,0]\n",
" # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -\n",
" # tile_spacing[0]\n",
" # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -\n",
" # tile_spacing[1]\n",
" out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp\n",
" in zip(img_shape, tile_shape, tile_spacing)]\n",
"\n",
" if isinstance(X, tuple):\n",
" assert len(X) == 4\n",
" # Create an output numpy ndarray to store the image\n",
" if output_pixel_vals:\n",
" out_array = numpy.zeros((out_shape[0], out_shape[1], 4),\n",
" dtype='uint8')\n",
" else:\n",
" out_array = numpy.zeros((out_shape[0], out_shape[1], 4),\n",
" dtype=X.dtype)\n",
"\n",
" #colors default to 0, alpha defaults to 1 (opaque)\n",
" if output_pixel_vals:\n",
" channel_defaults = [0, 0, 0, 255]\n",
" else:\n",
" channel_defaults = [0., 0., 0., 1.]\n",
"\n",
" for i in xrange(4):\n",
" if X[i] is None:\n",
" # if channel is None, fill it with zeros of the correct\n",
" # dtype\n",
" dt = out_array.dtype\n",
" if output_pixel_vals:\n",
" dt = 'uint8'\n",
" out_array[:, :, i] = numpy.zeros(out_shape,\n",
" dtype=dt) + channel_defaults[i]\n",
" else:\n",
" # use a recurrent call to compute the channel and store it\n",
" # in the output\n",
" out_array[:, :, i] = tile_raster_images(\n",
" X[i], img_shape, tile_shape, tile_spacing,\n",
" scale_rows_to_unit_interval, output_pixel_vals)\n",
" return out_array\n",
"\n",
" else:\n",
" # if we are dealing with only one channel\n",
" H, W = img_shape\n",
" Hs, Ws = tile_spacing\n",
"\n",
" # generate a matrix to store the output\n",
" dt = X.dtype\n",
" if output_pixel_vals:\n",
" dt = 'uint8'\n",
" out_array = numpy.zeros(out_shape, dtype=dt)\n",
"\n",
" for tile_row in xrange(tile_shape[0]):\n",
" for tile_col in xrange(tile_shape[1]):\n",
" if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n",
" this_x = X[tile_row * tile_shape[1] + tile_col]\n",
" if scale_rows_to_unit_interval:\n",
" # if we should scale values to be between 0 and 1\n",
" # do this by calling the `scale_to_unit_interval`\n",
" # function\n",
" this_img = scale_to_unit_interval(\n",
" this_x.reshape(img_shape))\n",
" else:\n",
" this_img = this_x.reshape(img_shape)\n",
" # add the slice to the corresponding position in the\n",
" # output array\n",
" c = 1\n",
" if output_pixel_vals:\n",
" c = 255\n",
" out_array[\n",
" tile_row * (H + Hs): tile_row * (H + Hs) + H,\n",
" tile_col * (W + Ws): tile_col * (W + Ws) + W\n",
" ] = this_img * c\n",
" return out_array"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
ramseylab/networkscompbio | class10_closeness_python3_template.ipynb | 1 | 27357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CS446/546 - Class Session 10 - Closeness centrality\n",
"\n",
"In this class session we are going to scatter-plot the harmonic-mean closeness centralities\n",
"of the vertices in the gene regulatory network (which we will obtain from Pathway Commons) with the vertices' degree centralities. We will get the geodesic path distances using `igraph`, which will use BFS for this graph."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to use `pandas`, `igraph`, `numpy`, and `timeit`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"import igraph\n",
"import numpy\n",
"import timeit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load in the SIF file for Pathway Commons, using `pandas.read_csv` and specifying the three column names `species1`, `interaction_type`, and `species2`:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"sif_data = pandas.read_csv(\"shared/pathway_commons.sif\",\n",
" sep=\"\\t\", names=[\"species1\",\"interaction_type\",\"species2\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Subset the data frame to include only rows for which the `interaction_type` column contains the string `controls-expression-of`; subset columns to include only columns `species1` and `species2` using the `[` operator and the list `[\"species1\",\"species2\"]`; and eliminate redundant edges in the edge-list using the `drop_duplicates` method. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"interac_grn = sif_data[sif_data.interaction_type == \"controls-expression-of\"]\n",
"interac_grn_unique = interac_grn[[\"species1\",\"species2\"]].drop_duplicates()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create an undirected graph in igraph, from the dataframe edge-list, using `Graph.TupleList` and specifying `directed=False`. Print out the graph summary using the `summary` instance method."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'IGRAPH UN-- 14208 110013 -- \\n+ attr: name (v)'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grn_igraph = igraph.Graph.TupleList(interac_grn_unique.values.tolist(), directed=False)\n",
"grn_igraph.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For one vertex at a time (iterating over the vertex sequence `grn_igraph.vs`), compute that vertex's harmonic mean closeness centrality using Eq. 7.30 from Newman's book. Don't forget to eliminate the \"0\" distance between a vertex and itself, in the results you get back from calling the `shortest_paths` method on the `Vertex` object. Just for information purposes, measure how long the code takes to run, in seconds, using `timeit.default_timer()`. "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"126.19425056292675\n"
]
}
],
"source": [
"N = len(grn_igraph.vs)\n",
"\n",
"# allocate a vector to contain the vertex closeness centralities; initialize to zeroes\n",
"# (so if a vertex is a singleton we don't have to update its closeness centrality)\n",
"closeness_centralities = numpy.zeros(N)\n",
"\n",
"# initialize a counter\n",
"ctr = 0\n",
"\n",
"# start the timer\n",
"start_time = timeit.default_timer()\n",
"\n",
"# for each vertex in `grn_igraph.vs`\n",
"for my_vertex in grn_igraph.vs:\n",
" \n",
" # compute the geodesic distance to every other vertex, from my_vertex, using the `shortest_paths` instance method;\n",
" # put it in a numpy.array\n",
" \n",
" # filter the numpy array to include only entries that are nonzero and finite, using `> 0 & numpy.isfinite(...)`\n",
" \n",
" # if there are any distance values that survived the filtering, take their element-wise reciprocals, \n",
" # then compute the sum, then divide by N-1 (following Eq. 7.30 in Newman)\n",
"\n",
" # increment the counter\n",
"\n",
" \n",
"# compute the elapsed time\n",
"ci_elapsed = timeit.default_timer() - start_time\n",
"print(ci_elapsed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram the harmonic-mean closeness centralities. Do they have a large dynamic range?"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2UPyENiZJmpJVQ71wkj8AfhQ4NMl2RmdHXQBcnuRs4HPAGW3xq4GTgW3Ao8BZAFW1K8m7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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb281920c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot\n",
"matplotlib.pyplot.hist(closeness_centralities)\n",
"matplotlib.pyplot.xlabel(\"Ci\")\n",
"matplotlib.pyplot.ylabel(\"Frequency\")\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scatter plot the harmonic-mean closeness centralities vs. the log10 degree. Is there any kind of relationship?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IONDsPUdGRmJ0dDS3NpuZDSJJj0fESKt6ue5TiIgHgQfrym6s+frnwJvzbIOZ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\nMDOzxP8Hr7+3xT7AvWgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb281bd1240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = matplotlib.pyplot.gca()\n",
"ax.scatter(grn_igraph.degree(), closeness_centralities)\n",
"ax.set_xscale(\"log\")\n",
"matplotlib.pyplot.xlabel(\"degree\")\n",
"matplotlib.pyplot.ylabel(\"closeness\")\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which protein has the highest harmonic-mean closeness centrality in the network, and what is its centrality value? use `numpy.argmax`"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.707169001197\n"
]
},
{
"data": {
"text/plain": [
"'CYP26A1'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(numpy.max(closeness_centralities))\n",
"grn_igraph.vs[numpy.argmax(closeness_centralities)][\"name\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print names of the top 10 proteins in the network, by harmonic-mean closeness centrality:, using `numpy.argsort`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grn_igraph.vs[numpy.argsort(closeness_centralities)[::-1][0:9].tolist()][\"name\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Answer should be:\n",
"`['CYP26A1', 'TCF3', 'LEF1', 'MYC', 'MAZ', 'FOXO4', 'MAX', 'PAX4', 'SREBF1']`"
]
},
{
"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": 1
}
| apache-2.0 |
paix120/DataScienceLearningClubActivities | Activity03/.ipynb_checkpoints/Hummingbird Migration by Latitude-checkpoint.ipynb | 1 | 2034342 | null | gpl-2.0 |
rbiswas4/Cadence | quality/LightCurveMetrics.ipynb | 1 | 462423 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/manual/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
" \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
]
}
],
"source": [
"from metrics import PerSNMetric\n",
"from efficiencyTable import EfficiencyTable"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from OpSimSummary import summarize_opsim as oss"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import sncosmo"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import numpy as np\n",
"import copy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lsst.sims.photUtils import BandpassDict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup : Bandpasses"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Catsim bandpasses\n",
"lsst_bp = BandpassDict.loadTotalBandpassesFromFiles()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"u\n",
"g\n",
"r\n",
"i\n",
"z\n",
"y\n"
]
}
],
"source": [
"# sncosmo Bandpasses required for fitting\n",
"throughputsdir = os.getenv('THROUGHPUTS_DIR')\n",
"\n",
"from astropy.units import Unit\n",
"bandPassList = ['u', 'g', 'r', 'i', 'z', 'y']\n",
"banddir = os.path.join(os.getenv('THROUGHPUTS_DIR'), 'baseline')\n",
"\n",
"for band in bandPassList:\n",
"\n",
" # setup sncosmo bandpasses\n",
" bandfname = banddir + \"/total_\" + band + '.dat'\n",
"\n",
"\n",
" # register the LSST bands to the SNCosmo registry\n",
" # Not needed for LSST, but useful to compare independent codes\n",
" # Usually the next two lines can be merged,\n",
" # but there is an astropy bug currently which affects only OSX.\n",
" numpyband = np.loadtxt(bandfname)\n",
" print band\n",
" sncosmoband = sncosmo.Bandpass(wave=numpyband[:, 0],\n",
" trans=numpyband[:, 1],\n",
" wave_unit=Unit('nm'),\n",
" name=band)\n",
" sncosmo.registry.register(sncosmoband, force=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"et = EfficiencyTable.fromDES_EfficiencyFile('example_data/SEARCHEFF_PIPELINE_DES.DAT')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lsst.sims.catUtils.mixins import SNObject"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"opsimHDF = os.path.join(os.getenv('HOME'), 'data', 'LSST', 'OpSimData', 'storage.h5')\n",
"summarydf = pd.read_hdf(opsimHDF, 'table')\n",
"# df = df.query('propID == [364, 366]')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"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>obsHistID</th>\n",
" <th>sessionID</th>\n",
" <th>propID</th>\n",
" <th>fieldID</th>\n",
" <th>fieldRA</th>\n",
" <th>fieldDec</th>\n",
" <th>filter</th>\n",
" <th>expDate</th>\n",
" <th>expMJD</th>\n",
" <th>night</th>\n",
" <th>...</th>\n",
" <th>wind</th>\n",
" <th>humidity</th>\n",
" <th>slewDist</th>\n",
" <th>slewTime</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>ditheredRA</th>\n",
" <th>ditheredDec</th>\n",
" <th>gamma</th>\n",
" <th>N0sq</th>\n",
" <th>alpha</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>316</td>\n",
" <td>1.676483</td>\n",
" <td>-1.082473</td>\n",
" <td>y</td>\n",
" <td>2771</td>\n",
" <td>49353.032079</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.620307</td>\n",
" <td>0.000000</td>\n",
" <td>21.084291</td>\n",
" <td>1.643930</td>\n",
" <td>-1.108924</td>\n",
" <td>0.039924</td>\n",
" <td>0.000002</td>\n",
" <td>0.039924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>372</td>\n",
" <td>1.694120</td>\n",
" <td>-1.033972</td>\n",
" <td>y</td>\n",
" <td>2810</td>\n",
" <td>49353.032525</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.049266</td>\n",
" <td>4.542362</td>\n",
" <td>21.088257</td>\n",
" <td>1.664257</td>\n",
" <td>-1.060423</td>\n",
" <td>0.039924</td>\n",
" <td>0.000002</td>\n",
" <td>0.039924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>441</td>\n",
" <td>1.708513</td>\n",
" <td>-0.985271</td>\n",
" <td>y</td>\n",
" <td>2848</td>\n",
" <td>49353.032971</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.049298</td>\n",
" <td>4.561422</td>\n",
" <td>21.091100</td>\n",
" <td>1.680878</td>\n",
" <td>-1.011722</td>\n",
" <td>0.039924</td>\n",
" <td>0.000002</td>\n",
" <td>0.039924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>505</td>\n",
" <td>1.720374</td>\n",
" <td>-0.936476</td>\n",
" <td>y</td>\n",
" <td>2887</td>\n",
" <td>49353.033417</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.049266</td>\n",
" <td>4.570186</td>\n",
" <td>21.092714</td>\n",
" <td>1.694604</td>\n",
" <td>-0.962927</td>\n",
" <td>0.039924</td>\n",
" <td>0.000002</td>\n",
" <td>0.039924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>587</td>\n",
" <td>1.730248</td>\n",
" <td>-0.887672</td>\n",
" <td>y</td>\n",
" <td>2925</td>\n",
" <td>49353.033864</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.049177</td>\n",
" <td>4.568530</td>\n",
" <td>21.093091</td>\n",
" <td>1.706054</td>\n",
" <td>-0.914123</td>\n",
" <td>0.039924</td>\n",
" <td>0.000002</td>\n",
" <td>0.039924</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 48 columns</p>\n",
"</div>"
],
"text/plain": [
" obsHistID sessionID propID fieldID fieldRA fieldDec filter expDate \\\n",
"0 1 1189 364 316 1.676483 -1.082473 y 2771 \n",
"1 2 1189 364 372 1.694120 -1.033972 y 2810 \n",
"2 3 1189 364 441 1.708513 -0.985271 y 2848 \n",
"3 4 1189 364 505 1.720374 -0.936476 y 2887 \n",
"4 5 1189 364 587 1.730248 -0.887672 y 2925 \n",
"\n",
" expMJD night ... wind humidity slewDist slewTime \\\n",
"0 49353.032079 0 ... 0 0 1.620307 0.000000 \n",
"1 49353.032525 0 ... 0 0 0.049266 4.542362 \n",
"2 49353.032971 0 ... 0 0 0.049298 4.561422 \n",
"3 49353.033417 0 ... 0 0 0.049266 4.570186 \n",
"4 49353.033864 0 ... 0 0 0.049177 4.568530 \n",
"\n",
" fiveSigmaDepth ditheredRA ditheredDec gamma N0sq alpha \n",
"0 21.084291 1.643930 -1.108924 0.039924 0.000002 0.039924 \n",
"1 21.088257 1.664257 -1.060423 0.039924 0.000002 0.039924 \n",
"2 21.091100 1.680878 -1.011722 0.039924 0.000002 0.039924 \n",
"3 21.092714 1.694604 -0.962927 0.039924 0.000002 0.039924 \n",
"4 21.093091 1.706054 -0.914123 0.039924 0.000002 0.039924 \n",
"\n",
"[5 rows x 48 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summarydf.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"summarydf = summarydf.query('propID == [364, 366]').query('night < 365')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create the summary instance\n",
"so = oss.SummaryOpsim(summarydf)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ss = so.simlib(fieldID=309)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"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>obsHistID</th>\n",
" <th>sessionID</th>\n",
" <th>propID</th>\n",
" <th>fieldID</th>\n",
" <th>fieldRA</th>\n",
" <th>fieldDec</th>\n",
" <th>filter</th>\n",
" <th>expDate</th>\n",
" <th>expMJD</th>\n",
" <th>night</th>\n",
" <th>...</th>\n",
" <th>humidity</th>\n",
" <th>slewDist</th>\n",
" <th>slewTime</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>ditheredRA</th>\n",
" <th>ditheredDec</th>\n",
" <th>gamma</th>\n",
" <th>N0sq</th>\n",
" <th>alpha</th>\n",
" <th>MJDay</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54984</th>\n",
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" <td>-1.089087</td>\n",
" <td>0.039780</td>\n",
" <td>0.000001</td>\n",
" <td>0.039780</td>\n",
" <td>49434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62201</th>\n",
" <td>62145</td>\n",
" <td>1189</td>\n",
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" <td>309</td>\n",
" <td>4.189756</td>\n",
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" <td>-1.089087</td>\n",
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" <tr>\n",
" <th>65712</th>\n",
" <td>65656</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>Y</td>\n",
" <td>7364902</td>\n",
" <td>49438.241925</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.054641</td>\n",
" <td>4.534727</td>\n",
" <td>21.193041</td>\n",
" <td>4.136860</td>\n",
" <td>-1.085780</td>\n",
" <td>0.039916</td>\n",
" <td>0.000002</td>\n",
" <td>0.039916</td>\n",
" <td>49438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65717</th>\n",
" <td>65661</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>Y</td>\n",
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" <td>49438.244152</td>\n",
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" <td>49438</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
" obsHistID sessionID propID fieldID fieldRA fieldDec filter \\\n",
"54984 54932 1189 364 309 4.189756 -1.082474 u \n",
"62174 62118 1189 364 309 4.189756 -1.082474 i \n",
"62201 62145 1189 364 309 4.189756 -1.082474 i \n",
"65712 65656 1189 364 309 4.189756 -1.082474 Y \n",
"65717 65661 1189 364 309 4.189756 -1.082474 Y \n",
"\n",
" expDate expMJD night ... humidity slewDist slewTime \\\n",
"54984 6337289 49426.348262 73 ... 0 0.054175 4.737670 \n",
"62174 7018234 49434.229565 81 ... 0 0.054641 4.659288 \n",
"62201 7019269 49434.241549 81 ... 0 0.054641 4.600334 \n",
"65712 7364902 49438.241925 85 ... 0 0.054641 4.534727 \n",
"65717 7365094 49438.244152 85 ... 0 0.088075 5.189096 \n",
"\n",
" fiveSigmaDepth ditheredRA ditheredDec gamma N0sq alpha \\\n",
"54984 23.535869 4.165341 -1.089087 0.039553 0.000001 0.039553 \n",
"62174 23.346593 4.230447 -1.089087 0.039780 0.000001 0.039780 \n",
"62201 23.504851 4.230447 -1.089087 0.039745 0.000001 0.039745 \n",
"65712 21.193041 4.136860 -1.085780 0.039916 0.000002 0.039916 \n",
"65717 21.203663 4.136860 -1.085780 0.039915 0.000002 0.039915 \n",
"\n",
" MJDay \n",
"54984 49426 \n",
"62174 49434 \n",
"62201 49434 \n",
"65712 49438 \n",
"65717 49438 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/manual/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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RjV0kfZ7UGjF3osfkx71I0l758TNIXYCuLbSMnAq8U+mCH9sD7wTmFx6/d+7CN4PULe3m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\nevb/AQVVqFXz27LDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103c59f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = so.cadence_plot(fieldID=309, mjd_center=49540)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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0+DpylBZfR47S4uvIUVp8HTlKi68jR2nx4DGV0yG+jhylxdeRo7T4OnKUFl9H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\nzMzMzKxn/x806wgwLkeULAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103c598d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 1st season by default\n",
"_ = so.cadence_plot(fieldID=309)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Metrics"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"qm = PerSNMetric(fieldID=309, t0=49540, summarydf=summarydf, lsst_bp=lsst_bp)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ok33l6GGaVMT0/92qSvfu3dmwYQM//PBDqokIeP1Hpk+fztVXX81ll13GqlWr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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15f301750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qm.lcplot()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ey8S2uKqMZCRWvg0BDB48mHHjxjF79uyg2z6/++679O7dm6+++ooLL7wwzBGa\neBNNlRH/zrr/AwYDPWKxMrJ7924qVarEkiVLqFChQkiOmVFlJNWMGTO47bbb+PHHH235eJOhzK4F\nlowQGxcggIkTJ9KtWzfmz59PmTJlsvXZ0aNH8/DDDzNnzpywf5sy8SXKkpHRwBC8O+ticsh20KBB\n/Prrr7zzzjs5Ok7g3J+kpKRj8+gymlPXs2dPfvnlF8aOHWsrtJp0WTKShVi4AO3atYtatWrx8ccf\nc/nll5/UMV5//XWeffZZvvvuO84666wQR2jiVbQkI/6dda1V9QERSQAejbVkZM+ePZx//vnMmzeP\nKlWqRPTcBw8epF69evTt25fOnTtH9Nwmd7Dl4OPAgw8+SMeOHU86EQG4++672bBhAzfeeCPTp0+3\n2fEm1sT8nXUffvghjRs3jngiAlCwYEHeeecd2rZtS7NmzewLjcnWnXVWGSH3fxuaOnUq3bt3Z+nS\npRQtWjRHxzp69ChXX301VatWZeTIkSGK0MSzaKmMBIrFO+tUlbp16/Lcc89x5ZVXOoujT58+rF27\nljFjxjiLwUQnu5smhh09epSePXsycuTIHCci4M2O//DDD5kwYQJjx44NQYTGRK3cmXVkYMGCBezZ\ns4cWLVo4jaN///4sW7aMSZMmOY3D5C6WjORy77//PiVKlKBt27YhO2aJEiX46KOPuP/++9myZUvI\njmtMtFDV2ap6jes4QunVV1/l3nvvJU8et5f1QoUK8corr9CtWzf27t3rNBaTe9gwDbm3NHvgwAGq\nVq3Kxx9/zKWXXhry4/fv35+FCxcyZcoUmx1vTlo0DtNkJLdeC3bt2kXFihVZt24dJUuWdB0OADfd\ndBNly5a1xdDMMTZME6Nef/116tSpE5ZEBODJJ59kx44dvPnmm2E5vjEmND799FNatmwZNYkIwIgR\nI3jnnXdsQUUTFKuMEP3fhtK73z8lJYXXX3+d8ePH07Bhw7Cd+6effqJ58+YsXbo022uXGANWGYmE\nyy67jN69e3P11VdnvXMEjRo1iqlTp/Lll19addXYOiNZyU0XoNSVED/99FNefvll5syZE/Zz9u3b\nl7Vr1/LZZ5+F/Vwm9lgyEl6///479evXZ9OmTRQoUMB1OMc5fPgwderUYciQIbRr1y7L/bO70JrJ\nXSwZyUJuugCJCCkpKdSvX5/+/ftH5JvQgQMHqF27NiNGjAjpRFkTHywZCa9nnnmGP/74g1dffdV1\nKOn6+uvjGhKQAAAgAElEQVSvueuuu1i5ciWFChUK+nNZLUFvcp+onzMiIoVEpKDrOHKL2bNns3fv\nXtq0aROR86XOjn/wwQfZv39/RM5pjMmaqvLhhx/SpUsX16FkqHnz5lx44YWMGjXKdSgmijlJRkQk\nj4h0EJHRIrIJ+A1YLyKbRGSMiLQXG2DM0Msvv8yDDz4Y0Vv4WrRoQf369XnmmWcidk5jTOZ++ukn\nkpOTady4setQMjV8+HCGDx9uSwWYDDkZphGROcBcYALwo6oe9LcXBOoC1wCXqerJr22evXhyTWlW\nRDjttNP4/fffOfXUUyN67o0bN1KnTh3mz59PpUqVInpuk3vZME34DBw4kH/++Yfnn3/edShZ6tWr\nFzt27ODtt98Oan8bpok9UTdnREQKpiYgOdknhPHkmguQiHD77bcH/Q861J577jnmzp3LxIkTnZzf\n5D6WjIRP3bp1efHFF2nSpInrULL0zz//ULVqVSZPnsxFF12U5f6WjMSeqEtGAESkCdAMKA0cBbYD\n36nqNAex5IoLkKqSJ08evv32Wy655BInMRw8eJCaNWvy0ksv0apVKycxmNzFkpHw+O2332jYsCFb\ntmwhb968rsMJymuvvcbHH3/MrFmzsrzV15KR2BN1E1hF5Am8ROQHYAzecM0KoLmIPOsiptygZs2a\nADRq1MhZDAULFmTEiBE88sgjHD582FkcxsS78ePHc/XVV+eaRATgzjvvZOfOnYwfP951KCbKuLqb\nZrmqDlTVCar6tapOU9UxqtobWOwopqi3fft2AOeLB7Vt25Zzzz2XV155xWkcxsSzL774gmuvvdZ1\nGNmSL18+nn/+eXr27MmhQ4dch2OiiKs5I/0AAb4H9uEN0xQFagNnquqjITxXIWA2UBAoAIxX1T5p\n9on60uy+ffsoW7Ysf/31V1SULpcvX06zZs1YvXo1JUqUcB2OiWI2TBN6O3bsoGLFimzdupXChQu7\nDifbrrrqKq688koefvjhDPexYZrYE3XDNKo6CPgWuAjoANwI1AcWAY+F+FwHgERVrYOX7CSKyGWh\nPEckTJo0ifr167sO45iaNWvSvn17nn76adehGBN3pkyZQvPmzXNlIgIwbNgwhgwZwq5du1yHYqJE\nXK3AKiJF8Kokt6rqyoDtUf9tqF27drRv357bb789ar4tbNu2jRo1arBw4ULOP/981+GYKGWVkdDr\n0qULzZo1o2vXrq5DOWn33nsvRYoUYcSIEem+b5WR2BOVd9OkR0QqAB+pakjb0IpIHrwhoYrAq6ra\nK837UX0B2rVrF+eddx4bNmzg1FNPjap/oIMHD2bZsmV8+umnrkMxUcqSkdA6evQoZ511Fj/++CPn\nnHOO63BOWuqXmQULFlCxYsUT3rdkJPZE3TBNRlT1dyDka5yraoo/THMOcLmIJIT6HOE0ZswYWrZs\nySmnnOI6lBM8/PDDzJs3j0WLFrkOxZi4sGjRIsqUKZOrExGAs846i0ceeYQnnnjCdSgmCuRzcVIR\nOUtVtwW8bok3n2OJqs4M13lV9W8RmQxcDCQFvjdgwIBjz6OtQ+Rnn33Gfffd5zqMdBUtWpQBAwbQ\nq1cvZs6c6fxOH+NeYOdVE3pTp06ldevWrsMIiUceeYQqVaqwYMECGjZs6Doc45Cru2nuBw6r6hsi\n8ihwANgPVAA2qurrITzXGcARVf1LRAoDXwEDVfXrgH2itjS7c+dOzj//fDZv3kzRokWjsnR55MgR\natWqxYgRI2LmImlCx4ZpQqthw4Y8++yzJCYmug4lJN5++23eeecd5syZc9yXmWi81pmcicZhmjeB\nAf7zFar6sqq+rar/AUK9klYZYKaI/AgsACYGJiLRbsKECbRo0YKiRYu6DiVD+fLlY8iQIfTp04eU\nlBTX4RgTs7Zv387PP/8c9Y3xsuPWW2/lr7/+YsKECa5DMQ65SkaGAoVE5Ca8W3oRka4iUgYIafc3\nVf1JVS9S1TqqWltVh4Xy+OE2btw4OnTo4DqMLF177bUUKFCA0aNHuw7FmJg1ffp0EhISKFCggOtQ\nQiZv3rw8++yzPPHEExw5csR1OMaRqLmbRkRuB3YAkyJdJ43W0mxycjJly5blo48+YvFib2HapKSk\nY/NZom1uy/Tp0+nWrRsrVqwgXz4n05FMFLJhmtC58847qVu3Lt26dXMdSkipKgkJCdx6663ccccd\ngA3TxKKovLVXRBoCZYEFqrrJ39Yc2KqqKyIcS1RegD755BPee+89pkyZ4jqUoKgqzZo14+abb+bO\nO+90HY6JEpaMhM55553H5MmTqV69uutQQm7+/Plcf/31rFmzhsKFC1syEoOibs6IiAzCW2m1IfCW\niKSu+zEbmOMipmj0+eef0759e9dhBE1EGDx4MIMGDbK+EybqiEghEVkgIj+KyEoRecZ1TNnx66+/\ncvDgQapVq+Y6lLBo1KgR9evX5+WXX3YdinHA1ZyRv1T1elXtraqtgIUi0hdI8R9x7/Dhw0ybNo22\nbdu6DiVbLr30UqpVq8Zbb73lOhRjjpPbW0PMnDmTZs2axfTt808//TTPPfccf//9t+tQTIS5SkYO\niMjpInKfiBRR1STgf8ADQH5HMUWVefPmUalSJcqUKeM6lGwbOHAgQ4YM4cCBA65DMeY4qrrPf1oA\nyAvkmuYoX3/9Nc2aNXMdRlhVr16dq666KsMl4k3scpWMvA60BM7Cr4So6k7gv0CfTD4XNyZNmpTr\nqiKpGjRoQN26dXn99ZAtF2NMSIhIHv82/23ArMAeVdFMVZk5cybNmzd3HUrYDRgwgCFDhrgOw0RY\nVNxNIyKn+YuSlVDV3Q7OH3WT1qpWrcpHH31EvXr1XIdyUn788Ueuuuoq1q1bR5EiRVyHYxyKxgms\nInIq3gKIj/uV2dTtUXctAFi+fDnt2rXjl19+cR1KRJQuXZpt27bZBNYYk9m1IFruv7wVeAG4xf8z\nrq1Zs4bk5GTq1q3rOpSTVqdOHRo3bszLL79Mz549XYdjzHFyW2uIWbNmxfwQTaDvv/+esmXLsmXL\nllw5VG082WkNES2VkYdU9YXUPx2cP6q+DY0cOZJVq1bl+mGOlStXkpCQwLp166KyyZ+JjGipjOTm\n1hDXX3897dq14+abb3YdSsSICN27d+fFF190HYoJkai7tddkbtKkSbRpE/LmxRFXvXp1WrZsyQsv\nxH2xy0SHXNkaQlWZM2cOl19+uetQIu7DDz9kw4YNrsMwEWCVEaLr29CePXsoU6YMmzdvpnjx4q7D\nybF169bRqFEjVq9eTcmSJV2HYxyIlspIMKLpWpBq9erVtGzZkt9//911KBElIvTu3Zvdu3fz2muv\nuQ7HhIBVRnKRmTNn0qBBg5hIRAAqVapEx44dGTp0qOtQjMmV4rUqAtCrVy/Gjh0bNxN345klI1Fm\n6tSptG7d2nUYIdWvXz/eeustNm3a5DoUY3KdeE5GTj/9dLp3787AgQNdh2LCLFqGaaqr6srUPx2c\nPypKs6p6rPdEjRo1XIcTUr179+avv/6ycmscsmGanClfvjzTp0+nSpUqrkOJqNTeNP/88w+VKlUi\nKSkpJnvyxJOobJQXTaLlArRq1SpatmzJ+vXrY27J5127dlG1alXmzZtH1apVXYdjIsiSkZO3fv16\nGjZsyJYtW2LumpCVwEZ5w4cP59tvv2XcuHGOozI5kSvmjIjIuSJyges4XPryyy9p3bp1TF50Tj/9\ndB577DGeeOIJ16EYk2ukDtHE4jUhOx544AEWLVrEggULXIdiwiRqkhHgEeBWEblfRN4TkStdBxRp\nsThfJNCDDz7IwoUL+e6771yHYkyuMHfuXC67LNf08gubwoUL079/fx5//HFblTVGRVMy8oWq9gHW\nq+otQCnXAUXSnj17mD9/Pi1atHAdStgULlyYp556il69etkFxZggfPPNN5aM+G677TY2b97MtGnT\nXIdiwiCakpEeInI/UMx/HZKVbkSknIjMEpEVIrJcRB4MxXFDbcaMGTRs2JBixYplvXMudsstt/D3\n338zfvx416EYE9V27drFhg0bqF27tutQokK+fPl45pln6N27N0ePHnUdjgmxaEpGHsXrEXGqiLyA\nN2wTCoeBR1S1BtAIeEBEqoXo2CEzefLkmFh1NSt58+Zl+PDh9OrVi8OHD7sOx5io9e2339KwYUPy\n5YuWFmLutW/fniJFivDhhx+6DsWEWNQkI6q6TlVXqurrqvoQ0DdEx92qqj/6z/cAq4CzQ3HsUFFV\npkyZEhfJCMCVV17JeeedZ7f5GpOJefPm2RBNGiLCsGHDePLJJ9m/f7/rcEwIRU0ykpaqrgj1MUWk\nAlAXry9F1Pjxxx8pWrQolStXdh1KxAwbNoxBgwbx119/uQ7FmKg0b948Gjdu7DqMqNO4cWMuvvhi\na6AXY6JmnRG/i2ZpvGZWpYHGqvpoCI9fDG8Y6GlV/SLNe07XFnj66afZuXMnI0eOdBaDC127dqVE\niRIMGzbMdSgmjGydkew7cOAAZ5xxBlu2bImZ1hDZFbjOSFpr167lkksuYeXKlZQqFVf3OuRqmV0L\nomkwcjBeEjIPOAX4OVQHFpH8wFjgg7SJSKoBAwYce56QkEBCQkKoTp+lyZMnM2jQoIidL1o8/fTT\n1KxZk3vvvZeKFSu6DseESFJSEklJSa7DyNWWLFnCBRdcELeJSFYqV67MzTffzIABA3jllVdch2NC\nIGoqIwD+xNJawF5VnRyiYwrwLrBTVdOdFOvy29DWrVupVq0a27Zto0CBAk5icGnw4MH88MMPjBkz\nxnUoJkysMpJ9zz33HJs2beKFFyLexDxqZFYZgX9XdU5KSoq59hmxKupWYBWR4iLSXUTuEJEiqdtV\ndZWqfgYcFZGeITpdY+BmIFFEfvAfrUJ07BybOHEiLVu2jMtEBKBHjx4sWrSIOXPmuA7FmKhhi51l\n7fTTT6dv37706NHD1i2KAa4msA4DzgFaAFMDExIAVf0S+DYUJ1LVeaqaR1XrqGpd//FlKI4dChMm\nTKBdu3auw3CmcOHCPPvsszz88MO2doAxQEpKCt988w1NmjRxHUrUe+CBB/jjjz+YNGmS61BMDrlK\nRn5S1d6q2gXo5D+Oo6rfRD6syNq7dy+zZ8+O6SXgg9GpUycKFSrEe++95zoUY5xbsWIFJUuWpHTp\n0q5DiXr58+dn1KhRPPLIIxw8eNB1OCYHXCUjx/6vUdUtwD+O4nBq2rRpNGjQgNNOO811KE6JCKNG\njaJv374kJye7DscYp+bOnWtVkWxo2bIl1atXZ9SoUa5DMTngKhl5XET+688ZqQscG/ATkbMcxRRx\n8T5EE6hBgwZcccUVDBkyxHUoxjhlyUj2jRw5kmHDhrFhQ0i6iBgHnNxNIyL9gEV4y7PXx1uI7A/g\nG+BMv1FeJOOJ+Az6I0eOUKZMGRYvXkz58uUjeu5otXnzZmrXrs2CBQvsVt8YYnfTBE9VKVeuHElJ\nSVSqVMlZHNEgq7tp0howYAArVqxg9OjRYYzK5ERm14KoubVXRCoCDYG7VDUxwueO+AUoKSmJRx99\nlCVLlkT0vNFuyJAhLF68mHHjxrkOxYSIJSPB++2337j00kvZvHkz3qoE8SVwjZqkpKRj6z0Fs/bT\n/v37qVGjBv/73/+48sorwxuoOSlRt+iZiJRS1T8Dt6nqL8AvIrLJRUyRNm7cODp06OA6jKjTo0cP\nqlevzrRp0+yCYuJO6hBNPCYikLMFJwsXLsxLL71Et27dWLZsGYUKFQptcCasXM0ZWS4iV6e+EJGC\nInI2gKrOdhRTxKgqX3zxBe3bt3cdStQpVKgQL774It26dbPZ8Sbu2HyRnGnTpg21atXimWeecR2K\nySZXychQ4DYReU5E8qjqQaCsiDwuIs87iililixZQuHChalWrZrrUKJS27ZtqVatGsOHD3cdiokh\nIlJORGaJyAoRWS4iD7qOKa3AoQlzcl588UVeeeUVVq9e7ToUkw2ukpE9qnodsBWYISJnq+oiVX0W\nON9RTBGTOkQTr6XYYLzwwguMHDmSX3/91XUoJnYcBh5R1Rp4k+cf8FtQRIWNGzeye/duW9o8h8qW\nLUu/fv245557SElJcR2OCZKrZKQRgKqOAPoCE0Skhf9eSFZejWaff/65DdFkoUKFCvTq1Yt77rnH\nlno2IaGqW1X1R//5HmAVcLbbqP41e/ZsmjZtSp48ri7LseOBBx5g//79vP32265DMUFy9X/9IRHp\nISJVVPU7oCXwsIgMwPv2ErNWrlzJ3r17qV+/vutQol6PHj3YuXOnrcxqQk5EKuAtKbDAbST/SkpK\nomnTpq7DiAl58+bljTfe4IknnmDLli2uwzFBcJKMqOo9flVkg/96J3A1XiLypIuYImXMmDFcd911\nNkQThHz58vHWW2/Rq1cvtm7d6jocEyNEpBgwBnjIr5BEhdmzZ9t8kRCqXbs2d999N926dXMdiglC\n1KwzkkpEGqjqwgifM2JrC9SuXZtXX32Vxo0bR+R8saBv374sW7aMCRMmWBKXC0XTOiMikh+YBExV\n1RPWDxcR7d+//7HXObnVNDs2bdpE7dq12b59uw3ThNCBAweoW7cuTz31FNdff73rcOJO4LoxAAMH\nDoyuRc8kiN/+wewTwngicqrVq1eTmJjIxo0b7YKTDYcOHaJhw4Z069aNO++803U4JpuiJRkRL5N9\nF9ipqo9ksI+TRc8++ugjxowZY4v9hcF3331Hhw4dWLZsGWeeeabrcOJaZtcCV78Rk0Skp4hUSfuG\niFQVkd5AzK03MnbsWK677jpLRLKpQIECvP/++zz++OP88ssvrsMxuVdj4GYgUUR+8B+tXAcFMGvW\nLJsvEiaXXHIJXbp0oXv37q5DMZlw9VvxSmAn8LKIbBGRNSKyVkS2AP8FtgEtMj1CLjRmzBg6duzo\nOoxcqWbNmvTr149OnTrZYmjmpKjqPFXNo6p1VLWu//jSdVzgJSPNmjVzHUbMGjRoED/88IP1rYli\nzueMiEhe4Az/5Q5VPeoghrCXZtetW8dll13Gpk2byJs3b1jPFatUlQ4dOlC+fHlrF56LRMswTTBc\nDNP88ccf1KtXj23btlnVNIwWLFjANddcw9KlSyldurTrcOJSNA7THKOqR1V1m/+IeCISKaNHj+a6\n666zRCQHRIS3336b8ePHM2bMGNfhGBMSs2bNIjEx0RKRMGvYsCF33XUXXbt2tbWLolDM/98vIm+L\nyDYR+cllHJ999pnN5g6BEiVKMGbMGO677z6WLl3qOhxjcmzmzJk2RBMh//nPf9i6dSuvvvqq61BM\nGs6HacJNRJoAe4D3VLVWBvuEtTS7du1amjRpYkM0IfTJJ5/Qp08fFi5caDPko5wN02RMVTn33HP5\n+uuvqVLlhPn8JgzWrFlD48aNSUpKsqX3Iyxqh2lEpHo62xJCeQ5VnQvsDuUxs8uGaEKvU6dO3Hzz\nzbRp04Y9e6Jm3SpjsmXdunWkpKRQuXJl16HEjSpVqjB06FA6d+7M/v37XYdjfK6HaT4Tkd7iKSIi\nLwHPOo4p5EaPHs0NN9zgOoyY89RTT1GrVi2uu+46Dh065DocY7It9S4aW8wvsm6//XZq1qzJQw89\n5DoU43OdjDQEygHfAQuBLcClTiMKsTVr1rB161Yuu+wy16HEHBHhtddeo0iRInTu3JnDh2O6rZGJ\nQTNnziQxMdF1GHEn9doxZ84c3n//fdfhGCCf4/MfAfYDhYFCwK+q6qTn84ABA449D+US0J999hkd\nO3a0IZowyZcvH5988gnXX389N954I5988gkFChRwHVZcS7sEtElfSkoKM2fO5LnnnnMdSlwqXrw4\no0ePplmzZlx44YXUrl3bdUhxzekEVhFZCkwAnsJba+Q14KCqhvS2E79D50QXE1hr167Nyy+/TJMm\nTcJyfOM5dOgQN9xwA0eOHOGzzz6jSJEirkMyPpvAmr6lS5fSsWNH1q5dG5HzmfR99NFH9OvXj0WL\nFnH66ae7DiemRe0EVuBOVe2nqodVdYuqXoOXnISMiHwMfAtUEZENInJ7KI+fmVWrVrFz505rihcB\nBQoUYPTo0ZQsWZIWLVqwc+dO1yEZk6mvv/6aFi1ibqHpXKdLly5ce+21dO7cmSNHjrgOJ265Tkba\niEj/gMd/gPNCeQJV7ayqZ6tqQVUtp6rvhPL4mUldW8QWM4qM/Pnz83//9380adKESy+9lDVr1rgO\nyZgMzZgxg+bNm7sOwwBDhw4lJSWFxx57zHUoccv1b8m9eGuA7AGOAlcBFVwGFEqfffaZ3UUTYSLC\n0KFDeeyxx2jSpAkzZsxwHZIxJzh06BDz5s2zyatRIl++fIwePZqvvvrKFkRzxOkEVlUdHvhaRIYB\n0xyFE1I//fQTycnJNGrUyHUocemuu+6icuXKdOnShXvuuYcnn3zSJhGbqLFgwQIqV65MyZIlXYdi\nfKeddhqTJk3isssuo0KFCrRu3dp1SHHFdWUkraJAWddBhMIHH3xAly5dbIjGoYSEBJYsWUJSUhIt\nWrTgt99+cx2SMYDNF4lWFStW5PPPP+fWW29l0aJFrsOJK65XYP0p4LECWA284DKmUEhJSeHjjz/m\npptuch1K3CtTpgwzZszgqquuon79+rz00ks2Sc04Z/NFolejRo148803adeund3pFEGub+2tEPDy\nCLBNVSO+clWob+ebPXs23bt3Z9myZSE7psm5VatW0a1bN7Zu3crw4cNp1aqVrXwZAXZr7/H++ecf\nzj77bLZv307hwoXDei5z8t5++20GDhzInDlzKF++vOtwYkLU3tqrqr8HPDa6SETC4cMPP7SqSBSq\nVq0aM2bM4JlnnqFHjx5cdtllTJ8+3dqJm4iaPXs2DRs2tEQkyt1xxx306NGDFi1asHnzZtfhxDwn\nE1hFJDmTt1VVT4lYMCF28OBBxo4dyw8//OA6FJMOEeGaa66hTZs2fPrpp3Tv3p3ixYvTu3dv2rdv\nb5NcTdhNnz6dK664wnUYJggPPfQQ+/btIyEhgVmzZlG2bExMaYxKrioj41W1ONBPVYuneeTaRARg\nwoQJ1K5dm3PPPdd1KCYTefPmpUuXLqxcuZK+ffsyYsQIKlWqxMiRI/nrr79ch2dimCUjuUufPn24\n8847SUhIYMOGDa7DiVmukpGLRORs4A4ROT3tw1FMIfHGG29w1113uQ7DBClPnjxce+21fPvtt3z8\n8ccsXLiQ8847j7vvvptFixbZEI4JqY0bN7J9+3bq1q3rOhSTDb179+b+++/nsssu4+eff3YdTkxy\ntc7I/4CvgfOBJem8H9JVWCPlt99+4/vvv2fChJCuaG8ipFGjRjRq1IitW7fy1ltv0alTJ4oVK8Yt\nt9xC586dOfvss12HaHK5GTNm0KxZM7vlPxd65JFHKFGiBImJiYwfP54GDRq4DimmOPkXoaovqmo1\n4B1VPS/tw0VMofD2229z0003UahQIdehmBwoXbo0ffv2Ze3atYwaNYqVK1dSo0YNEhMTeeWVV2wy\nmzlpM2bMsCGaXOy2227j9ddfp23btnz++eeuw4kpTm/tjRahuJ3vyJEjVKhQgalTp1KrVrrNgU0u\ntn//fr766itGjx7NlClTqFKlCm3atKFVq1bUq1fPJr5mwm7t9agqZcqU4bvvvuO883Ltdy4DLFmy\nhHbt2vHggw/Ss2dPWyIgSJldCywZITQXoLFjx/L888/z7bffhigqE60OHTrEnDlzmDp1KlOnTmXr\n1q0kJCTQtGlTmjRpQu3atcmXz2mnhahiyYhn2bJldOjQgXXr1oXl+CayNmzYwLXXXkvVqlV58803\nKVKkiOuQop4lI1nI6QVIVWnYsCF9+vShffv2IYzM5AabN29m1qxZzJkzh7lz57JhwwYuuugiGjRo\nQL169bjooouoWLFi3FZPoikZEZG3gTbAn6p6QgkznMnI888/z7p166wRWwzZv38/d999N0uXLmXM\nmDFUqVLFdUhRzZKRLOT0AjRr1izuu+8+Vq5caRPTDLt372bhwoUsXryYxYsX8+OPP7J9+3aqV69O\nzZo1qV69OhdccAFVqlShQoUKFChQwHXIYRVlyUgTvC7h70U6GWnVqhV33303HTp0CMvxjRuqymuv\nvUa/fv146aWX6NSpk+uQopYlI1nI6QWoZcuW3HDDDdx5550hjMrEkr///pvly5ezYsUKVq5cyerV\nq1mzZg2bNm2iTJkyVKhQgfLly1OuXDnOOeccypQpQ5kyZTjrrLMoVapUrp4UHU3JCBxrQzExksnI\ngQMHOPPMM9mwYQOnnXZayI9v3Pv+++/p1KkTjRs35sUXX6R48eKuQ4o6loxkIScXoEWLFnHttdfy\n66+/UrBgwRBHZmLd4cOH+eOPP1i/fj2///47GzduZMOGDWzZsoXNmzfz559/8ueff1KgQAHOOOMM\nSpQoQYkSJTj11FM59dRTKV68OMWKFaNo0aIUKVKEwoULU7hwYQoVKkShQoWOvS5SpMixR+C+kajk\nWTICM2fO5IknnmD+/PkhP7aJHnv27OHhhx9m1qxZvPPOO1x++eWuQ4oqmV0LbJZdDqSkpPDggw8y\naNAgS0TMScmfPz8VK1akYsWKGe6jqiQnJ7Njxw52797N7t27+eeff/j7779JTk4mOTmZffv2sWPH\nDg4cOMD+/fs5ePAg+/fvP+6xd+/eY3/u3buXAwcOUKRIEYoVK3bcIzXBKV68+AnPUx+nnHLKCduK\nFSsWt/NismKrrsaHYsWK8eabbzJhwgQ6derEDTfcwNNPP02xYsVchxb14iIZEZFWwCggL/Cmqg4N\nxXHfffddVJXbbrstFIczJl0iwimnnMIpp4S2U0JKSsqxxCQ5OfnYn3v27Dnuz+TkZHbt2sX69euP\nvU7vsXfvXgoVKnRcQpMbL8IDBgw49jwhIYGEhIQcH3P69OmMGDEix8cxucM111xD48aN6dGjBzVr\n1lUF7wgAACAASURBVOTVV1+ldevWrsOKuKSkJJKSkoLaN+aHaUQkL7AaaAFsAhYBnVV1VcA+2S7N\n7t69m+rVqzNx4kQuvvjiUIZsTK6UkpLCvn37TkhmmjZtGtfDNH/++SeVK1dm+/btMT9Z2Zxo2rRp\n3HfffdSpU4dRo0ZRrlw51yE5k9kwTTzc+tEAWKeqv6vqYeAToF1ODnjo0CGuv/56OnfubImIMb48\nefJQrFgxSpcuTeXKlalbt27UjZmLyMfAt0AVEdkgIreH+5zTp08nMTHREpE4deWVV7J8+XJq1apF\nnTp1eOqpp9i3b5/rsKJOPCQjZYHAVosb/W0nJSUlhdtuu43ixYszbNiwHAdnjIkcVe2sqmerakFV\nLaeq74T7nF9++SWtWrUK92lMFCtcuDADBgxgyZIlLF++nGrVqvH++++TkpLiOrSoEQ/DNNcBrVT1\nLv/1zUBDVe0esI9OnDgRESFPnjzkzZuXfPnyUahQIYoWLQpAcnIy8+fP591336VkyZJMnTqVwoUL\nO/mZjMlNou1umsyEepgmJSWF0qVLs2DBAlsC3hwzb948evbsyYEDBxg8eDCtW7eOiyXlM70WqGpM\nP4BGwJcBr/sAvdPso+k9ypYtq7Vq1dJatWpp48aN9Y477tDZs2frf/7zn3T379+/v6anf//+tr/t\nHzf7z5o1S/v373/s4V1m3F8Lgnn4sYbM4sWLtWrVqiE9pokNKSkpOm7cOK1Ro4ZecsklOnXqVE1J\nSXEdVlhldi2Ih8pIPrwJrM2BzcBCQjCB1RgTnHiujAwePJjt27czatSokB3TxJajR48yevRonn76\naQoVKkSvXr3o0KFDTPa3iusJrKp6BOgGfAWsBD4NTESMMSZcbL6IyUrevHnp1KkTy5YtO7akfOXK\nlRk2bBg7d+50HV7ExHxlJBhWGTEmfOK1MrJr1y4qVKjAtm3bbH6ZyZaFCxfy3//+lwkTJnDNNdfQ\ntWtXmjRpkuvnlcR1ZcQYY1yYMmUKzZo1s0TEZFuDBg147733WLt2LXXq1OHee++lcuXKDBgwgDVr\n1rgOLywsGTHGmDAYP34811xzjeswTC525pln0qNHD1asWMEnn3zCrl27aNq0KXXr1mXw4MGsXLmS\nWKnq2zANNkxjTDjF4zDNwYMHOeuss1izZg2lSpUKQWTGeI4ePcrcuXMZN24cn3/+OQULFqRNmza0\nbt2ayy+/nCJFirgOMUPWtTcLlowYEz7xmIx8+eWXDBo0iG+++SYEURmTPlVl6dKlTJ48ma+++oof\nfviBiy++mMTERJo2bUr9+vWjKjmxZCQLlowYEz7xmIzcf//9VKhQgV69eoUgKmOCk5yczNy5c5k1\naxZz587lp59+okaNGjRs2JAGDRpQr149qlat6qy7tiUjWbBkxJjwibdkJCUlhXPPPZcZM2ZwwQUX\nhCgyY7Jv3759LFmyhAULFrBo0SKWLFnC1q1bqVGjBrVr16ZGjRrUqFGDqlWrcs4555AnT3inkVoy\nkgVLRowJn3hLRubOnct9/7+9O4+zsfz/OP76WLJl37cxCNmziynrt++QbJUlRUpZIvIta33pp76p\npEV7SlpQqKxjCWPf1zGWGLIkSxNCZJbP748zNDSYGeec+yyf5+NxHs5yn/t6G2cun3Nd133fvXuz\nfft2N6Uyxn1Onz5NVFQU27ZtIzo6mh07drB7925Onz5N2bJlKVu2LKVLlyY0NJSQkBBCQkIoXrw4\nBQsWvOlixYqRG7BixBjPCbZipFevXoSGhjJkyBA3pTLG886cOUNMTAwxMTHs37+f/fv3c+jQIQ4e\nPMgvv/zC6dOnKVSoEEWLFqVQoUIULFiQAgUKkD9/fvLly0e+fPnIkycPuXPnJleuXOTMmZNbb72V\nHDlyXD6brBUjN2DFiDGeE0zFyMWLFylWrBgbN26kVKlSbkxmjLP++usvjh07xq+//sqJEyc4duwY\nsbGxxMbGcvLkSX7//XdOnz7N6dOn+eOPP/jjjz84e/Ys586dI3PmzGTPnp2TJ09esy8IvJPfG2OM\nQyIiIqhUqZIVIibgZMmS5fK0TVqoKn/99Rfnzp2jQIEC19zOihFjjHGTr7/+mocfftjpGMb4DBEh\na9asZM2a9frb2fSETdMY40nBMk1z8uRJQkND2b9/P/ny5XNzMmP8n12bxhhjPOyTTz6hTZs2VogY\nkw42MoKNjBjjScEwMhIXF0fZsmX54YcfqFmzpgeSGeP/bGTEGGM86LvvvqN06dJWiBiTTlaMGGPM\nTXrrrbcYMGCA0zGM8VtWjBhjzE2IiIjgt99+o3Xr1k5HMcZv2aG9xhiTTufPn6dv37689957jl18\nzJhAENAjIyLyoIhEi0iCiNhkrjFBTkTCRWSXiOwRkcE3u79XXnmFmjVrEh4e7o54xgStgC5GgCig\nHbDMm41GRkZ6s7l084ecltE9/CGjp4lIRuBdIByoBHQWkYrp3d/8+fN5//33efPNN6+7nT/87C2j\ne1jG9AvoYkRVd6nqT95u11f/sa/mDzkto3v4Q0YvqAvsVdWfVTUOmAK0SetOEhISGD16NN27d+f7\n77+nRIkS193eH372ltE9LGP62ZoRY0ywKA4cSvb4MFAvNW+8ePEiW7ZsYc6cOUyYMIEyZcqwbt26\nGxYixpjU8ftiREQWAkVSeGmYqs7ydh5jjM9K1dnMpkyZwqlTpzhy5Aj79u1jx44d7N69m3LlytG4\ncWNmzZpF9erVPZ3VmKASFGdgFZElwH9UddM1Xg/8H4IxDvKFM7CKSH1gpKqGJz0eCiSq6qvJtrG+\nwBgPulZf4PcjI2lwzc7QFzpKY4zHbQDKiUgocAToCHROvoH1BcY4I6AXsIpIOxE5BNQH5ohIhNOZ\njDHOUNV4oC8wH9gBfKOqO51NZYyBIJmmMcYYY4zvCuiREWOMMcb4PitGjDHGGOMoK0aMMcYY4ygr\nRowxxhjjKCtGjDHGGOMoK0aMMcYY4ygrRowxxhjjKCtGjDHGGOMoK0aMMcYY4ygrRowxxhjjKCtG\njDHGGOMoK0aMMcYY4ygrRowxxhjjqKAoRkQkj4hME5GdIrJDROo7nckY430iMlREokUkSkQmiUgW\npzMZY4KkGAHeBuaqakWgGrDT4TzGGC8TkVDgCaCmqlYFMgKdnMxkjHHJ5HQATxOR3MBdqtoNQFXj\ngdPOpjLGOOAPIA7ILiIJQHbgF2cjGWMgOEZGSgMnRGSCiGwSkU9EJLvToYwx3qWqvwNvAAeBI8Ap\nVf3R2VTGGAiOYiQTUBN4X1VrAueAIc5GMsZ4m4iUBQYAoUAx4FYR6eJoKGMMEATTNMBh4LCqrk96\nPI2rihERUa+nMiaIqKo4nQGoDaxS1VgAEfkOaAB8fWkD6wuM8axr9QUBPzKiqkeBQyJSPump5kB0\nCtu57TZixAi37s9TN3/IaRn9P6MP2QXUF5FsIiK4+oIdV28USD97JzMuWbKEESNGMGLECBo1anT5\n/pIlS3wmoz/8HAMp4/UEw8gIQD/gaxG5BYgBujucxxjjZaq6VUS+ADYAicAm4GNnUwWuxo0b07hx\nYwBEhMjISEfzGN8WFMWIqm4F6jidwxjjLFV9DXjN6RzGmCsF/DSNEy59G/B1/pDTMrqHP2QMVP7w\ns7eM7mEZ009uNI8TDERE7edgjGeICOobC1hvyPoCz0j6DDgdwzjsen2BjYwYY4wxxlFWjBhjjDHG\nUVaMGGOMMcZRVowYY4wxxlFBcWivMcb4osjIyMvn34iMjLx8pEPyc3QYEwzsaBpsBb0xnmRH06S6\n7YA94iSQ/24m9exoGmOMMcb4LJumCWKxsbHMmTOHAwcO8Ntvv1GkSBEqVKhAs2bNyJ07t9PxjDHG\nBAkbGQlCe/fupV27dpQpU4aZM2fy119/ERoayu+//8748eMpVaoUXbt2ZdeuXU5HNcYYEwRsZCSI\nqCrjxo3j//7v/xgyZAhffPEFOXPm/Md2J06c4LPPPuOuu+6ie/fujBgxghw5cjiQ2HjTRx99xIcf\nfgjAqVOnKF26NIsXL3Y4lQlEtnDXd40YMYJ8+fLRv39/AIYPH07hwoV5+umnPdquLWAlOBawqipD\nhw5l/vz5fPvtt5QrV+6G7zl69CgDBgxgz549zJgxgxIlSnghqXFafHw8TZs2ZfDgwdx77703vT9b\nwJrqtgN2kef1/m6B/Pf2RwcOHKB9+/Zs3LiRxMREypcvz/r168mbN+9N79sWsBpeeukl5syZw48/\n/piqQgSgSJEiTJ48mQ4dOlC/fn2ioqI8nNL4gqeffppmzZq5pRAxxviXUqVKkT9/frZs2cKCBQuo\nWbOmWwqRG7FpmiAwbdo0Pv/8c1auXEn+/PnT9F4RYfDgwZQoUYJWrVqxZs0aihYt6qGkxmmff/45\nhw4d4v3333c6ijHGIT169GDChAkcO3aMxx57zCtt2jQNgT1Nc+TIEWrUqMHMmTOpV6/eTe1r1KhR\nzJw5k6VLl5I9e3Y3JTS+YuPGjTz66KMsX76cPHnyuG2/Nk2T6rYDdrrCpmn8S1xcHFWqVCEhIYE9\ne/Yg4p5fX5umCVKqymOPPUbv3r1vuhABeP755ylXrhwDBw50Qzrja9577z1OnjxJkyZNqFGjBk8+\n+aTTkYwxDsicOTNNmzalQ4cObitEbsRGRgjckZGvv/6asWPHsmbNGjJnzuyWfZ4+fZoqVaowceJE\nmjZt6pZ9msBmIyOpbjtgRwhsZMS/JCYmUqtWLaZNm0bZsmXdtt+gHxkRkZ9FZJuIbBaRdU7n8Ybz\n588zbNgw3n77bbcVIgC5c+fmo48+okePHpw9e9Zt+zXGG0Qkj4hME5GdIrJDROo7nckYX7Jjxw7K\nlStH8+bN3VqI3EhQjIyIyH6glqr+fo3XA25k5H//+x+bNm1i2rRpHtl/t27dKFSoEK+//rpH9m8C\nhy+NjIjIRGCpqn4mIpmAHKp6OtnrNjLiATYyYuD6fUEwFSO1VTX2Gq8HVDFy7NgxKlWqxLp16zxW\n2f76669UqVKFjRs3Ehoa6pE2TGDwlWJERHIDm1W1zHW2sWLEA6wYMWDTNAAK/CgiG0TkCafDeNqb\nb75Jp06dPDrEVrRoUfr27csLL7zgsTaMcbPSwAkRmSAim0TkExGxw8KM8QHBMjJSVFV/FZGCwEKg\nn6ouT/Z6wIyMnDx5kttuu41NmzZRqlQpj7Z15swZypcvz9y5c6lRo4ZH2zL+y4dGRmoDq4EGqrpe\nRN4C/lDV/ybbxkZGPMBGRgxcvy8IipOeqeqvSX+eEJHvgbrA8uTbjBw58vJ9f74+wnvvvUfr1q09\nXogA5MyZk+HDhzNixAhmzpzp8faMf0h+3REfcxg4rKrrkx5PA4ZcvVGg9AXGOC0tfUHAj4wkDcNm\nVNUzIpIDWAC8qKoLkm0TECMj586do3Tp0ixbtozbb7/dK22eP3+eMmXKsHDhQqpUqeKVNo1/8ZWR\nEQARWQb0UNWfRGQkkE1VByd73UZGPMBGRgzYmpHCwHIR2QKsBWYnL0QCyRdffEFYWJjXChGAbNmy\nMWDAAEaPHu21No25Cf2Ar0VkK1AN+J/DeYwxBMHISGoEwsiIqlKtWjXeeecdmjRp4tW2//jjD8qU\nKcO6desoU+aaByqYIOVLIyM3YiMjnmEjIwZsZCQoLF++nPj4eEfmt3PlykXPnj0ZO3as19s2xhjj\n/2xkhMAYGenUqRMNGzakX79+jrT/yy+/ULVqVQ4cOEDOnDkdyWB8k42MpLrtgB0hsJERAzYyEvCO\nHj3KggUL6Nq1q2MZihcvTtOmTfnqq68cy2CMMcY/WTESACZOnMj9999P7ty5Hc3Rp08f3n//ffuW\nY4wxJk2sGPFzqsqXX37p6KjIJU2aNCEuLo6VK1c6HcUYY4wfsWLEz23ZsoVz587RsGFDp6MgIvTu\n3ZsPPvjA6SjGGGP8iC1gxb8XsA4cOJAcOXIwatQop6MAEBsbS9myZfn555/JkyeP03GMD7AFrKlu\nO2CnOG0BqwG7au8N+WsxEh8fT8mSJVm6dCnly5d3Os5lDz74IM2aNaNXr15ORzE+wIqRVLft1f+U\n//zzT5YvX86iRYuIiopi9+7dnDx5kgsXLnDrrbdSuHBhbr/9durUqUOTJk2oXbs2GTKkbzDdihED\ndjRNwPrxxx8pVaqUTxUiAN27d2fChAlOxzDGXCUhIYHZs2fTqVMnihYtyssvv0yOHDl46qmnmD9/\nPjExMcTGxhIdHc2kSZNo3749R44coWvXroSEhDBo0CAOHjzo9F/DBCAbGcF/R0Yee+wxqlWrxoAB\nA5yOcoX4+HhKlSrFwoULqVSpktNxjMNsZCTVbXtshODcuXN8/PHHjBs3joIFC9K9e3cefPBB8ufP\nn+p97Ny5k08++YSJEyfSpk0bXnvtNQoUKJCq99rIiIEAGBkRkawiksXpHL4kPj6emTNn0r59e6ej\n/EOmTJl45JFHbHTEGIdduHCBN954g7Jly7Jq1SomT57M2rVr6dWrV5oKEYCKFSsyduxY9u/fT548\neahcuTKTJk3yUHITbHxyZEREMgBtgc5AA1xFkwAJwGrga+AHd32F8ceRkUWLFjFkyBDWr19/440d\nsHv3bho3bszBgwfJnDmz03GMg2xkJNVtu22EQFWZPHkyw4YN44477mDUqFFUrVrVLfu+ZNOmTXTs\n2JGWLVsyZsyY6/6e28iIAf8cGYkEagFjgDKqWlRViwBlkp6rAyx1Lp7zpk+fzv333+90jGuqUKEC\nZcqUYd68eU5HMSaoxMTE0Lx5c9544w2++OILfvjhB7cXIgA1a9Zk/fr17N27lxYtWnDu3Dm3t2GC\nh6+OjGRR1b9udps0tOdXIyOJiYkUL17c546iudqnn37K7Nmz+f77752OYhxkIyOpbvumRwi+/fZb\n+vTpw5AhQxgwYACZMmVyU7prS0hI4IknnuCnn35i7ty55MqV6x/b2MiIAT89tFdE7gKaAkVwTc+c\nAFar6gIPtOVXxciKFSvo3bs3UVFRTke5rjNnzlCyZEl++uknChUq5HQc4xArRlLddrr/U1ZVRo8e\nzQcffMCsWbOoXr26m9NdX2JiIn379mXz5s0sWrSI7NmzX/G6FSMG/HCaRkSG4SpENgPTgJlANNBM\nREY7mc0XzJgxg3bt2jkd44Zy5sxJ27Zt7eJ5xnjYSy+9xOTJk1m9erXXCxGADBky8N5771G+fHk6\nduxIfHy81zMY/+aTIyMi0lpVZ17jtQdUdZqb2/OrkZFKlSrx+eefU7duXaej3NCyZcvo1asX0dHR\niPjFl2PjZjYykuq20zVC8Nlnn/HSSy+xatUqihQp4oFkqRcXF8d9991HaGgoH3744eXnbWTEgB+O\njADVReS/ItJKRJqKSCMRaSkiQ4A7nQ7npP379xMbG0vt2rWdjpIqd911F6rKihUrnI5iTMCJjIxk\n2LBhREREOF6IAGTOnJmpU6eyfPlyxo8f73Qc40d8shhR1VHAKqAm0B7oiOsImvXAs+nZp4hkFJHN\nIjLLbUEdMGfOHFq0aJHu0zJ7m4jw5JNP8tFHHzkdxRggcPqC2NhYHnnkESZOnEiFChWcjnNZzpw5\n+e677xg2bBgbNmxwOo7xEz45TeMJIjIQ1+HCOVW19VWv+c00TcuWLS+fPdFfXLp4XkxMTJpPtGT8\nn69N0/hqX5CW6QpV5f7776d06dK88cYbHk6WPtOnT+fZZ59l8+bN5M2b16ZpjF9O06RIREJFZFU6\n3lcCaAmMx3XyNL/0559/smLFCu655x6no6RJ/vz5adWqFRMnTnQ6iglygdIXfPnll+zbt4///e9/\nTke5pvvvv5+WLVvSu3dvp6MYP+BXxYiq/gzcm463vgk8ByS6NZCXLV68mFq1apE7d26no6RZz549\n+fDDD0lM9Ot/AuP//L4viI2NZdCgQXz66adkyeLbV8kYM2YMM2bMcDqG8QM+WYyISOGrHv9bRJ4T\nkaaqejKN+2oFHFfVzfjxNyGAiIgIWrRo4XSMdAkLCyN79uwsWOD208QYkyqB0hcMGTKEDh06UKtW\nLaej3FC2bNkoV64cALt27XI4jfFlnj89X/rcLyJxqvqJiPwHuADEAo1F5DZV/TgN+2oAtBaRlkBW\nIJeIfKGqXZNvNHLkyMv3GzduTOPGjW/27+B2Cxcu5Ntvv3U6RrqICE8//TTvvPMO4eHhTscxHhQZ\nGUlkZKTTMVLi933B6tWriYiIYMeOHU5HSbWtW7ciInTo0IE1a9b844RoJnClpS/wyQWsInILsF9V\ni4tIuKrOS/Zad1VN1+VgRaQR8Kyq3nfV8z6/gPXAgQPUqVOHo0eP+s2RNFe7cOECISEhrFixwqdP\nY2/cy9cWsIJv9gU3WsipqoSFhdGzZ0+6du16ze18kYjQuXNnsmfP/o9Dfm0Ba/DwxwWsrwJZRaQL\nrkN6EZEeIlIUuNkFE375qV+4cCHNmzf320IEIGvWrDzxxBO8++67TkcxBvysL5gxYwZnz56lS5cu\nTkdJl48++ojVq1czbtw4p6MYH+STIyMpEZHuwG/AbHd/dfGHkZGOHTsSHh5O9+7dnY5yUw4fPky1\natWIiYkhb968TscxXuCLIyPX4qsjI/Hx8VStWpWxY8f65bqxS3+3/fv306BBAz799FNatmx5xWsm\n8PnjyAgiUk9E2otIcYCkqZk/gUrOJvO+xMREFi1axL/+9S+no9y0EiVK0KpVqytOFW2Mub4vvviC\nwoUL+/16q9KlSzN9+nQeffRR1qxZ43Qc40N8cmREREYBtwP7gOrAYlV9TUQyAcdU1a1nzvL1kZGN\nGzfy8MMPs3PnTqejuMW2bdsIDw9n//79Pn9oorl5NjKS6rZTHCGIj4+nQoUKfP7559x1110OJLt5\nV//dIiIi6NatG7NmzaJ+/fo2MhIk/HFk5JSqPqiqg1U1HFgnIsNxnRvAb88PkF4LFy4MiFGRS6pV\nq0a1atXsar7GpMLkyZMpUaKE3xYiKWnRogWff/45YWFhTkcxPsJXi5ELIpJPRHqLSHZVjQQ+BJ4C\nMjsbzfuWLFlCs2bNnI7hVoMGDWLMmDF2EjRjriMhIYGXX36ZF154wekobteyZcvLR9V9+umnDqcx\nTvPVYuRj4N9AYZJGQlQ1FngXGOpgLq+Li4tj9erVAfWtCKBJkybkypWL7777zukoxvis6dOnkzdv\n3oD7MnJJdHQ0AK+99hqPPfYYZ86ccTiRcYpPFiOqGqeqk1V1pKpeEJE8SS/lUdUPHA3nZRs2bKBs\n2bLky5fP6ShuJSI8//zzvPTSSzZfbEwKVJVXX32VYcOGIeIXS27SbePGjWTIkIE77riDuXPnWp8Q\nhHyyGElBt6Q//etMP24QGRnpU2eAdKdWrVoBMGfOHIeTGON7Fi9ezPnz57n33vRcjsu/3HrrrYwf\nP5533nmHgQMH8q9//YvFixdbURJE/KUYCVqBXIyICMOHD2fUqFHW6Rhzlddee43nnnvOr090mFb3\n3nsvUVFRdOjQgf79+1OxYkVGjx7Nzz//7HQ042HB8yn3Q4G6XiS59u3bc/bsWSIiIpyOYozP2LJl\nC9u3b+ehhx5yOorXZc6cmSeffJJt27bx6aef8vPPP1O7dm3uvPNOxo4da4VJgPLJ84xcTUT6q+rb\nl/70wP69cm6B5BcNSj7ica2Lca1evZo+ffqwefNmj2dz0vTp03n55ZfZuHFjwM+NByM7z0iq2748\nQvjQQw9xxx13MGjQIEeyuNv1zrKamjOwXrx4kSVLljB16lRmzpxJsWLFaN26NS1btqROnTpkzJjR\nE7GNm12vL7BiBGc6oNT8Ar7yyiscP36cN99800upnJGYmEjt2rV5/vnnad++vdNxjJtZMZLqtlFV\nYmJiqFevHvv27SNXrlyOZHG3my1GkktISGDVqlXMnj2bOXPmcPToUZo2bUrz5s1p1KgR5cuXty81\nPsqKkRvv3yeLkfDwcHr16kXbtm29lMo5c+bMYfDgwWzdutW+5QQYK0ZS3TaqSs+ePSlUqBCjRo1y\nJIcnuLMYudrhw4f58ccfWbx4MZGRkfz111/Ur1+funXrUqNGDapVq0bx4sWtQPEBgVCMVFLVHZf+\n9MD+fa4YSUhIIF++fOzdu5eCBQt6MZkzVJW77rqLHj168Oijjzodx7iRFSOpbptffvmFKlWqsHv3\n7oD6vfdkMZKcqnLo0CHWrFnD+vXr2bp1K9u2bePs2bOULVuWkJAQSpQoQYECBcibNy9Zs2a9fMue\nPTt58uShQIEClCxZkty5b/YC8eZqfl+MeJovFiNbtmyhc+fOAXM9mtRYvXo1HTp04KeffiJbtmxO\nxzFuYsVIqtvmP//5D3Fxcbz9ttsHgB3lrWLkWk6fPk1MTAwHDx7kl19+ITY2lpMnT3LhwoXLtz//\n/JNTp05x4sQJDh48yC233EKlSpWoWrUqtWvXpn79+lSsWDGojm5yt4AoRkQkBMiuqrs8sG+fK0bG\njRvHtm3b+OSTT7yYynkPPPAAtWrVYujQoDrRbkCzYiTVbZMvXz62bdtG8eLFHcngKU4XI2mlqpw4\ncYLo6Gi2bdvG+vXrWb16NadPn6ZRo0bcc889tGjRgpCQEKej+pVAKUbeBC4Ah4D6wFequsBN+/a5\nYqRjx460bNmSbt26XXObQLRnzx7uvPNOduzYQaFChZyOY9zAipFUt02/fv145513HGnfk/ytGLmW\nw4cPs2TJEubPn8+8efMICQnhgQceoFOnTpQpU8bpeD4vUIqRRqq6VETuVdU5IvKwqrrlsq++Voyo\nKiVKlGD58uVB+QF/9tlniY2NZcKECU5HMW5gxciN1a5dm40bN3L48OGAGxWBwClGkktISGDFihVM\nnTqVb7/9lkqVKvH444/z4IMPkjVrVqfj+aRAKUZmAPOBWFX95lJx4qZ9+1Qxsm/fPsLCwvjlZopw\nvwAAIABJREFUl1+CcgX4mTNnqFixIt988w0NGzZ0Oo65SVaM3FixYsX49ddf/fI/5dQIxGIkuYsX\nLzJ79mw+/vhjNm3aRI8ePejXrx9FixZ1OppPuV5f4E8rcf4DRAK5ReRt4JnUvElEsorIWhHZIiI7\nROQVT4Z0hxUrVhAWFhaUhQhAzpw5GTNmDH369CE+Pt7pOCZAiEhJEVkiItEisl1EnnY6E7iG/v/6\n6y+nY5ibcMstt9C+fXvmzZvHypUrOXPmDJUrV+bxxx9nz549TsfzC35TjKjqXlXdoaofq2p/YHgq\n33cBaKKqdwDVgCYiEubJrDdrxYoVQT8i0LFjRwoXLszrr7/udBQTOOKAZ1S1Mq51Z0+JSEWHM/HS\nSy/x+OOPOx3DuEm5cuUYN24ce/bsoWTJkjRo0IAuXbqwe/dup6P5NL8pRq6mqtFp2PbPpLu3ABmB\n3z0Syk1WrlxJWJhP10seJyKMHz+esWPHsm3bNqfjmACgqkdVdUvS/bPATqCYk5n27dvHtGnTGDx4\nsJMxjAfkz5+fkSNHEhMTQ6VKlQgLC+PRRx/lwIEDTkfzSX5TjIhINhEpLSINRKS9iLyRhvdmEJEt\nwDFgiSdOnOYuJ0+e5ODBg1SvXt3pKI4LCQnh9ddfp2vXrly8eNHpOCaAiEgoUANY62SOESNG0K9f\nP/Lnz+9kDONBuXLlYvjw4ZdHSmrWrMnAgQM5efKk09F8it8UI8DLSbc7gPJAqs83oqqJSdM0JYC7\nRaSxRxK6werVq6lTpw6ZMmVyOopP6NatG6VLl2bgwIFORzEBQkRuBaYB/ZNGSByxdetWFi5caJ/t\nIJEnTx5GjRpFdHQ0f/75JxUqVODtt9+2L1pJ/OZ/PFUdmDS/WxU4oKpz0rGP0yIyB6iNazHsZSNH\njrx8/1pX0fWGVatWBf16keREhAkTJlCnTh2+/PJLHnnkEacjmRtIfnVqXyMimYHpuM5T9ENK23ir\nLxgyZAjPP/88OXPm9Mj+jW8qUqQIH374If369ePZZ5/l3Xff5dVXX6Vdu3YBd9BCWvoCnzy0V0Ry\nAo8C54ApydZ8XHo9HKiqqjdc3SgiBYB4VT0lItlwHR78oqouSraNzxza26RJEwYPHkx4eLhX8/i6\n7du306RJEyIiIqhdu7bTcUwa+MqhveLq6SfiOj1AikfjeasvWLx4MU8++SQ7duzglltuudS23x/i\nei2BfmjvzViwYAHPPfcc2bJlY/To0Y59EfYGvzvPiIh8CJwGSgLFgRYpFCQNVXVlKvZVFVcHlCHp\n9uXVRYyvFCNxcXHky5ePQ4cOkSdPHq/m8QczZ86kZ8+eLF68mIoVHT8IwqSSDxUjYcAyYBtw6Zdv\nqKrOS7aNx/uCxMRE6taty3PPPUfHjh2T5wvY/5StGLm+xMREJk2axH//+19CQ0MZMWIEd999d8CN\nlFyvL/DVaZooVX0PQESKAp2Az5JvkJpCJGm7KKCm2xN6wNatWwkNDbVC5Bpat27NqVOnuOeee4iM\njKRs2bJORzJ+RFVX4APr5CZNmkTGjBnp0KGD01GMj8iQIQMPP/wwHTt25KuvvqJHjx4ULFiQQYMG\ncd9995ExY0anI3qc47+Y13D5DECq+ivwh4NZvGbVqlU0aNDA6Rg+rWvXrrzwwguEhYWxatUqp+MY\nkybnz59n2LBhjB07NuC+9ZqblzlzZrp3786uXbsYOHAg//vf/yhfvjxvvfUWp06dcjqeR/nqNM1e\nYB6wCdgMlFHV6UmvFVbVY25uzyemaYL14njpERERQbdu3Xj++efp06eP40cfJV+oFRkZeXne18nF\n0L7CV6ZpUsPTfcErr7zCxo0bmTZtWkptB+x0hU3TpI+qsmbNGt555x0iIiJo164djz/+OA0bNvTL\nYtYf14y8AKzHdZbEOrjOB3AQWAkUVNWubm7P8WLk0sXxli1bZtMPqbR792769OnDb7/9xpgxY2je\nvLlP/IJa53olK0Zcjh07RuXKlVmzZg233XZbSm0H7OfGipGbd/z4cT7//HM+++wzEhMTefjhh3no\noYdS/Cz5Kr8rRlIiImWBesATqtrEzft2vBgJ9ovjpZeqMnXqVEaOHMmtt97K4MGDadu2raNzrNa5\nXsmKEZcnn3ySnDlz8sYbKZ+vMZA/N1aMuI+qsnbtWiZNmsQ333xDiRIleOCBB2jXrh0VKlTw6f8/\n/K4YEZFCqnr8Gq+57Wq9yfbpeDHyxRdfMGfOHL755huv5ggUiYmJfP/997zxxhscPXqUZ555hh49\nepAtWzavZ7HO9UpWjLgWp99zzz3s3r37mgvUA/lzY8WIZ8THx7N8+XKmTp3KzJkzyZ49O61atSI8\nPJy7776brFmzOh3xCv5YjBwHHlfVWUmPswD5VfWIh9pzvBh54oknqF69On379vVqjkC0Zs0aRo8e\nzdq1a3n22Wfp06ePV4sS61yvFOzFiKrSvHlz2rdvz1NPPXW9tgP2c2PFiOepKps2bWLu3LlEREQQ\nFRVFvXr1aNasGY0bN6Z27dpkzpzZ0Yz+WIz8B2gAxABDVDVRROoAzXCtGfmPm9tzvBi5/fbb+eab\nb+yaNG60bds2XnzxRdasWcPw4cPp0aPH5RNMeZJ1rlcK9mJk2rRpvPjii2zatOm6/xkE8ufGihHv\nO336NEuXLmXJkiVERkayd+9e6tatS1hYGA0bNqR+/frkypXLq5n8sRjpqaofichAoBXw8KVRERH5\nXlXbubk9R4uR48ePU758eWJjY4PieHJv27BhA8OHDycmJoaXX36ZDh06eHRe1TrXKwVzMXLu3Dkq\nVqzIl19+SaNGjW7UdsB+bqwYcd6pU6dYuXIlK1asYOXKlWzatIkyZcpQv3596tatS926dalUqZJH\nj0z0x2Jkgqp2T7p/JzAO1wjJjyLy3NVnUHVDe44WI9999x3jx49n7ty5Xs0QbH788UcGDRpElixZ\nGDt2LHfeeadH2rHO9UrBXIw8//zz7Nu3j0mTJqWm7YD93Fgx4nsuXrzItm3bWLNmDevWrWPdunUc\nOnSIatWqUbNmTWrUqEH16tWpUqWK26a5/bEY+QjYDcxW1Z9EJD+uU7pvAE6p6ltubs/RYuSZZ56h\nUKFCDB061KsZglFiYiJfffUVw4YNo1GjRrz66quUKFHCrW1Y53qlYC1Gdu7cyd13382WLVsoXrx4\natoO2M+NFSP+4cyZM2zevJnNmzezadMmtm7dyu7duylVqhRVq1alcuXKVK5cmUqVKlGuXLk0T3v7\nXTFyiYhkU9XzSfcFGAY8o6oF3NyOo8VI7dq1eeuttwgLC/NqhmB29uxZXn31VT744AMGDRrEgAED\n3LaexDrXKwVjMZKYmMhdd91Fly5d6NOnT2rbDtjPjRUj/uvixYvs3r2bqKgooqOjiY6OZufOnRw4\ncICSJUtSoUIFypUrR7ly5Shbtixly5alVKlSKa6P8ttiJCUiUldV17l5n44VI6dOnaJkyZL89ttv\nZMmSxasZDMTExPD000+zf/9+PvjggxvO66eGda5XCsZi5P3332fSpEksW7aMDBlSd9WNQP7cWDES\neC5evEhMTAy7d+9mz5497Nmzh3379hETE8ORI0coUqQIpUqVIjQ0lJCQEEqUKEHv3r39qxiRVPQI\nqdkmDe05VozMnDmTcePGsXDhQq+2b/6mqvzwww88/fTTNG/enDFjxpA/f/5078861ysFWzGyb98+\n6tWrx7Jly9J0delA/txYMRJc4uLiOHz4MD///DMHDhzg4MGDHDp0iPHjx/tdMbIUmA3MUNWfrnqt\nAtAWuFdV73ZTe44VI8888wwFCxZk2LBhXm3f/NOZM2d44YUXmDJlCmPGjKFLly7pOurGOtcrBVMx\nEhcXR1hYGA899BD9+/dPa9sB+7mxYsSAH07TJJ3krAvQGagCnAEEuBXYDnwNTFLVi25qz7FipEaN\nGrz//vseO7LDpN26det44oknKFasGB9++CGlSpVK0/utc71SMBUjQ4cOJSoqilmzZqW5kA3kz40V\nIwb8sBhJTkQyApcWrP6mqgkeaMORYiQ2NpbQ0FBiY2MdPzOeuVJcXBxjxozhjTfe4L///S9PPfVU\nqs8BY53rlYKlGJk+fToDBgxg06ZNFCxYMD1tB+znxooRA9fvC1K3sspBqpqgqseSbm4vRJy0dOlS\nGjRoYIWID8qcOTNDhw5l5cqVTJs2jbCwMHbs2OF0LOOj1q5dS69evZgxY0a6ChFjgp3PFyOBbMmS\nJTRp4tYLEBs3q1ChApGRkXTr1o1GjRrx8ssvExcX53Qs40Oio6Np164dn332GTVr1nQ6jjF+KeCL\nEREpKSJLRCRaRLaLyNNOZ7rEihH/kCFDBnr16sXGjRtZvnw5d955J9HR0U7HMukgIuEisktE9ojI\n4Jvd36pVq2jatCmvvfYa9913nzsiGhOUfLoYEZFKKTzXOI27icN1orTKQH3gKRFJ/fF2HnT48GFq\n1arldAyTSiEhIURERNCzZ08aNWrEW2+9ZXPdfiRp/dm7QDhQCeic3r7gwoULvPLKK7Rp04aJEyfy\n8MMPuzOqMUHHp4sR4FsRGSwu2UVkHDA6LTtQ1aOquiXp/llgJ1DMA1nTrEmTJnZhPD8jIjzxxBOs\nW7eOyZMnc++993LixAmnY5nUqQvsVdWfVTUOmAK0ScsOjh8/zptvvknFihVZv349q1evJjw83CNh\njQkmnrs8n3vUA14FVuM6rHcS0CC9OxORUKAGsNYN2W5a8+bNnY5g0qlMmTKsWLGCF154gZo1azJl\nyhQaNmzodCxzfcWBQ8keH8bVx1zTiRMn2LJlC6tXr+bHH39k27ZttG3blq+//poGDdLdFQWFyMhI\nIiMjAWjUqBEjR44EoHHjxjRu3NixXMY3+fShvUnnG3kJuAfIATyvqlPSua9bgUjgJVX94arXvHpo\nr6qSIUMGdu/eTfny5b3WrvGMOXPm8NhjjzF06FD69+9PhgwZbPomGV85tFdE7gfCVfWJpMcPA/VU\ntV+ybbRRo0bExsZy+PBhAO644w5q165N8+bNCQsLI0eOHJ7KFzSfm+SFSmRk5OXixAqVwHbdvkBV\nffYGbAVGAZmBosBMYGo69pMZmA8MuMbrOmLEiMu3JUuWqCft2rVLAU1MTPRoO8Z79u/fr3fccYcW\nKFBAXb9WwWvJkiVX/D4l/Tx8oT+pD8xL9ngoMPiqbTSl24gRI1L8u176+7lj+5Q+N+7cv21v23t7\n+7T0Bb4+MlJbVTdc9dwjqvplGvYhwEQgVlWfucY26s2fQ5kyZdi/f3/QfAsKFn/++SelSpXit99+\n4+DBg5QsWdLpSD7Bh0ZGMgG7gWbAEWAd0FlVdybbxqt9wVX5rE8wAe16fYGvrxm5V0TuTfY4Pb+p\nDYGHgW0isjnpuaGqOu+m06XT6dOnnWraeFD27Nk5fvw4GTJkoF69ekybNs3WFfgQVY0Xkb64Rkkz\nAp8mL0SMMc7x9WLkHH8XINmAVkCaToOpqivwoaOG4uPjSUxMdDqG8ZBL1yMZP348bdu25c0336RL\nly4OpzKXqGoEEOF0DmPMlXy6GFHVMckfi8jrwAKH4rjF2rVrKVWqFKdOnXI6ivGgli1bsnjxYu67\n7z5++uknRo4cma4rABtjTDDwmRGDVMqB6/A8vzV//nz+/e9/Ox3DeEGVKlVYs2YN8+bNo1u3bly8\n6JaLTBtjTMDx6WJERKKS3aJxLT572+lcN8OKkeBSuHBhlixZwpkzZwgPD7f1QsYYkwJfP5omNNnD\neOCYus6c6O52vLKCPjY2ljJlynD8+HGyZs1qK+cDVEpHRSQkJNC/f3+WL19OREQExYr5xEmAvcJX\njqZJDTuaxhjPuV5f4NMjI+o6bfOl22FPFCLe9OOPP3L33XeTJUsWp6MYL8uYMSPjxo2jc+fOdqE9\nY4y5ik8uYBWRM9d5WVU1l9fCuJFN0QQ3EWHIkCGULFmSpk2bMmXKFLtqszHG4LsjIzNUNSfwgqrm\nvOrml4WIqloxYgDo0qULU6ZMoVOnTowfP97pOMYY4zhfLUZqikgx4DERyXf1zelw6REVFUW2bNm4\n7bbbnI5ifECTJk1Yvnw5r7/+OgMGDCAuzq9nII0x5qb4ajHyIbAIqABsTOHmd+bOnUvLli3tXBPm\nsvLly7NmzRr27NlD06ZNOXLkiNORjDHGET5ZjKjqO6paEZigqqWvvjmdLz0uFSPGJJc3b15mzZrF\nv//9b2rVqsXcuXOdjmSMMV7n04f2eounD+c7efIkpUqV4tixY2TLlu1Sm3YYX4BK77/t0qVL6dq1\nK23atGH06NFkz57dA+m8zw7tTXXb1ieYgOa3h/YGioULF3L33XdfLkSMSUmjRo3YsmULsbGxVK9e\nnRUrVjgdyRhjvMKKES+wKRqTWnnz5uXrr7/m9ddfp0OHDvTs2ZOTJ086HcsYYzzKihEPS0xMJCIi\nghYtWjgdxfiRtm3bsmPHDjJlykSlSpUYP348CQkJTscyxhiPsGLEwzZs2ED+/PkpXdov190aB+XJ\nk4f33nuP2bNn8/nnn1OnTh0WLlxo6wqMMQHHihEPmz17Nvfdd5/TMYwfq1WrFsuXL2fYsGH07duX\n5s2b23oSY0xAsWLEw2bNmkWrVq2cjmH8nIjwwAMPsH37djp37kzXrl1p1qyZjZQYYwKCHdqL5w7n\nO3z4MNWrV+fYsWNkynTlZYDsML7A5Y1/27i4OCZNmsTrr79OpkyZ6N+/P506dfLJI7bs0N5Ut219\nggloQX1or4h8JiLHRCTK223PmTOHFi1a/KMQMeZmZc6cmW7duhEVFcUrr7zCtGnTCAkJ4ZlnnmH7\n9u1Ox/M5IvK6iOwUka0i8p2I5HY6kzHmbwFfjAATgHAnGrYpGuNpIkKLFi2YM2cOa9euJUeOHISH\nh1OrVi3efvttjh075nREX7EAqKyq1YGfgKEO5zHGJBMU0zQiEgrMUtWq13jd7UOzf/75J0WKFOHA\ngQPkzZs3pTZtSDZAOf1vm5CQwOLFi/nyyy+ZOXMmtWvXpmPHjrRt25aCBQt6PY+vTdOISDvgflV9\nOIXXbJrGGA+5Xl9gxQie6YB++OEHxo0bx6JFi66VyTqeABIZGUlkZOTl+40bNwagcePGl+874fz5\n88yZM4epU6cyb948atasSZs2bWjTpo3XDjf3wWJkFjBZVSel8JoVI8Z4iBUjDhQj3bp1o3bt2vTr\n1+9amazjMV51/vx5Fi5cyIwZM5g9ezb58+fn3nvvJTw8nLCwMLJkyeKRdr1VjIjIQqBICi8NU9VZ\nSdsMB2qq6v3X2IcVI8Z4iBUjqShGRowYcfnxzX6bjYuLo0iRImzZsoWSJUteK5N1PMYxiYmJbNiw\ngYiICObNm8f27dtp2LAhzZo1o3HjxtSoUSPdC6+TjxIBvPjiiz4xMiIijwJPAM1U9cI1tnFrX5DG\nfNYnmICSlr7AihHc/21o8eLFDB48mPXr118vk3U8xmecPHmSyMhIFi1axNKlSzl48CD169enYcOG\n3HnnndSrV49cuXKla9++ME0jIuHAG0AjVf3tOtvZyIgxHhLUIyMiMhloBOQHjgP/VdUJV23j1g6o\nX79+FC1alGHDhl0vl3U8xmf99ttvrFq1ipUrV7J69Wo2bdpESEgIderUoVatWtSsWZNq1aqlqkDx\nkWJkD3AL8HvSU6tVtU8K21kxYoyHBHUxkhru7IBUlZCQEObPn0+lSpWu16Z1PMZvxMfHEx0dzbp1\n69i0aRMbN24kOjqawoULU61aNapUqULFihWpWLEiFSpUIEeOHJff6wvFSGpZMWKM51gxcgPu7IDW\nrl1Lt27d2LlzJyLX7n+t4zH+LiEhgT179hAdHU1UVBQ7d+5kx44d7N27l/z581OuXDluu+02xo8f\nb8VI6tq2PsEENCtGbsCdHdAzzzxD7ty5GTly5I3atI7HBKSEhAQOHjxITEwMe/fupXfv3laMXIOv\nHhJujCdYMXID7uqAEhISKFmyJIsXL+b222+/UZtWjJigYNM0xhgI8mvTeNOyZcsoXLjwDQsRY4wx\nxvzNihE3mjJlCp07d3Y6hjHGGONXbJoG9wzNXrx4kWLFirFx40ZKlSqVmjZtmsYEBZumMcaATdN4\nRUREBBUrVkxVIWKMMcaYv1kx4iYff/wxPXr0cDqGMcYY43dsmoabH5o9ePAgNWrU4NChQ2TPnj21\nbdo0jQkKNk1jjAGbpvG4zz77jIceeijVhYgxxhhj/mYjI9zct6GEhARCQ0OZO3cuVaumeB2+a7Vp\nIyMmKNjIiDEGbGTEo2bOnEmJEiXSVIgYY4wx5m9WjNwEVeXll19m8ODBTkcxxhhj/JYVIzdh/vz5\n/PXXX7Ru3drpKMYYY4zfsjUjpG+eWFUJCwujX79+dOrUKVXvsYtimWBka0aMMWAXyruh9HRACxYs\noG/fvuzcuZOMGTN6KJkx/s+KEWMMXL8vyOTtMIHg7Nmz9OrVi3fffdcKEWOMMeYm2cgIaf821L9/\nf06dOsXEiRM9mMqYwGAjI8YYsEN7EZFwEdklIntE5KYOfZk1axZTp07lzTffdFc8Y4yXiMh/RCRR\nRPI5ncUY87eAL0ZEJCPwLhAOVAI6i0jFtO5HVXnttdfo2bMn06dPJ1++a/dllxap+jp/yGkZ3cMf\nMnqaiJQE/gUc8Ga7/vCzt4zuYRnTL+CLEaAusFdVf1bVOGAK0Ca1b05MTGTGjBncddddfPvtt6xd\nu5Y777zzuu/x1X/sq/lDTsvoHv6Q0QvGAoO83ag//Owto3tYxvQLhgWsxYFDyR4fBupda+OEhAR2\n7drFunXrWLx4MfPnz6d06dI8++yztGvXjkyZguFHZkxgEZE2wGFV3SbiF8tXjAkqwfA/a6pWo4WF\nhXH06FEOHTpESEgIdevWJSwsjP/7v/+jdOnSns5ojLlJIrIQKJLCS8OBocA9yTf3SihjTKoE/NE0\nIlIfGKmq4UmPhwKJqvpqsm0C+4dgjMOcPJpGRKoAi4A/k54qAfwC1FXV41dta32BMR4UtCc9E5FM\nwG6gGXAEWAd0VtWdjgYzxjhCRPYDtVT1d6ezGGNcAn6aRlXjRaQvMB/ICHxqhYgxQS2wv4EZ44cC\nfmTEGGOMMb4tGA7t9RoReV1EdorIVhH5TkRyJ3ttaNJJ13aJyD3X248XcrrtJHDuIiIlRWSJiESL\nyHYReTrp+XwislBEfhKRBSKSxweyZhSRzSIyyxczikgeEZmW9FncISL1fC1joLO+IP2sL3B7Rr/o\nD6wYca8FQGVVrQ78hGsFPyJSCeiI66Rr4cD7IuLIz95dJ4HzgDjgGVWtDNQHnkrKNQRYqKrlcS1C\nHOJgxkv6Azv4e7jf1zK+DcxV1YpANWAXvpcx0FlfkH7WF7iXX/QHVoy4kaouVNXEpIdrca3aB9dJ\n1iarapyq/gzsxXUyNifc1EngPEVVj6rqlqT7Z4GduM4R0xq4dBGgiUBbZxK6iEgJoCUwnr8PD/WZ\njEnfwO9S1c/AtWZKVU/jQxmDgfUF6Wd9gfv4U39gxYjnPAbMTbpfDNfJ1i45jOuXywkpnQTOqSwp\nEpFQoAauTrywqh5LeukYUNihWJe8CTwHJCZ7zpcylgZOiMgEEdkkIp+ISA58K2Owsb4gnawvuGl+\n0x9YMZJGSfNsUSnc7ku2zXDgoqpOus6unFo57NMrlkXkVmA60F9VzyR/Lelyqo7lF5FWwHFV3cw1\nTprldEZcR8jVBN5X1ZrAOa4agvWBjAHB+gLPsr7ALfymPwj4Q3vdTVX/db3XReRRXEN3zZI9/QtQ\nMtnjSyddcsLVWUpy5Tc1x4hIZlydz5eq+kPS08dEpIiqHhWRosDxa+/B4xoArUWkJZAVyCUiX/pY\nxsO4Tnu+PunxNFzrFY76UMaAYH2B51hf4DZ+0x/YyIgbiUg4rmG7Nqp6IdlLM4FOInKLiJQGyuE6\n+ZoTNgDlRCRURG7BtZhupkNZLhMRAT4FdqjqW8lemgl0S7rfDfjh6vd6i6oOU9WSqloa6AQsVtVH\nfCzjUeCQiJRPeqo5EA3MwkcyBgPrC9LP+gL38af+wM4z4kYisge4Bbh0ZsfVqton6bVhuOaO43EN\nO853JiWISAvgLf4+CdwrTmW5RETCgGXANv4eMhyKq6P+FggBfgY6qOopJzImJyKNgP+oamsRyYcP\nZRSR6rgW1d0CxADdcf1b+0zGQGd9QfpZX+D2fH7RH1gxYowxxhhH2TSNMcYYYxxlxYgxxhhjHGXF\niDHGGGMcZcWIMcYYYxxlxYgxxhhjHGXFiDHGGGMcZcWI+QcRyS0ivZM9LiYiUx3K8h8RSUw6fp+k\nk0VNEJFtIrIl6Rj/S9tGiuty6JuTbgWu2tf9Sfuqmey5bkmX0f5JRLpeI0MWEflGXJdZXyMipdLy\nfmP8lfUF/8hgfYGnqKrd7HbFDQgFonwgR0lgHrAfyJf03FO4Ts4EUBDYkGz7JUDNa+wrJ64TKa0C\naiU9lw/XSYDyJN1igDwpvLcPrms7gOsslVPS8n672c1fb9YX/OO91hd46GYjIyYlo4GySd8oXhWR\nUiISBa7rbYjIDyKyQET2i0hfEXlWXFeEXC0ieZO2KysiESKyQUSWiUiFdOQYCwy66rmKuDoaVPUE\ncEpEaid7PcWLVgGjkv5ef/H3WR3/DSxQ1VPqOvvgQiA8hfcmv9z2dP6+1khq32+Mv7K+4ErWF3iI\nFSMmJYOBGFWtoaqD+ecvdWWgHVAHeBn4Q11XhFwNXBqe/Bjop6q1cV2j4/20BBCRNrgu8LTtqpe2\n4rpAVcaka3vU4sqLfU1M6jifT7avmkBxVZ171b5Sezn3y5daV9V44LSI5E/D+43xV9YXXMn6Ag+x\nq/aalFzrG8UlS1T1HHBORE7huugSQBRQTURy4Lqq5VSRy7u6JdWNi2QHhgHJr4p6aUcEtz4XAAAB\n4UlEQVSf4fpGtAE4gGuoNSHptS6qekSSLj0uIo8AX+H6VtUthX0ZY67P+gLjFTYyYtLjr2T3E5M9\nTsRV4GYATiZ9m7p0q3z1TkRkXtI3l4+veqksrrnqrSKyH9dl1jeKSCFVTVDVgUn7bItrfvYnAFU9\nkvTnWWASUBfX/HBlIDJpX/WBGSJSi9RfQv0XXBeUQkQyAblVNTYN7zcmUFlfYH2BW1gxYlJyBtcv\nbloJgKqeAfaLyAPguiS4iFS7emNVDU/qSJ686vkoVS2sqqXVdYnuw7gWox0XkWxJ37YQkX8Bcaq6\nK2motkDS85mB+3AtvPtDVQsm29caoLWqbgQWAPeISJ6k+e1/ASldQTX5ZcEfABYl3U/t+43xV9YX\nXMn6Ag+xaRrzD6oaKyIrkxaqzcU1x3tpoZcmu08K9y897gJ8kDRfmxmYjOuS4OmKlOx+YWCeiCTi\n6pgeSXo+a9LzmXFdHnsh8Ml1d6r6u4iMAtYnPfVi0uIzRORFXKvzZwGfAl+K67LwsUCnG73fmEBg\nfYH1Bd4iqnrjrYwxxhhjPMSmaYwxxhjjKCtGjDHGGOMoK0aMMcYY4ygrRowxxhjjKCtGjDHGGOMo\nK0aMMcYY4ygrRowxxhjjKCtGjDHGGOOo/weA9O44VUzslQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14e05d9d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qm.lcplot(nightlyCoadd=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qm.writeLightCurve('coadded_lc.dat',nightlyCoadd=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cm = qm.cadence_Matrix(fieldID=309, mjd_center=49540, mjd_range=[-30., 50.])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"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>Filters</th>\n",
" <th>u</th>\n",
" <th>g</th>\n",
" <th>r</th>\n",
" <th>i</th>\n",
" <th>z</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>49510</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49511</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49512</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49513</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49514</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Filters u g r i z Y\n",
"49510 NaN NaN NaN NaN NaN NaN\n",
"49511 NaN NaN NaN NaN NaN NaN\n",
"49512 NaN NaN NaN NaN NaN NaN\n",
"49513 NaN NaN NaN NaN NaN NaN\n",
"49514 NaN NaN NaN NaN NaN NaN"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cm.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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p3S/W7AZa1O67vSxXxntdVk+6lRunbvVnSBqPiIVtFrGDpCUl13WvpB8qtVy+Q6l7Yad5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\nJahgAgAAAAAq8f93ti7BxEAghAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1127a39d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cm = qm.cadence_plot(fieldID=309, mjd_center=49540, mjd_range=[-30, 5])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qm = PerSNMetric(fieldID=309, t0=49540, summarydf=ss, lsst_bp=lsst_bp, efficiency=et)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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VNiGuVJqZVYorlWY24Qv1SNoTOA54facbrVq/YectV9XyQvUyVy7voAN0qXL7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\npAXrRAIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15f4ce2d0>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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VNiGuVJqZVYorlWY24Qv1SNoTOA54facbrVq/YectV9XyQvUyVy7voAN0qXL7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\npAXrRAIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15f4ce2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qm.SNCadence[0]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"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>obsHistID</th>\n",
" <th>sessionID</th>\n",
" <th>propID</th>\n",
" <th>fieldID</th>\n",
" <th>fieldRA</th>\n",
" <th>fieldDec</th>\n",
" <th>filter</th>\n",
" <th>expDate</th>\n",
" <th>expMJD</th>\n",
" <th>night</th>\n",
" <th>...</th>\n",
" <th>humidity</th>\n",
" <th>slewDist</th>\n",
" <th>slewTime</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>ditheredRA</th>\n",
" <th>ditheredDec</th>\n",
" <th>gamma</th>\n",
" <th>N0sq</th>\n",
" <th>alpha</th>\n",
" <th>MJDay</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54984</th>\n",
" <td>54932</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>u</td>\n",
" <td>6337289</td>\n",
" <td>49426.348262</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.054175</td>\n",
" <td>4.737670</td>\n",
" <td>23.535869</td>\n",
" <td>4.165341</td>\n",
" <td>-1.089087</td>\n",
" <td>0.039553</td>\n",
" <td>0.000001</td>\n",
" <td>0.039553</td>\n",
" <td>49426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62174</th>\n",
" <td>62118</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>i</td>\n",
" <td>7018234</td>\n",
" <td>49434.229565</td>\n",
" <td>81</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.054641</td>\n",
" <td>4.659288</td>\n",
" <td>23.346593</td>\n",
" <td>4.230447</td>\n",
" <td>-1.089087</td>\n",
" <td>0.039780</td>\n",
" <td>0.000001</td>\n",
" <td>0.039780</td>\n",
" <td>49434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62201</th>\n",
" <td>62145</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>i</td>\n",
" <td>7019269</td>\n",
" <td>49434.241549</td>\n",
" <td>81</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.054641</td>\n",
" <td>4.600334</td>\n",
" <td>23.504851</td>\n",
" <td>4.230447</td>\n",
" <td>-1.089087</td>\n",
" <td>0.039745</td>\n",
" <td>0.000001</td>\n",
" <td>0.039745</td>\n",
" <td>49434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65712</th>\n",
" <td>65656</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>Y</td>\n",
" <td>7364902</td>\n",
" <td>49438.241925</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.054641</td>\n",
" <td>4.534727</td>\n",
" <td>21.193041</td>\n",
" <td>4.136860</td>\n",
" <td>-1.085780</td>\n",
" <td>0.039916</td>\n",
" <td>0.000002</td>\n",
" <td>0.039916</td>\n",
" <td>49438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65717</th>\n",
" <td>65661</td>\n",
" <td>1189</td>\n",
" <td>364</td>\n",
" <td>309</td>\n",
" <td>4.189756</td>\n",
" <td>-1.082474</td>\n",
" <td>Y</td>\n",
" <td>7365094</td>\n",
" <td>49438.244152</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.088075</td>\n",
" <td>5.189096</td>\n",
" <td>21.203663</td>\n",
" <td>4.136860</td>\n",
" <td>-1.085780</td>\n",
" <td>0.039915</td>\n",
" <td>0.000002</td>\n",
" <td>0.039915</td>\n",
" <td>49438</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
" obsHistID sessionID propID fieldID fieldRA fieldDec filter \\\n",
"54984 54932 1189 364 309 4.189756 -1.082474 u \n",
"62174 62118 1189 364 309 4.189756 -1.082474 i \n",
"62201 62145 1189 364 309 4.189756 -1.082474 i \n",
"65712 65656 1189 364 309 4.189756 -1.082474 Y \n",
"65717 65661 1189 364 309 4.189756 -1.082474 Y \n",
"\n",
" expDate expMJD night ... humidity slewDist slewTime \\\n",
"54984 6337289 49426.348262 73 ... 0 0.054175 4.737670 \n",
"62174 7018234 49434.229565 81 ... 0 0.054641 4.659288 \n",
"62201 7019269 49434.241549 81 ... 0 0.054641 4.600334 \n",
"65712 7364902 49438.241925 85 ... 0 0.054641 4.534727 \n",
"65717 7365094 49438.244152 85 ... 0 0.088075 5.189096 \n",
"\n",
" fiveSigmaDepth ditheredRA ditheredDec gamma N0sq alpha \\\n",
"54984 23.535869 4.165341 -1.089087 0.039553 0.000001 0.039553 \n",
"62174 23.346593 4.230447 -1.089087 0.039780 0.000001 0.039780 \n",
"62201 23.504851 4.230447 -1.089087 0.039745 0.000001 0.039745 \n",
"65712 21.193041 4.136860 -1.085780 0.039916 0.000002 0.039916 \n",
"65717 21.203663 4.136860 -1.085780 0.039915 0.000002 0.039915 \n",
"\n",
" MJDay \n",
"54984 49426 \n",
"62174 49434 \n",
"62201 49434 \n",
"65712 49438 \n",
"65717 49438 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qm.summary.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.99995137946174906"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qm.discoveryMetric()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0029671277166204053"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qm.qualityMetric()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using a Pull Light curve from MAF"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import healpy as hp\n",
"import lsst.sims.maf.db as db\n",
"import lsst.sims.maf.utils as utils\n",
"import lsst.sims.maf.metrics as metrics\n",
"import lsst.sims.maf.slicers as slicers\n",
"import lsst.sims.maf.metricBundles as metricBundles"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"outDir ='LightCurve'\n",
"dbFile = 'enigma_1189_sqlite.db'\n",
"opsimdb = utils.connectOpsimDb(dbFile)\n",
"resultsDb = db.ResultsDb(outDir=outDir)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filters = ['u','g','r','i','z','y']\n",
"colors={'u':'cyan','g':'g','r':'y','i':'r','z':'m', 'y':'k'}\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set RA, Dec for a single point in the sky. in radians.\n",
"ra = np.radians(0.)\n",
"dec = np.radians(0.)\n",
"# SNR limit (Don't use points below this limit)\n",
"snrLimit = 5.\n",
"# Demand this many points above SNR limit before plotting LC\n",
"nPtsLimit = 6"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The pass metric just passes data straight through.\n",
"metric = metrics.PassMetric(cols=['filter','fieldID','finSeeing','fiveSigmaDepth',\n",
" 'expMJD','airmass', 'propID', 'night', 'filtSkyBrightness'])\n",
"slicer = slicers.UserPointsSlicer(ra,dec,lonCol='fieldRA',latCol='fieldDec')\n",
"sql = 'night < 366'\n",
"bundle = metricBundles.MetricBundle(metric,slicer,sql)\n",
"bg = metricBundles.MetricBundleGroup({0:bundle}, opsimdb,\n",
" outDir=outDir, resultsDb=resultsDb)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Querying database with constraint night < 366\n",
"Found 272657 visits\n",
"Running: [0]\n",
"Completed metric generation.\n",
"Running reduce methods.\n",
"Running summary statistics.\n",
"Completed.\n"
]
}
],
"source": [
"bg.runAll()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"llc = pd.DataFrame.from_records(bundle.metricValues.data[0])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"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>obsHistID</th>\n",
" <th>filtSkyBrightness</th>\n",
" <th>airmass</th>\n",
" <th>fieldRA</th>\n",
" <th>fieldDec</th>\n",
" <th>filter</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>expMJD</th>\n",
" <th>night</th>\n",
" <th>finSeeing</th>\n",
" <th>propID</th>\n",
" <th>fieldID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>239966</td>\n",
" <td>17.153304</td>\n",
" <td>1.168194</td>\n",
" <td>6.25556</td>\n",
" <td>0.003271</td>\n",
" <td>z</td>\n",
" <td>22.149947</td>\n",
" <td>49672.064912</td>\n",
" <td>319</td>\n",
" <td>0.772224</td>\n",
" <td>364</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149400</td>\n",
" <td>21.874528</td>\n",
" <td>1.153041</td>\n",
" <td>6.25556</td>\n",
" <td>0.003271</td>\n",
" <td>g</td>\n",
" <td>24.854518</td>\n",
" <td>49545.387207</td>\n",
" <td>192</td>\n",
" <td>0.782242</td>\n",
" <td>364</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>227675</td>\n",
" <td>17.617678</td>\n",
" <td>1.209316</td>\n",
" <td>6.25556</td>\n",
" <td>0.003271</td>\n",
" <td>z</td>\n",
" <td>22.658431</td>\n",
" <td>49646.061726</td>\n",
" <td>293</td>\n",
" <td>0.597362</td>\n",
" <td>364</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>148176</td>\n",
" <td>20.009584</td>\n",
" <td>1.293457</td>\n",
" <td>6.25556</td>\n",
" <td>0.003271</td>\n",
" <td>i</td>\n",
" <td>23.866753</td>\n",
" <td>49544.314227</td>\n",
" <td>191</td>\n",
" <td>0.758747</td>\n",
" <td>364</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>178722</td>\n",
" <td>19.972167</td>\n",
" <td>1.355178</td>\n",
" <td>6.25556</td>\n",
" <td>0.003271</td>\n",
" <td>i</td>\n",
" <td>23.843180</td>\n",
" <td>49577.210972</td>\n",
" <td>224</td>\n",
" <td>0.759127</td>\n",
" <td>364</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obsHistID filtSkyBrightness airmass fieldRA fieldDec filter \\\n",
"0 239966 17.153304 1.168194 6.25556 0.003271 z \n",
"1 149400 21.874528 1.153041 6.25556 0.003271 g \n",
"2 227675 17.617678 1.209316 6.25556 0.003271 z \n",
"3 148176 20.009584 1.293457 6.25556 0.003271 i \n",
"4 178722 19.972167 1.355178 6.25556 0.003271 i \n",
"\n",
" fiveSigmaDepth expMJD night finSeeing propID fieldID \n",
"0 22.149947 49672.064912 319 0.772224 364 2655 \n",
"1 24.854518 49545.387207 192 0.782242 364 2655 \n",
"2 22.658431 49646.061726 293 0.597362 364 2655 \n",
"3 23.866753 49544.314227 191 0.758747 364 2655 \n",
"4 23.843180 49577.210972 224 0.759127 364 2655 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llc.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x160448b10>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Big4wIcc5pUzx2vwT83eY2VEzOzy27DQzu9HM7jazG8xsqd+YIiIyrzbP8P8cuHDLsiuA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\nIVTwRUQKoYIvIlIIFXwRkUL8P8W/WA6YPUQPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x160448f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llc.expMJD.hist()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"q2 = PerSNMetric(t0=49580, summarydf=llc, lsst_bp=lsst_bp, efficiency=et, raCol='fieldRA',\n",
" decCol='fieldDec')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'obsHistID', u'sessionID', u'propID', u'fieldID', u'fieldRA', u'fieldDec', u'filter', u'expDate', u'expMJD', u'night', u'visitTime', u'visitExpTime', u'finRank', u'finSeeing', u'transparency', u'airmass', u'vSkyBright', u'filtSkyBrightness', u'rotSkyPos', u'lst', u'altitude', u'azimuth', u'dist2Moon', u'solarElong', u'moonRA', u'moonDec', u'moonAlt', u'moonAZ', u'moonPhase', u'sunAlt', u'sunAz', u'phaseAngle', u'rScatter', u'mieScatter', u'moonIllum', u'moonBright', u'darkBright', u'rawSeeing', u'wind', u'humidity', u'slewDist', u'slewTime', u'fiveSigmaDepth', u'ditheredRA', u'ditheredDec', u'gamma', u'N0sq', u'alpha', u'MJDay'], dtype='object')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss.columns"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'obsHistID', u'filtSkyBrightness', u'airmass', u'fieldRA', u'fieldDec', u'filter', u'fiveSigmaDepth', u'expMJD', u'night', u'finSeeing', u'propID', u'fieldID'], dtype='object')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llc.columns"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'fieldRA'"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.raCol"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6.25556"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.summary[q2.raCol].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"358.41718649085726"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.radeg"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.18741449478729227"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.decdeg"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'MWE(B-V)': 0.035068757832050323,\n",
" 'ModelSource': 'salt2-extended',\n",
" '_dec': 0.003271,\n",
" '_ra': 6.25556,\n",
" 'c': 0.0,\n",
" 'hostebv': 0.0,\n",
" 'hostr_v': 3.1000000000000001,\n",
" 'mwebv': 0.0,\n",
" 'mwr_v': 3.1000000000000001,\n",
" 't0': 49580.0,\n",
" 'x0': 1.0068661711630977e-05,\n",
" 'x1': 0.0,\n",
" 'z': 0.5}"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.SN.SNstate"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"metrics.py:148: 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",
" df['SNR'] = df['flux'] / df['fluxerr']\n",
"metrics.py:150: 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",
" df['DetectionEfficiency'] = df.apply(self.func, axis=1)\n",
"/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/core/frame.py:2891: 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",
" inplace=inplace, kind=kind, na_position=na_position)\n"
]
},
{
"data": {
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gDeBWM3s7pk0/YJiZ9ZPUHXjAzHabwFpR7wWjR49m+PDhzJo1i5ycnN32b9my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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1604b99d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"q2.lcplot(nightlyCoadd=False)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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m9nZMm/7AUDPrL+ko4C9mtscE1vJ6LxgzZgzDhw9n9uzZ5OTk7LF/27ZtdOjQ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\np3PnzkyaNImDB1OsWY0JGTZEk3FhYWGMHz+eqKgoBgwY4DpOSPJqMdI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h2BH1V1o4i0vtR2OReV9MVCb4E0a9YsHnzwwbA4/wvj5ptvpn///vTv35/Dhw+zYMECZs+ezdNP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\nWS/aeeZu5AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x166ff1550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"q2.lcplot(nightlyCoadd=True)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q2.writeLightCurve(fname='test.dat', nightlyCoadd=True)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"night band zpsys flux zp fluxerr time\r\n",
"197 i ab 7.38981535465e-15 0.0 1.12061110296e-10 49550.294916\r\n",
"199 y ab 4.16334214811e-12 0.0 3.3196763789e-10 49552.2878882\r\n",
"200 z ab 5.38047609363e-12 0.0 1.05603667637e-10 49553.3395135\r\n",
"201 y ab 1.57584572158e-11 0.0 3.22634345035e-10 49554.3756353\r\n",
"202 y ab 2.39528163608e-11 0.0 3.07289518501e-10 49555.277983\r\n",
"204 y ab 6.15882336785e-11 0.0 2.34661082246e-10 49557.3534095\r\n",
"211 z ab 3.06088533052e-10 0.0 3.06937163341e-10 49564.433548\r\n",
"221 u ab 1.56029563392e-11 0.0 4.89707769799e-11 49574.326309\r\n",
"222 u ab 1.46938990797e-11 0.0 6.34592850528e-11 49575.26335\r\n",
"224 i ab 7.64088967051e-10 0.0 4.26806497321e-11 49577.203024\r\n",
"225 i ab 7.75634346472e-10 0.0 4.24992888433e-11 49578.2069525\r\n",
"230 y ab 7.2989146669e-10 0.0 2.15075882766e-10 49583.194115\r\n",
"239 r ab 5.28844207371e-10 0.0 4.80448638946e-11 49592.277693\r\n",
"242 g ab 9.20280911049e-11 0.0 1.43309236615e-11 49595.173483\r\n",
"251 i ab 4.01952469103e-10 0.0 2.26067447291e-11 49604.187105\r\n",
"252 i ab 3.84096121974e-10 0.0 4.02448535325e-11 49605.159508\r\n",
"255 z ab 4.0348474097e-10 0.0 6.48469963374e-11 49608.1246967\r\n",
"256 z ab 3.97467421966e-10 0.0 5.92905748255e-11 49609.130089\r\n",
"257 z ab 3.91194149306e-10 0.0 7.8789301386e-11 49610.1313937\r\n",
"259 y ab 3.86491211188e-10 0.0 3.91296930794e-10 49612.188252\r\n",
"266 i ab 1.95901501575e-10 0.0 9.12537376645e-11 49619.1560455\r\n"
]
}
],
"source": [
"!cat test.dat"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"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>night</th>\n",
" <th>band</th>\n",
" <th>zpsys</th>\n",
" <th>flux</th>\n",
" <th>zp</th>\n",
" <th>fluxerr</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>199</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>4.163342e-12</td>\n",
" <td>0</td>\n",
" <td>3.319676e-10</td>\n",
" <td>49552.287888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>1.575846e-11</td>\n",
" <td>0</td>\n",
" <td>3.226343e-10</td>\n",
" <td>49554.375635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>2.395282e-11</td>\n",
" <td>0</td>\n",
" <td>3.072895e-10</td>\n",
" <td>49555.277983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>204</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>6.158823e-11</td>\n",
" <td>0</td>\n",
" <td>2.346611e-10</td>\n",
" <td>49557.353409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>230</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>7.298915e-10</td>\n",
" <td>0</td>\n",
" <td>2.150759e-10</td>\n",
" <td>49583.194115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>259</td>\n",
" <td>y</td>\n",
" <td>ab</td>\n",
" <td>3.864912e-10</td>\n",
" <td>0</td>\n",
" <td>3.912969e-10</td>\n",
" <td>49612.188252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" night band zpsys flux zp fluxerr time\n",
"1 199 y ab 4.163342e-12 0 3.319676e-10 49552.287888\n",
"3 201 y ab 1.575846e-11 0 3.226343e-10 49554.375635\n",
"4 202 y ab 2.395282e-11 0 3.072895e-10 49555.277983\n",
"5 204 y ab 6.158823e-11 0 2.346611e-10 49557.353409\n",
"11 230 y ab 7.298915e-10 0 2.150759e-10 49583.194115\n",
"19 259 y ab 3.864912e-10 0 3.912969e-10 49612.188252"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.coaddedLightCurve.reset_index()[q2.coaddedLightCurve.reset_index()['band'] == 'y']"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.086475461159803382"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.qualityMetric()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.99999999999999989"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q2.discoveryMetric()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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246u/mpmZmZmZWSU8ptLWU0VLZSd1P2MzGx4bMBlcTm0gHlNpNi/Mp5ZKs17c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\n63xwCtwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15bc21bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = q2.cadence_plot(summarydf=llc, racol='fieldRA', deccol='fieldDec', mjd_center=49580)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1659efb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Defaults to full season\n",
"_ = q2.cadence_plot(summarydf=llc, racol='fieldRA', deccol='fieldDec')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Demonstrate that a minimal definition of observation works"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xx = llc[['expMJD', 'filter', 'fiveSigmaDepth', 'fieldRA', 'fieldDec', 'fieldID']].copy(deep=True)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>expMJD</th>\n",
" <th>filter</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>fieldRA</th>\n",
" <th>fieldDec</th>\n",
" <th>fieldID</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
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" <td>22.149947</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>49545.387207</td>\n",
" <td>g</td>\n",
" <td>24.854518</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>6.255560</td>\n",
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" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>22.287935</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>49547.355973</td>\n",
" <td>r</td>\n",
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" <td>r</td>\n",
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" <td>0.003271</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>49646.080690</td>\n",
" <td>z</td>\n",
" <td>22.563434</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <th>11</th>\n",
" <td>49631.116856</td>\n",
" <td>r</td>\n",
" <td>24.829550</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
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" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>y</td>\n",
" <td>21.780857</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <td>49544.297329</td>\n",
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" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <tr>\n",
" <th>19</th>\n",
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" <td>6.255560</td>\n",
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" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>49545.404611</td>\n",
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" <td>24.901808</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>49608.115980</td>\n",
" <td>z</td>\n",
" <td>22.944295</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>49543.426070</td>\n",
" <td>z</td>\n",
" <td>22.418617</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>49595.179838</td>\n",
" <td>g</td>\n",
" <td>24.621747</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>49646.049013</td>\n",
" <td>z</td>\n",
" <td>22.653370</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>49555.285839</td>\n",
" <td>y</td>\n",
" <td>21.411571</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>49637.075923</td>\n",
" <td>i</td>\n",
" <td>23.701628</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>49684.027023</td>\n",
" <td>y</td>\n",
" <td>21.504122</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>49637.055415</td>\n",
" <td>i</td>\n",
" <td>23.627379</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>49645.133682</td>\n",
" <td>y</td>\n",
" <td>21.905190</td>\n",
" <td>6.255560</td>\n",
" <td>0.003271</td>\n",
" <td>2655</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>101</th>\n",
" <td>49637.074577</td>\n",
" <td>i</td>\n",
" <td>23.685725</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>49645.127742</td>\n",
" <td>y</td>\n",
" <td>21.904519</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>49619.163176</td>\n",
" <td>i</td>\n",
" <td>22.896463</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>49646.043874</td>\n",
" <td>z</td>\n",
" <td>22.697461</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>49583.190979</td>\n",
" <td>y</td>\n",
" <td>21.701455</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>49645.123239</td>\n",
" <td>y</td>\n",
" <td>21.884584</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>49645.119563</td>\n",
" <td>y</td>\n",
" <td>21.925221</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>49555.285393</td>\n",
" <td>y</td>\n",
" <td>21.391583</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>49653.997663</td>\n",
" <td>y</td>\n",
" <td>21.794320</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>49545.391689</td>\n",
" <td>g</td>\n",
" <td>24.922707</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>49646.152727</td>\n",
" <td>z</td>\n",
" <td>22.774476</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>49544.314673</td>\n",
" <td>i</td>\n",
" <td>23.836942</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>49544.296882</td>\n",
" <td>i</td>\n",
" <td>23.822951</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>49646.033043</td>\n",
" <td>z</td>\n",
" <td>22.536047</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>49604.177906</td>\n",
" <td>i</td>\n",
" <td>24.258893</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>49612.188252</td>\n",
" <td>y</td>\n",
" <td>21.768196</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>49631.110199</td>\n",
" <td>r</td>\n",
" <td>24.724469</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>49547.353288</td>\n",
" <td>r</td>\n",
" <td>24.454882</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>49631.127883</td>\n",
" <td>r</td>\n",
" <td>24.706460</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>49547.372248</td>\n",
" <td>r</td>\n",
" <td>24.448127</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>49608.133413</td>\n",
" <td>z</td>\n",
" <td>23.000778</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>49654.024117</td>\n",
" <td>r</td>\n",
" <td>24.956614</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>49583.197698</td>\n",
" <td>y</td>\n",
" <td>21.614014</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>49608.116427</td>\n",
" <td>z</td>\n",
" <td>22.902978</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>49595.167128</td>\n",
" <td>g</td>\n",
" <td>24.585773</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>49543.440574</td>\n",
" <td>z</td>\n",
" <td>22.296168</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>49595.180285</td>\n",
" <td>g</td>\n",
" <td>24.588526</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>49578.217204</td>\n",
" <td>i</td>\n",
" <td>23.756624</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>49631.117755</td>\n",
" <td>r</td>\n",
" <td>24.817052</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>49545.411397</td>\n",
" <td>g</td>\n",
" <td>24.918747</td>\n",
" <td>0.027626</td>\n",
" <td>0.003271</td>\n",
" <td>2656</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>131 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" expMJD filter fiveSigmaDepth fieldRA fieldDec fieldID\n",
"0 49672.064912 z 22.149947 6.255560 0.003271 2655\n",
"1 49545.387207 g 24.854518 6.255560 0.003271 2655\n",
"2 49646.061726 z 22.658431 6.255560 0.003271 2655\n",
"3 49544.314227 i 23.866753 6.255560 0.003271 2655\n",
"4 49577.210972 i 23.843180 6.255560 0.003271 2655\n",
"5 49554.373247 y 21.369012 6.255560 0.003271 2655\n",
"6 49672.081557 z 21.995196 6.255560 0.003271 2655\n",
"7 49543.439675 z 22.287935 6.255560 0.003271 2655\n",
"8 49547.355973 r 24.536172 6.255560 0.003271 2655\n",
"9 49631.109299 r 24.739914 6.255560 0.003271 2655\n",
"10 49646.080690 z 22.563434 6.255560 0.003271 2655\n",
"11 49631.116856 r 24.829550 6.255560 0.003271 2655\n",
"12 49609.137336 z 22.998605 6.255560 0.003271 2655\n",
"13 49557.355873 y 21.357556 6.255560 0.003271 2655\n",
"14 49645.135933 y 21.780857 6.255560 0.003271 2655\n",
"15 49631.128344 r 24.717211 6.255560 0.003271 2655\n",
"16 49574.326309 u 24.009828 6.255560 0.003271 2655\n",
"17 49555.288078 y 21.442207 6.255560 0.003271 2655\n",
"18 49544.297329 i 23.864832 6.255560 0.003271 2655\n",
"19 49646.033489 z 22.649448 6.255560 0.003271 2655\n",
"20 49545.404611 g 24.901808 6.255560 0.003271 2655\n",
"21 49608.115980 z 22.944295 6.255560 0.003271 2655\n",
"22 49543.426070 z 22.418617 6.255560 0.003271 2655\n",
"23 49595.179838 g 24.621747 6.255560 0.003271 2655\n",
"24 49646.049013 z 22.653370 6.255560 0.003271 2655\n",
"25 49555.285839 y 21.411571 6.255560 0.003271 2655\n",
"26 49637.075923 i 23.701628 6.255560 0.003271 2655\n",
"27 49684.027023 y 21.504122 6.255560 0.003271 2655\n",
"28 49637.055415 i 23.627379 6.255560 0.003271 2655\n",
"29 49645.133682 y 21.905190 6.255560 0.003271 2655\n",
".. ... ... ... ... ... ...\n",
"101 49637.074577 i 23.685725 0.027626 0.003271 2656\n",
"102 49645.127742 y 21.904519 0.027626 0.003271 2656\n",
"103 49619.163176 i 22.896463 0.027626 0.003271 2656\n",
"104 49646.043874 z 22.697461 0.027626 0.003271 2656\n",
"105 49583.190979 y 21.701455 0.027626 0.003271 2656\n",
"106 49645.123239 y 21.884584 0.027626 0.003271 2656\n",
"107 49645.119563 y 21.925221 0.027626 0.003271 2656\n",
"108 49555.285393 y 21.391583 0.027626 0.003271 2656\n",
"109 49653.997663 y 21.794320 0.027626 0.003271 2656\n",
"110 49545.391689 g 24.922707 0.027626 0.003271 2656\n",
"111 49646.152727 z 22.774476 0.027626 0.003271 2656\n",
"112 49544.314673 i 23.836942 0.027626 0.003271 2656\n",
"113 49544.296882 i 23.822951 0.027626 0.003271 2656\n",
"114 49646.033043 z 22.536047 0.027626 0.003271 2656\n",
"115 49604.177906 i 24.258893 0.027626 0.003271 2656\n",
"116 49612.188252 y 21.768196 0.027626 0.003271 2656\n",
"117 49631.110199 r 24.724469 0.027626 0.003271 2656\n",
"118 49547.353288 r 24.454882 0.027626 0.003271 2656\n",
"119 49631.127883 r 24.706460 0.027626 0.003271 2656\n",
"120 49547.372248 r 24.448127 0.027626 0.003271 2656\n",
"121 49608.133413 z 23.000778 0.027626 0.003271 2656\n",
"122 49654.024117 r 24.956614 0.027626 0.003271 2656\n",
"123 49583.197698 y 21.614014 0.027626 0.003271 2656\n",
"124 49608.116427 z 22.902978 0.027626 0.003271 2656\n",
"125 49595.167128 g 24.585773 0.027626 0.003271 2656\n",
"126 49543.440574 z 22.296168 0.027626 0.003271 2656\n",
"127 49595.180285 g 24.588526 0.027626 0.003271 2656\n",
"128 49578.217204 i 23.756624 0.027626 0.003271 2656\n",
"129 49631.117755 r 24.817052 0.027626 0.003271 2656\n",
"130 49545.411397 g 24.918747 0.027626 0.003271 2656\n",
"\n",
"[131 rows x 6 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xx"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q3 = PerSNMetric(t0=49580, summarydf=xx, lsst_bp=lsst_bp, efficiency=et, raCol='fieldRA',\n",
" decCol='fieldDec')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<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>time</th>\n",
" <th>band</th>\n",
" <th>flux</th>\n",
" <th>fluxerr</th>\n",
" <th>zp</th>\n",
" <th>zpsys</th>\n",
" <th>SNR</th>\n",
" <th>fiveSigmaDepth</th>\n",
" <th>DetectionEfficiency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>84</th>\n",
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" <th>4</th>\n",
" <td>49577.210972</td>\n",
" <td>i</td>\n",
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" <tr>\n",
" <th>40</th>\n",
" <td>49577.195076</td>\n",
" <td>i</td>\n",
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" <td>6.182001e-11</td>\n",
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" <th>128</th>\n",
" <td>49578.217204</td>\n",
" <td>i</td>\n",
" <td>7.757303e-10</td>\n",
" <td>6.359777e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>12.197445</td>\n",
" <td>23.756624</td>\n",
" <td>0.980863066305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>49592.277693</td>\n",
" <td>r</td>\n",
" <td>5.288442e-10</td>\n",
" <td>4.804486e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>11.007300</td>\n",
" <td>24.058825</td>\n",
" <td>0.977440877322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>49604.177906</td>\n",
" <td>i</td>\n",
" <td>4.021245e-10</td>\n",
" <td>4.009737e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>10.028698</td>\n",
" <td>24.258893</td>\n",
" <td>0.971468221461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>49604.209600</td>\n",
" <td>i</td>\n",
" <td>4.015318e-10</td>\n",
" <td>4.339009e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>9.253998</td>\n",
" <td>24.170085</td>\n",
" <td>0.960060775297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>49604.195079</td>\n",
" <td>i</td>\n",
" <td>4.018032e-10</td>\n",
" <td>4.392809e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>9.146840</td>\n",
" <td>24.156292</td>\n",
" <td>0.958431972159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>49604.165835</td>\n",
" <td>i</td>\n",
" <td>4.023504e-10</td>\n",
" <td>5.250583e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>7.662966</td>\n",
" <td>23.957700</td>\n",
" <td>0.91928675239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49605.172561</td>\n",
" <td>i</td>\n",
" <td>3.838602e-10</td>\n",
" <td>5.207630e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>7.371111</td>\n",
" <td>23.966039</td>\n",
" <td>0.898446610558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>49605.146455</td>\n",
" <td>i</td>\n",
" <td>3.843320e-10</td>\n",
" <td>6.137305e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>6.262228</td>\n",
" <td>23.784909</td>\n",
" <td>0.732320326635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>49609.123288</td>\n",
" <td>z</td>\n",
" <td>3.975091e-10</td>\n",
" <td>1.076489e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.692643</td>\n",
" <td>23.170611</td>\n",
" <td>0.147915195371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>49609.122842</td>\n",
" <td>z</td>\n",
" <td>3.975119e-10</td>\n",
" <td>1.104060e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.600454</td>\n",
" <td>23.143069</td>\n",
" <td>0.131063060097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>49608.132967</td>\n",
" <td>z</td>\n",
" <td>4.034365e-10</td>\n",
" <td>1.221543e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.302678</td>\n",
" <td>23.033086</td>\n",
" <td>0.0899290685553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>49595.166681</td>\n",
" <td>g</td>\n",
" <td>9.208227e-11</td>\n",
" <td>2.811439e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.275273</td>\n",
" <td>24.626269</td>\n",
" <td>0.0455871130483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>49595.179838</td>\n",
" <td>g</td>\n",
" <td>9.197747e-11</td>\n",
" <td>2.823113e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.258016</td>\n",
" <td>24.621747</td>\n",
" <td>0.0445034250721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>49608.133413</td>\n",
" <td>z</td>\n",
" <td>4.034339e-10</td>\n",
" <td>1.258372e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.205997</td>\n",
" <td>23.000778</td>\n",
" <td>0.0787720699832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>49595.180285</td>\n",
" <td>g</td>\n",
" <td>9.197391e-11</td>\n",
" <td>2.910569e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.159997</td>\n",
" <td>24.588526</td>\n",
" <td>0.0383478251473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>49595.167128</td>\n",
" <td>g</td>\n",
" <td>9.207871e-11</td>\n",
" <td>2.917961e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.155584</td>\n",
" <td>24.585773</td>\n",
" <td>0.0380707012218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>49609.137336</td>\n",
" <td>z</td>\n",
" <td>3.974230e-10</td>\n",
" <td>1.260820e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.152100</td>\n",
" <td>22.998605</td>\n",
" <td>0.0725523475965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>49609.136890</td>\n",
" <td>z</td>\n",
" <td>3.974257e-10</td>\n",
" <td>1.287304e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.087272</td>\n",
" <td>22.976003</td>\n",
" <td>0.065071222715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>49608.115980</td>\n",
" <td>z</td>\n",
" <td>4.035356e-10</td>\n",
" <td>1.325474e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>3.044464</td>\n",
" <td>22.944295</td>\n",
" <td>0.0601311267317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>49608.116427</td>\n",
" <td>z</td>\n",
" <td>4.035330e-10</td>\n",
" <td>1.376831e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>2.930883</td>\n",
" <td>22.902978</td>\n",
" <td>0.0504244586653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>49610.123399</td>\n",
" <td>z</td>\n",
" <td>3.912453e-10</td>\n",
" <td>1.494942e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>2.617127</td>\n",
" <td>22.813475</td>\n",
" <td>0.0296538331036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>49610.123845</td>\n",
" <td>z</td>\n",
" <td>3.912424e-10</td>\n",
" <td>1.514511e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>2.583292</td>\n",
" <td>22.799352</td>\n",
" <td>0.0274139477974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>49610.138942</td>\n",
" <td>z</td>\n",
" <td>3.911459e-10</td>\n",
" <td>1.637832e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>2.388193</td>\n",
" <td>22.714362</td>\n",
" <td>0.0189930104695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>49610.139389</td>\n",
" <td>z</td>\n",
" <td>3.911430e-10</td>\n",
" <td>1.649642e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>2.371078</td>\n",
" <td>22.706563</td>\n",
" <td>0.0185480363744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>49583.190532</td>\n",
" <td>y</td>\n",
" <td>7.299050e-10</td>\n",
" <td>4.083045e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.787649</td>\n",
" <td>21.722725</td>\n",
" <td>0.00652166818175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>49583.190979</td>\n",
" <td>y</td>\n",
" <td>7.299033e-10</td>\n",
" <td>4.163903e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.752931</td>\n",
" <td>21.701455</td>\n",
" <td>0.00613282424487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>49619.148915</td>\n",
" <td>i</td>\n",
" <td>1.959627e-10</td>\n",
" <td>1.189086e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.648011</td>\n",
" <td>23.061447</td>\n",
" <td>0.00165124901726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>49583.197251</td>\n",
" <td>y</td>\n",
" <td>7.298796e-10</td>\n",
" <td>4.430705e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.647322</td>\n",
" <td>21.634095</td>\n",
" <td>0.00495000275789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>49583.197698</td>\n",
" <td>y</td>\n",
" <td>7.298779e-10</td>\n",
" <td>4.513505e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.617098</td>\n",
" <td>21.614014</td>\n",
" <td>0.00461149347624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>49619.163176</td>\n",
" <td>i</td>\n",
" <td>1.958403e-10</td>\n",
" <td>1.384548e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>1.414471</td>\n",
" <td>22.896463</td>\n",
" <td>0.000948682825452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>49564.433548</td>\n",
" <td>z</td>\n",
" <td>3.060885e-10</td>\n",
" <td>3.069372e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.997235</td>\n",
" <td>22.032878</td>\n",
" <td>0.00179281143174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>49612.188252</td>\n",
" <td>y</td>\n",
" <td>3.864912e-10</td>\n",
" <td>3.912969e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.987718</td>\n",
" <td>21.768196</td>\n",
" <td>0.0017680680578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>49574.326309</td>\n",
" <td>u</td>\n",
" <td>1.560296e-11</td>\n",
" <td>4.897078e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.318618</td>\n",
" <td>24.009828</td>\n",
" <td>0.000327447080087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>49575.263350</td>\n",
" <td>u</td>\n",
" <td>1.469390e-11</td>\n",
" <td>6.345929e-11</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.231548</td>\n",
" <td>23.732270</td>\n",
" <td>0.000292619379922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>49557.349596</td>\n",
" <td>y</td>\n",
" <td>6.149433e-11</td>\n",
" <td>5.330854e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.115355</td>\n",
" <td>21.432803</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>49557.356768</td>\n",
" <td>y</td>\n",
" <td>6.167087e-11</td>\n",
" <td>5.715836e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.107895</td>\n",
" <td>21.357279</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>49557.355873</td>\n",
" <td>y</td>\n",
" <td>6.164883e-11</td>\n",
" <td>5.714374e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.107884</td>\n",
" <td>21.357556</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>49557.347805</td>\n",
" <td>y</td>\n",
" <td>6.145028e-11</td>\n",
" <td>5.859003e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.104882</td>\n",
" <td>21.330481</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>49557.348701</td>\n",
" <td>y</td>\n",
" <td>6.147232e-11</td>\n",
" <td>5.869402e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.104734</td>\n",
" <td>21.328560</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>49557.361714</td>\n",
" <td>y</td>\n",
" <td>6.179277e-11</td>\n",
" <td>5.976313e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.103396</td>\n",
" <td>21.309006</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>49555.288078</td>\n",
" <td>y</td>\n",
" <td>2.405797e-11</td>\n",
" <td>5.284568e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.045525</td>\n",
" <td>21.442207</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>49555.285839</td>\n",
" <td>y</td>\n",
" <td>2.403440e-11</td>\n",
" <td>5.436199e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.044212</td>\n",
" <td>21.411571</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>49555.285393</td>\n",
" <td>y</td>\n",
" <td>2.402971e-11</td>\n",
" <td>5.537460e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.043395</td>\n",
" <td>21.391583</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>49553.330838</td>\n",
" <td>z</td>\n",
" <td>5.353111e-12</td>\n",
" <td>1.389688e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.038520</td>\n",
" <td>22.889608</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>49553.348189</td>\n",
" <td>z</td>\n",
" <td>5.407841e-12</td>\n",
" <td>1.590478e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.034001</td>\n",
" <td>22.743794</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>49555.252622</td>\n",
" <td>y</td>\n",
" <td>2.368918e-11</td>\n",
" <td>7.933510e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.029860</td>\n",
" <td>21.002011</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>49554.379517</td>\n",
" <td>y</td>\n",
" <td>1.578887e-11</td>\n",
" <td>5.462624e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.028903</td>\n",
" <td>21.406311</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>49554.374142</td>\n",
" <td>y</td>\n",
" <td>1.574675e-11</td>\n",
" <td>5.645824e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.027891</td>\n",
" <td>21.370585</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>49554.373247</td>\n",
" <td>y</td>\n",
" <td>1.573975e-11</td>\n",
" <td>5.654029e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.027838</td>\n",
" <td>21.369012</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>49552.291835</td>\n",
" <td>y</td>\n",
" <td>4.178304e-12</td>\n",
" <td>7.689126e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.005434</td>\n",
" <td>21.035912</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>49552.291389</td>\n",
" <td>y</td>\n",
" <td>4.176612e-12</td>\n",
" <td>7.846948e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.005323</td>\n",
" <td>21.013892</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>49552.284236</td>\n",
" <td>y</td>\n",
" <td>4.149506e-12</td>\n",
" <td>7.971559e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.005205</td>\n",
" <td>20.996816</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>49552.284683</td>\n",
" <td>y</td>\n",
" <td>4.151197e-12</td>\n",
" <td>8.136669e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.005102</td>\n",
" <td>20.974596</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>49552.287370</td>\n",
" <td>y</td>\n",
" <td>4.161372e-12</td>\n",
" <td>8.467581e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.004914</td>\n",
" <td>20.931387</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>49552.287816</td>\n",
" <td>y</td>\n",
" <td>4.163062e-12</td>\n",
" <td>8.636013e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.004821</td>\n",
" <td>20.910037</td>\n",
" <td>0.0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>49550.294916</td>\n",
" <td>i</td>\n",
" <td>7.389815e-15</td>\n",
" <td>1.120611e-10</td>\n",
" <td>0</td>\n",
" <td>ab</td>\n",
" <td>0.000066</td>\n",
" <td>23.124046</td>\n",
" <td>0.000100026377805</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time band flux fluxerr zp zpsys SNR \\\n",
"84 49578.196701 i 7.755384e-10 5.639221e-11 0 ab 13.752580 \n",
"4 49577.210972 i 7.641964e-10 5.886288e-11 0 ab 12.982654 \n",
"40 49577.195076 i 7.639816e-10 6.182001e-11 0 ab 12.358161 \n",
"128 49578.217204 i 7.757303e-10 6.359777e-11 0 ab 12.197445 \n",
"79 49592.277693 r 5.288442e-10 4.804486e-11 0 ab 11.007300 \n",
"115 49604.177906 i 4.021245e-10 4.009737e-11 0 ab 10.028698 \n",
"57 49604.209600 i 4.015318e-10 4.339009e-11 0 ab 9.253998 \n",
"34 49604.195079 i 4.018032e-10 4.392809e-11 0 ab 9.146840 \n",
"100 49604.165835 i 4.023504e-10 5.250583e-11 0 ab 7.662966 \n",
"48 49605.172561 i 3.838602e-10 5.207630e-11 0 ab 7.371111 \n",
"62 49605.146455 i 3.843320e-10 6.137305e-11 0 ab 6.262228 \n",
"38 49609.123288 z 3.975091e-10 1.076489e-10 0 ab 3.692643 \n",
"80 49609.122842 z 3.975119e-10 1.104060e-10 0 ab 3.600454 \n",
"37 49608.132967 z 4.034365e-10 1.221543e-10 0 ab 3.302678 \n",
"42 49595.166681 g 9.208227e-11 2.811439e-11 0 ab 3.275273 \n",
"23 49595.179838 g 9.197747e-11 2.823113e-11 0 ab 3.258016 \n",
"121 49608.133413 z 4.034339e-10 1.258372e-10 0 ab 3.205997 \n",
"127 49595.180285 g 9.197391e-11 2.910569e-11 0 ab 3.159997 \n",
"125 49595.167128 g 9.207871e-11 2.917961e-11 0 ab 3.155584 \n",
"12 49609.137336 z 3.974230e-10 1.260820e-10 0 ab 3.152100 \n",
"78 49609.136890 z 3.974257e-10 1.287304e-10 0 ab 3.087272 \n",
"21 49608.115980 z 4.035356e-10 1.325474e-10 0 ab 3.044464 \n",
"124 49608.116427 z 4.035330e-10 1.376831e-10 0 ab 2.930883 \n",
"52 49610.123399 z 3.912453e-10 1.494942e-10 0 ab 2.617127 \n",
"76 49610.123845 z 3.912424e-10 1.514511e-10 0 ab 2.583292 \n",
"33 49610.138942 z 3.911459e-10 1.637832e-10 0 ab 2.388193 \n",
"70 49610.139389 z 3.911430e-10 1.649642e-10 0 ab 2.371078 \n",
"55 49583.190532 y 7.299050e-10 4.083045e-10 0 ab 1.787649 \n",
"105 49583.190979 y 7.299033e-10 4.163903e-10 0 ab 1.752931 \n",
"97 49619.148915 i 1.959627e-10 1.189086e-10 0 ab 1.648011 \n",
"47 49583.197251 y 7.298796e-10 4.430705e-10 0 ab 1.647322 \n",
"123 49583.197698 y 7.298779e-10 4.513505e-10 0 ab 1.617098 \n",
"103 49619.163176 i 1.958403e-10 1.384548e-10 0 ab 1.414471 \n",
"86 49564.433548 z 3.060885e-10 3.069372e-10 0 ab 0.997235 \n",
"116 49612.188252 y 3.864912e-10 3.912969e-10 0 ab 0.987718 \n",
"16 49574.326309 u 1.560296e-11 4.897078e-11 0 ab 0.318618 \n",
"92 49575.263350 u 1.469390e-11 6.345929e-11 0 ab 0.231548 \n",
"43 49557.349596 y 6.149433e-11 5.330854e-10 0 ab 0.115355 \n",
"81 49557.356768 y 6.167087e-11 5.715836e-10 0 ab 0.107895 \n",
"13 49557.355873 y 6.164883e-11 5.714374e-10 0 ab 0.107884 \n",
"45 49557.347805 y 6.145028e-11 5.859003e-10 0 ab 0.104882 \n",
"77 49557.348701 y 6.147232e-11 5.869402e-10 0 ab 0.104734 \n",
"83 49557.361714 y 6.179277e-11 5.976313e-10 0 ab 0.103396 \n",
"17 49555.288078 y 2.405797e-11 5.284568e-10 0 ab 0.045525 \n",
"25 49555.285839 y 2.403440e-11 5.436199e-10 0 ab 0.044212 \n",
"108 49555.285393 y 2.402971e-11 5.537460e-10 0 ab 0.043395 \n",
"75 49553.330838 z 5.353111e-12 1.389688e-10 0 ab 0.038520 \n",
"95 49553.348189 z 5.407841e-12 1.590478e-10 0 ab 0.034001 \n",
"30 49555.252622 y 2.368918e-11 7.933510e-10 0 ab 0.029860 \n",
"85 49554.379517 y 1.578887e-11 5.462624e-10 0 ab 0.028903 \n",
"88 49554.374142 y 1.574675e-11 5.645824e-10 0 ab 0.027891 \n",
"5 49554.373247 y 1.573975e-11 5.654029e-10 0 ab 0.027838 \n",
"61 49552.291835 y 4.178304e-12 7.689126e-10 0 ab 0.005434 \n",
"82 49552.291389 y 4.176612e-12 7.846948e-10 0 ab 0.005323 \n",
"56 49552.284236 y 4.149506e-12 7.971559e-10 0 ab 0.005205 \n",
"71 49552.284683 y 4.151197e-12 8.136669e-10 0 ab 0.005102 \n",
"59 49552.287370 y 4.161372e-12 8.467581e-10 0 ab 0.004914 \n",
"72 49552.287816 y 4.163062e-12 8.636013e-10 0 ab 0.004821 \n",
"90 49550.294916 i 7.389815e-15 1.120611e-10 0 ab 0.000066 \n",
"\n",
" fiveSigmaDepth DetectionEfficiency \n",
"84 23.891976 0.982020548524 \n",
"4 23.843180 0.970846927353 \n",
"40 23.788072 0.980670206794 \n",
"128 23.756624 0.980863066305 \n",
"79 24.058825 0.977440877322 \n",
"115 24.258893 0.971468221461 \n",
"57 24.170085 0.960060775297 \n",
"34 24.156292 0.958431972159 \n",
"100 23.957700 0.91928675239 \n",
"48 23.966039 0.898446610558 \n",
"62 23.784909 0.732320326635 \n",
"38 23.170611 0.147915195371 \n",
"80 23.143069 0.131063060097 \n",
"37 23.033086 0.0899290685553 \n",
"42 24.626269 0.0455871130483 \n",
"23 24.621747 0.0445034250721 \n",
"121 23.000778 0.0787720699832 \n",
"127 24.588526 0.0383478251473 \n",
"125 24.585773 0.0380707012218 \n",
"12 22.998605 0.0725523475965 \n",
"78 22.976003 0.065071222715 \n",
"21 22.944295 0.0601311267317 \n",
"124 22.902978 0.0504244586653 \n",
"52 22.813475 0.0296538331036 \n",
"76 22.799352 0.0274139477974 \n",
"33 22.714362 0.0189930104695 \n",
"70 22.706563 0.0185480363744 \n",
"55 21.722725 0.00652166818175 \n",
"105 21.701455 0.00613282424487 \n",
"97 23.061447 0.00165124901726 \n",
"47 21.634095 0.00495000275789 \n",
"123 21.614014 0.00461149347624 \n",
"103 22.896463 0.000948682825452 \n",
"86 22.032878 0.00179281143174 \n",
"116 21.768196 0.0017680680578 \n",
"16 24.009828 0.000327447080087 \n",
"92 23.732270 0.000292619379922 \n",
"43 21.432803 0.0005 \n",
"81 21.357279 0.0005 \n",
"13 21.357556 0.0005 \n",
"45 21.330481 0.0005 \n",
"77 21.328560 0.0005 \n",
"83 21.309006 0.0005 \n",
"17 21.442207 0.0005 \n",
"25 21.411571 0.0005 \n",
"108 21.391583 0.0005 \n",
"75 22.889608 0.0005 \n",
"95 22.743794 0.0005 \n",
"30 21.002011 0.0005 \n",
"85 21.406311 0.0005 \n",
"88 21.370585 0.0005 \n",
"5 21.369012 0.0005 \n",
"61 21.035912 0.0005 \n",
"82 21.013892 0.0005 \n",
"56 20.996816 0.0005 \n",
"71 20.974596 0.0005 \n",
"59 20.931387 0.0005 \n",
"72 20.910037 0.0005 \n",
"90 23.124046 0.000100026377805 "
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q3.lightcurve"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table id=\"table5841954256\"><thead><tr><th>index</th><th>time</th><th>band</th><th>flux</th><th>fluxerr</th><th>zp</th><th>zpsys</th><th>SNR</th><th>fiveSigmaDepth</th><th>DetectionEfficiency</th></tr></thead><tr><td>84</td><td>49578.196701</td><td>i</td><td>7.75538365405e-10</td><td>5.63922106496e-11</td><td>0.0</td><td>ab</td><td>13.7525795934</td><td>23.891976</td><td>0.982020548524</td></tr><tr><td>4</td><td>49577.210972</td><td>i</td><td>7.64196356584e-10</td><td>5.88628779774e-11</td><td>0.0</td><td>ab</td><td>12.9826536323</td><td>23.84318</td><td>0.970846927353</td></tr><tr><td>40</td><td>49577.195076</td><td>i</td><td>7.63981577519e-10</td><td>6.18200051961e-11</td><td>0.0</td><td>ab</td><td>12.3581610046</td><td>23.788072</td><td>0.980670206794</td></tr><tr><td>128</td><td>49578.217204</td><td>i</td><td>7.7573032754e-10</td><td>6.35977734421e-11</td><td>0.0</td><td>ab</td><td>12.1974447462</td><td>23.756624</td><td>0.980863066305</td></tr><tr><td>79</td><td>49592.277693</td><td>r</td><td>5.28844207371e-10</td><td>4.80448638946e-11</td><td>0.0</td><td>ab</td><td>11.0072995218</td><td>24.058825</td><td>0.977440877322</td></tr><tr><td>115</td><td>49604.177906</td><td>i</td><td>4.02124455277e-10</td><td>4.00973738694e-11</td><td>0.0</td><td>ab</td><td>10.0286980536</td><td>24.258893</td><td>0.971468221461</td></tr><tr><td>57</td><td>49604.2096</td><td>i</td><td>4.01531809615e-10</td><td>4.33900886246e-11</td><td>0.0</td><td>ab</td><td>9.25399837481</td><td>24.170085</td><td>0.960060775297</td></tr><tr><td>34</td><td>49604.195079</td><td>i</td><td>4.0180324533e-10</td><td>4.39280924679e-11</td><td>0.0</td><td>ab</td><td>9.14684027364</td><td>24.156292</td><td>0.958431972159</td></tr><tr><td>100</td><td>49604.165835</td><td>i</td><td>4.02350366191e-10</td><td>5.25058284955e-11</td><td>0.0</td><td>ab</td><td>7.66296576437</td><td>23.9577</td><td>0.91928675239</td></tr><tr><td>48</td><td>49605.172561</td><td>i</td><td>3.83860199365e-10</td><td>5.20763040109e-11</td><td>0.0</td><td>ab</td><td>7.37111065495</td><td>23.966039</td><td>0.898446610558</td></tr><tr><td>62</td><td>49605.146455</td><td>i</td><td>3.84332044583e-10</td><td>6.13730519363e-11</td><td>0.0</td><td>ab</td><td>6.26222800492</td><td>23.784909</td><td>0.732320326635</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>30</td><td>49555.252622</td><td>y</td><td>2.36891836477e-11</td><td>7.93351000339e-10</td><td>0.0</td><td>ab</td><td>0.0298596505678</td><td>21.002011</td><td>0.0005</td></tr><tr><td>85</td><td>49554.379517</td><td>y</td><td>1.57888726978e-11</td><td>5.46262375648e-10</td><td>0.0</td><td>ab</td><td>0.0289034599521</td><td>21.406311</td><td>0.0005</td></tr><tr><td>88</td><td>49554.374142</td><td>y</td><td>1.57467523657e-11</td><td>5.64582361322e-10</td><td>0.0</td><td>ab</td><td>0.0278909747176</td><td>21.370585</td><td>0.0005</td></tr><tr><td>5</td><td>49554.373247</td><td>y</td><td>1.57397465838e-11</td><td>5.65402917927e-10</td><td>0.0</td><td>ab</td><td>0.0278381063923</td><td>21.369012</td><td>0.0005</td></tr><tr><td>61</td><td>49552.291835</td><td>y</td><td>4.17830449003e-12</td><td>7.68912574756e-10</td><td>0.0</td><td>ab</td><td>0.00543404364451</td><td>21.035912</td><td>0.0005</td></tr><tr><td>82</td><td>49552.291389</td><td>y</td><td>4.17661170682e-12</td><td>7.84694845122e-10</td><td>0.0</td><td>ab</td><td>0.00532259353147</td><td>21.013892</td><td>0.0005</td></tr><tr><td>56</td><td>49552.284236</td><td>y</td><td>4.14950571962e-12</td><td>7.97155883514e-10</td><td>0.0</td><td>ab</td><td>0.00520538806202</td><td>20.996816</td><td>0.0005</td></tr><tr><td>71</td><td>49552.284683</td><td>y</td><td>4.15119723193e-12</td><td>8.13666937662e-10</td><td>0.0</td><td>ab</td><td>0.0051018384056</td><td>20.974596</td><td>0.0005</td></tr><tr><td>59</td><td>49552.28737</td><td>y</td><td>4.16137189875e-12</td><td>8.4675808906e-10</td><td>0.0</td><td>ab</td><td>0.00491447551846</td><td>20.931387</td><td>0.0005</td></tr><tr><td>72</td><td>49552.287816</td><td>y</td><td>4.16306184151e-12</td><td>8.63601329771e-10</td><td>0.0</td><td>ab</td><td>0.00482058294493</td><td>20.910037</td><td>0.0005</td></tr><tr><td>90</td><td>49550.294916</td><td>i</td><td>7.38981535465e-15</td><td>1.12061110296e-10</td><td>0.0</td><td>ab</td><td>6.59445130889e-05</td><td>23.124046</td><td>0.000100026377805</td></tr></table>"
],
"text/plain": [
"<Table rows=59 names=('index','time','band','flux','fluxerr','zp','zpsys','SNR','fiveSigmaDepth','DetectionEfficiency')>\n",
"array([ (84, 49578.196701, 'i', 7.755383654049765e-10, 5.639221064962588e-11, 0.0, 'ab', 13.75257959336839, 23.891976, 0.9820205485242536),\n",
" (4, 49577.210972, 'i', 7.641963565840871e-10, 5.886287797742404e-11, 0.0, 'ab', 12.982653632348438, 23.84318, 0.9708469273530312),\n",
" (40, 49577.195076, 'i', 7.639815775187586e-10, 6.182000519611042e-11, 0.0, 'ab', 12.358161004600282, 23.788072, 0.9806702067944797),\n",
" (128, 49578.217204, 'i', 7.757303275396231e-10, 6.359777344214444e-11, 0.0, 'ab', 12.197444746164157, 23.756624, 0.980863066304603),\n",
" (79, 49592.277693, 'r', 5.288442073709477e-10, 4.804486389458742e-11, 0.0, 'ab', 11.007299521781466, 24.058825, 0.9774408773219763),\n",
" (115, 49604.177906, 'i', 4.021244552770227e-10, 4.0097373869363306e-11, 0.0, 'ab', 10.028698053571754, 24.258893, 0.9714682214607208),\n",
" (57, 49604.2096, 'i', 4.015318096146626e-10, 4.339008862456936e-11, 0.0, 'ab', 9.253998374810827, 24.170085, 0.9600607752971246),\n",
" (34, 49604.195079, 'i', 4.0180324532957655e-10, 4.392809246791391e-11, 0.0, 'ab', 9.146840273637261, 24.156292, 0.9584319721592864),\n",
" (100, 49604.165835, 'i', 4.0235036619119026e-10, 5.250582849553943e-11, 0.0, 'ab', 7.662965764369788, 23.9577, 0.9192867523897155),\n",
" (48, 49605.172561, 'i', 3.838601993648443e-10, 5.207630401088651e-11, 0.0, 'ab', 7.37111065494584, 23.966039, 0.8984466105583383),\n",
" (62, 49605.146455, 'i', 3.8433204458323825e-10, 6.137305193631146e-11, 0.0, 'ab', 6.2622280049242205, 23.784909, 0.7323203266350712),\n",
" (38, 49609.123288, 'z', 3.975091326151192e-10, 1.0764893877995893e-10, 0.0, 'ab', 3.692643300717087, 23.170611, 0.14791519537108352),\n",
" (80, 49609.122842, 'z', 3.9751186698885636e-10, 1.1040602806667404e-10, 0.0, 'ab', 3.6004543769005033, 23.143069, 0.131063060097412),\n",
" (37, 49608.132967, 'z', 4.03436466840165e-10, 1.2215433593095584e-10, 0.0, 'ab', 3.3026782370475627, 23.033086, 0.08992906855528873),\n",
" (42, 49595.166681, 'g', 9.20822724772304e-11, 2.8114385125399264e-11, 0.0, 'ab', 3.2752725007683305, 24.626269, 0.04558711304825116),\n",
" (23, 49595.179838, 'g', 9.19774702730734e-11, 2.8231126292799225e-11, 0.0, 'ab', 3.258016322803729, 24.621747, 0.04450342507207419),\n",
" (121, 49608.133413, 'z', 4.034338623569116e-10, 1.258372496013037e-10, 0.0, 'ab', 3.2059971402357474, 23.000778, 0.07877206998320525),\n",
" (127, 49595.180285, 'g', 9.197391097176115e-11, 2.9105693674091083e-11, 0.0, 'ab', 3.1599972157211718, 24.588526, 0.038347825147289585),\n",
" (125, 49595.167128, 'g', 9.207871069751052e-11, 2.917960624932301e-11, 0.0, 'ab', 3.1555844143594918, 24.585773, 0.03807070122177608),\n",
" (12, 49609.137336, 'z', 3.9742297602173114e-10, 1.2608196685448988e-10, 0.0, 'ab', 3.152100065827761, 22.998605, 0.07255234759652361),\n",
" (78, 49609.13689, 'z', 3.9742571223878915e-10, 1.2873037244729286e-10, 0.0, 'ab', 3.087272293890942, 22.976003, 0.06507122271501468),\n",
" (21, 49608.11598, 'z', 4.0353562138552123e-10, 1.3254735252494533e-10, 0.0, 'ab', 3.04446383649629, 22.944295, 0.060131126731671855),\n",
" (124, 49608.116427, 'z', 4.0353301329661043e-10, 1.3768308214337226e-10, 0.0, 'ab', 2.9308830614091215, 22.902978, 0.05042445866528385),\n",
" (52, 49610.123399, 'z', 3.912452602928697e-10, 1.4949415991220617e-10, 0.0, 'ab', 2.6171273882714705, 22.813475, 0.02965383310357135),\n",
" (76, 49610.123845, 'z', 3.912424098439033e-10, 1.5145108258864168e-10, 0.0, 'ab', 2.5832922627998776, 22.799352, 0.027413947797351894),\n",
" (33, 49610.138942, 'z', 3.91145892849853e-10, 1.6378322032063485e-10, 0.0, 'ab', 2.3881927103650495, 22.714362, 0.018993010469491287),\n",
" (70, 49610.139389, 'z', 3.9114303423691356e-10, 1.6496419818459024e-10, 0.0, 'ab', 2.371078322092868, 22.706563, 0.018548036374414573),\n",
" (55, 49583.190532, 'y', 7.299050270627587e-10, 4.0830445439877144e-10, 0.0, 'ab', 1.7876489447991555, 21.722725, 0.006521668181750541),\n",
" (105, 49583.190979, 'y', 7.299033389827146e-10, 4.163902908029454e-10, 0.0, 'ab', 1.7529307361495075, 21.701455, 0.006132824244874484),\n",
" (97, 49619.148915, 'i', 1.9596268363157067e-10, 1.189085915993241e-10, 0.0, 'ab', 1.6480111402873985, 23.061447, 0.0016512490172645533),\n",
" (47, 49583.197251, 'y', 7.298795982955162e-10, 4.4307047582500575e-10, 0.0, 'ab', 1.6473216748113635, 21.634095, 0.0049500027578872705),\n",
" (123, 49583.197698, 'y', 7.298779024191725e-10, 4.5135054808257726e-10, 0.0, 'ab', 1.6170976318071004, 21.614014, 0.004611493476239525),\n",
" (103, 49619.163176, 'i', 1.9584031951921582e-10, 1.3845477743584385e-10, 0.0, 'ab', 1.414471375752728, 22.896463, 0.0009486828254516369),\n",
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" (90, 49550.294916, 'i', 7.389815354648024e-15, 1.1206111029572393e-10, 0.0, 'ab', 6.59445130888553e-05, 23.124046, 0.00010002637780523555)], \n",
" dtype=[('index', '<i8'), ('time', '<f8'), ('band', 'O'), ('flux', '<f8'), ('fluxerr', '<f8'), ('zp', '<f8'), ('zpsys', 'O'), ('SNR', '<f8'), ('fiveSigmaDepth', '<f8'), ('DetectionEfficiency', 'O')])"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q3.SNCosmoLC()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'MWE(B-V)': 0.035068757832050323,\n",
" 'ModelSource': 'salt2-extended',\n",
" '_dec': 0.003271,\n",
" '_ra': 6.25556,\n",
" 'c': 0.0,\n",
" 'hostebv': 0.0,\n",
" 'hostr_v': 3.1000000000000001,\n",
" 'mwebv': 0.0,\n",
" 'mwr_v': 3.1000000000000001,\n",
" 't0': 49580.0,\n",
" 'x0': 1.0068661711630977e-05,\n",
" 'x1': 0.0,\n",
" 'z': 0.5}"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q3.SN.SNstate"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.086475461159803382"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q3.qualityMetric()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.99999999999999989"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q3.discoveryMetric()"
]
},
{
"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
}
| mit |
eshlykov/mipt-day-after-day | optimizaion/kaggle/eshlykov-kaggle.ipynb | 1 | 100081 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Подключаем все необходимые библиотеки.\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy as sp\n",
"import scipy.misc as scm # Для logsumexp и imread.\n",
"%matplotlib inline\n",
"\n",
"import math\n",
"import tqdm # Для отображения прогресса."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_test(size=3000):\n",
" # 3000 - потому что не хочу запоминать, сколько всего картинок на самом деле.\n",
" test = np.array([]).reshape(0, 3072) # Размер картинки 32x32, всего 3 цвета, итого 3072.\n",
" for i in tqdm.tqdm(np.arange(size)):\n",
" # Нужен try для случая, если картинки не существует.\n",
" try:\n",
" img = scm.imread('test/test/img_{}.jpg'.format(i)).reshape(1, -1)\n",
" test = np.append(test, img, axis=0)\n",
" except:\n",
" # Ничего не надо делать, переходим к следующей картинке.\n",
" continue\n",
" return test / 127.5 - 1 # Отображаем все числа в отрезок [0; 1]."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_train(size=7000):\n",
" # 7000 - потому что не хочу запоминать, сколько всего картинок на самом деле.\n",
" sample = np.array([]).reshape(0, 3072) # Размер картинки 32x32, всего 3 цвета, итого 3072.\n",
" result = np.array([]).reshape(0, 2) # Векторы вида [0, 1] (indoor) либо [1, 0] (outdoor).\n",
" for i in tqdm.tqdm(np.arange(size)):\n",
" # Переберим теперь тип картинки.\n",
" for res, door in enumerate(['outdoor', 'indoor']):\n",
" # Нужен try для случая, если картинки не существует.\n",
" try:\n",
" img = scm.imread('train/train/{}_{}.jpg'.format(door, i)).reshape(1, -1)\n",
" sample = np.append(sample, img, axis=0)\n",
" # Заполняем вектор ответа - зависит лишь от типа картинки.\n",
" result = np.append(result, np.zeros((1, 2)), axis=0)\n",
" result[-1, res] = 1\n",
" except:\n",
" # Ничего не надо делать, переходим к следующей картинке.\n",
" continue\n",
" return sample / 127.5 - 1, result # Отображаем всечисла из выборки в отрезок [0; 1]."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████| 3000/3000 [01:34<00:00, 31.60it/s]\n"
]
}
],
"source": [
"# Считываем данные, которые будем исследовать.\n",
"test = read_test()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████| 7000/7000 [06:54<00:00, 16.87it/s]\n"
]
}
],
"source": [
"# Считываем данные, на которых будем обучаться.\n",
"sample, result = read_train()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2960, 3072) (6905, 3072) (6905, 2)\n"
]
}
],
"source": [
"# Просто посмотрим на их размер.\n",
"print(test.shape, sample.shape, result.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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6QHTM0trdRJRjUb21hZyGcrwndpynYdSNGA/Lemdl2fE2yp7Q/PUQEXm8Ydo5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+JzQzIE27axkuLiySbjZPrl94gVprNHJQM688BWbnPTTV6+7CVJux8zX9bxpVd+le8uWSKfOzsx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\nFPfTmfNPZ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"text/plain": [
"<matplotlib.figure.Figure at 0x210afad5dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Пример картинок из выборки. Вывод странненький, но это не наша проблема. =)\n",
"plt.figure(figsize=(10, 10))\n",
"for i in np.arange(10):\n",
" plt.subplot(1, 10, i + 1)\n",
" plt.imshow(np.reshape(sample[i], (32, 32, 3)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Будем совсем неразумно обучаться на всем train'е, так как тогда мы переобучимся,\n",
"то есть наш алгоритм \"подгониться\" под закономерности, присущие только train'у,\n",
"а на реальных данных будет неистово лажать. Так что train разделим на две части:\n",
"на 75% будем обучаться, а на 25% проверять, что мы лажаем не неистово.\n",
"\n",
"Если возьмем первые 25% от всего train'а, то может быть несбалансированное число\n",
"outdoor'ов и indoor'ов. Поэтому для train возьмем первые 75% outdoor'ов плюс\n",
"первые 75% indoor'ов. Тогда мы сохраним пропорции outdoor:indoor таким, какое оно\n",
"во всем train'е. Будет особенно клево, если и в исследуемых данных соблюдается так же\n",
"пропорция.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Выделяем outdoor'ы и indoor'ы.\n",
"sample_out = sample[result[:, 0] == 1]\n",
"sample_in = sample[result[:, 1] == 1]\n",
"result_out = result[result[:, 0] == 1]\n",
"result_in = result[result[:, 1] == 1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Считаем размер indoor- и outdoor-частей в train'е.\n",
"train_size_in = int(sample_in.shape[0] * 0.75)\n",
"train_size_out = int(sample_out.shape[0] * 0.75)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Разделяем outdoor'ы и indoor'ы на обучающую и тестовую часть.\n",
"x_train_out, x_test_out = np.split(sample_out, [train_size_out])\n",
"y_train_out, y_test_out = np.split(result_out, [train_size_out])\n",
"x_train_in, x_test_in = np.split(sample_in, [train_size_in])\n",
"y_train_in, y_test_in = np.split(result_in, [train_size_in])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Делаем общий train и test, смешивая indoor'ы и outdoor'ы.\n",
"x_train = np.vstack([x_train_in, x_train_out])\n",
"y_train = np.vstack([y_train_in, y_train_out])\n",
"x_test = np.vstack([x_test_in, x_test_out])\n",
"y_test = np.vstack([y_test_in, y_test_out])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для каждой картинки мы хотим найти вектор $(p_0, p_1)$, вероятностей такой, что $p_i$ - вероятность того, что картинка принадлежит классу $i$ ($0$ — outdoor, $1$ — indoor).\n",
"\n",
"Реализуя логистическую регрессию, мы хотим приближать вероятности к их настоящему распределению. \n",
"\n",
"Выражение выдает ответ вида $$ W x + b, $$\n",
"\n",
"\n",
"где $x$ — наш вектор картинки, а результат — числовой вектор размерности $2$ с какими-то числами. Для того, чтобы эти числа стали вероятностями от $0$ до $1$, реализуем функцию \n",
"$$\n",
"\\text{softmax}(W, b, x) = \\frac{e^{Wx+b}}{\\sum(e^{Wx+b})},\n",
"$$\n",
"и полученные значения будут как раз давать в сумме 1, и ими мы будем приближать вероятности. \n",
"\n",
"Оценивать качество нашей модели будем с помощью кросс-энтропии, см. https://en.wikipedia.org/wiki/Cross_entropy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сначала поймем, что $x$ - вектор размерности 3072, $W$ - матрица 2 на 3072, $b$ - вектор размерности 2.\n",
"\n",
"Положим $x'_i = x_i$ для $ i \\leqslant 3072 $ и $x'_{3073} = 1$. Получили вектор $x'$ размерности 3073. Положим $W'_{i,j} = W_{i,j}$ для $ i \\leqslant 2, j \\leqslant 3073$ и $W'_{i,3073}=b_i$ для $ i \\leqslant 2 $.\n",
"\n",
"Таким образом, к вектору $x$ просто дописали 1, а к матрице $W$ просто приписали вектор $b$ справа.\n",
"\n",
"Заметим теперь, что в точности верно равенство: $Wx+b=W'x'$. Теперь забьем на вектор $b$ и будем считать, что у нас есть матрица 10 на 3073, элементы которой надо оценить. Далее везде считаем $W' = W$ и $x' = x$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Градиентный спуск считается по формуле: $W_{k+1} = W_k - \\eta_k \\nabla L(W_k)$, где $\\eta_k$ — шаг, а $L$ — функция $\\text{loss}$. Значит, нам надо посчитать градиент функции $L$, то есь найти ее частные производные по всем 6146 переменным.\n",
"\n",
"Вспомним, как определяется $L$. Обозначим через $y$ вектор вида $(1, 0)$ либо $(0, 1)$, где 1 на $k$-м месте, где $k - 1$ — тип исследуемой картинки. Размерность $y$ равна 2. Сам вектор $y$ олицетворяет ответ для данной картинки.\n",
"\n",
"Тогда\n",
"$$ L(W) = -y_1 \\ln \\frac{e^{(Wx)_1}}{e^{(Wx)_1} + e^{(Wx)_2}} -y_{2} \\ln \\frac{e^{(Wx)_{2}}}{e^{(Wx)_1} + e^{(Wx)_2}} + \\frac{\\lambda}{2} \\sum_{i=1}^{2} \\sum_{j=1}^{3073} W_{i,j}^2. $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Последняя сумма — так называемый регуляризатор. Если у нас много признаков (у нас их 6146), то при логистической регресии может возникнуть переобучение. Добавляя все параметры в $\\text{loss}$, мы не сможем получить неестественного результата, когда какие-то параметры очень маленькие, а какие-то очень большие, потому что большие будут сильно увеличивать регуляризатор, а функция минимизируется. Таким образом, более вероятно получение подходящего результата.\n",
"\n",
"Это описано в курсе Machine Learning by Stanford University во втором уроке третьей недели. Ссылка: https://www.coursera.org/learn/machine-learning/lecture/4BHEy/regularized-logistic-regression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Теперь найдем производную по $W_{i,j}$: $$\n",
"\\frac{dL(W)}{dW_{i,j}} =\n",
"-y_1 \\frac{e^{(Wx)_1} + e^{(Wx)_2}}{e^{(Wx)_1}} \\cdot\n",
"\\frac{-e^{(Wx)_1} e^{(Wx)_i} x_j}\n",
"{e^{(Wx)_1} + e^{(Wx)_2}}\n",
"-y_{2} \\frac{e^{(Wx)_1} + e^{(Wx)_2}}{e^{(Wx)_{2}}} \\cdot\n",
"\\frac{-e^{(Wx)_{2}} e^{(Wx)_i} x_j}\n",
"{e^{(Wx)_1} + e^{(Wx)_2}} -\\\\\n",
"- y_i \\frac{e^{(Wx)_1} + e^{(Wx)_2}}{e^{(Wx)_i}} \\cdot\n",
"\\frac{e^{(Wx)_i} x_j (e^{(Wx)_1} + e^{(Wx)_2})}\n",
"{(e^{(Wx)_1} + e^{(Wx)_2})^2}\n",
"+ \\lambda W_{i,j}. $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Упростим немного: $$\n",
"\\frac{dL(W)}{dW_{i,j}} =\n",
"\\frac{ x_j e^{(Wx)_i} (y_1 + y_2) }\n",
"{e^{(Wx)_1} + e^{(Wx)_2}}\n",
"-y_i x_j\n",
"+ \\lambda W_{i,j}. $$\n",
"\n",
"Упрощая еще сильнее, приходим к окончательному ответу: $$\n",
"\\frac{dL(W)}{dW_{i,j}} =\\left( \\frac{e^{(Wx)_i}}{e^{(Wx)_1} + e^{(Wx)_2}} - y_i \\right) x_j\n",
"+ \\lambda W_{i,j}.\n",
"$$\n",
"Соответственно, если $j = 3073$, то есть дифференцируем по переменным $ W_{1, 3073} = b_1, \\ldots, W_{2, 3073} = b_2$, то коэффициент перед скобкой просто 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Перейдем к реализации."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def softmax(W, x):\n",
" # Функция logsumexp более стабтильно вычисляет функцию экспонент, почти\n",
" # избавляя нас от проблемы переполнения.\n",
" p = np.dot(x, W.T)\n",
" return np.exp(p - scm.logsumexp(p, axis=1).reshape(-1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def loss(y, softmax, W, l):\n",
" # Формула из Википедии по ссылке выше c добавленным регуляризатором.\n",
" return np.mean(-np.sum(y * np.log(softmax), axis=1)) + l * np.trace(W @ W.T) / (2 * y.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Считаем средний по всем картинкам градиент.\n",
"# Градиент у нас будет не вектор, как мы привыкли, а матрица 2x3073.\n",
"def gradients(W, x, y, l):\n",
" p = softmax(W, x)\n",
" grads = (p - y).T @ x + l * W\n",
" return grads / x.shape[0] # По максимимум матричных вычислений!"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Выбор шага по правилу Армихо из семинарского листочка.\n",
"def armijo(W, x, y, l, alpha=0.5, beta=0.5):\n",
" s = 1\n",
" grad = gradients(W, x, y, l)\n",
" dW = -grad # Направление спуска.\n",
" loss_1 = loss(y_train, softmax(W + s * dW, x), W, l)\n",
" loss_0 = loss(y_train, softmax(W, x), W, l)\n",
" while loss_1 > loss_0 + alpha * s * (grad * dW).sum():\n",
" s = beta * s\n",
" loss_1 = loss(y_train, softmax(W + s * dW, x), W, l)\n",
" loss_0 = loss(y_train, softmax(W, x), W, l)\n",
" return s"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def classify(x_train, x_test, y_train, y_test, iters, l):\n",
" # Как было замечено выше, W Размера 2 на 3072, а b размера 2, но мы приписываем b к W.\n",
" W = np.zeros((2, 3072))\n",
" b = np.zeros(2)\n",
"\n",
" # Для приписывания запишем b как вектор столбец и воспользуемся функцией hstack.\n",
" b = b.reshape(b.size, 1)\n",
" W = np.hstack([W, b])\n",
"\n",
" # Соответственно, нужно поменять x_train и x_test, добавив по 1 снизу.\n",
" fictious = np.ones((x_train.shape[0], 1))\n",
" x_train = np.hstack([x_train, fictious])\n",
" fictious = np.ones((x_test.shape[0], 1))\n",
" x_test = np.hstack([x_test, fictious])\n",
"\n",
" # Будем записывать потери на каждом шаге спуска.\n",
" losses_train = [loss(y_train, softmax(W, x_train), W, l)]\n",
" losses_test = [loss(y_test, softmax(W, x_test), W, l)]\n",
"\n",
" # Собственно, сам спуск.\n",
" for i in tqdm.tqdm(np.arange(iters)):\n",
" # Именно так - в Армихо подставляется alpha = l, а l = 0!\n",
" # Потому что я накосячил и не заметил! =)\n",
" eta = armijo(W, x_train, y_train, 0, l)\n",
" W = W - eta * gradients(W, x_train, y_train, l)\n",
" losses_train.append(loss(y_train, softmax(W, x_train), W, l))\n",
" losses_test.append(loss(y_test, softmax(W, x_test), W, l))\n",
"\n",
" # На выходе имеется оптимальное значение W и массивы потерь.\n",
" return W, losses_train, losses_test"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = 0.04 # Сработает лучше, чем вообще без регуляризатора (l = 0)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████| 100/100 [00:29<00:00, 3.34it/s]\n"
]
}
],
"source": [
"# Нам хватит и 100 итераций, переобучение начинается достаточно быстро.\n",
"W, losses_train, losses_test = classify(x_train, x_test, y_train, y_test, 100, l)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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xkwsu8GdhrVvnx01ycyE/3/f9x8f7lkpKih+cP+ooX8+UKb7uzz7z3XNXXunv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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x210b3063358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(losses_train, color='green', label='train')\n",
"plt.plot(losses_test, color='red', label='test')\n",
"plt.xlabel('Gradient descent iteration')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iters = np.argmin(losses_test) # На этой итиреации ошибка на тесте минимальна."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████| 82/82 [00:26<00:00, 3.12it/s]\n"
]
}
],
"source": [
"# Делаем столько итераций.\n",
"W, losses_train, losses_test = classify(x_train, x_test, y_train, y_test, iters, l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Посчитаем среднюю квадратичную ошибку на тесте, чтобы прикинуть, что будет на Kaggle."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Добавляем 1 к выборке.\n",
"nx_test = np.hstack([x_test, np.ones(x_test.shape[0]).reshape(x_test.shape[0], 1)])\n",
"probabilities = softmax(W, nx_test) # Считаем вероятности.\n",
"recognized = np.argmax(probabilities, axis=1) # Что распознано.\n",
"answers = np.argmax(y_test, axis=1) # Правильные ответы."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.35447343856070407"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(np.mean((recognized - answers) ** 2)) # Собственно, ошибка."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Теперь применяем найденную матрицу к исследумемым данным."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Добавляем 1 к выборке.\n",
"ntest = np.hstack([test, np.ones(test.shape[0]).reshape(test.shape[0], 1)])\n",
"probabilities = softmax(W, ntest) # Считаем вероятности.\n",
"ress = np.argmax(probabilities, axis=1).reshape(-1, 1) # Что распознано."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Осталось загнать все в табличку, чтобы ее записать в csv.\n",
"ids = np.arange(ress.size).reshape(-1, 1)\n",
"submit = np.hstack([ids, ress])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Заполняем csv-шник.\n",
"import csv\n",
"with open('submission.csv', 'w', newline='') as csvfile:\n",
" submission = csv.writer(csvfile, delimiter=',')\n",
" submission.writerow(['id', 'res'])\n",
" submission.writerows(submit)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вот и готово."
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| unlicense |
nsrchemie/code_guild | Untitled.ipynb | 1 | 1126 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def tuple_full():\n",
" count_dict = {}\n",
" filename = input(\"input filename \" )\n",
" file = open(filename, 'r')\n",
"\n",
" for line in file:\n",
" lst = line.split(\" \")\n",
" if lst[0] == \"From\":\n",
" \n",
"\n",
" if lst[1] in count_dict:\n",
" count_dict[lst[1]] += 1\n",
" else:\n",
" count_dict.update({lst[1]: 1})\n",
"\t\n",
"\tc_tup = count_dict.items()\n",
"\tprint(c_tup)"
]
}
],
"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
}
| mit |
cabrer7/PyWignerCUDA | instances/Wigner2D/Wigner2D_FFT_Caldeira_Free.ipynb | 1 | 474835 | {
"metadata": {
"name": "",
"signature": "sha256:e5687d62bc141ef34a776da75a95487ee283ea73934581ceb2a75b81da84e63b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"$x^2$ potential"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pickle\n",
"import numpy as np\n",
"import pycuda.gpuarray as gpuarray\n",
"from scipy.special import hyp1f1\n",
"import scipy.fftpack as fftpack\n",
"import pylab as plt\n",
"import time\n",
"\n",
"#-------------------------------------------------------------------------------------\n",
"from pywignercuda_path import SetPyWignerCUDA_Path\n",
"SetPyWignerCUDA_Path()\n",
"from GPU_Wigner2D_FFT import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Settings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class frame( GPU_Wigner2D_FFT ):\n",
" def __init__ (self):\n",
" X_gridDIM = 512\n",
" P_gridDIM = 512\n",
" \n",
" X_amplitude = 15\n",
" P_amplitude = 10\n",
" \n",
" kappa = 1.\n",
" dt= 0.005\n",
" \n",
" timeSteps = 1000\n",
" skipFrames = 200\n",
" \n",
" mass = 1. \n",
" # Diffusion parameter \n",
" D_Theta = 0.0143\n",
" D_Lambda = 0.000 \n",
" \n",
" # Damping parameters (implies another source of diffusion as well)\n",
" self.dampingFunction = 'CaldeiraLeggett'\n",
" gammaDamping = 0.07 #8*10**(-6)\n",
" epsilon = 0.01;\n",
" \n",
" #Gross Pitaevskii coefficient\n",
" self.grossPitaevskiiCoefficient = 0.0\n",
" \n",
" # Potential and derivative of potential\n",
" self.omega = 0\n",
" X2_constant = 0.5*mass*self.omega**2\n",
" \n",
" potentialString = '{0}*pow(x,2)'.format(X2_constant)\n",
"\n",
" dPotentialString = '2*{0}*x'.format(X2_constant)\n",
" \n",
" kinematicString = '0.5*p*p' #.format(mass=mass)\n",
" \n",
" self.fp_Damping_String = ' p*p/sqrt( p*p + {epsilon} ) '.format( epsilon=epsilon )\n",
" \n",
" self.SetTimeTrack( dt, timeSteps, skipFrames,\n",
" fileName = '/home/rcabrera/DATA/Wigner2D/X2/Free.hdf5' )\n",
" \n",
" GPU_Wigner2D_FFT.__init__(self,\n",
" X_gridDIM,P_gridDIM,X_amplitude,P_amplitude,\n",
" kappa,mass,D_Theta,D_Lambda,gammaDamping,potentialString,dPotentialString,kinematicString)\n",
" \n",
" \n",
" def Set_Initial_Condition_HarmonicOscillator(self):\n",
" \"\"\"\n",
" Sets self.PsiInitial_XP with the Wigner function of the Harmonic oscillator \n",
" \"\"\"\n",
" self.x_init = -5\n",
" self.p_init = 2.\n",
" n=0\n",
" omega = 1\n",
" self.W_init = self.Wigner_HarmonicOscillator(n, omega , self.x_init, self.p_init)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Run"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"instance = frame()\n",
"print '\t\t\t\t\t\t\t'\n",
"print ' \tWigner2D propagator with damping\t'\n",
"print '\t\t\t\t\t\t\t'\n",
"\n",
"instance.Set_Initial_Condition_HarmonicOscillator ()\n",
"\n",
"%time instance.Run( )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t\t\t\t\t\t\t\n",
" \tWigner2D propagator with damping\t\n",
"\t\t\t\t\t\t\t\n",
" X_gridDIM = 512 P_gridDIM = 512\n",
" dx = 0.05859375 dp = 0.0390625\n",
" dLambda = 0.209439510239 dTheta = 0.314159265359\n",
" \n",
" GPU memory Total 5.24945068359 GB\n",
" GPU memory Free 4.92453765869 GB\n",
" GPU memory Free post gpu loading "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4.89328765869 GB\n",
" ------------------------------------------------------------------------------- \n",
" Split Operator Propagator GPU with damping \n",
" ------------------------------------------------------------------------------- \n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0 %\n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 19 %\n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39 %\n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 59 %\n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 79 %\n",
" progress "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 99 %\n",
"CPU times: user 8.71 s, sys: 5.98 s, total: 14.7 s\n",
"Wall time: 15.9 s\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"0"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'Potential'\n",
"fig, ax = plt.subplots(figsize=(10, 3))\n",
"ax.plot( instance.X_range, instance.Potential(0,instance.X_range) )\n",
"#ax.set_xlim(-10,10)\n",
"ax.set_ylim(-1,60)\n",
"ax.set_xlabel('x')\n",
"ax.set_ylabel('V')\n",
"ax.grid('on')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Potential\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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onU5blwAAAOAp168DlkwmtWTJktwM2NSpU3Xq1Knc6yUlJRocHLy4MGbAAADAODHWvqXI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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0a24050>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def PlotWignerFrame( W_input , x_plotRange,p_plotRange):\n",
" W = W_input.copy()\n",
" W = fftpack.fftshift(W.real) \n",
" \n",
" dp = instance.dP\n",
" p_min = -instance.P_amplitude\n",
" p_max = instance.P_amplitude - dp \n",
" \n",
" #p_min = -dp*instance.P_gridDIM/2.\n",
" #p_max = dp*instance.P_gridDIM/2. - dp \n",
" \n",
" x_min = -instance.X_amplitude\n",
" x_max = instance.X_amplitude - instance.dX\n",
" \n",
" global_max = 0.17 # Maximum value used to select the color range\n",
" global_min = -0.31 # \n",
" \n",
" print 'min = ', np.min( W ), ' max = ', np.max( W )\n",
" print 'final time =', instance.timeRange[-1] ,'a.u. =',\\\n",
" instance.timeRange[-1]*( 2.418884326505*10.**(-17) ) , ' s '\n",
" \n",
" print 'normalization = ', np.sum( W )*instance.dX*dp\n",
"\n",
" zero_position = abs( global_min) / (abs( global_max) + abs(global_min)) \n",
" wigner_cdict = {'red' \t: \t((0., 0., 0.),\n",
"\t\t\t\t\t\t\t(zero_position, 1., 1.), \n",
"\t\t\t\t\t\t\t(1., 1., 1.)),\n",
"\t\t\t\t\t'green' :\t((0., 0., 0.),\n",
"\t\t\t\t\t\t\t(zero_position, 1., 1.),\n",
"\t\t\t\t\t\t\t(1., 0., 0.)),\n",
"\t\t\t\t\t'blue'\t:\t((0., 1., 1.),\n",
"\t\t\t\t\t\t\t(zero_position, 1., 1.),\n",
"\t\t\t\t\t\t\t(1., 0., 0.)) }\n",
" wigner_cmap = matplotlib.colors.LinearSegmentedColormap('wigner_colormap', wigner_cdict, 256)\n",
"\n",
" fig, ax = plt.subplots(figsize=(12, 5))\n",
"\n",
" cax = ax.imshow( W ,origin='lower',interpolation='none',\\\n",
" extent=[ x_min , x_max, p_min, p_max], vmin= global_min, vmax=global_max, cmap=wigner_cmap)\n",
"\n",
" ax.contour(instance.Hamiltonian ,\n",
" np.arange(0, 10, 1 ),origin='lower',extent=[x_min,x_max,p_min,p_max],\n",
" linewidths=0.25,colors='k')\n",
" \n",
" axis_font = {'size':'24'}\n",
" \n",
" ax.set_xlabel(r'$x$',**axis_font)\n",
" ax.set_ylabel(r'$p$',**axis_font)\n",
" \n",
" ax.set_xlim((x_plotRange[0] , x_plotRange[1] ))\n",
" ax.set_ylim((p_plotRange[0] , p_plotRange[1] ))\n",
" ax.set_aspect(1.)\n",
" #ax.grid('on')\n",
" cbar = fig.colorbar(cax, ticks=[-0.3, -0.2,-0.1, 0, 0.1, 0.2 , 0.3])\n",
" matplotlib.rcParams.update({'font.size': 18})\n",
" return fig"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_init = PlotWignerFrame( instance.W_init.real , (-12.,12) ,(-5,5) )\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"min = 1.83247742086e-236 max = 0.318169063665\n",
"final time = 5.0 a.u. = 1.20944216325e-16 s \n",
"normalization = 1.0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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slWbTumDv2XWbdQEAkBao6AJeZK/W2rfNPmfws7c82Ku5sX6OCbn2iq5zlga3\n19orqyawOoOuvSfXPHe2JtiDrQm79vG5VXPt8+a6nRPuawUApAwqukC6sc/AYLZNQJWCodTev+uc\nYcHtvTIy2t7AFm6WBmegNJ9t2hhMiLUH33DB1jmPrmlbiDTrQrivAQDgOVR0gVQTrn1BaruARKQQ\nZ84xsyyYc+0tDPZtt3HYA6W9hcBtOrJwrw/3HvaeXWeV136OvYIbaSoxZ/h1jgUA4DlUdAEvcrYu\nOIOdFBpK7WHW2dNqn8LLTBdmpg6zTx9m3zY9vfEEXfOZzlXO3Cq4kbadPbrOKq999oVIv3fR9gEA\nOj0qukAqcgZZ+/5o+8L179qDb7zBzjmDgqnoGpFaH5wVXft4nBVb581nzuPhenTdPtf5nDALAJ5D\nRRfwMueNZlJob26419irt/YZGezn2FsAzCwOJtCaG9rsVxi3AG6vNDsrryaw2ntwnYtM2M+xf03O\n6cXc2hViRQAGgJRF0AVSWSwVXKdogc+tN9f5GueP/w8fDu6zT0kW6SY2+1jdbhwz2+FaEcIt+evs\n0Y3UhhBtlgUAQMqjdQHwOufNaeFuZDOcx+3VXec5zinKnLMsRPuvtD1AO3t2zWpmzoqtszXBXq11\nm12hvTefEX4BIOVR0QVSXbRZGNzYg1+04Cu1DbsmUJp9JpDGs5qa8/PChVZnRdcZfJ1VYLcqcbiv\nK9J+AABSHEEX3ubs0bVPPxZvz6p9mWD71GRm2z7/brT+XDM2yb2qa39uD7JuMyi4vcatquv2PBzC\nLwB4Aq0LgBdEquC6tS5E6l119teaAOtcFtgZfO2fbT8vlnFLbSuvbr220Sq49nOcnxEp/MayHwCQ\ncmhdALwk2o1o4c6x30gWT9BzhmMj0vy94d7DPHfbdgvB4c4/mpALAPAUKrpAOnBWfN2qvM6pwqKx\nh+Nw7xlvm4Db83Crm7n96taPSyUXANIWFV3AayJVdZ1hVGq7bLAJsPbg5wytzs9wvsYt5EZbljhc\nz26kX81zt9fEg5ALAJ5ERRdIN86Q6nbDWqytDNHCstu5TpGm/ool6EYKufHehAYA8JR0r+i2c6mk\nzqu5uVmlpaU644wzlJOTo4EDB+qOO+5QfX19soeGjuQ280C0853Pfb7wN4jF8sjMbH2Y97I/zDH7\na+yzKrg9nGMNN3+u23YsXzsAwFOam+N/eInnKrq33XabysrKdO211+rOO+/UBx98oCVLlmjLli1a\ns2aNLMvNqxZYAAAfZUlEQVRK9hDRkcK1Mbjtj1SddYq1/9b53vGeF+5msliruJE+m4ALAJ5H64KH\nbNu2TWVlZZo8ebJWr17dsv/UU09VcXGxVq1apenTpydxhEiKSGHXcLYyRON2I5rzfdorUqh1O+42\n3mhfAyEXANKC1yq08WpX68KWLVs0a9YsjRw5UhdddJGKioq0du3aRI8tbs8//7wkad68eSH7i4qK\nlJubq4qKimQMC51BtFaGcGHRrR3A2drgbHGIpfUg2jn293f77GjjjvZ7AQBIC4FA/A8vsQKB+L6k\n119/XZMmTdKRI0faHDv//PP19NNP65xzzknYAONx1VVX6Y033lB9fb0yzQpV/3LRRRfpL3/5i/7+\n97+H7LcsS4F0/+9OuolWdQ133G1/pPeK9c9VpLl24w2y9OICQKdg+XyKM2IlfgyWpU2b4h/DBRdY\nSR97osRd0b3vvvv085//XHv27NG+ffv09ttv65FHHtHll1+u9957TxdeeKF+//vfH4uxRlVTU6M+\nffq0CbmS1L9/f9XW1roGdKSZWCqekSq84aq8bi0HsTxi+ZxoY29PxRoAAI+Lu0e3R48euvHGG1u2\nR48erdGjR+vuu+9WdXW17r//fn3nO9/Ru+++qzPPPDOhg42mvr5eWVlZrseys7NbzunZs2fIsbfe\neuuYjw2dUKz9tJHO64j/8Ua6gTKWAEvIBYC05ZHCbLvFHXR79eql/fv3twmLUrBqunz5cp1xxhn6\n0Y9+pF//+tcJGWSscnNzVVtb63rM7/fLsizl5ua2ObZ8+fKW5yNHjtTIkSOP2RjRicR6A1mk8+wh\nNFFXk1hmBmnvLA4AgGNiy5Yt2rJlS7KH0Ua6d2fG3aP7pz/9SYsXL9bKlSsjTtU1cuTIDv+GR+vR\n/eSTT/TVV1+F7KdHF5Liny0hEbMrtAcBFwBSQmfp0f3jH+Mfw0UXpXGP7rnnnqsTTzxR3/rWt7Rm\nzRo1NDS0OScQCLhWTo+1UaNGqampSZs2bQrZ7/f7tXXrVp133nkdPiakiEi9trGcH+8CFe1573jG\nBQCAWDAi7qC7YMEClZaW6ve//72uvPJK5eXl6ZJLLtH999+vtWvXauvWrZo5c6ZuuummNq9ds2ZN\nQgYdznXXXSfLsrR48eKQ/eXl5WpoaNCMGTOO6efDQ9obFqNNHRbL42g+EwAAG6YXi7M2ffbZZ6ui\nokJNTU2qqqrShg0btGHDBu3atavlnJNPPlnXXXedxowZozFjxuikk06SFLxxraqqKrFfgUNxcbGW\nLl2qa665RuPHj9eHH36osrIyXXzxxXrjjTfanE/rAmKSrFaFaAi3ANApdZbWhcrK+MdQWOid1oW4\ng+748eP1n//5nxoxYkTI/s8//7wl9G7YsEEfffRRy2/SwIED9fWvf12vvfaa/H5/4kbvorm5WYsX\nL9YzzzyjnTt36oQTTtB1112nkpIS13YKgi7i0hkCL+EWADq9zhJ033gj/jF84xtpHHRra2t17733\nqrq6Wpdffrnmz58f9ry33npL69ev14YNG7R161Y1NzerqTMEBRuCLtqtI/8sE24BIKV0lqC7dm38\nY7j88jQOusahQ4e0efNmXXzxxTGd//e//10jR45UdXV1ez7umCHoImESHXwJtwCQsjpL0P3DH+If\nwxVXeCfoxj2PrpGVlRVzyJWkE088UZMnT27vxwGdH8EUANDJeCSvtlu7K7peQUUXAAAkWmep6P7v\n/8Y/hm9+0zsV3binFwMAAABitXLlSo0cOVK5ubk66aSTVFRUFHYlWzd/+MMf9MMf/lDnn3++srOz\n5fP59Oabb8b0WoIuAACARyV7Ht3S0lLNmjVLeXl5WrJkiWbPnq1Vq1apsLBQ9fX1Mb3Hc889pxUr\nVigQCGjYsGGSFHF1Xrt29+gCAACgc0tmd2Ztba3uu+8+jRo1SmvXrm0Jp+eff74mTpyoJ554Qvfc\nc0/U9/mP//gPlZeXKzMzU4899pi2bt0a8xio6AIAAHhUMiu6v/3tb9XQ0KBbb701pAI7YcIEDRo0\nSBUVFTG9T35+vjIzM9s1BoIuAACARzU3x/9IlM2bN0uSxowZ0+bYBRdcoO3bt8fcvtBeBF0AAACP\nSmZFt6amRpZlqX///m2O9e/fX4FAQDU1NYn7QBf06AIAAHhUIiq0dXV1Ki0tjfn8uXPnKi8vr6Va\nm5WV1eac7OxsSTrmFV2CLgAAgEclIuju3btXJSUlwbUHopR8LcvSzJkzlZeXp9zcXEnB1XSdYdfv\n90tSyznHCkEXAADAo2JpRfh//69S27ZVhj1eUFCg5nYk5vz8fAUCAVVXV2vQoEEhx6qrq+Xz+ZSf\nnx/3+8aDoAsAAOBRseTTYcMKNWxYYcv26tUPJuSzR40apfLycr399tttgm5VVZVOP/30Y17R5WY0\nAAAAj0rmzWiTJk1STk6Oli5dGlIRfvnll7Vjxw7NmDEj5Pzdu3dr+/bt2r9/f8LGQEUXAADAo5K5\nYESfPn300EMP6Y477tC4ceM0bdo0VVdX6/HHH9fQoUM1b968kPPLyspUUlKiFStW6IYbbmjZ//77\n7+ull16SJP3xj3+UFFxWeP369ZKk4uJi9ezZ03UMBF0AAACPSvSSvvGaP3++evfurdLSUs2dO1e9\nevXStGnT9Oijj7ZpW7Asq+Vht2XLFv3oRz8KOW/58uUtz2fOnBk26FqBaLfPeZxlWQok8787AADA\ncyyfL+oMBcd8DJalX/0q/jFcf3302RVSBRVdAAAAj0r3Wh5BFwAAwKM8UphtN4IuAACAR1HRBQAA\ngCcRdAEAAOBJtC4AAADAk6joAgAAwJOo6EI7d+5M9hAAAAASjoouAAAAPImKLlRQUJDsIQAAACQc\nFV0AAAB4UrpXdH3JHgAAAABwLFDRBQAA8Kh0b13wVEW3urpajzzyiC699FLl5+ere/fuGj58uO66\n6y7t2bMn2cMDAADoUIFA/A8v8VRF9+WXX9aDDz6oCRMm6JprrlGPHj20adMmLV68WKtWrdLmzZvV\nt2/fZA8TAACgQ6R7RddTQXfs2LH661//qhNPPLFl34033qgLLrhARUVFeuyxx/TTn/40iSMEAADo\nOOkedD3VujBs2LCQkGtMnTpVkrRt27aOHhIAAEDS0LqQBnbt2iVJtC0AAIC0ku4V3bQIug888IAk\n6YYbbkjySAAAADqO1yq08eqUQbeurk6lpaUxnz937lzl5eW5Hnv88cf14osvavbs2SosLEzQCAEA\nADo/Krqd0N69e1VSUiLLshSI8l8Ry7I0c+ZM16C7bNky3XXXXZowYYKWLl0a9j127tx5tEMGAADo\ndKjodkIFBQVqPsr/gixfvlw333yzvvnNb+rXv/61MjIywp67ePHiluejR4/W6NGjj+qzAQBAeqmq\nqlJVVVWyh9FGuld0rUC0kmkKWr58uW666SZdeeWVeumll9S1a9ew51qWpUC6/ykAAAAJZfl8UX8q\nfczHYFn68Y/jH8N990X/iXqq8NT0YpL0i1/8QkVFRRo3bpx+97vfRQy5AAAA8K5O2brQXi+99JJu\nvPFG9erVS1OnTtXq1atDjvf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4WEuXLtU111yj8ePH68MPP1RZWZkuvvhiFvno5G677TZt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\nAADwJIIuAAAAPOn/A5EG0cGjUCjhAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da094fcd0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_init = PlotWignerFrame( instance.W_end , (-12.,12) ,(-5,5) )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"min = -3.80615540457e-08 max = 0.257950430789\n",
"final time = 5.0 a.u. = 1.20944216325e-16 s \n",
"normalization = 1.0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7W8OHD9eMGTM0YcKEZM8vkGeeeUaSdNttt0XsLyws1IIFC1ReXk7QRXyCVhrj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\nVE8BAAAg6eKp6P7jHxX6xz8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kHQ+9fgi6AAAAhkrlk9GmTJmirl27aunSpREV4RdeeEG7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\nEkEXAAAARiLoAgAAwEj/Hz1xs9qSN6hrAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0878710>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def PlotMarginals():\n",
" \n",
" W = fftpack.fftshift( instance.W_end )\n",
" \n",
" dp = instance.dP\n",
" p_min = -instance.P_amplitude\n",
" p_max = instance.P_amplitude - dp \n",
" \n",
" W0 = fftpack.fftshift(instance.W_init )\n",
" \n",
" marginal_x_init = np.sum( W0 , axis=0 )*dp\n",
" marginal_p_init = np.sum( W0 , axis=1 )*instance.dX\n",
"\n",
" marginal_x = np.sum( W, axis=0 )*dp\n",
" marginal_p = np.sum( W, axis=1 )*instance.dX\n",
"\n",
"\n",
" x_min = -instance.X_amplitude\n",
" x_max = instance.X_amplitude - instance.dX \n",
" #.......................................... Marginal in position\n",
"\n",
" plt.figure(figsize=(10,10))\n",
" plt.subplot(211)\n",
"\n",
" plt.plot(instance.X_range, marginal_x_init, '-',label='initial')\n",
" plt.plot(instance.X_range, marginal_x, label='final')\n",
" #plt.axis([x_min, 0*x_max, -0.01,6])\n",
" plt.xlabel('x')\n",
" plt.ylabel('Prob')\n",
"\n",
" plt.legend(loc='upper right', shadow=True)\n",
"\n",
" #.......................................... Marginal in momentum\n",
"\n",
" print 'p = ', np.sum( marginal_p*instance.P_range )*dp,\\\n",
" '->', np.sum( W*instance.P )*instance.dX*dp\n",
" print 'x = ', np.sum( W0*instance.X )*instance.dX*dp, \\\n",
" '->',np.sum( W*instance.X )*instance.dX*dp\n",
" \n",
" rangeP = np.linspace( p_min, p_max, instance.P_gridDIM )\n",
" \n",
" plt.subplot(212)\n",
" plt.plot(rangeP, marginal_p_init ,'-', label='initial')\n",
" plt.plot(rangeP, marginal_p , label='final')\n",
" plt.axis([p_min, p_max, -0.01, 1])\n",
" plt.xlabel('p')\n",
" plt.ylabel('Prob')\n",
"\n",
" plt.legend(loc='upper right', shadow=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"PlotMarginals()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"p = 0.994881866633 -> -8.77476570163\n",
"x = 9.99999999997 -> -6.48217421318\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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IFKlEytOSqJAoIlK5KCSKVBJXr9qmsKlb9/aODw1Vd7OISGWikChSSZw4Ybtp5XafIV+n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\niIiISIJFIhERERFJsEgkIiIiIon/A1edLu0DHTrPAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0967350>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"First Order Ehrenfest"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(10, 4))\n",
"\n",
"ax.plot( instance.timeRange , np.gradient(instance.X_average, instance.dt) , '-',\n",
" label = '$\\\\frac{d}{dt} \\\\langle x \\\\rangle $' ,color = 'red', linewidth=1.5)\n",
"\n",
"ax.plot( instance.timeRange , instance.P_average/instance.mass , '--' ,\n",
" label='$\\\\frac{1}{m}\\\\langle p \\\\rangle$', linewidth=1.5 )\n",
"\n",
"\n",
"#ax.set_xlim(0,3.5)\n",
"#ax.set_ylim(-1.,1.2)\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size':22})\n",
"ax.set_xlabel('t')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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5LW9rrVmzJmXLlk2276WXXiI0NJSwsDCefvrp+84NCgpKGqOQP39+/Pz8CAwM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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0ad8fd0>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(10, 4))\n",
"\n",
"ax.plot( instance.timeRange , np.gradient( instance.P_average , instance.dt) ,'-' , \n",
" label = '$\\\\frac{d}{dt} \\\\langle p \\\\rangle $' ,color = 'r' , linewidth=1.5)\n",
"\n",
"ax.plot( instance.timeRange , \n",
" - instance.dPotentialdX_average -2*instance.gammaDamping*instance.P_average , '--' ,\n",
" label = '$ \\\\langle \\\\frac{d}{dx}V \\\\rangle - 2 \\\\gamma \\\\langle p \\\\rangle $' ,linewidth=1.5)\n",
"\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size':22})\n",
"#ax.set_ylim(-0.8,0.8)\n",
"ax.set_xlabel('t')\n",
"#ax.set_ylabel(' ')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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DJyiVctvQB7FJE+jUCTp25F7jFxk+rjQdO0LHjlC/vnRTFDnT8nOLlssmhJZI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\nnSlbtizBwcE4OjrmmG7o0KFs3ryZa9e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z6rTGFStW0LZtW9zc3GjUqBFRUVHk5uZW6L1m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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0a51150>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Second Order Ehrenfest Theorems"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(10, 4))\n",
"\n",
"ax.plot( instance.timeRange , np.gradient( instance.X2_average , instance.dt) , '-',\n",
" label='$\\\\frac{d}{dt} \\\\langle x^2 \\\\rangle$' , color = 'red', linewidth=1.5)\n",
"\n",
"ax.plot( instance.timeRange , \\\n",
" 2*instance.XP_average/instance.mass, '--',label = '$\\\\frac{2}{m} \\\\langle xp \\\\rangle$',linewidth=1.5 )\n",
"\n",
"\n",
"#ax.set_xlim(0,3.5)\n",
"#ax.set_ylim(-1.,1.2)\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size':24})\n",
"ax.set_xlabel('t')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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6deLhhx8u1OtevnyZzp07s3v3bhwcCm8miCGKE2dnZ+Lj42+4LSUlBYvFcsPH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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d98bc24d0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange , np.gradient( instance.P2_average , instance.dt) , '-',\n",
" label = '$\\\\frac{d}{dt} \\\\langle p^2 \\\\rangle$',\n",
" color = 'red', linewidth=1.5)\n",
"\n",
"ax.plot( instance.timeRange , \\\n",
" -2*instance.PdPotentialdX_average\\\n",
" +2.*instance.D_Theta \\\n",
" -4*instance.gammaDamping*instance.P2_average\n",
" , '--',\n",
" label = '$- \\\\langle p\\\\frac{dV}{dx} +\\\\frac{dV}{dx} p \\\\rangle + 2 D_{\\\\theta}- 4\\\\gamma \\\\langle p^2 \\\\rangle $',\n",
" linewidth=1.5 )\n",
"\n",
"\n",
"#ax.set_xlim(-0.2,26)\n",
"#ax.set_ylim(-1.,1.2)\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=5, prop={'size':22})\n",
"ax.set_xlabel('t')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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N1atX586dOyQmJqY8afL69et88cUXFCpUiNOnT9O+fXv+85//EBwczIoVK1KK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\nHQAAAHAThHcAAADATfwfiABEx8WeL3AAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d98c4a150>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange , 2*np.gradient( instance.XP_average , instance.dt) ,\n",
" '-' ,label = '$\\\\frac{d}{dt} \\\\langle xp+px \\\\rangle$' , color = 'r' , linewidth=1.5 )\n",
"\n",
"ax.plot( instance.timeRange , \\\n",
" 2*instance.P2_average/instance.mass \\\n",
" -2*instance.XdPotentialdX_average \\\n",
" -4*instance.gammaDamping*instance.XP_average\n",
" , '--' , \n",
" label = '$\\\\frac{2}{m} \\\\langle p^2 \\\\rangle - 2 \\\\langle x \\\\frac{d}{dx}V \\\\rangle - 2 \\\\gamma \\\\langle xp + px \\\\rangle $'\n",
" ,linewidth=1.5)\n",
"\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=5, prop={'size':22})\n",
"#ax.set_ylim(- 12 , 7)\n",
"ax.set_xlabel('t')\n",
"ax.set_ylabel(' ')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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6+eLFi3nmmWfSzE/vOMXFxTFo0CA8PDyIj49n5MiRHD9+nPfff59y5cqRmJjI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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da095ded0>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange ,\n",
"np.sqrt(instance.X2_average - instance.X_average**2)*np.sqrt(instance.P2_average - instance.P_average**2)\n",
" , '-' , label = '$\\\\Delta x \\\\Delta p$' , linewidth=1.)\n",
"\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=5, prop={'size':22})\n",
"#ax.set_ylim(-50, 0)\n",
"ax.set_xlabel('t')\n",
"#ax.set_xlim(0,41000)\n",
"ax.set_ylabel(' ')\n",
"ax.grid();\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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j5k+t6y0i3qLIdpSOHTuycuVKc/H/nnLJfUt4eDgrVqzIu/ahhx7KK8I7dOiQ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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d9893e7d0>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange , instance.Hamiltonian_average \n",
" , '-' , label = '$Energy$' , linewidth=1.)\n",
"\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=5, prop={'size':22})\n",
"#ax.set_ylim(3.48 , 3.52)\n",
"ax.set_xlabel('t')\n",
"ax.set_ylabel(' ')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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XtHOn9Pbb5ihaVHroIalHD6lMGacrA4BrQwY8FQM4ALhTSoq0erXJ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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d98948410>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange , \\\n",
" np.sqrt(instance.P2_average - instance.P_average**2) \\\n",
" , '-' , label = '$p^2 $',linewidth=2.)\n",
"\n",
"#ax.plot( instance.timeRange , instance.X3_average - 2*gamma*instance.P_average , '-' ,\n",
"# label = '$-F-2\\gamma <P>$' ,linewidth=2.)\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size':22})\n",
"#ax.set_ylim(- 12 , 7)\n",
"ax.set_xlabel('t')\n",
"ax.set_ylabel(' ')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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LiIiIiPgMDWUUn5WXB0uWwIwZZsl9gEsvhYcfhvvvhxo1PBufiIiI+B/1O8VV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\nABYjmAEAAACAxf4fbPl0WM0MiDUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d98bda9d0>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax.plot( instance.timeRange , \\\n",
" np.sqrt(instance.X2_average - instance.X_average**2) \\\n",
" , '-' , label = '$x^2 $',linewidth=2.)\n",
"\n",
"#ax.plot( instance.timeRange , instance.X3_average - 2*gamma*instance.P_average , '-' ,\n",
"# label = '$-F-2\\gamma <P>$' ,linewidth=2.)\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size':22})\n",
"#ax.set_ylim(- 12 , 7)\n",
"ax.set_xlabel('t')\n",
"ax.set_ylabel(' ')\n",
"ax.grid();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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8AGPHwvffh7EjjgiHEF96aZjVkiTp1/a42BoyZAjDhw+nZ8+ejBs3bo9+plat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\nwOreHQ46KHHxSZIk7S/vOxVLzmxpJ5s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Ux48fa+rUqS+s5+fna3BwUB999JGBVBirLNsh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\nLQAAAAAIAcoWAAAAAITAPwAtYaR7daH7NgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d9893ee10>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Classical trajectory"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def dVdx(x):\n",
" return instance.dPotential(0,x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xp_init = np.array([ instance.x_init, instance.p_init ])\n",
"\n",
"trajectory = instance.SymplecticPropagator(\n",
" instance.dt, instance.timeSteps, xp_init, instance.gammaDamping ).T"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_min = -instance.X_amplitude\n",
"x_max = instance.X_amplitude - instance.dX \n",
"\n",
"p_min = -instance.dP*instance.P_gridDIM/2.\n",
"p_max = instance.dP*instance.P_gridDIM/2. -instance.dP \n",
"\n",
"print ' Quantum Ehrenfest trajectory vs classical trajectory'\n",
"print ' final time = ', instance.dt*instance.timeSteps\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"ax.plot( instance.X_average , instance.P_average , '-' ,color = 'g', \n",
" label = 'quantum (<X>,<P>)', linewidth=1.5 )\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('P')\n",
"ax.set_aspect(1.5)\n",
"\n",
"ax.set_xlim(-4.,4.)\n",
"ax.set_ylim(-4.,4.)\n",
"\n",
"ax.plot( trajectory[0] , trajectory[1] , '--' , color='r', \n",
" label = 'classical (X,P)', linewidth=1. )\n",
"\n",
"ax.contour(instance.Hamiltonian ,\n",
" np.arange(-45, 100, 2 ),origin='lower',extent=[x_min,x_max,p_min,p_max],\n",
" linewidths=0.25,colors='k')\n",
"\n",
"ax.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
"#ax.grid();\n",
"ax.set_aspect(1.)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Quantum Ehrenfest trajectory vs classical trajectory\n",
" final time = 5.0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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TJui+++7TypUrL+rZz4cOHdKCBQs0f/58/f7775KkwMBA9e3bV8OHD7+oumbPnq25c+fq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\nligGAAAgWaIYAACAZP0/2FzCZj/Gez0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5da0ade110>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"def NumpyPropertyOnly (obj):\n",
" \"\"\"\n",
" Class that will contain only picklable properties\n",
" \"\"\"\n",
" obj_ = dict()\n",
" for prop_name in dir(obj) :\n",
" prop = getattr(obj, prop_name)\n",
" if isinstance(prop, np.ndarray) : obj_[prop_name] = prop\n",
" return obj_"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#pickle.dump( NumpyPropertyOnly(instance) , open( \"X2.pickle\", \"wb\" ) )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Loading saved File"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W = instance.WignerFunctionFromFile(1*instance.skipFrames)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"instance.PlotWignerFrame( W.real , \n",
" plotRange=((-6.,6) ,(-6,6)),\n",
" global_color=(-0.2, 0.2),\n",
" energy_Levels=(0, 20, 1), aspectRatio=1.);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"min = -5.44084584972e-09 max = 0.350870096\n",
"normalization = 1.0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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KT9JjRgoAeShi/1LENtUBAjIAAICvCMhOCMj1IqlqXMmUbVEV5LTz/qY9CU+K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\n879vf1WgnjTaNzj0NwAyRJfjxquAvH79ek2dOlV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06vXXX9cLL7xgWzY7p1h0dXVp27Zt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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5d98b5e890>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
}
],
"metadata": {}
}
]
} | mit |
luizhsda10/Data-Science-Projectcs | Data Capstone Projects/Finance Project.ipynb | 1 | 2901577 | null | mit |
Jackporter415/phys202-2015-work | assignments/assignment10/ODEsEx03.ipynb | 1 | 13686 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Ordinary Differential Equations Exercise 3"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from scipy.integrate import odeint\n",
"from IPython.html.widgets import interact, fixed"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Damped, driven nonlinear pendulum"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The equations of motion for a simple [pendulum](http://en.wikipedia.org/wiki/Pendulum) of mass $m$, length $l$ are:\n",
"\n",
"$$\n",
"\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta\n",
"$$\n",
"\n",
"When a damping and periodic driving force are added the resulting system has much richer and interesting dynamics:\n",
"\n",
"$$\n",
"\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta - a \\omega - b \\sin(\\omega_0 t)\n",
"$$\n",
"\n",
"In this equation:\n",
"\n",
"* $a$ governs the strength of the damping.\n",
"* $b$ governs the strength of the driving force.\n",
"* $\\omega_0$ is the angular frequency of the driving force.\n",
"\n",
"When $a=0$ and $b=0$, the energy/mass is conserved:\n",
"\n",
"$$E/m =g\\ell(1-\\cos(\\theta)) + \\frac{1}{2}\\ell^2\\omega^2$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"### Basic setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Here are the basic parameters we are going to use for this exercise:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [],
"source": [
"g = 9.81 # m/s^2\n",
"l = 0.5 # length of pendulum, in meters\n",
"tmax = 50. # seconds\n",
"t = np.linspace(0, tmax, int(100*tmax))"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function `derivs` for usage with `scipy.integrate.odeint` that computes the derivatives for the damped, driven harmonic oscillator. The solution vector at each time will be $\\vec{y}(t) = (\\theta(t),\\omega(t))$."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "c7256bdd25791dfa8322d3b828cec74d",
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"-1.0000000000000024"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def derivs(y, t, a, b, omega0):\n",
" \"\"\"Compute the derivatives of the damped, driven pendulum.\n",
" \n",
" Parameters\n",
" ----------\n",
" y : ndarray\n",
" The solution vector at the current time t[i]: [theta[i],omega[i]].\n",
" t : float\n",
" The current time t[i].\n",
" a, b, omega0: float\n",
" The parameters in the differential equation.\n",
" \n",
" Returns\n",
" -------\n",
" dy : ndarray\n",
" The vector of derviatives at t[i]: [dtheta[i],domega[i]].\n",
" \"\"\"\n",
"\n",
" theta = y[0]\n",
" omega = y[1]\n",
" answer = []\n",
" for i in range(len(y)-1):\n",
" dy = -g/l*np.sin(theta)-a*omega-b*np.sin(omega0*t)\n",
" return dy\n",
" \n",
" \n",
"derivs(np.array([np.pi,1.0]), 0, 1.0, 1.0, 1.0)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "3509b75989fc0ec30fa07c7a9331e14e",
"grade": true,
"grade_id": "odesex03a",
"points": 2
}
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-43-60c303fcce69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mderivs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.0\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;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"assert np.allclose(derivs(np.array([np.pi,1.0]), 0, 1.0, 1.0, 1.0), [1.,-1.])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"nbgrader": {
"checksum": "eb552816913899d79298c64989e872d4",
"solution": true
}
},
"outputs": [],
"source": [
"def energy(y):\n",
" \"\"\"Compute the energy for the state array y.\n",
" \n",
" The state array y can have two forms:\n",
" \n",
" 1. It could be an ndim=1 array of np.array([theta,omega]) at a single time.\n",
" 2. It could be an ndim=2 array where each row is the [theta,omega] at single\n",
" time.\n",
" \n",
" Parameters\n",
" ----------\n",
" y : ndarray, list, tuple\n",
" A solution vector\n",
" \n",
" Returns\n",
" -------\n",
" E/m : float (ndim=1) or ndarray (ndim=2)\n",
" The energy per mass.\n",
" \"\"\"\n",
" # YOUR CODE HERE\n",
" raise NotImplementedError()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "3eda6ae22611b37df76850d7cdc960d0",
"grade": true,
"grade_id": "odesex03b",
"points": 2
}
},
"outputs": [],
"source": [
"assert np.allclose(energy(np.array([np.pi,0])),g)\n",
"assert np.allclose(energy(np.ones((10,2))), np.ones(10)*energy(np.array([1,1])))"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"### Simple pendulum"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use the above functions to integrate the simple pendulum for the case where it starts at rest pointing vertically upwards. In this case, it should remain at rest with constant energy.\n",
"\n",
"* Integrate the equations of motion.\n",
"* Plot $E/m$ versus time.\n",
"* Plot $\\theta(t)$ and $\\omega(t)$ versus time.\n",
"* Tune the `atol` and `rtol` arguments of `odeint` until $E/m$, $\\theta(t)$ and $\\omega(t)$ are constant.\n",
"\n",
"Anytime you have a differential equation with a a conserved quantity, it is critical to make sure the numerical solutions conserve that quantity as well. This also gives you an opportunity to find other bugs in your code. The default error tolerances (`atol` and `rtol`) used by `odeint` are not sufficiently small for this problem. Start by trying `atol=1e-3`, `rtol=1e-2` and then decrease each by an order of magnitude until your solutions are stable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "afb5bca3311c3e9c7ac5070b15f2435c",
"grade": true,
"grade_id": "odesex03c",
"points": 3
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the two plots and their tuning of atol, rtol."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Damped pendulum"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a `plot_pendulum` function that integrates the damped, driven pendulum differential equation for a particular set of parameters $[a,b,\\omega_0]$.\n",
"\n",
"* Use the initial conditions $\\theta(0)=-\\pi + 0.1$ and $\\omega=0$.\n",
"* Decrease your `atol` and `rtol` even futher and make sure your solutions have converged.\n",
"* Make a parametric plot of $[\\theta(t),\\omega(t)]$ versus time.\n",
"* Use the plot limits $\\theta \\in [-2 \\pi,2 \\pi]$ and $\\theta \\in [-10,10]$\n",
"* Label your axes and customize your plot to make it beautiful and effective."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"nbgrader": {
"checksum": "82dc6206b4de351b8afc48dba9d0b915",
"solution": true
}
},
"outputs": [],
"source": [
"def plot_pendulum(a=0.0, b=0.0, omega0=0.0):\n",
" \"\"\"Integrate the damped, driven pendulum and make a phase plot of the solution.\"\"\"\n",
" # YOUR CODE HERE\n",
" raise NotImplementedError()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Here is an example of the output of your `plot_pendulum` function that should show a decaying spiral."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [],
"source": [
"plot_pendulum(0.5, 0.0, 0.0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use `interact` to explore the `plot_pendulum` function with:\n",
"\n",
"* `a`: a float slider over the interval $[0.0,1.0]$ with steps of $0.1$.\n",
"* `b`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$.\n",
"* `omega0`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use your interactive plot to explore the behavior of the damped, driven pendulum by varying the values of $a$, $b$ and $\\omega_0$.\n",
"\n",
"* First start by increasing $a$ with $b=0$ and $\\omega_0=0$.\n",
"* Then fix $a$ at a non-zero value and start to increase $b$ and $\\omega_0$.\n",
"\n",
"Describe the different *classes* of behaviors you observe below."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "40364759d02737525e2503b814608893",
"grade": true,
"grade_id": "odesex03d",
"points": 3,
"solution": true
}
},
"source": [
"YOUR ANSWER HERE"
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mirthbottle/ghg | stock data.ipynb | 1 | 36509 | {
"metadata": {
"name": "",
"signature": "sha256:017e2491887e8b6ad5176e3e9efceef077b4536be671aa36bcb8f9bf3c558835"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scopes12 = pd.read_pickle(\"../CDPdata/2010to2014scopes12.pkl\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"companies = scopes12.index.levels[0].tolist()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(companies)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"361"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = pd.read_pickle(\"../CDPdata/sheet35_2014.pkl\")\n",
"pcols = p.columns.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"isin = p[[\"Organisation\",\"ISIN\", \"Ticker\"]]\n",
"isin=isin[isin[\"ISIN\"].notnull()].drop_duplicates(\"Organisation\")\n",
"isin= isin.set_index(\"Organisation\")\n",
"isin.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>ISIN</th>\n",
" <th>Ticker</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Organisation</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Volkswagen AG</th>\n",
" <td> DE0007664039</td>\n",
" <td> VOW3 GR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vontobel Holding AG</th>\n",
" <td> CH0012335540</td>\n",
" <td> VONN SW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vopak</th>\n",
" <td> NL0009432491</td>\n",
" <td> VPK NA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VP Bank Gruppe</th>\n",
" <td> LI0010737216</td>\n",
" <td> VPB SW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>W.W. Grainger, Inc.</th>\n",
" <td> US3848021040</td>\n",
" <td> GWW US</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
" ISIN Ticker\n",
"Organisation \n",
"Volkswagen AG DE0007664039 VOW3 GR\n",
"Vontobel Holding AG CH0012335540 VONN SW\n",
"Vopak NL0009432491 VPK NA\n",
"VP Bank Gruppe LI0010737216 VPB SW\n",
"W.W. Grainger, Inc. US3848021040 GWW US"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"isin.to_pickle(\"../CDPdata/2014orginfos.pkl\")\n",
"# isin = pd.read_pickle(\"../CDPdata/2014isin.pkl\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"isinsubset = isin.loc[companies]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# isinsubset.to_pickle(\"../CDPdata/isin_subset.pkl\")\n",
"len(isinsubset.index.tolist())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"361"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tsubset = pd.read_pickle(\"../CDPdata/tickers_subset.pkl\")\n",
"tsubset.to_excel(\"../CDPdata/tickers_subset.xlsx\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"isinsubset.to_excel(\"../CDPdata/isin_subset.xlsx\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"revenues = pd.read_csv(\"../CDPdata/subset_revenues.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# fyr 3, fyear 2013 means the year ending is March, 2014\n",
"revenues = revenues.rename(columns={\"fyear\":\"year\", \"isin\":\"ISIN\"})\n",
"revenues = revenues.set_index([\"ISIN\", \"year\"])\n",
"revenues.head()\n",
"len(revenues.index.levels[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"189"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scopes12 = pd.read_pickle(\"../CDPdata/2010to2014scopes12.pkl\")\n",
"scopes12.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Scope 1</th>\n",
" <th>Scope 2</th>\n",
" <th>has Scope 1and2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Organisation</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">ACC</th>\n",
" <th>2010</th>\n",
" <td> 11888436.00</td>\n",
" <td> 493000</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> 13717736.46</td>\n",
" <td> 473744</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> 15809662.00</td>\n",
" <td> 625162</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> 15383520.00</td>\n",
" <td> 565856</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> 15146444.00</td>\n",
" <td> 511476</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
" Scope 1 Scope 2 has Scope 1and2\n",
"Organisation year \n",
"ACC 2010 11888436.00 493000 1\n",
" 2011 13717736.46 473744 1\n",
" 2012 15809662.00 625162 1\n",
" 2013 15383520.00 565856 1\n",
" 2014 15146444.00 511476 1"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scopes12.loc[\"Gold Fields Limited\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Scope 1</th>\n",
" <th>Scope 2</th>\n",
" <th>has Scope 1and2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> 1308764.00</td>\n",
" <td> 5093511.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> 1377194.00</td>\n",
" <td> 5164897.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> 1009661.76</td>\n",
" <td> 4835939.60</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> 1220651.24</td>\n",
" <td> 4607613.11</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> 420296.00</td>\n",
" <td> 814968.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
" Scope 1 Scope 2 has Scope 1and2\n",
"year \n",
"2010 1308764.00 5093511.00 1\n",
"2011 1377194.00 5164897.00 1\n",
"2012 1009661.76 4835939.60 1\n",
"2013 1220651.24 4607613.11 1\n",
"2014 420296.00 814968.00 1"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"isinsubset = isinsubset.reset_index()\n",
"isinsubset = isinsubset.rename(columns={\"index\":\"Organisation\"})\n",
"isinsubset = isinsubset.set_index(\"Organisation\")\n",
"isinsubset.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>ISIN</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Organisation</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ACC</th>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACEA SpA</th>\n",
" <td> IT0001207098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AECI Ltd Ord</th>\n",
" <td> ZAE000000220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AFLAC Incorporated</th>\n",
" <td> US0010551028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AMG Advanced Metallurgical Group NV</th>\n",
" <td> NL0000888691</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" ISIN\n",
"Organisation \n",
"ACC INE012A01025\n",
"ACEA SpA IT0001207098\n",
"AECI Ltd Ord ZAE000000220\n",
"AFLAC Incorporated US0010551028\n",
"AMG Advanced Metallurgical Group NV NL0000888691"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghgrevenues = scopes12.join(isinsubset, how=\"outer\")\n",
"ghgrevenues.reset_index(inplace=True)\n",
"ghgrevenues.set_index([\"ISIN\", \"year\"], inplace=True)\n",
"ghgrevenues.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Organisation</th>\n",
" <th>Scope 1</th>\n",
" <th>Scope 2</th>\n",
" <th>has Scope 1and2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ISIN</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">INE012A01025</th>\n",
" <th>2010</th>\n",
" <td> ACC</td>\n",
" <td> 11888436.00</td>\n",
" <td> 493000</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> ACC</td>\n",
" <td> 13717736.46</td>\n",
" <td> 473744</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> ACC</td>\n",
" <td> 15809662.00</td>\n",
" <td> 625162</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> ACC</td>\n",
" <td> 15383520.00</td>\n",
" <td> 565856</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> ACC</td>\n",
" <td> 15146444.00</td>\n",
" <td> 511476</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" Organisation Scope 1 Scope 2 has Scope 1and2\n",
"ISIN year \n",
"INE012A01025 2010 ACC 11888436.00 493000 1\n",
" 2011 ACC 13717736.46 473744 1\n",
" 2012 ACC 15809662.00 625162 1\n",
" 2013 ACC 15383520.00 565856 1\n",
" 2014 ACC 15146444.00 511476 1"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghgrevenues.loc[\"ZAE000018123\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Organisation</th>\n",
" <th>Scope 1</th>\n",
" <th>Scope 2</th>\n",
" <th>has Scope 1and2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> Gold Fields Limited</td>\n",
" <td> 1308764.00</td>\n",
" <td> 5093511.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> Gold Fields Limited</td>\n",
" <td> 1377194.00</td>\n",
" <td> 5164897.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> Gold Fields Limited</td>\n",
" <td> 1009661.76</td>\n",
" <td> 4835939.60</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> Gold Fields Limited</td>\n",
" <td> 1220651.24</td>\n",
" <td> 4607613.11</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> Gold Fields Limited</td>\n",
" <td> 420296.00</td>\n",
" <td> 814968.00</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
" Organisation Scope 1 Scope 2 has Scope 1and2\n",
"year \n",
"2010 Gold Fields Limited 1308764.00 5093511.00 1\n",
"2011 Gold Fields Limited 1377194.00 5164897.00 1\n",
"2012 Gold Fields Limited 1009661.76 4835939.60 1\n",
"2013 Gold Fields Limited 1220651.24 4607613.11 1\n",
"2014 Gold Fields Limited 420296.00 814968.00 1"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# len(revenues.index.levels[0])\n",
"# ghgrevs = revenues.join(ghgrevenues)\n",
"def add_total_and_intensity(p):\n",
" p[\"1and2 total\"] = p[\"Scope 1\"] + p[\"Scope 2\"]\n",
" p[\"1and2 intensity\"] = p['1and2 total']/p[\"revt\"]\n",
" return p"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghgrevs.to_pickle(\"../CDPdata/ghgrevenues.pkl\")\n",
"ghgrevs[[\"1and2 total\", \"1and2 intensity\", \"cogs\", \"revt\", \"Organisation\"]].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>1and2 total</th>\n",
" <th>1and2 intensity</th>\n",
" <th>cogs</th>\n",
" <th>revt</th>\n",
" <th>Organisation</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ISIN</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">AT0000746409</th>\n",
" <th>2010</th>\n",
" <td> 2688664</td>\n",
" <td> 840.743211</td>\n",
" <td> 1729.712</td>\n",
" <td> 3197.961</td>\n",
" <td> VERBUND AG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> 3687587</td>\n",
" <td> 975.965223</td>\n",
" <td> 2344.788</td>\n",
" <td> 3778.400</td>\n",
" <td> VERBUND AG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> 4503481</td>\n",
" <td> 1444.617059</td>\n",
" <td> 1430.076</td>\n",
" <td> 3117.422</td>\n",
" <td> VERBUND AG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> 4140240</td>\n",
" <td> 1371.871635</td>\n",
" <td> 1448.004</td>\n",
" <td> 3017.950</td>\n",
" <td> VERBUND AG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AU000000WOW2</th>\n",
" <th>2010</th>\n",
" <td> 2968261</td>\n",
" <td> 57.419503</td>\n",
" <td> 37593.500</td>\n",
" <td> 51694.300</td>\n",
" <td> Woolworths Limited</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 100,
"text": [
" 1and2 total 1and2 intensity cogs revt \\\n",
"ISIN year \n",
"AT0000746409 2010 2688664 840.743211 1729.712 3197.961 \n",
" 2011 3687587 975.965223 2344.788 3778.400 \n",
" 2012 4503481 1444.617059 1430.076 3117.422 \n",
" 2013 4140240 1371.871635 1448.004 3017.950 \n",
"AU000000WOW2 2010 2968261 57.419503 37593.500 51694.300 \n",
"\n",
" Organisation \n",
"ISIN year \n",
"AT0000746409 2010 VERBUND AG \n",
" 2011 VERBUND AG \n",
" 2012 VERBUND AG \n",
" 2013 VERBUND AG \n",
"AU000000WOW2 2010 Woolworths Limited "
]
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rs = revenues.drop_duplicates([\"sedol\", \"datadate\"])\n",
"rs.loc[\"ZAE000018123\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>gvkey</th>\n",
" <th>indfmt</th>\n",
" <th>datafmt</th>\n",
" <th>consol</th>\n",
" <th>popsrc</th>\n",
" <th>fyr</th>\n",
" <th>datadate</th>\n",
" <th>cogs</th>\n",
" <th>revt</th>\n",
" <th>teq</th>\n",
" <th>exchg</th>\n",
" <th>sedol</th>\n",
" <th>conm</th>\n",
" <th>costat</th>\n",
" <th>fic</th>\n",
" <th>incorp</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> 17947</td>\n",
" <td> INDL</td>\n",
" <td> HIST_STD</td>\n",
" <td> C</td>\n",
" <td> I</td>\n",
" <td> 6</td>\n",
" <td> 20100630</td>\n",
" <td> 12295.1</td>\n",
" <td> 31565.3</td>\n",
" <td> 45448.9</td>\n",
" <td> 177</td>\n",
" <td> 6280215</td>\n",
" <td> GOLD FIELDS LTD</td>\n",
" <td> A</td>\n",
" <td> ZAF</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> 17947</td>\n",
" <td> INDL</td>\n",
" <td> HIST_STD</td>\n",
" <td> C</td>\n",
" <td> I</td>\n",
" <td> 12</td>\n",
" <td> 20101231</td>\n",
" <td> 10869.9</td>\n",
" <td> 18308.1</td>\n",
" <td> 46622.5</td>\n",
" <td> 177</td>\n",
" <td> 6280215</td>\n",
" <td> GOLD FIELDS LTD</td>\n",
" <td> A</td>\n",
" <td> ZAF</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> 17947</td>\n",
" <td> INDL</td>\n",
" <td> HIST_STD</td>\n",
" <td> C</td>\n",
" <td> I</td>\n",
" <td> 12</td>\n",
" <td> 20111231</td>\n",
" <td> 22804.0</td>\n",
" <td> 41876.8</td>\n",
" <td> 48061.5</td>\n",
" <td> 177</td>\n",
" <td> 6280215</td>\n",
" <td> GOLD FIELDS LTD</td>\n",
" <td> A</td>\n",
" <td> ZAF</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> 17947</td>\n",
" <td> INDL</td>\n",
" <td> HIST_STD</td>\n",
" <td> C</td>\n",
" <td> I</td>\n",
" <td> 12</td>\n",
" <td> 20121231</td>\n",
" <td> 15989.9</td>\n",
" <td> 28915.8</td>\n",
" <td> 53157.4</td>\n",
" <td> 177</td>\n",
" <td> 6280215</td>\n",
" <td> GOLD FIELDS LTD</td>\n",
" <td> A</td>\n",
" <td> ZAF</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> 17947</td>\n",
" <td> INDL</td>\n",
" <td> HIST_STD</td>\n",
" <td> C</td>\n",
" <td> I</td>\n",
" <td> 12</td>\n",
" <td> 20131231</td>\n",
" <td> 17961.4</td>\n",
" <td> 27900.6</td>\n",
" <td> 41827.5</td>\n",
" <td> 177</td>\n",
" <td> 6280215</td>\n",
" <td> GOLD FIELDS LTD</td>\n",
" <td> A</td>\n",
" <td> ZAF</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
" gvkey indfmt datafmt consol popsrc fyr datadate cogs revt \\\n",
"year \n",
"2010 17947 INDL HIST_STD C I 6 20100630 12295.1 31565.3 \n",
"2010 17947 INDL HIST_STD C I 12 20101231 10869.9 18308.1 \n",
"2011 17947 INDL HIST_STD C I 12 20111231 22804.0 41876.8 \n",
"2012 17947 INDL HIST_STD C I 12 20121231 15989.9 28915.8 \n",
"2013 17947 INDL HIST_STD C I 12 20131231 17961.4 27900.6 \n",
"\n",
" teq exchg sedol conm costat fic incorp \n",
"year \n",
"2010 45448.9 177 6280215 GOLD FIELDS LTD A ZAF NaN \n",
"2010 46622.5 177 6280215 GOLD FIELDS LTD A ZAF NaN \n",
"2011 48061.5 177 6280215 GOLD FIELDS LTD A ZAF NaN \n",
"2012 53157.4 177 6280215 GOLD FIELDS LTD A ZAF NaN \n",
"2013 41827.5 177 6280215 GOLD FIELDS LTD A ZAF NaN "
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xls = pd.ExcelFile('../CDPdata/northamerica compustat.xlsx')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = xls.parse(0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tickerrevs = p.rename(columns={\"Current ISO Country Code - Incorporation\": \"Country\",\"Data Year - Fiscal\": \"year\",\n",
" \"Revenue - Total\": \"revt\", \"Ticker Symbol\": \"Ticker\",\n",
" \"Cost of Goods Sold\": \"cogs\"})\n",
"tickerrevs = tickerrevs.reset_index().set_index([\"Ticker\", \"year\"])\n",
"tickerrevs.drop(\"index\", 1, inplace=True)\n",
"ghgrevs_nam = tickerrevs.join(ghgs)\n",
"ghgrevs_nam = ghgrevs_nam.reset_index().set_index([\"ISIN\", \"year\"])\n",
"ghgrevs_nam = add_total_and_intensity(ghgrevs_nam)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# scopes12 = scopes12.reset_index().set_index([\"Organisation\"])\n",
"# ghgs = scopes12.join(orginfos)\n",
"#ghgs.reset_index(inplace=True)\n",
"#ghgs[\"Ticker\"] = ghgs[\"Ticker\"].apply(lambda x: str(x).split(\" \")[0])\n",
"# ghgs = ghgs.reset_index().set_index([\"Ticker\",\"year\"])\n",
"ghgs.head()\n",
"# scopes12.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>Organisation</th>\n",
" <th>Scope 1</th>\n",
" <th>Scope 2</th>\n",
" <th>has Scope 1and2</th>\n",
" <th>ISIN</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Ticker</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">ACC</th>\n",
" <th>2010</th>\n",
" <td> ACC</td>\n",
" <td> 11888436.00</td>\n",
" <td> 493000</td>\n",
" <td> 1</td>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> ACC</td>\n",
" <td> 13717736.46</td>\n",
" <td> 473744</td>\n",
" <td> 1</td>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> ACC</td>\n",
" <td> 15809662.00</td>\n",
" <td> 625162</td>\n",
" <td> 1</td>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> ACC</td>\n",
" <td> 15383520.00</td>\n",
" <td> 565856</td>\n",
" <td> 1</td>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> ACC</td>\n",
" <td> 15146444.00</td>\n",
" <td> 511476</td>\n",
" <td> 1</td>\n",
" <td> INE012A01025</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 85,
"text": [
" Organisation Scope 1 Scope 2 has Scope 1and2 ISIN\n",
"Ticker year \n",
"ACC 2010 ACC 11888436.00 493000 1 INE012A01025\n",
" 2011 ACC 13717736.46 473744 1 INE012A01025\n",
" 2012 ACC 15809662.00 625162 1 INE012A01025\n",
" 2013 ACC 15383520.00 565856 1 INE012A01025\n",
" 2014 ACC 15146444.00 511476 1 INE012A01025"
]
}
],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghgrevs_gl = pd.read_pickle(\"../CDPdata/ghgrevenues.pkl\")\n",
"ghgrevs_gl.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 93,
"text": [
"Index([u'gvkey', u'indfmt', u'datafmt', u'consol', u'popsrc', u'fyr', u'datadate', u'cogs', u'revt', u'teq', u'exchg', u'sedol', u'conm', u'costat', u'fic', u'incorp', u'Organisation', u'Scope 1', u'Scope 2', u'has Scope 1and2', u'1and2 total', u'1and2 intensity'], dtype='object')"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"keepcols = [\"cogs\", \"revt\", \"Organisation\", \"Scope 1\", \"Scope 2\", \"has Scope 1and2\", \"1and2 total\", \"1and2 intensity\"]\n",
"ghgrevs = pd.concat([ghgrevs_gl[keepcols], ghgrevs_nam[keepcols]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(ghgrevs.index.levels[0])\n",
"# ghgrevs.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 102,
"text": [
"320"
]
}
],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
queirozfcom/python-sandbox | python3/notebooks/housing-conditions-in-copenhagen/logistic-regression.ipynb | 1 | 19695 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from keras.layers import Input, Dense\n",
"from keras.models import Model, Sequential\n",
"\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv(\"../housing-conditions-in-copenhagen/data.csv\",sep=\"\\s+\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"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>housing</th>\n",
" <th>influence</th>\n",
" <th>contact_with_neighbours</th>\n",
" <th>satisfaction</th>\n",
" <th>n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" housing influence contact_with_neighbours satisfaction n\n",
"1 1 1 1 1 21\n",
"2 1 1 1 2 21\n",
"3 1 1 1 3 28\n",
"4 1 1 2 1 14\n",
"5 1 1 2 2 19"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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>housing</th>\n",
" <th>influence</th>\n",
" <th>contact_with_neighbours</th>\n",
" <th>satisfaction</th>\n",
" <th>n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>72.00000</td>\n",
" <td>72.000000</td>\n",
" <td>72.000000</td>\n",
" <td>72.000000</td>\n",
" <td>72.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.50000</td>\n",
" <td>2.000000</td>\n",
" <td>1.500000</td>\n",
" <td>2.000000</td>\n",
" <td>23.347222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.12588</td>\n",
" <td>0.822226</td>\n",
" <td>0.503509</td>\n",
" <td>0.822226</td>\n",
" <td>17.666041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.75000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.50000</td>\n",
" <td>2.000000</td>\n",
" <td>1.500000</td>\n",
" <td>2.000000</td>\n",
" <td>19.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.25000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>31.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.00000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>86.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" housing influence contact_with_neighbours satisfaction n\n",
"count 72.00000 72.000000 72.000000 72.000000 72.000000\n",
"mean 2.50000 2.000000 1.500000 2.000000 23.347222\n",
"std 1.12588 0.822226 0.503509 0.822226 17.666041\n",
"min 1.00000 1.000000 1.000000 1.000000 3.000000\n",
"25% 1.75000 1.000000 1.000000 1.000000 10.000000\n",
"50% 2.50000 2.000000 1.500000 2.000000 19.500000\n",
"75% 3.25000 3.000000 2.000000 3.000000 31.750000\n",
"max 4.00000 3.000000 2.000000 3.000000 86.000000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_new = pd.concat([df,pd.get_dummies(df[\"housing\"],prefix=\"housing\")],axis=1).drop([\"housing\"],axis=1)\n",
"df_new = pd.concat([df_new,pd.get_dummies(df[\"influence\"],prefix=\"influence\")],axis=1).drop([\"influence\"],axis=1)\n",
"df_new = pd.concat([df_new,pd.get_dummies(df[\"satisfaction\"],prefix=\"satisfaction\")],axis=1).drop([\"satisfaction\"],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_new[\"contact_with_neighbours\"] = df_new[\"contact_with_neighbours\"].map(lambda v: 0 if v == 1 else 1)\n",
"\n",
"df_new = df_new.drop([\"n\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"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>contact_with_neighbours</th>\n",
" <th>n</th>\n",
" <th>housing_1</th>\n",
" <th>housing_2</th>\n",
" <th>housing_3</th>\n",
" <th>housing_4</th>\n",
" <th>influence_1</th>\n",
" <th>influence_2</th>\n",
" <th>influence_3</th>\n",
" <th>satisfaction_1</th>\n",
" <th>satisfaction_2</th>\n",
" <th>satisfaction_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>1</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" contact_with_neighbours n housing_1 housing_2 housing_3 housing_4 \\\n",
"1 0 21 1 0 0 0 \n",
"2 0 21 1 0 0 0 \n",
"3 0 28 1 0 0 0 \n",
"4 1 14 1 0 0 0 \n",
"5 1 19 1 0 0 0 \n",
"\n",
" influence_1 influence_2 influence_3 satisfaction_1 satisfaction_2 \\\n",
"1 1 0 0 1 0 \n",
"2 1 0 0 0 1 \n",
"3 1 0 0 0 0 \n",
"4 1 0 0 1 0 \n",
"5 1 0 0 0 1 \n",
"\n",
" satisfaction_3 \n",
"1 0 \n",
"2 0 \n",
"3 1 \n",
"4 0 \n",
"5 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = df_new[\"contact_with_neighbours\"].values.reshape(-1,1)\n",
"X = df_new.drop([\"contact_with_neighbours\"], axis=1).values"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"# sc = StandardScaler()\n",
"# X = sc.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((72, 1), (72, 11))"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.shape, X.shape"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indices = np.arange(72)\n",
"np.random.shuffle(indices)\n",
"num_validation_samples = int(0.2*72)\n",
"\n",
"y = y[indices]\n",
"X = X[indices]\n",
"\n",
"X_train = X[:-num_validation_samples]\n",
"y_train = y[:-num_validation_samples]\n",
"X_val = X[-num_validation_samples:]\n",
"y_val = y[-num_validation_samples:]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((58, 11), (58, 1), (14, 11), (14, 1))"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape,y_train.shape,X_val.shape, y_val.shape"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/felipe/tf-venv3/lib/python3.5/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, kernel_initializer=\"glorot_uniform\", activation=\"sigmoid\", input_dim=11)`\n",
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 58 samples, validate on 14 samples\n",
"Epoch 1/30\n",
"58/58 [==============================] - 0s - loss: 3.6872 - val_loss: 4.8987\n",
"Epoch 2/30\n",
"58/58 [==============================] - 0s - loss: 3.1931 - val_loss: 4.3503\n",
"Epoch 3/30\n",
"58/58 [==============================] - 0s - loss: 2.7570 - val_loss: 3.8347\n",
"Epoch 4/30\n",
"58/58 [==============================] - 0s - loss: 2.4010 - val_loss: 3.3106\n",
"Epoch 5/30\n",
"58/58 [==============================] - 0s - loss: 2.0814 - val_loss: 2.8140\n",
"Epoch 6/30\n",
"58/58 [==============================] - 0s - loss: 1.7635 - val_loss: 2.3486\n",
"Epoch 7/30\n",
"58/58 [==============================] - 0s - loss: 1.4866 - val_loss: 1.9665\n",
"Epoch 8/30\n",
"58/58 [==============================] - 0s - loss: 1.2881 - val_loss: 1.6689\n",
"Epoch 9/30\n",
"58/58 [==============================] - 0s - loss: 1.1107 - val_loss: 1.3813\n",
"Epoch 10/30\n",
"58/58 [==============================] - 0s - loss: 0.9732 - val_loss: 1.1473\n",
"Epoch 11/30\n",
"58/58 [==============================] - 0s - loss: 0.8849 - val_loss: 0.9791\n",
"Epoch 12/30\n",
"58/58 [==============================] - 0s - loss: 0.8177 - val_loss: 0.8943\n",
"Epoch 13/30\n",
"58/58 [==============================] - 0s - loss: 0.8037 - val_loss: 0.8291\n",
"Epoch 14/30\n",
"58/58 [==============================] - 0s - loss: 0.7842 - val_loss: 0.8239\n",
"Epoch 15/30\n",
"58/58 [==============================] - 0s - loss: 0.7753 - val_loss: 0.8058\n",
"Epoch 16/30\n",
"58/58 [==============================] - 0s - loss: 0.7701 - val_loss: 0.8029\n",
"Epoch 17/30\n",
"58/58 [==============================] - 0s - loss: 0.7877 - val_loss: 0.7680\n",
"Epoch 18/30\n",
"58/58 [==============================] - 0s - loss: 0.7633 - val_loss: 0.7721\n",
"Epoch 19/30\n",
"58/58 [==============================] - 0s - loss: 0.7587 - val_loss: 0.7935\n",
"Epoch 20/30\n",
"58/58 [==============================] - 0s - loss: 0.7576 - val_loss: 0.8060\n",
"Epoch 21/30\n",
"58/58 [==============================] - 0s - loss: 0.7571 - val_loss: 0.8120\n",
"Epoch 22/30\n",
"58/58 [==============================] - 0s - loss: 0.7453 - val_loss: 0.7862\n",
"Epoch 23/30\n",
"58/58 [==============================] - 0s - loss: 0.7415 - val_loss: 0.7701\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f461326bdd8>"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from keras.regularizers import l1_l2\n",
"reg = l1_l2(l1=0.01, l2=0.01)\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(1,\n",
" init='glorot_uniform',\n",
" activation='sigmoid', \n",
" input_dim=X.shape[1]))\n",
"\n",
"\n",
"rmsprop = keras.optimizers.RMSprop(lr=0.01)# y = sc.fit_transform(y)\n",
"sgd = keras.optimizers.SGD(lr=0.1)\n",
"model.compile(optimizer=rmsprop, loss='binary_crossentropy')\n",
"\n",
"early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')\n",
"\n",
"model.fit(X_train, \n",
" y_train,\n",
" epochs=30, \n",
" validation_data=(X_val, y_val),\n",
" callbacks=[early_stopping])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Global TF Kernel (Python 3)",
"language": "python",
"name": "global-tf-python-3"
},
"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
}
| mit |
ajhenrikson/phys202-2015-work | assignments/assignment13/GitHubRepos.ipynb | 1 | 3859 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Turning In Your GitHub Repos"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The purpose of this assignment is for you to \"turn in\" your Github repos. In addition to being used to turn in your project, this assignment will be assigned a grade that reflects your usage of Git/GitHub."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Homework GitHub repo"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Throughout the quarter you should have been pushing your weekly homework to a public Github repo.\n",
"\n",
"1. Make sure all of your homework is pushed to this repo.\n",
"2. In the following markkdown cell, paste the URL to that repo. This should be something like https://github.com/ellisonbg/phys202-2015-work"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "00acb1f05c2e0b17272076cd16e1d65b",
"grade": true,
"grade_id": "githubreposa",
"points": 5,
"solution": true
}
},
"source": [
"https://github.com/ajhenrikson/phys202-2015-work"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Project GitHub repo"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"To turn in the notebooks for your project, go through the following steps:\n",
"\n",
"1. Create a new public GitHub repo, named `phys202-project`. If you need a refresher on creating a repo, have a look at this [tutorial](https://help.github.com/articles/create-a-repo/).\n",
"2. Clone the repo onto dirac1. When you do this, you should do it in a directory that doesn't already have a directory with the name `phys202-project` and which itself is not another GitHub repo.\n",
"3. Copy your project materials into the new GitHub repo on dirac1.\n",
"4. Commit and push your changes to GitHub\n",
"5. In the following markdown cell, paste the URL to that repo. This should be something like https://github.com/ellisonbg/phys202-project\n",
"\n",
"Before turning this in you should check the following:\n",
"\n",
"* Make sure it will be obvious to me what each notebook contains and which order I should go through them in.\n",
"* If there are cells or notebook that take longer than 30-60 seconds to run, you should put a uppercase bold warning in a markdown cell immediately above the cell: **THIS CELL TAKES XXX MINUTES TO RUN**. I won't run these cells.\n",
"* You should make sure that your notebooks will run without error wth a cleared kernel, from the top to the bottom, *leaving out the long running cells*. This will require you to save the output of long running cells to disk and load them back for analysis and visualization purposes. Yes, I will run your code!"
]
},
{
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"https://github.com/ajhenrikson/phys202-project"
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| mit |
BradHub/SL-SPH | BEM_problem.ipynb | 1 | 267580 | {
"cells": [
{
"cell_type": "code",
"execution_count": 794,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math\n",
"import numpy\n",
"from matplotlib import pyplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BEM method"
]
},
{
"cell_type": "code",
"execution_count": 795,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Q = 2000/3 #strength of the source-sheet,stb/d\n",
"h=25.26 #thickness of local gridblock,ft\n",
"phi=0.2 #porosity \n",
"kx=200 #pemerability in x direction,md\n",
"ky=200 #pemerability in y direction,md\n",
"kr=kx/ky #pemerability ratio\n",
"miu=1 #viscosity,cp\n",
"\n",
"Nw=1 #Number of well\n",
"Qwell_1=2000 #Flow rate of well 1\n",
"Boundary_V=-400 #boundary velocity ft/day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boundary Discretization\n",
"we will create a discretization of the body geometry into panels (line segments in 2D). A panel's attributes are: its starting point, end point and mid-point, its length and its orientation. See the following figure for the nomenclature used in the code and equations below.\n",
"<img src=\"./resources/PanelLocal.png\" width=\"300\">\n",
"<center>Figure 1. Nomenclature of the boundary element in the local coordinates</center>\n",
"### Create panel and well class"
]
},
{
"cell_type": "code",
"execution_count": 796,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Panel:\n",
" \"\"\"Contains information related to a panel.\"\"\"\n",
" def __init__(self, xa, ya, xb, yb):\n",
" \"\"\"Creates a panel.\n",
" \n",
" Arguments\n",
" ---------\n",
" xa, ya -- Cartesian coordinates of the first end-point.\n",
" xb, yb -- Cartesian coordinates of the second end-point.\n",
" \"\"\"\n",
" self.xa, self.ya = xa, ya\n",
" self.xb, self.yb = xb, yb\n",
" \n",
" self.xc, self.yc = (xa+xb)/2, (ya+yb)/2 # control-point (center-point)\n",
" self.length = math.sqrt((xb-xa)**2+(yb-ya)**2) # length of the panel\n",
" \n",
" \n",
" # orientation of the panel (angle between x-axis and panel)\n",
" self.sinalpha=(yb-ya)/self.length\n",
" self.cosalpha=(xb-xa)/self.length\n",
" \n",
" self.Q = 0. # source strength\n",
" self.U = 0. # velocity component\n",
" self.V = 0. # velocity component\n",
" self.P = 0. # pressure coefficient\n",
"\n",
"class Well:\n",
" \"\"\"Contains information related to a panel.\"\"\"\n",
" def __init__(self, xw, yw,rw,Q):\n",
" \"\"\"Creates a panel.\n",
" \n",
" Arguments\n",
" ---------\n",
" xw, yw -- Cartesian coordinates of well source.\n",
" Q -- Flow rate of well source.\n",
" rw -- radius of well source.\n",
" \"\"\"\n",
" self.xw, self.yw = xw, yw\n",
" \n",
" self.Q = Q # source strength\n",
" self.rw = rw # velocity component\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a node distribution on the boundary that is refined near the corner with cosspace function"
]
},
{
"cell_type": "code",
"execution_count": 797,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cosspace(st,ed,N):\n",
" N=N+1\n",
" AngleInc=numpy.pi/(N-1)\n",
" CurAngle = AngleInc\n",
" space=numpy.linspace(0,1,N)\n",
" space[0]=st\n",
" for i in range(N-1):\n",
" space[i+1] = 0.5*numpy.abs(ed-st)*(1 - math.cos(CurAngle));\n",
" CurAngle += AngleInc\n",
" if ed<st:\n",
" space[0]=ed\n",
" space=space[::-1]\n",
" return space"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discretize boundary element along the boundary \n",
"Here we implement BEM in a squre grid"
]
},
{
"cell_type": "code",
"execution_count": 798,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N=80 #Number of boundary element\n",
"Nbd=20 #Number of boundary element in each boundary\n",
"Dx=1. #Grid block length in X direction\n",
"Dy=1. #Gird block lenght in Y direction\n",
"\n",
"#Create the array\n",
"x_ends = numpy.linspace(0, Dx, N) # computes a 1D-array for x\n",
"y_ends = numpy.linspace(0, Dy, N) # computes a 1D-array for y\n",
"interval=cosspace(0,Dx,Nbd)\n",
"rinterval=cosspace(Dx,0,Nbd)\n",
"#interval=numpy.linspace(0,1,Nbd+1)\n",
"#rinterval=numpy.linspace(1,0,Nbd+1)\n",
"\n",
"#Define the rectangle boundary\n",
"\n",
"\n",
"for i in range(Nbd):\n",
" x_ends[i]=0\n",
" y_ends[i]=interval[i]\n",
"\n",
"for i in range(Nbd):\n",
" x_ends[i+Nbd]=interval[i]\n",
" y_ends[i+Nbd]=Dy\n",
" \n",
"for i in range(Nbd):\n",
" x_ends[i+Nbd*2]=Dx\n",
" y_ends[i+Nbd*2]=rinterval[i]\n",
" \n",
"for i in range(Nbd):\n",
" x_ends[i+Nbd*3]=rinterval[i]\n",
" y_ends[i+Nbd*3]=0\n",
" \n",
"x_ends,y_ends=numpy.append(x_ends, x_ends[0]), numpy.append(y_ends, y_ends[0])\n",
"\n",
"#Define the panel\n",
"panels = numpy.empty(N, dtype=object)\n",
"for i in range(N):\n",
" panels[i] = Panel(x_ends[i], y_ends[i], x_ends[i+1], y_ends[i+1])\n",
" \n",
" \n",
"#Define the well\n",
"wells = numpy.empty(Nw, dtype=object)\n",
"\n",
"wells[0]=Well(Dx/2,Dy/2,0.025,Qwell_1)"
]
},
{
"cell_type": "code",
"execution_count": 799,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#for i in range(N):\n",
" # print(\"Panel Coordinate (%s,%s) sina,cosa (%s,%s) \" % (panels[i].xc,panels[i].yc,panels[i].sinalpha,panels[i].cosalpha))\n",
"#print(\"Well Location (%s,%s) radius: %s Flow rate:%s \" % (wells[0].xw,wells[0].yw,wells[0].rw,wells[0].Q))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot boundary elements and wells"
]
},
{
"cell_type": "code",
"execution_count": 800,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x19cb8278>"
]
},
"execution_count": 800,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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fc3uVxhI0OjHF5Gn3ORyZd11++Zns2vVm2jY7d77BFVec2UkR+U9Q8zbjoioi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\ncxjBFRzsjQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1b4c18d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot the panel\n",
"%matplotlib inline\n",
"\n",
"val_x, val_y = 0.3, 0.3\n",
"x_min, x_max = min(panel.xa for panel in panels), max(panel.xa for panel in panels)\n",
"y_min, y_max = min(panel.ya for panel in panels), max(panel.ya for panel in panels)\n",
"x_start, x_end = x_min-val_x*(x_max-x_min), x_max+val_x*(x_max-x_min)\n",
"y_start, y_end = y_min-val_y*(y_max-y_min), y_max+val_y*(y_max-y_min)\n",
"\n",
"size = 5\n",
"pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n",
"pyplot.grid(True)\n",
"pyplot.xlabel('x', fontsize=16)\n",
"pyplot.ylabel('y', fontsize=16)\n",
"pyplot.xlim(x_start, x_end)\n",
"pyplot.ylim(y_start, y_end)\n",
"\n",
"pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
" numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
" linestyle='-', linewidth=1, marker='o', markersize=6, color='#CD2305');\n",
"pyplot.scatter(wells[0].xw,wells[0].yw,s=100,alpha=0.5)\n",
"\n",
"pyplot.legend(['panels', 'Wells'], \n",
" loc=1, prop={'size':12})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boundary element implementation\n",
"<img src=\"./resources/BEMscheme2.png\" width=\"400\">\n",
"<center>Figure 2. Representation of a local gridblock with boundary elements</center>\n",
"\n",
"\n",
"\n",
"Generally, the influence of all the j panels on the i BE node can be expressed as follows:\n",
"\n",
"\\begin{matrix}\n",
"{{c}_{ij}}{{p}_{i}}+{{p}_{i}}\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}}=({{v}_{i}}\\cdot \\mathbf{n})\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}\n",
"\\end{matrix}\n",
"Where,\n",
"\n",
"${{c}_{ij}}$ is the free term, cased by source position.\n",
"\n",
"\n",
"<center>${{c}_{ij}}=\\left\\{ \\begin{matrix}\n",
" \\begin{matrix}\n",
" 1 & \\text{source j on the internal domain} \\\\\n",
"\\end{matrix} \\\\\n",
" \\begin{matrix}\n",
" 0.5 & \\text{source j on the boundary} \\\\\n",
"\\end{matrix} \\\\\n",
" \\begin{matrix}\n",
" 0 & \\text{source j on the external domain} \\\\\n",
"\\end{matrix} \\\\\n",
"\\end{matrix} \\right.$</center>\n",
"\n",
"$\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}\\text{ }}$ is the integrated effect of the boundary element source i on the resulting normal flux at BE node j. \n",
"\n",
"$\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}$ is the is the integrated effect of the boundary element source i on the resulting pressure at BE node j\n",
"\n",
"### Line segment source solution for pressure and velocity (Derived recently)\n",
"\n",
"The integrated effect can be formulated using line segment source solution, which givs:\n",
"\n",
"\\begin{equation}\n",
"\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}=B{{Q}_{w}}=P({{{x}'}_{i}},{{{y}'}_{i}})=-\\frac{70.60\\mu }{h\\sqrt{{{k}_{x}}{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\ln \\left\\{ {{({x}'-t\\cos {{\\alpha }_{j}})}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({y}'-t\\sin {{\\alpha }_{j}})}^{2}} \\right\\}dt}\\cdot {{Q}_{w}}\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
"\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}\\text{ }}={{v}_{i}}(s)\\cdot {{\\mathbf{n}}_{i}}=-{{u}_{i}}\\sin {{\\alpha }_{i}}+{{v}_{i}}\\cos {{\\alpha }_{i}}\n",
"\\end{equation}\n",
"\n",
"Where,\n",
"\n",
"\\begin{equation}\n",
"u\\left( {{{{x}'}}_{i}},{{{{y}'}}_{i}} \\right)={{A}_{u}}{{Q}_{j}}=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\frac{{{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}}}{{{\\left( {{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}})}^{2}}}dt}\\cdot {{Q}_{j}}\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
"v\\left( {{{{x}'}}_{i}},{{{{y}'}}_{i}} \\right)={{A}_{v}}{{Q}_{j}}=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\frac{{{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}}}{{{\\left( {{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}})}^{2}}}dt}\\cdot {{Q}_{j}}\n",
"\\end{equation}\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line segment source Integration function (Bij and Aij)"
]
},
{
"cell_type": "code",
"execution_count": 801,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Panel infuence factor Bij\n",
"def InflueceP(x, y, panel):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
"#Transfer global coordinate point(x,y) to local coordinate\n",
" x=x-panel.xa\n",
" y=y-panel.ya\n",
" L1=panel.length\n",
" \n",
"#Calculate the pressure and velocity influence factor\n",
" a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
" b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
" c=y*panel.cosalpha-x*panel.sinalpha\n",
" dp=70.6*miu/h/math.sqrt(kx*ky)\n",
" Cp = dp/a*(\n",
" (\n",
" b*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)\n",
" -L1*a*math.log((x-L1*panel.cosalpha)**2+kr*(y-L1*panel.sinalpha)**2)\n",
" +2*math.sqrt(kr)*c*math.atan((b-a*L1)/math.sqrt(kr)/c)\n",
" )\n",
" -\n",
" (\n",
" b*math.log(x**2+kr*y**2)\n",
" +2*math.sqrt(kr)*c*math.atan((b)/math.sqrt(kr)/c)\n",
" ) \n",
" )\n",
" #debug\n",
" #print(\"a: %s b:%s c:%s \" % (a,b,c))\n",
" #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
" #print(\"Magic angle:%s\"% angle)\n",
" return Cp\n",
"\n",
"def InflueceU(x, y, panel):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
"#Transfer global coordinate point(x,y) to local coordinate\n",
" x=x-panel.xa\n",
" y=y-panel.ya\n",
" L1=panel.length\n",
"\n",
"#Calculate the pressure and velocity influence factor\n",
" a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
" b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
" c=y*panel.cosalpha-x*panel.sinalpha\n",
" dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
" Cu = dv/a*(\n",
" ( \n",
" panel.cosalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(kr)*panel.sinalpha*math.atan((a*L1-b)/math.sqrt(kr)/c) \n",
" )\n",
" -\n",
" (\n",
" panel.cosalpha*math.log(x**2+kr*y**2)+2*math.sqrt(kr)*panel.sinalpha*math.atan((-b)/math.sqrt(kr)/c)\n",
" ) \n",
" ) \n",
" #print(\"a: %s b:%s c:%s \" % (a,b,c))\n",
" #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
" #print(\"Magic angle:%s\"% angle)\n",
" return Cu\n",
"\n",
"def InflueceV(x, y, panel):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
"#Transfer global coordinate point(x,y) to local coordinate\n",
" x=x-panel.xa\n",
" y=y-panel.ya\n",
" L1=panel.length\n",
"\n",
"#Calculate the pressure and velocity influence factor\n",
" a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
" b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
" c=y*panel.cosalpha-x*panel.sinalpha\n",
" dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
" Cv = dv/a*(\n",
" ( \n",
" panel.sinalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b-a*L1)/math.sqrt(kr)/c) \n",
" )\n",
" -\n",
" (\n",
" panel.sinalpha*math.log(x**2+kr*y**2)+2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b)/math.sqrt(kr)/c)\n",
" ) \n",
" ) \n",
" #print(\"a: %s b:%s c:%s \" % (a,b,c))\n",
" #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
" #print(\"Magic angle:%s\"% angle)\n",
"\n",
" return Cv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Well source function\n",
"\n",
"### Line source solution for pressure and velocity (Datta-Gupta, 2007)\n",
"\n",
"\\begin{equation}\n",
"P(x,y)=B{{Q}_{w}}=-\\frac{70.60\\mu }{h\\sqrt{{{k}_{x}}{{k}_{y}}}}\\ln \\left\\{ {{(x-{{x}_{w}})}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{w}})}^{2}} \\right\\}{{Q}_{w}}+{{P}_{avg}}\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
"\\frac{\\partial P}{\\partial x}=u=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\sum\\limits_{k=1}^{{{N}_{w}}}{{{Q}_{k}}}\\frac{x-{{x}_{k}}}{{{\\left( x-{{x}_{k}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{k}})}^{2}}}\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
"\\frac{\\partial P}{\\partial y}=v=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\sum\\limits_{k=1}^{{{N}_{w}}}{{{Q}_{k}}}\\frac{y-{{y}_{k}}}{{{\\left( x-{{x}_{k}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{k}})}^{2}}}\n",
"\\end{equation}"
]
},
{
"cell_type": "code",
"execution_count": 802,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Well influence factor\n",
"def InflueceP_W(x, y, well):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
" dp=-70.6*miu/h/math.sqrt(kx*ky)\n",
" Cp=dp*math.log((x-well.xw)**2+kr*(y-well.yw)**2)\n",
" return Cp\n",
"\n",
"def InflueceU_W(x, y, well):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
" dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
" Cu=dv*(x-well.xw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
" return Cu\n",
"\n",
"def InflueceV_W(x, y, well):\n",
" \"\"\"Evaluates the contribution of a panel at one point.\n",
" \n",
" Arguments\n",
" ---------\n",
" x, y -- Cartesian coordinates of the point.\n",
" panel -- panel which contribution is evaluated.\n",
" \n",
" Returns\n",
" -------\n",
" Integral over the panel of the influence at one point.\n",
" \"\"\"\n",
" dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
" Cv=dv*(y-well.yw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
" return Cv"
]
},
{
"cell_type": "code",
"execution_count": 803,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#InflueceV(0.5,1,panels[3])\n",
"#InflueceP(0,0.5,panels[0])\n",
"#InflueceU(0,0.5,panels[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BEM function solution\n",
"Generally, the influence of all the j panels on the i BE node can be expressed as follows:\n",
"\n",
"\\begin{matrix}\n",
"{{c}_{ij}}{{p}_{i}}+{{p}_{i}}\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}}=({{v}_{i}}\\cdot \\mathbf{n})\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}\n",
"\\end{matrix}\n",
"\n",
"Applying boundary condition along the boundary on above equation, a linear systsem can be constructed as follows:\n",
"\n",
"\\begin{matrix}\n",
"\\left[ {{{{H}'}}_{ij}} \\right]\\left[ {{P}_{i}} \\right]=\\left[ {{G}_{ij}} \\right]\\left[ {{v}_{i}}\\cdot \\mathbf{n} \\right]\n",
"\\end{matrix}\n",
"\n",
"!!!!!MY IMPLEMENTATION MAY HAS SOME PROBLEM HERE!!!!!!\n",
"\n",
"All the integration solution can be evaluated except on itself. Where,\n",
"\n",
"<center>$\n",
"\\left[ {{{{H}'}}_{ij}} \\right]=\\left\\{ \\begin{matrix}\n",
" \\begin{matrix}\n",
" {{H}_{ij}} & i\\ne j \\\\\n",
"\\end{matrix} \\\\\n",
" \\begin{matrix}\n",
" {{H}_{ij}}+\\frac{1}{2} & i=j \\\\\n",
"\\end{matrix} \\\\\n",
"\\end{matrix} \\right.\n",
"$</center>\n",
"\n",
"<img src=\"./resources/BEMscheme.png\" width=\"400\">\n",
"<center>Figure 3. Representation of coordinate systems and the principle of superstition with well source and boundary element source </center>\n",
"\n",
"As shown in Fig.3, the pressure and velocity at any point i in the local gridblock can be determined using Eqs. below. Applying principle of superposition for each BE node along the boundary (Fig. 3), boundary condition can be written as follows:\n",
"\n",
"\\begin{matrix}\n",
" {{P}_{i}}(s)=\\sum\\limits_{j=1}^{M}{{{B}_{ij}}{{Q}_{j}}} & \\text{constant pressure boundary} \\\\\n",
"\\end{matrix}\n",
"\n",
"\\begin{matrix}\n",
" {{v}_{i}}(s)\\cdot {{\\mathbf{n}}_{i}}=\\sum\\limits_{j=1}^{M}{{{A}_{ij}}{{Q}_{j}}} & \\text{constant flux boundary} \\\\\n",
"\\end{matrix}\n",
"\n",
"\n",
"The Pi and v ·n are the konwn boundary codition. The flow rate(strength) of boundary elements in Hij and Gij are the only unknown terms. \n",
"So we could rearrange the matrix above as linear system:\n",
"\n",
"<center>$\n",
"{{\\left[ \\begin{matrix}\n",
" {{A}_{ij}} \\\\\n",
" {{B}_{ij}} \\\\\n",
"\\end{matrix} \\right]}_{N\\times N}}{{\\left[ \\begin{matrix}\n",
" {{Q}_{j}} \\\\\n",
" {{Q}_{j}} \\\\\n",
"\\end{matrix} \\right]}_{N\\times 1}}={{\\left[ \\begin{matrix}\n",
" -{{u}_{i}}\\sin {{\\alpha }_{i}}+{{v}_{i}}\\cos {{\\alpha }_{i}} \\\\\n",
" {{P}_{i}} \\\\\n",
"\\end{matrix} \\right]}_{N\\times 1}}\n",
"$</center>"
]
},
{
"cell_type": "code",
"execution_count": 804,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def build_matrix(panels):\n",
" \"\"\"Builds the source matrix.\n",
" \n",
" Arguments\n",
" ---------\n",
" panels -- array of panels.\n",
" \n",
" Returns\n",
" -------\n",
" A -- NxN matrix (N is the number of panels).\n",
" \"\"\"\n",
" N = len(panels)\n",
" A = numpy.empty((N, N), dtype=float)\n",
" #numpy.fill_diagonal(A, 0.5)\n",
" \n",
" for i, p_i in enumerate(panels): #target nodes\n",
" for j, p_j in enumerate(panels): #BE source\n",
" #if i != j: ###Matrix construction\n",
" if i>=0 and i<Nbd or i>=3*Nbd and i<4*Nbd: \n",
" A[i,j] = -p_j.sinalpha*InflueceU(p_i.xc, p_i.yc, p_j)+p_j.cosalpha*InflueceV(p_i.xc, p_i.yc, p_j)\n",
" #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
" if i>=Nbd and i<2*Nbd or i>=2*Nbd and i<3*Nbd: \n",
" A[i,j] = -p_j.sinalpha*InflueceU(p_i.xc, p_i.yc, p_j)+p_j.cosalpha*InflueceV(p_i.xc, p_i.yc, p_j)\n",
" #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
"\n",
" return A\n",
"\n",
"def build_rhs(panels):\n",
" \"\"\"Builds the RHS of the linear system.\n",
" \n",
" Arguments\n",
" ---------\n",
" panels -- array of panels.\n",
" \n",
" Returns\n",
" -------\n",
" b -- 1D array ((N+1)x1, N is the number of panels).\n",
" \"\"\"\n",
" b = numpy.empty(len(panels), dtype=float)\n",
" \n",
" \n",
" for i, panel in enumerate(panels):\n",
" V_well=( -panel.sinalpha*Qwell_1*InflueceU_W(panel.xc, panel.yc, wells[0])+panel.cosalpha*Qwell_1*InflueceV_W(panel.xc, panel.yc, wells[0]) )\n",
" if i>=0 and i<Nbd: \n",
" b[i]=0+V_well\n",
" #b[i]=4000\n",
" #b[i]=84\n",
" if i>=Nbd and i<2*Nbd:\n",
" b[i]=-V_well\n",
" #b[i]=-42\n",
" if i>=2*Nbd and i<3*Nbd: \n",
" b[i]=-V_well\n",
" #b[i]=-42\n",
" if i>=3*Nbd and i<4*Nbd:\n",
" b[i]=0+V_well\n",
" #b[i]=84\n",
" return b"
]
},
{
"cell_type": "code",
"execution_count": 805,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
"d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:70: RuntimeWarning: divide by zero encountered in double_scalars\n",
"d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:102: RuntimeWarning: divide by zero encountered in double_scalars\n",
"d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:106: RuntimeWarning: divide by zero encountered in double_scalars\n"
]
}
],
"source": [
"#Qwell_1=300 #Flow rate of well 1\n",
"#Boundary_V=-227 #boundary velocity ft/day\n",
"\n",
"A = build_matrix(panels) # computes the singularity matrix\n",
"b = build_rhs(panels) # computes the freestream RHS"
]
},
{
"cell_type": "code",
"execution_count": 806,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# solves the linear system\n",
"Q = numpy.linalg.solve(A, b)\n",
"\n",
"for i, panel in enumerate(panels):\n",
" panel.Q = Q[i]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot results"
]
},
{
"cell_type": "code",
"execution_count": 807,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Visulize the pressure and velocity field\n",
"\n",
"#Define meshgrid\n",
"Nx, Ny = 50, 50 # number of points in the x and y directions\n",
"x_start, x_end = -0.01, 1.01 # x-direction boundaries\n",
"y_start, y_end = -0.01, 1.01 # y-direction boundaries\n",
"x = numpy.linspace(x_start, x_end, Nx) # computes a 1D-array for x\n",
"y = numpy.linspace(y_start, y_end, Ny) # computes a 1D-array for y\n",
"X, Y = numpy.meshgrid(x, y) # generates a mesh grid\n",
"\n",
"#Calculate the velocity and pressure field\n",
"p = numpy.empty((Nx, Ny), dtype=float)\n",
"u = numpy.empty((Nx, Ny), dtype=float)\n",
"v = numpy.empty((Nx, Ny), dtype=float)\n",
"\n",
"#for i, panel in enumerate(panels):\n",
" #panel.Q = 0.\n",
"\n",
"#panels[0].Q=100\n",
"#panels[5].Q=100\n",
"#Qwell_1=400\n",
"\n",
"\n",
"for i in range(Nx):\n",
" for j in range(Ny):\n",
" p[i,j] =sum([p.Q*InflueceP(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceP_W(X[i,j], Y[i,j], wells[0])\n",
" u[i,j] =sum([p.Q*InflueceU(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceU_W(X[i,j], Y[i,j], wells[0])\n",
" v[i,j] =sum([p.Q*InflueceV(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceV_W(X[i,j], Y[i,j], wells[0])\n",
" #p[i,j] =sum([p.Q*InflueceP(X[i,j], Y[i,j], p) for p in panels])\n",
" #u[i,j] =sum([p.Q*InflueceU(X[i,j], Y[i,j], p) for p in panels])\n",
" #v[i,j] =sum([p.Q*InflueceV(X[i,j], Y[i,j], p) for p in panels])\n",
" #p[i,j] =Qwell_1*InflueceP_W(X[i,j], Y[i,j], wells[0])\n",
" #u[i,j] =Qwell_1*InflueceU_W(X[i,j], Y[i,j], wells[0])\n",
" #v[i,j] =Qwell_1*InflueceV_W(X[i,j], Y[i,j], wells[0])"
]
},
{
"cell_type": "code",
"execution_count": 808,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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E3zdunEiTJvr2sWP0aEpM5HZMDN1mr151rsONG/RwunqVMQANG9JbZuZMunF+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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12120b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plots the streamlines\n",
"%matplotlib inline\n",
"\n",
"size = 6\n",
"pyplot.figure(figsize=(size, size))\n",
"pyplot.grid(True)\n",
"pyplot.title('Streamline field')\n",
"pyplot.xlabel('x', fontsize=16)\n",
"pyplot.ylabel('y', fontsize=16)\n",
"pyplot.xlim(-0.2, 1.2)\n",
"pyplot.ylim(-0.2, 1.2)\n",
"\n",
"\n",
"pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
" numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
" linestyle='-', linewidth=1, marker='o', markersize=6, color='#CD2305');\n",
"stream =pyplot.streamplot(X, Y, u, v,density=2, linewidth=1, arrowsize=1, arrowstyle='->') #streamline\n",
"#cbar=pyplot.colorbar(orientation='vertical')\n",
"\n",
"#equipotential=pyplot.contourf(X, Y, p1, extend='both')"
]
},
{
"cell_type": "code",
"execution_count": 809,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x22b3b5f8>"
]
},
"execution_count": 809,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ffErCjve1Dse/uGRIzzFMWb50u9i44y6VzHTConUAitKRjCTfklPJG+/h2pcH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\nZoXMGGNMXLNCZowxJq5ZITPGGBPX/j82KbS6WYxbxgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c40b630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 7\n",
"pyplot.figure(figsize=(size, size-1))\n",
"pyplot.title('Pressure field')\n",
"pyplot.xlabel('x', fontsize=16)\n",
"pyplot.ylabel('y', fontsize=16)\n",
"pyplot.xlim(0, 1)\n",
"pyplot.ylim(0, 1)\n",
"\n",
"pyplot.contour(X, Y, p, 15, linewidths=0.5, colors='k')\n",
"pyplot.contourf(X, Y, p, 15, cmap='rainbow',\n",
" vmax=abs(p).max(), vmin=-abs(p).max())\n",
"pyplot.colorbar() # draw colorbar"
]
},
{
"cell_type": "code",
"execution_count": 810,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x1c9c09b0>"
]
},
"execution_count": 810,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Yph9lFxzB21MWuB2GiQ8+IE9VZwHXAX/rx2P3aBShrx9P2GMiciaRvlMP8ICq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\nTURECkfhJiIihaNwExGRwlG4iYhI4fw/t3kM0QS9/tUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22b3def0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 7\n",
"pyplot.figure(figsize=(size, size-1))\n",
"pyplot.title('Total Velocity field')\n",
"pyplot.xlabel('x', fontsize=16)\n",
"pyplot.ylabel('y', fontsize=16)\n",
"pyplot.xlim(0, 1)\n",
"pyplot.ylim(0, 1)\n",
"\n",
"Vtotal= numpy.sqrt(u**2+v**2)\n",
"#Vtotal= numpy.abs(v)\n",
"pyplot.contour(X, Y, Vtotal, 15, linewidths=0.5, colors='k')\n",
"pyplot.contourf(X, Y, Vtotal, 15, cmap='rainbow')\n",
" #vmax=50, vmin=0)\n",
"pyplot.colorbar() # draw colorbar"
]
},
{
"cell_type": "code",
"execution_count": 811,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3991.53526449, 4697.23214482, 4427.94528366, 4213.10279637,\n",
" 4056.60881842, 3935.0524005 , 3835.61636196, 3758.7042195 ,\n",
" 3678.83215419, 3624.28803091, 3574.58365565, 3512.86339586,\n",
" 3477.97481989, 3446.47687207, 3393.90113729, 3365.99608357,\n",
" 3346.67520352, 3314.63917316, 3278.95525446, 3263.89905598,\n",
" 3249.45519765, 3219.13221798, 3199.8855574 , 3188.04140691,\n",
" 3175.46727089, 3157.2693224 , 3144.22991707, 3132.72142655,\n",
" 3123.92635749, 3118.96690841, 3108.32799206, 3098.56287193,\n",
" 3101.19357179, 3101.62102519, 3094.55662065, 3093.65095622,\n",
" 3110.17941018, 3111.68331515, 3115.93189462, 3141.31882334,\n",
" 3156.43775968, 3175.3078 , 3215.96547724, 3250.99279038,\n",
" 3305.71980505, 3375.05945207, 3470.19304338, 3601.64257033,\n",
" 3743.87730196, 3061.23226415])"
]
},
"execution_count": 811,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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By4AZwMzWkpQl3/0ujBkDhYqlmVklVLZGVU+9vUa1YAFstx2sWAEzZsC73lV2\nRGbWCHp1jcrq68orYdmyVKNykjKzqnGi6uXeegsuzM/s+MIXyo3FzKw1TlS93E9/CnPmwD77wJFH\nlh2NmdnaGql7unWDU0+FTTeFESPc28/MqsmdKXBnCjOz9eHOFGZmZnQiUUm6T9Jxkvp3Z0BmZmZF\nnalR/Y301PJ5kn4gafduisnMzGyVDieqiGgC9iAlq+OBJyU1S/o/kvp1U3zWDS64AC6+OP2ar5lZ\n1a1XZ4rc/Hcs8M/AAcAC0o8d/ndEPN+lEdZBb+pMMW9e+vXev/wFpk6FUaPKjsjMGlWlO1NExNKI\nuBo4Dfhf4B3A14EZkq7PTze3Cvr3f09J6mMfc5Iys8bQ6RqVpI2ATwBfAPYDniH95Pv1wD8C44Gn\nI2J0l0bajXpLjerZZ2H3fGVx+nQYPrzceMyssdWrRtXhG34lvRv4F+BTwMbALcDpETGlMNrPJL1C\nSlpWMeefnx48+9nPOkmZWePozJMp/gjMAy4kXYt6uY3xngXuf7uBWddasSLVoiQ444yyozEz67gO\nN/1J+ihwS0Ss6N6Q6q+3NP1FwMMPw3veU3YkZtYT1Kvpz49QovckKjOzrlTpXn+NRNKHJT0taUb+\nCXszM2sgPbpGJakP6WfoR5Our00DxkbE0y3Gc43KzKyTXKPqGqOAmRExKyKWAROAo0uOqa6cf82s\n0fX0RDUUmF14PSeX9QqLFsGwYfBv/+aEZWaNq6cnql7tpptg9uzV3dLNzBpRT/+F37nAjoXX2+ey\ntYwfP37VcFNTE01NTd0ZV7eLSA+eBfj0p8uNxcx6hubmZpqbm+u+3J7emaIv6RFPo4GXgQeBT0TE\nUy3G63GdKSZOhCOOgK23hhdegIEDy47IzHqayj1CqRFFxApJ/wpMJjVzXtYySfVUV1yR/n/5y05S\nZtbYenSiAoiIScBuZcdRTxGwdCn06wef/GTZ0ZiZvT09uumvo3pi0x/An/8MgwaVHYWZ9VR+hFId\n9dREZWbWnXzDr5mZGU5UZmZWcU5UZmZWaU5UPUgEfPOb8Otfw7JlZUdjZtY13JmCntOZYsYM2G03\nGDwY5s+HPj4NMbNu5M4U1mm//W36f8ghTlJm1nP4cNaD3Htv+v+BD5Qbh5lZV3Ki6kHuuy/9P/DA\ncuMwM+tKvkZFz7hGtXAhbLUVbLQRvPEGbNDjH45lZmXzQ2mtU/r1g8svh9dec5Iys57FNSp6Ro3K\nzKze3Otdca1fAAAN6ElEQVTPzMwMJyozM6s4JyozM6u0SiUqSf8k6QlJKySNbPHeOEkzJT0l6dBC\n+UhJj0maIenCQvmGkibkae6XtGM918XMzLpGpRIV8DjwEeB3xUJJI4BjgRHAYcDFkmoX8C4BToqI\n4cBwSWNy+UnAwojYFbgQOL8O8Zfi8cfhqKPgggvKjsTMrOtVKlFFxDMRMRNo2YvkaGBCRCyPiBeB\nmcAoSUOAQRExLY93FXBMYZor8/ANwOhuDb5E06fDbbetvuHXzKwnqVSiasdQYHbh9dxcNhSYUyif\nk8vWmCYiVgCvS9qy+0Otvzn5E9hhh3LjMDPrDnW/NVTSncA2xSIggG9ExG3duej23hw/fvyq4aam\nJpqamroxlK41b176v9125cZhZj1bc3Mzzc3NdV9u3RNVRHxoPSabCxTrC9vnsrbKi9PMk9QX2DQi\nFra1gGKiajRz8xoPHdr+eGZmb0fLk/izzz67LsutctNfsQZ0KzA29+TbCdgFeDAiXgEWSxqVO1cc\nD9xSmOaEPPxx4O46xV13TlRm1pNV6hFKko4B/hMYDLwOPBoRh+X3xpF68i0DTouIybl8P+AKYAAw\nMSJOy+X9gauBfYHXgLG5I0Zry23oRyg9+CA89xwcemh6MK2ZWT3U6xFKlUpUZWn0RGVmVgY/68/M\nzAwnKjMzqzgnKjMzqzQnKjMzqzQnqgb38MPw4Q/Dd75TdiRmZt3DP1re4GbNgjvugAEDyo7EzKx7\nuEbV4N56K/3fZJNy4zAz6y5OVA3ur39N/12jMrOeyomqwdUS1UYblRuHmVl3caJqcK5RmVlP50co\n0diPUHrhBXjqKRg2DPbcs+xozKw38bP+6qiRE5WZWVn8rD8zMzOcqMzMrOKcqMzMrNKcqMzMrNIq\nlagknS/pKUmPSrpR0qaF98ZJmpnfP7RQPlLSY5JmSLqwUL6hpAl5mvsl7Vjv9amHs8+GI4+EBx4o\nOxIzs+5RqUQFTAb2jIh9gJnAOABJewDHAiOAw4CLJdV6mlwCnBQRw4Hhksbk8pOAhRGxK3AhcH79\nVqN+HnwQbr8dXnut7EjMzLpHpRJVRNwVESvzyweA7fPwUcCEiFgeES+SktgoSUOAQRExLY93FXBM\nHj4auDIP3wCM7u74y+Abfs2sp6tUomrhRGBiHh4KzC68NzeXDQXmFMrn5LI1pomIFcDrkrbszoDL\n4ERlZj1d3X/mQ9KdwDbFIiCAb0TEbXmcbwDLIuJXXbno9t4cP378quGmpiaampq6cNHdx4nKzOql\nubmZ5ubmui+3ck+mkPQZ4PPAByNiaS47A4iIOC+/ngScBcwCpkTEiFw+FjgoIk6ujRMRUyX1BV6O\niK3bWGbDPplizz1h+nR44gk/QsnM6qtXPplC0oeBrwFH1ZJUdiswNvfk2wnYBXgwIl4BFksalTtX\nHA/cUpjmhDz8ceDuuqxEnf3kJ3DbbelZf2ZmPVGlalSSZgIbArU+bA9ExCn5vXGknnzLgNMiYnIu\n3w+4AhgATIyI03J5f+BqYN88v7G5I0Zry23YGpWZWVn8UNo6cqIyM+u8Xtn0Z2Zm1pITlZmZVZoT\nlZmZVZoTVYM7/HA44ghYvrzsSMzMuoc7U9C4nSkioE8+1Vi5EtTtlzTNzFZzZwpbp1otqm9fJykz\n67mcqBrY3/6W/m+4YblxmJl1JyeqBrZsWfrfr1+5cZiZdScnqgbmRGVmvUHdn55uXWfTTWHSpNUd\nKszMeiL3+qNxe/2ZmZXJvf7MzMxwojIzs4pzojIzs0pzojIzs0pzompg06fDmDHw1a+WHYmZWfep\nVKKS9G1Jf5T0iKRJkoYU3hsnaaakpyQdWigfKekxSTMkXVgo31DShDzN/ZJ2rPf6dLcFC2DyZJg6\ntexIzMy6T6USFXB+ROwdEfsCtwNnAUjaAzgWGAEcBlwsrXq63SXASRExHBguaUwuPwlYGBG7AhcC\n59dxPerCN/yaWW9QqUQVEW8WXm4MrMzDRwETImJ5RLwIzARG5RrXoIiYlse7CjgmDx8NXJmHbwBG\nd2fsZfCz/sysN6jckykkfRc4HngdODgXDwXuL4w2N5ctB+YUyufk8to0swEiYoWk1yVtGRELuzH8\nunKNysx6g7onKkl3AtsUi4AAvhERt0XEN4FvSjod+DdgfFctur03x49fvZimpiaampq6aLHdx4nK\nzOqpubmZ5ubmui+3so9QkrQDcHtE7CXpDCAi4rz83iTS9atZwJSIGJHLxwIHRcTJtXEiYqqkvsDL\nEbF1G8tqyEcozZ8Pjz4KgwfDfvuVHY2Z9Ta98hFKknYpvDwGeDoP3wqMzT35dgJ2AR6MiFeAxZJG\n5c4VxwO3FKY5IQ9/HLi721egzrbZJnVPd5Iys56sateozpU0nNSJYhbwBYCImC7pOmA6sAw4pVAF\nOhW4AhgATIyISbn8MuBqSTOB14CxdVsLMzPrMpVt+qunRm36MzMrU69s+jMzM2vJicrMzCrNicrM\nzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrN\nicrMzCrNicrMzCrNicrMzCqtkolK0lckrZS0ZaFsnKSZkp6SdGihfKSkxyTNkHRhoXxDSRPyNPdL\n2rHe62FmZm9f5RKVpO2BD5F+4bdWNgI4FhgBHAZcnH96HuAS4KSIGA4MlzQml58ELIyIXYELgfPr\ntAp119zcXHYIb4vjL08jxw6Ov7eoXKICfgh8rUXZ0cCEiFgeES8CM4FRkoYAgyJiWh7vKuCYwjRX\n5uEbgNHdGnWJGn1jd/zlaeTYwfH3FpVKVJKOAmZHxOMt3hoKzC68npvLhgJzCuVzctka00TECuD1\nYlOimZk1hg3qvUBJdwLbFIuAAL4JnElq9uuWRXfTfM3MrBspIsqOAQBJfwfcBSwhJZXtSTWnUcCJ\nABFxbh53EnAW6TrWlIgYkcvHAgdFxMm1cSJiqqS+wMsRsXUby67Gh2Bm1mAiotsrAXWvUbUlIp4A\nhtReS3oBGBkRiyTdCvxS0g9ITXq7AA9GREhaLGkUMA04HvhRnsWtwAnAVODjwN3tLNu1LTOziqpM\nompFkJvrImK6pOuA6cAy4JRYXRU8FbgCGABMjIhJufwy4GpJM4HXgLF1jN3MzLpIZZr+zMzMWlOp\nXn9lknR+vpn4UUk3Stq07JjWRdKHJT2db3Y+vex4OkPS9pLulvSkpMclfbHsmNaHpD6S/pCbpxuK\npM0kXZ+3+ycl/X3ZMXWGpC9LeiLf8P9LSRuWHVN7JF0mab6kxwplW0iaLOkZSXdI2qzMGNvTRvx1\nOW46Ua02GdgzIvYh3ac1ruR42iWpD/BjYAywJ/AJSbuXG1WnLAf+b0TsCfwDcGqDxV9zGqlJuhFd\nRGouHwHsDTxVcjwdJmk74N9I17H3Il3GqHrz/uWk/bXoDOCuiNiNdB29ysed1uKvy3HTiSqLiLsi\nYmV++QCp12GVjQJmRsSsiFgGTCDd5NwQIuKViHg0D79JOkgObX+qaslPUTkcuLTsWDorn/m+PyIu\nB8g3079Rclid1RfYWNIGwEBgXsnxtCsi7gUWtSguPpjgSlY/sKByWou/XsdNJ6rWnQj8puwg1qHl\nTdDFm50biqR3AvuQemg2ktpTVBrxQu9OwAJJl+emy/+WtFHZQXVURMwDLgBeIt3G8npE3FVuVOtl\n64iYD+nkDWj1FpoG0W3HzV6VqCTdmduza3+P5///WBjnG8CyiLimxFB7DUmbkB5xdVquWTUESUcA\n83OtUDTeDeUbACOB/4qIkaT7F88oN6SOk7Q5qTYyDNgO2ETSJ8uNqks04klPtx83q9w9vctFRLtP\nvZD0GVJTzgfrEtDbMxcoPhG+doN0w8hNNjcAV0fELWXH00kHAkdJOhzYCBgk6aqIOL7kuDpqDulx\nZQ/l1zcAjdQh5xDg+YhYCCDp18ABQKOdYM6XtE1EzM/PLv1T2QF1Vj2Om72qRtUeSR8mNeMcFRFL\ny46nA6YBu0galns7jSXd5NxIfg5Mj4iLyg6ksyLizIjYMSJ2Jn32dzdQkiI3N82WNDwXjaaxOoW8\nBOwvaUD+JYXRNEZnkJa171uBz+ThE4Cqn7CtEX+9jpu+jyrLNwZvSLo5GOCBiDilxJDWKW8kF5FO\nOC6rPWKqEUg6ELgHeJzU3BHAmYUbthuGpIOAr0TEUWXH0hmS9iZ1BOkHPA98NiIWlxtVx0k6i3SS\nsAx4BPhc7lhUSZKuAZqArYD5pMfA3QxcD+xAeiTcsRHxelkxtqeN+M+kDsdNJyozM6s0N/2ZmVml\nOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZVYikgfkX\nU6dK6lsoP1TSCkknlxmfWRn8CCWzipG0D+lH6H4QEWdK2gZ4FLg/Ij5abnRm9edEZVZBkr4E/D+g\n9nTqPYG9az9rYdabOFGZVZSk20m/8dMPOCQimsuNyKwcvkZlVl1XA/2BPzpJWW/mRGVWQfnXXi8C\nHgb2lvTFkkMyK40TlVk1XQn8hfST6xcB50r6u3JDMiuHr1GZVYykrwDnAgdHxL2S+pF6AfYH9uvO\nn/w2qyLXqMwqRNK+wHeBcyLiXoD88+qfAIYBPygxPLNSuEZlZmaV5hqVmZlVmhOVmZlVmhOVmZlV\nmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlV2v8HDNxTCTQfcDcAAAAASUVORK5C\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x19c3f908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.title('Darcy velocity on the outflow boundary, x component (ft/day)')\n",
"pyplot.xlabel('x', fontsize=16)\n",
"pyplot.ylabel('y', fontsize=16)\n",
"\n",
"pyplot.plot(y, u[49,:], '--', linewidth=2)\n",
"pyplot.plot(9.8425+y, u[:,49], '--', linewidth=2)\n",
"u[:,49]"
]
},
{
"cell_type": "code",
"execution_count": 812,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3991.53526449, 4697.23214482, 4427.94528366, 4213.10279637,\n",
" 4056.60881842, 3935.0524005 , 3835.61636196, 3758.7042195 ,\n",
" 3678.83215419, 3624.28803091, 3574.58365565, 3512.86339586,\n",
" 3477.97481989, 3446.47687207, 3393.90113729, 3365.99608357,\n",
" 3346.67520352, 3314.63917316, 3278.95525446, 3263.89905598,\n",
" 3249.45519765, 3219.13221798, 3199.8855574 , 3188.04140691,\n",
" 3175.46727089, 3157.2693224 , 3144.22991707, 3132.72142655,\n",
" 3123.92635749, 3118.96690841, 3108.32799206, 3098.56287193,\n",
" 3101.19357179, 3101.62102519, 3094.55662065, 3093.65095622,\n",
" 3110.17941018, 3111.68331515, 3115.93189462, 3141.31882334,\n",
" 3156.43775968, 3175.3078 , 3215.96547724, 3250.99279038,\n",
" 3305.71980505, 3375.05945207, 3470.19304338, 3601.64257033,\n",
" 3743.87730196, 3061.23226415])"
]
},
"execution_count": 812,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IF9KHky70XSStv/P+YlKPhGHAMLXz8QGtkXQ8sK4HJJU+pD7yk51UrNpyAjmT1NyzXkSs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\nYmZmVfX/AK3XNwu/Lu7oAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1bb29208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.title('Darcy velocity on the outflow boundary, y component (ft/day)')\n",
"\n",
"pyplot.plot(y, v[:,49], '--', linewidth=2)\n",
"pyplot.plot(9.8425+y, v[49,:], '--', linewidth=2)\n",
"v[49,:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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| mit |
aglorios/dark_money_predictions | .ipynb_checkpoints/Kalamazoo2-Copy0-Copy0-Copy0-Jed-checkpoint.ipynb | 1 | 2023838 | null | apache-2.0 |
Kappa-Dev/ReGraph | examples/Tutorial_graph_audit.ipynb | 1 | 233562 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Audit trails for graph objects in ReGraph (aka versioning)\n",
"\n",
"ReGraph implements a framework for the version control (VC) of graph transformations\n",
"\n",
"The data structure `VersionedGraph` allows to store the history of transformations of a graph object and perform the following VC operations:\n",
"\n",
"- _Rewrite_: perform a rewriting of the object with a commit to the revision history\n",
"- _Branch_: create a new branch (with a diverged version of the graph object)\n",
"- _Merge branches_: merge branches\n",
"- _Rollback_: rollback to a point in the history of transformations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from regraph import NXGraph\n",
"from regraph.audit import VersionedGraph\n",
"from regraph.rules import Rule\n",
"from regraph import print_graph, plot_rule, plot_graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a graph and pass it to the `VersionedGraph` wrapper that will take care of the version control."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"graph_obj = NXGraph()\n",
"g = VersionedGraph(graph_obj)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create a rule that adds to the graph two nodes connected with an edge and apply it. If we want the changes to be commited to the version control we rewrite through the `rewrite` method of a `VersioneGraph` object."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/raimon/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if not cb.iterable(width):\n",
"/home/raimon/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:676: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if cb.iterable(node_size): # many node sizes\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'a': array([1. , 0.12769283]), 'b': array([-1. , -0.12769283])}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rule = Rule.from_transform(NXGraph())\n",
"rule.inject_add_node(\"a\")\n",
"rule.inject_add_node(\"b\")\n",
"rule.inject_add_edge(\"a\", \"b\")\n",
"\n",
"rhs_instance, _ = g.rewrite(rule, {}, message=\"Add a -> b\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a new branch called \"branch\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"branch_commit = g.branch(\"branch\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Branches: ['master', 'branch']\n",
"Current branch 'branch'\n"
]
}
],
"source": [
"print(\"Branches: \", g.branches())\n",
"print(\"Current branch '{}'\".format(g.current_branch()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a rule that clones the node 'b' to the current vesion of the graph (branch 'branch')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x216 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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aFy9enIZoIiKZd6zncUbGzgPJrfIZc4Mc73mcgdH2TMTpivZo5GNnokHpKP8/AKvMbLmZFQF3AE9MGvMEcGfk9geBX+t6v4jMFa+2f41Q+MKMP+9g53cykCY5KZd/5Br+Z4FfAK8BP3TOHTCzL5nZrZFh3wGqzOwY8DfARctBRUTyUd/wKfpH3px+4CRjbpgj3f+cgUTJScuPm51zTwNPT3rsv8XcHgI+lI5ziYjkkqFQNwErYszNfHP24dC5DCRKjl7hKyIyS+FwmPPn+hgLz+69/L1c7qk9fEVEkuSc4+zZs5w+fZrW1lba2toYCZ2HjSOzmkovKFmZ/pBJUvmLiFzC+fPnJ8q+tbWVoaH4yzvz59cSDl9Df+Blkl3tA1AYqGBDbcaWe05L5S8iEqO/v5/W1taJwr9wIX4VT3l5OUuWLKGhoYGGhgYqKiro6F/OU0dvJRQeSPo8ZkGaKm9Jd/ykqfxFxNcGBwfjyr63tzfueElJCQ0NDROFv2DBgoueo6b8GlYsfD8nzv4kqX8AglbKDU3fImCFafs6ZkrlLyK+Mjw8TFtb20Thnz17Nu54UVER9fX1E2W/cOFCzBK9ScFbzIy3X/Z1wm6UN849OeWafyNIMFDE25c9wGWV29P2Nc2Gyl9E5rTR0VHa29snyr6rqyvueEFBAXV1dROz+6qqKgKBmf/0NmAF3ND0LU6ef5o97f9I98CrBKwQh8Mwwm6UFQtvZ2PdX7OodG26vrxZU/mLyJwyNjZGR0fHRNl3dnbGba4SCASora2dKPvFixcTDAbTcm4zo6nyvTRVvpfzQ8fpGdxPKDxIUXAB9fO2UhS8+JKRV1T+IpLXwuEwnZ2dE2Xf0dHB2Nhbb65mZtTU1Ez8gLauro6CgsxX34KSlZ4u5ZyOyl9E8opzju7u7omll21tbYyOxr89clVV1cTMvq6ujqKiIo/S5i6Vv4jkvHPnzsWttR8eHo47XllZOTGzb2hooKSkxKOk+UPlLyI5p6+vL67sBwbil09WVFTErbUvLy/3KGn+UvmLiOcGBgbi1tr39fXFHS8tLY0r+/nz53uUdO5Q+YtI1g0NDU3M6ltbWzl3Lv7dLYuLiy9aay/ppfIXkYwbGRmhvb19Ymbf3d0dd7ywsJD6+vqJmX1VVdW0L6yS1Kj8RSTtQqEQHR0dE2Xf2dlJ7OZ9wWCQ2traiZn94sWLZ/XCKpk9lb+IpCwcDnPmzJm4tfaTX1hVU1MzUfa1tbVpe2GVzI7KX0RmzDlHV1fXRNm3t7cTCsVvaFJdXR231r6w0Ls3MZOLqfxFZFrRTUxif0g7MjISN2bhwoUTZV9fX09xcbFHaSUZKn8RSej8+fNxyy8v3sRkftwLq8rKyjxKKrOh8hcR4K1NTKKFP3kTk7KyMpYsWTJx3b6iosKjpJIOKn8Rn4puYhIt+6k2MYleykm0iYnkL5W/iE+MjIzEXbPv6emJOx7dxCRa9slsYiL5S+UvMkeFQqG4F1Z1dXXFrbWP3cSkoaGB6upqrbX3EZW/yBwRu4lJa2srZ86cSbiJSfSafU1Njdba+5jKXyRPhcNhurq6Jmb27e3tObGJieQH/UkQyRPOOXp6eibKPtEmJosWLZqY2dfX12sTE5mSyl8kh507dy5urf3kTUwWLFgQ91bH2sREkqXyF8khfX19cWWfaBOT6GocbWIiqVD5i3gouolJtPATbWISW/baxETSReUvkkVDQ0O0tbVNzOwnb2JSVFQU98IqbWIimaLyF8mg6CYm0Zn95E1MCgoK4nas0iYmki0plb+ZLQIeA5qAN4APO+fOJhg3BuyL3D3lnLs1lfOK5KroJibRsp9qE5PozF6bmIhXUp353ws855z7ipndG7n/XxOMG3TObUrxXCI5J3YTk+ha+9gXVplZXNnX1NRorb3khFT/FN4GvCNy+xHg30hc/iJzQuwmJtG19lNtYhJda69NTCQXpVr+tc65NgDnXJuZ1UwxrsTMdgEh4CvOuZ+leF6RrOnp6bnkJiaVlZVxm5horb3kg2nL38yeBeoSHPrCDM6zzDnXamYrgF+b2T7n3PEE59oB7ABYtmzZDJ5eJH16e3snVuO0trYyODgYd3zevHlxL6zSJiaSY6ojk+2onc65nZMHTVv+zrmbpjpmZh1mVh+Z9dcDZ6Z4jtbIryfM7N+AzcBF5R8JuBOgubnZTT4ukgkXLlyIK/v+/v6449FNTKJlP2/ePI+SiiSlyznXPN2gVC/7PAHcCXwl8uvPJw8ws4XAgHNu2MyqgW3AP6R4XpFZGxwcjFtrf/78+bjjsZuYNDQ0UFlZ6VFSkcxJtfy/AvzQzO4CTgEfAjCzZuBu59yngCuBh8wsDAQYv+Z/MMXziiRtZGQkruwnb2JSWFgYt9Z+0aJFWmsvc15K5e+c6wZuTPD4LuBTkdsvAetTOY/ITEy3iUkwGKSurm6i7LWJifiRFhxL3hsbG+PMmTMTZT/VJiaxa+21iYn4ncpf8k50E5Poq2gTbWKyePHiiZm9NjERuZj+RkjOi25iEi37qTYxiV1rr01MRC5N5S85KbqJSfRjaGgo7viCBQviyr60tNSjpCL5yfflb2b09fVRUVHhdRRfS3YTk+iHfr9EUuP78hdvaBMTEW+p/IH77ruPX/7yl3R3d/P3f//3fOADH/A60pwzPDwcV/baxETEWyp/xpcCvvTSSxw+fJitW7dy/fXXU1Mz1XvUSTJGR0dpa2ubKPyurq6449FNTKJlr01MRLJL5Q/cddddAKxevZotW7bw8ssvc+ut2m9mJkKh0EVr7WNfWBUIBKirq5uY3dfU1OiFVSIeUvlP4pzTDDQJ4XCYzs7OibLv6Oi4aK29NjERyV362wg8/PDDfPGLX+To0aPs2bOHa6+91utIOcc5R3d390TZaxMTkfym8geKi4vZtm0bXV1dPPTQQ7reH3H27Nm4tzpOtIlJdDWONjERyS++L//odenPf/7zHifxXm9vb9xae21iIjJ3+b78/ezChQtxZa9NTET8Q+XvI0NDQ3Fr7bWJiYh/qfznMG1iIiJTUfnPIdFNTKIze21iIiJTUfnnsegmJtGy1yYmIpIslX8eid3EpLW1lfb29ri19trERESSpWbIYc65uLX2bW1tF6211yYmIjIbKv8co01MRCQbVP4e6+/vn5jZnz59WpuYiEhWqPyzLHYTk9bWVnp7e+OORzcxic7utYmJiGSCb8vfOUd7/2/Y2/F1OgdeYSw8TGGwguWVt3JVzd3MK74sLecZHh6OW2t/9uzZuOPaxEREvODL8u8ZPMAvjt3BYKiTUHgAGF8LPzzWw4HOnRzs/DZL5t/AO5d/h6LgxW9pEAqF2L9/P42NjVRXV8cdGx0dpb29faLstYmJiOQi35V/54U/8i9H3kMofCHh8bAbX01zuvfX/OzQDbxvzfNx/wAMDg7yzDPPcOzYMVatWsUtt9wSt4lJZ2fnRWvttYmJiOQaX5X/cOgcTx29bcrijzXmhukbfoPnTtzJ9lU/AcZX4jz11FP8/ve/59ixY7zxxht0dHTEzdy1iYmI5ANftdKR7u9NzOyTMeaGae17kd7hE3ScGuOb3/wmBw8epL+/n0AgQFtbG42NjaxevVqbmIhIXvFN+TvneLXj/sg1/pl83hj7Oh7kyL+vpaWlBeccpaWlE++Tc91113HDDTdkKLWISGb4pvz7Rl5nZOz89AMnCTPK6+ee4CMf+e8sW7aM0dFRent76e7uZnh4OANJRUQyzzflPxw6h83yyx0d66OsrIwbb7xx4rGRkRF6enpYtGhRuiKKiGSNb8o/GCgluqRz5p9bfNFjRUVF1NXVpZhKRMQbKa05NLMPmdkBMwubWfMlxt1sZofN7JiZ3ZvKOWdrXtEywi40/cAEFpWuS3MaERFvpbrgfD/wfuCFqQaYWRB4ANgOrAU+amZrUzzvjBUGy7l80QcxZvZ+9gWBCjbW/nWGUomIeCOl8nfOveacOzzNsGuAY865E865EeAHwG2pnHe21td+loDN7C2Pi4IVNM5/Z4YSiYh4IxsvNV0CvBlzvyXyWNYtKl3Lprr/QkGgLKnxBYEy/sPK72OmV+SKyNwy7Q98zexZINFPNr/gnPt5EudI9MY1CX/yamY7gB0Ay5YtS+KpZ25L/fiPHPa0/yNhN4Jj7KIxQSshYAXcfPmPqCmf8kcZIiK5qNrMdsXc3+mc2zl50LTl75y7KcUgLcDSmPuNQOsU59oJ7ARobm6e3dKcaZgZVzf8LU2Vt7C34+ucOPtTAlaEAWHGKAiUsL7mM6yp/iSlhYszEUFEJJO6nHPTzlqzsdTzD8AqM1sOnAbuAD6WhfNeUlXZem5YvpNty+6jd/gEofAARcFKKktWEzBtci4ic1uqSz1vN7MW4E+Bp8zsF5HHG8zsaQDnXAj4LPAL4DXgh865A6nFTp+i4HyqyzZRV7GVRaVrVfwi4gspzfydcz8Ffprg8VbgPTH3nwaeTuVcIiKSPlrGIiLiQyp/EREfUvmLiPiQyl9ExIdU/iIiPqTyFxHxIZW/iIgPqfxFRHzInMvIW+ikzMw6gZNZPm010JXlc85WvmTNl5ygrJmirJkxVdbLnHPTvjFZzpa/F8xsVzJviJQL8iVrvuQEZc0UZc2MVLPqso+IiA+p/EVEfEjlH++iDQ9yWL5kzZecoKyZoqyZkVJWXfMXEfEhzfxFRHzI1+VvZovM7FdmdjTy68JLjJ1vZqfN7BvZzBhz/mmzmtllZrbbzPaY2QEzuztHc24ys99GMu41s49kO2eyWSPjnjGzc2b2pAcZbzazw2Z2zMzuTXC82Mweixz/nZk1ZTtjTJbpsr7dzP5oZiEz+6AXGSM5psv5N2Z2MPJn8zkzu8yLnJEs02W928z2Rf7O/7uZrU36yZ1zvv0A/gG4N3L7XuCrlxh7P/DPwDdyNStQBBRHblcAbwANOZjzCmBV5HYD0AZU5uL3NHLsRuA/Ak9mOV8QOA6siPzevgqsnTTm08D/idy+A3gs29/HGWRtAjYAjwIfzOGcNwBlkdt/mePf0/kxt28Fnkn2+X098wduAx6J3H4EeF+iQWZ2NVAL/DJLuRKZNqtzbsQ5Nxy5W4w3/7NLJucR59zRyO1W4Aww7YtSMiCp33/n3HNAX7ZCxbgGOOacO+GcGwF+wHjmWLFfw+PAjWZmWcwYNW1W59wbzrm9QNiDfFHJ5HzeOTcQufsy0JjljFHJZO2NuVsOJP1DXL+Xf61zrg0g8mvN5AFmFgD+N/D5LGebbNqsAGa21Mz2Am8yPpNtzWJGSDJnlJldw/is5ngWsk02o6weWML472NUS+SxhGPc+H7Z54GqrKSbIkdEoqy5YKY57wL+NaOJppZUVjP7jJkdZ/x/svck++Qp7eGbD8zsWaAuwaEvJPkUnwaeds69mekJVRqy4px7E9hgZg3Az8zscedcR7oyQnpyRp6nHvh/wJ3OuYzMBtOV1SOJ/sBNntklMyYbciXHdJLOaWZ/DjQDf5bRRFNLKqtz7gHgATP7GPBF4M5knnzOl79z7qapjplZh5nVO+faIkV0JsGwPwWuN7NPM34dvcjM+p1zF/3wJQeyxj5Xq5kdAK5n/HJATuU0s/nAU8AXnXMvpzNfrHR+Tz3QAiyNud8ITP6fXHRMi5kVAAuAnuzES5gjKlHWXJBUTjO7ifEJwp/FXErNtpl+T38APJjsk/v9ss8TvPWv5J3AzycPcM593Dm3zDnXBHwOeDQTxZ+EabOaWaOZlUZuLwS2AYezlnBcMjmLgJ8y/r38URazTTZtVo/9AVhlZssj37M7GM8cK/Zr+CDwaxf56V+WJZM1F0yb08w2Aw8BtzrnvJwQJJN1Vczd9wJHk352L36KnSsfjF8bfS7yDXsOWBR5vBn4doLxn8S71T7TZgXeBexlfFXAXmBHjub8c2AU2BPzsSkXs0buvwh0AoOMz8bencWM7wGOMP4zkS9EHvsS48UEUAL8CDgG/B5Y4cWfzySz/knk+3cB6AYO5GjOZ4GOmD+bT+Tw9/R+4EAk5/PAumSfW6/wFRHxIb9f9hER8SWVv4iID6n8RUR8SOUvIuJDKn8RER9S+YuI+JDKX0TEh1T+ItOrI8IAAAAJSURBVCI+9P8By/a7am62X98AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'a': array([ 0.25050521, -0.06178004]),\n",
" 'b': array([-0.40100886, -0.93821996]),\n",
" 'b1': array([0.15050365, 1. ])}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"b\")\n",
"rule = Rule.from_transform(pattern)\n",
"rule.inject_clone_node(\"b\")\n",
"plot_rule(rule)\n",
"\n",
"rhs_instance, commit_id = g.rewrite(rule, {\"b\": rhs_instance[\"b\"]}, message=\"Clone b\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `rewrite` method of `VersionedGraph` returns the RHS instance of the applied and the id of the newly created commit corresponding to this rewrite."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RHS instance {'b': 'b1', 'b1': 'b'}\n",
"Commit ID: e916119f-61d5-4e38-ba6f-9eb921f16a46\n"
]
}
],
"source": [
"print(\"RHS instance\", rhs_instance)\n",
"print(\"Commit ID: \", commit_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Switch back to the 'master' branch"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"master\n"
]
}
],
"source": [
"g.switch_branch(\"master\")\n",
"print(g.current_branch())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a rule that adds a loop form 'a' to itself, a new node 'c' and connects it with 'a'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'a': array([ 0.12668879, -0.19332068]),\n",
" 'b': array([-1. , -0.61842372]),\n",
" 'c': array([0.87331121, 0.8117444 ])}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"a\")\n",
"rule = Rule.from_transform(pattern)\n",
"rule.inject_add_node(\"c\")\n",
"rule.inject_add_edge(\"c\", \"a\")\n",
"rule.inject_add_edge(\"a\", \"a\")\n",
"\n",
"rhs_instance, _ = g.rewrite(rule, {\"a\": \"a\"}, message=\"Add c and c->a\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new branch 'dev'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'718d78fe-e39c-4fa8-9ec2-6e16c9f72a70'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.branch(\"dev\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this branch remove an edge from 'c' to 'a' and merge two nodes together"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x216 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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SxpB7DGYWCxwGHgQagXLgWefc/j5t7gV2OufazOy/APc459aH5l12zt3W7TvVYxid2traqKio4ODBg0BP/aGwsJClS5cSExOR/1FE5Cai2WMoAmqdc3XOuU5gE7CubwPn3AfOubbQaACYF4HtyiiTnJzMpz/9aZ588klmz55Ne3s7v//973njjTdobGz0OjwRCYlEYpgLHO8z3hiaNpAXgDf7jCeaWYWZBczs8QjEIyNcWloan/3sZ3nwwQeZPHky58+fZ/v27bz11lu0trZ6HZ7IuBeJ6xjCnYMY9viUmT0HFAJ/2GdyhnPupJllA++b2V7n3JEwy24ANgBkZGQMPWrxXFZWFhkZGezbt49du3bR0NBAY2Mjubm5FBQUkJCQ4HWIImNNmpn1PQ6/0Tm38fpGkagx+IC/dc59JjT+1wDOuW9c1+4B4P8Af+icOzvAun4I/Mo5t+Vm21SNYey5evVqb/3BOUdCQgIFBQXk5uaq/iASIdGsMZQDi8wsy8zigWeAfmcXmVk+8F1gbd+kYGYpZpYQep8G3A3sR8adpKQkPvWpT/Hkk08yZ84cOjo62LFjB1u2bOH48eO3XoGIRExErmMws0eBbwOxwCvOub8zs5eACufcNjN7F1gJnAot0uCcW2tmpfQkjCA9Serbzrnv32p76jGMfceOHcPv93Px4kUA5s2bh8/nIyUlxePIREYvXeAmo14wGOytP3R2dmJmvfWHxMREr8MTGXWUGGTMaG9vp6KiggMHDuCcIz4+noKCApYvX676g8htUGKQMaelpYVAINB7zcPUqVPx+Xw6S01kkJQYZMxqaGjA7/dz4cIFoKf+UFJSQmpqqseRiYxsSgwypgWDQfbv309FRUVv/WHZsmUUFhaq/iAyACUGGRfa29uprKxk//79vfWH1atXs2LFCtUfRK6jxCDjyvnz5wkEAr3XPEyZMoWSkhIyMzO9DUxkBFFikHHp+PHj+P3+3nsuzZkzB5/Px/Tp0z2OTMR7SgwybgWDQQ4cOEBFRUXvMx+WLl3KmjVrSEpK8jg6Ee8oMci419HR0Vt/CAaDTJgwobf+EBsb63V4IlGnxCAS0traSiAQoKGhAYDJkydTUlJCVlaWx5GJRJcSg8h1Ghsb8fv9nD9/HoDZs2fj8/lIS0vzODKR6FBiEAkjGAxy8OBBKioqaG9vB2DJkiWsWbOG5ORkj6MTGV6DTQyReFCPyKgRExNDbm4uCxcuZNeuXezbt49Dhw5RV1dHfn4+K1euVP1Bxj31GGRcu3DhAjt37qS+vh6ASZMmUVJSQnZ2treBiQwDHUoSuQ0nTpzA7/fT0tICwKxZs/D5fMyYMcPjyEQiR4lB5DY55zh48CDl5eW99YfFixdTVFSk+oOMCaoxiNymazfiy8nJ4eOPP2bv3r0cPnyYuro68vLyWLVqFXFx+pORsU89BpEBXLx4kZ07d3L06FGgp/5QVFTEwoULPY5M5M7oUJJIhJw8eRK/38+5c+cASE9Px+fzMXPmTI8jE7k9SgwiEeSc49ChQ5SXl3P16lUAFi1aRFFRERMnTvQ4OpHBGWxiiMgN683sYTM7ZGa1ZvZimPkJZrY5NH+nmWX2mffXoemHzOwzkYhHJNLMjKVLl7J+/Xry8vKIjY2lpqaGzZs3U1lZSVdXl9chikTMkBODmcUC/ww8AuQCz5pZ7nXNXgDOO+cWAi8D3wotmws8AywHHgb+b2h9IiNSfHw8RUVFPP3002RnZ9PV1UVlZSWbN2+mpqaG0dgDF7leJHoMRUCtc67OOdcJbALWXddmHfBq6P0W4H4zs9D0Tc65DufcUaA2tD6REW3y5Mk88MADrF27lrS0NK5cucIHH3zA1q1bOXPmjNfhiQxJJBLDXOB4n/HG0LSwbZxzXcAFYPoglwXAzDaYWYWZVTQ1NUUgbJGhmzVrFk888QT33HMPycnJnD17lq1bt/L+++9z+fJlr8MTuV7ate/R0LAhXKNInJRtYaZd358eqM1glu2Z6NxGYCP0FJ9vJ0CR4WRmLF68mKysLHbv3s3u3bupra3l6NGj3HXXXdx1111MmDDB6zBFAJqjVXxuBOb3GZ8HnByojZnFAVOBlkEuKzIqTJgwgcLCQtavX09OTg7d3d3s2rWLzZs3c/jwYdUfZNSIRGIoBxaZWZaZxdNTTN52XZttwPOh958H3nc9fyXbgGdCZy1lAYuAsgjEJOKZSZMmcf/997Nu3TpmzJhBW1sbH374IT//+c85ffq01+GJ3NKQE0OoZvAV4G3gAPCac67azF4ys7WhZt8HpptZLfAXwIuhZauB14D9wFvAl51z3UONSWQkSE9P5/HHH+fee+9l4sSJNDc3s23bNt59910uXbrkdXgiA9IFbiJR0NXV1Vt/6OrqIjY2lpUrV5Kfn6/6g0SNrnwWGYEuX75MWVkZtbW1ACQlJVFUVMTixYvpOYNbZPgoMYiMYGfPnmXHjh2cPXsWgLS0NHw+H7Nnz/Y4MhnLlBhERoHa2lrKysp6r3nIysqiuLiYKVOmeByZjEV6HoPIKLBw4UIyMzPZs2cPVVVVHD16lGPHjvXWH+Lj470OUcYh9RhERogrV65QXl7O4cOHgZ76w5o1a1iyZInqDxIROpQkMko1NTWxY8eO3nsupaamUlpaypw5czyOTEY7JQaRUa6uro5AINBbf8jMzKS4uJipU6d6HJmMVqoxiIxy2dnZZGRksHfvXj7++GPq6+tpaGhgxYoVrF69WvUHGTbqMYiMAm1tbZSXl3Po0CEAEhMTKSwsZOnSpcTEROR5WzIO6FCSyBjU3NzMjh07eu+5lJqaSklJCfPmzfM4MhkNlBhExrCjR48SCAR677mUkZFBSUkJ06ZN8zgyGclUYxAZw7KysvrVHxoaGmhsbGT58uWsXr2ahIQEr0OUUUw9BpFRrq2tjYqKCg4ePAhAQkIChYWFLFu2TPUH6UeHkkTGmXPnzuH3+zl5sudZVykpKZSUlDB//vxbLCnjhRKDyDhVX19PIBDg4sWLAMyfPx+fz6f6g6jGIDJeZWZmMn/+fKqrq9m1axfHjx/vV39ITEz0OkQZ4dRjEBnDrl692lt/cM6RkJBAQUEBubm5qj+MQzqUJCK9Wlpa8Pv9nDhxAoBp06ZRUlJCRkaGx5FJNCkxiMgNjh07RiAQ4MKFCwDMmzcPn89HSkqKx5FJNAw2MQypL2lmqWb2jpnVhF5v+O0yszwz85tZtZntMbP1feb90MyOmllVaMgbSjwicnMLFizgqaeewufzER8fT2NjI1u2bOH3v/897e3tXocnI8SQegxm9vdAi3Pum2b2IpDinPuf17VZDDjnXI2ZzQEqgWXOuVYz+yHwK+fcltvZrnoMIkPX3t5OZWUl+/fvxzlHfHw8BQUFLF++XPWHMSoqPQZgHfBq6P2rwOPXN3DOHXbO1YTenwTOAjOGuF0RGaLExETuvvtuPv/5zzNv3jw6Ozvx+/28/vrrHDt2zOvwxENDTQzpzrlTAKHXmTdrbGZFQDxwpM/kvwsdYnrZzHQdv0iUpaSk8Oijj/Lwww8zbdo0Lly4wNtvv82vf/1rWlpavA5PPHDLQ0lm9i4wK8ysrwGvOuem9Wl73jkXtoplZrOBD4HnnXOBPtNO05MsNgJHnHMvDbD8BmADQEZGRoH+oxGJvGAwyP79+6msrKSjowMzY+nSpRQWFpKUlOR1eDJEZnYMaO4zaaNzbuMN7YZYYzgE3OOcO3Xti985tyRMuyn0JIVvOOdeH2Bd9wB/6Zx77FbbVY1BZHh1dHRQWVlJdXV1b/0hPz+fFStWEBsb63V4coeiVWPYBjwfev88sDVMIPHAz4EfXZ8UQskE63nS+ePAviHGIyIRkJCQQGlpKU899RQZGRl0dnayc+dOXn/9derr670OT4bZUHsM04HXgAygAXjKOddiZoXAl5xzXzSz54AfANV9Fv2Cc67KzN6npxBtQFVomcu32q56DCLRdfz4cfx+P62trQDMmTMHn8/H9OnTPY5MbocucBORiAoGgxw4cIDKysreax6u1R+Sk5M9jk4GQ4lBRIZFR0cHu3btorq6mmAwyIQJE8jPz2flypWqP4xwSgwiMqwuXLhAIBDoveZh8uTJFBcXk52d7XFkMhAlBhGJisbGRgKBQO81D7NmzaK0tJS0tDSPI5PrKTGISNQEg0EOHTpEeXl5b/1hyZIlrFmzRvWHEUQP6hGRqImJiWHZsmXk5OSwa9cu9u3bx6FDhzhy5Ehv/SEuTl83o4V6DCIScRcvXiQQCPRe8zBp0iSKi4vJycnxNrBxToeSRMRzJ0+exO/3c+7cOQDS09MpLS1lxgzdR9MLSgwiMiI453rrD1evXgVg0aJFFBUVMXHiRI+jG19UYxCREeHajfiys7Opqqpiz5491NTUcPToUfLy8li1apXqDyOMegwiElUXL15k586dHD16FICJEydSXFzMwoULPY5s7NOhJBEZ0U6dOoXf76e5uecu0DNnzqS0tJSZM2/6WBcZAiUGERnxnHMcPnyY8vJy2traAFi4cCFFRUVMmjTJ4+jGHtUYRGTEMzOWLFnSr/5QW1tLfX09q1atIi8vT/UHD6jHICIjxqVLl9i5cyd1dXUAJCcn99Yfeh7bIkOhQ0kiMmqdPn0av99PU1MTADNmzKC0tJT09HSPIxvdlBhEZFRzzlFTU0NZWVlv/SEnJ4fi4mLVH+6QagwiMqqZGYsXLyYrK4vdu3eze/dujhw50q/+MGHCBK/DHJPUYxCRUeHy5cuUlZVRW1sL9NQfioqKWLRokeoPg6RDSSIyJp05cwa/38/Zs2cBSEtLw+fzMXv2bI8jG/kGmxhiohGMiEikpKens27dOu677z4mTpxIc3Mzv/zlL3n33Xe5dOmS1+Hdkplx+fJlr8O4qSHVGMwsFdgMZAL1wNPOufNh2nUDe0OjDc65taHpWcAmIBXYBfyJc65zKDGJyNhnZixcuJDMzMze+kNdXV2/+kN8fLzXYY5aQ+0xvAi855xbBLwXGg/nqnMuLzSs7TP9W8DLoeXPAy8MMR4RGUfi4uIoKChg/fr1LFq0iGAwSFVVFZs3b+bgwYOM1EPl//AP/0BpaSlLlizhjTfe8DqcGww1MawDXg29fxV4fLALWk+16D5gy50sLyJyzcSJE7n33nt5/PHHSU9P5+rVq/z2t7/lZz/7GSdPnvQ6vBvExMSwY8cOtm3bxoYNG3rrJSPFUBNDunPuFEDodaC7XyWaWYWZBczs2pf/dKDVOdcVGm8E5g60ITPbEFpHxbWLXkRE+po5cybr1q3j/vvvZ9KkSZw7d45f/epX/OY3v+HixYteh9frhRd6Do4sWbKE1atXEwgEorXptGvfo6FhQ7hGt6wxmNm7wKwws752G8FkOOdOmlk28L6Z7QXCfUoD9vuccxuBjdBzVtJtbFtExpmcnBwWLFjAnj17qKqqor6+noaGBlauXEl+fv6Iqj8456J5um1zRM5Kcs494JxbEWbYCpwxs9kAodew/SHn3MnQax3wIZAPNAPTzOxacpoHjLw+n4iMSnFxcaxevZpnnnmGxYsXEwwG2b17N5s2beLAgQOe1h9+8IMfAFBTU0NVVRXFxcWexRLOUA8lbQOeD71/Hth6fQMzSzGzhND7NOBuYL/r+VQ+AD5/s+VFRIYiOTmZe+65hyeeeIJZs2bR3t7O7373O9544w1OnDjhSUwJCQncfffdPPbYY3z3u98dcc+gGNIFbmY2HXgNyAAagKeccy1mVgh8yTn3RTMrBb4LBOlJRN92zn0/tHw2/3G66sfAc865jlttVxe4icidqqurIxAI9F5LsGDBAkpKSpg6darHkQ0/XfksIjKA7u5u9u7dy8cff8wnn3xCTEwMK1asYPXq1SOq/hBpSgwiIrfQ1tZGRUUFBw8eBCAxMZHCwkKWLl1KTMzYuzGEEoOIyCA1Nzfj9/s5deoUACkpKfh8PubNm+dxZJGlxCAicpuOHj1KIBDovedSRkYGJSUlTJs2zePIIkPPYxARuU1ZWVlkZGSwb98+du3aRUNDA42NjeTm5lJQUEBCQoLXIUaFegwiImFcvXq1t/7gnCMhIYGCggJycyEVQQkAAAg1SURBVHNHbf1Bh5JERCLg3Llz+P3+3nsuTZs2DZ/Px/z58z2O7PYpMYiIRFB9fT2BQKD3nkvz58+npKSElJQUjyMbPNUYREQiKDMzs1/94fjx4/3qD4mJidTX19PS0kJ+fv6oftyoEoOIyCDFxMSwatUqFi9eTEVFBQcOHKC6upqamhqWLVtGVVUVly5dIi4ujlWrVnkd7h1TYhARuU2JiYn8wR/8Abm5uQQCARobG9m0aRP19fUkJCQwZcoUMjIybjjN9UrnSaqbvkfd+S10dF0gNiae1MTlrJr1VeZOvgezkVHUVmIQEblDqampPProo1RWVuL3+zlz5gwJCQm8/fbbJCUl8dxzz2FmdAXb+ff6/0p96zYcELx2S7huaPvkNKev7CQ+dgoPZv+Y9ElFnv5MMPS7q4qIjHtNTU3MmjWL9PR0Ojs7aWxs5Be/+AVvv/02XcF2fnnoEepbf0m36/iPpNBHV/AybZ+c5Nc1j3Hi4r978BP0px6DiMgQzZs3j7y8PJYvX86VK1eora3l1KlTdHR08LtjX6XlajXdrv2W6+kKXuXtI+tZv3wXE+PnRCHy8JQYRESGaMWKFaxYsaJ3PBgM0t3dTRet/NveF+i+9dME/mNZ10V100aK5v7tMEQ6ODqUJCISYTExMUyYMIEDTa8At3faatB1sL/pewTdJ8MT3CAoMYiIDJO61p8P6hDS9ZwL0txWNQwRDY4Sg4jIMOnoar2j5Qy742UjQYlBRGSYxMYk3tmCBnExSZEN5jYoMYiIDJMZyfnYHXzNdgc7mJa4ZBgiGpwhJQYzSzWzd8ysJvR6w92kzOxeM6vqM7Sb2eOheT80s6N95uUNJR4RkZFkZfpX7qDXYMydch9JE2YMS0yDMdQew4vAe865RcB7ofF+nHMfOOfynHN5wH1AG/CbPk3+6tp855x31RYRkQibObGASfHzuZ0zk+Jikrgr/c+HL6hBGGpiWAe8Gnr/KvD4Ldp/HnjTOdc2xO2KiIwKD2T/mAkxEwfVNs6SWTL9T5g9+e5hjurmhpoY0p1zpwBCrzNv0f4Z4KfXTfs7M9tjZi+b2fh4bp6IjBupScv47JI3SYhNIdYGKijHEBeTzNIZz1M6/++jGl84t3xQj5m9C8wKM+trwKvOuWl92p53zoV9aoWZzQb2AHOc67lyIzTtNBAPbASOOOdeGmD5DcAGgIyMjIJjx47d4kcTERk5OrrOc7D5x+w9+090dl8mhtjQDfU6WTD1EVbN+iozJ97yGTpDYmbHgOY+kzY65zbe0G4oT3Azs0PAPc65U6Ev+Q+dc2FL6Wb258By59yGAebfA/ylc+6xW21XT3ATkdHKuSCt7Yfp6G4h1pKYkpBJQlx0ngIXrSe4bQOeB74Zet16k7bPAn/dd4KZzQ4lFaOnPrFviPGIiIxoZjGkJC31OoybGmqN4ZvAg2ZWAzwYGsfMCs3se9camVkmMB+4/n6yPzGzvcBeIA34+hDjERGRIRpSj8E5dw64P8z0CuCLfcbrgblh2t03lO2LiEjk6cpnERHpR4lBRET6UWIQEZF+lBhERKQfJQYREelHiUFERPpRYhARkX6GdEsMr5hZExDNmyWl0f/+IqJ9MhDtl/C0X8KL9n5Z4Jy75YMeRmViiDYzqxjM/UXGE+2T8LRfwtN+CW+k7hcdShIRkX6UGEREpB8lhsG54X7lon0yAO2X8LRfwhuR+0U1BhER6Uc9BhER6UeJIQwze8rMqs0saGYDnjFgZg+b2SEzqzWzF6MZY7SZWaqZvWNmNaHXgR7h2m1mVaFhW7TjjJZbffZmlmBmm0Pzd4aeSTLmDWK/fMHMmvr8jnwx3HrGEjN7xczOmlnYB5FZj38K7bM9ZrY62jFeT4khvH3Ak8BvB2pgZrHAPwOPALnAs2aWG53wPPEi8J5zbhHwXmg8nKvOubzQsDZ64UXPID/7F4DzzrmFwMvAt6IbZfTdxt/E5j6/I98LM3+s+SHw8E3mPwIsCg0bgO9EIaabUmIIwzl3wDl36BbNioBa51ydc64T2ASsG/7oPLMOeDX0/lV6HsU6Xg3ms++7v7YA94ceYTuWjbe/iUFxzv0WaLlJk3XAj1yPADDNzGZHJ7rwlBju3FzgeJ/xRsI8pW4MSXfOnQIIvc4coF2imVWYWcDMxmryGMxn39vGOdcFXACmRyU67wz2b+JzoUMmW8xsfnRCG9FG3HfJkB7tOZqZ2bvArDCzvuac2zqYVYSZNqpP8brZPrmN1WQ4506aWTbwvpntdc4diUyEI8ZgPvsx9/sxCIP5mX8J/NQ512FmX6KnVzXeH/E74n5Xxm1icM49MMRVNAJ9/9uZB5wc4jo9dbN9YmZnzGy2c+5UqJt7doB1nAy91pnZh0A+MNYSw2A++2ttGs0sDpjKzQ8njAW33C+h58Rf8y+Mg9rLIIy47xIdSrpz5cAiM8sys3jgGWDMnoVDz8/2fOj988ANvSozSzGzhND7NOBuYH/UIoyewXz2fffX54H33di/aOiW++W6Y+drgQNRjG+k2gb8aejspBLgwrXDtp5xzmm4bgCeoCeLdwBngLdD0+cA2/u0exQ4TM9/xF/zOu5h3ifT6TkbqSb0mhqaXgh8L/S+FNgL7A69vuB13MO4P2747IGXgLWh94nA60AtUAZkex3zCNkv3wCqQ78jHwBLvY45Cvvkp8Ap4JPQ98oLwJeAL4XmGz1ncx0J/d0Ueh2zrnwWEZF+dChJRET6UWIQEZF+lBhERKQfJQYREelHiUFERPpRYhARkX6UGEREpB8lBhER6ef/A3tSneUEExpBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([ 1. , -0.6968324]), 'a_c': array([-1. , 0.6968324])}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"c\")\n",
"pattern.add_node(\"a\")\n",
"pattern.add_edge(\"c\", \"a\")\n",
"rule = Rule.from_transform(pattern)\n",
"rule.inject_remove_edge(\"c\", \"a\")\n",
"rule.inject_merge_nodes([\"c\", \"a\"])\n",
"plot_rule(rule)\n",
"\n",
"g.rewrite(rule, {\"a\": rhs_instance[\"a\"], \"c\": rhs_instance[\"c\"]}, message=\"Merge c and a\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Switch back to the 'master' branch."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"g.switch_branch(\"master\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a rule that clones a node 'a'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([-1. , 0.28258431]),\n",
" 'c': array([ 0.84888266, -0.38447044]),\n",
" 'a': array([0.10569474, 0.38496972]),\n",
" 'a1': array([ 0.04542259, -0.28308359])}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"a\")\n",
"rule = Rule.from_transform(pattern)\n",
"_, rhs_clone = rule.inject_clone_node(\"a\")\n",
"rhs_instance, rollback_commit = g.rewrite(rule, {\"a\": rhs_instance[\"a\"]}, message=\"Clone a\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new branch 'test'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'fc2b3516-b31f-4bf0-ad52-533813781d3f'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.branch(\"test\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this branch apply the rule that adds a new node 'd' and connects it with an edge to one of the cloned 'a' nodes"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([-0.67410961, -0.51722017]),\n",
" 'c': array([-0.03253862, -0.18702102]),\n",
" 'a': array([-0.4006808 , -0.12011358]),\n",
" 'a1': array([0.10732903, 0.12056189]),\n",
" 'd': array([1. , 0.70379289])}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"a\")\n",
"rule = Rule.from_transform(pattern)\n",
"rule.inject_add_node(\"d\")\n",
"rule.inject_add_edge(\"a\", \"d\")\n",
"g.rewrite(rule, {\"a\": rhs_instance[rhs_clone]}, message=\"Add d -> clone of a\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Switch back to 'master'"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"g.switch_branch(\"master\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove a node 'a'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([-6.73394186e-04, -1.00000000e+00]),\n",
" 'c': array([0.37144577, 0.86176356]),\n",
" 'a1': array([-0.37077238, 0.13823644])}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern = NXGraph()\n",
"pattern.add_node(\"a\")\n",
"rule = Rule.from_transform(pattern)\n",
"rule.inject_remove_node(\"a\")\n",
"rhs_instance, _ = g.rewrite(rule, {\"a\": rhs_instance[\"a\"]}, message=\"Remove a\")\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Merge the branch 'dev' into 'master'"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'309a5f73-aa58-4d11-845d-4efacd87679c'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.merge_with(\"dev\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([-0.32108623, 1. ]),\n",
" 'a1_c': array([ 0.32108623, -1. ])}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Merge 'test' into 'master'"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3d6dfa53-8edb-4602-b6c3-46349534d800'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.merge_with(\"test\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([-0.42604917, -0.72368097]),\n",
" 'a1_c': array([-0.02907932, 0.09656937]),\n",
" 'd': array([0.28941266, 1. ]),\n",
" 'a_1': array([ 0.16571582, -0.3728884 ])}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_graph(g.graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can inspect the version control object in more details and look at its attribute `_revision_graph`, whose nodes represent the commits and whose edges represent graph deltas between different commits (basically, rewriting rules that constitute commits). Here we can see that on the nodes of the revision graph are stored branch names to which commits belong and user specified commit messages."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Node ID: d87f0d16-155b-4854-bfe9-f6bfcdb7f946\n",
"Attributes: \n",
"\t {'branch': 'master', 'message': 'Initial commit', 'time': datetime.datetime(2020, 3, 30, 20, 57, 52, 620015)}\n",
"Node ID: 5ced4ea5-a107-42de-a58d-eb635dfa92b3\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 52, 633859), 'message': 'Add a -> b'}\n",
"Node ID: 0f1cbd2a-0de8-425f-964d-dbdf5d2e70bc\n",
"Attributes: \n",
"\t {'branch': 'branch', 'time': datetime.datetime(2020, 3, 30, 20, 57, 52, 830309), 'message': \"Created branch 'branch'\"}\n",
"Node ID: e916119f-61d5-4e38-ba6f-9eb921f16a46\n",
"Attributes: \n",
"\t {'branch': 'branch', 'time': datetime.datetime(2020, 3, 30, 20, 57, 53, 438862), 'message': 'Clone b'}\n",
"Node ID: 6d0e2417-b8c5-4289-bc00-a87904ef945f\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 53, 648498), 'message': 'Add c and c->a'}\n",
"Node ID: 718d78fe-e39c-4fa8-9ec2-6e16c9f72a70\n",
"Attributes: \n",
"\t {'branch': 'dev', 'time': datetime.datetime(2020, 3, 30, 20, 57, 53, 830322), 'message': \"Created branch 'dev'\"}\n",
"Node ID: 85c8360e-29c3-4e4d-afdd-f1ca523bc75b\n",
"Attributes: \n",
"\t {'branch': 'dev', 'time': datetime.datetime(2020, 3, 30, 20, 57, 54, 789783), 'message': 'Merge c and a'}\n",
"Node ID: 274f1558-a12c-4448-9fd2-7d63f4772a40\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 55, 105118), 'message': 'Clone a'}\n",
"Node ID: fc2b3516-b31f-4bf0-ad52-533813781d3f\n",
"Attributes: \n",
"\t {'branch': 'test', 'time': datetime.datetime(2020, 3, 30, 20, 57, 55, 467808), 'message': \"Created branch 'test'\"}\n",
"Node ID: c889ccbe-172d-4579-b02c-15a64b129e2d\n",
"Attributes: \n",
"\t {'branch': 'test', 'time': datetime.datetime(2020, 3, 30, 20, 57, 55, 482334), 'message': 'Add d -> clone of a'}\n",
"Node ID: 327527a7-e926-4624-bbbf-c3016795c7c2\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 55, 738230), 'message': 'Remove a'}\n",
"Node ID: 309a5f73-aa58-4d11-845d-4efacd87679c\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 55, 945093), 'message': \"Merged branch 'dev' into 'master'\"}\n",
"Node ID: 3d6dfa53-8edb-4602-b6c3-46349534d800\n",
"Attributes: \n",
"\t {'branch': 'master', 'time': datetime.datetime(2020, 3, 30, 20, 57, 56, 279136), 'message': \"Merged branch 'test' into 'master'\"}\n"
]
}
],
"source": [
"for n, attrs in g._revision_graph.nodes(data=True):\n",
" print(\"Node ID: \", n)\n",
" print(\"Attributes: \")\n",
" print(\"\\t\", attrs)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-03-30 20:57:52.620015 d87f0d16-155b-4854-bfe9-f6bfcdb7f946 master Initial commit\n",
"2020-03-30 20:57:52.633859 5ced4ea5-a107-42de-a58d-eb635dfa92b3 master Add a -> b\n",
"2020-03-30 20:57:52.830309 0f1cbd2a-0de8-425f-964d-dbdf5d2e70bc branch Created branch 'branch'\n",
"2020-03-30 20:57:53.438862 e916119f-61d5-4e38-ba6f-9eb921f16a46 branch Clone b\n",
"2020-03-30 20:57:53.648498 6d0e2417-b8c5-4289-bc00-a87904ef945f master Add c and c->a\n",
"2020-03-30 20:57:53.830322 718d78fe-e39c-4fa8-9ec2-6e16c9f72a70 dev Created branch 'dev'\n",
"2020-03-30 20:57:54.789783 85c8360e-29c3-4e4d-afdd-f1ca523bc75b dev Merge c and a\n",
"2020-03-30 20:57:55.105118 274f1558-a12c-4448-9fd2-7d63f4772a40 master Clone a\n",
"2020-03-30 20:57:55.467808 fc2b3516-b31f-4bf0-ad52-533813781d3f test Created branch 'test'\n",
"2020-03-30 20:57:55.482334 c889ccbe-172d-4579-b02c-15a64b129e2d test Add d -> clone of a\n",
"2020-03-30 20:57:55.738230 327527a7-e926-4624-bbbf-c3016795c7c2 master Remove a\n",
"2020-03-30 20:57:55.945093 309a5f73-aa58-4d11-845d-4efacd87679c master Merged branch 'dev' into 'master'\n",
"2020-03-30 20:57:56.279136 3d6dfa53-8edb-4602-b6c3-46349534d800 master Merged branch 'test' into 'master'\n"
]
}
],
"source": [
"# Pretty-print the history\n",
"g.print_history()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can rollback to some previous commit (commit where we first cloned the node 'a')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created the new head for 'dev'\n",
"Created the new head for 'master'\n"
]
}
],
"source": [
"g.rollback(rollback_commit)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Branches: ['master', 'branch', 'dev']\n",
"Current branch 'master'\n",
"Updated revision graph:\n",
"2020-03-30 20:57:52.620015 d87f0d16-155b-4854-bfe9-f6bfcdb7f946 master Initial commit\n",
"2020-03-30 20:57:52.633859 5ced4ea5-a107-42de-a58d-eb635dfa92b3 master Add a -> b\n",
"2020-03-30 20:57:52.830309 0f1cbd2a-0de8-425f-964d-dbdf5d2e70bc branch Created branch 'branch'\n",
"2020-03-30 20:57:53.438862 e916119f-61d5-4e38-ba6f-9eb921f16a46 branch Clone b\n",
"2020-03-30 20:57:53.648498 6d0e2417-b8c5-4289-bc00-a87904ef945f master Add c and c->a\n",
"2020-03-30 20:57:53.830322 718d78fe-e39c-4fa8-9ec2-6e16c9f72a70 dev Created branch 'dev'\n",
"2020-03-30 20:57:54.789783 85c8360e-29c3-4e4d-afdd-f1ca523bc75b dev Merge c and a\n",
"2020-03-30 20:57:55.105118 274f1558-a12c-4448-9fd2-7d63f4772a40 master Clone a\n",
"Current graph object\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Nodes:\n",
"\n",
"b : {}\n",
"a1 : {}\n",
"a : {}\n",
"c : {}\n",
"\n",
"Edges:\n",
"\n",
"a1 -> b : {}\n",
"a1 -> a1 : {}\n",
"a1 -> a : {}\n",
"a -> a1 : {}\n",
"a -> b : {}\n",
"a -> a : {}\n",
"c -> a1 : {}\n",
"c -> a : {}\n"
]
}
],
"source": [
"print(\"Branches: \", g.branches())\n",
"print(\"Current branch '{}'\".format(g.current_branch()))\n",
"print(\"Updated revision graph:\")\n",
"g.print_history()\n",
"print(\"Current graph object\")\n",
"plot_graph(g.graph)\n",
"print_graph(g.graph)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"g.switch_branch(\"branch\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created the new head for 'branch'\n"
]
}
],
"source": [
"g.rollback(branch_commit)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-03-30 20:57:52.620015 d87f0d16-155b-4854-bfe9-f6bfcdb7f946 master Initial commit\n",
"2020-03-30 20:57:52.633859 5ced4ea5-a107-42de-a58d-eb635dfa92b3 master Add a -> b\n",
"2020-03-30 20:57:52.830309 0f1cbd2a-0de8-425f-964d-dbdf5d2e70bc branch Created branch 'branch'\n",
"2020-03-30 20:57:53.648498 6d0e2417-b8c5-4289-bc00-a87904ef945f master Add c and c->a\n",
"2020-03-30 20:57:53.830322 718d78fe-e39c-4fa8-9ec2-6e16c9f72a70 dev Created branch 'dev'\n",
"2020-03-30 20:57:54.789783 85c8360e-29c3-4e4d-afdd-f1ca523bc75b dev Merge c and a\n",
"2020-03-30 20:57:55.105118 274f1558-a12c-4448-9fd2-7d63f4772a40 master Clone a\n"
]
}
],
"source": [
"g.print_history()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'master': '274f1558-a12c-4448-9fd2-7d63f4772a40', 'branch': '0f1cbd2a-0de8-425f-964d-dbdf5d2e70bc', 'dev': '85c8360e-29c3-4e4d-afdd-f1ca523bc75b'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'a': array([0.23148329, 1. ]), 'b': array([-0.23148329, -1. ])}"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(g._heads)\n",
"plot_graph(g.graph)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"g.switch_branch(\"master\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9+/ZF3HEhRDozm81X7V2IVi0Eb1NdqXcheNE5vyCPSxn/Gb+eW1dMWvs5N/wsH6v9q019to2SxVxSwMzMDL/5zW+Ym5vjmmuu4a677op3SEIkPa112HBR6MFhdjZkeCdzFPZ8H8xrP/MPys6o5Is3tMcwalnMJW14vV5eeeUV5ubmsNvt7Nu3L94hCZESgheJg70LH374ITabjb1791JSUsL09DQul4t+1wku6Qw060/+C/6ZLYh8bST5JzGtNW+88Qbj4+MUFhZy7733ylS8QmwBv9/P22+/jdPppKBg8Q6dhYWFxesLjRZ0qX9x0HudzKbMGEe6dpL8k9iJEyfo7e2NWIZRCBE7AwMDtLW18d5773Hy5ElmZmbw+/1YrVby8/P5vc/+DkV3R1+TeTUlWdfHONq1k+SfpC5evMiHH36IyWTivvvui7okoBBifUZHR2lra+Py5ctcuXIFp9OJ2+0GFg8Cc3NzaK2xWq00NDRwxx138OjnvsRlXx+XXS+gid5nEI3FlMONFX+6VR9l9f3Hbc9iw/r7+41lGD/xiU9QWVkZ54iESD4ul4u2tjY6OzuNRD89PR2xXXZ2NtXV1bS0tHD58mUyMzO55ZZb2L9/P83NzQDkz/0Huid/jV+vPflbzflU5+2P1cdZN0n+SWZycpJXX32VQCDAjTfeaPzyCSFWNjMzw6VLl7h8+TLd3d04HA4mJycjtrPZbFRVVVFXV8euXbtobm42pp4YHh7mV7/6FUVFRdx3331hcxUVZe1mb+W3eG/w+/gC7lXjsZiyeWDXCygVv2t0kvyTiMfjMZZh3LlzJ7fddlu8QxIi4bjdbtrb240z+t7e3qjzAGVkZIQl+qamJux2+4o3TZSXl/Poo4+Sl5cXtfP3popvAnBm4C8J6AU0/ohtzCoLk7LwYNOvKcvZu8lPujmS/JNEcBnGyclJSkpKZBlGIVhs0Lp8+TLt7e309PTQ29sbdXpoi8VCZWUltbW1NDQ00NzcTF1d3brvjovWJBaklOLmym9RV/gQHw79VzrHf4lJZaBQBPCTYcrm+h3fYHfpY9gsJev+rLEmyT9JvPXWW/T395Odnc2BAwfWPOeIEKnC5/Nx+fJlOjo66O7upre3l6GhIQKB8DttzGYzO3bsMBJ9Y2MjDQ0N27Z0aXHWHm7b8VfUqD+moMyPL+Am01JAQWZTXId5lpPknwTOnj3LpUuXMJvNPPDAA7IMo0h5Pp+Pnp4e2tvb6e7upqenh8HBQfz+8KEUk8nEjh07qKmpoaGhwRi+iedtz16vlyeeeILu7m6eeOIJamuvi1ssVyPJP8H19PRw4sQJYHEZxrKysjhHJERsBQIBHA4H7e3tdHV10dPTw8DAAAsLkR2zZWVl2O126uvraWxspLm5OaFmrtVac/jwYc6ePUsgEGBwcDBh1yaR5J/AxsfHjWUYW1tbaWhIjBWAhNgMp9NpJPre3l6cTiderzdiu6KiImpqati5c6eR6HNzc+MQ8dq9++67vPPOOwwNDVFcXIzL5Yp3SCuS5J+g3G43r7zyCgsLCzQ2NrJ3b3zvDBBiIwYHB+ns7KSjo8NI9HNzkbNfBufPCSb6pqYmCgsL4xDxxnV3d3PixAnef/99srKygMXPr7VOyJszJPknIJ/Px6uvvsrMzAw7duzg7rvvjndIQqxqdHSUjo6OsO7YmZnIictyc3Ox2+3s3LnTGKOPtr5vsgn2DeTl5TE7O0tWVhYTExNMTk4m5IFMkn8COnr0KMPDw+Tm5nL//fdjNm9gxightpDL5aKjo8O4l97hcDA1NRWxXVZWFna7nbq6OhobG2lsbKSioiIOEW+9m266icrKSmZmZigpKWH37t1YrdZtu8tovRIzqjR25swZOjs7ycjI4MCBA0b5KES8zMzMhDVNORwOJiYmIrazWq3Y7XZqa2vDmqbSSVZWFmVlZdTU1PClL30JrXXCzrQryT+BXL58mVOnThnLMF6toUSIrTA/P097ezsdHR1Goo+2vGGwOza0aaqmpiZhE912GRwcBKCiogKlVEKO9QdJ8k8Qw8PDHDlyBIA77rgjYW8PE6nD6/UaY/RdXV04HA5GRkaiNk1VVFRQV1dHfX290R2bqMMZ8RSa/BOd/OslgJmZGQ4dOoTf72fPnj1cf3385vgWqSnYHRtM9MHu2GhNU8FpEOrr62lqamLXrl2S6NdIkr9Ys4WFBQ4dOsTc3BzV1dV87GMfi3dIIskFAgGjOzaY6AcGBvD5IqcbLi8vNxJ9Y2Mju3btSqimqWQyPz+Py+XCbDYnxd1LkvzjKLgM49jYGAUFBbIMo1i3QCCA0+k0hm96enro7++P2h1bUlJCbW1t2L302dnZcYg6NQ0NDQGLB9Rk+H8syT+O3nnnHXp6esjMzOTAgQNkZsZvPU+RHPr7+2lvbw9L9PPz8xHbFRYWGk1Tu3btoqWlhby8vDhEnD6SacgHJPnHzaVLlzh79qyxDGNwUWghgoaHh41Ef+XKFfr6+owlBUPl5eUZTVONjY20tLQkZFNRqpPkL1bV39/PW2+9BcBdd91lrBQk0tf4+DhtbW10dXWtuqRgaHdsS0tLUowvpzqfz8fIyAgAO3bsiHM0ayPJf5tNTU3x2muvEQgEuOGGG9i9e3e8QxLbbGpqyjijX21JwWB3bHBJwWQ5q0w3o6OjBAIBiouL4zqd9HpI8t9GwWUYPR4PdXV13H777fEOSWyx0CUFg4k+2pKCVquV6upq4176lpYWqqurE7pJSHwk2YZ8QJL/tgkEAhw+fBiXy0VxcTGf/OQn5T92ipmfnzdmsAwm+uBQQKjgkoI7d+5k586dtLS0UFtbmxR3iIjoJPmLFR07doy+vj6ysrJkGcYUsLCwYCT6K1eu0NPTs2J37I4dO4wz+qamJurr66VpKoVorSX5i+jOnTvHhQsXjGUYE31BChHO5/Nx5coV2tvbjUR/tSUFl3fHJssYsNiYiYkJvF4vubm5SfV/W5L/FnM4HBw/fhyA/fv3U15eHueIxNUElxRsa2sz1o692pKCNTU1RqJvbGyU7tg0FGzuSpa7fIIk+W+h8fFxDh8+jNaaW265hV27dsU7JLFM6JKCPT099PX1RV1SsLi42Ej0wemKk+ksT2ydZBzyAUn+W2Zubs5YhnHXrl3ccsst8Q4p7Q0ODoZ1xzqdzqjdsQUFBdjtdhoaGmhoaKClpYX8/Pw4RCySgSR/YfD7/cYyjOXl5bIMYxyMjo7S1tZmJHqHwxG1Oza4pGB9fb2R6GUdBbFWs7OzTE9PY7Vak+73RpI/8IUvfIG2tjY8Hg+NjY0899xzFBUVbfj9jh49ytDQkLEMo9zZsbVcLpexAElvby+9vb1Ru2ODSwoGu2Obm5vlGozYlNDJ3JLt1m3JSsDTTz9ttMh/5zvf4amnnuLJJ5/c0Hu99957dHR0YLFYOHDggMyaGGPBJQU7OjqMM/poSwrabDaqqqrCumNlGg0Ra8k65AOS/AF4/vnn+dnPfobX62V2dpbm5uYNvU9XVxcnT54EkGUYY8DtdofdS9/b28vY2FjEdsElBYOJPrh2rDRNia2W9slfKXUAeBowAz/SWj+57PlM4HngFmAM+AOt9ZVY7Huz3nzzTZ599lmOHTtGWVkZP//5zzl48OC632dkZCRsGca6uroYR5ragksKBqdB6O3tNeZLCWU2m8PWjm1sbKS+vl4Svdh2CwsLjI2NYTKZknL4cNPJXyllBp4B7gOcwEml1Eta6wshm30VmNBaNyqlHgWeAv5gs/uOBZfLRUFBASUlJXg8Hp577rl1v8fs7CyHDh3C5/Oxe/dubrjhhi2INHUElxQMToPQ29vL8PBwRNNUMNHX1NQYiV6WFBSJYmhoCK01ZWVlSfk7GYuIbwM6tdZdAEqpF4CHgdDk/zDw3aWvfwX8QCmltNY6BvvflAcffJCf/vSn7N69G7vdTmtrK+++++6aX+/z+Th06BBut5uqqiruuuuuLYw2+fh8PmNJweB8N9GWFAyePYUuKdjU1CTdsSJhJfOQD8Qm+VcDjpDvncDy6SqNbbTWPqXUJFACjIZupJR6HHgcoLa2Ngahrc5isfCLX/xiQ68NLsM4OjpKfn4+9913X1oPPwS7Yzs7O9e1pGCwO1YujotkErzTJwGTf6lS6lTI9we11hFj2bFI/tHub1p+Rr+WbVgK8CBAa2tr3KuC1Zw8eZIrV65gtVrTchnGvr6+iLVjozVNFRUVGUsKNjY20tzcLN2xIqkFAoFEntZhVGvdutpGsUj+TqAm5Hs70L/CNk6llAUoACInNU8i7e3tvP/++yiluO+++1J+2bzlSwo6nU7m5uYitsvPzw9L9E1NTSn/sxHpZ2xsDJ/PR0FBAVlZWfEOZ0NikfxPAk1KqXqgD3gU+PyybV4CHgOOA58F3kiE8f6NGhwc5OjRo8DiMozV1dVxjii2gksKhib6mZmZiO2ys7Opra2ltraWpqYmmpqaZElBkRYSeMhnzTad/JfG8L8BHGLxVs/ntNbnlVJ/AZzSWr8E/A3wE6VUJ4tn/I9udr/xMjU1xauvvkogEOD6669nz5498Q5pU6ampmhra6Ozs9NI9LKkoBBXF7zYm4BDPmsWk/uTtNYvAy8ve+z/Dvl6Hvj9WOwrnrxeL6+88grz8/PU1tZyxx13xDukdQl2x3Z1ddHV1YXT6Vx1ScGGhgaam5ux2+1xiFiIxJTsd/qAdPiuWbItwzg/Px/WNLXSkoIZGRlUVlaGJfqampq0vmtJiKuZmprC7XZjs9mS+nqWJP81On78OE6nE5vNxgMPPJBQ9597vd6IpqmVlhSsqKgwlhRsbm6mrq4uKRtUhIiXVDjrB0n+a3L+/HnOnz+PyWTigQceIC8vL26x+Hw+uru76ejooKuri97eXoaGhqIuKVhZWUltbS11dXU0NzfT0NCQUActIZKRJP804XQ6OXbsGLC4DON2XuAJBAJGd2ww0UfrjoXFJQWXN03JkoJCxJ4k/zQwMTFhLMO4d+9eGhsbt2xfgUAAp9NpDN10d3ev2B0buqRgsGlKumOF2Hrz8/O4XC7MZnPS39actslf6wB900f4YPBpxubO4g94yDDnsrPw33Bd+dexUcOhQ4fwer00NDTEfBnGwcFBLl26ZCwSfrUlBUPXjm1ubpYlBYWIk9DFW5L9poi0TP4js+/x6uVH8fgn8QVmjccXAtNcHPkxbaM/xTy3C+/MH1JWVsv+/fs3dWdP6JKCwXvpoy0pmJeXZ6w0FeyOlTUBhEgcqTLkA2mY/Idm3uGfOh4OS/qhNAv49QJ+6yXUtU9zz3VvR9wNMzk5yZEjR4w7f0K5XK6IpqloSwpmZ2cbiT64dmxZWVnsPqgQIuZSobM3KK2S/7xvnJc7fm/FxB/G5EdZJ3l74I/4N3kvGQ/39PTw+uuvc/HiRSorK8nJyaGvr8+4l36l7tjlTVOypKAQycXv9zM8PAwkd2dvUFol/7bRnxDQkRdQVxLAy+DMcVzzHWSb6vj1r3/Na6+9xqVLlxgfHycrK4ujR4+GzVApSwoKkZqCvTPFxcUpcct02iR/rQOcHfqv+HXkTJRX4w8s8Ntj/wenflPH8ePHmZiYQGuN2WzG7/dTWFjI7bffbpzR19XVSaIXIgWl0ng/pFHyn/J0sRCIHHtflfLjyTpNRcXtxrCNzWbDarVSWFjIvffey4EDB2IfsBAioUjyT1Ie/yRqgx/Xkhng29/+NmazmdnZWfr6+ujr62NwcDApF24WQqyP1jqlLvZCGiV/iykbCKy6XdTXmm2YzWZgccbL/Px8FhYWyMzMpKamZpVXCyGSncvlwuPxkJOTkzKr0KVN8s/PrCOg/atvGEVp1g28/fbb/OM//iNKKWO5xoyMDLxeL/v3749hpEKIRJNqQz6QRsnfYsqmqeRR2kZ/giZybpyVX5fDDRV/ystHztPe3k5/fz9msxmTyURjYyN79+5lfn5e5tERIoVJ8k9y15d/nY6xv8Ov1578M82FVOfdzWOP7cNqtXL8+HEuXbqE1+tlfn6e9vZ2HA4H5eXl1NTUUFNTQ1lZWULP9S+EWB9J/kmuKGs3t1T+X5wZfBJfIHJ6heUspmweaAHC71YAABThSURBVHwBpUxkZmby5S9/mZqaGk6fPk17ezstLS00NTXR39/P8PAww8PDnD592ljysKamBrvdnrQLPAshwO12Mz09TUZGRkpNt5JWyR/gxoo/BeD0wBMEtC/qEJBZZWE2ZfBg44uUZt9kPG4ymfjkJz9JVlYWeXl53HrrrbS2tuLz+ejv76e3txeHw8H09DSdnZ10dnYCi9MtB6uC8vJyqQqESCKh6/Wm0v9dpbWOdwxRtba26lOnTm3Z+0/MXeLc8LN0jL+AwgSY0PiwmvO5vvyPaSn9IjbLykf56elpcnNzo/4yTE5O4nA4jPn3QxdayczMNKqCmpoaqQqESHDHjh3j3LlztLa2snfv3niHsyql1Gmtdeuq26Vr8g/yBdxMe3rxBdxYzfnkZzagVOw6dINVgcPhwOFwMDU1FfZ8aWkptbW1UhUIkaBefPFFRkdHeeihh5JiTq61Jv+0G/ZZzmLKpihr99a9v8VCbW0ttbW1wEdVgcPhoL+/n9HRUUZHRzlz5oxUBUIkmIWFBcbGxjCZTCnX0Jn2yX+7FRQUUFBQwHXXXYfP52NgYMAYIpqamuLy5ctcvnwZWKwKQq8VyJxBQmyv4eFhtNaUlZVFTO2e7FLr0yQZi8ViJPd9+/YxNTVlXDQOrQree+89MjMzqa6upra2FrvdLss2CrENQi/2phpJ/gkkPz+f6667LqIqCK4T0NXVRVdXFyBVgRDbIRXv7w+S5J+gQqsCgKmpqajXCt577z2sVmvYtQKpCoTYvEAgkHKTuYWS5J8k8vPzufbaa7n22mvx+XwMDg4aQ0TLq4KSkhJqamqora2VqkCIDRofH8fn85Gfn5+SN19I8k9CFosFu92O3W4HIquCsbExxsbGeP/996UqEGKDUnnIByT5p4TQqsDv94ddK3C5XFGrgpqaGnbs2CFVgRArkOQvkorZbDaqgjvvvJPp6WnjVtJoVUF1dbVxMMjJyYl3+EIkDEn+Iqnl5eVxzTXXcM0110StCrq7u+nu7gaguLjY6DaWqkCks6mpKdxuNzabjcLCwniHsyUk+aeRlaoCh8NBX18f4+PjjI+PS1Ug0l4q3+UTJMk/jS2vCgYHB40homhVQfBAUFFRIVWBSGmpPuQDkvzFErPZTHV1NdXV1dxxxx3MzMwYt5KGVgUffPABGRkZRrexVAUiFaVyZ2+QJH8RVW5ubtSqwOFwMDExwZUrV7hy5QogVYFILR6Ph4mJCcxmM6WlpfEOZ8tsKvkrpYqBXwA7gSvA57TWE1G28wMfLn3bq7X+zGb2K7ZXtKog2rWC0KogeDDIzc2Nd/hCrEtwvL+8vByz2RznaLbOZs/8vw28rrV+Uin17aXv/88o281prW+K8rhIQrm5uezZs4c9e/YQCATCuo2XVwVFRUXGgaCyslKqApHw0mHIBzaf/B8G9i99/WPgCNGTv0hRJpOJqqoqqqqqjKrA6XTS29tLX18fExMTTExMcPbsWSwWi1EV1NbWSlUgElI6XOyFzSf/HVrrAQCt9YBSaqXVDmxKqVOAD3hSa/0Pm9yvSFC5ubns3r2b3bt3G1VBcIhofHycnp4eenp6ACgsLDQuGldUVKR0iS2Sg9/vZ3h4GJAzf5RSh4Foh8A/X8d+arXW/UqpBuANpdSHWuvLUfb1OPA4YKx8JZJXaFVw++23Mzs7axwInE4nLpcLl8slVYFIGCMjIwQCAYqLi8nMzIx3OBtVunSyHXRQa31w+UarJn+t9b0rPaeUGlJKVS6d9VcCwyu8R//S311KqSPAzUBE8l8K8CAsruG7WmwiueTk5IRVBUNDQ0ZfQbSqIPRagVQFYjukyJDP6Has4fsS8Bjw5NLfv12+gVKqCHBrrT1KqVLgY8BfbnK/IsmZTCYqKyuprKzktttuC6sK+vr6jKrgww8/xGKxUFVVZQwR5eXlxTt8kaLSobM3aLPJ/0ng75VSXwV6gd8HUEq1Av9ea/01YA/wQ6VUADCxOOZ/YZP7FSlmparA4XAwNjZGb28vvb29gFQFYmtorVPlzH9NNpX8tdZjwKeiPH4K+NrS18eA6zezH5FellcFbrc76rWC0KogeDDIz8+Pd/giSblcLjweDzk5OWlxzUk6fEXCy87OpqWlhZaWFgKBAMPDw0ZfwfKqoKCgwLhoLFWBWI90OusHSf4iyZhMJioqKqioqIhaFUxOTjI5Ocm5c+ekKhDrkk7j/SDJXyS5aFVB8GAwOjoatSoIXiuwWOTXX3wkXTp7g+S3X6SM0Krg1ltvxe12G93G0aqCyspKY4hIqoL05na7mZqaIiMjg+Li4niHsy0k+YuUlZ2dTXNzM83NzVGrguDXx44dIz8/37iVVKqC9BN61p8u80/Jb7hICytVBcFrBVNTU5w7d45z585hNpvDrhUUFBTEO3yxxdLtYi9I8hdpanlVMDIyYnQbh1YFAPn5+caBoKqqSqqCFCTJX4g0ZDKZ2LFjBzt27KC1tZW5ubmwO4impqY4f/4858+fx2w2U1lZaQwRSVWQ/BYWFhgbG0MpRXn5SnNTph5J/kIsk5WVZVQFWuuwawUjIyM4nU6cTicgVUEqGB4eRmtNWVlZWv37pc8nFWIDlFIRVUHwWoHD4YhaFQQPBoWFhfEOX6xBOg75gCR/IdYlKyuLpqYmmpqa0FozMjJidBuHVgXHjx8nLy/POBBUV1en1VllMpHkL4RYl+AYcXl5Oa2trczPz4ddK5ienubChQtcuHDBmK8o2FcgVUFiCN4CDOnT3BUkyV+IGLHZbBFVQfBgMDw8TF9fH319fZw4cSKsKqiqqiIjIyPe4ael8fFxFhYWyM/PJzs7O97hbCtJ/kJsgdCq4JZbbmF+fj7sWsFKVUFNTQ1FRUXxDj9tpOuQD0jyF2Jb2Gw2GhsbaWxsRGtt9BL09vZGVAW5ublh1wqkKtg6kvyFENtGKUVZWRllZWXs3bs3oiqYmZnh4sWLXLx40ehMDvYVSFWwMcePH+db3/oW09PTAHzve9/j/vvvl+QvhIiflaqC4LWC/v5++vv7pSrYoPHxcR555BFefPFF9u3bh9/vZ2pqiunpadxuNzabLS0vwEvyFyKBRKsK+vr6rloVBA8G6TIb5XodP36ca665hn379gFgNpspKiqio6MDSL+7fIIk+QuRwGw2G7t27WLXrl1orY2Vy5ZXBe+88w65ubnY7XZqa2upqqrCarXGO/yEoLWO+ng6D/mAJH8hkoZSitLSUkpLS9m7dy8ejyfiWsGlS5e4dOmSVAUh9u3bx9e+9jWOHz/OnXfeaQz7SPIXQiSlzMzMiKogeCAYGhoKqwpycnLCrhWkU1VQXFzMiy++yJ/92Z8xOzuLyWTiiSeeYGJiArPZTGlpabxDjAu1UkkUb62trfrUqVPxDkOIpOTxeOjr6zNWMXO73cZzwVlMg93G6VgV9Pb28sorr1BRUcFnPvOZeIcTU0qp01rr1tW2kzN/IVJQZmYmDQ0NNDQ0RK0KBgYGGBgY4N13303LqiDdh3xAkr8QKS/0WsHNN9+M1+sNu1YwOzsbdq0gWBXU1NRQUlIS7/C3hCR/Sf5CpB2r1WpUBYBRFfT29kZUBdnZ2caBwG63p0RV4Pf7GRkZAdL3Nk+Q5C9E2ispKaGkpISbbrrJqAqcTie9vb243W7a2tpoa2sz1jYIHgyS9ULp6Ogofr+foqIiMjMz4x1O3EjyF0IYllcF4+PjRl/B0NAQg4ODDA4OcvLkSbKzs42+gurq6qRJpDLks0iSvxBiRcXFxRQXFxtVQWi38ezsLO3t7bS3t0dUBSUlJSil4h1+VJL8F0nyF0KsidVqpb6+nvr6emCxKggeCIIVQbAqyMrKCrtWkChVgdZakv8SSf5CiA0JVgU33njjqlVBeXm50VcQz6pgcnISj8dDdnY2eXl5cYkhUUjyF0Js2mpVwdDQEENDQ5w6dSquVYGc9X9Ekr8QIuZCq4KFhYWImUmjVQXBO4i2siqQ5P8RSf5CiC2VkZHBzp072blzJwATExNGX0G0qsButxtVgc1m29S+x9wf8uHwD+ibOoov4MZr01DXSFbxNTH4ZMlN5vYRQsRNtKogKLi2QXAVs/VUBbPefl69/IeMz10koL1o/B89GTBhNmdSnHUN9+/6O3KslbH+WHG11rl9JPkLIRJGsCpwOBwMDAwQCASM52w2W9i1gpWqghmvgxcvfhyPbxKNb8V9KSxkWgr5vT1vkmu1x/yzxIskfyFEUltYWKC/v98YIgqtCoCwawVlZWUopQhoP39//mamPb3hZ/srUJjJz6znc9eeRinTVn2UbbUts3oqpX4f+C6wB7hNax01WyulDgBPA2bgR1rrJzezXyFE6svIyKCuro66ujoAXC6X0W08MDDA8PAww8PDnD59GpvNht1uJ7uyB/fC8JoSP4DGj3thkL7pf8Ge/6mt/DgJZ7MXfM8Bvwf8cKUNlFJm4BngPsAJnFRKvaS1vrDJfQsh0khhYSGFhYXccMMNYVWBw+Fgenqazs5OMD0DebPret+FwAzvD/4XSf7robW+CKx2EeY2oFNr3bW07QvAw4AkfyHEhkSvCq5wwvvNDb3fwPRRtNYJOyXFVtiOQa5qwBHyvXPpMSGEiInCwkJartmJyZSxwXcw4Qusr2JIdque+SulDgPROiL+XGv92zXsI9qhNOpVZqXU48DjALW1tWt4ayGEWGQ22dB65bt7rkbjx2zaXE9BAilVSoVefz2otT64fKNVk7/W+t5NBuIEakK+twP9K+zrIHAQFu/22eR+hRBpxGKykZVRgXshanq5qlxrNSaVMj2vo2u522c7hn1OAk1KqXqllBV4FHhpG/YrhEgzN+z4BmaVta7XWEzZ3FD+H7YoosS1qeSvlHpEKeUE7gT+SSl1aOnxKqXUywB6sQ77BnAIuAj8vdb6/ObCFkKISC0lX9zAqzTNpX8Y81gS3aaSv9b6N1pru9Y6U2u9Q2v9wNLj/VrrT4ds97LWullrvUtr/f9uNmghhIgm01LE3XX/35rP/s0qi/11B7GaC7Y4ssSTMoNcQggB0FjyWTQ+jvb8CVr7CLAQsY0JK0qZubvuGRqKfzcOUcafJH8hRMppKnmUHbm3c274h7SN/hhQKBQaDWh2l36Fa8v/iPzMnXGONH5kbh8hRErzBeZxzbez4J/Gas6nwNaEJXVu64ywLXP7CCFEorOYbJRm3xDvMBJOakxjJ4QQYl0k+QshRBqS5C+EEGlIkr8QQqQhSf5CCJGGJPkLIUQakuQvhBBpSJK/EEKkIUn+QgiRhhJ2egel1AjQs4GXlgKjMQ4n1iTG2EmGOCXG2EmGOOMdY53Wumy1jRI2+W+UUurUWua1iCeJMXaSIU6JMXaSIc5kiBFk2EcIIdKSJH8hhEhDqZj8I1apT0ASY+wkQ5wSY+wkQ5zJEGPqjfkLIYRYXSqe+QshhFhF0id/pVSxUuo1pVTH0t9FK2z3l0qp80qpi0qpv1ZKqQSMsVYp9epSjBeUUjsTLcalbfOVUn1KqR9sV3wh+141TqXUTUqp40v/3meVUn+wTbEdUEq1KaU6lVLfjvJ8plLqF0vPv7Od/77riPHPln73ziqlXldK1SVajCHbfVYppZVScbmzZi1xKqU+t/TzPK+U+vl2x3hVWuuk/gP8JfDtpa+/DTwVZZt9wNuAeenPcWB/IsW49NwR4L6lr3OB7ESLcen5p4GfAz9I0H/vZqBp6esqYAAo3OK4zMBloAGwAh8A1yzb5uvAf1v6+lHgF9v8s1tLjPcEf++A/zURY1zaLg84CpwAWuPwe7iWn2UT8B5QtPR9+XbHebU/SX/mDzwM/Hjp6x8DvxtlGw3YWPxHygQygKFtiW7RqjEqpa4BLFrr1wC01jNaa/f2hbimnyNKqVuAHcCr2xTXcqvGqbVu11p3LH3dDwwDqza9bNJtQKfWuktr7QVeWIo1VGjsvwI+tZ0V6Fpi1Fr/S8jv3QnAvo3xrSnGJf8PiycC89sZXIi1xPm/AM9orScAtNbD2xzjVaVC8t+htR4AWPq7fPkGWuvjwL+weAY4ABzSWl9MpBhZPFt1KaVeVEq9p5T6nlLKnEgxKqVMwF8B39rGuJZby8/SoJS6jcWD/uUtjqsacIR871x6LOo2WmsfMAmUbHFcUfe/JFqMob4K/POWRhRp1RiVUjcDNVrr/7mdgS2zlp9lM9CslHpbKXVCKXVg26Jbg6RYwF0pdRioiPLUn6/x9Y3AHj46i3lNKfUJrfXRGIW46RhZ/Lf4OHAz0Av8AvgK8DexiA9iEuPXgZe11o6tPGGNQZzB96kEfgI8prUOxCK2q+0uymPLb6VbyzZbac37V0p9EWgF7t7SiKLsOspjRoxLJyD/mcX/G/G0lp+lhcWhn/0s5p43lVLXaa1dWxzbmiRF8tda37vSc0qpIaVUpdZ6YOk/e7TS6hHghNZ6Zuk1/wzcweKYYaLE6ATe01p3Lb3mH5ZijFnyj0GMdwIfV0p9ncVrElal1IzWesWLcnGKE6VUPvBPwHe01idiGd8KnEBNyPd2oH+FbZxKKQtQAIxvQ2zL9x8ULUaUUveyeKC9W2vt2abYglaLMQ+4DjiydAJSAbyklPqM1vrUtkW59n/vE1rrBaBbKdXG4sHg5PaEeHWpMOzzEvDY0tePAb+Nsk0vcLdSyqKUymDxbGY7h33WEuNJoEgpFRyb/iRwYRtiC1o1Rq31F7TWtVrrncA3gedjnfjXYNU4lVJW4DcsxvfLbYrrJNCklKpf2v+jS7GGCo39s8AbeulKYKLEuDSk8kPgM3Eao75qjFrrSa11qdZ659Lv4YmlWLcz8a8a55J/YPECOkqpUhaHgbq2NcqrifcV583+YXHM9HWgY+nv4qXHW4Ef6Y+uzP+QxYR/AfhPiRbj0vf3AWeBD4H/AVgTLcaQ7b9CfO72Wcu/9xeBBeD9kD83bUNsnwbaWby+8OdLj/0Fi8kJFm86+CXQCbwLNMTh57dajIdZvBki+HN7KdFiXLbtEeJwt88af5YK+E9LOedD4NF4xLnSH+nwFUKINJQKwz5CCCHWSZK/EEKkIUn+QgiRhiT5CyFEGpLkL4QQaUiSvxBCpCFJ/kIIkYYk+QshRBr6/wH0B1Nun0x7sAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'a': array([-0.72829653, -0.20635592]),\n",
" 'b': array([-0.37039546, 0.77598258]),\n",
" 'a1': array([0.62157182, 0.43037333]),\n",
" 'c': array([ 0.47712017, -1. ])}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_graph(g.graph)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'993bcf3d-00d5-48a2-ad66-bddb4529a278'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.merge_with(\"branch\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'b': array([ 1. , -0.46067039]),\n",
" 'c': array([-0.90693215, -0.3488259 ]),\n",
" 'a_a1': array([-0.09306785, 0.80949629])}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_graph(g.graph)"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
cifu9502/nrgcode | Runs/Untitled2.ipynb | 2 | 181 | {
"metadata": {
"name": "",
"signature": "sha256:068c426b3841f8a3da73160fcf78643a6472d77452297b98d396485fd2197440"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": []
} | mit |
aerijman/Transcriptional-Activation-Domains | Disorder.ipynb | 1 | 47815 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import numpy as np\n",
"import os,sys, re\n",
"import pandas as pd\n",
"from IPython.display import Markdown\n",
"import matplotlib\n",
"matplotlib.rcParams.update({'font.size': 20})\n",
"%pylab inline\n",
"sys.path.append(os.path.abspath('../libraries/'))\n",
"from summary_utils import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### I have generated iupred results for the whole proteome since we didn't have the scores."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def read_iupred_results(fileName):\n",
" '''\n",
" function read files containing the scores from iupred into \n",
" the hash results\n",
" INPUT: fileName\n",
" results (hash)\n",
" '''\n",
" results = {}\n",
" f = open(fileName, \"r\")\n",
" while True:\n",
" try:\n",
" k,v = next(f), next(f)\n",
" k = k.strip()[1:]\n",
" results[k] = v.strip().split(',')[:-1]\n",
" except StopIteration:\n",
" break\n",
" f.close()\n",
" return results\n",
"\n",
"\n",
"import os,re\n",
"files = [i for i in os.listdir('../scripts/iupred/') if re.search(\".results$\",i)]\n",
"\n",
"disorders = {}\n",
"for i in files:\n",
" d = read_iupred_results('../scripts/iupred/'+i)\n",
" disorders.update(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### These are old predictions that include sequences and scores from our deep learning model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# forgot to add the confidence of secondary_structure_oredictions...\n",
"path = '../scripts/fastas/'\n",
"tmp, tnp = [], []\n",
"for f in [i for i in os.listdir(path) if i[-11:]==\".output.csv\"]:\n",
" predsName = f + \".predictions.npz\"\n",
"\n",
" df = pd.read_csv(path + f, index_col=0)\n",
" tmp.append(df[['sequence','secondStruct','disorder']])\n",
" nf = np.load(path + predsName)\n",
" tnp.append(nf[nf.files[0]])\n",
"\n",
"predictions = np.hstack(tnp)\n",
"df = pd.concat(tmp)\n",
"\n",
"# finally join all fields into a single data structure to facilitate further analysis\n",
"df['predictions'] = predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Here I joined both datasets"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame([disorders]).T\n",
"idx = df2.index.intersection(df.index)\n",
"df2 = df2.loc[idx]\n",
"\n",
"df = pd.concat([df.loc[idx],df2], axis=1)\n",
"df.columns = ['sequence', 'secondStruct', 'disorder', 'predictions', 'iupred']\n",
"\n",
"del(df2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Have to define the set of Transcription factors or Nuclear proteins"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"175 in tf_full\n",
"132 in tf_short\n",
"4802 in total\n",
"812 in nuclear\n",
"\n"
]
}
],
"source": [
" ## SGD ##\n",
"# collect data from SGD \n",
"SGD = pd.read_csv('https://downloads.yeastgenome.org/curation/chromosomal_feature/SGD_features.tab', index_col=3, sep='\\t', header=None)\n",
"SGD = SGD[SGD[1]=='ORF'][4]\n",
"\n",
" ## TF ##\n",
"# Steve's list of TFs\n",
"# long list including potential NON-TF\n",
"tf_full = pd.read_csv('../data/TFs.csv')\n",
"tf_full = tf_full['Systematic name'].values\n",
"\n",
"# short list excluding potential False TF\n",
"tf_short = pd.read_csv('../data/TFs_small.csv')\n",
"tf_short = tf_short['Systematic name'].values\n",
"\n",
" ## Nuclear ##\n",
"# Are tf enriched in the Nucleus?\n",
"localization = pd.read_csv('../data/localization/proteomesummarylatestversion_localisation.csv', index_col=0)\n",
"X = localization.iloc[:,1] \n",
"nuclear = [i for i in set(X) if re.search(\"nucl\",i)]\n",
"X = pd.DataFrame([1 if i in nuclear else 0 for i in X], index=localization.index, columns=['loc'])\n",
"nuclear = X[X['loc']==1].index\n",
"\n",
"\n",
"total_idx = df.index.intersection(X.index)\n",
"nuclear_idx = nuclear.intersection(total_idx)\n",
"tf_full_idx = set(tf_full).intersection(total_idx)\n",
"tf_short_idx = set(tf_short).intersection(total_idx)\n",
"\n",
"print('{} in tf_full\\n{} in tf_short\\n{} in total\\n{} in nuclear\\n'.format(\n",
" len(tf_full_idx), len(tf_short_idx), len(total_idx), len(nuclear_idx)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Predict TADs from the proteome"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/app/easybuild/software/Python/3.6.5-foss-2016b-fh3/lib/python3.6/site-packages/h5py-2.7.1-py3.6-linux-x86_64.egg/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",
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# load NN model and weights\n",
"from keras.models import model_from_json\n",
"\n",
"# open json model and weights\n",
"with open(\"../models/deep_model.json\", \"r\") as json_file:\n",
" json_model = json_file.read()\n",
"\n",
"deep_model = model_from_json(json_model)\n",
"deep_model.load_weights(\"../models/deep_model.h5\")\n",
"\n",
"# set cutoff to predict TADs in the proteome\n",
"cutoff=0.8 \n",
"\n",
"results = np.zeros(shape=(df.shape[0],4))\n",
"for n,prot in enumerate(df.predictions):\n",
" results[n] = predict_motif_statistics(prot, cutoff)\n",
" \n",
"results = pd.DataFrame(results, index=df.index, columns = ['length', 'start_position', 'gral_mean', 'mean_longest_region'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### In parsed disorder scores there are null values that have to be excluded"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"fixed_disorder = []\n",
"for n,i in enumerate(df.iupred.values):\n",
" i = [t for t in i if t!=\"\"]\n",
" fixed_disorder.append(np.array(i).astype(float))\n",
" \n",
"df.iupred = fixed_disorder"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"lenCutoff = 5 # Threshold for defining a potential TAD (more than 5 contiguous residues with score.0.8)\n",
"flanking = 100 # How many points to consider\n",
"bins_tad = 20 # Pure legacy now. It's to show more clearly the TAD in the figure\n",
"\n",
"\n",
"# Build distribution of lengths to use building the null hypothesis\n",
"TADs_idx = results[results.length>lenCutoff].index.dropna().intersection(df.index)\n",
"lengths = np.array([len(i) for i in df.loc[TADs_idx].sequence.values])\n",
"lengths = np.hstack([lengths]*10) # allow for a bigger sampling to build null hypothesis\n",
"np.random.shuffle(lengths)\n",
"\n",
"# build disorder and helicity vectors\n",
"dis_vector = np.hstack(df.loc[TADs_idx].iupred.values)\n",
"dis_vector = np.hstack([np.ones(flanking), dis_vector, np.ones(flanking)]) # fix \"N\" and \"C\" terminal errors\n",
"\n",
"result_dis_pre = np.zeros(shape=(len(lengths), flanking))\n",
"result_dis_tad = np.zeros(shape=(len(lengths), bins_tad))\n",
"result_dis_post = np.zeros(shape=(len(lengths), flanking))\n",
"\n",
"##########################################\n",
"### Build null hypothesis distributions ##\n",
"##########################################\n",
"\n",
"\n",
"# random start sites\n",
"np.random.seed(42) # set random seed for reproducibility\n",
"rand_starts = np.random.uniform(low=101,high=len(dis_vector)-1000, size=len(lengths)).astype(int)\n",
"\n",
"# Null Hypothesis disOrder and helIcity\n",
"for n,(i,j) in enumerate(zip(rand_starts, lengths)):\n",
" result_dis_pre[n] = dis_vector[i-flanking:i]\n",
" result_dis_tad[n] = np.median(dis_vector[i-flanking:i+j-flanking]) \n",
" result_dis_post[n] = dis_vector[i+j-flanking:i+j]\n",
"\n",
"dis = np.hstack([result_dis_pre, result_dis_tad, result_dis_post]).T\n",
"medians_dis_random = np.array([np.percentile(i,50) for i in dis])\n",
"_25_dis_random = np.array([np.percentile(i,25) for i in dis])\n",
"_75_dis_random = np.array([np.percentile(i,75) for i in dis])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"lenCutoff = 5 # Threshold for defining a potential TAD (more than 5 contiguous residues with score.0.8)\n",
"flanking = 100 # How many points to consider\n",
"bins_tad = 20 # Pure legacy now. It's to show more clearly the TAD in the figure\n",
"\n",
"\n",
"TADs = results[results.length>lenCutoff]\n",
"\n",
"# use only the nuclear TADs\n",
"TADs = TADs.loc[TADs.index.intersection(tf_short_idx)]\n",
"\n",
"result_dis_pre = np.zeros(shape=(len(TADs), flanking))\n",
"result_dis_tad = np.zeros(shape=(len(TADs), bins_tad))\n",
"result_dis_post = np.zeros(shape=(len(TADs), flanking))\n",
"\n",
"# Null Hypothesis disOrder and helIcity\n",
"for n,(i,j,k) in enumerate(zip(TADs.start_position.values.astype(int), \n",
" TADs.length.values.astype(int), \n",
" TADs.index.dropna())):\n",
" \n",
" dis = np.array(df.iupred.loc[k]).astype(float)\n",
" dis = np.hstack([np.ones(flanking), dis, np.ones(flanking)]) # fix \"N\" and \"C\" terminal errors\n",
" hel = df.secondStruct.loc[k]\n",
" \n",
" i +=100 # part of fixing the \"N\" terminal\n",
" \n",
" result_dis_pre[n] = dis[i-flanking:i]\n",
" result_dis_tad[n] = np.median(dis[i:i+j])\n",
" result_dis_post[n] = dis[i+j:i+j+flanking]\n",
"\n",
"dis = np.hstack([result_dis_pre, result_dis_tad, result_dis_post]).T\n",
"medians_dis_tad = np.array([np.percentile(i,50) for i in dis])\n",
"_25_dis_tad = np.array([np.percentile(i,25) for i in dis])\n",
"_75_dis_tad = np.array([np.percentile(i,75) for i in dis])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def plotit(ax, medians, _25, _75, title):\n",
" ax.fill_between( np.arange(len(_25)),_75,_25, alpha=0.3, color='gray')\n",
" ax.plot(medians, label=\"50%\", lw=3, c='k')\n",
" ax.set_xticks([100,120])\n",
" ax.set_xticklabels([\"\", \"\"])\n",
" ax.text(40, -0.1, \"pre-tad\")\n",
" ax.text(100, -0.1, \"TAD\")\n",
" ax.text(150, -0.1, \"post-TAD\")\n",
" ax.set_title(title)\n",
" #ax.set_ylim(-0.01,1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 792x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(11,5))\n",
"ax = plt.subplot(1,2,1)\n",
"plotit(ax,medians_dis_tad, _25_dis_tad, _75_dis_tad, 'tads')\n",
"plt.ylim(0,1)\n",
"ax = plt.subplot(1,2,2)\n",
"plotit(ax,medians_dis_random, _25_dis_random, _75_dis_random, 'random')\n",
"plt.ylim(0,1)"
]
},
{
"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
}
| mit |
scienceguyrob/Docker | Images/music/samples/libROSA/LibROSA_Demo.ipynb | 1 | 13305 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Librosa demo\n",
"\n",
"This notebook demonstrates some of the basic functionality of librosa version 0.4.\n",
"\n",
"Following through this example, you'll learn how to:\n",
"\n",
"* Load audio input\n",
"* Compute mel spectrogram, MFCC, delta features, chroma\n",
"* Locate beat events\n",
"* Compute beat-synchronous features\n",
"* Display features\n",
"* Save beat tracker output to a CSV file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We'll need numpy for some mathematical operations\n",
"import numpy as np\n",
"\n",
"\n",
"# matplotlib for displaying the output\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.style as ms\n",
"ms.use('seaborn-muted')\n",
"%matplotlib inline\n",
"\n",
"\n",
"# and IPython.display for audio output\n",
"import IPython.display\n",
"\n",
"\n",
"# Librosa for audio\n",
"import librosa\n",
"# And the display module for visualization\n",
"import librosa.display"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audio_path = librosa.util.example_audio_file()\n",
"\n",
"# or uncomment the line below and point it at your favorite song:\n",
"#\n",
"# audio_path = '/path/to/your/favorite/song.mp3'\n",
"\n",
"y, sr = librosa.load(audio_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, librosa will resample the signal to 22050Hz.\n",
"\n",
"You can change this behavior by saying:\n",
"```\n",
"librosa.load(audio_path, sr=44100)\n",
"```\n",
"to resample at 44.1KHz, or\n",
"```\n",
"librosa.load(audio_path, sr=None)\n",
"```\n",
"to disable resampling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mel spectrogram\n",
"This first step will show how to compute a [Mel](http://en.wikipedia.org/wiki/Mel_scale) spectrogram from an audio waveform."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's make and display a mel-scaled power (energy-squared) spectrogram\n",
"S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128)\n",
"\n",
"# Convert to log scale (dB). We'll use the peak power as reference.\n",
"log_S = librosa.logamplitude(S, ref_power=np.max)\n",
"\n",
"# Make a new figure\n",
"plt.figure(figsize=(12,4))\n",
"\n",
"# Display the spectrogram on a mel scale\n",
"# sample rate and hop length parameters are used to render the time axis\n",
"librosa.display.specshow(log_S, sr=sr, x_axis='time', y_axis='mel')\n",
"\n",
"# Put a descriptive title on the plot\n",
"plt.title('mel power spectrogram')\n",
"\n",
"# draw a color bar\n",
"plt.colorbar(format='%+02.0f dB')\n",
"\n",
"# Make the figure layout compact\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Harmonic-percussive source separation\n",
"\n",
"Before doing any signal analysis, let's pull apart the harmonic and percussive components of the audio. This is pretty easy to do with the `effects` module."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y_harmonic, y_percussive = librosa.effects.hpss(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# What do the spectrograms look like?\n",
"# Let's make and display a mel-scaled power (energy-squared) spectrogram\n",
"S_harmonic = librosa.feature.melspectrogram(y_harmonic, sr=sr)\n",
"S_percussive = librosa.feature.melspectrogram(y_percussive, sr=sr)\n",
"\n",
"# Convert to log scale (dB). We'll use the peak power as reference.\n",
"log_Sh = librosa.logamplitude(S_harmonic, ref_power=np.max)\n",
"log_Sp = librosa.logamplitude(S_percussive, ref_power=np.max)\n",
"\n",
"# Make a new figure\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"plt.subplot(2,1,1)\n",
"# Display the spectrogram on a mel scale\n",
"librosa.display.specshow(log_Sh, sr=sr, y_axis='mel')\n",
"\n",
"# Put a descriptive title on the plot\n",
"plt.title('mel power spectrogram (Harmonic)')\n",
"\n",
"# draw a color bar\n",
"plt.colorbar(format='%+02.0f dB')\n",
"\n",
"plt.subplot(2,1,2)\n",
"librosa.display.specshow(log_Sp, sr=sr, x_axis='time', y_axis='mel')\n",
"\n",
"# Put a descriptive title on the plot\n",
"plt.title('mel power spectrogram (Percussive)')\n",
"\n",
"# draw a color bar\n",
"plt.colorbar(format='%+02.0f dB')\n",
"\n",
"# Make the figure layout compact\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chromagram\n",
"\n",
"Next, we'll extract [Chroma](http://en.wikipedia.org/wiki/Pitch_class) features to represent pitch class information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We'll use a CQT-based chromagram here. An STFT-based implementation also exists in chroma_cqt()\n",
"# We'll use the harmonic component to avoid pollution from transients\n",
"C = librosa.feature.chroma_cqt(y=y_harmonic, sr=sr)\n",
"\n",
"# Make a new figure\n",
"plt.figure(figsize=(12,4))\n",
"\n",
"# Display the chromagram: the energy in each chromatic pitch class as a function of time\n",
"# To make sure that the colors span the full range of chroma values, set vmin and vmax\n",
"librosa.display.specshow(C, sr=sr, x_axis='time', y_axis='chroma', vmin=0, vmax=1)\n",
"\n",
"plt.title('Chromagram')\n",
"plt.colorbar()\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MFCC\n",
"\n",
"[Mel-frequency cepstral coefficients](http://en.wikipedia.org/wiki/Mel-frequency_cepstrum) are commonly used to represent texture or timbre of sound."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Next, we'll extract the top 13 Mel-frequency cepstral coefficients (MFCCs)\n",
"mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)\n",
"\n",
"# Let's pad on the first and second deltas while we're at it\n",
"delta_mfcc = librosa.feature.delta(mfcc)\n",
"delta2_mfcc = librosa.feature.delta(mfcc, order=2)\n",
"\n",
"# How do they look? We'll show each in its own subplot\n",
"plt.figure(figsize=(12, 6))\n",
"\n",
"plt.subplot(3,1,1)\n",
"librosa.display.specshow(mfcc)\n",
"plt.ylabel('MFCC')\n",
"plt.colorbar()\n",
"\n",
"plt.subplot(3,1,2)\n",
"librosa.display.specshow(delta_mfcc)\n",
"plt.ylabel('MFCC-$\\Delta$')\n",
"plt.colorbar()\n",
"\n",
"plt.subplot(3,1,3)\n",
"librosa.display.specshow(delta2_mfcc, sr=sr, x_axis='time')\n",
"plt.ylabel('MFCC-$\\Delta^2$')\n",
"plt.colorbar()\n",
"\n",
"plt.tight_layout()\n",
"\n",
"# For future use, we'll stack these together into one matrix\n",
"M = np.vstack([mfcc, delta_mfcc, delta2_mfcc])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Beat tracking\n",
"\n",
"The beat tracker returns an estimate of the tempo (in beats per minute) and frame indices of beat events.\n",
"\n",
"The input can be either an audio time series (as we do below), or an onset strength envelope as calculated by `librosa.onset.onset_strength()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now, let's run the beat tracker.\n",
"# We'll use the percussive component for this part\n",
"plt.figure(figsize=(12, 6))\n",
"tempo, beats = librosa.beat.beat_track(y=y_percussive, sr=sr)\n",
"\n",
"# Let's re-draw the spectrogram, but this time, overlay the detected beats\n",
"plt.figure(figsize=(12,4))\n",
"librosa.display.specshow(log_S, sr=sr, x_axis='time', y_axis='mel')\n",
"\n",
"# Let's draw transparent lines over the beat frames\n",
"plt.vlines(librosa.frames_to_time(beats),\n",
" 1, 0.5 * sr,\n",
" colors='w', linestyles='-', linewidth=2, alpha=0.5)\n",
"\n",
"plt.axis('tight')\n",
"\n",
"plt.colorbar(format='%+02.0f dB')\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the beat tracker will trim away any leading or trailing beats that don't appear strong enough. \n",
"\n",
"To disable this behavior, call `beat_track()` with `trim=False`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print('Estimated tempo: %.2f BPM' % tempo)\n",
"\n",
"print('First 5 beat frames: ', beats[:5])\n",
"\n",
"# Frame numbers are great and all, but when do those beats occur?\n",
"print('First 5 beat times: ', librosa.frames_to_time(beats[:5], sr=sr))\n",
"\n",
"# We could also get frame numbers from times by librosa.time_to_frames()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Beat-synchronous feature aggregation\n",
"\n",
"Once we've located the beat events, we can use them to summarize the feature content of each beat.\n",
"\n",
"This can be useful for reducing data dimensionality, and removing transient noise from the features."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# feature.sync will summarize each beat event by the mean feature vector within that beat\n",
"\n",
"M_sync = librosa.util.sync(M, beats)\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"# Let's plot the original and beat-synchronous features against each other\n",
"plt.subplot(2,1,1)\n",
"librosa.display.specshow(M)\n",
"plt.title('MFCC-$\\Delta$-$\\Delta^2$')\n",
"\n",
"# We can also use pyplot *ticks directly\n",
"# Let's mark off the raw MFCC and the delta features\n",
"plt.yticks(np.arange(0, M.shape[0], 13), ['MFCC', '$\\Delta$', '$\\Delta^2$'])\n",
"\n",
"plt.colorbar()\n",
"\n",
"plt.subplot(2,1,2)\n",
"# librosa can generate axis ticks from arbitrary timestamps and beat events also\n",
"librosa.display.specshow(M_sync, x_axis='time',\n",
" x_coords=librosa.frames_to_time(librosa.util.fix_frames(beats)))\n",
"\n",
"plt.yticks(np.arange(0, M_sync.shape[0], 13), ['MFCC', '$\\Delta$', '$\\Delta^2$']) \n",
"plt.title('Beat-synchronous MFCC-$\\Delta$-$\\Delta^2$')\n",
"plt.colorbar()\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Beat synchronization is flexible.\n",
"# Instead of computing the mean delta-MFCC within each beat, let's do beat-synchronous chroma\n",
"# We can replace the mean with any statistical aggregation function, such as min, max, or median.\n",
"\n",
"C_sync = librosa.util.sync(C, beats, aggregate=np.median)\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"librosa.display.specshow(C, sr=sr, y_axis='chroma', vmin=0.0, vmax=1.0, x_axis='time')\n",
"\n",
"plt.title('Chroma')\n",
"plt.colorbar()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"librosa.display.specshow(C_sync, y_axis='chroma', vmin=0.0, vmax=1.0, x_axis='time', \n",
" x_coords=librosa.frames_to_time(librosa.util.fix_frames(beats)))\n",
"\n",
"\n",
"plt.title('Beat-synchronous Chroma (median aggregation)')\n",
"\n",
"plt.colorbar()\n",
"plt.tight_layout()"
]
}
],
"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": 0
} | gpl-3.0 |
tudarmstadt-lt/sensegram | hypernyms.ipynb | 1 | 8622 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 0. Download data\n",
"\n",
"Data can be found at http://ltdata1.informatik.uni-hamburg.de/joint/hyperwatset/konvens/ \n",
"\n",
"Download all files and put them into the ``data`` directory to run the code below.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 1.Generate vectors for synsets and hyper synsets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from gensim.models import KeyedVectors\n",
"import logging\n",
"from time import time\n",
"from os.path import exists\n",
"\n",
"\n",
"def try_print(w2v, test_word):\n",
" try:\n",
" for word, score in w2v.most_similar(test_word):\n",
" print(word, score)\n",
" except:\n",
" print(\"Warning: word '{}' not found.\".format(test_word))\n",
" \n",
" \n",
"def load_and_pickle(w2v_fpath, binary=False):\n",
" tic = time()\n",
" logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
" w2v_pkl_fpath = w2v_fpath + \".pkl\"\n",
"\n",
" if exists(w2v_pkl_fpath):\n",
" w2v = KeyedVectors.load(w2v_pkl_fpath)\n",
" else:\n",
" w2v = KeyedVectors.load_word2vec_format(w2v_fpath, binary=binary, unicode_errors='ignore')\n",
" w2v.init_sims(replace=True)\n",
" try_print(w2v, \"for\")\n",
" try_print(w2v, \"для\")\n",
" w2v.save(w2v_pkl_fpath)\n",
" \n",
" print(time()- tic, \"sec.\")\n",
"\n",
" return w2v, w2v_pkl_fpath\n",
"\n",
"w2v_en_original_fpath = \"data/GoogleNews-vectors-negative300.txt\" # standard google news word2vec model\n",
"w2v_ru_original_fpath = \"data/all.norm-sz500-w10-cb0-it3-min5.w2v\" # RDT word2vec model\n",
"\n",
"w2v_en, w2v_en_fpath = load_and_pickle(w2v_en_original_fpath)\n",
"w2v_ru, w2v_ru_fpath = load_and_pickle(w2v_ru_original_fpath, binary=True)\n",
"\n",
"from glob import glob \n",
"from vector_representations.build_sense_vectors import run\n",
"\n",
"for lang in [\"ru\", \"en\"]:\n",
" sensegram_fpaths = \"data/{}/*-sensegram.tsv\".format(lang)\n",
" w2v_fpath = w2v_ru_original_fpath if \"ru\" else w2v_en_original_fpath \n",
"\n",
" for inventory_fpath in glob(sensegram_fpaths):\n",
" run(inventory_fpath, w2v_fpath) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 2. Generate binary hypernyms "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import codecs\n",
"import operator\n",
"from multiprocessing import Pool\n",
"from vector_representations.dense_sense_vectors import DenseSenseVectors\n",
"from traceback import format_exc\n",
"from glob import glob \n",
"\n",
"\n",
"def generate_binary_hypers(output_dir, max_synsets=1, hyper_synset_max_size=10, hc_max=0):\n",
" output_fpath = output_dir + \".vector-link-s%d-hmx%d-hc%d.csv\" % (\n",
" max_synsets, hyper_synset_max_size, hc_max) \n",
" bin_count = 0\n",
" \n",
" out = codecs.open(output_fpath, \"w\", \"utf-8\")\n",
" log = codecs.open(output_fpath + \".log\", \"w\", \"utf-8\")\n",
" \n",
" for i, h_id in enumerate(dsv.pcz.data):\n",
" try:\n",
" if i % 10000 == 0: print(i)\n",
"\n",
" if \"h\" in h_id:\n",
" hypo_h_senses = dsv.pcz.data[h_id][0][\"cluster\"]\n",
" tmp = sorted(dsv.pcz.data[h_id][0][\"cluster\"].items(), key=operator.itemgetter(1), reverse=True)\n",
"\n",
" s_id = \"s\" + h_id[1:]\n",
" hypo_senses = dsv.pcz.data[s_id][0][\"cluster\"]\n",
" log.write(\"\\n{}\\t{}\\n\".format(\n",
" h_id, \", \".join(hypo_h_senses)\n",
" ))\n",
" log.write(\"{}\\n\".format(\n",
" \", \".join([\"{}:{}\".format(k,v) for k,v in tmp])\n",
" ))\n",
" log.write(\"{}\\t{}\\n\".format(\n",
" s_id, \", \".join(hypo_senses)\n",
" ))\n",
"\n",
" # save relations from the hierarchical context \n",
" for hypo_sense in hypo_senses:\n",
" for hc_num, hyper_sense in enumerate(hypo_h_senses):\n",
" if hc_num == hc_max: break\n",
" hypo_word = hypo_sense.split(\"#\")[0]\n",
" hyper_word = hyper_sense.split(\"#\")[0]\n",
" if hypo_word != hyper_word:\n",
" out.write(\"{}\\t{}\\tfrom-original-labels\\n\".format(hypo_word, hyper_word))\n",
" bin_count += 1\n",
"\n",
" # save binary relations from a synset\n",
" s_synsets = 0\n",
" for rh_id, s in dsv.sense_vectors.most_similar(h_id + \"#0\"):\n",
" if \"s\" in rh_id:\n",
" hyper_senses = dsv.pcz.data[rh_id.split(\"#\")[0]][0][\"cluster\"]\n",
" if len(hyper_senses) > hyper_synset_max_size: continue\n",
"\n",
" rh_str = \", \".join(hyper_senses)\n",
" log.write(\"\\t{}:{:.3f} {}\\n\".format(rh_id, s, rh_str))\n",
"\n",
" for hypo_sense in hypo_senses:\n",
" for hyper_sense in hyper_senses:\n",
" hypo_word = hypo_sense.split(\"#\")[0]\n",
" hyper_word = hyper_sense.split(\"#\")[0]\n",
" if hypo_word != hyper_word:\n",
" out.write(\"{}\\t{}\\tfrom-vector-linkage\\n\".format(hypo_word, hyper_word))\n",
" bin_count += 1\n",
" s_synsets += 1\n",
"\n",
" if s_synsets >= max_synsets: break\n",
" except KeyboardInterrupt:\n",
" break\n",
" except:\n",
" print(\"Error\", i, h_id)\n",
" print(format_exc())\n",
" out.close()\n",
" log.close()\n",
" \n",
" print(\"# binary relations:\", bin_count)\n",
" print(\"binary relations:\", output_fpath)\n",
" print(\"log of binary relations:\", output_fpath + \".log\")\n",
" \n",
" return bin_count, output_fpath\n",
" \n",
"\n",
"for pcz_fpath in glob(\"data/ru/*tsv\"):\n",
" print(pcz_fpath)\n",
" reload = True\n",
" try: dsv\n",
" except NameError: reload = True\n",
"\n",
" if reload:\n",
" dsv = DenseSenseVectors(\n",
" pcz_fpath=pcz_fpath,\n",
" word_vectors_obj=None,\n",
" save_pkl=True,\n",
" sense_dim_num=1000,\n",
" norm_type=\"sum\",\n",
" weight_type=\"score\",\n",
" max_cluster_words=20)\n",
"\n",
" for max_top_synsets in [1, 2, 3]:\n",
" for max_hyper_synset_size in [3, 5, 10, 20]:\n",
" for hc_max in [1, 3, 5]: \n",
" print(\"=\"*50)\n",
" print(\"max number of synsets:\", max_top_synsets)\n",
" print(\"max hyper synset size:\", max_hyper_synset_size)\n",
" print(\"hc_max:\", hc_max)\n",
" generate_binary_hypers(pcz_fpath, max_top_synsets, max_hyper_synset_size, hc_max)\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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
OzFlux/Peters-iPython-notebooks | test_ERAInterim2.ipynb | 1 | 11571 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: Qt4Agg\n"
]
}
],
"source": [
"%run basics\n",
"%matplotlib\n",
"import pysolar\n",
"import pytz"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-12.4952 131.15005\n"
]
}
],
"source": [
"site_name = \"HowardSprings\"\n",
"tower_name = \"../Sites/\"+site_name+\"/Data/Portal/\"+site_name+\"_2014_L3.nc\"\n",
"ds_tower = qcio.nc_read_series(tower_name)\n",
"site_timezone = ds_tower.globalattributes[\"time_zone\"]\n",
"site_latitude = float(ds_tower.globalattributes[\"latitude\"])\n",
"site_longitude = float(ds_tower.globalattributes[\"longitude\"])\n",
"tower_timestep = int(ds_tower.globalattributes[\"time_step\"])\n",
"print site_latitude,site_longitude\n",
"dt_tower = ds_tower.series[\"DateTime\"][\"Data\"]\n",
"Fsd_tower,f,a = qcutils.GetSeriesasMA(ds_tower,\"Fsd\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-12.75 131.25\n"
]
}
],
"source": [
"erai_name = \"../ECMWF/ECMWF_2014.nc\"\n",
"erai_file = netCDF4.Dataset(erai_name)\n",
"latitude = erai_file.variables[\"latitude\"][:]\n",
"longitude = erai_file.variables[\"longitude\"][:]\n",
"lat_resolution = abs(latitude[-1]-latitude[0])/(len(latitude)-1)\n",
"lon_resolution = abs(longitude[-1]-longitude[0])/(len(longitude)-1)\n",
"site_lat_index = int(((latitude[0]-site_latitude)/lat_resolution)+0.5)\n",
"site_lon_index = int(((site_longitude-longitude[0])/lon_resolution)+0.5)\n",
"erai_timestep = 180\n",
"print latitude[site_lat_index],longitude[site_lon_index]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get the time and convert to Python datetime object\n",
"erai_time = erai_file.variables[\"time\"][:]\n",
"time_units = getattr(erai_file.variables[\"time\"],\"units\")\n",
"dt_erai = netCDF4.num2date(erai_time,time_units)\n",
"hour_utc = numpy.array([dt.hour for dt in dt_erai])\n",
"#print dt_ecmwf[0],dt_ecmwf[-1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# variables are dimensioned [time,latitude,longitude]\n",
"Fsd_3d = erai_file.variables[\"ssrd\"][:,:,:]\n",
"Fsd_accum = Fsd_3d[:,site_lat_index,site_lon_index]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"site_tz = pytz.timezone(site_timezone)\n",
"# make utc_dt timezone aware\n",
"dt_erai_utc = [x.replace(tzinfo=pytz.utc) for x in dt_erai]\n",
"# get local time from UTC\n",
"erai_offset = datetime.timedelta(minutes=float(erai_timestep)/2)\n",
"dt_erai_loc = [x.astimezone(site_tz) for x in dt_erai_utc]\n",
"#dt_erai_loc = [x.astimezone(site_tz) for x in dt_erai_utc]\n",
"# NOTE: will have to disable daylight saving at some stage, towers stay on Standard Time\n",
"# PRI hopes that the following line will do this ...\n",
"#dt_ecmwf_loc = [x-x.dst() for x in dt_ecmwf_loc]\n",
"# make local time timezone naive to match datetimes in OzFluxQC\n",
"dt_erai_loc_ntz = [x.replace(tzinfo=None) for x in dt_erai_loc]\n",
"dt_erai_loc_cor = [x - erai_offset for x in dt_erai_loc_ntz]\n",
"dt_erai_utc_ntz = [x.replace(tzinfo=None) for x in dt_erai_utc]\n",
"dt_erai_utc_cor = [x - erai_offset for x in dt_erai_utc_ntz]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Downwelling shortwave in ERA-I is a cummulative value that is reset to 0\n",
"# at 0300 and 1500 UTC. Here we convert the cummulative values to\n",
"# 3 hourly values.\n",
"Fsd_erai_3hr = numpy.ediff1d(Fsd_accum,to_begin=0)\n",
"idx = numpy.where((hour_utc==3)|(hour_utc==15))[0]\n",
"Fsd_erai_3hr[idx] = Fsd_accum[idx]\n",
"Fsd_erai_3hr = Fsd_erai_3hr/(erai_timestep*60)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=plt.figure()\n",
"plt.plot(dt_tower,Fsd_tower,'b-')\n",
"plt.plot(dt_erai_loc_cor,Fsd_erai_3hr,'r+')\n",
"#plt.plot(dt_erai_loc,1000*numpy.sin(numpy.deg2rad(alt_solar_3hr)),'g^')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now we interpolate from the 3 hourly ERA-I time step to the tower time step using the solar altitude.\n",
"nth = erai_timestep/tower_timestep\n",
"Fsd_erai = numpy.zeros(len(Fsd_erai_3hr)*nth)\n",
"idx = numpy.array(range(0,len(Fsd_erai_3hr)))\n",
"for i in range(0,nth):\n",
" Fsd_erai[idx*nth+i] = Fsd_erai_3hr[idx]\n",
"# now get a datetime series at the tower time step\n",
"# tower time step as a datetime delta\n",
"tsdt = datetime.timedelta(minutes=tower_timestep)\n",
"# offset the start dateime to allow forhe period over which the first ERA-I\n",
"# value has been accumulated\n",
"start_date = dt_erai_loc_cor[0]-(nth-1)*tsdt\n",
"end_date = dt_erai_loc_cor[-1]\n",
"dt_erai_loc = [x for x in qcutils.perdelta(start_date,end_date,tsdt)]\n",
"# do the same fo UTC\n",
"start_date = dt_erai_utc_cor[0]-(nth-1)*tsdt\n",
"end_date = dt_erai_utc_cor[-1]\n",
"dt_erai_utc = [x for x in qcutils.perdelta(start_date,end_date,tsdt)]\n",
"#dt_erai_loc_1hr = [dt_erai_loc_cor[0]-datetime.timedelta(minutes=60)]+dt_erai_loc_1hr\n",
"#dt_erai_loc_1hr = dt_erai_loc_1hr+[dt_erai_loc_cor[-1]+datetime.timedelta(minutes=60)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print dt_erai_loc_cor[0]-(nth-1)*tsdt\n",
"print dt_erai_loc_cor[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=plt.figure()\n",
"plt.plot(dt_tower,Fsd_tower,'b-')\n",
"plt.plot(dt_erai_loc,Fsd_erai,'r+')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# get the solar altitude, we will use this later to interpolate the ERA Interim\n",
"# data from the ERA-I 3 hour time step to the tower time step.\n",
"# alt_solar is in degrees\n",
"alt_solar_3hr = numpy.array([pysolar.GetAltitude(site_latitude,site_longitude,dt) for dt in dt_erai_utc_cor])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#alt_solar_3hr = numpy.ma.masked_where(alt_solar_3hr<0,alt_solar_3hr)\n",
"coef_3hr = Fsd_erai_3hr/numpy.sin(numpy.deg2rad(alt_solar_3hr))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax1 = plt.subplot(311)\n",
"plt.plot(dt_erai_loc_cor,alt_solar_3hr,'bo')\n",
"ax2 = plt.subplot(312,sharex=ax1)\n",
"plt.plot(dt_erai_loc_cor,Fsd_erai_3hr,'bo')\n",
"ax3 = plt.subplot(313,sharex=ax1)\n",
"plt.plot(dt_erai_loc_cor,coef_3hr,'bo')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"alt_solar = numpy.array([pysolar.GetAltitude(site_latitude,site_longitude,dt) for dt in dt_erai_utc])\n",
"idx = numpy.where(alt_solar<=0)[0]\n",
"alt_solar[idx] = float(0)\n",
"coef = numpy.ma.zeros(len(coef_3hr)*nth)\n",
"idx = numpy.array(range(0,len(coef_3hr)))\n",
"for i in range(0,nth):\n",
" coef[idx*nth+i] = coef_3hr[idx]\n",
"Fsd_erai = coef*numpy.sin(numpy.deg2rad(alt_solar))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax1 = plt.subplot(211)\n",
"plt.plot(dt_tower,Fsd_tower,'b-')\n",
"plt.plot(dt_erai_loc_cor,Fsd_erai_3hr,'g.')\n",
"plt.plot(dt_erai_loc,Fsd_erai,'r+')\n",
"ax2 = plt.subplot(212,sharex=ax1)\n",
"plt.plot(dt_erai_loc_cor,coef_3hr,'bo')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# now get the true solar altitude\n",
"start_date = dt_erai_utc_cor[0]\n",
"end_date = dt_erai_utc_cor[-1]\n",
"dt_erai_utc_1hr = [x for x in qcutils.perdelta(start_date,end_date,tdhr)]\n",
"dt_erai_utc_1hr = [dt_erai[0]-datetime.timedelta(minutes=60)]+dt_erai_utc_1hr\n",
"dt_erai_utc_1hr = dt_erai_utc_1hr+[dt_erai[-1]+datetime.timedelta(minutes=60)]\n",
"alt_solar = numpy.array([pysolar.GetAltitude(site_latitude,site_longitude,dt) for dt in dt_erai_utc_1hr])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=plt.figure()\n",
"plt.plot(dt_erai_cor,alt_solar_3hr)\n",
"plt.plot(dt_erai_1hr,alt_solar_1hr,'r+')\n",
"plt.plot(dt_erai_1hr,alt_solar,'g^')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=plt.figure()\n",
"plt.plot(dt_erai_cor,1000*numpy.sin(numpy.deg2rad(alt_solar_3hr)),'b.')\n",
"#plt.plot(dt_erai_1hr,1000*numpy.sin(numpy.deg2rad(alt_solar)),'b^')\n",
"plt.plot(dt_erai_cor,Fsd_erai_3hr,'r+')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ratio = numpy.deg2rad(alt_solar)/numpy.deg2rad(alt_solar_1hr)\n",
"Fsd_erai_1hr = ratio*Fsd_erai_1hr\n",
"fig=plt.figure()\n",
"plt.plot(dt_tower,Fsd_tower,'b-')\n",
"plt.plot(dt_erai_1hr,Fsd_erai_1hr,'r+')\n",
"plt.ylim([0,1200])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.interpolate import InterpolatedUnivariateSpline\n",
"order = 1\n",
"s = InterpolatedUnivariateSpline(xi, yi, k=order)\n",
"y = s(x)"
]
}
],
"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
}
| gpl-3.0 |
endlesspint8/endlesspint8.github.io | code/hbo_greatest/hbo_greatest.ipynb | 1 | 3950401 | null | mit |
ivastar/clear | notebooks/grizli/grizli_retrieve_and_prep.ipynb | 1 | 14488 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## This notebook shows how to use `grizli` to\n",
"\n",
"retrieve and pre-process raw CLEAR G102/F105W and 3D-HST G141/F140W observations for a single CLEAR pointing (GS1).\n",
"\n",
"These series of notebooks draw heavily from Gabe Brammer's existing `grizli` notebooks, which are available at https://github.com/gbrammer/grizli/tree/master/examples, but with examples specific for the CLEAR survey."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import grizli\n",
"\n",
"try: \n",
" from mastquery import query, overlaps\n",
" use_mquery = True\n",
"except: \n",
" from hsaquery import query, overlaps\n",
" use_mquery = False\n",
"\n",
"import os\n",
"import numpy as np\n",
"from IPython.display import Image\n",
"from grizli.pipeline import auto_script\n",
"import glob\n",
"from glob import glob\n",
"import astropy\n",
"from grizli.prep import process_direct_grism_visit\n",
"from astropy.io import fits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Initialize Directories</center></h1>\n",
"\n",
"\n",
"### ***The following paths need to be changed for your filesystem.*** [HOME_PATH] is where the raw data, reduced data, and `grizli` outputs will be stored. [PATH_TO_CATS] is where the catalogs are stored and must include the following:\n",
" ### reference mosaic image (e.g., goodss-F105W-astrodrizzle-v4.3_drz_sci.fits)\n",
" ### segmentation map (e.g., Goods_S_plus_seg.fits)\n",
" ### source catalog (e.g., goodss-F105W-astrodrizzle-v4.3_drz_sub_plus.cat)\n",
" ### radec_catalog (e.g., goodsS_radec.cat)\n",
" ### 3DHST Eazy Catalogs (e.g., goodss_3dhst.v4.1.cats/*)\n",
" \n",
"the [PATH_TO_CATS] files are available on the team archive: https://archive.stsci.edu/pub/clear_team/INCOMING/for_hackday/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"field = 'GS1'\n",
"ref_filter = 'F105W'\n",
"\n",
"HOME_PATH = '/Users/rsimons/Desktop/clear/for_hackday/%s'%field\n",
"PATH_TO_CATS = '/Users/rsimons/Desktop/clear/Catalogs'\n",
"\n",
"# Create [HOME_PATH] and [HOME_PATH]/query_results directories if they do not already exist\n",
"if not os.path.isdir(HOME_PATH): os.system('mkdir %s'%HOME_PATH)\n",
"if not os.path.isdir(HOME_PATH + '/query_results'): os.system('mkdir %s/query_results'%HOME_PATH)\n",
"\n",
"# Move to the [HOME_PATH] directory\n",
"os.chdir(HOME_PATH)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Query MAST</center></h1>\n",
"\n",
"### Run an initial query for all raw G102 data in the MAST archive from the proposal ID 14227 with a target name that includes the phrase 'GS1' (i.e., GS1 pointing of CLEAR). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# proposal_id = [14227] is CLEAR\n",
"parent = query.run_query(box = None, proposal_id = [14227], instruments=['WFC3/IR', 'ACS/WFC'], \n",
" filters = ['G102'], target_name = 'GS1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next, find all G102 and G141 observations that overlap with the pointings found in the initial query."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Find all G102 and G141 observations overlapping the parent query in the archive\n",
"tabs = overlaps.find_overlaps(parent, buffer_arcmin=0.01, \n",
" filters=['G102', 'G141'], \n",
" instruments=['WFC3/IR','WFC3/UVIS','ACS/WFC'], close=False)\n",
"\n",
"footprint_fits_file = glob('*footprint.fits')[0]\n",
"jtargname = footprint_fits_file.strip('_footprint.fits')\n",
"\n",
"\n",
"# A list of the target names\n",
"fp_fits = fits.open(footprint_fits_file)\n",
"overlapping_target_names = set(fp_fits[1].data['target'])\n",
"\n",
"\n",
"# Move the footprint figure files to $HOME_PATH/query_results/ so that they are not overwritten\n",
"os.system('cp %s/%s_footprint.fits %s/query_results/%s_footprint_%s.fits'%(HOME_PATH, jtargname, HOME_PATH, jtargname, 'all_G102_G141'))\n",
"os.system('cp %s/%s_footprint.npy %s/query_results/%s_footprint_%s.npy'%(HOME_PATH, jtargname, HOME_PATH, jtargname, 'all_G102_G141'))\n",
"os.system('cp %s/%s_footprint.pdf %s/query_results/%s_footprint_%s.pdf'%(HOME_PATH, jtargname, HOME_PATH, jtargname, 'all_G102_G141'))\n",
"os.system('cp %s/%s_info.dat %s/query_results/%s_info_%s.dat'%(HOME_PATH, jtargname, HOME_PATH, jtargname, 'all_G102_G141'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Table summary of query\n",
"tabs[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Retrieve raw data from MAST</center></h1>\n",
"\n",
"\n",
"### We now have a list of G102 and G141 observations in the MAST archive that overlap with the GS1 pointing of CLEAR. \n",
"\n",
"### For each, retrieve all associated RAW grism G102/G141 and direct imaging F098M/F105W/F125W/F140W data from MAST.\n",
"\n",
"**For GS1, the retrieval step takes about 30 minutes to run and requires 1.9 GB of space.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Loop targ_name by targ_name\n",
"for t, targ_name in enumerate(overlapping_target_names):\n",
" if use_mquery:\n",
" extra = {'target_name':targ_name}\n",
" else:\n",
" extra = query.DEFAULT_EXTRA.copy()\n",
" extra += [\"TARGET.TARGET_NAME LIKE '%s'\"%targ_name]\n",
" \n",
" # search the MAST archive again, this time looking for \n",
" # all grism and imaging observations with the given target name\n",
" tabs = overlaps.find_overlaps(parent, buffer_arcmin=0.01, \n",
" filters=['G102', 'G141', 'F098M', 'F105W', 'F125W', 'F140W'], \n",
" instruments=['WFC3/IR','WFC3/UVIS','ACS/WFC'], \n",
" extra=extra, close=False)\n",
" if False:\n",
" # retrieve raw data from MAST\n",
" s3_status = os.system('aws s3 ls s3://stpubdata --request-payer requester')\n",
" auto_script.fetch_files(field_root=jtargname, HOME_PATH=HOME_PATH, remove_bad=True, \n",
" reprocess_parallel=True, s3_sync=(s3_status == 0))\n",
"\n",
" # Move the figure files to $HOME_PATH/query_results/ so that they are not overwritten\n",
" os.system('mv %s/%s_footprint.fits %s/query_results/%s_footprint_%s.fits'%(HOME_PATH, jtargname, HOME_PATH, jtargname, targ_name))\n",
" os.system('mv %s/%s_footprint.npy %s/query_results/%s_footprint_%s.npy'%(HOME_PATH, jtargname, HOME_PATH, jtargname, targ_name))\n",
" os.system('mv %s/%s_footprint.pdf %s/query_results/%s_footprint_%s.pdf'%(HOME_PATH, jtargname, HOME_PATH, jtargname, targ_name))\n",
" os.system('mv %s/%s_info.dat %s/query_results/%s_info_%s.dat'%(HOME_PATH, jtargname, HOME_PATH, jtargname, targ_name))\n",
"\n",
" os.chdir(HOME_PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The following directories are created from auto_script.fetch_files:\n",
" [HOME_PATH]/j0333m2742\n",
" [HOME_PATH]/j0333m2742/RAW\n",
" [HOME_PATH]/j0333m2742/Prep\n",
" [HOME_PATH]/j0333m2742/Extractions\n",
" [HOME_PATH]/j0333m2742/Persistance\n",
" \n",
" \n",
"RAW/ is where the downloaded raw and pre-processed data are stored.\n",
"\n",
"Prep/ is the general working directory for processing and analyses.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"PATH_TO_RAW = glob(HOME_PATH + '/*/RAW')[0]\n",
"PATH_TO_PREP = glob(HOME_PATH + '/*/PREP')[0]\n",
"\n",
"# Move to the Prep directory\n",
"os.chdir(PATH_TO_PREP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract exposure information from downloaded flt files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Find all pre-processed flt files in the RAW directory\n",
"files = glob('%s/*flt.fits'%PATH_TO_RAW)\n",
"# Generate a table from the headers of the flt fits files\n",
"info = grizli.utils.get_flt_info(files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``info`` table includes relevant exposure details: e.g., filter, instrument, targetname, PA, RA, DEC.\n",
" \n",
"Print the first three rows of the table."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"info[0:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we use `grizli` to parse the headers of the downloaded flt files in RAW/ and sort them into \"visits\". Each visit represents a specific pointing + orient + filter and contains the list of its associated exposure files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Parse the table and group exposures into associated \"visits\"\n",
"visits, filters = grizli.utils.parse_flt_files(info=info, uniquename=True)\n",
"\n",
"# an F140W imaging visit\n",
"print ('\\n\\n visits[0]\\n\\t product: ', visits[0]['product'], '\\n\\t files: ', visits[0]['files'])\n",
"\n",
"# a g141 grism visit\n",
"print ('\\n\\n visits[1]\\n\\t product: ', visits[1]['product'], '\\n\\t files: ', visits[1]['files'])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Pre-process raw data</center></h1>\n",
"\n",
"We are now ready to pre-process the raw data we downloaded from MAST.\n",
"\n",
"\n",
"### process_direct_grism_visit performs all of the necessary pre-processing:\n",
"\n",
"- Copying the flt files from Raw/ to Prep/\n",
"- Astrometric registration/correction\n",
"- Grism sky background subtraction and flat-fielding\n",
"- Extract visit-level catalogs and segmentation images from the direct imaging\n",
"\n",
"\n",
"\n",
"### The final products are:\n",
"\n",
"1. Aligned, background-subtracted FLTS\n",
"\n",
"2. Drizzled mosaics of direct and grism images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if 'N' in field.upper(): radec_catalog = PATH_TO_CATS + '/goodsN_radec.cat'\n",
"if 'S' in field.upper(): radec_catalog = PATH_TO_CATS + '/goodsS_radec.cat' \n",
"\n",
"product_names = np.array([visit['product'] for visit in visits])\n",
"filter_names = np.array([visit['product'].split('-')[-1] for visit in visits])\n",
"basenames = np.array([visit['product'].split('.')[0]+'.0' for visit in visits])\n",
"\n",
"# First process the G102/F105W visits, then G141/F140W\n",
"for ref_grism, ref_filter in [('G102', 'F105W'), ('G141', 'F140W')]:\n",
" print ('Processing %s + %s visits'%(ref_grism, ref_filter))\n",
" for v, visit in enumerate(visits):\n",
" product = product_names[v]\n",
" basename = basenames[v]\n",
" filt1 = filter_names[v]\n",
" field_in_contest = basename.split('-')[0]\n",
" if (ref_filter.lower() == filt1.lower()):\n",
" #Found a direct image, now search for grism counterpart\n",
" grism_index= np.where((basenames == basename) & (filter_names == ref_grism.lower()))[0][0]\n",
" if True:\n",
" # run the pre-process script\n",
" status = process_direct_grism_visit(direct = visit,\n",
" grism = visits[grism_index],\n",
" radec = radec_catalog, \n",
" align_mag_limits = [14, 23])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Examining outputs from the pre-processing steps</center></h1>\n",
"\n",
"## Astrometric Registration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.chdir(PATH_TO_PREP)\n",
"!cat gs1-cxt-09-227.0-f105w_wcs.log\n",
"Image(filename = PATH_TO_PREP + '/gs1-cxt-09-227.0-f105w_wcs.png', width = 600, height = 600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grism sky subtraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.chdir(PATH_TO_PREP)\n",
"Image(filename = PATH_TO_PREP + '/gs1-cxt-09-227.0-g102_column.png', width = 600, height = 600)"
]
},
{
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
andrzejkrawczyk/python-course | part_2/03.Wielodziedziczenie.ipynb | 1 | 7296 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Adam(): pass\n",
"class Ewa(): pass\n",
"class Eugeniusz(Adam, Ewa): pass\n",
"class Genowefa(Adam, Ewa): pass\n",
"class Maksymilian(Adam, Ewa): pass\n",
"class Agnieszka(Adam, Ewa): pass\n",
"\n",
"class Marek(Eugeniusz, Genowefa): pass\n",
"class Henryka(Maksymilian, Agnieszka): pass\n",
"class Andrzej(Marek, Henryka): pass\n",
"\n",
"help(Andrzej)\n",
"Andrzej.mro()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Wielodziedziczenie\n",
"* stare i nowe klasy w Python2\n",
"* dziedziczenie - technika do wykorzystywania ponownie kodu\n",
"* Deterministyczny algorytm C3 linearization\n",
"* słowo kluczowe `super`\n",
"* `classname.mro()` i `help(classname)`\n",
"* technika delegacji pracy do innego obiektu\n",
"* specjalizowanie działania klas\n",
"* mixin classes - budowanie obiektu z komponentów"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"class DostawcaBrazu(object):\n",
" def pobierz_material(self):\n",
" return \"Braz\"\n",
" \n",
"class Kowal():\n",
" \n",
" def stworz(self, *przedmioty):\n",
" material = DostawcaBrazu().pobierz_material()\n",
" print(\"Zaczynam prace\")\n",
" for przedmiot in przedmioty:\n",
" print(\"wykuwam z {0}: \".format(material), przedmiot)\n",
" print(\"Skonczylem prace\")\n",
" \n",
"Kowal().stworz(\"tarcza\", \"miecz\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"class DostawcaBrazu(object):\n",
" def pobierz_material(self):\n",
" return \"Braz\"\n",
" \n",
"class Kowal(object):\n",
" \n",
" def stworz(self, *przedmioty):\n",
" material = super().pobierz_material()\n",
" print(\"Zaczynam prace\")\n",
" for przedmiot in przedmioty:\n",
" print(\"wykuwam z {0}: \".format(material), przedmiot)\n",
" print(\"Skonczylem prace\")\n",
" \n",
"class BrazKowal(Kowal, DostawcaBrazu): \n",
" pass\n",
" \n",
"BrazKowal().stworz(\"tarcza\", \"miecz\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"class DostawcaStali(object):\n",
" \n",
" def pobierz_material(self):\n",
" return \"Stal\"\n",
" \n",
" \n",
"class StalowyKowal(Kowal, DostawcaStali):\n",
" pass\n",
"\n",
"\n",
"StalowyKowal().stworz(\"tarcza\", \"miecz\", \"zbroja\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import requests\n",
"class PodstawoweOperacje():\n",
" \n",
" def pobierz_dane_z_internetu(self):\n",
" return requests.get(url=\"http://www.trojmiasto.pl\")\n",
" \n",
" def parsuj_dane(self, dane):\n",
" print(\"Parsuje dane\")\n",
" print(dane)\n",
" return \"Dane sparsowane\"\n",
" \n",
" \n",
"class WykonajAsynchronicznaOperacje(PodstawoweOperacje):\n",
" \n",
" def wykonaj(self):\n",
" dane = super().pobierz_dane_z_internetu()\n",
" super().parsuj_dane(dane)\n",
" \n",
"WykonajAsynchronicznaOperacje().wykonaj()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import requests\n",
"class MockPodstawowychOperacji(PodstawoweOperacje):\n",
" \n",
" def pobierz_dane_z_internetu(self):\n",
" return \"Dane z internetuj\"\n",
" \n",
" def parsuj_dane(self, dane):\n",
" print(\"Parsuje dane\")\n",
" print(dane)\n",
" return \"Dane sparsowane\"\n",
" \n",
" \n",
"class MockWykonajAsynchronicznaOperacje(WykonajAsynchronicznaOperacje, MockPodstawowychOperacji):\n",
" pass\n",
" \n",
"MockWykonajAsynchronicznaOperacje().wykonaj() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class DoKwadratuMixin():\n",
" \n",
" def do_kwadratu(self, liczba):\n",
" return liczba ** 2\n",
" \n",
"class ObiektMatematyczny(DoKwadratuMixin):\n",
" pass\n",
"\n",
"print(ObiektMatematyczny().do_kwadratu(4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class ModelCacheMixin(object):\n",
" CACHE_PREFIX = None\n",
"\n",
" @classmethod\n",
" def set_cache(cls, id, value, timeout=30*60):\n",
" cache.set(cls.CACHE_PREFIX % id, value, timeout=timeout)\n",
"\n",
" @classmethod\n",
" def get_cached(cls, id, timeout=30*60):\n",
" cached_object = cache.get(cls.CACHE_PREFIX % id)\n",
" if cached_object:\n",
" return cached_object\n",
" else:\n",
" entity = cls.objects.get(id=id)\n",
" cls.set_cache(id, entity, timeout=timeout)\n",
" return entity\n",
"\n",
" @classmethod\n",
" def get_cache(cls, id):\n",
" return cache.get(cls.CACHE_PREFIX % id)\n",
"\n",
" @classmethod\n",
" def get_cached_or_none(cls, id):\n",
" try:\n",
" return cls.get_cached(id)\n",
" except cls.DoesNotExist:\n",
" return None\n",
"\n",
" @classmethod\n",
" def get_cached_or_404(cls, id):\n",
" try:\n",
" return cls.get_cached(id)\n",
" except cls.DoesNotExist:\n",
" raise Http404()\n",
"\n",
" @classmethod\n",
" def cache_delete(cls, id):\n",
" cache.delete(cls.CACHE_PREFIX % id)"
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
dm-wyncode/zipped-code | content/posts/how-to/remote_pair_coding.ipynb | 1 | 7656 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pair coding environment\n",
"\n",
"As graduates of Wyncode Academy, we want to be able to pair code while not sitting next to each other on the same network.\n",
"\n",
"At least one person does not edit text files in the terminal.\n",
"\n",
"Specifications:\n",
"\n",
"* ability to use voice chat\n",
"\n",
"**Trivially solved with any app that supports voice chatting.**\n",
"\n",
"* ability to see results of executed code: \n",
"\n",
"**Easily solved with shared tmux session after users are granted shell access via ssh.**\n",
"\n",
"* ability for all users to have immediately available sourced files that they can edit with any text editor of choice: \n",
"\n",
"*Not as trivial as the first 2 specs.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## possible solutions\n",
"\n",
"### combination of the following:\n",
"\n",
"* git: https://git-scm.com/\n",
"* gitolite: http://gitolite.com/gitolite/index.html\n",
" \n",
" gitolite is for hosting git repositories.\n",
" \n",
"* Python package watchdog: https://pypi.python.org/pypi/watchdog\n",
"\n",
" For watching a directory and initiating an automated series of steps after file changes. \n",
" \n",
" The file changes trigger a commit and push to the git repo from which all other users can pull. \n",
" \n",
" The pull can be initiated by using watchdog to watch the designated gitolite repo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### setup\n",
"\n",
"* Install a virtualenv with python [`watchdog`](https://pypi.python.org/pypi/watchdog) package installed.\n",
"* Set up a named repo for a project in the gitolite hosting git repository. This must be done by a gitolite administrator by editing `~/gitolite-admin/conf/gitolite.conf`. \n",
"\n",
"In this example let's call the new pair coding repo `$new_project_name`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_project_name = 'trial-setup'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possible to view the current repos with this command:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello dmmmd, this is gitolite@zip running gitolite3 v3.6.6-2-g8620d5f on git 1.9.1\r\n",
"\r\n",
" R W\tgitolite-admin\r\n",
" R W\tpair-coding-template\r\n",
" R W\tpractice\r\n",
" R W\ttest-project\r\n",
" R W\ttesting\r\n"
]
}
],
"source": [
"!./bin/gitolite-info"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* clone the template repo that will become the working project repo set up earlier"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'trial-setup'...\n",
"remote: Counting objects: 207, done.\u001b[K\n",
"remote: Compressing objects: 100% (95/95), done.\u001b[K\n",
"remote: Total 207 (delta 98), reused 190 (delta 94)\u001b[K\n",
"Receiving objects: 100% (207/207), 26.64 KiB | 0 bytes/s, done.\n",
"Resolving deltas: 100% (98/98), done.\n",
"Checking connectivity... done.\n"
]
}
],
"source": [
"!./bin/git-clone $new_project_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cloned directory contains scripts to set up the direcotry for pair coding.\n",
"\n",
"`cd` into the directory"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!cd $new_project_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Run the setup executable: \n",
"\n",
"```bash\n",
"./setup \"$new_project_name\"\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then run:\n",
"\n",
"```bash\n",
"./watchdog\n",
"```\n",
"\n",
"And the watching should begin. All file changes should result in a commit and push."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### More about the files\n",
"\n",
"#### watchdog file"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# %load watchdog"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"#!/bin/bash\n",
"\n",
"./automated_git.sh 2>&1 > watchdog.log & # start watchdog and redirect output to log and put in background\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# %load automated_git.sh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"#!/bin/bash\n",
"watchmedo shell-command \\\n",
" --patterns='*' \\\n",
" --ignore-patterns='.git *.log' \\ # avoid race condition when these files update from the update\n",
" --command='git add -A && git commit -m replaced_by_hook && git push origin master'\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The process with watchdog has to be manually killed. This is done with the following script:\n",
"\n",
"```bash\n",
"./get-watchdog-pid\n",
"```\n",
"\n",
"* Then kill the process:\n",
"\n",
"```bash\n",
"kill $pid\n",
"```\n",
"\n",
"* This will kill all the watchdog processes.\n",
"\n",
"```bash\n",
"./kill-all-watchdogs\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### expected result\n",
"\n",
"Every time a file other than those in the ignore-patterns is changed it will trigger a commit and push to the gitolite repo where it can be pulled by other users."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### todo\n",
"\n",
"* Set up vim key mapping so when a non-terminal edit has occurred the repo is pulled and vim files are reloaded."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extras\n",
"\n",
"* rst directory: contains an rst file to use with https://github.com/Rykka/InstantRst\n",
"\n",
"As that file is updated another user can copy and paste items from it. Sometimes it's easier than saying what should be typed. Can be used as a scratch pad, too. It is instantly updated to all those subscribed to the web page it produces."
]
}
],
"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": 2
}
| mit |
cjayb/mne-python | mne/viz/_brain/tests/test.ipynb | 2 | 1895 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mne\n",
"from mne.datasets import testing\n",
"data_path = testing.data_path()\n",
"raw_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw.fif'\n",
"subjects_dir = data_path + '/subjects'\n",
"subject = 'sample'\n",
"trans = data_path + '/MEG/sample/sample_audvis_trunc-trans.fif'\n",
"info = mne.io.read_info(raw_fname)\n",
"mne.viz.set_3d_backend('notebook')\n",
"mne.viz.plot_alignment(info, trans, subject=subject, dig=True,\n",
" meg=['helmet', 'sensors'], subjects_dir=subjects_dir,\n",
" surfaces=['head-dense'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import mne\n",
"from mne.datasets import testing\n",
"data_path = testing.data_path()\n",
"sample_dir = os.path.join(data_path, 'MEG', 'sample')\n",
"subjects_dir = os.path.join(data_path, 'subjects')\n",
"fname_stc = os.path.join(sample_dir, 'sample_audvis_trunc-meg')\n",
"stc = mne.read_source_estimate(fname_stc, subject='sample')\n",
"initial_time = 0.13\n",
"mne.viz.set_3d_backend('notebook')\n",
"brain = stc.plot(subjects_dir=subjects_dir, initial_time=initial_time,\n",
" clim=dict(kind='value', pos_lims=[3, 6, 9]),\n",
" time_viewer=True,\n",
" hemi='split')"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | bsd-3-clause |
Hexiang-Hu/mmds | week7/Quiz-Week7.ipynb | 2 | 17465 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Quiz Week 7B Basic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Q1.\n",
"\n",
"* Compute the Topic-Specific PageRank for the following link topology. Assume that __pages selected for the teleport set are nodes 1 and 2__ and that in the teleport set, the __weight assigned for node 1 is twice that of node 2__. Assume further that the teleport probability, (1 - beta), is 0.3. Which of the following statements is correct?\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.2 0.45 0.35 0. ]\n",
" [ 0.9 0.1 0. 0. ]\n",
" [ 0.2 0.1 0. 0.7 ]\n",
" [ 0.2 0.1 0.7 0. ]]\n",
"[ 0.25 0.25 0.25 0.25]\n",
"iteration#1:\n",
"r_new = [ 0.375 0.1875 0.2625 0.175 ]\n",
"iteration#2:\n",
"r_new = [ 0.33125 0.23125 0.25375 0.18375]\n",
"iteration#3:\n",
"r_new = [ 0.361875 0.2159375 0.2445625 0.177625 ]\n",
"iteration#4:\n",
"r_new = [ 0.35115625 0.22665625 0.25099375 0.17119375]\n",
"iteration#5:\n",
"r_new = [ 0.35865937 0.22290469 0.24274031 0.17569562]\n",
"iteration#6:\n",
"r_new = [ 0.35603328 0.22553078 0.24851772 0.16991822]\n",
"iteration#7:\n",
"r_new = [ 0.35787155 0.22461165 0.2435544 0.1739624 ]\n",
"iteration#8:\n",
"r_new = [ 0.35722815 0.22525504 0.24702872 0.17048808]\n",
"iteration#9:\n",
"r_new = [ 0.35767853 0.22502985 0.24437151 0.17292011]\n",
"iteration#10:\n",
"r_new = [ 0.3575209 0.22518749 0.24623156 0.17106006]\n",
"iteration#11:\n",
"r_new = [ 0.35763124 0.22513231 0.24487435 0.17236209]\n",
"iteration#12:\n",
"r_new = [ 0.35759262 0.22517093 0.2458244 0.17141205]\n",
"iteration#13:\n",
"r_new = [ 0.35761965 0.22515742 0.24514585 0.17207708]\n",
"iteration#14:\n",
"r_new = [ 0.35761019 0.22516688 0.24562083 0.1716021 ]\n",
"iteration#15:\n",
"r_new = [ 0.35761682 0.22516357 0.24528503 0.17193458]\n",
"iteration#16:\n",
"r_new = [ 0.3576145 0.22516589 0.24552009 0.17169952]\n",
"iteration#17:\n",
"r_new = [ 0.35761612 0.22516507 0.24535474 0.17186407]\n",
"iteration#18:\n",
"r_new = [ 0.35761555 0.22516564 0.24547049 0.17174832]\n",
"iteration#19:\n",
"r_new = [ 0.35761595 0.22516544 0.24538927 0.17182934]\n",
"iteration#20:\n",
"r_new = [ 0.35761581 0.22516558 0.24544612 0.17177249]\n",
"iteration#21:\n",
"r_new = [ 0.35761591 0.22516553 0.24540627 0.17181228]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# calculate topic specific page\n",
"\n",
"M = np.array([[0, 0.5, 0.5, 0],\n",
" [1, 0, 0, 0],\n",
" [0, 0, 0, 1],\n",
" [0, 0, 1, 0]\n",
" ])\n",
"p = 0.7\n",
"\n",
"teleport_M = np.array([[2.0/3, 1.0/3, 0, 0],\n",
" [2.0/3, 1.0/3, 0, 0],\n",
" [2.0/3, 1.0/3, 0, 0],\n",
" [2.0/3, 1.0/3, 0, 0]\n",
" ])\n",
"M_after = M*p + (1 - p)* teleport_M;\n",
"r_old = np.array([ 0.25 for i in xrange(4)])\n",
"\n",
"\n",
"r_1 = np.dot(M_after.T, r_old)\n",
"r_2 = np.dot(M_after.T, r_1)\n",
"print M_after\n",
"print r_old\n",
"\n",
"cnt = 0\n",
"while True:\n",
" cnt += 1\n",
" r_new = np.dot(M_after.T, r_old)\n",
" \n",
" print \"iteration#{0}:\".format(cnt)\n",
" print \"r_new = \" + str(r_new)\n",
" if np.sum( np.abs(r_new - r_old) ) < 10**-4 or cnt > 100:\n",
" break\n",
" \n",
" r_old = r_new"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Q2.\n",
"* The __spam-farm architecture__ described in Section 5.4.1 suffers from the problem that the target page has many links --- __one to each supporting page__. To avoid that problem, the spammer could use the architecture shown below:\n",
"\n",
"\n",
"\n",
"* There, k \"second-tier\" nodes act as __intermediaries__. The target page t has only to link to __the k second-tier pages__, and each of those pages links to m/k of the __m supporting pages__. Each of the supporting pages links only to t (although most of these links are not shown). Suppose the __taxation parameter is β = 0.85__, and x is the amount of PageRank supplied __from outside to the target page__. Let n be the total number of pages in the Web. Finally, let y be the __PageRank of target page t__. If we compute the formula for y in terms of k, m, and n, we get a formula with the form\n",
"\n",
"$$\n",
"y = a \\cdot x + \\frac{b \\cdot m}{n} + \\frac{c \\cdot k}{n}\n",
"$$\n",
"\n",
"* Note: __To arrive at this form__, it is necessary at the last step to drop a low-order term that is a fraction of 1/n. Determine coefficients a, b, and c, remembering that β is fixed at 0.85. Then, identify the value, correct to two decimal places, for one of these coefficients.\n",
"\n",
"\n",
"\n",
"* __Hint__: Here is an outline of the solution. \n",
" * Use w as the PageRank of each second-tier page and z as the PageRank of each supporting page. \n",
" * You can write three equations, one for y in terms of z, one for w in terms of y, and one for z in terms of w. \n",
" * For example, y equals x (the PageRank from outside) plus all the untaxed PageRank of each of the m supporting pages (a total of βzm), plus its share of the tax (which is (1-β)/n). \n",
" * Discover the equations for w and z, then substitute these in the equation for y to get an equation for y in terms of itself, from which you can solve for y.\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution 2.\n",
"* Let w be the PageRank of each of the second-tier pages, and let z be the PageRank of each of the supporting pages. Then the equations relating y, w, and z are:\n",
" 1. $y = x + \\beta \\cdot z \\cdot m + \\frac{ (1 - \\beta) }{n} $\n",
" 2. $w = \\frac{\\beta \\cdot y}{k} + \\frac{(1 - \\beta)}{n}$\n",
" 3. $z = \\frac{\\beta \\cdot w \\cdot k}{m} + \\frac{(1 - \\beta)}{n}$\n",
"\n",
"\n",
"* The first equation says that the PageRank of t is the external contribution x, plus βz (the amount of PageRank not taxed) times the number of supporting pages, plus (1-β)/n, which is the share of \"tax\" that every page gets. The second equation says that each second-tier page gets 1/k-th of the untaxed PageRank of t, plus its share of the tax. The third equation says each supporting page gets 1 part in m/k of the untaxed PageRank of the second-tier page that reaches that supporting page, plus its share of the tax.\n",
" * Begin by substituting for z in the first equation:\n",
" * $y = x + β2*k*w + \\frac{β*(1-β)*m}{n} + \\frac{(1-β)}{n}$\n",
"\n",
" * Now, substitute for w in the above:\n",
" * $y = x + β3*y + \\frac{β*(1-β)*m}{n} + \\frac{β2*(1-β)*k}{n} + \\frac{(1-β)}{n}$\n",
" * Neglect the last term (1-β)/n, per the directions in the statement of the problem. If we move the term β3y to the left, and note that β3 = (1-β)(1+β+β2), we get\n",
" * $y = \\frac{x}{(1-β3)} + \\frac{β}{(1+β+β2)} \\cdot \\frac{m}{n} + \\frac{β}{1+β+β2} \\cdot \\frac{k}{n}$\n",
" * For β = 0.85, these coefficients evaluate to:\n",
" * y = 2.59x + 0.33(m/n) + 0.28(k/n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quiz - Week7A Advanced"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Q1.\n",
"\n",
"* Suppose we have an LSH family h of __(d1,d2,.6,.4)__ hash functions. We can use __three functions__ from h and the AND-construction to form a (d1,d2,w,x) family, and we can use __two functions from h__ and the OR-construction to form a (d1,d2,y,z) family. Calculate w, x, y, and z, and then identify the correct value of one of these in the list below."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w = 0.216\n",
"x = 0.064\n",
"y= 0.84\n",
"z= 0.64\n"
]
}
],
"source": [
"import numpy as np\n",
"# And Construction\n",
"print \"w = {0}\".format(.6 ** 3)\n",
"print \"x = {0}\".format(.4 ** 3)\n",
"\n",
"# Or Construction\n",
"print \"y= {0}\".format(1 - (1 - .6)**2)\n",
"print \"z= {0}\".format(1 - (1 - .4)**2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Q2.\n",
"* Here are eight strings that represent sets:\n",
"\n",
"<pre>\n",
" s1 = abcef\n",
" s2 = acdeg\n",
" s3 = bcdefg\n",
" s4 = adfg\n",
" s5 = bcdfgh\n",
" s6 = bceg\n",
" s7 = cdfg\n",
" s8 = abcd\n",
"</pre>\n",
"\n",
"* Suppose our __upper limit on Jaccard distance is 0.2__, and we use the indexing scheme of Section 3.9.4 based on symbols appearing in the prefix (no position or length information). For each of __s1, s3, and s6__, determine __how many other strings that string will be compared with__, if it is used as the __probe__ string. Then, identify the true count from the list below."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s1: ab\n",
"s2: ac\n",
"s3: bc\n",
"s4: a\n",
"s5: bc\n",
"s6: b\n",
"s7: c\n",
"s8: a\n",
"===========================\n",
"So we got the group as: \n",
"a : [s1, s2, s4, s8]\n",
"b : [s1, s3, s4, s5]\n",
"c : [s2, s3, s4]\n"
]
}
],
"source": [
"import numpy as np\n",
"import math\n",
"jDist = 0.2\n",
"def get_symbol(st):\n",
" L = len(st)\n",
" pLen = int(math.floor(jDist * L)) + 1\n",
" return st[:pLen]\n",
"\n",
"print \"s1: \" + get_symbol('abcef')\n",
"print \"s2: \" + get_symbol('acdeg')\n",
"print \"s3: \" + get_symbol('bcdefg')\n",
"print \"s4: \" + get_symbol('adfg')\n",
"print \"s5: \" + get_symbol('bcdfgh')\n",
"print \"s6: \" + get_symbol('bceg')\n",
"print \"s7: \" + get_symbol('cdfg')\n",
"print \"s8: \" + get_symbol('abcd')\n",
"\n",
"\n",
"print \"===========================\\nSo we got the group as: \"\n",
"print \"a : [s1, s2, s4, s8]\"\n",
"print \"b : [s1, s3, s4, s5]\"\n",
"print \"c : [s2, s3, s4]\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Solution2.\n",
"* First, we index a string of length L on the symbols appearing in its prefix of length floor(0.2L+1). Thus, strings of length 5 and 6 are indexed on their first two symbols, while strings of length 4 are indexed on their first symbol only. Thus, the index for a consists of {s1, s2, s4, s8}; the index for b consists of {s1, s3, s5, s6}, the index for c consists of {s2, s3, s5, s7}, and no other symbol is indexed at all.\n",
"* For s1, we examine the indexes for a and b, which contains all strings but s7. Thus, s1 is compared with 6 other strings.\n",
"\n",
"* For s3, we examine the indexes for b and c, which together contain s1, s2, s3, s5, s6, and s7. Thus, s3 is compared with five other strings.\n",
"\n",
"* For s6, we examine only the index for b. Thus, s6 is compared only with the three other strings s1, s3, and s5. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Q3.\n",
"* Consider the link graph\n",
"\n",
"\n",
"* First, construct the L, the link matrix, as discussed in Section 5.5 on the HITS algorithm. Then do the following:\n",
"\n",
"1. Start by assuming the hubbiness of each node is 1; that is, the vector h is (the transpose of) [1,1,1,1].\n",
"2. Compute an estimate of the authority vector a=LTh.\n",
"3. Normalize a by dividing all values so the largest value is 1.\n",
"4. Compute an estimate of the hubbiness vector h=La.\n",
"5. Normalize h by dividing all values so the largest value is 1.\n",
"6. Repeat steps 2-5.\n",
"7. Now, identify in the list below the true statement about the final estimates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution 3.\n",
"* Here is the matrix L:\n",
"<pre>\n",
" 0\t1\t1\t0\n",
" 1\t0\t0\t0\n",
" 0\t0\t0\t1\n",
" 0\t0\t1\t0\n",
"</pre>\n",
"\n",
"* In what follows, all vectors will be written as rows, i.e., in transposed form. We start with h = [1,1,1,1] and compute LTh = [1,1,2,1]. Since the largest value is 2, we divide all values by 2, giving us the first estimate a = [1/2,1/2,1,1/2].\n",
"\n",
"* Next, we compute La = [3/2,1/2,1/2,1] and normalize by multiplying by 2/3 to get h = [1,1/3,1/3,2/3].\n",
"\n",
"* The next calculation of a from the estimate of h gives LTh = [1/3,1,5/3,1/3], and normalizing gives a = [1/5,3/5,1,1/5].\n",
"\n",
"* For the final estimate of h we compute La = [8/5,1/5,1/5,1], which after normalizing gives h = [1,1/8,1/8,5/8].\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Q4.\n",
"* Consider an implementation of the __Block-Stripe Algorithm__ discussed in Section 5.2 to compute page rank on a graph of N nodes (i.e., Web pages). Suppose each page has, on average, 20 links, and we divide the new rank vector into k blocks (and correspondingly, the matrix M into k stripes). Each stripe of M has one line per \"source\" web page, in the format:\n",
" [source_id, degree, m, dest_1, ...., dest_m]\n",
"* Notice that we had to add an additional entry, m, to denote the number of destination nodes in this stripe, which of course is no more than the degree of the node. Assume that all entries (scores, degrees, identifiers,...) are encoded using 4 bytes.\n",
"\n",
"* There is an additional detail we need to account for, namely, locality of links. As a very simple model, assume that we divide web pages into two disjoint sets:\n",
"\n",
"1. __Introvert__ pages, which link only to other pages within the same host as themselves.\n",
"2. __Extrovert__ pages, which have links to pages across several hosts.\n",
"* Assume a fraction x of pages (0 Construct a formula that counts the amount of I/O per page rank iteration in terms of N, x, and k. The 4-tuples below list combinations of N, k, x, and I/O (in bytes). Pick the correct combination.\n",
"* __Note.__ There are some additional optimizations one can think of, such as striping the old score vector, encoding introvert and extrovert pages using different schemes, etc. For the purposes of working this problem, assume we don't do any optimizations beyond the block-stripe algorithm discussed in class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Solution4.\n",
"* The number of bytes involved in __reading the old pagerank vector__ and __writing the new pagerank vector to disk__ = 4 (k+1) N \n",
" * For the M matrix: - The introvert pages will appear xN times and each row will have on average 23 entries (3 metadata and 20 destination links). \n",
"* __Total number of bytes read__ = 4*23 xN \n",
" * The extrovert pages will appear (1-x) kN times and each row will have 3 (metadata) + 20/k (destination links) entries on average. \n",
"* __Total number of bytes read__ = 4 * (3+20/k) * (1-x) kN \n",
"* __Total I/O per pagerank iteration__ (in GB, where 1GB ~ 10^9 = N bytes) = 4 [(k+1) N + 23 xN + (3k + 20) (1-x) N] / N = 4 [(k+1) + 23 x + (3k + 20) (1-x)] = 4 [21 + k + 3 (x + (1-x) k)]"
]
},
{
"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
}
| mit |
janvitek/can_R_learn_from_Julia | src/.ipynb_checkpoints/Means-R-checkpoint.ipynb | 1 | 9583 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Integer\"\n",
" user system elapsed \n",
" 0.527 0.002 0.530 \n",
"[1] \"Float\"\n",
" user system elapsed \n",
" 0.496 0.001 0.498 \n",
"[1] \"Logical\"\n",
" user system elapsed \n",
" 0.543 0.001 0.545 \n",
"[1] \"Complex\"\n",
" user system elapsed \n",
" 2.056 0.675 2.775 \n"
]
}
],
"source": [
"len = 100000 \n",
"x1 = 1:len ### x1 INTEGER vector\n",
"dim(x1) = c(len/2,2)\n",
"x2 = x1 + .1 ### x2 REAL vector\n",
"x3 = rep(TRUE,len) ### x3 LOGICAL vector\n",
"dim(x3) = c(len/2,2)\n",
"x4 = complex(r=1:len,i=1:len)\n",
"dim(x4) = c(len/2,2) ### x4 COMPLEX vector\n",
"\n",
"measure <- function(n, x) \n",
" { print(n); print(system.time(for (i in 1:1000) colMeans(x))) }\n",
" \n",
"### Examples of polymophism, colMeans works on all those datatypes \n",
"measure(\"Integer\",x1) # .53\n",
"measure(\"Float\", x2) # .49\n",
"measure(\"Logical\",x3) # .54\n",
"measure(\"Complex\",x4) #2.77"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## For performance colMeans is split in a dimension-polymorphic function in R\n",
"## and more special-cased function in C\n",
"colMeans <- function (x, na.rm = FALSE, dims = 1L) {\n",
" dn = dim(x)\n",
" id = 1:dims\n",
" n = prod(dn[id])\n",
" dn = dn[-id]\n",
" pdn = prod(dn)\n",
" z <- if (is.complex(x)) \n",
" .Internal(colMeans(Re(x),n,pdn,na.rm))+(0+1i)*.Internal(colMeans(Im(x),n,pdn,na.rm))\n",
" else .Internal(colMeans(x, n, pdn, na.rm))\n",
" if (length(dn) > 1L) {\n",
" dim(z) <- dn\n",
" dimnames(z) <- dimnames(x)[-id]\n",
" } else names(z) <- dimnames(x)[[dims + 1L]]\n",
" z\n",
"} "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"3"
],
"text/latex": [
"3"
],
"text/markdown": [
"3"
],
"text/plain": [
"[1] 3"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Integer\"\n",
" user system elapsed \n",
" 33.960 0.191 34.800 \n"
]
}
],
"source": [
"## A simplified version of colMeans written entirely in R\n",
"## using loops\n",
"colMeans2 <- function (x, na.rm = FALSE, dims = 1L) {\n",
" dn = dim(x)\n",
" id = 1:dims\n",
" n = prod(dn[id])\n",
" dn = dn[-id]\n",
" pdn = prod(dn)\n",
" res = 0\n",
" for (j in 0:(pdn-1)) {\n",
" sum = 0\n",
" cnt = 0\n",
" off = (j * n)\n",
" for (i in 1:n) {\n",
" v = x[[i+off]]\n",
" cnt = cnt+ 1\n",
" sum = sum+ v\n",
" }\n",
" res[j+1] = sum/cnt\n",
" }\n",
" res\n",
"}\n",
" \n",
"measure2 <- function(n, x) \n",
" { print(n); print(system.time(for (i in 1:1000) colMeans2(x))) }\n",
"### Warning this is SLOW!!! \n",
"#measure2(\"Integer\",x1) # 222.6\n",
"require(compiler)\n",
"enableJIT(3)\n",
"measure2(\"Integer\",x1) # 33.9"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"/* colSums(x, n, p, na.rm) and friends */\n",
"SEXP attribute_hidden do_colsum(SEXP call, SEXP op, SEXP args, SEXP rho)\n",
"{\n",
" SEXP x, ans = R_NilValue;\n",
" int type;\n",
" Rboolean NaRm, keepNA;\n",
"\n",
" checkArity(op, args);\n",
" x = CAR(args); args = CDR(args);\n",
" R_xlen_t n = asVecSize(CAR(args)); args = CDR(args);\n",
" R_xlen_t p = asVecSize(CAR(args)); args = CDR(args);\n",
" NaRm = asLogical(CAR(args));\n",
" if (n == NA_INTEGER || n < 0)\n",
" error(_(\"invalid '%s' argument\"), \"n\");\n",
" if (p == NA_INTEGER || p < 0)\n",
" error(_(\"invalid '%s' argument\"), \"p\");\n",
" if (NaRm == NA_LOGICAL) error(_(\"invalid '%s' argument\"), \"na.rm\");\n",
" keepNA = !NaRm;\n",
"\n",
" int OP = PRIMVAL(op);\n",
" switch (type = TYPEOF(x)) {\n",
" case LGLSXP: break;\n",
" case INTSXP: break;\n",
" case REALSXP: break;\n",
" default:\n",
" error(_(\"'x' must be numeric\"));\n",
" }\n",
"\n",
" if (OP == 0 || OP == 1) { /* columns */\n",
" PROTECT(ans = allocVector(REALSXP, p));\n",
" for (R_xlen_t j = 0; j < p; j++) {\n",
" R_xlen_t cnt = n, i;\n",
" LDOUBLE sum = 0.0;\n",
" switch (type) {\n",
" case REALSXP: {\n",
" double *rx = REAL(x) + (R_xlen_t)n*j;\n",
" if (keepNA)\n",
" for (sum = 0., i = 0; i < n; i++) sum += *rx++;\n",
" else \n",
" for (cnt = 0, sum = 0., i = 0; i < n; i++, rx++)\n",
" if (!ISNAN(*rx)) {cnt++; sum += *rx;}\n",
" break;\n",
" }\n",
" case INTSXP: {\n",
" int *ix = INTEGER(x) + (R_xlen_t)n*j;\n",
" for (cnt = 0, sum = 0., i = 0; i < n; i++, ix++)\n",
" if (*ix != NA_INTEGER) {cnt++; sum += *ix;}\n",
" else if (keepNA) {sum = NA_REAL; break;}\n",
" break;\n",
" }\n",
" case LGLSXP: {\n",
" int *ix = LOGICAL(x) + (R_xlen_t)n*j;\n",
" for (cnt = 0, sum = 0., i = 0; i < n; i++, ix++)\n",
" if (*ix != NA_LOGICAL) {cnt++; sum += *ix;}\n",
" else if (keepNA) {sum = NA_REAL; break;}\n",
" break;\n",
" }\n",
" }\n",
" if (OP == 1) sum /= cnt; /* gives NaN for cnt = 0 */\n",
" REAL(ans)[j] = (double) sum;\n",
" }\n",
" } else { /* rows */\n",
"\tPROTECT(ans = allocVector(REALSXP, n));\n",
"\n",
"\t/* allocate scratch storage to allow accumulating by columns\n",
"\t to improve cache hits */\n",
"\tint *Cnt = NULL;\n",
"\tLDOUBLE *rans;\n",
"\tif(n <= 10000) {\n",
"\t R_CheckStack2(n * sizeof(LDOUBLE));\n",
"\t rans = (LDOUBLE *) alloca(n * sizeof(LDOUBLE));\n",
"\t Memzero(rans, n);\n",
"\t} else rans = Calloc(n, LDOUBLE);\n",
"\tif (!keepNA && OP == 3) Cnt = Calloc(n, int);\n",
"\n",
"\tfor (R_xlen_t j = 0; j < p; j++) {\n",
"\t LDOUBLE *ra = rans;\n",
"\t switch (type) {\n",
"\t case REALSXP:\n",
"\t {\n",
"\t\tdouble *rx = REAL(x) + (R_xlen_t)n * j;\n",
"\t\tif (keepNA)\n",
"\t\t for (R_xlen_t i = 0; i < n; i++) *ra++ += *rx++;\n",
"\t\telse\n",
"\t\t for (R_xlen_t i = 0; i < n; i++, ra++, rx++)\n",
"\t\t\tif (!ISNAN(*rx)) {\n",
"\t\t\t *ra += *rx;\n",
"\t\t\t if (OP == 3) Cnt[i]++;\n",
"\t\t\t}\n",
"\t\tbreak;\n",
"\t }\n",
"\t case INTSXP:\n",
"\t {\n",
"\t\tint *ix = INTEGER(x) + (R_xlen_t)n * j;\n",
"\t\tfor (R_xlen_t i = 0; i < n; i++, ra++, ix++)\n",
"\t\t if (keepNA) {\n",
"\t\t\tif (*ix != NA_INTEGER) *ra += *ix;\n",
"\t\t\telse *ra = NA_REAL;\n",
"\t\t }\n",
"\t\t else if (*ix != NA_INTEGER) {\n",
"\t\t\t*ra += *ix;\n",
"\t\t\tif (OP == 3) Cnt[i]++;\n",
"\t\t }\n",
"\t\tbreak;\n",
"\t }\n",
"\t case LGLSXP:\n",
"\t {\n",
"\t\tint *ix = LOGICAL(x) + (R_xlen_t)n * j;\n",
"\t\tfor (R_xlen_t i = 0; i < n; i++, ra++, ix++)\n",
"\t\t if (keepNA) {\n",
"\t\t\tif (*ix != NA_LOGICAL) *ra += *ix;\n",
"\t\t\telse *ra = NA_REAL;\n",
"\t\t }\n",
"\t\t else if (*ix != NA_LOGICAL) {\n",
"\t\t\t*ra += *ix;\n",
"\t\t\tif (OP == 3) Cnt[i]++;\n",
"\t\t }\n",
"\t\tbreak;\n",
"\t }\n",
"\t }\n",
"\t}\n",
"\tif (OP == 3) {\n",
"\t if (keepNA)\n",
"\t\tfor (R_xlen_t i = 0; i < n; i++) rans[i] /= p;\n",
"\t else\n",
"\t\tfor (R_xlen_t i = 0; i < n; i++) rans[i] /= Cnt[i];\n",
"\t}\n",
"\tfor (R_xlen_t i = 0; i < n; i++) REAL(ans)[i] = (double) rans[i];\n",
"\n",
"\tif (!keepNA && OP == 3) Free(Cnt);\n",
"\tif(n > 10000) Free(rans);\n",
" }\n",
"\n",
" UNPROTECT(1);\n",
" return ans;\n",
"}"
]
}
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| cc0-1.0 |
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